The Wiley Encyclopedia of Health Psychology, 4 Volume Set [1st ed.] 9781119057840, 1119057841, 9781119057833, 1119057833

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The Wiley Encyclopedia of Health Psychology

The Wiley Encyclopedia of Health Psychology Volume 1 Biological Bases of Health Behavior Edited by

Robert H. Paul

Co edited by

Lauren E. Salminen Jodi Heaps

Editor‐in‐Chief

Lee M. Cohen

This edition first published 2021 © 2021 John Wiley & Sons Ltd All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. The right of Lee M. Cohen to be identified as the author of the editorial material in this work has been asserted in accordance with law. Registered Offices John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial Office 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging‐in‐Publication Data Names: Paul, Robert D., editor. Title: The Wiley encyclopedia of health psychology / edited by Robert Paul, St. Louis MO, USA ; editor-in-chief, Lee Cohen, The University of Mississippi, MS, USA. Description: First edition. | Hoboken : Wiley-Blackwell, 2021. | Includes index. | Contents: volume. 1. Biological bases of health behavior. Identifiers: LCCN 2020005459 (print) | LCCN 2020005460 (ebook) | ISBN 9781119057833 (v. 1 ; cloth) | ISBN 9781119057833 (v. 2 ; cloth) | ISBN 9781119057833 (v. 3 ; cloth) | ISBN 9781119057833 (v. 4 ; cloth) | ISBN 9781119675785 (adobe pdf) | ISBN 9781119675761 (epub) Subjects: LCSH: Clinical health psychology–Encyclopedias. Classification: LCC R726.7 .W554 2021 (print) | LCC R726.7 (ebook) | DDC 616.001/903–dc23 LC record available at https://lccn.loc.gov/2020005459 LC ebook record available at https://lccn.loc.gov/2020005460 Cover image and design: Wiley Set in 10/12.5pt ITC Galliard by SPi Global, Pondicherry, India

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Contents

List of Contributors

ix

Foreword xxvii Preface Volume 1: Biological Bases of Health

xxix

Preface Volume 1: Biological Basis of Behavior

xxxi

Editor‐in‐Chief Acknowledgments

xxxiii

Brain Development from Conception to Adulthood Jodi M. Heaps‐Woodruff and David Von Nordheim

1

Functional Anatomy: Types of Cells/Physiology Patrick W. Wright

5

Functional Neuroanatomy: Cortical–Subcortical Distinctions and Pathways David Von Nordheim and Jodi M. Heaps‐Woodruff

13

Neurobiology of Stress–Health Relationships R.L. Spencer, L.E. Chun, M.J. Hartsock, and E.R. Woodruff

21

Stress, Neurogenesis, and Mood: An Introduction to the Neurogenic Hypothesis of Depression Jacob Huffman and George T. Taylor

37

An Overview of Neuropsychiatric Symptoms and Pathogenesis in MS Emily Evans, Elizabeth Silbermann, and Soe Mar

43

Psychoneuroimmunology: Immune Markers of Psychopathology Rachel A. Wamser‐Nanney and Brittany F. Goodman

49

vi

Contents

Genetic Polymorphisms Mikhail Votinov and Katharina Sophia Goerlich

59

Epigenetics in Developmental Disorders Tiffany S. Doherty and Tania L. Roth

75

Epigenetics in Behavior and Mental Health Tiffany S. Doherty and Tania L. Roth

83

Diffusion MRI: Introduction Naiara Demnitz and Claire E. Sexton

91

Structural Neuroimaging of Hippocampal Subfields in Healthy Aging, Alzheimer’s Disease, Schizophrenia, and Major Depressive Disorder Lok‐Kin Yeung and Adam M. Brickman DTI Tractography Metrics: Fiber Bundle Length Stephen Correia The Use of Diffusional Kurtosis Imaging (DKI) to Define Pathological Processes in Stroke and Dementia Rachael L. Deardorff, Jens H. Jensen, and Joseph A. Helpern Functional Magnetic Resonance Imaging: fMRI Tricia Z. King Resting‐State Functional Magnetic Resonance Imaging and Neuropsychiatric Conditions Carissa L. Philippi, Tasheia Floyd, and Gregory Dahl

99 109

117 125

131

The Event‐Related Potential (ERP) Method and Applications in Clinical Research Amanda C. Eaton, Rebecca A. Meza, Kirk Ballew, and Suzanne E. Welcome

141

Magnetic Resonance Spectroscopy (MRS) in Psychiatric Diseases Alexander P. Lin, Sai Merugumala, Huijun Liao, and Napapon Sailasuta

149

Artificial Intelligence in Medicine: Can Computers Aid the Diagnosis of Alzheimer’s Disease? Christian Salvatore, Petronilla Battista, and Isabella Castiglioni

163

Therapeutic Effects of Repetitive Transcranial Magnetic Stimulation (rTMS) in Stroke: Moving Toward an Individualized Approach Jason L. Neva, Kathryn S. Hayward, and Lara A. Boyd

169

Connectomics and Multimodal Imaging Techniques in Clinical Research Olusola Ajilore and Alex Leow

181

Asperger Syndrome Laurent Mottron

187

Contents

vii

Neurobiology of Down Syndrome Stella Sakhon and Jamie Edgin

197

Vascular Dementia Paola García‐Egan, Jenna Haddock, and Robert H. Paul

205

Neurodegenerative Conditions: FTD Abigail O. Kramer, Andrea G. Alioto, and Joel H. Kramer

209

Traumatic Brain Injury (TBI) Matthew R. Powell and Michael McCrea

219

Chronic Traumatic Encephalopathy: A Long‐Term Consequence of Repetitive Head Impacts Michael L. Alosco, Alyssa Phelps, and Robert A. Stern

227

Neuropsychological Assessment of Cortical and Subcortical Cognitive Phenotypes Robert H. Paul, Carissa L. Philippi, and Gregory Dahl

237

Mild Traumatic Brain Injury and Posttraumatic Stress Disorder: Diagnostic and Neurobiological Signatures of Comorbidity Jacob Bolzenius

243

Caloric Restriction, Cognitive Function, and Brain Health Patrick J. Smith

253

Psychosocial: Substance Abuse—Street Drugs J. Megan Ross, Jacqueline C. Duperrouzel, Ileana Pacheco‐Colón, and Raul Gonzalez

261

The Effects of Prenatal Stress on Offspring Development Megan R. Gunnar and Colleen Doyle

275

Psychophysiology of Traumatic Stress and Posttraumatic Stress Disorder Michael G. Griffin, Brittany F. Goodman, Rebecca E. Chesher, and Natalia M. Kecala

287

Cognitive Reserve Sarah A. Cooley

293

APOE as a Risk Factor for Age‐Related Cognitive Impairment: Neuropsychological and Neuroimaging Findings Laura E. Korthauer and Ira Driscoll The Serotonin Transporter‐Linked Polymorphic Region (5‐HTTLPR) Polymorphism, Stress, and Depression Christina Di Iorio, Kimberly Johnson, Daniel Sheinbein, Samantha Kahn, Patrick England, and Ryan Bogdan

299

311

viii

Contents

The Neuroscience of Well‐Being: Part 1 (Conceptual Definitions) Justine Megan Gatt

325

Gene × Environment Interactions: BDNF Lauren E. Salminen and Robert H. Paul

331

The Insula in Women with Posttraumatic Stress Disorder Steven E. Bruce, Katherine R. Buchholz, and Wilson J. Brown

341

Language and Literacy Development Laura O’Hara, Rebecca A. Meza, and Amanda C. Eaton

353

The Neuroscience of Well‐Being: Part 2 (Potential Neural Networks) Justine Megan Gatt

361

Early Life Stress Madeline B. Harms and Seth D. Pollak

373

Alcohol‐Related Brain Disorders Rebecca N. Preston‐Campbell, Stephanie A. Peak, Paola García‐Egan, and Robert H. Paul

395

Index 403

List of Contributors

Amanda M. Acevedo, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Zeba Ahmad, Icahn School of Medicine at Mount Sinai, New York, NY, USA Leona S. Aiken, Department of Psychology, Arizona State University, Tempe, AZ, USA Olusola Ajilore, Department of Psychiatry, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA Damla E. Aksen, Department of Psychology, Binghamton University, Binghamton, NY, USA Kristi Alexander, Department of Psychology, University of Central Florida, Orlando, FL, USA Andrea G. Alioto, Palo Alto University, Palo Alto, CA, USA Department of Neurology, University of California, Davis, CA, USA Michael L. Alosco, Boston University Alzheimer’s Disease Center and Boston University CTE Center, Department of Neurology, Boston University School of Medicine, Boston, MA, USA Christina M. Amaro, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA Judith Andersen, Department of Psychology, University of Toronto, Mississauga, ON, Canada Aviva H. Ariel, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Danielle Arigo, Psychology Department, The University of Scranton, Scranton, PA, USA Jamie Arndt, Psychology Department, University of Missouri System, Columbia, MO, USA Robert C. Arnold, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA Melissa V. Auerbach, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Lisa Auster‐Gussman, Department of Psychology, University of Minnesota, Minneapolis, MN, USA Aya Avishai, Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Hoda Badr, Icahn School of Medicine at Mount Sinai, New York, NY, USA David E. Balk, Department of Health and Nutrition Sciences, Brooklyn College of the City University of New York, Brooklyn, NY, USA

x

List of Contributors

Kirk Ballew, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Department of Communication, University of Illinois at Urbana‐Champaign College of Liberal Arts and Sciences, Urbana, IL, USA Diletta Barbiani, Department of Neuroscience, University of Turin Medical School, Turin, Italy Hypoxia Medicine, Plateau Rosà, Switzerland Jeffrey E. Barnett, College of Arts and Sciences, Loyola University Maryland, Baltimore, MD, USA Petronilla Battista, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Institute of Bari, Italy Brittney Becker, Psychology Department, Texas A&M University System, College Station, TX, USA Alexandra A. Belzer, Colby College, Waterville, ME, USA Fabrizio Benedetti, Department of Neuroscience, University of Turin Medical School, Turin, Italy Hypoxia Medicine, Plateau Rosà, Switzerland Danielle S. Berke, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA Mamta Bhatnagar, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA Kelly Biegler, Department of Psychological Science, University of California, Irvine, CA, USA Susan J. Blalock, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Brittany E. Blanchard, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Jennifer B. Blossom, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA Ryan Bogdan, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA Jacob Bolzenius, Missouri Institute of Mental Health, University of Missouri‐St. Louis, St. Louis, MO, USA Bruce Bongar, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Dianna Boone, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Jerica X. Bornstein, Department of Human Development and Family Sciences, University of Texas at Austin, Austin, TX, USA Joaquín Borrego Jr., Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Kyle J. Bourassa, Department of Psychology, University of Arizona, Tucson, AZ, USA Lara A. Boyd, Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada Patrick Boyd, Department of Psychology, University of South Florida, Tampa, FL, USA Adam M. Brickman, Columbia University Medical Center, New York, NY, USA Athena Brindle, Department of Psychology, College of Science, The University of Texas at Arlington, Arlington, TX, USA

List of Contributors

xi

Jennifer L. Brown, Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA Kathleen S. Brown, Private Practice, Fort Myers, FL, USA Wilson J. Brown, Pennsylvania State University, The Behrend College, Erie, PA, USA Steven E. Bruce, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA Angela D. Bryan, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Katherine R. Buchholz, VA Boston Healthcare System, Boston, MA, USA National Center for Post Traumatic Stress Disorder, White River Junction, VT, USA Claire Burgess, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA Edith Burns, Medical College of Wisconsin, Milwaukee, WI, USA Rachel J. Burns, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada Johannes B.J. Bussmann, Erasmus MC University Medical Center Rotterdam, CA Rotterdam, South Holland, The Netherlands Fawn C. Caplandies, Department of Psychology, University of Toledo, Toledo, OH, USA Angela L. Carey, Psychology Department, University of Arizona, Tucson, AZ, USA Brandon L. Carlisle, Department of Psychiatry, University of California, San Diego, CA, USA Delesha M. Carpenter, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Allison J. Carroll, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Charles S. Carver, Department of Psychology, University of Miami, Coral Gables, FL, USA Center for Advanced Study in the Behavioral Sciences, Stanford University, Stanford, CA, USA Isabella Castiglioni, Dipartimento di Fisica “G. Occhialini”, Università degli Studi di Milano‐ Bicocca – Piazza della Scienza, Milan, Italy Gretchen B. Chapman, Department of Psychology, Rutgers University–New Brunswick, New Brunswick, NJ, USA Alyssa C.D. Cheadle, Psychology Department, Hope College, Holland, MI, USA Rebecca E. Chesher, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA William J. Chopik, Psychology Department, Michigan State University, East Lansing, MI, USA Nicholas J.S. Christenfeld, Department of Psychology, University of California San Diego, La Jolla, CA, USA L.E. Chun, University of Colorado, Boulder, CO, USA Briana Cobos, Department of Psychology,Texas State University, San Marcos, TX, USA Alex S. Cohen, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Jenna Herold Cohen, Department of Psychology, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA Lee M. Cohen, Department of Psychology, College of Liberal Arts, The University of Mississippi, Oxford, MS, USA Catherine Cook‐Cottone, Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, USA

xii

List of Contributors

Sarah A. Cooley, Department of Neurology, School of Medicine, Washington University in Saint Louis, St. Louis, MO, USA Stephen Correia, Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI, USA Patrick Corrigan, Department of Psychology, Illinois Institute of Technology College of Science, Chicago, IL, USA Juliann Cortese, School of Information, Florida State University, Tallahassee, FL, USA Riley Cropper, Stanford University Counseling and Psychological Services, Stanford Hospital and Clinics, Stanford, CA, USA Robert T. Croyle, National Cancer Institute, Bethesda, MD, USA Tegan Cruwys, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Sasha Cukier, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA Gregory Dahl, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Beth D. Darnall, Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA Savannah Davidson, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Elizabeth L. Davis, Department of Psychology, University of California, Riverside, CA,USA Rachael L. Deardorff, Center for Biomedical Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA Janet Deatrick, Department of Family & Community Health, University of Pennsylvania School of Nursing, Philadelphia, PA, USA Lauren Decaporale‐Ryan, Departments of Psychiatry, Medicine, & Surgery, University of Rochester Medical Center, Rochester, NY, USA Jordan Degelia, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Anita Delongis, Department of Psychology, University of British Columbia, Vancouver, BC, Canada Naiara Demnitz, Department of Psychiatry, University of Oxford, Oxford, UK Eliot Dennard, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Kristine M. Diaz, Walter Reed National Military Medical Center, Bethesda, MD, USA Sally S. Dickerson, Department of Psychology, Pace University, New York, NY, USA M. Robin DiMatteo, Department of Psychology, University of California Riverside, Riverside, CA, USA Sona Dimidjian, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Genevieve A. Dingle, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Laura J. Dixon, Department of Psychology, University of Mississippi, Oxford, MS, USA Michelle R. Dixon, Department of Psychology, Rutgers University Camden, Camden, NJ, USA Tiffany S. Doherty, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA Michael D. Dooley, Department of Psychology, University of California, Riverside, Riverside, CA, USA

List of Contributors

xiii

Tessa L. Dover, Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA Colleen Doyle, Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, USA Ira Driscoll, Department of Psychology, University of Wisconsin‐Milwaukee, Milwaukee, WI, USA Claudia Drossel, Department of Psychology, Eastern Michigan University College of Arts and Sciences, Ypsilanti, MI, USA Sean P.A. Drummond, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Christine Dunkel Schetter, Department of Psychology, University of California, Los Angeles, CA, USA William L. Dunlop, Psychology Department, University of California, Riverside, Riverside, CA, USA Jacqueline C. Duperrouzel, Florida International University, Miami, FL, USA Allison Earl, Department of Psychology, University of Michigan, Ann Arbor, MI, USA Amanda C. Eaton, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Ulrich W. Ebner‐Priemer, Karlsruhe Institute of Technology, Karlsruhe, Germany Robin S. Edelstein, Psychology Department, University of Michigan, Ann Arbor, MI, USA Jamie Edgin, Department of Psychology, The University of Arizona, Tucson, AZ, USA Bradley A. Edwards, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Sleep and Circadian Medicine Laboratory, Department of Physiology, School of Biomedical Sciences, Monash University, Melbourne, VIC, Australia Naomi I. Eisenberger, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Amber S. Emanuel, Psychology Department, University of Florida, Gainesville, FL, USA Patrick England, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA Ipek Ensari, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY, USA Emily Evans, Neurology Resident, Washington University in St. Louis, St. Louis, MO, USA James Evans, Department of Psychology, Binghamton University, Binghamton, NY, USA Anne M. Fairlie, Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Angelica Falkenstein, Department of Psychology, University of California, Riverside, Riverside, CA, USA Zachary Fetterman, Psychology Department, University of Alabama, Tuscaloosa, AL, USA Christopher Fink, Durham Veterans Affairs Medical Center, Durham, NC, USA North Carolina State University, Raleigh, NC, USA Alexandra N. Fisher, Psychology Department, University of Victoria, Victoria, BC, Canada Tabitha Fleming, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Tasheia Floyd, Washington University School of Medicine, St. Louis, MO, USA

xiv

List of Contributors

Lauren A. Fowler, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA Elinor Flynn, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Heather Walton Flynn, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA Elizabeth S. Focella, Department of Psychology, University of Wisconsin Oshkosh, Oshkosh, WI, USA Amy Frers, Department of Psychology and University Health Sciences Center, University of Colorado Denver, Denver, CO, USA Howard S. Friedman, Psychology Department, University of California Riverside, Riverside, CA, USA Paul T. Fuglestad, Department of Psychology, University of North Florida, Jacksonville, FL, USA Kentaro Fujita, Department of Psychology, The Ohio State University, Columbus, OH, USA Kristel M. Gallagher, Thiel College, Greenville, PA, USA Andrea M. Garcia, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA Paola García‐Egan, Department of Psychological Sciences, University of Missouri‐St. Louis, St. Louis, MO, USA Robert García, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA Casey K. Gardiner, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Robert J. Gatchel, Endowment Professor of Clinical Health Psychology Research, Department of Psychology, College of Science, The University of Texas at Arlington, Arlington, TX, USA Justine Megan Gatt, Neuroscience Research Australia, Sydney, NSW, Australia School of Psychology, University of New South Wales, Sydney, NSW, Australia Nicole K. Gause, Department of Psychology, University of Cincinnati, Cincinnati, OH, USA Ashwin Gautam, Department of Psychology, Binghamton University, Binghamton, NY, USA Fiona Ge, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA Andrew L. Geers, Department of Psychology, University of Toledo, Toledo, OH, USA Margaux C. Genoff, Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA James I. Gerhart, Department of Psychosocial Oncology, Cancer Integrative Medicine Program, Rush University Medical Center, Chicago, IL, USA Meg Gerrard, Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Frederick X. Gibbons, Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Arielle S. Gillman, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Marci E.J. Gleason, Department of Human Development and Family Sciences, University of Texas at Austin, Austin, TX, USA Teresa Gobble, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA

List of Contributors

xv

Department of Educational Psychology, University of Texas at Austin, Austin, TX, USA Katharina Sophia Goerlich, Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany Jamie L. Goldenberg, Department of Psychology, University of South Florida, Tampa, FL, USA Cesar A. Gonzalez, Mayo Clinic Minnesota, Rochester, MN, USA Raul Gonzalez, Florida International University, Miami, FL, USA Brittany F. Goodman, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA Janna R. Gordon, San Diego State University and SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA Edward C. Green, Department of Anthropology, The George Washington University, Washington, DC, USA Joseph P. Green, Department of Psychology, Ohio State University at Lima, Lima, OH, USA Michael G. Griffin, Department of Psychological Sciences and Center for Trauma Recovery, University of Missouri–St. Louis, St. Louis, MO, USA Steven Grindel, Medical College of Wisconsin, Milwaukee, WI, USA Kristina Gryboski, Project Hope, Millwood, VA, USA Suzy Bird Gulliver, Baylor and Scott and White Hospitals and Clinics, Waco, TX, USA School of Public Health, Texas A&M University, College Station, TX, USA Megan R. Gunnar, Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, USA Jenna Haddock, Department of Psychological Sciences, University of Missouri‐St. Louis, St. Louis, MO, USA Martin S. Hagger, Health Psychology and Behavioral Medicine Research Group, School of Psychology and Speech Pathology, Faculty of Health Sciences, Curtin University, Perth, WA, Australia Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland School of Applied Psychology and Menzies Health Institute Queensland, Behavioural Bases for Health, Griffith University, Brisbane, QLD, Australia Centre for Physical Activity Studies, School of Human, Health and Social Sciences, Central Queensland University, Rockhampton, QLD, Australia Jada G. Hamilton, Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA Paul K.J. Han, Center for Outcomes Research and Evaluation, Maine Medical Center Research Institute, Portland, ME, USA Grace E. Hanley, Psychology Department, University of California, Riverside, Riverside, CA, USA Madeline B. Harms, Department of Psychology, University of Wisconsin‐Madison, Madison, WI, USA Adam Harris, Department of Psychology and University Health Sciences Center, University of Colorado Denver, Denver, CO, USA Lauren N. Harris, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Peter R. Harris, School of Psychology, University of Sussex, Brighton & Hove, UK Philine S. Harris, School of Psychology, University of Sussex, Brighton & Hove, UK William Hart, Psychology Department, University of Alabama, Tuscaloosa, AL, USA M.J. Hartsock, University of Colorado, Boulder, CO, USA

xvi

List of Contributors

Kelly B. Haskard‐Zolnierek, Department of Psychology, Texas State University, San Marcos, TX, USA S. Alexandar Haslam, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Catherine Haslam, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Karen Hasselmo, Department of Psychology, University of Arizona, Tucson, AZ, USA Kathryn S. Hayward, Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada Stroke Division, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia NHMRC Centre for Research Excellence in Stroke Rehabilitation and Recovery, Parkville, VIC, Australia Jodi M. Heaps‐Woodruff, University of Missouri–St. Louis, St. Louis, MO, USA Missouri Institute of Mental Health, St. Louis, MO, USA Dietlinde Heilmayr, Psychology Department, Moravian College, Bethlehem, PA, USA Vicki S. Helgeson, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Joseph A. Helpern, Center for Biomedical Imaging, Department of Neuroscience, Department of Neurology, and Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA Garrett Hisler, Department of Psychology, Iowa State University, Armes, IA, USA Molly B. Hodgkins, Department of Psychological and Brain Sciences, University of Maine, Orono, ME, USA Michael Hoerger, Department of Psychology, Tulane University, New Orleans, LA, USA Jeanne S. Hoffman, US Department of the Army, Washington, DC, USA Stephanie A. Hooker, Psychology Department,University of Colorado at Denver – Anschutz Medical Campus, Aurora, CO, USA Vera Hoorens, Center for Social and Cultural Psychology, Katholieke Universiteit Leuven, Leuven, Belgium Arpine Hovasapian, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Jennifer L. Howell, Psychology Department, Ohio University, Athens, OH, USA Robert M. Huff, Department of Health Sciences, California State University, Northridge, Morgan Hill, CA, USA Jacob Huffman, Behavioral Neuroscience Program, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO, USA Lauren J. Human, Department of Psychology, McGill University, Montreal, QC, Canada Jeffrey M. Hunger, Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA Tanya Hunt, Department of Clinical Psychology, Palo Alto University, Palo Alto, California, USA Steven Hyde, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA Christina Di Iorio, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA

List of Contributors

xvii

Jamie M. Jacobs, Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA Lauren James, Department of Psychology, University of North Florida, Jacksonville, FL, USA Rebekah Jazdzewki, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Brooke N. Jenkins, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Jens H. Jensen, Center for Biomedical Imaging, Department of Neuroscience, and Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA Donna C. Jessop, School of Psychology, University of Sussex, Brighton & Hove, UK Jolanda Jetten, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Kimberly Johnson, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA Doerte U. Junghaenel, USC Dornsife Center for Self‐Report Science, University of Southern California, Los Angeles, CA, USA Vanessa Juth, The Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA Samantha Kahn, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA David A. Kalkstein, Department of Psychology, New York University, New York, NY, USA Alexander Karan, Department of Psychology, University of California, Riverside, CA, USA Natalia M. Kecala, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri–St. Louis, St. Louis, MO, USA Robert G. Kent de Grey, Department of Psychology, University of Utah, Salt Lake City, UT, USA Margaret L. Kern, Center for Positive Psychology, Graduate School of Education, University of Melbourne, Parkville, VIC, Australia Nathan A. Kimbrel, Department of Veterans Affairs’ VISN 6 Mid‐Atlantic MIRECC, Durham, NC, USA Durham Veterans Affairs Medical Center, Durham, NC, USA Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA Tricia Z. King, The Neuroscience Institute, Georgia State University, Atlanta, GA, USA Nancy Kishino, West Coast Spine Restoration, Riverside, CA, USA Ekaterina Klyachko, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA Brian Konecky, Boise VAMC Virtual Integrated Multisite Patient Aligned Care Team Hub, Boise, ID, USA Gerald P. Koocher, Department of Psychology, DePaul University, Chicago, IL, USA Laura E. Korthauer, Department of Psychology, University of Wisconsin‐Milwaukee, Milwaukee, WI, USA Abigail O. Kramer, Palo Alto University, Palo Alto, CA, USA Weill Institute for Neurosciences, University of California, San Francisco Memory and Aging Center, San Francisco, CA, USA Joel H. Kramer, Weill Institute for Neurosciences, University of California, San Francisco Memory and Aging Center, San Francisco, CA, USA Zlatan Krizan, Department of Psychology, Iowa State University, Armes, IA, USA

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List of Contributors

Monica F. Kurylo, Departments of Medicine and Public Health, University of Kansas Medical Center, Kansas City, KS, USA Onawa P. LaBelle, Psychology Department, University of Michigan, Ann Arbor, MI, USA Melissa H. Laitner, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Mark J. Landau, Department of Psychology, University of Kansas, Lawrence, KS, USA Shane Landry, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Sleep and Circadian Medicine Laboratory, Department of Physiology, School of Biomedical Sciences, Monash University, Melbourne, VIC, Australia Kevin T. Larkin, Department of Psychology, West Virginia University, Morgantown, WV, USA Jason M. Lavender, Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA Kristin Layous, Department of Psychology, California State University East Bay, Hayward, CA, USA Thanh Le, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Thad R. Leffingwell, Department of Psychology, Oklahoma State University, Stillwater, OK, USA Angela M. Legg, Department of Psychology, Pace University, New York, NY, USA Jana Lembke, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA Elizabeth Lemon, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Richie L. Lenne, Psychology Department, University of Minnesota Twin Cities, Minneapolis, MN, USA Alex Leow, Department of Psychiatry, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA Department of Bioengineering, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA Howard Leventhal, Department of Psychology, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA Linda J. Levine, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Melissa A. Lewis, Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Meng Li, Department of Health and Behavior Sciences, University of Colorado Denver, Denver, CO, USA Huijun Liao, Department of Radiology, Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Alexander P. Lin, Department of Radiology, Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Wendy P. Linda, School of Psychology, Fairleigh Dickinson University, Teaneck, NJ, USA Nikolette P. Lipsey, Psychology Department,University of Florida, Gainesville, FL, USA Dana M. Litt, Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA

List of Contributors

xix

Andrew K. Littlefield, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Brett Litz, Department of Psychology, Boston University & VA Boston Healthcare System, Boston, MA, USA Marci Lobel, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Todd Lucas, C. S. Mott Endowed Professor of Public Health Associate Professor of Epidemiology and Biostatistics Division of Public Health College of Human Medicine Michigan State University, Wayne State University, Detroit, MI, USA Mia Liza A. Lustria, School of Information, Florida State University, Tallahassee, FL, USA Steven Jay Lynn, Department of Psychology, Binghamton University, Binghamton, NY, USA Salvatore R. Maddi, Department of Psychological Science, University of California, Irvine, Irvine, CA, USA Renee E. Magnan, Washington State University Vancouver, Vancouver, WA, USA Brenda Major, Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA Vanessa L. Malcarne, San Diego State University, UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA UC San Diego Moores Cancer Center, San Diego, CA, USA Department of Psychology, San Diego State University, San Diego, CA, USA Kelsey A. Maloney, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Andrew W. Manigault, Psychology Department, Ohio University, Athens, OH, USA Traci Mann, Psychology Department, University of Minnesota Twin Cities, Minneapolis, MN, USA Soe Mar, Neurology and Pediatrics, Division of Pediatric Neurology, Washington University in St. Louis, St. Louis, MO, USA Shannon M. Martin, Department of Psychology, Central Michigan University, Mount Pleasant, MI, USA Allison Marziliano, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Katilyn Mascatelli, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Tyler B. Mason, Department of Psychology, University of Southern California, Los Angeles, CA, USA Neuropsychiatric Research Institute, Fargo, ND, USA Kevin S. Masters, Psychology Department, University of Colorado at Denver  –  Anschutz Medical Campus, Aurora, CO, USA Kevin D. McCaul, North Dakota State University, Fargo, ND, USA Tara P. McCoy, Psychology Department, University of California, Riverside, Riverside, CA, USA Michael McCrea, Brain Injury Research Program, Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA Scout N. McCully, Department of Psychological Sciences, Kent State University, Kent OH, USA Don McGeary, Department of Psychiatry, Department of Family and Community Medicine, The University of Texas Health Science Center San Antonio, San Antonio, TX, USA

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List of Contributors

Jessica McGovern, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Robert E. McGrath, School of Psychology, Farleigh Dickinson University, Teaneck, NJ, USA Michael P. Mead, North Dakota State University, Fargo, ND, USA Alan Meca, Department of Psychology, Old Dominion University, Norfolk, VA, USA Matthias R. Mehl, Psychology Department, University of Arizona, Tucson, AZ, USA Timothy P. Melchert, Department of Psychology, Marquette University, Milwaukee, WI, USA Paola Mendoza‐Rivera, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Maria G. Mens, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Sai Merugumala, Department of Radiology, Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Rebecca A. Meza, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Dara Mickschl, Medical College of Wisconsin, Milwaukee, WI, USA Tricia A. Miller, Santa Barbara Cottage Hospital, Santa Barbara, CA, USA Department of Psychology, University of California Riverside, Riverside, CA, USA Kyle R. Mitchell, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Mona Moieni, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Phillip J. Moore, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA DeAnna L. Mori, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA Sandra B. Morissette, Department of Psychology, The University of Texas at San Antonio College of Sciences, San Antonio, TX, USA Amber Morrow, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Ariel J. Mosley, Department of Psychology, University of Kansas, Lawrence, KS, USA Robert W. Motl, Department of Physical Therapy, University of Alabama at Birmingham, Birmingham, AL, USA Laurent Mottron, Department of Psychiatry, Faculty of Medicine, Universite de Montreal, Montreal, QC, Canada Anne Moyer, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Elaine M. Murphy, Population Reference Bureau, Washington, DC, USA Carolyn B. Murray, Psychology, University of California, Riverside, CA, USA Christopher S. Nave, Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA Department of Psychology, Rutgers University Camden, Camden, NJ, USA Eve‐Lynn Nelson, KU Center for Telemedicine & Telehealth, University of Kansas Medical Center, University of Kansas, Kansas City, KS, USA Steven M. Nelson, VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX, USA Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA

List of Contributors

xxi

Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA Elizabeth Neumann, Medical College of Wisconsin, Milwaukee, WI, USA Jason L. Neva, Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada Jody S. Nicholson, Department of Psychology, University of North Florida, Jacksonville, FL, USA Christina Nisson, Department of Psychology, University of Michigan, Ann Arbor, MI, USA Mary‐Frances O’Connor, Psychology Department, University of Arizona, Tucson, AZ, USA Laura O’Hara, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Gbolahan O. Olanubi, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Nils Olsen, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA Yimi Omofuma, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Elizabeth Ortiz‐Gonzalez, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Ileana Pacheco‐Colón, Florida International University, Miami, FL, USA Aliza A. Panjwani, Graduate School and University Center, City University of New York, New York, NY, USA Mike C. Parent, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Department of Educational Psychology, University of Texas at Austin, Austin, TX, USA Thomas J. Parkman, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Robert H. Paul, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Missouri Institute of Mental Health, St. Louis, MO, USA Stephanie A. Peak, Battelle Memorial Institute, Columbus, OH, USA Michael G. Perri, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Laura Perry, Department of Psychology, Tulane University, New Orleans, LA, USA Alyssa Phelps, Boston University Alzheimer’s Disease Center and Boston University CTE Center, Department of Neurology, Boston University School of Medicine, Boston, MA, USA Carissa L. Philippi, Department of Psychological Sciences, University of Missouri‐St Louis, St Louis, MO, USA L. Alison Phillips, Iowa State University, Ames, IA, USA Paula R. Pietromonaco, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA Craig P. Pollizi, Department of Psychology, Binghamton University, Binghamton, NY, USA Seth D. Pollak, Department of Psychology, University of Wisconsin‐Madison, Madison, WI, USA David B. Portnoy, US Food and Drug Administration, Silver Spring, MD, USA Rachel Postupack, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA

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List of Contributors

Matthew R. Powell, Division of Neurocognitive Disorders, Department of Psychiatry & Psychology, Mayo Clinic Minnesota, Rochester, MN, USA Candice Presseau, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA Sarah D. Pressman, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Rebecca N. Preston‐Campbell, Missouri Institute of Mental Health, St. Louis, MO, USA Isabel F. Ramos, Department of Psychology, University of California, Los Angeles, CA, USA Christopher T. Ray, College of Health Sciences, Texas Woman’s University, Denton, TX, USA Amanda L. Rebar, Centre for Physical Activity Studies, School of Human, Health and Social Sciences, Central Queensland University, Rockhampton, QLD, Australia Allecia E. Reid, Colby College, Waterville, ME, USA May Reinert, Department of Psychology, Pace University, New York, NY, USA Kelly E. Rentscher, Psychology Department, University of Arizona, Tucson, AZ, USA Tracey A. Revenson, Department of Psychology, Hunter College, New York, NY, USA C. Steven Richards, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Laura River, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Megan L. Robbins, Department of Psychology, University of California, Riverside, CA, USA Michael C. Roberts, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA John D. Robinson, Department of Surgery and Department of Psychiatry and Behavioral Sciences, Howard University College of Medicine/Hospital, Washington, DC, USA Theodore F. Robles, Psychology Department, University of California Los Angeles, CA, USA Catherine Rochefort, Department of Psychology, Southern Methodist University, Dallas, TX, USA Daniel G. Rogers, Department of Psychology, University of Mississippi, MS, USA John L. Romano, Department of Educational Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA Karen S. Rook, Department of Psychological Science, University of California, Irvine, CA, USA Ann Rosenthal, Medical College of Wisconsin, Milwaukee, WI, USA J. Megan Ross, Florida International University, Miami, FL, USA Tania L. Roth, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA Alexander J. Rothman, Department of Psychology, University of Minnesota, Minneapolis, MN, USA Ronald H. Rozensky, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Kristen L. Rudd, Department of Psychology, University of California, Riverside, Riverside, CA, USA Kirstie K. Russell, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada Arseny A. Ryazanov, Department of Psychology, University of California San Diego, La Jolla, CA, USA

List of Contributors

xxiii

Napapon Sailasuta, Centre for Addiction and Mental Health, Toronto, ON, Canada Stella Sakhon, Department of Psychology, The University of Arizona, Tucson, AZ, USA Lauren E. Salminen, Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA Christian Salvatore, Scuola Universitaria Superiore IUSS Pavia, Piazza della Vittoria, Pavia, Italy DeepTrace Technologies S.R.L., Via Conservatorio, Milan, Italy Peter M. Sandman, Department of Human Ecology, Rutgers University New Brunswick, New Brunswick, NJ, USA Keith Sanford, Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA David A. Sbarra, Department of Psychology, University of Arizona, Tucson, AZ, USA Michael F. Scheier, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Adam T. Schmidt, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Elana Schwartz, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Esther N. Schwartz, Department of Family and Community Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA Seth J. Schwartz, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA Ralf Schwarzer, Health Psychology, Freie Universität Berlin, Berlin, Germany Health Psychology, Australian Catholic University, Melbourne, VIC, Australia Richard J. Seime, Mayo Clinic Minnesota, Rochester, MN, USA Claire E. Sexton, Department of Psychiatry, University of Oxford, Oxford, UK Louise Sharpe, School of Psychology, Faculty of Science, The University of Sydney, Sydney, NSW, Australia Lindsay Sheehan, Department of Psychology, Illinois Institute of Technology College of Science, Chicago, IL, USA Paschal Sheeran, Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Daniel Sheinbein, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA James A. Shepperd, Psychology Department, University of Florida, Gainesville, FL, USA Brittany N. Sherrill, Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA Molin Shi, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Emily W. Shih, Department of Psychology, University of California, Riverside, CA, USA Ben W. Shulman, Psychology Department, University of California, Los Angles, CA, USA Elizabeth Silbermann, Neurology Resident, Washington University in St. Louis, St. Louis, MO, USA Department of Neurology, Oregon Health and Science University, Portland, OR, USA Roxane Cohen Silver, Department of Psychological Science, University of California, Irvine, Irvine, CA, USA Colleen A. Sloan, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA

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List of Contributors

Rachel Smallman, Psychology Department, Texas A&M University System, College Station, TX, USA Kathryn E. Smith, Neuropsychiatric Research Institute, Fargo, ND, USA Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA Patrick J. Smith, Duke University Medical Center, Durham, NC, USA Todd A. Smitherman, Department of Psychology, University of Mississippi, University, MS, USA Joshua M. Smyth, The Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA Dara H. Sorkin, Department of Psychological Science, University of California, Irvine, CA, USA R. L. Spencer, University of Colorado, Boulder, CO, USA Melissa Spina, Psychology Department, University of Missouri System, Columbia, MO, USA Bonnie Spring, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA Annette L. Stanton, Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA Ellen Stephenson, Department of Psychology, University of British Columbia, Vancouver, BC, Canada Robert A. Stern, Boston University Alzheimer’s Disease Center and Boston University CTE Center, Department of Neurology, Boston University School of Medicine, Boston, MA, USA Department of Neurosurgery and Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, USA Angela K. Stevens, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Courtney J. Stevens, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Elizabeth M. Stewart, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Danu Anthony Stinson, Psychology Department, University of Victoria, Victoria, BC, Canada Michelle L. Stock, Department of Psychological and Brain Sciences, George Washington University, Washington, DC, USA Arthur A. Stone, USC Dornsife Center for Self‐Report Science, University of Southern California, Los Angeles, CA, USA Department of Psychology, Dana and David Dornsife College of Letters Arts and Sciences, University of Southern California, Los Angeles, CA, USA Jeff Stone, Psychology Department, University of Arizona, Tucson, AZ, USA John A. Sturgeon, Center for Pain Relief, University of Washington, Seattle, WA, USA Kieran T. Sullivan, Department of Psychology, Santa Clara University, Santa Clara, CA, USA Amy Summerville, Psychology Department, Miami University, Oxford, OH, USA Jessie Sun, Center for Positive Psychology, Graduate School of Education, University of Melbourne, Parkville, VIC, Australia Department of Psychology, University of California, Davis, CA, USA Steve Sussman, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

List of Contributors

xxv

Trevor James Swanson, Department of Psychology, University of Kansas, Lawrence, KS, USA Allison M. Sweeney, Department of Psychology, University of South Carolina, Columbia, SC, USA Kate Sweeny, Department of Psychology, University of California, Riverside, Riverside, CA, USA Sergey Tarima, Medical College of Wisconsin, Milwaukee, WI, USA Carly A. Taylor, Boston Medical Center, Boston, MA, USA George T. Taylor, Behavioral Neuroscience Program, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Interfakultäre Biomedizinische Forschungseinrichtung (IBF) der Universität Heidelberg, Heidelberg, Germany A. Janet Tomiyama, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Victoria A. Torres, Department of Psychology, College of Liberal Arts, University of Mississippi, Oxford, MS, USA David R.M. Trotter, Department of Family and Community Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA Bert N. Uchino, Department of Psychology, University of Utah, Salt Lake City, UT, USA John A. Updegraff, Department of Psychological Sciences, Kent State University, Kent, OH, USA Jay Urbain, Milwaukee School of Engineering, Milwaukee, WI, USA Amy E. Ustjanauskas, San Diego State University, UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA UC San Diego Moores Cancer Center, San Diego, CA, USA Jason Van Allen, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Erika D. Van Dyke, Department of Sport Sciences, College of Physical Activity and Sport Sciences, West Virginia University, Morgantown, WV, USA Department of Psychology, Springfield College, Springfield, MA, USA Judy L. Van Raalte, Department of Psychology, Springfield College, Springfield, MA, USA Maryke Van Zyl‐Harrison, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Rachel Vanderkruik, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Elizabeth J. Vella, Department of Psychology, University of Southern Maine, Portland, ME, USA Birte von Haaren‐Mack, German Sport University Cologne, Köln, Nordrhein‐Westfalen, Germany David Von Nordheim, University of Missouri–St. Louis, St. Louis, MO, USA Missouri Institute of Mental Health, St. Louis, MO, USA Mikhail Votinov, Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany Institute of Neuroscience and Medicine (INM‐10), Jülich, Germany Rebecca K. Vujnovic, Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, USA Amy Wachholtz, Department of Psychology and University Health Sciences Center, University of Colorado Denver, Denver, CO, USA

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List of Contributors

Eric F. Wagner, Department of Epidemiology, Florida International University, Miami, FL, USA Ryan J. Walker, Psychology Department, Miami University, Oxford, Ohio, USA Rachel A. Wamser-Nanney, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA Britney M. Wardecker, Psychology Department, University of Michigan, Ann Arbor, MI, USA Melissa Ward‐Peterson, Department of Epidemiology, Florida International University, Miami, FL, USA Lisa Marie Warner, Health Psychology, Freie Universitat Berlin, Berlin, Germany Neil D. Weinstein, Department of Human Ecology, Rutgers University New Brunswick, New Brunswick, NJ, USA Suzanne E. Welcome, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Kristen J. Wells, San Diego State University and SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA Amy Wenzel, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA Private Practice, Rosemont, PA, USA Summer L. Williams, Department of Psychology, Westfield State University, Westfield, MA, USA Cynthia J. Willmon, Reno VA Sierra Nevada Health Care System, Reno, NV, USA Sara M. Witcraft, Department of Psychology, University of Mississippi, Oxford, MS, USA Stephen A. Wonderlich, Neuropsychiatric Research Institute, Fargo, ND, USA Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA E.R. Woodruff, University of Colorado, Boulder, CO, USA Patrick W. Wright, Neurology, Washington University in St. Louis, St. Louis, MO, USA Robert C. Wright, Department of Psychology, University of California, Riverside, CA, USA Shawna Wright, KU Center for Telemedicine & Telehealth, University of Kansas Medical Center, University of Kansas, Kansas City, KS, USA Tuppett M. Yates, Department of Psychology, University of California, Riverside, Riverside, CA, USA Julie D. Yeterian, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA Lok‐Kin Yeung, Columbia University Medical Center, New York, NY, USA Alex J. Zautra, Psychology Department, Arizona State University, Tempe, AZ, USA Colin A. Zestcott, Psychology Department, University of Arizona, Tucson, AZ, USA Peggy M. Zoccola, Psychology Department, Ohio University, Athens, OH, USA

Foreword

Until the 1970s, there were no books, journals, or university courses on health psychology. Although the field’s intellectual roots stretch back to the beginnings of psychology more than a century ago, its formal emergence depended on a convergence of influences (Friedman & Silver, 2007), including psychosomatic medicine, social‐psychological and socio‐anthropological perspectives on medicine, epidemiology, and medical and clinical psychology. Today, health psychology is a principal area of significant social science research and practice, with vital implications for the health and well‐being of individuals and societies. Understanding the explosive trajectory of health psychology is useful to appreciating the strengths of the field and to approaching this new encyclopedia, the Wiley Encyclopedia of Health Psychology. What is the nature of health? That is, what does it mean to be healthy? The way that this question is answered affects the behaviors, treatments, and resource allocations of individuals, families, health practitioners, governments, and societies. For example, if it is thought that you are healthy unless and until you contract a disease or suffer an injury, then attention and resources are primarily allocated toward “fixing” the problem through medications or surgical repairs. This is the traditional biomedical model of disease (sometimes called the “disease model”). Indeed, in the United States, the overwhelming allocation of attention and resources is to physicians (doing treatments) and to pharmaceuticals (prescription drugs and their development). In contrast, health psychology developed around a much broader and more interdisciplinary approach to health, one that is often termed the biopsychosocial model. The biopsychosocial model (a term first formally proposed by George Engel in 1968) brings together core elements of staying healthy and recovering well from injury or disease (Stone et al., 1987). Each individual—due to a combination of biological influences and psychosocial experiences—is more or less likely to thrive. Some of this variation is due to genetics and early life development; some depends on the availability and appropriate applications of medical treatments; some involves nutrition and physical activity; some depends on preparations for, perceptions of, and reactions to life’s challenges; and some involves exposure to or seeking out of healthier or unhealthier environments, both physically and socially. When presented in this way, it might seem obvious that health should certainly be viewed in this broader interdisciplinary way. However, by misdirecting its vast expenditures on health care, the United States gives its residents mediocre health at high cost (Kaplan, 2019). To approach these matters in a thorough manner, this encyclopedia includes four volumes, with Volume 1 focusing on the biological bases of health and health behavior, Volume 2 concentrating on the social bases, Volume 3 centering around the psychological and clinical aspects, and Volume 4 focused more broadly on crosscutting and applied matters.

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Foreword

Key parts of health depend on biological characteristics and how they interact with our experiences and environments. So, for example, in Volume 1, there are articles on injury to the brain, alcohol effects on the brain, nutrition, drug abuse, psychophysiology, and the tools and key findings of neuroscience. Note that even individuals with the same genes (identical twins) can and do have different health and recovery outcomes, and the articles delve into such ­complexities of health. Of course humans are also social creatures, and people’s growth, development, and health behaviors take place in social contexts. So, in Volume 2, there are articles on such fundamental matters as social support, coping, spirituality, emotion, discrimination, communication, psychosocial stress, and bereavement. Because our approaches to and conceptions of health are heavily influenced by society’s institutions and structures revolving around medical care, much of health psychology derives from or intersects with clinical psychology and applied behavioral medicine. Volume 3 covers such clinical topics as psycho‐oncology, depression, drug abuse, chronic disease, eating disorders, and the psychosocial aspects of coronary heart disease. Finally and importantly, there are a number of special and cross‐cutting matters that are considered in Volume 4, ranging from relevant laws and regulations to telehealth and health disparities. Taken together, the articles triangulate on what it truly means to be healthy. What are the most promising directions for the future that emerge from a broad and deep approach to health? That is, to where do these encyclopedia articles point? One key clue arises from a unique opportunity to enhance, extend, and analyze the classic Terman study of children who were followed and studied throughout their lives (Friedman & Martin, 2012). These studies revealed that there are lifelong trajectories to health, thriving, and longevity. Although ­anyone can encounter bad luck, a number of basic patterns emerged that are more likely to lead to good health. That is, for some individuals, certain earlier life characteristics and circumstances help propel them on pathways of healthier and healthier behaviors, reactions, relationships, and experiences, while others instead face a series of contingent stumbling blocks. There are multipart but nonrandom pathways across time linking personalities, health behaviors, social groups, education, work environments, and health and longevity. The present encyclopedia necessarily is a compendium of summaries of the relevant elements of health and thriving, but one that would and can profitably be used as a base to synthesize the long‐term interdependent aspects of health. In sum, this encyclopedia is distinctive in its explicit embrace of the biopsychosocial approach to health, not through lip service or hand‐waving but rather through highly detailed and extensive consideration of the many dozens of topics crucial to this core interdisciplinary understanding.

References Friedman, H. S., & Martin, L. R. (2012). The longevity project: Surprising discoveries for health and long life from the landmark eight‐decade study. New York: Penguin Plume Press. Friedman, H. S., & Silver, R. C. (Eds.) (2007). Foundations of health psychology. New York: Oxford University Press. Kaplan, R. M. (2019). More than medicine: The broken promise of American health. Cambridge, MA: Harvard University Press. Stone, G., Weiss, M., Matarazzo, J. D., Miller, N. E., Rodin, J., Belar, C. D., … Singer, J. E. (Eds.) (1987). Health psychology: A discipline and a profession. University of Chicago Press.

Howard S. Friedman University of California, Riverside August 15, 2019

Preface Volume 1: Biological Bases of Health

The field of health psychology is a specialty area that draws on how biology, psychology, behavior, and social factors influence health and illness. Despite the fact that the formal recognition of the field is only about a half of a century old, it has established itself as a major scientific and clinical discipline. The primary reason for this is that there have been a number of significant advances in psychological, medical, and physiological research that have changed the way we think about health, wellness, and illness. However, this is only the tip of the iceberg. We have the field of health psychology to thank for much of the progress seen across our ­current healthcare system. Let me provide some examples that illustrate the diversity of these important contributions. Starting with a broad public health perspective, health psychologists have been involved in how our communities are planned and how urban development has a significant and direct impact on our health behaviors. That is, if we live close to where we work, play, and shop, we are more likely to walk or bike to our destination rather than drive or take public transportation. Focusing on a smaller, but no less important, source of data, health psychologists are involved in using information obtained from our genes to help counsel individuals to make good, well‐informed health decisions that could have an impact on the individual immediately or in the future. Being well‐informed can direct a person toward the best possible path toward wellness, whether it be measures aimed at prevention, further monitoring, or intervention. Of course, there are many other contributions. For instance, data indicates that several of the leading causes of death in our society can be prevented or delayed (e.g., heart disease, respiratory disease, cerebrovascular disease, and diabetes) via active participation in psychological interventions. Knowing that we can improve health status by changing our behaviors seems like an easy “fix,” but we know that behavior change is tough. As such, it is not surprising that health psychologists have been involved in trying to improve upon treatment success by examining patient compliance. When we better understand what motivates and discourages people from engaging in treatment or pro‐health behaviors, we can improve upon compliance and help individuals adopt more healthy lifestyles. Health psychologists have also played a role  in shaping healthcare policy via identifying evidence‐based treatments. This work has direct effects on how individuals receive healthcare as well as what treatments are available/ reimbursable by insurance companies. Finally, there are also factors beyond medical care to consider (e.g., economic, educational) that can lead to differential health outcomes. Thus, health psychologists likewise examine ways to reduce health disparities, ensuring that the ­public and government officials are made aware of the impact of the social determinants of health.

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Preface Volume 1: Biological Bases of Health

I could go on and on, however, examples like this illustrate the significant impact this broad and exciting field has had (and will continue to have) on our understanding of health and wellness. Given the constantly changing nature of the field, it is not possible to be all inclusive; however, the aim of these four volumes is to provide readers an up‐to‐date overview of the field. Each entry is written to stand alone for those who wish to learn about a specific topic, and if the reader is left wanting more, suggested readings are provided to expand one’s knowledge. Volume I, Biological Bases of Health Behavior, includes entries that cover topics in the broad areas of neuroscience and biopsychology relevant to health behavior. General topics include degenerative and developmental conditions, emerging methodologies available in clinical research, functional anatomy and imaging, and gene × environment interactions. Volume II, Social Bases of Health Behavior, addresses topics related to theories and concepts derived from social psychology. Specifically, topics related to the self, social cognition, social perception, attitudes and attitude change, perception, framing, and pro‐health behaviors are included. Volume III, Clinical Health Psychology and Behavioral Medicine, covers the applied aspects of the field of health psychology including practical topics that clinical health psychologists face in the workplace, behavioral aspects of medical conditions, the impact of unhealthy behaviors, and issues related to the comorbidity of psychiatric disorders and chronic health concerns. Finally, Volume IV, Special Issues in Health Psychology, contains a wide array of ­topics that are worthy of special consideration in the field. Philosophical and conceptual issues are discussed, along with new approaches in delivering treatment and matters to consider when working with diverse and protected populations. It is my sincere hope that the Wiley Encyclopedia of Health Psychology will serve as a comprehensive resource for academic and applied psychologists, other health care professionals interested in the relationship of psychological and physical well‐being, and students across the health professions. I would very much like to thank and acknowledge all those who have made this work possible including Michelle McFadden, the Wiley editorial and production teams, the volume editors, and of course all of the authors who contributed their outstanding work. Lee M. Cohen, PhD

Preface Volume 1: Biological Basis of Behavior

The chapters of this volume focus on the neural and molecular mechanisms of the ­biopsychosocial model of behavioral health. The work detailed herein is timely, as the scientific pendulum is shifting away from the long‐standing research aimed at dismantling complex clinical phenotypes into biological components as the pathway to discover biological mechanisms. The common reductionistic approach has yielded no curative strategies for children and adults affected by the most vexing neuropsychiatric and neurodegenerative conditions. At the same time, a worsening incidence of behavioral health disorders and associated reductions in health‐adjusted life expectancies worldwide raises concern that behavioral health conditions represent a looming global health crisis. This volume summarizes the current state of science on the biological bases of behavioral health, with a focus on advanced conceptual models that prioritize the integration of numerous biological, psychological, and sociocultural interdependencies inherent to developmental conditions (e.g., autism spectrum disorder, Down syndrome, language‐based learning disorder), contextual factors that alter neurodevelopmental trajectories (e.g., obesity, illicit drug use, psychosocial stress, traumatic brain injury, infectious disease), and age‐related neurodegenerative conditions (e.g., cerebrovascular disease, Alzheimer’s disease). Technological advances are reviewed, including neuroimaging methods capable of visualizing the anatomical and physiological basis of behavioral health disorders that have high potential to improve ­diagnostic classification and monitoring of patient care outcomes (i.e., precision medicine). Technologies and computational innovations introduced during the past three decades ­following the “Decade of the Brain” in 1990 and completion of the Human Genome Project in 1993 offer a viable path to create much needed scientific breakthroughs that are difficult to achieve using traditional research methods designed more to confirm hypotheses than to ­foster new insights and discovery. Data science offers a new opportunity to identify nonlinear and linear patterns and highly dimensional interactions across explanatory variables that more accurately model the intrinsic features of the biopsychosocial determinants of behavior. Combined with new advances in diagnostic technologies (e.g., ligands for positron emission tomography) and intervention strategies (e.g., transcranial magnetic stimulation), the current scientific momentum has potential to transform behavioral health outcomes on a global scale. Robert Paul St. Louis, MO, USA

Editor‐in‐Chief Acknowledgments

I would like to publicly recognize and thank my family, Michelle, Ross, Rachel, and Becca for being supportive of my work life while also providing me with a family life beyond my wildest dreams. I love you all very much. I would also like to note my great appreciation and thanks to the faculty and staff of the Doctoral Training Program in Clinical Psychology at Oklahoma State University for providing me with an opportunity to expand my education and for providing extraordinary training. In particular, I would like to thank my late mentor Dr. Frank L. Collins Jr., Dr. Larry Mullins, Dr. John Chaney, and Patricia Diaz Alexander. Further, I am grateful to the University of Mississippi and Texas Tech University (and the colleagues and students I have had the privilege to work alongside) for affording me excellent working environments and the support to do the work I am honored to be a part of. Finally, I would like to recognize and thank the volume editors for their vision and perseverance to this project as well as to each of the contributors for their excellent entries and their dedication to this very important field. Lee M. Cohen, Ph.D. Editor‐in‐Chief

Brain Development from Conception to Adulthood Jodi M. Heaps‐Woodruff and David Von Nordheim Missouri Department of Mental Health, University of Missouri–St. Louis, St. Louis, MO, USA Missouri Institute of Mental Health, St. Louis, MO, USA

Brain Gray Matter and White Matter Development The development of the central nervous system (CNS) begins approximately 17 days after conception. Arranged in a flat disk with three layers, the cells that comprise the topmost layer, the ectoderm, forms the neural plate and provides the foundation of the developing nervous system. Within 22–25 days after conception, the cells of this plate fold into a groove that forms the neural tube, the precursor to the brain and spinal cord. Subsequent development leverages a pool of undifferentiated cells that lack functional specificity until the cells migrate to predetermined regions. This process is guided by progenitor cells that release attraction molecules to guide the formation of new synapses. Beyond the first year of life, brain cells undergo ongoing refinement of existing connections (Institute of Medicine and National Academy of Sciences, 1992) and programmed cell death to eliminate connections that are not regularly activated. In a comprehensive review, Sowell et al. (2003) describes the discordant pattern of gray and white matter development across the lifespan. Gray matter volumes increase exponentially ­during infancy until about 18 months after birth, when the rate continues to increase, albeit more slowly, until the late 20s. Peak gray matter volume is generally achieved in the third ­decade followed by progressive reductions in volume thereafter. By contrast, the volume of white matter increases into the fourth decade followed by progressive decline. Given the ­difference in developmental maturation of the gray and white matter, total brain volume is a poor index of brain integrity, especially during the third and fourth decade. The brain accounts for 2–3% of total body mass while consuming 20% of total glucose needs. These demands are even greater from birth through late adolescence. The metabolic rate of glucose in the brain (CGMR) doubles from birth to 6 months, with a fourfold increase The Wiley Encyclopedia of Health Psychology: Volume 1: Biological Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editor: Robert H. Paul. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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from birth through the second year of life. Glucose needs remain high until adolescence, with peak levels accounting for almost half of the total energy needs of the body (Goyal & Raichle, 2018). These data underscore the high resource demands of the developing brain in children and adolescents (Kuzawa et al., 2014).

Critical Time Windows of Brain Development Development does not occur in a vacuum. Genetic, epigenetic, and environmental factors (Bale, 2015; Fox, Levitt, & Nelson, 2010) are potentially powerful moderators of neurodevelopment. During prenatal development, environmental factors can produce permanent damage to brain structure and connections. Preliminary research suggests that targeted interventions applied during the first years of life have potential to mitigate these deleterious abnormalities, but the durability of treatment outcomes remains unclear. Many studies indicate that the prenatal environment strongly influences development, ­particularly maternal stress and immunity. A recent review by Lindsay, Buss, Wadhwa, and Entringer (2018) identifies associations between maternal anxiety, depression, elevated stress hormones, and alterations in both brain structure and function. Maternal diet may have a protective effect on prenatal development. Preliminary data indicate a link between certain high fat diets and reduced maternal stress. Additionally, an antioxidant‐rich diet may also ­mitigate the effects of maternal stress on fetal brain development. Similarly, diets high in ­antioxidants, nutrients, and certain fatty acids (polyunsaturated), along with exercise and engagement in mental activities, can stimulate adult neurogenesis (Poulose, Miller, Scott, & Shukitt‐Hale, 2017). A sensitive developmental window also occurs around ages 5–7, a time when many children residing in resourced countries are exposed to new pathogens and social stressors at school. During puberty, a great deal of physical and chemical change is taking place in the body. Some brain regions are nearing maturity, whereas others are continuing to undergo dynamic change. Early adolescence is considered the end of a critical period for language acquisition. However, the frontal and prefrontal cortices that mediate reasoning and judgment are still developing. These structures are particularly sensitive to alcohol and drugs both in terms of damage and increased propensity for substance use disorders in adulthood. Early adulthood represents yet another sensitive period, as realized by the onset of chronic mental illness during the second to third decade of life (e.g., schizophrenia).

Moderating Factors of Brain Development The impact of the gut microbial environment on brain health has been a fairly recent area of study. Research with germ‐free mice suggests altered neurogenesis in a number of brain structures relative to a control group. In both the hippocampus and the amygdala, the resulting alterations consist of increased volumes as the result of increased neurogenesis; however, in the hippocampus, fewer dendritic spines were observed, whereas there were more spines evident in the amygdala of germ‐free mice. Additionally, hypermyelination in the prefrontal cortex has also been reported in germ‐free mice. In addition to effects on brain structure, some bacteria have the ability to synthesize neurotransmitters (NTs). As examples, Lactobacillus and Bifidobacterium are able to synthesize gamma‐aminobutyric acid (GABA), and norepinephrine has been produced by species of Escherichia, Bacillus, and Saccharomyces. Other species,

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such as Bifidobacterium infantis, may indirectly influence serotonin levels by increasing the levels of precursor molecule, tryptophan. A strong empirical base is emerging that implicates the gut–brain axis in numerous brain disorders including anxiety, autism, schizophrenia, Alzheimer’s disease, and Parkinson’s disease (for review see Dinan & Cryan, 2017). Another recent and active area of research examines the role of microglia in brain development and pathology. Microglia function as the immune cells of the nervous system, activating in the presence of injury or inflammation. Microglia facilitate the clearing of toxins and dying cells and support neurodevelopmental processes including modulation of neuronal apoptosis, cell proliferation, and synaptic activity. In studies of schizophrenia and autism, elevated levels of activated microglia have been observed, along with evidence of perturbations in brain development. As with numerous other factors, it is unclear whether this abnormal microglia activity is responsible for altered brain development or whether the microglia are responding to other insults that have negatively impacted neurodevelopment (Bilimoria & Stevens, 2015). In this chapter we have reviewed the growth and development of the human brain, critical or sensitive periods in brain development, discordant gray and white matter maturation, and factors that influence neurodevelopment. The research literature in these areas is continuing to evolve to produce more complete explanatory models of brain development. Considerable research is needed on therapeutic targets to mitigate the negative effects of moderating factors on development to improve brain health across the lifespan.

Author Biographies Jodi M. Heaps‐Woodruff is a research neuropsychologist and health services researcher. She earned her PhD from the University of Missouri–St. Louis and is a research assistant professor at the Missouri Institute of Mental Health at the University of Missouri–St. Louis. Her research is focused on the cognitive aspects involved in recovery from mental illness and the impact of chronic disease on the brain. David Von Nordheim earned his Masters of Arts in Cognitive Psychology from the University of Nebraska–Omaha in May 2018. His research interests include memory, attention, word processing, and the cognitive psychology of addictive behaviors.

References Bale, T. L. (2015). Epigenetic and transgenerational reprogramming of brain development. Nature Reviews Neuroscience, 16(6), 332. Bilimoria, P. M., & Stevens, B. (2015). Microglia function during brain development: New insights from animal models. Brain Research, 1617, 7–17. Dinan, T. G., & Cryan, J. F. (2017). Gut instincts: Microbiota as a key regulator of brain development, ageing and neurodegeneration. The Journal of Physiology, 595(2), 489–503. Fox, S. E., Levitt, P., & Nelson, C. A., III (2010). How the timing and quality of early experiences influence the development of brain architecture. Child Development, 81(1), 28–40. Goyal, M. S., & Raichle, M. E. (2018). Glucose requirements of the developing human brain. Journal of Pediatric Gastroenterology and Nutrition, 66, S46–S49. Institute of Medicine and National Academy of Sciences. (1992). Discovering the Brain. Washington, DC: The National Academies Press. https://doi.org/10.17226/1785 Kuzawa, C. W., Chugani, H. T., Grossman, L. I., Lipovich, L., Muzik, O., Hof, P. R., … Lange, N. (2014). Metabolic costs and evolutionary implications of human brain development. Proceedings of the National Academy of Sciences, 111(36), 13010–13015.

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Lindsay, K. L., Buss, C., Wadhwa, P. D., & Entringer, S. (2018). The interplay between nutrition and stress in pregnancy: Implications for fetal programming of brain development. Biological Psychiatry, 85(2), 135–149. Poulose, S. M., Miller, M. G., Scott, T., & Shukitt‐Hale, B. (2017). Nutritional factors affecting adult neurogenesis and cognitive function. Advances in Nutrition (Bethesda, Md.), 8(6), 804–811. doi:10.3945/an.117.016261 Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003). Mapping cortical change across the human life span. Nature Neuroscience, 6(3), 309.

Suggested Reading Hair, N. L., Hanson, J. L., Wolfe, B. L., & Pollak, S. D. (2015). Association of child poverty, brain development, and academic achievement. JAMA Pediatrics, 169(9), 822–829. Hanson, J. L., Hair, N., Shen, D. G., Shi, F., Gilmore, J. H., Wolfe, B. L., & Pollak, S. D. (2013). Family poverty affects the rate of human infant brain growth. PLoS One, 8(12), e80954. Institute of Medicine and National Academy of Sciences. (1992). Discovering the Brain. Washington, DC: The National Academies Press. https://doi.org/10.17226/1785 Knickmeyer, R. C., Gouttard, S., Kang, C., Evans, D., Wilber, K., Smith, J. K., … Gilmore, J. H. (2008). A structural MRI study of human brain development from birth to 2 years. Journal of Neuroscience, 28(47), 12176–12182. Prado, E. L., & Dewey, K. G. (2014). Nutrition and brain development in early life. Nutrition Reviews, 72(4), 267–284. Stouffer, M. A., Golden, J. A., & Francis, F. (2016). Neuronal migration disorders: Focus on the ­cytoskeleton and epilepsy. Neurobiology of Disease, 92, 18–45.

Functional Anatomy: Types of Cells/Physiology Patrick W. Wright Neurology, Washington University in St. Louis, St. Louis, MO, USA

Neurons Neurons are the primary fundamental unit of the central nervous system (CNS) and provide the infrastructure for all major communication within the brain. Grossly, typical neuron ­morphology consists of a cell body (soma), dendrites, and axons. The soma contains the nucleus and other organelles. Arising from the cell body is the arborization of dendrites, which act as the primary synaptic target from other neurons. The axon is a long projection from the soma that communicates integrated information from the dendrites and acts on a downstream synapse. In humans, neurons can be classified by the number of processes that extend from the soma: bipolar, multipolar, and pseudounipolar neurons. Bipolar neurons have one axon and one dendrite that extend from the soma and can be found in the visual pathway (e.g., retinal bipolar cells). Multipolar neurons (e.g., Purkinje cells in the cerebellum) have one axon and multiple dendrites; they are the most common type found in the CNS. Pseudounipolar ­neurons have one soma process that bifurcates into two branches. Most sensory neurons are pseudounipolar. Neurons can be further classified by their context‐dependent structure determined by the neural circuit in which they participate. Interneurons, or local circuit neurons, have relatively short axons, whereas projection neurons have axons that extend to distant ­targets (Nicholls, Martin, Wallace, & Kuffler, 2001; Purves, 2008).

The Synapse The interface between neurons where information is transferred is known as the synapse, which consists of the axon terminal of the presynaptic cell, the intermediate extracellular space (the synaptic cleft), and the postsynaptic cell. The axon of presynaptic cell is not limited to forming The Wiley Encyclopedia of Health Psychology: Volume 1: Biological Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editor: Robert H. Paul. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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synapses with only the dendritic projection of the postsynaptic cell. Instead, axosecretory (axon–bloodstream), axoaxonic (axon–axon), axodendritic (axon–dendrite), and axosomatic (axon–soma) are examples of alternative synaptic interfaces. Formed synapses are primarily classified by the mode of information transfer: chemical synapses and electrical synapses ­ (Nicholls et al., 2001; Purves, 2008).

Synaptic Transmission Electrical synapse Electrical transmission at electrical synapses is mediated by gap junctions, which are channels that connect the intracellular space and cytoplasm of adjacent cells. Electrical synapses are inherently bidirectional, enabling the passage of electrical currents (carried by ions) as well as intracellular messengers to and from the postsynaptic cell. Each gap junction channel (­connexon) is made up of a pair of hexameric connexin proteins (e.g., connexin 36 [CX36], CX50), with each cell providing one of the pair. These gap junctions allow passive ionic ­current flow, providing an almost instantaneous route of transmission (Nicholls et al., 2001; Pereda, 2014; Purves, 2008). Chemical synapse Chemical synapses are the most common type of synapse. Information transfer at chemical synapses is facilitated by presynaptic synaptic vesicles containing neurotransmitters. Action potentials, most often originating at the “neck” of the axon (known as the axon hillock) where it connects to the neuronal soma, propagate down the length of the axon to the axon terminals. Myelin sheaths provided by neighboring oligodendrocytes are insulating adipose tissue that improve conductance rate by constraining electrochemical gradient‐associated depolarization to occur at the gaps between sheaths known as nodes of Ranvier; “white matter” is so named due to the white appearance of these myelin sheaths on axons. When the action potential arrives at the axon terminal, it activates voltage‐gated calcium (Ca2+) channels that are present along the presynaptic membrane. The subsequent influx of Ca2+ along its concentration gradient ([Ca2+]e ≈ 1–2 mM, [Ca2+]i ≈ 0.0001) enables vesicle fusion to the presynaptic membrane. These vesicles contain neurotransmitters that can then be exocytosed into the synaptic cleft. Released neurotransmitters will diffuse across the synaptic cleft via Brownian motion to the postsynaptic membrane containing metabotropic and ionotropic receptors; information transfer via this mechanism is inherently probabilistic, in contrast to electrical transmission via gap junctions as occurs at electrical synapses (Nicholls et al., 2001; Pereda, 2014; Purves, 2008). Synaptic vesicle exocytosis and recycling SNARE (soluble N‐ethylmaleimide‐sensitive factor [NSF] attachment protein receptor) protein complexes at the vesicle (v‐SNARE) and membrane (target‐localized, t‐SNARE) ­ ­enable docking of the vesicle to the inner membrane surface following binding of Ca2+ to the ­Ca2+‐sensing vesicular protein synaptotagmin‐1, enabling neuroexocytosis. The three‐vesicle pool model describes a reserve pool consisting of 80–90% of the total pool of vesicles, a recycling pool with 10–15%, and the readily releasable pool (RRP) at the active zone, the portion of the presynaptic membrane where vesicle exocytosis occurs that faces the postsynaptic ­density, with only 1–2% of the total vesicle population. The vesicles in the RRP are docked and primed (i.e., SNARE complex has formed) and are ready for fusion and neurotransmitter exocytosis in ≪1s in the presence of Ca2+. The vesicles of the recycling and reserve pools will release their

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neurotransmitter on the order of seconds to minutes and in response to moderate to intense (e.g., 5–10 Hz in frog neuromuscular junction or 30 Hz in Drosophila larval n ­ euromuscular ­junction) stimulation frequencies. The synaptic vesicle cycle consists of the endocytosis and subsequent recycling of vesicles that have bound to the presynaptic membrane and released neurotransmitter. This endocytosis of incorporated synaptic vesicles can be clathrin mediated for fully fused membranes or can consist of direct retrieval and recycling for vesicles that did not fuse completely for exocytosis (“kiss and run” fusion) (Gundelfinger, Kessels, & Qualmann, 2003; Kavalali, 2015; Nicholls et al., 2001; Purves, 2008; Rizzoli & Betz, 2005). The action potential The electrical signals that propagate over distances within the CNS and enable neurotransmitter release are called action potentials. Action potentials are an all‐or‐nothing event that are a consequence of the transmembrane electrochemical gradient of local sodium (Na+) and potassium (K+) ions. Prior to the evocation of an action potential, when the cell is at rest, resting membrane potential is ~−65 mV (range: ~−40 to −90 mV). At equilibrium, there is higher extracellular Na+ concentration than intracellular ([Na+]e ≈ 145 mM, [Na+]i ≈ 5–15 mM) and higher intracellular K+ concentration than extracellular ([K+]e ≈ 5 mM, [K+]i ≈ 140 mM). The resting membrane is most permeable to K+ compared with other ions, underlying the resting membrane potential being close to the equilibrium potential of K+ (~−58 mV). When a threshold potential is reached that is depolarized relative to rest (~−50 vs. ~−65 mV), an action potential is triggered. Voltage‐gated Na+ channels open, allowing an influx of Na+ into the cell along its electrochemical gradient; this is the primary depolarization phase of the action potential. Spike amplitude will peak at around ~+25 mV before the repolarization phase begins, in which voltage‐gated Na+ channels are beginning to inactivate and voltage‐gated K+ channels are opening. This efflux of K+ repolarizes the membrane potential to resting potential and overshoots it due to the slow closing of these channels. The shape and timing of action potentials can vary considerably across cell types, but the typical total duration of an action potential is 2–5 ms. Finally, Na+/K+‐ATPase, an active transporter pump that relies on energy from the hydrolysis of ATP, actively pumps 3 Na+ out of the cell for every 2 K+ brought in to return the cell to steady‐state resting potential and establish the electrochemical gradients necessary to facilitate subsequent action potentials (Bean, 2007). Following an action potential, the neuron enters a refractory period, in which it is either impossible (the absolute refractory period, initial ~1 ms following action potential) or more difficult (the relative refractory period, ~2–4 ms following the absolute refractory period) to attain threshold. This is due to the initial depolarization causing in the delayed action of K+ channels and inactivation of Na+ channels. This period of enforced silence limits the highest frequency of action potential occurrence (Nicholls et al., 2001; Purves, 2008). Excitatory and inhibitory postsynaptic potentials Small induced potential differences at the individual synapse level are known as postsynaptic potentials and are defined by the manner in which they affect the probability of action potential production. Excitatory postsynaptic potentials (EPSPs) increase the likelihood of action potential occurrence, whereas inhibitory postsynaptic potentials (IPSPs) decrease it. Generally, EPSPs and IPSPs can be differentiated by where their reversal potential, the potential at which current polarity is reversed, is relative to action potential threshold. For example, a net flow of cations causing depolarization (e.g., flow of Na+/K+) upon activation of an N‐methyl‐d‐aspartate (NMDA) glutamate receptor will have a reversal potential that is depolarized relative to resting membrane potential (e.g., 0 mV compared to −60 mV), inducing a depolarization that

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increases the probability of threshold being achieved. Alternatively, a hyperpolarizing inward flow of anions (e.g., chloride [Cl−]) due to a reversal potential that is more negative than ­resting potential will drive an IPSP and a reduction in likelihood that threshold will be achieved. It should be noted that a depolarizing flow of ions will still drive an IPSP if its reversal ­potential is negative relative to threshold potential, despite being positive relative to resting potential. It is the cumulative contribution of incident EPSPs and IPSPs from all synapses received by the postsynaptic cell, integrated spatially and temporally, that determines if ­membrane potential threshold is achieved and an action potential evoked (Nicholls et  al., 2001; Purves, 2008).

Ionotropic and Metabotropic Receptors Ionotropic receptors across the synaptic cleft at the postsynaptic membrane serve as ligand‐ gated ion channels. These allow for direct synaptic transmission driven by the binding of released presynaptic neurotransmitters. Metabotropic receptors, on the other hand, typically act through second messenger systems such as GTP‐binding proteins (G‐proteins) and are thus considered “slow” and indirect. These receptors are not themselves ion channels, as in the case of ionotropic receptors, and instead can modify the activity of ion channels or receptor proteins. G‐protein‐coupled receptors (GPCRs) can also commonly act at the level of gene expression, modulating transcription through, for example, the cyclic AMP‐protein kinase A pathway (Nicholls et al., 2001; Purves, 2008).

Neurotransmitters The chief low‐molecular‐weight (non‐peptide) CNS neurotransmitters can be delineated in three ways: the biogenic amines, amino acids, and acetylcholine. A single neuron can release multiple neurotransmitters, often at least one low‐molecular‐weight neurotransmitter as well as neuropeptides. Among the biogenic amines or monoamines include the catecholamines, which are derived from tyrosine and defined by the possession of a benzene ring with two adjacent hydroxyl groups: dopamine, norepinephrine (NE, noradrenaline), and epinephrine (adrenaline). Another class of biogenic amine is the tryptophan‐derived 5‐hydroxytryptamine (5‐HT, serotonin), an indoleamine (containing a benzene ring fused with a pyrrole ring). Relatively few neurons in the CNS release these biogenic amines, and most are localized to nuclei in the brain stem. The amino acid neurotransmitters include glycine, glutamate, and γ‐aminobutyric acid (GABA). A key component of synaptic transmission via neurotransmitter is clearance of transmitter from the synaptic cleft. This can be done by diffusion, degradation (e.g., acetylcholinesterase is an enzyme that hydrolyzes ACh to choline and acetate), or reuptake by glia or nerve terminals (Nicholls et al., 2001; Purves, 2008).

Glutamate Glutamate is an amino acid that serves as the primary excitatory neurotransmitter of the CNS. The four classes of glutamate receptors are NMDA, α‐amino‐3‐hydroxy‐5‐methyl‐4‐ isoxazole propionic acid (AMPA), kainate, and metabotropic glutamate receptors (mGluRs). The ionotropic channels are all nonselective cation channels, which enable consistent production of excitatory postsynaptic responses. The mGluRs are slower and can either increase or

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decrease postsynaptic excitability. Increased glutamate concentrations have been associated with depression and bipolar disorder. Elevated glutamate at the synaptic cleft, for example, following neural injury such as status epilepticus, trauma, or ischemic stroke, can be e­ xcitotoxic and lead to the destruction of postsynaptic neurons (Nicholls et  al., 2001; Purves, 2008; Raiteri, 2006).

GABA GABA and glycine are amino acids that are the principal inhibitory neurotransmitters in the CNS. The primary target receptors of GABA are GABAa receptor (GABAaR) and metabotropic GABAbR. GABAaR preferentially permits flow of Cl− ions (anions). Extracellular Cl− concentration is greater than intracellular concentration ([Cl−]e ≈ 110 mM, [Cl−]i ≈ 4–30 mM), so Cl− enters and consequently hyperpolarizes the cell, decreasing the likelihood of threshold potential being achieved and an action potential produced. Presynaptic GABAbR receptors suppress the activity of voltage‐gated Ca2+ channels, while postsynaptic GABAbR receptors activate inward rectifier K+ channel. GABAaR is unique in that it is sensitive to allosteric modulation. These receptors have binding sites that act as targets for anti‐anxiety benzodiazepines (e.g., diazepam [Valium] and chlordiazepoxide [Librium]) and anticonvulsant barbiturates (e.g., phenobarbital) (Nicholls et al., 2001; Purves, 2008; Raiteri, 2006).

Dopamine Dopamine is both an excitatory and inhibitory (dependent on target receptor) catecholamine with five identified metabotropic receptors (D1–5). Dopamine‐containing nuclei and their p ­ rojections are involved in regulating selection and execution of motor behaviors, problem solving, regulation of emotion, response to reward, and motivation. It is because of the influence of these pathways that antipsychotics for treating schizophrenia and bipolar disorder target dopamine receptors (e.g., aripiprazole [Abilify], a partial agonist of the D2 receptor, and olanzapine [Zyprexa], a D2 antagonist). Furthermore, the degeneration of dopaminergic neurons in the pars compacta of the substantia nigra is the defining feature of Parkinson’s disease. A reduction of presynaptic dopaminergic axon terminals drives a loss of dopamine in the basal ganglia, leading to downstream motor disorders such as difficulty in initiating movement and tremor at rest. l‐DOPA, the precursor for dopamine, can be used pharmacologically to improve dopamine concentrations and reduce associated motor deficits (Nemeroff & Owens, 2002; Nicholls et al., 2001; Purves, 2008; Raiteri, 2006; Schatzberg & Nemeroff, 2009).

Serotonin Serotonin is a predominantly excitatory monoamine (indoleamine) neurotransmitter. All ­serotonin (5‐HT) receptors except for the cation‐selective ionotropic 5‐HT3 receptor are metabotropic. Serotonin receptors can act broadly and influence activity across the cortex by modulating the release of many neurotransmitters (e.g., glutamate, GABA, ACh) through inhibition, facilitation, or stimulation of specific neurotransmitter exocytosis by a postsynaptic neuron. Serotonin projections originate at the raphe nuclei of the midbrain. Serotonergic neurons are involved with arousal and the sleep–wake cycle, mood, aggression, and the establishment of social dominance. The monoamines, including 5‐HT, are the primary neurotransmitters implicated in depression. Pharmacotherapy via antidepressants acts by either inhibiting

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monoamine oxidase, which degrades monoamine neurotransmitters and excites or inhibits pre‐/postsynaptic receptors that regulate neurotransmitter release and neuronal firing rates, or by blocking presynaptic monoamine transporter proteins (which remove neurotransmitter from the synaptic cleft). Examples of this final class of antidepressants include selective ­serotonin reuptake inhibitors (SSRIs) (e.g., fluoxetine [Prozac] and citalopram [Celexa]) and serotonin–norepinephrine reuptake inhibitors (SNRIs) (e.g., duloxetine [Cymbalta] and ­venlafaxine [Effexor]) (Nemeroff & Owens, 2002; Nicholls et  al., 2001; Purves, 2008; Schatzberg & Nemeroff, 2009).

Acetylcholine Acetylcholine (ACh) is an excitatory small‐molecule neurotransmitter that is involved with memory, learning, and cognitive performance. Cholinergic neurons project from the basal forebrain nucleus and innervate most of the cortex. Cholinergic receptors are classified based on their responsiveness to nicotine (“nicotinic” acetylcholine receptor [nAChR]) or muscarine (“muscarinic” acetylcholine receptor [mAChR]). nAChRs are cationic ionotropic receptors, whereas mAChRs are metabotropic. nAChRs are preferentially impacted by amyloid‐beta plaque deposition in Alzheimer’s disease (AD) (compared with mAChRs), more so than the effects seen due to normal aging, providing a potential therapeutic target for AD‐associated cognitive decline. AD patients are currently often prescribed drugs that are acetylcholinesterase inhibitors (e.g., donepezil [Aricept]), thus increasing the concentration of ACh (Nicholls et  al., 2001; Purves, 2008; Raiteri, 2006; Sadigh‐Eteghad, Majdi, Talebi, Mahmoudi, & Babri, 2015).

Norepinephrine and Epinephrine Norepinephrine (noradrenaline) and epinephrine (adrenaline) are excitatory catecholamines that share metabotropic noradrenergic receptors (α and β receptors). The primary source of norepinephrine in the brain is the locus coeruleus in the brain stem. Norepinephrine is involved with dysphoria, fatigue, apathy, and cognitive dysfunction. It regulates circadian patterns of melatonin synthesis in mammals. Epinephrine has the lowest concentration in the brain among the catecholamines and is found in the fewest neurons. No plasma membrane transporter ­specific for epinephrine has been identified. Epinephrine plays an important role in driving a sympathetic response in autonomic nervous system, but it also is involved in memory consolidation within the CNS (Cahill & Alkire, 2003; Nicholls et al., 2001; Purves, 2008; Raiteri, 2006; Schatzberg & Nemeroff, 2009).

Glia In addition to neurons, a family of non‐neuronal support cells, called glia, are present in the CNS. In the combined cortical gray and white matter, glia are more numerous, outnumbering neurons by >3.5 : 1 (Azevedo et al., 2009). Additionally, they lack axons and have more negative resting potentials (−90 vs. ~−65 mV) compared with neurons. Glia are involved with maintaining ion concentrations in the neuropil, regulating the uptake of neurotransmitters at or near the synaptic cleft, and controlling rate of action potential propagation among other support roles. Also, glial cells react to neuronal injury by replication and will propagate and occupy the

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injured area after all cellular debris has been removed. They can then secrete the extracellular matrix glycoprotein laminin to encourage neurite outgrowth or neoangiogenesis, for example, after ischemic stroke (Ji & Tsirka, 2012). The primary glial cell types are oligodendrocytes, microglia, ependymal cells, radial glial cells, and astrocytes (Nicholls et al., 2001; Purves, 2008).

Oligodendrocytes, Microglia, Ependymal Cells, and Radial Glial Cells Oligodendrocytes are primarily found within white matter regions in the brain where they form myelin sheaths around neuron axons. This subsequently aids in increasing speed of electrical transmission. Microglia are phagocytic, specialized macrophages that are involved in scavenging and clearing debris from sites of injury or normal cell turnover. They can release cytokines as part of an inflammatory response as well as prune synapses by monitoring synaptic transmission. Ependymal cells form an epithelial layer in CNS ventricular spaces. Their apical surfaces have cilia that circulate cerebrospinal fluid (CSF) in the ventricles. Modified ependymal cells, joined by gap junctions, surrounding a core of capillaries and connective tissue ­collectively make up the choroid plexus, which produces CSF. Finally, radial glial cells act to guide neuronal migration during development; their processes act as physical tracks that migrating neurons traverse along toward their target region and synapse (Fields et al., 2014; Nicholls et al., 2001; Purves, 2008).

Astrocytes Astrocytes interface with neurons and capillaries and can ensheath thousands of synapses. The two chief morphological classifications of astrocytes are fibrous and protoplasmic astrocytes. Fibrous astrocytes contain filaments and are found primarily within white matter. Their end feet envelop nodes of Ranvier. Protoplasmic astrocytes are abundant in gray matter and have end feet that surround synapses. Astrocytes serve a number of different functions in the CNS. They are involved with neurovascular coupling and can control local blood flow through release of arachidonic acid derivatives that can act on smooth muscle surrounding oxygen‐­ supplying arterioles to either constrict or dilate vessel diameter (Attwell et al., 2010). Astrocytes also modify the concentration of extracellular K+, neurotransmitters, and neuromodulatory substances. They can also modify the volume and geometry of the immediate extracellular space (Fields et al., 2014).

Author Biography Patrick W. Wright is currently a postdoctoral research fellow under Alan Koretsky, PhD, in the Laboratory of Functional and Molecular Imaging at the National Institute of Neurological Disorders and Stroke and the National Institutes of Health in Bethesda, Maryland. He received his BS in Biomedical Engineering from the University of Alabama at Birmingham in 2010. He subsequently earned his PhD in Biomedical Engineering from Washington University in St. Louis in 2016 with Joseph Culver, PhD, and Beau Ances, MD, PhD, serving as his thesis advisers. During graduate school, he developed an in vivo optical imaging system to study resting‐state cortical activity using concurrently measured hemodynamics and fluorescent calcium transients in mice. He also used clinical MRI data to examine the effects of early HIV infection on the brain.

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References Attwell, D., Buchan, A. M., Charpak, S., Lauritzen, M., Macvicar, B. A., & Newman, E. A. (2010). Glial and neuronal control of brain blood flow. Nature, 468(7321), 232–243. doi:10.1038/nature09613. PubMed PMID: 21068832; PubMed Central PMCID: PMCPMC3206737. Azevedo, F. A., Carvalho, L. R., Grinberg, L. T., Farfel, J. M., Ferretti, R. E., Leite, R. E., … Herculano‐ Houzel, S. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled‐up primate brain. Journal of Comparative Neurology, 513(5), 532–541. doi:10.1002/cne.21974. PubMed PMID: 19226510. Bean, B. P. (2007). The action potential in mammalian central neurons. Nature Reviews. Neuroscience, 8(6), 451–465. doi:10.1038/nrn2148. PubMed PMID: 17514198. Cahill, L., & Alkire, M. T. (2003). Epinephrine enhancement of human memory consolidation: Interaction with arousal at encoding. Neurobiology of Learning and Memory, 79(2), 194–198. PubMed PMID: 12591227. Fields, R. D., Araque, A., Johansen‐Berg, H., Lim, S. S., Lynch, G., Nave, K. A., … Wake, H. (2014). Glial biology in learning and cognition. The Neuroscientist, 20(5), 426–431. doi:10.1177/ 1073858413504465. PubMed PMID: 24122821; PubMed Central PMCID: PMCPMC4161624. Gundelfinger, E. D., Kessels, M. M., & Qualmann, B. (2003). Temporal and spatial coordination of exocytosis and endocytosis. Nature Reviews. Molecular Cell Biology, 4(2), 127–139. doi:10.1038/ nrm1016. PubMed PMID: 12563290. Ji, K., & Tsirka, S. E. (2012). Inflammation modulates expression of laminin in the central nervous system following ischemic injury. Journal of Neuroinflammation, 9, 159. doi:10.1186/ 1742‐2094‐9‐159. PubMed PMID: 22759265; PubMed Central PMCID: PMCPMC3414761. Kavalali, E. T. (2015). The mechanisms and functions of spontaneous neurotransmitter release. Nature Reviews. Neuroscience, 16(1), 5–16. doi:10.1038/nrn3875. PubMed PMID: 25524119. Nemeroff, C. B., & Owens, M. J. (2002). Treatment of mood disorders. Nature Neuroscience, 5(Suppl), 1068–1070. doi:10.1038/nn943. PubMed PMID: 12403988. Nicholls, J. G., Martin, A. R., Wallace, B. G., & Kuffler, S. W. (2001). From neuron to brain: A cellular and molecular approach to the function of the nervous system (4th ed., pp. xx). Sunderland, MA: Sinauer Associates, 807 p. Pereda, A. E. (2014). Electrical synapses and their functional interactions with chemical synapses. Nature Reviews. Neuroscience, 15(4), 250–263. doi:10.1038/nrn3708. PubMed PMID: 24619342; PubMed Central PMCID: PMCPMC4091911. Purves, D. (2008). Neuroscience (4th ed.). Sunderland, MA: Sinauer. Raiteri, M. (2006). Functional pharmacology in human brain. Pharmacological Reviews, 58(2), 162– 193. doi:10.1124/pr.58.2.5. PubMed PMID: 16714485. Rizzoli, S. O., & Betz, W. J. (2005). Synaptic vesicle pools. Nature Reviews. Neuroscience, 6(1), 57–69. doi:10.1038/nrn1583. PubMed PMID: 15611727. Sadigh‐Eteghad, S., Majdi, A., Talebi, M., Mahmoudi, J., & Babri, S. (2015). Regulation of nicotinic acetylcholine receptors in Alzheimers disease: A possible role of chaperones. European Journal of Pharmacology, 755, 34–41. doi:10.1016/j.ejphar.2015.02.047. PubMed PMID: 25771456. Schatzberg, A. F., & Nemeroff, C. B. (2009). The American Psychiatric Publishing textbook of psychopharmacology (4th ed., pp. xxxii). Washington, DC: American Psychiatric Publishing, 1616 p.

Functional Neuroanatomy: Cortical–Subcortical Distinctions and Pathways David Von Nordheim and Jodi M. Heaps‐Woodruff Missouri Department of Mental Health, Univerity of Missouri–St. Louis, St. Louis, MO, USA Missouri Institute of Mental Health, St. Louis, MO, USA

The cerebrum, also known as the forebrain, is the largest area of the mammalian brain and the most recently developed in evolutionary history. Consisting of all brain structures above the cerebellum, the cerebrum is further divided into the cerebral cortex, the brain’s convoluted outer surface, and the subcortical structures beneath. The cerebrum is responsible for the higher order processes that give rise to thought, emotion, and sensory experiences. The structures of the cerebral cortex are central to processing sensory, motor, and perceptual information. These structures are intricately connected, allowing information from ­disparate processing streams to combine, producing a harmonized response within a dynamic environment. The coordination between cortical and subcortical structures occurs via n ­ etworks of feedback loops designed to process continuously updated sensory information. To understand the coordination between subcortical structures and different regions of the cortex, we will describe the connections between the subcortical nuclei and the cortical–subcortical feedback loops.

The Thalamus The thalamus is a pivotal structure involved in coordinating communication between the ­cortex and subcortex. With the notable exception of olfaction, the thalamus is the first destination for all sensory information, earning its title as the “relay station” of the brain. The thalamus is divided into nuclei dedicated to processing sensory information and relaying information back to the relevant cortical areas. As an example, visual information initially travels from The Wiley Encyclopedia of Health Psychology: Volume 1: Biological Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editor: Robert H. Paul. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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the retina to the lateral geniculate nucleus (LGN) of the thalamus, where it is subsequently ­transmitted to the primary visual cortex of the occipital lobe. As described further in the ­chapter, the thalamus also processes incoming information from other subcortical structures.

Basal Ganglia The basal ganglia consist of a cluster of subcortical nuclei, including the striatum, the globus pallidus, ventral pallidum, substantia nigra, and subthalamic nucleus (STN). The striatum is divided into four nuclei: the caudate and putamen (dorsal striatum) and the nucleus accumbens (NAcc) and olfactory tubercule (ventral striatum). The dorsal and ventral striatal regions are innervated by both cortical and subcortical afferents. The caudate has been described as having a critical role in planning and execution of goal‐directed behavior (Grahn, Parkinson, & Owen, 2008). A recent functional connectivity study described an age‐related reduction in differentiation of connections between the caudate and cortical networks. Specifically, caudate areas in younger adults were connected to the frontal parietal control network (FPN), whereas these same areas were connected to the default network (DN) in older adults. This difference in connectivity is related to memory decline in adults over the age of 75. This same study also reported that reduction dopamine transporter density in the striatum predicted memory decline in older adults (Rieckmann, Johnson, Sperling, Buckner, & Hedden, 2018). The putamen is interconnected with the primary motor, primary somatosensory area, and premotor cortex. As such, damage observed in the putamen results in movement disorders. A  recent study determined that children with complex motor stereotypies (repetitive and rhythmic movements) displayed diminished size of the putamen compared with controls (Mahone et al., 2016). Additionally, connections between the cerebellum and the putamen are active in parallel with the putamen to primary motor cortex, likely coordinating the sequence and learning of motor movements (Penhune & Steele, 2012). The internal capsule, a dense white matter tract that carries sensory and motor information from the spinal cord to the frontal cortex, separates the caudate and putamen. Lesions in the internal capsule commonly result in motor impairments, though the location of the lesion may produce variations in symptomatology. Lesions in the internal capsule resulting from stroke are associated with poorer recovery (Kobayashi et al., 2015). The nuclei of the ventral striatum—the NAcc and the olfactory tubercle—are involved in components of behavioral reinforcement. The olfactory tubercle is located in both the ventral striatum and the olfactory cortex and, despite its name, does not appear to have a role in olfaction. The NAcc is often referred to as the “pleasure center” of the brain; however, recent research has characterized the function of the NAcc a bit differently. NAcc function is related to motivated actions toward relevant stimuli and suppression of actions that would deter unsuitable actions (Floresco, 2015). The structures of the ventral striatum are strongly coactive with the limbic system, a network of cortical and subcortical circuits that process emotions and episodic memories, implicating the role of emotion and memory in the development and maintenance of motivational behaviors.

Globus Pallidus The globus pallidus is composed of the internus (GPi) and the externus (GPe) regions. The GPi receives inputs from both the excitatory direct path via the striatum and inhibitory, indirect

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path via the STN. Inputs from the striatum inhibit the activity of the GPi, preventing the release of gamma‐aminobutyric acid (GABA) from GPi to the thalamus. The GPi is a target for deep brain stimulation of dystonias or movement disorders that produce uncontrolled contractions of muscles that result in irregular twisting and abnormal postures. The GPe once thought to have a singular function of suppressing motor activity via inputs from the STN recently has been described as having diversified projections through distinct subconnections (Gittis et al., 2014). The GPe is a fairly active area of research, as effective treatment of Parkinson’s disease can be achieved through deep brain stimulation of the GPe (Carvalho, SuKul, Bookland, Koch, & Connolly, 2014).

Subthalamic Nucleus Described in some detail above, the STN not only receives input via the indirect pathway but also receives direct input from cortical areas. A small structure subnuclei within the STN are associated with connectivity between motor, association networks, and the limbic system. Like the GPe, the STN is a potential target for deep brain stimulation. A recent functional connectivity study revealed that in Parkinson’s patients, the STN had developed increased connectivity in two particular cortical areas (motor and precuneus), whereas controls had a more dispersed pattern of connectivity (Fernández‐Seara et al., 2015). The final major structure of the basal ganglia, the substantia nigra, is a small nucleus located in the most ventral section of the subcortex. It has two distinct regions, the substantia nigra pars compacta (SNpc) and the substantia nigra pars reticulata (SNpr). The SNpc produces and releases the majority of dopamine in the brain. A decrease in SNpc dopamine is characteristic of Parkinson’s disease; however, the SNpc also plays a role in circuits between the striatum and prefrontal cortex and is implicated in the cognitive dysfunctions associated with schizophrenia (Yoon, Minzenberg, Raouf, D’Esposito, & Carter, 2013). The SNpr receives input from both the striatum (direct pathway) and the STN (indirect pathway). Inhibitory input from the striatum can prevent the SNpr from sending inhibitory GABA signals to the thalamus, increasing the potential for motor activity. Likewise, the SNpr can also receive excitatory glutamatergic signals from the STN, ultimately resulting in decreased potential for motor activity. As described above, cortical–subcortical communication originates within the sensory regions of the cortex. The subcortical structures organize and coordinate a response through either the direct excitatory stream or indirect inhibitory stream (see Figure 1). Specific feedback loops have been defined that characterize the patterns or circuits of communication between cortical and subcortical structures. The loops are detailed below, with brief examples of different disease processes that result from alterations in the communication patterns of the feedback loops.

Skeletomotor Loop The skeletomotor loop facilitates communication between the motor cortices and subcortical structures to produce movement. A consequence of dysfunction in the skeletomotor loop is impaired muscle movement, resulting in range of symptoms referred to as dyskinesias. Dyskinesias can be characterized as ranging from slow or hypokinetic movements resulting from excitatory pathway dysfunction to fast or hyperkinetic movements resulting from damage within the inhibitory pathway. Examples include bradykinesia, dystonia, athetosis, chorea, ­ballisms, and tics. Bradykinesias are hypokinesias that appear as slowed, clumsy, or inaccurate

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David Von Nordheim and Jodi M. Heaps‐Woodruff Cortex DLPFC

ACC

LOFC

Hyper direct path Indirect pathway

Glu STN

GABA

GPe

GABA, Enk

DA

SNc

Glu Caudate

PN

Putamen Cort

Str GABA, SP

Glu

MC

Direct pathway

GPi/SNr

DN

Cerebellum

Basal ganglia GABA

Glu

Thalamus

Glu

Figure 1  This diagram depicts a portion of the neural circuits and their interactions. Arrows represent signals from GABA, glutamergic and dopaminergic signals. The dashed grey lines represent the interconnected circuits between the basal ganglia and the cerebellum. The abbreviations go as follows: The Dorsolateral Prefrontal Cortex (DLPFC), Anterior Cingulate Cortex (ACC), Lateral Orbitofrontal Cortex (LOFC), Motor Cortex (MC), Globus Pallidus externus (GPe), Subthalamic Nucleus (STN), Substantia Nigra pars compacta (SNc), Globus Pallidus internus (GPi), Substantia Nigra pars reticulata (SNr), Pontine Nuclei (PN), Cerebellar Cortex (Cort), Dentate Nucleus (DN). Source: Original framework for this diagram can be found at Caligiore et al. (2017), courtesy of Daniele Caligiore. Reproduced with permission of Elsevier.

movements as well as poverty or lack of movement, resulting from impairments with initiated movements. Symptoms of dytonias include involuntary contractions of muscles, creating repetitive or twisting movements of various body parts, including the entire torso. Athetosis, a condition observed in athetoid cerebral palsy, consists of involuntary, writhing movements of the hands and feet. In some patients athetosis may affect arms, legs, neck, and even the tongue. Choreas or chorionic movements are characteristic of Huntington’s disease and include erratic movements that lack purpose and may include awkward movement of the facial muscles. Ballism, typically hemiballismus, is a severe form of movement disorder that involves involuntary, violent, or extreme motions that occur on one side of the body. Hemiballismus is the result of damage to the STN located on the side contralateral to the movements. Finally, tics are hyperkinesias that appear as rapid, repetitive involuntary movements often associated with conditions such as autism and Tourette’s syndrome.

Oculomotor Loop The oculomotor loop is a circuit including the frontal eye fields, prefrontal cortex, posterior parietal cortex, the caudate nucleus, GPi, SNpr, superior colliculus, and visual regions of the

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thalamus. One function of the oculomotor loop includes suppression of unnecessary eye movements and enables the eyes to fixate on a target. Signals from this loop originate in the posterior parietal lobe and dorsolateral prefrontal cortex (DLPFC), structures associated with spatial mapping and attentional processing. The thalamus ultimately initiates or inhibits eye movement through projections to the frontal and supplementary receptive fields of the eyes. Dysfunctions in some psychiatric disorders include inaccuracies in visual fixation. A current report indicates that schizophrenia and obsessive–compulsive disorder share both overlapping and unique patterns of dysfunction. Shared deficits appear to involve the DLPFC, anterior cingulate, and parietal circuits, whereas additional deficits in schizophrenia pointed to motor control and frontal eye field dysfunction (Damilou, Apostolakis, Thrapsanioti, Theleritis, & Smyrnis, 2016).

Executive–Associative Loops Numerous circuits between the prefrontal cortex and subcortical structures have been defined and are continually being redefined through the use of neuroimaging tools that provide new insights into functional connections. One loop, the DLPFC, is associated with cognitive processes, including outcome prediction, working memory, reasoning, inhibition, and ­ ­conscious decision making. The DLPFC loop has received a great deal of attention as it is implicated in both positive and negative behaviors associated with schizophrenia. Specifically, damage to this circuit is associated with substantial deficits in social cognition, including difficulties in reasoning and failure to follow rules (Garety, Kuipers, Fowler, Freeman, & Bebbington, 2001). The more ventrally positioned orbitofrontal cortex has both medial (mOFC) and lateral (lOFC) circuits that display distinct connectivity. These circuits are implicated in goal‐directed behaviors including reward learning, mood regulation, emotional reappraisal, and decision making. The mOFC is implicated in assigning value to stimuli, whereas the lOFC is implicated in appraising or calculating the value between stimuli (Fettes, Schulze, & Downar, 2017).

Limbic Loop The limbic loop is a circuit of nuclei including the anterior cingulate cortex (ACC), medial prefrontal cortices, NAcc, GPi, and thalamus. A variety of psychopathologies are associated with dysfunction in this circuit, including Tourette’s syndrome and obsessive–compulsive ­disorder (Devinsky, Morrell, & Vogt, 1995). Dysregulation of the thalamus is hypothesized to result in overstimulation of the ACC, resulting in repetitive, involuntary behaviors. On the other hand, thalamic dysregulation may also result in inadequate stimulation of the ACC. Hypostimulation of the ACC may contribute to the profound apathy observed in akinetic mutism, a disorder characterized by an indifference to hunger, thirst, or pain, a lack of spontaneous movement, and an inability to respond to questions and commands (Holroyd & Yeung, 2012).

Conclusion Because of the intricate connectivity between cortical and subcortical structures, the damage caused by neurological diseases or lesions is unlikely to be isolated entirely within the cortex

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or  subcortex. Additionally, the loops as presented, while distinct circuits, have further ­interconnections not described here. Certain pathologies, as described in this chapter, might result from lesions or lack a diffuse pattern of connectivity (Fernández‐Seara et  al., 2015; Rieckmann et al., 2018). As has been demonstrated in this chapter, damage to the cortex can result in dysfunction in subcortical communications; likewise, damage to subcortical structures will eventually lead to reciprocal impairment and possible atrophy in cortical structures. Despite our relatively thorough understanding of cortical–subcortical feedback loops, there is much that is not well understood, including the role of other neuromodulators in producing behavior or disease processes. Ongoing research using multimodal imaging and examination of the role of other body systems (e.g., the gut–brain axis, immune system) in disease states will provide greater understanding of the complexity of cortical–subcortical communication systems.

Author Biographies David Von Nordheim earned his Masters of Arts in Cognitive Psychology from the University of Nebraska–Omaha in May 2018. His research interests include memory, attention, word processing, and the cognitive psychology of addictive behaviors. His long‐term goal is to earn a PhD in cognitive neuroscience and conduct neuropsychological research with clinical populations. Jodi M. Heaps‐Woodruff is a research neuropsychologist and health services researcher. She earned her PhD from the University of Missouri–St. Louis and is a research assistant professor at the Missouri Institute of Mental Health at the University of Missouri–St. Louis. Her research is focused on the cognitive aspects involved in recovery from mental illness and the impact of chronic disease on the brain.

References Caligiore, D., Pezzulo, G., Baldassarre, G., Bostan, A. C., Strick, P. L., Doya, K., … Herreros, I. (2017). Consensus paper: Towards a systems‐level view of cerebellar function: the interplay between ­cerebellum, basal ganglia, and cortex. Cerebellum, 16, 203–229. https://doi.org/10.1007/s12311‐ 016‐0763‐3 Carvalho, K. S., Sukul, V. V., Bookland, M. J., Koch, S. A., & Connolly, P. J. (2014). Deep brain stimulation of the globus pallidus suppresses post‐traumatic dystonic tremor. Journal of Clinical Neuroscience, 21(1), 153–155. Damilou, A., Apostolakis, S., Thrapsanioti, E., Theleritis, C., & Smyrnis, N. (2016). Shared and distinct oculomotor function deficits in schizophrenia and obsessive compulsive disorder. Psychophysiology, 53(6), 796–805. Devinsky, O., Morrell, M. J., & Vogt, B. A. (1995). Contributions of anterior cingulate cortex to behaviour. Brain, 118(1), 279–306. Fernández‐Seara, M. A., Mengual, E., Vidorreta, M., Castellanos, G., Irigoyen, J., Erro, E., & Pastor, M. A. (2015). Resting state functional connectivity of the subthalamic nucleus in Parkinson’s disease assessed using arterial spin‐labeled perfusion f MRI. Human Brain Mapping, 36(5), 1937–1950. Fettes, P., Schulze, L., & Downar, J. (2017). Cortico‐striatal‐thalamic loop circuits of the orbitofrontal cortex: Promising therapeutic targets in psychiatric illness. Frontiers in Systems Neuroscience, 11, 25. Floresco, S. B. (2015). The nucleus accumbens: An interface between cognition, emotion, and action. Annual Review of Psychology, 66, 25–52.

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Garety, P. A., Kuipers, E., Fowler, D., Freeman, D., & Bebbington, P. E. (2001). A cognitive model of the positive symptoms of psychosis. Psychological Medicine, 31(2), 189–195. Gittis, A. H., Berke, J. D., Bevan, M. D., Chan, C. S., Mallet, N., Morrow, M. M., & Schmidt, R. (2014). New roles for the external globus pallidus in basal ganglia circuits and behavior. Journal of Neuroscience, 34(46), 15178–15183. Grahn, J. A., Parkinson, J. A., & Owen, A. M. (2008). The cognitive functions of the caudate nucleus. Progress in Neurobiology, 86(3), 141–155. Holroyd, C. B., & Yeung, N. (2012). Motivation of extended behaviors by anterior cingulate cortex. Trends in Cognitive Sciences, 16(2), 122–128. Kobayashi, Z., Akaza, M., Endo, H., Numasawa, Y., Tomimitsu, H., & Shintani, S. (2015). Deterioration of pre‐existing hemiparesis due to an ipsilateral internal capsule infarction after a contralateral stroke. Journal of the Neurological Sciences, 354(1–2), 140–141. Mahone, E. M., Crocetti, D., Tochen, L., Kline, T., Mostofsky, S. H., & Singer, H. S. (2016). Anomalous putamen volume in children with complex motor stereotypies. Pediatric Neurology, 65, 59–63. Penhune, V. B., & Steele, C. J. (2012). Parallel contributions of cerebellar, striatal and M1 mechanisms to motor sequence learning. Behavioural Brain Research, 226(2), 579–591. Rieckmann, A., Johnson, K. A., Sperling, R. A., Buckner, R. L., & Hedden, T. (2018). Dedifferentiation of caudate functional connectivity and striatal dopamine transporter density predict memory change in normal aging. Proceedings of the National Academy of Sciences, 115(40), 10160–10165. Yoon, J. H., Minzenberg, M. J., Raouf, S., D’Esposito, M., & Carter, C. S. (2013). Impaired prefrontal‐ basal ganglia functional connectivity and substantia nigra hyperactivity in schizophrenia. Biological Psychiatry, 74(2), 122–129.

Suggested Reading Corbetta, M., Ramsey, L., Callejas, A., Baldassarre, A., Hacker, C. D., Siegel, J. S., … Connor, L. T. (2015). Common behavioral clusters and subcortical anatomy in stroke. Neuron, 85(5), 927–941. Coyle, J. T., Price, D. L., & Delong, M. R. (1983). Alzheimer’s disease: A disorder of cortical cholinergic innervation. Science, 219(4589), 1184–1190. Gutman, B., Bright, J., Rummel, C., Rocha, C. S., Debove, I., Yasuda, C., … Wiest, R. (2018). Widespread cortical thinning in Parkinson’s disease: Findings from the ENIGMA‐Parkinson’s Disease Working Group (S45. 008). Neurology, 90(15 Supplement), S45.008. Heller, A. S. (2016). Cortical‐subcortical interactions in depression: From animal models to human ­psychopathology. Frontiers in Systems Neuroscience, 10, 20. Kiverstein, J., & Miller, M. (2015). The embodied brain: Towards a radical embodied cognitive neuroscience. Frontiers in Human Neuroscience, 9, 237. Leunissen, I., Coxon, J. P., Caeyenberghs, K., Michiels, K., Sunaert, S., & Swinnen, S. P. (2014). Subcortical volume analysis in traumatic brain injury: The importance of the fronto‐striato‐thalamic circuit in task switching. Cortex, 51, 67–81.

Neurobiology of Stress–Health Relationships R.L. Spencer, L.E. Chun, M.J. Hartsock, and E.R. Woodruff Department of Psychology and Department of Neuroscience, University of Colorado, Boulder, CO, USA

Stress and Health Overview The adverse effects that stress can have on physical and mental health are well established. Stress has a causal role in the development of some psychological disorders, such as major depressive disorder or posttraumatic stress disorder, and it can negatively impact all phases of physiological and psychological illness progression including precipitation of episode onset, symptom exacerbation, increased treatment resistance, delayed recovery, and increased relapse (Dimsdale, 2008; Reagan & Grillo, 2008). Recent life stress is reported to be a factor contributing to 60–80% of healthcare provider visits in the United States. Because most major stressors identified by members of our society are psychological rather than physiological in nature, understanding and mitigating the negative relationship between stress and health is a central issue for health psychology.

The Stress Concept The foundation for the scientific study of stress was firmly established by the physiologist Walter Cannon and the endocrinologist Hans Selye. Cannon, as a physiologist at Harvard Medical School (1900–1945), coined the term homeostasis to represent the elaborate physiological processes that operate in a coordinated fashion to ensure the stability of cellular and physiological functions in the body. Cannon was instrumental in determining the critical role that the sympathetic nervous system (SNS) plays in restoring homeostasis in the face of a wide range of environmental or internal disturbances. He described the pluripotent effects of SNS activation

The Wiley Encyclopedia of Health Psychology: Volume 1: Biological Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editor: Robert H. Paul. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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as mediating key physiological changes in the body, such as energy mobilization and increased cardiovascular function, that support the “fight‐or‐flight” response to physical threat. Hans Selye, while working (1936–1982) at McGill University and Universite de Montreal, extensively characterized in controlled laboratory experiments the physiological effects of stress. Whereas Cannon advanced the importance of the SNS, Selye determined that another key physiological mediator of the body’s stress response was increased secretion of glucocorticoid hormones as a result of hypothalamic–pituitary–adrenal (HPA) axis activation. Selye referred to stressors as the factors that cause stress and the stress response as the body’s adaptive response to stressors. He proposed the theory that a wide range of experiences (stressors) have the ability to exert a similar influence on the body (stress), which can lead to detrimental effects when sustained. However, the body can detect this stress state and effectively respond by mounting a coordinated stress response. Selye described this stress state as a nonspecific demand placed upon the body. Despite the various theoretical conundrums surrounding the nature of this stress state, researchers have made a number of important discoveries concerning the neurobiology of stress.

Types of Stressors The categorization of stressors into two general types, physiological and psychological, has proven useful for studying the specific underlying neurocircuitry that participates in the ­processing and response coordination to these stressors. Physiological stressors are those altered physiological states that elicit well‐characterized autonomic homeostatic adjustments, such as hypothermia, hypoxia, dehydration, tissue damage, hypotension, and hemorrhage. These stressors are also referred to as interoceptive or systemic stressors. An important finding of stress research has been that the two principal physiological stress response systems (SNS and HPA) are not only activated after a disruption of homeostasis has been detected but also by a variety of environmental or psychosocial situations that do not themselves disrupt physiological function. Some of these so‐called psychological (or processive) stressors are environmental stimuli that appear to be innately recognized as an immediate threat to physiological well‐being, such as physical attack, sudden loud noise, impending collision with a fast moving object, or teetering on the edge of a high precipice. However, physiological stress responses are also effectively triggered by many other experiential situations that do not reflect impending bodily harm, such as public speaking, novel social circumstances, traffic jams, relationship difficulties, financial insecurity, and job performance concerns. Moreover, psychological stressors may be self‐generated (e.g., traumatic experience recall and anticipation of future negative outcomes). Research has identified several underlying psychological factors that are shared by various psychosocial situations that are considered stressful, and these factors include lack of controllability, lack of predictability, novelty, and social‐evaluative threat. The ability to respond to psychological stressors with a physiological stress response confers considerable adaptive advantage to the organism. If the organism is able to initiate a physiological stress response not only to a direct physical insult (e.g., attack from a predator) but also to cues associated with the stressor (e.g., odors, sounds, or sight of a nearby predator), then the physiological stress response may be vital for the organism’s ability to avoid the stressor or may optimally prepare the organism for a subsequent encounter with the stressor. Moreover, this capability can draw upon prior experience (i.e., learning and memory), such that previously neutral environmental cues, when paired with stressors, will subsequently be sufficient to elicit physiological stress responses. The ability not only to respond to immediate physical stressors but also to anticipate and prepare for future stressors is a key aspect of evolutionary stress neurobiology. However, in modern society, many complex psychosocial stressors are not

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associated with impending physical threat to well‐being, and consequently the activation of physiological stress responses in these situations is not adaptive and contributes to the adverse effects of chronic stress on health.

Types of Stress Responses Physiological and psychological stressors elicit not only a physiological response but also ­psychological responses. The psychological responses often include generation of emotional states, such as fear, anxiety, or frustration, as well as shifts in attention (e.g., hypervigilance), decision‐making (e.g., bias), and memory processes. This psychological aspect of the stress response is essential for motivation and coordination of adaptive behavioral responses. The importance of behavioral responses to stress was featured early on in stress response physiology by Cannon as he framed the significance of the SNS physiological response in terms of behavior—the “fight‐or‐flight response.” Other basic behavioral responses elicited by stressors include freezing and withdrawal, but the psychological aspect of the response to stress may also motivate elaborate behavioral strategies to avoid or reduce stress. No physiological or psychological response alone, or in combination, is a dedicated response exclusively reserved for combatting stress. Each can occur in other contexts, and therefore the presence of these responses is not a definitive measure of stress, although they may be useful biomarkers associated with stress.

Neurobiology of the Body’s Response to Stress The ability of the body to detect stressors and then mount a coordinated stress response that effectively copes with the stressful situation is fundamentally a neurobiological process. It requires neural communication between sensory afferents and motor efferents. However, because there is not a physical dimension of stressfulness inherent in stimuli, the determination of stressfulness or threat requires integrative neural processing. The perception of and the coordinated response to stress depend on hierarchically organized neural systems in the brain. In the case of an appropriate response to physiological stressors, the underlying neural circuitry tends to be relatively simple. For example, a sudden drop in blood pressure results in reduced activity of baroreceptors located in the aortic arch and carotid artery sinus. These specialized sensory neurons project via the vagus and glossopharyngeal cranial nerves to the vasomotor control centers in the brain stem. The vasomotor control centers then direct an increase in activity of descending spinal cord premotor efferent neurons that innervate components of the SNS. Increased SNS activity then mediates a rapid increase in cardiac output and arteriole vasoconstriction. Superimposed on these evolutionarily primitive neurocircuits are more complex neural circuits and networks (e.g., components of the limbic system) that subserve detection of the wide range of psychological stressors and orchestration of the physiological and psychological responses directed toward reducing stress. The two primary physiological stress response systems are the SNS and the HPA axis. Each system has both a neuronal and a hormonal component. The two systems mediate complementary adaptive changes in virtually every cell and system of the body, and both systems operate on a complementary time scale. The SNS typically produces rapid (within seconds) but short‐lasting (minutes) actions, whereas the HPA axis typically produces delayed (45– 60 min) but long‐lasting (hours to days) actions. Individuals do not have voluntary control over the initiation or termination of activity within these two stress response systems, but they may learn strategies, such as relaxation techniques to modulate their activity.

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SNS The SNS (Figure  1) is one of two subdivisions of the autonomic nervous system (ANS) (Powley, 2013). The parasympathetic nervous system (PSNS) forms the other subdivision. The ANS is primarily a motor system that innervates and controls the activity of internal organs and glands throughout the body including the heart, lungs, liver, kidney, g ­ astrointestinal

PVN LC A5/C1 RN

IML

SG

EPI Sympathetic-adrenal-medullary Sympathetic preganglionic Sympathetic postganglionic Catecholamine premotor sympathetic Non-catecholamine premotor sympathetic

Figure 1  Neural pathways of the stress response. Depicted are examples of sympathetic preganglionic neuron projections from the intermediolateral cell column of the spinal cord (IML) to sympathetic postganglionic neurons in the sympathetic ganglion (SG) and to the adrenal medulla, where epinephrine (EPI) is released into the circulation from chromaffin cells. Also depicted is an example of sympathetic postganglionic neuron projections to skin and a section of a blood vessel, where norepinephrine is released from nerve terminals. Descending neural control originates in paraventricular nucleus of the hypothalamus (PVN), locus ceruleus (LC), A5 noradrenergic and C1 adrenergic cell groups (A5/C1), and raphe nuclei (RN). Source: Reprinted with permission from McCarty (2016). Reproduced with permission of Taylor & Francis.

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system, bladder, reproductive organs, salivary glands, and lacrimal glands. The ANS also ­regulates blood pressure and blood flow to various parts of the body by adjusting the diameter of arterioles, and it regulates the amount of light that enters the eye by controlling the diameter of the iris. The ANS comprises a series of two neuron units (preganglionic neuron and postganglionic neuron) that connect the spinal cord and brain stem with the targeted organs and glands. In general, the SNS and PSNS have opposing actions on the activity and function of the target organs, for example, increased SNS activity leads to an increase in heart rate, and increased PSNS activity leads to a decrease in heart rate. Combined the SNS and PSNS provide the critical balance that largely underlies physiological homeostasis. The SNS preganglionic neurons receive their primary inputs from descending brain stem neural pathways, which synapse onto the cell bodies of preganglionic sympathetic neurons located in the intermediolateral cell column of the thoracolumbar spinal cord. Axons of the preganglionic sympathetic neurons extend outside the spinal cord to synapse onto postganglionic cell bodies, many of which are located in sympathetic chain ganglia adjacent to the spinal column. The axons of postganglionic neurons terminate on target organs throughout the body. Preganglionic neurons release acetylcholine onto nicotinic cholinergic receptors present on postganglionic neurons, while postganglionic neurons release norepinephrine onto adrenergic receptors present on target organs. Norepinephrine release onto target organs triggers physiological changes associated with the fight‐or‐flight response, including pupil dilation, shunting of blood flow from the viscera to the skeletal muscles of the limbs, increased respiration, and heart rate acceleration. In addition to this direct neural control of SNS target organs, an endocrine component of the SNS provides hormonal control over target organs. The chromaffin cells of the adrenal medulla comprise this endocrine component. The axons of sympathetic preganglionic neurons in the thoracic spinal cord extend as part of the splanchnic nerve to terminate on chromaffin cells. Chromaffin cells, in response, secrete the catecholamine molecules epinephrine (also known as adrenaline) and norepinephrine into the systemic circulation. Catecholamine hormones cannot cross the blood–brain barrier and therefore act only in the periphery, with epinephrine binding all classes of adrenergic receptors (α1, α2, β1, β2, and β3 adrenergic receptors) and norepinephrine binding preferentially α1, α2, and β1 adrenergic receptors. Given the different receptor binding affinities of epinephrine and norepinephrine and the differential distribution of adrenergic receptor subtypes on target organs throughout the periphery, epinephrine and norepinephrine generate different effects. Epinephrine, for instance, is particularly effective in increasing heart rate, cardiac output, and blood glucose, while norepinephrine is most effective in increasing blood pressure and energy mobilization by breaking down fats (lipolysis). Although homeostasis depends on the selective activation of various SNS pre‐ and postganglionic neuron units for specific reflexive homeostatic adjustments, a more global SNS activation, including the hormonal component, is observed in response to psychological stressors.

HPA Axis The HPA axis (Figure 2) is a neuroendocrine system that controls the secretion of glucocorticoid hormones (Spencer & Deak, 2017). The principal glucocorticoid hormones (CORT) produced in vertebrates are cortisol (e.g., in humans, fish, dogs) and the closely related hormone corticosterone (e.g., in rats, mice, birds, and many reptiles). The HPA axis consists of three populations of cells and the hormonal signals that they use to communicate with each other and the rest of the body.

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Figure 2  The hypothalamic‐pituitary‐adrenal axis. Neuron cell bodies located in the paraventricular nucleus (PVN) of the hypothalamus release corticotropin releasing factor (CRF) and arginine vasopressin (AVP) from axon terminals located in the median eminence (ME) at the base of the hypothalamus. CRF and AVP are carried down the portal vein within the pituitary stalk to corticotroph cells located in the anterior pituitary. In response to CRF and AVP, corticotrophs secrete adrenocorticotropic hormone (ACTH) into the systemic circulation. ACTH stimulates the production of the endogenous glucocorticoid hormones cortisol and corticosterone (CORT) in the adrenal cortex. CORT is released into the systemic circulation and acts as the effector hormone of the HPA axis throughout the body. The brain’s central stress response neural networkprovides excitatory and inhibitory (+/−) neural input to the HPA axis.

The neuronal component of the HPA axis is composed of neurons whose cell bodies are located in the medial parvocellular portion of the paraventricular nucleus (PVN) of the hypothalamus. These neurons project to a region at the base of the hypothalamus (median ­eminence) that forms the junction between the brain and the pituitary stalk. These neurons are called hypophysiotropic neurons because they project to the interface region between the brain and pituitary (also known as hypophysis). These neurons secrete the neurohormone corticotropin‐releasing factor (CRF) (also known as corticotropin‐releasing hormone). CRF is carried by the portal vein within the pituitary stalk the short distance (~1 mm) down to the anterior pituitary. The second population of cells consists of endocrine cells, called corticotrophs, located in the anterior pituitary. When these cells are stimulated by CRF, they release adrenocorticotropic hormone (ACTH) into the general circulation. Some CRF neurons of the PVN also secrete vasopressin, which is co‐released with CRF and acts on corticotrophs to enhance ACTH release. The third population of cells consists of endocrine cells located in the outer cortex (fasciculata layer) of the adrenal gland. ACTH causes these adrenal cortical cells

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to produce CORT. CORT is the effector hormone of the HPA axis; it mediates the effects of the HPA axis throughout the body. CORT is a steroid molecule, which is lipid soluble, and therefore CORT readily crosses the blood–brain barrier. Consequently, CORT can influence all tissues throughout the body, including the brain. The HPA axis endocrine cells in the anterior pituitary and the adrenal cortex have very little intrinsic activity. As a result, CORT secretion ultimately depends on the activity of the hypothalamic CRF neurons and the net excitatory neural input that these neurons receive from other brain regions. In the absence of stress, CORT exhibits a large daily fluctuation (circadian rhythm) in secretion, with maximal circulating levels present around the habitual time of awakening. Superimposed on this circadian rhythm of CORT secretion is a dynamic increase in response to stress, with peak stress levels of CORT attained about 30 min after stressor onset. CORT exerts its effects throughout the body by activating two structurally similar receptor proteins, the mineralocorticoid receptor (MR) (which is also the primary receptor for the hormone aldosterone) and the glucocorticoid receptor (GR). These receptors are located inside cells rather than on the outer cell surface. These receptors function as transcription factors in the cell nucleus, and they act either to enhance or repress gene expression by directly binding specific sequences of DNA (glucocorticoid response elements) or indirectly by interacting with other transcription factor proteins (Granner, Wang, & Yamamoto, 2015). There are also some rapid non‐genomic effects of CORT, although the molecular mechanisms of these rapid effects have not yet been fully determined. Because the majority of CORT effects on physiological function depend on alteration of gene expression, most CORT effects are slow to develop, with the earliest actions evident about 45 min after an elevation in CORT. However, once initiated, these actions are relatively long lasting (for at least several hours) and therefore can persist, even after CORT levels have returned to low basal levels. GR is widely expressed in most cell types throughout the body. MR is more restricted in its expression but has high levels in the kidney, cardiac tissue, and hippocampus of the brain. CORT has a 10‐fold higher affinity for MR than GR, and therefore low circulating levels of CORT act primarily through MR, whereas higher levels, such as those present during stress, also activate GR. For this reason, GR is believed to be the primary mediator of the stress‐ related actions of CORT, whereas MR is important for mediating more “permissive” or system maintenance effects of CORT. These permissive actions are important, such that glucocorticoid insufficiency (e.g., Addison’s disease), as well as glucocorticoid excess, has pathological consequences. It is important to note that most synthetic glucocorticoids, which are widely prescribed for various medical conditions, primarily activate only GR. Stress‐induced CORT produces a number of effects that can be adaptive during times of stress. In the periphery this includes increased blood glucose levels, shift in fat depots, modulation of immune system function, and increased cardiovascular tone. In the brain CORT modulates the biochemical aspect of intercellular (neurotransmitter systems) and intracellular signaling function, thereby regulating neuronal activity, plasticity, and morphology. Consequently CORT regulates various aspects of brain function that may be adaptive during times of stress, including learning, memory, reward processes, emotions, sleep, and appetite.

Central Stress Response Neural Network The neural circuitry that underlies the rapid response to physiological stressors, such as a drop in blood pressure, as described above, has been well characterized. Determining the neural circuitry that underlies the physiological and psychological response to complex psychosocial stressors such as public speaking is not nearly as straightforward. However, considerable

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­rogress has been made in recent years at identifying relevant brain regions and their p ­connections (Ulrich‐Lai & Herman, 2009) (Figure 3). The direct neural control of the output neurons of the SNS (thoracolumbar spinal cord preganglionic neurons) and the HPA axis (CRF neurons in the PVN) is limited. The SNS receives direct input (premotor neurons) from descending spinal cord projection neurons whose cell bodies are located primarily in the medulla (e.g., nucleus of the solitary tract, ventral medullary reticular formation, and A5 noradrenergic group) and hypothalamus (non‐CRF neurons in the PVN and lateral hypothalamic area). There is, however, also an indirect cortical component provided by some neurons located in the ventromedial prefrontal cortex. The CRF neurons in the PVN receive direct neural input primarily from the brain stem and a few subcortical forebrain regions. Excitatory brain stem input is provided from adrenergic and noradrenergic afferents originating in the medulla (e.g., the nucleus of the solitary tract and the ventral medulla) and from some serotonergic afferents originating in the raphe nuclei. Both excitatory and inhibitory inputs are provided by other hypothalamic brain regions, the

Figure 3  Key neural elements of the central stress response neural network. HPA axis elements (PVN CRFneurons) and SNS elements (spinal cord preganglionic neurons) receive limited direct neural control (primarily from the BNST, hypothalamic subnuclei and brain stem nuclei) and more extensive indirect neural control from limbic system components. Many aspects of the network are modulated by brainstem monoaminergic neurons (NE, norepinephrine; Epi, epinephrine; DA, dopamine; 5HT, serotonin), and by CRF (corticotropin releasing factor) neurons (* indicates brain regions that contain CRF neurons). BNST, bednucleus of the stria terminalis; Ht, hypothalamus; POA, preoptic area; LH, lateral hypothalamus; GABAergic, gamma amino butyric acid releasing neurons; NTS, nucleus of the solitary tract; VM, ventral medullary reticular formation; LC, locus ceruleus.

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bed nucleus of the stria terminalis, and the thalamus. CRF neurons in the PVN are also strongly innervated by surrounding GABAergic neurons, and much of the activation and inhibition of CRF neurons may be a result of neural modulation of tonic GABAergic inhibitory tone. The PVN serves as a unique forebrain integrator of the physiological stress response as it contains both the motor neurons of the HPA axis (CRF hypophysiotropic neurons) and SNS premotor neurons. Other brain regions believed to engage in higher‐level stressor processing and stress response coordination are components of the classically described limbic center. These brain regions include the medial prefrontal cortex, lateral septum, hippocampal formation, and amygdala. The bed nucleus of the stria terminalis and subregions of the hypothalamus (e.g., preoptic, dorsomedial, and lateral) are an important interface between these limbic structures and the SNS and HPA axis. In the context of stress neurobiology, the medial prefrontal cortex is important for situation‐ specific decision making and control of emotionally motivated behaviors, including the acquisition of conditioned fear extinction learning. The medial prefrontal cortex is also necessary for the detection of stressor controllability and coordination of its protective effects. The ­hippocampus is especially important for formation and retrieval of contextual memories associated with stressful experiences, whereas the amygdala is critical for forming learned associations between specific environmental cues (e.g., sounds and light patterns) and stressful experiences. The amygdala is also important for generation of negative emotions and coordination of stereotypic defensive behaviors, such as freezing. The lateral septum is important for modulating affective tone, mood, and associated behaviors. The medial prefrontal cortex and the ventral hippocampus also contribute to the timely shutoff of the HPA axis response to stressors. Strong modulatory regulation of the central stress response neural network is provided by the ascending brain stem monoamine neurotransmitter systems and by the central CRF ­neuropeptide system (Feldman, Meyer, & Quenzer, 1997). Many noradrenergic neurons (which secrete norepinephrine) are clustered in the brain stem locus coeruleus and project widely throughout the brain. Increased activity in the noradrenergic system contributes to general arousal, and many of these neurons are stress reactive. Adrenergic neurons (which secrete ­epinephrine) have a more restricted projection to the forebrain but innervate the PVN. Dopaminergic neurons (which secrete dopamine) originating in the ventral tegmental area of the midbrain provide extensive projections to the striatum and medial prefrontal cortex. This mesolimbic dopaminergic system is fundamental for reward processes, but the system is also stress reactive and contributes to emotion modulation. Serotonergic neurons (which secrete serotonin) originating in the raphe nuclei also project widely throughout the forebrain. Subsets of these serotonergic neurons in the dorsal raphe nuclei are also very stress reactive. In addition to the CRF hypophysiotropic neurons located in the PVN, there are CRF ­neurons located in strategic portions of the stress response network, such as the hypothalamus, bed nucleus of the stria terminalis, and the amygdala, as well as CRF interneurons located throughout the neocortex and hippocampus. CRF central neurotransmission contributes to SNS and HPA axis activation and regulation of anxiety‐related behaviors. The central stress response network is responsible not just for activation of the SNS and HPA axis but also for generating the psychological stress response. Although there is not a specific stress emotion, there are a number of negative emotional states associated with many stressors, such as fear, anxiety, frustration, and anger. As noted above, the psychological response to stress also includes shifts in attention and decision‐making. These shifts are especially dependent on altered operation of the prefrontal cortex. For example, uncontrollable stress increases norepinephrine and dopamine release in the medial prefrontal cortex, leading to an acute reduction in medial prefrontal cortex function. This reduced function contributes

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to a shift in behavioral control away from top‐down reflective behaviors to more bottom‐up reactive behaviors (Arnsten, 2015). Conversely, many of the protective effects of controllable stress depend on medial prefrontal cortex‐mediated suppression of stress‐induced dorsal raphe nuclei serotonergic activity (Maier, Jose, Baratta, Evan, & Watkins, 2006). Stress also ­produces robust effects on learning and memory, but the stress–memory relationship is not unidirectional, as stress can interfere with some memory processes but facilitate others. For example, stress can enhance formation of emotional memories while attenuating formation and retrieval of declarative or spatial memories. Stress also shifts reward incentives from a long‐term to short‐term perspective. In general, all of the orchestrated neural network events associated with stress are directed toward shifting physiological and psychological resources in a manner adaptive for coping with stress.

Regulation of the Stress Response Stress Response Adaptation In human experience, all stress responses are superimposed on a background of previous stressor exposure and adaptation. The effects of that adaptation on the expression of subsequent stress responses may be a critical factor in the overall impact of stress on the individual. The deleterious effects of chronic stress on organismal well‐being (e.g., disruptions in metabolism, cardiovascular function, inflammation, and depression‐like behaviors) largely stem from the cumulative effects of the stress response over time. A key principle of stress neurobiology that has emerged from studies of stress response adaptation is that repeated exposure of an experimental subject to a particular stressor typically leads to the expression of a diminished hormonal and neural response (habituation) when the subject is then challenged again with the same stressor (homotypic stressor), but often expression of an augmented response (sensitization, also referred to as facilitation) when the subject is subsequently challenged with a different novel stressor (heterotypic stressor) (McCarty, 2016). The rate and degree of stress response habituation generally depends on the type, severity, and duration of the stressor, but the end result is a progressively diminished physiological response to the homotypic stressor. Importantly, this habituation is quite stressor selective, such that even relatively small changes in stressor properties, context, or duration can interfere with the expression of habituation. A sensitized response to challenge with a novel stressor is evident not only after repeated exposure to a homotypic stressor but also after exposure to chronic variable stress, a procedure in which a subject is exposed to a variety of stressors on an unpredictable schedule. Single‐­ session exposure to some relatively intense acute stressors is also sufficient to produce an exaggerated response to subsequent challenge with a novel stressor. Sensitization of the response to a novel stressor is manifest not only by an exaggerated HPA axis or SNS response but also by increased activation of the dorsal raphe nuclei serotonergic system, potentiated activity in the locus coeruleus, and increased norepinephrine release in the PVN and cortex. Both impaired stress response habituation and increased stress response sensitization pose increased health risk. Several features of stress response habituation and sensitization described above indicate that the underlying adaptive processes critically occur within central neurocircuits that control physiological stress responses rather than within the anatomical elements of the SNS and HPA axis themselves. First, the stressor selectivity of stress response habituation rules out a generalized decreased responsiveness of the SNS and HPA axis to all stressor input.

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Second, the preganglionic neurons of the SNS or the PVN CRH neuron of the HPA axis lack the direct multimodal neural input necessary to discriminate between many stressors. Third, neuronal activity in stress‐responsive brain areas shows parallel adaptive changes with the SNS and HPA axis response. The specific mechanisms that underlie this central stress response neurocircuit adaptation remain to be determined, but recent progress indicates that it is a multidimensional process. Neurocircuit components that may be especially important for the expression of this adaptation are the thalamic paraventricular nucleus, medial prefrontal cortex, and basolateral amygdala. Sensitization may depend on upregulation of generalized stressor responsiveness of serotonergic, noradrenergic, and dopaminergic systems. Although stress response adaptation fundamentally depends on neuroplastic changes within the central stress response neural ­network, additional adaptation within the HPA axis may fine‐tune the expression of HPA axis stress response adaptation.

Sex Differences Women have a greater prevalence of stress‐related mood disorders (e.g., depression and some anxiety‐related disorders such as posttraumatic stress disorder) compared with men. This prevalence difference may be due in part to sex differences in the physiological and psychological response to stress. Female rodents display greater stress‐induced neuroactivation than males in some brain regions (e.g., bed nucleus of the stria terminalis, medial preoptic hypothalamic area, and the hippocampus) and lesser neuroactivation in others (e.g., amygdala, raphe nuclei, and thalamic nuclei). In the medial prefrontal cortex, female rats exhibit a smaller initial but longer‐lasting pattern of neuroactivation than males. In an fMRI study of men and women exposed to negative stimuli, sex differences in blood oxygenation level‐dependent signal were evident in a variety of limbic brain regions, and these sex differences varied with female ­menstrual cycle phase (Goldstein, Jerram, Abbs, & Whitfield‐Gabrieli, 2010). There are also some sex differences in neuronal intracellular signal processing in response to acute stress (e.g., in the prefrontal cortex and locus coeruleus) that may contribute to sex differences not only in immediate neural activity but also in neural adaptation to stress. Female rodents typically exhibit greater stress‐induced HPA axis activation compared with males. These HPA axis sex differences depend on both organizational and activational effects of gonadal steroids. Sex differences in HPA axis activity are much less consistently observed in human research. Some studies have found greater stress‐induced ACTH and CORT in men compared with women, but other studies have found that while men have greater basal salivary CORT levels, women have equal if not greater stress‐induced salivary CORT, suggesting that women overall have a greater increase in stress‐induced CORT.

Developmental and Lifespan Factors Stress occurs throughout the lifespan, and its effects vary depending on the organism’s age (Lupien, McEwen, Gunnar, & Heim, 2009). There are important prenatal, childhood, and adolescent critical periods of stress response system development. The extent to which the individual is subject to a stressful environment during these critical periods may program development to be adaptive for a lifetime of excessive stress. For example, in rodent studies, maternal stress results in offspring that as adults exhibit HPA axis hyperreactivity, social withdrawal, and deficits in prefrontal cortex‐dependent and hippocampus‐dependent learning and memory tasks.

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Neonatal stress exposure of rats and mice can also result in increased stress reactivity as an adult. However, the detrimental effects of neonatal stress can be mitigated by good maternal care (e.g., appropriate amount of grooming and licking). Human studies also find long‐lasting effects of early life stress on HPA axis function. During adolescence, rodents and humans show a prolonged HPA axis response to acute stress compared with adults. In the rat the prolonged CORT response is due to increased PVN CRF activity and increased adrenal sensitivity to ACTH. In response to chronic stress, adolescent rats also have higher CORT levels and impaired habituation to a repeated stressor, resulting in a greater likelihood of developing depression‐like behavior. Aged animals also exhibit prolonged CORT release in response to some acute stressors, and this may be due to decreased hippocampal functioning, resulting in decreased inhibitory ­control of the HPA axis. In regard to chronic stress, it has been found that aged animals do not adapt to a repeated stressor as well as young rats. Similar findings have also been reported in human studies of the elderly.

Mechanisms by Which Stress Impacts Health Research indicates that the greatest consequence of stress on physical and mental health is not a result of the immediate physiological and psychological impact of an acute stress response, but rather a result of the cumulative and emergent consequence of chronic or repeated stress. In animal studies, some of these effects of chronic stress have been shown to persist for months after chronic stress has ended.

Physiological Health The long‐term alteration of SNS and HPA axis basal and reactive function is a major mediator of the adverse effects of chronic stress on physiological health. Every physiological system has cellular and molecular elements that are directly modulated by catecholamines and glucocorticoids. A shift in the appropriate balance of these modulatory effects directly contributes to systems level pathology. Biomedical research has made tremendous progress in determining many of these specific mechanisms. In addition to the specific regulatory effects that the SNS and HPA axis exert on each physiological system, abnormal catecholamine and glucocorticoid profiles contribute to three processes that can negatively affect all physiological systems. These three processes are increased inflammation, metabolic imbalance, and circadian dysregulation. Catecholamines lead to increased production of plasma and tissue‐specific proinflammatory cytokines (e.g., interleukin‐1β and interleukin‐6). Although pharmacological levels of glucocorticoids have potent anti‐inflammatory and immunosuppressive effects that are beneficial in treating individuals with autoimmune disorders and tissue transplant recipients, physiological levels of CORT exert a more complex regulatory influence on immune system function (Spencer, Dhabhar, & Kalman, 2001). For example, the permissive effects of CORT prior to stress exposure are necessary for mounting an appropriate proinflammatory response to pathogens, but post‐stress glucocorticoids have an important anti‐inflammatory influence while promoting certain aspects of acquired immunity. The dysregulation of these immune processes results not only in elevated risk for infection and increased autoimmune expression, but a shift toward chronic tissue and systemic inflammation that negatively impact all physiological ­systems. This includes the exacerbation of certain inflammatory‐related disorders such as ­rheumatoid arthritis, celiac disease, inflammatory bowel diseases, and asthma.

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The chronic activation of the stress response systems is associated with a host of metabolic perturbations including insulin resistance, elevated blood sugar, and adipose tissue redistribution (Bose, Oliván, & Laferrère, 2009). SNS activation acutely promotes negative energy ­balance, characterized by lipolysis in adipocytes via β‐adrenergic receptors, increased triglyceride hydrolysis, free fatty acid mobilization, and brown adipose tissue thermogenesis. Conversely, increased CORT tends to promote positive energy balance in part by antagonizing catecholamine action (e.g., downregulation of β‐adrenergic receptor expression), adipogenesis, lipogenesis, and inhibition of brown adipose tissue uncoupling protein‐1. Chronic CORT exposure also increases the salience of palatable foods, thereby increasing their consumption that is beneficial for survival in an environment with limited food resources, but contributes to unhealthy diets and metabolic disruption in modern society. The various depots of adipose tissue are distinguished by their anatomical location and the circulatory system into which they drain. A decreased ratio of subcutaneous white adipose ­tissue relative to visceral white adipose tissue is a predictor of negative health outcomes associated with obesity. Increased visceral adiposity is associated with chronic stress in many different species including humans. Increased glucocorticoids can promote visceral obesity even when circulating CORT levels appear normal as a result of a stress‐induced upregulation within ­visceral fat of an enzyme (11β‐hydroxysteroid dehydrogenase type 1) that converts inactive glucocorticoids into locally active glucocorticoids (Bose et al., 2009). 11β HSD‐1 knockout mice are protected against high fat diet‐induced metabolic disruptions and obesity. Stress‐ induced disruption of metabolic balance directly contributes to the metabolic syndrome, which is defined as a cluster of conditions that increases risk of cardiovascular disease, stroke, and type 2 diabetes. Metabolic syndrome symptoms include elevated blood sugar, abnormal levels of cholesterol and triglycerides, excess abdominal fat, and high blood pressure. The optimal function of every physiological system depends on appropriate daily timing of various aspects of its operation (Dickmeis, Weger, & Weger, 2013). These daily fluctuations in operation (circadian rhythms) are maintained and coordinated by a so‐called master biological clock located in the suprachiasmatic nucleus of the hypothalamus. CORT is one of the primary intercellular signals used by the suprachiasmatic nucleus to regulate each tissue’s unique circadian rhythms. CORT circadian secretion can act as an entrainment factor that contributes to the rhythmic expression of clock genes within many peripheral tissues, and rhythmic clock gene expression then gives rise to tissue‐specific circadian operation. Chronic stress can disrupt the body’s circadian rhythms not only as a result of disturbed sleep patterns but also by leading to a loss of normal circadian CORT secretion and the resultant misalignment of circadian clocks in tissues throughout the body.

Mental Health The SNS and HPA axis stress response systems also contribute to the negative effects of chronic stress on mental health. Although the SNS does not have direct effects on brain function, it can indirectly affect the brain through alteration of the activity of visceral sensory afferent nerves, many of which travel with the vagus nerve and project to the brain stem. Chronic elevation of glucocorticoids produces a number of molecular and cellular changes in the brain that are associated with cognitive and emotional disruptions observed in cases of impaired mental health. For example, elevated glucocorticoids lead to dendritic atrophy in the ­hippocampus and prefrontal cortex while increasing dendritic arborization in the basolateral amygdala. These neuronal morphological changes may enhance amygdala‐dependent ­emotional learning while reducing the appropriate contextualization and situation‐specific

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modulation of that emotional learning. Chronic elevation of glucocorticoids also changes the functional activity of most neurotransmitter systems by altering levels of neurotransmitter synthetic enzymes (e.g., serotonin tryptophan hydroxylase II and tyrosine hydroxylase), ­ neurotransmitter receptors (e.g., serotonin 2A and NMDA receptors), and degradative ­ enzymes (e.g., monoamine oxidase). However, some of the changes in brain function that emerge with chronic stress are independent of HPA axis and SNS activity. These changes may be directly related to altered activity of components of the central stress response network, as well as additional factors such as altered epigenetic factors and neuroinflammation.

Author Biographies Prof. R.L. Spencer received a PhD in biopsychology from University of Arizona in 1986 and is currently a professor of neuroscience at the University of Colorado Boulder. His research focuses on the neurobiology of stress response adaptation and circadian regulation of brain function. Dr. L.E. Chun received a PhD in neuroscience from University of Colorado Boulder in 2017 and is currently a postdoctoral researcher at the University of Colorado Boulder. Her research focuses on sex differences in the influence of stress on circadian neurobiology. M.J. Hartsock received a MA in neuroscience from University of Colorado Boulder in 2017 and is currently a doctoral student at the University of Colorado Boulder. His research focuses on neural and hormonal mechanisms of circadian regulation of brain function. Dr. E.R. Woodruff received a PhD in neuroscience from University of Colorado Boulder in 2017 and is currently a postdoctoral researcher at the University of Colorado Boulder. Her research focuses on neurohormonal and circadian regulation of prefrontal cortex‐dependent emotional learning.

References Arnsten, A. F. T. (2015). Stress weakens prefrontal networks: Molecular insults to higher cognition. Nature Neuroscience, 18(10), 1376–1385. doi:10.1038/nn.4087 Bose, M., Oliván, B., & Laferrère, B. (2009). Stress and obesity: The role of the hypothalamic‐pituitary‐ adrenal axis in metabolic disease. Current Opinion in Endocrinology, Diabetes, and Obesity, 16(5), 340–346. doi:10.1097/MED.0b013e32832fa137 Dickmeis, T., Weger, B. D., & Weger, M. (2013). The circadian clock and glucocorticoids—Interactions across many time scales. Molecular and Cellular Endocrinology, 380(1‐2), 2–15. doi:10.1016/j. mce.2013.05.012 Dimsdale, J. E. (2008). Psychological stress and cardiovascular disease. Journal of the American College of Cardiology, 51(13), 1237–1246. doi:10.1016/j.jacc.2007.12.024 Feldman, R. S., Meyer, J. S., & Quenzer, L. F. (1997). Principles of neuropsychopharmacology. Sunderland, MA: Sinauer Associates, Inc. Goldstein, J. M., Jerram, M., Abbs, B., Whitfield‐Gabrieli, S., & Makris, N. (2010). Sex differences in stress response circuitry activation dependent on female hormonal cycle. Journal of Neuroscience, 30(2), 431–438. doi:10.1523/JNEUROSCI.3021‐09.2010 Granner, D. K., Wang, J.‐C., & Yamamoto, K. R. (2015). Regulatory actions of glucocorticoid hormones: From organisms to mechanisms. Advances in Experimental Medicine and Biology, 872, 3–31. doi:10.1007/978‐1‐4939‐2895‐8_1 Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews. Neuroscience, 10(6), 434–445. doi:10.1038/nrn2639

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Maier, S. F., Jose, A., Baratta, M. V., Evan, P., & Watkins, L. R. (2006). Behavioral control, the medial prefrontal cortex, and resilience. Dialogues in Clinical Neuroscience, 8(4), 397–406. McCarty, R. (2016). Learning about stress: Neural, endocrine and behavioral adaptations. Stress (Amsterdam, Netherlands), 19(5), 1–30. doi:10.1080/10253890.2016.1192120 Powley, T. L. (2013). Central control of autonomic function. The organization of the autonomic ­nervous system. In L. R. Squire, D. Berg, F. E. Bloom, S. du Lac, A. Ghosh, & N. C. Spitzer (Eds.), Fundamental neuroscience (4th ed.). Waltham, MA: Academic Press. Reagan, L. P., Grillo, C. A., & Piroli, G. G. (2008). The As and Ds of stress: Metabolic, morphological and behavioral consequences. European Journal of Pharmacology, 585(1), 64–75. doi:10.1016/j. ejphar.2008.02.050 Spencer, R. L., & Deak, T. (2017). A users guide to HPA axis research. Physiology and Behavior, 178, 43–65. doi:10.1016/j.physbeh.2016.11.014 Spencer, R. L., Dhabhar, F. S., & Kalman, B. A. (2001). Role of endogenous glucocorticoids in immune system function: Regulation and counterregulation. In B. S. McEwen & H. Maurice Goodman (Eds.), Handbook of physiology. Section 7 (Vol. 4, pp. 381–423). New York, NY: Oxford University Press. Ulrich‐Lai, Y. M., & Herman, J. P. (2009). Neural regulation of endocrine and autonomic stress responses. Nature Reviews. Neuroscience, 10(6), 397–409.

Stress, Neurogenesis, and Mood: An Introduction to the Neurogenic Hypothesis of Depression Jacob Huffman1,2 and George T. Taylor1,3 Behavioral Neuroscience Program, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA 2 Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO, USA 3 Interfakultäre Biomedizinische Forschungseinrichtung (IBF) der Universität Heidelberg, Heidelberg, Germany 1

Introduction The prevalence of depression in the United States has steadily increased over the years with 6.7% of adults and 11.4% of adolescents diagnosed with major depressive disorder (MDD) in 2014 (NIMH, 2015). MDD is a complex clinical condition resulting from diverse neurobiological anomalies (Fried, Nesse, Zivin, Guille, & Sen, 2014; Milaneschi et  al., 2016). A  recent n ­ europathogenic model of MDD highlights the stress response and reduced ­neurogenesis in the dentate gyrus (DG) as essential underpinning components to the onset and progression of depressive symptoms. This model is referred to as the neurogenic hypothesis of depression (NHD; Jacobs, Van Praag, & Gage, 2000; Snyder, Soumier, ­ Brewer, Pickel, & Cameron, 2011).

Functional Significance of the Dentate Gyrus The DG is composed of molecular, granule, and polymorphic layers. Of primary interest to the NHD models is the granule layer, which is composed of a large number of tightly grouped cells. Granule cells have unique properties contributing to neurogenesis and exhibit generative

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capacity as a function of progenitor cells that emerge from the subgranular zone. Interestingly, granule cell counts are static in healthy adult animals (Rapp & Gallagher, 1996), suggesting that ongoing migration of these cells from the subgranular zone is required to maintain a homeostatic level (Lyons et  al., 2010). Newly derived granule cells mature and establish ­synaptic connectivity with adjacent cells (Amaral, Scharfman, & Lavenex, 2007). These cells are excitatory in nature and are densely populated in efferent/afferent regions of the entorhinal cortex and hippocampus. Not surprisingly, altered hippocampal neurogenesis is correlated with impaired performance on memory tasks in animal models of depression (Jessberger et al., 2009; Parihar, Hattiangady, Kuruba, Shuai, & Shetty, 2011).

Stress Response and Neurogenesis Elevated cortisol (CORT) suppresses neurogenesis in the DG, likely due to the stimulation of NMDA receptors (Anacker et  al., 2013). Granule cells and progenitor cells are sensitive to glucocorticoid levels (Alahmed & Herbert, 2008). Elevated levels of CORT inhibit the ­ ­differentiation of progenitor cells into mature granule cells (Brummelte & Galea, 2010; Wong & Herbert, 2006). Chronic stress reduces neurogenesis and the total number of mature granule cells. Behaviorally, individuals with chronic stress exhibit high levels of CORT, reduced neurogenesis, and impaired memory performance (Montaron et al., 2006). Interestingly, ­neurogenic processes decline with normal aging (Cowen, Takase, Fornal, & Jacobs, 2008), which may contribute to memory impairments and sensitization to stressful stimuli in older individuals.

BNDF Versus Glucocorticoids BDNF is a one of four growth factors in the neurotrophin family, including nerve growth ­factor, neurotrophin‐3, and neurotrophin‐4. Growth factors facilitate cellular maturation and differentiation. BDNF expression increases long‐term potentiation, a key component to ­sustained cell signaling and adequate memory function (Gartner, 2006). BDNF also assists cellular activation presynaptically by modifying incoming signals from upstream neurons ­ (Kuczewski et al., 2008). Of particular importance to the current chapter, BDNF regulates NMDA receptor activation postsynaptically (Caldeira et al., 2007) and is essential for preventing excess signaling from driving excitotoxic degradation of mature granule cells. Reductions in BDNF disrupt NMDA activation (Xu et al., 2000). The level of signal modulation may be an important facet of neurogenic processes and helps explain why granule cells are particularly vulnerable to excitotoxicity. Indeed, injections of BDNF directly into the ­hippocampus enhances neurogenesis and dendritic branching (Kramár et al., 2012; Scharfman et al., 2005). Snyder et al. (2011) showed that neurogenesis helps to buffer the effects of stress by controlling CORT production through mature granule cells in the DG. The implications are the hippocampus and HPA axis are dependent on neurogenesis to adapt effectively in the context of chronic stress. Observable reductions in BDNF may be the first sign of HPA ­dysregulation typical of stress‐induced depressive behaviors.

Acute and Chronic BDNF Activation The mechanisms driving different changes in BDNF expression under acute and chronic stress are not well understood. In a study by Murakami, Imbe, Morikawa, Kubo, and Senba (2005),

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BDNF expression was reduced considerably more with acute compared with chronic stress. CORT levels continued to rise during sustained stress, whereas BDNF levels remained steady. These data, coupled with findings from Snyder et  al. (2011), indicate that stress‐induced reductions in neurogenesis follow initial downregulation of BDNF.

BDNF and Antidepressant Efficacy Fluoxetine enhances the differentiation of progenitor cells and prevents cell death in the DG (Lee et al., 2001). Another antidepressant, tianeptine, reduces apoptosis in the DG (Lucassen, Fuchs, & Czéh, 2004). Chronic administration of venlafaxine, mirtazapine, and fluoxetine reduces depressive‐like behaviors and increases BDNF levels (Zhang, Gu, Chen, & Dong, 2010). Of importance, only venlafaxine and mirtazapine reduced serum levels of CORT. The observation that all drugs reduced depressive‐like behaviors and increased BDNF levels, but did not decrease CORT, suggests BDNF plays a primary role in the success of antidepressant treatments. Fluoxetine displays unique properties relevant to the NHD model. Specifically, fluoxetine enhances neurogenesis, improves depressive‐like behaviors, and helps to maintain drug efficacy without extensive influence on CORT secretion (Lee et al., 2001; Zhang et al., 2010). Evidence also suggests that chronic CORT administration induces hyperactivity, anxiety, and depressive‐like behaviors similar to that seen with chronic stress, which can be reversed following fluoxetine administration (David et al., 2009). These findings were reduced to fluoxetine’s ability to reverse CORT‐induced disruptions in cellular proliferation, rather than progenitor cell differentiation or maturation. The implication is that fluoxetine modulates depressive‐like behaviors through the indirect downregulation of CORT secretion via modified cellular ­processes derived from BDNF. Indeed, the well‐substantiated link between stress and CORT, per the NHD model, has led to the contention that the effects of BDNF on depressive‐like behavior acts to indirectly aid the mechanisms of antidepressant medication, rather directly superseding the effects of CORT. Evidence suggests that lower levels of BDNF in the DG are not sufficient to produce depressive‐like behavior (Adachi, Barrot, Autry, Theobald, & Monteggia, 2008; Larsen, Mikkelsen, Hay‐Schmidt, & Sandi, 2010). However, genetic depletion of BDNF expression hinders the efficacy of antidepressant medication (Ibarguen‐Vargas et al., 2009). Postmortem analysis has shown that BDNF is higher in the DG of patients who were using antidepressant medication at the time of death (Chen, Dowlatshahi, Macqueen, Wang, & Young, 2001).

Summary According to the NHD, reduced neurogenesis in the DG disrupts the HPA axis with ­downstream effects on CORT secretion and BDNF expression. With chronic stress, continuously elevated CORT decreases BDNF and suppresses the differentiation of progenitor cells into mature granule cells. Furthermore, excess CORT inhibits the migration of progenitor cells, disrupting homeostatic regulation of mature granule cell homeostasis. Chronic stress creates a feedforward cycle that restricts recovery. Here, we contend that BDNF is an initial alteration in a sequence of physiological responses to stress in the DG (rather than CORT). This is likely consequent to the failure of proper stress adaptation, producing chronic hyperactivation of the entorhinal cortex, excessive stimulation of granule cells, and abnormal HPA negative feedback.

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Author Biographies Jacob Huffman is currently working on a PhD at the University of Missouri–St. Louis. His research focuses on the steroidal and immunological contributions of acute/chronic stress and ischemic stroke on models of depression‐like behaviors. He is a certified surgeon for the micro‐arterial stroke model, middle cerebral artery occlusion, which mimics a focal cerebral ischemic event in humans. He also collaborates with Washington University Medical School on steroidal, anesthetic, and viral models of prenatal and postnatal neurotoxicity. George T. Taylor is a professor of psychology and director of the behavioral neuroscience graduate program. He received his PhD from the University of New Mexico in animal ­psychology, and postdoctoral work was conducted at Rockefeller University in New York City in neurophysiology. He also has held appointments as a guest scientist at Bordeaux (France) Medical University in neuroendocrinology, at the German Cancer Research Center–Heidelberg in experimental pathology, and at Tokyo University in biology. Research in his laboratory employs animal models for the study of neuroendocrinology–psychopathology relations. Ongoing projects include neurosteroidal influences on cognition and learning and memory behaviors, steroid receptor functions in the hippocampus, neurobiology of acute/chronic stress and depression‐like behaviors, and hormones as neuroprotective agents in stroke. His lab collaborates with colleagues at Heidelberg University Medical School, Washington University Medical School, and University of Missouri–Columbia Veterinary School, allowing his graduate students to gain further research experiences in biomedical settings.

References Adachi, M., Barrot, M., Autry, A. E., Theobald, D., & Monteggia, L. M. (2008). Selective loss of brain‐ derived neurotrophic factor in the dentate gyrus attenuates antidepressant efficacy. Biological Psychiatry, 63(7), 642–649. Alahmed, S., & Herbert, J. (2008). Strain differences in proliferation of progenitor cells in the dentate gyrus of the adult rat and the response to fluoxetine are dependent on corticosterone. Neuroscience, 157(3), 677–682. Amaral, D. G., Scharfman, H. E., & Lavenex, P. (2007). The dentate gyrus: Fundamental neuroanatomical organization (dentate gyrus for dummies). Progress in Brain Research, 3–790. Anacker, C., Cattaneo, A., Luoni, A., Musaelyan, K., Zunszain, P. A., Milanesi, E., … Price, J. (2013). Glucocorticoid‐related molecular signaling pathways regulating hippocampal neurogenesis. Neuropsychopharmacology, 38(5), 872–883. Brummelte, S., & Galea, L. (2010). Chronic high corticosterone reduces neurogenesis in the dentate gyrus of adult male and female rats. Neuroscience, 168(3), 680–690. Caldeira, M. V., Melo, C. V., Pereira, D. B., Carvalho, R. F., Carvalho, A. L., & Duarte, C. B. (2007). BDNF regulates the expression and traffic of NMDA receptors in cultured hippocampal neurons. Molecular and Cellular Neuroscience, 35(2), 208–219. Chen, B., Dowlatshahi, D., Macqueen, G. M., Wang, J., & Young, L. (2001). Increased hippocampal BDNF immunoreactivity in subjects treated with antidepressant medication. Biological Psychiatry, 50(4), 260–265. Cowen, D. S., Takase, L. F., Fornal, C. A., & Jacobs, B. L. (2008). Age‐dependent decline in hippocampal neurogenesis is not altered by chronic treatment with fluoxetine. Brain Research, 1228, 14–19. David, D. J., Samuels, B. A., Rainer, Q., Wang, J. W., Marsteller, D., Mendez, I., … Artymyshyn, R. P. (2009). Neurogenesis‐dependent and‐independent effects of fluoxetine in an animal model of ­anxiety/depression. Neuron, 62(4), 479–493.

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Fried, E. I., Nesse, R. M., Zivin, K., Guille, C., & Sen, S. (2014). Depression is more than the sum score of its parts: individual DSM symptoms have different risk factors. Psychological Medicine, 44(10), 2067–2076. Gartner, A. (2006). Hippocampal long‐term potentiation is supported by presynaptic and postsynaptic tyrosine receptor kinase B‐mediated phospholipase C signaling. Journal of Neuroscience, 26(13), 3496–3504. Ibarguen‐Vargas, Y., Surget, A., Vourc’h, P., Leman, S., Andres, C. R., Gardier, A. M., & Belzung, C. (2009). Deficit in BDNF does not increase vulnerability to stress but dampens antidepressant‐like effects in the unpredictable chronic mild stress. Behavioural Brain Research, 202(2), 245–251. Jacobs, B. L., Van Praag, H., & Gage, F. H. (2000). Adult brain neurogenesis and psychiatry: A novel theory of depression. Molecular Psychiatry, 5(3), 262. Jessberger, S., Clark, R. E., Broadbent, N. J., Clemenson, G. D., Consiglio, A., Lie, D. C., … Gage, F. H. (2009). Dentate gyrus‐specific knockdown of adult neurogenesis impairs spatial and object recognition memory in adult rats. Learning & Memory, 16(2), 147–154. Kramár, E. A., Chen, L. Y., Lauterborn, J. C., Simmons, D. A., Gall, C. M., & Lynch, G. (2012). BDNF upregulation rescues synaptic plasticity in middle‐aged ovariectomized rats. Neurobiology of Aging, 33(4), 708–719. Kuczewski, N., Porcher, C., Ferrand, N., Fiorentino, H., Pellegrino, C., Kolarow, R., … Gaiarsa, J. (2008). Backpropagating action potentials trigger dendritic release of BDNF during spontaneous network activity. Journal of Neuroscience, 28(27), 7013–7023. Larsen, M. H., Mikkelsen, J. D., Hay‐Schmidt, A., & Sandi, C. (2010). Regulation of brain‐derived neurotrophic factor (BDNF) in the chronic unpredictable stress rat model and the effects of chronic antidepressant treatment. Journal of Psychiatric Research, 44(13), 808–816. Lee, H. J., Kim, J. W., Yim, S. V., Kim, M. J., Kim, S. A., Kim, Y. J., … Chung, J. H. (2001). Fluoxetine enhances cell proliferation and prevents apoptosis in dentate gyrus of maternally separated rats. Molecular Psychiatry, 6(6), 725–728. Lucassen, P. J., Fuchs, E., & Czéh, B. (2004). Antidepressant treatment with tianeptine reduces ­apoptosis in the hippocampal dentate gyrus and temporal cortex. Biological Psychiatry, 55(8), 789–796. Lyons, D. M., Buckmaster, P. S., Lee, A. G., Wu, C., Mitra, R., Duffey, L. M., … Schatzberg, A. F. (2010). Stress coping stimulates hippocampal neurogenesis in adult monkeys. Proceedings of the National Academy of Sciences, 107(33), 14823–14827. Milaneschi, Y., Lamers, F., Peyrot, W. J., Abdellaoui, A., Willemsen, G., Hottenga, J. J., … Boomsma, D. I. (2016). Polygenic dissection of major depression clinical heterogeneity. Molecular Psychiatry, 21(4), 516–522. Montaron, M., Drapeau, E., Dupret, D., Kitchener, P., Aurousseau, C., Moal, M. L., … Abrous, D. (2006). Lifelong corticosterone level determines age‐related decline in neurogenesis and memory. Neurobiology of Aging, 27(4), 645–654. Murakami, S., Imbe, H., Morikawa, Y., Kubo, C., & Senba, E. (2005). Chronic stress, as well as acute stress, reduces BDNF mRNA expression in the rat hippocampus but less robustly. Neuroscience Research, 53(2), 129–139. NIMH. (2015). Major depression among adults. Retrieved from http://www.nimh.nih.gov/health/ statistics/prevalence/major‐depression‐among‐adults.shtml. Parihar, V. K., Hattiangady, B., Kuruba, R., Shuai, B., & Shetty, A. K. (2011). Predictable chronic mild stress improves mood, hippocampal neurogenesis and memory. Molecular Psychiatry, 16(2), 171–183. Rapp, P. R., & Gallagher, M. (1996). Preserved neuron number in the hippocampus of aged rats with spatial learning deficits. Proceedings of the National Academy of Sciences, 93(18), 9926–9930. Scharfman, H., Goodman, J., Macleod, A., Phani, S., Antonelli, C., & Croll, S. (2005). Increased ­neurogenesis and the ectopic granule cells after intrahippocampal BDNF infusion in adult rats. Experimental Neurology, 192(2), 348–356. Snyder, J. S., Soumier, A., Brewer, M., Pickel, J., & Cameron, H. A. (2011). Adult hippocampal neurogenesis buffers stress responses and depressive behaviour. Nature, 476(7361), 458–461.

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Wong, E., & Herbert, J. (2006). Raised circulating corticosterone inhibits neuronal differentiation of progenitor cells in the adult hippocampus. Neuroscience, 137(1), 83–92. Xu, B., Gottschalk, W., Chow, A., Wilson, R. I., Schnell, E., Zang, K., … Reichardt, L. F. (2000). The role of brain‐derived neurotrophic factor receptors in the mature hippocampus: Modulation of long‐term potentiation through a presynaptic mechanism involving TrkB. The Journal of Neuroscience, 20(18), 6888–6897. Zhang, Y., Gu, F., Chen, J., & Dong, W. (2010). Chronic antidepressant administration alleviates frontal and hippocampal BDNF deficits in CUMS rat. Brain Research, 1366, 141–148.

An Overview of Neuropsychiatric Symptoms and Pathogenesis in MS Emily Evans1, Elizabeth Silbermann1,2, and Soe Mar3 Neurology Resident, Washington University in St. Louis, St. Louis, MO, USA Department of Neurology, Oregon Health and Science University, Portland, OR, USA 3 Neurology and Pediatrics, Division of Pediatric Neurology, Washington University in St. Louis, St. Louis, MO, USA 1

2

Pathophysiology of MS Multiple sclerosis (MS) is a chronic inflammatory disorder affecting the central nervous s­ ystem. MS affects almost 2.3 million people worldwide and approximately 250,000–400,000 people in the United States. MS primarily affects the white matter with pathology characterized by the inflammatory plaque. Inflammatory plaques exist throughout the brain, spinal cord, and optic nerves with their locations corresponding to a myriad of clinical symptoms including weakness, numbness, bowel and bladder symptoms, vision loss, or other neuropsychiatric symptoms. Inflammation in MS occurs in several phases and manifests with considerable variability. Our initial understanding of the pathophysiology of this disease focused on the inflammatory plaque. Inflammatory plaques are often characterized by their temporal pattern and their degree of inflammation. Early inflammatory plaques feature monocytes and lymphocytes with T‐cell predominant perivascular cuffing and demyelination. Early on, the blood–brain barrier remains intact with limited inflammation. Between six and twenty weeks of disease progression, lesions begin to show inflammatory infiltrates. Chronic plaques, on the other hand, ­feature hypo‐cellularity and glial scarring with varying degrees of active inflammation present at the plaque margin (Wu & Alvarez, 2011). The robust presence of T cells in inflammatory lesions prompted investigation into their involvement in neuronal destruction. Several studies in animal models of MS have demonstrated that autoreactive CD4 cells and to a lesser extent CD8 cells lead to a phenotype similar to MS. There are ongoing investigations into the role of a particular lineage of CD4 T cells, TH17 cells, which secrete IL17, a proinflammatory cytokine. In experimental autoimmune

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encephalomyelitis (EAE), a commonly utilized animal model of MS, TH17 cells, participate in the robust inflammatory response seen in early CNS lesions (Ontaneda, Hyland, & Cohen, 2012). Further, human studies have shown that both the peripheral blood and cerebral spinal fluid (CSF) of patients with MS contain a greater proportion of TH17 cells than do controls. TH17 cells are found in perivascular infiltrates that further support their role in allowing ­penetration of the blood–brain barrier. B cells are also involved in CNS damage. While acute plaques feature robust active inflammation, chronic plaques often feature complement deposition and plasma cells (Weissert, 2013). This involvement is not entirely surprising; the presence of oligoclonal bands in the CSF of patients is highly suggestive of, although not specific for, a diagnosis of MS. In addition, B‐cell follicles have been reported in the meninges of patients with secondary progressive MS and have also been reported underlying pathology in the corresponding cortical gray matter. The exact target of these clonal B cells and their antibodies remains unclear but is an important area of ongoing research. While MS was initially defined by abnormalities noted in plaques, there is now substantial evidence that pathology exists in normal‐appearing white matter (NAWM) and dirty‐appearing white matter (DAWM) (Moore et al., 2008). These extra‐lesional areas feature perivascular and parenchymal inflammation and have disruption of the blood–brain barrier that promotes proinflammatory cascades, fibrinogen deposition, and resultant demyelination. Similarly, DAWM also features demyelination and axonal loss. While the implications of NAWM and DAWM remain unclear, it does provide further evidence that MS exists as a more global inflammatory process as opposed to a clearly focal process. In addition to white matter disease, postmortem studies have provided robust evidence that MS also involves gray matter structures (Gilmore et  al., 2009). The cortex is frequently ­disrupted in MS with cortical pathology demonstrating demyelination, inflammation, and cell death. Similar to white matter lesions, this damage occurs both within discrete lesions as well as in normal‐appearing gray matter. The functioning of areas such as the prefrontal cortex, cingulate cortex, and hippocampus is preferentially affected, which explains the dysfunction in domains of executive function, learning, information processing, and memory displayed by MS patients. Finally, disruption of deep gray matter structures, including the thalamus and basal ganglia, is also seen. Atrophy in both the cortex and deeper gray matter regions leads to more global cognitive dysfunction that ultimately contributes to significant morbidity and potentially mortality in MS patients.

Cognitive Impairment in MS Cognitive impairment is a common complaint among MS patients and leads to considerable disability. The true prevalence of cognitive impairment varies by study; however, it is generally accepted that between 40 and 65% of MS patients experience it (Bobholz & Rao, 2003). Cognitive changes in MS are not clearly tied to disease duration or physical disability. In fact, cognitive changes can occur very early in the disease course and can even precede the diagnosis. Cognitive issues can worsen acutely during a relapse but persist even between relapses and often progress over time. Cognitive impairment seems to be more profound in the progressive subtypes of MS than in the relapsing subtypes. The cognitive impairment in MS is not global, but rather preferentially affects individual cognitive domains. For example, processing speed is commonly slow in patients with MS. Processing speed involves one’s ability to take in new information, manipulate it, and apply it to his environment. Attentional domains are also very commonly affected, especially sustained and

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shifting attention. When tested, patients with MS score poorly on tasks requiring them to divide their attention and attend to multiple tasks simultaneously. Other very commonly affected domains include visual memory, executive functioning, visual perception, and long‐term ­memory. Visual perception is often confounded by poor visual acuity as many patients with MS have suffered optic neuritis and have residual deficits in visual acuity. However visual perception deficits also occur independently of visual acuity loss. With regard to long‐term memory, the problem with memory is usually one of encoding memories and reflective of the poor attention of said patients. Conversely domains like language processing and naming are usually spared. Cognitive impairment has real consequences for patients with MS and results in decreased productivity at work, high rates of unemployment, disrupted social relationships, and less ability to live independently. Cognitive deficits correlate with objective difficulties completing instrumental activities of daily living, such as managing one’s finances and medications, and with worsened scores on subjective quality of life (QOL) measures as reported from a patient’s perspective (Cutajar et  al., 2000). QOL can be quantified using such tests as the Multiple Sclerosis Quality of Life Inventory or SF36 (part of health status questionnaire). Cognitive deficits can also interfere with the patient–physician therapeutic alliance or patient adherence to prescribed pharmacological or non‐pharmacological treatment regimens. Cognitive impairment can be measured in various ways. Traditional clinical measures of screening cognition such as the Mini‐Mental State Examination (MMSE) or Montreal Cognitive Assessment (MoCA) are insensitive to the specific deficits affected in MS. If these are the only screens used to assess MS patients, their deficits and complaints can go under‐­ recognized and underappreciated for years. The gold standard for diagnosing cognitive impairment in MS patients is formal neuropsychiatric testing, which involves exposing patients to a battery of tests assessing multiple domains of cognition. However, such testing is time consuming, costly, and not available in all regions. A recent focus in the field has been on developing more clinic‐friendly cognitive screening assessments. Several validated screening assessments are now available, and there has been considerable debate regarding which are the “best” screens. Each varies in regard to sensitivities and specificities depending on the specific domain of cognition one is interested in analyzing. Two of the most commonly utilized assessments include the Minimal Assessment of Cognitive Function in MS (MACFIMS) and Brief International Cognitive Assessment for MS (BICAMS). The MACFIMS is a ninety‐minute battery, consisting of seven tests that was created by the consortium of MS centers that has been validated both in MS patients and in healthy controls (Benedict et al., 2006). The BICAMS is a fifteen‐minute test created by an international expert consensus committee and includes the symbol digit modalities test (SDMT), California Verbal Learning Test second edition (CVLT‐II), and revised Brief Visuospatial Memory Test (BVMT‐R) (Langdon et al., 2012). The BICAMS is designed to be administered by any healthcare practitioner and would not require formal neuropsychiatric training. It is currently undergoing validation studies in multiple countries. One recent focus in the field has been on developing a computerized or Internet‐based screening test to ameliorate some of the cost and availability issues of our current screening methods. Several such screens have been developed and validated, such as the thirty‐minute Cognitive Stability Index (Younes et al., 2007), but none have yet been widely incorporated into clinical practice. Pathophysiologically, it is not known what the exact cause of the cognitive dysfunction in MS is. Likely it is a combination of both gray matter and white matter damage (as detailed earlier in the pathophysiology of MS section) and the resultant atrophy associated with these inflammatory changes in the brain.

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The cognitive changes in MS do correlate with quantifiable radiographic changes such as MRI patterns of cerebral atrophy. Both gray and white matter atrophy have been implicated. A recent review article published in Lancet (Rocca et al., 2015) compiled a table of more than thirty recently published studies reporting associations between cognitive function and radiographic measures of either white matter lesions, gray matter lesions, or total brain atrophy. For example, associations have been demonstrated between cognitive impairment and total lesion areas, T1 lesion volumes, T2 lesion volumes, FLAIR lesion volumes, the size of the corpus callosum, and the third ventricular width (Rocca et al., 2015). Moreover, cortical lesions burden has been associated with cognitive disability in MS. No one measure has been shown to fully explain or predict cognitive performance, and research remains ongoing in the field. Several agents including disease‐modifying therapies aimed at tempering the inflammatory process in the CNS have been shown to decrease acute clinical relapses and decrease radiographic lesion burden among patients with MS. It is possible that these therapies slow the rate of progressive cognitive impairment; however no studies yet exist that clearly demonstrate this. No pharmacological therapies have yet been proven to reverse cognitive impairment among MS patients. Nonetheless several agents are used for symptomatic treatment of cognitive impairment in MS. These include stimulants (such as methylphenidate, amphetamine, and modafinil), cholinesterase inhibitors (such as donepezil, galantamine, and rivastigmine), and N‐methyl‐d‐aspartate (NMDA) receptor antagonists (such as memantine). A recent Cochrane review of the literature concluded that there is no convincing evidence that any of these agents were effective (He, Zhou, Guo, Hao, & Wu, 2011). Further investigations into pharmacotherapy to treat MS‐related cognitive impairment are warranted. Cognitive rehabilitation is a non‐pharmacological form of treatment being increasingly explored to combat MS‐related cognitive impairment. Cognitive rehabilitation is an individualized treatment regimen, which combines restorative techniques with compensatory activities. Restorative techniques are designed to help restore normal brain functioning to a previously damaged area and focuses on repeated exposures and practice to harness the brain’s natural repair mechanisms and plasticity. Conversely compensatory techniques focus on finding ways around the cognitive difficulties a patient has by utilizing what remaining domains a patient has. For example, behavioral interventions like employing reminders such as personal organizers, calendars, or alarms can be useful for patients with attentional or working memory difficulties. Studies have showed that cognitive rehabilitation strategies can both improve a patient’s quality of life and lead to improved performance on memory tasks.

Depression and Fatigue Both mood disorders and fatigue are very prevalent in MS patients with estimates that up to sixty and ninety percent of patients suffer from these conditions, respectively. Depression and other emotional disorders are associated with reduced functioning, reduced adherence to medical therapies, and overall decreased quality of life. The lifetime prevalence of major depression in patients with MS is elevated and estimated at 46–54% versus 16.2% in the general public (Minden et al., 2014). In addition, MS patients are more likely to have clinical diagnoses of anxiety, and suicide is twice as common in the MS population relative to controls. Recent studies suggest a relationship between depression and demyelination, particularly when demyelination involves the limbic system. Functional MRI studies have shown differences in activation between the prefrontal cortex and the amygdala in patients with concurrent MS and depression. Similar studies have also shown patients with concurrent MS and affective disorders had overall higher global disease burdens suggesting a link between structural damage and comorbid psychiatric disease.

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Fatigue is also highly prevalent in MS patients. Patients complain of both cognitive and global fatigue. Researchers have demonstrated an association between cognitive fatigue and slower information processing (Andreasen, Spliid, Andersen, & Jakobsen, 2010). The impact of global fatigue, however, has been more difficult to quantify. Radiographic studies have ­correlated global fatigue with preferential frontoparietal cortical atrophy and disruption of frontal and parietal pathways (Sepulcre et al., 2009). Recent studies have utilized fMRI s­ canning to explore MS subjects undergoing cognitive evaluation of working memory and attention via Paced Auditory Serial Addition Test (PASAT) or the Paced Visual Serial Addition Test (PVSAT). Subjects with MS have demonstrated more sustained and widespread cortical activation despite worse task performance. It was hypothesized that this may contribute to “neural fatigue.” The interplay between fatigue and cognitive dysfunction requires further investigation. Screening for fatigue and affective disorders like depression is very important, because when untreated, such conditions lead to disability. Independent evaluation of these disorders is challenging as fatigue, depression, and cognitive impairment are intricately linked. A few screening tools are currently available. Fatigue can be assessed using validated scales such as the Modified Fatigue Impact Scale (MFIS), Fatigue Severity Scale (FSS), or the Chalder Fatigue Scale. Depression can be assessed using the Beck Depression Inventory. In addition, the General Health Questionnaire can be useful in evaluating more globally for difficulty in regulating emotions. Finally, the Center for Neurologic Study Emotional Lability Scale can be useful in detecting pseudobulbar affect, which can be another important consideration in evaluating depression in MS patients. Clinicians must carefully consider the confounding impact of other related factors, such as insomnia, restless legs syndrome, and circadian rhythm abnormalities, all of which are experienced by MS patients and can influence both fatigue and depression.

Author Biographies Emily Evans, MD, completed her undergraduate studies at Tulane University and earned a medical degree from the Perelman School of Medicine at the University of Pennsylvania. She completed her residency training in neurology at Washington University in Saint Louis. She is currently a clinical fellow in neuroimmunology at the John L. Trotter MS Center associated with Washington University in Saint Louis where she is specializing in the clinical care of patients with MS and the conduct of clinical trials related to MS. Elizabeth Silbermann, MD, completed her undergraduate studies at Brown University and earned a medical degree at Warren Alpert Medical School, Providence, RI. She completed a residency in neurology at Washington University School of Medicine in Saint Louis, where she distinguished herself as a neurology chief resident. She is currently a neuroimmunology fellow at Oregon Health and Science University. She is the recipient of a National MS Society sponsored Sylvia Lawry Physician Fellowship grant and is currently involved in Phase I, Phase II, and observational trials of drug and rehabilitation interventions. Soe Mar, MD, completed her undergraduate and medical school training in Myanmar. She completed her residency and fellowship training at the Albert Einstein College of Medicine in New York, NY. She is the program director of the Pediatric Neurology Residency Training Program at Saint Louis Children’s Hospital (SLCH) associated with Washington University in Saint Louis. She is also the director of the Pediatric Multiple Sclerosis & Demyelinating Disease Center at SLCH, which is one of twelve nationwide National MS Society (NMMS) recognized pediatric MS centers. She has been the recipient of numerous distinguished teaching awards and published more than seventy papers relating not only to MS but also to ­leukodystrophies, headache disorders, and CNS lymphomas to name a few.

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References Andreasen, A. K., Spliid, P. E., Andersen, H., & Jakobsen, J. (2010). Fatigue and processing speed are related in multiple sclerosis. European Journal of Neurology, 17(2), 212–218. doi:10.1111/ j.1468‐1331.2009.02776.x Benedict, R. H. B., Cookfair, D., Gavett, R., Gunther, M., Munschauer, F., Garg, N., & Weinstock‐ Guttman, B. (2006). Validity of the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS). Journal of the International Neuropsychological Society: JINS, 12(4), 549–558. doi:10.1017/S1355617706060723 Bobholz, J. A., & Rao, S. M. (2003). Cognitive dysfunction in multiple sclerosis: A review of recent developments. Current Opinion in Neurology, 16(3), 283–288. doi:10.1097/01.wco.0000073928.19076.84 Cutajar, R., Ferriani, E., Scandellari, C., Sabattini, L., Trocino, C., Marchello, L. P., & Stecchi, S. (2000). Cognitive function and quality of life in multiple sclerosis patients. Journal of Neurovirology, 6, S186–S190. Gilmore, C. P., Donaldson, I., Bö, L., Owens, T., Lowe, J., & Evangelou, N. (2009). Regional variations in the extent and pattern of grey matter demyelination in multiple sclerosis: A comparison between the cerebral cortex, cerebellar cortex, deep grey matter nuclei and the spinal cord. Journal of Neurology, Neurosurgery, and Psychiatry, 80(2), 182–187. doi:10.1136/jnnp.2008.148767 He, D., Zhou, H., Guo, D., Hao, Z., & Wu, B. (2011). Pharmacologic treatment for memory disorder in multiple sclerosis. Cochrane Database of Systematic Reviews, (10), CD008876. doi:10.1002/ 14651858.CD008876.pub2 Langdon, D., Amato, M., Boringa, J., Brochet, B., Foley, F., Fredrikson, S., et  al. (2012). Recommendations for a Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS). Multiple Sclerosis Journal, 18(6), 891–898. doi:10.1177/1352458511431076 Minden, S. L., Feinstein, A., Kalb, R. C., Miller, D., Mohr, D. C., Patten, S. B., et al. (2014). Evidence‐ based guideline: Assessment and management of psychiatric disorders in individuals with MS: Report of the guideline development subcommittee of the American academy of neurology. Neurology, 82(2), 174–181. doi:10.1212/WNL.0000000000000013 Moore, G. R. W., Laule, C., MacKay, A., Leung, E., Li, D. K. B., Zhao, G., … Paty, D. W. (2008). Dirty‐appearing white matter in multiple sclerosis: Preliminary observations of myelin phospholipid and axonal loss. Journal of Neurology, 255(11), 1802–1811. doi:10.1007/s00415‐008‐0002‐z Ontaneda, D., Hyland, M., & Cohen, J. A. (2012). Multiple sclerosis: New insights in pathogenesis and novel therapeutics. Annual Review of Medicine, 63, 389–404. doi:10.1146/annurev‐med‐042910‐ 135833 Rocca, M. A., Amato, M. P., De Stefano, N., Enzinger, C., Geurts, J. J., Penner, I. K., … Filippi, M. (2015). Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. The Lancet Neurology. doi:10.1016/S1474‐4422(14)70250‐9 Sepulcre, J., Masdeu, J. C., Goñi, J., Arrondo, G., Vélez de Mendizábal, N., Bejarano, B., & Villoslada, P. (2009). Fatigue in multiple sclerosis is associated with the disruption of frontal and parietal pathways. Multiple Sclerosis (Houndmills, Basingstoke, England), 15(3), 337–344. doi:10.1177/ 1352458508098373 Weissert, R. (2013). The immune pathogenesis of multiple sclerosis. Journal of Neuroimmune Pharmacology, 8(4), 857–866. doi:10.1007/s11481‐013‐9467‐3 Wu, G. F., & Alvarez, E. (2011). The immunopathophysiology of multiple sclerosis. Neurologic Clinics. doi:10.1016/j.ncl.2010.12.009 Younes, M., Hill, J., Quinless, J., Kilduff, M., Peng, B., Cook, S. D., & Cadavid, D. (2007). Internet‐ based cognitive testing in multiple sclerosis. Multiple Sclerosis (Houndmills, Basingstoke, England), 13(8), 1011–1019. doi:10.1177/1352458507077626

Psychoneuroimmunology: Immune Markers of Psychopathology Rachel A. Wamser‐Nanney and Brittany F. Goodman Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA

Overview In recent years, the role of inflammation as a disease mechanism for both physical and ­psychological disorders has been increasingly recognized. Immune activation appears to play a critical role in numerous physical disorders and has been observed in conditions such as cardiovascular disease, diabetes, cancer, asthma, and obesity. Accumulating evidence suggests that inflammation also may be a key factor in the development and treatment of psychological disorders (Dantzer, O’Connor, Freund, Johnson, & Kelley, 2008; Raison, Capuron, & Miller, 2006). Although research regarding inflammation and psychological disorders is still emerging, abnormal levels of immune markers have been associated with psychiatric conditions including major depressive disorder (MDD), bipolar disorder (BPD), schizophrenia, and posttraumatic stress disorder (PTSD). The immune system may be a potential treatment target for these disorders. This paper will provide a brief overview of inflammation and describe how inflammation may be a factor in the development of psychopathology. We will then characterize the prior research regarding the role of immune markers in four psychological disorders where inflammation has been more extensively examined: MDD, BPD, schizophrenia, and PTSD.

Inflammation Inflammation is the immune system’s response to both internal and external stimuli that are potentially harmful to the organism. Acute inflammation is protective, preventing spread of harmful agents to nearby tissue and initiating the healing process for damaged tissue. However, chronic inflammation becomes toxic, resulting in neuronal death, and has been linked to The Wiley Encyclopedia of Health Psychology: Volume 1: Biological Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editor: Robert H. Paul. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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­ eurodegenerative disorders and psychopathology (Dantzer et al., 2008). Cytokines are one n family of proteins that are heavily involved in the inflammatory response and are routinely measured to assess levels of inflammation. Cytokines include interferons, interleukins (IL), and growth factors that have a diverse set of functions and can be proinflammatory or anti‐ inflammatory or both. Inflammation research is therefore complicated, as inflammation can be helpful or harmful, depending on the context (Yirmiya & Goshen, 2011). As the brain is responsible for processes integral to the immune response, cytokines are expressed in the brain. The vast majority of the inflammation research has utilized peripheral indices of inflammation, such as measuring levels of cytokines in the body, as opposed to directly studying immune markers in the brain. Peripheral inflammation indices are thought to be a proxy for central nervous system (CNS) inflammation due to the significant interconnections between the peripheral and central immune systems. Proinflammatory cytokines, such as IL‐6, IL‐1, and tumor necrosis factor‐alpha (TNF‐α), can be transported across the blood– brain barrier (BBB) and thus are directly able to alter CNS functioning. Peripheral proinflammatory signals can also be propagated across the BBB by cross‐talk between the peripheral and central immune systems. While there is a considerable amount of interaction between the central and peripheral immune systems, the exact degree to which peripheral inflammation indicates neuroinflammation remains unclear. The immune system has been found to be critically involved in brain development and functioning. Cytokines play a role in shaping brain development through cell signaling, neuronal growth and development, and modulation of neurotransmission (Yirmiya & Goshen, 2011). Proinflammatory cytokines aid in the migration of neurons and regulate the process of pruning during development (Dantzer et  al., 2008). Indeed, animal studies and nonhuman ­primate models have found that immune activation in the prenatal and early postnatal period is related to brain volume and neurogenesis. Levels of inflammatory markers have been inversely linked with brain volumes. For example, higher levels of immune markers such as IL‐6 have been associated with reduced hippocampal gray matter volume. Immune cells, such as T cells and microglia, are critical for brain and cognitive functioning and help maintain brain plasticity, particularly hippocampal neurogenesis. The brain and the immune system are strongly interconnected; yet, the precise nature of these relationships is complex and not fully understood. Future research in both animal and human models that examines these complex interactions will continue to ­provide additional insights regarding the effects of immune activation on the brain and behavior.

Immune Markers and Psychopathology The robust links between the immune system and the brain create numerous opportunities for inflammation to be involved in the development and maintenance of psychopathology. Peripheral immune modulators can induce psychiatric symptoms in both animal and human models (Dantzer et al., 2008). Treatments with proinflammatory cytokines can result in “sickness behavior,” a constellation of depressive‐like symptoms including decreased appetite, weight loss, fatigue, sleep disturbances, and social withdrawal. In animal models, the injection of lipopolysaccharide (LPS), a marker for the immune system to identify a foreign body as toxic, induces inflammation and results in depressive‐like symptoms. Immune activation via LPS is associated with depression, anxiety, and memory impairment as well as higher levels of serum IL‐1b and TNF‐α. Therefore, there appears to a direct link between inflammation and the emergence of psychopathology. Physical health conditions associated with immune system abnormalities such as obesity, diabetes, rheumatoid arthritis, and multiple sclerosis are risk factors for mood disorders.

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Similarly, depression is frequently observed among patients who have been diagnosed with a medical condition that is associated with cytokine activation, such as viral infections, rheumatoid arthritis, cancer, and neurodegenerative diseases. Other research has found that approximately half of individuals with hepatitis C and cancer develop depressive symptoms associated with increased levels of IL‐6 and TNF‐α (Dantzer et al., 2008). In addition to depression, a 30‐year population study reported that having an autoimmune disease or being hospitalized from a serious infection increased the risk of developing schizophrenia by 29 and 60%, respectively (Benros et al., 2011). Although there are alternative explanations for why individuals with a physical health condition may develop a psychological disorder (i.e., situational stress, difficulties coping with illness, or lifestyle changes as a result of illness), the relationship between physical and psychological conditions suggests that inflammation is likely important in the etiology of psychopathology. The exact mechanisms by which inflammation results in psychopathology are unclear; however, stress may be a critical factor (Raison et al., 2006). The stress response system includes the release of immune markers such as TNF‐α and IL‐6, which increase the release of corticotropin‐releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and cortisol by acting directly on hypothalamic and pituitary cells. The seminal work of Kiecolt‐Glaser and colleagues has demonstrated that stress, induced experimentally or in response to an external event, is related to compromised immune function and physical health conditions. Stress predicts higher rates of illness following pathogen exposure, protracted wound healing, poorer response to immunization, and alterations in several indices of immune functioning (Glaser & Kiecolt‐ Glaser, 2005). Dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis, the major stress response system, is also associated with depression. Stress may activate the immune response in the CNS via enhanced neuronal activity. The large and compelling literature regarding stress and the immune system has established clear links between stress, the brain, and the immune system, although again, the precise nature of these relationships still merits attention. Another explanation for how inflammation may increase the risk for psychopathology is the ability of cytokines to regulate hippocampal neurogenesis. Hippocampal neurogenesis has been implicated as a key mechanism in the pathophysiology and treatment of depression, through the modulation of brain‐derived neurotrophic factor (BDNF). Selective serotonin reuptake inhibitors (SSRIs) upregulate the expression of BDNF in the hippocampus, which promotes the proliferation and survival of neural progenitor cells, neural stem cells that differentiate according to location and function. Inflammation may also create a vulnerability for psychological illness via serotonin (5‐HT). Serotonin is a neurotransmitter that is heavily involved in mood functioning and has been implicated in the development and treatment of numerous psychological disorders. Inflammatory cytokines have been found to alter the metabolism and release of central 5‐HT. Proinflammatory cytokines IL‐1B and TNF‐α have also been shown to activate 5‐HT ­transporters. Therefore, immune‐driven alterations in 5‐HT are likely an important factor in understanding how inflammation may contribute to psychiatric illnesses.

Inflammation and Psychiatric Conditions Major Depressive Disorder The inflammation hypothesis posits that MDD is associated with sustained activation of the  immune system, specifically increased production of proinflammatory cytokines and acute  phase proteins (Maes, 1999). Numerous studies have suggested that depression is

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accompanied by immune dysregulation. Depressed patients have exhibited increased serum levels of numerous immune markers including IL‐1, IL‐1B, IL‐2, IL‐6, interferon gamma (IFN‐γ), and TNF‐α. One meta‐analysis of 58 studies found that IL‐6, CRP, and TNF‐α were significantly associated with MDD, whereas IL‐1B was not tied to MDD (Haapakoski, Mathieu, Ebmeier, Alenius, & Kivimäki, 2015). It is important to note that elevated levels of inflammation were not consistently reported in all studies or for all immune markers and there was extensive heterogeneity in study‐specific estimates. Children and adolescents diagnosed with MDD have also exhibited elevated levels of immune markers such as IL‐6, IFN‐γ, and IL‐1B. Increased levels of IL‐1B, IL‐2, and IL‐10 have been reported among unmedicated children with MDD. Elevated protein expression levels of IL‐1B and TNF‐α in the prefrontal cortex were observed in adolescents who completed suicide than controls. Immune activation may differ depending on the presence of suicidality. Most individuals who have attempted suicide or had suicidal ideation have shown an imbalance of the immune system. IL‐6 and TNF‐α may be significantly higher, whereas IL‐2 may be lower among suicidal patients versus depressed patients without suicidality. Although further research is needed in this area to draw conclusions regarding suicidality and inflammation, suicidality should be assessed in the context of MDD inflammation research. Relatively little research has investigated the influence of treatment on inflammation; however, cytokines may be reduced to normal levels following successful antidepressant treatment.

Bipolar Disorder Inflammatory markers, combined with oxidative damage and neurotrophins, are theorized to be one of the three key pillars in a multifactorial approach to predicting BPD among those at risk (Brietzke et  al., 2012). Immune alterations have been associated with many aspects of BPD including symptom severity, mood episodes, stages, and medication effects. TNF‐α, IL‐6, and IL‐8 have been shown to be elevated during manic and depressive phase, whereas IL‐2, IL‐4, and IL‐6 were increased during manic episodes. Other markers, such as IL‐1B and IL‐1, may be normal among individuals with BPD. A large meta‐analysis of thirty studies concluded that IL‐4, IL‐10, and TNF‐α are elevated in patients with BPD compared with healthy controls (Modabbernia, Taslimi, Brietzke, & Ashrafi, 2013). Interestingly, levels of IL‐2, IFN‐γ, and IL‐4 were unrelated to medication status. Phasic differences were present for TNF‐α and IL‐6, but not IL‐4 and IL‐10. Levels of TNF‐α and IL‐6 appear to normalize during the depressive phase, whereas other markers such as IL‐2R remain abnormal. Larger studies examining phasic differences are needed to elucidate trait and state markers of BPD. The research with child and adolescent samples is too limited to draw conclusions at this time; however, CRP may be related to mania symptom severity. Regarding treatment effects, a meta‐analysis reported that medication did not significantly impact markers such as IL‐2, IL‐4, and IFN‐γ (Modabbernia et al., 2013). Medication effects of other cytokines could not be determined due to the small number of studies. While there is substantial support, the immune system being involved in BPD, at this time, it is unclear which markers are most related to specific phases and the impact of treatment on the immune system.

Schizophrenia The development of schizophrenia may be due, in part, to immune dysregulation (Miller, Buckley, Seabolt, Mellor, & Kirkpatrick, 2011). Early work revealed that prenatal infections

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with bacterial or viral agents were associated with an increased risk of adult‐onset schizophrenia. A substantial portion of cases diagnosed with schizophrenia spectrum disorders had mothers exposed to influenza in the first trimester of pregnancy than controls. Further, increased maternal levels of proinflammatory cytokines have been related to schizophrenia in offspring. A number of reviews describe the mechanisms by which infection alters neurodevelopment that increases the risk of developing schizophrenia (e.g., Miller et al., 2011). However, the risk of developing schizophrenia from infection is not restricted to the prenatal period. Exposure to neurotropic virus in early childhood is associated with increased risk of subclinical psychosis during adolescence. In a 30‐year population study, childhood autoimmune conditions were related to subclinical psychotic experiences in adolescents and schizophrenia in adults (Benros et al., 2011). Here, the risk of schizophrenia had a positive linear relationship with the number of severe infections among those with a history of autoimmune disease, and autoimmune conditions were more prevalent in individuals with schizophrenia than controls. One meta‐analytic study revealed that the risk of schizophrenia was nearly double among adult survivors of childhood CNS viral infections (Khandaker, Zimbron, Dalman, Lewis, & Jones, 2012). Interestingly, the prevalence of autoimmune conditions is increased in people with schizophrenia as well as their unaffected first‐degree relatives. Increased levels of inflammatory markers have been recorded in those with schizophrenia and associated symptoms. First‐episode psychosis and acute psychotic relapse are linked with increased concentrations of IL‐6, TNF‐α, IL‐1β, and IFN‐γ and decreased levels of IL‐10. Several meta‐analyses have concluded that schizophrenia is related to altered immune functioning, with a propensity toward increased production of proinflammatory cytokines (e.g., Miller et al., 2011). It is important to note that the literature may be more conflicting, as some studies have found both decreased proinflammatory markers (e.g., IL‐2, IFN‐γ) and increased serum and cerebral spinal fluid (CSF) anti‐inflammatory cytokines (e.g., IL‐10). Nonetheless, there appears to be a dominance of proinflammatory findings. Miller and colleagues reported that levels of inflammation between controls, first‐episode patients, and acutely relapsed patients were similar, suggesting that abnormal cytokine levels in schizophrenia are not a result of antipsychotic treatment. Comparatively little work has been conducted with child populations, although children with schizophrenia may have comparable levels of TNF‐α to controls, both before and after clozapine treatment. More studies are needed in child populations as childhood‐onset schizophrenia may present with a different inflammatory profile than adult‐ onset schizophrenia. In a longitudinal study of inflammatory markers and subsequent psychotic disorders, the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort revealed that higher concentrations of IL‐6 at age 9 corresponded to a twofold increased risk of developing a ­psychotic disorder at age 18 (Khandaker, Pearson, Zammit, Lewis, & Jones, 2014). A robust dose–response relationship was also reported between IL‐6 levels in childhood and later risk of psychotic experiences in young adulthood, even after controlling for sex, body mass, and psychological and behavioral problems. Initial CRP levels were not related to later psychosis. Additional longitudinal studies will help to confirm whether which markers are tied to the development of schizophrenia and related psychosis. The hypothesized immune‐mediation etiology for schizophrenia is supported by genome‐ wide association studies that report significant relationships between schizophrenia and markers close to the major histocompatibility complex region, which contains many immune‐related genes, including inflammatory mediators. Genome‐wide studies have identified numerous genetic loci associated with schizophrenia, many of which represent genes expressed in the brain and immune cells involved in adaptive immunity. In a recent review, inflammation‐related

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genes such as SERPINA3 and IFITM were consistently increased in schizophrenic brains (Trépanier, Hopperton, Mizrahi, Mechawar, & Bazinet, 2016). These findings suggest increased expression for immune‐mediating genes in schizophrenia. Further support for the role of the immune system in schizophrenia is found in treatment studies. Immunotherapy has led to symptomatic improvement in some cases of first‐episode psychosis. TNF‐α, IL‐1β, IFN‐γ, and IL‐10 may normalize after remission of symptoms from antipsychotic treatment. Treatment of acute psychosis has been related to significant reductions in IL‐1β, IL‐4, and IL‐6 and increases in IL‐12. Thus, it appears that changes in cytokines are likely following successful treatment.

Posttraumatic Stress Disorder Individuals with PTSD are at an increased risk for a myriad of health issues, including ­autoimmune disorders, diabetes, and cardiovascular disease. However, the etiology of this relationship is unknown, and studies regarding inflammation and PTSD appear more ­conflicting than other disorders. The first meta‐analysis regarding PTSD and inflammation was recently conducted by Passos et al. (2015), which included 20 studies that contrasted inflammatory markers among individuals with PTSD compared with healthy controls. Concentrations of IL‐6, IL‐1B, and IFN‐γ were higher in the PTSD group than controls, with large effect sizes for IL‐1B and IL‐6 and a medium effect size for IFN‐γ. PTSD duration was positively related to IL‐1B, and symptom severity was tied to IL‐6. Higher levels TNF‐α were observed in the unmedicated PTSD group. In the studies that excluded individuals with comorbid MDD, levels of TNF‐α, IL‐1B, and IL‐6 remained elevated. Consequently, use of medication and comorbid conditions may account for conflicting results. It is important to note that research studies with trauma‐exposed individuals, who may or may not have PTSD, have also reported higher levels of CRP, IL‐1B, IL‐6, and TNF‐α, suggesting full PTSD diagnostic criteria need not be met for immune dysfunction to manifest. Coelho, Viola, Walss‐Bass, Brietzke, and Grassi‐Oliveira (2014) reviewed the nascent literature regarding child maltreatment and inflammation. A history of child maltreatment was related to increased levels of CRP, fibrinogen, TNF‐α, and IL‐6. CRP was the most robust marker. However, many of the studies included individuals with MDD, only one study was specific to PTSD, and most of the studies utilized small samples (ns 30). More diffusion encoding directions permits better characterization of the underlying white matter. The details of the MRI sequences required for such measurements vary across scanners and research settings and are beyond the scope of this chapter. The ADC values in each image voxel can be expressed as a second‐order tensor that can then be decomposed into three non‐negative eigenvalues (λ1 >  λ2  >   λ3) and their corresponding eigenvectors (ε1, ε2, and ε) that respectively capture the magnitude and direction of diffusion in each imaging voxel. The eigenvectors are in the frame of reference of the underlying tissue and are therefore independent of the scanner axes. The dominant fiber orientation within an image voxel (ε1) is the orientation with the largest magnitude of diffusion (λ1) (Basser et al., 1994a, 1994b; Buxton, 2002; Mori & Zhang, 2006). The eigenvalues can then be mathematically combined into scalar metrics describing various aspects of diffusion in each imaging voxel. Commonly used DTI scalar metrics include mean diffusivity (MD), or the average magnitude of diffusion in each voxel; axial diffusivity (AD or DA) or rate of diffusion in the principal diffusion direction (i.e., λ1); radial diffusivity (RD) or off‐axis diffusivity (i.e., λ2,, λ3); and fractional anisotropy (FA) or the degree to which diffusion is directionally restricted in each image voxel. These scalar metrics have values between 0.0 and 1.0. Higher values of FA indicate greater directional restriction of water diffusion (e.g., greater white matter structural coherence), whereas higher MD values indicate greater isotropic diffusion possibly related to lower fiber coherence. MD, RD, and DA are in units of mm2/s, whereas FA is dimensionless. These scalar metrics are typically displayed as grayscale images. Formulas and sample images for MD, DA, RD, and FA are provided below. Other scalar metrics such as relative anisotropy and volume ratio appear less frequently in published research and will not be discussed further here.

MD = (λ1 + λ2 + λ3)/3  

DTI Tractography Metrics: Fiber Bundle Length

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AD (or DA) = λ1  

RD = (λ2 + λ3)/2  

FA

1 2

1

2

2 

 

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DTI is sensitive to multiple factors that impact the structural integrity of cerebral white ­ atter. This includes developmental maturation (Vertes & Bullmore, 2015) and aging m (Marner, Nyengaard, Tang, & Pakkenberg, 2003; McLaughlin et al., 2007; Tang, Nyengaard, Pakkenberg, & Gundersen, 1997) as well as acquired conditions causing white matter injury, edema, demyelination, and degeneration (e.g., traumatic brain injury, ischemia, multiple sclerosis, Alzheimer’s disease, etc.). The scalar metrics listed above are sensitive to these and other pathologies causing alterations of white matter structural integrity although the individual metrics are not specific to any particular pathologic process. For example, Wallerian degeneration seen in Alzheimer’s disease has been associated with increased diffusivity measures (DA, MD, and RD) and decreased fractional anisotropy (Alves et al., 2012). A similar pattern of change in FA, MD, and RD was found in widespread white matter regions in retired athletes with history of mild traumatic brain injuries (Tremblay et al., 2014). Increased diffusivity has been shown in inflammation (Logygensky et al., 2010). Increased RD has been shown to be sensitive to myelin changes as in multiple sclerosis (Ontaneda et  al., 2014). Myriad other human and animal studies show sensitivity of DTI scalar metrics to various developmental and pathological factors that impact white matter integrity. DTI data can also be displayed using tractography. This method incorporates the scalar and vector information to provide indirect visual information about the orientation and path of white matter fibers as they course through the cerebrum. The resulting images are visualized mathematical representations (i.e., integral curves; a.k.a. streamtubes) of the underlying white matter anatomy based on the acquired diffusion signal. Typical tractography color‐coding convention is that red indicates fibers or tracts running in the left–right direction, blue indicates inferior–superior direction, and green indicates anterior–posterior (Pajevic & Pierpaoli, 1999), although this convention is not always followed. Tractography can be accomplished based on the diffusion tensor (deterministic tractography, e.g., Mori, Crain, Chacko, & van Zijl, 1999; Zhang, Demiralp, & Laidlaw, 2003) or can be performed without tensor computation using probabilistic methods (probabilistic ­tractography,

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Coronal section through cerebral tractography model. (https://www.researchgate.net/figure/51021601_fig12_FIG‐16‐Whole‐brain‐nontensor‐probabilistic‐ tractography‐results‐displayed‐as‐a‐coronal)

Behrens, Berg, Jbabdi, Rushworth, & Woolrich, 2007; Behrens, Johansen‐Berg, et al., 2003). In deterministic methods, the visualized fibers are actually integral curves that have been propagated through three‐dimensional image space based on seed points. The curves follow the spatial orientation of the principal eigenvector. The algorithm is set to terminate curve propagation when FA falls below a prespecified value (often 100 peer‐reviewed publications, has written numerous book chapters, serves as an ad hoc reviewer for several journals, and has received several honors and awards. Ms. Alyssa Phelps, BA completed her undergraduate studies at Cornell University where she graduated with honors in psychology. Her undergraduate research focused on integrative ­neuroethology and cognitive neuroscience. Since graduating, Alyssa transitioned to the Boston University (BU) Alzheimer’s Disease (AD) and CTE Center where she is an intern working on the Longitudinal Examination to Gather Evidence of Neurodegenerative Disease (LEGEND) Study. In the future, Alyssa hopes to pursue a doctoral degree in clinical psychology.

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Dr. Robert L. Stern, PhD received his PhD in clinical psychology from the University of Rhode Island. He completed his predoctoral internship training under Dr. Edith Kaplan at the Boston VA Medical Center and his postdoctoral fellowship at the University of North Carolina School of Medicine. He is currently professor of Neurology, Neurosurgery, and Anatomy and Neurobiology at Boston University (BU) School of Medicine, where he is also director of the Clinical Core of the NIH‐funded BU Alzheimer’s Disease Center and director of Clinical Research for the BU Chronic Traumatic Encephalopathy (CTE) Center. A major focus of his research involves the long‐term effects of repetitive head impacts in athletes, including the neurodegenerative disease, CTE. He is the lead co‐principal investigator of a $16 million NIH grant for a multicenter longitudinal study to develop methods of diagnosing CTE during life as well as examining potential risk factors of the disease. Dr. Stern’s other current major area of funded research involves the diagnosis and treatment of Alzheimer’s disease. He has published on various aspects of cognitive assessment and is the senior author of the ­ Neuropsychological Assessment Battery (NAB), as well as the Boston Qualitative Scoring System for the Rey–Osterrieth Complex Figure. He has received numerous NIH and other national grants, and he is a Fellow of both the National Academy of Neuropsychology and the American Neuropsychiatric Association. Dr. Stern has over 175 peer‐reviewed publications, is on several journal editorial boards, and is the co‐editor of two upcoming books: Sports Neurology (part of the Handbook in Clinical Neurology series published by Elsevier) and The Oxford Handbook of Adult Cognitive Disorders. He is a member of the medical advisory boards of several b ­ iotech/ pharma companies as well as the Mackey‐White Health and Safety Committee of the NFL Players Association and the Medical Scientific Committee for the NCAA Student‐Athlete Concussion Injury Litigation.

References Adler, C. M., DelBello, M. P., Weber, W., Williams, M., Duran, L. R. P., Fleck, D., … Divine, J. (2016). MRI evidence of neuropathic changes in former college football players. Clinical Journal of Sport Medicine. doi:10.1097/JSM.0000000000000391 Alosco, M. L., Kasimis, A. B., Stamm, J. M., Chua, A. S., Baugh, C. M., Daneshvar, D. H., … Stern, R. A. (2017). Age of first exposure to American football and long‐term neuropsychiatric and cognitive outcomes. Translational Psychiatry, 7, e1236. doi:10.1038/tp.2017.197 Alosco, M. L., Koerte, I. K., Tripodis, Y., Mariani, M., Chua, A. S., Jarnagin, J., … Stern, R. A. (2017). White matter signal abnormalities in former national football league players. Alzheimers Dement (Amst), 10, 56–65. doi:10.1016/j/dadm.2017.10.003 Alosco, M. L., Mez, J., Kowall, N. W., Stein, T. D., Goldstein, L. E., Cantu, R. C., … AC, M. K. (2016). Cognitive reserve as a modifier of clinical expression in chronic traumatic encephalopathy: A preliminary examination. The Journal of Neuropsychiatry and Clinical Neurosciences, appineuropsych16030043. doi:10.1176/appi.neuropsych.16030043 Alosco, M. L., Tripodis, Y., Jarnagin, J., Baugh, C. M., Martin, B., Chaisson, C. E., … Stern, R. A. (2017). Repetitive head impact exposure and later‐life plasma total tau in former NFL players. Alzheimers Dement (Amst), 7, 33–40. doi:10.1016/j.dadm.201611003 Armstrong, R. A., McKee, A. C., Stein, T. D., Alvarez, V. E., & Cairns, N. J. (2016). A quantitative study of tau pathology in eleven cases of chronic traumatic encephalopathy. Neuropathology and Applied Neurobiology. doi:10.1111/nan.12323 Asken, B. M., Sullan, M. J., Dekosky, S. T., Jaffee, M. S., & Bauer, R. M. (2017). Research gaps and controversies in chronic traumatic encephalopathy. JAMA Neurology, 74(10), 1255–1262. doi:10.1001/jamaneurol.2017.2396

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Barrio, J. R., Small, G. W., Wong, K. P., Huang, S.‐C., Liu, J., Merrill, D. A., … Kepe, V. (2015). In vivo characterization of chronic traumatic encephalopathy using [F‐18]FDDNP PET brain imaging. Proceedings of the National Academy of Sciences of the United States of America, 112(16), E2039–E2047. doi:10.1073/pnas.1409952112 Bieniek, K. F., Ross, O. A., Cormier, K. A., Walton, R. L., Soto‐Ortolaza, A., Johnston, A. E., … Dickson, D. W. (2015). Chronic traumatic encephalopathy pathology in a neurodegenerative disorders brain bank. Acta Neuropathologica, 130(6), 877–889. doi:10.1007/s00401‐015‐1502‐4 Cherry, J. D., Stein, T. D., Tripodis, Y., Alvarez, V. E., Huber, B. R., Au, R., … McKee, A. C. (2017). CCL11 is increased in the CNS in chronic traumatic encephalopathy but not in Alzheimer’s disease. PLoS One, 12(9), e0185541. doi:10.1371/journal.pone.0185541 Cherry, J. D., Tripodis, Y., Alvarez, V. E., Huber, B., Kiernan, P. T., Daneshvar, D. H., … Stein, T. D. (2016). Microglial neuroinflammation contributes to tau accumulation in chronic traumatic encephalopathy. Acta Neuropathologica Communications, 4(1), 112. doi:10.1186/s40478‐016‐0382‐8 Corsellis, J. A., Bruton, C. J., & Freeman‐Browne, D. (1973). The aftermath of boxing. Psychological Medicine, 3(3), 270–303. Coughlin, J. M., Wang, Y., Munro, C. A., Ma, S., Yue, C., Chen, S., … Pomper, M. G. (2015). Neuroinflammation and brain atrophy in former NFL players: An in vivo multimodal imaging pilot study. Neurobiology of Disease, 74, 58–65. doi:10.1016/j.nbd.2014.10.019 Ford, J. H., Giovanello, K. S., & Guskiewicz, K. M. (2013). Episodic memory in former professional football players with a history of concussion: An event‐related functional neuroimaging study. Journal of Neurotrauma, 30(20), 1683–1701. doi:10.1089/neu.2012.2535 Gardner, R. C., Hess, C. P., Brus‐Ramer, M., Possin, K. L., Cohn‐Sheehy, B. I., Kramer, J. H., … Rabinovici, G. D. (2016). Cavum septum pellucidum in retired American pro‐football players. Journal of Neurotrauma, 33(1), 157–161. doi:10.1089/neu.2014.3805 Goswami, R., Dufort, P., Tartaglia, M. C., Green, R. E., Crawley, A., Tator, C. H., … Davis, K. D. (2016). Frontotemporal correlates of impulsivity and machine learning in retired professional ­athletes with a history of multiple concussions. Brain Structure & Function, 221(4), 1911–1925. doi:10.1007/s00429‐015‐1012‐0 Hampshire, A., MacDonald, A., & Owen, A. M. (2013). Hypoconnectivity and hyperfrontality in retired American football players. Scientific Reports, 3, 2972. doi:10.1038/srep02972 Hart, J., Jr., Kraut, M. A., Womack, K. B., Strain, J., Didehbani, N., Bartz, E., … Cullum, C. M. (2013). Neuroimaging of cognitive dysfunction and depression in aging retired National Football League players: a cross‐sectional study. JAMA Neurology, 70(3), 326–335. doi:10.1001/2013. jamaneurol.340 Iverson, G. L., Keene, C. D., Perry, G., & Castellani, R. J. (2017). The need to separate chronic traumatic encephalopathy neuropathology from clinical features. Journal of Alzheimer’s Disease, 61(1), 17–28. doi:10.3233/jad‐170654 Jordan, B. D. (2013). The clinical spectrum of sport‐related traumatic brain injury. Nature Reviews. Neurology, 9(4), 222–230. doi:10.1038/nrneurol.2013.33 Koerte, I. K., Hufschmidt, J., Muehlmann, M., Tripodis, Y., Stamm, J. M., Pasternak, O., … Shenton, M. E. (2016). Cavum septi pellucidi in symptomatic former professional football players. Journal of Neurotrauma, 33(4), 346–353. doi:10.1089/neu.2015.3880 Lin, A. P., Ramadan, S., Stern, R. A., Box, H. C., Nowinski, C. J., Ross, B. D., & Mountford, C. E. (2015). Changes in the neurochemistry of athletes with repetitive brain trauma: Preliminary results using localized correlated spectroscopy. Alzheimer’s Research & Therapy, 7(1), 13. doi:10.1186/ s13195‐015‐0094‐5 Ling, H., Morris, H. R., Neal, J. W., Lees, A. J., Hardy, J., Holton, J. L., … Williams, D. D. (2017). Mixed pathologies including chronic traumatic encephalopathy account for dementia in Retired Association Football (Soccer) players. Acta Neuropathologica, 133(3), 337–352. doi:10.1007/ s00401‐017‐1680‐3 Martland, H. S. (1928). Punch drunk. JAMA, 91(15), 1103–1107.

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McKee, A. C., Cairns, N. J., Dickson, D. W., Folkerth, R. D., Keene, C. D., Litvan, I., … Gordon, W. A. (2016). The first NINDS/NIBIB consensus meeting to define neuropathological criteria for the diagnosis of chronic traumatic encephalopathy. Acta Neuropathologica, 131(1), 75–86. doi:10.1007/ s00401‐015‐1515‐z McKee, A. C., Stern, R. A., Nowinski, C. J., Stein, T. D., Daneshvar, D. H., Alvarez, V. E., … Cantu, R. C. (2013). The spectrum of disease in chronic traumatic encephalopathy. Brain, 136(Pt 1), 43–64. doi:10.1093/brain/aws307 Mez, J., Daneshvar, D. H., Kiernan, P. T., Abdolmohammadi, B., Alvarez, V. E., Huber, B. R., … AC, M. K. (2017). Clinicopathological evaluation of chronic traumatic encephalopathy in players of American football. JAMA, 318(4), 360–370. doi:10.1001/jama.2017.8334 Mez, J., Solomon, T. M., Daneshvar, D. H., Murphy, L., Kiernan, P. T., Montenigro, P. H., … McKee, A. C. (2015). Assessing clinicopathological correlation in chronic traumatic encephalopathy: Rationale and methods for the UNITE study. Alzheimer’s Research & Therapy, 7(1), 62. doi:10.1186/s13195‐015‐0148‐8 Millspaugh, J. A. (1937). Dementia pugilistica. United States Naval Medicine Bulletin, 35, 297–303. Mitsis, E. M., Riggio, S., Kostakoglu, L., Dickstein, D. L., Machac, J., Delman, B., … Gandy, S. (2014). Tauopathy PET and amyloid PET in the diagnosis of chronic traumatic encephalopathies: Studies of a retired NFL player and of a man with FTD and a severe head injury. Translational Psychiatry, 4, e441. doi:10.1038/tp.2014.91 Montenigro, P. H., Alosco, M. L., Martin, B. M., Daneshvar, D. H., Mez, J., Chaisson, C. E., … Tripodis, Y. (2016). Cumulative head impact exposure predicts later‐life depression, apathy, executive dysfunction, and cognitive impairment in former high school and college football players. Journal of Neurotrauma. doi:10.1089/neu.2016.4413 Montenigro, P. H., Baugh, C. M., Daneshvar, D. H., Mez, J., Budson, A. E., Au, R., … Stern, R. A. (2014). Clinical subtypes of chronic traumatic encephalopathy: Literature review and proposed research diagnostic criteria for traumatic encephalopathy syndrome. Alzheimer’s Research & Therapy, 6(5), 68. doi:10.1186/s13195‐014‐0068‐z Olsson, B., Lautner, R., Andreasson, U., Öhrfelt, A., Portelius, E., Bjerke, M., … Zetterberg, H. (2016). CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: A systematic review and meta‐ analysis. Lancet Neurology, 15(7), 673–684. doi:10.1016/S1474‐4422(16)00070‐3 Omalu, B. I., DeKosky, S. T., Minster, R. L., Kamboh, M. I., Hamilton, R. L., & Wecht, C. H. (2005). Chronic traumatic encephalopathy in a National Football League player. Neurosurgery, 57(1), 128– 134. discussion 128–134 Schultz, V., Stern, R. A., Tripodis, Y., Stamm, J., Wrobel, P., Lepage, C., … Koerte, I. K. (2017). Age at first exposure to repetitive head impacts is associated with smaller thalamic volumes in former professional American football players. Journal of Neurotrauma, 34, 1–8. doi:10.1089/neu.2017.5145 Small, G. W., Kepe, V., Siddarth, P., Ercoli, L. M., Merrill, D. A., Donoghue, N., … Barrio, J. R. (2013). PET scanning of brain tau in retired national football league players: Preliminary findings. The American Journal of Geriatric Psychiatry, 21(2), 138–144. doi:10.1016/j.jagp.2012.11.019 Stamm, J. M., Bourlas, A. P., Baugh, C. M., Fritts, N. G., Daneshvar, D. H., Martin, B. M., … Stern, R. A. (2015). Age of first exposure to football and later‐life cognitive impairment in former NFL players. Neurology, 84(11), 1114–1120. doi:10.1212/WNL.0000000000001358 Stamm, J. M., Koerte, I. K., Muehlmann, M., Pasternak, O., Bourlas, A. P., Baugh, C. M., … Shenton, M. E. (2015). Age at first exposure to football is associated with altered corpus callosum white matter microstructure in former professional football players. Journal of Neurotrauma, 32(22), 1768–1776. doi:10.1089/neu.2014.3822 Stein, T. D., Alvarez, V. E., & McKee, A. C. (2015). Concussion in chronic traumatic encephalopathy. Current Pain and Headache Reports, 19(10), 47. doi:10.1007/s11916‐015‐0522‐z Stein, T. D., Montenigro, P. H., Alvarez, V. E., Xia, W., Crary, J. F., Tripodis, Y., … McKee, A. C. (2015). Beta‐amyloid deposition in chronic traumatic encephalopathy. Acta Neuropathologica, 130(1), 21–34. doi:10.1007/s00401‐015‐1435‐y

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Stern, R. A., Daneshvar, D. H., Baugh, C. M., Seichepine, D. R., Montenigro, P. H., Riley, D. O., … McKee, A. C. (2013). Clinical presentation of chronic traumatic encephalopathy. Neurology, 81(13), 1122–1129. doi:10.1212/WNL.0b013e3182a55f7f Stern, R. A., Tripodis, Y., Baugh, C. M., Fritts, N. G., Martin, B. M., Chaisson, C., … Taylor, D. D. (2016). Preliminary study of plasma exosomal tau as a potential biomarker for chronic traumatic encephalopathy. Journal of Alzheimer’s Disease, 51(4), 1099–1109. doi:10.3233/JAD‐151028 Strain, J., Didehbani, N., Cullum, C. M., Mansinghani, S., Conover, H., Kraut, M. A., … Womack, K. B. (2013). Depressive symptoms and white matter dysfunction in retired NFL players with concussion history. Neurology, 81(1), 25–32. doi:10.1212/WNL.0b013e318299ccf8 Strain, J. F., Womack, K. B., Didehbani, N., Spence, J. S., Conover, H., Hart, J., … Cullum, C. M. (2015). Imaging correlates of memory and concussion history in retired National Football League Athletes. JAMA Neurology, 72(7), 773–780. doi:10.1001/jamaneurol.2015.0206 Terry, D. P., Adams, T. E., Ferrara, M. S., & Miller, L. S. (2015). FMRI hypoactivation during verbal learning and memory in former high school football players with multiple concussions. Archives of Clinical Neuropsychology, 30(4), 341–355. doi:10.1093/arclin/acv020 Victoroff, J. (2013). Traumatic encephalopathy: Review and provisional research diagnostic criteria. NeuroRehabilitation, 32(2), 211–224. doi:10.3233/NRE‐130839

Suggested Reading Mahar, I., Alosco, M. L., & McKee, A. C. (2017). Psychiatric phenotypes in chronic traumatic encephalopathy. Neuroscience and Biobehavioral Reviews, 83, 622–630. doi:10.1016/j.neubiorev. 2017.08.023 McKee, A. C., Cairns, N. J., Dickson, D. W., Folkerth, R. D., Dirk Keene, C., Litvan, I., … Bieniek, K. F. (2016). The first NINDS/NIBIB consensus meeting to define neuropathological criteria for the diagnosis of chronic traumatic encephalopathy. Acta Neuropathologica, 131(1), 75–86. doi:10.1007/ s00401‐015‐1515‐z Mez, J., Daneshvar, D. H., Kiernan, P. T., Abdolmohammadi, B., Alvarez, V. E., Huber, B. R., … McKee, A. C. (2017). Clinicopathological evaluation of chronic traumatic encephalopathy in players of American football. JAMA, 318(4), 360–370. doi:10.1001/jama.2017.8334 Montenigro, P. H., Baugh, C. M., Daneshvar, D. H., Mez, J., Budson, A. E., Au, R., … Stern, R. A. (2014). Clinical subtypes of chronic traumatic encephalopathy: Literature review and proposed research diagnostic criteria for traumatic encephalopathy syndrome. Alzheimer’s Research & Therapy, 6(5), 68. doi:10.1186/s13195‐014‐0068‐z Stern, R. A., Daneshvar, D. H., Baugh, C. M., Seichepine, D. R., Montenigro, P. H., Riley, D. O., … McKee, A. C. (2013). Clinical presentation of chronic traumatic encephalopathy. Neurology, 81(13), 1122–1129. doi:10.1212/WNL.0b013e3182a55f7f

Neuropsychological Assessment of Cortical and Subcortical Cognitive Phenotypes Robert H. Paul1,2, Carissa L. Philippi1, and Gregory Dahl1 1

Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA 2 Missouri Institute of Mental Health, St. Louis, MO, USA

Introduction For more than a century, neuropsychological assessment has provided an objective method to quantify brain integrity. This approach overcomes limitations of subjective report of cognitive status, which are vulnerable to bias. The severity of cognitive impairment is often inflated in the context of concurrent mood disruption. By contrast, individuals with neurological brain injury are prone to underestimate the severity of cognitive impairment. Lack of insight into cognitive impairment is frequently associated with Alzheimer’s disease (AD), yet the feature is neither universal nor specific to the neuropathogenic mechanisms of AD. For example, recent studies indicate poor self‐awareness of cognitive impairment among younger adults infected with human immunodeficiency virus (HIV). The inherent limitations of self‐reported cognitive function underscore the importance of objectively defined assessment regardless of the underlying disease or mental health condition.

Anatomical Substrates of Neuropsychological Performance The field of neuropsychology is based on the central tenet that biology and behavior are ­intertwined and inseparable. These relationships were established through empirical studies of individuals with natural and iatrogenic brain damage (Kolb & Wishaw, 2015). More recently, neuroimaging using high‐resolution magnetic resonance imaging (MRI) and positron emission tomography (PET) allows in vivo examination of brain–behavior links. Advanced imaging The Wiley Encyclopedia of Health Psychology: Volume 1: Biological Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editor: Robert H. Paul. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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t­echniques such as MRI and PET (among other modalities) have revolutionized the brain sciences, yet neuroimaging studies neither define nor reliably predict functional capacity. ­ Reliable estimates of the functional integrity of the underlying anatomy require a comprehensive approach to cognitive testing using measures sensitive to the primary networks of the brain (Yeo, Krienen, Chee, & Buckner, 2014). Inconsistencies exist in the literature, but most groups agree that these networks can be defined as five to seven relatively unique cognitive domains (attention, information processing speed/psychomotor speed, working memory/executive function, language, learning and memory, visuospatial, and motor). Analyses of the behavioral patterns observed within and across these domains produce rich insights into brain structure and function. This process is referred to as cognitive phenotyping. Cognitive testing alone cannot reliably determine the etiology of isolated neuronal damage. However, the sensitivity of cognitive phenotyping to detect abnormalities in regional or brain‐ wide networks is quite good. Neuronal disruption that is largely confined to the cellular layers of the cortical ribbon is expressed behaviorally as deficit driven by the affected cortical region (e.g., language, memory, and motor). By contrast, neuronal damage predominately affecting the subcortical white matter fasciculi or the deep subcortical gray matter nuclei (caudate, ­globus pallidus, putamen, thalamus) produces a pattern of deficits most consistent with frontal lobe dysfunction (Cummings, 1993). Such anatomical specificity for associated cognitive impairment is most likely to be observed during the early stages of neurodegenerative conditions. By contrast, advanced stages of neurological conditions typically involve both cortical and subcortical brain regions and therefore produce a mixed pattern of cognitive symptoms.

Cortical Versus Subcortical Cognitive Phenotypes The cortical cognitive phenotype includes amnestic memory loss, apraxia, agnosia, and anomic aphasia. This pattern was originally modeled after the prototypical expression of AD. Processing speed, working memory, and executive function are also affected in AD, but the level of impairment is typically less severe than the early and profound impairment in memory retention. Other neurodegenerative conditions that produce a cortical cognitive phenotype include semantic dementia, argyrophilic grain disease, and posterior cortical atrophy (Benson, Davis, & Snyder, 1988; Ferrer, Santpere, & van Leeuwen, 2008; Hodges, Patterson, Oxbury, & Funnell, 1992). The exact nature of the accompanying phenotype differs in these conditions, yet each shares a predilection for early involvement of cortical gray matter regions and a cognitive signature consistent with at least one cortical syndrome. An exception to this model can be seen among individuals with the frontal variant of frontotemporal dementia (FTD). Executive dysfunction is present early in FTD, and the behavioral deficits associated with poor behavioral regulation dominate the clinical presentation. Individuals under 65 with a marked change in personality and self‐regulation are likely to meet diagnostic criteria for FTD. In contrast to the cortical phenotype, subcortical cognitive phenotypes are characterized by abnormalities in attention, working memory, executive function, information processing, and psychomotor speed. This pattern reflects a predilection for disease mechanisms to impact deep subcortical gray matter nuclei (e.g., small vessel ischemia), white matter fasciculi (e.g., multiple sclerosis [MS]), or both gray and white matter regions (HIV) (Cummings, 1993; Rao, 1986; Woods, Moore, Weber, & Grant, 2009). Parkinson’s disease, Huntington’s disease, and “normal aging” also produce a cognitive pattern consistent with a subcortical profile. Neuropathology may exist in cortical regions (e.g., Parkinson’s disease) or in gray–white ­matter junctions (MS), but the preponderance of cellular damage is concentrated in white

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matter tracts and/or the basal ganglia. An interesting example of disconnection is evident among individuals with damage to the cerebellum (e.g., from stroke). The cerebellum is ­connected to the dorsolateral prefrontal cortex by a long‐range fasciculus, and when damaged, affected individuals exhibit significant executive dysfunction (Strick, Dum, & Fiez, 2009). These cases of distant/remote brain damage provide clinical evidence that the behavioral expression of frontal impairment results from damage to the subcortical neural pathways. Individuals with a subcortical cognitive phenotype often exhibit memory impairment, but the profile differs from what is observed in AD. Subcortical memory dysfunction is characterized by shallow learning and poor delayed recall of newly learned information. The restricted learning curve is a downstream consequence of poor attention, working memory, and/or executive skills needed to organize and learn the information. Long‐term free recall is reduced as a function of the poor initial learning, but individuals usually do not exhibit a total loss of information. As such, subcortical etiologies spare recognition memory (discrimination of targets from foils) except when performance is confounded by positive or negative response sets (e.g., cases of stimulus‐bound individuals). By contrast, individuals with AD do not benefit from recognition trials because the critical brain structures necessary for encoding and retention are damaged (i.e., medial temporal lobe). The ability to detect these unique memory phenotypes, therefore, is dependent on the specific memory measures included in the test battery. Memory tests that do not include a recognition trial will not differentiate retrieval from retention deficits.

Limitations to Phenotype Modeling Neuropsychological phenotyping is not a perfect science. As described in the example of ­memory profiles above, the pattern of results is highly influenced by test selection (sensitivity, coverage), limited time windows to complete testing (e.g., when embedded in a demanding research study), access to appropriate normative data, and availability of alternate and equivalent forms for repeat testing (Kolb & Wishaw, 2015; Lezak, Howieson, Bigler, & Tranel, 2012). Additionally, stimulus confusion resulting from the administration of multiple tests can artificially create behavioral failures due to poor test administration procedures. A related problem is confusion that occurs when language measures are administered during the interval between final learning trials and delayed recall trials on verbal memory tests. These details are not intuitive, yet the quality of data available for phenotyping is intimately linked to sound implementation procedures. Unfortunately, efforts to fully standardize the process (e.g., NIH Toolbox; Gershon et al., 2013) have not been widely adopted, largely because the value of such an approach remains undefined. We expect change in the future, with more ready uptake of standardized cognitive assessments and application of predictive modeling using artificial intelligence algorithms (e.g., machine learning).

Opportunities for Future Research in Cognitive Phenotyping Neuropsychological tests generate many indices of brain function. Depending on final test selections, the total number of dependent variables obtained from a comprehensive evaluation can easily exceed 50 or more indices. Structural and functional neuroimaging assessments generate many more variables of interest. Aggregating these outcomes into global measures by cognitive domain (e.g., executive function, information processing) or brain region (frontal lobe volume, white matter volume) to reduce the total number of dependent variables reduces

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the sensitivity of the outcome measures as areas of relative weakness are masked by relative strengths. An alternative approach is to simply ignore select data outputs based on a priori hypotheses related to regions believed to be relevant to the clinical or research question. This approach avoids reduced and restricted sensitivity due to averaging, but the outcomes are entirely dependent on the accuracy of the a priori hypotheses. It can be argued that this traditional data‐reduction approach to clinical neuroscience has contributed to the slow pace of innovation in the field. Thus, a new approach is greatly needed. Artificial intelligence systems adapted from private industry offer a fresh approach. Rather than intentional omission of potentially important indices of brain function, artificial intelligence incorporates all data input features into models to define the latent variance structure. Groups, including ours, apply these methods to integrate multimodal neuroimaging data, comprehensive neuropsychological performance indices, and laboratory markers, without aggregation of scores by cognitive domains or brain regions and without discarding data based on assumptions. Work conducted to date clearly defines that value of this approach to define the most salient variables that define group classification, as well as inform which variables can be adjusted to reduce risks associated with specific conditions. It is expected that data‐driven approaches using artificial intelligence algorithms will transform the field and establish new paths for personalized medicine, inclusive of treatment and cure strategies across a broad ­spectrum of neurological conditions.

Author Biographies Robert H. Paul is a board‐certified clinical neuropsychologist. He serves as director of the Missouri Institute of Mental Health and professor of psychological sciences at the University of Missouri–St. Louis. His research program is focused on mechanisms of brain dysfunction in health conditions that primarily impact brain structures located deep beneath the surface of the cerebral cortex. He has special interest in clinical neuroscience capacity building in resource‐limited environments. Carissa L. Philippi, PhD, is an assistant professor in the Department of Psychological Sciences at the University of Missouri–St. Louis. Her research takes a dimensional approach to understanding the behavioral (e.g., negative self‐related thought) and neural correlates of neuropsychiatric conditions, such as depression. For example, in a recent study, she identified associations between subclinical depression severity and resting‐state functional connectivity of the anterior cingulate cortex that mirrored neural circuit dysfunction in major depression. Gregory Dahl is a graduate research assistant in the Behavioral Neuroscience Program at the University of Missouri–St. Louis. He completed his undergraduate work in Psychology and Philosophy in May of 2015 and began working with client data/statistics for a not‐for‐profit mental health clinic. Upon entry to graduate school, he began working with Professor Philippi on the cognitive and affective neurobiology of self. His research interests include the neurobiology of memory and the cognitive/behavioral/environmental factors that can affect memory.

References Benson, D., Davis, R., & Snyder, B. (1988). Posterior cortical atrophy. Archives of Neurology, 45(7), 789–793. doi:10.1001/archneur.1988.00520310107024 Cummings, J. (1993). Frontal‐subcortical circuits and human behavior. Archives of Neurology, 50(8), 873–880. doi:10.1001/archneur.1993.00540080076020

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Ferrer, I., Santpere, G., & van Leeuwen, F. W. (2008). Argyrophilic grain disease. Brain, 131(6), 1416–1432. doi:10.1093/brain/awm305 Gershon, R. C., Wagster, M. V., Hendrie, H. C., Fox, N. A., Cook, K. F., & Nowinski, C. J. (2013). NIH toolbox for assessment of neurological and behavioral function. Neurology, 80(11 Supplement 3), S2–S6. doi:10.1212/WNL.0b013e3182872e5f Hodges, J. R., Patterson, K., Oxbury, S., & Funnell, E. (1992). Semantic dementia: Progressive fluent asphasia with temporal lobe atrophy. Brain, 115(6), 1783–1806. doi:10.1093/brain/115.6.1783 Kolb, B., & Wishaw, I. (2015). Fundamentals of human neuropsychology (7th ed.). New York, NY: Worth Publishers. Lezak, M. D., Howieson, D. B., Bigler, E. D., & Tranel, D. (2012). Neuropsychological assessment. New York, NY: Oxford University Press. Rao, S. M. (1986). Neuropsychology of multiple sclerosis: A critical review. Journal of Clinical and Experimental Neuropsychology, 8(5), 503–542. doi:10.1080/01688638608405173 Strick, P. L., Dum, R. P., & Fiez, J. A. (2009). Cerebellum and nonmotor function. Annual Review of Neuroscience, 32(1), 413–434. doi:10.1146/annurev.neuro.31.060407.125606 Woods, S. P., Moore, D. J., Weber, E., & Grant, I. (2009). Cognitive neuropsychology of HIV‐associated neurocognitive disorders. Neuropsychology Review, 19(2), 152–168. doi:10.1007/s11065‐009‐9102‐5 Yeo, B. T. T., Krienen, F. M., Chee, M. W. L., & Buckner, R. L. (2014). Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex. NeuroImage, 88, 212–227. doi:10.1016/j.neuroimage.2013.10.046

Mild Traumatic Brain Injury and Posttraumatic Stress Disorder: Diagnostic and Neurobiological Signatures of Comorbidity Jacob Bolzenius Missouri Institute of Mental Health, University of Missouri–St. Louis, St. Louis, MO, USA

Traumatic brain injury (TBI) is one of the most common conditions of acquired brain ­dysfunction, with estimates reporting 1.7 million cases per year in the United States alone (Faul & Coronado, 2015). While treatment for and survivability from more severe TBI has improved markedly over recent decades, mild TBI (mTBI), also commonly described as “­concussion,” presents a fairly unique symptom profile and recovery prognosis. Moreover, prevalence of this condition in certain stressful scenarios (e.g., military service, assault, motor vehicle accidents) leaves the individual prone to psychiatric disorders such as posttraumatic stress disorder (PTSD). This commonly comorbid disorder challenges clinical judgment and treatment strategies, particularly as patients progress past the acute injury phase. Optimized methods for identification of mTBI and PTSD, especially when presenting together, have thus been the subject of intense investigation over recent years. This chapter aims to delineate characteristics of both mTBI and PTSD and review evidence regarding the neurobiological bases of these disorders that may guide clinical care decisions.

Diagnosis and Characteristics of mTBI Severity of TBI is classified as mild, moderate, or severe based on several criteria, including loss of consciousness (LOC), alteration of consciousness or posttraumatic amnesia (PTA), Glasgow Coma Scale (GCS) score, level of care or hospitalization required, and presence of acute neuroimaging abnormalities if available (Department of Veterans Affairs/Department of ­ The Wiley Encyclopedia of Health Psychology: Volume 1: Biological Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editor: Robert H. Paul. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Defense, 2016). mTBI is defined as a GCS of 13–15, LOC less than 30 min, and alteration of consciousness or PTA less than 24 hr. Most cases have unremarkable neuroimaging findings (i.e., CT or MRI) and require no significant hospitalization or prolonged care. The accuracy of these criteria and immediate care decisions, however, are largely dependent on self‐report as well as information from collateral sources, if present. The most common causes of civilian mTBI include falls and motor vehicle accidents (Bazarian et al., 2009). Among military populations in modern conflicts, blast injuries or blast‐related injuries represent the most common cause of mTBI (Helmick et al., 2015; Terrio et al., 2009). Unlike moderate to severe TBI, which leads to marked and prolonged cognitive deficits, cognitive and behavioral difficulties from mTBI tend to resolve within a couple weeks. Meta‐ analyses have suggested that reductions in cognitive function are evident in the acute phase of mTBI, particularly in memory, fluency, and processing speed. However, these changes are dependent on the post‐injury interval and tend to resolve with time, usually returning to near average within about 90 days (Belanger, Curtiss, Demery, Lebowitz, & Vanderploeg, 2005; Frencham, Fox, & Maybery, 2007; Schretlen & Shapiro, 2003). Injury‐related litigation has been identified as one key factor to residual or worsening cognitive performance beyond this time point (Belanger et al., 2005). While the majority of individuals with mTBI fully recover, around 10–15% continue to experience symptoms past the 3‐month window related to the injury. These chronic symptoms are known as post‐concussion syndrome (PCS) (Iverson & Lange, 2011), recently reclassified as major or mild neurocognitive disorder due to TBI in the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5) (American Psychiatric Association, 2013). Symptoms of PCS may take weeks to months to emerge following mTBI and can include a host of secondary effects, including orthopedic pain, headaches, psychiatric conditions, and sensory dysfunction (Leddy, Sandhu, Sodhi, Baker, & Willer, 2012). Treatment options include a variety of pharmacologic, physical, and psychological methods, with evidence supporting the use of early, brief psychological intervention including symptom attribution education, complemented by resumption of physical activity and medication when appropriate (Mittenberg, Canyock, Condit, & Patton, 2010). Work is ongoing to identify biomarkers that can detect biological correlates of continued cognitive or subjective symptoms in these individuals. Volumetric MRI studies have described cortical thinning (A. P. Michael et al., 2015) and global and focal atrophy to both white and gray matter at 1‐year post‐injury that relates to cognitive changes and PCS symptoms (Zhou et  al., 2013). Diffusion tensor imaging (DTI) has demonstrated sensitivity to sequelae of mTBI and ability to distinguish patients from controls (Hulkower, Poliak, Rosenbaum, Zimmerman, & Lipton, 2013). The advantage of DTI resides in the sensitivity to microstructural detail of neuronal components that are affected by the shearing forces (e.g., traumatic axonal injury) sustained during mTBI, particularly to white matter. As a result, DTI has become the neuroimaging method of choice for examining brain markers of mTBI, especially as this scan sequence is already commonly available on most commercial MRI scanners. Research has suggested that DTI may be more sensitive than cognitive testing to subacute mTBI changes and that these changes normalize longitudinally as injury‐related physiological disruption normalizes (Mayer et al., 2010). In individuals with symptoms of PCS, DTI abnormalities in the subacute stage after mTBI have been related to greater white matter structural impairment at 6‐month follow‐up in patients compared with non‐PCS controls (Messé et al., 2012). This is supported by animal research confirming the link between DTI changes and altered neuronal histopathology in mTBI models (Bennett, Mac Donald, & Brody, 2012; Hylin et  al., 2013). Additional structural MRI sequences such as susceptibility‐weighted imaging (SWI) and fluid‐attenuated inversion recovery (FLAIR) provide help detecting ­

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micro‐hemorrhages, and together usage of SWI and/or DTI detects mTBI abnormalities in up to one‐third of those with negative CT scans on day of injury (Bigler, 2013; Shenton et al., 2012). Functional MRI (fMRI) sequences have also shown promise in differentiating mTBI from controls (Bryer, Medaglia, Rostami, & Hillary, 2013), suggesting that functional ­network integrity can be leveraged as a marker of both acute and chronic mTBI.

Diagnosis and Characteristics of PTSD PTSD is a disorder characterized by emotional, behavioral, and cognitive symptoms that develops in response to a severe stressor or threat to life or well‐being. Some of the most ­common clinical assessment tools for PTSD updated for DSM‐5 criteria include the Clinician‐ Administered PTSD Scale (CAPS‐5) (Weathers et  al., 2013), the PTSD Symptom Scale— Interview  (PSS‐I‐5) (Foa et  al., 2013), and the Structured Clinical Interview for DSM‐5 (SCID‐5) (First, Williams, Karg, & Spitzer, 2015). Symptoms of PTSD are grouped diagnostically among s­everal criteria clusters. These include reexperiencing or intrusive thoughts, avoidance behaviors, negative mood or altered cognitive ability, and arousal or reactivity. These diagnostic clusters reflect a change implemented in the DSM‐5, which separated avoidance and negative alterations in cognition and mood, thus requiring identifiable presence of both of these symptoms for diagnosis. Symptoms can extend well beyond the acute recovery period and lead to continued cognitive difficulties, observable brain abnormalities, and altered quality of life and disability (Seedat, Lochner, Vythilingum, & Stein, 2006). PTSD is especially ­prevalent among military veterans, presenting in up to 20% of those returning from service (Hoge et  al., 2004), and is associated with poor post‐deployment health and occupational outcomes compared with non‐symptomatic counterparts (Hoge et al., 2008). While PTSD is generally viewed and treated as a disorder of fear conditioning dysregulation, it is also characterized by changes to objective neuropsychological performance. Individuals with PTSD exhibit greater attention to, and more difficulty disengaging from, emotionally laden stimuli (Christianson, 1992; Paunovi, Lundh, & Ost, 2002; Vythilingam et al., 2007). PTSD has been linked to long‐term changes in several cognitive domains such as verbal and visual memory compared with controls (Johnsen & Asbjørnsen, 2008; Vasterling, Aslan, et al., 2018). Further, detriments in areas of executive function are shown to affect memory formation and retrieval for both neutral and emotionally laden content (LaGarde, Doyon, & Brunet, 2010; Williams et al., 2007). In addition to these various types of explicit memory, implicit memory shows differential effects in PTSD, with evidence of facilitated priming for high valence stimuli compared with controls (Kleim, Ehring, & Ehlers, 2011; T. Michael & Ehlers, 2007; T. Michael, Ehlers, & Halligan, 2005). Attention bias to trauma‐related words in an emotional Stroop task relates to a diagnosis of PTSD (El Khoury‐Malhame et al., 2011) as well as PTSD severity (Fleurkens, Rinck, & van Minnen, 2011). Additionally, these individuals exhibit impaired reward processing as effort expenditure to reach a positive reward varies greatly from patterns demonstrated by controls (Hopper et al., 2007). Thus, there is sufficient evidence substantiating the effects of altered cognitive functions, particularly as they relate to emotionally laden content. PTSD disrupts normal function of the hypothalamic–pituitary–adrenal axis. These are marked by decreased cortisol and increased levels of corticotropin‐releasing hormone that ­create a sensitized negative feedback inhibitory loop (Sherin & Nemeroff, 2011). To identify the neurocircuitry behind the disorder, prior work has demonstrated links to various markers of brain volume, integrity, and network connectivity. This includes disruptions in frontal

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­etworks necessary for inhibiting vigilance to trauma‐related stimuli and extinguishing n response patterns, overactive amygdala function that facilitates the learned and habituated aspects of the fear response, and decreased hippocampal activation leading to impaired association with and retention of less emotionally volatile information (Rauch, Shin, & Phelps, 2006). Volumetric MRI studies have related PTSD to smaller volumes in the hippocampus (Smith, 2005; Villarreal et al., 2002), anterior cingulate cortex (Kitayama & Quinn, 2006; Woodward et al., 2006), and amygdala (Karl et al., 2006) compared with healthy controls. DTI research has demonstrated consistent white matter disruptions associated with PTSD, particularly in tracts related to emotional regulation (Long et al., 2013; Schuff et al., 2011). Functional neuroimaging, including PET and fMRI, have also provided evidence for network disruption and altered blood flow patterns in response to emotional stimuli in individuals with PTSD (Bremner et al., 1999; Lanius et al., 2003; Patel, Spreng, Shin, & Girard, 2012). Elevated activation of the amygdala and hippocampus has been observed using fMRI while encoding and recalling negative stimuli in PTSD relative to trauma‐exposed non‐PTSD individuals (Brohawn, Offringa, Pfaff, Hughes, & Shin, 2010).

Characteristics of Comorbid mTBI and PTSD Overlapping presentation of symptoms represents one of the chief challenges of comorbid PTSD and mTBI. Clinical determination of differential diagnosis is challenged by the need to rely on self‐reported symptom complaints. Acute injury details may be irretrievable due to memory loss related to the traumatic event, making attribution of deficits ambiguous between sequelae of psychological and brain trauma. Both disorders share chronic symptom complaints including insomnia, slowed or foggy thinking, headache, and difficulties with attention, and additional comorbidities may arise including depression, substance use, and somatic problems. Due to variations in injury‐related factors such as loss or alteration of consciousness that impact the subjective experience of the event, individuals with mTBI tend to be at higher risk for development of PTSD than more severe head injuries (Klein, Caspi, & Gil, 2016). Military populations in particular are at elevated risk for exposure to trauma resulting in mTBI and PTSD. A large‐scale study demonstrated a threefold increase in the rate of PTSD among veterans exhibiting positive symptoms of TBI compared with those exhibiting no symptoms (Carlson et  al., 2010). Studies focusing on mTBI have shown that a significant ­portion of service members (up to 40% or higher) experiencing symptoms of mTBI also exhibit symptoms of PTSD (Hoge et al., 2008). These elevated rates are likely due to combat environments creating pre‐injury states of heightened stress and hypervigilance that expose the individual to PTSD‐inducing stimuli prior to onset of brain trauma (Kennedy et al., 2007). Additionally, variations in psychiatric profiles, including higher premorbid PTSD symptoms and lower cognitive function, military combat, and post‐deployment stress, predict PTSD development following trauma exposure (Boasso, Steenkamp, Larson, & Litz, 2016; Sørensen, Andersen, Karstoft, & Madsen, 2016). Disentangling these disorder profiles and identifying optimal treatment regimens remain a critical goal, as history of TBI is a prominent risk factor for suicidal ideation (Wisco et al., 2014) and nearly two dozen US veterans die from suicide each day (Department of Veterans Affairs, 2016). Research shows that presence of PTSD can impact the presentation and severity of the mTBI and vice versa. TBI and PTSD are both independently related to subjective post‐injury symptom reporting (Vasterling et al., 2012). Symptoms of PCS in particular overlap notably with those of PTSD; thus treatment of some nonspecific symptoms (e.g., anger, impaired

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concentration) (Vasterling, Jacob, & Rasmusson, 2018) may improve outcomes of either c­ondition. In the acute phase, symptoms of PTSD and mTBI (e.g., confusion, disrupted attention, disorientation) can resemble each other, and in the post‐acute stage, PCS may be endorsed by individuals with non‐trauma disorders (Donnell, Kim, Silva, & Vanderploeg, 2012) or even healthy controls (Iverson & Lange, 2010). Studies have described independent associations between both mTBI and PTSD with PCS, with evidence supporting an additive burden of adversity for those with both mTBI and PTSD (Brenner et  al., 2010; Brenner, Vanderploeg, & Terrio, 2009). Data suggest that psychological distress related to PCS and PTSD are predictors of cognitive status regardless of TBI history, but history of mTBI is linked to higher rates of clinical morbidity (Donnelly, Donnelly, Warner, Kittleson, & King, 2017). Further, rates of PTSD are nearly twice as likely one year after mTBI compared with non‐TBI civilian trauma (Bryant et  al., 2010). Objective cognitive profiles are difficult to ascribe to one single condition, as both affect several of the same domains (e.g., executive function, learning, memory) (Scott et al., 2015), though some research suggests PTSD relates to poorer performance in cognitive domains of reaction time, learning, and memory after adjusting for TBI status (Vasterling et al., 2012). Subjective complaints regarding cognitive function often exceed measurable dysfunction (Spencer, Drag, Walker, & Bieliauskas, 2010; Zatzick et al., 2010). Few neuroimaging studies have focused on both mTBI and PTSD. One study suggested that PTSD severity was more strongly related to measures of white matter integrity than mTBI, but subclinical symptoms of mTBI moderate the development of PTSD (Bazarian et al., 2013). Research has also shown that PTSD relates to white matter changes using DTI after accounting for mTBI status (Davenport et al., 2016; Schuff et al., 2011). Another study demonstrated greater white matter integrity in PTSD than mTBI, particularly in high‐­ complexity regions (Davenport, Lim, & Sponheim, 2014), supporting the idea of mTBI as a disorder of diffuse brain disruption (Bolzenius et al., 2018; Hu et al., 2016). Other methods, such as single‐photon emission computed tomography (SPECT), have revealed greater regional activation in PTSD compared to a mixed‐severity TBI group, suggesting that PTSD symptoms in the presence of TBI produce a different signature than when considering either condition alone (Amen et al., 2015). fMRI research has also implicated altered connectivity between prefrontal and hippocampal regions that relates to disruptive reexperiencing of PTSD symptoms. Other studies report altered connectivity between prefrontal and basal ganglia regions, providing a potential neural substrate associated with abnormalities in working memory and maintenance networks that exacerbate PTSD symptoms(Spielberg, McGlinchey, Milberg, & Salat, 2015). Overall, these studies present intriguing interactions between symptom profiles of mTBI and PTSD, but additional research is needed to further tease apart the unique contributions of each condition. Advances in computational data science and predictive modeling algorithms based on artificial intelligence offers the potential to delineate the biology, symptoms, and treatments for PTSD and mTBI as separate entities.

Author Biography Jacob D. Bolzenius earned a PhD (2014) in Psychology with an emphasis in behavioral ­neuroscience from the University of Missouri–St. Louis and has held several appointments, currently at the University of Missouri‐St. Louis. His research has focused on using novel neuroimaging techniques, including diffusion tensor imaging and volumetric shape analysis, to assess biological correlates of brain disease or insult (e.g., traumatic brain injury, healthy

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aging). Currently he is applying diffusion basis spectrum imaging methods to infer markers of cellular inflammation and neuronal integrity associated with delirium in older adults. This research has promise to validate in vivo cellular pathology associated with brain disease that can inform the understanding of brain disease progression in later life to apply to prevention and treatment strategies.

References Amen, D. G., Raji, C. A., Willeumier, K., Taylor, D., Tarzwell, R., Newberg, A., & Henderson, T. A. (2015). Functional neuroimaging distinguishes posttraumatic stress disorder from traumatic brain injury in focused and large community datasets. PLoS One, 10(7), e0129659. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM‐5®), fifth edition. Washington, DC: Author. Bazarian, J. J., Donnelly, K., Peterson, D. R., Warner, G. C., Zhu, T., & Zhong, J. (2013). The relation between posttraumatic stress disorder and mild traumatic brain injury acquired during operations enduring freedom and iraqi freedom. The Journal of Head Trauma Rehabilitation, 28(1), 1–12. Bazarian, J. J., McClung, J., Shah, M. N., Cheng, Y. T., Flesher, W., & Kraus, J. (2009). Mild traumatic brain injury in the United States, 1998–2000. Brain Injury, 19(2), 85–91. Belanger, H. G., Curtiss, G., Demery, J. A., Lebowitz, B. K., & Vanderploeg, R. D. (2005). Factors moderating neuropsychological outcomes following mild traumatic brain injury: A meta‐analysis. Journal of the International Neuropsychological Society, 11(3), 215–227. Bennett, R. E., Mac Donald, C. L., & Brody, D. L. (2012). Diffusion tensor imaging detects axonal injury in a mouse model of repetitive closed‐skull traumatic brain injury. Neuroscience Letters, 513(2), 160–165. Bigler, E. D. (2013). Neuroimaging biomarkers in mild traumatic brain injury (mTBI). Neuropsychology Review, 23(3), 169–209. Boasso, A. M., Steenkamp, M. M., Larson, J. L., & Litz, B. T. (2016). Posttraumatic stress symptoms across the deployment cycle: A latent transition analysis. Journal of Psychiatric Research, 83, 54–60. Bolzenius, J. D., Velez, C. S., Lewis, J. D., Bigler, E. D., Wade, B. S. C., Cooper, D. B., … Tate, D. F. (2018). Diffusion imaging findings in US service members with mild traumatic brain injury and posttraumatic stress disorder. The Journal of Head Trauma Rehabilitation, 33(6), 393–402. Bremner, J. D., Narayan, M., Staib, L. H., Southwick, S. M., McGlashan, T., & Charney, D. S. (1999). Neural correlates of memories of childhood sexual abuse in women with and without posttraumatic stress disorder. The American Journal of Psychiatry, 156(11), 1787–1795. Brenner, L. A., Terrio, H., Homaifar, B. Y., Gutierrez, P. M., Staves, P. J., Harwood, J. E., … Warden, D. (2010). Neuropsychological test performance in soldiers with blast‐related mild TBI. Neuropsychology, 24(2), 160–167. Brenner, L. A., Vanderploeg, R. D., & Terrio, H. (2009). Assessment and diagnosis of mild traumatic brain injury, posttraumatic stress disorder, and other polytrauma conditions: Burden of adversity hypothesis. Rehabilitation Psychology, 54(3), 239–246. Brohawn, K. H., Offringa, R., Pfaff, D. L., Hughes, K. C., & Shin, L. M. (2010). The neural correlates of emotional memory in posttraumatic stress disorder. Biological Psychiatry, 68(11), 1023–1030. Bryant, R. A., O’Donnell, M. L., Creamer, M., McFarlane, A. C., Clark, C. R., & Silove, D. (2010). The psychiatric sequelae of traumatic injury. The American Journal of Psychiatry, 167(3), 312–320. Bryer, E. J., Medaglia, J. D., Rostami, S., & Hillary, F. G. (2013). Neural recruitment after mild traumatic brain injury Is task dependent: A meta‐analysis. Journal of the International Neuropsychological Society, 19(7), 751–762. Carlson, K. F., Nelson, D., Orazem, R. J., Nugent, S., Cifu, D. X., & Sayer, N. A. (2010). Psychiatric diagnoses among Iraq and Afghanistan war veterans screened for deployment‐related traumatic brain injury. Journal of Traumatic Stress, 23(1), 17–24.

Mild Traumatic Brain Injury and Posttraumatic Stress Disorder

249

Christianson, S. A. (1992). Emotional stress and eyewitness memory: A critical review. Psychological Bulletin, 112(2), 284–309. Davenport, N. D., Lamberty, G. J., Nelson, N. W., Lim, K. O., Armstrong, M. T., & Sponheim, S. R. (2016). PTSD confounds detection of compromised cerebral white matter integrity in military veterans reporting a history of mild traumatic brain injury. Brain Injury, 30(12), 1491–1500. Davenport, N. D., Lim, K. O., & Sponheim, S. R. (2014). White matter abnormalities associated with military PTSD in the context of blast TBI. Human Brain Mapping, 36(3), 1053–1064. Department of Veterans Affairs. (2016). Suicide among veterans and other Americans 2001–2014. Washington, DC: Office of Suicide Prevention. Department of Veterans Affairs/Department of Defense. (2016). VA/DoD clinical practice guideline for the management of concussion‐mild traumatic brain injury. Retrieved from https://www. healthquality.va.gov/guidelines/Rehab/mtbi/. Donnell, A. J., Kim, M. S., Silva, M. A., & Vanderploeg, R. D. (2012). Incidence of postconcussion symptoms in psychiatric diagnostic groups, mild traumatic brain injury, and comorbid conditions. The Clinical Neuropsychologist, 26(7), 1092–1101. Donnelly, K., Donnelly, J. P., Warner, G. C., Kittleson, C. J., & King, P. R. (2017). Longitudinal study of objective and subjective cognitive performance and psychological distress in OEF/OIF Veterans with and without traumatic brain injury. The Clinical Neuropsychologist, 3, 1–20. El Khoury‐Malhame, M., Lanteaume, L., Beetz, E. M., Roques, J., Reynaud, E., Samuelian, J. C., … Khalfa, S. (2011). Attentional bias in post‐traumatic stress disorder diminishes after symptom amelioration. Behaviour Research and Therapy, 49(11), 796–801. Faul, M., & Coronado, V. (2015). Epidemiology of traumatic brain injury. Handbook of Clinical Neurology, 127, 3–13. First, M. B., Williams, J. B. W., Karg, R. S., & Spitzer, R. L. (2015). Structured clinical interview for DSM‐5 disorders, clinician version (SCID‐5‐CV). Arlington, VA: American Psychiatric Association. Fleurkens, P., Rinck, M., & van Minnen, A. (2011). Specificity and generalization of attentional bias in sexual trauma victims suffering from posttraumatic stress disorder. Journal of Anxiety Disorders, 25(6), 783–787. Foa, E. B., McLean, C. P., Zang, Y., Zong, J., Rauch, S., Porter, K., … Kauffman, B. (2013). Psychometric properties of the Posttraumatic Stress Disorder Symptoms Scale Interview for  DSM‐5  (PSSI‐5). Psychological Assessment, 28, 1159–1165. Frencham, K. A. R., Fox, A. M., & Maybery, M. T. (2007). Neuropsychological studies of mild traumatic brain injury: A meta‐analytic review of research since 1995. Journal of Clinical and Experimental Neuropsychology, 27(3), 334–351. Helmick, K. M., Spells, C. A., Malik, S. Z., Davies, C. A., Marion, D. W., & Hinds, S. R. (2015). Traumatic brain injury in the US military: epidemiology and key clinical and research programs. Brain Imaging and Behavior, 9(3), 358–366. Hoge, C. W., Castro, C. A., Messer, S. C., McGurk, D., Cotting, D. I., & Koffman, R. L. (2004). Combat duty in Iraq and Afghanistan, mental health problems, and barriers to care. NEJM, 351, 13–22. Hoge, C. W., McGurk, D., Thomas, J. L., Cox, A. L., Engel, C. C., & Castro, C. A. (2008). Mild traumatic brain injury in U.S. soldiers returning from Iraq. NEJM, 358(5), 453–463. Hopper, J. W., Pitman, R. K., Su, Z., Heyman, G. M., Lasko, N. B., Macklin, M. L., … Elman, I. (2007). Probing reward function in posttraumatic stress disorder: Expectancy and satisfaction with monetary gains and losses. Journal of Psychiatric Research, 42(10), 802–807. Hu, H., Zhou, Y., Wang, Q., Su, S., Qiu, Y., Ge, J., … Xiao, Z. (2016). Association of abnormal white matter integrity in the acute phase of motor vehicle accidents with post‐traumatic stress disorder. Journal of Affective Disorders, 190, 714–722. Hulkower, M. B., Poliak, D. B., Rosenbaum, S. B., Zimmerman, M. E., & Lipton, M. L. (2013). A decade of DTI in traumatic brain injury: 10 Years and 100 articles later. American Journal of Neuroradiology, 34(11), 2064–2074.

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Hylin, M. J., Orsi, S. A., Zhao, J., Bockhorst, K., Perez, A., Moore, A. N., & Dash, P. K. (2013). Behavioral and histopathological alterations resulting from mild fluid percussion injury. Journal of Neurotrauma, 30(9), 702–715. Iverson, G. L., & Lange, R. T. (2010). Examination of “postconcussion‐like” symptoms in a healthy sample. Applied Neuropsychology, 10(3), 137–144. Iverson, G. L., & Lange, R. T. (2011). Post‐concussion syndrome. In M. Schoenberg & J. Scott (Eds.), The little black book of neuropsychology (pp. 745–764). Boston, MA: Springer. Johnsen, G. E., & Asbjørnsen, A. E. (2008). Consistent impaired verbal memory in PTSD: A meta‐analysis. Journal of Affective Disorders, 111(1), 74–82. Karl, A., Schaefer, M., Malta, L. S., Dörfel, D., Rohleder, N., & Werner, A. (2006). A meta‐analysis of structural brain abnormalities in PTSD. Neuroscience and Biobehavioral Reviews, 30(7), 1004–1031. Kennedy, J. E., Jaffee, M. S., Leskin, G. A., Stokes, J. W., Leal, F. O., & Fitzpatrick, P. J. (2007). Posttraumatic stress disorder and posttraumatic stress disorder‐like symptoms and mild traumatic brain injury. Journal of Rehabilitation Research and Development, 44(7), 895–920. Kitayama, N., & Quinn, S. (2006). Smaller volume of anterior cingulate cortex in abuse‐related posttraumatic stress disorder. Journal of Affective Disorders, 90(2‐3), 171–174. Kleim, B., Ehring, T., & Ehlers, A. (2011). Perceptual processing advantages for trauma‐related visual cues in post‐traumatic stress disorder. Psychological Medicine, 42(1), 173–181. Klein, E., Caspi, Y., & Gil, S. (2016). The relation between memory of the traumatic event and PTSD: Evidence from studies of traumatic brain injury. The Canadian Journal of Psychiatry, 48(1), 28–33. LaGarde, G., Doyon, J., & Brunet, A. (2010). Memory and executive dysfunctions associated with acute posttraumatic stress disorder. Psychiatry Research, 177(1), 144–149. Lanius, R. A., Williamson, P. C., Hopper, J., Densmore, M., Boksman, K., Gupta, M. A., … Menon, R. S. (2003). Recall of emotional states in posttraumatic stress disorder: An fMRI investigation. Biological Psychiatry, 53(3), 204–210. Leddy, J. J., Sandhu, H., Sodhi, V., Baker, J. G., & Willer, B. (2012). Rehabilitation of concussion and post‐concussion syndrome. Sports Health, 4(2), 147–154. Long, Z., Duan, X., Xie, B., Du, H., Li, R., Xu, Q., … Chen, H. (2013). Altered brain structural connectivity in post‐traumatic stress disorder: A diffusion tensor imaging tractography study. Journal of Affective Disorders, 150(3), 798–806. Mayer, A. R., Ling, J., Mannell, M. V., Gasparovic, C., Phillips, J. P., Doezema, D., … Yeo, R. A. (2010). A prospective diffusion tensor imaging study in mild traumatic brain injury. Neurology, 74(8), 643–650. Messé, A., Caplain, S., Pélégrini‐Issac, M., Blancho, S., Montreuil, M., Lévy, R., … Benali, H. (2012). Structural integrity and postconcussion syndrome in mild traumatic brain injury patients. Brain Imaging and Behavior, 6(2), 283–292. Michael, A. P., Stout, J., Roskos, P. T., Bolzenius, J., Gfeller, J., Mogul, D., & Bucholz, R. (2015). Evaluation of cortical thickness after traumatic brain injury in military veterans. Journal of Neurotrauma, 32(22), 1751–1758. Michael, T., & Ehlers, A. (2007). Enhanced perceptual priming for neutral stimuli occurring in a traumatic context: Two experimental investigations. Behaviour Research and Therapy, 45(2), 341–358. Michael, T., Ehlers, A., & Halligan, S. L. (2005). Enhanced priming for trauma‐related material in posttraumatic stress disorder. Emotion (Washington, DC), 5(1), 103–112. Mittenberg, W., Canyock, E. M., Condit, D., & Patton, C. (2010). Treatment of post‐concussion syndrome following mild head injury. Journal of Clinical and Experimental Neuropsychology, 23(6), 829–836. Patel, R., Spreng, R. N., Shin, L. M., & Girard, T. A. (2012). Neurocircuitry models of posttraumatic stress disorder and beyond: A meta‐analysis of functional neuroimaging studies. Neuroscience and Biobehavioral Reviews, 36(9), 2130–2142.

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251

Paunovi, N., Lundh, L. G., & Ost, L. G. (2002). Attentional and memory bias for emotional information in crime victims with acute posttraumatic stress disorder (PTSD). Journal of Anxiety Disorders, 16(6), 675–692. Rauch, S. L., Shin, L. M., & Phelps, E. A. (2006). Neurocircuitry models of posttraumatic stress d ­ isorder and extinction: Human neuroimaging research—Past, present, and future. Biological Psychiatry, 60(4), 376–382. Schretlen, D. J., & Shapiro, A. M. (2003). A quantitative review of the effects of traumatic brain injury on cognitive functioning. International Review of Psychiatry, 15(4), 341–349. Schuff, N., Zhang, Y., Zhan, W., Lenoci, M., Ching, C., Boreta, L., … Neylan, T. C. (2011). Patterns of altered cortical perfusion and diminished subcortical integrity in posttraumatic stress disorder: An MRI study. NeuroImage, 54(S1), S62–S68. Scott, J. C., Matt, G. E., Wrocklage, K. M., Crnich, C., Jordan, J., Southwick, S. M., … Schweinsburg, B. C. (2015). A quantitative meta‐analysis of neurocognitive functioning in posttraumatic stress disorder. Psychological Bulletin, 141(1), 105–140. Seedat, S., Lochner, C., Vythilingum, B., & Stein, D. J. (2006). Disability and quality of life in post‐traumatic stress disorder. PharmacoEconomics, 24(10), 989–998. Shenton, M. E., Hamoda, H. M., Schneiderman, J. S., Bouix, S., Pasternak, O., Rathi, Y., … Zafonte, R. (2012). A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging and Behavior, 6(2), 137–192. Sherin, J. E., & Nemeroff, C. B. (2011). Post‐traumatic stress disorder: The neurobiological impact of psychological trauma. Dialogues in Clinical Neuroscience, 13(3), 263–278. Smith, M. E. (2005). Bilateral hippocampal volume reduction in adults with post‐traumatic stress disorder: A meta‐analysis of structural MRI studies. Hippocampus, 15(6), 798–807. Sørensen, H. J., Andersen, S. B., Karstoft, K.‐I., & Madsen, T. (2016). The influence of pre‐deployment cognitive ability on post‐traumatic stress disorder symptoms and trajectories: The Danish USPER follow‐up study of Afghanistan veterans. Journal of Affective Disorders, 196, 148–153. Spencer, R. J., Drag, L. L., Walker, S. J., & Bieliauskas, L. A. (2010). Self‐reported cognitive symptoms following mild traumatic brain injury are poorly associated with neuropsychological performance in OIF/OEF veterans. Journal of Rehabilitation Research and Development, 47(6), 521–530. Spielberg, J. M., McGlinchey, R. E., Milberg, W. P., & Salat, D. H. (2015). Brain network disturbance related to posttraumatic stress and traumatic brain injury in veterans. Biological Psychiatry, 78(3), 210–216. Terrio, H., Brenner, L. A., Ivins, B. J., Cho, J. M., Helmick, K., Schwab, K., … Warden, D. (2009). Traumatic brain injury screening: Preliminary findings in a US army brigade combat team. The Journal of Head Trauma Rehabilitation, 24(1), 14–23. Vasterling, J. J., Aslan, M., Lee, L. O., Proctor, S. P., Ko, J., Jacob, S., & Concato, J. (2018). Longitudinal associations among posttraumatic stress disorder symptoms, traumatic brain injury, and neurocognitive functioning in army soldiers deployed to the Iraq war. Journal of the International Neuropsychological Society, 24(4), 311–323. Vasterling, J. J., Brailey, K., Proctor, S. P., Kane, R., Heeren, T., & Franz, M. (2012). Neuropsychological outcomes of mild traumatic brain injury, post‐traumatic stress disorder and depression in Iraq‐ deployed US Army soldiers. The British Journal of Psychiatry, 201(3), 186–192. Vasterling, J. J., Jacob, S. N., & Rasmusson, A. (2018). Traumatic brain injury and posttraumatic stress disorder: Conceptual, diagnostic, and therapeutic considerations in the context of co‐occurrence. The Journal of Neuropsychiatry and Clinical Neurosciences, 30(2), 91–100. Villarreal, G., Hamilton, D. A., Petropoulos, H., Driscoll, I., Rowland, L. M., Griego, J. A., … Brooks, W. M. (2002). Reduced hippocampal volume and total white matter volume in posttraumatic stress disorder. Biological Psychiatry, 52(2), 119–125. Vythilingam, M., Blair, K. S., McCaffrey, D., Scaramozza, M., Jones, M., Nakic, M., … Blair, R. J. (2007). Biased emotional attention in post‐traumatic stress disorder: A help as well as a hindrance? Psychological Medicine, 37(10), 1445–1455.

252

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Weathers, F. W., Blake, D. D., Schnurr, P. P., Kaloupek, D. G., Marx, B. P., Keane, T. M. (2013). The clinician‐administered PTSD scale for DSM‐5 (CAPS‐5). Interview available from the National Center for PTSD at www.ptsd.va.gov Williams, J. M., Barnhofer, T., Crane, C., Herman, D., Raes, F., Watkins, E., & Dalgleish, T. (2007). Autobiographical memory specificity and emotional disorder. Psychological Bulletin, 133(1), 122–148. Wisco, B. E., Marx, B. P., Holowka, D. W., Vasterling, J. J., Han, S. C., Chen, M. S., … Keane, T. M. (2014). Traumatic brain injury, PTSD, and current suicidal ideation among Iraq and Afghanistan U.S. veterans. Journal of Traumatic Stress, 27(2), 244–248. Woodward, S. H., Kaloupek, D. G., Streeter, C. C., Martinez, C., Schaer, M., & Eliez, S. (2006). Decreased anterior cingulate volume in combat‐related PTSD. Biological Psychiatry, 59(7), 582–587. Zatzick, D. F., Rivara, F. P., Jurkovich, G. J., Hoge, C. W., Wang, J., Fan, M.‐Y., … Mackenzie, E. J. (2010). Multisite investigation of traumatic brain injuries, posttraumatic stress disorder, and self‐ reported health and cognitive impairments. Archives of General Psychiatry, 67(12), 1291–1300. Zhou, Y., Kierans, A., Kenul, D., Ge, Y., Rath, J., Reaume, J., … Lui, Y. W. (2013). Mild traumatic brain injury: Longitudinal regional brain volume changes. Radiology, 267(3), 880–890.

Caloric Restriction, Cognitive Function, and Brain Health Patrick J. Smith Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA

Introduction An increasingly large body of evidence suggests that lifestyle factors, including diet and ­exercise, play an important role in either mitigating or accelerating the neuropathological processes, leading to the development of Alzheimer’s disease (AD) and other forms of late life dementia (Solfrizzi & Panza, 2014). Although previously conceived of as a disease developing in the later stages of life, AD in particular has been found to develop over the course of decades (Jack et al., 2010), emphasizing the importance of chronic lifestyle practices on the progression of subtle, insidious neuropathological changes. Among the lifestyle characteristics examined, physical activity and dietary practices have been most extensively studied (Scarmeas et al., 2009). Although many studies have examined patterns of dietary behavior, such as the Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diet, caloric restriction has consistently been an area of interest, owing to a large body of animal evidence demonstrating that it is the only known intervention to consistently increase lifespan across species. In addition, several lines of evidence among humans suggest that more limited intake of energy may confer reduced risk of dementia. For example, multiple studies have suggested that lower weight in midlife is associated with ­substantially lower risk of AD and other forms of dementia. Similarly, several randomized trials have provided evidence, albeit preliminary, that intentional caloric restriction may improve cognitive performance (Witte, Fobker, Gellner, Knecht, & Floel, 2009) and improve neuroimaging markers of brain health (Espeland et al., 2016). Nevertheless, multiple studies have suggested that lower weight, particularly underweight, is associated with worse outcomes among older adults, in contrast to the protective effects demonstrated in midlife.

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Epidemiological Data Accumulating evidence suggests that lower levels of caloric intake may be associated with improved cognitive performance and reduced rates of dementia. Although several recent examinations have been conducted in humans, this relationship has been studied extensively among animals. Despite relatively limited evidence in humans linking caloric restriction directly to brain outcomes, multiple, indirect lines of evidence support the notion that restricted energy intake may have salubrious effects. For example, multiple studies have demonstrated that being overweight or obese, particularly in midlife, is a risk factor for dementia (Kivipelto et al., 2006). Gustafson, Rothenberg, Blennow, Steen, and Skoog (2003) found that every 1.0 increase in BMI at age 70 was associated with a 36% increased risk of AD over an 18‐year ­follow‐up of nondemented, Swedish women. Similar results were found in a sample of Japanese men, with every 2.9 point increase in BMI conferring a 21% increased risk of vascular dementia (VaD) (Kalmijn et al., 2000). Although genetic factors no doubt play a prominent role in the development of AD, twin studies nevertheless demonstrate a similar relationship, with midlife obesity conferring more than a threefold increased risk of dementia (Xu et al., 2011). Although the mechanisms linking obesity to dementia are likely multifactorial (Craft, 2009), recent studies have suggested that the presence of obesity in midlife is associated with neuropathological correlates of dementia, including neurofibrillary tangles (Chuang et al., 2016) and total brain volume loss (Debette et al., 2011). Despite these findings, it must also be noted that not all studies have shown an association between obesity and increased risk of dementia, a ­phenomenon that has come to be referred to as the “obesity paradox” (Driscoll et al., 2016). Independent of studies examining obesity, a separate line of evidence suggests that more modest caloric intake is associated with improved longevity and that excess energy intake is associated with adverse cognitive outcomes. For example, among nearly 1,000 participants in the Washington Heights‐Inwood Columbia Aging project, Luchsinger, Tang, Shea, and Mayeux (2002) found that greater caloric intake was associated with increased risk of AD, particularly among individuals with APOE‐4 genotype. A well‐known association has also been documented between chronic, markedly diminished caloric intake and improved longevity among Okinawans, who have a high prevalence of centenarians and relatively low rates of dementia (Willcox et al., 2007). In an archival analysis of centenarian Okinawans, participants even reported a 10.9% energy deficit during their 30s, a pattern that was maintained over ­subsequent decades of life.

Randomized Trials Relatively few randomized trials have directly tested the impact of caloric restriction in human participants. In one of the most positive findings reported, Witte et al. (2009) examined the effects of caloric restriction on cognitive performance among 50 healthy, elderly subjects at either normal weight or mildly overweight. Participants were randomly assigned to either caloric restriction, increased intake of unsaturated fatty acids (UFAs), or a control condition. Following 3 months of treatment, individuals in the caloric restriction group exhibited increased verbal memory scores, and these improvements were correlated with decreased ­levels of fasting insulin and C‐reactive protein. Notably, these results were strongest among subjects with the best adherence. Serum levels of brain‐derived neurotrophic factor (BDNF), a growth factor thought to mediate the effects of aerobic fitness on brain function, were also assessed in this trial, but were not altered with treatment and were not associated with ­alterations in cognitive performance.

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Martin et al. (2007) conducted a similar study among 48 overweight adults, aged 25–50 years. Participants were randomly assigned to one of four groups: caloric restriction, caloric restriction and exercise, a low‐calorie diet condition, or to a control group (weight maintenance). At baseline, 3 months, and again at 6 months, cognitive tests were administered assessing verbal and visual memory, as well as concentration/attention. Overall, cognitive function changed very little across any group, and no consistent pattern of change emerged. In addition, changes in cognitive performance were not related to changes in caloric intake (indexed by daily energy deficit) or weight loss. Several other randomized trials provide preliminary evidence that caloric restriction may confer cognitive and/or brain benefits in various patient populations. Horie et  al. (2016) found that a 12‐month nutritional counseling intervention among obese individuals with mild cognitive impairment was successful in improving cognitive performance across multiple ­subtests. Cognitive improvements were associated with changes in insulin resistance, CRP, leptin, and intake of energy, fat, and carbohydrates. We previously examined this relationship in a sample of 120 middle‐aged adults with hypertension and overweight or obesity (Smith et al., 2010). Participants were randomized for 4 months to a one of three groups: (a) a multicomponent weight loss intervention consisting of aerobic exercise and the DASH diet with caloric restriction, (b) the DASH diet without weight loss, and (c) a usual care control group. Results suggested that combined weight loss group showed improvements on several tests of executive function, learning, and memory, as well as psychomotor speed, with the DASH diet group alone showing more modest benefits on one measure of psychomotor speed alone compared with controls. In one of the longest randomized trials to examined caloric restriction, Espeland et  al. (2016) examined the potential persistent effects of the Action for Health in Diabetes clinical trial 10 years after randomization. The trial was designed to promote caloric restriction among adults with diabetes. In order to examine this relationship, the authors conducted a per protocol analysis, incorporating only individuals who exhibited at least modest weight loss. At the time of the 10‐year follow‐up, participants in the intervention group exhibited 28% lower white matter volume compared with control participants, who underwent a diabetes support and education program. In contrast, improvements in cognitive performance were relatively modest, only being observed on one subtest of processing speed. In a relatively small trial, Prehn et al. (2017) examined changes in brain structure and function among 37 obese, postmenopausal women, randomized to an intensive 12‐week low‐­ calorie diet or a control group. In a per protocol analysis, the authors excluded individuals in the intervention group who did not lose at least 10% body weight, as well as participants in the control group who lost more than 5% of body weight. Participants in the intervention group exhibited improved memory performance paralleled by increased gray matter volume in the inferior frontal gyrus and hippocampus, as well as augmented hippocampal resting‐state ­functional connectivity to parietal areas. The effects appeared to be specific to transient negative energy balance and were not detected after subsequent weight maintenance. An overlapping but conceptually distinct question is whether weight loss is associated with improvements in neurocognitive performance. Although many studies achieve weight loss through a combination of exercise and dietary modification, more modest weight loss may be achieved through exercise alone. In addition, some studies have examined the impact of weight loss through bariatric surgery and its impact on cognitive change, although these studies are beyond the scope of the present review. In one of the few systematic reviews to examine intentional weight loss and cognitive function changes, Siervo et al. (2011) combined all ­randomized trials examining weight loss and cognitive function changes, including data from 12 studies, seven of which incorporated a control group in the study design. Of note, although the

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­ ajority of studies included were behavioral weight loss interventions, gastric bypass studies m were also included. The authors found that intentional weight loss was associated with small improvements in memory and attention/executive functioning across a heterogeneous and group of trials, both in terms of intervention, sample, and cognitive assessment. Despite these positive findings, several quasi‐randomized (Bryan & Tiggemann, 2001) and randomized (Martin et al., 2007) trials have failed to find cognitive benefits with caloric restriction.

Whole Diet Interventions While only a small number of studies has examined caloric restriction and cognitive function specifically, it should be noted that a growing body of evidence suggests that overall healthier dietary patterns, which often include modest caloric reductions, have shown preliminary ­evidence of mitigating cognitive decline (Martinez‐Lapiscina, Clavero, Toledo, Estruch, et al., 2013; Smith et al., 2010). As noted above, our study examining the DASH diet, with and without caloric restriction and weight loss, was associated with modest improvements in ­cognitive performance, which were associated with weight loss and improved fitness (Smith et al., 2010). In addition, results from a single site among the larger multi‐site PREMEDI study demonstrated that participants randomized to receive the MeDI diet exhibited better cognitive performance than controls (Martinez‐Lapiscina, Clavero, Toledo, Estruch, et  al., 2013) and these benefits were maintained among some participants, who showed a reduced risk of mild cognitive impairment during subsequent follow‐ups (Martinez‐Lapiscina, Clavero, Toledo, San Julián, et al., 2013). These studies have provided preliminary evidence that intentional modification of dietary intake may confer cognitive improvements (Smith & Blumenthal, 2010), a question that is now being examined in several ongoing randomized trials.

Mechanisms There are several mechanisms linking caloric restriction to cognitive and brain health. Although the majority of existing evidence has been derived from animal models of neurodegenerative illnesses, a growing body of evidence in humans has extended these findings, providing ­preliminary evidence linking underlying changes in neuroinflammation, cerebrovascular health, and neural signaling processes to cognitive performance (Murphy, Dias, & Thuret, 2014).

Neuroinflammation A growing body of evidence suggests that neuroinflammation and microglial activation in response to injury may play an important role in the development of late life cognitive ­dysfunction and AD (Grammas, 2011). Caloric restriction and exercise‐induced weight loss appear to improve peripheral inflammatory markers, and animal models of caloric restriction suggest that reduced caloric intake may mitigate amyloid burden in mouse models of AD. Moreover, increasing evidence suggests that neuroinflammatory cytokines may be an important regulator of neuronal plasticity, synaptogenesis, and neurogenesis, suggesting that basal elevations of neuroinflammation may counteract neuronal proliferation associated with ­behavior modification.

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Cerebrovascular Health As noted earlier, obesity and associated vascular comorbidities are associated with substantially increased risk of stroke, cerebrovascular burden, and AD in later life. Not surprisingly, preliminary evidence suggests that subtle MRI evidence of microvascular dysfunction is evident even among younger obese samples (Guo, Bakshi, & Lin, 2015) and that weight loss may improve microvascular function (Haltia et  al., 2007) particularly among diabetic patients (Espeland et al., 2016). Moreover, microvascular function has been shown to mediate the association between dietary intake and cognitive performance in some samples of older adults (Gu et al., 2016), underscoring its potential importance among individuals engaging in chronic caloric restriction.

Neurohumoral/Growth Factor Modulation Although few studies have examined it directly, preliminary evidence suggests that caloric restriction may increase expression of growth factors known to be important for neural plasticity and neurogenesis, most notably BDNF. Animal models of caloric restriction have suggested an upregulation of BDNF following reduced caloric intake, and, although few human studies have examined BDNF changes following caloric restriction (Witte et  al., 2009), emerging evidence from other behavioral interventions suggests that BDNF expression may play an important role in neurogenesis, particularly in perihippocampal areas that are seminal in AD progression. Indeed, animal models have demonstrated that BDNF expression following caloric restriction is associated with enhanced hippocampal neurogenesis. In one of the few human studies to examine this, Araya, Orellana, and Espinoza (2008) found that only 3 months of caloric restriction enhanced BDNF expression among insulin‐resistant overweight and obese subjects, suggesting that BDNF may be possible with dietary modification. A closely related neural mechanism is enhanced neurohumoral responsivity, particularly enhanced insulin sensitivity and insulin‐like growth factor (IGF‐1) expression, which have been implicated as important components of the neuropathological processes underlying dementia, with particularly detrimental effects to the hippocampus. Insulin and IGF‐1 appear to act as neurotrophins by inhibiting apoptosis, and insulin resistance has been increasingly recognized as a risk factor for AD, with novel treatments currently being designed to enhance cerebral glucose utilization. In one of the longest randomized trials ever to examine this question, Colman et al. (2014) examined the long‐term effects of caloric restriction vs. ad libitum eating patterns in a sample of 30 adult, male, rhesus monkeys. The monkeys were followed for 20 years, and detailed accounting of chronic disease development and causes of death were examined. In their study, not only did monkeys randomized to caloric restriction live longer, but also they exhibited better glucose homeostasis, lower incidence of cancer and diabetes, and exhibited more preserved brain volumes compared with the control monkeys, particularly gray matter volumes in the frontal lobes.

Oxidative Stress One of the most enduring theories linking caloric restriction to successful aging is its ­presumed amelioration of oxidative stress, which remains a central mechanism of age‐related decline. Indeed, genes facilitating oxidative stress responses and DNA repair are ­preferentially

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upregulated in the aging prefrontal cortex (Lu et al., 2004), implicating a central role in cognitive decline. Numerous animal studies have demonstrated that calorically limited mouse samples exhibit improved longevity and lower rates of oxidative damage, as e­ videnced by reduced accumulation of oxidatively damaged proteins, lipids, and DNA (Finkel & Holbrook, 2000). Although very few human studies have examined alterations in oxidative stress directly, Harvie et  al. (2011) found that a 6‐month randomized trial comparing caloric restriction with intermittent fasting yielded comparable improvements in markers of oxidative stress. A related line of inquiry examines the impact of caloric restriction on sirtuins (Bishop, Lu, & Yankner, 2010), a family of proteins involved in cellular response to environmental and dietary stress. Specifically, these NAD‐dependent deacetylases and monoadenosine diphosphate‐­ ribosyl transferases influence the regulation of lipid and glucose metabolism, DNA repair, and insulin secretion and control cell survival. Although beyond the scope of the ­present review, sirtuins appear to be an important molecular mechanism linking caloric restriction to improved longevity (Mair & Dillin, 2008).

Future Directions The existing body of evidence linking caloric restriction to cognitive function provides ­compelling preliminary data suggesting that limited energy intake may confer neuroprotective effects. Despite the available evidence, there are strikingly few large, well‐controlled randomized controlled testing the benefits of caloric restriction among overweight or obese adults, and the vast majority of mechanistic data have been conducted in non‐human s­ amples, limiting the ability to draw conclusions regarding the putative neurobiological links between diet and brain function. Larger randomized trials examining caloric restriction among obese adults are needed to gain a more comprehensive understanding of the potential impact of dietary strategies to mitigate cognitive decline. Future studies could also benefit from delineating the unique effects of weight loss through caloric restriction with and without exercise, as many of the available studies utilized both treatment modalities to achieve larger treatment improvements in weight. A related, understudied aspect of caloric restriction is the potential benefit of intermittent fasting on cognitive and brain outcomes, which has been shown to produce comparable weight loss benefits when compared with traditional caloric restriction (Davis et  al., 2016), reduces cardiovascular risk factors, is associated with reduced risk of age‐related cognitive deficits, and may be easier for patients to adhere to. Finally, future randomized trials would benefit from careful assessment of mechanisms linking caloric restriction to cognitive function, including neuroimaging assessments of structural and functional outcomes, markers of neuroinflammation, neurotrophic expression, and oxidative stress.

Author Biography Dr. Patrick J. Smith is an assistant professor in the Department of Psychiatry and Behavioral Sciences at Duke University Medical Center. Dr. Smith obtained his PhD in clinical psychology from Duke University with an emphasis in health and neuropsychology, as well as an MPH in biostatistics from the University of North Carolina at Chapel Hill. His research examines the impact of lifestyle modification, including aerobic exercise and dietary modification, on cardiovascular and cognitive outcomes.

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References Araya, A. V., Orellana, X., & Espinoza, J. (2008). Evaluation of the effect of caloric restriction on serum BDNF in overweight and obese subjects: Preliminary evidences. Endocrine, 33(3), 300–304. Bishop, N. A., Lu, T., & Yankner, B. A. (2010). Neural mechanisms of ageing and cognitive decline. Nature, 464(7288), 529–535. Bryan, J., & Tiggemann, M. (2001). The effect of weight‐loss dieting on cognitive performance and psychological well‐being in overweight women. Appetite, 36(2), 147–156. Chuang, Y. F., An, Y., Bilgel, M., Wong, D. F., Troncoso, J. C., O’Brien, R. J., … Thambisetty, M. (2016). Midlife adiposity predicts earlier onset of Alzheimer’s dementia, neuropathology and ­presymptomatic cerebral amyloid accumulation. Molecular Psychiatry, 21(7), 910–915. Colman, R. J., Beasley, T. M., Kemnitz, J. W., Johnson, S. C., Weindruch, R., & Anderson, R. M. (2014). Caloric restriction reduces age‐related and all‐cause mortality in rhesus monkeys. Nature Communications, 5, 3557. Craft, S. (2009). The role of metabolic disorders in Alzheimer disease and vascular dementia: Two roads converged. Archives of Neurology, 66(3), 300–305. Davis, C. S., Clarke, R. E., Coulter, S. N., Rounsefell, K. N., Walker, R. E., Rauch, C. E., … Ryan, L. (2016). Intermittent energy restriction and weight loss: A systematic review. European Journal of Clinical Nutrition, 70(3), 292–299. Debette, S., Seshadri, S., Beiser, A., Au, R., Himali, J. J., Palumbo, C., … DeCarli, C. (2011). Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology, 77(5), 461–468. Driscoll, I., Gaussoin, S. A., Wassertheil‐Smoller, S., Limacher, M., Casanova, R., Yaffe, K., … Espeland, M. A. (2016). Obesity and structural brain integrity in older women: The Women’s Health Initiative Magnetic Resonance Imaging Study. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 71(9), 1216–1222. Espeland, M. A., Erickson, K., Neiberg, R. H., Jakicic, J. M., Wadden, T. A., Wing, R. R., … Bryan, R. N. (2016). Brain and white matter hyperintensity volumes after 10 years of random assignment to lifestyle intervention. Diabetes Care, 39(5), 764–771. Finkel, T., & Holbrook, N. J. (2000). Oxidants, oxidative stress and the biology of ageing. Nature, 408(6809), 239–247. Grammas, P. (2011). Neurovascular dysfunction, inflammation and endothelial activation: Implications for the pathogenesis of Alzheimer’s disease. Journal of Neuroinflammation, 8, 26. Gu, Y., Vorburger, R. S., Gazes, Y., Habeck, C. G., Stern, Y., Luchsinger, J. A., … Brickman, A. M. (2016). White matter integrity as a mediator in the relationship between dietary nutrients and cognition in the elderly. Annals of Neurology, 79(6), 1014–1025. Guo, J., Bakshi, V., & Lin, A. L. (2015). Early shifts of brain metabolism by caloric restriction preserve white matter integrity and long‐term memory in aging mice. Frontiers in Aging Neuroscience, 7, 213. Gustafson, D., Rothenberg, E., Blennow, K., Steen, B., & Skoog, I. (2003). An 18‐year follow‐up of overweight and risk of Alzheimer disease. Archives of Internal Medicine, 163(13), 1524–1528. Haltia, L. T., Viljanen, A., Parkkola, R., Kemppainen, N., Rinne, J. O., Nuutila, P., & Kaasinen, V. (2007). Brain white matter expansion in human obesity and the recovering effect of dieting. The Journal of Clinical Endocrinology and Metabolism, 92(8), 3278–3284. Harvie, M. N., Pegington, M., Mattson, M. P., Frystyk, J., Dillon, B., Evans, G., … Cutler, R. G. (2011). The effects of intermittent or continuous energy restriction on weight loss and metabolic disease risk markers: A randomized trial in young overweight women. International Journal of Obesity, 35(5), 714–727. Horie, N. C., Serrao, V. T., Simon, S. S., Gascon, M. R. P., Santos, A. X. D., Zambone, M. A., … Cercato, C. (2016). Cognitive effects of intentional weight loss in elderly obese individuals with mild cognitive impairment. The Journal of Clinical Endocrinology and Metabolism, 101(3), 1104–1112. Jack, C. R., Jr., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., … Trojanowski, J. Q. (2010). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurology, 9(1), 119–128.

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Patrick J. Smith

Kalmijn, S., Foley, D., White, L., Burchfiel, C. M., Curb, J. D., Petrovitch, H., … Launer, L. J. (2000). Metabolic cardiovascular syndrome and risk of dementia in Japanese‐American elderly men. The Honolulu‐Asia aging study. Arteriosclerosis, Thrombosis, and Vascular Biology, 20(10), 2255–2260. Kivipelto, M., Ngandu, T., Laatikainen, T., Winblad, B., Soininen, H., & Tuomilehto, J. (2006). Risk score for the prediction of dementia risk in 20 years among middle aged people: A longitudinal, population‐based study. Lancet Neurology, 5(9), 735–741. Lu, T., Pan, Y., Kao, S. Y., Li, C., Kohane, I., Chan, J., & Yankner, B. A. (2004). Gene regulation and DNA damage in the ageing human brain. Nature, 429(6994), 883–891. Luchsinger, J. A., Tang, M. X., Shea, S., & Mayeux, R. (2002). Caloric intake and the risk of Alzheimer disease. Archives of Neurology, 59(8), 1258–1263. Mair, W., & Dillin, A. (2008). Aging and survival: The genetics of life span extension by dietary restriction. Annual Review of Biochemistry, 77, 727–754. Martin, C. K., Anton, S. D., Han, H., York‐Crowe, E., Redman, L. M., Ravussin, E., & Williamson, D. A. (2007). Examination of cognitive function during six months of calorie restriction: Results of a randomized controlled trial. Rejuvenation Research, 10(2), 179–190. Martinez‐Lapiscina, E. H., Clavero, P., Toledo, E., Estruch, R., Salas‐Salvadó, J., & Julián, B. S. (2013). Mediterranean diet improves cognition: The PREDIMED‐NAVARRA randomised trial. Journal of Neurology, Neurosurgery, and Psychiatry, 84(2), 1318–1325. Martinez‐Lapiscina, E. H., Clavero, P., Toledo, E., Julián, B. S., Sanchez‐Tainta, A., Corella, D., … Martínez‐Gonzalez, M. Á. (2013). Virgin olive oil supplementation and long‐term cognition: The PREDIMED‐NAVARRA randomized, trial. The Journal of Nutrition, Health & Aging, 17(6), 544–552. Murphy, T., Dias, G. P., & Thuret, S. (2014). Effects of diet on brain plasticity in animal and human studies: Mind the gap. Neural Plasticity, 2014, 563160. Prehn, K., Jumpertz von Schwartzenberg, R., Mai, K., Zeitz, U., Witte, A. V., Hampel, D., … Flöel, A. (2017). Caloric restriction in older adults‐differential effects of weight loss and reduced weight on brain structure and function. Cerebral Cortex, 27(3), 1765–1778. Scarmeas, N., Luchsinger, J. A., Schupf, N., Brickman, A. M., Cosentino, S., Tang, M. X., & Stern, Y. (2009). Physical activity, diet, and risk of Alzheimer disease. JAMA: The Journal of the American Medical Association, 302(6), 627–637. Siervo, M., Arnold, R., Wells, J. C., Tagliabue, A., Colantuoni, A., Albanese, E., … Stephan, B. C. M. (2011). Intentional weight loss in overweight and obese individuals and cognitive function: A systematic review and meta‐analysis. Obesity Reviews, 12(11), 968–983. Smith, P. J., & Blumenthal, J. A. (2010). Diet and neurocognition: Review of evidence and methodological considerations. Current Aging Science, 3(1), 57–66. Smith, P. J., Blumenthal, J. A., Babyak, M. A., Craighead, L., Welsh‐Bohmer, K. A., Browndyke, J. N., … Sherwood, A. (2010). Effects of the dietary approaches to stop hypertension diet, exercise, and caloric restriction on neurocognition in overweight adults with high blood pressure. Hypertension, 55(6), 1331–1338. Solfrizzi, V., & Panza, F. (2014). Mediterranean diet and cognitive decline. A lesson from the whole‐diet approach: What challenges lie ahead? Journal of Alzheimer’s Disease, 39(2), 283–286. Willcox, B. J., Willcox, D. C., Todoriki, H., Fujiyoshi, A., Yano, K., He, Q., … Suzuki, M. (2007). Caloric restriction, the traditional Okinawan diet, and healthy aging: The diet of the world’s longest‐lived people and its potential impact on morbidity and life span. Annals of the New York Academy of Sciences, 1114, 434–455. Witte, A. V., Fobker, M., Gellner, R., Knecht, S., & Floel, A. (2009). Caloric restriction improves memory in elderly humans. Proceedings of the National Academy of Sciences of the United States of America, 106(4), 1255–1260. Xu, W. L., Atti, A. R., Gatz, M., Pedersen, N. L., Johansson, B., & Fratiglioni, L. (2011). Midlife overweight and obesity increase late‐life dementia risk: A population‐based twin study. Neurology, 76(18), 1568–1574.

Psychosocial: Substance Abuse—Street Drugs J. Megan Ross, Jacqueline C. Duperrouzel, Ileana Pacheco‐Colón, and Raul Gonzalez Department of Psychology, Florida International University, Miami, FL, USA

Introduction Drug use is a significant public health problem that impacts millions of individuals worldwide. In the United States, approximately 27 million individuals (12 years old and older) reported consumption of an illicit drug in the past 30 days—this translates to approximately 1 in 10 people in 2014. The percentage of individuals who have used an illicit drug in the past 30 days has increased each year since 2002 (Substance Abuse and Mental Health Services Administration [SAMHSA], 2015). Drugs of abuse impact neurotransmission, often influence neuropsychological performance, and confer risk of addiction. It has been well documented that substance use and abuse negatively impacts neuropsychological functioning. Since a review of every drug of abuse is beyond the scope of this chapter, we will focus on the most commonly used illegal psychoactive drugs. Whenever possible, this chapter will rely on findings from large‐scale studies, review papers, and meta‐analyses, as well as any pertinent novel, recent, and high impact research findings. Before undertaking a review of the literature on neuropsychological consequences of ­substance use, there are several methodological challenges and limitations inherent in such work that are worth considering. One such pervasive challenge is heterogeneity of the participant samples. Participants within and across studies often have diverse patterns of substance use, vary on whether or not they meet criteria for a substance use disorder, often use multiple substances, and vary in prevalence of other comorbid conditions often common among ­substance users (e.g., psychiatric disorders, traumatic brain injury, HIV). Characteristics of substance users can vary on an array of domains that include length of abstinence, number and severity of polysubstance use, amount and frequency of use, and diagnosis and severity of

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substance use disorders. For example, the neuropsychological effects of a particular drug may dissipate after a prolonged period of abstinence (e.g., cannabis; Schreiner & Dunn, 2012), or neuropsychological effects become more prominent after a prolonged period of abstinence (e.g., cocaine; Woicik et al., 2009). Perhaps one of the most intractable challenges to date is definitively determining whether neuropsychological impairments are a result of a substance use disorder or if these impairments precede a substance use disorder. Quasi‐experimental designs are the only ethical research methods to use when studying the effects of chronic ­substance use on neuropsychological functioning in humans. As such, longitudinal designs and twin studies are some of the strongest research designs to assess whether neuropsychological deficits result from or predate substance use. With these issues in mind, we will focus on meta‐analyses, longitudinal studies, twin studies, and recent novel studies whenever possible.

Benzodiazepines Benzodiazepines (BZD) are one of the most widely used therapeutic drugs to treat anxiety and sleep disorders, with an estimated 3% of individuals endorsing prescribed lifetime use in 2014 (SAMHSA, 2015). Among high school seniors, use of Xanax (the most common nonmedically abused BZD) and perceived BZD availability have decreased over the past decade (SAMHSA, 2015). Introduced during the 1960s, BZD became favored over prior sedatives and barbiturates because of their improved safety and low risk of lethality. BZD produces anxiolytic, anticonvulsant, sedative‐hypnotic, and anesthetic effects, leading it to become synonymous with terms such as sedatives and tranquilizers. Created with the intention for only short‐term use, BZD physiological and pharmacological dependence, withdrawal, and negative side effects are common even at low therapeutic doses. BZDs exert their effects on the central nervous system (CNS) by facilitating the rapid binding of GABA to its receptors with high concentrations in the amygdala, frontal cortex, insula, hippocampus, and cerebellum (Sieghart, 1994). Acute subjective effects of BZD administration include sedation, slowing, relaxation, and anterograde amnesia, which are directly associated with neuropsychological functioning (Buffett‐ Jerrott & Stewart, 2002). Acutely, BZDs have been found to elicit deficits in many cognitive domains including ­processing speed, explicit and implicit memory, attention, and executive functions (Buffett‐ Jerrott & Stewart, 2002). However, differences in BZD chemical potency, bioavailability, and half‐lives make it difficult to generalize results across individual studies. To examine long‐term BZD use effects, meta‐analyses have provided greater insight on how cognitive functioning is influenced by prolonged use. Barker, Greenwood, Jackson, and Crowe (2004a, 2004b) evaluated cognitive performance in 12 neuropsychological domains and found moderate to large effects across all domains that included sensory processing, nonverbal memory, speed of processing, attention/concentration, general intelligence, working m ­ emory, psychomotor speed, visuospatial, problem solving, verbal memory, motor control/performance, and verbal reasoning in 13 studies. Additionally, Barker and colleagues (2004a) conducted another meta‐analysis to assess whether neuropsychological functioning in long‐term users improves after BZD cessation. Results revealed small to moderate effect sizes in 5 of 11 domains, indicating an improvement in visuospatial, attention/concentration, general intelligence, psychomotor speed, and ­nonverbal memory performance. However, despite improvements, small to large negative effects were observed in verbal memory, psychomotor speed, speed of processing, motor

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c­ontrol, visuospatial, general intelligence, attention/concentration, and nonverbal memory compared with controls, despite discontinued BZD use for 6 months. Similarly, BZD half‐life metabolism has been found to negatively influence cognition. In a single study conducted by Helmes and Ostbye (2015), BZD half‐life classification (i.e., short, intermediate, and long acting) was associated with differences in neuropsychological performance. Specifically, intermediate BZDs were associated with poorer performance in judgment and language comprehension and long‐acting BZDs with poorer language comprehension and verbal short‐term memory. These results suggest that larger cognitive deficits are observed in BZDs with longer half‐lives, but no deficits are observed in shorter‐acting BZDs. Despite specific cognitive impairments associated with BZD use, risk of global cognitive decline and incident dementia is only slightly higher compared with nonusers (Gray et al., 2016). When comparing BZD users, a slight increase in individual risk was only associated with low BZD exposure and not with higher levels of exposure. These results further suggest that the risk for developing more severe neurocognitive disorders such as Alzheimer’s or dementia is only slightly elevated for those using low‐dose BZDs (Gray et al., 2016). Overall, evidence supports that acute and prolonged BZD use affects neurocognitive ­performance persistently. Despite evidence suggesting discontinuation of BZD use improves cognitive performance, residual effects have been observed and need to be further explored.

Cannabis Cannabis is the most commonly used illicit drug (per federal regulations) in the United States with over 45% of Americans reporting lifetime use in 2014 (SAMHSA, 2015). With growing efforts over the last decade to legalize cannabis for recreational and medicinal use, risk perception has steadily declined among users and nonusers (Volkow, Baler, Compton, & Weiss, 2014). Due to its widespread use among various ages, cannabis and its neuropsychological effects have been avidly researched and debated. The genus Cannabis contains three subspecies, Cannabis sativa, Cannabis indica, and Cannabis ruderalis, all which contain over 400 compounds influencing its analgesic, sedative, and stimulatory properties. In 1964, delta‐9‐tetrahydrocannabinol (Δ9_THC) was discovered to be the primary psychoactive constituent of cannabis, promoting interest in research and leading to the discovery of the endogenous cannabinoids. The discovery of the first cannabinoid receptor (CB1) in the mammalian brain in 1988 and the second (CB2) in the 1990s has led to a better understanding of how THC exerts its psychoactive effects by binding to CB1 receptors. CB1 receptors are located throughout the body with the densest concentrations located in the CNS. They are most abundant in the hippocampus, amygdala, basal ganglia, and cerebellum, structures known to influence learning, memory, reasoning, and emotional ­processing (Glass, Faull, & Dragunow, 1997), but can be found throughout cortex. The psychoactive effects of cannabis on the CNS vary depending on route of administration and dose. The most common method of use, smoking, results in peak plasma concentrations within 3–10 min, and psychoactive effects lasting approximately 2–3 hr. Acute intoxication has been shown to affect neuropsychological functioning; however, results examining specific cognitive domains and age ranges provide varying results. In addition, many studies have provided evidence that regular and long‐term cannabis use results in neurocognitive functioning deficits, even after the acute effects of the drug have subsided. Due to heterogeneity in study design and limitations in methodology, single studies and research reviews provide a fragmented picture of cannabis’ effects. To control for these

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l­imitations, the utilization of meta‐analytic techniques has allowed for consensus among ­findings. Grant, Gonzalez, Carey, Natarajan, and Wolfson (2003) examined the non‐acute (i.e., residual, long term, permanent) effects of cannabis use in 11 studies and were the first to highlight small observable effect sizes providing evidence for subtle overall cognitive ­differences in heavy cannabis users compared with nonusers. Schreiner and Dunn (2012) replicated these results and extended them with numerous additional studies published since the original meta‐analysis. They found positive effects globally and in six cognitive domains including abstraction/executive, attention, forgetting/retrieval, learning, motor, and verbal language, indicating deficits after acute cannabis intoxication (i.e., minimum 25‐day abstinence). Further examining prolonged cannabis abstinence effects, Schreiner and Dunn (2012) found no significant effects of cognitive impairment after 6 months of discontinued use, demonstrating impairments may only be limited to recent cessation influenced by THC metabolite presence. However, there is ongoing debate on the various factors that may influence neuropsychological performance in cannabis users. Two that have received significant attention are sex and age of initial use. Sex differences observed in episodic memory and age of cannabis use initiation suggest that females may be at higher risk for developing neurocognitive vulnerabilities and maintaining use compared with males (Crane, Schuster, Mermelstein, & Gonzalez, 2015). Similarly, an earlier age of cannabis use onset has been postulated by others to result in worse neurocognitive functioning. Strong support for this idea emerged recently from the largest longitudinal study to date assessing the relationship between cannabis use and neurocognitive functioning (Meier et al., 2012). Importantly, baseline assessments of neurocognitive functioning were available for all participants prior to cannabis use onset. The authors found that neuropsychological decline was associated with persistent cannabis use, only when persistent cannabis use began before age 18. However, studies of monozygotic and dizygotic twin pairs have reported no neurocognitive differences between twin pairs discordant for history of ­cannabis use (Jackson et al., 2016). Recently neuroimaging techniques including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have been used to further delineate the neurobiological effects of cannabis use. In a systematic review conducted by Martin‐Santos et al. (2010), acute and chronic cannabis exposure was associated with decreased blood flow during resting global acquisition in prefrontal cortex (PFC) and anterior cingulate cortex (ACC) compared with nonusers. Similarly, in a review of 43 structural and fMRI studies, the authors concluded that THC exposure was associated with reduced hippocampal volume and lower prefrontal cortical, cerebellar, and striatal blood oxygen level‐dependent (BOLD) ­signal compared with controls during cognitive tasks (Batalla et al., 2013). These findings indicate a possible inefficient neural network influenced by cannabis use, despite comparable task outcomes with nonusers. Finally, evidence from recent neuroimaging studies is suggesting altered reward processing and motivation among regular cannabis users (e.g, Volkow et al., 2014). The preponderance of evidence demonstrates that acute cannabis use on neuropsychological functioning influences cognitive domains including learning and memory. Deficits are also observed among current heavy cannabis users when not intoxicated with less ­frequent users displaying minimal or no deficits, although there is ongoing debate on the extent of these deficits in this population. While current neuropsychological results have suggested cannabis use impairments may dissipate over time, future neuroimaging studies and further examining prolonged neural changes after cessation need to be explored to ­support prior evidence.

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Cocaine Cocaine is a psychoactive alkaloid that is found in the leaves of the coca bush, native to Southwest America. In 2013, cultivation of the coca bush was at its lowest in the last three decades, creating a decrease in the availability of cocaine. In 2014, approximately 4.3 million American adults reported using cocaine in the past year, and 1.5 million reported using cocaine in the past month (SAMHSA, 2015). Typically, cocaine is consumed in one of two forms, which are cocaine hydrochloride, a powered form of cocaine that is usually ingested intranasally, or crack cocaine, a free‐base form of cocaine that is typically smoked. It is important to note that cocaine can cause ischemic and hemorrhagic cerebrovascular damage that can lead to neuropsychological deficits (Levine et al., 1990). Similarly to other psychostimulants, acute effects of cocaine on neuropsychological functioning results in better attention (Johnson et  al., 1998), speed of information processing (Higgins et  al., 1990), and inhibitory control (Fillmore, Rush, & Hays, 2006). However, cocaine use can cause long‐term neuronal and neuropsychological damage. Brain imaging studies suggest cocaine‐dependent individuals have lower gray matter volumes in the premotor cortex, orbitofrontal cortex, temporal cortex, thalamus, and cerebellum compared with healthy controls. These deficits were associated with poorer performance on tasks of executive function and fine motor performance (Sim et  al., 2007). Ersche, Williams, Robins, and Bullmore (2013) conducted a voxel‐wise meta‐analysis of MRI studies to determine structural brain abnormalities among individuals addicted to stimulants (i.e., cocaine, amphetamines, or methamphetamines). Although the meta‐analysis included individuals addicted to cocaine, amphetamines, or methamphetamines, a majority of the studies (n = 10) included only participants who were addicted to cocaine. The meta‐analysis contained 16 studies composed of 494 stimulant‐dependent individuals compared with 428 healthy controls. Stimulant‐addicted individuals had significant gray matter volume decreases in five specific regions: the insula, ventromedial PFC, inferior frontal gyrus, pregenual anterior cingulate gyrus, and anterior thalamus. The regions with gray matter volume decreases are primarily involved with executive function. These findings generally support research that suggests executive function deficits among cocaine‐dependent individuals. A qualitative review conducted by Jovanovski, Erb, and Zakzanis (2005) of the neuropsychological effects of cocaine use compared the effect sizes across studies to determine the most consistent neuropsychological deficits. Across 15 studies that included 481 cocaine users and 586 controls, the most consistent deficit observed among cocaine users was in attention and executive functioning. Attention deficits had the largest effect sizes followed by working memory, visual memory, and executive functioning. Small effect sizes were observed for deficits in language function (including verbal fluency and sensory‐perceptual function). It must be noted that the cocaine‐dependent individuals included in this meta‐analysis have a wide range of number of days of abstinence (0–1,075). One notable study conducted by Woicik and colleagues (2009) documented differences in neuropsychological performance among individuals with current cocaine use disorders and matched healthy controls in measures of attention, executive function, and verbal memory. However, these deficits were more pronounced in the individuals with cocaine use disorders who have a negative urine status for cocaine. These findings suggest that neuropsychological deficits for cocaine‐dependent individuals may manifest after a longer period of abstinence, but more work is needed to understand the underlying cause for this observed effect. Currently, the evidence generally suggests that the largest deficits associated with cocaine use are in the areas of attention and executive functioning, while small effects have been

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observed in memory and language functioning. These findings are supported by neuroimaging studies that have observed decreased gray matter volume primarily in the PFC. However, as documented by Woicik and colleagues (2009), these findings may be more pronounced after a longer period of abstinence from cocaine use. Future studies that evaluate the association of cocaine use and neuropsychological functioning may benefit from also examining cocaine‐dependent individuals who have been abstinent from cocaine use for an extended period of time.

MDMA (Ecstasy) 3,4‐Methylenedioxymethamphetamine (MDMA), or “ecstasy,” has a similar chemical s­ tructure to both stimulants and hallucinogens and has been classified as an “entactogen.” In the United States, 6.6% of Americans ages 12 and older have used ecstasy at least once in their lifetime (SAMHSA, 2015). A significant challenge to the study of the neuropsychological effects of MDMA is that ecstasy pills are expected to contain MDMA; however this is not always the case. Many ecstasy pills (also known as molly, candy, and rolling) contain other psychoactive substances or may not contain any MDMA at all. Specifically, out of 1,214 tablets sold as ecstasy, only 39% of the tablets contained pure MDMA, 46% did not contain any MDMA at all, and the remaining 15% contained both MDMA and other substances (e.g., caffeine, dextromethorphan, methylenedioxyamphetamine, methylenedioxyethylamphetamine, and ­ methamphetamine; Tanner‐Smith, 2006). Attention performance has been measured during acute intoxication of MDMA and has yielded mixed results. Studies suggest no impairment in attention (Vollenweider, Gamma, Liechti, & Huber, 1998), though one study found that acute intoxication of MDMA can result in deficits in divided attention and tracking performance (Kuypers, Wingen, Samyn, Limbert, & Ramaekers, 2007). Impairments in memory performance have also been reported; however, these impairments appear to return to baseline levels after intoxication (Kuypers & Ramaekers, 2005). Kuypers, Wingen, Heinecke, Formisano, and Ramaekers (2011) designed a study to determine the neural substrates associated with memory impairment during acute intoxication of MDMA via MRI. Results suggest that impairments with memory encoding may be due to the influence of MDMA on middle frontal gyrus functioning. Several meta‐analyses have been conducted that address the neuropsychological sequelae of MDMA. Verbaten (2003) examined only studies with between‐group designs between 1975 and 2002. This meta‐analysis contained 14 studies, and the only inclusion criteria for the meta‐analysis were that participants had to be abstinent from MDMA and other drugs for at least one week prior to the assessment. Results suggest that MDMA users performed significantly worse on measures of short‐term memory, long‐term memory, and attention. Kalechstein, De La Garza, Mahoney, Fantegrossi, and Newton (2007) conducted a similar meta‐analysis of studies conducted until 2004, with lenient inclusion criteria (i.e., any studies that evaluated neuropsychological effects of MDMA with matched controls) resulting in 23 studies and strict inclusion criteria (i.e., matched controls on demographic and premorbid ­factors and MDMA users were abstinent at evaluation and not treatment seeking) resulting in 11 studies. MDMA users performed significantly worse in all domains in both conditions. Other meta‐analyses have reached similar conclusions, with small to moderate effect sizes across all domains and a negative association between performance and length of MDMA use (Zakzanis, Campbell, & Jovanovski, 2007). Recently, a meta‐analysis by Murphy et al. (2012) was conducted to determine the effects of MDMA on visuospatial memory. Liberal criteria for

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inclusion in the meta‐analysis were applied (i.e., any study that used visuospatial memory tasks and participants who were abstinent during the evaluation), yielding 52 studies. Moderate effect sizes were observed for MDMA users performing significantly worse on recall or recognition of spatial distribution of stimulus elements, recognition of figures, and reproduction or production of figures. Notable prospective studies have been recently conducted that included only MDMA‐naive participants at baseline and followed the participants to determine the effects of MDMA after initiation of use. The study included 149 participants who had never used MDMA at ­baseline—109 participants were administered the 1‐year follow‐up. During the 1‐year follow‐ up period, 43 subjects did not use any illicit substances, while 23 participants used MDMA more than 10 times. The remaining subjects, who used MDMA more than once but less than 10 times, were not included in the analysis. Change scores were compared between MDMA users and nonusers. MDMA users performed significantly worse on immediate and delayed recall of visual paired associations; however, there was no other area of cognitive decline (Wagner, Becker, Koester, Gouzoulis‐Mayfrank, & Daumann, 2013). Using the same sample at a 2‐year follow‐up, immediate and delayed recall of visual paired associates declined significantly among MDMA users, and no significant decline was observed in other areas of ­neuropsychological functioning (Wagner et al., 2015). Generally, the evidence supports that MDMA may cause decline in visual memory and learning impairment among users. Although notable, the small sample sizes among some studies limit the generalizability of these findings. A larger sample size may reveal small or moderate changes in cognitive domains other than visual memory and learning. Future studies in this area would therefore benefit from larger sample sizes. Additionally, meta‐analyses suggest ­cognitive decline in several domains, though these findings are divergent among prospective studies.

Methamphetamine Methamphetamine (e.g., ice, crank, crystal meth) is a potent and highly addictive stimulant of the CNS, the effects of which can last up to 12 hr. Methamphetamine can be consumed in a variety of ways, including snorting, smoking, and injection. Recent national estimates indicate that 1.3 million people (0.5% of the population) reported using methamphetamine in the past year and 569,000 (0.2%) reported using it in the past month, figures similar to those from previous years (SAMHSA, 2015). Methamphetamine’s lipophilicity allows it to cross the blood–brain barrier faster and thus penetrate the CNS to a greater extent than its parent compound amphetamine. Methamphetamine stimulates the release of dopamine while simultaneously blocking, to some extent, synaptic reuptake of dopamine, both of which result in increased levels of dopamine in reward regions of the brain. The acute effects of methamphetamine may include autonomic arousal, tachycardia, suppression of appetite, euphoria, feelings of increased physical and mental capacity, elevated self‐esteem, increased libido, insomnia, and irritability. Administration of moderate doses of methamphetamine can lead to enhanced attention and concentration (Johnson et al., 2005). Long‐term use induces neurotoxicity in several neurotransmitter systems, particularly the nigrostriatal dopaminergic pathways, thus altering the function of prefrontal–striatal circuits. A meta‐analysis, which included 18 studies and a total of 487 participants with lifetime methamphetamine abuse/dependence and 464 healthy controls, revealed that chronic ­

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­ ethamphetamine users showed impaired neuropsychological performance in the domains of m learning, executive functions, memory, speed of information processing, and motor skills ­relative to healthy controls, which is likely related to the dysfunction in the neural circuits underlying these processes (Scott et al., 2007). These effect sizes were broadly in the medium range, such that methamphetamine users performed worse than controls in measures of attention/working memory, visuoconstruction, and language (Scott et al., 2007). Neuroimaging studies have found a range of structural and metabolic abnormalities in the brains of chronic methamphetamine abusers. Structurally, methamphetamine abusers showed enlarged striatal volumes, suggesting an initial compensatory (inflammatory) response (Chang, Alicata, Ernst, & Volkow, 2007). However, a study found that greater usage ultimately led to a decrease in striatal volume, associated with greater cognitive impairment (Chang et  al., 2005). Smaller hippocampal volumes have also been linked to poorer episodic memory performance among chronic users (Thompson et  al., 2004). PET studies have found reduced dopamine transporter (DAT) density as well as reduced D2 dopamine receptors in the striatum of methamphetamine users (Chang et  al., 2007). Though changes in DAT density subside with prolonged abstinence, the psychomotor and learning impairments associated with them do not, which suggests either insufficient recovery or the involvement of other unidentified neural networks (Chang et  al., 2007). Furthermore, H1 magnetic resonance spectroscopy studies have revealed lower concentrations of the metabolite N‐acetylaspartate (NAA), a marker for neuronal integrity and density, and creatine, which reflects cellular energy metabolism, in the basal ganglia of individuals who recently abstained from chronic methamphetamine use (Chang et al., 2007). The etiology and consequences of these abnormalities are not yet clear. Studies have employed fMRI techniques to detect differences in the brain activity of methamphetamine abusers, which mostly involve hypoactivity of frontal areas. Notably, Salo, Fassbender, Buonocore, and Ursu (2013) found that methamphetamine abusers showed little to no activation in the dorsolateral PFC relative to controls during a variant of the Stroop task that measured trial‐to‐trial reaction time adjustments. PFC activity was negatively correlated with reaction time adjustments, such that less activity was related to longer reaction times and thus worse performance (Salo et al., 2013). These findings suggest a failure to adapt a behavioral response based on prior experience, which is a hallmark behavior of addiction (Salo et al., 2013). Hypoactivity of frontal and other areas in methamphetamine users has also been linked to deficits in socioemotional processing (Payer et  al., 2008) and decision making (Paulus, Hozack, Frank, Brown, & Schuckit, 2003). Together, these findings suggest that long‐term methamphetamine abuse leads to learning and memory impairments, as well as executive dysfunction. Neuroimaging findings suggest that these deficits are linked to abnormalities in prefrontal–striatal circuits, most notably abnormalities in gray matter volumes of subcortical structures and hypoactivity of frontal areas. Further research is needed to determine whether these impairments will improve with prolonged abstinence.

Opiates Opiate is extracted from the seeds of the opium poppy plant, with majority of the illegal opiates being grown in Afghanistan. Opioids are the synthetic form of opiates. Approximately 4.8 million Americans reported using heroin in their lifetime during 2014, a figure that has remained steady since 2013 (SAMHSA, 2015). Prescription opioid addiction, such as Vicodin

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and Oxycontin, has become a national crisis in the United States due to the increases in opioid abuse and overdose deaths. The number of deaths related to opioid abuse has almost tripled since 2010 (Hedegaard, Chen, & Warner, 2015). Neuroimaging studies have documented detrimental effects of opiate use on brain structure and function. General cortical atrophy and enlargement of external and internal CSF spaces (Pezawas et al., 1998) have been shown among individuals addicted to opiates. Specifically, decreases in gray matter density have been observed in the prefrontal, anterior cingulate, ­insular, and temporal cortices among opiate abusers compared with healthy controls (Lyoo et al., 2006). Gruber, Silveri, and Yurgelun‐Todd (2007) reviewed the neuropsychological consequences of opiate use. Results suggest that neuropsychological deficits exist both acutely and residually in measures of attention, concentration, recall, visuospatial, and psychomotor speed. Executive function appears to suffer the greatest impact from opiate use. A recent meta‐analysis on the neuropsychological effects of opiate use revealed that the most robust impairments were in measures of verbal working memory, risk‐taking (i.e., delay discounting and decision making), and verbal fluency. This review analyzed 20 studies with 767 chronic opioid users and 15,196 healthy controls compared on 7 neuropsychological domains: short‐term verbal working memory, short‐term visuospatial working memory, long‐term memory, attention, cognitive impulsivity, non‐planning impulsivity, and cognitive flexibility (Baldacchino, Balfour, Passetti, Humphris, & Matthews, 2012). Research on opioid addiction has focused on the neuropsychological impairments associated with methadone maintenance therapy (MMT), the most commonly used form of replacement therapy for opioid dependence. Neuropsychological deficits have been observed among patients on MMT including attention, working memory, memory, psychomotor speed, problem solving, and decision making (Darke, Sims, McDonald, & Wickes, 2000; Mintzer & Stitzer, 2002). When comparing patients on MMT to abstinent heroin abusers, patients on MMT had significantly poorer performance on measures of processing speed, visuospatial perception, cognitive flexibility, working memory, and logical reasoning (Verdejo, Toribio, Orozco, Puente, & Pérez‐García, 2005). These findings overwhelmingly suggest that there are neuropsychological impairments associated with opioid dependence. These cognitive impairments include verbal working memory, risk‐taking, and verbal fluency. In addition, patients on MMT may experience cognitive impairments in processing speed, visuospatial perception, cognitive flexibility, working memory, and logical reasoning.

Discussion Substance use is associated with numerous negative outcomes, including social, emotional, and economic consequences that make it a significant public health problem. The use of neuropsychological tools in the context of substance use disorders has helped us to gain a better understanding of how critical cognitive processes are affected by substances of abuse, acutely and in the longer term. This chapter presents a complex picture of consequences from use of various substances. The heterogeneity in findings is likely due to a variety of issues, including each drugs own unique pharmacological properties as well as the characteristics of participant samples. For example, did the study include individuals recently out of recovery, a community sample of users, individuals that used a certain amount of a drug, or individuals that met criteria for a substance use disorder? Regardless, as has been described in this chapter, adverse effects

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on neurocognitive functioning are common consequence of substance use. Indeed, our review suggests that substance abuse is generally associated with neuropsychological deficits. However, as noted, these deficits vary across substances, neurocognitive domains, pattern, severity, and duration. Collectively, in most cases, more severe, frequent, and symptomatic substance use (e.g., a diagnosable substance use disorder) is associated with poorer neurocognitive outcomes. Recovery of neurocognitive functioning with abstinence is a more complex issue. Whereas abstinence from some substances may result in normalization of neurocognitive functioning over time (e.g., memory functioning among cannabis users), other deficits seem to persist or progressively worsen despite abstinence with other substances (e.g., cocaine and BZD). Another important issue to consider, which often looms in the background and has received progressively more attention in recent years, is whether any of the neurocognitive deficits observed among substance users may have predated use; rather than a consequence of use, they may serve as a risk factor for addiction. From a theoretical standpoint, measures of inhibitory control, decision making, and other aspects of executive functioning are ­plausible candidates for neurocognitive deficits that may predate heavy use. However, without prospective longitudinal studies that adequately assess neurocognitive functioning before and after initiation of substance use, this issue cannot be adequately resolved. Although such studies have been conducted by several research groups or among specific cohorts (e.g., the Dunedin cohort; CEDAR; Pittsburgh Youth Study), many of these were not specifically designed to examine changes in neurocognitive functioning over time. The newly launched Adolescent Brain Cognitive Development (ABCD) project aims to examine neurocognitive abilities and brain structure and function, alongside many other relevant factors, among a representative sample of 10,000 children starting at ages 9–10 and throughout adolescence and early adulthood. Substance use characteristics will also be carefully assessed in this p ­ roject to provide a clearer picture of how the brain looks and functions prior to and after initiation of substance use and how it continues to change among those that use relative to those that do not. In the process, the results of this project will address many of these lingering issues. Despite dramatic advances in the scientific study of the neuropsychology of substance use, there are plenty of areas in which future studies could help elucidate discrepant findings and further our understanding of substance use disorders. As noted, prospective longitudinal studies with large sample sizes will continue to move the field forward. Similarly, more sophisticated meta‐analyses that carefully consider how heterogeneity of samples and specific substance use characteristics influence findings will be valuable to the scientific literature.

Author Biographies J. Megan Ross graduated from Florida International University with a doctorate in clinical psychology. She is currently a postdoctoral fellow at the Institute for Behavioral Genetics at the University of Colorado, Boulder. Her research focuses on predictors and moderators of risk‐taking behavior among adolescents. Jacqueline C. Duperrouzel graduated with a BS in Psychology from the Florida State University and received her MS in Clinical Psychology from Florida International University (FIU). She is currently a graduate student at FIU pursuing a PhD in Clinical Science, where her current research interests focus on examining the neuropsychological effects of early‐onset cannabis use and factors that contribute to substance use disorder development. Ileana Pacheco‐Colón graduated with a BS in Psychology from Brown University. She is currently a graduate student pursuing a PhD in Clinical Psychology at Florida International

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University, where her research focuses on the effects of adolescent cannabis use on ­neuropsychological functioning and motivation. Raul Gonzalez is professor of psychology, psychiatry, and immunology at Florida International University (FIU). He graduated with his PhD in Clinical Psychology from the San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, with a specialization in clinical neuropsychology. He is the director of the Substance Use and HIV Neuropsychology and affiliated with the Center for Children and Families at FIU. His research broadly examines risks for and consequences of substance use, with a focus on cannabis.

References Baldacchino, A., Balfour, D., Passetti, F., Humphris, G., & Matthews, K. (2012). Neuropsychological consequences of chronic opioid use: A quantitative review and meta‐analysis. Neuroscience & Biobehavioral Reviews, 36(9), 2056–2068. Barker, M. J., Greenwood, K. M., Jackson, M., & Crowe, S. F. (2004a). Cognitive effects of long‐term benzodiazepine use. CNS Drugs, 18(1), 37–48. Barker, M. J., Greenwood, K. M., Jackson, M., & Crowe, S. F. (2004b). Persistence of cognitive effects after withdrawal from long‐term benzodiazepine use: A meta analysis. Archives of Clinical Neuropsychology, 19, 437–454. Batalla, A., Bhattacharyya, S., Yücel, M., Fusar‐Poli, P., Crippa, J. A., Nogué, S., … Martin‐Santos, R. (2013). Structural and functional imaging studies in chronic cannabis users: A systematic review of adolescent and adult findings. PLoS One, 8(2), e55821. Buffett‐Jerrott, S. E., & Stewart, S. H. (2002). Cognitive and sedative effects of benzodiazepine use. Current Pharmaceutical Design, 8(1), 45–58. Chang, L., Alicata, D., Ernst, T., & Volkow, N. (2007). Structural and metabolic brain changes in the striatum associated with methamphetamine abuse. Addiction, 102(1), 16–32. Chang, L., Cloak, C., Patterson, K., Grob, C., Miller, E. N., & Ernst, T. (2005). Enlarged striatum in abstinent methamphetamine abusers: A possible compensatory response. Biological Psychiatry, 57(9), 967–974. Crane, N. A., Schuster, R. M., Mermelstein, R. J., & Gonzalez, R. (2015). Neuropsychological sex differences associated with age of initiated use among young adult cannabis users. Journal of Clinical and Experimental Neuropsychology, 23(2), 117–137. Darke, S., Sims, J., McDonald, S., & Wickes, W. (2000). Cognitive impairment among methadone maintenance patients. Addiction, 95(5), 687–695. Ersche, K. D., Williams, G. B., Robbins, T. W., & Bullmore, E. T. (2013). Meta‐analysis of structural brain abnormalities associated with stimulant drug dependence and neuroimaging of addiction vulnerability and resilience. Current Opinion in Neurobiology, 23(4), 615–624. Fillmore, M. T., Rush, C. R., & Hays, L. (2006). Acute effects of cocaine in two models of inhibitory control: Implications of non‐linear dose effects. Addiction, 101(9), 1323–1332. Glass, M., Faull, R. L. M., & Dragunow, M. (1997). Cannabinoid receptors in the human brain: A  detailed anatomical and quantitative autoradiographic study in the fetal, neonatal and adult human brain. Neuroscience, 77(2), 299–318. Grant, I., Gonzalez, R., Carey, C. L., Natarajan, L., & Wolfson, T. (2003). Non‐acute (residual) neurocognitive effects of cannabis use: A meta‐analytic study. Journal of the International Neuropsychological Society, 9(5), 679–689. Gray, S. L., Dublin, S., Yu, O., Walker, R., Anderson, M., Hubbard, R. A., … Larson, E. B. (2016). Benzodiazepine use and risk of incident dementia or cognitive decline: Prospective population based study. British Medical Journal, 352. Gruber, S. A., Silveri, M. M., & Yurgelun‐Todd, D. A. (2007). Neuropsychological consequences of opiate use. Neuropsychology Review, 17(3), 299–315.

272

J. Megan Ross et al.

Hedegaard, H., Chen, L. H., & Warner, M. (2015). Drug‐poisoning deaths involving heroin: United States, 2000–2013. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics. Helmes, E., & Ostbye, T. (2015). Associations between benzodiazepine use and neuropsychological test scores in older adults. Canadian Journal on Aging, 34(2), 207–214. Higgins, S. T., Bickel, W. K., Hughes, J. R., Lynn, M., Capeless, M. A., & Fenwick, J. W. (1990). Effects of intranasal cocaine on human learning, performance and physiology. Psychopharmacology, 102(4), 451–458. Jackson, N. J., Isen, J. D., Khoddam, R., Irons, D., Tuvblad, C., Iacono, W. G., & Baker, L. A. (2016). Impact of adolescent marijuana use on intelligence: Results from two longitudinal twin studies. Proceedings of the National Academy of Sciences of the United States of America, 113(5), E500–E508. Johnson, B., Overton, D., Wells, L., Kenny, P., Abramson, D., Dhother, S., … Bordnick, P. (1998). Effects of acute intravenous cocaine on cardiovascular function, human learning, and performance in cocaine addicts. Psychiatry Research, 77(1), 35–42. Johnson, B. A., Roache, J. D., Ait‐Daoud, N., Wallace, C., Wells, L. T., & Wang, Y. (2005). Effects of isradipine on methamphetamine‐induced changes in attentional and perceptual‐motor skills of ­cognition. Psychopharmacology, 178(2‐3), 296–302. Jovanovski, D., Erb, S., & Zakzanis, K. K. (2005). Neurocognitive deficits in cocaine users: A quantitative review of the evidence. Journal of Clinical and Experimental Neuropsychology, 27(2), 189–204. Kalechstein, A. D., De La Garza, R., II, Mahoney, J. J., III, Fantegrossi, W. E., & Newton, T. F. (2007). MDMA use and neurocognition: A meta‐analytic review. Psychopharmacology, 189(4), 531–537. Kuypers, K. P., & Ramaekers, J. G. (2005). Transient memory impairment after acute dose of 75mg 3.4‐methylene‐dioxymethamphetamine. Journal of Psychopharmacology, 19(6), 633–639. Kuypers, K. P., Wingen, M., Heinecke, A., Formisano, E., & Ramaekers, J. G. (2011). MDMA intoxication and verbal memory performance: a placebo‐controlled pharmaco‐MRI study. Journal of Psychopharmacology, 25(8), 1053–1061. Kuypers, K. P., Wingen, M., Samyn, N., Limbert, N., & Ramaekers, J. G. (2007). Acute effects of ­nocturnal doses of MDMA on measures of impulsivity and psychomotor performance throughout the night. Psychopharmacology, 192(1), 111–119. Levine, S. R., Brust, J. C., Futrell, N., Ho, K.‐L., Blake, D., Millikan, C. H., … Selwa, J. F. (1990). Cerebrovascular complications of the use of the crack form of alkaloidal cocaine. New England Journal of Medicine, 323(11), 699–704. Lyoo, I. K., Pollack, M. H., Silveri, M. M., Ahn, K. H., Diaz, C. I., Hwang, J., … Renshaw, P. F. (2006). Prefrontal and temporal gray matter density decreases in opiate dependence. Psychopharmacology, 184(2), 139–144. Martin‐Santos, R., Fagundo, A. B., Crippa, J. A., Atakan, Z., Bhattacharyya, S., Allen, P., … McGuire, P. (2010). Neuroimaging in cannabis use: A systematic review of the literature. Psychological Medicine, 40(03), 383–398. Meier, M. H., Caspi, A., Ambler, A., Harrington, H., Houts, R., Keefe, R. S., & Moffitt, T. E. (2012). Persistent cannabis users show neuropsychological decline from childhood to midlife. Proceedings of the National Academy of Sciences of the United States of America, 109(40), E2657–E2664. Mintzer, M. Z., & Stitzer, M. L. (2002). Cognitive impairment in methadone maintenance patients. Drug and Alcohol Dependence, 67(1), 41–51. Murphy, P. N., Bruno, R., Ryland, I., Wareing, M., Fisk, J. E., Montgomery, C., & Hilton, J. (2012). The effects of ‘ecstasy’(MDMA) on visuospatial memory performance: Findings from a systematic review with meta‐analyses. Human Psychopharmacology: Clinical and Experimental, 27(2), 113–138. Paulus, M. P., Hozack, N., Frank, L., Brown, G. G., & Schuckit, M. A. (2003). Decision making by methamphetamine‐dependent subjects is associated with error‐rate‐independent decrease in ­prefrontal and parietal activation. Biological Psychiatry, 53(1), 65–74. Payer, D. E., Lieberman, M. D., Monterosso, J. R., Xu, J., Fong, T. W., & London, E. D. (2008). Differences in cortical activity between methamphetamine‐dependent and healthy individuals performing a facial affect matching task. Drug and Alcohol Dependence, 93(1), 93–102.

Psychosocial: Substance Abuse—Street Drugs

273

Pezawas, L. M., Fischer, G., Diamant, K., Schneider, C., Schindler, S. D., Thurnher, M., … Kasper, S. (1998). Cerebral CT findings in male opioid‐dependent patients: Stereological, planimetric and linear measurements. Psychiatry Research: Neuroimaging, 83(3), 139–147. Salo, R., Fassbender, C., Buonocore, M. H., & Ursu, S. (2013). Behavioral regulation in methamphetamine abusers: An fMRI study. Psychiatry Research: Neuroimaging, 211(3), 234–238. Schreiner, A. M., & Dunn, M. E. (2012). Residual effects of cannabis use on neurocognitive performance after prolonged abstinence: A meta‐analysis. Experimental and Clinical Psychopharmacology, 20(5), 420. Scott, J. C., Woods, S. P., Matt, G. E., Meyer, R. A., Heaton, R. K., Atkinson, J. H., & Grant, I. (2007). Neurocognitive effects of methamphetamine: A critical review and meta‐analysis. Neuropsychology Review, 17(3), 275–297. Sieghart, W. (1994). Pharmacology of benzodiazepine receptors: An update. Journal of Psychiatry and Neuroscience, 19(1), 24–29. Sim, M. E., Lyoo, I. K., Streeter, C. C., Covell, J., Sarid‐Segal, O., Ciraulo, D. A., … Renshaw, P. F. (2007). Cerebellar gray matter volume correlates with duration of cocaine use in cocaine‐dependent subjects. Neuropsychopharmacology, 32(10), 2229–2237. Substance Abuse and Mental Health Services Administration. (2015). 2014 National survey on drug use and health: Detailed tables. Rockville, MD: Center for Behavioral Health Statistics and Quality. Tanner‐Smith, E. E. (2006). Pharmacological content of tablets sold as “ecstasy”: Results from an online testing service. Drug and Alcohol Dependence, 83(3), 247–254. Thompson, P. M., Hayashi, K. M., Simon, S. L., Geaga, J. A., Hong, M. S., Sui, Y., … London, E. D. (2004). Structural abnormalities in the brains of human subjects who use methamphetamine. The Journal of Neuroscience, 24(26), 6028–6036. Verbaten, M. N. (2003). Specific memory deficits in ecstasy users? The results of a meta‐analysis. Human Psychopharmacology: Clinical and Experimental, 18(4), 281–290. Verdejo, A., Toribio, I., Orozco, C., Puente, K. L., & Pérez‐García, M. (2005). Neuropsychological functioning in methadone maintenance patients versus abstinent heroin abusers. Drug and Alcohol Dependence, 78(3), 283–288. Volkow, N. D., Baler, R. D., Compton, W. M., & Weiss, S. R. (2014). Adverse health effects of marijuana use. New England Journal of Medicine, 370(23), 2219–2227. Volkow, N. D., Wang, G. J., Telang, F., Fowler, J. S., Alexoff, D., Logan, J., … Tomasi, D. (2014). Decreased dopamine brain reactivity in marijuana abusers is associated with negative emotionality and addiction severity. Proceedings of the National Academy of Sciences of the United States of America, 111(30), E3149–E3156. Vollenweider, F. X., Gamma, A., Liechti, M., & Huber, T. (1998). Psychological and cardiovascular effects and short‐term sequelae of MDMA (“ecstasy”) in MDMA‐naive healthy volunteers. Neuropsychopharmacology, 19(4), 241–251. Wagner, D., Becker, B., Koester, P., Gouzoulis‐Mayfrank, E., & Daumann, J. (2013). A prospective study of learning, memory, and executive function in new MDMA users. Addiction, 108(1), 136–145. Wagner, D., Tkotz, S., Koester, P., Becker, B., Gouzoulis‐mayfrank, E., & Daumann, J. (2015). Learning, memory, and executive function in new MDMA users: A two‐year follow‐up study. Frontiers in Neuroscience, 9, 445. Woicik, P. A., Moeller, S. J., Alia‐Klein, N., Maloney, T., Lukasik, T. M., Yeliosof, O., … Goldstein, R. Z. (2009). The neuropsychology of cocaine addiction: Recent cocaine use masks impairment. Neuropsychopharmacology, 34(5), 1112–1122. Zakzanis, K. K., Campbell, Z., & Jovanovski, D. (2007). The neuropsychology of ecstasy (MDMA) use: A quantitative review. Human Psychopharmacology: Clinical and Experimental, 22(7), 427–435.

The Effects of Prenatal Stress on Offspring Development Megan R. Gunnar and Colleen Doyle Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, USA

Introduction In recent decades, research from a wide range of disciplines has shown prenatal development provides a foundation for learning and health throughout the lifespan. A growing body of work suggests that variation in the prenatal environment can produce individual differences and program regulatory systems that subsequently influence developmental organization and outcomes in infancy, childhood, and adulthood. This research provides evidence of long‐term educational, behavioral, and psychological sequelae of prenatal stress (PS). In this work, prenatal stress is often used as an umbrella term for maternal experiences that include physiological and psychological responses to perceived threat or challenge, as well as negative mood states (frustration, anxiety, depression). The central hypothesis guiding PS research is that maternal experiences influence the in utero environment, alter the fetus’s developing central nervous system (CNS), and subsequently set probabilistic parameters for future brain and behavior development. Although epidemiological, correlational, and quasi‐experimental research with human subjects supports this fetal origins hypothesis, confounding variables prevent these studies from providing definitive proof. Instead, strong causal evidence for PS effects comes ­primarily from animal experiments. This area of research underscores the importance of delineating the effects and mechanisms of stress during the prenatal period in order to provide the optimal foundation for child development. The goals of this article are to (a) outline the essential features of normal prenatal brain development in humans, (b) review the primary methods for studying brain development, (c) summarize findings on the effects and mechanisms of PS, and (d) consider factors that might buffer against PS and promote resilience. Finally, conclusions are discussed in the context of implications for efforts to support the health of pregnant women and their children. The Wiley Encyclopedia of Health Psychology: Volume 1: Biological Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editor: Robert H. Paul. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Prenatal Brain Development Brain development unfolds through a series of complex and dynamic processes involving the interaction of genes and the environment (see Stiles, 2008). Human prenatal CNS development is organized into the embryonic period (conception—gestational week [GW] 8) and the fetal period (GW9—birth). The first step in embryonic brain development is the emergence of stem cells called neural progenitor cells at GW2. Through the process of gastrulation, neural progenitors become capable of producing all types of cells for the brain and CNS. Following their transformation, the region of neural progenitor cells is called the neural plate. During GW3, the first brain structure, the neural tube, begins to form. Through the process of ­neurulation, ridges rise along the sides of the neural plate, arch together, and fuse. Before closure of the tube, the anterior end expands to form three primary brain vesicles that are ­precursors of the forebrain, midbrain, and hindbrain. By GW8, these subdivide to form five secondary brain vesicles that are the precursors of the medulla, pons, midbrain, diencephalon, and telencephalon. From GW4–8 the embryo grows in size tenfold. During this time of rapid development, neurogenesis, or neuron production, begins. Between GW6 and GW20, the brain produces about 250,000 cells per minute. At the start of the fetal period, neuron production begins to drive dramatic changes in the brain’s gross morphology. The smooth structure of the immature brain is transformed as gyral and sulcal folding emerge. Between GW8 and GW22, the longitudinal fissure that separates the right and left hemispheres develops. Additional primary sulci form between GW14 and GW26, secondary sulci emerge between GW30 and GW35, and the development of tertiary sulci extends from GW36 into the postnatal period. These foldings allow the fetal brain to dramatically increase cortical surface area. By GW20, most of the billions of neurons in the human brain have been produced. Neural migration ensues in two main waves, with peaks at GW8–10 and GW12. Through migration, neurons radially form the six‐layered neocortex in an “inside out” fashion, so that deeper layers are populated and mature first, and more superficial layers are populated and mature last. Cajal–Retzius cells control the positioning of n ­ eurons by producing a molecule, reelin, that signals neurons to stop migrating. Around GW16 neurons reach their final position and begin extending axons and dendrites, which allow them to communicate with other neurons via synapses. Few synapses are formed until GW28, when the pace accelerates to approximately 40,000 synapses per minute. Through synaptic communication neurons establish functional neural circuits that mediate sensory and motor processing and underlie behavior. Around GW32–34, myelination begins. This involves the production of the fatty sheath that insulates nerve fibers and allows for cells to transmit information faster. Along with cell proliferation, naturally occurring cell death plays an essential role in CNS development. Necrotic cell death eliminates cells that are damaged due to injury, and apoptosis eliminates cells that initiate an intrinsic suicide program. Since neurons that form connections with other neurons are more likely to survive, apoptosis is hypothesized to regulate the establishment of neural circuits. Apoptosis also eliminates cells like neural progenitor cells once their transitory functions are complete. During prenatal development normative cell death eliminates 50% or more of neurons. Brain maturation begins a new phase at birth characterized by exuberant connectivity and refinement of neural circuitry. Between birth and the third year of life, gray matter increases from 50% of adult brain volume to 90%, and white matter myelination progresses to an adult‐like pattern. Finally, synaptic pruning begins refining neural connectivity via plasticity processes that are influenced by the child’s experience and environment (Greenough, Black, & Wallace, 1987).

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Methods of Studying Fetal Brain Development Animal models Because experimental studies of typical and atypical fetal brain development are neither easy nor ethical with human subjects, animal models provide an essential tool for understanding PS effects. Animal experiments allow researchers to systematically manipulate the type, timing, and intensity of the stressor and test for differential effects on offspring development at the behavioral, anatomical, and cellular level of the brain. Stimuli used to stress the mothers include random light or noise, restraint, or removal from the home cage. Additionally, animal models provide an opportunity to tease apart pre‐ and postnatal influences on offspring development through cross‐fostering designs. Although animal studies have used many mammals, the majority have been conducted on rodents. As a result, researchers often grapple with issues of translation when interpreting findings (Gottlieb & Lickliter, 2004). Fetal physiological monitoring For over seven decades researchers have studied indices of fetal physiological functioning, including fetal heart rate (FHR), fetal heart rate variability (FHRV), fetal movement (FM), and “coupling,” or the association between FHRV and FM (see DiPietro, Costigan, & Voegtline, 2015). Over the course of pregnancy, these indices change in a predictable way that is reflective of brain maturation. This is because the heart is innervated by fibers from both branches of the autonomic nervous system, which interact to regulate FHR and FHRV; ­sympathetic innervation increases FHR, while parasympathetic innervation reduces FHR. Early in gestation, FHR is predominately under the control of the sympathetic nervous ­system. As the CNS develops, the maturation of the parasympathetic branch allows progressive activity of the vagus nerve to slow the heart down and modulate FHR in coordination with motor functioning. As a result, over the course of pregnancy, FHR decreases and FHRV and cardiac‐ motor coupling increases. Researchers often use fetal physiological monitoring to characterize the adaptation and individual developmental trajectory of the CNS in the fetus. Typically, 30–50 min of fetal monitoring using electrodes applied to the mother’s stomach is required to detect within‐individual stability and measure variations that emerge during fetal rest‐activity cycles. There is evidence of rank‐order stability of FHRV over gestation, suggesting continuity in individual differences in brain development is detectable during pregnancy. Additionally, by comparing typically progressing pregnancies with pregnancies characterized by intrauterine growth restriction and other conditions, researchers have identified deviations in fetal indices that reflect these conditions. Similarly, researchers have used fetal indices to examine possible deviations in fetal CNS development that are associated with PS effects. Finally, differences in fetal indices, independent of maternal factors, have been used to predict offspring behavioral and psychological development. Fetal MRI Since the development of magnetic resonance imaging (MRI) in the 1980s, researchers have used this technology to study fetal brain development (see Prayer et al., 2006). MRI uses a powerful magnetic field (MF) and radiofrequency (FF) pulse to align magnetically active nuclei (i.e., protons) and then knock the nuclei out of alignment. As the nuclei recover their alignment, they emit a radio signal that is converted into an image. Although MRI can generate detailed images depicting fetal brain anatomy, until recently it was not widely used due to safety concerns. Unlike other imaging methods, MRI does not involve radiation, which can directly kill cells or cause genetic mutations. Pregnant women cannot be scanned if they have

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metal devices or implants. However, primary safety concerns for the fetus include risk due to exposure to MF or RF pulse or to increased heat or noise caused by the MRI. In addition to animal studies examining these concerns, several follow‐up studies of children studied with fetal MRI have been carried out. Although no significant adverse effects have been discovered, researchers typically do not use MRI to study normal brain development until after GW18, when the major steps of organ production and development have been completed. Currently, the most significant challenge to using MRI to study brain development is fetal motion, which can cause ghosting or blurring of the acquired MRI image. In life‐threatening situations, the use of a sedative can reduce fetal motion; however, this is not a practical approach for general use. Instead, the ongoing development of faster imaging and motion correction techniques are essential to advancing fetal MRI methodology. Currently, research using this approach is in the nascent stage. Because of the rapid pace of fetal brain maturation, changes that can be detected with fetal MRI occur on the order of weeks or even days. As a result, it is difficult to study fetal brain development at a precise developmental stage. Therefore, in order to fully leverage fetal MRI to study how PS may alter brain development, an increased understanding of temporal and regional variability of events in normal brain maturation is needed.

Effects of Prenatal Stress on Offspring Brain and Behavior Development The delineation of PS effects depends on the definition of stress itself (see Gunnar & Davis, 2013). Stress is conceptualized as a state of disharmony or threatened homeostasis that sets in motion physiological and behavioral responses to reestablish homeostasis. Distress is a negative psychological response to the perception of threatening, difficult, or stressful experiences that can include a variety of cognitive and affective states, such as uncertainty, frustration, anxiety, and depression. Thus PS is equated with negative emotional states. This is problematic for ­several reasons. First, PS can involve physical challenges that do not involve negative emotions, such as the pregnant woman doing physically demanding work that activates stress hormones. Second, not all emotionally negative states activate stress biology (Hennessy & Levine, 1979), suggesting that associations between mood dysregulation and fetal outcomes may involve biology beyond activation of stress‐mediating systems. Third, because it is the interpretation of the event that mediates the impact of objective conditions on stress biology, when PS is defined by the event (i.e., exposure to a natural disaster), there can be a wide variety of individual responses. Notably, several researchers have shown PS is neither specific to pregnancy nor stress. Correlations among self‐report measures of stress and symptoms of depression and anxiety are high during pregnancy and remain stable through the first 2 years after delivery. These results suggest that measures of PS may gauge factors like personality traits, coping skills, and social support. Additionally, many environmental stressors, such as low socioeconomic status, ­marital/family conflict, and paternal death or incarceration, are likely to persist after ­pregnancy. Since these factors can influence maternal caregiving, investigators must measure and control for postnatal levels of stress in order to differentiate pre‐ and postnatal influences on child development. To test for PS effects on offspring brain and behavior development, investigators have looked for converging evidence from animal and human studies. Here we summarize results in both domains of research. Animal studies Results from animal models show offspring exposed to PS exhibit heightened anxiety and fear of novelty, diminished exploration and social interaction, deficits in attention, learning and

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memory, delays in motor development, and disturbances in motor behavior (stereotyped, non‐directed motor behavior). These findings have been widely replicated in rats and rhesus monkeys using different stress paradigms. Also, PS alterations to offspring behavior have been associated with changes in brain development, including alterations to the hippocampus and amygdala, two areas of the brain that support learning, memory, and emotion regulation. These alterations include reduced neurogenesis, synaptic density, and dendritic length and complexity. Additionally, research shows PS alters the neurodevelopmental trajectory of the amygdala, such that animals exposed to PS exhibit initially reduced and subsequently increased amygdala volume. Offspring exposed to stress at the end of gestation also shows hypermyelination. Finally, animal experiments show that PS induces alterations to hypothalamic–­pituitary– adrenocortical (HPA) axis functioning including elevated basal corticosterone levels, heightened HPA axis reactivity to acute stress, and decreased negative feedback of the HPA axis. These physiological alterations are associated with decreased binding capacity of both mineralocorticoid and glucocorticoid receptors in the brain. Additionally, animal models suggest some sex‐specific effects of PS. While males exposed to PS show deficits on tasks involving learning and spatial working memory, there are ­conflicting reports of learning deficits and learning enhancements for females. Male offspring with learning deficits show decreased neurogenesis in the dentate gyrus of the hippocampus, while female offspring with increased anxiety show decreased neurogenesis in the ventral hippocampus. Most research on stress posits that mild forms of stress can have a positive effect on development and the organism’s ability to respond to stress in the future. Similarly, animal models show differential effects of PS depending on exposure level. Although daily maternal restraint of 1 hr will reduce dendritic length and complexity in the brains of adult male rat offspring, restraint of only 30 min will increase dendritic length and complexity. There are no known equivalent studies with adult female rats. Other studies have shown enhanced learning ability of both male and female offspring following mild PS. Additionally, animal models show that fetuses are affected differently when subjected to a random versus predictable stress protocol. In response to a random stressor, glucocorticoid levels increased in maternal and fetal blood after each stress exposure. In contrast, levels no longer increased by the third day of a predictable stress protocol. These results suggest researchers should assess whether animals (and correspondingly, humans) adapt to certain types of PS experiences. Animal designs that cross‐foster pups from a stressed mother onto a control mother (and vice versa) have shown independent effects of pre‐ and postnatal influences on offspring development. Results from these studies suggest that chronic, unpredictable stress at the end of pregnancy can increase anxious behavior of mothers until after pups are weaned. During this time, persisting PS effects can decrease maternal caretaking behavior and increase glucocorticoids passed to offspring through milk. After cross‐fostering, PS pups reared by control mothers showed reduced anxiety, and control pups reared by stressed mothers showed increased anxiety. Additionally, male PS rats reared by control mothers showed normalized HPA axis functioning. However, rearing by a control mother did not ameliorate learning deficits in PS male offspring. These results suggest that PS effects on learning may be prenatally mediated. Quasi‐experimental human studies Prospective studies involving pregnant women who experienced natural or man‐made d ­ isasters have found persisting effects on child outcomes attributed to PS. Notable programs of research have followed children whose mothers experienced an ice storm, hurricane and tropical storms, major floods and earthquakes, or an act of terrorism or war. These studies have attempted to

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disentangle the effects of objective stressors (i.e., days without power), subjective reactions (e.g., distress, perceived stress), and physiological stress reactivity (e.g., cortisol over 24 hr). Additionally, retrospective studies have examined PS effects in children whose mothers experienced disasters like the Dutch Hunger Winter of 1944–1945. Results from these quasi‐experimental studies have linked PS to increased prevalence rates of neurodevelopmental disorders (attention‐deficit hyperactivity disorder, autism, schizophrenia), psychiatric disorders (depression, anxiety), difficult temperament, increased behavior problems, and deficits in attention, language, and motor development. Elevated subjective distress (and not objective stress) was associated with increased internalizing problems (anxiety, depression, social withdrawal) that remained stable from ages 4–11 years old. High subjective distress was also associated with increased externalizing problems (aggression, hyperactivity) that diminished over development relative to published norms. Although exposure to severe stress during an ice storm was associated with IQ, memory, and language deficits at 2, 5, 8, and 11 years old, moderate levels of stress were associated with enhanced cognitive abilities. Recent findings have shown brain differences might be associated with PS and/or behavioral alterations in these cohorts. Objective but not subjective PS predicted smaller right hippocampus volumes in males at age 11; no effects of PS were detected for females. In comparison to age‐matched unexposed controls, PS exposure related to the Dutch Hunger Winter was associated with smaller brain volume in males but not females at age 68. Altogether, findings from quasi‐experimental studies echo results of animal models showing PS effects may depend on stressor type, timing, and intensity, as well as offspring sex. Correlational human studies Early correlational research on PS effects focused on offspring birth outcomes (gestational age at delivery, birth weight, Apgar scores) that are thought to reflect adverse in utero exposures and fetal development. This work led to a widely replicated finding linking PS to shorter gestation and low birth weight. Although the broader interest of this research was the possible developmental sequelae of birth outcomes, initially very few longitudinal, prospective datasets were available to assess behavioral and psychological functioning beyond the neonatal period. Today, several datasets allow researchers to examine long‐term outcomes. Although mounting correlational evidence suggests that PS is associated with persisting educational, behavioral, and psychological problems, caution is required in assuming causation. Results of correlational human research on PS effects are varied. However, major findings that have been replicated by several different investigators may be organized and interpreted within the developmental psychopathology principles of equifinality and multifinality (Cicchetti & Rogosch, 1996). Equifinality suggests that a single developmental outcome (i.e., ADHD) may be influenced by many different factors. Accordingly, findings have linked a single type of offspring outcome (inattention/hyperactivity) to multiple types of PS (elevated levels of maternal stress, depression, anxiety, trait anxiety, and pregnancy‐specific anxiety). Other outcomes that have been independently linked to these five types of PS include difficult temperament, increased negative behavioral reactivity, and increased anxiety. Also, lower verbal IQ has been independently associated with both elevated maternal anxiety and depression. In contrast, the principle of multifinality suggests that one developmental factor can lead to many different outcomes. Accordingly, high maternal anxiety has been associated with different offspring outcomes, including different types of internalizing and externalizing problems, and poor performance on executive function and decision‐making tasks. Correlational research findings also suggest that offspring sex and intensity and timing of PS exposure may induce different effects on development, some of which may be positive. Overall,

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PS findings suggest males may be more vulnerable to adverse in utero effects, with female outcomes being more variable. However, some reports contradict this. Although elevated ­ maternal anxiety and cortisol early in pregnancy have been associated with deficits in infant cognition, moderate and high levels of PS late in pregnancy have been associated with enhanced infant cognitive maturation and abilities. Additionally, the timing of PS exposure has been related to alterations in offspring brain structure and connectivity development. Studies assessing PS effects in middle childhood have linked reductions in offspring gray matter volume to high levels of PS in the second (but not first or third) trimesters, including maternal anxiety and depression. Also, high levels of maternal stressful life events in the second trimester have been associated with precocious white matter tract development in the uncinate fasciculus, an area of the brain linked to behavioral disorders, including antisocial behavior and autistic spectrum ­conditions. Some investigators have interpreted this neurodevelopmental profile of reduced gray matter and precocious white matter as indicative of aberrant accelerated brain maturation induced by PS. Finally, correlational research has shown an association between PS and altered offspring amygdala development. Increased maternal prenatal cortisol levels have been associated with increased offspring amygdala volume at age 10. Also, elevated maternal depressive symptoms have been associated with atypical amygdala functional connectivity in 6‐month olds that is ­consistent with patterns observed in adolescents and adults with major depressive disorder. Correlational studies that have measured PS in the pre‐ and postnatal periods show that although many effects of PS are attenuated after controlling for postpartum measures, PS is independently associated with adverse child outcomes. Fetal studies provide additional, proximal evidence supporting putative effects of PS on offspring CNS development. Human and animal research has shown rapid fetal response to experimental manipulations designed to change maternal psychological state (both for induced stress and induced relaxation). Further, within the context of expectable prenatal CNS development (decreased FHR, increased FHRV, and cardiac‐motor coupling), maternal distress (anxiety, depression, and negative mood) has been associated with variations in fetal indices and their rate of change over the course of gestation. Several studies suggest that PS is associated with an accelerated pace of fetal CNS maturation and more mature neural integration at birth. It is possible that maternal self‐report of PS and offspring outcomes in the pre‐ and postnatal period reflect a shared genetic contribution, instead of a causal influence of the prenatal environment. Comparisons between children conceived via in vitro fertilization using embryos from the mother and those conceived with donor embryos have been used to tease apart genetic and putative PS effects on in utero development (Rice et al., 2010). Results suggest that offspring inattention/hyperactivity may be due to a shared inheritance of characteristics, rather than PS effects on the in utero environment. In contrast, lower birth weight, shortened gestation, and offspring antisocial behavior were linked to PS in related and unrelated dyads, suggesting an in utero environment effect. PS was also linked to offspring anxiety; however this association was not significant after accounting for postnatal measures of maternal anxiety and depression. These findings underscore the importance of considering confounding genetic and postnatal factors in PS research.

Mechanisms of PS Effects Despite strong evidence of PS effects from animal models, and compelling results from human studies, our understanding of the biological mediators of PS is incomplete. Research examining possible mechanisms of PS has focused on the endocrine, immune, and vascular systems, which play a major role in the response and adaptation to stress.

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Maternal endocrine system The maternal HPA axis has been the most widely investigated mediator of PS effects. PS is hypothesized to produce alterations in offspring brain and behavior development through the action of glucocorticoids. In human studies, however, few data show the HPA axis is the smoking gun behind PS effects. As previously reviewed, measures of maternal HPA axis functioning and PS or distress measures have independently been associated with offspring outcomes. However, with few exceptions researchers have failed to find an association between maternal stress hormones and measures of maternal distress in human studies (see Beijers, Buitelaar, & de Weerth, 2014). Animal experiments, however, do show stress‐induced maternal glucocorticoids mediate some offspring outcomes. By adrenalectomizing pregnant rats prior to the initiation of stress, and injecting them with either saline or maintenance levels of glucocorticoids, researchers were able to block glucocorticoid production in response to stress. Stress exposure in the absence of stress hormones prevented alterations typically observed in offspring, including increased anxiety, learning deficits, and HPA dysregulation. Moreover, when investigators administered additional glucocorticoids to adrenalectomized pregnant rats to simulate a stress response, they were able to restore increased anxiety and HPA dysregulation in offspring. However, this manipulation did not restore offspring learning deficits. These findings suggest that glucocorticoids mediate some PS effects seen in animal models and that other biological mechanisms likely play a role. Accordingly, animal and human studies suggest that maternal thyroid hormone (TH) may partially mediate PS effects. TH is a biologically plausible mechanism for PS effects because TH deficiency is associated with mood disturbance, and maternal TH also helps to regulate multiple prenatal neurodevelopment processes, including neurogenesis, neuronal migration, axonal and dendritic growth, synaptogenesis, and myelination. Insufficient maternal TH ­levels, especially early in development when the fetus relies entirely on maternal TH production, are associated with alterations in offspring brain development and persisting behavioral deficits. Research has shown children of women with untreated TH deficiency at the beginning of the second trimester exhibit lower IQ and deficits in language, attention, and learning that echo PS effects seen in animal and human studies. Maternal immune system Recent research has highlighted a complex interplay between the maternal HPA axis and immune system and suggested that maternal inflammation during pregnancy is a likely mechanism of PS effects. Maternal inflammation may alter offspring development through small immune signaling proteins called cytokines. Animal models show that cytokines are released into the blood in response to infection, immune activation, and stress. Additionally, animal and human studies have shown that PS is positively correlated with higher circulating levels of proinflammatory cytokines and that cytokines in maternal circulation can cross the placenta and access the fetal brain. Research suggests cytokine signaling pathways may help regulate offspring neurodevelopmental events including neuronal differentiation and migration and axon and synapse formation. Animal and human studies have linked many types of infections and pathogens to offspring outcomes, prompting the hypothesis that maternal immune activation may be a common mechanism underlying these effects. Additionally, rodent models that use agonists of innate immune system receptors to stimulate a proinflammatory cascade of cytokines in the maternal immune system (in the absence of an actual pathogen) have provided evidence that maternal immune activation and not direct fetal infection alters offspring development. Recent research suggests the balance of maternal pro‐ and anti‐inflammatory cytokine

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production may influence offspring outcomes; manipulating the maternal immune system to overexpress IL‐10, an anti‐inflammatory cytokine prevented behavioral abnormalities in offspring. Maternal vascular system Although there is no direct vascular connection between mother and offspring, maternal ­autonomic responses (heart rate, blood pressure) and hemodynamics (circulation of blood) influence the offspring via the placenta. Animal research has shown that PS releases adrenaline and noradrenaline into circulation, which can reduce placental blood flow and fetal oxygenation. Through prolonged hypoxia, or oxygen deficiency, PS can alter offspring brain and behavior development. Human studies suggest that due to the vasculature of the developing brain, the areas most vulnerable to hypoxic injury are subcortical structures, including the amygdala, hippocampus, thalamus, and hypothalamus. Furthermore, research shows these ­tissues may be most vulnerable to hypoxia prior to GW24. Hypoxia has been associated with offspring outcomes linked to measures of PS and distress, such as learning and behavioral problems, as well as peripheral nerve damage, hearing deficits, and seizures. Altogether, these findings suggest that maternal vascular mechanisms may be most likely to mediate PS effects on development in the first and second trimester of pregnancy. The placenta As previously indicated, the “maternal–placental–fetal unit” is a complex, dynamic system that facilitates the exchange of hormones, cytokines, oxygen, and nutrients between mother and offspring. As a result, the placenta is a likely mediator of PS effects. The placenta is the first fetal organ to develop, and for the majority of pregnancy, it is approximately one cell thick. During a normally progressing pregnancy, only a small amount of maternal stress hormones reach the fetal brain. Approximately 90% of circulating corticosteroids are sequestered by a corticosteroid‐binding globulin (CBG) before they access the fetus, and most of the remaining 10% is converted to inactive metabolites by the placental enzyme 11β‐hydroxy steroid de‐hydrogenase‐2 (11β‐HSD‐2). However, these dynamics shift at the end of gestation, allowing a surge in fetal cortisol levels to drive maturation of the fetal lungs and possibly help initiate labor. Research suggests that chronic PS reduces the level of CBG in rodents and downregulates the activity of 11β‐HSD‐2 in rodents and humans. Thus, altered placental functioning as a result of PS may play a role in offspring outcomes by allowing elevated levels of glucocorticoids to cross the placental barrier. Additionally, the placenta plays a fundamental role in organizing the development of the fetal CNS through the production of placental corticotrophin‐releasing hormone (pCRH). pCRH has been shown to regulate the maturation of the fetal HPA axis and other systems and modify the timing of labor onset and delivery. Maternal cortisol stimulates pCRH production, which in turn stimulates cortisol production. This positive feedback loop can progressively amplify pCRH and cortisol production during pregnancy. Thus, research suggests pCRH trajectory (i.e., rate of production acceleration), rather than absolute concentration of pCRH, may be an important indicator of PS alterations to fetal development. Notably, because the placenta produces pCRH in primates, but not other mammals, researchers are unable to study this mechanism in rodent models. An emerging body of work has suggested that placental factors may mediate sex‐specific PS‐related offspring outcomes. As a result of sex‐specific differences in the structure and function of the placenta, research suggests that female fetuses may make multiple adaptations in response to an adverse in utero environment, while males may show fewer signs of in utero

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adaptation (see Clifton, 2010). These findings align with results from animal and human ­literature showing that males may be more vulnerable to adverse PS‐related outcomes, while females show a more varied response. Offspring regulatory systems Animal and human studies suggest that PS disrupts normal development of homeostatic regulatory systems in the offspring, leading to persisting dysregulation of the offspring’s own endocrine, autonomic, and immune systems. This dysregulation is hypothesized to subsequently play a role in inducing adverse behavioral outcomes. Findings from animal work ­support this hypothesis. Mechanistically, PS has been shown to alter the number of glucocorticoid receptors in the offspring hippocampus, an area important in terminating the stress response. Rodent and nonhuman primates exposed to PS show a heightened, prolonged elevation of corticosterone in response to stress, and several investigators have replicated this finding in humans. Similar “programming” effects have been reported for the immune system. Injections of IL‐6 into pregnant mice induced chronically elevated levels of IL‐6 in adult offspring. Additionally, high levels of objective PS during an ice storm were associated with an immune profile biased toward a proinflammatory response in 13‐year‐old children.

Toward Resilience and Intervention Not all women and children who experience high stress during pregnancy show biological evidence of this stress. Multiple factors may influence maternal and offspring susceptibility to stress effects, including genetic predispositions, individual differences in personality and ­temperament, and lifestyle factors. Research suggests that variation in the interpretation of a stimulus as stressful depends on the characteristics of the individual experiencing the stimulus. Accordingly, researchers interested in PS effects are beginning to assess maternal personality traits, as well as individual differences in maternal threat perception and reaction. Additionally, research suggests that environment and lifestyle factors also contribute to maternal resilience. Social support has been shown to “buffer” or reduce levels of self‐reported stress during pregnancy. Lifestyle factors like exercise have been shown to promote resilience and healthy outcomes for pregnant women and their children. Notably, research also shows that postnatal caregiving experiences can play an important role in reducing or reversing PS effects. As reviewed here, fostering prenatally stressed (PS) pups onto control mothers after birth has been shown to prevent adverse offspring outcomes.

Conclusions For many generations, the human brain was considered to be a tabula rasa, or blank slate, at birth. However, a growing body of research suggests brain and behavior development are already underway by delivery. Undoubtedly, animal models and other recently developed methods have been crucial to opening a new window into early brain growth that previously has been inaccessible. Our growing understanding of both typical and atypical fetal brain development has provided an important foundation for research investigating the “fetal ­origins” hypothesis that maternal health, emotions, and stress levels have long‐term effects on offspring outcomes. Research shows that PS interferes with normal CNS development and induces brain perturbations that result in persisting effects on offspring functioning. Affected regions of the brain identified in this literature include the amygdala, hippocampus, and

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­ ncinate fasciculus. Additionally, PS is associated with reduced gray matter volume and hyperu myelination. Central findings on offspring behavioral outcomes have associated PS with increased offspring anxiety, fear, and inhibition in the face of novelty, as well as deficits in attention, learning, memory, and motor maturation. Importantly, moderate PS has been associated with enhanced cognitive and motor maturation and ability level. Besides PS intensity, research shows that PS effects may depend on stress type, timing of exposure, and offspring sex. Although results are varied, findings suggest that PS exposure in early or mid‐gestation is associated with increased risk for adverse offspring outcomes. It is possible the offspring is more vulnerable during early gestation when disruption to the rapidly developing architecture of the brain may have cascading effects. Currently, there is no unifying definition of PS, and many investigators confound PS with distress. This may help explain why human studies have had difficulty detecting a biological mediator of PS effects. However, research suggests that major regulatory systems that play a role in the adaptation and response to stress, including the maternal endocrine, immune, and vascular system, are likely mechanisms of PS effects. Maternal experiences during the pregnancy have profound effects on development, which are not yet well understood by researchers, clinicians, and policy makers. The growing PS literature may identify new opportunities to support the health and resilience of women and their children. However, scientists have the duty to deliver findings to women in a clear way that does not promote scaremongering. Importantly, correlational research may be suggestive but is not strong evidence of a causal relationship. Based on currently available evidence, advisory panels such as the March of Dimes recommend that stress during pregnancy should be reduced, if possible. This might be accomplished by helping pregnant women build a strong support network, form a plan to cope with anticipated challenges, practice stress reduction techniques (mediation, yoga), and maintain healthy habits (sleep, exercise, nutrition). As PS research shows, supporting the health of women during pregnancy can help promote the ­optimal development of their children over the lifespan.

Author Biographies Megan R. Gunnar is a regents professor and Distinguished McKnight University professor at the University of Minnesota. She is the director and chair of the Institute of Child Development, the interim director of the Center for Early Education and Development, and the associate director of the Center for Neurobehavioral Development. She received her PhD in Developmental Psychology at Stanford University and then completed a postdoctoral fellowship in stress neurobiology at Stanford Medical School. In 1979 she came to the U of M as an assistant professor moving through the ranks to full professor by 1990. Professor Gunnar has spent her career studying how stress affects human brain and behavioral development and the processes that help children regulate stress. She is the recipient of lifetime achievement awards from the American Psychological Association, Division 7 Developmental Psychology, and the Society for Research in Child Development and a lifetime mentor award from the Association for Psychological Science. Nationally she is a member of the Harvard National Scientific Council on the Developing Child that translates developmental science into language that communicates with policy makers. Internationally she is a member of the Canadian Institute for Advanced Research’s Program on Child and Brain Development, a group working on how early experiences “gets under the skin” to influence lifelong health and well‐being. In addition, she chairs the Research Advisory Council for the Minnesota Children’s Museum and is a consultant on stress and development for the Greater Minneapolis Crisis Nursery.

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Colleen Doyle is a doctoral student and National Science Foundation Graduate Research Fellow at the University of Minnesota, Institute of Child Development. Colleen received her undergraduate education at the University of Chicago and earned a Masters of Fine Arts in poetry at Boston University, before switching careers to examine the human experience empirically. Prior to beginning graduate work in developmental psychology, Colleen worked as a research assistant with Dr. Catherine Monk at Columbia University Medical Center studying the effects of fetal programming. Currently, Colleen’s research interests focus on understanding how in utero developmental processes influence fetal central nervous system maturation, infant brain and behavioral development, and health and well‐being throughout the lifespan.

References Beijers, R., Buitelaar, J. K., & de Weerth, C. (2014). Mechanisms underlying the effects of prenatal psychosocial stress on child outcomes: Beyond the HPA axis. European Child & Adolescent ­ Psychiatry, 23(10), 943–956. Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8(04), 597–600. Clifton, V. L. (2010). Review: Sex and the human placenta: Mediating differential strategies of fetal growth and survival. Placenta, 31(Suppl), S33–S39. DiPietro, J. A., Costigan, K. A., & Voegtline, K. M. (2015). Studies in fetal behavior: Revisited, renewed, and reimagined. Monographs of the Society for Research in Child Development, 80(3), 1–94. doi:10.1111/mono.v80.3 Gottlieb, G., & Lickliter, R. (2004). The various roles of animal models in understanding human development. Social Development, 13(2), 311–325. Greenough, W. T., Black, J. E., & Wallace, C. S. (1987). Experience and brain development. Child Development, 58(3), 539–559. Gunnar, M. R., & Davis, E. P. (2013). The effects of stress on early brain and behavioral development. In J. L. R. Rubenstein & P. Rakic (Eds.), Comprehensive developmental neuroscience: Neural circuit development and function in the heathy and diseased brain (pp. 447–465). Amsterdam, the Netherlands: Academic Press. Hennessy, J. W., & Levine, S. (1979). Stress, arousal, and the pituitary‐adrenal system: A psychoendocrine hypothesis. Progress in Psychobiology and Physiological Psychology, 8, 133–178. Prayer, D., Kasprian, G., Krampl, E., Ulm, B., Witzani, L., Prayer, L., & Brugger, P. C. (2006). MRI of normal fetal brain development. European Journal of Radiology, 57(2), 199–216. Rice, F., Harold, G. T., Boivin, J., Van den Bree, M., Hay, D. F., & Thapar, A. (2010). The links between prenatal stress and offspring development and psychopathology: Disentangling environmental and inherited influences. Psychological Medicine, 40(02), 335–345. Stiles, J. (2008). The fundamentals of brain development: Integrating nature and nurture. Cambridge, MA: Harvard University Press.

Psychophysiology of Traumatic Stress and Posttraumatic Stress Disorder Michael G. Griffin, Brittany F. Goodman, Rebecca E. Chesher, and Natalia M. Kecala Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri–St. Louis, St. Louis, MO, USA

Diagnostic criteria for posttraumatic stress disorder (PTSD) were introduced in third edition of Diagnostic and Statistical Manual for Mental Disorders (DSM‐III) (American Psychiatric Association [APA], 1980). Of course, scientific studies of trauma reactions extend back to Janet’s work at the turn of the last century and Kardiner’s (1941) description of “physioneurosis” among veterans of World War I. Seminal studies targeting psychophysiological biomarkers of traumatic stress reactions served a key role in mitigating initial skepticism from clinicians, scientists, and the community that questioned the validity of PTSD as a distinct diagnostic entity (Yehuda & McFarlane, 1995). The DSM‐III diagnostic criteria included three symptom clusters composed of reexperiencing, avoidance, and hyperarousal symptoms. Most of the biological research into the disorder was focused on the hyperarousal symptoms, such as exaggerated startle and hyperreactivity to trauma cues. Historically, the DSM diagnostic system has defined physiological reactivity in PTSD in broad terms, such as “heightened startle reactivity” and “increased physiological reactivity to trauma‐related cues or reminders of the traumatic event.” The current conceptualization of PTSD as detailed in the DSM‐5 (APA, 2013) includes four clusters: (a) intrusion or reexperiencing symptoms, such as intrusive thoughts or memories, nightmares, or flashbacks to the traumatic experience; (b) avoidance symptoms, which include avoidance of thoughts, feelings, people, or situations connected to the trauma; (c) negative mood and cognitions arising from the traumatic experience, including negative self‐ appraisal, self‐blame, shame, detachment from others, and a loss of interest in previously enjoyable activities; and (d) hyperarousal symptoms, such as hypervigilance to trauma cues, exaggerated startle response, irritability and anger, and difficulties with sleep.

The Wiley Encyclopedia of Health Psychology: Volume 1: Biological Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editor: Robert H. Paul. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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The current conceptualization of PTSD is highly informed by results from structured, empirical investigations of psychophysiological alterations that follow a traumatic event (Pole, 2007). However, a disproportionate number of these studies only included male combat ­veterans, the results of which do not readily generalize to females or other trauma populations. This trend is beginning to shift, with new studies aiming to establish explanatory models of PTSD from cardiac activity (heart rate [HR], heart rate variability [HRV]), sweat gland activity (skin conductance [SC]), and facial electromyogram (EMG) activity of facial muscles as indices of negative emotional states (e.g., frontalis or corrugator) or startle response (orbicularis oculi). Broadly, the early psychophysiological studies of PTSD utilized one or more of the following paradigms: (a) resting physiological state, (b) responses to standard trauma cues (e.g., sound of bombs exploding, gunfire, or video of car crashes) or personalized trauma cues (e.g., scripts of the individual’s traumatic event), or (c) startle reactivity to sudden loud auditory stimuli (typically pure tones). The large collection of early work by Pitman, Orr, and colleagues contributed significantly to the knowledge base of psychophysiological trauma reactions in all three domains. Of special note was their work on the reactions to personalized trauma cues that was derived from Lang’s bio‐information theory of emotion networks (Lang, 1979). Specifically, this led to the development of an auditory script‐driven imagery paradigm of trauma and neutral cues that were recorded and played back in the psychophysiological laboratory while measurement of ­autonomic nervous system reactivity was recorded (e.g., Pitman, Orr, Forgue, de Jong, & Claiborn, 1987). An important meta‐analysis of the early psychophysiological research (Pole, 2007) reported significantly larger effect sizes for higher physiological reactivity when comparing individuals with PTSD to trauma‐exposed participants without PTSD. Additionally, a psychophysiological signature emerged, characterized by the absence of SC habituation to startling tones and a facial EMG pattern of negative emotional state in response to idiographic trauma cues. Notably, HR reactivity was the most consistent indicator of PTSD hyperreactivity, consistent with observations that elevated HR immediately following a traumatic event predicts the risk of PTSD (e.g., Bryant, Creamer, O’Donnell, Silove, & McFarlane, 2011).

Recent Developments in the Psychophysiology of Traumatic Stress and PTSD Recent studies have focused on the role of the parasympathetic nervous system in PTSD. The vast majority of prior studies focused on the sympathetic nervous system, with little attention directed at the parasympathetic system, largely due to the insensitivity of measures (e.g., SC and HR) to parasympathetic tone and drive. However, it is now possible to quantify parasympathetic nervous system activity through measures such as HRV (Task Force of the European Society of Cardiology and the North American Society for Pacing and Electrophysiology, 1996). Innovative work by Porges (2011) provides a theoretic framework (i.e., polyvagal theory) for this line of inquiry. A second area of current interest is fear processing and the elucidation of brain fear circuits that drive PTSD symptoms. These research directions emphasize brain mechanisms that align with the National Institute of Mental Health initiative “Research Domain Criteria (RDoC)” to delineate linkages between biological factors, neural circuits, and clinical phenotypes (Insel et al., 2010).

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The Polyvagal Theory and Traumatic Stress Based on phylogenetic development of the nervous system, the polyvagal theory suggests that an individual’s reaction to a challenge or life threat depends on activation of the underlying system. Two separate vagal systems have been identified: the unmyelinated vagus that is phylogenetically older and originates in the dorsal motor nucleus and the myelinated vagus that originates in the nucleus ambiguus (NA). According to this theory, the sympathetic nervous system activates the “fight‐or‐flight” response. The older vagal system is associated with immobilization or freezing behavior, while the newer pathway is linked to social aspects of survival and control of HR. Output from this system follows a respiratory rhythm that allows for noninvasive monitoring of respiratory sinus arrhythmia (RSA) as a measure of HRV ­secondary to breathing dynamics. The polyvagal theory suggests that the activation of these systems follows the Jacksonian principle of dissolution, such that when an organism is faced with a challenge, the phylogenetically newest system is engaged first, followed by the older system if needed to secure safety. Neuroception, the unconscious detection of threat through temporal brain regions, determines which system is to be engaged. Consistent with this theory, trauma may induce a deficit in neuroception, which leads to incorrectly interpreting a safe situation as dangerous. Trauma survivors may then rely on the sympathetic nervous system more readily or have a lowered threshold for engagement of the sympathetic nervous system. Future RSA‐targeted studies will help inform the role of neuroception in PTSD. The polyvagal theory describes a harmonized synchrony between the sympathetic and parasympathetic systems. Specifically, the myelinated vagus serves as a “vagal break” on HR via the SA node. The myelinated vagus maintains HR at levels that are optimal for restoration and regrowth. When faced with a challenge, the vagal brake is released allowing for increased HR and mobilization of needed resources. Prolonged threat activates the sympathetic nervous system via the adrenal system. Persistence of the threat leads to freezing, fainting, or dissociative behaviors governed by the unmyelinated vagal system (Porges, 2011). Faulty neuroception may lead to chronic activation of sympathetic drive and maladaptive social behavior. Preliminary evidence suggests engagement of the myelinated vagal system via music therapy leads to better regulation of the viscera and an increase spontaneous social behavior (Porges, 2011). Specific findings examining alterations in the parasympathetic nervous system show that individuals with PTSD exhibit decreased HRV during trauma recall (Keary, Hughes, & Palmieri, 2009) at baseline, and during affective induction (Hauschildt, Peters, Moritz, & Jelinek, 2011). Further, there is evidence for higher basal HR with PTSD that may be driven by a reduction in parasympathetic input (Hopper, Spinazzola, Simpson, & van der Kolk, 2006). While promising, additional studies of the polyvagal theory and the role of altered vagal/parasympathetic responses are needed.

Traumatic Stress and the Psychophysiology of Fear Processing Theoretical models of fear processing and PTSD emphasize the role of the amygdala (Norrholm & Jovanoovic, 2018). A traumatic event represents an unconditioned stimulus that elicits an unconditioned fear response, driven by activation of the sympathetic nervous system. While some traumatic events are repetitive, a single event is sufficient to elicit a

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c­ onditioned physiological and emotional fear response even in the context of overt safety. For example, a combat trauma survivor may have an extreme physiological reaction to fireworks, despite the absence of an imminent threat or danger. Psychophysiology and neuroimaging research demonstrate heightened amygdala activation in response to trauma cues in PTSD (Shin, Rauch, & Pitman, 2006). In addition, hyporeactivity in the medial prefrontal cortex (mPFC) inhibits amygdala activation in participants with PTSD, along with smaller volume and decreased functionality of the hippocampus (Shin et al., 2006). Without inhibition from the mPFC, hyperactivity in the amygdala creates the fear and anxiety symptoms in PTSD (Davis, 1992). The “learning” conceptualization of PTSD proposes that the conditioned response is ­reinforced by avoidance of trauma cues, preventing extinction through operant conditioning. There is little empirical research to support avoidance as a mechanism inhibiting extinction in PTSD (Lissek & van Meurs, 2015). However, extinction learning in PTSD is not retained, as measured using physiological reactivity on the day following extinction learning. This deficit in retention corresponds to reactivity in the mPFC (Lissek & van Meurs, 2015). Non‐associative learning theories have also been suggested as a means to explain hyperarousal symptoms in PTSD. Individuals with PTSD exhibit poor habituation to repeated stimuli such as loud tones (Pole, 2007). There is also evidence of sensitization in PTSD with enhanced responses following repeated activation, for example, with greater HR responses to repeated loud tones and greater amygdala activation while viewing fearful or neutral faces (Lissek & van Meurs, 2015).

Future Directions Two developing areas for psychophysiological investigation in PTSD are (a) reorientation toward mechanisms of physiological recovery (allostasis) (Ottaviani, 2018) and away from an exclusive focus on reactivity. Recently, Goodman and Griffin (2018) used HR recovery to significantly improve the ability to predict the risk for developing PTSD in a longitudinal design and (b) the use of psychophysiological indices as biomarkers of fear extinction in treatment studies. Clinical trials using a virtual reality environment (Diemer, Muhlberger, Pauli, & Zwanzger, 2014) and startle measurement demonstrate the high sensitivity of psychophysiological measures to treatment response (Griffin, Resick, & Galovski, 2012; Robinson‐Andrew et al., 2014). Pretreatment psychophysiological reactivity also predicts posttreatment outcome (Norrholm et  al., 2016). Finally, measurement and biofeedback using HRV is an effective treatment option for PTSD (Tan, Dao, Farmer, Sutherland, & Gevirtz, 2011; Zucker, Samuelson, Muench, Greenberg, & Gevirtz, 2009). Similarly, targeted training to manage visceral activity elicited by a visual threat allows for the creation of new associations and realignment of fear network response (Norrholm & Jovanoovic, 2018). Application of these strategic interventions represents a potentially transformative focus of new clinical research studies.

Author Biographies Michael G. Griffin, PhD, has research interests in the area of traumatic stress and p ­ osttraumatic stress disorder (PTSD) and biological alterations associated with the disorder. He is interested in studying psychophysiological and psychoendocrine changes in sexual and p ­ hysical assault survivors.

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Brittany F. Goodman, MA, is a doctoral candidate with research interests related to risk ­factors for PTSD development and testing the polyvagal theory in trauma populations. Rebecca E. Chesher, MA, is a doctoral candidate with research interests in PTSD and understanding psychological and physiological factors that occur with the dysregulation of sleep. Natalia M. Kecala, MA, has research interests in peritraumatic reactions in PTSD and how maladaptive coping strategies may be a risk factor for the disorder.

References American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Arlington, VA: American Psychiatric Publishing. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing. Bryant, R. A., Creamer, M., O’Donnell, M., Silove, D., & McFarlane, A. C. (2011). Heart rate after trauma and the specificity of fear circuitry disorders. Psychological Medicine, 41(12), 2573–2580. doi:10.1017/s0033291711000948 Davis, M. (1992). The role of the amygdala in fear‐potentiated startle: Implications for animal models of anxiety. Trends in Pharmacological Sciences, 13, 35–41. Diemer, J., Muhlberger, A., Pauli, P., & Zwanzger, P. (2014). Virtual reality exposure in anxiety disorders: Impact on psychophysiological reactivity. World Journal of Biological Psychiatry, 15, 427–442. Goodman, B. F., & Griffin, M. G. (2018). Prospectively predicting PTSD status with heart rate reactivity and recovery in interpersonal violence survivors. Psychiatry Research, 259, 270–276. doi:10.1016/ j.psychres.2017.10.036 Griffin, M. G., Resick, P. A., & Galovski, T. E. (2012). Does physiologic response to loud tones change following cognitive‐behavioral treatment for posttraumatic stress disorder? Journal of Traumatic Stress, 25, 25–32. Hauschildt, M., Peters, M. J. V., Moritz, S., & Jelinek, L. (2011). Heart rate variability in response to affective scenes in posttraumatic stress disorder. Biological Psychology, 88, 215–222. Hopper, J. W., Spinazzola, J., Simpson, W. B., & van der Kolk, B. A. (2006). Preliminary evidence of parasympathetic influence on basal heart rate in posttraumatic stress disorder. Journal of Psychosomatic Research, 60, 83–90. Insel, T. R., Cuthbert, B. N., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., … Wang, P. (2010). Research Domain Criteria (RDoC): Toward a new classification framework for research on mental disorders. American Journal of Psychiatry, 167, 748–751. doi:10.1176/appi.ajp.2010.09091379 Kardiner, A. (1941). The traumatic neuroses of war. New York, NY: Paul Hoeber. Keary, T. A., Hughes, J. W., & Palmieri, P. A. (2009). Women with posttraumatic stress disorder have larger decreases in heart rate variability during stress tasks. International Journal of Psychophysiology, 73, 257–264. Lang, P. J. (1979). A bio‐informational theory of emotional imagery. Psychophysiology, 16, 495–512. Lissek, S., & van Meurs, B. (2015). Learning models of PTSD: Theoretical accounts and psychobiological evidence. International Journal of Psychophysiology, 98(3, Part 2), 594–605. doi:10.1016/j. ijpsycho.2014.11.006 Norrholm, S. D., & Jovanoovic, T. (2018). Fear processing, psychophysiology, and PTSD. Harvard Review of Psychiatry, 26, 129–141. doi:10.1097/HRP.0000000000000189 Norrholm, S. D., Jovanovic, T., Gerardi, M., Breazeale, K. G., Price, M., Davis, M., … Tuerk, P. W. (2016). Baseline psychophysiological and cortisol reactivity as a predictor of PTSD treatment ­outcome in virtual reality exposure therapy. Behaviour Research and Therapy, 82, 28–37. Ottaviani, C. (2018). Brain‐heart interactions in perseverative cognition. Psychophysiology, 55, e13082. doi:10.1111/psyp.13082

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Pitman, R. K., Orr, S. P., Forgue, D. F., de Jong, J. B., & Claiborn, J. M. (1987). Psychophysiologic assessment of posttraumatic stress disorder imagery in Vietnam combat veterans. Archives of General Psychiatry, 44, 970–975. doi:10.1001/archpsyc.1987.01800230050009 Pole, N. (2007). The psychophysiology of posttraumatic stress disorder: A meta‐analysis. Psychological Bulletin, 133(5), 725–746. doi:10.1037/0033‐2909.133.5.725 Porges, S. W. (2011). The polyvagal theory: Neurophysiological foundations of emotions, attachment, c­ommunication, and self‐regulation. New York, NY: W.W. Norton & Company, Inc. Robinson‐Andrew, E. J., Duval, E. R., Nelson, C. B., Echiverri‐Cohen, A., Giardino, N., Defever, A., … Rauch, S. A. M. (2014). Changes in trauma‐potentiated startle with treatment of posttraumatic stress disorder in combat veterans. Journal of Anxiety Disorders, 28, 358–362. Shin, L. M., Rauch, S. L., & Pitman, R. K. (2006). Amygdala, medial prefrontal cortex, and hippocampal function in PTSD. Annals of the New York Academy of Sciences, 1071, 67–79. doi:10.1196/ annals.1364.007 Tan, G., Dao, T. K., Farmer, L., Sutherland, R. J., & Gevirtz, R. (2011). Heart rate variability (HRV) and posttraumatic stress disorder (PTSD): A pilot study. Applied Psychophysiology and Biofeedback, 36, 27–35. Task Force of the European Society of Cardiology and the North American Society for Pacing and Electrophysiology. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 17, 151–178. doi:10.1111/j.1542‐474X.1996.tb00275.x Yehuda, R., & McFarlane, A. C. (1995). Conflict between current knowledge about posttraumatic stress disorder and its original conceptual basis. American Journal of Psychiatry, 152, 1705–1713. Zucker, T. L., Samuelson, K. W., Muench, F., Greenberg, M. A., & Gevirtz, R. N. (2009). The effects of respiratory sinus arrhythmia biofeedback on heart rate variability and posttraumatic stress ­disorder symptoms: A pilot study. Applied Psychophysiology and Biofeedback, 34, 134–156.

Suggested Reading Ney, L. J., Wade, M., Reynolds, A., Zuj, D. V., Dymond, S., Matthews, A., & Felmingham, K. L. (2018). Critical evaluation of current data analysis strategies for psychophysiological measures of fear conditioning and extinction in humans. International Journal of Psychophysiology, 134, 95–107. Pineles, S. L., & Orr, S. P. (2018). The psychophysiology of PTSD. In C. B. Nemeroff & C. R. Marmar (Eds.), Post‐traumatic stress disorder. Oxford University Press. Pitman, R. K., Rasmusson, A. M., Koenen, K. C., Shin, L. M., Orr, S. P., Gilbertson, M. W., … Liberzon, I. (2012). Biological studies of post‐traumatic stress disorder. Nature Reviews. Neuroscience, 13, 769–787. Porges, S. W. (2001). The polyvagal theory: Phylogenetic substrates of a social nervous system. International Journal of Psychophysiology, 42, 123–146.

Cognitive Reserve Sarah A. Cooley Department of Neurology, School of Medicine, Washington University in Saint Louis, St. Louis, MO, USA

Introduction The relationship between neuropathology and clinical presentation is imperfect. An ­often‐cited postmortem study of older individuals reported substantial Alzheimer’s pathology in the brain tissue of individuals who did not have a corresponding degree of cognitive impairment prior to death (Katzman et al., 1988). The concept of cognitive reserve (CR) was introduced as an important explanation for this disconnect in pre‐ and postmortem disease phenotypes. The following chapter reviews the theoretical basis of CR and putative neural mechanisms underlying the construct. Potential pathways to enhance CR throughout the lifespan are discussed.

Brain Reserve Versus Cognitive Reserve Katzman et al. (1988) reported greater brain weight and neuronal cell counts in cognitively normal individuals with neuropathological evidence of Alzheimer’s disease (AD) compared with cognitively impaired individuals with a similar degree of neuropathology. This observation became the basis for the “passive” model of reserve, otherwise known as brain reserve (Satz, 1993). The brain reserve model highlights the importance of premorbid brain structure integrity, which naturally varies across individuals. Those with higher reserve (more robust neuronal integrity) prior to an injury or onset of disease are less likely to express symptoms compared with individuals with lower reserve. The brain reserve model is often referenced as a quantitative model because biological measures of brain integrity are summed to calculate a quantitative index. For example, neuronal/synapse count (Wilson et al., 2013), head circumference (Perneczky et al., 2010), and brain/intracranial volume (ICV) (Sumowski et al., 2013) have been utilized as markers of brain reserve. Not all studies report significant associations

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between these markers and clinical outcomes (Jenkins, Fox, Rossor, Harvey, & Rossor, 2000; Tate et al., 2011), raising concern that additional factors contribute to individual outcomes despite similar levels of neuropathology. CR was proposed as an alternative model to brain reserve. CR is described as an “active” model that emphasizes brain function (versus brain structure) and dynamic versus static mechanisms (Stern, 2002). Similar to brain reserve, a person with high CR would be expected to perform better on testing or have a better clinical outcome compared with an individual with lower CR at any given level of neuropathology. Importantly, when compared with those with low CR, individuals with higher CR experience a much faster disease progression after the expression of initial symptoms. These results suggest that a critical threshold governs disease manifestation, such that clinical evidence of severe cognitive dysfunction or dementia is delayed until the threshold of pathology has been met (Hall et al., 2009). CR is quantified using proxy measures including education (Stern et  al., 1994), occupational attainment (Stern et al., 1994), premorbid IQ (Armstrong et al., 2012), literacy (Manly, Schupf, Tang, & Stern, 2005), leisure activities (Scarmeas, Levy, Tang, & Stern, 2001), social networks (Bennett, Schneider, Tang, Arnold, & Wilson, 2006), and bilingualism (Gollan, Salmon, Montoya, & Galasko, 2011). Nucci, Mapelli, and Mondini (2012) demonstrated additional gain using an aggregate measure of multiple CR proxies, referred to as the Cognitive Reserve Index.

Evidence for CR Stern et al. (1994) conducted one of the first studies of CR and risk of AD in a large group of older adults. Results from this study indicated that individuals with low CR ( ε3 > ε2), the magnitude of the association between the ε4 allele and AD risk is weaker in these groups compared with Caucasians, suggesting that other genetic or environmental factors may be contributory (Farrer et al., 1997). APOE encodes a lipid transporter that is expressed throughout the brain and the liver. The ApoE protein binds to low‐density lipoprotein (LDL) receptors. The E4 isoform of ApoE has lower stability than E3 and E2, making it more likely to form destabilized protein conformations (Hatters, Peters‐Libeu, & Weisgraber, 2006). This destabilized conformation contributes to altered lipid binding, protein misfolding, and protein aggregation, suggesting potential mechanisms for neurodegenerative processes (Hatters et al., 2006). The Wiley Encyclopedia of Health Psychology: Volume 1: Biological Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editor: Robert H. Paul. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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AD is associated with profound disturbance in cognition, with a clinical diagnosis of ­probable AD requiring deficits in two or more cognitive domains. Cognition progressively declines over the course of the disease, resulting in significant functional impairments. Elucidating how APOE modulates cognition and brain integrity can inform our understanding of the pathological progression of AD. Furthermore, identifying neuropsychological and neuroimaging phenotypes in relation to APOE status may help identify novel targets for prevention and treatment efforts. It is particularly important to study the effects of APOE across the lifespan, as the magnitude and direction of its effects on cognition and brain integrity differ from young adulthood through older age. Thus, we aim to provide a ­comprehensive overview of the relationship between APOE, cognition, and neuroimaging findings across the human lifespan.

APOE Genotype and Cognition Given the profound impact of AD on episodic memory, studies of APOE and cognitive a­ bilities have naturally focused on this aspect of cognitive performance. However, accumulating ­evidence suggests that APOE may also impact executive functions, processing speed, and ­general intellectual abilities (Wisdom, Callahan, & Hawkins, 2011).

Young Adulthood Although APOE ε4 is associated with higher risk of AD, it may actually be associated with better cognitive performance earlier in the lifespan. Fetuses carrying the ε4 allele are less likely to be stillborn (Becher, Keeling, McIntosh, Wyatt, & Bell, 2006), while APOE ε2 has been associated with higher risk of perinatal death. Infant APOE ε4 carriers also have higher scores on an index of mental development at 24 months compared with ε2 and ε3 carriers (Wright et al., 2003). These findings suggest that the ApoE E4 isoform may exert a beneficial effect very early in brain development. During younger adulthood, commonly defined as ages 18–39 years, ε4 carriers outperform noncarriers across a range of cognitive tests. For example, young adult ε4 carriers (aged 22.8 ± 4) have better episodic memory for a word list than ε3 or ε2 carriers (Mondadori et al., 2007). Among younger individuals (aged 24.7 ± 5.4) recovering from traumatic brain injury, APOE ε4 carriers score higher on standardized neuropsychological tests of attention, executive functioning, and episodic memory encoding (Han et al., 2007). Young ε4 carriers also have higher IQs (Yu, Lin, Chen, Hong, & Tsai, 2000), better verbal fluency (Alexander et al., 2007), and greater educational achievement (Hubacek et al., 2001) than noncarriers. Although not all studies of young adults find differences between APOE ε4 carriers and noncarriers, collectively, the literature suggests that the ε4 allele may be advantageous during young adulthood. Han and Bondi have proposed that the effects of APOE may be an example of antagonistic pleiotropy (Han et al., 2007). Antagonistic pleiotropy is an evolutionary biology term referring to alleles that have differential effects on fitness at different points in the lifespan. Thus, APOE may confer a benefit during young adulthood but may be deleterious during older adulthood. It remains unclear when APOE ε4 carriers begin to show cognitive impairment relative to noncarriers. Studying middle‐aged individuals can shed light on the transition of the effects of APOE from young adulthood to older age.

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Middle Adulthood Studies investigating the effects of APOE on cognition in middle age have yielded mixed results. This variability may be partially attributed to differing demographic characteristics and definitions of middle age. For instance, a baseline assessment of 452 middle‐aged participants in the Wisconsin Registry for Alzheimer’s Prevention (WRAP)—all of whom have at least one parent with AD—found no association between APOE and neuropsychological performance (Sager, Hermann, & La Rue, 2005). Similarly, the PATH Through Life project cohort of middle‐aged adults (N  =  6,560) reported no effect of APOE on cognition despite age‐related declines in episodic memory, working memory, and processing speed in the same cohort (Jorm et al., 2007). In contrast to these null findings, some evidence suggests that middle‐aged ε4 carriers may show subtle differences in cognitive performance. No clear pattern of affected cognitive domains has emerged. Middle‐aged ε4 carriers score more poorly on learning and memory tasks, verbal delayed recall, sustained attention, and working memory (Caselli et  al., 2001; Flory, Manuck, Ferrell, Ryan, & Muldoon, 2000). Whether there is a dose‐dependent relationship of the ε4 and cognition during middle age is an area of ongoing research. The age at which APOE ε4 may transition from benefitting cognition to being detrimental remains an area of active research. Large‐scale, longitudinal studies are needed to assess the trajectories of cognitive change in APOE ε4 carriers and noncarriers. In one such study, ε4 homozygotes began declining on verbal memory measures in their 50s, while noncarriers maintained their memory abilities until their 70s (Caselli et al., 2011). Another longitudinal study found that for APOE ε4 carriers younger than 57 years, verbal memory actually increased over the 4‐year follow‐up interval. In contrast, ε4 carriers older than 57 experienced a significant decline in immediate recall abilities (Jochemsen, Muller, van der Graaf, & Geerlings, 2012). Together, these studies suggest that APOE ε4 may begin to have a detectable adverse effect on cognition in the 50s. The changes in underlying neurobiological processes may begin even earlier, making a research into early biomarkers for AD a fertile area for future research.

Older Adulthood The deleterious effects of APOE ε4 on cognition in older adulthood are robust. The APOE ε4 allele negatively affects several cognitive domains in a dose‐dependent manner. The most recent meta‐analysis to answer this question included 77 studies of 40,972 cognitively healthy adults (Wisdom et al., 2011). It assessed the effects of APOE on global cognitive ability and seven separate cognitive domains: attention, episodic memory, executive functioning, perceptual speed, primary memory, verbal ability, and visuospatial skill. APOE ε4 carriers performed significantly worse on measures of global cognitive ability, episodic memory, executive functioning, and ­perceptual speed compared with non‐ε4 carriers but did not differ on attention or visuospatial functioning. The largest effect sizes were observed for episodic memory. Furthermore, these effects were moderated by age, with older ε4 carriers having larger deficits in episodic memory and global cognitive ability compared with noncarriers. These findings indicate that the adverse relationship between APOE ε4 and cognition may be magnified during the aging process.

Mild Cognitive Impairment and Alzheimer’s Disease Presence of the APOE ε4 allele is known to increase risk of developing AD, but it may also affect progression of the disease. Mild cognitive impairment (MCI) is a clinical diagnosis given

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to individuals who have significant cognitive impairment in one or more domains but are able to maintain independence in their activities of daily living. People with MCI may have impairments in memory (amnestic MCI) or another cognitive domain (non‐amnestic MCI). The factors that increase the risk of conversion from MCI to dementia are as yet uncertain, although they may include cardiovascular risk factors, depressed mood, type 2 diabetes, and traumatic brain injury (Cooper, Sommerlad, Lyketsos, & Livingston, 2015). A 2011 meta‐analysis of prospective cohort studies of individuals with MCI found that ­carriers of a single APOE ε4 allele were 2.29 times more likely to convert, while ε4 homozygotes were 3.94 times to convert to AD‐type dementia compared with noncarriers (Elias‐Sonnenschein, Viechtbauer, Ramakers, Verhey, & Visser, 2011). Compared with noncarriers, ε4 carriers progressed more quickly and at an earlier age than noncarriers.

Summary of Neuropsychological Findings Taken together, the available evidence supports the idea that the APOE ε4 allele is associated with better cognition in young adulthood but poorer cognition later in life. Not only does APOE ε4 increase a risk for dementia, but also it is associated with faster progression of age‐ related cognitive impairment. Longitudinal studies indicate that cognition may begin to decline during middle age in ε4 carriers, maintaining a steeper trajectory of decline into older adulthood. Thus, efforts to prevent or delay the progression of AD pathology likely need to target middle‐aged individuals, before gross decline in cognitive ability manifests. Additionally, understanding the interaction of APOE with protective factors such as cognitive reserve (e.g., cognitive resilience in the face of neuropathological damage) will be an important ­avenue for future research.

Neuroimaging: Relationships Between APOE and Brain Integrity There has been a surge of imaging genetics studies over the past two decades, including studies employing structural magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), MRI arterial spin labeling (ASL), and positron emission tomography (PET). These neuroimaging modalities shed light on gross brain morphology, activity of the brain during memory tasks or while at rest, WM microstructural properties, patterns of regional blood flow, and presence of AD neuropathology, respectively. Neuroimaging represents a powerful noninvasive method of detecting subtle differences associated with APOE genotype that may precede detectable cognitive impairment.

Magnetic Resonance Imaging Regional brain volumes Development of AD is associated with significant regional brain atrophy, with the most ­pronounced volume loss in the medial temporal lobes (MTL). The majority of studies investigating how APOE genotype impacts brain volume have thus focused on the hippocampus and other MTL structures. Homozygous ε4 AD patients have smaller volumes of the right ­hippocampus, amygdala, and entorhinal cortex compared with patients who are ε3/ε4 and ε3/ε3 (Lehtovirta et al., 1995) and may show greater atrophy over time. In healthy older adults, longitudinal studies have consistently reported that the rate of ­hippocampal volume loss is greater in ε4 carriers than noncarriers. In at least two of these

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s­ tudies (Cohen, Small, Lalonde, Friz, & Sunderland, 2001; Crivello et al., 2010), however, the magnitude of the decline in hippocampal volume was not related to any cognitive changes over the same interval. In healthy, middle‐aged individuals, findings are equivocal. One sample of 60–64‐year‐olds showed no volumetric differences associated with APOE genotype (Cherbuin et  al., 2008). In contrast, another report demonstrated smaller hippocampal ­volumes in ε4 ­carriers younger than 65, with no differences in individuals older than 65 (Lind et al., 2006). Collectively, literature suggests that APOE seems to moderate hippocampal volume loss during aging. APOE ε4‐associated hippocampal atrophy may be related to neuronal cell death, loss of neuropil, or degeneration of fibers terminating in the hippocampus. Furthermore, it remains unclear whether hippocampal atrophy drives the higher AD risk among ε4 carriers or is a by‐product of some other neurodegenerative process. Task‐based functional MRI (fMRI) fMRI measures blood oxygenation level‐dependent (BOLD) response in brain tissue. Higher BOLD response reflects increased neural activity in a given region. Comparing BOLD response while performing a cognitive task and during a control condition allows inference about APOE‐related differences in regional brain activation during the specific tasks. fMRI studies using episodic memory paradigms are equivocal. Some report higher (Bondi, Houston, Eyler, & Brown, 2005), while others report lower (Fleisher, Podraza, et al., 2009) BOLD signal in ε4 carriers. Differences in the memory tasks, participant age, and family history of AD, among other factors, may be contributing to the inconsistencies among the reports. Imaging studies focusing on healthy, young adult ε4 carriers typically report higher MTL ­activation during memory tasks (Filippini et  al., 2009). Additionally, a lifespan study found overactivation in young ε4 carriers compared with noncarriers, while healthy older adults showed a pattern of task‐based deactivation (Filippini et al., 2011). This evidence seems to suggest that younger APOE ε4 carriers depend on compensatory mechanisms to maintain performance, while early pathology in older ε4 carriers leads to lower recruitment of relevant brain areas. Healthy, adult ε4 carriers also have higher activation compared with noncarriers on tasks requiring working memory (Wishart et al., 2006), verbal fluency (Smith et al., 2002), semantic categorization (Persson et al., 2008), and executive control (Trachtenberg et al., 2012). Thus, differential patterns of activation extend beyond the MTL to include frontal, parietal, and posterior cingulate regions. One interpretation of such findings is that ε4 carriers must recruit a broader network of brain regions to maintain performance. Longitudinal studies are needed to assess APOE ε4‐related change in activation patterns over time. Functional brain networks In addition to probing task‐related differences in BOLD activation, fMRI can be used to ­characterize patterns of activity when the brain is at rest. During resting‐state fMRI, synchronous patterns of coactivity between discrete brain regions reliably emerge. These so‐called resting‐state networks overlap with functionally relevant regions known to be involved in ­visual processing, motor functioning, auditory processing, attention, and memory. Resting‐ state networks are neuronal in origin, and functional connectivity within these networks reflects underlying structural connectivity (Greicius, Supekar, Menon, & Dougherty, 2009). MCI and AD involve loss of functional brain connectivity in several resting‐state networks. The best‐documented differences are in the default mode network (DMN), which includes the posterior cingulate, medial prefrontal, and inferior parietal cortices, and the hippocampus. DMN activity is anticorrelated with activity of brain regions recruited during cognitively demanding tasks. Integrity and activity of this network is thought to support episodic memory

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and self‐referential thought. A limited number of studies have examined resting‐state ­functional connectivity in APOE ε4 carriers. Two studies of healthy young and middle‐aged adults have reported that ε4 carriers have higher DMN connectivity (Filippini et  al., 2009; Fleisher, Sherzai, et al., 2009), but studies in healthy elderly and early AD show lower connectivity in ε4 carriers. This includes functional disconnection between the hippocampus and posterior cingulate cortex, precuneus, dorsal anterior cingulate cortex, and middle temporal cortex (Sheline et  al., 2010). Disrupted functional connectivity is more pronounced in older ­individuals and is observed even in the absence of amyloid plaques (Sheline et al., 2010). More research is needed to delineate the effects of APOE genotype on functional connectivity of the DMN and other resting‐state networks. Characterizing longitudinal changes in ­network connectivity and dynamic interactions between networks will inform our understanding of connectivity loss with AD progression. White matter integrity Although early brain imaging studies focused on gray matter pathology in AD patients, the disease is also associated with profound changes in WM (Gunning‐Dixon & Raz, 2000). DTI is a method of imaging the diffusion of water in brain tissue that provides information about the direction of WM tracts as well as their microstructural properties. Thus, DTI metrics may be more sensitive indices of WM disruption than WM hyperintensities. Compared with healthy older adults, AD patients show a more posterior profile of WM abnormalities, including alterations in the parahippocampal gyrus, temporal WM, splenium of the corpus callosum, and posterior cingulum (see Chua, Wen, Slavin, & Sachdev, 2008, for review). WM microstructural alterations in APOE ε4 carriers appear to parallel the differences seen in AD patients. ε4 carriers have lower fractional anisotropy (FA) and higher mean diffusivity (MD), measures of microstructural disorganization, in the MTL and other regions (Honea, Vidoni, Harsha, & Burns, 2009). Preliminary evidence suggests that these microstructural differences are connected to cognitive abilities. For example, one study of 92 healthy older adults found that higher FA in the entorhinal cortex was associated with better memory ­performance in ε4 but not ε3 carriers (Westlye et  al., 2012). Integrating DTI with other ­neuroimaging modalities will provide a more comprehensive description of brain network integrity and is an important area for future research. Cerebral perfusion ASL is a noninvasive method of quantifying cerebral blood flow with MRI. Deficits in cerebral perfusion increase with AD progression and serve as an index of cortical dysfunction (Alsop, Dai, Grossman, & Detre, 2010). Findings related to cerebral perfusion in APOE ε4 carriers have been mixed. For instance, a small study (N = 24) found that ε4 carriers have higher cerebral perfusion at rest but attenuated changes in MTL during memory encoding (Fleisher, Podraza, et al., 2009). Another study found a similar pattern of ε4‐associated resting hyperperfusion in MCI patients, while ε4 carriers with AD had lower perfusion than ε3 carriers (Kim et al., 2013). This is consistent with an interpretation that ε4 carriers have greater metabolic demands at rest to compensate for their functional impairments early in the disease process, while this compensatory mechanism collapses in the face of more significant pathological burden in AD.

Positron Emission Tomography (PET) AD neuropathology comprises two main processes: extracellular deposition of beta‐amyloid (Aβ) plaques and intracellular accumulation of hyperphosphorylated tau protein in neurofibrillary tangles. Traditionally, these hallmarks of AD pathology were only detectable upon

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­ ostmortem examination. The development of novel PET ligands has aided understanding p of  the progression of AD neuropathology in vivo. For example, Pittsburgh compound B (11C‐PiB) binds to Aβ in the cortex, permitting visualization of deposition of Aβ plaques. In healthy older adults and patients with MCI or AD, there is a gene–dose relationship between number of APOE ε4 alleles and higher 11C‐PiB retention in the frontal, temporal, and parietal cortices (Drzezga et al., 2009). Furthermore, longitudinal imaging studies report that older ε4 carriers have a greater rate of PiB retention compared with ε4 noncarriers over a 2–3 year follow‐up interval (Villemagne et al., 2011). More recently, development of a novel tau ligand (18F‐THK523) has permitted in vivo imaging of neurofibrillary tangles. The effects of APOE genotype on 18F‐THK523 retention have not yet been explored, but this remains an exciting area for future research. In addition to in vivo characterization of hallmarks of AD neuropathology, PET can be used to assess glucose metabolism using a fludeoxyglucose (18F‐FDG) tracer. Patients with AD have significantly lower regional cerebral glucose metabolism, and lower 18F‐FDG uptake may predict which patients with MCI will convert to AD (Chételat et  al., 2003). Among AD patients, APOE ε4 carriers consistently show lower cerebral glucose metabolism compared with non‐ε4 carriers, particularly within temporal, frontal, parietal, and posterior cingulate cortical regions (Lehmann et al., 2014). Differences in cerebral glucose metabolism are observed before the onset of cognitive symptoms, with healthy older ε4 carriers showing hypometabolism in the posterior cingulate, temporal, parietal, and prefrontal ­cortices (Langbaum et al., 2010). In summary, APOE ε4 carriers show neuropathological signs characteristic of AD even before the onset of clinical symptoms, including higher deposition of Aβ plaques visualized via PET PiB imaging and cerebral hypometabolism in frontal, temporal, parietal, and posterior cingulate cortices.

Summary of Neuroimaging Findings Collectively, neuroimaging literature suggests a role for APOE ε4 in brain integrity t­ hroughout the lifespan. Consistent with evidence that APOE ε4 is associated with better cognition early in life before becoming deleterious in older adulthood, younger ε4 carriers have higher task‐ based activation and resting‐state functional connectivity in studied brain networks. Older ε4 carriers show the opposite patterns, including lower functional connectivity, smaller MTL volumes, disorganization of WM microstructure, cerebral hypometabolism, and greater deposition of Aβ plaques in the regions of interest. Ongoing longitudinal imaging studies will soon be able to answer important questions about the trajectory and magnitude of age‐related changes that may serve as neuroimaging biomarkers of risk for AD.

Summary and Conclusions Characterizing the effects of APOE, the best‐established genetic risk factor for AD on ­neuropsychological functioning and brain health remains an area of active research. APOE appears to have a differential role on cognition and underlying brain integrity across the lifespan. Younger ε4 carriers, albeit exhibiting better cognitive performance, seem to require greater recruitment of functional brain resources. In older age, APOE ε4 becomes deleterious and is associated with cognitive decline as well as poorer brain integrity. It is possible that the ε4 allele was evolutionarily conserved because of its beneficial effects early in the lifespan; with humans today living decades longer than our ancestors, the adverse effects of APOE are

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now observed during aging. Alternatively, interactions between APOE and other AD risk ­factors (both genetic and environmental) may explain the conflicting patterns of findings across the lifespan. Given that older age is the biggest risk factor for AD, general age effects and inclusion of individuals in the early stages of age‐related cognitive decline may confound the studies of older adults. Studying middle age as a possible transitional period will provide a rich source of information about the effects of APOE on cognition. By investigating the additive or synergistic effects of APOE with other genetic risk factors for AD, we may advance our understanding of the complex interplay between genes, brain integrity, and cognitive health. Furthermore, using multimodal imaging to create integrated descriptions of brain integrity will increase our ability to predict who is at greatest risk for MCI and AD. This is essential to identify those who may benefit most from early prevention and treatment efforts, reducing the global burden of AD.

Author Biographies Laura E. Korthauer, MA, is a PhD candidate in the Clinical Psychology Program at the University of Wisconsin‐Milwaukee, with an emphasis in clinical neuropsychology. Her research focuses on genetic contributions to structural and functional brain networks that may serve as preclinical biomarkers of age‐related cognitive decline. Ira Driscoll, PhD, is an associate professor of psychology and neuroscience at the University of Wisconsin‐Milwaukee. Dr. Driscoll’s primary research focus revolves around brain changes as early predictors of cognitive deficits and dementia and the role for hormones and genetic background as modulators of age‐related cognitive decline.

References Alexander, D. M., Williams, L. M., Gatt, J. M., Dobson‐Stone, C., Kuan, S. A., Todd, E. G., … Gordon, E. (2007). The contribution of apolipoprotein E alleles on cognitive performance and dynamic neural activity over six decades. Biological Psychology, 75(3), 229–238. doi:10.1016/j.biopsycho. 2007.03.001 Alsop, D. C., Dai, W., Grossman, M., & Detre, J. A. (2010). Arterial spin labeling blood flow MRI: Its role in the early characterization of Alzheimer’s disease. Journal of Alzheimer’s Disease, 20(3), 871– 880. doi:10.3233/JAD‐2010‐091699 Alzheimer’s Association. (2015). 2015 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 11(3), 332–384. Becher, J. C., Keeling, J. W., McIntosh, N., Wyatt, B., & Bell, J. (2006). The distribution of apolipoprotein E alleles in Scottish perinatal deaths. Journal of Medical Genetics, 43(5), 414–418. doi:10.1136/ jmg.2005.033936 Bertram, L., McQueen, M. B., Mullin, K., Blacker, D., & Tanzi, R. E. (2007). Systematic meta‐analyses of Alzheimer disease genetic association studies: The AlzGene database. Nature Genetics, 39(1), 17–23. doi:10.1038/ng1934 Bondi, M. W., Houston, W. S., Eyler, L. T., & Brown, G. G. (2005). fMRI evidence of compensatory mechanisms in older adults at genetic risk for Alzheimer disease. Neurology, 64(3), 501–508. doi:10.1212/01.WNL.0000150885.00929.7E Caselli, R. J., Dueck, A. C., Locke, D. E., Hoffman‐Snyder, C. R., Woodruff, B. K., Rapcsak, S. Z., & Reiman, E. M. (2011). Longitudinal modeling of frontal cognition in APOE ε4 homozygotes, heterozygotes, and noncarriers. Neurology, 76(16), 1383–1388. doi:10.1212/WNL. 0b013e3182167147

APOE as a Risk Factor for Age‐Related Cognitive Impairment

307

Caselli, R. J., Osborne, D., Reiman, E. M., Hentz, J. G., Barbieri, C. J., Saunders, A. M., … Alexander, G. E. (2001). Preclinical cognitive decline in late middle‐aged asymptomatic apolipoprotein E‐e4/4 homozygotes: A replication study. Journal of the Neurological Sciences, 189(1‐2), 93–98. Cherbuin, N., Anstey, K. J., Sachdev, P. S., Maller, J. J., Meslin, C., Mack, H. A., … Easteal, S. (2008). Total and regional gray matter volume is not related to APOE*E4 status in a community sample of middle‐aged individuals. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 63(5), 501–504. Chételat, G., Desgranges, B., de la Sayette, V., Viader, F., Eustache, F., & Baron, J. C. (2003). Mild cognitive impairment: Can FDG‐PET predict who is to rapidly convert to Alzheimer’s disease? Neurology, 60(8), 1374–1377. Chua, T. C., Wen, W., Slavin, M. J., & Sachdev, P. S. (2008). Diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease: A review. Current Opinion in Neurology, 21(1), 83–92. doi:10.1097/WCO.0b013e3282f4594b Cohen, R. M., Small, C., Lalonde, F., Friz, J., & Sunderland, T. (2001). Effect of apolipoprotein E genotype on hippocampal volume loss in aging healthy women. Neurology, 57(12), 2223–2228. Cooper, C., Sommerlad, A., Lyketsos, C. G., & Livingston, G. (2015). Modifiable predictors of dementia in mild cognitive impairment: A systematic review and meta‐analysis. American Journal of Psychiatry, 172(4), 323–324. Crivello, F., Lemaître, H., Dufouil, C., Grassiot, B., Delcroix, N., Tzourio‐Mazoyer, N., … Mazoyer, B. (2010). Effects of ApoE‐epsilon4 allele load and age on the rates of grey matter and hippocampal volumes loss in a longitudinal cohort of 1186 healthy elderly persons. NeuroImage, 53(3), 1064– 1069. doi:10.1016/j.neuroimage.2009.12.116 Drzezga, A., Grimmer, T., Henriksen, G., Mühlau, M., Perneczky, R., Miederer, I., … Kurz, A. (2009). Effect of APOE genotype on amyloid plaque load and gray matter volume in Alzheimer disease. Neurology, 72(17), 1487–1494. doi:10.1212/WNL.0b013e3181a2e8d0 Elias‐Sonnenschein, L. S., Viechtbauer, W., Ramakers, I. H., Verhey, F. R., & Visser, P. J. (2011). Predictive value of APOE‐ε4 allele for progression from MCI to AD‐type dementia: A meta‐analysis. Journal of Neurology, Neurosurgery, and Psychiatry, 82(10), 1149–1156. doi:10.1136/ jnnp.2010.231555 Farrer, L. A., Cupples, L. A., Haines, J. L., Hyman, B., Kukull, W. A., Mayeux, R., … van Duijn, C. M. (1997). Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease: A meta‐analysis. Journal of the American Medical Association, 278(16), 1349–1356. Filippini, N., Ebmeier, K. P., MacIntosh, B. J., Trachtenberg, A. J., Frisoni, G. B., Wilcock, G. K., … Mackay, C. E. (2011). Differential effects of the APOE genotype on brain function across the lifespan. NeuroImage, 54(1), 602–610. doi:10.1016/j.neuroimage.2010.08.009 Filippini, N., MacIntosh, B. J., Hough, M. G., Goodwin, G. M., Frisoni, G. B., Smith, S. M., … Mackay, C. E. (2009). Distinct patterns of brain activity in young carriers of the APOE‐epsilon4 allele. Proceedings of the National Academy of Sciences of the United States of America, 106(17), 7209–7214. doi:10.1073/pnas.0811879106 Fleisher, A. S., Podraza, K. M., Bangen, K. J., Taylor, C., Sherzai, A., Sidhar, K., … Buxton, R. B. (2009). Cerebral perfusion and oxygenation differences in Alzheimer’s disease risk. Neurobiology of Aging, 30(11), 1737–1748. doi:10.1016/j.neurobiolaging.2008.01.012 Fleisher, A. S., Sherzai, A., Taylor, C., Langbaum, J. B., Chen, K., & Buxton, R. B. (2009). Resting‐state BOLD networks versus task‐associated functional MRI for distinguishing Alzheimer’s disease risk groups. NeuroImage, 47(4), 1678–1690. doi:10.1016/j.neuroimage.2009.06.021 Flory, J. D., Manuck, S. B., Ferrell, R. E., Ryan, C. M., & Muldoon, M. F. (2000). Memory performance and the apolipoprotein E polymorphism in a community sample of middle‐aged adults. American Journal of Medical Genetics, 96(6), 707–711. Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2009). Resting‐state functional connectivity reflects structural connectivity in the default mode network. Cerebral Cortex, 19(1), 72–78. doi:10.1093/cercor/bhn059

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Laura E. Korthauer and Ira Driscoll

Gunning‐Dixon, F. M., & Raz, N. (2000). The cognitive correlates of white matter abnormalities in normal aging: A quantitative review. Neuropsychology, 14(2), 224–232. Han, S. D., Drake, A. I., Cessante, L. M., Jak, A. J., Houston, W. S., Delis, D. C., … Bondi, M. W. (2007). Apolipoprotein E and traumatic brain injury in a military population: Evidence of a neuropsychological compensatory mechanism? Journal of Neurology, Neurosurgery & Psychiatry, 78(10), 1103–1108. doi:10.1136/jnnp.2006.108183 Hatters, D. M., Peters‐Libeu, C. A., & Weisgraber, K. H. (2006). Apolipoprotein E structure: Insights into function. Trends in Biochemical Sciences, 31(8), 445–454. doi:10.1016/j.tibs.2006.06.008 Honea, R. A., Vidoni, E., Harsha, A., & Burns, J. M. (2009). Impact of APOE on the healthy aging brain: A voxel‐based MRI and DTI study. Journal of Alzheimer’s Disease, 18(3), 553–564. doi:10.3233/JAD‐2009‐1163 Hubacek, J. A., Pitha, J., Skodová, Z., Adámková, V., Lánská, V., & Poledne, R. (2001). A possible role of apolipoprotein E polymorphism in predisposition to higher education. Neuropsychobiology, 43(3), 200–203. Jochemsen, H. M., Muller, M., van der Graaf, Y., & Geerlings, M. I. (2012). APOE ε4 differentially influences change in memory performance depending on age. The SMART‐MR study. Neurobiology of Aging, 33(4), 832. e815–822. doi:10.1016/j.neurobiolaging.e2011.07.016 Jorm, A. F., Mather, K. A., Butterworth, P., Anstey, K. J., Christensen, H., & Easteal, S. (2007). APOE genotype and cognitive functioning in a large age‐stratified population sample. Neuropsychology, 21(1), 1–8. https://doi.org/10.1037/0894‐4105.21.1.1 Kim, S. M., Kim, M. J., Rhee, H. Y., Ryu, C. W., Kim, E. J., Petersen, E. T., & Jahng, G. H. (2013). Regional cerebral perfusion in patients with Alzheimer’s disease and mild cognitive impairment: Effect of APOE epsilon4 allele. Neuroradiology, 55(1), 25–34. doi:10.1007/s00234‐012‐1077‐x Langbaum, J. B., Chen, K., Caselli, R. J., Lee, W., Reschke, C., Bandy, D., … Reiman, E. M. (2010). Hypometabolism in Alzheimer‐affected brain regions in cognitively healthy Latino individuals carrying the apolipoprotein E epsilon4 allele. Archives of Neurology, 67(4), 462–468. doi:10.1001/ archneurol.2010.30 Lehmann, M., Ghosh, P. M., Madison, C., Karydas, A., Coppola, G., O’Neil, J. P., … Rabinovici, G. D. (2014). Greater medial temporal hypometabolism and lower cortical amyloid burden in ApoE4‐ positive AD patients. Journal of Neurology, Neurosurgery & Psychiatry, 85(3), 266–273. doi:10.1136/jnnp‐2013‐305858 Lehtovirta, M., Laakso, M. P., Soininen, H., Helisalmi, S., Mannermaa, A., Helkala, E. L., … Hartikainen, P. (1995). Volumes of hippocampus, amygdala and frontal lobe in Alzheimer patients with different apolipoprotein E genotypes. Neuroscience, 67(1), 65–72. Lind, J., Larsson, A., Persson, J., Lars‐GöranNilsson, M. I., LarsBäckman, R. A., Cruts, M., … Nyberg, L. (2006). Reduced hippocampal volume in non‐demented carriers of the apolipoprotein E ɛ4: Relation to chronological age and recognition memory. Neuroscience Letters, 396(1), 23–27. https://doi.org/10.1016/j.neulet.2005.11.070 Christian R. A. Mondadori, Dominique J. ‐F. deQuervain, Andreas Buchmann, Henrietta Mustovic, M. Axel Wollmer, Conny F. Schmidt, Peter Boesiger, Christoph Hock, Roger M. Nitsch, Andreas Papassotiropoulos, Katharina Henke (2007). Better memory and neural efficiency in young apolipoprotein E ε4 carriers. Cerebral Cortex, 17(8): 1934–1947, https://doi.org/10.1093/cercor/bhl103 Persson, J., Lind, J., Larsson, A., Ingvar, M., Sleegers, K., Van Broeckhoven, C., … Nyberg, L. (2008). Altered deactivation in individuals with genetic risk for Alzheimer’s disease. Neuropsychologia, 46(6), 1679–1687. doi:10.1016/j.neuropsychologia.2008.01.026 Sager, M. A., Hermann, B., & La Rue, A. (2005). Middle‐aged children of persons with Alzheimer’s disease: APOE genotypes and cognitive function in the Wisconsin Registry for Alzheimer’s Prevention. Journal of Geriatric Psychiatry and Neurology, 18(4), 245–249. doi:10.1177/ 0891988705281882 Sheline, Y. I., Morris, J. C., Snyder, A. Z., Price, J. L., Yan, Z., D’Angelo, G., … Mintun, M. A. (2010). APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Aβ42. Journal of Neuroscience, 30(50), 17035–17040. doi:10.1523/JNEUROSCI.3987‐10.2010

APOE as a Risk Factor for Age‐Related Cognitive Impairment

309

Smith, C. D., Andersen, A. H., Kryscio, R. J., Schmitt, F. A., Kindy, M. S., Blonder, L. X., & Avison, M. J. (2002). Women at risk for AD show increased parietal activation during a fluency task. Neurology, 58(8), 1197–1202. Trachtenberg, A. J., Filippini, N., Cheeseman, J., Duff, E. P., Neville, M. J., Ebmeier, K. P., … Mackay, C. E. (2012). The effects of APOE on brain activity do not simply reflect the risk of Alzheimer’s disease. Neurobiology of Aging, 33(3), 618. e611‐618.e613. doi:10.1016/j.neurobiolaging.2010.11.011 Villemagne, V. L., Pike, K. E., Chételat, G., Ellis, K. A., Mulligan, R. S., Bourgeat, P., … Rowe, C. C. (2011). Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease. Annals of Neurology, 69(1), 181–192. doi:10.1002/ana.22248 Westlye, E. T., Hodneland, E., Haász, J., Espeseth, T., Lundervold, A., & Lundervold, A. J. (2012). Episodic memory of APOE ε4 carriers is correlated with fractional anisotropy, but not cortical thickness, in the medial temporal lobe. NeuroImage, 63(1), 507–516. doi:10.1016/j.neuroimage. 2012.06.072 Wisdom, N. M., Callahan, J. L., & Hawkins, K. A. (2011). The effects of apolipoprotein E on non‐ impaired cognitive functioning: A meta‐analysis. Neurobiology of Aging, 32(1), 63–74. doi:10.1016/ j.neurobiolaging.2009.02.003 Wishart, H. A., Saykin, A. J., Rabin, L. A., Santulli, R. B., Flashman, L. A., Guerin, S. J., … McAllister, T. W. (2006). Increased brain activation during working memory in cognitively intact adults with the APOE epsilon4 allele. American Journal of Psychiatry, 163(9), 1603–1610. doi:10.1176/ ajp.2006.163.9.1603 Wright, R. O., Hu, H., Silverman, E. K., Tsaih, S. W., Schwartz, J., Bellinger, D., … Hernandez‐Avila, M. (2003). Apolipoprotein E genotype predicts 24‐month bayley scales infant development score. Pediatric Research, 54(6), 819–825. doi:10.1203/01.PDR.0000090927.53818.DE Yu, Y. W., Lin, C. H., Chen, S. P., Hong, C. J., & Tsai, S. J. (2000). Intelligence and event‐related potentials for young female human volunteer apolipoprotein E epsilon4 and non‐epsilon4 carriers. Neuroscience Letters, 294(3), 179–181.

The Serotonin Transporter‐Linked Polymorphic Region (5‐HTTLPR) Polymorphism, Stress, and Depression Christina Di Iorio, Kimberly Johnson, Daniel Sheinbein, Samantha Kahn, Patrick England, and Ryan Bogdan BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA

Introduction Diathesis–stress theories postulate that genetic vulnerability promotes the development of depression in the context of stress. The intuitive appeal of this theory was supported by twin research by Kendler and colleagues (Kendler et al., 1995) showing that risk for depression was substantially elevated among individuals whose co‐twin had depression, but only if they themselves were recently exposed to a severe stressful life event. However, it was not until 2003 that molecular genetic variation was first associated with vulnerability to the depressogenic effects of stress (Caspi et al., 2003). Specifically, Caspi and colleagues reported evidence that the short allele of the serotonin transporter‐linked polymorphic region (5‐HTTLPR) polymorphism, which had been previously linked to reduced serotonin transporter expression as well as putative intermediate depressive phenotypes (Hariri et al., 2002; Heils et al., 1996; Lesch et al., 1996), confers vulnerability to stress‐related depression. This initial report of a gene × environment (G × E) interaction spurred multiple subsequent replication and extension attempts that have collectively produced conflicting findings with even meta‐analyses reaching opposing conclusions (Duncan & Keller, 2011; Karg, Burmeister, Shedden, & Sen, 2011; Munafo, Durrant, Lewis, & Flint, 2009; Risch et al., 2009; Sharpley, Palanisamy, Glyde, Dillingham, & Agnew, 2014; Taylor & Munafo, 2016; Table 1). These opposing results have led to great controversy within the literature, with divergent interpretations (Caspi, Hariri, Holmes, Uher, &

The Wiley Encyclopedia of Health Psychology: Volume 1: Biological Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editor: Robert H. Paul. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Table 1  Meta‐analyses of the 5‐HTTLPR × stress interaction and depression. Meta‐analysis

Data coding

  Munafo, Durrant, et al. (2009)   15 studies 11,158 participants

Depression: Evaluated main effects No evidence of G × E, OR: 1.16, Depression diagnoses of 5‐HTTLPR and 95% CI: 0.89–1.49 Results did and self‐reported stress as well as their not differ with random effects depression symptoms interaction using models or with different SLE and severity fixed effect model coding Stress: Presence (1) pooling odds ratios or absence (0) of (ORs). Random stressful life event. effect models Also tested 2 or implemented when more stressful life significant events relative to association observed 0–1. Stress included in the context of lifetime trauma between study exposure, heterogeneity maltreatment during childhood Genotype: Short allele dominance, i.e., S carriers (S/S, or S/L) vs. L homozygotes (L/L) Depression: Evaluated main effects No evidence of GxE, OR: 1.01, Depression diagnosis of 5‐HTTLPR and 95% CI: 0.94–1.10 Results did (interview‐based stress as well as their not differ when sexes were diagnosis or >85% interaction using considered separately. Findings on a measure of random effects comparable in dominant and depression model pooling ORs. recessive models symptoms) Also evaluated Stress: Stressful life presence of gene‐ events (i.e., 0, 1, 2, environment ≥3). correlation (rGE). Genotype: Number of Analyses conducted S alleles (additive): 0 with sexes combined (L/L), 1 (S/L), and (14 studies) and 2 (S/S). Also separated (10 studies) compared models of S dominance (i.e., S carriers vs. L homozygotes) or recessive (i.e., S homozygotes vs. L Carriers)

  Risch et al. (2009)   14 studies 14,250 participants

Meta‐analytic method

Conclusion

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Table 1  (Continued ) Meta‐analysis

Data coding

  Karg et al. (2011)   54 studies 40,749 participants

Depression: P values extracted from Depression diagnosis effects reported in and self‐reported each article (using a depression symptoms variety of genotype and severity coding, stress Stress: Childhood measures, and maltreatment, depression measures) specific medical   conditions, and SLE Analyses conducted Genotype: As coded in across all forms of original study stress reported and stratified by stressor type (i.e., childhood maltreatment, specific medical conditions, and SLE), as well as method of stress assessment (i.e., objective, interview, and self‐report).

  Mak, Kong, Mak, Sharma, and Ho (2013)   4 studies 642 participants

Meta‐analytic method

Conclusion

Significant G × E: p  20 geriatric medicine fellows. Her research topics are immunology of aging, successful aging, self‐management of chronic illness (e.g., diabetes, CHF, joint impairment), patient expectations and timelines for outcomes, risks for hospital readmission after elective surgery, and multimorbidity in geriatric populations.

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Jenna Herold Cohen, MS, 5th year, Doctoral Candidate in Clinical Psychology at Rutgers University. Her research interests include factors that aid in adaptive cancer survivorship. Her dissertation will improve knowledge of factors affecting fear of recurrence among patients who have completed treatment for lymphoma. This work is being conducted under the supervision of Howard Leventhal, PhD, with committee members from Memorial Sloan Kettering Cancer Center (Smita Banerjee, PhD) and clinical faculty at Rutgers University.

References Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. Bolman, C., Arwert, T. G., & Völlink, T. (2011). Adherence to prophylactic asthma medication: Habit strength and cognitions. Heart & Lung: The Journal of Acute and Critical Care, 40(1), 63–75. Brandes, K., & Mullan, B. (2014). Can the common‐sense model predict adherence in chronically ill patients? A meta‐analyses. Health Psychology Review, 8, 129–153. Brewer, N. T., Hallman, W. K., & Kipen, H. M. (2008). The symmetry rule: A seven‐year study of symptoms and explanatory labels among Gulf War veterans. Risk Analysis, 28(6), 1737–1748. doi:10.1111/j.1539‐6924.2008.01118.x Brooks, T. L., Leventhal, H., Wolf, M. S., O’Conor, R., Morillo, J., Martynenko, M., … Federman, A. D. (2015). Strategies used by older adults with asthma for adherence to inhaled corticosteroids. Journal of General Internal Medicine, 29(11), 1506–1512. Bunde, J., & Martin, R. (2006). Depression and prehospital delay in the context of myocardial infarction. Psychosomatic Medicine, 68(1), 51–57. Cameron, L., Leventhal, E. A., & Leventhal, H. (1995). Seeking medical care in response to symptoms and life stress. Psychosomatic Medicine, 57(1), 37–47. Carver, C. S., & Scheier, M. F. (1982). Control theory: A useful conceptual framework for personality– social, clinical, and health psychology. Psychological Bulletin, 92(1), 111–135. Gardner, B. (2015). A review and analysis of the use of ‘habit’ in understanding, predicting and influencing health‐related behavior. Health Psychology Review. doi:10.1080/17437199.2013.876238 Grosmaitre, P., Le Vavasseur, O., Yachouh, E., Courtial, Y., Jacob, X., Meyran, S., & Lantelme, P. (2013). Significance of atypical symptoms for the diagnosis and management of myocardial infarction in elderly patients admitted to emergency departments. Archives of Cardiovascular Diseases, 106(11), 586–592. Hagger, M. S., Koch, S., Chatzisarantis, N. L. D., & Orbell, S. (2017). The common sense model of self‐regulation: Meta‐analysis and test of a process model. Psychological Bulletin. doi:10.1037/ bul0000118 Halm, E. A., Mora, P., & Leventhal, H. (2006). No symptoms, no asthma: The acute episodic disease belief is associated with poor self‐management among inner‐city adults with persistent asthma. Chest, 129(3), 573–580. Horne, R., Chapman, S. C. E., Parham, R., Freemantle, N., Forbes, A., & Cooper, V. (2013). Understanding patients’ adherence‐related beliefs about medicines prescribed for long‐term conditions: A meta‐analytic review of the Necessity‐Concerns Framework. PLoS One. doi:10.1371/jourjal.poe.0080633 Horowitz, C. R., Rein, S. B., & Leventhal, H. (2004). A story of maladies, misconceptions and mishaps: Effective management of heart failure. Social Science & Medicine, 58(3), 631–643. Howell, E. A., Balbierz, A., Wang, J., Parides, M., Zlotnick, C., & Leventhal, H. (2012). Reducing postpartum depressive symptoms among black and Latina mothers: a randomized controlled trial. Obstetrics and gynecology, 119(5), 942–949.

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Idler, E. L., & Benyamini, Y. (1997). Self‐rated health and mortality: A review of twenty‐seven community studies. Journal of Health and Social Behavior, 38, 21–37. Kaptein, A. A., Hughes, B. M., Scharloo, M., Fischer, M. J., Snoei, L., Weinman, J., & Rabe, K. F. (2008). Illness perceptions about asthma are determinants of outcome. Journal of Asthma, 45(6), 459–464. Leventhal, H. (1970). Findings and theory in the study of fear communications. Advances in Experimental Social Psychology, 5, 119–186. Leventhal, H., Phillips, L. A., & Burns, E. A. (2016). The common‐sense model of self‐regulation (CSM): A dynamic framework for understanding illness self‐management. Journal of Behavioral Medicine, 39, 935–946. Lowe, R., & Norman, P. (2017). Information processing in illness representation: Implications from an associative‐learning framework. Health Psychology, 36(3), 280–290. Meyer, D., Leventhal, H., & Guttmann, M. (1985). Common‐sense models of illness: The example of hypertension. Health Psychology, 4(2), 115. Moss‐Morris, R., Weinman, J., Petrie, K., Horne, R., Cameron, L., & Buick, D. (2002). The revised illness perception questionnaire (IPQ‐R). Psychology & Health, 17(1), 1–16. Omer, Z. B., Hwang, E. S., Esserman, L. J., Howe, R., & Ozanne, E. M. (2013). Impact of ductal carcinoma in situ terminology on patient treatment preferences. JAMA Internal Medicine, 173(19), 1830–1831. Phillips, L. A., Leventhal, H., & Burns, E. (2017). Choose (and use) your tools wisely: “Validated” measures and advanced analyses can provide invalid evidence for/against a theory. Journal of Behavioral Medicine. doi:10.1007/s10865‐016‐9807‐x Phillips, L. A., Leventhal, H., & Leventhal, E. A. (2012). Physicians’ communication of the common‐ sense self‐regulation model results in greater reported adherence than physicians’ use of interpersonal skills. British Journal of Health Psychology, 17(2), 244–257. Phillips, L. A., Leventhal, H., & Leventhal, E. A. (2013). Assessing theoretical predictors of long‐term medication adherence: Patients’ treatment‐related beliefs, experiential feedback and habit development. Psychology & Health, 28(10), 1135–1151. Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health belief model. Health Education Quarterly, 15(2), 175–183. Rottmann, B. (2016). Searching for the best cause: Roles of mechanism beliefs, autocorrelation, and exploitation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(8), 1233– 1256. doi:10.1037/xlm0000244 Tannenbaum, M. B., Hepler, J., Zimmerman, R. S., Saul, L., Jacobs, S., Wilson, K., & Albarracín, D. (2015). Appealing to fear: A meta‐analysis of fear appeal effectiveness and theories. Psychological Bulletin, 141(6), 1178–1204. Tannenbaum, M. L., Leventhal, H., Breland, J. Y., Yu, J., Walker, E. A., & Gonzalez, J. S. (2015). Successful self‐management among non‐insulin‐treated adults with Type 2 diabetes: A self‐­ regulation perspective. Diabetes Medicine, 32, 1504–1512. doi:10.1111/dme.12745

Suggested Reading Brooks, T. L., Leventhal, H., Wolf, M. S., O’Conor, R., Morillo, J., Martynenko, M., … Federman, A. D. (2015). Strategies used by older adults with asthma for adherence to inhaled corticosteroids. Journal of General Internal Medicine, 29(11), 1506–1512. Leventhal, H., Weinman, J., Leventhal, E., & Phillips, L. A. (2008). Health psychology: The search for pathways between behavior and health. Annual Review of Psychology, 59, 477–505. doi:10.1146/ annurev.psych.59.103006.093643 Levine, J. C., Burns, E., Whittle, J., Fleming, R., Knudson, P., Flax, S., & Leventhal, H. (2016). Randomized trial of technology‐assisted self‐monitoring of blood glucose by low‐income seniors:

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Improved glycemic control in type 2 diabetes mellitus. Journal of Behavioral Medicine, 39(6), 1001–1008. Phillips, L. A., Cohen, J., Burns, E. A., Abrams, J., & Renninger, S. (2016). Self‐management of chronic illness: The role of ’habit’ vs reflective factors in exercise and medication adherence. Journal of Behavioral Medicine, 39(6), 1076–1091. doi:10.1007/s10865‐016‐9732‐z Phillips, L. A., Leventhal, H., & Leventhal, E. A. (2012). Physicians’ communication of the Common‐ Sense Self‐Regulation Model results in greater reported adherence than physicians’ use of interpersonal‐skills. British Journal of Health Psychology, 17(2), 244–257. doi:10.1111/j.2044‐8287.2011. 02035.x

Implicit Processes and Health Behavior Change Aya Avishai and Paschal Sheeran Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Prominent theories in health psychology, including the health belief model, protection moti­ vation theory, social cognitive theory, the transtheoretical model, and the theory of planned behavior, all assume that changing people’s conscious beliefs (e.g. behavioral intentions, risk appraisals) will promote health behavior change. However, meta‐analyses indicate that a medium‐to‐large change in intention engenders only a small‐to‐medium change in behavior and that a large change in risk appraisal has only a small effect on behavior. Dual‐process mod­ els of health behavior explain this modest impact of conscious beliefs on behavior by pointing out that health behavior is governed not only by conscious beliefs but also by implicit pro­ cesses that could activate conflicting behavioral schemata. More particularly, the reflective– impulsive model (RIM) proposes that there are two information‐processing modes: reflective and impulsive. The reflective mode is slow and based on rules of language and logic. The key processes are reasoning (e.g., from knowledge about the consequences of an action to a deci­ sion to act) and intending. The impulsive mode, on the other hand, draws upon the store of associations that the person has acquired over many experiences. The key impulsive process is spreading activation whereby perceptual input activates related elements in the associative store. This mode of information processing is fast and occurs outside of awareness. Accordingly, the defining feature of implicit processes is that people do not realize, nor intend, their impact on health decisions and actions. The present entry focuses on the implications of implicit processes for interventions to pro­ mote health behavior change. In an effort to organize the large and disparate literature on this topic, Figure 1 presents a framework for intervening with implicit processes. The organizing principle concerns the nature and target of the intervention (what factor is modified and how) and suggests that three overarching intervention approaches can be identified:

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Intervening with Implicit Processes

Context Change

• Decision simplification • Provision of prompts

Training Impulse/Reflection

• Attentional bias modification • Evaluative conditioning • Approach bias training • Working memory training

Self-Regulation

• If-then plans

Figure 1  A framework for intervening with implicit processes.

• Context change interventions alter features of the physical or social setting in order to change health behaviors. Context change interventions are of two broad types: inter­ ventions geared at simplifying decisions and interventions that install prompts. The provision of prompts can guide behavior via the activation of goals or norms or by increasing self‐monitoring. • Training impulse/reflection involves practice‐based interventions that are geared at modi­ fying either impulsive responses (i.e., attentional bias, implicit affect, or approach bias) or the person’s reflective control over impulsive responding. • Self‐regulation interventions give people tools to better align their thoughts, feelings, and actions with their health goals. Forming if‐then plans or implementation intentions (Gollwitzer & Sheeran, 2006) is the most extensively researched and best validated of these tools.

Changing the Context Decision Simplification Simplifying decisions reduces the burden on the reflective system and helps to ensure that implicit choices are healthful. Health decisions can be simplified by the options that are made available, the ease with which those options can be selected, and the default options that are already in place. Correlational studies indicate that the availability of fresh fruit, vegetables, and low‐fat milk is inversely related to overweight/obesity and that the availability of ciga­ rettes is positively associated with smoking rates. When an intervention increased the availabil­ ity of healthy food and drinks in vending machines by 50%, the proportion of healthy items that was purchased also increased (French et al., 2010). Introducing variety can also improve health decisions: When individuals can choose a different fruit to one they had consumed earlier, then fruit is more likely to be selected than a sweet. The ease with which an option can be chosen also is important. An experiment among coffee shop consumers found that when the cognitive demands involved in making healthy eating decisions were lowered, there was a decline in the proportion of high‐calorie items purchased (Allan, Johnston, & Campbell, 2015). Defaults also simplify decisions by creating a standard, preselected

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option that does not require people to make an active choice. For instance, a country’s default could be that all residents are registered as organ donors (and residents opt out if they do not want to register) or that residents are not registered as donors by default (and opt in if they wish to register). When countries with the different defaults are compared, countries with an opt‐out default have higher rates of donor registration than countries with an opt‐in default.

Provision of Prompts Changing self‐reported (explicit) goals and norms and increasing conscious monitoring of behavior are known to promote health behaviors. The provision of prompts can also instigate these processes implicitly. For instance, Papies and colleagues primed the goal of eating health­ ily among patrons of a butcher’s store by putting a sign about a low‐fat recipe at the entrance (or not). Restrained eaters ate fewer meat snacks that were available on the store counter when the sign was present than when it was absent. Similarly, a flyer about low‐fat recipes also decreased snack purchases (by 75%) among overweight/obese patrons of a grocery store (see Papies, 2016, for a review). Social norms also exert an implicit influence on health behavior. For instance, people eat more when they are in pairs or in groups of four than when they are alone, and people are largely unaware of how dining companions influence their eating behavior—citing taste and hunger as key influences instead. Portion size, plate size, and the amount of food on the plate can each activate consumption norms. Including photographs of vegetables on the bottom of food trays in a school cafeteria doubled the proportion of students eating green beans and tripled the proportion eating carrots (Reicks, Redden, Mann, Mykerezi, & Vickers, 2012). Injunctive norms or social pressure can also be primed. People entering a hospital were twice as likely to engage in hand hygiene when a picture of a pair of eyes was placed over the sanitary gel dispenser (King et al., 2015). Subtle prompts can also promote self‐monitoring. Geier, Wansink, and Rozin (2012) gave participants containers of potato chips and had them watch a movie. Participants who encountered a red potato chip following a fixed num­ ber of regular chips (and thus could monitor their consumption) ate half as many chips as participants who were given only regular chips. Similarly, in a study that examined eating wings at a sports bar, participants whose food debris was left on their plate ate less than those whose finished wings and bones were removed by the waitstaff (Wansink & Payne, 2007).

Training Impulse–Reflection People who smoke, drink, or overeat tend to show biased attention to relevant stimuli, more positively evaluate those stimuli on implicit measures, and exhibit greater approach motivation in relation to those stimuli. Interventions have targeted both the respective impulses (atten­ tional retraining for attentional bias, evaluative conditioning [EC] for implicit affect, and approach bias training for motivational bias) as well as people’s reflective control over such impulses (working memory [WM] training).

Attentional Retraining Attentional retraining modifies paradigms such as the Stroop and visual dot probe (VDP) so that participants gain practice at not giving attention to unhealthy stimuli. A single session of attentional retraining with heavy drinkers decreased attention to alcohol relative to soft drinks

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(Schoenmakers, Wiers, Jones, Bruce, & Jansen, 2007), indicating that attentional bias could be altered. Craving for alcohol and preference behavior were not affected by the intervention, however. Repeated sessions appeared to have stronger effects. In the Alcohol Attention‐ Control Training Program (AACTP), participant bias is assessed, goals for attentional bias reduction are set, and participants’ progress is monitored. Hazardous and harmful drinkers who undertook the program demonstrated a reduction in alcohol attentional bias as well as a reduction in alcohol consumption over 3 months (Fadardi & Cox, 2009). Schoenmakers et al. (2010) undertook the first small RCT of repeated attentional retraining in alcohol‐dependent patients. Results showed an increase in attentional bias in the control group and a significant decrease in attentional bias for alcohol stimuli in the experimental group. The experimental group was also discharged from treatment sooner and relapsed later than the control group. Attentional retraining also proved effective in reducing chocolate consumption. However, a recent double‐blind RCT observed no reliable effect on smoking cessation outcomes.

Evaluative Conditioning EC tasks repeatedly pair a focal stimulus (e.g., beer) with a highly valenced stimuli (e.g., illness) in order to alter the underlying affective associations. Participants who completed an EC task that paired beer with negative affect exhibited less positive attitudes, had fewer cravings, and consumed less beer after training than did those in the control group (Houben, Schoenmakers, & Wiers, 2010). In a second study, participants who underwent EC had more negative implicit attitudes regarding alcohol and also drank less alcohol during the subsequent week compared with controls. EC was also effective in altering eating attitudes and behavior. After training to associate snacks with unhealthy outcomes, participants’ implicit attitudes toward those snacks were more negative, and they were less likely to choose a snack as a parting gift after the experi­ ment (Hollands, Prestwich, & Marteau, 2011). Learning to associate fruit with positive images also increased the proportion of fruit selected in a behavioral choice task.

Approach Bias Training Repeated consumption of unhealthy substances forges associations between those stimuli and approach responses. Approach bias training targets this automatic motivational tendency. Go/ no‐go, stop signal, and approach/avoidance tasks have each been used in approach bias train­ ing. Heavy drinkers who undertook a go/no‐go task that paired beer with “no‐go” responses exhibited more negative implicit attitudes toward alcohol and also consumed less alcohol (Houben, Nederkoorn, Wiers, & Jansen, 2011). A go/no‐go task involving palatable foods engendered a significant reduction in weight over 1 month, and training was especially effec­ tive among participants with higher BMI at the outset of the intervention (Veling, van Koningsbruggen, Aarts, & Stroebe, 2014). Stop signal training consistently presents behavioral stop signals in close temporal proximity to high‐calorie foods in order to create strong associations between these foods and response inhibition. Stop signals proved effective in reducing snack consumption in multiple studies, and the observed effects were especially pronounced when participants were hungry or habitu­ ally consumed the relevant snack food. Wiers, Eberl, Rinck, Becker, & Lindenmeyer (2011) examined the impact of approach/ avoidance training on relapse rates among patients with alcoholism. Patients were instructed to respond to pictures of alcohol by making an avoidance movement (pushing the joystick) and to pictures of nonalcoholic soft drinks by making an approach movement (pulling the

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joystick). Approach/avoidance training reduced approach bias and implicit attitudes toward alcohol and also led to better treatment outcomes 1 year later.

Working Memory Training Automatic impulses have greater influence on behavior among participants with low WM capacity and when WM is compromised by cognitive load. The implication is that WM train­ ing could reduce the impact of antagonistic implicit processes on health behaviors. Houben, Wiers, and Jansen (2011) reported that 25 sessions of WM training (one session every 2 days) significantly increased memory span. Training also reduced weekly alcohol consumption both 1week and 5 weeks post‐training. WM training proved especially effective in reducing alcohol consumption among participants who had strong implicit preferences for alcohol at the outset. Although there is much debate about the magnitude and mechanisms of WM training transfer effects, research is ongoing to test the impact of WM training on other health behaviors.

Self‐Regulation If‐then plans or implementation intentions are a key self‐regulation tool in health psychology. Implementation intentions provide a structure in which people (a) identify key opportunities for, or obstacles to, performing health behaviors, (b) specify a way to respond to each opportunity and obstacle, and then (c) formalize a link between the opportunity or obstacle and the response: If (opportunity/obstacle) arises, then I will (respond in this way)!

Meta‐analyses indicate that forming implementation intentions promotes effective self‐­ regulation (Gollwitzer & Sheeran, 2006) and leads to greater physical activity and healthy eating. Implementation intentions are an effective intervention for health behavior change because the opportunities or obstacles specified in plans become highly accessible and thus people are in a good position to identify their moment to act when they encounter it. Moreover, strong associations are forged between the opportunity/obstacle and the specified response, meaning that people are in a good position to seize that moment and respond exactly as they had spelled out in advance. Evidence indicates that forming if‐then plans endows responses with key features of auto­ maticity (i.e., swift and effortless responding). Thus, although implementation intentions are formed in a conscious act of will, automatic processes are recruited that serve to promote the initiation of the planned responses. This marriage of explicit and implicit processes that charac­ terizes the operation of if‐then plans has been termed “strategic automaticity.” Neurophysiological evidence supports this characterization. Whereas action control by conscious intentions oper­ ates in a “top‐down” manner, if‐then plans operate in “bottom‐up” fashion. The stimulus control of behavior observed in implementation intentions is akin to the operation of habits. The previous section reviewed evidence that extensive practice‐based training can help to overcome the unwanted impact of implicit goals, attitudes, and social influence on health behaviors. However, if‐then plans also offer a simple and effective strategy for regulating unwanted implicit influence. For instance, Gollwitzer, Sheeran, Trötschel, and Webb (2011) observed that forming if–then plans about how to drive safely protected driving behavior from the antagonistic impact of priming the goal of being fast. Another study measured implicit attitudes toward chocolate and had participants form if‐then plans to instigate reflection

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whenever they were tempted to eat some (“If I am tempted to have chocolate, then I ask myself, ‘do I really want to do this?’”). Findings showed that if‐then plans led to reduced chocolate consumption over the subsequent week. Whereas positive implicit attitudes predicted greater consumption among control participants, this association was actually reversed for participants who formed if‐then plans (Sheeran, 2013). If–then plans also proved effective in reducing the implicit influence of binge drinker stereotypes on young people’s alcohol consumption in a field experiment over 1 month (Rivis & Sheeran, 2013). In sum, implicit processes are key to the effectiveness of implementation intentions in changing health behaviors by automatizing the initiation of healthful actions and controlling disruptive automatic influences.

Conclusion Implicit processes have important implications for behavior change interventions. Implicit pro­cesses can be harnessed in order to promote healthful decisions (e.g., via context change interventions). Moreover, when implicit processes threaten to engender risk behavior, training impulse–reflection and forming if‐then plans can prove effective in attenuating their influence. Although research on intervening with implicit processes is still in its early stages and much remains to be done to test interventions over extended periods among both representative samples and patients, this approach holds considerable promise. A key implication of dual‐­ process theories is that behavior change interventions will be most effective if they target not only conscious beliefs but also implicit processes and exploit potential synergies between beliefs and implicit processes.

Acknowledgments This entry was made possible through the support of a grant from the John Templeton Foundation (#23145). The opinions are the authors’ and do not necessarily reflect the views of the John Templeton Foundation.

Author Biographies Aya Avishai is a PhD student in the Department of Psychology and Neuroscience at the University of North Carolina at Chapel Hill. Paschal Sheeran is a professor in the Department of Psychology and Neuroscience at the University of North Carolina at Chapel Hill. His research takes a self‐regulation perspective on emotion control and health behavior change and has been published in Psychological Bulletin, Psychological Science, and Health Psychology.

References Allan, J. L., Johnston, M., & Campbell, N. (2015). Snack purchasing is healthier when the cognitive demands of choice are reduced: A randomized controlled trial. Health Psychology, 34(7), 750–755. doi:10.1037/hea0000173 Fadardi, J. S., & Cox, W. M. (2009). Reversing the sequence: Reducing alcohol consumption by over­ coming alcohol attentional bias. Drug and Alcohol Dependence, 101(3), 137–145. doi:10.1016/j. drugalcdep.2008.11.015

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French, S. A., Hannan, P. J., Harnack, L. J., Mitchell, N. R., Toomey, T. L., & Gerlach, A. (2010). Pricing and availability intervention in vending machines at four bus garages. Journal of Occupational and Environmental Medicine, 52(Suppl. 1), S29–S33. doi:10.1097/JOM.0b013e3181c5c476 Geier, A., Wansink, B., & Rozin, P. (2012). Red potato chips: Segmentation cues can substantially decrease food intake. Health Psychology, 31(3), 398–401. doi:10.1037/a0027221 Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta‐anal­ ysis of effects and processes. Advances in Experimental Social Psychology, 38, 69–119. doi:10.1016/ S0065‐2601(06)38002‐1 Gollwitzer, P. M., Sheeran, P., Trötschel, R., & Webb, T. L. (2011). Self‐regulation of priming effects on behavior. Psychological Science, 22(7), 901–907. doi:10.1177/0956797611411586 Hollands, G. J., Prestwich, A., & Marteau, T. M. (2011). Using aversive images to enhance healthy food choices and implicit attitudes: An experimental test of evaluative conditioning. Health Psychology, 30(2), 195–203. doi:10.1037/a0022261 Houben, K., Nederkoorn, C., Wiers, R. W., & Jansen, A. (2011). Resisting temptation: Decreasing alcohol‐related affect and drinking behavior by training response inhibition. Drug and Alcohol Dependence, 116(1–3), 132–136. doi:10.1016/j.drugalcdep.2010.12.011 Houben, K., Schoenmakers, T. M., & Wiers, R. W. (2010). I didn’t feel like drinking but I don’t know why: The effects of evaluative conditioning on alcohol‐related attitudes, craving and behavior. Addictive Behaviors, 35(12), 1161–1163. doi:10.1016/j.addbeh.2010.08.012 Houben, K., Wiers, R. W., & Jansen, A. (2011). Getting a grip on drinking behavior: Training working memory to reduce alcohol abuse. Psychological Science, 22(7), 968–975. doi:10.1177/095679761 1412392 King, D., Vlaev, I., Everett‐Thomas, R., Fitzpatrick, M., Darzi, A., & Birnbach, D. J. (2015). “Priming” hand hygiene compliance in clinical environments. Health Psychology.. doi:10.1037/hea0000239 Papies, E. K. (2016). Health goal priming as a situated intervention tool: How to benefit from noncon­ scious motivational routes to health behavior. Health Psychology Review, 10(4), 408–424. Reicks, M., Redden, J. P., Mann, T., Mykerezi, E., & Vickers, Z. (2012). Photographs in lunch tray compartments and vegetable consumption among children in elementary school cafeterias. JAMA, 307(8), 784–785. doi:10.1001/jama.2012.170 Rivis, A., & Sheeran, P. (2013). Automatic risk behavior: Direct effects of binge drinker stereotypes on drinking behavior. Health Psychology, 32(5), 571–580. doi:10.1037/a0029859 Schoenmakers, T., Wiers, R. W., Jones, B. T., Bruce, G., & Jansen, A. M. (2007). Attentional re‐training decreases attentional bias in heavy drinkers without generalization. Addiction, 102(3), 399–405. doi:10.1111/j.1360‐0443.2006.01718.x Schoenmakers, T. M., de Bruin, M., Lux, I. M., Goertz, A. G., Van Kerkhof, D. T., & Wiers, R. W. (2010). Clinical effectiveness of attentional bias modification training in abstinent alcoholic patients. Drug and Alcohol Dependence, 109(1–3), 30–36. doi:10.1016/j.drugalcdep.2009.11.022 Sheeran, P. (2013, April). Self‐control over automatic attitudes and behaviour. Keynote address pre­ sented at the Implicit Bias and Philosophy Conference, Sheffield, UK. Veling, H., van Koningsbruggen, G. M., Aarts, H., & Stroebe, W. (2014). Targeting impulsive processes of eating behavior via the internet. Effects on body weight. Appetite, 78(C), 102–109. doi:10.1016/j. appet.2014.03.014 Wansink, B., & Payne, C. R. (2007). Counting bones: Environmental cues that decrease food intake. Perceptual and Motor Skills, 104(1), 273–276. doi:10.2466/PMS.104.1.273‐276 Wiers, R. W., Eberl, C., Rinck, M., Becker, E. S., & Lindenmeyer, J. (2011). Retraining automatic action tendencies changes alcoholic patients’ approach bias for alcohol and improves treatment outcome. Psychological Science, 22(4), 490–497. doi:10.1177/0956797611400615

Suggested Reading Marteau, T. M., Hollands, G. J., & Fletcher, P. C. (2012). Changing human behavior to prevent disease: The importance of targeting automatic processes. Science, 337(6101), 1492–1495. doi:10.1126/ science.1226918

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Sheeran, P., Gollwitzer, P. M., & Bargh, J. A. (2013). Nonconscious processes and health. Health Psychology, 32(5), 460–473. doi:10.1037/a0029203 Wansink, B. (2006). Mindless eating: Why we eat more than we think. New York: Bantam. Wiers, R. W., Gladwin, T. E., Hofmann, W., Salemink, E., & Ridderinkhof, K. R. (2013). Cognitive bias modification and cognitive control training in addiction and related psychopathology: Mechanisms, clinical perspectives, and ways forward. Clinical Psychological Science, 1(2), 192–212. doi:10.1177/ 2167702612466547

Intimate Relationships and Physical Health Fiona Ge*, Jana Lembke, and Paula R. Pietromonaco Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA

Romantic relationships are connected to physical health in many ways. Simply being in a romantic relationship generally predicts better health outcomes, but the quality of the relationship, individual differences such as attachment style (expectations that partners will or will not be supportive and responsive to one’s needs), and couples’ behavioral interaction styles also play a role in health. We review how each of these broad categories of predictors is associated with people’s voluntary health‐related behaviors, health‐related physiological responses, and health and disease outcome and discuss applications of this research for medical practice.

Relationship Status and Quality People who are in a happy relationship tend to evidence better health than those who are not in a relationship or are in a poor‐quality relationship. According to a recent meta‐analysis, marital quality is linked to health via multiple pathways, including physiological markers, health behaviors, and direct health outcomes (Robles, Slatcher, Trombello, & McGinn, 2014).

Physiological Health Correlates of Relationship Quality Research examining relationship quality and endocrine function has focused on the hypothalamic–pituitary–adrenal (HPA) axis, a major stress response system that governs the release of cortisol, a steroid hormone. Cortisol is released into the bloodstream in response to environmental stressors, and it affects metabolic, immune, and nervous system functioning. Over time, the continued release of cortisol may have adverse effects on the body, which can increase *The first and second authors contributed equally to this entry. The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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s­ usceptibility to illness and accelerate chronic disease. Conversely, a flattened cortisol slope (low reactivity) in response to a stressor also serves as an index of psychological maladjustment and is typically associated with self‐regulatory problems under chronic stress. Consistent with this idea, poorer marital quality has been related to flatter cortisol responses throughout the day for men and women. Women in more satisfying relationships, however, show greater morning cortisol levels and a steeper decline in cortisol levels throughout a typical work day, indicating better physiological recovery from work than women who were less satisfied with their marriages. Although partners’ daily cortisol levels tend to be related, greater relationship satisfaction may shield spouses from the effects of each other’s cortisol responses to stress. For instance, women’s cortisol levels are less closely associated with their husband’s cortisol levels when the women were more satisfied in their relationship. Additionally, spouses who reported greater marital satisfaction showed less convergence in cortisol patterns during a discussion of relationship conflict (Laws, Sayer, Pietromonaco, & Powers, 2015). Thus, high‐quality relationships may minimize the extent to which one partner’s physiological stress responses spill over to those of their partner, although the mechanisms through which this process occurs remain to be specified. Functioning of the cardiovascular system is another biological indicator of health, and lower reactivity is generally better. Overall, individuals in high‐quality marriages have lower cardiovascular reactivity than single individuals and even more so than individuals in low‐quality marriages. Similarly, marital dissatisfaction has been associated with higher blood pressure among individuals with mild hypertension. These findings indicate that relationship quality— not just relationship status—matters for health. A recent survey based on a US national sample found that greater marital adjustment was associated with less interleukin‐6, a pro‐inflammatory cytokine and a marker of inflammation, particularly for younger women (Whisman & Sbarra, 2012). For men, in contrast, being married buffered against elevations in C‐reactive protein, another marker of inflammation, compared with men who were unmarried. It has also been found that older, happily married individuals may evidence stronger antibody responses than those who are unmarried, bereaved, or in an unhappy marriage. Furthermore, it has been found that people who perceive that their relationship contains both positive and negative aspects have greater inflammation compared with those who perceived their relationship in a primarily positive way, suggesting that relationship ambivalence has negative consequences for health.

Relationship Quality and Health Behavior Being in a relationship can motivate health‐enhancing behaviors, perhaps because those behaviors are viewed as a shared goal for both members of the couple. Furthermore, individuals in a relationship may have greater support in seeking healthcare and following treatment regimens. For instance, people who were married or had a partner were 1.5–2 times more likely to attend cardiac rehabilitation than their unpartnered counterparts (Molloy, Hamer, Randall, & Chida, 2008). In some cases, however, relationships do not promote better health. Having a partner with unhealthy habits—such as smoking—may hinder the other partner’s attempts to break similar habits. Satisfaction with one’s relationship, not just relationship status, also relates to health. For instance, while married individuals generally show healthier sleep patterns than unmarried individuals, being in a more satisfying marriage is associated with fewer sleep disturbances in women. Couples in more satisfying marriages also are more likely to use healthcare services than couples in distressed marriages. In contrast, greater relationship dissatisfaction tends to promote health‐compromising behaviors, such as greater alcohol consumption.

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Relationship Quality, Mortality, and Disease Recovery Mortality risk is generally lower among those who are married versus unmarried, who marry later in life, or who have been married for a longer period. In addition, being in a relationship has been linked to lower morbidity and higher survival rates after the diagnosis of different diseases or surgery, such as cardiovascular disease, coronary heart disease, hospital‐diagnosed infectious diseases, and coronary artery bypass grafting. Relationship quality has also been linked to physical health outcomes. For example, greater marital quality predicted a better survival rate over 8 years for patients with heart failure, ­especially among women (see Robles et al., 2014). Similarly, greater relationship negativity predicted a higher risk of coronary heart disease. Additionally, the risk of coronary artery calcification was higher when both partners had positive and negative (ambivalent) perceptions of each other than when one partner had ambivalent perceptions and the other perceived the partner as purely positive (Uchino, Smith, & Berg, 2014).

Attachment and Health Based on day‐to‐day experiences in close relationships from childhood through adulthood, people develop attachment orientations (e.g., expectations, beliefs, goals) that guide how they think, feel, and behave with close partners. These attachment orientations also promote different ways of perceiving and regulating stress, which in turn affect health‐related physiological patterns. Overreacting to and poorly recovering from relationship stressors—a tendency observed in insecurely attached individuals—can exhaust physiological systems and eventually produce health problems.

Physiological Health Correlates of Attachment Both attachment avoidance and anxiety are associated with disruptions in the HPA axis, but because anxiously attached people are particularly sensitive to relationship stressors and are continually monitoring the relationship for signs of threat, they are susceptible to heightened HPA responses. For example, anxiously attached individuals exhibit elevated HPA axis activity during travel separations, a situation that threatens anxious people’s need for their partner’s closeness (see Pietromonaco, DeVito, Ge, & Lembke, 2015). Anxiously attached individuals also show increased cortisol levels in response to generalized stressors in experimental settings, and because they underestimate their partner’s availability and care, they take longer than nonanxious individuals to recover from the stressor even when presented with support from their partner. Recent evidence sheds light on the interplay of both partner’s attachment on stress responses. Couples consisting of an anxious wife and an avoidant husband showed increased cortisol reactivity in anticipation of a discussion of relationship conflict followed by a sharp decline before the discussion began (Beck, Pietromonaco, DeBuse, Powers, & Sayer, 2013). This pattern was accompanied by less constructive behaviors during the conflict and was distinct from patterns observed in other attachment pairings. These findings, together with other recent work (see Pietromonaco & Powers, 2015), underscore the importance of the situational context (e.g., both partners’ characteristics) in understanding the link between attachment and physiological responses. Attachment insecurity (anxiety, avoidance, or both) is associated with cardiovascular risk factors. Attachment avoidance predicts elevated cardiovascular reactivity in the face of

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a­ ttachment‐related stressors, a likely consequence of avoidant people’s defensive, repressive, and distancing coping strategies. Although a partner’s presence should mitigate insecure individuals’ sympathetic nervous system arousal in the face of stress, this does not seem to be the case. Avoidant and anxious individuals who were anticipating a stressful situation showed greater cardiovascular reactivity to stress in their partner’s presence versus absence, suggesting that they were not comforted by their partner once the attachment system was activated, perhaps because they were anticipating not only the stressor but also the possibility of being rejected by their partners. Attachment insecurity may impact health via immune functioning, which can be measured via speed of wound healing, inflammatory cytokines (IL‐6), and T‐cell counts. While this area of research is just developing, immune functioning seems to be impaired by attachment insecurity. Other work suggests that the link between attachment and immune functioning is moderated by gender. It is possible that some markers of immune functioning vary with one type of attachment insecurity but not the other or that other moderating variables are involved (e.g., relationship quality, situational aspects). Research building on these initial findings will yield a better understanding of the link between attachment insecurity and immune system dysregulation.

Attachment and Health Behavior Attachment insecurity is linked to a variety of health‐compromising behaviors, such as riskier sexual behavior, greater substance use, lower levels of exercise and higher rates of smoking, less participation in preventative healthcare, and poorer relationships with physicians. Factors such as self‐esteem and communication styles may partly explain the link between attachment and health behavior (see Pietromonaco et  al., 2015). For example, securely attached university students held higher self‐esteem than those who were insecurely attached, which in turn predicted their greater engagement in health behavior. Communication styles may explain the link between attachment and risky sexual behavior: anxiously attached individuals were less likely to talk with their partners about AIDS‐related issues, which predicted riskier sexual behavior.

The Dyadic Influence of Attachment on Health Research integrating work on attachment and social support has found that people low in attachment anxiety reported better health outcomes when they perceived their partners as more supportive, but individuals high in attachment anxiety did not benefit from their partners’ greater support (Stanton & Campbell, 2014). Relatedly, individuals who were anxiously attached and who perceived lower social support showed greater severity of inflammatory bowel disease severity. Because anxious individuals’ relationship fears may cause them to underestimate their partner’s support, these findings indicate that individuals’ perceptions of relationship support matter for health, not just the amount of support. Another line of research has taken into account the interplay between both partners’ attachment styles in relation to health. When caregivers and patients with Alzheimer’s disease (AD) were both high in anxious attachment, the patients reported greater physical symptoms (e.g., pain, nausea); when caregivers were low in anxious attachment, patients’ attachment anxiety was unrelated to their reported physical symptoms (Monin, Schulz, & Kershaw, 2013). Caregivers’ low attachment anxiety may buffer AD individuals’ perceptions of their symptoms, particularly for those who are anxiously attached.

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Negative Relationship Interactions Although high‐quality relationships provide a source of support for individuals’ health and well‐being, relationship interactions—when they go wrong—can be a major source of stress (Robles et al., 2014).

Physiological Correlates of Conflict Research on relationship conflict and endocrine responses has focused on the stress hormone cortisol. Greater positive behaviors during a conflict have been associated with increased cortisol levels, while spouses who displayed mainly negative behaviors showed no change in cortisol levels during the conflict, a nonresponsive pattern that is often linked to poorer outcomes. Another potentially problematic response pattern is heightened cortisol reactivity, which has been found among men who coped with stress by seeking support, women who received less spousal support, and men and women whose partners had greater need for social support. Relationship‐oriented factors also contribute to individuals’ endocrine reactivity in response to a conflict. Greater demand–withdrawal communication patterns, characterized by wives’ criticism and blaming and husbands’ avoidance and disengagement, are associated with greater cortisol reactivity in response to a conflict for both spouses. Similarly, husbands’ withdrawal in response to wives’ negative behaviors was associated with wives’ greater cortisol reactivity (Kiecolt‐Glaser et al., 1996). Negative relationship interactions evoke greater cardiovascular reactivity, such as blood pressure, heart rate, and cardiac output. Hostile interactions are linked to couples’ increased blood pressure, whereas positive or neutral interactions are not linked to a change in blood pressure, implying that the negative aspects of relationship interactions typically outweigh the positive or neutral aspects of the discussions in influencing physiological reactivity. In fact, dispositionally hostile husbands (vs. low hostile husbands) paired with hostile wives evidenced greater resistance to blood flow through the cardiovascular system throughout a conflict discussion (Broadwell & Light, 2005). Other studies examining support provisions during conflict interactions found that low spousal support adversely impacts blood pressure (Heffner et al., 2004). Engaging in marital conflict has been associated with dysregulation of the immune system, which has negative implications for health such as impaired wound healing (Robles et  al., 2014). Conflicts characterized by hostile and negative behaviors are especially likely to elicit poorer immune responses. Social cognitive factors, such as the extent to which people think about their feelings and the circumstances during a conflict discussion, have been associated with immune responses such that high‐level cognitive processing seems to buffer against inflammation in the face of stressful events (Graham et al., 2009).

Positive Relationship Interactions Endocrine Pathway Positive relationship interactions—such as showing physical affection or support—are important for health, likely because they reduce stress and protect against cortisol increases. Daily displays of intimacy are one way that relationship interactions confer health benefits. In one study, dual‐career couples reported time spent on intimacy and provided multiple daily saliva

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samples for cortisol analysis over a 1‐week period. Greater amounts of intimacy on a given day appeared to increase positive affect, which in turn reduced cortisol, suggesting that positive affect generated by intimacy aids in stress reduction (Ditzen, Hoppmann, & Klumb, 2008). Similarly, husbands’ supportive behaviors during heated conflict interactions helped downregulate stress hormones in wives, perhaps because husbands’ constructive engagement during conflict reduces wives’ perceived threat and speeds recovery from physiological stress.

Cardiovascular Pathway Simply being in a loving, low‐conflict relationship supports physiological patterns necessary for good cardiovascular health for both men and women. For instance, the extent to which partners viewed their romantic relationships as positive, important, and secure predicts life satisfaction and reductions in nighttime blood pressure, especially for women. In a sample of 250 healthy older adults, frequent spousal interaction reduced progression of carotid artery thickening for men in well‐adjusted marriages (Janicki, Kamarck, Shiffman, Sutton‐Tyrrell, & Gwaltney, 2005). Perceiving one’s partner as supportive in times of stress also promotes physiological responses correlated with better cardiovascular health. One study of 94 married couples found that perceptions of informational support from one’s partner helped reduce the effects of momentary, everyday life stress on systolic and diastolic blood pressure. This finding suggests that providing useful advice or helping with problem solving protects against blood pressure increases that are associated with cardiovascular risk. Other work suggests that it is not the type of support received during a conflict, but whether partners were happy with the support that is associated with blood pressure and cortisol reactivity (Heffner et al., 2004). Relationships also may impact cardiovascular health via loving touch. A history of frequent physical contact between partners has been associated with higher oxytocin production and lower cardiovascular reactivity. Experimentally induced touch has a similar effect: cohabiting couples who engaged in handholding followed by a 20‐s hug showed attenuated blood pressure and heart rate reactivity in response to a public speaking task compared with those in a no‐touch control group (Grewen, Anderson, Girdler, & Light, 2003). Also, evidence suggests that positive physical contact (but not verbal support) can buffer against physiological responding to a stressor. Physical contact between partners can even be used as intervention to improve health. Compared with a no‐treatment control group, couples participating in a 4‐week‐long “warm touch” intimacy‐building intervention showed enhanced oxytocin, lower stress hormones, and, among husbands, lower systolic blood pressure (Holt‐Lunstad, Birmingham, & Light, 2008). Clearly, warm and affectionate relationships contribute to lower sympathetic nervous system reactivity to acute stressors, a correlate of good cardiovascular health.

Immune Pathway Provisions of support confer benefits for physical health via inflammatory responses over and above individual differences. For example, wives in more well‐adjusted marriages showed lower blood levels of IL‐6 (a marker of inflammation), even after controlling for significant demographic and health‐related variables, and reports of partner support (but not negative partner interactions) were uniquely associated with IL‐6 (Whisman & Sbarra, 2012). Another way to measure immune functioning is via the body’s ability to heal from injury. Neurochemical activity related to positive relationship functioning aids in wound healing: compared with individuals who showed lower levels of oxytocin, individuals exhibiting the highest levels (i.e., upper quartile) of oxytocin healed faster from experimentally induced

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wounds (Gouin et al., 2010). Greater oxytocin and vasopressin also were associated with more positive communication behaviors and fewer negative behaviors during a social support marital interaction. Thus, supportive marital interactions may promote greater oxytocin and vasopressin response, which counteract stress and aid in wound healing.

Recommendations for Future Research and Conclusion More work is needed to determine the conditions under which physiological reactivity to momentary relationship stressors translates to observable, long‐term effects on health. For example, attachment is associated with subtle biological stress response patterns that likely translate to concrete health outcomes over time, but limited work has directly tested this link. Also, it is unclear how long it takes for psychophysiological responses (e.g., heightened cortisol reactivity) to produce a diagnosable disease. Because serious health problems such as cancer and arteriosclerosis can take years to develop, it will be important to examine the connection between relationship processes and health over time. Additional work is needed on clinical samples such as couples in which one partner has a chronic illness. Recent research by Manne, Siegel, Kashy, and Heckman (2014) illustrated the potential for conducting basic psychological research with applications for medical practice: in a study of 254 women with breast cancer and their husbands, how couples viewed the cancer (e.g., going through it as a team) affected their intimacy, and this association was mediated by how they communicated (i.e., having greater perceived self‐disclosure and responsiveness). Medical treatments for chronic illness that include a relationship component hold promise: A meta‐analysis found that patients receiving a couple‐oriented intervention showed greater improvements in their depressive symptoms, marital functioning, and pain than when only usual medical care was prescribed (Martire, Schulz, Helgeson, Small, & Saghafi, 2010). This research points to relationship interventions as potential nonpharmacological medical treatments. Indeed, research at the nexus of health psychology and relationship science has the promise to be translated to medical practice and lay a groundwork for developing new, low‐ risk methods of improving physical health.

Author Biographies Fiona Ge is a PhD student in social psychology at the University of Massachusetts Amherst. She is interested in studying how social interactions between romantic partners shape individuals’ emotional, physiological, and social well‐being. She is also interested in examining how communication patterns among romantic couples vary by culture. Jana Lembke is a social psychology graduate student at the University of Massachusetts Amherst. Her research focuses on the role of positive partner interactions in relationship well‐ being. She is currently examining how couples reconnect with each other following instances of relationship conflict and how these types of interactions may contribute to relationship satisfaction. Paula R. Pietromonaco is a professor in the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst. Her research focuses on attachment, emotion, and health. Her current work examines how couple members influence each other’s psychological, behavioral, and physiological responses to stress and the extent to which these processes contribute to their emotional and physical health over time.

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References Beck, L. A., Pietromonaco, P. R., DeBuse, C. J., Powers, S. I., & Sayer, A. G. (2013). Spouses’ attachment pairings predict neuroendocrine, behavioral, and psychological responses to marital conflict. Journal of Personality and Social Psychology, 105(3), 388–424. http://doi.org/10.1037/a0033056 Broadwell, S. D., & Light, K. C. (2005). Hostility, conflict and cardiovascular responses in married couples: A focus on the dyad. International Journal of Behavioral Medicine, 12(3), 142–152. http:// doi.org/10.1207/s15327558ijbm1203_3 Ditzen, B., Hoppmann, C., & Klumb, P. (2008). Positive couple interactions and daily cortisol: On the stress‐protecting role of intimacy. Psychosomatic Medicine, 70(8), 883–889. http://doi.org/ 10.1097/PSY.0b013e318185c4fc Gouin, J.‐P., Carter, C. S., Pournajafi‐Nazarloo, H., Glaser, R., Malarkey, W. B., Loving, T. J., … Kiecolt‐Glaser, J. K. (2010). Marital behavior, oxytocin, vasopressin, and wound healing. Psychoneuroendocrinology, 35(7), 1082–1090. http://doi.org/10.1016/j.psyneuen.2010.01.009 Graham, J. E., Glaser, R., Loving, T. J., Malarkey, W. B., Stowell, J. R., & Kiecolt‐Glaser, J. K. (2009). Cognitive word use during marital conflict and increases in proinflammatory cytokines. Health Psychology, 28(5), 621–630. http://doi.org/10.1037/a0015208 Grewen, K. M., Anderson, B. J., Girdler, S. S., & Light, K. C. (2003). Warm partner contact is related to lower cardiovascular reactivity. Behavioral Medicine, 29(3), 123–130. http://doi.org/ 10.1080/08964280309596065 Heffner, K. L., Kiecolt‐Glaser, J. K., Loving, T. J., Glaser, R., & Malarkey, W. B. (2004). Spousal support satisfaction as a modifier of physiological responses to marital conflict in younger and older couples. Journal of Behavioral Medicine, 27(3), 233–254. Holt‐Lunstad, J., Birmingham, W. A., & Light, K. C. (2008). Influence of a “warm touch” support enhancement intervention among married couples on ambulatory blood pressure, oxytocin, alpha amylase, and cortisol. Psychosomatic Medicine, 70(9), 976–985. http://doi.org/10.1097/PSY. 0b013e318187aef7 Janicki, D. L., Kamarck, T. W., Shiffman, S., Sutton‐Tyrrell, K., & Gwaltney, C. J. (2005). Frequency of spousal interaction and 3‐year progression of carotid artery intima medial thickness: The Pittsburgh healthy heart project. Psychosomatic Medicine, 67(6), 889–896. http://doi.org/10.1097/01. psy.0000188476.87869.88 Kiecolt‐Glaser, J. K., Newton, T., Cacioppo, J. T., MacCallum, R. C., Glaser, R., & Malarkey, W. B. (1996). Marital conflict and endocrine function: Are men really more physiologically affected than women? Journal of Consulting and Clinical Psychology, 64(2), 324–332. http://doi.org/10.1037/ 0022‐006X.64.2.324 Laws, H., Sayer, A., Pietromonaco, P. R., & Powers, S. I. (2015). Longitudinal changes in spouses’ HPA responses: Convergence in cortisol patterns during the early years of marriage. Health Psychology, 34(11), 1076. Manne, S. L., Siegel, S., Kashy, D., & Heckman, C. J. (2014). Cancer‐specific relationship awareness, relationship communication, and intimacy among couples coping with early‐stage breast cancer. Journal of Social and Personal Relationships, 31(3), 314–334. http://doi.org/10.1177/0265407 513494950 Martire, L. M., Schulz, R., Helgeson, V. S., Small, B. J., & Saghafi, E. M. (2010). Review and meta‐analysis of couple‐oriented interventions for chronic illness. Annals of Behavioral Medicine, 40(3), 325– 342. http://doi.org/10.1007/s12160‐010‐9216‐2 Molloy, G. J., Hamer, M., Randall, G., & Chida, Y. (2008). Marital status and cardiac rehabilitation attendance: A meta‐analysis. European Journal of Cardiovascular Prevention & Rehabilitation, 15(5), 557–561. http://doi.org/10.1097/HJR.0b013e3283063929 Monin, J. K., Schulz, R., & Kershaw, T. S. (2013). Caregiving spouses’ attachment orientations and the physical and psychological health of individuals with Alzheimer’s disease. Aging & Mental Health, 17(4), 508–516. http://doi.org/10.1080/13607863.2012.747080

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Pietromonaco, P. R., DeVito, C., Ge, F., & Lembke, J. (2015). Health and attachment processes. In J. A. Simpson & W. S. Rholes (Eds.), Attachment theory and research: New directions and emerg­ ing themes (pp. 287–318). New York, NY: Guilford Press. Pietromonaco, P. R., & Powers, S. I. (2015). Attachment and health‐related physiological stress processes. Current Opinion in Psychology, 1, 34–39. http://doi.org/10.1016/j.copsyc.2014.12.001 Robles, T. F., Slatcher, R. B., Trombello, J. M., & McGinn, M. M. (2014). Marital quality and health: A meta‐analytic review. Psychological Bulletin, 140(1), 140–187. http://doi.org/10.1037/ a0031859 Stanton, S. C. E., & Campbell, L. (2014). Psychological and physiological predictors of health in romantic relationships: An attachment perspective. Journal of Personality, 82(6), 528–538. http:// doi.org/10.1111/jopy.12056 Uchino, B. N., Smith, T. W., & Berg, C. A. (2014). Spousal relationship quality and cardiovascular risk dyadic perceptions of relationship ambivalence are associated with coronary‐artery calcification. Psychological Science, 25(4), 1037–1042. http://doi.org/10.1177/0956797613520015 Whisman, M. A., & Sbarra, D. A. (2012). Marital adjustment and interleukin‐6 (IL‐6). Journal of Family Psychology, 26(2), 290–295. http://doi.org/10.1037/a0026902

Suggested Reading Loving, T. J., & Slatcher, R. B. (2013). Romantic relationships and health. In J. A. Simpson & L. Campbell (Eds.), The Oxford handbook of close relationships (pp. 617–637). New York, NY: Oxford University Press. Robles, T. F. (2014). Marital quality and health implications for marriage in the 21st century. Curr­ ent Directions in Psychological Science, 23(6), 427–432. doi:http://doi.org/10.1177/0963721 414549043 Wright, B. L., & Loving, T. J. (2011). Health implications of conflict in close relationships. Social and Personality Psychology Compass, 5(8), 552–562. http://doi.org/10.1111/j.1751‐9004.2011 .00371.x

Medical Decision Making Meng Li1 and Gretchen B. Chapman2 Department of Health and Behavioral Sciences, University of Colorado Denver, Denver, CO, USA 2 Department of Psychology, Rutgers University–New Brunswick, New Brunswick, NJ, USA 1

Medical decision making encompasses decisions made in all health and medicine contexts by patients, healthcare workers, health policy makers, and the general public. The study of medical decision making follows research on judgment and decision making more generally: It compares actual or descriptive decision making to normative or rational models of decision making, where the latter provide a benchmark for how decisions should be made. It also explores interventions that can improve decision making or guide choice behavior in a particular direction (i.e., toward selection of a particular healthy option). Medical decisions are often difficult because they entail choosing among options with uncertain outcomes; they require trade‐offs between competing goals, often with overwhelming amounts of information; they involve taking on immediate costs for long‐term benefits; they may also require strategic interactions among multiple agents. Below we characterize medical decision making by discussing some selective theoretical and empirical issues related to each of these topics.

Risk and Uncertainty Many medical decisions involve uncertain outcomes. For example, a patient must decide whether to undergo surgery, not knowing if the surgery will be successful. A physician must decide whether to recommend a preventive measure, such as cholesterol‐lowering medication, not knowing whether the patient would be fine even without the medication. According to normative expected utility theory, the utility of each outcome (e.g., stroke) should be weighted by the probability that the outcome will occur. Consequently, it is critical that decision makers be able to assess, at least implicitly, the probability of key outcomes and to weight the utility of outcomes in proportion to their probability. In a number of cases, however, outcomes are systematically over‐ or underweighted, and low likelihood events tend to be overweighted and The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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thus have more influence on decisions than they should. This phenomenon is illustrated in the American response to the 2014 Ebola outbreak in West Africa. Although contracting Ebola in the United States was an extremely low likelihood event (only four cases of Ebola were diagnosed in the United States, only two of which were contracted in the United States), the response of some US public officials was indicative of a much higher level of risk. For example, in October 2014, the governors of New York and New Jersey announced a mandatory quarantine for any healthcare workers returning from treating Ebola patients in West Africa to the United States via a New York or New Jersey airport. It appears that decision makers draw a large distinction between an outcome with a 0% likelihood and one with a small positive likelihood. For example, people will pay more to reduce the risk of an adverse event from a toxin from 5 in 10,000 to 0 in 10,000 than they will pay for a risk reduction from 15 in 10,000 to 10 in 10,000, even though both are the same net reduction in risk (Viscusi, Magat, & Huber, 1987). Similarly, decision makers draw a large distinction between 100% and a proportion that is slightly smaller than 100%. Consequently, 100% of a small category can seem larger than 50% of a large category, whether the percentages refer to probability, effectiveness, disease coverage, or something else (Li & Chapman, 2009, 2013).

Difficult Trade‐Offs A medical decision can be especially difficult if it encapsulates a trade‐off between two important but opposing goals or attributes. Trade‐offs in medical decision making are prevalent, such as in the choice between quality and length of life in end‐of‐life decisions or in the design of screening programs that entail either greater false alarms or potential misses of true pathologies. According to normative multi‐attribute utility theory, each attribute should be weighted according to its importance, and an overall utility (goodness) for each option could be computed by summing the weighted utility of all attributes. However, real‐life decisions often violate this normative prescription, and the weight that people assign to attributes can shift depending on irrelevant factors, such as how the problem is described. One often‐debated issue in health policy settings involves the trade‐off between efficiency and equality, such as in the allocation of scarce medical resources, including transplant organs, scarce vaccines, etc. For example, quality‐adjusted life year (QALY) is a widely used metric to assess benefits in medical options. The rationale behind the use of QALYs is that more life years saved represents more net benefit, regardless of whose QALYs are saved (QALYs entail younger people being prioritized, as saving them produces more QALYs). People also hold another important goal in the allocation of medical resources: equality in receiving such resources. People place great value on equality, to the point that they are willing to save fewer lives to achieve equal allocation of lifesaving resources (Ubel, Baron, & Asch, 2001). However, the preference for equity at the expense of efficiency is also volatile and subject to change depending on how the question is posed. For instance, in the choice between younger and older lives when not all lives can be saved, people usually prefer to save younger lives, demonstrating a preference for efficiency (Li, Vietri, Galvani, & Chapman, 2010), but such preferences fluctuate systematically. When people read descriptions on allocation outcomes (e.g., 500 20‐year old people will be saved; 500 60‐year old people will be saved), they prefer to save younger individuals; however, when they read general principles on how different lives should be prioritized under scarcity (e.g., “younger people should be valued more,” “older people should be valued more,” or “all lives should be valued equally regardless of age”), people demonstrate greater preference for equal allocation across young and old recipients (Li, 2012).

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Another example in which preference for efficiency versus equality can be shifted with a fairly subtle change in description comes from a study by Colby, DeWitt, and Chapman (2015). Participants allocated 6 transplant kidneys to 12 potential recipients, 6 of whom had a high chance of transplant success. When the 12 recipients were presented in one group, participants tended to allocate the kidneys efficiently, giving them to the 6 recipients most likely to benefit from them. However, when the 12 recipients were presented in 2 groups of 6 each, participants tended to spread the kidneys across the 2 groups, even though this meant sacrificing efficiency.

Information Overload All decisions entail a comparison and selection among multiple options, even if some options are not salient (e.g., choosing nothing, doing nothing). But what happens when there are a large number of options available, accompanied by a large amount of information about them? This is the situation many Americans face after the recent ratification of the Patient Protection and Affordable Care Act (ACA), which allows them to select from among dozens of healthcare plans. Providing too many healthcare plans can decrease decision quality. For example, Johnson, Hassin, Baker, Bajger, and Treuer (2013) presented participants with premium, co‐pay, and deductible information for multiple health plans and found that participants frequently showed near‐chance levels of performance in selecting the lowest cost plan because they gave too much weight to out‐of‐pocket expenses and deductibles. Although Johnson et al.’s participants made hypothetical choices, other researchers who analyzed data from actual health plan choices also found that consumers frequently chose dominated health plans—plans that were more expensive but provided no better benefits than alternative plans—because consumers focused too heavily on deductibles (Bhargava, Loewenstein, & Sydnor, 2015). Thus, choosing from a large choice set makes for a difficult psychological task, sometimes resulting in the decision maker violating normative principles such as dominance. Therefore, medical decisions can be improved by simplifying choice sets or providing smart defaults to decision makers.

Discounting Future Outcomes Frequently, medical decisions involve delayed outcomes. For example, a calorie‐dense diet and low levels of physical activity lead to increased risk of obesity and associated comorbidities such as diabetes and heart disease. However, these consequences are delayed, often by years or decades. Decision makers value delayed outcomes less than equivalent immediate outcomes; consequently, it is difficult for decision makers to forgo the immediate temptations of a piece of cheesecake or the TV episode instead of a trip to the gym. According to normative discounted utility theory, delayed outcomes should be discounted in a consistent manner, and thus people should not change their preferences between a larger later reward (e.g., the long‐ term benefit of weight loss) and a smaller sooner reward (e.g., the enjoyment of a piece of cheesecake) at different time points, that is, if you decided that the long‐term benefit of weight loss is more important than the enjoyment of cheesecake that you can have soon, you should be able to stick to this decision at any given time. However, preference between these two types of rewards frequently changes with time: People often choose the larger later reward initially (such as on New Year’s Day), when they are temporally distant from both rewards, but later fall for the temptation when it becomes available immediately (as the dessert tray rolls by). This inconsistent choice marks the typical pattern of self‐control failure.

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As a solution to this problem, decision makers will sometimes take advantage of the ­opportunity to precommit or bind themselves to the earlier preference for the larger later reward. For example, Schwartz et al. (2014) invited grocery store shoppers to lay their money on the line to precommit to buying more healthy foods such as fresh produce. The shoppers agreed to forfeit the discount they already received for buying healthy foods if they did not meet their healthy foods purchase goal. Indeed, these shoppers increased their healthy food purchases relative to a control group who merely indicated hypothetically whether they would precommit. Similarly, dieters who do not keep desserts in the house are taking advantage of a precommitment device, as are people who prepay for a gym membership in the hopes of motivating themselves to work out frequently to “get their money’s worth.” Another solution to the problem of discounted future outcomes is bundling temptation with a desired behavior, so one is allowed to indulge in a pleasure only when one engages in a necessary but unpleasant health behavior. For example, Milkman, Minson, and Volpp (2014) found that people visited the gym more often if they could listen to addictive audio books only when they were at the gym.

Strategic Behavior Some medical decisions are made in a strategic setting where the choices made by an individual affect the outcomes of others while at the same time the decisions of others affect the outcomes experienced by the individual. Take vaccination as an example. Because of herd immunity, an individual’s vaccination protects not only herself from infection but also her social contacts, because she is now less likely to spread the virus to others. Consequently, if enough individuals in a population are vaccinated, the unvaccinated members are protected from infection. Thus, vulnerable individuals who cannot receive vaccination themselves (e.g., infants) can be cocooned in this fashion. Because of this positive externality from vaccination, an individual’s decision about whether to vaccinate may be affected by how many others in the population are vaccinating (Galvani, Reluga, & Chapman, 2007). This strategic interaction can be modeled using game theory (Chapman et al., 2012): individuals can “defect” by getting a free ride, that is, forgoing vaccination because they receive protection from others who vaccinated, or they can “cooperate,” that is, vaccinating in part to protect those around them. Free riding may contribute to the anti‐vaccination movement because those opposed to vaccination can forgo immunization without bearing much risk of contracting infectious disease, as long as the majority of the surrounding population is immunized. When critical numbers of those opposed to vaccination are clustered in one geographical region, however, herd immunity can dip below the threshold that is necessary to prevent the spread of disease, making the larger population vulnerable to an outbreak, such as the measles outbreak in a theme park in California in 2015. Other medical decisions that entail externalities include overuse of antibiotics, which does not affect the user negatively but contributes to the development of resistant strains of bacteria, and the over‐ordering of expensive medical tests for which the physician does not pay but which will drive up healthcare costs and insurance premiums.

Numeracy Normative models of decision making assume that decision makers will make informed decisions, provided that they are in possession of the relevant facts. However, decision makers vary in the capacity to comprehend and use information to approach choices. For example, numer-

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acy, or the capacity to process numerical information, is essential in medical decision making, as such decisions frequently involve numbers (e.g., risk probabilities, efficacy information, cost, time). Numeracy levels among Americans are worrisome, with many people, even those who are highly educated, are particularly lacking in the ability to process percentages and fractions. These basic numerical competencies are critical in the medical context, such as when one needs to calculate medication dosage based on a child’s body weight. A recent review (Reyna, Nelson, Han, & Dieckmann, 2009) concluded that patients who have low numeracy are more likely to misinterpret graphs containing medical information, more easily influenced by factors irrelevant to the decision at hand (such as how the information is presented), and more likely to overestimate small risks and have poorer health outcomes when the health condition requires a high degree of self‐management. Perhaps more disturbingly, medical professionals do not necessarily possess the level of numeracy that is required to correctly understand risk information. And the lack of capacity to comprehend risk information can incur serious consequences. For example, despite the commonly held belief that cancer screening saves lives, some cancer screening tests can produce a large number of false positives, encouraging overtreatment for nonprogressive cancer while yielding little or no life‐saving benefit, and inflating the 5‐year survival rate. Therefore, to evaluate the effectiveness of a screening test in saving lives, a reduced mortality rate should be used as the gold standard, instead of improved survival rates. Unfortunately, a study by Wegwarth, Schwartz, Woloshin, Gaissmaier, and Gigerenzer (2012) showed that about half of physicians do not share this knowledge and instead incorrectly believe that increased early detection constitutes evidence that such screening saves lives. This lack of numerical competency among physicians may have contributed to the ill‐deserved enthusiasm toward cancer screening tests and the resulting financial and health toll of their overuse.

Healthy Nudges Given the myriad ways in which actual medical decisions deviate from normative models of decision making, researchers and clinicians alike consider what can be done to improve medical decisions. Decision psychologists and behavioral economists have recently developed nudges (Thaler & Sunstein, 2008), or changes to the decision environment that facilitate selection of the healthy option, while maintaining freedom of choice. One well‐known nudge is the default effect, or the tendency for people to stick with the option they will get automatically if they take no explicit action. For example, people prescheduled for a flu shot appointment (which they can cancel if they do not want it) are more likely to get vaccinated than are those who are not prescheduled (Chapman, Li, Colby, & Yoon, 2010). Although both groups have a choice to have a flu shot appointment or not, for the prescheduled group, the default is having a flu shot appointment, and for the comparison group the default is not having an appointment. Because most people tend to stick with their default status, and because having a flu shot appointment is a strong predictor of actually getting a flu shot, vaccination rates increased by 36% (from 33 to 45%) in the group with a default vaccination appointment relative to the group where an appointment has to be actively scheduled. There are other types of nudges toward healthy behavior. For example, because people tend to conform their behavior to that of the group, giving people information about what others do will nudge them toward that same behavior. Social norms have been used to reduce caloric intake (by printing recommended calorie intake on menus, combined with menu calorie labeling; Roberto, Larsen, Agnew, Baik, & Brownell, 2010), to increase children’s vegetable intake

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at school lunch (by placing photographs of vegetables on wells of the lunch tray; Reicks, Redden, Mann, Mykerezi, & Vickers, 2012), and to encourage walking (by informing participants who wear pedometers how much others are walking; Chapman, Colby, Convery, & Coups, 2015). Another example of a healthy nudge is the position effect, in which food options displayed at prominent, easy‐to‐reach positions are chosen more frequently. Researchers have used the position effect to encourage healthy eating in cafeterias and restaurants (e.g., Rozin, Scott, & Dingley, 2011). In summary, medical decision making includes not only decisions that are made in strictly medical settings but also decisions made in everyday life that impact health. Research on medical decision making can inform health professionals as well as the general public about the pitfalls in medical decisions, how to overcome them, and how to use decision biases to the advantage of people’s health.

Author Biographies Meng Li is an assistant professor in the Department of Health and Behavioral Sciences at the University of Colorado Denver. Her work, which has been featured on JAMA, Lance, Psychological Science, and other top journals, utilizes decision biases to “nudge” people toward healthy behavior, such as vaccination, healthy diet, and hand sanitizer use. Her more recent work explores policy issues, including resource allocation on money and health, price transparency in healthcare, and work–life balance policies. Gretchen B. Chapman is a professor of psychology and a member of the Institute for Health and the Center for Cognitive Science at Rutgers University. Her research combines judgment and decision making with health psychology to examine preventive health behaviors such as vaccination. She conducts field studies to evaluate decision theoretic interventions designed to encourage health behavior and laboratory research to examine basic processes in decisions under uncertainty, strategic interactions, and allocation of scarce resources.

References Bhargava, S., Loewenstein, G., & Sydnor, J.R. (2015). Do individuals make sensible health insurance decisions? Evidence from a menu with dominated options. NBER Working Paper No. w21160. Reterived from http://ssrn.com/abstract=2604841 Chapman, G. B., Colby, H., Convery, K., & Coups, E. J. (2015). Goals and social comparisons promote walking behavior. Medical Decision Making, 36(4), 472–478. doi:10.1177/0272989X15592156 Chapman, G. B., Li, M., Colby, H., & Yoon, H. (2010). Opting in versus opting out of influenza vaccination. The Journal of the American Medical Association, 304(1), 43–44. Chapman, G. B., Li, M., Vietri, J., Ibuka, Y., Thomas, D., Yoon, H., & Galvani, a. P. (2012). Using game theory to examine incentives in influenza vaccination behavior. Psychological Science, 23, 1008–1015. doi:10.1177/0956797612437606 Colby, H., DeWitt, J., & Chapman, G. B. (2015). Grouping promotes equality: The effect of recipient grouping on allocation of limited medical resources. Psychological Science, 26, 1084–1089. doi:10.1177/0956797615583978 Galvani, A. P., Reluga, T. C., & Chapman, G. B. (2007). Long‐standing influenza vaccination policy is in accord with individual self‐interest but not with the utilitarian optimum. Proceedings of the National Academy of Sciences of the United States of America, 104, 5692–5697. doi:10.1073/ pnas.0606774104

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Johnson, E. J., Hassin, R., Baker, T., Bajger, A. T., & Treuer, G. (2013). Can consumers make affordable care affordable? The value of choice architecture. PLoS One, 8(12). doi:10.1371/journal.pone.0081521 Li Meng (2012). How do people value life? (Dissertation). Retrieved from https://rucore.libraries. rutgers.edu/rutgers‐lib/37432/ Li, M., & Chapman, G. B. (2009). “100% of anything looks good”: The appeal of one hundred percent. Psychonomic Bulletin & Review, 16(1), 156–162. doi:10.3758/PBR.16.1.156 Li, M., & Chapman, G. B. (2013). A big fish or a small pond? Framing effects in percentages. Organizational Behavior and Human Decision Processes, 122(2), 190–199. doi:10.1016/j.obhdp.2013.07.003 Li, M., Vietri, J., Galvani, A. P., & Chapman, G. B. (2010). How do people value life? Psychological Science, 21, 163–167. doi:10.1177/0956797609357707 Milkman, K. L., Minson, J. a., & Volpp, K. G. M. (2014). Holding the hunger games hostage at the gym: An evaluation of temptation bundling. Management Science, 60, 283–299. doi:10.1287/ mnsc.2013.1784 Reicks, M., Redden, J. P., Mann, T., Mykerezi, E., & Vickers, Z. (2012). Photographs in lunch tray compartments and vegetable consumption among children in elementary school cafeterias. JAMA, 307(8), 784–785. doi:10.1001/jama.2012.170 Reyna, V. F., Nelson, W. L., Han, P. K., & Dieckmann, N. F. (2009). How numeracy influences risk comprehension and medical decision making. Psychological Bulletin, 135(6), 943–973. Roberto, C. A., Larsen, P. D., Agnew, H., Baik, J., & Brownell, K. D. (2010). Evaluating the impact of menu labeling on food choices and intake. American Journal of Public Health, 100(2), 312–318. doi:10.2105/AJPH.2009.160226 Rozin, P., Scott, S., & Dingley, M. (2011). Nudge to nobesity I: Minor changes in accessibility decrease food intake. Judgement and Decision Making, 6(4), 323–332. Retrieved from http://www.sjdm. org/journal/11/11213/jdm11213.pdf Schwartz, J., Mochon, D., Wyper, L., Maroba, J., Patel, D., & Ariely, D. (2014). Healthier by precommitment. Psychological Science, 25(January), 538–546. doi:10.1177/0956797613510950 Thaler, R., & Sunstein, C. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press. Ubel, P. A., Baron, J., & Asch, D. A. (2001). Preference for equity as a framing effect. Medical Decision Making, 21(3), 180–189. doi:10.1177/0272989X0102100303 Viscusi, W. K., Magat, W. A., & Huber, J. (1987). An investigation of the rationality of consumer valuation of multiple health risks. RAND Journal of Economics, 18(4), 465–479. Wegwarth, O., Schwartz, L. M., Woloshin, S., Gaissmaier, W., & Gigerenzer, G. (2012). Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States. Annals of Internal Medicine, 156(5), 340–349. doi:10.7326/0003‐4819‐156‐5‐201203060‐00005

Suggested Reading Betsch, C., Böhm, R., & Chapman, G. B. (2015). Interventions to counter vaccine hesitancy—Using behavioral insights to increase vaccination policy effectiveness. Policy Insights From the Behavioral and Brain Sciences, 2(1), 61–73. Li, M., & Chapman, G. B. (2013). Nudge to health: Harnessing decision research to promote health behavior. Social Psychology Compass, 7(3), 187–198. Milkman, K. L., Rogers, T., & Bazerman, M. H. (2008). Harnessing our inner angels and demons: What we have learned about want/should conflicts and how that knowledge can help us reduct short‐ sighted decision making. Perspectives on Psychological Science, 3(4), 324–338. Reyna, V. F., Nelson, W. L., Han, P. K., & Dieckmann, N. F. (2009). How numeracy influences risk comprehension and medical decision making. Psychological Bulletin, 135(6), 943–973. Thaler, R., & Sunstein, C. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press.

Message Framing Scout N. McCully and John A. Updegraff Department of Psychological Sciences, Kent State University, Kent, OH, USA

Doctors, health psychologists, and medical professionals implore patients to adopt and maintain health behaviors, but effective persuasion often proves difficult. Message framing offers a relatively simple and versatile way to tailor a health message to increase its effectiveness. Message framing is built upon the idea that all decisions regarding health behaviors carry consequences; generally, engaging in a health behavior will yield more positive consequences than negative consequences, while failing to do so will yield more negative outcomes than positive outcomes. As such, messages about health behaviors can be framed to emphasize what can be gained by performing the behavior (gain framed) or what can be lost by not performing the behavior (loss framed). For example, a message about the flu shot could read, “If you get a flu shot, you will reduce your risk of getting the flu this year” (gain framed), or it could read, “If you do not get a flu shot, you will be at risk of getting the flu this year” (loss framed). The gain‐framed message emphasizes the positive consequences of getting a flu shot, while the loss‐framed message highlights the negative consequences associated with not getting a flu shot. Message framing developed as an extension of prospect theory (Tversky & Kahneman, 1981), which suggests that people will make different choices when outcomes are framed as gains versus losses. People are more likely to make risky decisions when faced with a loss, as they would rather gamble for the possibility of no losses (risking greater losses along the way) than accept certain loss. Alternatively, people are risk averse when contemplating a gain, as they would rather maintain a certain gain—even a small gain—than risk losing it all. Rothman and Salovey (1997) extended prospect theory to health behavior messages, proposing that gain‐ and loss‐framed messages would differentially affect behavior depending on the behavior the message endorsed. After years of examining message framing across various behaviors, people, and situations, researchers have found no general advantage of one frame over the other (Gallagher & Updegraff, 2012). However, research suggests that in specific circumstances, gain‐framed messages produce greater behavior change than loss‐framed messages, while in other circumstances, loss‐framed messages prevail. Thus, message framing research has focused on understanding the moderators or conditions that make either gain‐ or loss‐framed messages more effective in producing behavior change. The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Moderators of Message Framing Effects Moderators of message framing effects can be organized into three categories: behavior‐related moderators, person‐related moderators, and context‐related moderators (see Table  1 for a summary). In this entry, we use the term behavior‐related moderator to refer to a characteristic of the target health behavior that affects the relative persuasiveness of a gain‐ or loss‐framed message. A person‐related moderator is a state or trait characteristic of the target individual or audience member that influences the relative effectiveness of a particular frame. A context‐ related moderator refers to a characteristic of the situation in which the message is presented that changes the effect of message frames.

Behavior‐Related Moderators The most influential and widely studied moderator of framing effects is the behavior‐related moderator of prevention versus detection behaviors. As mentioned above, prospect theory (Tversky & Kahneman, 1981) proposes that when faced with a loss, people are more likely to make risky decisions, but when faced with a gain, people are risk averse. Rothman and Salovey (1997) theorized that illness detection behaviors (e.g., mammograms, cancer screenings) carry an element of risk because such tests may reveal a health threat. Alternatively, behaviors focused on promotion of health or prevention of illness (e.g., physical activity, sunscreen use) carry little risk because they generally support and improve health. Thus, loss‐framed messages should be more effective at promoting high‐risk detection behaviors, whereas gain‐framed messages should be more effective at promoting low‐risk prevention behaviors. After two decades of research on framing effects in prevention and detection behaviors, a meta‐analysis concluded that indeed gain‐framed messages tended to be more effective than loss‐framed messages in promoting prevention behaviors, but loss‐framed messages were no more effective than gain‐framed messages in promoting detection behaviors (Gallagher & Updegraff, 2012). For prevention behaviors, gain‐framed messages produced significantly higher rates of behavior in the domains of smoking, skin cancer prevention, and physical activity. Effects of message framing were weak for safe sex, diet, oral health, and vaccination, but in no case was a loss‐framed message significantly more effective than a gain‐framed message in prevention behaviors. Although the effect size of the gain‐frame advantage (r  =  .083) was Table 1  Recommendations for framing health messages. Recommended message frame Moderator Behavior‐related moderators Prevention vs. detection Person‐related moderators Motivational orientation Culture Perceived susceptibility Self‐efficacy Context‐related moderators Autonomy vs. heteronomy Color

Gain frame

Loss frame

Prevention

n/a

Approach oriented Individualistic Low susceptibility n/a

Avoidance oriented Collectivistic High susceptibility High self‐efficacy

Prime autonomy n/a

Prime heteronomy Red

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r­ elatively weak, Gallagher and Updegraff (2012) suggest that given the complexity of health behavior determinants, even a relatively weak effect may work additively with other factors to produce meaningful changes in behavior. In contrast to prevention behaviors, detection behaviors were not significantly affected by message framing. However, in examining specific domains of detection behaviors, the meta‐analysis found that loss‐framed messages were marginally more effective in promoting breast cancer detection behaviors than gain‐framed messages. Behavior‐related moderators provide high‐level recommendations for framing, particularly useful when little or no information about the target audience is available. A notable example would be a message created for mass production (e.g., public service announcements), which could be made most effective by framing it according to the behavior’s inherent risk. Based on Gallagher and Updegraff’s (2012) findings, mass‐media messages promoting preventive health behaviors (e.g., physical activity, nutrition, sunscreen use, condom use, oral healthcare) should emphasize the positive outcomes that can be gained by adopting and maintaining these behaviors. When encouraging detection behaviors, healthcare professionals can rely on person‐ and context‐related moderators to help determine the most effective message frame.

Person‐Related Moderators Where behavior‐related moderators offer broad recommendations for framing, person‐related moderators allow healthcare professionals to tailor framed messages to a patient when characteristics of that person are known. A number of person‐related variables have been found to moderate the effect of message frame on health behaviors including a person’s motivational orientation, culture, perceived susceptibility, and self‐efficacy. Motivational orientation refers to the degree that a person is predominantly motivated by cues of reward (approach oriented) versus cues of punishment or threat (avoidance oriented). Because gain‐framed messages emphasize the reward to be gained by performing a behavior, researchers theorized that gain‐framed messages would be more congruent and thus more persuasive for approach‐oriented individuals. Alternatively, loss‐framed messages emphasize the punishment or negative consequences of not adopting a behavior and thus should be a better match for avoidance‐oriented individuals. Indeed, research indicates that approach‐oriented people are more responsive to gain‐framed messages, whereas avoidance‐oriented people are more responsive to loss‐framed messages. For example, in the domain of oral health behavior, participants who were primarily approach oriented flossed more after reading a gain‐ framed article, and participants who were primarily avoidance oriented flossed more after reading a loss‐framed article (Sherman, Mann, & Updegraff, 2006). Thus, a clinician may assess motivational orientation using a short survey and then maximize a message’s effectiveness by framing it to be congruent with the patient’s predominant orientation. In addition to moderators borne from individual differences, cultural difference factors may also play a role in the relative effectiveness of gain‐ and loss‐framed messages. In a study assessing the moderating influence of culture on flossing behavior, researchers found that people with low exposure to US culture, measured by proportion of life spent in United States and whether parents were born in the United States, flossed more after seeing a loss‐framed message compared with a gain‐framed message (Brick et al., 2015). While culturally congruent messages resulted in greater flossing, culturally incongruent messages led to the same levels of flossing as seeing no message. Why might this be? Researchers have found systematic differences in motivational orientation across cultures, with collectivistic East Asian and Latino cultures tending to be more

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avoidance oriented and individualistic White British and American cultures tending to be more approach oriented. It is possible that because of these cultural orientation propensities, loss‐ framed messages are more effective among people from (or primarily exposed to) collectivistic cultures, whereas gain‐framed messages are more effective among people from (or primarily exposed to) individualistic cultures. Indeed, support for this explanation comes from a study that examined differences in how White and East Asian individuals living in Britain responded to framed messages promoting oral health (Uskul, Sherman, & Fitzgibbon, 2009). White British individuals were more persuaded by a gain‐framed message, whereas East Asian individuals were more persuaded by a loss‐framed message. Clinically, cultural background can be measured with simple demographic questions often obtained during intake, and as such offers a simple and practical way for health professionals to tailor framed messages to patients, and also may suggest ways to address cultural and ethnic disparities in health. Perceived susceptibility to a health condition has been studied as another person‐related moderator of framing effects, particularly in detection behaviors. Although detection behaviors were originally proposed as inherently risky, people’s perceptions of the risk of these behaviors likely fall along a spectrum, with some perceiving very little risk and others perceiving great risk. Specifically, people who perceive themselves to be more susceptible to a disease may be more likely to perceive the detection behavior as risky. Indeed, research on mammography testing indicates that among those with high perceived susceptibility, loss‐ framed messages are more effective than gain‐framed messages but that message frame does not affect behavior when perceived susceptibility is low (Gallagher, Updegraff, Rothman, & Sims, 2011). In the context of oral health (Updegraff, Brick, Emanuel, Mintzer, & Sherman, 2015), moderation by perceived susceptibility was also observed: a gain‐framed message was more effective for those who perceived low susceptibility to oral health problems, and a loss‐framed message was somewhat more effective for those who perceived high susceptibility. Finally, self‐efficacy has also been proposed as a moderator of message frame, such that loss‐ framed messages appear to be more effective for those with high confidence in their ability to perform a behavior. However, this finding is currently limited to studies of self‐examinations and smoking cessation; similar studies have failed to replicate this moderation in the context of other behaviors such as immunization (Covey, 2014). Although person‐related moderators may be difficult for clinicians to apply because they require pre‐assessment and tailoring, they can offer valuable framing guidance when behavior‐related cues are unclear.

Context‐Related Moderators In addition to behavior‐ and person‐related moderators, context‐related moderators may influence the relative effectiveness of gain‐ and loss‐framed messages. Theoretically, context‐ related moderators could be manipulated within a persuasion situation to increase the effectiveness of a framed message, unlike relatively stable factors like behavior‐ and person‐related moderators. One example of a context‐related moderator is the role of perceived autonomy versus heteronomy in health decision making. Autonomy refers to the experience of engaging in actions based on one’s personal interests and values, whereas heteronomy refers to engaging in actions based on some pressure or coercion from other people. Pavey and Churchill (2014) found that after priming autonomy (i.e., having people generate sentences with words like freedom, choice, decision), gain‐framed message produced greater behavior change than loss‐ framed messages, but after priming heteronomy (i.e., having people generate sentences with words like pressure, control, must), loss‐framed messages were more effective.

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Additionally, the use of colors may moderate framing effects. In particular, the use of the color red—which often acts as a cue of threat, due to its association with blood and danger— has been shown to increase the persuasiveness of loss‐framed messages (Gerend & Sias, 2009). The domain of context‐related moderators requires further study, but it offers the unique benefit of allowing researchers and clinicians to amplify the strength of a framed message by modifying the context in which it is presented. For example, if the target behavior is prevention focused, and the target audience is primarily approach oriented, the clinician could choose a gain‐framed message and theoretically amplify it by emphasizing the individual’s autonomy and freedom of choice. However, researchers have only begun to investigate the role of context‐related moderators, and additional empirical work is needed to replicate and extend these findings.

Mediators of Message Framing Effects Why do gain‐ or loss‐framed messages have a differential impact on persuasion and behavior? While much is known about the moderators of framing effects, much less is known about the psychological mechanisms—that is, mediators—that account for these effects. Indeed, there may be many processes that make a gain‐ or loss‐framed message more effective, including conscious cognitive processes as well as more implicit or emotional processes. Most of the research that has looked at mediators of framing effects has focused on conscious cognitive processes, such as changes in consciously articulated attitudes or intentions toward the advocated health behavior, but many studies have failed to show that these factors consistently explain effects on behavior (see Gallagher & Updegraff, 2012 for review). It is possible, then, that more implicit or emotional reactions may explain the effects of framing on behavior. However, to date, two mediators seem to have the greatest potential to explain the influences of framed messages on behavior: elaboration and the subjective sense of “feeling right.” Specifically, research has found that people tend to elaborate on a message more—that is, they process the message at a greater depth—when the message frame is congruent with their motivational orientation. For example, people receiving congruent messages were better able to discern strong and weak arguments about the importance of flossing than people who received incongruent messages (Updegraff, Sherman, Luyster, & Mann, 2007). In addition, congruently framed messages may be more likely to lead a person to have a subjective sense of “feeling right” about their reactions to a message than incongruently framed messages (Cesario, Higgins, & Scholer, 2008). Interestingly, both the elaboration and the “feeling right” accounts highlight the importance of basing a framed health messages on strong, convincing information. If recipients are more likely to think deeply about and “feel right” about their reactions to a congruently framed health message, then incorporating unconvincing or flimsy information into the message may have the unintended effect of decreasing the effectiveness of the framing. Thus, the mediation of framing effects is complex and requires further study but is an important direction for future research.

Applying Message Framing Message framing offers a relatively simple and versatile way for clinicians and health professionals to present health messages in a maximally persuasive way. Specifically, the moderators

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discussed above offer a number of ways to decide how to determine the frame of the message. If a message is to be mass‐produced, or if no information about the recipient is known, the message crafter should consider whether the behavior is a detection or prevention behavior and frame the message accordingly; that is, if the behavior is a prevention or promotion behavior, the clinician should gain‐frame the message. However, if more information is known about the audience, the clinician may use some of these characteristics to determine an appropriate message frame. A recipient who is avoidance oriented or perceives high susceptibility may be more strongly persuaded by a loss‐framed message. Alternatively, a person from an individualistic culture or with low perceived susceptibly may be better persuaded by a gain‐ framed message. Under certain circumstances, having information about a number of moderators can pose difficult clinical decisions if those pieces of information propose conflicting message frames. For example, if the behavior is a detection behavior, and the target individual perceives high susceptibility, but the individual is also approach oriented and from an individualistic culture, which frame should be used? The high perceived susceptibility suggests a loss‐framed message, but the motivational and cultural orientations suggest a gain‐framed message. Clearly, conflicting information poses challenges for the clinician hoping to put this information to good use. Additional research that examines multiple moderators in concert is needed to determine relative strength and weights of these various moderators so that clinicians can make better informed decisions when selecting message frames. Message framing is a particularly versatile application, as it can be applied through any number of clinically relevant media, from brochures and signs to videos to live doctor–patient conversations. With a simple modification to the way in which an action (or lack of action) is paired with a consequence, healthcare professionals may be able to increase the effectiveness of their communications and help patients make healthier decisions.

Author Biographies Scout N. McCully is a doctoral student in psychological sciences at Kent State University. Her work seeks to improve people’s success in adopting and maintaining health behaviors, with a focus on simple, scalable, theory‐driven interventions. John A. Updegraff is professor of psychological sciences at Kent State University. His research examines the effectiveness of tailoring persuasive health messages to match the ways in which people think about health behaviors and their environments in general. A central goal of his work is to develop theoretical frameworks and strategies for communicating health information in ways that are personally relevant and effective in promoting long‐term behavior change.

References Brick, C., McCully, S. N., Updegraff, J. A., Ehret, P. J., Areguin, M. A., & Sherman, D. K. (2015). Impact of cultural exposure and message framing on oral health behavior: Exploring the role of message memory. Medical Decision Making, 6(7), 834–843. doi:10.1177/0272989X15570114 Cesario, J., Higgins, E. T., & Scholer, A. A. (2008). Regulatory fit and persuasion: Basic principles and remaining questions. Social and Personality Psychology Compass, 2(1), 444–463. doi:10.1111/j.1751‐ 9004.2007.00055.x

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Covey, J. (2014). The role of dispositional factors in moderating message framing effects. Health Psychology, 33(1), 52–65. doi:10.1037/a0029305 Gallagher, K. M., & Updegraff, J. A. (2012). Health message framing effects on attitudes, intentions, and behavior: A meta‐analytic review. Annals of Behavioral Medicine, 43(1), 101–116. doi:10.1007/ s12160‐011‐9308‐7 Gallagher, K. M., Updegraff, J. A., Rothman, A. J., & Sims, L. (2011). Perceived susceptibility to breast cancer moderates the effect of gain‐ and loss‐framed messages on use of screening mammography. Health Psychology, 30(2), 145–152. doi:10.1037/a0022264 Gerend, M. A., & Sias, T. (2009). Message framing and color priming: How subtle threat cues affect persuasion. Journal of Experimental Social Psychology, 45(4), 999–1002. doi:10.1016/j.jesp. 2009.04.002 Pavey, L., & Churchill, S. (2014). Promoting the avoidance of high‐calorie snacks: Priming autonomy moderates message framing effects. PLoS One, 9(7). doi:10.1371/journal.pone.0103892 Rothman, A. J., & Salovey, P. (1997). Shaping perceptions to motivate healthy behavior: The role of message framing. Psychological Bulletin, 121(1), 3–19. doi:10.1037/0033‐2909.121.1.3 Sherman, D. K., Mann, T., & Updegraff, J. A. (2006). Approach/avoidance motivation, message framing, and health behavior: Understanding the congruency effect. Motivation and Emotion, 30, 165– 169. doi:10.1007/s11031‐006‐9001‐5 Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458. Updegraff, J. A., Brick, C., Emanuel, A. S., Mintzer, R. E., & Sherman, D. K. (2015). Message framing for health: Moderation by perceived susceptibility and motivational orientation in a diverse sample of Americans. Health Psychology, 34(1), 20–29. doi:10.1037/hea0000101 Updegraff, J. A., Sherman, D. K., Luyster, F. S., & Mann, T. L. (2007). The effects of message quality and congruency on perceptions of tailored health communications. Journal of Experimental Social Psychology, 43(2), 249–257. doi:10.1016/j.jesp.2006.01.007 Uskul, A. K., Sherman, D. K., & Fitzgibbon, J. (2009). The cultural congruency effect: Culture, regulatory focus, and the effectiveness of gain‐ vs. loss‐framed health messages. Journal of Experimental Social Psychology, 45(3), 535–541. doi:10.1016/j.jesp.2008.12.005

Suggested Reading Gallagher, K. M., & Updegraff, J. A. (2012). Health message framing effects on attitudes, intentions, and behavior: A meta‐analytic review. Annals of Behavioral Medicine, 43, 101–116. Rothman, A. J., Bartels, R. D., Wlaschin, J., & Salovey, P. (2006). The strategic use of gain‐and loss‐ framed messages to promote healthy behavior: How theory can inform practice. Journal of Communication, 56, S202–S220. Rothman, A. J., & Salovey, P. (1997). Shaping perceptions to motivate healthy behavior: The role of message framing. Psychological Bulletin, 121, 3–19.

Message Tailoring Mia Liza A. Lustria and Juliann Cortese School of Information, Florida State University, Tallahassee, FL, USA

Tailoring as a Health Communication Strategy Much of the world’s disease burden is associated with a few modifiable health behaviors like diet and physical activity and risky or unhealthy behaviors such as smoking, alcohol abuse, unsafe sex, use of firearms, and drug use. Most people recognize risks associated with these unhealthy behaviors and the potential benefits of adopting healthy behaviors in lowering their risk for chronic illness. Yet many continue to make unhealthy lifestyle choices even when they experience undesired consequences for their actions (or inaction) or even if they have a genetic predisposition toward a particular chronic disease (King, Mainous, Carnemolla, & Everett, 2009). Health psychologists recognize that a number of factors can shape health behaviors and that these factors vary from one individual to another. These factors are driven by reciprocal influences of one’s experiences, beliefs, values and attitudes, motives and expectations, environment and circumstances, personality traits, mental and emotional states, patterns of behavior, and habits, among others. Health messages primarily designed to raise awareness without considering the complexity of behavior change are deemed to have limited impact on individual actions and decision making. It is for this reason that interpersonal communication continues to be the ideal channel for promoting individual behavior change because of the ability to adjust communication on the fly and to deliver personalized recommendations. Unfortunately, the reach and accessibility of health counselors adequately trained in delivering evidence‐based personalized recommendations is limited. Message tailoring, a health communication strategy for disseminating theoretically informed individualized recommendations, seeks to address this need to deliver more efficacious health behavior change strategies on a mass scale.

The Tailoring Process Message tailoring is defined as a “multidimensional communication strategy that involves developing individualized messages based on a pre‐assessment of key individual‐difference The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Mia Liza A. Lustria and Juliann Cortese Assessment Individual assessment

Message creation Tailoring algorithm

Administered Phone interview FtF interview Self-Administered Print-based survey Computer-based survey

Library of tailored messages

Dissemination

Mode of delivery

First generation Tailored materials Interpersonal feedback print materials desktop computer kiosks Second-generation Tailored materials Wed-based technology Mobile technology

Figure 1  A basic message tailoring system.

variables or characteristics linked to the underlying model of behavior change” (Lustria et al., 2013, p. 1040). It refers to various message design strategies for creating health messages that resonate at an individual level by using knowledge about a person’s unique needs and preferences (Kreuter & Wray, 2003). Information about the individual is typically assessed through a survey of relevant factors (e.g., personal, psychosocial) and theoretically informed determinants of the targeted health behavior. This data is then evaluated by an expert system that uses tailoring algorithms to prioritize content and recommendations based on user input. At the most basic level, this process involves selecting messages most relevant for the individual from a database of tailored content. A typical tailoring system includes (a) a way to collect information about the individual (e.g., paper‐based, telephone‐based, or computer‐based assessment), (b) a system to analyze responses (usually via computer algorithms), (c) an extensive message library, and (d) a way to disseminate the tailored messages (e.g., via print media, electronic, or web‐based technologies; Figure 1). As a communication strategy, message tailoring takes into account the heterogeneity of factors that determine an individual’s health behaviors and the variety of pathways to success. It is not to be mistaken for simple personalization or the inclusion of personal identifiers, the main goal of which is to draw attention to the health message (e.g., names). It is also different from targeted messaging or creating messages that might resonate with specific groups of people that share common characteristics (e.g., teenage mothers, dementia patients, breast cancer survivors). Rather, in tailored messaging, individual‐level assessment enables the creation and delivery of highly personalized recommendations and plans for modifying simple to complex health behaviors based on an analysis of individual drivers for (and barriers to) change. In contrast to generic messages, tailored messages are designed to directly address factors relevant to the individual and promote recommendations that take the individual’s context into consideration. The following is an example of a generic health message aimed at preventing weight gain found on typical websites like the Centers for Disease Control and Prevention (adapted from Centers for Disease Control and Prevention, 2015): If you’re currently at a healthy weight, you’re already one step ahead of the game. To stay at a healthy weight, it’s worth doing a little planning now. Or maybe you are overweight but aren’t ready to lose weight yet. If this is the case, preventing further weight gain is a worthy goal. As people age, their body composition gradually shifts — the proportion of muscle decreases and the

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proportion of fat increases. This shift slows their metabolism, making it easier to gain weight. In addition, some people become less physically active as they get older, increasing the risk of weight gain. The good news is that choosing a lifestyle that includes good eating habits and daily physical activity can prevent weight gain. To learn more…

In contrast, the following is an example of how a tailored health message differs from the generic message just provided: Hi Pam! Based on your current age, weight and height, you are slightly at risk for obesity. This extra weight can but stress on your bones, joints, and organs, which may partly explain your lethargy and difficulty in sustaining your daily activities. Too much body fat also raises your blood pressure and cholesterol ‐ this can be very dangerous for people like you who have a family history of heart disease and stroke. Here’s the good news – you can achieve a healthy weight by adopting good eating habits and daily physical activity. Being a busy career woman and mom, we understand that this is easier said than done. So we’ve created a few simple steps for you to reach your goal. We’ll start by recommending easy‐to‐prepare meals to help lower your daily caloric intake to _____ and a few daily exercises that you can do in 10 minutes or less each day. Start here…

Message Tailoring Works A wide evidence base promotes the use of tailored messaging over one‐size‐fits‐all generic messaging. Compared with non‐tailored messages, tailored information commands greater attention and is processed more intently, recalled more readily, and perceived as more personally relevant and more positively by health consumers (Kreuter, Strecher, & Glassman, 1999). More importantly, several studies and meta‐analyses provide empirical evidence for the benefits of tailored versus non‐tailored interventions across a wide variety of health outcomes such as physical fitness, nutrition, mental health, immunization, early detection or screening, sexual health, alcoholism, and chronic disease (e.g., Chodosh et  al., 2005; Davies, Morriss, & Glazebrook, 2014; Kessels, Ruiter, Brug, & Jansma, 2011). Overall, various meta‐analyses have pointed to small‐ and medium‐size effects of tailoring on behavior change. Noar, Benac, and Harris (2007) conducted a meta‐analytic review of 57 tailored print behavior change interventions and found a mean effect of r = .074 of tailoring on health behaviors. The variables that were found to significantly moderate the effect included (a) type of comparison condition, (b) health behavior, (c) type of participant population (both type of recruitment and country of sample), (d) type of print material, (e) number of intervention contacts, (f) length of follow‐up, (g) number and type of theoretical concepts tailored on, and (h) whether demographics and/or behavior were tailored on. Krebs, Prochaska, and Rossi (2010) also conducted a meta‐analysis to evaluate the mean effect of 88 computer‐tailored interventions focusing on smoking cessation, physical activity, eating a healthy diet, and receiving regular mammograms. They found clinically and statistically significant overall effects across each of the four behaviors. Effect sizes decreased after intervention completion, and dynamically tailored interventions were found to have increased efficacy over time as compared with those based on one assessment only. Tailored interventions can range from simple, tailored risk assessments or tailored health messaging to more complex and highly individualized health programs, treatment plans, or recommended actions. The most basic tailored interventions involve risk assessment followed by short motivational interventions focused on promoting the targeted health behavior. These

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brief tailored interventions typically involve single assessments at baseline, after which ­participants either immediately receive tailored advice via a webpage or print out or are scheduled to receive tailored messages via email or postal mail. For health interventions targeting more complex lifestyle changes or behavior modification, tailoring may be done iteratively or delivered in modules and may be enhanced through interactive features such as goal setting, skills building, tracking and monitoring, and expert and social support tools to help participants achieve their goals (Lustria, Cortese, Noar, & Glueckauf, 2009). In one example, Woolford, Clark, Strecher, and Resnicow (2010) used mobile phone texting to tailor healthy behavior messages to obese adolescents. Baseline measurement was used to assess behaviors, and then text messages were sent automatically through a computer application that chose from a series of appropriate messages in a library of tailored content. Participants were enrolled in a weight management program, and the tailored reminders helped keep them on track with their weight‐loss goals. Lustria et  al. (2013) conducted a meta‐analysis of 40 randomized controlled trials and quasi‐experimental studies comparing tailored versus non‐tailored web‐based interventions. The findings indicated that web‐based tailored interventions lead to significantly greater improvement in health outcomes as compared with control conditions both at posttest and at follow‐up. The researchers concluded that web‐based interventions benefited from the inclusion of several different modalities that added an interactive element to interventions, such as audio, video, discussion, and chat, which is ideal for improving user engagement and supporting patients’ self‐management and skills building.

How Does Message Tailoring Work? Hawkins, Kreuter, Resnicow, Fishbein and Dijkstra (2008) propose that tailoring improves the efficacy of health communication by enhancing (a) the cognitive preconditions for message processing or acceptance and (b) message impact by selectively modifying the behavioral determinants of desired health outcomes. Petty and Cacioppo’s elaboration likelihood model (ELM) lays the theoretical groundwork for how tailoring can enhance persuasion. ELM is a useful way of looking at how attitudes can be influenced, shaped, or changed as a result of exposure to mass‐mediated persuasive messages. According to this theory, attitudes may be influenced as a result of high elaboration, one’s thoughtful consideration of the merits of a persuasive message (central route processing), or a simple cue in the persuasion context (peripheral route processing; Petty & Cacioppo, 1986). When people are motivated (e.g., when the topic is interesting or perceived to be personally relevant) and have the ability to process messages actively and more thoughtfully (e.g., have prior knowledge, no distractions, message is comprehensible), they are more likely to scrutinize the information and make decisions based on its merits. However, not all situations are conducive for thoughtful reflection, as in the case of a brief message communicated via a televised public service announcement. In situations of low elaboration like this, persuasion may still occur through the influence of heuristic cues like the appearance of the source or the source’s perceived trustworthiness. Attitudes formed via the central route are thought to be more enduring, more resistant to counter‐persuasion, and more predictive of behavior (Petty, Briñol, & Priester, 2009), and thus messages designed to induce higher elaboration are ideal for persuasion. While the valence of a persuasion attempt can be influenced by a number of factors, it is clear that an individual’s motivation and ability to process the message are critical to the outcome. Much of the research on this topic has shown a significant

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relationship between tailoring and increased attention and perceived ­ personal relevance. Petty et al. (2009, p. 145) note that tailoring “can influence attitudes by serving as a peripheral cue when elaboration is low, by biasing thoughts when elaboration is high and by enhancing the amount of information processing when elaboration is moderate.” The efficacy of tailored interventions is also bolstered when message tailoring is guided by research and by health behavior theories and models (Webb, Joseph, Yardley, & Michie, 2010). Theory and research can inform tailoring in a number of ways by helping prioritize relevant constructs for tailoring (e.g., perceived risk, attitudes, beliefs) and by informing the selection of specific messaging strategies and behavior change techniques (e.g., stage matching, timing, delivery mode) most effective for particular population groups. The most common theories used to guide message tailoring are described below. Prochaska’s transtheoretical model (TTM) has been used extensively and successfully in smoking cessation programs tailored based on patients’ readiness to quit smoking. The TTM suggests that a patient’s responsiveness to a behavioral intervention is influenced by where they are in the “stages of change”: precontemplation, contemplation, preparation, action, maintenance, and termination (J. O. Prochaska & DiClemente, 1983). The model is suited particularly well for guiding tailoring in health interventions related to areas to which people may exhibit various degrees of commitment (i.e., physical activity, nutrition, and smoking cessation; J. M. Prochaska & Prochaska, 2014). Certainly, stage‐matched tailoring has proven to be effective in a number of interventions related to smoking, condom use, cancer screening, and hypertension control, among others (Stanczyk et al., 2014). Knowledge about an individual’s readiness for change can be very useful in crafting and scheduling the delivery of motivational messages and reminders with the desired goal of moving someone onto latter stages or effecting more lasting change. Another health communication theory often used to guide message tailoring is the health belief model. The health belief model is a predictive model that examines the likelihood that an individual will engage in a health‐promoting behavior (Rosenstock, 1974). The theory suggests that likelihood of engagement in a health‐conscious behavior can be predicted by examining an individual’s perceived susceptibility, seriousness of the health concern, benefits versus barriers of the healthy choice, perceived threat, self‐efficacy, and cues to action. Because all of these factors have the opportunity to affect change, presenting the individual with tailored message focused in any of these areas can result in a choice to engage in a health‐promoting behavior. Often one of the easiest ways to influence behavior is to present a behavior that can be imitated or modeled. This approach is the focus of social cognitive theory, which states that individuals can gain knowledge by observation. Though not developed in the health communication field, the theory has much to offer in this area (Bandura, 1998). Tailored messages may be created in such a way as to present scenarios that can be modeled by the viewer through stories, anecdotes, and testimonials. Cortese and Lustria (2012) created a web‐based tailored intervention to educate teens about sexual health and decision making. Message tailoring drew heavily from constructs in the health belief model and social cognitive theory. Overall, messages were created to raise perceptions of risk (perceived severity), address individual concerns about what it will take to adopt the recommended behavior (e.g., money, effort, time; perceived barriers), and build confidence that taking the recommended action can reduce risk (perceived benefits). Tailored testimonials were presented to model safe sex behaviors and healthy decision making and to increase perceived self‐efficacy to carry out the prescribed behaviors. Results of the randomized controlled trial comparing the tailored site with a generic site showed that those in the

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tailored condition had higher satisfaction with the site and elaborated on the health messages more deeply as compared with those exposed to the non‐tailored site. The theory of reasoned action and planned behavior (TRA/TPB), another predictive model, links together beliefs, attitudes, and behavioral intentions to predict behavior (Fishbein & Ajzen, 1975). A number of tailoring studies have used TRA/TPB successfully. Webb et  al. (2010) conducted a meta‐analysis to examine the use of theory‐based message tailoring and found that studies identifying TPB as their theoretical frame (9 out of 85 studies) reported larger effect sizes compared with all other tailored interventions included in the review. To exemplify how TPB can inform tailoring, Hurling et al. (2007) conducted a randomized controlled trial to evaluate the efficacy of a tailored physical activity program using web and mobile phone technology. Participants in the real‐time tailored condition received tailored feedback, tailored solutions to address their perceived barriers, a weekly exercise plan with SMS and email reminders, and access to a message board for social support. Participants in both groups were issued wrist‐worn accelerometers to monitor their level of physical activity for 9 weeks. Results showed that the tailored group reported a significantly greater increase over baseline than the control group for perceived control and intention/expectation to exercise. Participants in the tailored condition also lost a greater percent of their body fat than the control group.

The Future of Message Tailoring Thoughts about applying the process of mass customization (Davis, 1989) to health messaging was first introduced in the late 1980s and early 1990s. Kreuter, Farrell, Olevitch, and Brennan (2000) attributed the turn toward mass customization to three innovations: (a) increasing popularity of audience segmentation in developing health education materials, (b) the introduction of new models of individual health behavior change (e.g., TTM), and (c) advances in computer technology. Of these three, the application of computers for processing individual data and for generating individual reports provided the tipping point for mass production of customized content. First‐generation tailored interventions were largely print based or delivered via desktop computers or kiosks and typically involved a single, simple tailoring assessment. Over time, the sophistication of tailored interventions has greatly increased concomitant with advances in information and communication technologies. These advances have benefited message tailoring by increasing reach, improving delivery, and improving assessment. In particular, web technologies have made it possible to scale the production and delivery of tailored messages to multiple individuals at relatively low cost. Internet delivery improves the reach of effective behavior change interventions to potentially hard‐to‐reach population groups and provides wider access to expert care and feedback. Synchronous and asynchronous technologies (e.g., email, SMS, chat) present multiple options for the delivery of tailored content, reminders, and feedback. Users can access tailored content at any time and receive “just‐ in‐time” information when and where they need it the most. Users also have more options for scheduling and receiving tailored reminders with the ability to personalize notification settings. In addition to the availability of multiple delivery modes (e.g., web and mobile technologies), tailored messages may be communicated via different formats (e.g., audio, video, simulations, games, virtual reality). Multimodal options are beneficial for addressing the needs of participants with different literacy levels, learning styles, and technological competencies. Interactive technologies (e.g., e‐journaling, quizzes, video, simulations, discussion forums)

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provide excellent platforms for implementing effective behavior change strategies, such as tracking and monitoring, goal setting, skills building, and providing tailored feedback and social support. As an example, Stanczyk et al. (2014) conducted a randomized controlled trial comparing the efficacy of a stage‐matched tailored smoking cessation intervention with tailored video or tailored text and a generic control. Video tailoring was found to be more effective in increasing 7‐day point prevalence abstinence than the control condition and resulted in significantly higher prolonged abstinence rates among smokers with a low readiness to quit. Social media also have the potential for amplifying access to tailored interventions through cross‐platform communication strategies and content repurposing (e.g., re‐tweeting, blogging). Web 2.0 technologies like social networks, microblogging tools, and video‐sharing sites can expand messaging opportunities while also providing highly interactive, dynamic, and flexible content that may improve user engagement. Perhaps one of the most exciting areas of development in tailored messaging is the use of mobile devices and sensing technologies. Mobile technologies are particularly suited for persuasion as people are deeply attached to their mobile devices and have them wherever they go, making it easy to deliver persuasive messages when and where they need it the most. The delivery of reminders, tailored motivational messages, and coping tips could be triggered during opportune moments—for example, when the mobile system senses a user’s location as a place where temptation typically lies. The new generation of smart phone and wearable technologies (e.g., Apple Watch, Fitbit, Nike FuelBand) have also enhanced users’ ability to track and monitor progress on their health goals without requiring manual data entry. These advances are paving the way for more synchronous and interactive tailoring based on real‐time tracking through ubiquitous and unobtrusive sensing (Wangberg & Psychol, 2013). Coupled with advances in artificial intelligence and data processing methods, researchers may one day be able to fully automate the tailoring process, conduct iterative, real‐time tailoring, and lower the burden for user assessment.

Author Biographies Mia Liza A. Lustria, PhD, is an associate professor and chair of the undergraduate program at the School of Information, Florida State University. She has a successful record of publications and funded research in consumer health informatics aimed at improving access to healthcare and education through the use of digital and interactive media. Her current work focuses on theory‐based message tailoring and the design and evaluation of persuasive technologies for use in behavior change interventions. Juliann Cortese, PhD, is an associate professor in the School of Communication, Florida State University. Her area of research focuses on the uses and effects of online health content, having examined online tailoring, mediated social support, and mental processes engaged in while examining online content. She has published in top ranking journals including Patient Education and Counseling, Journal of Health Communication, Journal of the American Society for Information Science and Technology, and Human Communication Research.

References Bandura, A. (1998). Health promotion from the perspective of social cognitive theory. Psychology and Health, 13(4), 623–649. doi:10.1080/08870449808407422

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Centers for Disease Control and Prevention. (2015, 15 May). Healthy weight. Retrieved from http:// www.cdc.gov/healthyweight/prevention/index.html Chodosh, J., Morton, S. C., Mojica, W., Maglione, M., Suttorp, M. J., Hilton, L., … Shekelle, P. (2005). Meta‐analysis: Chronic disease self‐management programs for older adults. Annals of Internal Medicine, 143(6), 427–438. Cortese, J., & Lustria, M. L. A. (2012). Can tailoring increase elaboration of health messages delivered via an adaptive educational site on adolescent sexual health and decision making? Journal of the American Society for Information Science and Technology, 63(8), 1567–1580. doi:10.1002/asi.22700 Davies, E. B., Morriss, R., & Glazebrook, C. (2014). Computer‐delivered and web‐based interventions to improve depression, anxiety, and psychological well‐being of university students: A systematic review and meta‐analysis. Journal of Medical Internet Research, 16(5), e130. doi:10.2196/jmir.3142 Davis, S. (1989). From future perfect: Mass customizing. Planning Review, 17(2), 16–21. doi:10.1108/ eb054249 Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An intro to theory and research. Reading, MA: Addison‐Wesley. Hawkins, R. P., Kreuter, M., Resnicow, K., Fishbein, M., & Dijkstra, A. (2008). Understanding tailoring in communicating about health. Health Education Research, 23(3), 454–466. doi:10.1093/her/ cyn004 Hurling, R., Catt, M., De Boni, M., Fairley, B. W., Hurst, T., Murray, P., … Barkhuus, L. (2007). Using Internet and mobile phone technology to deliver an automated physical activity program: Randomized controlled trial. Journal of Medical Internet Research, 9(2), e7. doi:10.2196/jmir.9.2.e7 Kessels, L. T. E., Ruiter, R. A. C., Brug, J., & Jansma, B. M. (2011). The effects of tailored and threatening nutrition information on message attention. Evidence from an event‐related potential study. Appetite, 56(1), 32–38. doi:10.1016/j.appet.2010.11.139 King, D. E., Mainous, A. G., III, Carnemolla, M., & Everett, C. J. (2009). Adherence to healthy lifestyle habits in US adults, 1988–2006. The American Journal of Medicine, 122(6), 528–534. doi:10.1016/j. amjmed.2008.11.013 Krebs, P., Prochaska, J. O., & Rossi, J. S. (2010). A meta‐analysis of computer‐tailored interventions for health behavior change. Preventive Medicine, 51(3–4), 214–221. doi:10.1016/j.ypmed.2010.06.004 Kreuter, M. W., Farrell, D., Olevitch, L., & Brennan, L. (2000). Tailoring health messages: Customizing communication with computer technology. Mahwah, NJ: Lawrence Erlbaum Associates. Kreuter, M. W., Strecher, V. J., & Glassman, B. (1999). One size does not fit all: The case for tailoring print materials. Annals of Behavioral Medicine, 21(4), 276–283. doi:10.1007/BF02895958 Kreuter, M. W., & Wray, R. J. (2003). Tailored and targeted health communication: Strategies for enhancing information relevance. American Journal of Health Behavior, 27(Suppl. 3), S227–S232. Lustria, M. L. A., Cortese, J., Noar, S. M., & Glueckauf, R. L. (2009). Computer‐tailored health interventions delivered over the web: Review and analysis of key components. Patient Education and Counseling, 74(2), 156–173. doi:10.1016/j.pec.2008.08.023 Lustria, M. L. A., Noar, S. M., Cortese, J., van Stee, S., Glueckauf, R. L., & Lee, J. (2013). A meta‐analysis of web‐delivered tailored health behavior change interventions. Journal of Health Communication, 18(9), 1039–1069. doi:10.1080/10810730.2013.768727 Noar, S. M., Benac, C. N., & Harris, M. S. (2007). Does tailoring matter? Meta‐analytic review of tailored print health behavior change interventions. Psychological Bulletin, 133(4), 673–693. doi:10.1037/0033‐2909.133.4.673 Petty, R. E., Briñol, P., & Priester, J. R. (2009). Mass media attitude change: Implications of the Elaboration Likelihood Model of persuasion. In J. Bryant & M. B. Oliver (Eds.), Media effects: Advances in theory and research (3rd ed., pp. 125–164). New York, NY: Routledge. Petty, R. E., & Cacioppo, J. T. (1986). The Elaboration Likelihood Model of persuasion. In B. Leonard (Ed.), Advances in experimental social psychology (Vol. 19, pp. 123–205). San Diego, CA: Academic Press. Prochaska, J. M., & Prochaska, J. O. (2014). A stage approach to enhancing adherence to treatment. In D. I. Mostofsky (Ed.), The handbook of behavioral medicine (pp. 58–75). West Sussex: Wiley.

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Prochaska, J. O., & DiClemente, C. C. (1983). Stages and processes of self‐change of smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51(3), 390–395. PMID: 6863699 Rosenstock, I. M. (1974). Historical origins of the Health Belief Model. Health Education & Behavior, 2(4), 328–335. doi:10.1177/109019817400200403 Stanczyk, N., Bolman, C., van Adrichem, M., Candel, M., Muris, J., & de Vries, H. (2014). Comparison of text and video computer‐tailored interventions for smoking cessation: Randomized controlled trial. Journal of Medical Internet Research, 16(3), 35–52. doi:10.2196/jmir.3016 Wangberg, S. C., & Psychol, C. (2013, December 2–5). Personalized technology for supporting health behaviors. Paper presented at the 2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom), Budapest, Hungary. Webb, L. T., Joseph, J., Yardley, L., & Michie, S. (2010). Using the Internet to promote health behavior change: A systematic review and meta‐analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12(1), e4. doi:10.2196/jmir.1376 Woolford, S. J., Clark, S. J., Strecher, V. J., & Resnicow, K. (2010). Tailored mobile phone text messages as an adjunct to obesity treatment for adolescents. Journal of Telemedicine and Telecare, 16(8), 458–461. doi:10.1258/jtt.2010.100207

Suggested Reading Hawkins, R. P., Kreuter, M., Resnicow, K., Fishbein, M., & Dijkstra, A. (2008). Understanding tailoring in communicating about health. Health Education Research, 23(3), 454–466. doi:10.1093/her/ cyn004 Kreuter, M. W., & Wray, R. J. (2003). Tailored and targeted health communication: Strategies for enhancing information relevance. American Journal of Health Behavior, 27(Suppl. 3), S227–S232. Lustria, M. L. A., Cortese, J., Noar, S. M., & Glueckauf, R. L. (2009). Computer‐tailored health interventions delivered over the web: Review and analysis of key components. Patient Education and Counseling, 74(2), 156–173. doi:10.1016/j.pec.2008.08.023 Lustria, M. L. A., Noar, S. M., Cortese, J., van Stee, S., Glueckauf, R. L., & Lee, J. (2013). A meta‐analysis of web‐delivered tailored health behavior change interventions. Journal of Health Communication, 18(9), 1039–1069. doi:10.1080/10810730.2013.768727 Noar, S. M., Benac, C. N., & Harris, M. S. (2007). Does tailoring matter? Meta‐analytic review of tailored print health behavior change interventions. Psychological Bulletin, 133(4), 673–693. ­ doi:10.1037/0033‐2909.133.4.673

Naturalistic Observation of Social Interactions Angela L. Carey, Kelly E. Rentscher, and Matthias R. Mehl Psychology Department, University of Arizona, Tucson, AZ, USA

People’s social relationships and daily social interactions can impact health in profound ways. For example, in a meta‐analytic review, Holt‐Lunstad, Smith, and Layton (2010) found that across 148 studies totaling 308,849 participants, individuals with strong social relationships had a 50% greater likelihood of survival relative to those with poorer social relationships. The magnitude of this effect in fact exceeded many well‐known mortality risk factors (e.g., obesity, smoking), providing further evidence that social interactions not only influence quality of life but can also have important implications for human survival. It is therefore just as necessary and important to include social interactions in research on health as it is other mortality risk factors. Interestingly, when considering existing social health research, there appears to be a discrepancy in the precision researchers tend to afford independent versus dependent variables in what might be called the “social health equation,” or the prediction of health outcomes by psychosocial variables (Mehl, Robbins, & Deters, 2012). On the criterion side, researchers measure important health outcomes using an assortment of high‐fidelity methods to assess health functioning as objectively and precisely as possible (e.g., endocrinological and immunological biomarkers, markers of gene, or cellular functioning). However, on the predictor side, health‐relevant psychosocial constructs tend to be measured with considerably less fidelity, objectivity, and precision. This is evidenced by a general inclination to rely heavily on self‐ report methods or, more specifically, what participants (can) tell about their psychological and social functioning. Self‐reports are undoubtedly valuable given that they provide important information and can be obtained efficiently and at low cost; at the same time, it is clear that they do not afford a level of fidelity and precision that can rival that of objective health outcome measures. For example, the theoretical and empirical distinctness of subjective (i.e., experiential) and objective (i.e., observable) aspects of moral behaviors illustrate the need for  greater precision when measuring health‐relevant social constructs. With respect to The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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health‐relevant processes, it seems clear that the subjective experience of gratitude differs in important ways from the behavioral expression of gratitude. Whereas the former is first and foremost a result of the interplay of inner cognitive processes, the latter is critically shaped by outer, situational, and cultural factors. For example, subjective experience of gratitude can promote self‐esteem and reduce negative emotions such as envy, regret, and resentment, thereby affecting “downstream” constructs such as happiness and depression. Behavioral expressions of gratitude, on the other hand, including enhanced empathy toward others, reduced aggression, and further, the act itself (e.g., inviting an acquaintance to lunch to express thanks for assisting when the babysitter unexpectedly canceled) can lead to more opportunities for social interaction and development of new relationships. Gratitude has been tied to health outcomes such as enhanced sleep quality and duration, as well as improved physiological health. It has also been found to attenuate stress responses, which in turn can improve cardiovascular function and reduce risk of heart disease. Gratitude is often assessed, however, via self‐report only, which makes it challenging to truly disentangle experienced and observed gratitude and to determine how the two independently relate to health outcomes. Given this, it is important to be able to separate these two components measurement‐wise, which can only be accomplished through the combination of first‐person self‐report and third‐person observational assessments. Analyzing behavioral observations in conjunction with subjective reports of gratitude enables social scientists to achieve a deeper understanding of the interaction between what is behaviorally occurring and what is subjectively experienced in people’s daily lives, thus increasing measurement fidelity and precision in operationalizing health‐relevant constructs. In short, the field of health psychology could benefit substantially from a multi‐method approach to studying daily social interactions that complements self‐reports with observational assessments.

Ambulatory Assessment Methods Although the majority of research investigating social interactions and their influence on health continues to be based on self‐report surveys, the last 20 years have seen a steady increase in the use of ambulatory assessment methods that can capture aspects of participants’ daily lives “in vivo.” Ambulatory assessments are defined as the use of field methods to assess the ongoing behavior, physiology, experience, and environmental aspects of people in naturalistic or unconstrained settings. Ambulatory assessment uses ecologically valid tools to understand biopsychosocial processes as they unfold naturally in time and in context (www.ambulatory‐assessment.org). Ambulatory assessments have several key advantages (Mehl & Conner, 2012). First, ambulatory assessments take place directly within participants’ everyday lives; they maximize generalizability across a wide range of real‐world contexts. Second, they capture individuals’ in‐the‐moment or close‐in‐time behaviors, thoughts, and feelings and thereby bypass memory‐related biases that can undermine the validity of global and retrospective self‐reports (e.g., recall‐related errors resulting from serial position effects, mood effects, and heuristics and estimation strategies). A third advantage of ambulatory assessments is that their typical high density of longitudinal measurements renders them uniquely suited to studying how individuals’ behaviors, thoughts, and feelings change over time in ways that cannot be adequately modeled within a laboratory setting. When experiences are assessed in real time (or as close to their occurrence as possible), participants’ responses tend to more accurately represent what is occurring in the moment and are less influenced by applicable heuristics or schemata. In this  way, intensive longitudinal ambulatory assessments are ideally suited for identifying

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i­ndividualized patterns of behavior and behavior change over time. Ambulatory assessment methods are also often referred to (with similar but not always identical meaning) as ecological momentary assessment, intensive longitudinal, experience sampling, and diary methods (see Mehl and Conner (2012) for further information).

Ambulatory Assessment of Physiology and Experience Ambulatory monitoring allows for the sampling of physiological activity in daily life. Modern‐ day mobile technology allows for the reliable measurement of biological signals via portable signal recording devices that capture activity such as blood pressure, electrodermal activity, body temperature, and hormones, as a person goes about their everyday life. If researchers’ primary interest involves the momentary sampling of experiences in everyday life, daily diary assessments are some of the most commonly used methods. These typically involve once‐a‐day assessments via Internet surveys for a prolonged period of time (e.g., a few weeks) in order to assess events or experiences that can easily be recalled at the end of the day, such as emotions in response to daily stressors. With experience sampling methods (ESM), participants complete self‐report assessments repeatedly throughout the day, mainly through use of mobile technology, at random (i.e., signal‐continent sampling) or predetermined (i.e., interval‐continent sampling) times, or when a target event such as an argument with a romantic partner has occurred (i.e., event‐contingent sampling).

Assessment of Behavior There is tremendous value in obtaining subjective reports of individuals’ daily thoughts and experiences using each of the aforementioned ambulatory methods. Yet, one important lesson from psychometrics is that people are not always in the position to accurately report what they do. Sometimes they may actively censor information that they do not want to report; other times (and probably more often), they bias their self‐perceptions in socially desirable ways for psychological self‐protection. However, there are also other times when people want to honestly and accurately report information, but they simply cannot, due to limitations inherent to human information processing; for example, it is nearly impossible to recall the number of times one sighs or laughs in a day (and it would be highly impractical to be able to do so). In circumstances such as this, some of the same concerns pertaining to global and retrospective self‐reports may also apply to momentary self‐reports. Although ambulatory self‐reports can provide more accurate representations of individuals’ perceived daily lives than retrospective or global assessments in the laboratory can, they still require individuals to report on their thoughts and behaviors and therefore are inherently limited to capturing what surpasses the perceptual threshold and enters into working memory. This can be problematic given that participants’ responses to self‐report scales can be affected by recent events or experiences, thereby rendering them potentially susceptible to momentary influences including mood effects, socially desirable responding (i.e., impression management and self‐deception), and, perhaps most importantly, lack of awareness. Another potential concern is measurement reactivity, whereby participants’ behaviors and/or responses might systematically change as a result of prolonged and repeated momentary self‐report assessments. For example, repeatedly reporting on one’s behavioral expression(s) of gratitude might render participants mindful of their grateful behavior (or lack thereof) and consequently alter future expressions of gratitude. It is also possible that participants may lose motivation and become more careless with their responses over time leading to increased measurement error.

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Naturalistic Observation of Social Interactions Naturalistic observation methods extend the area of ambulatory assessment to real‐world observational methods. Naturalistic observation methods are well suited to addressing some of the limitations inherent in momentary self‐reports. Furthermore, these observational methods aim to capture individuals’ behavior from the perspective of an observer and can thereby offer a complementary approach to the study of everyday social interactions through the eyes (or the mind) of the “actor.” Researchers have directly observed behavior in the real world through use of small cameras placed in participants’ homes, as well as live videographers. In one study at the Sloan Center on Everyday Lives of Families (CELF) at the University of California, Los Angeles, videographers with ambulatory cameras accompanied participants to work, in transit, in public, and as they reunited with their families at the end of the day. This study collected more than 1,400 hr of video recordings from 32 different dual‐earner families over the course of 2 weekdays and 2 weekend days, as they normally lived their lives (e.g., Ochs, Graesch, Mittman, Bradbury, & Repetti, 2006). This study has yielded several important findings, one of which is that job stressors (collected via daily diary assessments of experiences at work) appear to be linked with increases in expressed marital anger and withdrawal (derived from the video recordings). Additionally, self‐reported negative mood mediated the association between experienced job stress and observed at‐home marital behavior (e.g., Story & Repetti, 2006). In adopting a multi‐method approach that combined self‐report and observational methods, this research provided real‐world evidence of “spillover effects,” in which stressors experienced outside of the home impact behavior, cognitions, and emotions inside the home. Although video‐based methods produce large amounts of data that allow for detailed and nuanced investigations of participants’ naturalistically observed daily social interactions, they are very labor and resource intensive and afford a certain amount of intrusiveness (i.e., having cameras and often even members of the research team present in the setting). An issue that accompanies direct observational techniques such as these is the potential that participants’ awareness of being video recorded might produce evaluation apprehension and, as a consequence, behavioral reactivity, in which the participants alter their behavior in response to being observed.

The Electronically Activated Recorder (EAR) One methodology that can alleviate some of these potential issues while preserving the ability to observationally capture social interactions during daily life is the Electronically Activated Recorder (EAR) (Mehl, Pennebaker, Crow, Dabbs, & Price, 2001). The EAR is a portable digital audio recorder designed to intermittently capture snippets of ambient sounds from individuals’ momentary environments, providing acoustic logs of their daily lives as they naturally unfold. The EAR allows researchers to unobtrusively capture behavior in real time, bypassing many of the previously mentioned limitations that often accompany self‐report methods. In its most recent version, the EAR is a software application (the “iEAR”) that runs on iPod touch devices and can be downloaded freely from Apple’s App Store. To protect the iPod device from impact, it is handed to participants in a protective case with a clip so that participants can wear the EAR by attaching it to a belt, the outside of their clothing, or a bag as they go about their normal daily lives. The EAR’s sampling rate can be freely programmed, and sampling patterns that have proven useful in prior EAR research are 30 s every 12 min (or 5 times per hour; the default sampling pattern) and 50 s every 9 min. This translates roughly

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into recording 5 and 10%, respectively, of the participants’ day, leaving 95 and 90%, r­ espectively, of their day private (i.e., unrecorded). In studies where there are special privacy considerations, such as using the EAR in the workplace, weekend monitoring (i.e., recording Friday evening through Sunday evening) may be preferable and the only way to obtain good compliance and valid information. In cases where there are no out‐of‐the‐ordinary privacy concerns (e.g., among students or older, retired adults), monitoring on both weekdays and weekend days is preferable and can provide more generalizable information about participants’ typical patterns of social interaction. The current version of the EAR, that is, the iEAR app, also offers a randomization feature that allows researchers to randomize the length of the interval between each recording. At 100% randomization, recording times are fully random; at 50% randomization yields random recordings within half and one and a half times the programmed interval; for example, with a sampling rate of 30 s every 12 min, 30‐s recordings will occur randomly between 6 and 18 min after the previous recording. Because of the electronic nature of the recording, participants cannot tell when the EAR is recording. The ability to set a “blackout period,” which is a prespecified period of guaranteed non‐recording, is another feature of the current iEAR app. It is recommended to adjust the blackout period to participants’ reported bedtime and wake‐up time to maximize the obtained information and the protection of participants’ privacy. When it comes to determining a desired sampling pattern (with respect to frequency and length of recording), researchers should first consider the natural base rate of their targeted behavior (i.e., how often does it happen in a day?); low base rate behaviors (e.g., crying, conflict) can require more frequent and longer‐term sampling than higher base rate behaviors (e.g., watching TV, talking to the partner) to ensure that sufficient variability in the target behavior is represented in the recordings. With respect to deciding on the length of an individual recording sample (e.g., 30, 50 s, 2 min), researchers need to consider how much information coders need to clearly identify the (presence or degree of) construct. For example, if the researcher is interested in demand–withdraw interactions, a complex or “molar” psychological construct, longer recording segments (e.g., 2–3 min) might be necessary to reliably infer the pattern from a couple’s way of discussing a problem. Alternatively, if the researcher is mostly interested in relatively narrow or “molecular” behaviors, such as whether or not a person is talking, laughing, sighing, or watching TV in any given recording segment, relatively short recordings (e.g., 30 s) will likely provide sufficient information. In our research, we have successfully employed both 50‐ and 30‐s recording segments but have found 30‐s segments to be usually preferable for both pragmatic and psychometric reasons (Mehl et al., 2012). Our default sampling rate of 30‐s recordings every 12 min yields quite a high sampling density (approximately 5 samples per hour) that should provide good resolution for between and many within‐person analyses; and, consistent with findings from decades of thin‐slice research, we have found 30‐s sound bites to yield surprisingly rich information about a person’s behavior and social context at a given moment.

How Can the EAR Sound Files be Analyzed? To extract relevant information from the EAR sound files, researchers can use a psychological rating or behavior counting approach. With the psychological rating approach, trained expert raters listen to the recordings and judge the degree to which they indicate the presence of a construct of interest (e.g., social support). To date, the majority of EAR studies have utilized a behavior counting approach. Here, trained coders make binary “behavior present” versus “behavior absent” codings for each recorded sound file using a standardized coding system

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referred to as the Social Environment Coding of Sound Inventory (SECSI) (Alisic, Barrett, Bowles, Conroy, & Mehl, 2015; Mehl et al., 2012). The SECSI captures a person’s daily social environment, behaviors, interactions, and affect using the following four core categories: the participant’s current (a) location (e.g., home, in transit, in public), (b) activity (e.g., watching TV, doing housework, eating), (c) social interaction (e.g., alone versus with others, talking to someone in person, talking on phone), and (d) emotional expression (e.g., crying, sighing, laughing). Conceptually, the EAR captures information about the types of social environments individuals are in (i.e., person–environment effects at the level of situation selection) and how they interact within those environments (i.e., person–environment effects at the level of person– situation interaction). In addition, all of the participants’ utterances are transcribed verbatim, providing the ability to extract information about individuals’ everyday language use from the sound files (e.g., via computerized text analysis). The language data can in turn be utilized to examine how natural language use reflects psychological states and traits; for example, individuals’ personal pronoun use has been investigated as an automatic, nonconscious linguistic marker of interpersonal focus. To assess the psychometric properties of the EAR data, inter‐coder reliability estimates are obtained either from double codings (i.e., independent sets of codings by two trained coders) or from codings accompanying a standard set of training EAR recordings. It is important that reliability estimates are calculated for the targeted unit of analysis. That is, if a researcher analyzes the EAR data at the aggregate level (e.g., percentage of sound files in a day in which the participant laughed), then the reliability should be reported for the aggregate measure; if a researcher analyzes each EAR sound file individually (within person), then an estimate of the coding of an individual sound file should be provided. In our lab, we first test inter‐coder reliability at the level of the sound file by having all coders of a study code a standard set of training EAR recordings; this initial reliability check is part of the coder training and enables us to calibrate coders. Following this, we submit every sound file to an independent double coding and then estimate the reliability of the average measure composed of the two sets of codings. In our experience, it is reasonable to rely on single codings for narrow and highly concrete behaviors (e.g., whether or not the participant is talking or whether or not the TV is switched on); broader and more abstract psychological behaviors (e.g., disclosure, support, conflict) are more difficult to code reliably and thus tend to warrant a double‐coding approach.

Ethical Considerations Pertaining to the EAR Method Importantly, recording ambient sounds around participants raises ethical and legal concerns. EAR studies conducted within our laboratory routinely implement a series of safeguards to protect participants’ privacy and ensure confidentiality of the data. First, data collection is limited to a fraction of a person’s day; using the default sampling pattern (i.e., 30 s every 12 min), only 5% of a participant’s total day is recorded, leaving 95% of the day private. Limiting the recordings to a brief 30 s yields sound files that are long enough to reliably extract behavioral information, yet also short enough to capture as little contextualized personal information as possible. Additionally, the latest version of the iEAR features a privacy button that, upon pressing it, guarantees participants a prespecified (by the researcher) time of non‐ recordings (e.g., 5 min; a countdown timer provides visual feedback for how much longer privacy is guaranteed). Another safeguard, and one of the most important, is that all participants are given the opportunity to listen to their EAR recordings and to delete any sound files that they do not want on record before investigators access any of the data. What makes the EAR unique from other ambulatory assessment methods designed to assess behavior is that it differs in its assessment perspective. With traditional self‐report‐based

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a­mbulatory assessment methods, the participant is the agent and responds with subjective experiential accounts of an event, whereas the EAR adopts the perspective of a bystander or observer, yielding corresponding objective behavioral accounts (i.e., “participant as object”). In this sense, the EAR alleviates the burden placed on participants to interrupt their day and respond to a series of questions. It does, however, place the burden of potential psychological discomfort on participants: the knowledge that they are being intermittently recorded could cause participants to feel that their privacy is being violated or feel something akin to evaluation apprehension. Most participants experience some awareness when first equipped with the EAR, but this heightened self‐consciousness tends to decrease within the first couple of hours and stays relatively low after that (Mehl & Holleran, 2007). One of the more serious challenges, however, concerns the issue of potentially recording bystanders. In the United States there are relatively few restrictions regarding the recording of behavior in public places, but some states only permit recordings of private conversations if all interactants have knowledge of the recording device. In our studies, we usually equip the EAR with a neon triangle‐shaped alert sign that displays a picture of a microphone, alerting people around the participants that the device may be recording. Participants are further encouraged to openly mention the EAR in conversations with others. This safeguard is aimed at minimizing expectations of privacy for bystanders, thereby indirectly eliciting their passive consent (i.e., through bystanders “behaving” in the awareness of being recorded). For further protection, all sound files are coded by staff who are trained and certified to conduct research with human subjects, and any identifying information that is captured in the sound files is omitted from the transcripts. Participants also always have the option to delete any sound files that may contain information about bystanders. Finally, in most studies, it is recommendable to protect the sound files through a Certificate of Confidentiality that the National Institutes of Health issues (upon a simple application and at no cost) to protect study data from forced third‐party disclosure.

Health and Social Interaction Research Utilizing the EAR Methodology As an unobtrusive observational method, the EAR has the unique advantage of being able to capture health‐relevant behaviors that are rarely studied, as they may reside largely outside of conscious awareness and therefore tend to evade self‐report assessment. Coupled with other more traditional momentary assessment methods, the EAR provides a comprehensive representation of health‐relevant social processes as they naturally occur in everyday life. For example, 13 rheumatoid arthritis patients wore the EAR for 2 separate weekends spaced approximately 1 month apart to test the degree to which naturalistically observed sighing in daily life served as a behavioral indicator of self‐reported depression and physical pain symptoms (Robbins, Mehl, Holleran, & Kasle, 2011). Sighing emerged as significantly and substantially related to patients’ levels of depression but was not associated with their reported pain and number of symptom “flare” days. A second study tracked spontaneous swearing in the daily interactions of breast cancer and rheumatoid arthritis patients to investigate its potential role in coping with chronic illness (Robbins, Focella, et al., 2011). Interestingly, naturalistically observed swearing in the presence of other people, but not swearing when alone, was associated with decreases in self‐reported emotional support and increases in depressive symptoms—suggesting that swearing can undermine emotional support with potential consequences for psychological adjustment. Importantly, both sighing and swearing are health‐related behaviors that are difficult if not impossible to study with ambulatory self‐report methods and

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are, by nature, optimally assessed from an (unobtrusive) observational perspective. Together, these findings highlight the utility of the EAR method in capturing largely unconscious behaviors that have potentially important implications for health. The EAR has also been used to assess the impact of parents’ psychological health on familial environments. For example, Slatcher and Trentacosta (2011) examined the association between parental depressive symptoms and child problem behaviors. Thirty‐five two‐parent families with children between the ages of 3 and 5 years were recruited into the study. Both parents completed a depression questionnaire, and the EAR was administered at two separate time points to capture the children’s behavior and language. The children wore the EAR for two 1‐day periods that took place 1 year apart from each other. Fathers’ depressive symptoms correlated positively with children’s crying, acting out, and watching TV at EAR Time 1, as well as children’s watching TV at EAR Time 2. Mothers’ depressive symptoms correlated positively with children’s crying at EAR Time 2, and both mothers’ and fathers’ depressive symptoms were positively associated with their children’s negative behavioral symptoms at Time 2. In addition, mothers’ and fathers’ depressive symptoms uniquely predicted increases in children’s anger word use from Time 1 to Time 2. In more recent research, the EAR has been used to investigate family interactions as they relate to physical health, or more specifically, to examine the associations between naturalistically observed family conflict and youth asthma symptoms (Tobin et  al., 2015). Fifty‐four adolescents with asthma wore the EAR for a 4‐day period to capture conflict with parental figures and audible symptoms of asthma (i.e., wheezing), and both adolescents and parental figures completed daily diary reports of conflict and asthma symptoms. EAR‐observed family conflict was strongly associated with both self‐reported asthma symptoms and coder‐observed wheezing, providing evidence that everyday conflict can have important implications for health symptoms such as asthma, particularly in the context of family interactions outside of the laboratory. Finally, the examples of research findings presented here are not intended to represent an exhaustive review of existing EAR research within the field of social health psychology. Rather, these examples serve to illustrate the utility of combining both subjective experiential reports with behavioral observations in order to gain a deeper understanding of how daily social interactions impact health and, in some cases, how health impacts social interaction.

Mobile Sensing Methods A relatively new and rapidly developing approach to objectively documenting social interactions in daily life is mobile sensing methods. With the fast‐paced development of mobile devices, that is, smartphones and wearables, coupled with the growing number of people who now own and depend on them, there is no question that objective assessment of aspects of social interactions will soon be at least partially absorbed by mobile sensing. Generally speaking, mobile sensing applications are used to make inferences about users’ behavior, emotions, environments, and daily life patterns through computer integration of the data that comes from both interactions with the actual user interface of mobile devices (e.g., duration of phone calls, number of text messages) and the multiple sensors that are embedded in smartphones (e.g., GPS, Bluetooth, accelerometers). Wearable devices such as portable smart watches will also be increasingly incorporated into research on account of the behavioral (e.g., physical activity) and physiological (e.g., heart rate) information they gather. Mobile sensing a­ pplications

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have much to offer in the amount of data they can collect and in the ways in which they open the field up to the objective characterization of a person’s moment‐to‐moment social environment (e.g., location via GPS, solitary vs. social via Bluetooth, sedentary vs. active via accelerometers), and research within social and health psychology is swiftly moving in this direction. It will undoubtedly continue on its trajectory of becoming the new gold standard method for “big data collection,” given that these devices can automatically store and transmit vast amounts of real‐world user interaction data essentially instantaneously. Mobile sensing methods can potentially provide mass amounts of real‐world data across thousands of participants, thereby critically reducing the resource burden for the researcher (i.e., studies can be run remotely at scale rather than locally in person). An example of one such mobile sensing method is EmotionSense (Rachuri et al., 2010). EmotionSense is a platform for creating mobile applications that can easily be installed onto off‐the‐shelf smartphones, with a focus on gathering not only data pertaining to participants’ emotions, as the name would suggest, but also proximity and patterns of conversations with others. The EmotionSense apps are able to collect experience sampling self‐reports as well as passive measurement of physical activity, mobility, and socializing via internal sensors and settings that can be configured for researchers’ specific experience sampling studies. Many mobile sensing applications sound ideal for conducting real‐world research, but there are also limitations to utilizing mobile sensing as a methodology for naturalistic observation of social interactions. One of the most immediate concerns to address is the question of how to appropriately synthesize and interpret data derived from mobile sensing methods. Even the most advanced social sensing tools still struggle with extracting information at a level that is useful and readily interpretable for researchers. Physical activity is typically downloaded as a raw stream, meaning that artifacts have to be filtered out and activity states (e.g., sitting, standing, walking, running) have to be algorithmically computed. In a similar way, raw GPS data provides relatively little psychological information and requires post‐processing to label the coordinates and make psychological sense of frequented locations (e.g., identification of home or the work place). Additionally, self‐reported activity often differs from accelerometer‐derived activity, and because people do not always carry their phones with them, further errors can be introduced into the existing algorithms. Intensity of physical activity also presents a crucial issue; mobile devices often struggle with basic extraction of information regarding intensity level of activity, and this is particularly important given that intensity provides significant health‐relevant information, above and beyond the knowledge that someone has simply been active. Another issue that accompanies mobile sensing methods involves some ethical concerns. Guaranteeing protection of participants’ privacy is difficult; there is still the challenge of finding ways to ensure that participants’ data, which largely resides online, will remain both de‐ identified and stored safely and securely. Moving beyond these pragmatic concerns, mobile sensing methods also differ widely from other forms of naturalistic observation in terms of what they can and cannot collect (or algorithmically produce) regarding daily social interactions. These apps can likely provide more, and potentially better quality, information regarding electronic forms of communication (e.g., text messaging, online social network use), and this increased accuracy greatly benefits the study of computer‐mediated social interactions and their relation to health. However, due to privacy reasons, mobile sensing may face more challenges tracking in‐person social interaction. There is no question regarding whether smartphone sensing applications have been effectively established as proof of concept. However, for this area of research to move forward in a

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way that can significantly benefit social and health psychologists, more research is needed to ensure that algorithms are producing data with a level of precision that is acceptable to health researchers. In other words, what researchers urgently need are thorough psychometric validations of sensor data as they accumulate in the context of people’s natural use of their smartphones and in the context of existing mobile sensing applications. Much work is being done to move the field of mobile sensing forward, and it will inevitably introduce impactful changes regarding the collection of naturalistic observation data and the assessment of social interactions and health. Implemented (and validated the right way), these methods can further help reduce the discrepancy that exists between the fidelity of the measures on the “social” side and the fidelity of the measures on the “health” side of the “social‐health equation” (i.e., the prediction of health outcomes from psychosocial variables). Recent EAR research has demonstrated that assessing social interactions and health through use of both momentary self‐report (e.g., rheumatoid arthritis “flare” days; parental depressive symptoms) and behavioral observation (e.g., swearing in the presence of others; child problem behavior in the home) can undoubtedly produce a more nuanced picture of how social and health constructs manifest and interact within the real world. Further, adopting a combined naturalistic approach affords researchers the ability to disentangle whether and when a person’s experiential report of an interaction may differ from what is captured observationally and what such discrepancies might indicate. The ability to capture and make sense of any such theoretical and empirical distinctness is an example of how this combination of ambulatory assessment methods can serve to increase precision and fidelity of health‐ relevant psychosocial variables, ensuring a methodologically sound approach. When both sides of the “social‐health equation” are held to comparable levels of precision, researchers can more comprehensively assess how real‐world social interactions impact health on a variety of levels and ultimately, with the assistance of rapidly evolving mobile technologies, determine how researchers can utilize this knowledge to improve and maintain health and well‐being.

Author Biographies Angela L. Carey, MA, is a social psychology doctoral student at the University of Arizona. Her research centers around social interactions and social support influences on coping in the context of major life upheavals (e.g., divorce). Her work utilizes the Electronically Activated Recorder (EAR) in combination with other methods (e.g., daily dairies, linguistic analysis) to study how different types of real‐world social interactions change over time and in relation to self‐reported adjustment and well‐being. Kelly E. Rentscher, MA, is a clinical psychology doctoral candidate at the University of Arizona. Her research involves a multi‐method approach to studying how couples cope with stress and chronic illness, how relationship processes affect health over time (and vice versa), and how couple‐focused interventions can target these processes to improve health outcomes. Her research employs linguistic analysis, observational, and naturalistic methodologies in laboratory and real‐world settings. Matthias R. Mehl, PhD, is associate professor of psychology at the University of Arizona. He has developed the Electronically Activated Recorder (EAR) as a methodology for the unobtrusive naturalistic observation of daily life and explores other novel methods for studying daily life. In 2012, he coedited the Handbook of Research Methods for Studying Daily Life (Guilford Press). He is a founding member of the Society for Ambulatory Assessment.

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References Alisic, E., Barrett, A., Bowles, P., Conroy, R., & Mehl, M. R. (2015). Topical review: Families coping with child trauma: A naturalistic observation methodology. Journal of Pediatric Psychology, 41(1), 117–127. Holt‐Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: A meta‐ analytic review. PLoS Medicine, 7(7), e1000316. doi:10.1371/journal.pmed.1000316 Mehl, M. R., & Conner, T. S. (Eds.) (2012). Handbook of research methods for studying daily life. New York, NY: Guilford Press. Mehl, M. R., & Holleran, S. E. (2007). An empirical analysis of the obtrusiveness of and participants’ compliance with the electronically activated recorder (EAR). European Journal of Psychological Assessment, 23(4), 248–257. doi:10.1027/1015‐5759.23.4.248 Mehl, M. R., Pennebaker, J. W., Crow, D. M., Dabbs, J., & Price, J. H. (2001). The electronically activated recorder (EAR): A device for sampling naturalistic daily activities and conversations. Behavior Research Methods, Instruments, & Computers, 33, 517–523. doi:10.3758/BF03195410 Mehl, M. R., Robbins, M. L., & Deters, F. G. (2012). Naturalistic observation of health‐relevant social processes: The electronically activated recorder methodology in psychosomatics. Psychosomatic Medicine, 74(4), 410–417. doi:10.1097/PSY.0b013e3182545470 Ochs, E., Graesch, A. P., Mittmann, A., Bradbury, T., & Repetti, R. L. (2006). Video ethnography and ethnoarchaeological tracking. In M. Pitt‐Catsouphes, E. E. Kossek, & S. Sweet (Eds.), The work and family handbook: Multi‐disciplinary perspectives, methods, and approaches (pp. 387–409). Mahwah, NJ: Erlbaum. Rachuri, K. K., Musolesi, M., Mascolo, C., Rentfrow, P. J., Longworth, C., & Aucinas, A. (2010). EmotionSense: A mobile phones based adaptive platform for experimental social psychology research. Proceedings of the 12th ACM international conference on Ubiquitous Computing, (pp. 281– 290). doi: 10.1145/1864349.1864393 Robbins, M. L., Focella, E. S., Kasle, S., Lõpez, A. M., Weihs, K. L., & Mehl, M. R. (2011). Brief report: Naturalistically observed swearing, emotional support and depressive symptoms in women coping with illness. Health Psychology, 30(6), 789–792. doi:10.1037/a0023431 Robbins, M. L., Mehl, M. R., Holleran, S. E., & Kasle, S. (2011). Naturalistically observed sighing and depression in rheumatoid arthritis patients: A preliminary study. Health Psychology, 30(1), 129–133. doi:10.1037/a0021558 Slatcher, R. B., & Trentacosta, C. J. (2011). A naturalistic observation study of the links between parental depressive symptoms and preschoolers’ behaviors in everyday life. Journal of Family Psychology, 25(3), 444–448. https://doi.org/10.1037/a0023728 Story, L. B., & Repetti, R. L. (2006). Daily occupational stressors and marital behavior. Journal of Family Psychology, 20, 690–700. doi:10.1037/0893‐3200.20.4.690 Tobin, E. T., Kane, H. S., Saleh, D. J., Naar‐King, S., Poowuttikul, P., Secord, E., … Slatcher, R. B. (2015). Naturalistically observed conflict and youth asthma symptoms. Health Psychology, 34(6), 622–631. doi:10.1037/hea0000138

Suggested Reading Conner, T. S., & Mehl, M. R. (2015). Ambulatory assessment: Methods for studying everyday life. In R. A. Scott, S. M. Kosslyn, & M. Stephen (Eds.), Emerging trends in the social and behavioral sciences: An interdisciplinary, searchable, and linkable resource (pp. 1–15). Thousand Oaks, CA: SAGE Publications. doi:10.1002/9781118900772.etrds0010 Mehl, M. R., & Conner, T. S. (Eds.) (2012). Handbook of research methods for studying daily life. New York, NY: Guilford Press. Mehl, M. R., Robbins, M. L., & Deters, F. G. (2012). Naturalistic observation of health‐relevant social processes: The electronically activated recorder methodology in psychosomatics. Psychosomatic Medicine, 74(4), 410–417. doi:10.1097/PSY.0b013e3182545470

Optimism and Physical Health Maria G. Mens1, Michael F. Scheier1, and Charles S. Carver2 Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA 2 Department of Psychology, University of Miami, Coral Gables, FL, USA

1

Optimists are people who expect good things to happen to them. Pessimists are people who expect bad things to happen to them. Folk wisdom has long held that this difference among people is important in many aspects of living. In this case, folk wisdom appears to be right. A substantial body of research suggests that optimists experience better physical health than pessimists.

Defining Optimism Defining optimism in terms of expectancies links the optimism construct to a long tradition of expectancy‐value theories of motivation. These theories hold that people pursue only goals they both value and expect to obtain. Expectancies reflect a person’s confidence that sought after goals will be attained and are particularly important when impediments arise in goal pursuit. If a person is confident about eventual success, he or she will persist despite difficulties. However, if confidence wanes, the person is likely to disengage effort, sometimes disengaging from the goal itself. Expectancies exist at many levels of generality. A student can have expectancies about her ability to get to class on time, expectancies about her ability to learn a topic, and expectancies about her likelihood of finding a fulfilling career. The principles of expectancy‐value theories pertain equally well to expectancies that are specific and expectancies that are more general. They should even apply to the most broad or general kinds of expectancies, which characterize optimists and pessimists. The “confidence” that is at issue in optimism is simply broader in scope, pertaining to most situations in life rather than just one or two.

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Measuring Optimism Research on the effects of optimism has flourished over the past 30 years. This research has taken several different routes to assessing optimism, leading to somewhat distinct literatures. The most common approach asks people whether they think that good or bad things will generally happen to them in the future. In this method, individuals are asked to respond to statements such as “I am optimistic about my future” (e.g., Scheier, Carver, & Bridges, 1994). Most of the research linking optimism to physical health has used this approach to measuring expectancies. Researchers who study optimism often refer to optimists and pessimists as though they were distinct groups. However, this usage is often a verbal convenience. All approaches to the measurement of optimism provide scores that vary continuously across large numbers of people. Thus, as often conceived, people actually range from very optimistic to very pessimistic, with most falling somewhere between. Interestingly, research exists (reviewed later in the chapter) that suggests that optimism and pessimism might not represent opposite poles of the same dimension, but actually might reflect two separate but related constructs. Use of separate terms in this context reflects more than a verbal convenience, but even here people vary in how optimistic or pessimistic they are. For now, we will treat optimism and pessimism as bipolar end points on a single dimension. This is how most of the researchers treated optimism and pessimism in the work that we review.

Effects of Optimism on Physical Health Early Research on Optimism Initial research on the benefits of optimism focused on relationships between optimism and subjective well‐being. This research clearly demonstrated that optimists experience less distress when faced with various stressors, such as undergoing coronary artery bypass surgery (e.g., Scheier et al., 1989) or being diagnosed with cancer (e.g., Carver et al., 1993). Over time, researchers became increasingly interested in whether optimism might also predict better physical health outcomes. Early research on optimism and physical health focused on the effects of optimism on short‐term health outcomes, as the main motivation was simply to test as efficiently as possible whether such relationships exist. These studies consistently found that optimists have better physical health than pessimists. An example of this type of short‐term health outcome is rehospitalization after coronary artery bypass surgery. Rehospitalization after bypass surgery is fairly common for a variety of reasons (e.g., wound infection, myocardial infarction, need for subsequent surgery), but it is quite costly in terms of healthcare expenditures and patients’ well‐being. Scheier et al. (1999) investigated whether optimists might respond better to surgery and be less likely than pessimists to need rehospitalization. To test this idea, they administered a measure of optimism to over 300 men and women who were scheduled to undergo bypass surgery. Six months later, they found that optimists were less likely to have been rehospitalized.

Larger Epidemiological Studies More recently, a number of epidemiological studies have substantially extended the research linking optimism and physical health. Typically, these studies enroll samples of initially healthy

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participants and measure the incidence (number of new cases) of disease over many years. Long periods of follow‐up time are necessary, as many diseases, such as cardiovascular disease and cancer, take years and even decades to develop. Furthermore, because most people do not develop the disease, studies have to recruit large numbers of participants in order to have adequate statistical power to detect associations. Participant numbers in these studies have ranged from the 1,000s to upward of 100,000. One of the largest epidemiological studies to investigate the effects of optimism was conducted by Tindle et  al. (2009). They administered a measure of optimism to over 95,000 initially healthy Caucasian and African American women. Eight years later, they found that optimists were less likely to have developed coronary heart disease and were less likely to have died from any cause, as compared with pessimists.

Pathways to Cardiovascular Disease Research on disease incidence and mortality strongly suggests that optimism plays a role in susceptibility to cardiovascular diseases, such as coronary heart disease and stroke. Research on various biological markers further supports this idea, demonstrating that optimism is related to markers that may represent physiological pathways to the development of future cardiovascular diseases. One physiological pathway implicated in a number of cardiovascular diseases is atherosclerosis, or the development of plaque, which narrows and hardens the arteries. Atherosclerosis is a progressive condition, normally increasing with age. Matthews et al. (2004) investigated whether optimism might slow this progression. They followed a sample of middle‐aged women for 10–13 years, measuring the intima–media thickness of the carotid artery as a metric of atherosclerosis. They found that optimists had a slower progression of atherosclerosis. In fact, the individuals highest in optimism showed almost no progression of atherosclerosis at all. Another important risk factor for cardiovascular disease is high blood pressure, which can damage the heart and the arteries over time. Several studies have shown that pessimists have higher systolic and diastolic blood pressure throughout the day (e.g., Räikkönen & Matthews, 2008) and tend to have larger blood pressure responses to stressors (e.g., Terrill, Ruiz, & Garofalo, 2010). In addition to atherosclerosis and blood pressure, optimism has also been linked to a number of other risk factors for cardiovascular disease such as obesity, cholesterol, and triglyceride levels (Boehm, Williams, Rimm, Ryff, & Kubzansky, 2013b), as well as metabolic syndrome, which is a cluster of conditions (i.e., high blood pressure, high blood sugar levels, excess abdominal fat, and high cholesterol levels) that put an individual at risk for cardiovascular diseases and diabetes (Cohen, Panguluri, Na, & Whooley, 2010).

Non‐cardiovascular Disease Outcomes and Immune Function Although optimism has shown strong associations with cardiovascular outcomes, evidence linking optimism to other diseases, particularly cancer and HIV, has been more equivocal. Some studies have found that optimism is related to lower mortality in patients with cancer (Allison, Guichard, & Gilain, 2000) and slower disease progression in patients with HIV (e.g., Ironson et al., 2005). However, other studies have not found that optimism predicts cancer and HIV‐related outcomes (e.g., Tindle et  al., 2009), and a recent meta‐analysis failed to find a significant association between optimism and cancer (Rasmussen, Scheier, & Greenhouse, 2009).

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The inconsistent relationship between optimism and cancer/HIV outcomes may be p ­ artially attributable to the nuanced relationship between optimism and various aspects of immunity. The immune system consists of multiple interacting components, and psychological factors can have positive and negative effects on different aspects of the immune system. These effects can further be moderated by specific contexts and situations. In terms of the positive effects of optimism on immune outcomes, multiple studies have demonstrated that optimism is associated with lower levels of circulating pro‐inflammatory cytokines and indicators of the inflammatory process, such as interleukin‐6, homocysteine, fibrinogen, and intercellular adhesion molecule 1 (e.g., Roy et al., 2010). High levels of circulating inflammatory markers are considered an index of systemic or chronic low‐grade inflammation. Such systemic inflammation is thought to be involved in a number of diseases, including cardiovascular disease, arthritis, and diabetes (e.g., Libby & Theroux, 2005). Thus, chronic inflammation may be another biological pathway by which optimism influences the development and progression of cardiovascular diseases, as well as other diseases. In contrast to the effects on inflammation, optimism has shown more qualified relationships with indicators of cell‐mediated immunity (e.g., circulating levels of T cells, natural killer cell cytotoxicity, delayed‐type hypersensitivity skin test). Cell‐mediated immunity is particularly important for combating viruses that enter human cells, such as the HIV virus, and for the removal of cells that have become cancerous. Research suggests that optimism may be related to better cell‐mediated immunity under some conditions, namely, when individuals face brief, controllable stressors. Optimism may be related to diminished levels of cell‐mediated immunity, though, when people face more prolonged and uncontrollable stressors (for a review, see Segerstrom, 2005). One of the first studies to identify this qualified relationship was conducted by Cohen et al. (1999). They followed a sample of young women for 3 months, assessing the experience of stressors weekly. Among women who experienced only a brief stressor, lasting 1 week or less, optimism was associated with higher levels of circulating T cells. The opposite pattern was found among women who experienced a stressor for multiple weeks. For these women, optimism was associated with lower levels of circulating T cells and lower natural killer cell cytotoxicity. Explanations for this pattern have varied. Some authors have argued that optimists are particularly vulnerable to disappointment when their positive expectations are disconfirmed (Tennen & Affleck, 1987). This disappointment could then explain the negative relationship between optimism and immunity when stressors prove difficult to resolve or control (e.g., Cohen et al., 1999). Although possible, this explanation is inconsistent with a large body of research suggesting that optimists experience less distress when they encounter negative outcomes, for example, when they are diagnosed with breast cancer (Carver et al., 1993). Segerstrom and colleagues proposed an alternative explanation for the negative relationship between optimism and immune response in some circumstances (Segerstrom, Castañeda, & Spencer, 2003). Specifically, they argued that optimists are more likely to engage actively in trying to resolve stressors and to persist in these efforts despite difficulties. During a prolonged stressor, this persistence strategy may have costs reflected in a sustained physiological stress response and reduced cell‐mediated immunity. However, this strategy likely has long‐term benefits in terms of eventual stressor resolution. Pessimists, who are more likely to give up dealing with a stressor, may not incur these physiological costs during a prolonged stressor, but they may also be less likely to deal with the stressor adaptively. Subsequent research has generally supported this second explanation (Segerstrom et  al., 2003). For example, Solberg Nes, Segerstrom, and Sephton (2005) gave optimists a difficult

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task while monitoring physiological indicators of stress. Optimists were more likely to persist at the task, but this persistence was associated with higher levels of cortisol and greater skin conductance. If the negative effects of optimism on immunity are attributable to greater persistence, more research will still be needed to determine whether this association explains the inconsistent relationship between optimism and cancer/HIV outcomes. Similarly, more research will be needed to demonstrate which of the multiple plausible physiological pathways actually underlie the effect of optimism on cardiovascular outcomes. In the future, research will likely need to move beyond simply demonstrating associations between optimism and physical heath. Instead, research should focus on documenting how optimism influences specific physiological pathways, which ultimately result in the development and progression of specific diseases. Another fruitful area for future research will be to investigate the effect of optimism on a wider array of physical health outcomes. The effects of optimism on various physiological pathways (e.g., metabolic syndrome, inflammatory markers) strongly indicate that optimism contributes to other diseases. New research is beginning to support this idea. Initial research has found that optimism may play a role in diabetes management, being associated with better glycemic control among individuals with diabetes (Brody, Kogan, Murry, Chen, & Brown, 2008). Additionally, studies have shown that optimism is related to several pregnancy‐related outcomes, such as birthweight and gestational age (Lobel, DeVincent, Kaminer, & Meyer, 2000), as well as in vitro fertilization success (Bleil et al., 2012).

Remaining Questions Psychological and Behavioral Mechanisms We have discussed several physiological pathways that might underlie the relationship between optimism and specific disease outcomes. What these physiological pathways do not explain is how optimism exerts physiological effects in the first place. In this regard, optimism has a number of effects on behavioral and psychological factors, which may account for the relationship between optimism and health. However, little research has actually tested whether these effects mediate the association between optimism and health. One of the most likely reasons optimists experience better health is they engage in more positive health behaviors, while pessimists tend to engage in health‐damaging behaviors. A number of studies have shown that optimists have a better diet (e.g., more fruits and vegetables), are more physically active, and are more likely to consume alcohol in moderation (e.g., Steptoe, Wright, Kunz‐Ebrecht, & Iliffe, 2006). In contrast, pessimists are more likely to be smokers (e.g., Steptoe et al., 2006) and to suffer from substance abuse problems (e.g., Carvajal, Garner, & Evans, 1998). Few studies have formally tested whether these health behaviors mediate the relationship between optimism and physical health, but some evidence suggests that these health behaviors have physiological effects. Boehm, Williams, Rimm, Ryff, and Kubzansky (2013a) found that the tendency of optimists to eat a better diet and smoke less had a beneficial impact on their serum levels of antioxidants (see also Steptoe et al., 2006; Tindle et al., 2009). The tendency of optimists to engage in proactive efforts to maintain their health is thought to be a manifestation of a broader tendency to engage in active, approach‐oriented coping strategies. A number of studies have found that optimists utilize coping strategies that directly address the stressor (e.g., seeking information about the problem) or directly alter cognitions about the

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stressor (e.g., positively reframing a stressful situation). Pessimists, in contrast, utilize avoidance coping strategies such as denying that a problem exists (for a meta‐analytic review of the different coping strategies used by optimists and pessimists, see Solberg Nes & Segerstrom, 2006). These coping strategies have a pervasive influence on how optimists and pessimists experience stressors. As mentioned previously, optimists experience less distress and higher levels of subjective well‐being when confronted with a variety of stressors, perhaps especially so for those that are acute in nature. Decades of research have shown that stress impairs physical health, while subjective well‐being is associated with better health and longevity (e.g., Diener & Chan, 2011). The tendency of optimists to engage in adaptive methods of coping that minimize distress may ultimately contribute to their physical health as well. Another potential mechanism concerns the effects of optimism on social relationships. Optimism is associated with greater social network size and to ties with others that cross age, educational, and racial boundaries (Andersson, 2012). Optimists are also more satisfied with their relationships and report greater social support than pessimists (e.g., Assad, Donnellan, & Conger, 2007). As both social network size and perceived social support predict physical health (for review see Cohen, 2004), these factors may also contribute to the relationship between optimism and health.

Dimensionality of Optimism Throughout this chapter, we have been referring to optimism as a single bipolar dimension ranging from optimism to pessimism. Many people working in the field construe optimism and pessimism in this fashion and analyze their studies accordingly, but as noted earlier on, researchers have also explored the possibility that optimism and pessimism may be somewhat distinct constructs. This view is consistent with the fact that factor analysis of generalized optimism scales often yields two separate components—one measuring expectancies for positive outcomes and one measuring expectancies for negative outcomes. Psychometrically, it remains unclear whether the distinction between optimism and pessimism is meaningful or merely an artifact of how optimism is measured. Recent research on physical health has contributed to the discussion of dimensionality, suggesting that when analyzed separately, optimism and pessimism may have differential effects on physical health outcomes. In fact, most of the research linking optimism to inflammatory outcomes suggests that it is mainly pessimism driving this effect, not optimism (e.g., Roy et al., 2010). Pessimism was also found to be a stronger predictor than optimism of in vitro fertilization success (Bleil et al., 2012). These findings raise the possibility that the detrimental impact of pessimism on health is stronger than any salutary effects of optimism, but much more research is needed to answer this question definitively.

Conclusion A substantial body of research suggests that optimism has a pervasive influence on physical health. The effects of optimism have been observed on short‐term health outcomes, such as rehospitalization after surgery, and long‐term health outcomes, such as mortality. This research clearly demonstrates that optimism is related to the development and progression of cardiovascular diseases, as well as a number of plausible physiological pathways to the development of these diseases. Optimism has shown a less consistent relationship with HIV/cancer outcomes, which may be due to the qualified relationship between optimism and immunity.

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More recent research has begun to investigate a wider array of physical health outcomes as well. Several questions remain concerning the relationship between optimism and physical health, including what psychological and behavioral factors allow optimism to create health‐ related physiological responses, as well as how best to conceptualize optimism. As time passes, the answers to these and other questions are likely to be resolved. Research on optimism and physical health has flourished for decades. There is no reason to expect that the tide of research will recede anytime soon.

Acknowledgment Preparation of this chapter was facilitated by support from the National Center for Complementary and Alternative Medicine (AT007262) and the Templeton Foundation. We would like to thank Nabila Fahin Jahan for reading an earlier draft of the paper.

Author Biographies Maria G. Mens is a graduate student at Carnegie Mellon University. She is interested in the health effects of optimism and pessimism and the processes that underlie a sense of purpose in life and the links between purpose in life and well‐being. Michael F. Scheier is a professor of psychology at Carnegie Mellon University. His work falls at the intersection of personality, social, and health psychology. He does research on the associations between personality and health, including optimism and pessimism, as well as work on coping with stress, adjustment to chronic disease, and goal adjustment processes and well‐being. Charles S. Carver is a professor of psychology at the University of Miami. His work at various times has addressed topics in personality, social psychology, health psychology, and experimental psychopathology. His current research mostly examines individual differences in impulsiveness versus constraint, how those differences are produced, and what their consequences are for effective self‐regulation.

References Allison, P. J., Guichard, C., & Gilain, L. (2000). A prospective investigation of dispositional optimism as a predictor of health‐related quality of life in head and neck cancer patients. Quality of Life Research, 9, 951–960. http://dx.doi.org/10.1023/A:1008931906253 Andersson, M. A. (2012). Dispositional optimism and the emergence of social network diversity. The Sociological Quarterly, 53(1), 92–115. http://dx.doi.org/10.1111/j.1533‐8525.2011.01227.x Assad, K. K., Donnellan, M. B., & Conger, R. D. (2007). Optimism: An enduring resource for romantic relationships. Journal of Personality and Social Psychology, 93(2), 285–297. http://dx.doi. org/10.1037/0022‐3514.93.2.285 Bleil, M. E., Pasch, L. A., Gregorich, S. E., Millstein, S. G., Katz, P. P., & Adler, N. E. (2012). Fertility treatment response: Is it better to be more optimistic or less pessimistic? Psychosomatic Medicine, 74(2), 193–199. http://dx.doi.org/10.1097/PSY.0b013e318242096b Boehm, J. K., Williams, D. R., Rimm, E. B., Ryff, C., & Kubzansky, L. D. (2013a). The association between optimism and serum antioxidants in the Midlife in the United States Study. Psychosomatic Medicine, 75(1), 2–10. http://dx.doi.org/10.1097/PSY.0b013e31827c08a9

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Boehm, J. K., Williams, D. R., Rimm, E. B., Ryff, C., & Kubzansky, L. D. (2013b). Relation between optimism and lipids in midlife. The American Journal of Cardiology, 111(10), 1425–1431. http:// dx.doi.org/10.1016/j.amjcard.2013.01.292 Brody, G. H., Kogan, S. M., Murry, V. M., Chen, Y. F., & Brown, A. C. (2008). Psychological functioning, support for self‐management, and glycemic control among rural African American adults with diabetes mellitus type 2. Health Psychology, 27(1S), S83–S90. http://dx.doi.org/10.1037/02 78‐6133.27.1.S83 Carvajal, S. C., Garner, R. L., & Evans, R. I. (1998). Dispositional optimism as a protective factor in resisting HIV exposure in sexually active inner‐city minority adolescents. Journal of Applied Social Psychology, 28(23), 2196–2211. http://dx.doi.org/10.1111/j.1559‐1816.1998.tb01367.x Carver, C. S., Pozo, C., Harris, S. D., Noriega, V., Scheier, M. F., Robinson, D. S., … Clark, K. C. (1993). How coping mediates the effect of optimism on distress: A study of women with early stage breast cancer. Journal of Personality and Social Psychology, 65(2), 375–390. http://dx.doi. org/10.1037/0022‐3514.65.2.375 Cohen, B. E., Panguluri, P., Na, B., & Whooley, M. A. (2010). Psychological risk factors and the metabolic syndrome in patients with coronary heart disease: Findings from the Heart and Soul Study. Psychiatry Research, 175(1‐2), 133–137. Cohen, F., Kearney, K. A., Zegans, L. S., Kemeny, M. E., Neuhaus, J. M., & Stites, D. P. (1999). Differential immune system changes with acute and persistent stress for optimists vs pessimists. Brain, Behavior, and Immunity, 13(2), 155–174. http://dx.doi.org/10.1006/brbi.1998.0531 Cohen, S. (2004). Social relationships and health. American Psychologist, 59(8), 676–684. http://dx. doi.org/10.1037/0003‐066X.59.8.676 Diener, E., & Chan, M. Y. (2011). Happy people live longer: Subjective well‐being contributes to health and longevity. Applied Psychology. Health and Well‐Being, 3(1), 1–43. http://dx.doi.org/ 10.1111/j.1758‐0854.2010.01045.x Ironson, G., Balbin, E., Stuetzle, R., Fletcher, M. A., O’Cleirigh, C., Laurenceau, J.‐P., … Solomon, G. (2005). Dispositional optimism and the mechanisms by which it predicts slower disease progression in HIV: Proactive behavior, avoidant coping, and depression. International Journal of Behavioral Medicine, 12(2), 86–97. http://dx.doi.org/10.1207/s15327558ijbm1202_6 Libby, P., & Theroux, P. (2005). Pathophysiology of coronary artery disease. Circulation, 111(25), 3481–3488. http://dx.doi.org/10.1161/CIRCULATIONAHA.105.537878 Lobel, M., DeVincent, C. J., Kaminer, A., & Meyer, B. A. (2000). The impact of prenatal maternal stress and optimistic disposition on birth outcomes in medically high‐risk women. Health Psychology, 19(6), 544–553. http://dx.doi.org/10.1037/0278‐6133.19.6.544 Matthews, K. A., Raikkonen, K., Sutton‐Tyrrell, K., & Kuller, L. H. (2004). Optimistic attitudes protect against progression of carotid atherosclerosis in healthy middle‐aged women. Psychosomatic Medicine, 66(5), 640–644. http://dx.doi.org/10.1097/01.psy.0000139999.99756.a5 Räikkönen, K., & Matthews, K. A. (2008). Do dispositional pessimism and optimism predict ambulatory blood pressure during schooldays and nights in adolescents? Journal of Personality, 76(3), 605–630. http://dx.doi.org/10.1111/j.1467‐6494.2008.00498.x Rasmussen, H. N., Scheier, M. F., & Greenhouse, J. B. (2009). Optimism and physical health: A meta‐ analytic review. Annals of Behavioral Medicine, 37(3), 239–256. http://dx.doi.org/10.1007/ s12160‐009‐9111‐x Roy, B., Diez‐Roux, A. V., Seeman, T., Ranjit, N., Shea, S., & Cushman, M. (2010). The association of optimism and pessimism with inflammation and hemostasis in the Multi‐Ethnic Study of Atherosclerosis (MESA). Psychosomatic Medicine, 72(2), 134–140. http://dx.doi.org/10.1097/ PSY.0b013e3181cb981b Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from neuroticism (and trait anxiety, self‐mastery, and self‐esteem): A re‐evaluation of the Life Orientation Test. Journal of Personality and Social Psychology, 67, 1063–1078. Scheier, M. F., Matthews, K. A., Owens, J. F., Magovern, G. J., Lefebvre, R. C., Abbott, R. A., & Carver, C. S. (1989). Dispositional optimism and recovery from coronary artery bypass surgery:

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The beneficial effects on physical and psychological well‐being. Journal of Personality and Social Psychology, 57(6), 1024–1040. http://dx.doi.org/10.1037/0022‐3514.57.6.1024 Scheier, M. F., Matthews, K. A., Owens, J. F., Schulz, R., Bridges, M. W., Magovern, G., Sr., & Carver, C. S. (1999). Optimism and re‐hospitalization following coronary artery bypass graft surgery. Archives of Internal Medicine, 159(8), 829–835. http://dx.doi.org/10.1001/archinte.159.8.829 Segerstrom, S. C. (2005). Optimism and immunity: Do positive thoughts always lead to positive effects? Brain, Behavior, and Immunity, 19(3), 195–200. http://dx.doi.org/10.1016/j.bbi.2004.08.003 Segerstrom, S. C., Castañeda, J. O., & Spencer, T. E. (2003). Optimism effects on cellular immunity: Testing the affective and persistence models. Personality and Individual Differences, 35(7), 1615– 1624. http://dx.doi.org/10.1016/S0191‐8869(02)00384‐7 Solberg Nes, L., & Segerstrom, S. C. (2006). Dispositional optimism and coping: A meta‐analytic review. Personality and Social Psychology Review, 10(3), 235–251. http://dx.doi.org/10.1207/ s15327957pspr1003_3 Solberg Nes, L., Segerstrom, S. C., & Sephton, S. E. (2005). Engagement and arousal: Optimism’s effects during a brief stressor. Personality and Social Psychology Bulletin, 31(1), 111–120. http:// dx.doi.org/10.1177/0146167204271319 Steptoe, A., Wright, C., Kunz‐Ebrecht, S. R., & Iliffe, S. (2006). Dispositional optimism and health behavior in community‐dwelling older people: Associations with healthy ageing. British Journal of Health Psychology, 11(1), 71–84. http://dx.doi.org/10.1348/135910705X42850 Tennen, H., & Affleck, G. (1987). The costs and benefits of optimistic explanations and dispositional optimism. Journal of Personality, 55(2), 377–392. http://dx.doi.org/10.1111/j.1467‐6494.1987. tb00443.x Terrill, A. L., Ruiz, J. M., & Garofalo, J. P. (2010). Look on the bright side: Do the benefits of optimism depend on the social nature of the stressor? Journal of Behavioral Medicine, 33(5), 399–414. http:// dx.doi.org/10.1007/s10865‐010‐9268‐6 Tindle, H. A., Chang, Y. F., Kuller, L. H., Manson, J. E., Robinson, J. G., Rosal, M. C., … Matthews, K. A. (2009). Optimism, cynical hostility, and incident coronary heart disease and mortality in the Women’s Health Initiative. Circulation, 120(8), 656–662. http://dx.doi.org/10.1161/ CIRCULATIONAHA.108.827642

Suggested Reading Carver, C. S., & Scheier, M. F. (2014). Dispositional optimism. Trends in Cognitive Sciences, 18, 293– 299. doi:10.1016/j.tics.2014.02.003 Carver, C. S., & Scheier, M. F. (2017). Optimism, coping, and well‐being. In C. Cooper & J. C. Quick (Eds.), Wiley handbook of stress and health: A guide to research and practice (pp. 401–414). Chichester, UK: Wiley‐Blackwell. Scheier, M. F., Carver, C. S., & Armstrong, G. H. (2012). Behavioral self‐regulation, health, and illness. In A. S. Baum, T. A. Revenson, & J. E. Singer (Eds.), Handbook of health psychology (2nd ed., pp. 79–98). New York, NY: Psychology Press. Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from neuroticism (and trait anxiety, self‐mastery, and self‐esteem): A re‐evaluation of the Life Orientation Test. Journal of Personality and Social Psychology, 67, 1063–1078. Segerstrom, S. C., Carver, C. S., & Scheier, M. F. (2017). Optimism. In M. Eid & M. D. Robinson (Eds.), The happy mind: Cognitive contributions to well‐being (pp. 195–214). New York, NY: Springer.

Patient Adherence Kelly B. Haskard‐Zolnierek and Briana Cobos Department of Psychology, Texas State University, San Marcos, TX, USA

Introduction and Definition The primary factor in achieving optimal health and the effectiveness of a medical treatment plan is determined by the patient’s adherence toward their treatment regimen. Adherence is defined as the extent to which a patient’s behavior corresponds with the agreed‐upon treatment guidelines set forth by a healthcare provider. The terms “compliance” and “persistence” are essentially synonymous with the term “adherence”; however, adherence is preferred because compliance suggests that the patient is passively obeying the guidelines of the medical provider. Persistence describes the degree to which the patient follows the guidelines or behavior modifications recommended by a provider for a predetermined duration of time. Adherence includes various health‐related behaviors such as taking medication, filling prescriptions, attending follow‐up appointments, self‐management of a chronic and/or acute disease, receiving immunizations, and/or modifying one’s lifestyle behaviors. These behaviors are intended to provide optimal health when followed. The terms “adherence” and “concordance” similarly describe a situation in which a treatment plan is negotiated and collectively determined between the patient and physician. The primary objective of concordance is to establish a therapeutic relationship between the patient and physician. However, “adherence” is the preferred term because it suggests an agreement between a patient and a healthcare provider, that is, negotiated, planned, and discussed with a collaborative and therapeutic effort to determine a treatment plan and/or behavior modifications aimed at providing optimal health for the patient. In contrast, nonadherence constitutes the patient failing to follow the recommended treatment regimen. An individual can be nonadherent by not taking their medication, discontinuing their medications before the recommended time set forth by their healthcare provider, not taking their medication dosage correctly, and/or disregarding certain behavior modifications that are part of the treatment plan. Furthermore, an individual can be nonadherent by not filling a prescription, which is referred to as “nonfulfillment.”

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Intentional nonadherence is another form of nonadherence in which an individual actively decides not to follow a healthcare provider’s treatment recommendations or guidelines. Within this type of nonadherence, the individual usually takes an active approach in deciding whether to adhere or not adhere to their treatment plan. Individuals’ beliefs and knowledge of their health condition and treatment regimen play an important role in their decision. For example, the individual may actively decide to discontinue medication due to aversive symptoms subsiding. Alternatively, an individual can be unintentionally nonadherent toward treatment. This type of nonadherence can occur when the individual forgets to take a medication or lacks knowledge or understanding about how to take medication or adhere to a treatment plan. This has less to do with the individual’s belief or knowledge. Instead, this type of nonadherence is due to unforeseen circumstances, such as the complexity of a treatment plan causing the individual to forget certain behavior modifications or medication dosages. Therefore, nonadherence could ultimately lead the individual to not experience the benefits of their treatment plan and can further worsen an individual’s well‐being.

Rates of Nonadherence Rates of nonadherence vary depending on regimen, disease, disease category (i.e., acute versus chronic), and other factors. On average, rates of nonadherence range from 25 to 50%, with greater rates of nonadherence in lifestyle change regimens (e.g., making major dietary changes or maintaining a consistent exercise regimen). Rates of nonadherence tend to be higher for more complex regimens such as those that require multiple doses per day or have more complicated dosing instructions, such as associated dietary restrictions. In addition, rates of nonadherence to chronic diseases can be greater due to the long‐term nature of these regimens as compared with adherence to short‐term regimens for acute diseases. A meta‐analysis of 569 studies published from 1948 through 1998 reported the highest rates of adherence in HIV and cancer and the lowest rates in pulmonary diseases, diabetes, and sleep disorders (DiMatteo, 2004a). Other meta‐analytic work has shown that across 116 studies, patients with serious illnesses such as end‐stage renal disease or heart disease who are in poorer health (either objectively or according to self‐ratings) are less adherent (DiMatteo, Haskard, & Williams, 2007). These variations in rates of adherence across diseases could be due to the complex nature of some disease regimens, other factors such as adverse side effects, and other restrictions on one’s activities and lifestyle. This nonadherence could also be due to personal factors such as depression, cognitive deficits, and pessimism that can accompany serious illnesses.

Consequences of Nonadherence There are various causes of nonadherence to treatment regimens. For example, contributing factors toward nonadherence can include the following: adverse medication side effects, a long duration of treatment, frequency of expected intake of medication, or the complexity of the treatment. Nonadherence is an important healthcare issue because of the associated financial, health, and other consequences. Patients who are nonadherent are more likely to utilize healthcare resources, thus increasing healthcare costs. The economic burden of nonadherence in the United States is estimated to be between $290 and $300 billion dollars each year. This burden is caused by individuals who are unable

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to modify health or lifestyle behaviors, patients unable to attend medical visits, secondary illnesses that arise due to uncontrolled chronic health conditions, and unnecessary hospitalizations or emergency room visits because of uncontrolled health conditions (DiMatteo, Haskard Zolnierek, & Martin, 2012). Nonadherence could also cause either additional economic burden in the US workforce by loss of productivity at work or an increased rate of employee absenteeism. Nonadherence has been associated with higher costs in various diseases, wherein patients face greater annual healthcare costs because of their nonadherence (Iuga & McGuire, 2014). Conversely, increased rates of adherence provide economic benefits. An increase in adherence rates would reduce the use and cost of healthcare resources needed during relapses, emergencies, and/or hospitalizations that could have been prevented by the patient following their treatment regimen. Therefore, nonadherence represents a serious issue for healthcare professionals, patients, and the healthcare industry, in which optimal health outcomes are exclusively determined by the individual’s adherence toward a treatment regimen. Nonadherence toward a treatment regimen can have further adverse effects on an individual’s health outcomes by causing additional health complications or secondary illnesses. The consequences of nonadherence on an individual’s health vary based on the type of illness and the treatment regimen. For example, types of health conditions that require strict adherence toward their treatment regimen and medication include kidney transplantation, end‐ stage renal disease, and HIV. The primary cause of kidney transplant rejection for patients is nonadherence toward their immunosuppressant medication (Cukor, Rosenthal, Jindal, Brown, & Kimmel, 2009). Individuals who are nonadherent toward their medication are at an increased risk of rejecting their organ or experiencing kidney failure. Furthermore, for individuals with end‐stage renal disease, adherence to hemodialysis regimens is crucial in order to decrease the individual’s risk of earlier mortality or further complications. Individuals diagnosed with HIV face an increased rate of drug resistance to their antiretroviral medication if they are not strictly adherent toward their medication regimen as well. As a result, nonadherence causes an increased risk of mortality and future adverse effects such as faster progression of the virus. Health conditions that are not solely dependent on medication have been shown to have suboptimal adherence rates. For example, treatment regimens for individuals with diabetes or primary hypertension usually include a complex treatment regimen that includes medication, as well as diet and lifestyle modifications in order to obtain optimal health. Nonadherence to treatment regimens for these health conditions can have an adverse effect on an individual’s health over time. Individuals diagnosed with diabetes and are nonadherent can experience adverse complications such as ketoacidosis (diabetic coma), stroke, kidney disease, blindness, damage to the nerves, the development of heart disease, and an increased risk of early mortality. Furthermore, individuals diagnosed with primary hypertension have a treatment plan that usually consists of antihypertensive medication as well as lifestyle change. Nonadherence to antihypertensive medication and behavior change can lead to the development of heart disease, kidney damage, congestive heart failure, and stroke. Therefore, adherence to the agreed‐ upon treatment plan is critical to decrease an individual’s risk of developing secondary illnesses or adverse complications and to increase the likelihood of optimal health outcomes. Another consequence of nonadherence is erosion of the physician–patient relationship. Ideally, a patient and physician work together in a collaborative manner using open communication to reach the common goal of good health outcomes for the patient. If a patient does not admit to struggles with adherence, it may be difficult for the physician to trust the patient, and the quality of their relationship can be negatively affected.

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Measurements of Adherence There are multiple methods of measuring adherence to treatment, but unfortunately no “gold standard” measurement method exists. Numerous measurement approaches can be used based on the setting and context of the measurement (e.g., clinical settings versus research contexts). Measurement approaches are typically grouped into two categories: direct and indirect methods. Direct methods include direct observation of patients taking their medication and measurement of levels of the medication or metabolite in blood or urine. Direct approaches can be very accurate but may also be more invasive, expensive, and time consuming. Indirect measures include patient self‐reports either via interview or questionnaire, family member reports, pill counts, pharmacy refill records, or electronic monitors (e.g., measurement via special pill bottle caps such as MEMScap™ that record the time and date each time a pill bottle is opened). Each of these measures has both benefits and drawbacks. Pharmacy refill records, for example, can be inaccurate because they do not directly show ingestion of medication or behavior such as incorrect timing of doses. Electronic monitors not only can be very accurate but also are a more expensive way to measure adherence and also do not indicate ingestion of medication. Although self‐report measures can be subject to biases in memory or the desire to present oneself in the best possible light, they do tend to be the simplest, most common, and least expensive approach to adherence measurement. In general, a multimethod approach to measuring adherence is recommended.

Predictors of Nonadherence Researchers have attempted to understand the factors that influence patient nonadherence and the factors that may promote adherence to treatment. Predictors of nonadherence have been grouped into several categories: patient‐related factors, regimen‐related factors, provider– patient interaction level factors, and healthcare system‐related factors (Osterberg & Blaschke, 2005). Patient‐related factors that predict nonadherence include such elements as mental health, beliefs and attitudes, understanding, motivation, and social support. Patients with poor mental health (particularly depression) tend to be less adherent. Meta‐analytic work reveals a strong relationship between nonadherence and depression across 31 studies of patients with chronic diseases (Grenard et al., 2011). This association may be due to a pessimistic attitude, forgetfulness, or lack of motivation that often accompanies depression. Furthermore, patients who do not believe in the benefit of their regimen or have negative attitudes toward it may be less adherent. Such patients make a conscious choice not to follow their treatment regimen, as they are not convinced that it will help them or alleviate their symptoms. In addition, patients who do not understand their regimen or how to follow it correctly may be unintentionally nonadherent. When patients first receive instructions on how to take medications, they may not understand the instructions due to poor health literacy (i.e., an inability or struggle to understand and process health‐related information). Also, patients who are lacking in social support, particularly that who comes from a loving family, may also be less adherent (DiMatteo, 2004b). A patient’s support network may play many roles in promoting adherence, including giving reminders to the patient, encouraging the patient in their health‐related goals, and providing tangible support such as driving the patient to medical appointments. There are several regimen‐related factors that may be associated with nonadherence. These include side effects, frequency of dosing, and complexity of the regimen. A review of 61

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studies reported that in diseases such as diabetes and HIV, greater dose frequency and more complex regimens (e.g., more medications, special requirements associated with medication taking) are associated with poorer adherence (Ingersoll & Cohen, 2008). Physician–patient communication is also associated with patient adherence. A meta‐analysis of 106 studies (Zolnierek & DiMatteo, 2009) reported that patients of physicians who communicated effectively had 19% higher adherence. Mutual trust, open communication, sharing in the process of making medical decisions, and partnership are all interpersonal factors in the medical provider–patient relationship that are central to achievement of patient adherence. Healthcare system factors may also play a role in nonadherence. Numerous studies have indicated that the cost of medications may predict nonadherence, as patients delay refills or take their medication less frequently due to cost issues (Briesacher, Gurwitz, & Soumerai, 2007). The information–motivation–strategy model has been proposed to explain three basic factors that are central to achievement of adherence (DiMatteo et al., 2012). First, patients do not understand their regimen and how to follow it. They lack the information and understanding needed to adhere. Communication with their physician may have been ineffective, or they may have difficulty understanding the regimen due to poor health literacy or forgetting the instructions they were given. Second, patients may not be motivated and committed to adherence to their treatment regimen. This lack of motivation or commitment could be due to their beliefs about the treatment regimen or their negative attitudes about it or to a lack of support that encourages the patient’s adherence. Third, patients may lack the resources and strategies to support adherence. These strategies could include tools to help them remember to take their medication or a plan for getting to the pharmacy for refills. Understanding the predictors of adherence is helpful in designing interventions to improve adherence.

Interventions to Improve Adherence As discussed, adherence is influenced by multiple factors, and researchers have proposed a variety of interventions in an effort to reduce rates of nonadherence. Interventions have been proposed on multiple levels, including patient, provider, and system‐related levels. For example, a policy‐level intervention might involve reducing out‐of‐pocket costs of medications. Unfortunately, fewer than half of published interventions have been found to improve adherence (Haynes, Ackloo, Sahota, McDonald, & Yao, 2008; van Dulmen et al., 2007). Evidence suggests that multifaceted interventions that address multiple barriers to adherence may be most effective. Strategies that have been found to be effective in multicomponent interventions include more intensive communication with and counseling of patients, providing reminders, and providing closer follow‐up (Haynes et al., 2008). A recent review of 182 randomized clinical trials of interventions to improve medication adherence reported that the most successful interventions were complex and personalized and involved several approaches to improving adherence (Nieuwlaat et  al., 2014). Examples of these approaches included increased support from family members or personalized care, education, and communication from healthcare providers such as pharmacists. Educational interventions with monitoring, support, and follow‐up were most strongly associated with improvements in adherence in a systematic review (Viswanathan et al., 2012). These authors compared interventions in various categories of chronic disease and stratified by patient, provider, and system‐level interventions. Specific examples of successful interventions included asthma self‐management and case management interventions for depression

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(Viswanathan et al., 2012). Other techniques that have been successful include reducing daily dosing frequency and providing reminders in the form of alarms or alerts, which can be helpful with the unintentional aspect of nonadherence. In promoting patient adherence, it will continue to be important to be aware of and address barriers at the patient, provider, and system levels. In addition, future research should continue to study the most successful components of interventions and improve these interventions to enhance adherence and health outcomes.

Author Biographies Kelly B. Haskard‐Zolnierek is an associate professor of psychology at Texas State University. Her research involves two main areas: patients’ adherence to medical recommendations and medical visit communication in the provider–patient relationship. Her current research involves evaluating mobile health interventions to improve patient adherence. Dr. Haskard‐ Zolnierek has coauthored a book on patient adherence, published in 2010 by Oxford University Press, and a 2009 meta‐analysis of the relationship between physician communication skills and patient adherence. Briana Cobos completed her master of arts degree at Texas State University in psychological research. Her general research area is in health psychology. Ms. Cobos’ research interests include predictors of nonadherence in individuals diagnosed with both a mental health condition and a chronic illness. She has coauthored various papers with a focus in health psychology and mental health conditions.

References Briesacher, B. A., Gurwitz, J. H., & Soumerai, S. B. (2007). Patients at‐risk for cost‐related medication nonadherence: A review of the literature. Journal of General Internal Medicine, 22(6), 864–871. doi:10.1007/s11606‐007‐0180‐x Cukor, D., Rosenthal, D., Jindal, R., Brown, C., & Kimmel, P. (2009). Depression is an important contributor to low medication adherence in hemodialyzed patients and transplant recipients. Kidney International, 75(11), 1223–1229. doi:10.1038/ki.2009.51 DiMatteo, M. R. (2004a). Variations in patients’ adherence to medical recommendations: A quantitative review of 50 years of research. Medical Care, 42(3), 200–209. doi:10.1097/01.mlr.0000114908. 90348.f9 DiMatteo, M. R. (2004b). Social support and patient adherence to medical treatment: A meta‐analysis. Health Psychology, 23(2), 207–218. doi:10.1037/0278‐6133.23.2.207 DiMatteo, M. R., Haskard, K. B., & Williams, S. L. (2007). Health beliefs, disease severity, and patient adherence: A meta‐analysis. Medical Care, 45(6), 521–528. DiMatteo, M. R., Haskard Zolnierek, K. B., & Martin, L. R. (2012). Improving patient adherence: A three‐factor model to guide practice. Health Psychology Review, 6, 74–91. doi:10.1080/17437199. 2010.537592 Grenard, J. L., Munjas, B. A., Adams, J. L., Suttorp, M., Maglione, M., McGlynn, E. A., & Gellad, W. F. (2011). Depression and medication adherence in the treatment of chronic diseases in the United States: A meta‐analysis. Journal of General Internal Medicine, 26(10), 1175–1182. doi:10.1007/ s11606‐011‐1704‐y Haynes, R. B., Ackloo, E., Sahota, N., McDonald, H. P., & Yao, X. (2008). Interventions for enhancing medication adherence. Cochrane Database of Systematic Reviews, (2), CD000011. doi:10.1002/ 14651858.CD000011.pub3

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Ingersoll, K. S., & Cohen, J. (2008). The impact of medication regimen factors on adherence to chronic treatment: A review of literature. Journal of Behavioral Medicine, 31(3), 213–224. doi:10.1007/ s10865‐007‐9147‐y Iuga, A. O., & McGuire, M. J. (2014). Adherence and health care costs. Risk Management Healthcare Policy, 7, 35–44. doi:10.2147/RMHP.S19801 Nieuwlaat, R., Wilczynski, N., Navarro, T., Hobson, N., Jeffery, R., … Haynes, R. B. (2014). Interventions for enhancing medication adherence. Cochrane Database of Systematic Reviews, (11), CD000011. doi:10.1002/14651858.CD000011.pub4 Osterberg, L., & Blaschke, T. (2005). Adherence to medication. New England Journal of Medicine, 353(5), 487–497. doi:10.1056/NEJMra050100 van Dulmen, S., Sluijs, E., van Dijk, L., de Ridder, D., Heerdink, R., & Bensing, J. (2007). Patient adherence to medical treatment: A review of reviews. BMC Health Services Research, 7, 55. doi:10.1186/1472‐6963‐7‐55 Viswanathan, M., Golin, C. E., Jones, C. D., Ashok, M., Blalock, S. J., Wines, R. C., et  al. (2012). Interventions to improve adherence to self‐administered medications for chronic diseases in the United States: A systematic review. Annals of Internal Medicine, 157(11), 785–795. doi:10.7326/ 0003‐4819‐157‐11‐201212040‐00538 Zolnierek, K. B., & DiMatteo, M. R. (2009). Physician communication and patient adherence to treatment: A meta‐analysis. Medical Care, 47(8), 826–834. doi:10.1097/MLR.0b013e31819a5acc

Suggested Reading Atreja, A., Bellam, N., & Levy, S. R. (2005). Strategies to enhance patient adherence: Making it simple. Medscape General Medicine, 7(1), 4. DiMatteo, M. R., Haskard Zolnierek, K. B., & Martin, L. R. (2012). Improving patient adherence: A three‐factor model to guide practice. Health Psychology Review, 6, 74–91. Martin, L. R., Williams, S. L., Haskard, K. B., & DiMatteo, M. R. (2005). The challenge of patient adherence. Therapeutics and Clinical Risk Management, 1(3), 189–199. Osterberg, L., & Blaschke, T. (2005). Adherence to medication. New England Journal of Medicine, 353(5), 487–497.

Patient Satisfaction Summer L. Williams1 and Tricia A. Miller2 Department of Psychology, Westfield State University, Westfield, MA, USA Department of Psychology, University of California, Riverside, CA, USA

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Introduction The field of patient satisfaction is extensive as an area of empirical research and covers many constructs and facets of the patient experience in medical care. Literature reviews and meta‐ analytic studies have explored patient satisfaction in relation to a multitude of external variables such as patient health outcomes and domains within the clinical care context. Many aspects of healthcare delivery have focused on the patient as a consumer of health services and likewise an essential evaluator of the quality of that healthcare experience, wherein the satisfaction of the patient becomes a critical element in the quest for improved healthcare delivery and health outcomes. Patients’ perceptions of care have become increasingly more important and valued as key indicators of outcome measures. Health researchers are keen to the importance of patient satisfaction, and thus they are engaged in ongoing efforts toward quality patient satisfaction measurement and translating patient satisfaction findings to inform health policy and make system‐level changes.

Assessment and Measurement of Patient Satisfaction Patient satisfaction as a healthcare outcome is typically measured via self‐report, in which patients report their preferences, expectations, and level of satisfaction with the medical care they received within a variety of domains, including access to and cost of care, empathy, competence, attention and information given by the provider, continuity of care, attention to psychosocial issues by the provider, and overall quality of care (Hall & Dornan, 1988). Typically patients report their perceptions of care immediately after their visit or some time later by phone, mail, online survey, or an in‐person interview. One of the most widely used measures of patient satisfaction is the Patient Satisfaction Questionnaire, developed by the

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RAND corporation, to assess general or global satisfaction with care as well as patient satisfaction with a number of individual domains such as technical quality, interpersonal manner, communication, financial aspects, time spent with doctor, and accessibility and convenience (Ware, Snyder, & Wright, 1976). This 50‐item measure has been shortened in subsequent versions to include 18 items, known as the Patient Satisfaction Questionnaire‐18 (PSQ‐18), for ease of use in healthcare settings (Marshall & Hays, 1994). Later measures of patient satisfaction extended to include more detailed aspects of the medical interview and include the Medical Interview Satisfaction Scale (MISS‐29) and refinements of it, developed for use in British general practice consultations (Meakin & Weinman, 2002; Wolf, Putnam, James, & Stiles, 1978). One of the most recent and widely used measures of patient satisfaction is the Consumer Assessment of Healthcare Providers and Systems— Clinician and Group Surveys (CG‐CAHPS) developed by Dyer, Sorra, Smith, Cleary, and Hays (2012). This measure extends questions of patient satisfaction beyond the clinician level and into the healthcare system level by looking at aspects of care such as access. One of the major early concerns with assessing patient satisfaction was the lack of standardization across studies in its measurement, with some measures yielding low reliability and the validity of the measuring instruments generally unknown. Additional challenges to measuring patient satisfaction include recent concerns over whether patients have the expertise, medical knowledge, and health literacy to provide an accurate assessment of their medical care (Manary, Boulding, Staelin, & Glickman, 2013) and questions as to the accuracy of patients’ recall of their medical experiences (Aharony & Strasser, 1993). Perhaps though one of the toughest challenges to measuring patient satisfaction has been concern over the high reported levels of satisfaction across studies (Williams, Coyle, & Healy, 1998). The concern lies in whether patients are accurately reporting their levels of satisfaction and, if so, what factors might be driving such high satisfaction scores across studies. Social psychological concepts have been applied to understanding high satisfaction scores. Cognitive consistency theory asserts that patients are likely to report satisfaction with their care as a way of justifying the time and effort they have spent seeking that care (LeVois, Nguyen, & Attkisson, 1981). Patients especially prone to this bias are those who continue their care despite adverse situations in the healthcare setting. It may be that dissatisfaction with care is only reported when the patient experiences an extreme negative event (Williams, 1994).

Key Research Findings in Patient Satisfaction Due to the sheer number of empirical studies on patient satisfaction, the literature and findings can often be unclear. Meta‐analytic studies within the area of patient satisfaction have become paramount in cutting through the diffuse nature of the literature in order to document the important role that patient satisfaction plays in the medical care forum and in the quality of care provided. Early research on patient satisfaction suggested that it may be hard to discern patients’ true attitudes toward their care because patients often fear unfavorable treatment in the future if their provider learns that they expressed dissatisfaction (e.g., through self‐report) (Ley, 1972). In one of the first meta‐analytic studies conducted addressing patient satisfaction, Hall and Dornan (1988) uncovered a bit of reluctance on the part of patients to evaluate their healthcare providers and to report on satisfaction with their medical care. The authors determined that patients’ reluctance to report on satisfaction was related to patients’ viewing their care as subpar in some regard yet not wanting to criticize their providers. Of the 221 studies included in the

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meta‐analysis, patients were overall more satisfied with their care when their physicians were still in training. This finding begs the question, what is it about physicians in training that makes their patients more satisfied? It is within the physician–patient relationship that the research has focused on correlates of patient satisfaction to gain a better understanding of the mediators involved; one key mediator of patient satisfaction is provider–patient communication. The importance of physician–patient communication skills is now, more than ever, a central component in medical school curricula and residency training programs thanks to a multitude of research demonstrating that beneficial patient outcomes, including patient satisfaction, are linked to good communication skills in providers (Williams, Weinman, & Dale, 1998). Aspects of physician communication such as instrumental communication (i.e., task‐directed skill) and discussion of psychosocial issues (i.e., socio‐emotional communication) with the patient have been found to be most related to and predictive of patient satisfaction (Inui, Carter, Kukull, & Haigh, 1982; Roter, Hall, & Katz, 1987). Other communication barriers such as lack of warmth and friendliness from the provider, failure to take the patient’s concerns into account, unclear expectations and explanations, and ambiguous diagnosis and causation of illness have been found to decrease satisfaction (Jackson, Chamberlin, & Kroenke, 2001). Additionally, providers who use medical jargon or fail to consider a patient’s level of health literacy may also be inadvertently contributing to their patient’s decreased satisfaction (Roter et al., 1997). Providers who take the time to educate their patients in a comprehensible manner will likely result in many positive clinical outcomes, including not only patient satisfaction but also reduced patient anxiety, patients’ enhanced ability to cope with symptoms, enhanced recovery after surgery, and increased patient adherence (Sitzia & Wood, 1997). It stands to reason that providers in training, who are practicing, developing, and honing their communication skills that involve the aforementioned aspects of communication, would produce more satisfied patients. However, provider communication alone does not speak to the entire relationship between aspects of the care visit and patient satisfaction. Other studies have linked increased patient satisfaction to the sheer amount of time spent in the clinical encounter, specifically during the physical exam as opposed to the history‐taking portion of the visit (Robbins et  al., 1993). These physician practice behaviors have an impact on patient satisfaction that goes beyond just how “good” of a communicator the provider is. Patients have entered into more collaborative partnerships with their providers and expect discussion of health education and treatment effects, among other information on which they might base their future health behaviors and decisions (Robbins et al., 1993).

Other Correlates of Patient Satisfaction Patient adherence to treatment guidelines is one health outcome that has been positively correlated with satisfaction (Jha, Orav, Zheng, & Epstein, 2008). This makes sense: a patient is more likely to adhere when a common understanding of treatment and self‐care expectations has been reached, and this association likely goes back to effective provider communication and establishment of collaboration with the patient (Manary et al., 2013). Other researchers have explored various patient demographics and health status (i.e., age, gender, socioeconomic, mental health, etc.) and their possible influence on a patient’s satisfaction. The most consistent determinant characteristic is age, such that older patients are generally more satisfied than younger patients (Hall & Dornan, 1990; Zahr, William, & El‐Hadad, 1991). Part of the reasoning behind this finding may hinge upon the expectations of older and

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younger patients. Cartwright and Anderson (1981) found that older patients expect less ­information from their physicians. With the advent of the Internet and accessibility of medical information, younger patients today might expect more information from their providers especially when dealing with cancer; younger patients may even be going into the interactions with more knowledge than older patients (Norum, Grev, Moen, Balteskard, & Holthe, 2003). A mediator of this finding might be the education level of the patient. Greater satisfaction is associated with lower levels of education (Hall & Dornan, 1990). On the other hand, demographics such as sex and race seem to reveal mixed results in relation to patient satisfaction. Pascoe and Attkisson (1983) demonstrated that US Whites, as a whole, tend to be more satisfied with their care than non‐Whites, yet the authors indicate that the interaction of ethnicity and socioeconomic status can confuse the results. Hall, Irish, Roter, Ehrlich, and Miller (1994) reported that within routine medical care visits, lower satisfaction was associated with younger female physicians and the least amount of satisfaction was found among male patients who were examined by younger female physicians. Other findings show no significant relation with sex and race (Marple, Lucey, Kroenke, Wilder, & Lucas, 1997). It is clear that the relationship between demographic variables and patient satisfaction is complex. Social and cultural issues in healthcare have pointed to the need for further examination of the various social inequalities involved in patient care and the recognition that patient distrust of providers, discrimination, mental health disparities, diversity in the clinical encounter, limited English proficiency, stereotypes, and bias operate within the bounds of the clinical encounter and very likely affect patient satisfaction in a multitude of ways (Smedley, Stith, & Nelson, 2003). Emerging as another patient factor related to satisfaction is the health status, both mental and physical, of the patient. Jackson et al. (2001) report various studies that have shown psychological distress, depression, and personality disorders to be associated with lower levels of satisfaction. Often these psychological factors are comorbid with chronic physical illness (Linn & Greenfield, 1982). Providers may be quite unaware of the expectations patients bring with them to their visits and may likewise not be comfortable addressing patients’ psychosocial concerns in general (Jackson et al., 2001; Roter et al., 1987).

Future Directions in the Patient Satisfaction Literature Although quantitative studies have provided much in the way of concrete correlates and outcomes in the field of patient satisfaction research, there is an ever‐pressing need for more qualitative research as well. In their review of the literature, Aharony and Strasser (1993) point to the void in studies looking at patients’ qualitative comments about their care. The authors suggest that there is still much to be done in content analyzing the written remarks of patients and those verbalized during interviews and focus groups. Such qualitative research might bring about a better understanding of the cognitive and affective processes that a patient moves through in evaluating his/her care, as well as allowing for more ethnographically accurate and representative data on the patient experience (Aharony & Strasser, 1993). There are also ways in which the methodology of patient satisfaction research could be improved. Moving from correlational designs to those that would allow for more methodological and statistical control is essential. The research paradigm should progress into theory‐ driven hypotheses, larger sample sizes, and factor analysis and regression models that would examine the direct and indirect impact of dependent variables (e.g., overall satisfaction; Aharony & Strasser, 1993).

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To address the concern over patients being inaccurate reporters of their satisfaction, survey instruments should focus on the patient–provider interactions, as well as on the overall care team of the patient, and evaluate all interactions within that framework. This is where patient‐ reported measures seem to be the most credible (Manary et al., 2013). Still the largest barrier remaining for this area of research is agreement on a common definition of “patient satisfaction.” The underpinnings of this research need cohesive grounding in theory in order to drive accurate and reliable data comparisons across studies (Manary et al., 2013).

Author Biographies Summer Williams is an assistant professor of psychology at Westfield State University in Western Massachusetts. Her areas of research interest include health communication, patient satisfaction and adherence, health outcomes and health inequities, and social and psychological determinants of health. She has recently published chapters in Assessment in Health Psychology and the Handbook of Health Behavior Change. Tricia A. Miller received her PhD in psychology from the University of California at Riverside. Her research focuses on the social psychological process of health and medical care delivery including patients’ health literacy. She also studies the effect of provider–patient communication on the promotion and maintenance of treatment adherence and patients’ overall health outcomes.

References Aharony, L., & Strasser, S. (1993). Patient satisfaction: What we know about and what we still need to explore. Medical Care Review, 50(1), 49–79. Cartwright, A., & Anderson, R. (1981). General practice revisited. London, UK: Tavistock. Dyer, N., Sorra, J. S., Smith, S. A., Cleary, P. D., & Hays, R. D. (2012). Psychometric properties of the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) clinician and group adult visit survey. Medical Care, 50, S28–S34. doi:10.1097/MLR.0b013e31826cbc0d Hall, J. A., & Dornan, M. C. (1988). What patients like about their medical care and how often they are asked: A meta‐analysis of the satisfaction literature. Social Science and Medicine, 27(9), 935–939. doi :10.1016/0277‐9536(88)90284‐5 Hall, J. A., & Dornan, M. C. (1990). Patient sociodemographic characteristics as predictors of patient satisfaction with medical care. Social Science and Medicine, 6, 811–818. doi:10.1016/0277‐9536 (90)90205‐7 Hall, J. A., Irish, J. T., Roter, D. L., Ehrlich, C. M., & Miller, L. H. (1994). Satisfaction, gender, and communication in medical visits. Medical Care, 32, 1216–1231. Inui, T. S., Carter, W. B., Kukull, W. A., & Haigh, V. H. (1982). Outcome‐based doctor‐patient interaction analysis: I. Comparison of techniques. Medical Care, 20(6), 535–549. Jackson, J. L., Chamberlin, J., & Kroenke, K. (2001). Predictors of patient satisfaction. Social Science and Medicine, 52, 609–620. doi:10.1016/S0277‐9536(00)00164‐7 Jha, A. K., Orav, E. J., Zheng, J., & Epstein, A. M. (2008). Patients’ perception of hospital care in  the United States. New England Journal of Medicine, 359, 1921–1931. doi:10.1056/ NEJMsa0804116 LeVois, M., Nguyen, T. D., & Attkisson, C. C. (1981). Artifact in client satisfaction assessment experience in community mental health settings. Evaluation and Program Planning, 4, 139–150. doi: 10.1016/0149‐7189(81)90004‐5

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Ley, P. (1972). Complaints made by hospital staff and patients: A review of the literature. Bulletin of British Psychologists, 25, 115–120. Linn, L. S., & Greenfield, S. (1982). Patient suffering and patient satisfaction among the chronically ill. Medical Care, 20, 425–431. Manary, M. P., Boulding, W., Staelin, R., & Glickman, S. W. (2013). The patient experience and health outcomes. New England Journal of Medicine, 368(3), 201–203. doi:10.1056/NEJMp1211775 Marple, R., Lucey, C., Kroenke, K., Wilder, J., & Lucas, C. (1997). A prospective study of concerns and expectations in patients presenting with common symptoms. Archives of Internal Medicine, 157, 1482–1488. Marshall, G. N., & Hays, R. D. (1994). The Patient Satisfaction Questionnaire short‐form (PSQ‐18). Santa Monica, CA: RAND. Meakin, R., & Weinman, J. (2002). The ‘Medical Interview Satisfaction Scale’ (MISS‐21) adapted for British general practice. Family Practice, 19(3), 257–263. doi:10.1093/fampra/19.3.257 Norum, J., Grev, A., Moen, M., Balteskard, L., & Holthe, K. (2003). Information and communication technology in oncology. Patients’ and relatives’ experiences and suggestions. Supportive Care in Cancer, 11(5), 286–293. doi:10.1007/s00520‐002‐0437‐1 Pascoe, G., & Attkisson, C. (1983). The evaluation ranking scale: A new methodology for assessing satisfaction. Evaluation and Program Planning, 6, 335–347. doi:10.1016/0149‐7189(83)90013‐7 Robbins, J. A., Bertakis, K. D., Helms, L. J., Azari, R., Callahan, E. J., & Creten, D. A. (1993). The influence of physician practice behaviors on patient satisfaction. Family Medicine, 25(1), 17–20. Roter, D. L., Hall, J. A., & Katz, N. R. (1987). Relations between physicians’ behaviors and analogue patients’ satisfaction, recall, and impressions. Medical Care, 25(5), 437–451. Roter, D. L., Stewart, M., Putnam, S. M., Lipkin, M., Jr., Stiles, W., & Inui, T. S. (1997). Communication patterns of primary care physicians. Journal of the American Medical Association, 277, 350–356. doi:10.1001/jama.1997.03540280088045 Sitzia, J., & Wood, N. (1997). Patient satisfaction: A review of issues and concepts. Social Science and Medicine, 45(12), 1829–1843. doi:10.1016/S0277‐9536(97)00128‐7 Smedley, B. D., Stith, A. Y., & Nelson, A. R. (Eds.) (2003). Unequal treatment: Confronting racial and ethnic disparities in healthcare. Washington, DC: National Academy Press. Ware, J. E., Snyder, M. K., & Wright, W. R. (1976). Development and validation of scales to measure patient satisfaction with medical care services. Springfield, VA: National Technical Information Service. Williams, B. (1994). Patient satisfaction: A valid concept? Social Science and Medicine, 38, 509–516. doi:10.1016/0277‐9536(94)90247‐X Williams, B., Coyle, J., & Healy, D. (1998). The meaning of patient satisfaction: An explanation of high reported levels. Social Science and Medicine, 47(9), 1351–1359. doi:10.1016/S0277‐9536 (98)00213‐5 Williams, S., Weinman, J., & Dale, J. (1998). Doctor‐patient communication and patient satisfaction: A review. Family Practice, 15(5), 480–492. Wolf, M. H., Putnam, S. M., James, S. A., & Stiles, W. B. (1978). The medical interview satisfaction scale: Development of a scale to measure patient perceptions of physician behavior. Journal of Behavioral Medicine, 1(4), 391–401. Zahr, L. K., William, S. G., & El‐Hadad, A. (1991). Patient satisfaction with nursing care in Alexandria, Egypt. International Journal of Nursing Studies, 28, 337–342. doi:10.1016/0020‐7489(91)90060‐G

Suggested Reading Hall, J. A., & Dornan, M. C. (1988). What patients like about their medical care and how often they are asked: A meta‐analysis of the satisfaction literature. Social Science and Medicine, 27(9), 935–939. doi :10.1016/0277‐9536(88)90284‐5

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Hall, J. A., Roter, D. L., & Katz, N. R. (1988). Meta‐analysis of correlates of provider behavior in ­medical encounters. Medical Care, 26(7), 657–675. Haskard, K. B., Williams, S. L., DiMatteo, M. R., Rosenthal, R., White, M. K., & Goldstein, M. G. (2008). Physician and patient communication training in primary care: effects on participation and satisfaction. Health Psychology, 27(5), 513–522. doi:10.1037/0278‐6133.27.5.513 Sitzia, J., & Wood, N. (1997). Patient satisfaction: A review of issues and concepts. Social Science and Medicine, 45(12), 1829–1843. doi:10.1016/S0277‐9536(97)00128‐7 Williams, S., Weinman, J., & Dale, J. (1998). Doctor‐patient communication and patient satisfaction: A review. Family Practice, 15(5), 480–492.

Personality and Coping Jamie M. Jacobs1 and Charles S. Carver2,3 Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA 2 Department of Psychology, University of Miami, Coral Gables, FL, USA 3 Center for Advanced Study in the Behavioral Sciences, Stanford University, Stanford, CA, USA 1

How do people deal with threats and challenges pertaining to acute and chronic diseases? Answers to this question generally invoke the concept of coping: attempts to remove or diminish a stressor or limit its impact. Coping responses vary widely, based in part on the duration of the disease (acute vs. chronic), its severity and prognosis, and the person’s overall interpretation, or appraisal, of the illness and its implications. Coping can also be affected by personality, such that a given stressor elicits different coping responses from different people. In this way, personality and situational coping both play roles in subsequent mental and physical health outcomes (Sharpe, Martin, & Roth, 2011; also see Kern & Friedman, 2011; Carver & Connor‐Smith, 2010). This chapter outlines several aspects of personality and coping, describes how they relate to each other, and suggests how they may relate to outcomes in the context of physical health and illness.

Personality The psychology of personality includes a very broad range of ideas (see Carver & Scheier, 2012), but two themes stand out. One is that personality incorporates some internal processes or dynamics that influence the person’s thoughts, feelings, and actions. The other is that, despite having the same basic functions, people differ from one another. There are many viewpoints on the nature of the underlying systems and also on what individual differences are most important. Here we outline two viewpoints that have been influential in health psychology.

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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The Five‐Factor Model What is generally termed the five‐factor model is a structural framework for describing ­individual differences. It places personality on five broad trait dimensions, commonly labeled extraversion, neuroticism, agreeableness, conscientiousness, and openness to experience (e.g., McCrae & John, 1992). Each person is assumed to occupy a position on each of the five traits, though that “position” is less a fixed point, perhaps, than a central tendency with variability around it (e.g., Fleeson, 2001). This model was not only developed mainly through studies of how people are described in natural language, but links have also developed between this view of personality and analyses of childhood temperament (e.g., Hambrick & McCord, 2010; McCrae et al., 2000). Extraversion is a dimension of approach and engagement with incentives, as reflected in descriptors such as active, assertive, energetic, and enthusiastic (McCrae & John, 1992). Some views of extraversion also emphasize qualities of sociability (Ashton, Lee, & Paunonen, 2002), and virtually all views incorporate an element of positive affect (Ozer & Benet‐Martinez, 2006). Neuroticism is the tendency to respond to threat, frustration, and loss with negative emotions (McCrae & John, 1992). Individuals high in neuroticism are vulnerable to anxiety, moodiness, and general emotional distress (see Carver & Connor‐Smith, 2010). Another term for neuroticism is emotional instability (McCrae & John, 1992). Agreeableness refers to investment in establishing and maintaining social relationships (Jensen‐Campbell & Graziano, 2001). Agreeableness facilitates compassion (Ozer & Benet‐ Martinez, 2006), and people high on this trait are more forgiving and trusting than those low on it (McCrae & John, 1992). Conscientiousness concerns thoroughness and trustworthiness. It includes qualities such as planfulness, persistence, and organization (McCrae & John, 1992), competence, self‐efficacy, and self‐discipline (Bartley & Roesch, 2011). Conscientiousness implies being perseverant in goals and tasks (Ozer & Benet‐Martinez, 2006). People with this trait act in ways that facilitate achievement striving (McCrae & John, 1992). The final factor generally termed openness to experience incorporates qualities such as curiosity and imagination (McCrae & John, 1992). Persons who are high on openness are flexible and willing to involve themselves in new and atypical experiences (McCrae, 1996). Openness to experience often overlaps with qualities such as creativity and inspiration (Ozer & Benet‐ Martinez, 2006).

Optimism and Expectancy‐Value Process Models Another individual difference variable that figures prominently in the coping literature is optimism (see Carver, Scheier, & Segerstrom, 2010 for a review). Optimism, which does not fit neatly into the five‐factor model, derives from a different research tradition, following from expectancy‐value models of motivation. Optimism consists of a generalized favorable versus unfavorable expectancy for one’s future. As is true of all expectancy‐value models, this construct assumes that a favorable expectancy promotes greater motivation, resulting in greater persistence and generally better outcomes. Optimism forms a lens, or cognitive filter, that influences life at multiple levels. It affects people’s appraisals of challenging or stressful situations and their subsequent reactions (both behavioral and physiological) to those situations. Optimism is not the view that adversity will not occur; rather, it incorporates the belief that adversity and threat can be overcome

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(Fowler & Geers, 2015). Whether people are optimistic or pessimistic influences the extent to which they experience distress in the context of adversity, and it influences the manner in which people cope.

Optimism and the Five‐Factor Model Although standing somewhat apart from the five‐factor model, optimism shares qualities with four of the five traits of that model. Optimism is positively associated with extraversion, agreeableness, and conscientiousness and negatively associated with neuroticism. The one factor with which optimism is not associated is openness to experience. In general, neuroticism and extraversion are the most correlated of the big five traits with optimism. However, optimism is not simply low neuroticism and high extraversion; rather, it reflects a more complex profile (Sharpe, Martin, & Roth, 2011).

Coping Coping is generally defined as the cognitive and behavioral efforts made by an individual to manage or reduce external or internal demands associated with a stressor that seem to tax or exceed the individuals’ resources (Lazarus & Folkman, 1987). Coping takes many forms, and a variety of distinctions have been made within the realm of coping. Coping may be cognitive or behavioral. It may effective or ineffective. Some would say that coping consists of intentional use of strategies, but others would say that some coping consists of involuntary reactions. Several distinctions are elaborated upon in the following sections (for more detail, see Carver & Connor‐Smith, 2010).

Problem‐Focused Versus Emotion‐Focused Coping A distinction was made early in the literature on coping between problem‐focused and emotion‐focused coping (Lazarus & Folkman, 1987). Problem‐focused coping comprises active efforts to remove the stressor or reduce its impact by engaging in such activities as problem solving, conflict resolution, information seeking, and decision making. Emotion‐focused coping comprises a set of efforts to manage and reduce the emotional distress provoked by the stressor (see Gross, 2014). Problem‐focused behaviors are most commonly used, and are thought to be more useful, in situations where the stressor is controllable. Emotion‐focused coping is more common in situations of low controllability. Emotion‐focused coping is more diverse than problem‐focused coping. Included in this category are activities as diverse as relaxation, meditation, music therapy, physical exercise, massage, cognitive restructuring, and humor. Also included in this category are avoidant strategies such as self‐distraction, shopping, wishful thinking, and retreating into substance use. These responses are all intended to regulate stress‐induced emotions. Many emotion‐focused responses are quite useful. Even distraction away from a threat can be useful in the short term, especially if a person has already taken suitable action. Some emotion‐focused coping responses, however—particularly those with avoidant properties—are not effective in the long term and can have adverse consequences for longer‐term health and well‐being. Problem‐focused and emotion‐focused coping categories are distinguishable in principle, but they often are used jointly (Lazarus, 2006). They can also facilitate one another. Although

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problem‐focused coping is aimed at removing the stressor or blunting its impact, a secondary consequence of effective problem‐focused coping is a reduction in distress. Although emotion‐focused coping is aimed at emotion regulation, the reduction in distress can also make it easier to turn to problem‐focused coping.

Engagement Versus Disengagement Coping A particularly important distinction is the one made between engagement and disengagement coping. Engagement coping, also known as approach coping, is geared toward dealing with the stressor and its emotional consequences (Skinner, Edge, Altman, & Sherwood, 2003). Disengagement coping involves trying to avoid or escape from dealing with the threat. Engagement coping encompasses both problem‐focused and some of the emotion‐focused techniques. Emotion‐focused techniques that would be viewed as engagement coping would include support seeking, emotion regulation, acceptance of the reality of the challenge, and cognitive restructuring. Disengagement techniques are avoidant in nature, including reactions such as denial, wishful thinking, and substance use. Often people use disengagement coping to distance themselves from the stressor. While this can be effective for avoiding distress in the short term, it does not address the situation that is producing the distress. More important, it could worsen long‐term outcomes. For instance, a person with a cancer diagnosis who avoids scheduling an appointment for a scan may evade distress temporarily. But that evasion ultimately allows an opportunity for the cancer to progress. This may result in more aggressive treatment and greater overall stress and threat to life. Certain disengagement coping mechanisms can cause other problems, such as drugs, alcohol, and overeating. Disengagement coping may ultimately lead to an abandonment of the goals with which the stressor is interfering in order to avoid experiencing or exacerbating any associated negative emotions (Carver, Scheier, & Weintraub, 1989).

Proactive Coping A final variation in the concept of coping we will note here is what is termed proactive coping. Proactive coping is anticipating and preparing for obstacles or stressful situations before they arise (Schwarzer & Taubert, 2002). Proactive coping is goal oriented and adaptive, and those who cope proactively are motivated to succeed. This tendency to expect and prepare for challenges plays a large role in the reduction of associated emotional distress and may be dictated by personality traits (Hambrick & McCord, 2010).

Personality and Coping How do aspects of personality relate to aspects of coping? Personality is not the same as coping, but personality can influence what sorts of coping a person engages in, at several stages in the stress process. Most fundamentally, personality can influence how people appraise and interpret potential stressors. A person who perceives a great threat will turn to coping responses more quickly than a person who perceives a lesser threat. Since appraisals also influence choice of coping response, differences in appraisal affect what actions follow. People also vary in which coping responses come naturally to them. Thus, even given the same appraisal, people may cope in different ways.

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The following sections address this issue from the perspective of the two views on personality that were described earlier in the chapter. First we consider the five‐factor model.

Five‐Factor Model and Coping A recent meta‐analysis of personality traits and coping (see Connor‐Smith & Flachsbart, 2007) showed a number of associations between coping tendencies and traits from the five‐factor model. For example, the trait of extraversion was fairly consistently related to use of problem‐ focused coping strategies and cognitive restructuring. These findings are consistent with the view that extraversion is a dimension of engagement with incentives and pursuit of goals. In contrast to this, the meta‐analysis found that neuroticism related inversely to problem solving and cognitive restructuring. Neuroticism also was positively associated with several maladaptive emotion‐focused coping strategies, such as wishful thinking and withdrawal. On the positive side, neuroticism was also found to predict greater support seeking, which can be helpful in coping with health difficulties and chronic illness. Interestingly, persons higher in neuroticism report greater emotional reactivity to negative life events and are more likely to develop anxiety and depression in response to such events (Lahey, 2009). Coping style probably plays a role in this relationship, given evidence that persons high in neuroticism use more disengagement‐related strategies than problem‐focused coping behaviors (Watson & Hubbard, 1996). Consistent with this pattern, people high in neuroticism also tend not to engage in proactive coping. The trait of conscientiousness is associated with properties such as self‐discipline, competence, self‐efficacy, and striving toward achievement. Not surprisingly, then, it is strongly associated with the use of problem‐focused, approach coping (Bartley & Roesch, 2011), including proactive coping. People high in conscientiousness also report greater positive affect. This relationship is likely mediated by the use of problem‐focused coping in situations of daily stress (Bartley & Roesch, 2011). Although the term proactive coping is generally reserved for cases of preparation for a particular stressor, there is a sense in which general efforts to live healthy lives represent proactive coping. Such health‐promoting behaviors are an important part of managing well‐being. Persons who are emotionally stable, introverted, and conscientious are more likely to engage in health‐promoting behaviors such as physical activity, a healthy diet, immunizations, safe driving, sleep, weight control, and not smoking (Korotkov, 2008).

Optimism and Coping Another personality dimension that has been linked to coping is optimism. Solberg Nes and Segerstrom (2006) conducted a meta‐analysis of optimism effects. Optimism is systematically associated with greater use of approach coping tendencies, strategies that are active and problem focused. Optimism is also related to the use of social support, both for emotional reasons and for instrumental assistance. In contrast to these tendencies, pessimism is associated with avoidance strategies, essentially efforts to escape the stressor and sometimes to escape even acknowledging the stressor (e.g., Solberg Nes & Segerstrom, 2006). As one example, in a sample of HIV‐positive men and women coping with the loss of their partners to AIDS, optimism was positively associated with reports of using active coping strategies and negatively associated with reports of using avoidant coping strategies and falling into hopelessness (Rogers, Hanson, Levy, Tate, & Sikkema, 2005).

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In a study of newly diagnosed breast cancer patients (Carver et al., 1993), optimism was associated with greater use of a range of engagement coping at time‐appropriate points in treatment. That is, correlations of optimism with report of planning and active coping emerged prior to surgery but not afterward. Correlations with acceptance and use of humor were present across the first 6 months after treatment, and with positive reframing across the first 3 months. Optimism was also inversely associated with disengagement coping—denial and behavioral disengagement—across the full year after treatment. Although similar effects have emerged in diverse contexts (Carver et al., 2010), there do appear to be boundaries on such effects of optimism on coping. Very recent research has investigated the role of what has been termed comparative optimism, which statistically is nearly independent of trait optimism as discussed here. Comparative optimism is assessed by items asking about the likelihood that respondents will experience problems in a given life domain compared with other people who are otherwise like them. Comparative optimists say those events are less likely than do other people. Trait optimists who were also comparative optimists minimized the degree of threat posed by particular health challenges; those who were trait optimists but not comparative optimists did not (Fowler & Geers, 2015).

Adjustment and Health Coping, Adjustment, and the Role of Personality Coping is associated with the health perceptions and quality of life of patients dealing with chronic illness. Across many diseases and health conditions, coping tendencies are associated with both mental and physical outcomes. There are two ways in which coping can have beneficial effects on well‐being. First, it does so to the extent that it removes or eliminates the threat (thereby reducing distress). Second, it does so to the extent that it directly reduces distress. Which of these classes of coping is more appropriate depends on the context. In the context of a controllable stressor, problem‐focused behaviors such as active problem solving and changing one’s circumstances are helpful (see Carver & Connor‐Smith, 2010 in further readings). In the context of an uncontrollable stressor, these responses are less useful; emotion regulation, use of social support resources, and controlled emotional expression are most beneficial (Penley, Tomaka, & Wiebe, 2002). Reliance on an emotion‐focused strategy in the context of a controllable stressor may be less productive, and vice versa. Behaviors that are either pointlessly ruminative or avoidant in nature—e.g., venting, denial, and self‐blame—tend to predict poorer adjustment regardless of context (e.g., Moskowitz, Hult, Bussolari, & Acree, 2009; also see Austenfeld & Stanton, 2004 in further readings). This selection and matching of the most beneficial coping strategy (or strategies) to a given stressor is one way to think about how coping influences adjustment. Within this viewpoint, the role of personality is principally to influence choice of coping responses. A second way in which personality may play a role here is to moderate the effect of coping, such that once a coping response is selected, personality may influence how well the individual implements that response and how well it works for that individual. For example, problem‐ solving behaviors may be more successfully implemented by people who are conscientious (see Carver & Connor‐Smith, 2010 in further readings).

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Coping, Personality, and Health Regardless of whether the role of personality is to influence selection of coping response or to influence the execution of the response, there are at least two pathways by which coping per se may have an effect on health. One path involves the level of wear and tear created on physical systems as a result of ineffective coping with threats and challenges. There is little dispute over the fact that chronic, elevated stress can affect physical health and disease processes via a range of physiological pathways such as the hypothalamic–pituitary–adrenocortical axis and the sympathetic nervous system (e.g., Schneiderman, Ironson, & Siegel, 2005). Effective coping can dampen physiological stress responses, proactive coping can prevent stress responses altogether, and dysfunctional coping can worsen stress responses (Carver & Vargas, 2011). With repeated or prolonged physiological activation, people become physically exhausted and more susceptible to illness. The key issue here may be the extent to which the stress is invoked repeatedly or chronically. Meta‐analyses suggest that persons coping with conditions such as HIV, prostate cancer, and diabetes who implement engagement coping have better physical and mental health than those who do so to a lesser degree (e.g., Moskowitz et al., 2009). On the other hand, poorer physical health outcomes are observed among persons who opt for disengagement coping (Moskowitz et al., 2009; also see Austenfeld & Stanton, 2004). A second pathway by which coping affects health is a behavioral mechanism. This pathway follows from the fact that some kinds of effective coping involve engaging in health‐promoting behaviors and avoiding health‐damaging behaviors. In this case, the effect on health is intrinsic to the behavior itself. This pathway, of course, is also influenced by personality. The likelihood of engaging in healthy lifestyle behaviors is greater among persons who are optimistic than pessimistic (e.g., Carver et al., 2010). Of the five‐factor model, conscientiousness, agreeableness, and extraversion are also linked to health‐promoting behaviors (e.g., Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007).

Concluding Comments This review is by no means complete or comprehensive. Coping in a health context is a multidimensional process, influenced by the healthcare environment, family factors, and life changes, along with physical demands such as pain and treatment side effects. Variation in these demands illustrates how different coping responses may be required for various components of a stressor. In part for this reason, it is challenging to predict coping responses from personality dispositions alone. There is much more to be said about coping and personality in the context of different health stressors, as well as the inevitable variation based on demographic differences such as age, gender, ethnicity, socioeconomic status, and stage of disease. More research is needed on the extent to which coping is stable across situations. The concept of proactive coping is another area in need of further investigation, for example, to understand whether proactive coping follows from particular personality profiles, or whether it develops over time in response to experiences of adversity. Finally, it would seem desirable to continue to hone programs to teach effective coping, thereby helping patients enhance their use of engagement coping techniques. Enhanced coping would, in turn, increase positive health behaviors and improve health outcomes from morbidity to mortality in the context of clinical settings.

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Author Biographies Dr. Jamie M. Jacobs is a clinical psychologist at the Massachusetts General Hospital Cancer Center and Behavioral Medicine Service and an Assistant Professor of Psychology in the Department of Psychiatry at Harvard Medical School. Her expertise includes effects of stress on health outcomes and the role of coping and stress management in buffering physiological stress and disease processes in the context of cancer. She is also interested in psychological factors that predict nonadherence to cancer treatment regimens, in order to guide intervention development to improve adherence and symptom management. Charles S. Carver is distinguished professor of psychology at the University of Miami. His background is in personality and social psychology, but he has also been active in health psychology and experimental psychopathology. One aspect of his research has focused on coping processes and optimism as a personality disposition, in the context of major health threats.

References Ashton, M. C., Lee, K., & Paunonen, S. V. (2002). What is the central feature of extraversion? Social attention versus reward sensitivity. Journal of Personality and Social Psychology, 83, 245–252. doi:10.1037/0022‐3514.83.1.245 Austenfeld, J. L., & Stanton, A. L. (2004). Coping through emotional approach: A new look at emotion, coping, and health‐related outcomes. Journal of Personality, 72, 1335–1364. doi:10.1111/j.1467‐ 6494.2004.00299.x Bartley, C. E., & Roesch, S. C. (2011). Coping with daily stress: The role of conscientiousness. Personality and Individual Differences, 50, 79–83. doi:10.1016/j.paid.2010.08.027 Carver, C., Scheier, M., & Segerstrom, S. (2010). Optimism. Clinical Psychology Review, 30, 879–889. doi:10.1016/j.cpr.2010.01.006 Carver, C. S., & Connor‐Smith, J. (2010). Personality and coping. Annual Review of Psychology, 61, 679–704. doi:10.1146/annurev.psych.093008.100352 Carver, C. S., Pozo, C., Harris, S. D., Noriega, V., Scheier, M. F., Robinson, D. S., … Clark, K. C. (1993). How coping mediates the effect of optimism on distress: A study of women with early stage breast cancer. Journal of Personality and Social Psychology, 65, 375–390. doi:10.1037/0022‐ 3514.65.2.375 Carver, C. S., & Scheier, M. F. (2012). Cybernetic control processes and the self‐regulation of behavior. In R. M. Ryan (Ed.), The Oxford handbook of human motivation (pp. 28–42). New York, NY: Oxford University Press. Carver, C. S., Scheier, M. F., & Weintraub, J. K. (1989). Assessing coping strategies: A theoretically based approach. Journal of Personality and Social Psychology, 56, 267–283. doi:10.1037/0022‐ 3514.56.2.267 Carver, C. S., & Vargas, S. (2011). Stress, coping, and health. In H. S. Friedman (Ed.), The Oxford handbook of health psychology (pp. 165–191). New York, NY: Oxford University Press. Connor‐Smith, J. K., & Flachsbart, C. (2007). Relations between personality and coping: A meta‐analysis. Journal of Personality and Social Psychology, 93, 1080–1107. doi:10.1037/0022‐3514.93.6.1080 Fleeson, W. (2001). Toward a structure‐ and process‐integrated view of personality: Traits as density distributions of states. Journal of Personality and Social Psychology, 80, 1011–1027. doi:10.1037/0022‐ 3514.80.6.1011 Fowler, S. L., & Geers, A. L. (2015). Dispositional and comparative optimism interact to predict avoidance of a looming health threat. Psychology & Health, 30, 456–474. doi:10.1080/08870446.2014. 977282 Gross, J. J. (Ed.) (2014). Handbook of emotion regulation (2nd ed.). New York, NY: Guilford Press.

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Hambrick, E. P., & McCord, D. M. (2010). Proactive coping and its relation to the five‐factor model of personality. Individual Differences Research, 8, 67–77. Jensen‐Campbell, L. A., & Graziano, W. G. (2001). Agreeableness as a moderator of interpersonal conflict. Journal of Personality, 69, 323–362. doi:10.1111/1467‐6494.00148 Kern, M. L., & Friedman, H. S. (2011). Personality and pathways of influence on physical health. Social and Personality Psychology Compass, 5, 76–87. doi:10.1111/j.1751‐9004.2010.00331.x Korotkov, D. (2008). Does personality moderate the relationship between stress and health behavior? Expanding the nomological network of the five‐factor model. Journal of Research in Personality, 42, 1418–1426. doi:10.1016/j.jrp.2008.06.003 Lahey, B. B. (2009). Public health significance of neuroticism. American Psychologist, 64, 241. doi:10.1037/a0015309 Lazarus, R. S. (2006). Emotions and interpersonal relationships: Toward a person‐centered conceptualization of emotions and coping. Journal of Personality, 74, 9–46. doi:10.1111/j.1467‐6494.2005. 00368.x Lazarus, R. S., & Folkman, S. (1987). Transactional theory and research on emotions and coping. European Journal of Personality, 1, 141–169. doi:10.1002/per.2410010304 McCrae, R. R. (1996). Social consequences of experiential openness. Psychological Bulletin, 120, 323– 337. doi:10.1037/0033‐2909.120.3.323 McCrae, R. R., Costa, P. T., Jr., Ostendorf, F., Angleitner, A., Hřebíčková, M., Avia, M. D., … Smith, P. B. (2000). Nature over nurture: Temperament, personality, and life span development. Journal of Personality and Social Psychology, 78, 173–186. doi:10.1037/0022‐3514.78.1.173 McCrae, R. R., & John, O. P. (1992). An introduction to the five‐factor model and its applications. Journal of Personality, 60, 175–215. doi:10.1111/j.1467‐6494.1992.tb00970.x Moskowitz, J. T., Hult, J. R., Bussolari, C., & Acree, M. (2009). What works in coping with HIV? A meta‐analysis with implications for coping with serious illness. Psychological Bulletin, 135, 121–141. doi:10.1037/a0014210 Nes, L. S., & Segerstrom, S. C. (2006). Dispositional optimism and coping: A meta‐analytic review. Personality and Social Psychology Review, 10, 235–251. doi:10.1207/s15327957pspr1003_3 Ozer, D. J., & Benet‐Martinez, V. (2006). Personality and the prediction of consequential outcomes. Annual Review of Psychology, 57, 401–421. doi:10.1146/annurev.psych.57.102904.190127 Penley, J. A., Tomaka, J., & Wiebe, J. S. (2002). The association of coping to physical and psychological health outcomes: A meta‐analytic review. Journal of Behavioral Medicine, 25, 551–603. doi: 10.1023/A:1020641400589 Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science, 2, 313–345. doi:10.1111/ j.1745‐6916.2007.00047.x Rogers, M. E., Hanson, N. B., Levy, B. R., Tate, D. C., & Sikkema, K. J. (2005). Optimism and coping with loss in bereaved HIV‐infected men and women. Journal of Social and Clinical Psychology, 24, 341–360. Schneiderman, N., Ironson, G., & Siegel, S. D. (2005). Stress and health: Psychological, behavioral, and biological determinants. Annual Review of Clinical Psychology, 1, 607–628. doi:10.1146/annurev. clinpsy.1.102803.144141 Schwarzer, R., & Taubert, S. (2002). Tenacious goal pursuits and striving toward personal growth: Proactive coping. In E. Frydenberg (Ed.), Beyond coping: Meeting goals, visions and challenges (pp. 19–35). London: Oxford University Press. Sharpe, J. P., Martin, N. R., & Roth, K. A. (2011). Optimism and the big five factors of personality: Beyond neuroticism and extraversion. Personality and Individual Differences, 51, 946–951. doi:10.1016/j.paid.2011.07.033 Skinner, E. A., Edge, K., Altman, J., & Sherwood, H. (2003). Searching for the structure of coping: A review and critique of category systems for classifying ways of coping. Psychological Bulletin, 129, 216–269. doi:10.1037/0033‐2909.129.2.216

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Watson, D., & Hubbard, B. (1996). Adaptational style and dispositional structure: Coping in the context of the five‐factor model. Journal of Personality, 64, 737–774. doi:10.1111/j.1467‐6494.1996. tb00943.x

Suggested Reading Austenfeld, J. L., & Stanton, A. L. (2004). Coping through emotional approach: A new look at emotion, coping, and health‐related outcomes. Journal of Personality, 72, 1335–1364. doi:10.1111/j.1467‐ 6494.2004.00299.x Carver, C. S., & Connor‐Smith, J. (2010). Personality and coping. Annual Review of Psychology, 61, 679–704. doi:10.1146/annurev.psych.093008.100352 Carver, C. S., & Scheier, M. F. (2012). Cybernetic control processes and the self‐regulation of behavior. In R. M. Ryan (Ed.), The Oxford handbook of human motivation (pp. 28–42). New York, NY: Oxford University Press. Connor‐Smith, J. K., & Flachsbart, C. (2007). Relations between personality and coping: A meta‐ analysis. Journal of Personality and Social Psychology, 93, 1080–1107. doi:10.1037/0022‐3514.93. 6.1080 Kern, M. L., & Friedman, H. S. (2011). Personality and pathways of influence on physical health. Social and Personality Psychology Compass, 5, 76–87. doi:10.1111/j.1751‐9004.2010.00331.x

Personality and Health Behaviors Christopher S. Nave1,2 and Michelle R. Dixon2 Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA 2 Department of Psychology, Rutgers University Camden, Camden, NJ, USA

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Introduction Health behaviors are positive or negative in valence and are associated with an individual’s overall health. There are two types of health behaviors: health‐promoting behaviors and health‐compromising behaviors. Health‐promoting behaviors include all behaviors that enhance or promote one’s health (Sarafino & Smith, 2011). Health‐promoting behaviors are considered beneficial for individuals to engage in and are frequently recommended by physicians, dietitians, and nutritionists in addition to being used in health behavior interventions. Examples of health‐promoting behaviors include maintaining a healthy diet, exercising on a regular basis, and engaging in preventative health habits. Preventative health habits include wearing sunscreen daily, regularly visiting the doctors, drinking adequate amounts of water, and reducing salt intake (Sarafino & Smith, 2011). Individuals who engage in health‐promoting behaviors can enhance one’s quality of life, increase confidence, decrease illness, and even increase lifespan. Health‐compromising behaviors are harmful or detrimental to one’s health, are considered destructive for people to engage in, and should be eliminated from daily life or minimized as much as possible (Sarafino & Smith, 2011). Examples of health‐compromising behaviors include smoking tobacco, eating foods high in sugar, using illegal drugs, engaging in excessive alcohol use or binge drinking, and participating in risky sexual behavior. There are many factors that can influence health behaviors. One influence on health behaviors is personality. Personality is described as a pattern of thought, emotions, and behavior that each individual possesses, while personality traits are any individual difference variable that can be used to predict behavior. Personality traits are a key predictor of important life outcomes like divorce, occupational attainment, mortality at comparable or even greater levels than IQ, and socioeconomic status (Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). Personality traits show a remarkable amount of stability throughout the lifespan (Ferguson, 2010) though there are opportunities for personality change and growth (Srivastava, John, Gosling, & Potter, 2003).

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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A thorough, systematic lexical and empirical approach to studying personality traits has yielded five broad factors, known as the Big Five (McCrae & Costa, 1987). The Big Five includes extraversion, agreeableness, conscientiousness, neuroticism and openness, or intellect/culture (McCrae & Costa, 1987). Extraversion refers to individuals who are outgoing, talkative, and social. Agreeableness refers to individuals who are tolerant and kind. Conscientiousness refers to individuals who are highly organized and achievement orientated. Neuroticism refers to individuals who are anxious and moody, though many researchers study the positive end of this trait and call it emotional stability. Lastly, openness refers to individuals who are open to new experiences and curious. Other researchers (e.g., Goldberg, 1990) believe the fifth factor is best described as intellect/culture, with more emphasis on cognitive ability and IQ.

The Big Five and Health Behaviors An abundance of research has demonstrated reliable associations between personality traits and health behaviors, many of which that can be organized around the Big Five. Support exists for a health behavior model of personality, where specific health behaviors have increasingly been demonstrated to mediate the relationship between personality and health outcomes (Hampson, Edmonds, Goldberg, Dubanoski, & Hillier, 2015). The following subsections break down a sampling of what we know about personality and its relationship with health behaviors.

Conscientiousness Conscientious individuals are those who show self‐discipline, plan their behaviors, aim for achievement, and practice impulse control. A variety of meta‐analyses, large longitudinal studies, surveys, and experimental studies convincingly show that conscientiousness is related to a large number of health‐promoting and health‐compromising behaviors (Bogg & Roberts, 2013). Previous research has shown that conscientiousness is related to individuals maintaining a healthy diet by consistently eating foods like fruits, vegetables, lean meats, and whole grains and avoiding foods that are high in sugar and unhealthy fats (Bogg & Roberts, 2004). Engaging in frequent exercise is related to conscientiousness although the relationship is not very strong. In a meta‐analysis that looked at conscientiousness and activity levels, the results showed that activity level was least related to conscientiousness in comparison with the other health behaviors, though conscientious individuals tend to possess better fitness levels and lower body mass indices than those who are low in conscientiousness (Bogg & Roberts, 2004). Another health‐promoting behavior conscientious people engage in is more periodic health screening and greater attendance at scheduled doctor appointments (Armon & Toker, 2013). In addition to going to doctors more often, conscientious people are also more likely to engage in medication adherence, an effect that nearly doubles in magnitude when examining younger conscientious individuals (Molloy, O’Carroll, & Ferguson, 2014). Medication adherence is a particularly important health‐promoting behavior because medication is not effective if the patient is not taking it. Conscientiousness is also related to individuals avoiding health‐compromising behaviors. People who are high in conscientiousness are less likely to smoke cigarettes or use drugs (Bogg & Roberts, 2004). Even conscientious ratings of children assessed by elementary school teachers predict low or no usage of tobacco later in life (Hampson, Goldberg, Vogt, & Dubanoski, 2006). Conscientious individuals are also less likely to consume alcohol to excess (Bogg &

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Roberts, 2013) and are unlikely to engage in risky driving as measured by number of ­accidents, driving violations, speeding tickets, DUIs, and not using seatbelts (Bogg & Roberts, 2004; Raynor & Levine, 2009). Individuals who are high in conscientiousness tend not to engage in risky sexual behaviors (e.g., not using contraceptives or having a large number of sexual partners) and tend to not participate in behaviors that lead to incarceration, conviction, or detention (Bogg & Roberts, 2004). A seminal meta‐analysis on conscientiousness and health behaviors found that out of all the healthy behaviors tested, frequency in alcohol consumption and driving while under the influence of alcohol were the most negatively correlated with conscientiousness (Bogg & Roberts, 2004). In summary, conscientiousness is related to the engagement in almost all health‐promoting behaviors and the nonengagement in nearly all health‐compromising behaviors. People who are high in conscientiousness are more likely to practice healthy behaviors and avoid unhealthy behaviors.

Extraversion Extraverted individuals tend to be energetic and assertive and take pleasure in talking with others. Findings for how extraversion and sociability influence health behaviors are unclear, particularly with respect to health‐promoting behaviors. One area where there is a consistent pattern of results for health‐promoting behaviors is in coping with stressful life events. Extraverted individuals tend to manage stress better than introverted individuals and use a variety of coping mechanisms to manage stress (DeLongis & Holtzman, 2005; McCrae & Costa, 1986). Extraversion is related to a number of health‐compromising behaviors. In two large longitudinal studies, individuals high in extraversion tend to drink more at midlife and participate in excessive alcohol use such as binge drinking and frequent drinking (Hampson et  al., 2006). Other research shows that those high in extraversion are less likely to limit consumption of alcoholic drinks or use designated drivers (Raynor & Levine, 2009). Tobacco use has also been shown to be moderately positively associated with extraversion (Raynor & Levine, 2009). One possible explanation for the relationships between extraversion and alcohol and tobacco use may be because highly extraverted people are social and enjoy going to social gatherings that involve alcohol or tobacco use. In a large survey study examining college students, extraversion was the strongest predictor of risky sexual practices including number of sexual partners and not using condoms in the last 30 days. Extraverted college students also slept less compared with those lower in extraversion (Raynor & Levine, 2009). Those high in extraversion tend to get less sleep than those who are low in extraversion, perhaps due to their social life and various campus activities (Raynor & Levine, 2009). Highly extraverted people are also less likely to participate in periodic health screenings (Armon & Toker, 2013). Lack of health screenings is a health‐compromising behavior because regular heath checkups allow individuals to prevent illness rather than treat illness after it has already been contracted. Overall, the evidence for extraversion shows the trait to be related to engaging in various health‐compromising behaviors.

Agreeableness Individuals who are high in agreeableness are those who tend to trust others, show kindness, and express affection. Agreeableness is related to a number of different heath behaviors. Agreeableness is related to possessing a high body mass index (Sutin, Ferrucci, Zonderman, & Terracciano, 2011). Body mass index is a measurement that primary care physicians use to determine if an individual’s weight is healthy or not. Individuals who have a higher body mass

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index than normal are considered overweight (Sarafino & Smith, 2011). An individual becomes overweight by engaging in health‐compromising behaviors such as not maintaining a healthy diet or regularly exercising. One study found that people high in agreeableness are slightly more likely to use drugs and alcohol (Turiano, Whiteman, Hampson, Roberts, & Mroczek, 2012). In a large survey study of college students, agreeableness was not related to alcohol or tobacco use but was slightly negatively correlated to binge drinking and number of sexual partners (Raynor & Levine, 2009). Another study found that agreeableness is related to the avoidance of risky driving (Booth‐Kewley & Vickers, 1994), which makes conceptual sense given that agreeableness is the opposite of hostility and aggression. Overall, agreeableness is related to engagement in health‐promoting behaviors and avoiding health‐compromising behaviors. Research is unclear on the relationship between agreeableness and substance use. More research on agreeableness needs to be done in order to find conclusive evidence with respect to health‐promoting and health‐compromising behaviors.

Openness and Culture/Intellect Openness refers to an individual’s ability to be highly imaginative and insightful. Out of all the Big Five personality traits, openness seems to be the least related to health behaviors. There are, however, a few health behaviors that previous research has found to be related to openness. Those who are highly open tend to practice healthy eating habits. Conversely, individuals high in openness are less likely to participate in periodic health screenings (Armon & Toker, 2013). One study found that substance use is related to openness (Booth‐Kewley & Vickers, 1994). Another study involving college students found that openness was not related to any heath behaviors (Raynor & Levine, 2009). The relationships between openness and the above stated health behaviors are very weak relationships. One explanation for the slight correlations may be due to openness being related to creativity. Creative people may be more open to unique healthy foods, holistic medicine, and recreational drug use such as marijuana. Nevertheless the relationship between openness and health behaviors is not very strong. Other researchers consider intellect, not openness, to be a major factor of the Big Five (e.g., Goldberg, 1990). Research examining childhood IQ has shown associations between lower IQ and smoking later in adulthood (Hart et al., 2004) and higher IQ and smoking cessation in midlife (Taylor et al., 2003). Other research from a prospective longitudinal study shows that highly intelligent individuals often engage in many of the same unhealthy behaviors compared with individuals of average intelligence (Friedman & Markey, 2003).

Neuroticism and Emotional Stability Neuroticism refers to an individual’s anxiety levels, emotional instability, and sadness. Neuroticism is only related to health‐compromising behaviors. Individuals high in neuroticism have been shown to be more likely to engage in substance use (Turiano et al., 2012). One reason for this relationship could be that the anxiety neurotic people experience influences their substance use. Highly neurotic individuals are not only more likely to smoke tobacco, but they are also more inclined to smoke large quantities of cigarettes (Booth‐Kewley & Vickers, 1994). When neuroticism is paired with being conscientious, however, research suggests that this combination, described as healthy neuroticism, predicts lower levels of smoking behavior (Weston & Jackson, 2015). One study found that low emotional stability (i.e., neuroticism) is related to alcohol use when combined with high levels of extraversion. This study found that children who rated high in emotional instability and extraversion predicted greater

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alcohol use 40 years later (Hampson et al., 2006). The relationship between alcohol use and neuroticism only occurs when combined with extraversion. Another study of college students found no relationship between neuroticism and alcohol use (Raynor & Levine, 2009). Highly neurotic individuals are also more likely to experience fluctuations with their weight (Sutin et al., 2011). This relationship may be related to the conception that people who are emotionally unstable may be more likely to eat unhealthy foods as a coping mechanism. Weight fluctuations are considered unhealthy because it usually means that the individuals are not consistently engaging in health‐promoting behaviors related to eating and exercising. Neurotics demonstrate higher levels of daily stress and negative affect (Mroczek & Almeida, 2004). Hostility, a subcomponent to neuroticism, has been related to smoking, insufficient physical activity, and overeating (Niaura et al., 2000). Finally, those who are very high or very low in neuroticism are less likely to participate in periodic health screening. Individuals that fall right in the middle on the neuroticism scale are more likely to participate in periodic health screening (Armon & Toker, 2013). Overall, neuroticism has a wide‐ranging set of associations with health‐promoting behaviors (negative associations) and with compromising behaviors.

Other Personality Traits Although the Big Five encompasses a variety of personality traits, there are personality and individual variables not captured by the Big Five that are related to health behaviors. In the Dunedin birth cohort study, high negative emotionality and low constraint at age 18 predicted various health risk behaviors (i.e., unsafe sexual behavior, alcohol dependence, dangerous driving habits, violence conviction) at age 21 (Caspi et al., 1997). Honesty‐humility is one of six factors of the HEXACO model of personality and refers to an individual who is honest, loyal, and sincere (Ashton & Lee, 2009). One study found that honesty‐humility was a significant negative predictor of exercise habits such that those who rated lower in honesty‐humility reported more frequent physical exercise (MacCann, Todd, Mullan, & Roberts, 2015). Other traits related to health habits include perfectionism, which is the tendency to strive for flawlessness. One study found that perfectionism is related to engaging in preventative health behaviors (Williams & Cropley, 2014). Dispositional optimism is related to less smoking and alcohol use and more physical activity among older participants (Steptoe, Wright, Kunz‐Ebrecht, & Iliffe, 2006). Finally, aggression has been associated with smoking habits such that individuals who tend to express hostility also tend to smoke more (Welch & Poulton, 2009).

Conclusion Research has shown that personality traits, particularly the Big Five, are in fact predictors of health behaviors. The association between personality and health behaviors does not mean that it is guaranteed that an individual with a certain personality trait will definitely engage in a specific health behavior. It simply means that the individual is more inclined to behave a certain way if they possess a specific personality trait. Recent health behavior models look to use personality and health behaviors to help understand important health outcomes like general health, cardiovascular risk, and mortality (Hampson et  al., 2015). Some correlations between health behaviors and personality traits are strong although many are weak. Health behaviors are influenced by a number of diverse factors, with personality as just one of these factors. With that being said, it is likely important to consider personality when implementing health interventions. People with varying levels of a personality trait may mean that a “one size

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fits all” for altering health behaviors is inadequate. Individuals may find it difficult to change their personality, but they can certainly take their personality into consideration when trying to change their health behaviors. As such, knowing associations between personality and health behaviors could improve intervention‐based research by allowing researchers to tailor programs better designed for those with certain personality characteristics. For example, group‐ based therapies to change behavior (e.g., diet, weight, addiction) may be better suited to patients high in extraversion and sociability compared with highly introverted or highly neurotic individuals. Therapies that involve complicated adherence strategies would likely benefit from including participants with high conscientiousness compared with those with low conscientiousness. Future research is needed to understand and unpack the processes and mechanisms behind associations of personality and health behaviors (Hampson, 2012). Understanding the relationship between personality and health behaviors is important because health behaviors (e.g., smoking, drinking, wearing a seatbelt) can be analyzed as the mechanism by which personality is related to many important health outcomes (e.g., lung cancer, alcoholism, longevity; Hampson, 2012). Personality traits seem to be important individual difference variables to help understand who engages in particular health behaviors, which in turn leads to particular health outcomes (e.g., conscientious individuals wear seatbelts more, which in turn may help predict why conscientious people live longer, on average, than individuals low in conscientiousness). As such, it is important for researchers to continue to examine how personality relates to a variety of health‐promoting and health‐compromising behaviors.

Author Biographies Christopher S. Nave, PhD, Associate Director, Master of Behavioral and Decision Sciences, University of Pennsylvania. Major research interests include examining associations between personality, health, and behavior across the lifespan, using multi‐method approaches to studying individual differences, person perception and impression management, and situational assessment. Michelle R. Dixon, MA Student, Rutgers University, Camden. Interests include health psychology and the relationships between health, personality, and behavior.

References Armon, G., & Toker, S. (2013). The role of personality in predicting repeat participation in periodic health screening. Journal of Personality, 81, 452–464. doi:10.1111/jopy.12021 Ashton, M. C., & Lee, K. (2009). The HEXACO‐60: A short measure of the major dimensions of personality. Journal of Personality Assessment, 91, 340–345. doi:10.1080/00223890902935878 Bogg, T., & Roberts, B. W. (2004). Conscientiousness and health‐related behaviors: A meta‐analysis of the leading behavioral contributors to mortality. Psychological Bulletin, 130, 887–919. doi:10.1037/ 0033‐2909.130.6.887 Bogg, T., & Roberts, B. W. (2013). The case for conscientiousness: Evidence and implications for a personality trait marker of health and longevity. Annals of Behavioral Medicine, 45, 278–288. doi:10.1007/s12160‐012‐9454‐6 Booth‐Kewley, S., & Vickers, R. R., Jr. (1994). Associations between major domains of personality and health behavior. Journal of Personality, 62, 281–298. doi:10.1111/j.1467‐6494.1994.tb00298.x Caspi, A., Begg, D., Dickson, N., Harrington, H., Langley, J., Moffitt, T. E., & Silva, P. A. (1997). Personality differences predict health‐risk behaviors in young adulthood: Evidence from a

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longitudinal study. Journal of Personality and Social Psychology, 73, 1052–1063. doi:10.1037/0022‐3514.73.5.1052 DeLongis, A., & Holtzman, S. (2005). Coping in context: The role of stress, social support, and personality in coping. Journal of Personality, 73, 1–24. doi:10.1111/j.1467‐6494.2005.00361.x Ferguson, C. J. (2010). A meta‐analysis of normal and disordered personality across the life span. Journal of Personality and Social Psychology, 98, 659–667. doi:10.1037/a0018770 Friedman, H. S., & Markey, C. N. (2003). Paths to longevity in the highly intelligent Terman cohort. In C. E. Finch, J.‐M. Robine, & Y. Christen (Eds.), Brain and longevity (pp. 165–175). New York: Springer. Goldberg, L. R. (1990). An alternative “description of personality”: The big‐five factor structure. Journal of Personality and Social Psychology, 59, 1216–1229. doi:10.1037/0022‐3514.59.6.1216 Hampson, S. E. (2012). Personality processes: Mechanisms by which personality traits “get outside the skin”. Annual Review of Psychology, 63, 315–339. doi:10.1037/0022‐3514.59.6.1216 Hampson, S. E., Edmonds, G. W., Goldberg, L. R., Dubanoski, J. P., & Hillier, T. A. (2015). A life‐span behavioral mechanism relating childhood conscientiousness to adult clinical health. Health Psychology, 34, 887–895. doi:10.1037/hea0000209 Hampson, S. E., Goldberg, L. R., Vogt, T. M., & Dubanoski, J. P. (2006). Forty years on: Teachers’ assessments of children’s personality traits predict self‐reported health behaviors and outcomes at midlife. Health Psychology, 25, 57–64. doi:10.1037/0278‐6133.25.1.57 Hart, C. L., Taylor, M. D., Davey Smith, G., Whalley, L. J., Starr, J. M., Hole, D. J., … Deary, I. J. (2004). Childhood IQ and cardiovascular disease in adulthood: Prospective observational study linking the Scottish Mental Survey 1932 and the Midspan studies. Social Science and Medicine, 59, 2131–2138. doi:10.1016/j.socscimed.2004.03.016 MacCann, C., Todd, J., Mullan, B. A., & Roberts, R. D. (2015). Can personality bridge the intention‐ behavior gap to predict who will exercise? American Journal of Health Behavior, 39, 140–147. doi:10.5993/AJHB.39.1.15 McCrae, R. R., & Costa, P. T., Jr. (1986). Personality, coping, and coping effectiveness in an adult sample. Journal of Personality, 54, 385–405. doi:10.1111/j.1467‐6494.1986.tb00401.x McCrae, R. R., & Costa, P. T., Jr. (1987). Validation of the five‐factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52, 81–90. doi:10.1037/ 0022‐3514.52.1.81 Molloy, G. J., O’Carroll, R. E., & Ferguson, E. (2014). Conscientiousness and medication adherence: A meta‐analysis. Annals of Behavioral Medicine, 47, 92–101. doi:10.1007/s12160‐013‐9524‐4 Mroczek, D. K., & Almeida, D. M. (2004). The effect of daily stress, personality, and age on daily negative affect. Journal of Personality, 72, 355–378. doi:10.1111/j.0022‐3506.2004.00265.x Niaura, R., Banks, S. M., Ward, K. D., Stoney, C. M., Spiro, A., III, Aldwin, C. M., … Weiss, S. T. (2000). Hostility and the metabolic syndrome in older males: The normative aging study. Psychosomatic Medicine, 62, 7–16. Raynor, D. A., & Levine, H. (2009). Associations between the five‐factor model of personality and health behaviors among college students. Journal of American College Health, 58, 73–82. doi:10.3200/JACH.58.1.73‐82 Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science, 2(4), 313–345. Sarafino, E., & Smith, T. (2011). Health psychology: Biopsychosocial interactions (7th ed.). New York, NY: Wiley. Srivastava, S., John, O. P., Gosling, S. D., & Potter, J. (2003). Development of personality in early and middle adulthood: Set like plaster or persistent change? Journal of Personality and Social Psychology, 84, 1041–1053. doi:10.1037/0022‐3514.84.5.1041 Steptoe, A., Wright, C., Kunz‐Ebrecht, S. R., & Iliffe, S. (2006). Dispositional optimism and health behavior in community‐dwelling older people: Associations with health ageing. British Journal of Health Psychology, 11, 71–84. doi:10.1348/135910705X42850

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Sutin, A. R., Ferrucci, L., Zonderman, A. B., & Terracciano, A. (2011). Personality and obesity across the adult life span. Journal of Personality and Social Psychology, 101, 579–592. doi:10.1037/ a0024286 Taylor, M. D., Hart, C. L., Davey Smith, G., Starr, J. M., Hole, D. J., Whalley, L. J., … Deary, I. J. (2003). Childhood mental ability and smoking cessation in adulthood: Prospective observational study linking the Scottish Mental Survey 1932 and the Midspan studies. Journal of Epidemiology and Community Health, 57, 464–465. doi:10.1136/jech.57.6.464 Turiano, N. A., Whiteman, S. D., Hampson, S. E., Roberts, B. W., & Mroczek, D. K. (2012). Personality and substance use in midlife: Conscientiousness as a moderator and the effects of trait change. Journal of Research in Personality, 46, 295–305. doi:10.1016/j.jrp.2012.02.009 Welch, D., & Poulton, R. (2009). Personality influences on change in smoking behavior. Health Psychology, 28, 292–299. doi:10.1037/a0013471 Weston, S. J., & Jackson, J. J. (2015). Identification of the healthy neurotic: Personality traits predict smoking after disease onset. Journal of Research in Personality, 54, 61–69. doi:10.1016/j. jrp.2014.04.008 Williams, C. J., & Cropley, M. (2014). The relationship between perfectionism and engagement in preventive health behaviours: The mediating role of self‐concealment. Journal of Health Psychology, 19, 1211–1221. doi:10.1177/1359105313488971

Suggested Reading Bogg, T., & Roberts, B. W. (2013). The case for conscientiousness: Evidence and implications for a personality trait marker of health and longevity. Annals of Behavioral Medicine, 45, 278–288. doi:10.1007/s12160‐012‐9454‐6 Friedman, H. S., & Kern, M. L. (2014). Personality, well‐being, and health. Annual Review of Psychology, 65, 719–742. doi:10.1146/annurev‐psych‐010213‐115123 Hampson, S. E., & Friedman, H. S. (2008). Personality and health: A lifespan perspective. In O. P. John, R. W. Robins, & L. A. Pervin (Eds.), Handbook of personality: Theory and research (3rd ed., pp. 770–794). New York, NY: Guilford.

Personality and Health Outcomes Dietlinde Heilmayr1 and Howard S. Friedman2 Psychology Department, Moravian College, Bethlehem, PA, USA Psychology Department, University of California, Riverside, CA, USA 1

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Introduction The fundamental question in the study of personality and health is who gets sick and who stays well. This question of connections between individual differences and physical health dates back to Hippocrates and Galen, who proposed that four bodily humors—phlegm, black bile, yellow bile, and blood—were the underlying components in health and individuality and excess or deficiency of any humor would have negative consequences for both personality and health. Though replaced by modern physiology, the ancient Greeks’ observations of the connections between psychosocial and physical health influence science to this day. At the beginning of the twentieth century, Walter Cannon’s work on the “fight‐or‐flight” response linked perceptions, emotions, and physiological reactions. Subsequent research at Johns Hopkins University found that “irregular and uneven” people were more prone to morbidity and mortality than wary and self‐reliant types, or cool and clever types (Betz & Thomas, 1979). Such efforts combined with the developing field of psychosomatic medicine to set the stage for modern research. Attention was piqued in the broader scientific community in the 1970s, with research on Type A behavior pattern. A Type A person is hasty, competitive, impulsive, and impatient and is hypothesized to be prone to coronary disease. However, imprecise conceptualization and inadequate assessment hindered rigorous scientific advances; the field of personality and health needed a more comprehensive approach. Such an approach involves multifactor predictors, multifactor outcomes, and models of the specific, multiple pathways to health across time (Friedman & Kern, 2014). We now know that there is at best a weak and non‐unique connection between Type A and disease proneness (Booth‐Kewley & Friedman, 1987) although elements of Type A behavior (especially hostility) are indeed part of a disease‐prone pattern. Some people tend to select into (put themselves into) stressful, hostile environments and evoke negative reactions from others. This cyclical process of selecting into and evoking

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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­ egative interactions and associated unhealthy behaviors shifts people onto a trajectory toward n illness and is known as “disease proneness.” Alternatively, someone may have a good match with his or her environment, creating psychological and physiological homeostasis, and a virtuous circle of behaviors. Such a person is said to have a self‐healing personality, and tends to be conscientious, secure, and sociable, and self‐selects into healthy environments and elicits positive reactions from others (see Friedman, 1991). This type of person is not necessarily stress‐free, but views difficulties as challenges rather than threats, and tends to have good coping patterns and psychosocial resources. The self‐sustaining appraisals, decisions, and actions of a person with the self‐healing personality set him or her on a healthy pathway to long life (see Friedman, 1991; Friedman & Martin, 2011).

Key Considerations in Defining Outcomes and Contexts The constructs of “disease‐prone” and “self‐healing” personalities—with their emphases on multiple predictors and multiple outcomes—highlight important considerations when studying personality and health. Many flawed studies rely solely on self‐reported health as the measure of health, sometimes boldly (and invalidly) drawing conclusions about objective health. Self‐reported health is highly correlated with subjective well‐being, and so people who tend to be cheerful are more likely to report good health than individuals who have a more serious or cautious view of life. Although self‐reported personality predicts self‐reported health, which in turn predicts mortality risk (Idler & Benyamini, 1997), studying self‐reported personality and health is not at all the same thing as studying validly assessed personality and objective health; this confusion has led to many problems in this field (Friedman & Kern, 2014). The most revealing studies avoid such problems by measuring multiple aspects of personality, assessing stress and coping, monitoring health behaviors, and tracking disease and mortality across time. For example, using the Type A classification, a hardworking corporate businesswoman who has climbed her way up the ranks with determination and drive would likely be told (incorrectly) that she is at risk of coronary disease. However, knowing that she has a supportive social network, relishes the challenges she faces at work, and has the resources to prevail over challenges, we can place her in the broader context of her surroundings and life path; research suggests she is on a pathway to good health (see Friedman & Martin, 2011).

Six Pathways to Health or Illness The self‐healing and disease‐prone personalities encapsulate broadly defined trajectories of health. For instance, one unhealthy trajectory may involve an imprudent adventurer camping in a lightning storm while another may involve an impulsive neurotic coping with stress by binging on TV and junk food, but both pathways predict premature mortality as a function of personality. Differentiating multiple pathways yields a clearer understanding of causes across time. In general, there are six ways of conceptualizing the pathways from personality to health or disease. First, personality may disrupt the body’s physiological regulation through emotional instability and chronic negative affect. Stress and the long‐term breakdown of homeostasis are linked with disease, though the pathways are not yet fully specified. Personality is a predictor and a moderator of stress perceptions, appraisals, and coping mechanisms: some personalities are not only more likely to encounter stressful challenges but are also more likely to both

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appraise situations as especially stressful and to lack adequate coping responses. Importantly, challenge in and of itself is not necessarily bad. Indeed, challenge, curiosity, and novelty are established elements of thriving. Rather, it is the appraisal of and reaction to challenge and the degree and length of disruption that puts some people at higher risk of disease. The second, and perhaps the most direct pathway, involves health behaviors. Certain personalities are more likely to engage in healthy behaviors—everything from eating well, not smoking, and being active to wearing a seatbelt and keeping a regular sleep cycle. Other individuals are more likely to engage in unhealthy behaviors. A key question is whether changing a behavior will change long‐term disease risk. Altering some behaviors, such as smoking, will have a big impact on health (as long as other unhealthy behaviors are not adopted instead), while others are erroneously thought to be causal but are simply correlational. Often, predictors are not key elements of causal processes (e.g., see Friedman & Kern, 2014). Third, personality predicts (and often plays a causal role in) social support, social networks, quality of relationships, and amount of interpersonal conflict, all of which are relevant to health. Good social relations are healthy, but poor relationships can cause stress and influence unhealthy behaviors—for example, divorce and marital conflict predict mortality risk (Sbarra, Law, & Portley, 2011). Separating from a spouse may bring about financial burdens, emotional challenges, the loss of social support, and a need to restructure one’s identity and social network. These challenges may pass quickly if coped with appropriately but may also have deleterious long‐term consequences. Though the precise mechanisms through which social interactions affect health are not yet fully specified, close, high‐quality relationships are usually important to a healthy life trajectory. A fourth pathway linking personality and health involves biological third variables, including genetics, neurological differences, and central nervous system changes due to early environments. These biological variables predispose individuals toward certain personalities and physiological reactions. For example, people born with Down syndrome not only are at increased risk of early mortality and congenital heart disease but also differ predictably in personality from individuals without Down syndrome. In such cases, personality is correlated with health and longevity, but personality is not a causal element in this association (and so changing the personality will not affect health). Fifth is the key pathway of situation selection and evocation. Certain individuals gravitate toward certain situations and evoke responses from others that fit with their personalities. This pathway reinforces trajectories toward health or disease, where personality facilitates entering healthy or unhealthy situations. The processes of situation selection and evocation suggest that many events that seem to be bad fortune—such as being bullied—are actually predictable to some degree. For example, a hostile or socially unskilled individual may create trouble and thus elicit an unhealthy environment. Lastly, we can reverse the causal arrow and look at how health predicts—and sometimes alters—personality. Illness can cause changes relevant to depression, anger, worrying, and more. For example, the immune response to infection can cause lethargy. Similarly, some medications or treatment regimens change personality. Although this pathway is not currently well studied, being treated for or coping with chronic illnesses or handicaps may shift personality. A vicious circle of negative outcomes may result. These multiple pathways between personality and health are not mutually exclusive; they function together, making it important to understand the full picture. Without a broad understanding of how these pathways work across time, interventions may be misguided and may eventually fail. For instance, early research on Type A led the American Heart Association to recommend screening and treatment for depression in patients with cardiovascular disease, but

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treating these patients for depression does not by itself decrease mortality risk (Friedman & Kern, 2014). Caution is needed to study the full causal pathways to ensure that interventions target the true causal elements of illness. Making people happier will not necessarily make them healthier, and worrying is health protective for some individuals in some circumstances (e.g., Weston & Jackson, 2015). It depends on the situation and the life trajectory of an individual.

Personality Trajectories What are the pathways to health? How do individuals who thrive and stay healthy live their lives? In particular, what is the relevance of the five broad domains used by researchers to describe personality: conscientiousness, extraversion, agreeableness, openness, and neuroticism?

Conscientiousness Conscientiousness is the best‐established trait predicting health. People who are orderly, achievement motivated, prudent, responsible, and planful are more likely to stay on healthy pathways to long life (Friedman & Martin, 2011; Goodwin & Friedman, 2006). The original finding that conscientiousness in childhood predicts risk of dying at any age (Friedman et al., 1993) has been supported by many other subsequent studies (e.g., Chapman, Fiscella, Kawachi, & Duberstein, 2010). Conscientiousness predicts, and often affects, health through several pathways that work together to produce a cycle of healthy habits, appraisals, and coping responses. People high in conscientiousness are more likely to engage in protective health behaviors and are less likely to engage in riskier behaviors such as smoking (see Bogg & Roberts, 2004). However, behavioral pathways do not fully explain the healthy outcomes of conscientious people (Martin, Friedman, & Schwartz, 2007). Highly conscientious people often find their way to less stressful lives. For example, the most conscientious children in the Terman lifespan study (a nine‐decade study of personality and health) were the least likely later to be sent to the most difficult US frontline during World War II as adults (Duggan & Friedman, 2014), avoiding the subsequent disruption in family life and career that combat often brings. When they do encounter stress, they may have better coping abilities. The difference in trajectories means that conscientious people are more likely to be productively engaged in society, with better careers, educations, and incomes than their less conscientious counterparts (Hampson, Goldberg, Vogt, & Dubanoski, 2007). When conscientious people do experience stress, such as a career setback, their personality is more likely to attenuate the negative effects, perhaps because they are more likely to engage in problem‐focused coping rather than through substance abuse, problem‐denying, or expressing negative emotions (Connor‐Smith & Flachsbart, 2007). Taken together, these studies suggest that the pathway from conscientiousness to health occurs across the lifespan through selection into stable situations (marriages, careers), evocation of positive responses from ­others, and healthy habits that maintain homeostasis.

Extraversion Extraverts tend to be dominant, sociable, and cheerful and are inclined to elicit positive social interactions. Extraversion provides a good example of why considering the facets (or sub‐ traits) of the Big Five is informative in understanding personality and health; the facets

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(dominance, sociability, and positivity) relate to health differentially—part of the reason why extraversion is inconsistently linked to physical health (Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). The facet of dominance predicts positive job outcomes, but its relation to health depends on the situation. When combined with positive affect and agreeableness, dominance plays an important role in good leadership, but when combined with hostility, dominance may increase risk of heart disease, unstable relationships, and poor mental health. Further, in stable environments, where dominance can be established, it may be adaptive to take the lead; however, being dominant when the hierarchy is easily threatened can be risky (for a study of monkeys: Manuck, Kaplan, Adams, & Clarkson, 1988). These findings illustrate how the link between personality and health is tied to environmental contexts and other personality variables. When considering the importance of social relationships to health, one might assume that the sociability facet of extraversion will be predictive of good health. However, although sociability is generally linked with good subjective health and social outcomes, there are mixed results for objective health outcomes. For example, in the Terman sample, extraversion predicted more social ties and higher social competence, but not physical health or mortality (Friedman, Kern, & Reynolds, 2010). Sociability may be linked to health through better coping strategies, social relationships, approach‐oriented temperament, and positive engagement with others (Connor‐Smith & Flachsbart, 2007). However, these extraverts may also be drawn toward unhealthy situations. Extraverted college students, for instance, are more likely to smoke, drink, get poor sleep, and have multiple sex partners, thus increasing risk of morbidity and mortality (Munafo, Zetteler, & Clark, 2007). A highly debated topic is that of positive affect (good feelings) and health. There is a small positive correlation of trait positive affect with self‐reported health, symptom control, and survival of people with chronic diseases (Howell, Kern, & Lyubomirsky, 2007), but the causal link is unclear, and it would therefore be unwise to intervene with positivity interventions in an attempt to save lives at this stage of understanding. Although individuals with high positive affect are more likely to exercise, to have healthy behaviors and healthy relationships, and to perceive stressors as challenging rather than threatening, these are correlations. Being optimistic in the face of challenge may increase someone’s willingness to persist with treatment or attain a goal, but there is no scientific evidence that positive thinking will heal broken bones, unclog arteries, fight cancer, or undo a diagnosis. Positivity interventions may promote health so far as they encourage healthy behaviors or decrease chronic stress but may raise the risks of blaming the victim and encouraging unproductive or irrelevant responses. Being too positive may put one at increased risk by underestimating one’s susceptibility to morbidity or mortality. In the Terman study, children rated as cheerful (sense of humor, cheery, optimistic) were at greater risk of dying at any age, partially due to risky behaviors in adulthood (Friedman et al., 1993). Dispositional optimism may steer individuals toward unrealistic thoughts, or the “above average effect” (illusory superiority), as is reflected in a study of individuals trying to lose weight, where dispositional optimism predicted confidence in ability to lose weight but did not predict actual weight loss (Benyamini & Raz, 2007). Taken together, it appears that optimism combined with conscientiousness is protective as it drives individuals toward achievement, but optimism combined with impulsivity and unrealistic expectations may become detrimental.

Agreeableness Agreeableness is the tendency to be cooperative, trusting, kind, and generous. People who are agreeable are more likely to use adaptive coping mechanisms (Connor‐Smith & Flachsbart,

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2007). However, research on the relationship between agreeableness and health is mixed. Some studies suggest that agreeable people have better self‐reported health and live longer lives, but other studies have found no relationship (Iwasa et al., 2008). Though agreeableness does not reliably predict morbidity or mortality (Martin et al., 2007), the combination of agreeableness with other traits may have explanatory power. For instance, hostility involves both high neuroticism and low agreeableness, which predicts poor health and increased mortality risk (Booth‐Kewley & Friedman, 1987), likely through chronic stress, and poor health behaviors and social skills. In a meta‐analysis of 27 studies, high hostility was associated with higher BMI, more alcohol use, smoking, and markers of heart disease (Bunde & Suls, 2006). Conscientiousness may also work in tandem with agreeableness in predicting health. High agreeableness and low conscientiousness may be risky, as the individual may be considered a “pushover” and fails to achieve personal goals in exchange for doing what others want them to do (Chapman et al., 2010). As previously discussed, though, low agreeableness is also risky. Therefore, agreeableness may be most predictive of good health when it is paired with protective traits and social contexts.

Openness Openness involves intellectual curiosity, willingness to experiment, and creativity. People who are open are more likely to cope resourcefully and effectively (Connor‐Smith & Flachsbart, 2007). In the MIDUS study (a longitudinal study of health and well‐being in the United States), it is found that low levels of openness in patients relate to stroke, hernia, tuberculosis, and bone problems (Goodwin & Friedman, 2006). A subsequent study found that high levels of openness relate to decreased odds of a stroke, heart condition, blood pressure, and arthritis diagnosis (Weston, Hill, & Jackson, 2014). The intellect facet may play a particularly strong role in predicting health. For instance, higher intellect predicts health status and educational attainment (Hampson et  al., 2007). However, some studies of personality and longevity have found no relation between openness and mortality (Maier & Smith, 1999). Openness is the least understood trait in its relation to health, as the pathways are not yet clearly specified, and the objective outcomes from study to study are at odds with one another.

Neuroticism Neuroticism, the tendency to experience the world as distressful, is frequently studied and further reveals the complex relationship of personality and health. Neurotics are prone to anxiety and depression and are emotionally unstable. It has long been known that people with higher levels of hostility, anxiety, and depression are more likely to be ill, but this is not necessarily a causal pathway, and prospective studies do not always show the same relationship; the commonsense view that neuroticism (“worrying”) leads to disease and premature mortality requires significant qualification (Friedman & Kern, 2014). Consider the strong relationship between neuroticism and subjective well‐being. This association makes it difficult to disentangle the effects of neuroticism on objective health outcomes because subjective well‐being is related to self‐rated health, symptom report, and medical visits. In other words, individuals high on neuroticism are more likely to feel and report symptoms, which can lead to further distress even when there are no objective health differences (Costa & McCrae, 1987). Though neuroticism may sometimes predict increased risk of

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­ ortality, various studies find no relation between neuroticism and mortality (e.g., Iwasa m et  al., 2008). Given these conflicting findings, it is likely that the effect of neuroticism on objective health is moderated by other personality or environmental factors (e.g., Taga, Friedman, & Martin, 2009). There is also little evidence that intervening on neuroticism increases longevity. In fact, it may even be risky to intervene in some situations, such as what Friedman terms “healthy neuroticism” (Friedman, 2000), or the combination of high neuroticism with high conscientiousness. Healthy neurotics may be particularly vigilant about their health (Friedman, 2000). Though healthy neurotics likely have lower subjective well‐being, more doctor visits, and more psychosomatic symptoms, they may be more willing to engage in health protective behaviors. Healthy neuroticism may also have a particularly profound effect in stressful situations. For example, conscientiously neurotic individuals diagnosed with diseases most related to smoking were more likely to quit smoking after disease diagnosis, but did not show any smoking differences before disease onset (Weston & Jackson, 2015). In the Terman sample, neuroticism predicted lower health and increased mortality risk for women, but decreased risk for widowed men (Taga et al., 2009). That is, men who lost their wives and were particularly anxiety prone lived longer than the carefree men who lost their wives. Together, these studies suggest that when experiencing a stressful situation, high levels of neuroticism may induce increased anxiety, and conscientiousness may make one more successful at channeling that anxiety into health‐promoting behaviors. Neuroticism is a good example of the importance of using a lifespan perspective to study the relationship between personality and health, as neuroticism can also be the outcome of illness. For instance, neurological conditions and inflammatory responses may cause depression. When depression is the outcome of disease, high neuroticism may cause a negative spiral when the individual fails to enact adaptive coping mechanisms. There is limited evidence that decreasing neuroticism reaps objective health gains. Recall that though treating depression decreases the risk of a secondary cardiac event, it does not reduce mortality risk (Rutledge, Redwine, Linke, & Mills, 2013). The association between depression and mortality may often be confounded by hostility (Lemogne et  al., 2010). Treating depression may only deter or delay disease so far as it motivates individuals to adopt healthier behaviors (such as increased physical activity), social networks, or coping skills. As neuroticism is health protective for some individuals, intervening on neuroticism may be risky, thus highlighting the importance of targeted interventions that understand the full causal chain.

Interventions and Future Directions Interventions that address personality to improve health should simultaneously target individual, community, and societal levels in order to launch people onto healthy paths and maintain their adherence to these healthy pathways. Because early character affects social relationships, careers, and environmental choice, all of which relate to health in later years, we need further understanding of how to shift people onto these healthy trajectories early and how to maintain these pathways, rather than intervening on short‐term stressors at any instance throughout life. Such interventions need to be rigorously tested over time to determine their impact on well‐being, disease, and, ultimately, mortality. The most promising trait for health intervention is conscientiousness. We know that having a high level of conscientiousness is healthy, and we know many key pathways from high

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c­ onscientiousness to longevity. Can we increase conscientiousness and sustain these changes? Personality is relatively stable but shows patterns of meaningful change across the lifespan, suggesting that change is possible. Different facets of conscientiousness may change differentially, as well. For instance, industriousness has been found to change early in life, impulse control and reliability changes across the lifespan, and orderliness is relatively stable (Jackson et al., 2009). Whether change is possible may also depend on contextual factors, such as age or stage of life. It may, for instance, be most effective to intervene during transitionary phases of life, when a person is shifting habits and behaviors.

Conclusion Understanding who gets sick and who stays well is an integral part of healthcare. By identifying which individuals are more susceptible to illness, we may be more likely to prevent illness and intervene in a more targeted manner appropriate to a specific demographic. After people fall ill, understanding personality influences can be important in predicting who will cooperate with treatment and stay on pathways that maximize chances for recovery. In disentangling the effects of personality on health and longevity, it is important to remember that personality interacts with psychosocial contexts. We cannot say that, for everyone, neuroticism is bad or that agreeableness is good. We must consider each individual as embedded in his or her environment across time and the ways in which personality traits interact with each other. In doing so, we can address how to shift people onto healthy life trajectories.

Author Biographies Dietlinde Heilmayr is an assistant professor of health psychology at Moravian College. She earned her BA from Hendrix College and her PhD from the University of California, Riverside. Her service experiences with AmeriCorps, Fulbright, and Bike and Build inform her applied scholarly interests broadly focused on nature‐based health interventions. In particular, Heilmayr focuses on the rigorous assessment and exploration of community gardening as a health intervention. She teaches a variety of courses including health psychology and experimental methods and data analysis and was the recipient of the Distinguished Graduate Teaching Award in 2017. Howard S. Friedman is a distinguished professor of psychology at the University of California, Riverside. His book, The Longevity Project: Surprising Discoveries for Health and Long Life from the Landmark Eight‐Decade Study, summarizes his 25‐year scientific study of the pathways to health and long life. His major scientific awards include the James McKeen Cattell Fellow career award from the Association for Psychological Science and the “Outstanding Contributions to Health Psychology” senior award from the American Psychological Association (Div. 38).

References Benyamini, Y., & Raz, O. (2007). “I can tell you if I’ll really lose all that weight”: Dispositional and situated optimism as predictors of weight loss following a group intervention. Journal of Applied Social Psychology, 37(4), 844–861. doi:10.1111/j.1559‐1816.2007.00189.x

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Betz, B. J., & Thomas, C. B. (1979). Individual temperament as a predictor of health or premature disease. The Johns Hopkins Medical Journal, 144(3), 81–89. Bogg, T., & Roberts, B. W. (2004). Conscientiousness and health‐related behaviors: A meta‐analysis of the leading behavioral contributors to mortality. Psychological Bulletin, 130(6), 887–919. Booth‐Kewley, S., & Friedman, H. S. (1987). Psychological predictors of heart disease: A quantitative review. Psychological Bulletin, 101(3), 343–362. doi:10.1037//0033‐2909.101.3.343 Bunde, J., & Suls, J. (2006). A quantitative analysis of the relationship between the Cook‐Medley Hostility Scale and traditional coronary artery disease risk factors. Health Psychology, 25(4), 493– 500. doi:10.1037/0278‐6133.25.4.493 Chapman, B. P., Fiscella, K., Kawachi, I., & Duberstein, P. R. (2010). Personality, socioeconomic status, and all‐cause mortality in the United States. American Journal of Epidemiology, 171(1), 83–92. doi:10.1093/aje/kwp323 Connor‐Smith, J. K., & Flachsbart, C. (2007). Relations between personality and coping: A meta‐ analysis. Journal of Personality and Social Psychology, 93(6), 1080–1107. doi:10.1037/0022‐3514. 93.6.1080 Costa, P. T., & McCrae, R. R. (1987). Neuroticism, somatic complaints, and disease: Is the bark worse than the bite? Journal of Personality, 55(2), 299–316. doi:10.1111/j.1467‐6494.1987.tb00438.x Duggan, K. A., & Friedman, H. S. (2014). Lifetime biopsychosocial trajectories of the Terman gifted children: Health, well‐being, and longevity. In D. K. Simonton (Ed.), The Wiley handbook of genius (pp. 488–507). Oxford, UK: Wiley‐Blackwell. Friedman, H. S. (1991). The self‐healing personality. Why some people achieve health and others succumb to illness. New York, NY: Henry Holt. Friedman, H. S. (2000). Long‐term relations of personality and health: Dynamisms, mechanisms, tropisms. Journal of Personality, 68(6), 1089–1107. Friedman, H. S., & Kern, M. L. (2014). Personality, well‐being, and health. The Annual Review of Psychology, 65(18), 1–24. doi:10.1146/annurev‐psych‐010213‐115123 Friedman, H. S., Kern, M. L., & Reynolds, C. A. (2010). Personality and health, subjective well‐being, and longevity. Journal of Personality, 78(1), 179–216. doi:10.1111/j.1467‐6494.2009.00613.x Friedman, H. S., & Martin, L. R. (2011). The longevity project: Surprising discoveries for health and long life from the landmark eight decade study. London: Hay House, Inc. Friedman, H. S., Tucker, J. S., Tomlinson‐Keasey, C., Schwartz, J. E., Wingard, D. L., & Criqui, M. H. (1993). Does childhood personality predict longevity? Journal of Personality and Social Psychology, 65(1), 176–185. doi:10.1037/0022‐3514.65.1.176 Goodwin, R. D., & Friedman, H. S. (2006). Health status and the five‐factor personality traits in a nationally representative sample. Journal of Health Psychology, 11(5), 643–654. doi:10.1177/ 1359105306066610 Hampson, S. E., Goldberg, L. R., Vogt, T. M., & Dubanoski, J. P. (2007). Mechanisms by which childhood personality traits influence adult health status: Educational attainment and healthy behaviors. Health Psychology, 26(1), 121–125. doi:10.1037/0278‐6133.26.1.121 Howell, R. T., Kern, M. L., & Lyubomirsky, S. (2007). Health benefits: Meta‐analytically determining the impact of well‐being on objective health outcomes. Health Psychology Review, 1(1), 83–136. doi:10.1080/17437190701492486 Idler, E. L., & Benyamini, Y. (1997). Self‐rated health and mortality: A review of twenty‐seven community studies. Journal of Health and Social Behavior, 38, 21–37. Iwasa, H., Masui, Y., Gondo, Y., Inagaki, H., Kawaai, C., & Suzuki, T. (2008). Personality and all‐cause mortality among older adults dwelling in a Japanese community: A five‐year population‐based prospective cohort study. The American Journal of Geriatric Psychiatry, 16(5), 399–405. doi:10.1097/ JGP.0b013e3181662ac9 Jackson, J. J., Bogg, T., Walton, K. E., Wood, D., Harms, P. D., Lodi‐Smith, J., … Roberts, B. W. (2009). Not all conscientiousness scales change alike: A multimethod, multisample study of age differences in the facets of conscientiousness. Journal of Personality and Social Psychology, 96(2), 446– 459. doi:10.1037/a0014156

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Lemogne, C., Nabi, H., Zins, M., Cordier, S., Ducimetière, P., Goldberg, M., & Consoli, S. M. (2010). Hostility may explain the association between depressive mood and mortality: Evidence from the French GAZEL cohort study. Psychotherapy and Psychosomatics, 79(3), 164–171. doi:10.1159/ 000286961 Maier, H., & Smith, J. (1999). Psychological predictors of mortality in old age. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 54(1), 44–54. doi:10.1093/geronb/54B.1.P44 Manuck, S. B., Kaplan, J. R., Adams, M. R., & Clarkson, T. B. (1988). Studies of psychosocial influences on coronary artery atherogenesis in cynomolgus monkeys. Health Psychology, 7(2), 113–124. doi:10.1037/0278‐6133.7.2.113 Martin, L. R., Friedman, H. S., & Schwartz, J. E. (2007). Personality and mortality risk across the life span: The importance of conscientiousness as a biopsychosocial attribute. Health Psychology, 26(4), 428–436. doi:10.1037/0278‐6133.26.4.428 Munafo, M. R., Zetteler, J. I., & Clark, T. G. (2007). Personality and smoking status: A meta‐analysis. Nicotine & Tobacco Research, 9(3), 405–413. doi:10.1080/14622200701188851 Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science, 2(4), 313–345. doi:10.1111/j.1745‐6916.2007.00047.x Rutledge, T., Redwine, L. S., Linke, S. E., & Mills, P. J. (2013). A meta‐analysis of mental health treatments and cardiac rehabilitation for improving clinical outcomes and depression among patients with coronary heart disease. Psychosomatic Medicine, 75(4), 335–349. doi:10.1097/ PSY.0b013e318291d798 Sbarra, D. A., Law, R. W., & Portley, R. M. (2011). Divorce and death: A meta‐analysis and research agenda for clinical, social, and health psychology. Perspectives on Psychological Science, 6(5), 454– 474. doi:10.1177/1745691611414724 Taga, K. A., Friedman, H. S., & Martin, L. R. (2009). Early personality traits as predictors of mortality risk following conjugal bereavement. Journal of Personality, 77(3), 669–690. doi:10.1111/j.1467‐ 6494.2009.00561.x Weston, S. J., Hill, P. L., & Jackson, J. J. (2014). Personality traits predict the onset of disease. Social Psychological and Personality Science, 1–9. doi:10.1177/1948550614553248 Weston, S. J., & Jackson, J. J. (2015). Identification of the healthy neurotic: Personality traits predict smoking after disease onset. Journal of Research in Personality, 54, 61–69. doi:10.1016/j.jrp. 2014.04.008

Suggested Reading Bogg, T., & Roberts, B. W. (2004). Conscientiousness and health‐related behaviors: A meta‐analysis of the leading behavioral contributors to mortality. Psychological Bulletin, 130(6), 887–919. Friedman, H. S. (1991). The self‐healing personality. Why some people achieve health and others succumb to illness. New York, NY: Henry Holt. Friedman, H. S., & Martin, L. R. (2011). The longevity project: Surprising discoveries for health and long life from the landmark eight decade study. London: Hay House, Inc.

Personality Hardiness Salvatore R. Maddi Department of Psychological Science, University of California, Irvine, Irvine, CA, USA

Introduction Some psychologists consider resilience to emphasize how people can remain the same despite the imposition on them of stressful circumstances. One conclusion from this position is that it is helpful to avoid stressors. In contrast, personality hardiness emphasizes resilience as the process of change, growth, and development that is provoked by dealing with rather than avoiding stressful circumstances (e.g., Maddi, 2002; Maddi & Khoshaba, 2005). In this regard, an assumption behind personality hardiness is that life is by its nature changing and stressful, so involving oneself in this process helps one grow and develop toward greater fulfillment. This assumption derives from the existential position (Kierkegaard, 1954) that everything we do in life is the result of making decisions. As life is an ongoing process of change, each decision with which we are faced involves choosing the unknown future or the familiar past. Although choosing the future is developmentally best, it brings anxiety because the outcome cannot be predicted in advance. But if we decide to avoid anxiety by choosing the past (i.e., what we already know and are doing), we encounter the existential guilt of missed opportunity, boredom, and emptiness of living. Based on this dilemma, the hardiness approach assumes that, in one’s ongoing life, the best way to establish a pattern of choosing the future, rather than the past, is to develop the personality pattern of hardiness. Hardiness is considered a pattern of attitudes and strategies that help one do the hard work of choosing the future rather than the past (Maddi, 2002). The more than 40 years of research, consulting, and counseling that emerged from this conceptualization has solidified this position. Early on, Maddi’s (1965) work involved showing that people who express more interest in change are also likely to be more creative in their work. But in specifying the personality characteristics involved more completely and the particular effects of stressors on functioning, it seemed important to carry out a relevant experiment, rather than just doing correlational research.

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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The Illinois Bell Telephone Project In the 1070s, Maddi was a consultant for Illinois Bell Telephone (IBT). At that time, the US telephone industry was a federally regulated monopoly, whose member companies only had to provide cheap and reliable telephone service and did not have to worry about their bottom lines. But it became clear that this situation had to change, as the beginning of the Internet was opening up the field and encouraging international competition. The US government made clear that the telephone industry would need to be deregulated in order to motivate competition for greater possibilities and income in the newly developing circumstances. With the help of IBT decision makers, Maddi and his research team in 1974 began yearly testing of 450 managers in a longitudinal research study to determine the effects of the deregulation on them. Each yearly testing included not only the set of relevant questionnaires but also interviews. Also available were yearly job performance evaluations done by the company. Six years into the study (in 1980), the federal deregulation occurred, and its effects are still regarded as a major disruption on the industry and its companies. Indeed, in the year following the deregulation, nearly half of the managers in our sample were terminated. As to the research study, we continued to test the managers, whether they continued or were terminated, with the questionnaires, interviews, and job evaluations each year for the following 6 years. The study is still regarded as a classical natural experiment. The study results showed that, in the 6 years following the deregulation of the telephone industry, nearly two‐thirds of the sample fell apart in various ways. They showed clear signs of major anxiety, depression, and anger, undermining performance at work. These signs included violence, data loss, inability to make decisions, and decision‐making avoidance. And in home life, they also showed signs of being undermined, such as divorces, substance abuse, and even suicides. In contrast, the other third of the sample not only survived but also thrived following the deregulation. At work, they either rose up in the ranks, or if they were among those terminated, they found significant jobs in other organizations or even started their own companies. In all this, they were able to give and get social support with their significant others (at work or at home) and were able to grow and develop by dealing effectively with the stress of deregulation. Of particular importance in developing hardiness assessment was the comparison of managers who reacted resiliently to the deregulation with those who did not (Maddi & Kobasa, 1984). In the 6 years before this upheaval, the managers who reacted well to the deregulation were much stronger than the others in the attitudes of commitment, control, and challenge. No matter how bad things might get, they wanted to stay involved with people and contexts (commitment), continue to struggle to have an influence on their outcomes (control), and learn how to grow and develop by trying to deal effectively with stressors (challenge). In contrast, the managers who fell apart under the deregulation had even before it showed definite signs of playing it safe by avoiding stressors and, if unsuccessful in this avoidance, by sinking into alienation and powerlessness. These results led to conceptualizing hardiness as the attitudes (the three Cs of commitment, control, and challenge) and strategies (problem solving rather than avoidance coping, socially supportive rather conflict‐laden interactions, and beneficial rather than overindulgent self‐care) associated with resilience. It appeared that hardy attitudes provided the courage and motivation to be able to do the hard work of hardy strategies that turned stressors into growth advantages. All of this is quite consistent with the existential view, well summarized by Nietzsche (1968, Nietzsche’s Twilight of the Idols, p. 254) as “whatever doesn’t kill me makes me stronger.”

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Also, the IBT data on interviews and questionnaires completed prior to the deregulation permitted a study (Khoshaba & Maddi, 1999) on the early life experiences of managers and the effects of these experiences on performance after the deregulation. Content analyses of the data showed that the managers who thrived after the deregulation had reported remembering a more disruptive, stressful early family life and having been selected by their parents as the hope of the family. They indicated having accepted this role and working hard to justify it. These results are consistent with the assumption that hardiness can be learned rather than being inborn. It also indicates that parents can help their children grow in hardiness by teaching them that life is by its nature stressful, and involving themselves in this process, rather than denying it, is the way to grow and develop toward a better life. In contrast, parents who are overprotective and controlling may be jeopardizing their children’s growth toward hardiness.

Further Evolution of the Hardiness Approach In the years since the IBT study, there have been many additional research studies and consulting work on the hardiness approach. In this process, our various versions of the hardiness questionnaire have been translated into more than 15 Asian and European languages and are also used in English in numerous countries. There have also been reviews of hardiness research (see Suggested Reading). In the research studies covered in these reviews, it has been shown that the hardy attitudes (the three Cs of commitment, control, and challenge) are positively related to each other and to the hardy strategies of problem‐solving (rather than avoidance) coping, socially supportive (rather than conflict‐laden) interactions, and beneficial (rather than overindulgent) self‐care. This hardiness pattern has been positively associated with enhanced performance, conduct, mood, and health in samples of college and high‐school students and working adults in military, firefighting, sports, and business contexts (e.g., Maddi, 2002). Some specific topics and studies are considered below. In the process of research and consulting, there has been systematic improvement of hardy attitudes measurement. The original questionnaire for the three Cs of commitment, control, and challenge was 50 items in length. Over the years, the measure has been shortened and improved. The current version (Maddi, 2001) is the 18‐Likert‐item Personal Views Survey, III‐R (third edition, revised). Examples of items are, for commitment, “I often wake up eager to take on life wherever it left off”; for control, “When I make plans, I’m certain I can make them work”; and for challenge, “Changes in routine provoke me to learn.” Cronbach’s alpha coefficients for all three Cs range in the.70’s and for total hardiness are even higher. The three Cs are positively correlated, and each of them shows a high correlation with the total hardiness score. Also, consistent with hardiness theorizing, Sinclair and Tetrick (2000) have found that factor analysis of the hardiness test items yields the three empirically related first‐order factors of commitment, control, and challenge and that these factors are indeed positively related to the second‐order factor of hardiness. This pattern has supported the conceptually emphasized use of a total hardiness score as necessary. In another study (Maddi & Harvey, 2005), no cross‐cultural or demographic differences were found in hardiness. By now, there are many especially significant studies on the role of hardiness in various performance, conduct, and health activities. For example, in samples of bus drivers (Bartone, 1989), lawyers (Kobasa, 1982), and nurses (Keane, Ducette, & Adler, 1985), hardiness was found to be positively correlated with measures of performance and satisfaction and negatively correlated with anxiety and depression. There are also similar findings in American employees experiencing the culture shock of work missions abroad (Atella, 1989) and in foreign

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i­mmigrants to the United States (Kuo & Tsai, 1986). In a study of sports performance (Maddi & Hess, 1992), hardiness levels of male, high‐school varsity basketball players were measured in the summer, and then their performance was obtained in the ensuing season. Hardiness predicted six out of seven indices of performance in the expected direction, even though all the subjects were varsity players. Also, there have been studies concerning hardiness and military personnel. For example, Bartone (1999) found that soldiers high in hardiness adjusted better to health and life stressors than did those who were low in hardiness. Further, among soldiers returning from operational deployments, there was a positive relationship between hardiness and readjustment to their former lives (Britt, Adler, & Bartone, 2001). Hardiness was also a positive predictor of performance success in soldiers undergoing special forces training among Norwegian Naval Academy cadets (Bartone, Johnsen, Eid, Brun, & Laberg, 2002). Similarly, hardiness was positively correlated with both retention and performance excellence in US Military Academy cadets (Maddi, Matthews, Kelly, Villarreal, & White, 2012). In addition, studies support a negative relationship between hardiness and conduct problems, such as alcohol and gambling abuse. For example, Maddi, Wadhwa, and Haier (1996) found that among high‐school graduates about to enter college, hardiness had a negative relationship with the reported frequency with which alcohol and drugs were used. In this study, the reported frequency of alcohol and drug use was objectively validated through urine screens. Also found in a sample of college students was a negative relationship between hardiness and gambling (Maddi et al., 2015).

Comparison of Hardiness and Other Possible Predictors Understandably, questions have arisen as to whether hardiness is little more than yet another personality variable. This concern has led to several empirical studies comparing hardiness and other possible predictors of performance. One other possible predictor that has been proposed is negative affectivity, or neuroticism (e.g., Hull, Van Treuren, & Virnelli, 1987). Maddi and Khoshaba (1994) conducted a relevant study in which hardiness and an accepted measure of negative affectivity were entered into a regression analysis as independent variables in an attempt to predict the clinical scales of the Minnesota Multiphasic Personality Inventory (MMPI). With the effects of negative affectivity controlled, hardiness was still a robust negative predictor of the MMPI clinical scales. Another alternative predictor that might be confounded with hardiness is optimism. To test this possibility, Maddi and Hightower (1999) conducted three related studies that compared the relative influence of hardiness and optimism with transformational and regressive coping. Studying undergraduate students, the first two studies differed in the tests used to assess transformational and regressive coping styles. Nonetheless, the results showed consistently that, by comparison with optimism, hardiness was the better positive predictor of transformational coping and negative predictor of regressive coping. In the third study, the same approach was used in a sample of women who had breast lumps and were arriving at a specialty clinic for diagnosis of whether or not the lumps were cancerous. Under this life‐threatening stressor, optimism showed similar effects on coping as did hardiness, but only hardiness was a negative predictor of regressive coping. Taken together, these three studies show that hardiness is a better predictor of effective coping and that optimism, by comparison, may be laced with naïve complacency.

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Another explanatory alternative to hardiness is grit, which is the courage provided by having a definite goal that affects performance under stress and will never be given up (Duckworth & Quinn, 2009). By comparison, hardiness is the existential courage to continue to learn from and change under life’s stressors (Maddi, 2002). To investigate the relative predicted power of hardiness and grit, a sample of cadets at the US Military Academy were tested for hardiness and grit several weeks before their training began, and then their performance evaluations were followed during the 4 years of this intentionally stressful training. The study results showed that hardiness was a better predictor than grit of cadet retention and performance (Maddi et al., 2012, 2015).

Hardiness Practice Applications There is a growing need for hardiness assessment and training, due to the increased rate of social, technological, and cultural change being fueled by our transition from industrial to information societies (e.g., Peters, 1988). In an effort to adapt to the pressures of change, companies are continually restructuring (e.g., decentralizing, merging, downsizing, upsizing), which influences the lives of employees. In an attempt to help individuals deal well and grow with these changes, Maddi and Khoshaba have developed hardiness assessment and training procedures that can be used in consultation with individuals and organizations. As to hardiness assessment, there is now the HardiSurvey III‐R, a valid and reliable 65‐item questionnaire that measures the vulnerability factors of stress, strain, and regressive coping, along with the resilience factors of hardiness attitudes, problem‐solving coping, and supportive social interactions (Maddi & Khoshaba, 2001). The vulnerability and resilience factors of the test taker are compared with each other and with available norms, leading to a wellness ratio. This test can be taken on our website (www.HardinessInstitute.com) and provides a comprehensive report concerning your resilience or vulnerability under stress. As to hardiness training, there is now a comprehensive workbook (Khoshaba & Maddi, 2008; Maddi & Khoshaba, 2005) that includes instructions, exercises, case studies, and evaluation procedures concerning how to engage in problem‐solving (rather than avoidance) coping, socially supportive (rather than conflict‐laden) interactions, and beneficial (rather than overindulgent) self‐care. Also shown is how to use what is learned through these procedures to deepen your hardy attitudes of commitment, control, and challenge, so that once the training is over, you will have the courage and strategies to do the hard work involved in improving your functioning in everyday living. This workbook can be used by trainees on their own, or with the supervision of a certified hardiness trainer. There are some studies supporting the effectiveness of the hardiness training procedure. In one study (Maddi, Kahn, & Maddi, 1998), IBT managers who went through hardiness training in the year following the stressful deregulation of the telephone industry improved in their performance and felt less anxious and overwhelmed, despite the huge disruption of the company. In this process, the IBT managers also increased in their hardy attitudes and simultaneously decreased in their self‐reported strain. This beneficial pattern of results was still present at a 6‐month follow‐up test. Further, a recent study (Maddi, Harvey, Khoshaba, Fazel, & Resurreccioin, 2009) showed that undergraduate students who went through a hardiness training course based on the workbook, by comparison with a carefully developed control group, not only increased in hardiness but also in grade‐point average at graduation.

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Concluding Remarks In terms of conceptualization and empirical support, it appears that the hardiness approach has growing validity. This encourages its use in assessment and training to increase the likelihood that individuals and organizations will be able to turn stressful circumstances from potential disasters into growth opportunities instead.

Author Biography The son of Sicilian immigrants, Salvatore R. Maddi was born in New York in 1933. He received a PhD in clinical psychology with honors from Harvard University in 1960. Having taught at the University of Chicago (1960–1986) and the University of California, Irvine (1986–2015), he has developed the hardiness approach that shows how people can develop the courage, motivation, and capabilities to turn stressful circumstances into growth opportunities. He has won many awards, the latest of which is the 2012 American Psychological Foundation Gold Medal.

References Atella, M. (1989). Crossing boundaries: Effectiveness and health among western managers living in China (Unpublished doctoral dissertation). University of Chicago. Bartone, P. T. (1989). Predictors of stress related illness in city bus drivers. Journal of Occupational Medicine, 31, 657–663. Bartone, P. T. (1999). Hardiness protects against war‐related stress in army reserve forces. Consulting Psychology Journal, 51, 72–82. doi:10.1037/1061‐4087.51.2.72 Bartone, P. T., Johnsen, B. H., Eid, J., Brun, W., & Laberg, J. C. (2002). Factors influencing small‐unit cohesion in Norwegian Navy officer cadets. Military Psychology, 4, 1–22. doi:10.1207/S1532787 6MP1401_01 Britt, T. W., Adler, A. B., & Bartone, P. T. (2001). Deriving benefits from stressful events: The role of engagement in meaningful work and hardiness. Journal of Occupational Health Psychology, 6, 53–63. doi:10.1037/1076‐8998.6.1.53 Duckworth, A. L., & Quinn, P. D. (2009). Development and validation of the short grit scale. Journal of Personality Assessment, 91, 166–174. doi:10.1080/00223890802634290 Hull, J. G., Van Treuren, R. R., & Virnelli, S. (1987). Hardiness and health: A critique and alternative approach. Journal of Personality and Social Psychology, 53, 518–530. doi:10.1037/0022‐3514.53.3.518 Keane, A., Ducette, J., & Adler, D. (1985). Stress in ICU and non‐ICU nurses. Nursing Research, 34, 231–236. Khoshaba, D. M., & Maddi, S. R. (1999). Early experiences in hardiness development. Consulting Psychology Journal, 51, 106–116. doi:10.1037/1061‐4087.51.2.106 Khoshaba, D. M., & Maddi, S. R. (2008). HardiTraining: Managing stressful change (4ed. ed.). Newport Beach, CA: Hardiness Institute. Kierkegaard, S. (1954). The sickness unto death. New York, NY: Doubleday. Kobasa, S. C. (1982). Commitment and coping in stress resistance among lawyers. Journal of Personality and Social Psychology, 42, 707–717. doi:10.1037/0022‐3514.42.4.707 Kuo, W. H., & Tsai, Y. (1986). Social networking, hardiness, and immigrant’s mental health. Journal of Health and Social Behavior, 27, 133–149. Retrived from http://www.jstor.org/stable/2136312 Maddi, S. R. (1965). Motivational aspects of creativity. Journal of Personality, 33, 330–347. doi:10.1111/ j.1467‐6494.1965.tb01390.x

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Maddi, S. R. (2001). HardiSurvey III‐R: Test development and internet instruction manual. Newport Beach, CA: Hardiness Institute. Maddi, S. R. (2002). The story of hardiness: Twenty years of theorizing, research, and practice. Consulting Psychology Journal, 54, 175–185. doi:10.1037/1061‐4087.54.3.173 Maddi, S. R., & Harvey, R. H. (2005). Hardiness considered across cultures. In P. T. Wong & L. C. J. Wong (Eds.), Handbook of multicultural perspectives on stress an coping (pp. 403–420). New York, NY: Springer. Maddi, S. R., Harvey, R. H., Khoshaba, D. M., Fazel, M., & Resurreccioin, N. (2009). Hardiness facilitates performance in college. Journal of Positive Psychology, 4, 566–577. doi:10.1080/ 17439760903157133 Maddi, S. R., & Hess, M. (1992). Personality hardiness and success in basketball. International Journal of Sports Psychology, 23, 360–368. Maddi, S. R., & Hightower, M. (1999). Hardiness and optimism as expressed in coping patterns. Consulting Psychology Journal, 51, 95–105. doi:10.1037/1061‐4087.51.2.95 Maddi, S. R., Kahn, S., & Maddi, K. L. (1998). The effectiveness of hardiness training. Consulting Psychology Journal, 50, 78–86. doi:10.1037/1061‐4087.50.2.78 Maddi, S. R., & Khoshaba, D. M. (1994). Hardiness and mental health. Journal of Personality Assessment, 63, 265–274. doi:10.1207/s15327752jpa6302_6 Maddi, S. R., & Khoshaba, D. M. (2001). HardiSurvey III‐R: Test development and internet instruction manual. Newport Beach, CA: Hardiness Institute. Maddi, S. R., & Khoshaba, D. M. (2005). Resilience at work: How to succeed no matter what life throws at you. New York, NY: Amacom. Maddi, S. R., & Kobasa, S. C. (1984). The hardy executive: Health under stress. Homewood, IL: Dow Jones‐Irwin. Maddi, S. R., Matthews, M. D., Kelly, D., Villarreal, B., & White, M. (2012). Hardiness and grit predict performance and retention of USMA cadets. Military Psychology, 24(1), 19–28. Maddi, S. R., Savino, S. C. M., Bach, S. C., Saifabad, N., Shirmohammadi, M., & Brown, S. D. (2015). Hardiness is negatively related to gambling. Manuscript submitted for publication. Maddi, S. R., Wadhwa, P., & Haier, R. J. (1996). Relationship of hardiness to alcohol and drug use in adolescents. American Journal of Drug and Alcohol Abuse, 22, 247–257. doi:10.3109/ 00952999609001657 Peters, T. (1988). Thriving on chaos. New York, NY: Knopf. Sinclair, R. R., & Tetrick, L. E. (2000). Implications of item wording for hardiness structure, relations to neuroticism, and stress buffering. Journal of Research in Personality, 34, 1–5. doi:10.1006/ jrpe.1999.2265

Suggested Reading Bartone, P. T. (1999). Hardiness protects against war‐related stress in army reserve forces. Consulting Psychology Journal, 51, 72–82. doi:10.1037/1061‐4087.51.2.72 Funk, S. C. (1992). Hardiness: A review of theory and research. Health Psychology, 11, 335–345. doi:10.1037/0278‐6133.11.5.335 Maddi, S. R. (2002). The story of hardiness: Twenty years of theorizing, research, and practice. Consulting Psychology Journal, 54, 175–185. doi:10.1037/1061‐4087.54.3.173 Orr, E., & Westman, M. (1990). Hardiness as a stress moderator: A review. In M. Rosenbaum (Ed.), Learned resourcefulness: On coping skills, self‐control, and adaptive behavior (pp. 64–94). New York, NY: Springer‐Verlag. Ouellette, S. C. (1993). Inquiries into hardiness. In L. Goldberger & S. Bresnitz (Eds.), Handbook of stress: Theoretical and clinical aspects (2nd ed., pp. 77–100). New York, NY: Free Press.

Physical Activity Monitoring Birte von Haaren‐Mack1, Johannes B.J. Bussmann2, and Ulrich W. Ebner‐Priemer3 German Sport University Cologne, Köln,, Nordrhein‐Westfalen, Germany 2 Erasmus MC University Medical Center Rotterdam, CA, Rotterdam, South Holland, The Netherlands 3 Karlsruhe Institute of Technology, Karlsruhe, Germany

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Although behavior is central to psychology in almost any definition of the field, research on actual behavior has reached a minimum in the field of psychology. This has been criticized several times, for example, by Baumeister and colleagues’ seminal paper titled “Psychology as the Science of Self‐Reports and Finger Movements: Whatever Happened to Actual Behavior?” In this chapter we will focus on the assessment of actual behavior. More precisely, we will focus on the assessment of physical behavior as an important subdomain of behavior—which encom­ passes overt postures, movements, and physical activities in everyday life—and its resultant physiological consequences (e.g., energy expenditure [EE]).

Measurement Principles and Technology With the technological developments of the last 20 years, ambulatory activity monitor­ ing—defined as a measurement strategy to assess physical activity, posture, and movement patterns continuously in everyday life—has become more and more feasible and is therefore increasingly applied to various research questions and clinical (feedback) applications. Activity monitors based on accelerometry have become most popular for assessing ambula­ tory activity; accelerometry can be used in almost every target group with relatively low participant burden and has contributed to advanced knowledge in epidemiological activity research over recent years. Accelerosensors measure acceleration—that is the change in velocity over time. They are built into accelerometers, devices that are specially designed to measure acceleration, and into a variety of other products, like smartphones, cars, and cameras. Accelerosensors can The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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be used to describe the frequency, intensity, type, and time of physical activity. Although not the main focus of this chapter, it is important to know that accelerometer signals can also be used to quantify movement characteristics, such as walking speed, step length, and smoothness of movement. Furthermore, most current accelerosensors also measure a com­ ponent of gravitational acceleration; because the magnitude of this component depends on the angular position of the sensor, these accelerosensors can also provide information on the angular position of the body segment the accelerometer is attached to. As a result, accelerosensors can also be used to automatically detect body postures and movements (Chen, Janz, Zhu, & Brychta, 2012). Most modern devices are based on a piezoelectric (sensing element causes displacement of seismic mass), capacitive (proof mass between two electrodes), or piezoresistive (pie­ zoresistors in the cantilever beam and proof mass) microscopic technology (microelectro­ mechanical systems [MEMS]). The main advantages of the new technology are increased sensitivity, reduced size and costs, expanded onboard memory, extended battery life, and wireless communication (Chen et al., 2012). Due to technological advancements, data can be collected for a longer period at higher time resolutions, and unfiltered raw data can be stored instead of integrating and filtering data across time intervals (epochs). This major improvement enhances the comparability of data assessed with different sensors and ena­ bles the reanalyzes of data with improved algorithms because the analysis is conducted offline (Chen et al., 2012). Although technological developments rendered counts as primary accelerometer output unnecessary, many accelerometers still have counts per unit time as their main output. Counts are arbitrary units without a physical meaning, but they express the volume of movement within a defined time interval (epoch). The term “volume” indicates that the number of counts per time interval depends on the amount (or duration) of physical activ­ ity within that time interval and its intensity. Although all of the sensors measure the same underlying construct, namely, acceleration, results are hardly comparable between studies and devices from different manufacturers and even between different generations of the same accelerometer (Chen et  al., 2012). Differences in outcomes are due to differences regarding transducers, sampling frequencies, amplifiers, and filtering (Chen et al., 2012). Thus, when conducting an accelerometer study, it is highly recommended to use a sensor that records unfiltered raw such as acceleration (m/s2) or the gravitational constant (g  =  9.81 m/s2). When measuring raw signals, the sampling and storage rate have to be considered carefully. For example, accelerometers should provide a sampling rate that, according to the Nyquist principle, should be at least twice the highest frequency of assessed movements (Chen et  al., 2012); thus, for example, for an arm movement of 25 Hz, the sampling rate has to be at least 50 Hz. Common accelerometers have sampling rates between 1 and 100 Hz (Chen et al., 2012). In sum, when choosing a suitable accelerometer, the choice should strongly depend on the research question, the topic or outcome of interest, and the target group’s behavioral charac­ teristics of interest (for a comparison in accelerometers, see Yang & Hsu, 2010). For example, children’s movement behavior is spontaneous with short intense intervals of high intensity, while older adults’ movement behavior is characterized by low intensity activity and long peri­ ods of sedentary behavior. Furthermore, factors such as reliability, validity, sensitivity, range, resolution, accuracy, and the precision of a device are important in the decision regarding which accelerometer to use. Finally, aspects such as battery length, capacity for data storage, and options of wear position should be considered (Chen et al., 2012; Matthews, Hagströmer, Pober, & Bowles, 2012).

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Fields of Application Accelerometry can be applied to various fields, for example, public health, elderly/falls research, rehabilitation, and other medical specialities. For the purpose of the present entry, we will focus on exemplary applications in the field of clinical and health psychology. The important role of decreased or increased physical activity in many psychological disor­ ders such as attention deficit hyperactivity disorder (ADHD), schizophrenia, major depres­ sion, or bipolar disorders has been underlined by Tryon in the early 2000s. For example, psychomotor retardation is a defining criterion in major depressive disorders. Volkers et  al. (2003) reported a lower level of motor activity during waking hours and a higher level of motor activity during sleep. Furthermore, the authors observed longer periods of inactivity during waking hours and decreased immobility during sleep. Reichert et al. (2015) detected motor retardation of depressed patients both in gait and gestures. Furthermore, lower levels of motor activity amplitude and frequency indicated a reduced and decelerated activity denot­ ing that patients walked both less and slower. In addition, ambulatory activity monitoring has advanced knowledge regarding situa­ tion‐specific characteristics of hyperactivity in children with ADHD, which could not have been identified with self‐report measures. For example, in the early 1980s, Porrino and colleagues have already found increased activity during structured school tasks but not during free time in children with ADHD compared with healthy children. Teicher (1995) examined the distribution of activities during the day and showed that children with ADHD generally had few periods of low‐level activity. They assumed that ADHD is char­ acterized more by the relative absence of periods of rest than the presence of extreme activity. Using an interactive ambulatory activity feedback system, Tryon, Tryon, Kazlausky, Gruen, and Swanson (2006) were able to decrease motor excess in children with ADHD during school periods. Two systematic reviews of Reed and colleagues between 2006 and 2009 revealed that momentary and regular physical activity have been repeatedly associated with positive mood in studies using a between‐subject perspective and retrospective self‐report measures. By using accelerometry to assess unstructured physical activity and by using electronic diaries to repeat­ edly assess mood within subjects in everyday life, ambulatory assessment studies have recently contributed to advanced knowledge on the activity–affect association. For example, Schwerdtfeger’s studies between 2008 and 2012 revealed that mood was associated with the preceding physical activity level and momentary mood state was associated with subsequent activity level. Furthermore, the activity–affect association appeared to be more complex in inactive people (von Haaren et al., 2013).

Objective Assessment Versus Self‐Report Although many self‐report (e.g., questionnaires) and objective measures (e.g., accelerometers) aim to measure the same construct, namely, the amount or volume of physical activity, they are low to moderately correlated (Prince et  al., 2008). Furthermore, the discrepancies between subjective and objective physical activity data are well illustrated in the results of studies analyz­ ing the achievement of physical activity guidelines suggesting tremendously higher achievement of health‐related exercise targets with subjective measures compared with accelerometer‐based studies (Gabriel, Morrow, & Woolsey, 2012). Gabriel et  al. (2012) have identified several

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aspects that point out why the use of self‐report instruments may induce different results ­compared with objective measures of physical activity: social desirability, reporting bias, misclas­ sification bias and recall bias, differences between self‐reports (with respect to psychometric properties, mode of administration, recall time frame), and differences in physical activity definitions. First, reporting bias and recall bias are relevant problems when using self‐reports, as they involve problems with cognitive processes that are necessary to gather activity information stored in the memory (Gabriel et al., 2012). In general, participants tend to overestimate the amount of physical activity performed (Prince et al., 2008). Second, misclassification bias describes the problems of methods to adequately classify ­participants regarding their physical activity level (e.g., an insufficiently active person is catego­ rized as meeting the guidelines). Therefore, it can lead to the underestimation of activity–health associations (Gabriel et al., 2012). Finally, even differences between self‐report instruments may impact results. For example, an interviewer‐administered questionnaire with a shorter recall time frame may be more useful and valid in older adults, although it may not be practical and valid in another target group (Gabriel et al., 2012). In addition, social desirability that characterizes the tendency to present oneself based on perceived cultural norms was associated with overreporting of physical activ­ ity in self‐report measures (Prince et al., 2008). Instead of questioning the use of self‐report per se, it may be important to understand that questionnaires and objective measures derived from accelerometry sometimes focus on con­ ceptually different aspects of physical activity (Gabriel et al., 2012). For example, “fishing” as a hobby might contribute to the amount of physical activity from the perspective of question­ naires, but will not significantly contribute to the amount of physical activity as captured by activity monitors.

Two Perspectives: Within Versus Between In general, research in physical behavior can focus on a between‐subject or within‐subject perspective. This is important to mention, as there is often confusion about these two per­ spectives. For example, a between‐subject investigation on affect and physical activity can show that persons with a higher activity level have higher mood ratings. However, it does not evidence that a within‐subject relation is true also, which means that a person has higher mood ratings when performing exercise compared with non‐exercise situations. Between‐ subject investigations often focus on the frequency, intensity, type, and time of components of physical behavior (e.g., walking or being upright) and its relationship with health targets. These studies generally consider person‐specific (e.g., age, sex, cultural, and social back­ ground) and environmental (e.g., region, facilities) variables of interest. However, they completely neglect the time dependency of activity. Said more simply, if my interest is in understanding why people do physical exercise, I need to assess in which specific situations they start exercising. Examples for within‐subject investigations are dose–response relations or the reciprocal associations between physical behavior and psychological variables such as stress, emotions, self‐concept, motives, and barriers to being physically active. Furthermore, physical behavior is addressed and evaluated in intervention studies and in studies that exam­ ine physiological variables that depend on or are confounded by physical activity, such as heart rate or heart rate variability.

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Measurement Issues Number of Measurement Days Although it is known that physical activity varies between days, it is unclear how many days are exactly needed to account for between‐day variability. The results of studies that tried to iden­ tify how many assessment days are necessary vary from 3 to 5 days in adults and 4–9 days in children (Trost, Mciver, & Pate, 2005) or from three 24 hr periods to 10 days or 2 weeks. Because physical activity varies between weekend and weekdays, at least one weekend day is required (Trost et al., 2005). It must be noted, however, that many factors will affect between‐ day variability and—because of that—the number of measurement days. Examples of this are the capacity of a person to vary his/her physical behavior (e.g., due to type of work, a person’s health/fitness) and the physical behavior outcome of interest; some outcomes (e.g., ratio of usage left/right arm) might be more stable between days than others (e.g., number of steps taken).

Data Processing Current software for initializing accelerometers generally allow user‐friendly setting of start and end time. Depending on the research question, some authors recommend programming the start of recording at midnight of the first day the sensor has to be worn (Matthews et al., 2012). Even though data processing of accelerometer data is much easier nowadays, it is important to realize that initializing accelerometers and analyzing data still need some time and, more importantly, requires skilled personnel (Matthews et al., 2012). Data check To detect problems during the course of a study and eventually be able as a researcher to repeat data collection, data should be controlled in terms of quantity and quality immediately after data recording (Heil, Brage, & Rothney, 2012). The quality of accelerometer data can be limited by implausible data (e.g., due to malfunction of the sensor, outliers exceeding specific thresholds, tampering by participants) and missing data over a predetermined time interval (Chen et al., 2012; Heil et al., 2012). Today, preset user settings of the software can support the identification of these data quality aspects. Non‐wear periods To identify periods during which the sensor was not worn, different recommendations are reported in the literature: non‐wear times can be detected based on thresholds of a predeter­ mined number of counts (e.g., zero counts over a predetermined time period). Recommended time periods to consider for zero counts vary considerably between 10 and 90 min (Choi, Liu, Matthews, & Buchowski, 2011). The identification of non‐wear periods is not trivial, as zero counts can have different reasons such as not only sleep or sitting quietly or malfunction of the sensor but also removal of the sensor without a reason, or due to specific activities that do not allow wearing the sensor, such as swimming, other water activities, or showering (sensors are not waterproof) or heavy contact sports (risk of breaking; Mâsse et al., 2005). Only removal of the sensor would be “true” non‐wear time. To be able to identify times during which the sensor was not worn (see Data check) and to facilitate the interpretation of the accelerometer data, we suggest that participants should record the following information depending on the research question: time at which the

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s­ ensor is removed in the evening, bed/sleep time, wake time in the morning, time of attach­ ment of the sensor in the morning, and times and activities during which the sensor was removed during the day. Identifying a valid day Researchers should think about how many hours of wear time they consider reasonable. Studies often require a wear time of 10 hr/day as a precondition for a valid day (Mâsse et al., 2005). In children, the required wear time for a valid day can be reduced by trend due to shorter waking times (Mâsse et al., 2005). In addition, it might be reasonable to differentiate between weekend and weekdays in terms of required wear time for a valid day (Mâsse et al., 2005). Using data imputation to account for invalid days, the values of valid days and those of partly valid days are used to extrapolate values for the invalid days (Mâsse et al., 2005).

Outcome Variables With the development of saving unfiltered raw acceleration data, accelerometry allows gener­ ating manifold outcome variables ranging from counts to movement‐specific or gait‐specific variables (e.g., walking speed), energy expenditure, body posture, and movement categories such as walking, jogging, standing, climbing stairs, etc. While ambulatory monitoring is an objective technique to assess physical activity, posture, and movement, it is not automatically guaranteed that the method generates reliable and valid outcome variables. Thus, a major challenge of ambulatory activity monitoring is to operationalize parameters that truly, uniquely express the aspect of physical behavior that is of interest. For example, in a literature review in 2006 on biomechanical exposure at work, Mathiassen pointed out that an overall measure of activity level may not reflect the negative effect of repetitive operations (behavior) at work on musculoskeletal disorders. Instead, the distribution (variation) of specific activities over the day is interesting (see Suggested reading). Several categorizations exist, but Heil et al. (2012) suggest the following categorization of outcome variables: • Movement‐based volume variables are variables that are not transformed into physio­ logical units, for example, acceleration in standard physical units (g, m/s2), counts/min, counts/day. • Time‐based amount variables (minutes of moderate to vigorous physical activity [MVPA]/ day, sedentary behavior/day). • Energy expenditure‐based variables (total EE/day, AEE/day, EE during MVPA). • Activity‐based variables (steps, walking time, jogging/week).

Influencing Factors To account for between‐day variability, we mentioned considering the minimum number of measurement days in the “number of measurement days” section above. However, there are multiple systematic factors influencing physical activity behavior in everyday life. Besides influencing factors such as age, gender, weight, sociocultural status, and many others that are assessed on the between‐person level, there are also aspects that have to be considered on a within‐subject level when conducting ambulatory activity monitoring. First, physical activity levels are higher or lower depending on the weather (and the temperature) and the

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season. Usually people are more physically active during spring and summer compared with autumn and winter. Second, physical activity levels vary between weekend and weekdays (Trost et al., 2005) and can also vary between vacations and times during which individuals have to work or go to school (Heil et al., 2012). Furthermore, the placement of the monitor is not only relevant to gather the behavior of interest but also a factor that can influence the results (Trost et al., 2005). Placement at the waist is recommended for whole body ambula­ tory activities of the large skeletal muscles, such as walking during everyday life, because a placement close to the center of mass is supposed (Yang & Hsu, 2010). Other placement options such as thigh (sedentary behavior/posture, differentiation between sitting and standing), wrist (detect subtle activity during sleep), or ankle (step count and gait pattern detection) can be used depending on the research question (Matthews et al., 2012; Yang & Hsu, 2010).

Reliability, Validity, and Reactivity Ambulatory activity assessment can improve both validity and generalizability, as it enables the assessment of real behavior in the natural environment instead of assessing behavior in an arti­ ficial laboratory setting. Looking at the literature reveals, unfortunately, considerable discrep­ ancies regarding validity and reliability. For example, the results for reliability of accelerometers obtained in laboratory settings have been reported to be high with values between 97.5 and 99.4% in studies by Tryon. However, Esliger and Tremblay (2006) examined the reliability of three common accelerometers and found high discrepancies between devices in intra‐ and inter‐instrument variability (range: CV = 0.5 to >40%). Results of validation studies show that accelerometers under‐ or overestimate energy expenditure of specific activities depending on the investigated target group, the type of devices that were used, and the activities that had to be performed in these studies (Calabró, Lee, Saint‐Maurice, Yoo, & Welk, 2014; Trost et al., 2005). Accelerometers still show con­ siderable discrepancies with the criterion measures such as indirect calorimetry in low intensity activities, a domain that is of growing interest for researchers (Calabró et  al., 2014). Furthermore, postures and movement patterns are misclassified when patterns are similar such as in walking and climbing stairs and in situations involving various patterns at the same time. From a methodological perspective, the validity outcomes of accelerometers depend on the validation protocols (how extended the protocols are; real life vs. laboratory), the population specificity, the reference instruments and outcome variables (energy expenditure, posture, activity type classification), the accelerometer device, the psychometric topic of study, and the applied statistics. Definitely, more studies are necessary to obtain a more extensive picture on these issues. Common problems regarding validity in monitoring movement behavior in eve­ ryday life are (a) a lack of insight into the setting, (b) the control of confounding variables, (c) personal background and motivation for performing or refraining from activities, and (d) sen­ sor placement. For example, irregular walking patterns may either represent a lack of balance while walking on a flat surface or an appropriate adaptation to an uneven pavement. In addition, an upcom­ ing exam can cause a low activity level as can laziness of a person. Furthermore, various move­ ments cannot be detected adequately or require additional sensors, such as arm movements, cycling, or swimming. Moreover, the assessment of static activities, the carrying of additional load, and the differentiation between sedentary behavior and activities of low intensity are

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problematic. For example, the differentiation between sitting and standing requires either various sensors, an integrated inclinometer or the placement of one sensor at the thigh that in turn induces less accurate data for whole body movements. Thus, the combination of acceler­ ometry with electronic diaries to assess context information such as temperature, social con­ tact, location, or light may help to identify situations and interpret the observed behavior (Ebner‐Priemer, Koudela, Mutz, & Kanning, 2013). The findings for reactivity to ambulatory activity monitoring are heterogeneous as well. For example, in one of Ebner‐Priemer’s studies in 2007, participants reported that they were mini­ mally affected by 24‐hr ambulatory activity monitoring. In addition, participants’ ratings of distress and reactivity caused by the device were minimal. Several studies of Clemes and col­ leagues between 2008 and 2009 revealed that wearing an unsealed pedometer and displaying daily steps induced the highest reactivity compared with wearing an unsealed pedometer with­ out displaying recorded steps or wearing a sealed pedometer. Dösegger et al. (2014) revealed an overall reactivity effect of approximately 5% in children and adolescents in response to accelerometers, while they reported no reactivity in other samples, for example, in a young active student sample. Additionally, van Sluijs, van Poppel, Twisk, and van Mechelen (2006) reported measurement effects in response to self‐report but not in response to accelerometer measures in a randomized controlled trial. Furthermore, wearing a multichannel system did not affect the amount of wheelchair driving in patients with spinal cord injuries; however, patients reported some burden (Bussmann et al., 2010). Due to the heterogeneous findings, it is not trivial to give general recommendations regard­ ing reactivity. Allowing for at least 1 day of familiarization and a random assignment of start days, as recommended by Dösegger et al. (2014) when measuring habitual physical activity with accelerometry, it might not be feasible for a highly burdensome multiple device assess­ ment procedure, which sometimes even lasts less than 24 hr. However, interviewing the par­ ticipants after the monitoring period about burden and reactivity might be helpful.

The Future of Ambulatory Activity Monitoring in Psychology: Combining Accelerometry With Electronic Diaries For psychology research, the association between psychological variables such as motiva­ tional processes, habits, and/or affective states is important to explain physical behavior in everyday life. Researchers often face the problem that the behavior of interest is rare: many people perform few active episodes because they are mainly sedentary. From a statistical point of view, a minimal number of active episodes are required to obtain sufficient variance or covariation. Recent studies addressing the association between affective states and physi­ cal activity in everyday life found a significant association between positive affect and physi­ cal activity but did not find an association between negative affect and physical activity. These results may be explained by different time courses of positive affect, negative affect, and physical activity, more specifically the effects on negative affective states that might not be parallel but shifted. Activity‐triggered e‐diaries, sometimes also called interactive multimodal ambulatory monitoring, may be advantageous to understanding the relationship between actual physical behavior and affective states. By changing the affective state assessment strategy from a ran­ dom or fixed mode to an interactive mode, interesting episodes are detected in real time and are used as triggers for the assessment of psychological variables and contextual information. Using this approach, variance can be enhanced, as affective states can be assessed during

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episodes of high physical activity as well as during episodes of low physical activity (Ebner‐ Priemer et al., 2013). Due to the fast technological developments in information technology and telemedicine, the assessment of physical behavior using accelerometers and connecting them with other sen­ sors, smartphones, and technical applications is in its infancy. Multiparametric sensor fusion and the use of additional signals such as skin temperature can deliver useful information (e.g., whether a sensor is worn or not) and thus enhance the differentiation between inactivity and compliance. Additional contextual information that can be obtained via smartphones or Global Positioning System (GPS) signals delivers useful information about the situation, the social interaction, or the interaction with other devices. Since some activities take place at specific locations, activity classification can be improved. The various functions of smartphones such as video and audio options will offer unlimited opportunities for researchers but will also demand a reconsideration of how participants can be protected in terms of ethical fundamentals in future studies.

Author Biographies Birte von Haaren‐Mack is a postdoctoral research associate at the Department of Health and Social Psychology of the German Sport University Cologne. The focus of her research is exam­ ining psychophysiological effects of physical activity on stress and the relationship between physical activity and affect in everyday life; to assess these constructs in everyday life, she uses the methodology of ambulatory assessment, especially electronic diaries, accelerometry, and ambulatory ECG. Johannes B.J. Bussmann is an associate professor at the Department of Rehabilitation Medicine, Erasmus MC, Rotterdam, Netherlands. The focus of his research is understanding the consequences of chronic conditions on physical behavior, the development and validation of objective devices and outcomes related to physical behavior, and the development and evaluation of treatment strategies. He is the president of the International Society for the Measurement of Physical Behavior (ISMPB) and board member of the Society of Ambulatory Assessment. Ulrich W. Ebner‐Priemer is a professor at the Institute for Sport and Sport Science at the Karlsruhe Institute of Technology (KIT), Germany. The focus of his research is characterized by its methodological focus on ambulatory assessment. Phenomena of interest are studied in everyday life (real life) in real time using psychophysiological methods (objective) and time‐ sensitive analysis (dynamics). He is currently the president of the SAA.

References Bussmann, J. B. J., Kikkert, M. A., Sluis, T. A. R., Bergen, M. P., Stam, H. J., & Vaccaro, A. R. (2010). Effect of wearing an activity monitor on the amount of daily manual wheelchair propulsion in per­ sons with spinal cord injury. Spinal Cord. doi:http://doi.org/10.1038/sc.2009.72 Calabró, M., Lee, J., Saint‐Maurice, P. F., Yoo, H., & Welk, G. J. (2014). Validity of physical activity monitors for assessing lower intensity activity in adults. International Journal of Behavioral Nutrition and Physical Activity, 11(1), 119. http://doi.org/10.1186/s12966‐014‐0119‐7 Chen, K., Janz, K. F., Zhu, W., & Brychta, R. J. (2012). Redefining the roles of sensors in objective physical activity monitoring. Medicine and Science in Sports and Exercise, 44(Suppl 1), S13–S23. http://doi.org/10.1249/MSS.0b013e3182399bc8

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Choi, L., Liu, Z., Matthews, C., & Buchowski, M. (2011). NIH public access. Medicine & Science in Sports & Exercise, 43(2), 357–364. http://doi.org/10.1249/MSS.0b013e3181ed61a3 Dösegger, A., Ruch, N., Jimmy, G., Braun‐Fahrländer, C., Mäder, U., Hänggi, J., … Bringolf‐Isler, B. (2014). Reactivity to accelerometer measurement of children and adolescents. Medicine & Science in Sports & Exercise, 46(6), 1140–1146. http://doi.org/10.1249/MSS.0000000000000215 Ebner‐Priemer, U. W., Koudela, S., Mutz, G., & Kanning, M. (2013). Interactive multimodal ambulatory monitoring to investigate the association between physical activity and affect. Frontiers in Psychology, 3, 596. http://doi.org/10.3389/fpsyg.2012.00596 Esliger, D. W., & Tremblay, M. S. (2006). Technical reliability assessment of three accelerometer models in a mechanical setup. Medicine and Science in Sports and Exercise, 38(12), 2173–2181. http://doi. org/10.1249/01.mss.0000239394.55461.08 Gabriel, K. K. P., Morrow, J. R., & Woolsey, A. L. T. (2012). Framework for physical activity as a com­ plex and multidimensional behavior. Journal of Physical Activity & Health, 9(Suppl 1), S11–S18. Heil, D. P., Brage, S., & Rothney, M. P. (2012). Modeling physical activity outcomes from wearable monitors. Medicine and Science in Sports and Exercise, 44, 50–60. http://doi.org/10.1249/ MSS.0b013e3182399dcc Mâsse, L. C., Fuemmeler, B. F., Anderson, C. B., Matthews, C., Trost, S. G., Catellier, D. J., & Treuth, M. (2005). Accelerometer data reduction: A comparison of four reduction algorithms on select out­ come variables. Medicine and Science in Sports and Exercise, 37, S544–S554. http://doi. org/10.1249/01.mss.0000185674.09066.8a Matthews, C., Hagströmer, M., Pober, D. M., & Bowles, H. R. (2012). Best practices for using physical activity monitors in population‐based research. Medicine and Science in Sports and Exercise, 44(Suppl 1), S68–S76. http://doi.org/10.1249/MSS.0b013e3182399e5b Prince, S., Adamo, K., Hamel, M., Hardt, J., Gorber, S., & Tremblay, M. (2008). A comparison of direct versus self‐report measures for assessing physical activity in adults: A systematic review. International Journal of Behavioral Nutrition and Physical Activity, 5, 56. http://doi.org/10.1186/1479‐5868‐5‐56 Reichert, M., Lutz, A., Deuschle, M., Gilles, M., Hill, H., Limberger, M. F., & Ebner‐Priemer, U. W. (2015). Improving motor activity assessment in depression: Which sensor placement, analytic strategy and diurnal time frame are most powerful in distinguishing patients from controls and monitoring treatment effects. PLoS One, 10(4), e0124231. http://doi.org/10.1371/journal. pone.0124231 Teicher, M. H. (1995). Actigraphy and motion analysis: New tools for psychiatry. Harvard Review of Psychiatry, 3(1), 18–35. Trost, S. G., Mciver, K. L., & Pate, R. R. (2005). Conducting accelerometer‐based activity assessments in field‐based research. Medicine and Science in Sports and Exercise, 37(Suppl. 11), 531–543. http://doi.org/10.1249/01.mss.0000185657.86065.98 Tryon, W. W., Tryon, G. S., Kazlausky, T., Gruen, W., & Swanson, J. M. (2006). Reducing hyperactivity with a feedback actigraph: Initial findings. Clinical Child Psychology and Psychiatry, 11(4), 607–617. http://doi.org/10.1177/1359104506067881 van Sluijs, E. M. F., van Poppel, M. N. M., Twisk, J. W. R., & van Mechelen, W. (2006). Physical activity measurements affected participants’ behavior in a randomized controlled trial. Journal of Clinical Epidemiology, 59(4), 404–411. http://doi.org/10.1016/j.jclinepi.2005.08.016 Volkers, A. C., Tulen, J. H. M., Van Den Broek, W. W., Bruijn, J. A., Passchier, J., & Pepplinkhuizen, L. (2003). Motor activity and autonomic cardiac functioning in major depressive disorder. Journal of Affective Disorders, 76(1–3), 23–30. http://doi.org/10.1016/S0165‐0327(02)00066‐6 von Haaren, B., Loeffler, S. N., Haertel, S., Anastasopoulou, P., Stumpp, J., Hey, S., & Boes, K. (2013). Characteristics of the activity‐affect association in inactive people: An ambulatory assessment study in daily life. Frontiers in Psychology, 4(April), 163. http://doi.org/10.3389/fpsyg.2013.00163 Yang, C.‐C., & Hsu, Y.‐L. (2010). A review of accelerometry‐based wearable motion detectors for physical activity monitoring. Sensors, 10(8), 7772–7788. http://doi.org/10.3390/s100807772

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Suggested Reading Clemes, S. A., & Parker, R. A. A. (2009). Increasing our understanding of reactivity to pedometers in adults. Medicine and Science in Sports and Exercise, 41(21), 674–680. http://doi.org/10.1249/ MSS.0b013e31818cae32 Crouter, S. E., Churilla, J. R., & Bassett, D. R. (2006). Estimating energy expenditure using accelerom­ eters. European Journal of Applied Physiology, 98, 601–612. http://doi.org/10.1007/s00421‐ 006‐0307‐5 Ebner‐Priemer, U. W., & Trull, T. J. (2009). Ambulatory assessment: An innovative and promising approach for clinical psychology. European Psychologist, 14(2), 109–119. http://doi.org/ 10.1027/1016‐9040.14.2.109 Mathiassen, S. E. (2006). Diversity and variation in biomechanical exposure: What is it, and why would we like to know? Applied Ergonomics, 37(4), 419–427. http://doi.org/10.1016/j. apergo.2006.04.006 Tryon, W. W. (2006). Activity measurement. In M. Hersen (Ed.), Clinician´s handbook of adult behavioral assessment (pp. 85–120). New York, NY: Academic Press.

Physical and Social Pain: Twin Challenges to Well‐Being in Adult Life John A. Sturgeon1, Alex J. Zautra2, and Beth D. Darnall3 Center for Pain Relief, University of Washington, Seattle, WA, USA Psychology Department, Arizona State University, Tempe, AZ, USA 3 Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA 1

2

Your pain is the breaking of the shell that encloses your understanding. —Kahlil Gibran

Shared Mechanisms Between Social and Physical Pain Pain is a multidimensional construct, consisting of sensory, emotional, and cognitive aspects. Given its complex nature, the experience of pain is subject to a variety of physical and psychological factors. Additionally, pain has a profound and mutually influential relationship with the social world. Social factors may be considered a “double‐edged sword” in the context of pain. For instance, social factors have the capacity to increase vulnerability to pain‐related dysfunction or to contribute to healthier responses to pain. Recent evidence supports the model of shared neural activation between nonphysical pain arising from social causes (e.g., exclusion, ostracism, and loss) and physical pain. In a seminal study, Eisenberger, Lieberman, and Williams (2003) reported that, after being exposed to social exclusion experimentally, participants demonstrated greater levels of activation of both the dorsal anterior cingulate cortex (ACC) and the right ventral prefrontal cortex, two brain regions previously associated with pain‐related emotional distress. Subsequent research has also implicated the anterior insula, which appears to play a key role in the experience of physical and social pain (Eisenberger, 2012). Craig (2015) has found substantial evidence that pain is an emotion that is indicative of the brain’s self‐regulatory response to disturbance in homeostasis. Like other feelings, pain has both sensory and motor components integrated within the

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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anterior insula and adjacent neural structures. These patterns of activation have been i­ mplicated in social rejection in experimental and real‐life settings. For instance, individuals who had recently been rejected by a romantic partner evidenced neural activation similar to physical pain when shown pictures of the person who had rejected them (Kross, Berman, Mischel, Smith, & Wager, 2011). Additionally, similar patterns of regional activation have been noted in individuals viewing pictures of recently lost loved ones, suggesting that social pain is not specific to social rejection and can include other socially based sources of distress, such as loss. In essence, when we refer to hurt that arises from social interactions, we are not speaking metaphorically. Social and physical pain belong within the same family of neurophysiological responses that alert the mind to modify its current status. Researchers have explored the potential evolutionary value of social pain, suggesting that social pain may have promoted physical safety: if an organism is separated from its group, it is exposed to greater predatory and other environmental threats (MacDonald & Leary, 2005). Thus, exclusion or rejection from one’s group may have developed to signal imminent danger, similar to the experience of acute physical pain, which signals potential tissue damage and serves a biologically necessary purpose. Linguistically, socially painful experiences are described similarly to physical pain, suggesting that they are often been experienced in a similar fashion. Importantly, these theoretical assertions have been supported by experimental data. For instance, social exclusion has been found to promote greater physical pain (Eisenberger, Jarcho, Lieberman, & Naliboff, 2006). Furthermore, in this same study, baseline cutaneous heat pain sensitivity was directly related to the level of social distress after exclusion. This bidirectional relationship provides further evidence that, in the evolutionary sense, prosocial ties confer safety and that the loss of social relationships is indexed neurally by the human “alarm system”—pain—to signal a threat. The immune system is implicated in social and physical pain, thereby revealing an additional shared mechanism. Indeed, pro‐inflammatory cytokines—markers of the immune system— have been associated with physical pain and may underlie mood difficulties in chronic pain. The inflammatory response is a primary biological response to tissue damage and serves to protect a wound through tissue swelling, slowing use of damaged areas with pain registers, and slowing psychomotor energy expenditures through signaling fatigue. Responding as if the body were harmed, increased circulation of pro‐inflammatory cytokines has been found following social stressors such as social exclusion (Slavich, Way, Eisenberger, & Taylor, 2010). Additionally, researchers found that the pro‐inflammatory cytokine interleukin‐6 (IL‐6) mediated the relationship between social exclusion and later feelings of depression (Eisenberger, Inagaki, Rameson, Mashal, & Irwin, 2009). Interestingly, when inflammation was induced in participants via exposure to an endotoxin, they reported greater feelings of social disconnection and depression (Eisenberger, Inagaki, Mashal, & Irwin, 2010), again underscoring a bidirectional relationship between a physiological response to a threat or harm (inflammation) and social exclusion. In fact, these authors reported that the relationships between inflammatory markers and depressed mood were fully explained by perceived social disconnection. Recent intervention studies showed that targeting these shared underlying systems may address both physical and social pain. DeWall and colleagues (Dewall et al. 2010) found that a 3‐week course of acetaminophen, a common nonsteroidal anti‐inflammatory analgesic thought to act on the central nervous system, also decreased feelings of social pain in response to social exclusion through reduced activity in the dorsal ACC and anterior insula. Individuals facing chronic social stress or isolation may be vulnerable to reliance on analgesic opioids to cope with this social pain. Indeed, polymorphisms of the μ‐opioid receptor gene, traditionally implicated in physical pain processes, have been found to be predictive of greater social pain in response to

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exclusion, as well as predicting greater ratings of physical pain unpleasantness (Way, Taylor, & Eisenberger, 2009). Accordingly, a greater emphasis on social therapeutic targets may be most effective for the dual purpose of treating pain and mitigating addiction risks.

Social Contributors to Vulnerability and Distress in Chronic Pain Traditionally, pain studies have focused on intraindividual processes, such as cognition and emotion. However, several factors originating in the social environment may alter both the experience of pain and subsequent coping responses to pain. It has been suggested that one of the etiologic factors in chronic physical pain may be chronic social pain, caused by ostracism, rejection, conflict, or isolation. These factors may increase vulnerability to pain and heightened emotional or behavioral responses to pain. For example, feeling ostracized or rejected may contribute to greater pain intensity following noxious thermal cutaneous stimulation (Eisenberger et  al., 2006). Feelings of loneliness also appear to predispose individuals to greater pain by increasing negative thoughts about pain. Similarly, individuals with chronic pain who face more frequent stressful or conflictual interactions with others tend to report greater pain intensity (Faucett & Levine, 1991). Further, specific interpersonal communications may exacerbate pain: receiving social feedback of a personal failure predicts greater pain intensity than feedback of success after an intelligence test (Hout, Vlaeyen, Peters, Engelhard, & Hout, 2000). Thus, the presence of social stress or conflict appears to have negative implications for pain perception. The consequences of negative social factors may also have long‐term consequences by altering individual trajectories of the development of pain. Perhaps the best‐known example of this phenomenon is the effect of significant social stressors in early life; exposure to traumatic events, particularly episodes of abuse, predisposes individuals to risks for future chronic pain (Davis, Luecken, & Zautra, 2005) and to medically unexplained pain later in life. Although the effect sizes from meta‐analytic studies are relatively modest, the effect is nevertheless robust across studies and populations (Davis et al., 2005). It has been hypothesized that early and severe social stressors may sensitize the central nervous system, thereby kindling a diathesis for poorer outcomes later in life (Rutten et al., 2013). Alternatively, early life trauma may increase exposure to future stressful social environments, thereby prolonging these negative effects (Davis et al., 2005). Though the exact mediators are unclear, exposure to early and severe social stress appears to have profound implications for pain, both immediately and in the long term. In addition to the direct effects of social stress on pain perception, the social environment may indirectly predispose individuals to poorer pain outcomes through changes in behavior, cognition, and emotional states. Feelings of social exclusion undermine effective coping strategies and self‐esteem and potentiate aggression against others. Exclusion also discourages prosocial behavior and efforts to reconnect with others after exclusion, thereby entrenching a pattern of social avoidance that compromises the potential benefits of an extant social support network (Maner, DeWall, Baumeister, & Schaller, 2007). Notably, these patterns have effects lasting beyond the initial social struggles; similar to “pain memories,” the recall of a socially painful experience may be sufficient to inspire social and emotional pain, increase feelings of aggression, and lead an individual to view a social relationship less favorably (Klages & Wirth, 2014). These findings are notable, as prior evidence suggests that a possible mechanism ­promoting long‐term vulnerability to mood disturbance in chronic pain is a heightened and persistent fear of rejection (Ehnvall, Mitchell, Hadzi‐Pavlovic, Malhi, & Parker, 2009).

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Social threat has been a recent focus of empirical inquiry, particularly with regard to how it affects pain‐related learning and coping. At present, the evidence in this area is mixed. Some experimental studies have found that a threatening social context, defined as a situation in which environmental cues signal either social threat (e.g., angry faces) or potential physical pain, facilitates the acquisition of pain‐related fear (Karos, Meulders, & Vlaeyen, 2015). However, the fear elicited from these social contexts may have domain‐specific effects: in an experimental setting, fear of physical pain, but not fear of social pain, was significantly predictive of greater pain intensity ratings in response to a cold water pain induction task, while only fear of social pain was found to predict social pain ratings after a social exclusion paradigm (Riva, Williams, & Gallucci, 2014). In both of these studies, the effects of a threatening social context had indirect effects on pain perception. More specifically, it was fear of pain, rather than general perception of a threat, that predicted increased pain intensity. These studies highlight the importance of proximal factors, such as current mood states, in the pain experience. Social problems are known risk factors for mood dysregulation, higher levels of depression, anger, and negative affect (see Sturgeon, Zautra, & Arewasikporn, 2014). In turn, these negative emotional states may increase pain perception; this effect has been noted primarily for high‐activation negative emotional states such as fear, anxiety, and anger. There is also evidence for a potentiating effect of depression on pain intensity, though this effect may be due to cognitive appraisals of pain, such as viewing pain in a catastrophic light (Berna et al., 2010). The mechanisms underlying the effects of negative mood on pain remain undefined but have been theorized to include increased muscle tension and overtaxing of the endogenous opioid system, which is responsible for pain perception and control in the body. In addition to interpersonal conflict or threat, other social risks factors, such as limited or absent social resources or relationships, contribute to worsened pain and poorer long‐term health for individuals with chronic pain. Chronic pain is associated with a susceptibility to social withdrawal (avoidance) when pain is high, which may decrease exposure to potential sources of positive social engagement, worsening the emotional consequences of pain (see Sturgeon et al., 2014). Isolation is also a risk factor for several pain‐relevant difficulties, such as depression and decreased physical function. More generally, there is also evidence that loneliness may affect genome‐wide transcriptional activity, leading to dysregulated glucocorticoid activity and potentially predisposing individuals facing chronic difficulties with loneliness to long‐term inflammatory disease (Cole et al., 2007). Given that inflammatory dysregulation may contribute to difficulties with chronic pain in some instances, social isolation may be a significant risk factor for both the incidence of chronic pain and poorer adaptive responses once chronic pain has developed. In sum, social stressors, whether due to social conflict or challenge or to a lack of positive social interactions, constitute a significant source of vulnerability for individuals facing pain in the short and long term.

Social and Environmental Factors Promoting Effective Pain Adaptation Although challenges in one’s social environment can worsen the experience of pain, there is also evidence that the social world can promote positive adaptation to pain. Resilience to pain has been defined by an individual’s capacity to effectively maintain meaningful function, recover from the deleterious effects of pain, and develop new capacities or knowledge after managing pain for a period of time (see Sturgeon & Zautra, 2010). A key contributor to resilience is social relationships, and earlier definitions highlighted the importance of individuals’

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utilization of social resources to recover from stress. In this vein, the frequency, nature, and quality of social interactions can powerfully affect an individual’s ability to cope with chronic pain. An important aspect of pain resilience is positive emotion, which is often fostered through positive and meaningful engagements with other people. Positive affect has been shown to promote resilient coping with chronic pain, as it has demonstrated myriad benefits for individuals experiencing acute or chronic pain. Positive emotions facilitate recovery from stress, bolster immune function, and improve cognitive flexibility and problem solving under stress (Fredrickson, 2001). Positive emotions have also been found to attenuate both clinical and experimental pain and to protect against pain catastrophizing (Hood, Pulvers, Carrillo, Merchant, & Thomas, 2012). Importantly, positive affect confers enduring protection, as it may inhibit the generalization of pain‐related fear, a known risk factor in the prolongation of pain from acute to chronic states. Thus it stands to reason that individuals who effectively glean positive emotions from their relationships with others may be more resistant to the deleterious effects of pain. Indeed, positive or enjoyable interpersonal events have been shown to increase positive affect and protect against later states of fatigue (see Sturgeon et  al., 2014). Furthermore, extraversion, the personality trait most closely related to positive social engagement, is predictive of more effective pain coping in arthritis populations and has been shown to contribute to resilience in arthritis populations. The positive effects of social factors may also directly influence pain experience and coping. In general, individuals who are more willing and able to seek out social support tend to exhibit greater individual resilience and report greater life satisfaction and lower levels of depression. Individuals with chronic pain also report reduced pain and show less activation of the central nervous system during a pain induction procedure when they are in the presence of their significant other (Montoya, Larbig, Braun, Preissl, & Birbaumer, 2004). These effects are observed even in the absence of a significant other: viewing a photo of a loved one has been shown to dampen ratings for experimentally induced pain (Eisenberger et al., 2011; Younger, Aron, Parke, Chatterjee, & Mackey, 2010). The potent effect of love on pain may be attributable to the attenuation of the common neural networks underlying physical and social pain, the engagement of neural networks associated with reward (Younger et al., 2010), or the interpretation of a loved one as a sign of physical safety (Eisenberger et al., 2011). Social support may also engage prefrontal cortex activity associated with prosocial behavior, which may serve as a protective factor against pain by buffering pain‐relevant negative emotional states (Onoda et al., 2009). In the long term, social support also appears to have a protective effect, as individuals with rheumatoid arthritis receiving greater levels of social support report less pain and functional disability 5 years later (for a review, see Evers, Zautra, & Thieme, 2011). The effects of social support are decidedly more nuanced than they might initially appear, however. Beyond the mere availability of friends and family who may provide support during periods of stress, an individual’s perception of the quality of their social relationships may influence or determine whether such support fosters resilient coping. While both the quality and quantity of social support predict successful adaptation to pain in fibromyalgia, the quality of social support appears to play a greater role in determining physical and psychological functionality (Franks, Cronan, & Oliver, 2004). A social network that is too large or focused on aiding an individual in coping with their pain may actually reduce self‐sufficiency and long‐ term pain coping (Franks et al., 2004). Indeed, certain individuals may value extra emotional or instrumental support over the reduction of pain or emotional distress. Thus, individuals who value social validation above continued meaningful function may successfully achieve these goals and inadvertently lead themselves to less adaptive methods of coping with pain.

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Indeed, women with fibromyalgia whose primary goals relate to social validation (rather than self‐sufficiency) report lower levels of perceived social support, higher levels of interpersonal conflict, and more negative life events (Hamilton, Karoly, & Zautra, 2005). Such findings point to the need to assess each individual’s social values, needs, and priorities within the context of pain. Similarly, the quality of interactions with one’s social network plays a meaningful role in the promotion of effective pain adaptation. When solicitous responses to pain are more frequent (i.e., asking a spouse for help in managing pain), individuals with chronic pain commonly demonstrate greater vulnerability to disability, greater pain intensity, and poorer overall adjustment to pain. Conversely, more mindful and empathic spousal responses are associated with greater perceptions of spousal support. Though this area of research is relatively nascent, the value of emotional support may be enhanced by mutual prosocial behavior between an individual coping with pain and a loved one. This type of interaction is predictive of greater well‐ being, lower distress, and greater perceptions of relationship quality for both members of a romantic relationship.

Social Interventions for People with Chronic Pain Can the troubled social relations of those with chronic pain conditions be reversed and the potential for positive social relations realized through carefully designed interventions? In some quarters the prevalent thought is that capacity for social connections is fixed in childhood, or at the latest, in early adulthood. Others have argued that social development can be advanced even into adulthood (e.g., Division of Behavioral and Social Research, National Institute on Aging, 2013; https://www.nia.nih.gov/research/dbsr). A research team led by one of the authors of this chapter (www.socialintelligenceinstitute.org) has developed an online social intelligence (SI) intervention that focuses on enhancing capacities of adults to develop the skills to sustain positive social relationships and the awareness of the value of sustainable social connections. The SI training program has been guided by the objective of the humanization of relationships, with lesson plans informed by latest evidence from social neuroscience and related fields with attention to cognitions that facilitate healthy social connections. However, the approach extends beyond cognitive models and behavioral principles to include attention to evidence of barriers to social–emotional development from adverse experiences in childhood and adult life and ways to move beyond those stressful experiences. Recent empirical evidence shows this type of program offers a promising resource for chronic pain patients and those who care for them (Zautra, Zautra, Gallardo, & Velasco, 2015).

Summary Though models of pain have evolved over the years to incorporate a greater number of nonbiological processes, including cognition, emotion, and behavior, relatively less attention has been paid to social factors that may influence perception and adaptation to pain. The social environment is a powerful influence on the experience of pain and may either promote or inhibit effective coping, depending on the circumstances. As the understanding of the neural, genetic, biological, and psychological factors at play in pain becomes more sophisticated, future models of pain and its relationships to all aspects of human functioning can be further enhanced by acknowledging the social context in which pain is experienced.

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Author Biographies John A. Sturgeon is an acting assistant professor and clinical psychologist at the Center for Pain Relief at the University of Washington Medical School. His research examines the physiological, psychological, and social factors that contribute to resilience and vulnerability in chronic pain and stress, as well as psychological interventions designed to promote effective adaptation to chronic pain. Alex J. Zautra was a professor of clinical psychology at Arizona State University Foundation. He dedicated his career to the study of resilience and ways of enhancing adaptation to stress. His latest work included the development of interventions that further the humanization of social relations. His most recent book, with coauthors Reich and Hall, is Handbook of Adult Resilience by Guilford Press. He also authored Emotions, Stress, and Health published by Oxford University Press. Beth D. Darnall is a clinical associate professor in the Department of Anesthesiology, Perioperative, and Pain Medicine in the Stanford University School of Medicine and faculty in the Stanford Systems Neuroscience and Pain Laboratory. Her research broadly focuses on psychosocial dimensions of pain, characterizing the psychophysical impacts of pain‐related stressors and developing targeted psychological interventions. She is the author of Less Pain, Fewer Pills (Bull Publishing).

References Berna, C., Leknes, S., Holmes, E. A., Edwards, R. R., Goodwin, G. M., & Tracey, I. (2010). Induction of depressed mood disrupts emotion regulation neurocircuitry and enhances pain unpleasantness. Biological Psychiatry, 67, 1083–1090. doi:10.1016/j.biopsych.2010.01.014 Cole, S. W., Hawkley, L. C., Arevalo, J. M., Sung, C. Y., Rose, R. M., & Cacioppo, J. T. (2007). Social regulation of gene expression in human leukocytes. Genome Biology, 8, R189. doi:10.1186/ gb‐2007‐8‐9‐r189 Craig, A. (2015). How do you feel?: An interoceptive moment with your neurobiological self. Princeton, NJ: Princeton University Press. Davis, D. A., Luecken, L. J., & Zautra, A. J. (2005). Are reports of childhood abuse related to the experience of chronic pain in adulthood? A meta‐analytic review of the literature. Clinical Journal of Pain, 21, 398–405. Dewall, C. N., Macdonald, G., Webster, G. D., Masten, C. L., Baumeister, R. F., Powell, C., … Eisenberger, N. I. (2010). Acetaminophen reduces social pain: Behavioral and neural evidence. Psychological Science, 21, 931–937. doi:10.1177/0956797610374741 Ehnvall, A., Mitchell, P. B., Hadzi‐Pavlovic, D., Malhi, G. S., & Parker, G. (2009). Pain during depression and relationship to rejection sensitivity. Acta Psychiatrica Scandinavica, 119, 375–382. doi:10.1111/j.1600‐0447.2008.01316.x Eisenberger, N. I. (2012). The pain of social disconnection: Examining the shared neural underpinnings of physical and social pain. Nature Reviews Neuroscience, 13, 421–434. doi:10.1038/nrn3231 Eisenberger, N. I., Inagaki, T. K., Mashal, N. M., & Irwin, M. R. (2010). Inflammation and social experience: An inflammatory challenge induces feelings of social disconnection in addition to depressed mood. Brain, Behavior, and Immunity, 24, 558–563. doi:10.1016/j.bbi.2009.12.009 Eisenberger, N. I., Inagaki, T. K., Rameson, L. T., Mashal, N. M., & Irwin, M. R. (2009). An fMRI study of cytokine‐induced depressed mood and social pain: The role of sex differences. Neuroimage, 47, 881–890. doi:10.1016/j.neuroimage.2009.04.040 Eisenberger, N. I., Jarcho, J. M., Lieberman, M. D., & Naliboff, B. D. (2006). An experimental study of shared sensitivity to physical pain and social rejection. Pain, 126, 132–138. doi:10.1016/j. pain.2006.06.024

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Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003). Does rejection hurt? An fMRI study of social exclusion. Science, 302, 290–292. doi:10.1126/science.1089134 Eisenberger, N. I., Master, S. L., Inagaki, T. K., Taylor, S. E., Shirinyan, D., Lieberman, M. D., & Naliboff, B. D. (2011). Attachment figures activate a safety signal‐related neural region and reduce pain experience. Proceedings of the National Academy of Sciences of the United States of America, 108, 11721–11726. doi:10.1073/pnas.1108239108 Evers, A., Zautra, A., & Thieme, K. (2011). Stress and resilience in rheumatic diseases: A review of 25 years of research. Nature Reviews Rheumatology, 7, 409–415. doi:10.1038/nrrheum.2011.80 Faucett, J. A., & Levine, J. D. (1991). The contributions of interpersonal conflict to chronic pain in the presence or absence of organic pathology. Pain, 44, 35–43. doi:10.1016/0304‐3959(91)90144‐m Franks, H. M., Cronan, T. A., & Oliver, K. (2004). Social support in women with fibromyalgia: Is quality more important than quantity? Journal of Community Psychology, 32, 425–438. doi:10.1002/ jcop.20011 Fredrickson, B. L. (2001). The role of positive emotions in positive psychology. The broaden‐and‐build theory of positive emotions. American Psychologist, 56, 218–226. doi:10.1037//0003‐066x.56.3.218 Hamilton, N. A., Karoly, P., & Zautra, A. J. (2005). Health goal cognition and adjustment in women with fibromyalgia. Journal of Behavioral Medicine, 28, 455–466. doi:10.1007/s10865‐005‐9013‐8 Hood, A., Pulvers, K., Carrillo, J., Merchant, G., & Thomas, M. (2012). Positive traits linked to less pain through lower pain catastrophizing. Personality & Individual Differences, 52, 401–405. doi: 10.1016/j.paid.2011.10.040 Hout, J. H., Vlaeyen, J. W., Peters, M. L., Engelhard, I. M., & Hout, M. A. (2000). Does failure hurt? The effects of failure feedback on pain report, pain tolerance and pain avoidance. European Journal of Pain, 4, 335–346. doi:10.1053/eujp.2000.0195 Karos, K., Meulders, A., & Vlaeyen, J. W. (2015). Threatening social context facilitates pain‐related fear learning. Journal of Pain, 16, 214–225. doi:10.1016/j.jpain.2014.11.014 Klages, S. V., & Wirth, J. H. (2014). Excluded by laughter: Laughing until it hurts someone else. Journal of Social Psychology, 154, 8–13. doi:10.1080/00224545.2013.843502 Kross, E., Berman, M. G., Mischel, W., Smith, E. E., & Wager, T. D. (2011). Social rejection shares somatosensory representations with physical pain. Proceedings of the National Academy of Sciences of the United States of America, 108, 6270–6275. doi:10.1073/pnas.1102693108 MacDonald, G., & Leary, M. R. (2005). Why does social exclusion hurt? The relationship between social and physical pain. Psychological Bulletin, 131, 202–223. doi:10.1037/0033‐2909.131.2.202 Maner, J. K., DeWall, C. N., Baumeister, R. F., & Schaller, M. (2007). Does social exclusion motivate interpersonal reconnection? Resolving the “porcupine problem”. Journal of Personality and Social Psychology, 92, 42. doi:10.1037/0022‐3514.92.1.42 Montoya, P., Larbig, W., Braun, C., Preissl, H., & Birbaumer, N. (2004). Influence of social support and emotional context on pain processing and magnetic brain responses in fibromyalgia. Arthritis and Rheumatism, 50, 4035–4044. doi:10.1002/art.20660 Onoda, K., Okamoto, Y., Nakashima, K., Nittono, H., Ura, M., & Yamawaki, S. (2009). Decreased ventral anterior cingulate cortex activity is associated with reduced social pain during emotional support. Social Neuroscience, 4, 443–454. doi:10.1080/17470910902955884 Riva, P., Williams, K. D., & Gallucci, M. (2014). The relationship between fear of social and physical threat and its effect on social distress and physical pain perception. Pain, 155, 485–493. doi:10.1016/j.pain.2013.11.006 Rutten, B. P., Hammels, C., Geschwind, N., Menne‐Lothmann, C., Pishva, E., Schruers, K., … Wichers, M. (2013). Resilience in mental health: Linking psychological and neurobiological perspectives. Acta Psychiatrica Scandinavica, 128, 3–20. doi:10.1111/acps.12095 Slavich, G. M., Way, B. M., Eisenberger, N. I., & Taylor, S. E. (2010). Neural sensitivity to social rejection is associated with inflammatory responses to social stress. Proceedings of the National Academy of Sciences of the United States of America, 107, 14817–14822. doi:10.1073/pnas. 1009164107

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Sturgeon, J., & Zautra, A. J. (2010). Resilience: A new paradigm for adaptation to chronic pain. Current Pain & Headache Reports, 2010(14), 105–112. doi:10.1007/s11916‐010‐0095‐9 Sturgeon, J., Zautra, A. J., & Arewasikporn, A. (2014). A multilevel structural equation modeling analysis of vulnerabilities and resilience resources influencing affective adaptation to chronic pain. Pain, 155, 292–298. doi:10.1016/j.pain.2013.10.007 Way, B. M., Taylor, S. E., & Eisenberger, N. I. (2009). Variation in the μ‐opioid receptor gene (OPRM1) is associated with dispositional and neural sensitivity to social rejection. Proceedings of the National Academy of Sciences of the United States of America, 106, 15079–15084. Younger, J., Aron, A., Parke, S., Chatterjee, N., & Mackey, S. (2010). Viewing pictures of a romantic partner reduces experimental pain: Involvement of neural reward systems. PLoS One, 5, e13309. doi:10.1371/journal.pone.0013309 Zautra, E. K., Zautra, A. J., Gallardo, E. C., & Velasco, L. (2015). Can we learn to treat one another better? A test of a social intelligence curriculum. PLoS One, 1–17. doi:10.1371/journal.pone. 0128638

Suggested Reading Evers, A., Zautra, A., & Thieme, K. (2011). Stress and resilience in rheumatic diseases: A review of 25 years of research. Nature Reviews Rheumatology, 7, 409–415. doi:10.1038/nrrheum.2011.80 Infurna, F., Rivers, C., Reich, J. W., & Zautra, A. J. (2015). Childhood trauma and personal mastery: Their influence on emotional reactivity to everyday events in a community sample of middle‐aged adults. PLoS One, 10(4), 1–21. doi:10.1371/journal.pone.0121840 Sturgeon, J., & Zautra, A. J. (2010). Resilience: A new paradigm for adaptation to chronic pain. Current Pain & Headache Reports, 2010(14), 105–112. doi:10.1007/s11916‐010‐0095‐9 Sturgeon, J., Zautra, A. J., & Arewasikporn, A. (2014). A multilevel structural equation modeling analysis of vulnerabilities and resilience resources influencing affective adaptation to chronic pain. Pain, 155, 292–298. doi:10.1016/j.pain.2013.10.007 Zautra, A., Infurna, F. J., Zautra, E., Gallardo, C., & Velasco, L. (2016). The humanization of social relations: Nourishment for resilience in mid‐life. In A. D. Ong & C. E. Löckenhoff (Eds.), Bronfenbrenner series on the ecology of human development. Emotion, aging, and health (pp. 207– 227). Washington, DC: American Psychological Association. https://doi.org/10.1037/14857‐011 Zautra, E. K., Zautra, A. J., Gallardo, E. C., & Velasco, L. (2015). Can we learn to treat one another better? A test of a social intelligence curriculum. PLoS One, 1–17. doi:10.1371/journal.pone. 0128638

Physician–Patient Communication Tricia A. Miller and M. Robin DiMatteo Department of Psychology, University of California Riverside, Riverside, CA, USA

Introduction While impressive technological advances abound in modern medicine, effective physician– patient communication remains one (if not the) central component in the process and delivery of quality healthcare. Effective communication plays a critical role in the physician–patient relationship; communication allows for the exchange of meaningful information and ideas in order to achieve patients’ therapeutic goals (Beck, Daughtridge, & Sloane, 2002; DiMatteo, 1994). To provide the best care, physicians must communicate trust and empathy (both verbally and nonverbally) in order for patients to feel comfortable disclosing medical information and sharing their experiences of living with illness (DiMatteo, 2004; DiMatteo, Hay, & Prince, 1986). In addition, research suggests that patients prefer and are more satisfied with a patient‐ centered approach to care (Cousin, Mast, Roter, & Hall, 2012). Patient‐centered care encompasses a communication style in which physicians seek to understand patients’ perspectives, attend to patients’ psychosocial needs, and involve and collaborate with patients, especially in any decision making (Cousin et al., 2012). Numerous studies, spanning over 40 years examining physician–patient communication, suggest positive and significant associations with various patient outcomes, including adherence to treatment recommendations, satisfaction, and physical and psychological health (Mallinger, Griggs, & Shields, 2005). Despite this empirical evidence, physician–patient communication is frequently judged to be inadequate (Beck et al., 2002). Some physicians overestimate their ability to effectively communicate (Tongue, Epps, & Forese, 2005), perhaps because of the complexity of the time‐limited medical interaction itself or other variables (e.g., patients’ type of illness or disease) that may influence the physician–patient relationship. This entry will highlight the published literature on medical communication, including both the benefits and challenges associated with effective physician–patient communication. In addition, the effects of communication training and strategies for improving physicians’ communication skills will be discussed.

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Problems in Physician–Patient Communication The communication problems in the physician–patient relationship include problems with diagnosis, a lack of patient involvement, or the inadequate provision of information to the patient (Stewart, 1995; Williams, Davis, Parker, & Weiss, 2002). Many of these problems can arise during history taking or even during the discussion of how the patient’s illness or disease should be appropriately managed (Stewart, 1995). Problems may also arise from a lack of communication skills on the part of either the physician or the patient. For patients, low levels of health literacy are particularly challenging, leading to misunderstanding of medical directives, nonadherence, and poor health outcomes (Zhang, Terry, & McHorney, 2014). Studies suggest that due to ineffective communication during the medical visit, physicians fail to recognize 50% of psychosocial and psychiatric problems that exist among their patients (Davenport, Goldberg, & Millar, 1987). More than half of patients’ problems and concerns are neither elicited by the physician nor disclosed by the patient, and about half of the time, physicians and their patients do not agree on the issue or medical problem presented during the medical visit (Starfield et  al., 1981; Stewart, McWhinney, & Buck, 1979). Some studies also demonstrate that physicians interrupt their patients within 18 s of the patient beginning to speak in an effort to describe their medical issue. As a result, patients often leave the medical interaction dissatisfied with the communication and with the information they received from their physicians (Frankel & Beckman, 1989). These studies suggest that the problems in physician–patient communication are very common; research should continue to focus attention on communication skills training at all levels of medical education.

Physician–Patient Communication and Improved Outcomes The benefits of effective communication are many. Studies examining the physician–patient relationship have found that effective communication has the potential to help regulate patients’ emotions, facilitate the complete comprehension of medical information, and allow physicians to efficiently identify and understand their patients’ specific needs, perceptions (including health beliefs), and treatment expectations (Ha, Anat, & Longnecker, 2010). Early research on effective physician–patient communication has identified positive and significant associations with patients’ sense of control, autonomy, and ability to tolerate and adjust to pain (Roter, 1983). Some more recent studies have observed decreases in the length of hospital stays and fewer referrals and decreases in the cost of individual medical visits and malpractice litigation as a result of effective communication (Eastaugh, 2004; Little et al., 2001). Physicians with adept communication skills can improve patient adherence. Patients typically make their health decisions based on their own beliefs, which may or may not be accurate, in the value and expected efficacy of the recommended treatment (DiMatteo, 2004). When effective communication allows physicians to be part of this evaluative process, it can be carried out in a way that is evidence based and avoids irrational or anecdotal conclusions. Additionally, interpersonal aspects of communication can affect patients’ responses to medical recommendations. Rapport and the communication of emotional support through nonverbal communication skills (e.g., facial expressions, body posture and movements, touch, eye contact, physical distance and orientation, vocal cues) can also help to facilitate and ensure trust within the therapeutic relationship (DiMatteo, 1994; Mast & Cousin, 2013). Research suggests that if the verbal information being communicated by physicians does not align with the

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nonverbal behaviors that patients expect, the result can be a decrease in the level of support and empathy felt by patients (Ong, Visser, Lammes, & de Haes, 2000). Effective communication is also related to physician satisfaction. Studies suggest that patients who are more satisfied (as a result of effective communication) tend to have physicians with greater job satisfaction and less work‐related stress and who feel less burnout (Maguire & Pitceathly, 2002). Some researchers even suggest a possible reciprocal relationship between communication and physician–patient satisfaction. It is possible that patients who are more satisfied with the communication during the medical visit behave in ways that reflect this satisfaction and thus positively influence their physicians’ overall satisfaction and vice versa.

Collaborative Partnership and Improving Communication One of the best‐known ways of improving communication in the physician–patient relationship is to ensure an exchange in which both physicians and patients collaborate and engage in shared decision making (Ha et al., 2010). This type of collaborative partnership requires physicians to offer treatment choices and allows patients to share in both the responsibility and control over any decisions made. Through the process of negotiation, care options are evaluated and tailored to the context of patients’ situations and needs, rather than a standardized treatment protocol (Ha et al., 2010). Research suggests, however, that patients differ in the degree and type of participation they prefer. For example, older and/or more seriously ill emergency patients desire to be less involved in their care compared with patients who are younger and/or less seriously ill (Jahng, Martin, Golin, & DiMatteo, 2005). This research not only suggests the importance of collaborative partnership through effective communication but also highlights the need for prescreening of patients’ involvement preferences in order to maximize the effectiveness of medical care delivery. Many techniques exist to help physicians improve their communication skills. Eastaugh (2004) detailed three effective ways to improve communication. First, physicians must focus on their patients’ concerns by exhibiting empathy through actively listening and making eye contact with their patients. Physicians must also engage their patients by using open‐ended questions and avoiding using medical jargon or interrupting their patients; this sets the groundwork for a true collaborative partnership. Second, physicians must take the time to properly educate their patients (e.g., clearly defining and describing unfamiliar terms, using the “teach‐back” method, and providing patients with written medical information in the form of pamphlets and brochures) to ensure complete comprehension of the prescribed treatment plan. This technique is especially important given some empirical evidence that suggests that physicians often overestimate the amount of information they provide. For example, physicians in one study believed that they provided at least 6 min of patient education, but after careful observation and analysis, it was found that the physicians provided only 40 s of education (Eastaugh, 2004). Lastly, physicians must seek to enlist their patients as collaborative partners in the decision‐making process. Doing so can empower patients to question and voice their prior misconceptions and ultimately learn new disease management techniques (Eastaugh, 2004).

Communication Skills Training Health psychologists and medical educators have sought to determine how best to teach communication skills to physicians and medical students in order to improve patient care (Haskard

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et al., 2009). Many training programs have been developed and evaluated (e.g., Communication Skills Mentors Program introduced by the Bayer Institute for Healthcare Communication), and the assessment of physicians’ communication skills has recently been added to medical certification programs (Haskard et al., 2009). Effective communication may not be easy, but it is a trainable skill; intervention studies show that training in communication skills can make a significant difference in patient outcomes. Communication skills such as information giving and active listening, for example, can be enhanced through training (Fallowfield, Jenkins, Farewell, & Solis‐Trapala, 2003). In a quantitative meta‐analysis, researchers found that communication skills training for physicians resulted in substantial and significant improvements in patient adherence; the odds of patient adherence were 1.62 times higher when physicians received communication training compared with when physicians were not trained (Zolnierek & DiMatteo, 2009). Additionally, training both physicians and patients to be effective communicators was related to increases in physician and patient satisfaction, willingness to recommend the physician, and the amount of health behavior counseling provided by physicians (Haskard et al., 2009). If only physicians or patients were trained in communication skills, however, physicians’ level of stress increased, and their satisfaction decreased (Haskard et al., 2009). Currently, there is much debate over whether or not these improved behaviors may lapse over time, given that medical students’ skills in talking with patients tend to decline as their medical education progresses (Ha et al., 2010). Thus, in order to deal with the challenges of shifting patient expectations, the increases in complex treatments, and the many constraints imposed by accountable/managed care, it is imperative that physicians practice optimal communication skills and seek ongoing performance feedback (Tongue et al., 2005).

Conclusion Effective physician–patient communication fosters more positive health outcomes and a higher quality of healthcare for patients. Most complaints and malpractice suits against physicians are related to issues of communication and not necessarily clinical competency. Patients want physicians who can skillfully diagnose, detect problems early, prevent medical crises and expensive interventions, and provide better support; effective communication can help to facilitate and ensure these outcomes (Levinson, Lesser, & Epstein, 2010). Continued efforts must be made to identify the types of communication skills (or communication training interventions) that are most effective in improving communication and the degree to which communication training can reduce healthcare disparities and improve care for all patients. Policy makers and health systems leaders must recognize that in order for physicians to provide patient‐centered care, they must be trained in the skills that enable them to communicate most effectively (Levinson et al., 2010). Finally, communication skills must be taught at all levels of medical education, and physicians must actively seek out, practice, and receive feedback on effective communication techniques toward the goal of better patient care.

Author Biographies Tricia A. Miller received her PhD in psychology from the University of California at Riverside. Her research focuses on the social psychological process of health and medical care delivery including patients’ health literacy. She also studies the effect of provider–patient ­communication

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on the promotion and maintenance of treatment adherence and patients’ overall health outcomes. M. Robin DiMatteo, PhD, is Distinguished Professor of Psychology Emerita at the University of California, Riverside. She received her PhD degree from Harvard University and has served as a resident consultant in health policy at the RAND Corporation. Dr. DiMatteo has been elected Fellow of the American Association for the Advancement of Science and received grant funding from numerous agencies and foundations including the Investigator Award in Health Policy Research from the Robert Wood Johnson Foundation.

References Beck, R. S., Daughtridge, R., & Sloane, P. D. (2002). Physician‐patient communication in the primary care office: A systematic review. Journal of the American Board of Family Practice, 15(1), 25–38. Cousin, G., Mast, M. S., Roter, D. L., & Hall, J. A. (2012). Concordance between physician communication style and patient attitudes predicts patient satisfaction. Patient Education and Counseling, 87(2), 193–197. doi:10.1016/j.pec.2011.08.004 Davenport, S., Goldberg, D., & Millar, T. (1987). How psychiatric disorders are missed during medical consultations. Lancet, 2, 439–440. DiMatteo, M. R. (1994). The physician‐patient relationship: Effects on the quality of healthcare. Clinical Obstetrics and Gynecology, 37(1), 149–161. DiMatteo, M. R. (2004). Variations in patients’ adherence to medical recommendations: A quantitative review of 50 years of research. Medical Care, 43, 200–209. doi:10.1097/01.mlr.0000114908.90348.f9 DiMatteo, M. R., Hays, R. D., & Prince, L. M. (1986). Relationship of physicians’ nonverbal communication skill to patient satisfaction, appointment noncompliance, and physician workload. Health Psychology, 5(6), 581–594. Eastaugh, S. R. (2004). Reducing litigation costs through better patient communication. Physician Executive, 30(3), 36–39. Fallowfield, L., Jenkins, V., Farewell, V., & Solis‐Trapala, I. (2003). Enduring impact of communication skills training: results of a 12‐month follow‐up. British Journal of Cancer, 89, 1445–1449. doi:10.1038/sj.bjc.6601309 Frankel, R., & Beckman, H. (1989). Evaluating the patient’s primary problem(s). In M. Stewart & D. Roter (Eds.), Communicating with medical patients (pp. 86–98). Newbury Park, CA: Sage Publications. Ha, J. F., Anat, D. S., & Longnecker, N. (2010). Doctor‐patient communication: A review. The Ochsner Journal, 10, 38–43. doi:10.1043/TOJ‐09‐0040.1 Haskard, K. B., Williams, S. L., DiMatteo, M. R., Rosenthal, R., White, M. K., & Goldstein, M. G. (2009). Physician and patient communication training in primary care: Effects on participation and satisfaction. Health Psychology, 27(5), 513–522. doi:10.1037/0278‐6133.27.5.513 Jahng, K. H., Martin, L. R., Golin, C. E., & DiMatteo, M. R. (2005). Preferences for medical collaboration: Patient‐physician congruence and patient outcomes. Patient Education and Counseling, 57(3), 308–314. doi:10.1016/j.pec.2004.08.006 Levinson, W., Lesser, C. S., & Epstein, R. M. (2010). Developing physician communication skills for patient‐centered care. Health Affairs, 29(7), 1310–1318. doi:10.1377/hlthaff.2009.0450 Little, P., Everitt, H., Williamsen, I., Moore, M., Could, C., … Payne, S. (2001). Observational study of effect of patient centeredness and positive approach on outcome of general practice consultations. BMJ, 323(7318), 908–911. doi:10.1136/bmj.323.7318.908 Maguire, P., & Pitceathly, C. (2002). Key communication skills and how to acquire them. BMJ, 325(7366), 697–700. Mallinger, J. B., Griggs, J. J., & Shields, C. G. (2005). Patient‐centered care and breast cancer survivors’ satisfaction with information. Patient Education and Counseling, 57(3), 342–349. doi:10.1016/j. pec.2004.09.009

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Mast, M. S., & Cousin, G. (2013). The role of nonverbal communication in medical interactions: Empirical results, theoretical bases, and methodological issues. In L. R. Martin & M. R. DiMatteo (Eds.), The oxford handbook of health communication, behavior change and treatment adherence (pp. 38–53). Oxford, UK: Oxford University Press. Ong, L. M., Visser, M. R., Lammes, F. B., & de Haes, J. C. (2000). Doctor‐patient communication and cancer patients’ quality of life and satisfaction. Patient Education and Counseling, 41(2), 145–156. doi:10.1016/S0738‐3991(99)00108‐1 Roter, D. L. (1983). Physician/patient communication: Transmission of information and patient effects. Medicine, 32, 260–265. Starfield, B., Wray, C., Hess, K., Gross, R., Birk, P. S., & D’Lugoff, B. C. (1981). The influence of patient‐ practitioner agreement on outcome of care. American Journal of Public Health, 71, 127–131. Stewart, M. A. (1995). Effective physician‐patient communication and health outcomes: A review. Canadian Medical Association Journal, 152(9), 1423–1433. Stewart, M. A., McWhinney, I. R., & Buck, C. W. (1979). The doctor/patient relationship and its effect upon outcome. The Journal of the Royal College of General Practitioners, 29, 77–82. Tongue, J. R., Epps, H. R., & Forese, L. L. (2005). Communication skills for patient‐centered care. The Journal of Bone and Joint Surgery, 87(3), 652–658. Williams, M. V., Davis, T., Parker, R. M., & Weiss, B. D. (2002). The role of health literacy in patient‐ physician communication. Family Medicine, 34(5), 383–389. Zhang, N. J., Terry, A., & McHorney, C. A. (2014). Impact of health literacy on medication adherence: A systematic review and meta‐analysis. Annals of Pharmacotherapy, 48, 741–751. doi:10.1177/ 1060028014526562 Zolnierek, K. B., & DiMatteo, M. R. (2009). Physician communication and patient adherence to treatment: A meta‐analysis. Medical Care, 47, 826–834. doi:10.1097/MLR.0b013e31819a5acc

Suggested Reading Martin, L. R., & DiMatteo, M. R. (2013). The Oxford handbook of health communication, behavior change, and treatment adherence. New York, NY: Oxford University Press. Martin, L. R., Haskard‐Zolnierek, K. B., & DiMatteo, M. R. (2010). Health behavior change and treatment adherence. New York, NY: Oxford University Press. Miller, T. A., & DiMatteo, M. R. (2015). Communication in the aging community. In B. A. Benson (Ed.), Psychology and geriatrics: Integrated care for an aging nation (pp. 67–89). New York, NY: Elsevier. Rotar, D., & Hall, J. A. (2006). Doctors talking with patients/patients talking with doctors: Improving communication in medical visits. Connecticut, CT: Praeger Publishers.

Placebo and Nocebo Effects Andrew L. Geers and Fawn C. Caplandies Department of Psychology, University of Toledo, Toledo, OH, USA

Historical Context Prior to the scientific era of medicine, the remedies used for medical care were largely ineffective or, worse, harmful to recipients. In the few cases in which therapeutic ingredients had value, they were inappropriately administered, given in the wrong dose, or combined with so many diluting and contrary substances that the remedies were rendered useless (Shapiro & Shapiro, 1997). Examples of early ingredients include the eating of live frogs, cast‐off snake skin, spider webs, wood lice, human bones, and goat intestines and drinking the blood of an enemy. Medical interventions involved activities such as dehydration, bloodletting, magnetism, exorcism, and attaching leeches to the body. Although such treatments appear peculiar now, there was considerable belief in their utility and frequent reports of treatment efficacy. Many medical scholars have concluded that the benefits experienced in early medicine were, to a large extent, the product of what has been called placebo effects. The word placebo entered the English language in the thirteenth century when it was translated from Latin as meaning “to please.” Between the eighteenth and nineteenth century, the word placebo developed into a term for fake treatments that are administered to placate patients. While the administration of placebos was openly viewed as the unsavory practice of disreputable practitioners, placebo treatments remained a common medical tool. Perspectives on placebos shifted in the middle of the twentieth century when concern about bias in medical research led to the scientific practice of administering inert treatments to control participants in randomized controlled trials (RCTs). The use of placebo control groups in RCTs was spurred on by an influential paper, “The Powerful Placebo,” in which Henry Beecher (1955) concluded that approximately 35% of patients in research trials benefit by simply being given a placebo. Since this time, the use of placebo control groups has become the gold standard in clinical research, and more placebos have been dispensed to participants in RCTs than any other drug or medical treatment. Even though placebo treatments have been administered throughout recorded human history and lay theories of placebo effects abound, empirical exploration into the topic is relaThe Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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tively recent. Initial experimentation on placebo effects began in the 1960s and has flourished during the past two decades. This increased attention can be observed in publication records, with fewer than 25 empirical publications in PubMed focused on placebo effects in the year 1992 to more than 200 in the year 2011. The interest in placebo effects likely came about for a number of reasons, including pioneering neurobiological studies linking placebo effects to detectable changes in the brain and a broader recognition of the importance of psychological mechanisms in health outcomes. With this influx of research has emerged the perspective that placebo effects, like pharmacologically active treatments, are fundamental to successful healthcare (Colloca & Benedetti, 2005). Rather than discounting placebo effects, there is now a push to find strategies to enhance them. From a broader scientific perspective, placebo effects provide a rich opportunity to study the interplay of complex psychological and neurological processes. Further, work on placebo effects is transdisciplinary and integrates diverse scholarly fields including psychology, anthropology, neuroscience, ethics, chemistry, and countless branches of medicine.

Reconceptualizing Placebo Effects Placebo effects have traditionally been defined as the result of inert treatments. As data mounted, however, such definitions proved problematic. For example, if placebo effects are defined as the consequences of inert agents, how does this explain the genuine psychobiological changes that are observed in numerous placebo studies? Recent research supports the position that the phenomenon actually centers on expectations and the meaning ascribed to the medical situation rather than to the inertness of a treatment. Contemporary definitions of placebo effects now describe them as psychological or physiological changes that are directly attributed to receiving a substance or undergoing a procedure, but that are not the direct consequence of the inherent power of that substance or procedure (Stewart‐Williams & Podd, 2004). When defined in this way, placebo effects are not only a result of the administration of inert agents. Instead, they are a component of all medical treatments—the component that can be attributed to an individual’s treatment expectations and interpretations (Benedetti, 2009). In discussing placebo effects, it is important to address three common points of confusion. First, improvements observed in placebo control conditions in RCTs are not a direct indicator of placebo effects because placebo‐controlled RCTs are designed to assess treatment effects by comparing a treatment group to a placebo group. To isolate placebo effects, researchers need an additional group, termed a no‐placebo control group, in which participants are not given either an active or inert treatment. The difference between a placebo group and a no‐placebo control group corresponds to the placebo effect. Without this third condition, it is unclear if any improvement observed in the placebo group was the sole result of placebo effects or the result of confounding factors such as spontaneous remission, history effects, and regression to the mean. Second, placebo effects are sometimes classified as a non‐specific effect. However, research has uncovered much about the causes of placebo effects, as well as the precise psychological and biochemical mechanisms underlying placebo effects. Thus, placebo effects have demonstrated specificity and do not fit the non‐specific category. Third, and relatedly, as with medical treatment effects, there is a wide array of pathways by which placebo effects emerge. As such, rather than only one placebo effect, there appear to be many types of placebo effects. This final point is notable as it speaks to the long‐standing question of the extent of placebo effects’

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power in medical care. Attempts to estimate the magnitude of placebo effects began with Beecher’s (1955) influential paper on the power of placebo effects and have been extended in more recent meta‐analytic reviews. As there are various and distinct processes responsible for placebo effects, however, there is likely no single answer to this magnitude question.

Nocebo Effects Nocebo effects refer to negatively valenced placebo effects and result from the anticipation of unpleasant treatment outcomes. As with placebo effects, contemporary research finds nocebo effects across symptom domains and in both clinical and experimental studies (Colloca, Arve Flaten, & Meissner, 2013). Because of the ethical issues involved in providing negative symptom expectations to patients, nocebo effects have been investigated less frequently than placebo effects. Although nocebo and placebo effects may involve some distinct processes, nocebo effects are often deemed a variant of the broader placebo effect phenomenon. Keeping with this perspective, much of what is discussed herein pertains to both placebo and nocebo effects. One notable way nocebo effects diverge from placebo effects is the occurrence of unpleasant treatment side effects in patients. Researchers have found that side effect warnings provided by practitioners, direct‐to‐consumer medical advertising, and informed consent protocols can all produce nocebo responses. For example, Myers, Cairns, and Singer (1987) found that listing gastrointestinal side effects on an informed consent document significantly increased the frequency of gastrointestinal symptoms reported by unstable angina patients. Notably, nocebo side effects do not always manifest, and they can be moderated by variables including worry and, in pain studies, beliefs about how one reacts to pain.

Placebo Effect Research The basic experimental procedure to test for placebo effects is to provide a treatment efficacy message to one group of participants (e.g., this pill will reduce your pain) and then compare the responses of this group with a second group who was not given this efficacy message. To isolate the impact of this manipulation on treatment outcomes, inert treatments are typically administered to all participants. Studies using this design find that the verbal information provided about a treatment is central in determining the illness experience and the symptomatic expressions of disease. Experiments have uncovered placebo effects across cultures and ages and with samples ranging from healthy volunteers to patients undergoing surgery. Although placebo effects occur in various domains, they are particularly likely to arise in studies of pain, anxiety, and depression. Placebo effects have been found with a wide variety of dependent measures, including subjective assessments of moods, itch, sleep quality, pain, and drug cravings. Although important, findings on subjective measures are not incontrovertible evidence for placebo effects. This is because subjective measures are prone to biases including demand characteristics, experimenter expectancy effects, and response shifts. Importantly, placebo effects have been exhibited on other dependent variables including cognitive processing measures such as reaction time, word generation, Stroop interference, and implicit learning; behavioral measures such as sleep latency, alcohol consumption, talking time by socially anxious individuals, and motor performance in Parkinson’s disease patients; and physiological measures including salivary ­cortisol, startle eyeblink reflex, bronchoconstriction in asthmatics, and beta‐band frequency

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during sleep. The reach of placebo effects, however, is not unlimited. Placebo effects do not alter disease parameters such as the size of cancerous tumors. In addition to treatment efficacy information altering the effects of inactive treatments, it also changes the efficacy of active treatments. For example, many researchers have studied placebo effects by using the balanced placebo design in which treatment efficacy information (e.g., stating that a pill contains caffeine or not) and treatment agency (e.g., administering caffeine pills or not) are independently manipulated. Although results from balanced placebo designs vary, many find that stating that a treatment is efficacious amplifies the effectiveness of active treatments. In a conceptually similar vein, studies employing the open–hidden paradigm (Colloca & Benedetti, 2005) compare the impact of pharmacologically active treatments when patients are aware and unaware of the timing of a treatment administration (e.g., automatic intravenous drip hidden from view). This work has revealed that when the dose of an active treatment is covertly given, its specific efficacy can be substantially reduced—that is, without the awareness of their administration, active drugs are less effective. Despite the remarkable progress in research on placebo effects, many uncertainties remain. Perhaps most notably, the magnitude and direction of placebo effects can be unpredictable. Some of these inconsistencies may be attributable to the diversity in treatment domains, samples, verbal efficacy manipulations, and contexts used in placebo studies.

Psychological Mechanisms Theoretical dialogue regarding psychological processes underlying placebo effects have largely focused on two factors: expectations and classical conditioning. Classical conditioning explanations posit that placebo effects can be conditioned responses, with active medications as the unconditioned stimuli and the methods or techniques used to administer or accompany treatments as the conditioned stimuli. A substantial body of human and animal laboratory work demonstrates that conditioning can produce placebo effects. Also, increasing the number of conditioning trials results in stronger placebo effects, prior experience with an effective treatment strengthens placebo effects, and placebo effects can emerge from social observational learning (Colloca et al., 2013). Even though placebo research has given considerable attention to conditioning, expectations are generally viewed as the primary psychological mechanism underlying placebo effects. In fact, theoretical and experimental work now points to the view that conditioning is an associative process by which expectancies develop. Expectancy explanations hold that the placebo effects are driven by the belief that receiving a treatment results in a particular response or outcome. One influential approach is response expectancy theory (Kirsch, 1999), which posits that placebo effects are caused by anticipated automatic reactions. That is, expectations are said to serve a preparatory function and directly stimulate expectancy‐based responses. An extensive array of work supports the position that expectancies cause placebo effects. For example, the same inert treatment can lead to arousal or sedation depending upon the expectancy message provided. Further, modifying the characteristics of an expectancy message can change the magnitude of placebo effects. For example, double‐blind expectations (e.g., you may or may not be given an active drug) often produce weaker placebo effects than deceptive expectations (e.g., you have been given an active drug). Investigations also find that self‐reported expectations can mediate the association between treatment efficacy messages and placebo effects (Stewart‐Williams & Podd, 2004). What are the pathways by which expectations bring about placebo effects? It appears there are at least four overlapping pathways (see Benedetti, 2009; Colloca et  al., 2013; Geers &

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Miller, 2014). First, expectations can produce beneficial outcomes by reducing anxiety and decreasing the activation of threat‐related centers in the brain. Thus, sometimes expectations lead to placebo effects because expecting an effective treatment lessens anxiety. In the case of nocebo effects, negative expectations increase rather than decrease threat and anxiety. Second, other work suggests that anticipating improvements from a treatment increases positive feelings through opioid and dopaminergic reward mechanisms in the brain. That is, expectations engender placebo responding by activating endogenous reward processes. Third, expectations can act as an interpretive guide, directing attention, detection, weighting, attributions, behavior, and recall of somatic experiences. Finally, conditioning and nonconscious priming studies indicate that placebo effects can arise from cues activating simple associative responses.

Neurobiological Mechanisms One of the fastest growing areas of placebo research concerns the neurobiological pathways involved in the effect. This line of work clearly overlaps with the aforementioned work on psychological mechanisms. Research in this area began with a landmark study by Levine, Gordon, and Fields (1978) in which the opioid antagonist naloxone was found to block placebo responses in a pain paradigm. These results were the first to implicate the endogenous opioid system in placebo pain relief (termed placebo analgesia). Since this time, opioid, cannabinoid, and cholecystokinin endogenous mechanisms have been connected to placebo analgesia. Brain imagining studies have been critical in clarifying the neurobiological mechanisms underlying placebo effects (for reviews, see Benedetti, 2009; Colloca et al., 2013). For example, experimental work indicates that placebo analgesics work by decreasing activity in the same pain‐processing regions of the brain (e.g., the anterior cingulate cortex and the prefrontal cortex) as pharmacological pain treatments. Thus, placebos appear to produce change through some of the same pathways as the drugs they are often tested against. Several studies have revealed that in addition to changing activity in brain networks, placebo treatments can involve the descending inhibition of spinal nociceptive processes. Other work reveals that, in contrast to placebo expectations, nocebo expectations are associated with greater activity in the anterior cingulate cortex and the prefrontal cortex and stronger pain‐related activity in the spinal cord. Outside of the arena of pain, researchers have found that in patients suffering from Parkinson’s disease, placebo effects in motor performance are associated with dopamine release in the striatum.

Moderators of Placebo Effects One of the most vexing puzzles in the placebo literature has been why some people display a placebo response in a given context and others do not. In early placebo research this issue was often framed in terms of locating the “type” of individuals who responds to a treatment expectation—labeled placebo responders. The possibility that there is a placebo‐prone personality has been frequently studied. Despite these efforts, few if any individual difference variables reliably predict placebo responding across contexts (Geers, Kosbab, Helfer, Weiland, & Wellman, 2007). For example, relationships between placebo effects and personality variables such as introversion, optimism, empathy, social desirability, and self‐esteem occur in some contexts, but not others. Relatedly, individuals responding to one placebo manipulation do

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not reliably respond to a different placebo manipulation. That said, much of this research has focused on personality differences, whereas a new wave of research is assessing whether genetic and biological factors predict placebo responding (Hall, Loscalzo, & Kaptchuk, 2015). Other lines of research have focused on situational factors that alter the success of placebo treatments. Many contextual factors alter the magnitude of placebo effects. These factors include aspects of the healthcare practitioner (e.g., white lab coat, a practitioner’s belief in treatment efficacy), properties of the treatment (e.g., number, size, color, brand, cost, and taste of pills), and properties of the administration procedure (e.g., injection, oral). Another factor that appears to moderate placebo effects is whether an active or passive placebo is employed. Active placebos produce side effects that are detectable by individuals, whereas passive placebos do not. Active placebos provide cues to the recipient that a treatment is effective and may lead to a stronger belief in treatment effectiveness than passive placebos. Recently, research has begun to consider the interactive effects of individual differences and situational factors in producing placebo effects. This work suggests that placebo effects are determined by the match between the context and the person’s current motives, prior experiences, and chronic tendencies. For example, Colloca and Benedetti (2009) induced placebo effects in three groups, one through a verbal message, one via conditioning, and one via observational learning. In this study, empathy motivation predicted placebo responses, but only when the placebo effect was induced through observational learning.

Placebo Effects in Modern Times Placebo effects may be commonplace in modern treatments for physical and mental health. For example, in controlled clinical trials, some standard treatments, including cough suppressants and dietary supplements, often do not differ from placebo controls—suggesting that they possess a strong placebo component (Moerman, 2002). There is also considerable data indicating that the benefits of many contemporary and alternative medicines, which amount to approximately 34 billion dollars annually, benefit substantially from placebo effects (Moerman, 2002). Evidence consistent with this notion comes from a series of clinical trials with chronic pain patients that compared real acupuncture with sham acupuncture. Although no differences were found between the two groups, patient beliefs about the efficacy of acupuncture were a reliable predictor of pain relief (Bausell, Lao, Bergman, Lee, & Berman, 2005). In terms of mental health, Kirsch found that approximately 70% of antidepressant effects can be attributed to placebo effects (Kirsch, 1999). Psychotherapy is another arena in which placebo effects are likely to be strong. Difficulties in developing precise placebo control groups for psychotherapy, however, leave the exact contribution of placebo effects in this domain unknown. Finally, recent surveys across a variety of countries indicate that many physicians continue to use placebo treatments in routine practice. For example, a survey of 679 practicing internists and rheumatologists in the United States found that 58% reported prescribing (mainly active) placebo treatments on a regular basis (Tilburt, Emanuel, Kaptchuk, Curlin, & Miller, 2008).

Clinical Translation A difficulty in the placebo effect literature has been determining how to harness the placebo phenomenon to improve patient care. This problem arises because research generally employs

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deceptive expectation manipulations to generate placebo effects, whereas the overt use of deception is not permitted by medical codes of conduct. The fact that placebo effects can be conditioned provides some opportunities for translation. For example, researchers have paired placebos and active drugs given to participants and then removed portions of the active drug over time (Doering & Rief, 2012). These studies suggest that placebos can be used to maintain drug responses after the initial associative pairing. Another intriguing development in translation is the creation of the “open label” paradigm in which participants are explicitly informed that they have been administered a placebo and are additionally told that placebos yield beneficial effects. In open label experiments, participants have improved from placebo treatments even when they were aware that their treatment was inert (Kam‐Hansen et al., 2014). Another approach to enhancing placebo effects in medical care arises from the notion that all treatments have a placebo (e.g., expectation) component. Thus, rather than directly administering inert treatments, benefits can arise from altering contextual factors that strengthen the placebogenic portion of a standard treatment. For example, research indicates that when individuals have an opportunity to choose between placebo treatments, they may exhibit larger placebo effects compared with participants assigned a placebo treatment (Geers et al., 2013). This work suggests that in some cases physicians could increase placebo responding by encouraging greater patient involvement. The patient–provider interaction provides another opportunity for enhancing placebo effects in medical settings (Benedetti, 2009). Evidence for the importance of the therapeutic encounter in placebo responding comes from a study in which patients with irritable bowel syndrome were randomly assigned to a wait list group, a placebo acupuncture group, or a group that received placebo acupuncture with a supportive practitioner (Kaptchuk et al., 2008). The supportive practitioners were instructed to listen to participants and to be warm, confident, and thoughtful. The results revealed that participants with the supportive practitioner reported greater improvement than the other two groups 3 weeks later. These data suggest that practitioners can play a key role in strengthening the placebo component of treatments. Although aspects of the patient–provider relationship seem to alter placebo effects, more research is needed to clarify the specific factors that best enhance placebo responding in such interactions.

Conclusion Over the last several decades, the concept of placebo effects has evolved into a topic of scientific inquiry in its own right. Research reveals that placebo effects influence meaningful physical and mental health outcomes and this phenomenon is not static but has many moderators. Research is now uncovering the psychobiological processes responsible for placebo effects. This work bridges disciplines and points to avenues by which to improve patient care, as well as our understanding of the interplay of psychological and neurological processes.

Author Biographies Andrew L. Geers is a professor of psychology at the University of Toledo. He is interested in the advancement and application of personality and social psychology theory within health and medical contexts. His primary research interests are in the areas of placebo effects, optimism, health behavior, and assimilation and contrast models. He received his BA from the University of Cincinnati and his PhD in experimental psychology from Ohio University.

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Fawn C. Caplandies is a graduate student in social psychology at the University of Toledo. She received her BA from Hartwick College in 2013. Her primary research interests are in placebo effects, expectations, and the psychophysiology of stress and coping.

References Bausell, R. B., Lao, L., Bergman, S., Lee, W. L., & Berman, B. M. (2005). Is acupuncture analgesia an expectancy effect? Preliminary evidence based on participants’ perceived assignments in two placebo‐ controlled trials. Evaluation & the Health Professions, 28, 9–26. doi:10.1177/0163278704273081 Beecher, H. K. (1955). The powerful placebo. Journal of the American Medical Association, 159, 1602– 1606. doi:10.1001/jama.1955.02960340022006 Benedetti, F. (2009). Placebo effects: Understanding the mechanisms in health and disease. New York, NY: Oxford University Press. Colloca, L., Arve Flaten, M., & Meissner, K. (Eds.) (2013). Placebo and pain: From bench to bedside. Boston, MA: Academic Press. Colloca, L., & Benedetti, F. (2005). Placebos and painkillers: Is mind as real as matter? Nature Reviews Neuroscience, 6, 545–552. doi:10.1038/nrn1705 Colloca, L., & Benedetti, F. (2009). Placebo analgesia induced by social observational learning. Pain, 144, 28–34. doi:10.1016/j.pain.2009.01.033 Doering, B. K., & Rief, W. (2012). Utilizing placebo mechanisms for dose reduction in pharmacotherapy. Trends in Pharmacological Sciences, 33, 165–172. doi:10.1016/j.tips.2011.12.001 Geers, A. L., Kosbab, K., Helfer, S. G., Weiland, P. E., & Wellman, J. A. (2007). Further evidence for individual differences in placebo responding: An interactionist perspective. Journal of Psychosomatic Research, 62, 563–570. doi:10.1016/j.jpsychores.2006.12.005 Geers, A. L., & Miller, F. G. (2014). Understanding and translating the knowledge about placebo effects: The contribution of psychology. Current Opinion in Psychiatry, 27, 326–331. doi:10.1097/ YCO.0000000000000082 Geers, A. L., Rose, J. P., Fowler, S. L., Rasinski, H. M., Brown, J. A., & Helfer, S. G. (2013). Why does choice enhance treatment effectiveness? Using placebo treatments to demonstrate the role of personal control. Journal of Personality and Social Psychology, 105, 549–566. doi:10.1037/a0034005 Hall, K. T., Loscalzo, J., & Kaptchuk, T. J. (2015). Genetics and the placebo effect: The placebome. Trends in Molecular Medicine, 21, 285–294. doi:10.1016/j.molmed.2015.02.009 Kam‐Hansen, S., Jakubowski, M., Kelley, J. M., Hoaglin, D. C., Kaptchuk, T. J., & Burstein, R. (2014). Altered placebo and drug labeling changes the outcome of episodic migraine attacks. Science Translational Medicine, 6, 218–225. doi:10.1126/scitranslmed.3006175 Kaptchuk, T. J., Kelley, J. M., Conboy, L. A., Davis, R. B., Kerr, C. E., Jacobson, E. E., … Lembo, A. J. (2008). Components of placebo effect: Randomised controlled trial in patients with irritable bowel syndrome. British Medical Journal, 336, 999–1003. doi:10.1136/bmj.39524.439618.25 Kirsch, I. (1999). How expectancies shape experience. Washington, DC: American Psychological Association. Levine, J. D., Gordon, N. C., & Fields, H. L. (1978). The mechanisms of placebo analgesia. Lancet, 2, 654–657. doi:10.1016/S0140‐6736(78)92762‐9 Moerman, D. (2002). Meaning, medicine, and the ‘placebo effect’. Cambridge, UK: Cambridge University press. doi:10.1017/CBO9780511810855 Myers, M. G., Cairns, J. A., & Singer, J. (1987). The consent form as a possible cause of side effects. Clinical Pharmacology & Therapeutics, 42, 250–253. doi:10.1038/clpt.1987.142 Shapiro, A. K., & Shapiro, E. (1997). The powerful placebo effect. Baltimore, MA: John Hopkins University Press. Stewart‐Williams, S., & Podd, J. (2004). The placebo effect: Dissolving the expectancy versus conditioning debate. Psychological Bulletin, 130, 324–340. doi:10.1037/0033‐2909.130.2.324

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Tilburt, J. C., Emanuel, E. J., Kaptchuk, T. J., Curlin, F. A., & Miller, F. G. (2008). Prescribing “placebo treatments”: Results of national survey of U.S. internists and rheumatologists. British Medical Journal, 337, 7678–7683. doi:10.1136/bmj.a1938

Suggested Reading Benedetti, F. (2014). Placebo effects: Understanding the mechanisms in health and disease (2nd ed.). New York, NY: Oxford University Press. Colloca, L., Arve Flaten, M., & Meissner, K. (Eds.) (2013). Placebo and pain: From bench to bedside. Oxford, UK: Elsevier. Finniss, D. G., Kaptchuk, T. J., Miller, F., & Benedetti, F. (2010). Biological, clinical, and ethical advances of placebo effects. Lancet, 375(9715), 686–695. doi:10.1016/S0140‐6736(09)61706‐2 Geers, A. L., & Miller, F. G. (2014). Understanding and translating the knowledge about placebo effects: The contribution of psychology. Current Opinion in Psychiatry, 27, 326–331. doi:10.1097/ YCO.0000000000000082 Stewart‐Williams, S., & Podd, J. (2004). The placebo effect: Dissolving the expectancy versus conditioning debate. Psychological Bulletin, 130, 324–340. doi:10.1037/0033‐2909.130.2.324

Positive Affect Brooke N. Jenkins, Amanda M. Acevedo, and Sarah D. Pressman Department of Psychological Science, University of California Irvine, Irvine, CA, USA

Positive affect (PA), such as feelings of happiness or joy, has been tied to many health and physiological benefits. For example, a meta‐analysis of the benefits of PA on mortality found that individuals high in PA were 18% less likely to die within the follow‐up periods of over 20 years (Chida & Steptoe, 2008). This effect size is similar in magnitude to the effects of smoking cessation programs for smokers on mortality indicating that it may be critical to uncover the dynamics of the PA–health relation. This entry will start by outlining the methodological issues when studying PA, briefly review the literature, and then discuss potential mechanisms connecting PA to health. Finally, we end with a discussion of positive psychology interventions (PPIs) in a health context and future directions for this field.

Methodological Themes and Issues in the Study of PA and Health Definitions of PA vary with an array of adjective and question types utilized to assess this characteristic. Thus, it is critical for researchers to think carefully about which types of PA are assessed by their measurement tools. The Positive and Negative Affect Schedule (PANAS) (Watson, Clark, & Tellegen, 1988) and the Profile of Mood States (POMS) questionnaire (Curran, Andrykowski, & Studts, 1995) are popular self‐report methods used to assess PA in health studies. These measures assess either state or trait affect by asking participants to report on how they have felt in a recent period of time or how they generally feel, respectively. In the case of large medical studies, researchers often rely on alternative methodologies to assess PA. Because of time constraints, for example, some researchers use single‐item measures of happiness or similar emotions. While these measures are not preferred, since they do not capture the full spectrum of the PA experience, they have been tied to future health outcomes. An alternative self‐report measurement approach, especially in archival data approaches, is to use the positive items from depression scales as a source of PA (e.g., Center for Epidemiological The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Studies Depression Scale [CESD]). While this approach has proved useful in predicting future health such as risk of AIDS mortality and stroke (e.g., Moskowitz, 2003), it is difficult to interpret what forms of positivity are most beneficial in these studies. The CESD PA items assess other positive constructs beyond PA (e.g., optimism, self‐esteem), and since these have also been tied to wellness, we are left with a question as to what the active ingredients for good health are in these studies. Related to this issue of “active ingredients” is the question of which types of items should be assessed and/or included in measures of PA and whether these components should be considered independently. The PANAS and POMS discussed above measure high arousal feelings of positivity to a large extent (e.g., vigor, excitement, interest). While these specific subcomponents have been tied to better health (e.g., longer lifespan, decreased cold infection; Cohen, Alper, Doyle, Treanor, & Turner, 2006; Pressman & Cohen, 2012), in excess, they can be harmful. In contrast, low arousal feelings of calm and peacefulness may be helpful for populations experiencing pain or high stress. Recent research has also examined specific positive emotions like love, desire, amusement, pride, and awe and shown that these do have distinct physiological profiles as well as distinctive behavioral correlates and signals. Although there is little research on the types of PA most helpful to different health outcomes at this point, there are many reasons to think that PA type matters and that, in some cases, examining broad measures of PA might obscure interesting subtype differences in predictions (Pressman, Jenkins, Kraft, Rasmussen, & Scheier, 2017). Another consideration when picking a PA measurement tool in the context of health is duration. Instructions for affect assessment are often not considered carefully when scales are chosen, but how long PA lasts may have important implications for the health or physiological outcome at hand. For example, strong changes in state PA (i.e., transient emotions and moods) may influence physiology, for example, when assessed in a laboratory setting with acute challenges or manipulations of affect. Trait (i.e., stable, dispositional) PA, on the other hand, is likely the stronger predictor of future longevity and disease because it indicates how an individual typically feels. With that said, there is a high correlation between state and trait PA, which is likely responsible for studies showing state PA ties to survival. Of possible future interest, although not assessed in the literature broadly at this point, is the possibility that variability in state PA (i.e., fluctuation as opposed to average levels) may also be important. Also of interest in measurement is the issue of whether there are limitations to the reliance on self‐reports in this field, given that there are known response biases and social desirability concerns, among other possible problems. To address this, some researchers use alternative methods like obtaining writing and audio samples and coding positive words and laughter as markers of emotional style (Pressman & Cohen, 2012). Others have relied on observable expressions of positivity by coding facial expressions in photos or in person and have found them to be tied to mortality and disease outcomes. While there are limitations to these methods as well (e.g., language and culture norm differences), they do provide convergent validity and some indication of the generalizability of PA–health findings beyond self‐report. One final important issue in the measurement of PA is the role of negative affect (NA). Negative traits have been extensively related to health and, depending on the measurement, can be strongly correlated with PA (Watson et al., 1988). Thus, it is critical for researchers to answer the question of whether these effects are distinct phenomena. While rare in the NA– health field, PA researchers broach this problem by controlling for NA in their PA studies and/or contrasting the relative effects. This can be difficult, as many trait measures of affect have highly correlated PA/NA scales, creating multicollinearity issues in analyses. Thus, many

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researchers choose the PANAS to resolve this, which was specifically designed to have low overlap between valence dimensions, or rely on alternative negative measures (e.g., depressive symptoms), which overlap less with PA. There are also methodological concerns specific to the health component of the PA–health question. One important concern is the conceptual and measurement overlap that exists when assessing health by self‐report. Many health studies rely on participants to self‐assess their health using questionnaires like the 36‐item Short‐Form Health Survey (SF‐36). While known to be a good and valid predictor of objective health, the SF‐36 actually includes items about happiness within it creating some clear overlap and interpretation problems. Researchers should therefore be cautious when picking self‐report health scales. This is especially important when comparing self‐report health scales with PA scales that assess energy (e.g., vigor, active) since these types of PA scales may tap elements of physical fitness and health. This can be partly addressed by controlling for these types of outcomes in analyses. Finally, it is important to remember that physiological and behavioral outcomes are not equivalent to health. While assessments of cortisol, blood pressure, and immune function as well as health behaviors (e.g., sleep, exercise) are key hypothesized mediators in the broader PA–health relation, when interpreting these outcomes, it is important to not reach too far since the level of change in parameters may not reach clinical significance.

PA–Health Review The following sections consider the impacts of PA on the health outcomes most studied in conjunction with PA. When examining the influence of PA on future health, the gold standard is to survey participants on both PA and health (e.g., medical records, physicals, medications, fitness) at baseline and then follow up with them after a period of time has passed. While this prospective longitudinal design does not allow us to infer causality, it does give us more confidence that PA may be leading to future health if we control for health and its connection with baseline PA as best we can.

Longevity/Mortality It is well established that PA is related to lower rates of mortality (e.g., Chida & Steptoe, 2008; Diener & Chan, 2011; Pressman & Cohen, 2005). A typical study design can be found by Kawamoto and Doi (2002) who evaluated older adults on baseline self‐rated happiness and then assessed mortality rates 3 years later. Similar to other studies, individuals reporting higher happiness and cheerfulness at baseline were more likely to still be alive at follow‐up. Beyond the typical self‐report strategy, studies coding positive words in writing samples from autobiographies as an indication of PA have also found years of extended life benefit especially among those using more activated words. Similarly, coding of positive facial expressions in baseball player cards found a 7‐year lifespan benefit for those with the most intense smiles even after controlling for relevant demographic variables. Although these non‐self‐report studies could not assess baseline health, attempts to do so by coding health words did not cancel out this effect, and we can assume that baseball players in their prime were in similarly good health given their professional athlete status. Thus, across numerous studies and PA measurement approaches, we see PA‐related lifespan benefits. While the consistency is impressive, there are some studies that have not shown these benefits. However, these studies are typically different from the above in some way. For example,

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studies that do not find such links assess child cheerfulness, middle‐aged women, and older adults in assisted living facilities. Thus, PA does not hold equal benefit at all life stages, and perhaps other factors are more important predictors of longevity in these groups (e.g., current health status and/or health behaviors). However, given the paucity of research on these groups, more research is needed before making any firm conclusions.

Survival PA–health researchers have also investigated the effects of PA on survival in populations with life‐threatening disease. For example, one study assessed women with HIV for positive expectations, finding meaning, and PA using words adapted from the POMS and found PA was related to longer survival. However, this study did not separate the effects of PA from expectations and meaning, which made it difficult to know what drove these benefits. Related, Moskowitz (2003) found that for every standard deviation increase in PA (based on the CESD subscale), HIV+ men were 28% less likely to die during follow‐up. These results were independent of CESD, NA, and other health‐related factors relevant to this population. These results are consistent with other positive findings in samples undergoing heart surgery or with congestive heart failure. Despite these benefits, the effects of PA in unhealthy populations are mixed. For example, the PA subscale of the NEO‐PI (Neuroticism, Extraversion, Openness Personality Inventory) predicted 11‐year survival in people with coronary artery disease after accounting for relevant demographic and health factors. However, the effect of PA became nonsignificant when the NEO‐PI depressive subscale was added into the model. Similarly, PA assessed using the Mood Adjectives Checklist did not predict 1‐year survival in recently diagnosed cancer patients with varying levels of cancer type, stage, and severity while depression (assessed with the CESD) did. It has been previously hypothesized that these mixed results are due to the limits of PA (Pressman & Cohen, 2005). That is, early‐stage diseases that can be benefited by healthy behavior, support, and lower stress may be helped by high PA, whereas diseases that are far along (e.g., failing kidneys, late‐stage cancer) may not benefit since at that point the pathways connecting PA to better health may be less potent. There is also the possibility that too much PA may be indicative of a failure to realistically cope with the negative aspects of a life‐threatening disease, in which case affect balance may be a useful pursuit to examine in future studies.

Morbidity Prospective studies assessing PA at baseline and then following up on rates of morbidity find that individuals higher in PA less frequently develop illness. For example, high PA is associated with a decreased likelihood of future coronary heart disease, injury, and stroke. While most studies are observational, experimental studies that exposed participants to cold viruses found that individuals with greater PA (averaged over 2 weeks) were less likely to develop a cold or flu (Cohen et al., 2006). These results were independent of NA. This study’s objective illness measures (e.g., mucus weight, viral cultures) and experimental design are instrumental in clarifying that even when high PA individuals get exposed to a virus, they are less likely to get sick (as opposed to the alternative explanation that high PA individuals are less likely to be exposed to a virus). Together with the naturalistic findings, these results support that PA may confer resiliency to illness and disease. The strength of this literature is the impressive consistency (i.e., studies support an array of morbidity benefits). The weakness is that few morbidity

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­ utcomes have been replicated, calling for future studies to generate more data and increase o our confidence regarding these positive associations.

Symptom Report, Severity, and Pain PA is consistently associated with an array of self‐reported health outcomes such as decreased symptom report, reduced reports of symptom severity, and less pain (e.g., DuBois et  al., 2015; Finan & Garland, 2015; Sturgeon & Zautra, 2013). While most studies are cross‐sectional, studies manipulating PA through, for example, loving‐kindness meditation training (Fredrickson, Cohn, Coffey, Pek, & Finkel, 2008) or smiling (Pressman, Acevedo, Aucott, & Kraft, under review) in healthy samples have shown reductions in illness symptoms and perceived pain, respectively. Similar results have been found in studies of patient populations, such as those suffering from chronic pain (e.g., Finan & Garland, 2015; Sturgeon & Zautra, 2013) and cardiovascular disease (e.g., DuBois et  al., 2015). These findings are more consistent across populations than the findings reviewed in previous sections. However, there may be reason to think PA can bias self‐reports in a way that might not match underlying objective symptoms. For example, individuals high in trait PA who got sick following experimental exposure to an influenza virus reported fewer subjective symptoms than was expected given the objective markers of disease (e.g., mucus weight) suggesting a symptom reporting bias (Cohen et al., 2006). This study highlights the importance of measuring and reporting both subjective and objective markers of symptoms and/or pain when possible to clarify whether the relationship between PA and health is due to biased subjective reports or actual, biological resilience.

How Does PA “Get Under the Skin” and Benefit Health and Well‐Being? Main Effect Model of PA Most researchers studying PA and health presume that PA benefits health through behavioral (e.g., better sleep, diet, exercise, medication adherence; Boehm & Kubzansky, 2012; Pressman & Cohen, 2005), physiological (e.g., cardiovascular, immune), and social (e.g., more social support and integration) pathways. These pathways are multidirectional (e.g., more social support may lead to better health behaviors, better health behaviors lead to healthier physiology). Newer studies in this area of research explore important pathways like inflammation, diet, and vagal tone (e.g., Kok et al., 2013) revealing consistent evidence that PA has direct connections to many of these pathways that provide a compelling explanation for how PA could influence health.

Stress‐Buffering Model of PA In contrast to the main effect model, PA may improve health by improving an individual’s ability to handle stress, either by altering stress perceptions, reactions, or recovery (Pressman & Cohen, 2005). Under this model, PA is beneficial to health because it counters the negative health sequelae and physiological arousal tied to stressful experiences. For example, high PA individuals may have resources to help them cope better with stressors and recover faster. Although evidence supports that PA is beneficial during stress, it is unlikely that it is only

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­ eneficial during these times and, as explained with the main effect model, that PA even durb ing non‐stressful times has health‐enhancing benefits.

Broaden‐and‐Build Theory The broaden‐and‐build theory suggests that the evolutionary advantage of PA is to allow us to expand our resources by facilitating approach behaviors (Fredrickson, 2001) in contrast to NA, which narrows focus to dispel danger. For example, emotions such as joy, interest, and excitement may lead to actions like exploration, creativity, or play. These behaviors can build resources such as skills, knowledge, or social ties that will endure after the positive emotion has passed (Fredrickson, 2001) and benefit individuals during future situations. In the context of health, the broadened behaviors and cognitions experienced as a result of PA may provide individuals with physical resources, knowledge, or social connections that enable them to perform better health behaviors or reduce stress. Taken together, these models show that happiness is not a black box that pushes out health, but that there are many reasons why PA should be good for an individual. Importantly, many of these paths do not make sense when considering the argument that PA is simply the lack of NA. For example, not being negative (but also not being positive) would not confer protection or recovery against stress. Similarly, the absence of NA would not encourage individuals to explore and build resources, while the presence of PA would. Certainly, further work is necessary with well‐designed experimental and longitudinal studies to help us unpack these pathways; however, there are already several detailed conceptual models to build off of that will allow us to better understand the PA–health link.

Does Increasing PA Improve Health? Now that we understand the multitude of interconnections between PA and health, we can ask the question: Can increasing PA improve health? This may be difficult given that many researchers consider PA (especially trait PA) to be relatively stable due to genetic factors and/ or unchangeable circumstances (Lyubomirsky, Sheldon, & Schkade, 2005). Nevertheless, evidence also indicates that a reasonably large portion of PA may be unfixed (Lyubomirsky et al., 2005), and a meta‐analysis of over 50 PPIs revealed that, overall, PPIs have a small‐to‐medium effect on increasing PA (Sin & Lyubomirsky, 2009). So, if we can successfully increase PA, will this initiate improvement in health? While there are only a few studies on this topic to date, they are promising. For example, researchers found a simple positive writing intervention led to fewer health center visits and better self‐reported health and health behavior, even when the writing intervention was only 2 min each day for 2 days. A randomized controlled trial provided an educational intervention or an education plus PA induction intervention to a hypertensive African American sample. Individuals in the latter group had significantly higher medication adherence. While not typically assessing PA as an outcome, studies on mindfulness (which likely increases low arousal PA) have shown immunological, cardiovascular, and neurological changes (Kok et al., 2013). Similarly, increased PA after a loving‐kindness meditation intervention led to increases in perceived social connectedness, which, in turn, increased vagal tone (Kok et al., 2013). While studies have not yet tested major differences in mortality, morbidity, or survival from PPIs, research has shown that these methods are acceptable for diseased populations. Although more research is needed to establish the benefits of PPIs, current results are promising. One important unknown, however, is what the active ingredients are

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that are responsible for health improvements. PA is a multifaceted construct, and it is likely that different kinds of PA are more or less helpful for future health and for those facing disease. Thus, researchers should take a nuanced and cautious approach as they enter this new territory.

Conclusion The PA–health relationship is multifaceted and requires refined knowledge of how to measure and conceptualize both PA and health. Understanding these complex issues allows us to investigate how PA influences health outcomes like longevity/mortality, morbidity, and survival, as well as symptom report, severity, and pain. Several pathways, theories, and models can explain this PA–health link, and researchers can use this knowledge to fine‐tune the effectiveness of interventions to target specific health outcomes. Future research in the area of PA–health will make great strides in our understanding of the role affect can play in health in particular by examining how different arousal components of PA influence health differentially, expanding research on the types of illnesses PA may impact, refining our understanding of the pathways of the PA–health link, and testing the bounds of the health benefits of PPIs.

Author Biographies Brooke N. Jenkins is an assistant professor at Chapman University. Her work examines the effects of emotions on health. She is interested in methods for assessing emotions over time and how emotion variability influences physiological and behavioral health outcomes. She investigates new methods for assessing this variability, and in her research she works with a number of different populations including healthy children undergoing surgery and children with cancer, as well as healthy adults. Amanda M. Acevedo is a graduate student at the University of California, Irvine. Her research program lies at the intersection of culture, emotion, and health. She is interested in how positive emotions and cultural factors might confer resilience during stress in general and in the context of pain in particular. She uses cardiovascular stress reactivity/recovery methods to examine how emotions and cultural factors might buffer stress in the lab. Sarah D. Pressman is an associate professor of psychology and social behavior at the University of California, Irvine. Dr. Pressman’s work examines how positive emotions are beneficial for physical health and the pathways by which positive psychosocial factors “get under the skin” to influence biology. She is especially interested in the role of positive psychosocial factors in protecting individuals from the harmful effects of stress and whether specific emotions confer resilience in the face of adversity.

References Boehm, J. K., & Kubzansky, L. D. (2012). The heart’s content: The association between positive psychological well‐being and cardiovascular health. Psychological Bulletin, 138, 655–691. doi:10.1037/a0027448 Chida, Y., & Steptoe, A. (2008). Positive psychological well‐being and mortality: A quantitative review of prospective observational studies. Psychosomatic Medicine, 70, 741–756. doi:10.1097/PSY. 0b013e31818105ba

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Cohen, S., Alper, C. M., Doyle, W. J., Treanor, J. J., & Turner, R. B. (2006). Positive emotional style predicts resistance to illness after experimental exposure to rhinovirus or influenza a virus. Psychosomatic Medicine, 68, 809–815. doi:10.1097/01.psy.0000245867.92364.3c Curran, S. L., Andrykowski, M. A., & Studts, J. L. (1995). Short Form of the Profile of Mood States (POMS‐SF): Psychometric information. Psychological Assessment, 7, 80–83. doi:10.1037/1040‐ 3590.7.1.80 Diener, E., & Chan, M. Y. (2011). Happy people live longer: Subjective well‐being contributes to health and longevity. Applied Psychology. Health and Well‐Being, 3, 1–43. doi:10.1111/j.1758‐0854. 2010.01045.x DuBois, C. M., Lopez, O. V., Beale, E. E., Healy, B. C., Boehm, J. K., & Huffman, J. C. (2015). Relationships between positive psychological constructs and health outcomes in patients with cardiovascular disease: A systematic review. International Journal of Cardiology, 195, 265–280. doi:10.1016/j.ijcard.2015.05.121 Finan, P. H., & Garland, E. L. (2015). The role of positive affect in pain and its treatment. The Clinical Journal of Pain, 31, 177–187. doi:10.1097/AJP.0000000000000092 Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden‐and‐build theory of positive emotions. American Psychologist, 56, 218–226. doi:10.1037/0003‐ 066X.56.3.218 Fredrickson, B. L., Cohn, M. A., Coffey, K. A., Pek, J., & Finkel, S. M. (2008). Open hearts build lives: Positive emotions, induced through loving‐kindness meditation, build consequential personal resources. Journal of Personality and Social Psychology, 95, 1045–1062. doi:10.1037/a0013262 Kawamoto, R., & Doi, T. (2002). Self‐reported functional ability predicts three‐year mobility and mortality in community‐dwelling older persons. Geriatrics and Gerontology International, 2, 68–74. doi:10.1046/j.1444‐1586.2002.00024.x Kok, B. E., Coffey, K. A., Cohn, M. A., Catalino, L. I., Vacharkulksemsuk, T., Algoe, S. B., … Fredrickson, B. L. (2013). How positive emotions build physical health: Perceived positive social connections account for the upward spiral between positive emotions and vagal tone. Psychological Science, 24, 1123–1132. doi:10.1177/0956797612470827 Lyubomirsky, S., Sheldon, K. M., & Schkade, D. (2005). Pursuing happiness: The architecture of sustainable change. Review of General Psychology, 9, 111–131. doi:10.1037/1089‐2680.9.2.111 Moskowitz, J. T. (2003). Positive affect predicts lower risk of AIDS mortality. Psychosomatic Medicine, 65, 620–626. doi:10.1097/01.PSY.0000073873.74829.23 Pressman, S. D., Acevedo, A. M., Aucott, K., & Kraft, T. L. (under review). Smiling through the pain: The clinical benefits of facial expression during needle injection. Pressman, S. D., & Cohen, S. (2005). Does positive affect influence health? Psychological Bulletin, 131, 925–971. doi:10.1037/0033‐2909.131.6.925 Pressman, S. D., & Cohen, S. (2012). Positive emotion word use and longevity in famous deceased psychologists. Health Psychology, 31, 297–305. doi:10.1037/a0026071 Pressman, S. D., Jenkins, B. N., Kraft, T. L., Rasmussen, H., & Scheier, M. (2017). The whole is not the sum of its parts: Specific types of positive affect influence sleep differentially. Emotion, 17(5), 778–793. Sin, N. L., & Lyubomirsky, S. (2009). Enhancing well‐being and alleviating depressive symptoms with positive psychology interventions: A practice‐friendly meta‐analysis. Journal of Clinical Psychology, 65, 467–487. doi:10.1002/jclp Sturgeon, J. A., & Zautra, A. J. (2013). Psychological resilience, pain catastrophizing, and positive ­emotions: Perspectives on comprehensive modeling of individual pain adaptation topical collection on psychiatric management of pain. Current Pain and Headache Reports, 17. doi:10.1007/ s11916‐012‐0317‐4 Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070. doi:10.1037/0022‐3514.54.6.1063

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Suggested Reading Boehm, J. K., Peterson, C., Kivimaki, M., & Kubzansky, L. (2011). A prospective study of positive psychological well‐being and coronary heart disease. Health Psychology, 30, 259–267. doi:10.1037/ a0023124 Chida, Y., & Steptoe, A. (2008). Positive psychological well‐being and mortality: A quantitative review of prospective observational studies. Psychosomatic Medicine, 70, 741–756. http://doi.org/ 10.1097/PSY.0b013e31818105ba Cohen, S., Alper, C. M., Doyle, W. J., Treanor, J. J., & Turner, R. B. (2006). Positive emotional style predicts resistance to illness after experimental exposure to rhinovirus or influenza a virus. Psychosomatic Medicine, 68, 809–815. http://doi.org/10.1097/01.psy.0000245867.92364.3c Moskowitz, J. T. (2003). Positive affect predicts lower risk of AIDS mortality. Psychosomatic Medicine, 65, 620–626. http://doi.org/10.1097/01.PSY.0000073873.74829.23 Pressman, S. D., & Cohen, S. (2005). Does positive affect influence health? Psychological Bulletin, 131, 925–971. http://doi.org/10.1037/0033‐2909.131.6.925

The Precaution Adoption Process Model Neil D. Weinstein1, Peter M. Sandman1, and Susan J. Blalock2 Department of Human Ecology, Rutgers University New Brunswick, New Brunswick, NJ, USA Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 1

2

To understand why many young adults put themselves at risk for AIDS, it seems logical to assess their beliefs about HIV and AIDS. A questionnaire based on some popular theories of health behavior might ask them about the likelihood that they will have sexual contact with someone who is HIV positive, their chance of becoming infected by this person, the effectiveness of vari­ ous precautions, the social consequences of taking precautions, and other related topics. This research strategy makes sense today. But in 1987, when the public was first hearing about AIDS, young adults would be completely unable to answer most of the questionnaire. The riski­ ness of their behavior would vary: some would have had many sexual partners; others would have had few or none. Some would use condoms, and others would not. Yet, these behaviors could not be explained or predicted by their beliefs about AIDS. They had not yet formed such beliefs. As this example shows, theories that try to explain health behavior by focusing on beliefs about the costs and benefits of particular actions are relevant only to people who have been engaged sufficiently by the health threat to have formed such beliefs. The precaution adoption process model (PAPM) seeks to identify the stages that people need to pass through in order to commence health protective behaviors—including stages prior to being engaged by the issue—and to determine the factors that cause people to move from one stage to the next.

How Stage Theories Approach the Issue of Explaining and Changing Behavior Many theories of individual health behavior, such as those focusing on the perceived pros and cons of action, specify and use a single equation to predict behavior. These theories ­acknowledge The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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quantitative differences among people in their positions on different variables and, ­consequently, in their likelihood of action. The goal of interventions is to maximize the vari­ ables that increase the value of the prediction equation. Any action‐promoting variable that has not already reached its maximum value is an appropriate target for intervention. Advocates of stage theories, like PAPM, claim that there are qualitative differences among people and question whether changes in health behaviors can be described by a single predic­ tion equation. They suggest, in effect, that we must develop a series of explanatory equations, one for each stage transition. This is a much more complicated goal than finding a single pre­ diction rule, but it offers the possibility of improving prediction, accuracy, and intervention effectiveness and efficiency. Stage theories have four principal elements and assumptions (Weinstein, Rothman, & Sutton, 1998): 1 A category system to define the stages Stages are theoretical constructs. An ideal or “prototype” must be defined for each stage even if few people perfectly match this ideal. Furthermore, stages can be useful constructs even if the actual boundaries between stages are not as clear‐cut as the theories suggest. 2 An ordering of the stages Stage theories assume that before people act, they usually pass through all the stages in order. Forward progression, however, is neither inevitable nor irreversible. There is no minimum length of time people must spend in a particular stage. In fact, people may sometimes progress so rapidly that, for practical purposes, they can be said to skip stages (e.g., when a doctor rec­ ommends a new vaccine and the patient gets the shot without any further deliberation). Some stages may lie outside the route to action. An example is a stage representing people who have decided not to act. 3 Common barriers to change facing people in the same stage Knowing the stage of an individual or group is helpful in designing an intervention program only if people at that stage must address similar types of issues before they can progress to the next stage. Thus, interventions can be tailored on the basis of stage. 4 Different barriers to change facing people in different stages If factors producing movement toward action were the same regardless of a person’s stage, the  same intervention could be used for everyone, and the concept of stages would be superfluous. A completely specified stage theory would include both the criteria that define stages and factors that govern movement between stages. Although stage definitions are meant to apply across behaviors, the specific barriers to progress may be behavior or hazard specific. Factors that enter into decisions to lose weight, for example, may be quite different from those that affect decisions to use condoms. A model that proposes a particular sequence of stages in the change process could be correct about these stages even if it has not identified all the determi­ nants of each transition from one stage to the next. At present, the PAPM does not provide detailed information about barriers at each stage. It is a conceptual framework or skeleton that needs to be fleshed out with information about why each stage transition occurs.

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The Precaution Adoption Process Model Description of the Model The PAPM attempts to explain how a person comes to a decision to take action and how he or she translates that decision into action. Adoption of a new precaution or cessation of a risky behavior requires deliberate steps unlikely to occur outside of conscious awareness. The PAPM applies to these types of actions, not to the gradual development of habitual behaviors, such as exercise and diet, in which health considerations may play little role (though it would apply to the initiation of a new exercise program or a new diet). Nor does the PAPM explain the com­ mencement of risky behaviors—such as a teenager accepting her first cigarette—which seem to be better explained in terms of a “willingness” to act than in terms of any decision to act (Gibbons, Gerrard, Blanton, & Russell, 1998). All PAPM stages prior to action are defined in terms of mental states, rather than in terms of factors external to the person, such as current or past behaviors. Neither are PAPM stages defined by criteria that are salient only to health professionals. For example, having less than 20% of calories in one’s diet come from fat might be a behavioral goal of importance to a dieti­ cian, but laypeople are unlikely to be aware of or be able to track their progress toward such a goal. Their goals, and the appropriate definitions of stages for them, are more likely to center on particular foods, such as increasing the consumption of fish to three meals a week, and PAPM stages are defined in terms of goals that reflect lay objectives. The present formulation of the PAPM (Weinstein & Sandman, 1992) identifies seven stages along the path from lack of awareness to action (see Figure 1). At some initial point in time, people are unaware of the health issue (stage 1). When they first learn something about the issue, they are no longer unaware, but they are not yet engaged by it either (stage 2). People who reach the decision making stage (stage 3) have become engaged by the issue and are considering their response. This decision making process can result in one of three outcomes: They may suspend judgment, remaining in stage 3 for the moment. They may decide to take no action, moving to stage 4 and halting the precaution adoption process, at least for the time being. Or they may decide to adopt the precaution, moving to stage 5. For those who decide to adopt the precaution, the next step is to initiate the behavior (stage 6). A seventh stage, if relevant, indicates that the behavior has been maintained over time (stage 7). The stages have been labeled with numbers, but these numbers have no more than ordinal values. They would not even have ordinal value if stage 4 were included, since it is not a stage on the path to action. The numbers should never be used to calculate correlation coefficients, the mean stage for a sample, or conduct regression analyses with stage treated as a continuous variable. Such calculations assume that the stages represent equal‐spaced intervals along a sin­ gle underlying dimension, which violates a fundamental assumption of stage theory. Although

Stage 1 unaware of issue

Stage 2 unengaged by issue

Stage 3 undecided about acting

Stage 5 decided to act

Stage 4 decided not to act

Figure 1  Stages of the precaution adoption process model.

Stage 6 acting

Stage 7 maintenance

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not shown in Figure 1, movement backward toward an earlier stage can also occur, without necessarily going back through all the intermediate stages, although it is not possible to go from later stages to stage 1. On the surface, the PAPM resembles another stage theory, the transtheoretical model devel­ oped by Prochaska and DiClemente (1983). However, even those stages with similar names in the two theories are defined according to quite different criteria. For example, the PAPM refers primarily to mental states, whereas the TTM emphasizes days or months until intended action. We are not aware of any research directly comparing the two theories.

Justification for the PAPM Stages There should be good reasons to propose the separate stages that make up a stage theory. What is the justification for the stages in the PAPM? Stage 1 (unaware) Much health research deals with well‐known hazards, like smoking, AIDS, and high‐fat diets. In such cases, asking someone about his or her beliefs and plans is quite reasonable; most peo­ ple have considered the relevance of these threats to their own lives. But if people have never heard of a hazard or a potential precaution, they cannot have formed opinions about it. The reluctance of respondents to answer survey questions about less familiar issues suggests that investigators ought to allow people to say that they “do not know” or have “no opinion” rather than forcing them to state a position. Participants in many health behavior investiga­ tions are not given this opportunity. Even when participants are permitted to say that they “do not know,” these responses are often coded as missing or are collapsed into another category. To say “I do not know” indicates something important and is real data that should not be discarded. Media often have a major influence in getting people from stage 1 of the PAPM to stage 2 and from stage 2 to stage 3 and much less influence thereafter. This and other factors that may be important in producing different transitions are given in Table 1 and in Weinstein (1988). These are suggestions for consideration, not core assumptions of the PAPM. Table 1  Factors likely to determine progress between stages. Stage transition

Factor

Stage 1 to stage 2 Stage 2 to stage 3

Media messages about the hazard and precaution Media messages about the hazard and precaution Communications from significant others Personal experience with hazard Beliefs about hazard likelihood and severity Beliefs about personal susceptibility Beliefs about precaution effectiveness and difficulty Behaviors and recommendations of others Perceived social norms Fear and worry Time, effort, and resources needed to act Detailed “how‐to” information Reminders and other cues to action Assistance in carrying out action

Stage 3 to stage 4 or stage 5

Stage 5 to stage 6

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Stage 2 (unengaged) versus stage 3 (undecided) Once people have heard about a health precaution and have begun to form opinions about it, they move out of stage 1. However, so many issues compete for their limited time and atten­ tion that people can know a moderate amount about a hazard or a precaution without ever considering whether they need to do anything about it. We believe that this condition of awareness without personal engagement is quite common. In a 1986 survey of radon testing (Weinstein, Sandman, & Klotz, 1987), for example, 50% of respondents in a high‐risk region said they had never thought about testing their own homes even though all indicated that they knew what radon was, and most correctly answered more than half of the questions on a knowledge test. The PAPM suggests further that it is important to distinguish between people who have never thought about an action and those who have given the action some consideration but are undecided. There are several reasons for making this distinction. People who have thought about acting are likely to be more knowledgeable. Also, getting people to think about an issue may require different sorts of communications (and entail different sorts of obstacles) than getting them to adopt a particular conclusion. Thus, whether a person has or has not thought about taking action appears to be an important distinction. Stage 3 (undecided) versus stage 4 (decided not to act) and stage 5 (decided to act) Research reveals important differences between people who have not yet formed opinions and those who have made decisions. People who have come to a definite position on an issue, even if they have not yet acted on their opinions, have different responses to information and are more resistant to persuasion than people who have not formed opinions (Cialdini, 1988; Ditto & Lopez, 1992; Nisbett & Ross, 1980, chapter 8). This widely recognized tendency to adhere to one’s own position has been termed “confirmation bias.” It manifests itself in a variety of ways. According to Klayman (1995), these include overconfidence in one’s beliefs, searches for new evidence that are biased to favor one’s beliefs, biased interpretations of new data, and insufficient adjustment of one’s beliefs in light of new evidence. For these reasons, the PAPM holds that it is significant when people say that they have decided to act or have decided not to act and that the implications of someone saying that they have decided not to act are not the same as saying it is “unlikely” they will act. We believe that cost–benefit theories of health behavior, such as the health belief model, the theory of reasoned action, protection motivation theory, and subjective expected utility the­ ory, are dealing mainly with factors that govern how people who get to stage 3 decide what to do. Factors these theories focus on are certainly important, but they relate mostly to this one phase of the precaution adoption process. These theories also overlook another possibility that people faced with a difficult decision might get stuck and quit trying to make up their minds, moving back to stage 2. Determinants of this regression to an earlier stage might be different from the factors that lead people toward stages 4 or 5. Perceived susceptibility is one factor that can influence what people decide and is included in most theories of health behavior (Connor & Norman, 1995). People are reluctant to acknowl­ edge personal susceptibility to harm even when they acknowledge risks faced by others. Consequently, overcoming this reluctance is a major barrier to getting people to decide to act. Stage 5 (decided to act) versus stage 6 (acting) The distinction between decision and action is common to most stage theories. For example, Schwarzer’s health action process approach (Schwarzer, 1992) distinguishes between an initial motivation phase during which people develop an intention to act, based on beliefs about risk,

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outcomes, and self‐efficacy, and the volition phase in which they plan the details of action, initiate action, and deal with the difficulties of carrying out that action. Even Ajzen’s (Ajzen, 1985) theory of planned behavior, which is not a stage theory, separates intentions from actions. Protection motivation theory (Rogers & Prentice‐Dunn, 1997) is also not a stage theory, but its developers implicitly recognize the need for sequencing interventions. According to these authors “[Protection Motivation Theory] experiments always present information in the same order: threatening information followed by coping information” (p. 116). These researchers also speak of first developing motivation and then developing coping skills. A growing body of research suggests that there are important gaps between intending to act and carrying out this intention and that helping people develop specific implementation plans can reduce these barriers. The PAPM suggests that detailed implementation information would be uninteresting to people in early stages. Yet, for people who have decided to act, such information is often essential to produce the transition from decision to action. This claim is echoed by temporal construal theory (Trope & Liberman, 2003), which asserts that decisions about action are based initially on abstract construals of the options but become more focused on concrete details as the actual choice approached. Stage 6 (acting) versus stage 7 (maintenance) For any health behavior that is more than a one‐time action, adopting the behavior for the first time is different from repeating the behavior at intervals, or developing a habitual pattern of response. Once a woman gets her first mammogram, for example, she will have acquired both more information in general and personal experience (perhaps both positive and negative). These will influence the decision to be rescreened. Similarly, a man who stops smoking or loses weight must deal with the acute withdrawal experience and/or the glow of success in the early stage of taking action but must address different challenges in the maintenance stage. The distinction between action and maintenance is widely recognized (e.g., Marlatt & Gordon, 1985; Meichenbaum & Turk, 1987). Stages of inaction One value of the PAPM is its recognition of differences among the people who are neither acting nor intending to act. People in stage 1 (unaware), stage 2 (unengaged), stage 3 (unde­ cided), and stage 4 (decided not to act) all fit in this broad category. Those in stage 1 need basic information about the hazard and recommended precautions. People in stage 2 need something that makes the threat and action personally relevant. Individualized messages and contact with friends and neighbors who have considered action should help these individuals move to the next stage. Another powerful influence on the transition from stage 2 to stage 3 is the awareness that others are making up their minds and that one is obliged to have some opinion on this current issue of the day. As stated earlier, people who have thought about and rejected action, stage 4, are a particu­ larly difficult group. Evidence shows that they can be quite well informed (Blalock et al., 1996; Weinstein & Sandman, 1992) and, as noted earlier, they will tend to dispute or ignore infor­ mation that challenges their decision not to act.

Criteria for Applying Stage‐Based Interventions A variety of issues should be considered to determine the practical utility of the PAPM or any other stage theory.

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Superiority Over Unstaged Messages The value of a stage theory depends on the extent to which it leads to interventions that are more effective than generic messages. One might ask, “Why not just employ combination treatments that contain elements for people at each stage?” There are several problems with this suggestion. First, a combination treatment will be much longer than a stage‐matched treatment. Media time is expensive, speakers usually have a limited length of time for their presentations, and audiences have a limited attention span. Thus, attempting to replace a stage‐focused intervention with a multi­ faceted combination treatment would involve substantial costs. Second, people are most likely to be engaged by a treatment that matches their stage, and a mismatched treatment may lose their attention. Thus, members of the general public who have already decided to act may be put off by basic risk information and may fail to attend to the subsequent, detailed procedural information describing what they do need. Nevertheless, if only a single message can be given to a mixed‐stage audience, a combination intervention would probably be the most appropriate.

Stage Assessment A second criterion is the ability to identify stages accurately and efficiently. The PAPM requires only a simple process to assess a person’s stage, so it can be used easily in individual and small‐ group settings. Clinicians could integrate this assessment without disrupting their practices. Similarly, a filtering question on a website can assess stage and send visitors to the page most relevant to their stage. Even in a large audience, a show of hands might be used to determine the distribution of stages present.

Delivery of Stage‐Targeted Messages The feasibility of delivering stage‐targeted messages in different situations varies greatly. If communication is one on one, delivering the message appropriate for an individual is relatively easy. In group settings, messages can be chosen to fit the overall audience, though not individual members. In mass communications, a stage approach is more often practicable with print than with broadcast media. Within print channels, pam­ phlets and magazines offer more opportunities for stage targeting than newspapers; within broadcasting, cable offers more opportunities for stage targeting than networks. The Internet makes it possible for individual users to choose different information path­ ways depending on their self‐perceived information needs. This should provide unprec­ edented opportunities for low‐cost message targeting, though evidence on the cost of such tailored messaging is scanty. There is evidence, though, that people in different PAPM stages perceive themselves as needing different types of information (Weinstein, Lyon, Sandman, & Cuite, 1998). The ability to deliver targeted messages to members of a group also depends on the range of stages present in that group. The greater the range of stages, the more difficult it is to choose a single message. For a mass audience, the most efficient way to encourage a new health protective action may be with a comprehensive broadcast message that ignores stage or assumes everyone to be at a very early stage. As the issue matures, however, distinctive audi­ ences, separable by stage, merit distinctive messages, and print or “narrowcasting” becomes the medium of choice for mass communications.

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The Difficulty of Behavior Change A final important criterion concerns the difficulty of the action being advocated and the expected resistance of the audience to the behavior change recommendation. When a behavior is easy and resistance is low, stage may matter little. In such situations, interventions and mes­ sages needed to help people progress from stage to stage can be brief, and several may be combined into a single comprehensive treatment. In contrast, when change is difficult and resistance is high, there is a greater need to have separate messages for each stage. Note that although some precautions—such as changing to a fluoride‐containing toothpaste—are easy to carry out, the ease of others may vary greatly from one person to the next. Health profes­ sionals should never assume that a behavior is easy without considering carefully the obstacles that may exist.

Using the PAPM to Develop and Evaluate Behavior Change Interventions Blalock and colleagues used the PAPM in three studies conducted from 1994 to 2000 that focused on osteoporosis prevention (Blalock et al., 2002). Osteoporosis is a metabolic bone disorder that results in decreased bone density and increased susceptibility to fracture (Riggs & Melton, 1986). Precautions recommended to reduce the risk of developing osteoporosis include adequate calcium intake and weight‐bearing exercise (Kling, Clarke, & Sandhu, 2014). Their research was designed to better understand the factors that (a) discriminate among women in different stages with respect to calcium intake and exercise and (b) predict different types of stage transitions (Blalock et al., 1996). This information was used to develop stage‐ based educational interventions. These studies provide examples of the necessary steps in using the PAPM to develop and evaluate behavior change interventions. The first step involves identifying and clearly defining the behavior of interest. Although the PAPM focuses on the adoption of specific health behaviors (e.g., “daily walking for at least 30 min”), it may also be used to intervene at a broader behavioral category level (e.g., “increas­ ing exercise”). In either case, care should be taken to define the target behavior(s) in terms that are meaningful to laypeople. Although Blalock and colleagues defined the target behavior in terms of a specific daily calcium intake, a value that has little meaning to most laypeople, they overcame this problem by providing study participants with feedback that informed them of their current calcium intake. This step would not have been needed if the behavior criterion had been simpler, such as “using a calcium supplement.” Second, a system must be developed to classify individuals according to their current stage. The stage classification system allows health professionals to assess the distribution of stages within a target population at a particular point in time, guiding the design of both individual and community‐level interventions. As described in the AIDS example at the beginning of this chapter, if awareness and knowledge of a health threat change over time, the effectiveness of different types of interventions is likely to change as well. Thus, monitoring temporal changes in the distribution of people across stages makes it possible to design dynamic interventions that accommodate the dynamic nature of the behavior change process. Third, it is necessary to have at least a preliminary understanding of the factors that ­influence different types of stage transitions. This understanding is needed to tailor interventions to people, or groups of people, who are in different stages of change. Early work by Blalock et al. (1996) suggested that to move people, or groups of people, from stages 1 and 2 (unaware,

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unengaged) to stage 3 (undecided), interventions should focus on increasing awareness of the health problem of interest, behavioral recommendations to minimize risk, and potential ben­ efits associated with adopting the behavioral recommendations—including the effectiveness of the recommended behaviors in terms of risk reduction (i.e., precaution effectiveness). The cross‐sectional and prospective research carried out by Blalock and colleagues examined between‐stage differences. Significant differences between stages on particular variables sug­ gest that these variables are worthy of additional attention—and of inclusion in interventions at early stages of research—but they are not proof of causation. Furthermore, if interventions succeed in altering these variables but fail to move people to stages closer to action, this does not prove that the stages themselves are invalid. The variables that actually cause each stage transition must be identified empirically. Fourth, intervention strategies are needed to address variables associated with different stage transitions. For example, media campaigns and informational materials may be able to increase awareness of a health problem, behavioral recommendations, and the benefits associ­ ated with action. However, more intensive interventions are often needed to help individuals acquire the skills and resources needed to support behavior change efforts. The intensity of the intervention required will depend on the behavior of interest and what barriers need to be overcome. For example, Blalock and colleagues used a combination of written materials and telephone counseling focused on helping women identify potential barriers to action and develop strategies to overcome the barriers identified. This approach led to a significant increase in calcium intake among women who were thinking about or trying to increase their calcium intake at baseline. However, a similar intervention focused on exercise had no effect on exercise level. Fifth, health educators must specify how the effectiveness of the intervention will be determined. Will it be considered effective if it results in stage progression, even if the proportion of people in the action and maintenance stages remains the same? Or, is success contingent upon behavior change in the target group? If a behavior is difficult to change and people are in early stages, the PAPM suggests that a single, one‐shot intervention—especially an intervention that focuses on movement to the next stage—should not be judged solely by whether it changes behavior. Finally, educators must determine the timeframe for follow‐up assessments. The PAPM and other stage theories suggest that the behavior change process is dynamic. Intervention‐induced changes in beliefs and behavior may be transient, so intervention effects may be missed if only long‐term follow‐up assessments are used. Although long‐term behavior change is generally desired, a stage model perspective raises the possibility that even transient changes may be steps in the right direction, helping us to understand the barriers at different stages and increas­ ing the success of subsequent behavior change attempts.

Research Using the PAPM The PAPM has been applied to many types of health behaviors, including osteoporosis preven­ tion (e.g., Blalock et al., 1996), cancer screening (e.g., Glanz, Steffen, & Taglialatela, 2007), hepatitis B vaccination (Hammer, 1998), home radon testing (e.g., Weinstein, Lyon, et al., 1998), smoking cessation (Borrelli et  al., 2002), and red meat consumption (Sniehotta, Luszczynska, Scholz, & Lippke, 2005). As discussed earlier in this chapter, stage theories— with their numerous assumptions about stages and about the changing barriers between stages—are complex. Given the limited, though growing, number of studies relating to the PAPM and the variety of behaviors examined, it is not yet possible to reach firm conclusions

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about the model’s validity or its helpfulness for designing interventions. For further inform­ ation on the ways in which the PAPM is being used, the problems researchers encounter in these investigations, and a detailed analysis of stage‐matched field study to encourage home radon testing, see Weinstein, Sandman, and Blalock (2008).

Conclusion and Future Directions Most other (non‐stage) theories of individual health behavior regard adoption of new precau­ tions as involving only one step, from inaction to action (or, perhaps, inaction to intention), and the variables typically claimed to produce this step clearly characterize it as a judgment about the relative costs and benefits of action. The PAPM does not reject the variables identified by these theories. Rather, it sees the theories as describing just one part of the precaution adoption process, the stage when people are actively weighing options and deciding what to do. The PAPM, however, shows that other issues important to behavior change arise before people ever think seriously about action and still different issues arise after people have decided to act. Because the PAPM is not composed of a short list of variables, it does not offer a simple process for designing interventions. Rather, it is a framework that can be used to guide inter­ ventions by identifying barriers that inhibit movement from one stage to the next. As addi­ tional research is conducted, we will learn more about barriers at each stage and will see how consistent these barriers are from one health behavior to the next.

Acknowledgments This chapter is adapted from Weinstein et al. (2008).

Author Biographies Neil D. Weinstein is a professor emeritus of Rutgers, The State University of New Jersey, where he was a member of the Departments of Human Ecology and Psychology. His major areas of scholarship include risk perception, unrealistic optimism about personal risk, commu­ nication of risk information, stage theories, the precaution adoption process model, smokers’ understanding of the risks of smoking, the relationship between perceived personal risk and health behavior, and theory testing. Peter M. Sandman was a Rutgers University professor from 1977 to 1995 and is one of the preeminent risk communication and reputation management speakers and consultants in the world today. Dr. Sandman works in three risk communication venues: helping arouse concern when people are insufficiently upset about serious risks, helping diminish concern when peo­ ple are excessively upset about small risks, and helping guide concern when people are appro­ priately upset about serious risks. Susan J. Blalock is a professor in the Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill. A hallmark of her research involves the application of behavio­ ral science theories to understand the mechanisms through which intervention effects are obtained. Her current research focuses on how patients process information concerning medi­ cation risks and benefits and the effect this type of information has on patient judgments and decisions regarding medication use.

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References Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11–39). Heidelberg: Springer Verlag. http:// dx.doi.org/10.1007/978‐3‐642‐69746‐3_2 Blalock, S. J., DeVellis, B. M., Patterson, C. C., Campbell, M. K., Orenstein, D. R., & Dooley, M. A. (2002). Effects of an osteoporosis prevention program incorporating tailored educational materials. American Journal of Health Promotion, 16, 146–156. http://dx.doi.org/10.4278/0890‐1171‐ 16.3.146 Blalock, S. J., DeVellis, R. F., Giorgino, K. B., DeVellis, B. M., Gold, D. T., Dooley, M. A., … Smith, S. L. (1996). Osteoporosis prevention in premenopausal women: Using a stage model approach to examine the predictors of behavior. Health Psychology, 15, 84–93. http://dx.doi.org/10.1037/0278‐ 6133.15.2.84 Borrelli, B., McQuaid, E. L., Becker, B., Hammond, K., Papandanatos, G., Fritz, G., & Abrams, D. (2002). Motivating parents of kids with asthma to quit smoking: The PAQS project. Health Education Research, 17, 659–669. http://dx.doi.org/10.1093/her/17.5.659 Cialdini, R. B. (1988). Influence: Theory and practice. Glenview, IL: Scott Foresman. Connor, M., & Norman, P. (1995). Predicting health behavior. Philadelphia, PA: Open University Press. Ditto, P. H., & Lopez, D. F. (1992). Motivated skepticism: Use of differential decision criteria for preferred and nonpreferred conclusions. Journal of Personality and Social Psychology, 63, 568–584. http://dx.doi.org/10.1037/0022‐3614.63.4.568 Gibbons, F. X., Gerrard, M., Blanton, H., & Russell, D. W. (1998). Reasoned action and social reaction: Willingness and intention as independent predictors of health risk. Journal of Personality and Social Psychology, 74, 1164–1181. http://dx.doi.org/10.1037/0022‐3514.74.5.1164 Glanz, K., Steffen, A. D., & Taglialatela, L. A. (2007). Effects of colon cancer risk counseling for first‐ degree relatives. Cancer, Epidemiology, Biomarkers and Prevention, 16, 1485–1491. http://dx.doi. org/10.1158/1055‐9965.EPI‐06‐0914 Hammer, G. P. (1998). Factors associated with hepatitis B vaccine acceptance among nursing home workers. Unpublished doctoral dissertation, Department of Health Policy and Management, Johns Hopkins University. Klayman, J. (1995). Varieties of confirmation bias. In J. Busemeyer & R. Hastie (Eds.), The psychology of learning and motivation. Vol. 32: Decision making from a cognitive perspective. New York, NY: Academic Press. Kling, J. M., Clarke, B. L., & Sandhu, N. (2014). Osteoporosis prevention, screening, and treatment: A review. Journal of Women’s Health, 23, 563–572. http://dx.doi.org/10.1089/jwh.2013.4611 Marlatt, G. A., & Gordon, J. R. (1985). Relapse prevention: Maintenance strategies in the treatment of addictive behaviors. New York, NY: Guilford. Meichenbaum, D., & Turk, D. C. (1987). Facilitating treatment adherence: A practitioner’s handbook. New York, NY: Plenum. http://dx.doi.org/10.1007/978‐1‐4684‐5359‐1 Nisbett, R., & Ross, L. (1980). Human inference: Strategies and shortcomings of social judgment. Englewood Cliffs, NJ: Prentice‐Hall. http://dx.doi.org/10.2307/2184495 Prochaska, J. O., & DiClemente, C. C. (1983). Stages and processes of self‐change in smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51, 390–395. http:// dx.doi.org/10.1037//0022‐006X.51.3.390 Riggs, B. L., & Melton, L. J. I. (1986). Involutional osteoporosis. New England Journal of Medicine, 314, 1676–1686. http://dx.doi.org/10.1056/NEJM198606263142605 Rogers, R. W., & Prentice‐Dunn, S. (1997). Protection motivation theory. In D. Gochman (Ed.), Handbook of health behavior research. Vol. 1. Determinants of health behavior: Personal and social (pp. 113–132). New York, NY: Plenum. Schwarzer, R. (1992). Self‐efficacy in the adoption and maintenance of health behaviors: Theoretical approaches and a new model. In R. Schwarzer (Ed.), Self‐efficacy: Thought control of action (pp. 217–242). Washington, DC: Hemisphere.

506

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Sniehotta, F. F., Luszczynska, A., Scholz, U., & Lippke, S. (2005). Discontinuity patterns in stages of the precaution adoption process model: Meat consumption during a livestock epidemic. British Journal of Health Psychology, 10, 221–235. http://dx.doi.org/10.1348/135910705X26137 Trope, Y., & Liberman, N. (2003). Temporal construal theory. Psychological Review, 100, 403–421. http://dx.doi.org/10.1037/0033‐295X.110.3.403 Weinstein, N. D. (1988). The precaution adoption process. Health Psychology, 7, 355–386. Weinstein, N. D., Lyon, J. E., Sandman, P. M., & Cuite, C. L. (1998). Experimental evidence for stages of precaution adoption. Health Psychology, 17, 445–453. http://dx.doi.org/10.1037/0278‐6133. 17.5.445 Weinstein, N. D., Rothman, A., & Sutton, S. (1998). Stage theories of health behavior. Health Psychology, 17, 290–299. http://dx.doi.org/10.1037/0278‐6133.17.3.290 Weinstein, N. D., & Sandman, P. M. (1992). A model of the precaution adoption process: Evidence from home radon testing. Health Psychology, 11, 170–180. http://dx.doi.org/10.1037/0278‐ 6133.11.3.170 Weinstein, N. D., Sandman, P. M., & Klotz, M. L. (1987). Public response to the risk from radon, 1986. New Brunswick, NJ: Environmental Communications Research Program, Rutgers University. Weinstein, N. D., Sandman, P. S., & Blalock, S. J. (2008). In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), The precaution adoption process model (pp. 123–147). San Francisco, CA: Jossey‐Bass.

Prejudice and Stereotyping in Healthcare Elizabeth S. Focella Department of Psychology, University of Wisconsin Oshkosh, Oshkosh, WI, USA

In the social psychological literature, stereotypes are defined as beliefs, usually overgeneralizations, associating particular traits and attributes with members of a particular group (e.g., “members of this group are lazy”). Prejudice, in contrast, is an attitude (usually unfavorable, e.g., dislike) toward a person based solely on that person’s presumed group membership. Both stereotyping and prejudice may be considered a form of bias. Literature in social psychology has long been interested in the concepts of stereotyping and prejudice as a way to understand intergroup relations. More recently, stereotyping and prejudice have been studied in the context of healthcare to help explain the apparent health disparities between groups, particularly between Whites (a term that will be used hereafter to refer to a diverse group of people who identify as White, Caucasian, or European American and people of European descent who may or may not be American) and Blacks (a term that will be used hereafter to refer to a diverse group of people who identify as Black or African American and people of African descent who may or may not be American). Researchers in a variety of disciplines, including psychology, medicine, and public health, find that even when controlling for factors such as access to healthcare, low socioeconomic status (SES), and disease severity, members of low‐power groups (Blacks in particular) still have worse health outcomes, including increased risk of mortality. Among the potential sources for this disparity, some research documents the presence of differential treatment in numerous areas of healthcare, including medicine, surgery, primary care, and mental health, and for a variety of conditions and procedures, including coronary artery disease, renal disease and kidney transplantation, stroke, cancer, HIV/AIDS, medication for pain management, and advice for smoking cessation (e.g., see Geiger, 2003). Among a number of explanations for these disparities in treatment and outcomes, stereotyping and prejudice in healthcare has received compelling empirical support (for a review, see Chapman, Kaatz, & Carnes, 2013; Dovidio & Fiske, 2012).

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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A substantial portion of literature concerning stereotyping and prejudice in healthcare examines racial/ethnic bias, particularly against Black patients (van Ryn & Burke, 2000). This is likely due to the fact that Blacks have poorer health outcomes, including risk of mortality, than other racial and ethnic groups. Hence, the literature on bias against Blacks represents a large portion of our empirical knowledge on the topic of bias in healthcare. The literature examining stereotyping and prejudice in healthcare does, however, document prejudice and stereotyping for other racial and ethnic groups, including Hispanic and Native American patients, and exists beyond examination of racial and ethnic bias, including bias toward other low‐power groups including people who identify as lesbian, gay, bisexual, or transgender (LGBT), women, the elderly, and people who are overweight, are diagnosed with mental illness, and abuse drugs and alcohol. At times, people are able to consciously access and report their feelings (prejudice) and beliefs (stereotypes) about particular groups of people. In such cases, prejudices and stereotypes are conscious, or explicit. In the past few decades, however, research has shown that in some cases, people hold stereotypes and prejudices that they are not consciously aware of (i.e., nonconscious), or are implicit. The literature on prejudice in healthcare examines both explicit and implicit stereotyping and prejudice among clinicians.

Explicit Stereotyping and Prejudice In the context of healthcare, the literature documenting clinicians’ explicit bias is comparatively less robust than literature documenting implicit bias. This may be the case because most physicians and other healthcare providers intend to treat their patients fairly and may not wish to endorse stereotyping or prejudice directed at these groups, thus making the prevalence of explicit stereotyping and prejudice less pronounced among healthcare providers. When explicit stereotypes are documented, they are typically directly related to concerns about compliance and adherence to treatment. Explicit negative attitudes (i.e., dislike) are typically less reported than stereotypes and are often expressed toward groups for which bias is more socially accepted (e.g., people who are overweight, people who are drug users). The literature finds that racial and ethnic groups including Blacks, Hispanics, and Native Americans, as well as members of other groups, including persons who are overweight and former injecting drug users, are explicitly stereotyped by healthcare providers as noncompliant. For example, one study found that medical and nursing students reported awareness of stereotypes associating Hispanics and Native Americans with noncompliance (Bean et  al., 2014). Similarly, other studies show that healthcare providers stereotype Blacks, people who are overweight, and former injecting drug users as less likely to adhere to treatment. Further, the aforementioned study of medical and nursing students found that medical and nursing students reported awareness of stereotypes associating Hispanics and Native Americans with risky health behavior and difficulty understanding and/or communicating health‐related information. While the stereotype of noncompliance is associated with several groups, other stereotypes are more group specific. In particular, overweight patients face more unique stereotypes in addition to noncompliance, including perceptions that they lack motivation and are lazy, self‐indulgent, and physically exhausting to care for. Other more general negative stereotypes toward overweight patients include being lower in social attractiveness, as well as physically unattractive. Other well‐documented explicit stereotypes in healthcare relate to perceptions of controllability, blame, and fault for the patient’s health state. These beliefs are often associated with patients who abuse drugs and alcohol, are overweight, are diagnosed with mental illness, and

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are current or former injecting drug users. Interestingly, these negative attitudes toward those who abuse substances may be influenced by beliefs about controllability and social mores. Literature on healthcare providers’ beliefs about overweight patients, as well as those coping with mental illness (e.g., those with schizophrenia) and substance abuse, shows that a portion of healthcare providers express the belief that those patients are responsible for their current state. For example, as documented in some literature, nurses have expressed the belief that their overweight patients’ failure to lose weight can be attributed to noncompliance with treatment recommendations and lack of motivation. Further, the literature suggests a link between these negative stereotypes and the clinician’s preexisting beliefs about personal responsibility. One study shows that clinicians who identify as conservative, as opposed to liberal, may have more negative attitudes toward patients who are injecting drug users. Mediation analyses indicated that conservative clinicians’ negative attitudes toward injecting drug users are due to the belief that injecting drug use is in the patient’s control (Brener, Hippel, Kippax, & Preacher, 2010). Taken together, the literature suggests that negative attitudes toward these groups of patients can be characterized by negative personal attributions for the patients’ state. In relation to stereotyping, literature documents the possibility that healthcare providers may, at times, overly attribute their patients’ physical ailments to the patients’ more salient condition. For example, studies show that nurses in particular express the belief that their overweight patients’ ailments are likely the result of their patients’ weight and that nurses caring for patients who are injecting drug users express the belief that their patients’ ailments are caused by their injecting drug user status. Importantly, this occurs regardless of whether the ailment is in fact due to the patients’ obesity or drug use. The tendency to overly rely on a patient’s group membership when making a determination about their treatment and diagnoses is a form of stereotyping and presents a potential for diagnostic overshadowing, in which a patients’ symptoms and ailments are blamed on another, more obvious condition. In diagnostic overshadowing, a disease or otherwise harmful or life‐threatening condition is not detected because it is “overshadowed” by a more obvious and more salient condition—a circumstance that delays treatment for the true cause of the patient’s ailments and possibly an increased likelihood of death. Diagnostic overshadowing occurs for people with a variety of conditions including people who are overweight, have intellectual disabilities, have physical disabilities, and are diagnosed with mental illness. In addition to explicit stereotyping of patients, the literature also documents the existence of prejudice. In a study about prejudice toward schizophrenic patients, researchers found that healthcare providers showed greater desire for social distance from a schizophrenic, compared with a non‐schizophrenic patient. Other research documents some evidence of explicit prejudice against Black patients, including physicians’ lower feelings of affiliation with Black, compared with White patients. Further, prejudice toward those who are overweight among healthcare providers is widely documented, including some healthcare providers’ feelings of disgust, repulsion, and pity for their obese patients. Physicians also express embarrassment at the prospect of discussing sexuality and sexually related healthcare with LGBT patients. The literature also documents more general negative affective responses of healthcare providers toward those who abuse drugs and alcohol, as well as those with mental disorders.

Implicit Stereotyping and Prejudice Implicit bias can be assessed in a number of ways including psychophysical reactions, neuroimaging, and computerized reaction time tasks that measure participants’ associations of ­particular

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groups of people with certain attributes as well as general evaluative concepts like “good” or “bad.” Implicit bias is most often assessed through a particular reaction time task known as the Implicit Association Test (IAT). The IAT is administered on a computer, in which participants are asked to categorize a set of words or images. The IAT measures the strength of associations between particular concepts (typically groups of people such as Blacks and Whites) and evaluations (such as good or bad) or stereotypes (such as noncompliant). The notion behind the IAT is that when a person holds an implicit association between a group of people and a stereotype or evaluation (e.g., bad), they should be able to categorize that group of people with words related to the stereotype or evaluation more quickly and with greater accuracy compared with when they are asked to categorize that stereotype or evaluation with other groups of people. For example, if a clinician holds an association between Blacks and noncompliance, they should be able to more easily categorize Blacks with words related to noncompliance compared with when they are asked to categorize Whites with noncompliance. The literature documenting implicit stereotypes and prejudice among healthcare providers documents a number of empirical findings. First, numerous studies on implicit bias in healthcare show that both physicians and other clinicians such as nurses hold an implicit, and often strong, preference for Whites relative to Blacks. The literature also documents a similar bias against those who are overweight. On a “fat–thin” IAT, healthcare providers show an implicit preference for thin, compared with fat, people. The literature also finds a similar preference among physicians and nurses toward people who are higher, compared with lower, in SES, and there is some discussion in the literature about physicians’ implicit as well as explicit bias against LGBT older adults (e.g., see Foglia & Fredrikson‐Goldsen, 2014). Interestingly, the implicit bias that physicians and nurses exhibit toward Blacks and those who are overweight is similar to that of the larger population, suggesting that current medical education does little to reduce implicit bias. The literature on implicit bias toward patients also shows evidence of stereotyping. Often, these implicit stereotypes pertain to noncompliance. Numerous studies show that physicians, nurses, and medical students implicitly associate Blacks, for example, as being noncompliant, medically uncooperative, and uncooperative generally. Literature shows that Hispanics are also implicitly stereotyped as noncompliant, as well as implicitly associated with health risk (Bean, Stone, Moskowitz, Badger, & Focella, 2013). Notably, the literature also shows that Blacks are not only implicitly associated with diseases that are epidemiologically relevant for Blacks (e.g., sickle cell anemia) but that they are also implicitly associated with conditions and social behaviors with no inherently biological or genetic relationship with Blacks including obesity and drug use (Moskowitz, Stone, & Childs, 2012).

The Influence of Stereotyping and Prejudice on Clinical Decision Making and Interactions with Patients Some recent empirical studies not only document the existence of prejudice and stereotyping among clinicians but also show that these biases influence their clinical decision making, including treatment, as well as their interactions with patients. In a landmark study by Green et al. (2007), physicians read a clinical vignette of either a Black or White patient presenting to the emergency department with an acute coronary syndrome. These physicians were later asked to make recommendations of whether to treat the patient with thrombolysis. Green et al. (2007) found that while physicians reported no explicit preference for Whites relative to Blacks or differences between Whites and Blacks on perceived cooperativeness, they did exhibit

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an implicit preference favoring Whites over Blacks as well as implicit stereotypes of Blacks as less cooperative with medical procedures and less cooperative more generally. Importantly, they identified a link between physicians’ implicit bias and their treatment recommendations— as physicians’ implicit pro‐White bias increased, the lower their likelihood of treating Black patients with thrombolysis. Other studies show similar findings, providing evidence that bias influences clinical decision making. For example, literature shows that implicit bias against the mentally ill can predict overdiagnosis, while explicit bias predicts more negative prognoses (Peris, Teachman, & Nosek, 2008). Additionally, research finds that perceptions of cooperativeness are predictive of treatment recommendations. This may disadvantage members of minority groups including Blacks, Hispanics, people who are overweight, and people who are mentally ill, who are stereotyped (implicitly as well as explicitly) as being less medically cooperative relative to Whites, the thin, and those not labeled as mentally ill. Further, it is widely observed that White patients are more likely to receive pain treatment than non‐White patients. This disparity in treatment, research indicates, can be predicted by physicians’ implicit pro‐White bias. For example, as pediatricians’ implicit pro‐White bias increases, the lower their likelihood of prescribing narcotic medication to Black patients (Sabin & Greenwald, 2012). To explain why pro‐White bias influences pain treatment, some research has explored the possibility of an empathy gap—namely, that physicians with a stronger implicit pro‐White bias are less able to empathize with their non‐White patients. One study in particular (Drwecki, Moore, Ward, & Prkachin, 2011) showed that the more participants (including undergraduate students, physicians, and nurses) empathized with Whites compared with Blacks, the less pain treatment participants gave to Blacks relative to Whites. This work suggests that the widely observed disparity in pain treatment between Whites and Blacks is due, in part, to clinicians’ lowered ability to empathize with, and thus assess, their Black patients’ pain. Stereotyping and prejudice not only influence treatment recommendations, but they also impact the way that clinicians interact with their patients. For example, some research shows that nurses who have implicit prejudice against those who are overweight are less likely to make eye contact with an overweight patient (Persky & Eccleston, 2011). Other studies show that healthcare providers’ (including physicians’ and genetic counselors’) pro‐White bias predicts using less emotionally responsive communication when counseling non‐White patients and, when interacting with Black patients, more clinical verbal dominance, lower patient positive affect, and poorer ratings of interpersonal care. When interacting with White patients, in contrast, physicians and other healthcare providers exhibit lower levels of verbal dominance, receive more positive ratings of nonverbal effectiveness, and have higher ratings of interpersonal care. With regard to patient stereotyping, race and compliance stereotyping is associated with longer visits, slower speech, less patient‐centeredness, and poorer ratings of interpersonal care with Black patients. When interacting with White patients, in contrast, physicians exhibit less verbal dominance and typically have shorter visits, faster speech, and higher clinician positive affect. Importantly, several studies show that medical visits with minority patients compared with White patients are less patient‐centered, which is predicted by implicit pro‐White bias. The more pro‐White bias on the part of the clinician, the less patient‐centered their care of their minority patients (e.g., see Cooper et al., 2012). Other sophisticated research on the topic of stereotyping and prejudice in healthcare finds interesting nuances and implications of the physician–patient interaction on patient health. For example, research exploring patients’ perceptions of discrimination on the part of their

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clinicians finds that the more patients’ perceive discrimination and bias from their healthcare providers, the lower their adherence to their clinicians’ treatment recommendations, which may have negative impacts on their health (e.g., see Bird, Bogart, & Delahanty, 2004). In addition, research in social psychology finds that while people might be low in explicit prejudice, they could nonetheless harbor nonconscious, or implicit, bias. In the context of healthcare, clinicians who possess egalitarian beliefs (i.e., who have low explicit prejudice), but who have implicit prejudice (termed aversive racists), may have particularly poor interactions with their patients. Notably, research shows that many healthcare providers, including physicians, tend to have low explicit bias, but nonetheless hold implicit biases. In interactions with a minority group member, people who are aversive racists tend to send mixed messages—while their low explicit prejudice tends to express itself in overt behavior (e.g., positive verbal content), their high implicit prejudice tends to “leak out” in their nonverbal behavior (e.g., less eye contact, distancing nonverbal behaviors). Since the minority interaction partner attends to the nonverbal cues in making judgments about the quality of the interaction, the minority person tends to have more negative ratings of the interaction and of their interaction partner. In fact, a study by Penner et al. (2010) found that Black patients even had more positive interactions with physicians who were high in both explicit and implicit bias than physicians who had low explicit but high implicit bias (i.e., aversive racists). Taken together, the work on explicit and implicit bias suggests that the conscious (explicit) and the unconscious (implicit) bias held by clinicians interact to influence clinical decision making, the quality of clinicians’ interactions with patients, and patients’ perceptions of bias.

Failures to Show a Link Between Stereotyping/Prejudice and Clinical Recommendations While several studies have documented that stereotyping and prejudice influence physicians’ treatment of patients, some have failed to find a direct effect of biases on treatment recommendations. For example, while several studies find that clinicians explicitly and implicitly stereotype their overweight patients as less adherent to treatment, some of these studies find that the extent to which clinicians hold this noncompliance stereotype has no predictive influence on treatment recommendations. Further, a subset of studies documenting explicit and implicit bias toward Black, Hispanic, and mentally ill patients failed to find effects of these biases on clinical decision making including physicians’ treatment recommendations. This literature that documents bias but shows no impact of bias on treatment suggests that further study is needed to understand the specific conditions under which bias may be predictive of clinical decision making, as well as the more nuanced impact of bias on healthcare. For example, although several studies do not show a direct effect of race (or other low‐ power group status, e.g., mental illness) on clinical recommendations, several show that clinicians’ perceptions of uncooperativeness and doubts about adherence alter treatment recommendations. Namely, they show that lower perceived adherence is predictive of lower likelihood to recommend treatment. These results suggest that differences in treatment are not necessarily directly influenced by race or other group status but that they may be indirectly influenced through stereotypes, including lower perceptions of cooperativeness and adherence. In addition, in the studies that fail to show a link between stereotyping and prejudice and treatment recommendations, there is often a standard of care that is typically followed. With little ambiguity and thus less discretion, it might be difficult to capture bias. Further, authors

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of some studies that fail to show a link between explicit or implicit bias and treatment recommendations note that participants might have been aware and/or were suspicious of the study’s purposes. With greater attention paid to race as a variable of interest, participants may have been particularly sensitive to make sure that their minority patients were given treatment as opposed to not. In Green et al.’s (2007) study, for example, 67 physicians were excluded from the analyses because they expressed awareness of the nature of the study. Yet, when these 67 “aware” physicians were included in the results, Green et al. found an interaction effect. As unaware physicians’ pro‐White bias increased, their likelihood of recommending thrombolysis to Black patients decreased. In contrast, increase in pro‐White bias among aware physicians was associated with more thrombolysis recommendations for Black patients.

When Are Stereotyping and Prejudice More Likely? The extant literature suggests situations in which stereotyping and prejudice are more likely to bias clinical decisions. First, situations marked with ambiguity are more likely to elicit bias. Research shows that in cases with clinical uncertainty, where a medical procedure is discretionary, or treatment guidelines are not well defined, the likelihood of clinician bias is greater. In addition, previous research shows that being under high cognitive load makes people, including physicians, more likely to behave according to stereotypes and prejudice. Similarly, when people are in time‐pressured situations, which necessitate that they simultaneously attend to numerous tasks, they tend to be more likely to engage in stereotyping and to act according to their biases. Unfortunately, the medical context, which is often one marked by limited time and high cognitive load, may be especially likely to elicit bias.

Reducing the Occurrence and Impact of Stereotyping and Prejudice in Healthcare To ameliorate the impact of stereotyping and prejudice in healthcare, the extant literature suggests several solutions. First, hiring a more diverse set of physicians may alleviate some of the observed disparities. Previous research finds that racially concordant medical interactions (those in which the physician and patient are the same race) are marked with less perceived discrimination, greater trust, greater satisfaction, and more effective communication (e.g., Cooper et al., 2003). Hence, increasing racial minority patients’ access to racially concordant physicians may help to produce more productive medical visits for racial minority patients. The literature also suggests that workshops may help by making physicians, nurses, and medical students aware of their implicit bias and how it may influence their clinical decision making and their interactions with patients. Finally, bias‐reduction strategies that have been successful in laboratory and some field settings may also be effective in the context of healthcare to reduce prejudice and stereotyping. These include taking the perspective of the minority patient (i.e., perspective taking), perceiving the patient as an individual as opposed to a member of their minority group (i.e., individuation), fostering a sense of common identity between the provider and the patient (i.e., common identity), and considering the ways in which the patient does not fit with stereotypes of his or her minority group (i.e., counterstereotyping). Individuation and perspective taking in particular have been shown to reduce biases in medical treatment, including pain treatment (see Chapman et  al., 2013). Specifically, this research finds that inducing perspective taking increases physicians’ empathy for their minority patients,

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improving their ability to assess their patients’ pain and, consequently, increase the provision of treatment. Importantly, these bias‐reduction strategies may have a place in medicine to alleviate the impact of prejudice and stereotyping in healthcare.

Suggestions for Future Research There are several directions for future research that will enhance our understanding of stereotyping and prejudice in healthcare. For example, while some bias‐reduction strategies have been shown to be effective, further research is needed to understand how, and under what circumstances, these known strategies may be useful specifically in the healthcare context. In addition, given that some literature has failed to find a link between stereotyping and prejudice and clinical recommendations, further research is needed to determine the specific conditions under which stereotyping and prejudice will influence providers’ recommendations and interactions with patients and the instances in which it will not. Future research may also examine the role of clinicians’ expectations on patient health outcomes. For example, if a clinician has low expectations (based on existing prejudice and stereotypes) that a patient will commit to therapy or treatment recommendations, some research suggests that they may be less likely to counsel the patient or to suggest a more challenging, yet perhaps more effective, course of treatment. As a result, the patient may experience worse health outcomes. Future research should more thoroughly examine the extent to which these expectations influence interactions with patients and how they influence the type and quality of treatment recommendations. Further, research has focused more on the existence of stereotyping and prejudice among clinicians, but less on examining patients’ own lived experience of stereotyping and prejudice. Not only would it be beneficial to obtain a qualitative understanding of these experiences, but it would also be beneficial to understand the cumulative effect of these experiences on patients, how these experiences may influence patients’ perception of and participation in healthcare, and how these experiences impact patients’ subsequent interactions with clinicians. Given that a great deal of work examines stereotyping and prejudice in short‐term studies, future work may also include more long‐term studies that examine how negative interactions with clinicians, and the healthcare system more generally, may influence patients’ subsequent participation in healthcare. Importantly, a more thorough investigation of patient–provider communication is warranted. Medical decisions are not made in a vacuum—rather, they derive from an interaction between the clinician and the patient. Future work may investigate how the nuances of these interactions shape the patients’ attitudes and behavior toward the physician and how these interactions may influence the physician’s clinical decision making. Research that investigates the more nuanced and reciprocal nature of patient–provider interaction (e.g., using non‐ obtrusive observation methods such as the Electronically Activated Recorder; Mehl & Robbins, 2012) may be particularly effective at understanding prejudice and stereotyping in the dynamic, and multifaceted, context of healthcare.

Author Biography Dr. Elizabeth S. Focella is an assistant professor of psychology at the University of Wisconsin Oshkosh. Her research examines attitudes and behavior change, chiefly in the context of health

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promotion. One major focus of her research examines prejudice and prejudice reduction, particularly in healthcare. Another line of her work, in cognitive dissonance, examines how people are motivated to change when they are made aware of the discrepancy between their pro‐ health attitudes and their unhealthy behavior.

References Bean, M. G., Focella, E. S., Covarrubias, R., Stone, J., Moskowitz, G. B., & Badger, T. A. (2014). Documenting nursing and medical students’ stereotypes about Hispanic and American Indian patients. Journal of Health Disparities Research and Practice, 7(3), 14–22. Retrived from http:// digitalscholarship.unlv.edu/jhdrp/vol7/iss4/2 Bean, M. G., Stone, J., Moskowitz, G. B., Badger, T. A., & Focella, E. S. (2013). Evidence of nonconscious stereotyping of Hispanic patients by nursing and medical students. Nursing Research, 62(5), 362–367. doi:10.1097/NNR.0b013e31829e02ec Bird, S. T., Bogart, L. M., & Delahanty, D. L. (2004). Health‐related correlates of perceived discrimination in HIV care. AIDS Patient Care and STDs, 18(1), 19–26. doi:10.1089/108729104322740884 Brener, L., Hippel, W. V., Kippax, S., & Preacher, K. J. (2010). The role of physician and nurse attitudes in the health care of injecting drug users. Substance Use & Misuse, 45(7‐8), 1007–1018. doi:10.3109/10826081003659543 Chapman, E. N., Kaatz, A., & Carnes, M. (2013). Physicians and implicit bias: How doctors may unwittingly perpetuate healthcare disparities. Journal of General Internal Medicine, 28(11), 1504–1510. doi:10.1007/s11606‐013‐2441‐1 Cooper, L. A., Roter, D. L., Carson, K. A., Beach, M. C., Sabin, J. A., Greenwald, A. G., & Inui, T. S. (2012). The associations of clinicians’ implicit attitudes about race with medical visit communication and patient ratings of interpersonal care. American Journal of Public Health, 102(5), 979–987. doi:10.2105/ajph.2011.300558 Cooper, L. A., Roter, D. L., Johnson, R. L., Ford, D. E., Steinwachs, D. M., & Powe, N. R. (2003). Patient‐centered communication, ratings of care, and concordance of patient and physician race. Annals of internal medicine, 139(11), 907–915. Dovidio, J. F., & Fiske, S. T. (2012). Under the radar: How unexamined biases in decision‐making processes in clinical interactions can contribute to health care disparities. American Journal of Public Health, 102(5), 945–952. doi:10.2105/ajph.2011.300601 Drwecki, B. B., Moore, C. F., Ward, S. E., & Prkachin, K. M. (2011). Reducing racial disparities in pain treatment: The role of empathy and perspective‐taking. Pain, 152(5), 1001–1006. doi:10.1016/j. pain.2010.12.005 Foglia, M. B., & Fredriksen‐Goldsen, K. I. (2014). Health disparities among LGBT older adults and the role of nonconscious bias. Hastings Center Report, 44(s4), S40–S44. doi:10.1002/hast.369 Geiger, H. J. (2003). Racial and ethnic disparities in diagnosis and treatment: A review of the evidence and a consideration of causes. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, 40(10), 417–454. doi:10.5860/choice.40‐5843 Green, A. R., Carney, D. R., Pallin, D. J., Ngo, L. H., Raymond, K. L., Iezzoni, L. I., & Banaji, M. R. (2007). Implicit bias among physicians and its prediction of thrombolysis decisions for Black and White patients. Journal of General Internal Medicine, 22(9), 1231–1238. doi:10.1007/ s11606‐007‐0258‐5 Mehl, M. R., & Robbins, M. L. (2012). Naturalistic observation sampling: The Electronically Activated Recorder (EAR). In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methods for studying daily life (pp. 176–192). New York, NY: Guilford Press. Moskowitz, G. B., Stone, J., & Childs, A. (2012). Implicit stereotyping and medical decisions: Unconscious stereotype activation in practitioners’ thoughts about African Americans. American Journal of Public Health, 102(5), 996–1001. doi:10.2105/AJPH.2011.300591

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Penner, L. A., Dovidio, J. F., West, T. V., Gaertner, S. L., Albrecht, T. L., Dailey, R. K., & Markova, T. (2010). Aversive racism and medical interactions with Black patients: A field study. Journal of Experimental Social Psychology, 46(2), 436–440. doi:10.1016/j.jesp.2009.11.004 Peris, T. S., Teachman, B. A., & Nosek, B. A. (2008). Implicit and explicit stigma of mental illness: Links to clinical care. The Journal of Nervous and Mental Disease, 196(10), 752–760. doi:10.1097/ nmd.0b013e3181879dfd Persky, S., & Eccleston, C. P. (2011). Medical student bias and care recommendations for an obese versus non‐obese virtual patient. International Journal of Obesity, 35(5), 728–735. doi:10.1038/ ijo.2010.173 Sabin, J. A., & Greenwald, A. G. (2012). The influence of implicit bias on treatment recommendations for 4 common pediatric conditions: Pain, urinary tract infection, attention deficit hyperactivity disorder, and asthma. American Journal of Public Health, 102(5), 988–995. doi:10.2105/ajph.2011.300621 van Ryn, M., & Burke, J. (2000). The effect of patient race and socio‐economic status on physicians’ perceptions of patients. Social Science & Medicine, 50(6), 813–828. doi:10.1016/s0277‐9536(99) 00338‐x

Suggested Reading Dovidio, J. F., & Fiske, S. T. (2012). Under the radar: How unexamined biases in decision‐making processes in clinical interactions can contribute to health care disparities. American Journal of Public Health, 102(5), 945–952. doi:10.2105/ajph.2011.300601 Nelson, A. (2002). Unequal treatment: confronting racial and ethnic disparities in health care. Journal of the National Medical Association, 94(8), 666–668. Shavers, V. L., Fagan, P., Jones, D., Klein, W. M., Boyington, J., Moten, C., & Rorie, E. (2012). The state of research on racial/ethnic discrimination in the receipt of health care. American Journal of Public Health, 102(5), 953–966. doi:10.2105/AJPH.2012.300773

The Prototype‐Willingness Model Frederick X. Gibbons1, Michelle L. Stock2, and Meg Gerrard1 Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Department of Psychological and Brain Sciences, George Washington University, Washington, DC, USA

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Basic Components The prototype‐willingness model (PWM) (Gerrard, Gibbons, Houlihan, Stock, & Pomery, 2008; Gibbons, Gerrard, & Lane, 2003; Gibbons, Stock, Gerrard, & Finneran, 2015) was created with a goal of improving the predictive and explanatory power of existing health behavior theories but with a specific target in mind: health risk behavior among adolescents. It is a modified dual‐process model. Like other dual‐process theories, the PWM maintains that health decision making involves two kinds of information processing. The two types are operative in the two pathways to health behavior described in the model. The reasoned pathway involves analytic processing. It is the deliberative route to behavior reflecting the fact that some risky behaviors are the result of a consideration of options and expected outcomes associated with those behaviors. The proximal antecedent in this path is behavioral intention, which is defined as an intention (plan) to engage in a particular behavior. The social reaction path reflects a belief that certain behaviors, especially among adolescents, are more reactive than reasoned—that is, a response to a set of circumstances that may not have been sought or even anticipated. This path involves more heuristic processing: Deliberation is truncated, especially consideration of consequences; affect has more impact, and cognition less impact; images are influential; and heuristics play an important role. Both pathways include attitudes and subjective norms as distal antecedents; the social reaction path, however, also includes two constructs that are unique to the model, willingness and prototypes. Behavioral willingness is defined as an openness to risk opportunity. It captures the reactive aspect of behavior, which is considerable for adolescents. It differs from an intention—it is not a plan of action or a goal state. Very few people plan on driving under the influence of drugs, for example, but some are willing to do so. Whether the behavior happens, then, is largely The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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dependent on whether the risk opportunity occurs (Gibbons et al., 2004). Willingness is influenced by affect and social influence (Gibbons et al., 2004), as well as heuristics (e.g., prototypes). Outcomes associated with the social reaction path typically have included risky health behaviors. However, there is no theoretical reason why the behavior has to be negative; as long as the behavior has a reactive component and an associated image, the PWM is potentially appropriate. Assessment of willingness includes a hypothetical scenario; for example, “Imagine you’re at a party sometime in the next month and there are some drugs there you can have if you want. We’re not suggesting you would ever be in this kind of situation, but try to imagine it happening.” Next, the respondent is asked how willing she/he would be under these circumstances to do each of several behaviors that increase in risk (e.g., “try some … use enough to get high … take some to use later”). Responses are aggregated. A basic assumption behind the construct of willingness is that many adolescents are curious about risky behaviors. Peer pressure can be important, but there is more to substance use or risky sex than an inability to “just say no”; willingness is different from resistance efficacy. When individuals are asked about their willingness, various heuristics are instantiated. This happens quickly, but it does require some thought. Thus, the social reaction pathway—and this is true for much health decision making—relies heavily on heuristics, but it is a conscious more than an unconscious process, and it is seldom automatic. Prototypes are the images that individuals have of the type of person who engages in a behavior. They include two primary dimensions: favorability and similarity. The most common method of favorability assessment involves adjective descriptors. For example, wording for adolescents (age ~12–14) would be: We want to ask about your images of people who do different behaviors. We’re not talking about any particular person, and we’re not saying these people are all alike. We want to know what you think these people are like. Please think about the type of person your age who____. How much do you think each of these adjectives describes that type of person?

This prompt is followed by a list of adjectives, preferably a mix of positive and negative (e.g., popular, selfish, considerate). These prototype (and willingness) items have consistently produced valid and reliable indices, even among children as young as 8 (Andrews, Hampson, Barckley, Gerrard, & Gibbons, 2008).

Research: Age and Experience Two recent meta‐analyses have reviewed some of the studies prompted by the PWM. One provided an analysis of the general model (Todd, Kothe, Mullan, & Monds, 2016), whereas the second examined a more specific issue: prototype similarity versus favorability (van Lettow, de Vries, Burdorf, & van Empelen, 2016). Both analyses reached essentially the same two general conclusions: PWM constructs, specifically prototypes and willingness, add significantly to the prediction of health behaviors, and the model has utility and potential in terms of interventions. Below are some issues raised in those meta‐analyses.

Adolescents A number of studies have indicated that willingness is usually a better predictor than intention among adolescents for risky behaviors, including substance use (e.g., cigarettes and drinking).

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This varies with age, however. Two prospective studies examined the relative predictive power of willingness versus intention annually from age 12 through 18 and found the anticipated pattern for smoking: controlling for previous behavior, willingness exceeded intention through age 16, at which point the smoking became more intentional for the next 2 years (Pomery, Gibbons, Reis‐Bergan, & Gerrard, 2009). From then on, smoking was mostly habitual, and neither willingness nor intention predicted significant variance beyond previous behavior. However, another analysis in the same paper with nonsmokers age 12–19 showed that initiation of smoking was also predicted better by willingness up to age 19. Consistent with these results, Todd et al. (2016) found that age moderated the prototype‐to‐willingness and willingness‐to‐behavior relations, leading them to conclude that the PWM works better for adolescents than adults.

Adults Some behaviors are risky or rare enough that the image associated with them is pronounced and impactful; consequently, they are less likely than drinking or smoking to become intentional or habitual, even into adulthood. These behaviors include sexual “cheating,” drunk driving, and heavy drug use, among others. More generally, many risk behaviors are intentional up to a certain level, but people have their limits, and risk above that threshold often becomes willingness based. More PWM studies with adults are likely in the future.

Type of Behavior Health Risk Todd et  al.’s list of behaviors examined in PWM studies included health promotion (e.g., exercise, vaccination) and health risk. The model did best in predicting substance use: Willingness accounted for 20% of the variance in behavior, whereas intention accounted for only 8%. As Todd et al. suggested, substance use is a risky behavior with a significant social reaction component. On the other hand, willingness explained less variance than intention in cigarette use, which has much less of a social component. This pattern varies considerably by age, however—much more reactive in early adolescence and then more reasoned in late adolescence and early adulthood (Pomery et al., 2009).

Health Promotion Gerrard et al. (2002) examined the prospective effects of a healthy prototype, the adolescent nondrinker, and the more common, unhealthy prototype, the adolescent drinker, on adolescents’ willingness to drink and their actual consumption. As expected, the drinker image predicted drinking indirectly, via willingness to drink, and the abstainer image predicted directly. However, there was also an indirect path from the abstainer image to drinking through an image contemplation measure (“How often have you thought about this type of person?”). These results are consistent with a dual‐process account. First, nondrinkers had a favorable nondrinker image, and that image directly predicted their abstinence, suggesting that the nondrinker image was a goal for them. Second, a favorable nondrinker image was associated with more image contemplation, which also suggests more reasoned processing; in contrast, the drinker image involved heuristic processing. Other healthy images that have been examined include condom users, exercisers, healthy eaters, sun protectors, and (substance) abstainers.

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Comparing Willingness and Intention The distinction between willingness and intention is central to the PWM and to its status as a dual‐process model. Clearly, many health behaviors have a reasoned component to them, and although the intention–behavior correlation is often less than predicted, it is reliable. For this reason, the PWM maintains that for many risky behaviors, analyses that include both willingness and intention are more likely to effectively predict behavior than intention alone. By the same token, for many health‐related behaviors, willingness is not relevant, especially among adults (e.g., drinking or smoking).

Antecedents In spite of their overlap, intention and willingness have distinct antecedents. In survey research, for example, Gibbons et al. (2004) found that parents’ substance use predicted their children’s intentions and especially their behavioral expectations to use substances (e.g., “My parents smoke, I probably will also”), but it did not predict willingness to use. Social influence (i.e., perception of friends’ use) and risk images, on the other hand, predicted both willingness and intention, but the relations were stronger for willingness. Other studies have found that endorsement of the heuristic “If it’s illegal, it must be effective” predicted willingness but not intention to use performance‐enhancing drugs and also that positive and negative alcohol portrayals by actors in movies affected adolescents’ willingness to drink but not their intentions (Gibbons et al., 2016).

Manipulations Several studies have also demonstrated differences in how intention and willingness are affected by experimental manipulations. Two such studies examined a behavior with a significant willingness–intention distinction: casual sex. One study found that men who initially reported no intention to engage in casual sex increased in willingness (but not intention) to engage in the behavior after exposure to tempting photos (women posing provocatively in bikinis; Roberts, Gibbons, Kingsbury, & Gerrard, 2013). A second study replicated this effect for “cheating” using supraliminal primes (provocative photos interspersed with control photos). This study found that those in committed relationships reported lower willingness to engage in casual sex than those who were single, as expected, but these committed men and women had significantly higher willingness than intention. Apparently, for some people with no intention to cheat but some willingness, whether that willingness turns into casual sex behavior may depend on “risk opportunity.”

Prototypes Dimensions Both favorability and similarity are important aspects of risk images (prototypes); when both are high, willingness is most likely. In their meta‐analysis, van Lettow et al. (2016) found that in a majority of studies that assessed both prototype similarity and favorability, similarity was a stronger predictor for willingness. However, studies that also included a similarity by favorability interaction term showed that the interaction was a stronger predictor than either dimension by itself. In fact, the “diagonals”—those who are high in similarity but low in favorability, or vice‐versa—are an interesting group. Presumably, those in the latter category are considering

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starting the behavior (“contemplators”). On the other hand, those in the high‐similarity/ low‐favorability category clearly do not approve of the behavior and, presumably, are either concerned they may start engaging or are already engaging and want to stop.

Relations With Intention and Willingness Some studies have found evidence of a prototype‐to‐intention path. Although rare when both willingness and intention are included in analyses, this relation is not inconsistent with the model. Health behavior often is goal based, and one goal can be image acquisition. Exercising is an obvious example of a healthy behavior with an image that can act as a goal. But (normatively) negative images can also be goals: admission into a desired social group, “dirts” or “druggies,” for example. In this case, the group has a negative image that can be attractive (for some) and therefore increase the image‐to‐intention relationship. Finally, prototype influence is moderated by social comparison tendencies. Those who compare a lot are more responsive to images, so the prototype/willingness relationship is stronger for them.

Subjective Norms and Attitudes Norms Some form of subjective norm construct can be found in most health behavior models, sometimes divided into descriptive and injunctive (people’s perceptions of what others are doing versus what others want them to do). We have found that peer pressure (injunctive norms) is often not a significant factor in terms of health risk willingness, whereas perceived prevalence (descriptive norms) usually is. The model describes paths from subjective norms to both intention and willingness, but that may not be true for both types of norms. Engaging in a behavior because you believe others want or expect you to do it requires some thought and reasoning (what do they want? will this please them?), whereas doing a behavior mostly for conformity reasons (i.e., “everyone’s doing it”) requires less reasoning and more reacting. Future research examining whether injunctive norms lead to intentions whereas descriptive norms lead to willingness would be informative.

Attitudes A similar dual‐process argument could be made for attitudes. Using perceived risk as an example, perceptions of danger (e.g., “driving after drinking is dangerous”) are usually higher than perceptions of personal vulnerability, which often reflect some optimistic bias (e.g., “I could have several drinks and drive home; I’ve done it before”). Perceived danger should be more strongly related to intention, and personal vulnerability more to willingness. Similarly, attitudes include both cognitive and affective components; the latter should be more strongly related to willingness than the former.

Interventions: Laboratory Studies Perceived Risk The PWM has been the theoretical basis for several interventions and preventive interventions as well as laboratory analogue studies examining the utility of components of the model, mostly

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prototypes and willingness, for possible future interventions. One recent example of this involved the “absent‐exempt” heuristic, which is a perception that some people develop if they have been engaging in a risk behavior for some time and nothing bad has happened to them that they may be somehow immune to the consequences. A series of studies showed that absent‐exempt thinking is enhanced among high‐risk individuals by (downward) social comparison with a low‐risk individual who has experienced the associated negative consequence (e.g., an STD), even though they had engaged in very little risk behavior (Stock, Gibbons, Beekman, & Gerrard, 2015). Most important, this increase in absent‐exempt thinking was accompanied by an increase in willingness to have casual sex among high‐risk individuals. This finding has intervention implications: PSAs that include the theme “It only takes once!” are common (especially for sexual behavior), which creates a real possibility of iatrogenic effects—increases in risk‐taking after the intervention, due to absent thinking, among those already at high risk.

Dual Processing There was also evidence of dual processing in these studies. Risk willingness increased significantly in all three studies, but risk intention did not change. Also, when reasoned processing was instantiated before the comparison opportunity, it eliminated the absent‐exempt effect. Most interesting, however, was the fact that in addition to reporting lower perceived vulnerability and higher willingness (but not intention), high‐risk participants also reported significantly more intention to get tested for an STD. In other words, the absent‐exempt heuristic was associated with an increase in risk willingness and an increase in health promotion intentions—an example of reasoned action accompanying social reaction in the same individuals simultaneously.

Manipulating Prototypes Safe Sex Images Many studies have manipulated components of the PWM to test prototype malleability and the impact of these manipulations on willingness and behavior. They have addressed a range of behaviors besides substance use and UV exposure, including exercise, diet, sleeping, safe sex, and nonprescription drug use. Most have focused on risk prototypes, but a sizable number were prompted by Gerrard et  al. (2002) in showing that images of people who engage in healthy behavior can also motivate change. In the first prototype manipulation study, college students read a bogus newspaper article reporting the results of a personality survey, indicating that the typical person who does not use condoms (experimental group) or does not vote (control) is “more selfish” and “less responsible” than those who do (Blanton et al., 2001). As expected, students in the condom condition reported less willingness to have unprotected sex than did those in the voter condition.

Prototype Visualization Another prototype paradigm uses a visualization strategy. Ouellette, Hessling, Gibbons, Reis‐ Bergan, and Gerrard (2005) had participants write about typical exercisers and non‐exercisers. As expected, exerciser prototypes were much more favorable; more important, a follow‐up revealed that those in the exerciser condition significantly increased their exercising, relative to those in the non‐exerciser condition. An extension of this study based on terror management

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theory employed a mortality salience induction to examine why prototypes affect behavior and found that making mortality salient, combined with contemplating an exerciser prototype, increased the extent to which participants said they valued exercising.

Interventions UV Protection A series of UV protection lab studies has also successfully altered prototypes. These studies employed a UV filter camera that produces a facial photo that graphically depicts existing skin damage due to previous UV exposure. The prediction was that the photos would make images of tanning and tanners more negative. Thus, the same motivation behind the risk behavior (appearance) was used to motivate abandoning it. One study showed that the photographs reduced prototype favorability and tanning willingness while increasing perceived vulnerability to skin damage; these changes mediated subsequent declines in tanning booth use among those who received the photos (Gibbons et  al., 2005). A more recent study examined the impact of the UV photos from a dual‐processing perspective by asking women to focus on either their thoughts or their feelings when they viewed the UV photos in order to encourage analytic or heuristic processing about the risk (Walsh, Stock, Peterson, & Gerrard, 2014). As expected, older women in the cognitive focus condition had lower sun exposure willingness and higher perceived skin cancer vulnerability than those in the affective focus group, suggesting that altering type of processing regarding risk can lead to a reduction in risk behavior. Another UV intervention focused on a high‐risk population: road maintenance workers (Stock et al., 2009). Participants were shown UV facial photographs and then watched an educational video on UV risk and protection. There were two significant paths from the intervention to the outcome measures (changes in self‐reported UV protection and tanning assessed objectively with a spectrophotometer): a direct path and an indirect path mediated by changes in risk cognitions suggested by the PWM. Thus, the most effective strategy for this high‐risk population included both reasoned (educational) and heuristic components.

Strong African American Families (SAAF) Work on family process by Gene Brody and colleagues led to the development of the SAAF Program, which is a family‐centered alcohol preventive intervention based partly on the PWM. It includes two components. One component, which involved the parents, targeted the reasoned path by promoting parent–child communication. The second, more heuristic, arm was directed at the children. It was designed to make their drinker images more negative and educate them on the differences between planned and reactive behavior, thereby reducing their willingness to drink. The intervention was successful in decreasing the favorability of the children’s drinker images and their intention and willingness to drink while increasing reports of parent–child communication. The desired effect on alcohol consumption at a 24‐month follow‐up also was significant (Gerrard et al., 2006).

Dual Processing The SAAF intervention also demonstrated the efficacy of combining a heuristic and an analytic approach to shaping adolescents’ decision making. In the reasoned path, the effect of changes in parenting on adolescents’ drinking was mediated by changes in their intention; in

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the social reaction path, the effect of changes in the children’s prototypes on their drinking were mediated by changes in willingness. The social reaction path was stronger, mostly because the willingness/behavior relationship was stronger than the intention–behavior relationship. Importantly, however, a structural equation model that included both the reasoned and the social reaction paths provided a significantly better fit of the data than models with either path alone, once again illustrating the utility of a dual‐processing approach to intervention.

Smoking Prevention Click City is an online prevention program that targets adolescents’ prototypes and willingness and intentions to use tobacco. One study demonstrated that the intervention was most efficacious for children who were most at risk, that is, those who had tried smoking at baseline and/ or had a family member who smoked. A follow‐up replicated these results: Students in the program reported smaller increases in the favorability of their smoker prototypes and their willingness and intentions to smoke than those in the control condition (Andrews et al., 2014).

Future Research Directions Smoker Prototypes and Smoking Cessation The first PWM prototype study examined the relation between smoker images and success at smoking cessation (Gibbons, Gerrard, Lando, & McGovern, 1991). Results indicated that smokers’ images of other smokers became more negative during the course of their quit group, and the more negative they became, the more likely the smokers were to quit. A subsequent study found that a negative smoker image was a better predictor of quitting than was a positive former smoker image (Gibbons & Eggleston, 1996). Along similar lines, overweight images have been shown to be stronger predictors of weight loss efforts than are thin images, and negative smoker prototypes are more motivating for nonsmokers to avoid smoking than positive smoker prototypes are for nonsmokers to start. These results suggest the desire to stop being, or avoid becoming, a negative exemplar may be a stronger motivator than the desire to become a positive (healthy) exemplar. More such cessation studies are needed.

Willingness and Prototype Development Finally, examining factors that affect the development of images and willingness would have value from both an applied and theoretical perspective. We know that risk images are influential for children as young as 8, and we know that the media (especially movies) are an important source for these images (Gibbons et  al., 2010). We also know that by age 13 or 14, adolescents can articulate the difference between intention and willingness. Further examination of this developmental process is called for; the same is true for the study of factors that can effect change in willingness and prototypes.

Conclusion Several conclusions can be drawn from the PWM literature. First, willingness and intention are clearly related, but there is considerable evidence suggesting that they are different constructs,

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with discriminable antecedents and consequences. Second, analogue studies have demonstrated that risk and nonrisk prototypes are malleable and that reducing risk prototype favorability and/or increasing nonrisk prototype favorability can reduce risk willingness and actual risk behavior. Finally, there is evidence of increasing acceptance of the dual‐processing perspective upon which the PWM is based, along with enthusiasm for the utility of this perspective for explaining, predicting, and changing health behavior, especially among adolescents.

Author Biographies Frederick X. Gibbons is a health–social psychologist and a professor in the Department of Psychological Sciences at the University of Connecticut. He received his PhD in psychology from the University of Texas. His research interests focus on the application of social psychology theory and principles to the study of health behavior. This research has examined disparities in health status and factors associated with onset and cessation of health risk behavior. It has included survey and lab (experimental) studies, along with interventions and preventive interventions. Michelle L. Stock is an associate professor in the Department of Psychology at The George Washington University. She received her PhD in psychology from Iowa State University. Her program of research includes a focus using the prototype‐willingness model to provide a framework for understanding cognitive (heuristic and reasoned), affective, and situational factors that influence risky health decisions. Meg Gerrard is a research professor in the Department of Psychological Sciences at the University of Connecticut. She received her PhD in psychology from the University of Texas. Throughout her career, her research has focused on the prediction of adolescent risk behaviors (i.e., alcohol and substance use, risky sexual behaviors, and smoking) and the prevention of these behaviors. She and her colleague (Rick Gibbons) developed the prototype‐willingness model of adolescent health behavior in an effort to explain and explore non‐intentional adolescent risk behavior.

References Andrews, J. A., Gordon, J. S., Hampson, S., Gunn, B., Christiansen, S., & Slovic, P. (2014). Long‐term efficacy of Click City® tobacco: A school‐based tobacco prevention program. Nicotine & Tobacco Research, 16, 33–41. doi:10.1093/ntr/ntt106 Andrews, J. A., Hampson, S. E., Barckley, M., Gerrard, M., & Gibbons, F. X. (2008). The effect of early cognitions on cigarette and alcohol use during adolescence. Psychology of Addictive Behaviors, 22, 96–106. doi:10.1037/0893‐164X.22.1.96 Blanton, H., Vanden‐Eijnden, R., Buunk, B. P., Gibbons, F. X., Gerrard, M., & Bakker, A. (2001). Accentuate the negative: Social images in the prediction and promotion of condom use. Journal of Applied Social Psychology, 31, 274–295. doi:10.1111/j.1559‐1816.2001.tb00197.x Gerrard, M., Gibbons, F. X., Brody, G. H., Murry, V. M., Cleveland, M. J., & Wills, T. A. (2006). A theory‐based dual‐focus alcohol intervention for preadolescents: The Strong African American Families Program. Psychology of Addictive Behaviors, 20, 185–195. doi:10.1037/0893‐164X. 20.2.185 Gerrard, M., Gibbons, F. X., Houlihan, A. E., Stock, M. L., & Pomery, E. A. (2008). A dual‐process approach to health risk decision making: The prototype‐willingness model. Developmental Review, 28, 29–61. doi:10.1016/j.dr.2007.10.001

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Gerrard, M., Gibbons, F. X., Reis‐Bergan, M., Trudeau, L., VandeLune, L., & Buunk, B. (2002). Inhibitory effects of drinker and nondrinker prototypes on adolescent alcohol consumption. Health Psychology, 21, 601–609. doi:10.1037/0278‐6133.21.6.601 Gibbons, F. X., & Eggleston, T. J. (1996). Smoker networks and the ‘typical smoker’: A prospective analysis of smoking cessation. Health Psychology, 15, 469–477. doi:10.1037/0278‐6133.15.6.469 Gibbons, F. X., Gerrard, M., Lando, H. A., & McGovern, P. G. (1991). Social comparison and smoking cessation: The role of the “typical smoker”. Journal of Experimental Social Psychology, 27, 239–258. doi:10.1016/0022‐1031(91)90014‐W Gibbons, F. X., Gerrard, M., & Lane, D. J. (2003). A social‐reaction model of adolescent health risk. In J. M. Suls & K. A. Wallston (Eds.), Social psychological foundations of health and illness (pp. 107– 136). Oxford, England: Blackwell. Gibbons, F. X., Gerrard, M., Lane, D. J., Mahler, H. I. M., & Kulik, J. A. (2005). Using UV photography to reduce use of tanning booths: A test of cognitive mediation. Health Psychology, 24, 358– 363. doi:10.1037/0278‐6133.24.4.358 Gibbons, F. X., Gerrard, M., VandeLune, L. S., Wills, T. A., Brody, G., & Conger, R. D. (2004). Context and cognition: Environmental risk, social influence, and adolescent substance use. Personality and Social Psychology Bulletin, 30, 1048–1061. doi:10.1177/0146167204264788 Gibbons, F. X., Kingsbury, J. H., Wills, T. A., Finneran, S. D., Dal Cin, S., & Gerrard, M. (2016). Impulsivity moderates the effects of movie alcohol portrayals on adolescents’ willingness to drink. Psychology of Addictive Behaviors, 30(3), 325–334. Gibbons, F. X., Pomery, E. A., Gerrard, M., Sargent, J. D., Weng, C. Y., Wills, T. A., … Yeh, H. C. (2010). Media as social influence: Racial differences in the effects of peers and media on adolescent alcohol cognitions and consumption. Psychology of Addictive Behaviors, 24, 649–659. doi:10.1037/ a0020768 Gibbons, F. X., Stock, M. L., Gerrard, M., & Finneran, S. (2015). The prototype‐willingness model. In M. Conner & P. Norman (Eds.), Predicting and changing health behaviour: Research and practice with social cognition models (3rd ed., pp. 189–224). Berkshire, UK: Open University Press. Ouellette, J. A., Hessling, R., Gibbons, F. X., Reis‐Bergan, M., & Gerrard, M. (2005). Using images to increase exercise behavior: Prototypes vs. possible selves. Personality and Social Psychology Bulletin, 31, 610–620. doi:10.1177/0146167204271589 Pomery, E. A., Gibbons, F. X., Reis‐Bergan, M., & Gerrard, M. (2009). From willingness to intention: Experience moderates the shift from reactive to reasoned behavior. Personality and Social Psychology Bulletin, 35, 894–908. doi:10.1177/0146167209335166 Roberts, M. E., Gibbons, F. X., Kingsbury, J. H., & Gerrard, M. (2013). Not intending but somewhat willing: The influence of visual primes on risky sex decisions. British Journal of Health Psychology, 19, 553–565. doi:10.1111/bjhp.12055 Stock, M. L., Gerrard, M., Gibbons, F. X., Dykstra, J. L., Mahler, H. I. M., Walsh, L. A., & Kulik, J. A. (2009). Sun protection intervention for highway workers: Long‐term efficacy of UV photography and skin cancer information on men’s protective cognitions and behavior. Annals of Behavioral Medicine, 38, 225–236. doi:10.1007/s12160‐009‐9151‐2 Stock, M. L., Gibbons, F. X., Beekman, J., & Gerrard, M. (2015). It only takes once: The absent‐exempt heuristic and reactions to comparison‐based sexual risk information. Journal of Personality and Social Psychology, 109, 35–52. doi:10.1037/a0039277 Todd, J., Kothe, E., Mullan, B., & Monds, L. (2016). Reasoned versus reactive prediction of behavior: A meta‐analysis of the prototype willingness model. Health Psychology Review, 10(1), 1–24. doi:10. 1080/17437199.2014.922895 van Lettow, B., de Vries, H., Burdorf, A., & van Empelen, P. (2016). Quantifying the strength of the associations of prototype perceptions with behaviour, behavioural willingness and intentions: A meta‐analysis. Health Psychology Review, 10(1), 25–43. doi:10.1080/17437199.2014.941997 Walsh, L. A., Stock, M. L., Peterson, L., & Gerrard, M. (2014). Women’s sun protection cognitions in response to UV photography: The role of age, cognition, and affect. Journal of Behavioral Medicine, 37, 553–563. doi:10.1007/s10865‐013‐9512‐y

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Suggested Reading Gerrard, M., Gibbons, F. X., Brody, G. H., Murry, V. M., Cleveland, M. J., & Wills, T. A. (2006). A theory‐based dual‐focus alcohol intervention for preadolescents: The Strong African American Families Program. Psychology of Addictive Behaviors, 20, 185–195. doi:10.1037/0893‐164X.20. 2.185 Gibbons, F. X., Stock, M. L., Gerrard, M., & Finneran, S. (2015). The prototype‐willingness model. In M. Conner & P. Norman (Eds.), Predicting and changing health behaviour: Research and practice with social cognition models (3rd ed., pp. 189–224). Berkshire, UK: Open University Press.

Psychosocial Factors in Coronary Heart Disease Elizabeth J. Vella Department of Psychology, University of Southern Maine, Portland, ME, USA

Cardiovascular disease represents the leading cause of death globally, which includes mortality due to stroke and coronary heart disease (CHD); of these two forms of cardiovascular disease, CHD accounts for more deaths annually (World Health Organization, 2015). The primary features of CHD include plaque development in the coronary arteries (atherosclerosis), heart attack (myocardial infarct), and acute chest pain (angina; Labarthe, 1998). The traditional risk factors for CHD include age, obesity, high cholesterol, high blood pressure, inactive lifestyle, smoking, excessive alcohol consumption, and family history of the disease (World Heart Federation, 2015). Epidemiologic evidence suggests that traditional risk factors of CHD may account for 58–75% of new cases (Beaglehole & Magnus, 2002). Other predictors of CHD may include stress‐related psychosocial factors at a person level (e.g., dispositional hostility and depression) and/or environmental level (e.g., chronic work‐related stress and lack of social support; Albus, 2010).

What Is a Psychosocial Risk Factor? Simply stated, a psychosocial risk factor should be conceptualized as a psychological attribute that impacts social behaviors in a way that elevates likelihood of illness, such as emotion dispositions of anger that increase antagonistic social interactions, thereby leading to exacerbated stress levels and social isolation, eventually giving rise to disease onset. The impact of psychosocial risk factors on illness onset is often both direct (e.g., stress‐induced elevations in markers of inflammation that hasten atherosclerotic plaque formation) and indirect (e.g., stress‐induced changes in lifestyle behaviors that contribute to unhealthy diet, smoking, and excessive alcohol consumption as maladaptive means of coping). When studying psychosocial risk factors for CHD, researchers must measure and control for the presence of traditional risk factors in their

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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statistical models to effectively be able to ascertain that the psychosocial risk factors in question are predictive of CHD above and beyond the traditional risk factors aforementioned.

Early Research on the Type A Behavior Pattern and Trait Hostility In the 1950s, cardiologists Friedman and Rosenman began to take notice that a pattern of personality characteristics seemed to typify their CHD patients. These physicians termed the personality style as the Type A behavior (TAB) pattern, characterized by a strong sense of time urgency, hard‐driving competitiveness, ambitiousness, hostility, and tendencies to experience anger and become aggressive. They conducted a wide‐scale assessment of TAB entitled the Western Collaborative Group Study (WCGS) to determine if a reliable relationship exists between this set of personality traits and the severity of coronary dysfunction; 3,154 men aged 39–59 were administered the structured interview to assess TAB and then followed for 8.5 years (Rosenman et al., 1975). Results indicated that Type A individuals were more than twice as likely to subsequently develop clinically significant CHD symptoms than Type B (essentially calm demeanor, even tempered) counterparts. However, the Multiple Risk Factor Intervention Trial (MRFIT) called the TAB construct into question by failing to replicate WCGS results. Reliable differences in the administration and coding of the structured interview to assess TAB have been reported between WCGS and MRFIT, which may account for these discrepant results (Sherwitz & Brand, 1990). Further, the multifaceted nature of the Type A construct may increase the probability of inconsistencies in the literature. Importantly, subsequent analyses of data from WCGS and MRFIT indicated that the structured interview‐derived subscale score entitled “potential for hostility,” an interactive style of expressive speech and experience of anger, was a better predictor of CHD in both datasets than the global Type A score (Dembroski, MacDougall, Costa, & Grandits, 1989; Hecker, Chesney, Black, & Frautschi, 1988). Therefore, the toxic elements of TAB appear to concern a particular interactive style separate from the qualities of ambitiousness, competition, and time urgency. More recently, evidence suggests a reliable association between trait hostility/anger and CHD outcomes. For example, a meta‐analytic report across 21 articles longitudinally examining 71,606 initially healthy participants and 18 articles longitudinally studying 8,120 CHD patients found that elevated ratings of anger/hostility were linked to an average 20% increase in disease risk, in terms of risk of eventual diagnosis amid initially healthy participants and risk of additional coronary events/CHD mortality for the patient samples (Chida & Steptoe, 2009). Interestingly, the risk profile was more pronounced amid the latter samples of participants already diagnosed with CHD, suggesting that the mere diagnosis and experience of CHD as a condition may promote bouts of anger and hostility in a way that contributes to poorer prognosis. Specifically, anger/hostility ratings predicted a 24% increased risk of additional coronary events/CHD mortality amid patients compared with a 19% increased risk of CHD diagnosis amid initially healthy participants. Currently, the hostility construct remains a point of controversy as a risk factor for the development of CHD, yielding a mixture of results claiming significant and insignificant associations. A clear problem in the literature that may explain many of these discrepancies across studies concerns the multidimensionality of the hostility construct (cognitive mistrustfulness, emotional anger, and behavioral aggression). This problem is compounded by variability in measurement instruments: some assessment tools for measuring hostility are more reliably predictive of disease onset than others (e.g., cynical mistrustfulness and the experience of

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anger versus anger expression through aggression), a quality that adds confusion to the ­medical science literature.

Depression Clinical depression is diagnosed based upon criteria specified in the Diagnostic and Statistical Manual published by the American Psychological Association, including such symptoms as depressed mood, feelings of worthlessness, fatigue, changes in sleep patterns, and suicidal ideation. The relationship between clinical depression and CHD diagnosis/mortality is more reliable and robust than the associations apparent for hostility/TAB. For example, a recent meta‐analysis across 30 prospective cohort studies including a total of 893,850 participants indicated that depression was associated with a 30% increased risk of CHD diagnosis (Gan et al., 2014). Similar to the hostility construct, the timeframe of depression onset has been found to be meaningfully related to CHD prognosis: patients who experienced recurrent–persistent depression pre‐CHD diagnosis and maintained depression levels post‐CHD diagnosis were 1.5–2 times more likely to be re‐hospitalized for a coronary event or experience coronary mortality, respectively, than their non‐depressed counterparts (Leung et  al., 2012). These findings indicate that clinically depressed adults are at an elevated risk for CHD diagnosis and that they furthermore exhibit poorer CHD prognoses compounded by a higher mortality risk due to coronary complications.

Environmental Factors: Work Stress A long‐standing history of research has illustrated work‐related stress to contribute to disease outcomes, ranging the full gamut from acute bouts of insomnia to chronic medical conditions, such as CHD (Ganster & Rosen, 2013). A variety of theoretical orientations have been offered to organize research assessments, including the job strain model, the effort–reward imbalance model, and the organizational injustice model (e.g., Kivimäki et  al., 2006). The job strain model proposes the demand–control hypothesis that occupations characterized as high demand–low control represent a toxic combination in terms of giving rise to chronically elevated stress levels that degrade immune function and lead to adverse health outcomes. In short, this model stipulates that employees who are burdened with oftentimes unpredictable waves of high volume responsibilities (elevated demand) without appropriate decision latitude for managing the workload (poor control) are at risk for work‐related illness and eventual burnout. Along similar lines, the effort–reward imbalance model stipulates that work environments whereby employees are disproportionately challenged relative to compensation and recognition are likewise at risk for stress‐induced illness. Finally, the organizational injustice model argues that stress‐induced illnesses may arise in workplaces whenever employees reliably report being treated unfairly by their supervisors. Each model grapples with different ways by which workers may experience heightened stress levels on a daily basis. Evidence supports all three models of conceptualizing the work stress–CHD relationship, as indicated by a meta‐analytic report of 14 prospective cohort studies involving 83,014 employees, whereby elevations in work stress predicted an average 50% increase in CHD diagnosis/mortality (Kivimäki et al., 2006). Although all three models were supported in this meta‐analysis, the evidence was significantly stronger in support of the effort–reward imbalance and organizational injustice models relative to the job strain model, after controlling for

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the variance pertaining to traditional CHD risk factors. Irrespective, there is a clear and strong pattern of evidence that elevated stress in the workplace predicts adverse coronary outcomes over time.

Environmental Factors: Social Support Having the opportunity to discuss stressful life experiences with a trusted confidant can be helpful in reducing emotional strain and promoting problem‐focused coping, envisioning potential resolutions to the circumstances underlying the challenge/threat in question. Social support resources elicit the tendency to perceive that a weight has been lifted from one’s shoulders through self‐disclosure, thereby enhancing the sensation of self‐efficacy (ability to effectively handle the stressful situation) and reducing the maladaptive tendency of catastrophizing (rumination of difficulties that leads to perceived hopelessness—common when people feel overwhelmed by their life stressors and alone in their path of coping). If chronically elevated stress levels increases risk of CHD diagnosis/mortality, it stands to reason that stress‐ reducing social environments would confer protective benefit. Sources of social support are typically defined in the health sciences literature in terms of being “functional” or “structural” in scope. Types of functional support include the following categories: emotional, informational (e.g., receiving advice), financial, appraisal (someone helping to evaluate a situation), and instrumental (e.g., receiving assistance in completing a project), whereas structural support pertains to the number of individuals in one’s social network in terms of marital status, proximity of friends and family, community memberships, and the frequency of social contacts (e.g., number of times visiting with friends per month). The pattern of evidence in both etiologic (initially healthy samples) and prognostic (samples of CHD patients) longitudinal studies has been significant for functional social support but insignificant for structural social support, whereby low functional support predicted elevated relative risk of heart attack amid initially healthy samples and, in particular, a 59% increased risk of mortality amid CHD patients after controlling for traditional risk factors in a meta‐analysis (Barth, Schneider, & von Känel, 2010). These findings highlight the prominence of functional social support with regard to the quality of supportive social interactions, rather than the structural quantity of social interactions, when predicting health outcomes.

Environmental Factors: Socioeconomic Status Typically assessed by annual income and/or years of education, socioeconomic status (SES) represents an environmental attribute worthy of consideration as a psychosocial risk factor for CHD. There are a variety of putative mechanisms that may connect low SES to CHD outcomes, such as indirect effects of inadequate resources to maintain a healthy lifestyle and direct effects of stress‐induced influences on coronary dysfunction. For example, individuals of a lower class standing may consume a cheaper, highly processed diet conducive to the development of obesity, type 2 diabetes, hypertension, and elevated cholesterol. Likewise, the economic burden of poverty often compels individuals to work two to three jobs to make ends meet, thereby exacerbating perceptual stress levels as a function of workload and reducing time spent with family and friends—effectively degrading social support resources for stress management. Thus, lower SES tends to co‐occur with elevated work stress and poor social support, eliciting potential synergistic effects on disease outcome, suggesting a need for

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t­heoretical orientations designed to guide research investigations involving psychosocial risk factors for CHD morbidity and mortality. Meta‐analytic evidence involving 70 studies has indicated significant effects across income, occupation, and education whereby individuals in the lowest socioeconomic positions exhibited elevated risk of incident heart attack relative to their more affluent counterparts, with the effects most apparent in terms of income (a staggering 71% increase in relative risk of heart attack; Manrique‐Garcia, Sidorchuk, Hallqvist, & Moraldi, 2011). Further, prospective evidence following 66,500 initially healthy adults across 8 years found low SES predicted CHD mortality after controlling for traditional risk factors. SES was measured in terms of three occupational classes (professional/managerial, skilled manual/non‐manual workers, and semi‐ routine/unskilled workers) with each step down from professional/managerial associated with a 24% increase in CHD mortality (Lazzarino, Hamer, Stamatakis, & Steptoe, 2013). Moreover, a synergistic effect was observed whereby psychological distress augmented the adverse effects of low SES: the combination of low SES and high psychological distress was linked to a 33% increase in CHD mortality relative to high SES and low psychological distress counterparts (Lazzarino et al., 2013).

Piecing Together the Puzzle: Integrative Theoretical Frameworks The evidence to date reveals that a variety of psychosocial risk factors predict elevated CHD morbidities and mortalities to a varying degree after controlling for the presence of traditional risk factors, including dispositional factors such as hostility and depression (e.g., Chida & Steptoe, 2009; Gan et al., 2014) and environmental factors such as occupational strain and low social support (e.g., Barth et al., 2010; Kivimäki et al., 2006). However, emergent findings increasingly show that these psychosocial risk factors do not exist in a vacuum, but rather dynamically interact with one another, thereby requiring more comprehensive theoretical models to guide future research. For example, the transactional model of hostility stipulates that the cynical mistrustfulness associated with this disposition serves as a driving force to create a social environment conducive to antagonistic behaviors that degrade social support resources and exacerbates emotional strain (e.g., Vella, Kamarck, Flory, & Manuck, 2012). Finally, the reserve capacity model was designed to explain associations between SES and health outcomes, whereby lower SES directly increases perceptual stress levels while simultaneously reducing social support resources for coping (the reserve capacity); in turn, both of these SES‐induced developments of increased stress and decreased coping reserve are thought to directly elevate daily negative emotions/cognitions and reduce positive emotions/cognitions, collectively altering physiological processes that underlie CHD risk (Gallo & Matthews, 2003). Numerous issues pervade this literature and are worthy of mention as limitations for consideration to improve subsequent research. First, whether dispositional or environmental in scope, all psychosocial risk factors reviewed here feature the same common denominator of elevated stress levels that may impact upon health status via direct and indirect pathways. As such, researchers should focus their attention on incorporating standardized measures of perceptual stress levels in concert with the psychosocial risk factors in question to disentangle the pathways leading to CHD outcome. Second, most of these psychosocial risk factors are multifaceted in scope, suggesting a need to concentrate efforts on measurement issues to reliably delineate the relative degrees to which psychosocial risk factor attributes predict the development and prognosis of CHD. Finally, just as it is known that functional social support may

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improve one’s ability to effectively cope with stressful life experiences, reducing risk of CHD morbidity and mortality, researchers should also counterbalance their investigations by incorporating other psychosocial “protective” factors that may confer significant health benefit as part of their theoretical models (e.g., frequency of positive emotions and trait optimism). Future research on psychosocial risk factors of CHD should make targeted efforts to ascertain interactive pathways that include traditional risk factors to maximally predict health outcomes and enhance scientific understanding of these provocative associations.

Author Biography Dr. Elizabeth J. Vella is an associate professor of psychology at the University of Southern Maine. Her research interests include the link between psychosocial factors and cardiovascular risk and the physiological mechanisms that may explain these associations, as well as the implications for stress management interventions in improving quality of life and reducing physiological responses to stressors among at risk populations. She has authored numerous articles in scientific journals and presented her research at academic conferences.

References Albus, C. (2010). Psychological and social factors in coronary heart disease. Annals of Medicine, 42, 487–494. doi:10.3109/07853890.2010.515605 Barth, J., Schneider, S., & von Känel, R. (2010). Lack of social support in the etiology and prognosis of coronary heart disease: A systematic review and meta‐analysis. Psychosomatic Medicine, 72, 229–238. doi:10.1097/PSY.0b013e3181d01611 Beaglehole, R., & Magnus, P. (2002). The search for new risk factors for coronary heart disease: Occupational therapy for epidemiologists? International Journal of Epidemiology, 31, 1117–1122. doi:10.1093/ije/31.6.1117 Chida, Y., & Steptoe, A. (2009). The association of anger and hostility with future coronary heart disease: A meta‐analytic review of prospective evidence. Journal of the American College of Cardiology, 53(11), 936–946. doi:10.1016/j.jacc.2008.11.044 Dembroski, T. M., MacDougall, J. M., Costa, P. T., & Grandits, G. A. (1989). Components of hostility as predictors of sudden death and myocardial infarction in the Multiple Risk Factor Intervention Trial. Psychosomatic Medicine, 51, 514–522. Gallo, L. C., & Matthews, K. A. (2003). Understanding the association between socioeconomic status and physical health: Do negative emotions play a role? Psychological Bulletin, 129(1), 10–51. doi:10.1037/0033‐2909.129.1.10 Gan, Y., Gong, Y., Tong, X., Sun, H., Cong, Y., Dong, X., … Lu, Z. (2014). Depression and risk of coronary heart disease: A meta‐analysis of prospective cohort studies. BMC Psychiatry, 14, 371–382. doi:10.1186/s12888‐014‐0371‐z Ganster, D. C., & Rosen, C. C. (2013). Work stress and employee health: A multidisciplinary review. Journal of Management, 39(5), 1085–1122. doi:10.1177/0149206313475815 Hecker, H. L., Chesney, M. A., Black, G. W., & Frautschi, N. (1988). Coronary‐prone behaviors in the Western Collaborative Group Study. Psychosomatic Medicine, 50, 153–164. Kivimäki, M., Virtanen, M., Elovainio, M., Kouvonen, A., Väänänen, A., & Vahtera, J. (2006). Work stress in the etiology of coronary heart disease: A meta‐analysis. Scandanavian Journal of Work Environment Health, 32(6), 431–442. doi:10.5271/sjweh.1049 Labarthe, D. R. (1998). Coronary heart disease. In J. Colilla (Ed.), Epidemiology and prevention of cardiovascular diseases: A global challenge (pp. 43–72). Gaithersburg, MD: Aspen Publishers, Inc.

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Lazzarino, A. I., Hamer, M., Stamatakis, E., & Steptoe, A. (2013). Low socioeconomic status and psychological distress as synergistic predictors of mortality from stroke and coronary heart disease. Psychosomatic Medicine, 75, 311–316. doi:10.1097/PSY.0b013e3182898e6d Leung, Y. W., Flora, D. B., Gravely, S., Irvine, J., Carney, R. M., & Grace, S. L. (2012). The impact of premorbid and postmorbid depression onset on mortality and cardiac morbidity among patients with coronary heart disease: Meta‐analysis. Psychosomatic Medicine, 74, 786–801. doi:10.1097/ PSY.0b013e31826ddbed Manrique‐Garcia, E., Sidorchuk, A., Hallqvist, J., & Moraldi, T. (2011). Socioeconomic position and incidence of acute myocardial infarction: A meta‐analysis. Journal of Epidemiology and Community Health, 65(4), 301–309. doi:10.11 36/jech.2009.104075 Rosenman, R. H., Brand, R. J., Jenkins, C. D., Friedman, M., Straus, R., & Wurm, M. (1975). Coronary heart disease in the Western Collaborative Group Study: Final follow‐up of 8.5 years. Journal of the American Medical Association, 233, 872–877. doi:10.1001/jama.1975.03260080034016 Sherwitz, L., & Brand, R. (1990). Interviewer behaviors during structured interviews to assess Type A/B behavior in the Western Collaborative Group Study and the Multiple Risk Factor Intervention Trial. Journal of Psychopathology and Behavioral Assessment, 12(1), 27–47. Vella, E. J., Kamarck, T. W., Flory, J. D., & Manuck, S. (2012). Hostile mood and social strain during daily life: A test of the transactional model. Annals of Behavioral Medicine, 44(3), 341–352. doi:10.1007/s12160‐012‐9400‐7 World Health Organization. (2015). Cardiovascular diseases fact sheet. Retrieved from http://www. who.int/mediacentre/factsheets/fs317/en/. World Heart Federation. (2015). Cardiovascular disease risk factors. Retrieved from http://www.world‐ heart‐federation.org/cardiovascular‐health/cardiovascular‐disease‐risk‐factors/.

Suggested Reading Everson‐Rose, S. A., & Lewis, T. T. (2005). Psychosocial factors and cardiovascular diseases. Annual Review of Public Health, 26, 469–500. doi:10.1146/annurev.publhealth.26.021304.144542 Krantz, D. S., & McCeney, M. K. (2002). Effects of psychological and social factors on organic disease: A critical review of research on coronary heart disease. Annual Review of Psychology, 53, 341–369. doi:10.1146/annurev.psych.53.100901.135208 Matthews, K. A. (2005). Psychological perspectives on the development of coronary heart disease. American Psychologist, 60(8), 783–796. doi:10.1037/0003‐066X.60.8.783

Relationship Dissolution and Health Karen Hasselmo, Kyle J. Bourassa, and David A. Sbarra Department of Psychology, University of Arizona, Tucson, AZ, USA

Humans are inherently social beings, and intimate relationships are a particularly important part of our lives. The beneficial effects of intimate relationships may operate directly by encouraging healthy behaviors (e.g., high‐quality sleep or a healthy diet) or more indirectly by buffering against the effects of psychological stress. Conversely, the absence of relationships, or being in a low‐quality relationship, is associated with a variety of negative health outcomes. This entry focuses on health outcomes associated with romantic separations. Specifically, we report on the epidemiological data connecting romantic separations with morbidity and mortality, explore the potential behavioral physiological mechanisms linking these outcomes with the dissolution of the relationship (e.g., cardiovascular and immune system processes, health behaviors), and consider the psychological variables that may explain the associations of interest, including forgiveness, rumination, and psychological distancing. We end with future directions for research and intervention development. When intimate relationships end, most people are resilient and cope well over time. The end of an intimate relationship can, however, set the stage for adverse effects on well‐being— whether it is divorce after decades of marriage or the breakup with a first love.1 For the subset of people who are affected negatively by a separation experience, these effects can exert their negative influence across a range of health‐relevant physiological systems.

Morbidity and Mortality Following Dissolution The bird’s‐eye view of intimate relationship dissolution offers one perspective on the connection between separations and poor health. In large samples of separated and/or divorced people, average findings indicate an increased risk for all‐cause morbidity and mortality (Sbarra, Law, & Portley, 2011). For example, in a long‐term study of the Uppsala birth cohort, being divorced placed both men and women in the highest risk groups for early mortality, with a 46 and 27% higher relative mortality risk than their married counterparts, respectively (Donrovich, Drefahl, & Koupil, 2014). The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Research has begun to connect specific disease processes to marital status changes. In a sample of over 3.5 million participants, for example, married participants had lower odds of any cardiovascular disease (OR  =  0.95), whereas divorced participants had higher odds (OR  =  1.05) relative to single adults (Alviar, Rockman, Guo, Adelman, & Berger, 2014). Losses in bone density are also connected to divorce or separation; divorced men, relative to those who are married, demonstrated a 0.33 standard deviation reduction in lumbar spinebone mass index. This number was similar (0.36) even for those men who remarried. Infectious diseases are also associated with marital status. In a cohort of over 5.5 million men and women followed for hospital contacts over three decades, divorced participants had a 48% greater chance of contracting a hospital‐diagnosed infectious disease when compared with married participants (Nielsen, Davidsen, Hviid, & Wohlfahrt, 2014). This increased infection risk was sustained in a 15‐year follow‐up. Other research demonstrates that rates of illness peak around the time of divorce and may not return to their baseline levels. Beyond physical health, marital status is predictive of mood and anxiety disorders across the world. In a study conducted by the World Mental Health Survey Initiative on a sample of 35,500 participants, the odds ratio of any mood or anxiety disorder increased 23% for divorced participants relative to married adults, and this effect was much stronger for women (OR = 2.6; McDowell et al., 2014). These epidemiological findings demonstrate the importance of studying health outcomes following a separation but offer few hints regarding what processes might underlie the association between dissolution of intimate relationships and negative health outcomes.

Health‐Relevant Responses to Relationship Dissolution A thorough account of how relationship breakups can ultimately affect physical health links psychological responses to separations with biologically plausible mechanisms of action (cf. Miller, Chen, & Cole, 2009). Physiological responding can be studied to investigate emotions and emotional responses, as well as an intermediate health‐relevant index used to link emotional reactions with long‐lasting, systemic changes in the body. For example, cortisol may play a role in the distress response following a social stressor, but it may also function as the mediator of the relationship between stress and a pathological inflammatory response. Thus, care must be taken in discussing physiological responses in general versus physiological responses that have clear implications for health. Miller et al. (2009) have advanced a conceptual approach to studying how biobehavioral responses to life events are associated with and may regulate (and be regulated by) disease‐relevant molecular and cellular responses. This approach highlights the role of biological intermediaries, or, as Miller et al. (2009) stated, “Once a robust linkage between a psychosocial factor and a clinical health outcome is identified, the next step is to determine what biological processes convey those effects into the physical environment of disease pathogenesis” (p. 504). Related to divorce, Miller et al.’s (2009) first criterion is satisfied. Beyond the meta‐analytic findings we described above, other studies demonstrate that divorce can result in a proliferation of stress that mediates the association between the end of marriage and poor self‐rated health up to a decade later. We do not yet know, however, the biological intermediaries that translate the psychosocial stress of divorce into poor physical health. We have some clues as to how these effects might operate but lack clear‐cut, disease‐relevant pathways. In the following sections, we review what is known with respect to cardiovascular, immune, and endocrine outcomes. We also discuss the role of relationship dissolution in shaping people’s health behaviors, as these behaviors are associated with changes in each of these systems.

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Cardiovascular Responding Cardiovascular processes are one possible link between intimate relationship dissolution and negative health outcomes. A dysregulated cardiovascular response to stress is thought to be an indicator of—if not causally linked to—the development of cardiovascular disease. It is well established that an exaggerated response to environmental stress leads to rises in future blood pressure, and there is evidence to suggest hyperreactivity is reliably associated with a host of other cardiovascular issues, such as atherosclerosis (i.e., buildup of plaque in the arteries) and increased cardiac hypertrophy (i.e., thickening of heart muscles; Chida & Steptoe, 2010). Conversely, hyporeactivity, or a blunted response to stress, is also linked with negative health outcomes, including obesity, depression, and poor self‐rated health, possibly due to its association with behaviors driven by disordered motivation, such as heightened focus on rewarding stimuli. As a result, both hyper‐ and hyporeactivity may indicate health risk following a stressful separation experience. For example, when asked to reflect over their relationship history and recent separation experience, divorced adults who scored high in anxious attachment style and who spoke in a verbally immediate way (i.e., indicating a pattern of continual engagement with the dissolved relationship) evidenced significantly higher blood pressure at the start of a divorce‐related experimental task (Lee, Sbarra, Mason, & Law, 2011). In a similar investigation, high levels of divorce‐related emotional intrusion were associated with elevated blood pressure, and men who find thinking about their separation especially difficult evidence substantial blood pressure increases when asked to do so (Sbarra, Law, Lee, & Mason, 2009). Heart rate variability, as indexed by respiratory sinus arrhythmia (RSA) (Porges, 1995), is a second active area of research in cardiac responding. RSA represents the action of the vagus nerve, which exerts inhibitory control over the heart. Higher RSA is adaptive in certain situations, as it helps prepare the body to free‐metabolic resources for coping responses driven by the sympathetic nervous system. Low RSA is predictive of greater cardiovascular disease risk; moreover, context inappropriate changes in RSA are thought to index poor self‐regulation (Porges, 1995). Regarding RSA reactivity in divorced adults, Sbarra and Borelli (2012) found that adults who reported having avoidant attachment styles, conceptualized as an ability to achieve emotional distance from their relationships, and who were successful at regulating their emotions during a divorce‐related task, as indexed by high levels of RSA reactivity during the task, evidence the greatest improvements in their self‐concept over the course of three months (Sbarra & Borelli, 2012). Further research involving RSA and adults’ adjustment following divorced considered a genetic polymorphism in the serotonin transporter gene (SERT). The “short” variation of this allele results in less available serotonin in the brain, and adults with the genetic polymorphism who also reported difficulty adjusting to their separation experience had significantly decreased levels of RSA during a task that asked them to reflect on their divorce experience (Hasselmo, Sbarra, O’Connor, & Moreno, 2015).

Immune Functioning A second health‐relevant psychophysiological pathway through which the end of an intimate relationship may exert its negative effects is via the immune system. Much like the cardiovascular pathway, the immune system has important functions for survival and health, and higher levels of social support are generally associated with an increase in physiological functioning in both the cellular and humoral divisions of the immune system (Uchino, Cacioppo, & Kiecolt‐ Glaser, 1996). Conversely, divorced/separated adults experience higher rates of mortality due to infectious diseases compared with their married counterparts. Psychoneuroimmunologists

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investigate the immune correlates of social influence by looking at both numbers of important immune cells and functionality of the system itself, both of which are required to mount an appropriate immune response (Uchino et  al., 1996). Intimate relationship dissolution may influence the immune system through both of these routes. For example, when compared with married adults on various measure of immune system functioning, recently divorced participants showed decreased functioning on several immune parameters (Kiecolt‐Glaser et al., 1987). A weakened immune system following the chronic stress of divorce or separation may lead to the higher rate of infectious diseases in this population. The mechanism that links the psychosocial effects of divorce or separation and the immune system is not yet understood, but two candidate pathways have emerged. One route through which the stress of separation from a partner influences immune parameters is the endocrine system, specifically the various hormones this system releases in response to stress. Cortisol, a steroid hormone, has a particularly diverse range of effects within the body, including the ability to bind to the white blood cells of the immune system and regulate both their distribution throughout the body and their functional responses (Segerstrom & Miller, 2004). Cortisol plays an important role in preparing the body to respond to stress, but if levels are elevated chronically, it can lead to the development of chronic inflammatory diseases such as arthritis and asthma, among others. Preliminary data from Powell et al. (2002) investigated 20 women who were undergoing a separation event and 20 who were in stable marriages. They found that separating women had elevated salivary cortisol, suggesting a link between relationship dissolution and cortisol levels. Although this data is encouraging, more specific studies linking the cortisol pathway to divorce in particular are needed. An exaggerated cortisol response can be deleterious in and of itself, but it can also set the stage for other problematic immune responses. In particular, it can permit inflammation over time. Similar to the hyperactivation of the cardiovascular system following the dissolution of an intimate relationship, the immune system can be over‐activated in response to the chronic psychological stress associated with a breakup. The immune response to stress often initiates with the release of chemical messengers, known as cytokines, which alert the rest of the body to the presence of an intruder. If these cytokines are pro‐inflammatory, they set off a cascade of processes in the body that promote inflammation. Although these pro‐inflammatory cytokines are adaptive at first, they can become dysregulated during the chronic stress following a distressing event. One model suggests that glucocorticoid receptors in the brain, which receive the inflammatory message of these cytokines and then downregulate the signal, become desensitized to the pro‐inflammatory cytokines when they are chronically proliferating (Miller, Cohen, & Ritchey, 2002). Without this glucocorticoid regulation, the inflammatory response persists, elevating risk for a range of chronic inflammatory diseases, including Type 2 diabetes, various autoimmune diseases, and cardiovascular disease. This pro‐inflammatory pathway receives considerable attention in the broad literature on stress and health but has yet to be studied as people adapt to a divorce or a romantic breakup.

Health Behaviors Although it is possible that the psychological stress of divorce is associated directly with changes in the aforementioned physiological systems, it is important to recognize that lifestyle habits following a breakup also play a powerful role in shaping the responses in these systems. In this way, health behaviors may be important in understanding the intermediate steps from relationship dissolution to morbidity and mortality. For example, social relationships promote

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important behaviors like seeking medical care and adherence to care while also encouraging self‐care like exercising, healthy eating, and avoiding illicit drug use. A recent study of adherence to doctor‐provided cancer prevention guidelines noted that the number one indicator of reductions in all‐cancer incidence and cancer mortality was following the prescribed guidelines (Kabat, Matthews, Kamensky, Hollenbeck, & Rohan, 2015). As being married increases adherence to medical advice, this may affect health at multiple levels for divorced or separated adults. Similarly, the loss of this social pressure in the form of a spouse or partner may disrupt these healthy lifestyle behaviors. Furthermore, there is epidemiological evidence that divorce or separation promotes the adoption of unhealthy habits like substance abuse. Women in particular are vulnerable to decreases in body mass, increased likelihood of smoking relapse, and decreases in the amount of healthy fruits and vegetables consumed following divorce. The cessation of positive health behaviors and the development of unhealthy ones result in an increased risk for the promotion and maintenance of cardiovascular problems and deleterious changes in the immune functioning. Sleep is an additional lifestyle factor that may affect health following the end of marriage. Disturbances in sleep can lead to dysregulated endocrine and autonomic nervous system functioning and even directly to higher mortality risk. Research suggests that divorced people have a higher incidence of sleep disturbance than partnered individuals. An early study of 70 subjects undergoing a separation period revealed these participants exhibited less delta wave sleep during their separation experience than their age‐matched, married counterparts, indicating lighter overall sleep (Cartwright & Wood, 1991). Epidemiological studies reveal that being separated or divorced is associated with an elevated risk for severe insomnia and sleep maintenance problems (Hajak, 2001). Another study focusing on 367 middle‐aged women’s relationship histories over 6–8 years noted that continuously married or cohabitating participants have better sleep quality (measured both by self‐report and a number of objective measures) than women who were unpartnered or gained/lost a partner during this same time period (Troxel et al., 2010). More recently, research on divorced adults suggests that sleep disturbances following separation predict future increases in blood pressure (Krietsch, Mason, & Sbarra, 2014). These increases were especially noteworthy in participants who continued to report disturbances 10 weeks after the separation and who evidenced the greatest increases in future blood pressure (Krietsch et al., 2014). Sleep highlights another pathway through which cardiovascular functioning may increase morbidity and mortality following a separation experience.

Integrating Across Multiple Domains: Allostatic Load If the presence of indicators of health risk across a variety of health‐relevant physiological systems are chronic and/or severe enough, their effects may accrue to produce accumulated wear and tear on the body, termed allostatic load (McEwen, 2000). Allostasis describes a physiological or behavioral adaptation to environmental changes in order to restore normality, or homeostasis (McEwen, 2000). Excessive cardiovascular, neuroendocrine, and immune activations in response to stress can promote vascular remodeling, initiate atherosclerotic plaque growth, and alter gene expression in a manner that contributes to disease pathogenesis (Miller et al., 2009). These processes may lead to the increased morbidity and mortality rates noted among populations affected by divorce and separation, though this area requires future research. To the extent that the cardiovascular and immune systems stay activated over time, that wear and tear may lead to illness and early death. What psychological processes are associated with the prolonged, hyperactivation of these physiological systems? We consider this question in the final section of the chapter.

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Psychological Processes and Health Outcomes In terms of psychological characteristics associated with adjustment to divorce, it is well known that individual differences in attachment anxiety are associated with poor outcomes when people perceive a threat to their relationship and/or their security within the relationship. In a study mentioned above in relation to blood pressure responses, Lee et al. (2011) studied language as a behavioral manifestation of attachment‐related hyperactivation. People higher in attachment anxiety who spoke about their separation experience in a highly immediate, experiential, and self‐focused manner demonstrated greater increases in systolic and diastolic blood pressure when thinking about their relationship history and separation experience relative to those people lower in attachment anxiety. People at high risk for poor outcomes following marital separation appear to employ coping strategies that are associated with a high degree of physiological activation, and this study, focused on blood pressure reactivity, provides an example of the ways in which emotion regulation strategies around attachment themes can provide insights into processes that confer risk for poor distal health outcomes. Other research on emotion regulation following divorce supports this idea. For example, an intervention for recently divorced people (Sbarra, Boals, Mason, Larson, & Mehl, 2013) focused on the use of expressive writing (EW; writing about emotional feelings) compared with control writing (continuously writing about nonemotional issues). The results indicated that EW increased distress related to the divorce 3‐month follow‐up for people high on rumination when compared with low ruminators who were in the EW condition and all people in the control writing condition. After 8 months, however, all people in the EW condition and low ruminators in the control writing condition were equivalent in their distress, whereas high ruminators, purportedly at the most risk for negative outcomes following divorce, evidenced the lowest distress. The results suggest that allowing people who are at risk for rumination to focus on actively structuring their time (rather than “digging more deeply” into the emotions associated with their separation) reduces their psychological distress following marital separation. Together, the findings related to attachment anxiety and rumination suggest potential psychological mechanisms linking marital separation to poor health outcomes. People who have a hard time distancing themselves from their psychological experiences show excessive cardiovascular responding, which, as noted above, is associated with the development of cardiovascular disease. Conceptually, this work fits well with the larger literature on self‐distanced reflection and evidence indicating that people who recount their experiences in a blow‐by‐blow manner rather than reconstrue their experiences to find meaning are at heightened risk for mood disorders (see Kross & Ayduk, 2011). In addition to attachment anxiety and rumination, forgiveness is another important psychological variable associated with adults’ recover from divorce. Rye et al. (2005) developed an intervention focused on facilitating forgiveness for an ex‐spouse using previous research indicating that forgiveness predicted improved well‐being, as well as reduced depressive symptoms and both state and trait anger. The intervention was based on a prior intervention designed to increase forgiveness for previous wrongs in a romantic relationship in college women, which resulted in higher levels of forgiveness and well‐being for participants. Among divorcing adults, this team tested two versions of their forgiveness intervention (secular versus religious forgiveness) and a no‐treatment control using a randomized control trial (RCT) over 8 weeks of treatment and a 6‐week follow‐up period. Participants in both the secular and religious intervention conditions showed improvements in forgiveness of their ex‐partner compared

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with control, though only the secular condition evidenced decreased levels of depression. This program of research and evidence from a laboratory experiment that less forgiveness results in higher skin conductance, heart rate, and blood pressure compared with baseline (van Oyen Witvliet, Ludwig, & Vander Lann, 2001) suggests that interventions designed to target forgiveness following divorce and romantic breakups may yield benefits in health outcomes. Beyond self‐distanced reflection and forgiveness, other variables and processes may serve as potential explanatory pathways leading to health‐relevant biological changes. In a prospective study of breakups following nonmarital dissolution (Mason, Law, Bryan, Portley, & Sbarra, 2012), improvements in self‐concept clarity (knowing who you are as a person after a separation) were associated with increases in future psychological well‐being. There was no evidence in this study that people begin to feel better, who then report a greater sense of who they are after their breakup; instead, the direction of the effect seems to operate from self‐concept clarity to improved psychological well‐being. In a recent experimental study, Larson and Sbarra (2015) found that people who simply reflected on their separation experience multiple times over nine weeks increased their sense of self‐concept clarity, which, in turn, explained decreases in loneliness and breakup‐related emotional distress over the entire study period. Self‐concept clarity was a key variable in early accounts of the psychological response to divorce (Weiss, 1975), yet no studies to date have examined this variable with respect to biomarkers of interest.

Conclusions Intimate relationships are an important part of people’s lives and can be beneficial for psychological and physical health. When relationships end, however, people are broadly vulnerable to morbidity and mortality from all causes, potentially due to changes in cardiovascular and immune responding. Alterations in these so‐called biological intermediaries appear to be the result of changes in health behaviors, including sleep, medical adherence, self‐care, healthy eating, and exercise. There are several potential psychological mechanisms that may link the end of an intimate relationship to distal health outcomes. Examples provided in this entry include experiential over‐involvement, rumination, forgiveness, and self‐concept clarity. Because of the health risks conferred by romantic separations, it seems essential to target those most at greatest risk for poor outcomes. Future interventions after romantic separation and divorce should focus on affecting the health behaviors and psychological processes proposed to mediate the link between romantic separation and health outcomes, with the goal of reducing the health burden associated with separation. In short, romantic separation and divorce are common stressful events, and these experiences are associated with negative, health‐relevant consequences for a subset of people.

Author Biographies Karen Hasselmo, MA, is a doctoral candidate at the University of Arizona, and her research centers on psychological and physiological outcomes of relationship dissolution. Kyle J. Bourassa, MA, is a doctoral candidate at the University of Arizona, and his research centers on dyadic processes in intact relationships, as well as how prior relationship characteristics might affect people’s well‐being following romantic separation.

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David A. Sbarra, PhD, is an associate professor of psychology at the University of Arizona. His research focuses on social relationships and health, as well as how people recover from difficult social transitions.

Note 1 Although there are obvious differences between the end of marriage and a nonmarital breakup, in this entry, we consider literatures relevant to both. Where the category matters, we make the distinction clear.

References Alviar, C. L., Rockman, C., Guo, Y., Adelman, M., & Berger, J. (2014). Association of marital status with vascular disease in different arterial territories: A population based study of over 3.5 million subjects. Journal of the American College of Cardiology, 63(12), A1328. doi:10.1016/ S0735‐1097(14)61328‐0 Cartwright, R. D., & Wood, E. (1991). Adjustment disorders of sleep: The sleep effects of a major stressful event and its resolution. Psychiatry Research, 39, 199–209. doi:10.1016/0165‐1781(91)90088‐7 Chida, Y., & Steptoe, A. (2010). Greater cardiovascular responses to laboratory mental stress are associated with poor subsequent cardiovascular risk status a meta‐analysis of prospective evidence. Hypertension, 55(4), 1026–1032. doi:10.1161/HYPERTENSIONAHA.109.146621 Donrovich, R., Drefahl, S., & Koupil, I. (2014). Early life conditions, partnership histories, and mortality risk for Swedish men and women born 1915–1929. Social Science & Medicine (1982), 108, 60–67. doi:10.1016/j.socscimed.2014.02.036 Hajak, G. (2001). Doxepin shows promise in primary insomnia. Inpharma, 1302, 25. doi:10.2165/00 128413‐200113020‐00032 Hasselmo, K., Sbarra, D. A., O’Connor, M. F., & Moreno, F. A. (2015). Psychological distress following marital separation interacts with a polymorphism in the serotonin transporter gene to predict cardiac vagal control in the laboratory. Psychophysiology. doi:10.1111/psyp.12409 Kabat, G. C., Matthews, C. E., Kamensky, V., Hollenbeck, A. R., & Rohan, T. E. (2015). Adherence to cancer prevention guidelines and cancer incidence, cancer mortality, and total mortality: A prospective cohort study. American Journal of Clinical Nutrition, 101(3), 558–569. doi:10.3945/ ajcn.114.094854 Kiecolt‐Glaser, J. K., Fisher, L. D., Ogrocki, P., Stout, J. C., Speicher, C. E., & Glaser, R. (1987). Marital quality, marital disruption, and immune function. Psychosomatic Medicine, 49(1), 13–34. Krietsch, K. N., Mason, A. E., & Sbarra, D. a. (2014). Sleep complaints predict increases in resting blood pressure following marital separation. Health Psychology, 33(10), 1204–1213. doi:10.1037/ hea0000089 Kross, E., & Ayduk, O. (2011). Making meaning out of negative experiences by self‐distancing. Current Directions in Psychological Science, 20, 187–191. doi:10.1177/0963721411408883 Larson, G. M., & Sbarra, D. A. (2015). Participating in research on romantic breakups promotes emotional recovery via changes in self‐concept clarity. Social Psychological and Personality Science, 6(4), 399–406. doi:10.1177/1948550614563085 Lee, L. A., Sbarra, D. A., Mason, A. E., & Law, R. W. (2011). Attachment anxiety, verbal immediacy, and blood pressure: Results from a laboratory‐analogue study following marital separation. Personal Relationships, 18(2), 285–301. doi:10.1111/j.1475‐6811.2011.01360.x. doi:10.1016/j. ijpsycho.2004.11.007

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Mason, A. E., Law, R. W., Bryan, A. E., Portley, R. M., & Sbarra, D. A. (2012). Facing a breakup: Electromyographic responses moderate self‐concept recovery following a romantic separation. Personal Relationships, 19(3), 551–568. McDowell, R. D., Ryan, A., Bunting, B. P., O’Neill, S. M., Alonso, J., Bruffaerts, R., … Tomov, T. (2014). Mood and anxiety disorders across the adult lifespan: A European perspective. Psychological Medicine, 44(04), 707–722. doi:10.1017/S0033291713001116 McEwen, B. S. (2000). The neurobiology of stress: from serendipity to clinical relevance. Brain Research, 886(1), 172–189. doi:10.1016/S0006‐8993(00)02950‐4 Miller, G., Chen, E., & Cole, S. (2009). Health psychology: Developing biologically plausible models linking the social world and physical health. Annual Review of Psychology, 60, 501–524. doi:10.1146/ annurev.psych.60.110707.163551 Miller, G. E., Cohen, S., & Ritchey, A. K. (2002). Chronic psychological stress and the regulation of pro‐inflammatory cytokines: A glucocorticoid‐resistance model. Health Psychology, 21(6), 531–541. doi:10.1037/0278‐6133.21.6.531. doi:10.1111/j.1532‐5415.2000.tb03901.x Nielsen, N. M., Davidsen, R. B., Hviid, A., & Wohlfahrt, J. (2014). Divorce and risk of hospital‐diagnosed infectious diseases. Scandinavian Journal of Public Health, 42(7), 705–711. doi:10.1177/ 1403494814544398 Porges, S. W. (1995). Cardiac vagal tone: A physiological index of stress. Neuroscience and Biobehavioral Reviews, 19(2), 225–233. doi:10.1016/0149‐7634(94)00066‐A Powell, L. H., Lovallo, W. R., Matthews, K. A., Meyer, P., Midgley, A. R., Baum, A., … Ory, M. G. (2002). Physiologic markers of chronic stress in premenopausal, middle‐aged women. Psychosomatic Medicine, 64(3), 502–509. Rye, M. S., Pargament, K. I., Pan, W., Yingling, D. W., Shogren, K. A., & Ito, M. (2005). Can group interventions facilitate forgiveness of an ex‐spouse? A randomized clinical trial. Journal of Consulting and Clinical Psychology, 73(5), 880. doi:10.1037/0022‐006X.73.5.880 Sbarra, D. A., Boals, A., Mason, A. E., Larson, G. M., & Mehl, M. R. (2013). Expressive writing can impede emotional recovery following marital separation. Clinical Psychological Science, 1(2), 120– 134. doi:10.1055/s‐0029‐1237430.Imprinting Sbarra, D. A., & Borelli, J. L. (2012). Heart rate variability moderates the association between attachment avoidance and self‐concept reorganization following marital separation. International Journal of Psychophysiology, 88(3), 253–260. doi:10.1016/j.ijpsycho.2012.04.004 Sbarra, D. A., Law, R. W., Lee, L. A., & Mason, A. E. (2009). Marital dissolution and blood pressure reactivity: Evidence for the specificity of emotional intrusion‐hyperarousal and task‐rated emotional difficulty. Psychosomatic Medicine, 71(5), 532–540. doi:10.1097/PSY.0b013e3181a23eee Sbarra, D. A., Law, R. W., & Portley, R. M. (2011). Divorce and death: A meta‐analysis and research agenda for clinical, social, and health psychology. Perspectives on Psychological Science, 6(5), 454– 474. doi:10.1177/1745691611414724 Segerstrom, S. C., & Miller, G. E. (2004). Psychological stress and the human immune system: A­ meta‐analytic study of 30 years of inquiry. Psychological Bulletin, 130(4), 601–630. doi:10.1037/0033‐2909.130.4.601 Troxel, W. M., Buysse, D. J., Matthews, K. A., Kravitz, H. M., Bromberger, J. T., Sowers, M., & Hall, M. H. (2010). Marital/cohabitation status and history in relation to sleep in midlife women. Sleep, 33(7), 973–981. Uchino, B., Cacioppo, J., & Kiecolt‐Glaser, J. K. (1996). The relationship between social support and physiological processes: A review with emphasis on underlying mechanisms and implications for health. Psychological Bulletin, 119(3), 488–531. doi:10.1037/0033‐2909.119.3.488 van Oyen Witvliet, C., Ludwig, T. E., & Vander Laan, K. L. (2001). Granting forgiveness or harboring grudges: Implications for emotion, physiology, and health. Psychological Science, 12(2), 117–123. doi:10.1111/1467‐9280.00320 Weiss, R. S. (1975). Marital separation. New York, NY: Basic Books.

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Suggested Reading Miller, G., Chen, E., & Cole, S. (2009). Health psychology: Developing biologically plausible models linking the social world and physical health. Annual Review of Psychology, 60, 501–524. doi:10.1146/ annurev.psych.60.110707.163551 Sbarra, D. A., Hasselmo, K., & Bourassa, K. J. (2015). Divorce and health: Beyond individual differences. Current Directions in Psychological Science, 24(2), 109–113. doi:10.1177/0963721414559125 Sbarra, D. A., Law, R. W., & Portley, R. M. (2011). Divorce and death: A meta‐analysis and research agenda for clinical, social, and health psychology. Perspectives on Psychological Science, 6(5), 454–474. doi:10.1177/1745691611414724

Risk Perception Vera Hoorens Center for Social and Cultural Psychology, Katholieke Universiteit Leuven, Leuven, Belgium

Risk perception is people’s judgment of future outcomes that may occur if they or other people follow a given course of action. Although risks may involve desirable as well as undesirable outcomes, the term is mostly used to describe undesirable outcomes such as health and safety hazards or natural or man‐made disasters. Some researchers distinguish between risk perception and risk evaluation. By risk perception they mean the estimation of the risk’s magnitude; by risk evaluation they mean judgments of risk acceptability, assumed to depend not only on the perceived magnitude of the risk but also on other risk characteristics. Other researchers use the term “risk perception” in a broader sense, including judgments of risk magnitude as well as of any other characteristic that might affect the risk’s acceptability. Risk perception in both senses is to be distinguished from risk assessment, which is a formal and systematic risk analysis undertaken by risk experts (such as actuaries) in the context of financial or policy decisions. Understanding how people perceive and evaluate risks is important to understand health and safety behavior and responses to risks of man‐made and natural disasters. To explain why people are willing (or unwilling) to pay for insurance, for instance, or to explain why they freely enter objectively dangerous situations but feel unsafe in objectively non‐dangerous ones, one needs to understand how people perceive the risks involved. Not surprisingly, risk perception and evaluation have been studied by researchers from a wide variety of disciplines, including psychology, economics, criminology, and sociology. Early research often started with the assumption (sometimes implicit) that people intuitively follow normative statistical or logical rules while judging risk magnitude. From a normative point of view, the magnitude of a given individual’s risk is a joint function of the perceived valence and extremity of the outcomes that he or she may experience (in more technical terms, outcomes’ expected subjective utility) and these outcomes’ perceived likelihood. Although many studies have focused on such individual risk perception (e.g., a vacationer judging her risk of skiing accidents), people may also form impressions of collective risks (e.g., a travel guide judging the risk of skiing accidents in her group of vacationers). Normatively, judgments

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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of the magnitude of overall risk should then also depend on the number of individuals who are likely to be affected. Assuming that people can and sometimes do intuitively follow normative rules, researchers in the domain of individual risk perception have studied how people estimate event likelihoods, how they arrive at expected subjective utilities, and how they combine likelihood and utility information toward an overall judgment of a risk’s magnitude. In addition to these questions, researchers in the domain of collective risk perception have also examined how people perceive the number of individuals affected and how they integrate this information in their general risk perception. More recent studies on risk perception are rooted in the view that people can apprehend reality in two fundamentally distinct manners: in an analytical manner, or in an experiential manner. From this perspective, people are thought to sometimes base their evaluation of the risks that are associated with a given activity on a systematic consideration of the associated outcomes and their likelihoods, but in other cases they base their risk evaluation on the affective state they experience while considering the activity. The analytical manner is deliberative, verbal, systematical, and slow, whereas the experiential manner is affective, nonverbal, intuitive, and quick. Although researchers who support the dual‐process approach continue to value research on likelihood estimation, expected subjective utility, and the combination of the two, they also examine how affective states determine risk perception and strive to identify the circumstances in which either systematic risk analysis or affect‐driven risk perception occurs. Among the circumstances that have been shown to favor affect‐driven risk perception are strong emotional reactions, time pressure, and reduced cognitive resources (e.g., Finucane, Alhakami, Slovic, & Johnson, 2000).

Likelihood Estimation One can estimate the likelihood of an event by observing how often, out of the cases in which it might have occurred, it has indeed occurred. People sometimes try to engage in this exercise, but in many cases they do so incompletely or use a totally different approach. When relative frequencies are used to estimate probabilities, one should weight each nonoccurrence as heavily as each occurrence. Five occurrences of an infection clearly represent a different likelihood of infection if 20 individuals were exposed than if 200 individuals were exposed. However, people focus on occurrences to the neglect of nonoccurrences. When likelihood or frequency information is presented to them in the form of a proportion or a fraction (e.g., a 1 out of 100 chance, a 10 out of 1,000 chance), they tend to focus on the numerator (in the examples just given, 1 or 10, respectively) to the neglect of the denominator (in the examples just given, 100 or 1,000, respectively). Thus, a 10 out of 1,000 chance seems larger than a 1 out of 100 chance even though the probability is identical in each case. One implication of this phenomenon is that likelihoods that are expressed in a probability format (i.e., as a value from 0 to 1 or as a percentage) seem smaller than likelihoods that are expressed in a frequency format with a denominator above 1. One impressive demonstration of this phenomenon was produced by Slovic, Monahan, and MacGregor (2000). They asked mental health professionals to judge if a given patient should be released from the hospital given the risk of the patient committing acts of violence. Half of the participants read that according to another expert, “20 out of 100” comparable patients committed acts of violence, whereas the other half read that according another expert, comparable patients had a “20% likelihood” of committing acts of violence. Participants were much less willing to release the patient in the former case than in the latter, indicating that they considered the likelihood of violent actions higher in the former case than in the latter.

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Even if people try to follow normative statistical rules, their estimates of how often events happen versus not happen is relatively easy to influence. For instance, support theory implies that people derive an estimate of an event’s likelihood from the support that they can find for the hypothesis that the event will occur relative to support for the alternative hypothesis that it will not occur (Rottenstreich & Tversky, 1997). The support for the focal or the alternative hypothesis depends, among other variables, on the ways in which people can imagine that the event will happen (or not happen). Events seem more likely when specific instances are provided (when the events are “unpacked”) than when the event is merely described through a general label. For instance, when people estimate their likelihood of contracting a disease on their vacation, their estimate will be higher when they consider Dengue, typhoid fever, cholera, diarrhea, hepatitis, and malaria than when they merely consider the general condition of “diseases that one may contract while on vacation.” One robust finding in the domain of likelihood estimation is that people often disregard normative rules of probability estimation and instead use simple rules of thumb, generally called heuristics (Furnham & Boo, 2011; Nisbett & Ross, 1980; Tversky & Kahneman, 1974). The availability heuristic implies that people base their judgment of the likelihood of an outcome on the ease with which they can remember or imagine that outcome. The representativeness heuristic implies that people base their judgment of the likelihood of a given action leading to a given outcome on the resemblance between the action and the potential outcome. For instance, people tend to believe that extreme or impressive actions provoke severe outcomes and that small, simple actions provoke no more than mundane outcomes. A third heuristic, the anchoring‐and‐adjustment heuristic, implies that people start from an initial value (the “anchor”) that they adjust to accommodate the specific risk information they may have. The anchor is whatever numerical value is active in working memory and hence may or may not be related to the specific risk at hand. Heuristics allow quick likelihood estimations that in many cases are sufficiently accurate to allow well‐balanced decisions. In many other cases, however, they lead to systematic error. These errors tend to occur when factors other than outcome frequency critically determine an outcome’s availability or its representativeness for the action under consideration, or when the anchor that is being used is unrelated to, or no more than remotely related to, the risk under consideration. Outcomes do not always resemble the actions that provoke them (e.g., seemingly unimpressive actions may have weighty consequences), such that the resemblance between actions, and outcomes does not reflect the strength of the association between them. Similarly, the ease by which people can remember or imagine an outcome depends on a variety of outcome characteristics, such as the vividness and the extensiveness with which they have been covered in the media or in the arts. People therefore overestimate the likelihood of outcomes that receive vivid media coverage. Finally, the anchor may be unrelated to the risk at hand. Because adjustments from initial anchors tend to be insufficient, irrelevant values may thus unduly affect likelihood perceptions.

Expected Subjective Utility People do not judge events by comparing their valence and extremity to some absolute zero level, but to a psychological reference point. The choice of a particular reference point thus crucially determines if, and to which extent, an outcome will be experienced as a loss or as gain. The reference point may be the current state of affairs, a previously experienced or expected state of affairs, or any other baseline that happens to be cognitively available. “Objective” gains may even be perceived as losses and “objective” losses as gains

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if they fall below the reference point that happens to be active. For instance, a patient suffering from a chronic disease may view the need for further treatment as a loss when comparing it the status quo but as a gain when comparing it to the deterioration that may occur without any treatment or when comparing it to the health status of other patients who are even worse off. The expected subjective utility of an outcome is not a strictly increasing monotonic function of the distance between that outcome and the reference point. The further an outcome is from the reference point, the less a further departure will enhance the extremity of its utility. For instance, the difference between paying $500 versus $1,000 for a medical treatment “feels” larger than the difference between paying $5,000 versus $5,500. In addition, losses are generally weighted heavier than gains, and the subjective utility function of losses is generally steeper than the subjective utility function of gains. Unexpectedly being refunded $500 feels like a gain, and unexpectedly having to pay an additional $500 feels like a loss, but the expected subjective utility of the loss will be more extreme than the expected subjective utility of the gain. One additional determinant of the expected subjective utility of an outcome is how easily people can imagine the relevant outcomes. Thus, vivid information about potential outcomes does not only enhance the subjective likelihood of these outcomes but may also make them seem more extreme. For instance, people are willing to pay more for flight insurance protecting them against losses caused by “terrorism” than for flight insurance protecting them against all losses, regardless of their cause, because terrorist attacks are easier to imagine than “any cause of loss” (Johnson, Hershey, Meszaros, & Kunreuther, 1993). The unpacking effect, which occurs in likelihood estimation, also has its equivalent in expected subjective utility, with instance‐based descriptions of events leading to more extreme judgments than general event labels (Van Boven & Epley, 2003). In one study, for instance, people read about an oil refinery that released toxins in the atmosphere and thereby caused an increase in health problems in the surrounding community. The participants’ task was to recommend the magnitude of compensation awarded to each victim and to advise on how long the refinery should be closed. Participants recommended larger compensations and a longer closure if the health hazards were described as “asthma, lung cancer, throat cancer, and all other varieties of respiratory diseases” rather than as “all varieties of respiratory diseases” (Van Boven & Epley, 2003, Study 1).

Combining Likelihoods and Utilities Inspired by normative expectancy‐value models, many researchers have assumed that a multiplicative function describes how people combine their estimates of outcome likelihood and outcome severity to arrive at a general estimation of risk magnitude. However, this multiplicative function has seldom been observed. One reason seems to be that people are rather insensitive to differences among relatively high probabilities. When comparing risks involving outcomes that are moderately to highly likely, they tend to solely focus on differences in severity despite differences between the outcomes’ likelihood (Weinstein, 2000). Similarly, people seem insensitive to differences among very low probability events (Kunreuther, Novemsky, & Kahneman, 2001). People seem to particularly disregard probabilities when the outcomes provoke strong emotions. In one study, participants indicated how much they would be willing to pay to eliminate a cancer risk due to arsenic in drinking water (Sunstein, 2003). The likelihood of getting

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c­ ancer due to arsenic pollution was described as being either 1 in 100,000 or 1 in 1,000,000. The cancer risk was described either in nonemotional terms or in a vivid and therefore frightening manner. In the former case, the number of participants willing to pay was considerably higher in the 1 in 100,000 risk condition than in the 1 in 1,000,000 risk condition. In the latter case, the participants were equally willing to pay in both conditions. When events seem very serious or provoke fear, people solely focus on the outcome’s subjective utility to the neglect of these outcomes’ probability (Sunstein, 2003). In one demonstration of “probability neglect,” participants imagined an experiment involving either a chance of experiencing a painful electric shock or having to pay a penalty of $20. Importantly, some participants were told that the chance of having to undergo the shock or to pay the penalty was 1%; others were told that the chance was 99%. When asked how much they would be willing to pay to avoid participating in the experiment, participants were willing to pay much more to avoid a 99% chance of having to pay $20 than to avoid a 1% chance of having to pay it ($18 versus $1). In contrast, they were not willing to pay much more to avoid a 99% chance of undergoing a painful shock than to avoid a 1% chance of undergoing it ($10 versus $7). Finally, some evidence suggests that likelihood and extremity estimations are not independent from each other. A trade‐off may occur, in that people assume that rare health hazards are serious and that more common health hazards are not so serious (Jemmott, Ditto, & Croyle, 1986). Of course, this rule of thumb may in many circumstances be accurate (if extremely serious hazards were very common, little would be left of humanity by now).

Perceiving the Numbers Involved The manner in which people consider the number of potential victims in their judgments resembles the manner in which they arrive at expected subjective utilities. Perhaps the best documented phenomenon in this context is “psychic numbing” (also called “psychophysical numbing”), meaning that the subjective value of an additional life saved or lost diminishes as more lives are involved (e.g., Slovic, 2007). For instance, if people find it reasonable to spend a fixed amount of money to save one life, they do not find it reasonable to spend X times that amount to save X lives. Saving one life feels important, but saving an additional life given many others already saved does not seem to make such a difference. Reference points also play a role in the judgment of numbers involved. One reference point that seems to be of psychological relevance is the population to which the individuals at risk belong. A given number of individuals at risk may seem smaller when the potential victims come from a large population than when they come from a small population, so that people are less willing to take action to save the potential victims in the former case than in the latter. For instance, Fetherstonhaugh, Slovic, Johnson, and Friedrich (1997, Study 1) had people express their preference for a program that would provide drinkable water to refugee camp and thus save 4,500 lives in a camp inhabited by 11,000 refugees or in a camp inhabited by 250,000 refugees. Participants showed a clear‐cut preference for the program in the smaller camp than for the program in the larger camp, even though the absolute number of lives saved was identical. Fetherstonhaugh et al. (1997, Study 3) also asked a group of participants how many lives would need to be saved to warrant an investment of 10 million dollars in life‐saving medical treatments. When the population at risk consisted of, say, 15,000 individuals, the median number of lives needed was 9,000. When the population at risk consisted of 290,000 individuals, the median number of lives needed was 100,000.

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From Risk Magnitude to Risk Acceptability The role of a large variety of outcome characteristics other than severity and likelihood has been examined as potential determinants of risk evaluation. These include, but are not limited to, how voluntarily people engage in the risky course of action; how much control they can still exert on the action’s outcome given that it occurs; how well the potential outcome is known to the individuals who are at risk for it, to scientists, or to society as a whole; and how many people are exposed to the risk or may fall victim to it. Risk evaluation furthermore involves an evaluation of the expected or potential beneficial outcomes of the risky action. These additional determinants of risk evaluation, above and beyond the perception of risk magnitude, explain why people sometimes respond very differently to equally large risks. For instance, people tend to find risks that they can control more acceptable than risks they cannot control and tend to find the risks of actions that benefit them more acceptable than the risks of action that do not benefit them. Researchers in the domain of risk evaluation have tried not only to examine the influence of each of these factors on risk evaluation but also to identify the underlying risk dimensions. Their studies have yielded different risk structures, partly due to differences in the selections of risks being included, but among the dimensions that were frequently found are the number of people exposed, the familiarity of the risk, and the extent to which the outcome is dreaded (e.g., because it cannot be controlled or because it is fatal when it occurs; for a succinct review, see Breakwell, 2014, pp. 37–43). The familiarity dimension suggests that people may start out by judging new and unknown risks as unacceptable but gradually come to accept these risks as they develop a feeling of familiarity with them—even if they is no “objective” reason to assume that characteristics of the risk other than its subjective familiarity have changed (Lima, Barnett, & Vala, 2005). Risk perception is supposed to be among the key determinants of risk, health, and safety behaviors. One debated issue in this context is whether people mainly act upon absolute risk perceptions (their perceptions of their own personal risks) or upon comparative risk perceptions (their perception of how their own personal risks compare with other people’s risks).

The Affect Heuristic As noted above, the dual‐process approach suggests that people do not always engage in systematic likelihood estimations, subjective utility judgments, or judgments of other risk dimensions. Instead, they sometimes simply rely on how they feel toward a potential outcome or to the risky activity that entails the risk at hand. When the outcome provokes strong affect in them, they perceive the risk as large. When they hold favorable feelings toward an activity, they view the risks that are associated with the activity as small and/or acceptable and the benefits that are associated with it as large. When they have unfavorable feelings toward an activity, they view the risks that are associated with the activity as large and/or unacceptable and the benefits that are associated with it as small. This manner of appraising risks has been coined the “affect heuristic” (Finucane et al., 2000). One characteristic of affect‐based risk perception is that the qualitative (rather than quantitative) nature of feelings renders this type of risk perception relatively insensitive to the scope and likelihood of the relevant outcomes. Although the experiential system seems highly sensitive to the difference between the occurrence or nonoccurrence of an outcome, and to the

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difference between certainty and uncertainty, it is rather insensitive to further variations in utility and likelihood. Extreme cases of probability neglect may therefore also be considered evidence of risk perceptions sometimes being affect‐based rather than based on a likelihood‐ utility calculation (Hsee & Rottenstreich, 2004). In one study, for instance, participants were asked to indicate how much they would be willing to pay for a set of second‐hand Madonna CDs. People in whom a “calculation mindset” had been induced were willing to pay significantly more for a 10‐CD box than for a 5‐CD box, whereas people in whom a “feeling mindset” had been induced were insensitive to the number of CDs that the box allegedly held (Hsee & Rottenstreich, 2004, Study 1). One consequence of the “affect heuristic” is that people often view risks and benefits as being negatively correlated (with higher risks associated with small benefits and lower risks associated with large benefits). This perception is remarkable because risks and benefits are in many cases positively correlated. Activities that entail large potential benefits tend to carry large risks, and activities that carry no more than small risks often produce no more than limited benefits. Another implication of the affect heuristic is that any intervention that changes how an activity makes people feel will affect the perceived magnitude of the risks that are associated with it, as well as the activity’s perceived benefits. Because this type of intervention may be related to the perceived benefits or to the magnitude of the risks themselves, enhancing the perceived benefits of an activity may reduce people’s perception of the risks associated with it, while reducing the perceived risks of the activity may enhance its perceived benefits. For instance, Finucane et  al. (2000, Study 2) provided participants with information stressing either the high benefit, the low benefit, the low risk, or the high risk of various technologies (food additives, natural gas, nuclear power). Before and after reading the information, participants rated the benefits and the risks of the technologies. When the information said that the benefits of the technologies were high, it lowered participants’ ratings of the risks associated with these technologies. When the information said that the risks were low, it enhanced participants’ ratings of the technologies’ benefits. Information saying that the risks were low generally enhanced ratings of the technologies’ benefits.

Coda: Your Risk and Mine Are Not Created Equal One intriguing phenomenon in risk perception through analytical processes is that systematic differences exist between people’s perceptions of their own risks and the risks of others. These differences occur both in perceptions of likelihood and in perceptions of outcome severity. Intriguingly, they seem to counteract each other. Likelihood estimations are characterized by comparative optimism, also called unrealistic optimism, with most people assuming that they are less likely than others to experience negative outcomes and more likely to experience positive events (for a brief introduction, see Shepperd, Waters, Weinstein, & Klein, 2015). Comparative optimism also occurs when people spontaneously imagine events that may happen in their or someone else’s future, rather than estimating the likelihood of outcomes that are presented to them (Hoorens, Smits, & Shepperd, 2008). Severity estimations, in contrast, seem characterized by comparative pessimism, with most people assuming that if negative outcomes happen to them, they will suffer more than others would (Blanton, Axsom, McClive, & Price, 2001). It is as yet an open question whether self‐other differences characterize affect‐ driven risk perception as much as likelihood‐utility‐based risk perception.

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Author Biography Vera Hoorens is a full professor of social psychology in the Centre for Social and Cultural Psychology of Leuven University, Belgium. Her research in the field of risk perception and risk communication mainly focuses on perceived self‐other differences in the likelihood and experience of life events. She also studies and publishes more broadly on social comparison, verbal communication, and the perception of groups and individuals. She has a professional guilty pleasure, namely, sixteenth‐century history.

References Blanton, H., Axsom, D., McClive, K. P., & Price, S. (2001). Pessimistic bias in comparative evaluations: A case of perceived vulnerability to the effects of negative life events. Personality and Social Psychology Bulletin, 27, 1627–1636. doi:10.1177/01461672012712006 Breakwell, G. M. (2014). The psychology of risk. Cambridge, UK: Cambridge University Press. Fetherstonhaugh, D., Slovic, P., Johnson, S., & Friedrich, J. (1997). Insensitivity to the value of human life: A study of psychophysical numbing. Journal of Risk and Uncertainty, 14, 283–300. doi:10.1023/A:1007744326393 Finucane, M. L., Alhakami, A., Slovic, P., & Johnson, S. M. (2000). The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making, 13, 1–17. Furnham, A., & Boo, H. C. (2011). A literature review of the anchoring effect. The Journal of Socio‐ Economics, 40, 35–42. doi:10.1016/j.socec.2010.10.008 Hoorens, V., Smits, T., & Shepperd, J. A. (2008). Comparative optimism in the spontaneous generation of future life‐events. British Journal of Social Psychology, 47, 441–451. doi:10.1348/0144666 07X236023 Hsee, C. K., & Rottenstreich, Y. (2004). Music, pandas, and muggers: On the affective psychology of value. Journal of Experimental Psychology: General, 133, 23–30. doi:10.1037/0096‐3445.133.1.23 Jemmott, J. B., Ditto, P. H., & Croyle, R. T. (1986). Judging health status: Effects of perceived prevalence and personal relevance. Journal of Personality and Social Psychology, 50, 899–905. doi:10.1037/0022‐3514.50.5.899 Johnson, E. J., Hershey, J., Meszaros, J., & Kunreuther, H. (1993). Framing, probability distortions, and insurance decisions. Journal of Risk and Uncertainty, 7, 35–51. doi:10.1007/978‐94‐ 011‐2192‐7_3 Kunreuther, H., Novemsky, N., & Kahneman, D. (2001). Making low probabilities useful. Journal of Risk and Uncertainty, 23, 103–120. doi:10.1023/A:1011111601406 Lima, M. L., Barnett, J., & Vala, J. (2005). Risk perception and technological development at a societal level. Risk Analysis, 25, 1229–1239. doi:10.1111/j.1539‐6924.2005.00664.x Nisbett, R. E., & Ross, L. (1980). Human inference: Strategies and shortcomings of social judgment. Englewood Cliffs, NJ: Prentice‐Hall. Rottenstreich, Y., & Tversky, A. (1997). Unpacking, repacking, and anchoring: Advances in support theory. Psychological Review, 104, 406–415. doi:10.1037/0033‐295X.104.2.406 Shepperd, J. A., Waters, E. A., Weinstein, N. D., & Klein, W. M. P. (2015). A primer on unrealistic optimism. Current Directions in Psychological Science, 24, 232–237. doi:10.1177/0963721414568341 Slovic, P. (2007). “If I look at the mass I will never act”: Psychic numbing and genocide. Judgment and Decision Making, 2, 79–95. Slovic, P., Monahan, J., & MacGregor, D. G. (2000). Violence risk assessment and risk communication: The effects of using actual cases, providing instruction, and employing probability versus frequency formats. Law and Human Behavior, 24, 271. doi:10.1023/A:1005595519944 Sunstein, C. R. (2003). Terrorism and probability neglect. Journal of Risk and Uncertainty, 26, 121– 136. doi:10.1023/A:1024111006336

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Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131. doi:10.1126/science.185.4157.1124 Van Boven, L., & Epley, N. (2003). The unpacking effect in evaluative judgments: When the whole is less than the sum of its parts. Journal of Experimental Social Psychology, 39, 263–269. doi:10.1016/ S0022‐1031(02)00516‐4 Weinstein, N. D. (2000). Perceived probability, perceived severity, and health‐protective behavior. Health Psychology, 19, 65–74. doi:10.1037/0278‐6133.19.1.65

Suggested Reading Breakwell, G. M. (2014). The psychology of risk. Cambridge, UK: Cambridge University Press. Finucane, M. L., Alhakami, A., Slovic, P., & Johnson, S. M. (2000). The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making, 13, 1–17. Kahneman, D. (2003). A perspective on judgment and choice: mapping bounded rationality. American Psychologist, 58, 697. doi:10.1037/0003‐066X.58.9.697 Slovic, P. E. (2000). The perception of risk. London, UK: Earthscan Publications.

Rumination Andrew W. Manigault and Peggy M. Zoccola Psychology Department, Ohio University, Athens, OH, USA

Interest in the role of rumination and related repetitive thought constructs in mental and physical illness and health has grown exponentially in the last several decades. Initial research focused on the role of ruminative thinking in the etiology and symptomatology of mental health disorders. More recently, researchers and clinicians have extended their focus to the physical health parameters associated with rumination, including physiological processes and biomarkers, somatic symptoms, and health‐relevant behaviors. Overall, rumination can be considered a risk factor for a variety of detrimental mental and physical health outcomes. This entry first reviews several prominent theoretical models and definitions of rumination and then presents a conceptual model in an effort to organize the understanding of rumination’s role in health. Afterward, we review the evidence for the relationship between rumination and health‐ relevant physiological systems and biomarkers, including blood pressure, cortisol, and immune functioning. The links between rumination and somatic symptoms, health behaviors, and other disease‐relevant outcomes are also presented. The entry concludes with a discussion of intervention and treatment efforts aimed at reducing rumination and suggestions for future directions in research and practice.

Models and Definitions of Rumination It is imperative that those who find themselves interested in rumination and its rich literature bear in mind that the meaning of rumination may change based upon theoretical context. Multiple theoretical models and definitions of rumination appear across disparate literatures. Among such theoretical models of rumination are the response styles theory, stress‐reactive model, attentional scope model, and goal process theory. Although there are important distinctions between these models, as reviewed below, there are also important unifying dimensions and some conceptual overlap. For instance, many models highlight the repetitive nature of ruminative thought that is often past focused and characterized by negative valence or content. The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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One of the most prominent and widely adopted models of rumination is Nolen‐Hoeksema’s response styles theory. According to this theory, rumination is repetitive thinking about the “causes, consequences, and symptoms of one’s negative affect” (p. 117; Smith & Alloy, 2009). Response styles theory posits that individuals who ruminate on the causes of their depressive symptoms tend to think more negatively, which consequently interferes with psychological functioning and maintains depressive symptoms. The theory also proposes that distraction from symptoms may alleviate the effects of rumination on depression. Supporting this account, some research shows that nondepressed individuals are more likely to experience a depressive episode if they report a tendency to ruminate over depressive symptoms relative to distraction from symptoms (Just & Alloy, 1997). Although response styles theory has received empirical support from a wide body of literature, some criticism persists. For example, rumination research grounded in this theory often makes use of questionnaires that have been criticized for their overlap with measures of depression, worry, and even positive forms of rumination (Smith & Alloy, 2009). Despite some criticism and mixed findings regarding the role of rumination in depressive symptoms, response styles theory remains one of the major approaches to health‐related rumination research. A theoretical extension of response styles theory is the stress‐reactive model of rumination (Robinson & Alloy, 2003). According to this model, rumination, or repetitive thoughts pertaining to negative inferences, following a stressful life event puts individuals at risk for developing depression or having depressive episodes of longer duration. Thus, in this model, the content of rumination pertains specifically to thoughts (including negative affect) associated with a stressor. A minor departure from response styles theory, the stress‐reactive model also captures ruminative thoughts before those that co‐occur with negative affect. Although evidence supports the role of stress‐reactive rumination in depression, this theoretical model of rumination has been criticized for being too specific (e.g., not capturing non‐stressor‐related ruminative thoughts; Smith & Alloy, 2009). Inspired by cognitive findings, the attentional scope model of rumination focuses on the cognitive mechanisms of rumination that contribute to its repetitiveness and negative valence (Whitmer & Gotlib, 2013). This model suggests that negative mood can affect not only what thoughts are readily available but also how likely one is to exert control over these thoughts. Specifically, negative mood is thought to narrow one’s attention such that it will be more difficult to inhibit the target of one’s attentional focus or pay attention to novel stimuli. This model also makes a distinction between the cognitive impairments associated with dispositional rumination and state rumination. For example, although ruminative tendencies are associated with difficulties with inhibiting irrelevant information and updating working memory, state rumination seems to be associated with widespread deficits in cognitive control. A major advantage of this model is that it attempts to reconcile some of the seemingly contradictory findings of the rumination literature by discriminating between types of attention that may promote repetitive thinking and those that do not. Other approaches to rumination have focused on the role of goals—in particular, frustrated and blocked goals—in prompting ruminative thinking. For example, goal process theory posits that ruminative thoughts emerge as a result of one’s failure to progress toward a goal (Martin, Tesser, & McIntosh, 1993). In this framework, rumination is defined as a class of conscious thoughts concerning one’s goal, which recur in the absence of immediate demands requiring the thoughts. This theory suggests that the experience or threat of failure is more important for rumination to occur than the feeling of sadness. A benefit of this approach is that it emphasizes conditions that lead to rumination as well as the cognitions that maintain it. Furthermore, goal process theory is more inclusive than other accounts;

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stressful events and depressed mood may both constitute blocked goals and thus fit within this framework. In sum, this brief review of rumination models highlights the need for a more comprehensive definition of rumination and perhaps more discriminatory use of terminology. Although the name may be shared, all types of rumination are not equivalent. The aforementioned conceptual models lack agreement with regard to the specific content and causes of rumination. Further, each model aims to predict different outcomes (e.g., response styles theory aims to predict the occurrence and duration of depressive episodes; the attentional scope model aims to identify the underlying cognitive mechanisms of rumination). Thus, they fail to provide a unified definition of rumination because they were never meant to do so. Despite theoretical discrepancies, it is important to note that the conceptual models reviewed here are not in full contradiction; across models, rumination is generally considered to be negatively valenced, repetitive, conscious, and past oriented. When considering rumination’s relationship to psychological and physical health disorders, it may be useful to take a comprehensive conceptual approach while also stipulating how rumination differs from other related constructs (e.g., negative affect, sadness). A broader definition of rumination, or one that examines it across psychological disorders (and among individuals with and without psychopathology), may be valuable for identifying common causes, mechanisms, and thus points of intervention that have wider leverage.

Rumination and Health The effects of rumination (broadly defined) extend beyond psychological processes. In particular, the link between rumination and physical health has been the subject of growing scientific interest. For example, the perseverative cognition hypothesis suggests that rumination may exacerbate or perpetuate physiological changes and symptoms that occur in response to stressors (Brosschot, Gerin, & Thayer, 2006). That is, normally occurring responses to stressors (e.g., rises in heart rate, blood pressure, and hormone secretion) may be accentuated or occur for longer periods of time if accompanied by ruminative thinking. Research on rumination and stress‐related physiological parameters has yielded especially interesting results pertaining to stress recovery (i.e., a return to physiological resting states in the absence of the stressor). Given that poor cardiovascular recovery has been linked to serious health threats such as hypertension, some have proposed that the way in which one recovers from a stressful event is a better or at least equivalent predictor of long‐term health outcomes than stress reactivity (i.e., the magnitude of the physiological changes that occur when the stressor is present). In other words, although it is adaptive to react in stressful situations, negative health outcomes may result from prolonged reactions in the absence of a stressor. Rumination may therefore impact health by maintaining the stress response beyond its adaptive range. In addition, rumination may influence other cognitions, emotions, and behaviors that are critical for health and well‐being. For example, if rumination maintains negative mood or depression, it may also negatively affect exercise and diet. In addition, rumination might also exacerbate the perception of negative symptoms (e.g., greater pain, poor sleep quality). In sum, the relationship between rumination and health is varied and operates through numerous pathways. A broad, conceptual overview of the effects of rumination on physical health is presented in Figure 1. The following section presents a non‐exhaustive summary of studies that have tested some of the pathways outlined in this conceptual model linking rumination to physical health.

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Rumination + Stressors

Physiology

Somatic Symptoms

Mood

Disease

Behavior

Well Being

Figure 1  A conceptual model of the effects of rumination on physical health.

Rumination and Physiological Parameters Autonomic Nervous System and Cardiovascular Activity The study of the effect of rumination on autonomic and cardiovascular activity is robust and growing. Among others, angry, stress‐related, and goal‐dependent rumination measures have been examined in relation to blood pressure, heart rate, heart rate variability, and endothelin‐1. Although there is some experimental and correlational evidence that rumination may lead to alterations in these parameters, empirical findings tend to vary depending on how rumination and cardiovascular outcomes are measured. Thus again, as is the case for definitions of rumination, the effect of rumination on cardiovascular outcomes depends to some degree on theoretical context. Such a relationship is logical given that any given definition of rumination may or may not capture relevant aspects of the construct relating to cardiovascular outcomes. Several lines of research show that stress‐related rumination predicts delayed cardiovascular recovery from stress. For example, undergraduates who report ruminating over a stressful arithmetic task take longer to return to baseline levels of blood pressure (Radstaak, Geurts, Brosschot, Cillessen, & Kompier, 2011). Rumination has also been linked to heart rate variability, or the variation in time between heartbeats, in some instances. Heart rate variability is a marker of physiological arousal that predicts cardiovascular health outcomes such as myocardial infarctions. In one study of heart rate variability, women without depressive rumination tendencies who engaged in state rumination following a stressful recall task had impaired heart rate variability recovery compared with those who did not ruminate following the task (Key, Campbell, Bacon, & Gerin, 2008). Thus, there is some consensus that stress‐related and anger‐related measures of rumination predict poor cardiovascular recovery. Rumination may also affect other parameters that are important for cardiovascular health, including endothelin‐1. Endothelin‐1 is a vasoconstrictor, which can contribute to atherosclerosis, plaque rupture, and acute coronary events. For example, one study demonstrated that angry rumination tendencies predict greater increases in endothelin‐1 in response to an anger recall task among individuals suffering from coronary heart disease (Fernandez et al., 2010). This finding seemingly holds after controlling for numerous other health covariates. Taken together, multiple lines of research point to an association between rumination and cardiovascular activation.

Neuroendocrine/Immune Activity The relationship between rumination and health correlates extends to neuroendocrine and immune systems. Among other stress hormones, cortisol is often used in research because it is

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easy to sample (e.g., via saliva) and affects the body in a variety of ways (e.g., metabolism, analgesia, immune/digestive/reproductive function). Cortisol reallocates resources from biological processes like digestion to prepare one’s body to react in a stressful situation. Although adaptive in the short term, the effects of cortisol (e.g., suppressing non‐specific immune responses) can be degenerative in the long term (e.g., increasing susceptibility to infections). Thus, demonstrating an effect of rumination on cortisol responses has important implications for physiological health. The literature on the relationship between cortisol and rumination is relatively new and inconsistent, but growing. Inconsistent findings regarding this relationship can be attributed, at least in part, to variation in measures of rumination (e.g., stress related vs. depressive, state vs. trait) and types of stressor tasks used (e.g., social evaluative vs. nonevaluative; Zoccola & Dickerson, 2012). Nevertheless, recent findings provide strong evidence in favor of a causal link between stress‐related rumination and cortisol recovery (Zoccola, Figueroa, Rabideau, Woody, & Benencia, 2014). In this experiment, participants were randomly assigned to a guided rumination or distraction condition following a stressful speech task. Participants who were guided through ruminative thinking produced higher concentrations of salivary cortisol 40 min and 1 hr after the stressful speech task relative to participants who were distracted. In sum, stress‐related rumination may prolong cortisol exposure, but effects seem to depend on study design issues. Rumination may also impact the immune system, but research on this topic is in its infancy. Using a sample of older adults, one study demonstrated that rumination is positively associated with several immune measures including leukocytes count, lymphocytes count, CD19+ B‐lymphocyte count, and polyclonal T‐cell activation (Thomsen et al., 2004). Given that high leukocyte counts are associated with increased risk of ischemic disease, rumination may negatively impact physical health via its effect on the immune system. Experimental evidence also links rumination to acute increases in inflammatory markers (Zoccola et  al., 2014). Much more work is needed to confirm the role of rumination in influencing immune function.

Rumination and Somatic Complaints The effects of rumination extend beyond physiology to symptom perception and somatic complaints. Mounting research suggests that rumination affects how one perceives, experiences, and reports health symptoms. Ruminative thinking may amplify the symptoms that one perceives and contribute to distress. For example, some research indicates that rumination is associated with health anxiety in college students, and this cognitive style predicts greater health anxiety above and beyond negative affect (as cited in Sansone & Sansone, 2012). Dispositional rumination also predicts increased somatic complaints (e.g., fatigue, nausea, and pain) in children and adolescents (as cited in Sansone & Sansone, 2012), as well as physical symptoms and overall subjective health ratings in young adults (Thomsen et  al., 2004). Rumination has also been linked to hypochondriasis, which is characterized by a dysfunctional preoccupation that one has a severe illness despite medical reassurance to the contrary (as cited in Sansone & Sansone, 2012). Perceived and experienced somatic complaints can greatly influence quality of life and are highly relevant to health behaviors, such as treatment seeking and adherence, and health outcomes. Thus, the perception of symptoms can be as important as physiological processes with regard to health outcomes. Within the pain perception framework in particular, rumination often has been integrated with other types of thoughts, such as feelings of helplessness and pessimism, which aggravate

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symptom perception. The sum of these aggravating cognitions is referred to as “catastrophizing.” Pain catastrophizing is commonly measured with self‐report questionnaires that include pain‐focused rumination items or subscales. Several lines of research indicate that those who score higher on measures of pain catastrophizing report greater emotional distress, greater pain intensity, and more negative attitudes toward pain. Edwards, Bingham, Bathon, and Haythornthwaite (2006) suggest that catastrophizing leads to such outcomes by increasing attention to pain, amplifying pain processing in the central nervous system, and interfering with pain‐coping behaviors. When rumination is examined independently of the other components of pain catastrophizing, results are generally similar. For instance, findings from a recent experiment suggest that rumination is the best predictor of pain perception and distress in healthy adults (as cited in Sansone & Sansone, 2012). In this experiment, participants who scored high in pain‐related rumination tendencies reported the greatest pain intensity resulting from inflation of a blood pressure cuff. In other studies, rumination predicted postoperative pain in athletes undergoing knee surgery, disability in chronic pain patients, pain levels in fibromyalgia and lower back pain patients, and psychological distress for palliative care patients (as cited in Sansone & Sansone, 2012). Thus, rumination and its related constructs (e.g., catastrophizing) are related to a wide spectrum of pain‐related outcomes that are important for health and well‐being. Rumination has also been studied in relation to sleep. Sleep is a physiologic recuperative state that entails significant health repercussions when disturbed (e.g., cognitive impairments, depressed mood, impaired cardiovascular, neuroendocrine and immune function). Given that rumination is associated with an inability to disengage from irrelevant information, it is possible that those who tend to ruminate maintain high cognitive arousal before falling asleep and experience delayed sleep onset, insomnia, and lower‐quality sleep. Results from several studies support this notion. For instance, one study in which participants were asked to report sleep quality and dispositional rumination demonstrated that rumination is negatively associated with sleep quality even after controlling for negative mood (Thomsen, Yung Mehlsen, Christensen, & Zachariae, 2003). In another study of college students, both state and trait measures of rumination may predict sleep onset latency, or time it takes to fall asleep (Zoccola, Dickerson, & Lam, 2009). Taken together, this body of work indicates that repetitive thinking generally predicts poor subjective sleep quality. Further, this relationship appears stable across types of ruminative thinking and thus constitutes an area of consensus for the rumination literature.

Health Behaviors Logically, if ruminators experience more severe symptoms and are less likely to seek medical attention, then one should find empirical evidence for the relationship between rumination and healthcare utilization. Supporting this claim, Lyubomirsky, Kasri, Chang, and Chung (2006) found that individuals who ruminate may take longer than non‐ruminators to seek help when experiencing symptoms of illness. In this study, women were asked to imagine finding a change in their breast and reported their intentions to seek medical attention. Women with ruminative tendencies were less likely to call a doctor immediately relative to women who do not ruminate. Furthermore, an independent sample of breast cancer survivors was asked to report the time lapse between noticing breast cancer symptoms and diagnosis. On average, women who ruminate took more than twice as long to visit a healthcare professional for breast cancer symptoms relative to non‐ruminators. Given that the progression of a tumor into

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­ etastatic cancer is partly a factor of time, seeking medical attention for signs of cancer early m greatly increases one’s chance of survival. Thus, for diseases such as cancer, rumination may increase the likelihood of fatalities by delaying diagnosis. Although some evidence suggests that rumination negatively affects intent to seek medical attention and delays diagnosis, the relationship between rumination and health behaviors is not consistent across age groups and physical ailments. For instance, a study by Thomsen et al. (2004) showed that, among the elderly, the tendency to ruminate predicted telephone consultations with healthcare providers but is unrelated to in‐person visits. Furthermore, rumination was unrelated to healthcare utilization in the young sample. The authors suggest that the positive relationship between rumination and health behaviors is more pronounced in individuals that are likely to need medical attention (e.g., the elderly). Further, given that rumination predicted phone call consultations but not in‐person consultations in the elderly sample, rumination may lead individuals to seek reassurance from healthcare providers (e.g., are physical ailments indicative of serious problems?). As such, rumination may in some cases be beneficial. For specific populations, rumination appears to increase healthcare utilization. One should note that this area of research is still in infancy and thus does not allow clear conclusions regarding the relationship between rumination and healthcare utilization behaviors. Future research in this area would gain from investigating the interplay of relevant moderators. Beyond healthcare utilization, rumination may also affect emotion‐focused coping behaviors, which can be detrimental to physical health outcomes (e.g., drug use). In a longitudinal study, ruminating adolescents were more likely to misuse substances in response to a negative event relative to non‐ruminators (Skitch & Abela, 2008). Additionally, individuals who experience work‐related affective rumination generally report eating more unhealthy foods after work relative to non‐ruminators (Cropley, Michalianou, Pravettoni, & Millward, 2012). Taken together, this area of research indicates that rumination may impact physical health by deterring healthcare utilization and promoting unhealthy coping behaviors.

Physical Illness and Disease Rumination ultimately may play a role in disease development. As previously mentioned, there are various pathways through which rumination may contribute to physical illness and well‐ being. As a result of rumination, individuals may engage in fewer pro‐health behaviors, exhibit increased and prolonged physiologically stress responses, experience aggravated symptoms leading to treatment delays or avoidance, sleep poorly, or engage in unhealthy coping behaviors. All of these possible effects of rumination entail consequences of their own, which are relevant to the progression of physical illness. For example, it is thought that the prolonged stress recovery associated with rumination may lead to weakened arteries, plaque buildup, and other risk factors of cardiovascular disease (Larsen & Christenfeld, 2009). Some research supports a direct link between rumination and related repetitive thoughts (worry) and clinical disease‐related outcomes (e.g., cancer, coronary heart disease). For example, worry—future‐oriented repetitive thinking—is associated with coronary heart disease (Kubzansky et  al., 1997). In a longitudinal study of over 2000 healthy men, participants reported at the beginning of the study the extent to which they worry. Over the subsequent 20 years, incidences of coronary heart disease and myocardial infarction were recorded. This study showed that men who tended to worry about social conditions were more likely to develop coronary heart disease and to experience a nonfatal myocardial infarction relative to those who reported little to no worry about social conditions. It is not clear from this finding

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if worry can lead to coronary heart disease or if risk factors for coronary heart disease lead one to report more worry, hence this avenue of research deserves further inquiry. Rumination may also play an important role in cancer prognosis. For example, Thomsen et al. (2013) found that in a sample of individuals who recently received a colon cancer diagnosis, rumination predicted increased depressive, intrusive, and avoidance symptoms at baseline and 8‐week follow‐up. These results further support the notion that rumination may negatively affect disease prognosis.

Prevention and Intervention As reviewed above, the potential impact of rumination on health is broad and consequential. Thus, some researchers have focused on identifying means to reduce the extent to which individuals ruminate or engage in other perseverative cognitions. A recent meta‐analysis by Querstret and Cropley (2013) suggests that both cognitive behavioral and mindfulness‐based interventions may be effective in reducing both rumination and worry. Cognitive behavioral interventions focus on changing one’s thinking style so that it may be more concrete, constructive, and active. Mindfulness‐based interventions aim to teach coping skills such as meditation while promoting the generalization these skills (e.g., acceptance, present moment awareness) to daily experiences. A recent experiment shows that relative to an inactive control, women with cancer who underwent an 8‐week mindfulness‐based stress reduction program showed a decrease in self‐reported rumination (Campbell, Labelle, Bacon, Faris, & Carlson, 2011). In sum, mindfulness‐based and cognitive behavioral interventions may effectively reduce ruminative thinking.

Summary and Conclusions As discussed in relation to psychopathology, biological parameters, somatic health, and illness, rumination may exacerbate or perpetuate the inherent components of each of these areas so as to result in negative health‐related processes and outcomes. In addition to extending physiological responses beyond their adaptive range of action, rumination may magnify one’s perception of poor health and thus aggravate psychological distress resulting from normally occurring symptoms. Via these pathways, rumination can promote pathogenesis and impair well‐being. Nevertheless, each of the pathways linking rumination to health entails important moderators; not all populations or measures of rumination are equivalent in relation to health outcomes. Specifically, the multitude of moderators that contribute to empirical findings linking rumination to health should not be ignored. A promising yet relatively untouched venue of research lies in a systematic review of such moderators (e.g., definitions of rumination, state vs. trait, age, gender). Although some outcomes have been reviewed across moderators (e.g., cortisol recovery), others have not (e.g., utilization of healthcare facilities). We encourage such research. Specific to rumination definitions, researchers would benefit from identifying “active ingredients” of rumination contributing to its potentially negative impact on physical health. For example, the content (e.g., stress, anger, and sadness), temporal focus, intentionality (e.g., voluntary vs. involuntary), level of construal (abstract vs. concrete), and other characteristics of ruminative thinking may not contribute equally to its impact on health. Examining the ­correlates and consequences of rumination across multiple kinds of ruminative thinking and

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multiple outcomes may be valuable for identifying common causes, mechanisms, and thus points of intervention that have wider leverage. For example, it is unclear if cognitive behavioral and mindfulness interventions are equally efficient in reducing stress‐related, depressive, and angry rumination. In sum, future rumination research should maximize generalizability while balancing the complex interplay of moderators that contribute to rumination’s relationship to health. Rumination is worthwhile research subject due to its broad impact. As reviewed in the present entry, our everyday experiences, our physical health, and our well‐being are at the mercy of our repetitive thoughts.

Author Biographies Andrew W. Manigault is a doctoral student of experimental health psychology at Ohio University. Andrew Manigault studies the effects of mindfulness and other psychological factors on stress responses. His research focuses on the potential for mindfulness and meditation to affect the frequency of repetitive thoughts and ultimately physiological processes, with specific emphasis on the identifying underlying components of mindfulness, which may drive its positive health effects. He studies at Ohio University under the tutelage of Dr. Peggy Zoccola. Peggy M. Zoccola is an assistant professor of psychology at Ohio University. Dr. Zoccola studies mechanisms contributing to persistent stress‐related physiological activation and associated health consequences. Questions addressed by her research include the following: Do individuals who ruminate have increased cortisol and inflammation in response to psychosocial stressors? Are some individuals at increased risk for rumination? Are certain kinds of stressors or contexts more likely to elicit ruminative thinking and cortisol? How can we best measure or manipulate ruminative thought?

References Brosschot, J. F., Gerin, W., & Thayer, J. F. (2006). The perseverative cognition hypothesis: A review of worry, prolonged stress‐related physiological activation, and health. Journal of Psychosomatic Research, 60(2), 113–124. doi:10.1016/j.jpsychores.2005.06.074 Campbell, T. S., Labelle, L. E., Bacon, S. L., Faris, P., & Carlson, L. E. (2011). Impact of Mindfulness‐ Based Stress Reduction (MBSR) on attention, rumination and resting blood pressure in women with cancer: A waitlist‐controlled study. Journal of Behavioral Medicine, 35(3), 262–271. doi:10.1007/s10865‐011‐9357‐1 Cropley, M., Michalianou, G., Pravettoni, G., & Millward, L. J. (2012). The relation of post‐work ruminative thinking with eating behaviour. Stress and Health, 28(1), 23–30. doi:10.1002/smi.1397 Edwards, R. R., Bingham, C. O., Bathon, J., & Haythornthwaite, J. A. (2006). Catastrophizing and pain in arthritis, fibromyalgia, and other rheumatic diseases. Arthritis Care & Research, 55(2), 325–332. doi:10.1002/art.21865 Fernandez, A. B., Soufer, R., Collins, D., Soufer, A., Ranjbaran, H., & Burg, M. M. (2010). Tendency to angry rumination predicts stress‐provoked endothelin‐1 increase in patients with coronary artery disease. Psychosomatic Medicine, 72(4), 348–353. doi:10.1097/PSY.0b013e3181d71982 Just, N., & Alloy, L. B. (1997). The response styles theory of depression: Tests and an extension of the theory. Journal of Abnormal Psychology, 106(2), 221–229. http://doi.org/10.1037/0021‐8 43X.106.2.221

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Key, B. L., Campbell, T. S., Bacon, S. L., & Gerin, W. (2008). The influence of trait and state rumination on cardiovascular recovery from a negative emotional stressor. Journal of Behavioral Medicine, 31(3), 237–248. doi:10.1007/s10865‐008‐9152‐9 Kubzansky, L. D., Kawachi, I., Spiro, A., Weiss, S. T., Vokonas, P. S., & Sparrow, D. (1997). Is worrying bad for your heart? A prospective study of worry and coronary heart disease in the normative aging study. Circulation, 95(4), 818–824. doi:10.1161/01.CIR.95.4.818 Larsen, B. A., & Christenfeld, N. J. S. (2009). Cardiovascular disease and psychiatric comorbidity: The potential role of perseverative cognition. Cardiovascular Psychiatry and Neurology, e791017. doi:10.1155/2009/791017 Lyubomirsky, S., Kasri, F., Chang, O., & Chung, I. (2006). Ruminative response styles and delay of seeking diagnosis for breast cancer symptoms. Journal of Social and Clinical Psychology, 25(3), 276– 304. doi:10.1521/jscp.2006.25.3.276 Martin, L. L., Tesser, A., & McIntosh, W. D. (1993). Wanting but not having: The effects of unattained goals on thoughts and feelings. In D. M. Wegner & J. W. Pennebaker (Eds.), Handbook of mental control (pp. 552–572). Englewood Cliffs, NJ: Prentice‐Hall, Inc. Querstret, D., & Cropley, M. (2013). Assessing treatments used to reduce rumination and/or worry: A systematic review. Clinical Psychology Review, 33(8), 996–1009. doi:10.1016/j.cpr.2013.08.004 Radstaak, M., Geurts, S. A. E., Brosschot, J. F., Cillessen, A. H. N., & Kompier, M. A. J. (2011). The role of affect and rumination in cardiovascular recovery from stress. International Journal of Psychophysiology, 81(3), 237–244. doi:10.1016/j.ijpsycho.2011.06.017 Robinson, M. S., & Alloy, L. B. (2003). Negative cognitive styles and stress‐reactive rumination interact to predict depression: A prospective study. Cognitive Therapy and Research, 27(3), 275–291. doi:10.1023/A:1023914416469 Sansone, R. A., & Sansone, L. A. (2012). Rumination: Relationships with physical health. Innovations in Clinical Neuroscience, 9(2), 29. Skitch, S. A., & Abela, J. R. Z. (2008). Rumination in response to stress as a common vulnerability factor to depression and substance misuse in adolescence. Journal of Abnormal Child Psychology, 36(7), 1029–1045. doi:10.1007/s10802‐008‐9233‐9 Smith, J. M., & Alloy, L. B. (2009). A roadmap to rumination: A review of the definition, assessment, and conceptualization of this multifaceted construct. Clinical Psychology Review, 29(2), 116–128. doi:10.1016/j.cpr.2008.10.003 Thomsen, D. K., Jensen, A. B., Jensen, T., Mehlsen, M. Y., Pedersen, C. G., & Zachariae, R. (2013). Rumination, reflection and distress: An 8‐month prospective study of colon‐cancer patients. Cognitive Therapy and Research, 37(6), 1262–1268. doi:10.1007/s10608‐013‐9556‐x Thomsen, D. K., Mehlsen, M. Y., Hokland, M., Viidik, A., Olesen, F., Avlund, K., … Zachariae, R. (2004). Negative thoughts and health: Associations among rumination, immunity, and health care utilization in a young and elderly sample. Psychosomatic Medicine, 66(3), 363–371. Thomsen, D. K., Yung Mehlsen, M., Christensen, S., & Zachariae, R. (2003). Rumination: Relationship with negative mood and sleep quality. Personality and Individual Differences, 34(7), 1293–1301. doi:10.1016/S0191‐8869(02)00120‐4 Whitmer, A. J., & Gotlib, I. H. (2013). An attentional scope model of rumination. Psychological Bulletin, 139(5), 1036–1061. doi:10.1037/a0030923 Zoccola, P. M., & Dickerson, S. S. (2012). Assessing the relationship between rumination and cortisol: A review. Journal of Psychosomatic Research, 73(1), 1–9. doi:10.1016/j.jpsychores.2012.03.007 Zoccola, P. M., Dickerson, S. S., & Lam, S. (2009). Rumination predicts longer sleep onset latency after an acute psychosocial stressor. Psychosomatic Medicine, 71(7), 771–775. doi:10.1097/ PSY.0b013e3181ae58e8 Zoccola, P. M., Figueroa, W. S., Rabideau, E. M., Woody, A., & Benencia, F. (2014). Differential effects of poststressor rumination and distraction on cortisol and C‐reactive protein. Health Psychology, 33(12), 1606–1609. doi:10.1037/hea0000019

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Suggested Reading Brosschot, J. F., Gerin, W., & Thayer, J. F. (2006). The perseverative cognition hypothesis: A review of worry, prolonged stress‐related physiological activation, and health. Journal of Psychosomatic Research, 60(2), 113–124. doi:10.1016/j.jpsychores.2005.06.074 Segerstrom, S. C., Stanton, A. L., Alden, L. E., & Shortridge, B. E. (2003). A multidimensional structure for repetitive thought: What’s on your mind, and how, and how much? Journal of Personality and Social Psychology, 85(5), 909–921. doi:10.1037/0022‐3514.85.5.909 Smith, J. M., & Alloy, L. B. (2009). A roadmap to rumination: A review of the definition, assessment, and conceptualization of this multifaceted construct. Clinical Psychology Review, 29(2), 116–128. doi:10.1016/j.cpr.2008.10.003 Watkins, E. R. (2008). Constructive and unconstructive repetitive thought. Psychological Bulletin, 134(2), 163. doi:10.1037/0033‐2909.134.2.163

Screening Behavior Leona S. Aiken Department of Psychology, Arizona State University, Tempe, AZ, USA

Medical screening refers to the identification of undetected disease or risk for disease among asymptomatic, apparently healthy individuals. The driving force in medical screening from a public health perspective is the reduction of morbidity and mortality through early detection and treatment. More broadly, the identification of risk for disease may lead to health behavior change. Of the 10 great public health achievements of 2001–2010 in the United States (Centers for Disease Control and Prevention, 2011), two encompass screening across the lifespan: lifesaving treatment and intervention for newborns through “improvements in technology and endorsement of a uniform newborn‐screening panel of diseases” (p. 620) and reduction in colorectal, female breast, and cervical cancer through improved cancer screening. This entry targets the main social factors that relate to screening uptake, among them social norms, close interpersonal relationships, social networks, and racial/ethnic disparities. Genetic testing, with its impact on families, is considered. An overview of screening as a broad public phenomenon is provided to contextualize the review of social forces. The drive for universal and repeated screening emanating from health‐related federal agencies and private medical organizations, operationalized through screening guidelines, is long standing. Yet recent writing in the medical community has raised questions about the risks versus benefits of screening, and ethical issues abound. These issues are included here as critical background for health psychology, as the field engages in both the study of screening behavior and in intervention research to increase screening. (A limitation was imposed on number of references per entry in this volume. A more complete reference list for this entry is available from the author.)

Varieties and Targets of Screening Foci of screening are many and varied. Genetic screening has a long history. In newborns and infants, the earliest essentially universal genetic screening was and is for phenylketonuria (PKU), which results in severe mental retardation. Screening for PKU, begun in the 1960s, is now carried

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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out throughout the United States. Since PKU is treatable if detected very early, it is an exemplar of human biochemical genetics applied to health. Newborn infants in the United States are genetically screened for 29 disorders recommended by the American College of Medical Genetics. The US federal Newborn Screening Saves Lives Reauthorization Act of 2014 extends appropriations for infant genetic screening through 2019. Genetic screening of the fetus targets incurable conditions like cystic fibrosis and Huntington’s disease. Genetic screening and counseling of future parents inform decisions about whether to have children in the face of possible genetic disease transmission. Screening of children and adults is available for multitude of genetic disorders that place one at high risk of developing a disease. Given sequencing of the genome and new technologies that make genetic analysis accessible, private companies now offer direct‐to‐consumer genetic screening for multiple genes that might have some implication for disease risk. Cancer screening has been a central focus since the 1980s, when mammography screening became increasingly available and the National Cancer Institute and American Cancer Society recommended universal screening. Now melanoma and colorectal screening are public health targets; controversial prostate cancer screening is now in the public focus. Screening for a host of other conditions and diseases is available, among them alcohol and substance abuse cardiovascular disorders, metabolic, nutritional, and endocrine conditions, sexually transmitted diseases, and musculoskeletal disorders. The US Preventive Services Task Force (USPSTF), convened by the Agency for Healthcare Research and Quality (AHRQ), provides evidenced‐based recommendations for clinical preventive services, including over 60 screening tests. The USPSTF employs a grading system for screening test utility: A and B (recommends, high certainty of substantial/moderate net benefit), C (recommends selective provision for individual patients), D (recommends against, no net benefit or harms outweigh benefits), and I (insufficient current evidence). Only a third of reviewed screening tests reviewed receive A or B grades. The C grade places a substantial shared decision burden on physicians and patients.

Health Psychology and Screening Health psychology has long been active in research on individual, social, and system‐wide factors associated with screening uptake, as well as interventions to encourage screening for multiple diseases among diverse populations. This involvement dovetails with advances in medical screening methodologies. The American Psychologist (February–March 2015) devoted a complete issue to cancer and psychology, considering both traditional screening for common cancers and newer screening approaches for genetic risk. Psychology has been active for over 40 years in cancer screening for common cancers, including breast, cervical, colorectal, and prostate cancer (Wardle, Robb, Vernon, & Waller, 2015), but only little involved in newer genetic screening (McBride, Birmingham, & Kinney, 2015). Genetic screening raises psychological issues ranging from the decision to undergo screening to the impact of positive screening outcomes for both the individual and their relatives.

Intraindividual Factors and Screening: Perceived Risk and Attitudes As previously indicated, this overview focuses mainly on social factors that influence screening uptake and social impacts associated with screening outcomes. Much health psychology‐based research on both the correlates of screening uptake and interventions to increase screening

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uptake has been driven by classic models of health behavior—the health belief model (HBM), the theories of reasoned action (TRA) and planned behavior (TPB), protection motivation theory (PMT), the health action process approach (HAPA), the transtheoretical model of change (TTM), and social cognitive theory (SCT). Overall, these models focus on intraindividual, mainly cognitive factors that relate to screening—perceived risk, attitudes (benefits, barriers), outcome expectancies, self‐efficacy, and perceived behavioral control and planning.

Perceived Risk Perceived risk for disease is a central driving force in screening uptake and a core component in interventions to increase screening. Health behavior models treat perceived risk for disease as a distal driving force in a chain of constructs that result in health protective behavior (Aiken, Gerend, Jackson, & Ranby, 2012). Aiken et al. (2012) provide an extensive treatment of the relationship of perceived risk to screening, both from the perspective of perceived risk as a correlate of screening and the role of perceived risk in interventions to increase screening.

Attitudes Attitudes toward screening are represented by multiple constructs across health models, including perceived benefits versus barriers in the HBM and positive versus negative outcome expectations in the TRA/TPB. In general, attitudes constitute a more proximal factor than perceived risk in motivating health behavior. Many older women have been socialized into having regular cervical cancer screening as they became sexually active, into having regular mammograms beginning in their early 40s, and, along with men, into having colorectal screening by age 50. These individuals have already weathered winds of change in screening recommendations over the years—for example, the ongoing debate since the early 1990s as to whether women in their 40s should receive mammograms. For these individuals, ceasing screening requires an active decision, whereas continuing screening sustains their ongoing practice through adulthood (Torke, Schwartz, Holtz, Montz, & Sachs, 2013). Many elderly individuals continue screening, valuing peace of mind and sensing a moral obligation to continue screening to care for their health. Physicians are more likely to discuss the pros than the cons of screening with older adults (Torke et al., 2013). In the United States, Medicare funding for senior healthcare includes regular mammograms and regular screening for cervical and vaginal, colorectal, and prostate cancer. In contrast, the USPSTF, which operates independent of US Department of Health and Human Services, recommends against PSA testing for prostate cancer, against routine colonoscopy above age 75, and against routine cervical screening over age 65 and reports insufficient evidence of benefits versus risks of mammography screening above age 75.

Barriers to Screening: An Overview Myriad barriers to screening exist. In a review of barriers to mammography screening, Sarma (2015) provided a broadly applicable social ecological model to capture the interplay between the individual and the environment in relation to screening. The model employed a three‐tier organization of barriers: healthcare system‐level, social, and individual barriers. At the healthcare system level, nonadherence to screening recommendations was associated with lack of a healthcare provider (HCP) recommendation, lack of a usual HCP, and economic barriers,

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including lack of insurance coverage, lack of access to screening services in terms of both transportation and insufficient facilities, difficulty in getting a mammogram appointment, and language barriers. Social barriers included lack of social support for screening from others and nonadherence to guidelines in the social network; broader cultural impacts included fatalism with regard to contracting and dying from cancer. On the individual level, a lack of accurate knowledge about screening, negative expectations about screening, including the receipt of a cancer diagnosis, distrust of the medical system, and competing priorities all were negatively associated with screening.

Social Factors and Screening Collectively across health behavior models, social factors are far less well represented than intraindividual factors. Social factors represented in current models include subjective norms in TRA and TPB and socio‐structural factors in SCT.

Social Norms and Screening Broadly conceived, two classes of social norms operate on behavior: descriptive norms that characterize predominant behavior by others in a given situation and injunctive norms that reflect behavior that is approved versus disapproved by society. Injunctive norms are most often represented in screening literature as subjective norms in TRA/TPB—specifically, what significant others believe that a target individual should do, weighted by the target individual’s wish to comply. How accurately subjective norms predict screening intention and actual screening uptake varies widely across studies. In a meta‐analysis of prediction of screening by the TRA/TPB (Cooke & French, 2008), attitudes toward screening exhibited a stronger relationship to screening intention than subjective norms. In contrast, Smith‐McLallen and Fishbein (2008) found subjective norms to be the strongest predictor of screening intentions, above and beyond attitudes and perceived behavioral control. One complexity of research on subjective norms and screening is that multiple sources of norms may be folded into a single scale of subjective norms, including the physician, friends and family, and, specifically, an individual’s spouse; these sources relate differentially to screening uptake. In addition, screening behaviors are performed in private, in contrast to observable health protective behaviors like exercise, which are subject to social evaluation. For further discussion of norm theory in relation to health behavior, see Reid, Cialdini, and Aiken (2010). Given the extensive literature on breast and cervical screening, much of what is known about cancer screening uptake is based on women. PsycINFO contains over five times the number of peer‐reviewed articles on breast and cervical screening (beginning in 1958) than on prostate screening (beginning in 1994). What this literature collectively illustrates is the complexity of the relationship of subjective norms (i.e., injunctive norms of what significant others believe one should do). Influence from significant others has been observed to support screening uptake on the one hand but to induce reactance and resistance to screening on the other hand. In a study of men’s uptake of colorectal and prostate screening, subjective norms that aggregated partners, families, and people in general were positively related to whether men had never, intermittently, or regularly been screened (Sieverding, Matterne, & Ciccarello, 2010). However, subjective norms were negatively related to screening in the next year among those who had previously never or intermittently been screened. This result was interpreted in terms of reactance to pressure for screening from significant others, particularly wives. This finding highlights the complexity of the relationship of injunctive norms to behavior.

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The descriptive norm of what others actually do contributes to the prediction of screening intentions above and beyond injunctive norms (Smith‐McLallen & Fishbein, 2008). Yet descriptive norms have complex relationships to behavior. While the descriptive norm of high perceived prevalence of a particular behavior may encourage the behavior, low perceived prevalence may well discourage behavior. Hence, social norms‐based marketing that bemoans the fact that few people are engaged in an important health protective behavior (a common theme in public health messaging) actually discourages the behavior. For example, communication addressing the low prevalence of cancer screening discouraged men’s intention for screening (Sieverding, Decker, & Zimmerman, 2010).

Spousal Influence on Screening Spouses exhibit high concordance in health behaviors, attributed in part to assortative mating but also to mutual influence on health behavior change (see review by Jackson, Steptoe, & Wardle, 2015). Manne, Kashy, Weinberg, Boscarino, and Bowen (2012) noted substantial concordance between spouses in screening practices. They employed an interdependence model that characterized spouses’ mutual influence on colorectal screening based on each spouse’s perceived risk of colorectal cancer (CRC), perceived benefits of versus barriers to colorectal screening (i.e., decisional balance), and marital relationship quality. Apparent spousal impact on screening uptake varies across populations. Among elderly participants in the national Medical Expenditure Panel Survey, for example, residing with one’s spouse was positively associated with screening uptake (Lau & Kirby, 2009). Yet the complexity of the spousal association is evident in considering diverse populations. Among immigrant Latinas, for example, being married has been associated with higher uptake of routine medical checkups, a factor positively related to screening. In contrast among Latinas from a range of countries including Puerto Rico and Mexico and countries in Central and South America, attending an educational intervention on mammography and cervical screening with their husbands was associated with lower screening uptake, attributable to patriarchal control of women’s health decisions (Shelton, Jandorf, Thelemaque, King, & Erwin, 2012.

Social Network and Screening Social networks play a role in adult health (Martire & Franks, 2014). Research on social networks and screening addresses diverse populations, among whom Mexican Americans and Asians residing in the United States are major foci. Early on, social networks were positively associated with screening among older Mexican American women. Yet more recent research indicates weaker association of social integration with screening uptake that varies across Latina groups in the United States (Suarez et al., 2000). Among Vietnamese American women, who are at high risk for cervical cancer and have low screening rates, recommendation from friends for cervical screening was associated with increased screening (Nguyen‐Truong et al., 2012). Structural characteristics of communities, specifically community cohesion characterized by residential stability and available community ties, are positively related to peer referrals for mammography (Southwell et al., 2010).

Genetic Screening As characterized by the National Institutes of Health, genetic screening associated with disease is organized into six broad classes related to health: (a) newborn testing for disorders that are

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treatable early in life, (b) diagnostic testing to confirm or disconfirm the presence of a genetic or chromosomal condition, (c) carrier testing to identify individuals who have a gene mutation that can be passed to offspring, (d) prenatal testing of the fetus in service of decision making about pregnancy continuation, (e) predictive and presymptomatic testing for diseases that may appear later in life, and (f) preimplantation testing of the embryo for procedures such as in vitro fertilization. There is a large expanding literature on factors associated with the decision to be screened (see review by Sweeny, Ghane, Legg, Huynh, & Andrews, 2014) and on the impact of test results on interpersonal relationships, particularly within the family. The inheritance pattern of most cancer genetic mutations is autosomal dominant; there is a 50% risk of offspring inheriting the mutation if one parent is a carrier. Whether the disease itself is manifest in a carrier depends on gene penetrance. For BRCA1 and BRCA2 carriers, an oft‐cited estimate of cumulative risk of breast cancer development is 57 and 49%, respectively, by age 70; for ovarian cancer, 40 and 18%, respectively (Chen & Parmigiani, 2007). Given high familial risk, family factors operate in both the decision to be screened for genetic mutations and in communication of screening results (see Katapodi, Northouse, Milliron, Liu, & Merajver (2013) for a summary). Discussion of genetic screening is positively associated with family cohesiveness and hardiness and negatively related to family conflict. Whether the decision to undergo screening is considered a personal or family matter differs across families. Multiplex genetic susceptibility testing (MGST) is a form of genetic testing in which a single genetic sample is used to test for multiple gene variants that may alter the chances of developing particular diseases. A notion underlying such testing is that personalized medicine can be based on personal genetic profiles. “The future for the field is proffered as one in which individuals and health care providers might use genomic risk information to facilitate decision‐ making, personalize treatment approaches and motivate lifestyle improvements and adherence to screening recommendations” (Alford et al., 2011, p. 85). In 2007, the National Institutes of Health implemented the Multiplex Initiative to assess public response to genetic testing. Healthy young adults, all of whom had access to healthcare, were offered web‐based information about genetic testing, free genetic multiplex testing for eight common diseases, and a personalized risk report. In all, 35% of those reached at baseline refused to participate; another 13% were ineligible. Only 6% of those initially reached at baseline actually received screening, having completed a complex multistep educational protocol required before screening. Consistent with previous findings concerning participation in genetic research, men had lower participation than women, and African Americans had lower participation than Whites (Alford et al., 2011). A vision for health psychology is that advances in genomic technology, including understanding of epigenetic alterations of genetic material by environment and behavior, will lead to translational research that lowers the threat of genetically associated disease. McBride et al. (2015) identify three important research areas for health psychologists in the genetic testing sphere: (a) effective communication about gene–environment interactions, (b) provider– patient communication about the impact of genetic risk for disease development, and (c) design of behavioral risk‐reduction interventions for those at genetic risk. The field of genetic counseling is already actively involved in these areas of research.

Social Stigma, Discrimination, and Screening Social stigma may operate as a deterrent to screening or disclosure of screening—for example, screening for sexually transmitted diseases (Balfe & Brugha, 2010) and for blood diseases such

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as hepatitis B transmitted via bodily fluid from an infected person. Earnshaw, Bogart, Dovidio, and Williams (2013) provide a comprehensive model to account for racial/ethnic disparities in HIV. While HIV testing among Blacks and Hispanics exceeds that for Whites, minority individuals are tested at a later stage of disease progression. Avoidance of testing is attributed to anticipated discrimination as a result of a positive HIV diagnosis, as well as to the expectation that the fact of being tested itself will lead to stigmatization. DiMillo et al. (2015) identified distinct spheres of discreditable stigma (that is, stigma that is not immediately perceivable by others) that are produced by genetic screening. Women carrying the BRCA1 or BRCA2 gene feared that they would be viewed by others as weak and unable to carry out work responsibilities and that their children might suffer stigmatization due to their potential for also carrying the genetic mutation. Discrimination in the workplace and discrimination in access to insurance are two spheres of serious concern with regard to genetic testing. In the United States, the federal Genetic Information Nondiscrimination Act of 2008 (GINA) contains both employment and health insurance provisions but leaves much uncovered. Employment provisions apply only to companies with 15 or more employees. Access to life insurance, disability insurance, and long‐term care insurance and the use of genetic test results to determine health insurance payments are not covered.

Racial/Ethnic Disparities Racial/ethnic disparities are evident in screening uptake in the United States. According to the 2010 National Health Interview Survey (NHIS), almost identical percentages of White and Black women received mammograms (73% each) and Pap tests (83, 85%, respectively) in accord with USPSTF recommendations. Hispanic women lagged behind both White and Black women for mammograms (70%) and Pap tests (79%). Overall, Asian women fell substantially behind White, Black, and Hispanic women for mammograms (64%) and Pap tests (75%). CRC screening uptake was higher among Whites than Blacks (60% versus 55%) and substantially lower among Hispanics and Asians (47% each). For all tests, having health insurance, higher education, a usual source of healthcare, and being US born were associated with higher screening rates. Among immigrants, being in the United States for fewer than 10 years was associated with lower screening (Centers for Disease Control and Prevention, 2012; see also Wardle et al., 2015).

Physician Recommendation Physician recommendation is a gatekeeper for access to screening, among the most powerful correlates of screening uptake. Physician recommendation is also a marker variable for barriers to screening, including lack of health insurance and lack of access to regular healthcare. Physician recommendation has been scrutinized as a putative cause of lower screening rates among racial/ethnic minorities. Discrepancies in rate of physician recommendation across racial/ethnic groups vary across studies, and close attention to the databases and case selection on which outcomes are based is warranted. The NHIS has been employed over time to assess differences in physician referral for screening tests as a function of race/ethnicity. Based on the 2005 NHIS, Wallace, Baltrus, Wallace, Blumenthal, and Rust (2013) reported a lower rate of physician recommendation for colorectal screening among Black than among White study participants, all of whom had no family history of CRC. With age, gender, education, income,

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health insurance, and number of doctor visits in the past year controlled, the probability of recommendation was less than half for Blacks than for Whites.

Current Issues in Screening Screening research in health psychology has for decades followed the medical community’s endorsement of screening, associated with ever‐refined technology for detection of disease before it is manifested in observable symptoms. Health psychology has researched putative determinants of and deterrents to screening uptake and has developed and evaluated interventions to increase screening rates. Yet now a new theme has emerged in the international medical community: that some screening procedures have health risks and screening yields false positives, over‐diagnosis, and over‐treatment for diseases that may never become life threatening. The screening message to the public has been that early detection leads to better treatment outcomes and longer survival. Yet lead‐time bias in screening refers to the detection of disease earlier than it would be diagnosed as a function of symptoms, while the course of disease remains the same. Thus, oft‐cited 5‐year survival rates after detection are not an accurate measure of the impact of screening on saving lives. In the current medical climate, joint decision making between physician and patient is the model. There are great emotional issues in screening decisions (Rosenbaum, 2014) coupled with a call for greater understanding of the interplay between affective science and cancer control (Ferrer, Green, & Barrett, 2015). Genetic screening brings a host of ethical issues—informed consent, ownership of genetic samples, disclosure of genetic findings, the impact of genetic screening on access to health insurance, and discrimination in the workplace. These issues are made more complex by direct‐ to‐consumer marketing. The concern with over‐screening is reflected in a recent funding initiative the National Cancer Institute to reduce over‐screening older adults for breast, cervical, and colorectal cancers. In sum, as health psychology continues to participate in screening research, the complexity of screening from individual, public health, social, and economic perspectives appears to increase exponentially over time.

Author Biography Leona S. Aiken is president’s professor of psychology at Arizona State University (ASU); she chairs the PhD concentration in quantitative methods in psychology. She is coauthor of Multiple Regression: Testing and Interpreting Interactions and of Applied Multiple Regression/ Correlation Analysis for the Behavioral Sciences. Her methodological work considers measured and latent continuous variable interactions. Her substantive research focuses on women’s health protective behaviors across the lifespan. She is past president of the Society of Multivariate Experimental Psychology.

References Aiken, L. S., Gerend, M. A., Jackson, K. M., & Ranby, K. W. (2012). Subjective risk and health protective behavior. In A. Baum, T. Revenson, & J. Singer (Eds.), Handbook of health psychology (2nd ed., pp. 113–145). New York, NY: Taylor & Francis.

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Alford, S. H., McBride, C. M., Reid, R. J., Larson, E. B., Baxevanis, A. D., & Brody, L. C. (2011). Participation in genetic testing research varies by social group. Public Health Genomics, 14, 85–93. doi:10.1159/000294277 Balfe, M., & Brugha, R. (2010). Disclosure of STI testing activities by young adults: The influence of emotions and social networks. Sociology of Health & Illness, 32, 1041–1058. doi:10.1111/j.1467‐9566. 2010.01281.x Centers for Disease Control and Prevention. (2011). Ten great public health achievements—United States, 2001–2010. Morbidity and Mortality Weekly Report, 60, 619–623. Centers for Disease Control and Prevention. (2012). Cancer screening—United States, 2010. Morbidity and Mortality Weekly Report, 61, 41–45. Chen, S., & Parmigiani, G. (2007). Meta‐analysis of BRCA1 and BRCA2 penetrance. Journal of Clinical Oncology, 25, 1329–1333. doi:10.1200/JCO.2006.09.1066 Cooke, R., & French, D. P. (2008). How well do the theory of reasoned action and planned behavior predict intentions and attendance at screening programmes? A meta‐analysis. Psychology and Health, 23, 745–765. doi:10.1080/08870440701544437 DiMillo, J., Samson, A., Thériault, A., Lowry, S., Corsini, L., Verma, S., & Tomiak, E. (2015). Genetic testing: When prediction generates stigmatization. Journal of Health Psychology, 20, 393–400. doi:10.1177/1359105313502566 Earnshaw, V. A., Bogart, L. M., Dovidio, J. F., & Williams, D. R. (2013). Stigma and racial/ethnic HIV disparities: Moving toward resilience. American Psychologist, 68, 225–236. doi:10.1037/a0032705 Ferrer, R. A., Green, P. A., & Barrett, L. F. (2015). Affective science perspectives on cancer control: Strategically crafting a mutually beneficial research agenda. Perspectives on Psychological Science, 10, 328–345. doi:10:1177/1745691615576755 Jackson, S. E., Steptoe, A., & Wardle, J. (2015). The influence of partner’s behavior on health behavior change. JAMA Internal Medicine, 175, 385–392. doi:10.1001/jamainternmed.2014.7554 Katapodi, M. C., Northouse, L. L., Milliron, K. J., Liu, G., & Merajver, S. D. (2013). Individual and family characteristics associated with BRCA1/2 genetic testing in high‐risk families. Psycho‐Oncology, 22, 1336–1343. doi:10.1002/pon.3139 Lau, D. T., & Kirby, J. B. (2009). The relationship between living arrangement and preventive care use among community‐dwelling elderly persons. American Journal of Public Health, 99, 1315–1321. doi:10.2105.AJPH.2008.151142 Manne, S., Kashy, D., Weinberg, D. S., Boscarino, J. A., & Bowen, D. J. (2012). Using the interdependence model to understand spousal influence on colorectal cancer screening intentions: A structural equation model. Annals of Behavioral Medicine, 43, 320–329. doi:10.1007/s12160‐012‐9344‐y Martire, L. M., & Franks, M. M. (2014). The role of social networks in adult health: Introduction to the special issue. Health Psychology, 33, 501–504. doi:10/1037/hea0000103 McBride, C. M., Birmingham, W. C., & Kinney, A. Y. (2015). Health psychology and translational genomic research: Brining innovation to cancer‐related behavioral interventions. American Psychologist, 72, 91–104. doi:10.1037/a0036568 Nguyen‐Truong, C. K. Y., Lee‐Lin, F., Leo, M. C., Gedaly‐Duff, V., Nail, L. M., Wang, P., & Tran, T. (2012). A community‐based approach to understanding pap testing adherence among ietnamese American Immigrants. Journal of Obstetrics, Gynecologic, and Neonatal Nursing, 41, E26–E40. doi:10.1111/j.1552‐6909.2012.01414.x Reid, A. E., Cialdini, R. B., & Aiken, L. S. (2010). Social norms and health behavior. In A. Steptoe (Ed.), Handbook of behavioral medicine research: Methods and application (pp. 263–275). New York, NY: Springer. doi:10.1007/978‐0‐387‐09488‐5 Rosenbaum, L. (2014). Invisible risks, emotional choices: Mammography and medical decision making. New England Journal of Medicine, 371, 1549–1552. doi:10.1056/NEJMrns1409003 Sarma, E. A. (2015). Barriers to screening mammography. Health Psychology Review, 9, 42–62. doi:10.1 080/17437199.2013.766831 Shelton, R. C., Jandorf, L., Thelemaque, L., King, S., & Erwin, D. O. (2012). Sociocultural determinants of breast and cervical cancer screening adherence: An examination of variation among

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i­mmigrant Latinas by country of origin. Journal of Health Care for the Poor and Underserved, 23, 1768–1792. doi:10.1353/hpu.2012.0191 Sieverding, M., Decker, S., & Zimmermann, F. (2010). Information about low participation in cancer screening demotivates other people. Psychological Science, 21, 941–943. doi:10.1177/ 0956797610373936 Sieverding, M., Matterne, U., & Ciccarello, L. (2010). What role do social norms play in the context of men’s cancer screening intention and behavior? Application of an extended theory of planned behavior. Health Psychology, 29, 72–81. doi:10.1037/a0016941 Smith‐McLallen, A., & Fishbein, M. (2008). Predictors of intentions to perform six cancer‐related behaviours: Roles for injunctive and descriptive norms. Psychology, Health & Medicine, 13, 389–401. doi:10.1080/13548500701842933 Southwell, B. G., Slater, J. S., Rothman, A. J., Friedenberg, L. M., Allison, T. R., & Nelson, C. L. (2010). The availability of community ties predicts likelihood of peer referral for mammography: Geographic constraints on viral marketing. Social Science and Medicine, 71, 1627–1635. doi:10.1016/j.socscimed.2010.08.809 Suarez, L., Ramirez, A. G., Villarreal, R., Marti, J., McAlister, A., Talavera, G. A., … Perez‐Stable, E. J. (2000). Social networks and cancer screening in four U.S. Hispanic groups. American Journal of Preventive Medicine, 19, 47–52. Sweeny, K., Ghane, A., Legg, A. M., Huynh, H. P., & Andrews, S. E. (2014). Predictors of genetic testing decisions: A systematic review and critique of the literature. Journal of Genetic Counseling, 23, 263–288. Torke, A. M., Schwartz, P. H., Holtz, L. R., Montz, K., & Sachs, G. A. (2013). Older adults and forgoing cancer screening. JAMA Internal Medicine, 173, 526–531. doi:10.001/jamaintermed. 2013.2903 Wallace, D. A. C., Baltrus, P. T., Wallace, T. c., Blumenthal, D. S., & Rust, S. G. (2013). Black white disparities in receiving a physician recommendation for colorectal cancer screening and reasons for undergoing screening. Journal of Health Care for the Poor and Underserved, 24, 1115–1124. doi:10.1353/hpu.2013.0132 Wardle, J., Robb, K., Vernon, S., & Waller, J. (2015). Screening for prevention and early diagnosis of cancer. American Psychologist, 70, 119–133. doi:10.1037/a0037357

Suggested Reading Fairfield, K. M., Gerstein, B. S., Levin, C. A., Stringfellow, V., Wierman, H. R., & McNaughton‐Collins, M. (2015). Decisions about medication use and cancer screening across age groups in the United States. Patient Education and Counseling, 98, 338–343. doi:10.1016/pec.2014.11.012 Kiviniemi, M. T., Bennett, A., Zaiter, M., & Marshall, J. R. (2011). Individual‐level factors in colorectal cancer screening: A review of the literature on the relation of individual‐level health behavior constructs and screening behavior. Psycho‐Oncology, 20, 1023–2033. doi:10.1002/pon.1865 Marquez, B., Elder, J. P., Arredondo, E. M., Madanat, H., Ji, M., & Ayala, G. X. (2014). Social network characteristics associated with health promoting behaviors among Latinos. Health Psychology, 33, 544–553. doi:10.1037/HEA0000092 Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2007). The constructive, destructive, and reconstructive power of social norms. Psychological Science, 28, 429–434. doi:10.1111/j.1467.9280.2007.01917.x Ye, J., Williams, S. D., & Xu, Z. (2009). The association between social networks and colorectal cancer screening in American males and females: Data from the 2005 Health Information National Trends Survey. Cancer Causes and Control, 20, 1227–1233. doi:10.1007/s10552‐009‐9335‐x

Selective Exposure in the Domain of Health Zachary Fetterman and William Hart Psychology Department, University of Alabama, Tuscaloosa, AL, USA

Consider the following scenario. Carl was diagnosed with severe sleep apnea. Carl’s doctor suggested a treatment called continuous positive airway pressure (CPAP) to manage his condition. Carl had heard anecdotally that this treatment was uncomfortable and ineffective, and he had a preferred treatment in mind prior to receiving his doctor’s recommendation. His doctor suggested he read more about his options and provided Carl with some reading material on the benefits of CPAP and the possible shortcomings of his preferred non‐recommended treatment. Carl never read this information. Instead, Carl sought out Internet sources that validated his preexisting preference. Carl continued to stubbornly believe that he knew the best treatment despite his doctor’s advice. From Carl’s perspective, he had done extensive research on the issue, and the research clearly confirmed his views on treatment. In the end, the interaction between Carl and his doctor ended without a resolution. Unfortunately, untreated sleep apnea is associated with increased mortality risk (Young et al., 2008). It is possible that Carl could have experienced a healthier and longer life if he had been more open and willing to expose himself to alternative views about available treatment options. Carl’s actions reflect a common human behavior termed selective exposure. Selective exposure refers to the tendency to selectively seek out viewpoint supportive information and neglect viewpoint inconsistent information (Festinger, 1957; Fischer & Greitemeyer, 2010; Hart et al., 2009). It ensures that people will know more about why they might be right than why they might be wrong. Reviews of selective exposure suggest that this effect can be large (Fischer & Greitemeyer, 2010; Frey, 1986; Hart et  al., 2009) and takes place in various domains including health, consumer decision making, politics, morality and values, and business decision making (Knobloch‐Westerwick, Johnson, & Westerwick, 2013). Selective exposure is similar to but distinguishable from two related phenomena. First, selective exposure refers to the relative preference for supportive over conflicting information, which distinguishes it from mere information avoidance. Second, the selective exposure of interest here is

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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driven by people’s preferences for supportive over conflicting information, which distinguishes it from selective exposure driven by the availability of information in one’s environment (a phenomenon called de facto selective exposure). In this entry we will focus on selective exposure in the domain of health, but we will often refer to research without a clear health outcome that nonetheless sheds light on the mechanisms that guide selective exposure. Our assumption is that these mechanisms also operate in health‐related selective exposure. Ultimately, understanding selective exposure and its manifestations in the context of health may allow scientists and practitioners to contribute to broad efforts to improve the health and well‐being of both individuals and populations.

Selective Exposure and Health Evidence suggests that people might selectively gather information that supports (vs. contradicts) their health choices. In one of the earliest studies relating selective exposure to health (Brock & Balloun, 1967), participants were asked to listen to recordings of health messages related to the smoking–cancer link; however, these messages were obscured by static. Participants were told that they could remove the static and more clearly hear the messages by repeatedly pressing a button. Smokers pressed the button more than nonsmokers to remove the static from a message discrediting the smoking–cancer link, and nonsmokers pressed the button more than smokers to remove static from a message validating the smoking–cancer link. Thus, people appear to have strategically clarified the message only when it would validate their own smoking‐related behavior. A more recent study assessed the relationship between participants’ tendency to engage in various health behaviors (consumption of organic food, coffee, and vegetables as well as exercise habits) and the time they spent reading articles in support of or opposition to each of these various health behaviors (Knobloch‐Westerwick et al., 2013). Across all studied health behaviors, the frequency of engaging in a behavior was positively associated with time spent reading information supporting (vs. challenging) that behavior. Other research in more naturalistic settings confirms the role of selective exposure in the health domain (e.g., Noguchi, Albarracin, Durantini, & Glasman, 2007). In one unfortunate example, individuals who are most at risk for contracting HIV appear to be the least willing to attend HIV intervention programs (Noguchi et al., 2007). Although evidence suggests that selective exposure sometimes takes place in health decision making (Hart et al., 2009; Knobloch‐Westerwick et al., 2013), the observed effects can be small. Presumably, health‐related selective exposure effects might be dependent on the presence of personal and situational variables that bolster or weaken selective exposure. Next, we discuss the processes that seem to influence selective exposure and how these processes might be at work in health‐related decision‐making scenarios.

Mechanisms and Moderators Feeling Good or Avoiding Feeling Bad One long‐standing assumption surrounding selective exposure is that it manifests, at least in part, as a means to make people feel good or avoid feeling bad. Indeed, early research conceptualized selective exposure as a way to cope with or avoid decisional remorse (i.e., dissonance;

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e.g., Festinger 1957, 1964). By selectively seeking congenial information and avoiding uncongenial information, individuals can effectively reduce the unpleasantness of feeling foolish (e.g., “I made a stupid choice!”) and promote the pleasantness of feeling wise (e.g., “I made a smart choice!”). Research reveals that selective exposure tends to be amplified when invalidation might particularly sting and validation might particularly satisfy (Fischer & Greitemeyer, 2010; Frey, 1986; Hart et al., 2009). As a few examples, selective exposure is amplified when individuals (a) are highly committed to an attitude or behavior (Jonas & Frey, 2003); (b) are dealing with information relevant to enduring, personally defining values (McFarland & Warren, 1992); (c) are reminded of their mortality (Jonas, Greenberg, & Frey, 2003); (d) are forced to choose information that is difficult to refute (Frey, 1986); (e) do not feel self‐affirmed (Klein & Harris, 2009) or are placed in a negative mood (Jonas, Graupmann, & Frey, 2006); and (f) care a great deal about cognitive consistency (Hart, Adams, Burton, Shreves, & Hamilton, 2012). These findings provide insight into the factors that might influence selective exposure in health‐related decision‐making scenarios. As a few examples, health‐related decision‐making scenarios that evoke negative moods (Penner et al., 2010), prompt concerns about one’s own mortality (e.g., a health diagnosis that makes death salient; Bozo, Tunca, & Šimšek, 2009), or challenge a person’s autonomy to make their own choices (e.g., a pushy healthcare provider; Fitzsimons & Lehmann, 2004) should be particularly likely to inspire defensive attempts to validate one’s own health views. In these scenarios, addressing defensiveness is likely necessary to ensure evenhanded information gathering. Fortunately, research has found that defensiveness can be tempered, and selective exposure reduced, by validating a person’s autonomy and sense of self‐worth (Howell & Shepperd, 2012).

High Confidence An additional mechanism proposed to underlie selective exposure is confidence. Various studies converge on the idea that increased decisional certainty, or confidence in one’s attitudes, beliefs, and behaviors, is associated with an increased tendency to engage in selective exposure (Fischer, Fischer, Weisweiler, & Frey, 2010). For example, in one experiment (Fischer et al., 2010), some participants were led to feel reduced confidence in their decision because they made their decision under suboptimal, highly distracting conditions. Other participants were allowed to make an immediate decision before any distraction or were able to deliberate in a distraction‐free environment. Participants in the distracted group, relative to participants in the other two non‐distracted groups, showed less confidence in their decision and reduced selective exposure. There are at least two reasons why high confidence might enhance selective exposure. First, it could be the case that highly confident individuals are more motivated to maintain their views than are less confident individuals (Frey, 1986). For highly confident individuals, confronting information that suggests the need for a change in attitudes, beliefs, or behaviors is likely to be perceived as counterproductive (e.g., “If it ain’t broke, don’t fix it!”: Frey, 1986). Second, it could be the case that highly confident individuals are prone to misperceive supportive information as higher in quality than unsupportive information (Fischer & Greitemeyer, 2010). Imagine feeling extremely certain that refined sugar consumption is linked with obesity. You are likely to perceive any information that denies this link as invalid (e.g., ignorant, biased). Conversely, you are likely to perceive information that supports the link as reliable and possibly worth a read. The confidence mechanism contributing to selective exposure seems particularly important to consider in the context of health‐related information. Research suggests that people tend to

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be overconfident about their knowledge of various health topics (Weinstein & Lyon, 1999). In light of this overconfidence, it may be particularly important to address individuals’ overconfidence and accordingly minimize selective exposure.

Cognitive Economy or Laziness Selective exposure may also be motivated by efficiency. Information search is often conducted with a desire to conserve energy, and selecting supportive information is generally an effective way to preserve cognitive resources (Fischer, 2011). Disconfirming information is more difficult to process because it is harder to integrate into one’s existing knowledge structures (Ditto & Lopez, 1992). Consistent with this idea, robbing people of energy increases their tendency to demonstrate selective exposure. In one study on this issue (Fischer, Greitemeyer, & Frey, 2008), participants either completed an easy task (watching a video of a politician speaking with words occasionally appearing on the bottom of the screen) or an energy‐zapping version of the same task (watching the same video but with the instructions not to look at the words and to regularly redirect their attention from the words to the politician). All participants then indicated views on political and economic issues. As anticipated, participants who completed the energy‐zapping task demonstrated increased selective exposure relative to those who did the easy task. Other research suggests that personal factors linked to “cognitive miserliness” predict enhanced selective exposure (Hart et al. 2009, Hart, Adams, Burton, Shreves, & Hamilton, 2012). Selective exposure as a result of cognitive economy may be particularly prevalent in patient populations. Research shows that individuals with various health conditions report increased fatigue relative to controls (Stone, Richards, A’Hern, & Hardy, 2000). Consider this correlation in relation to our scenario involving Carl. Carl was suffering from sleep apnea and presumably often fatigued. This diminished energy should, in theory, make Carl even more motivated than a well‐rested individual to preserve cognitive resources and thus engage in selective exposure. A related barrier that potentially perpetuates selective exposure in the domain of health is the public’s regular exposure to competing health information. Nutritional science, for example, often offers contradictory suggestions (e.g., high protein, low‐carb diet vs. vegetarian diet vs. balanced diet based on moderation). In light of this lack of consensus, individuals may feel inclined to preserve their energy by avoiding exhaustive, potentially unproductive information search and instead indulge themselves in confirmatory messages.

Accuracy Motivation (Wanting to be Right) Accuracy motivation refers to a desire to accurately appraise stimuli, reach correct conclusions, and discern truth independent of whether these appraisals and conclusions are consistent with existing thoughts, attitudes, and behaviors. Research shows that increases in accuracy motivation tend to reduce selective exposure because they can weaken the three mechanisms that promote this biased information gathering. For example, factors that enhance concerns for accuracy have been shown to encourage people to forsake immediate hedonic satisfaction (gained through selective exposure) for more long‐term satisfaction that comes from making the most informed choice (Jonas & Frey, 2003), to question their preexisting confidence (Fischer, Greitemeyer, et al., 2008), and to inspire greater willingness to expend energy processing new information (Fischer, 2011). Available research suggests strategies that may effectively induce accuracy concerns in health domains and reduce selective exposure. For example, encouraging people to confront the discrepancy between what they are doing and what they should be doing seems to inspire

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more evenhanded gathering of health information. In one study (Knobloch‐Westerwick et al., 2013), participants who perceived a larger discrepancy between their stated health behaviors and their health goals showed a reduced tendency to selectively prefer information that supported (vs. challenged) their current behavior. Additionally, existing research suggests that accuracy motivation may be ignited by encouraging individuals to consider higher‐order goals (Fischer, Jonas, Frey, & Schulz‐Hardt, 2005; Fischer, Schulz‐Hardt, & Frey, 2008). For example, encouraging individuals to consider their desirable health‐related goals (e.g., achieve a state of health that allows for increased experience of family, fun, and leisure) prior to information gathering may promote more evenhanded information gathering.

Conclusion Selective exposure can have deleterious consequences for health decision making (Noguchi et al., 2007). Failure to fully consider contradictory evidence about one’s treatment options, diet alternatives, views on exercise, and ideas about sleep could result in poor health literacy as well as suboptimal health choices and outcomes. Evidence suggests that selective exposure may occur in health‐related decision making, and it seems likely that the extent of this bias is influenced by at least four mechanisms: (a) wanting to feel good or avoid feeling bad, (b) being highly confident, (c) desiring cognitive economy, and (d) desiring accuracy.

Author Biographies Zachary Fetterman is a doctoral candidate at the University of Alabama, currently completing clinical internship at Wilford Hall Ambulatory Surgical Center. His interests include bias at individual and system levels. William Hart is an assistant professor in social psychology at the University of Alabama. Will received his PhD from the University of Florida in 2009. He is interested in judgment and decision making and goal pursuit.

References Bozo, Ö., Tunca, A., & Šimšek, Y. (2009). The effect of death anxiety and age on health‐promoting behaviors: A terror‐management theory perspective. The Journal of Psychology, 143(4), 377–389. doi:10.3200/JRLP.143.4.377‐389 Brock, T. C., & Balloun, J. L. (1967). Behavioral receptivity to dissonant information. Journal of Personality and Social Psychology, 6(4), 413–428. doi:10.1037/h0021225 Ditto, P. H., & Lopez, D. F. (1992). Motivated skepticism: use of differential decision criteria for preferred and nonpreferred conclusions. Journal of Personality and Social Psychology, 63(4), 568– 584. doi:10.1037/0022‐ Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press. Festinger, L. (1964). Conflict, decision, and dissonance. Stanford, CA: Stanford University Press. Fischer, P. (2011). Selective exposure, decision uncertainty, and cognitive economy: A new theoretical perspective on confirmatory information search. Social and Personality Psychology Compass, 5(10), 751–762. doi:10.1111/j.1751‐9004.2011.00386.x Fischer, P., Fischer, J., Weisweiler, S., & Frey, D. (2010). Selective exposure to information: How different modes of decision making affect subsequent confirmatory information processing. British Journal of Social Psychology, 49(4), 871–881.

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Fischer, P., & Greitemeyer, T. (2010). A new look at selective‐exposure effects: An integrative model. Current Directions in Psychological Science, 19(6), 384–389. doi:10.1177/0963721410391246 Fischer, P., Greitemeyer, T., & Frey, D. (2008). Self‐regulation and selective exposure: The impact of depleted self‐regulation resources on confirmatory information processing. Journal of Personality and Social Psychology, 94(3), 382–395. doi:10.1037/0022‐3514.94.3.382 Fischer, P., Jonas, E., Frey, D., & Schulz‐Hardt, S. (2005). Selective exposure to information: The impact of information limits. European Journal of Social Psychology, 35, 469–492. doi:10.1002/ ejsp.264 Fischer, P., Schulz‐Hardt, S., & Frey, D. (2008). Selective exposure and information quantity: How different information quantities moderate decision makers’ preference for consistent and inconsistent information. Journal of Personality and Social Psychology, 94, 231–244. doi:10.1037/0022‐ 3514.94.3.382 Fitzsimons, G. J., & Lehmann, D. R. (2004). Reactance to recommendations: When unsolicited advice yields contrary responses. Marketing Science, 23(1), 82–94. doi:10.1287/mksc.1030.0033 Frey, D. (1986). Recent research on selective exposure to information. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 19, pp. 41–80). New York: Academic Press. Hart, W., Adams, J. M., Burton, K. A., Shreves, W. B., & Hamilton, J. (2012). Shaping reality vs. hiding from reality: Reconsidering the effects of trait need for closure on information search. Journal of Research in Personality, 46, 489–496. doi:10.1016/j.jrp.2012.05.004 Hart, W., Albarracín, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009). Feeling validated versus being correct: A meta‐analysis of selective exposure to information. Psychological Bulletin, 135(4), 555–588. doi:10.1037/a0015701 Howell, J. L., & Shepperd, J. A. (2012). Reducing information avoidance through affirmation. Psychological Science, 23(2), 141–145. doi:10.1177/0956797611424164 Jonas, E., & Frey, D. (2003). Information search and presentation in advisor‐client interactions. Organizational Behavior and Human Decision Processes, 91, 154–168. doi:10.1016/S0749‐ 5978(03)00059‐1 Jonas, E., Graupmann, V., & Frey, D. (2006). The influence of mood on the search for supporting versus conflicting information: Dissonance reduction as a means of mood regulation? Personality and Social Psychology Bulletin, 32, 3–15. doi:10.1177/0146167205276118 Jonas, E., Greenberg, J., & Frey, D. (2003). Connecting terror management and dissonance theory: Evidence that mortality salience increases the preference for supporting information after decisions. Personality and Social Psychology Bulletin, 29(9), 1181–1189. doi:10.1177/0146167203254599 Klein, W. M., & Harris, P. R. (2009). Self‐affirmation enhances attentional bias toward threatening components of a persuasive message. Psychological Science, 20(12), 1463–1467. doi:10.1111/j.1467‐ 9280.2009.02467.x Knobloch‐Westerwick, S., Johnson, B. K., & Westerwick, A. (2013). To your health: Self‐regulation of health behavior through selective exposure to online health messages. Journal of Communication, 63(5), 807–829. doi:10.1111/jcom.12055 McFarland, S. G., & Warren, J. C., Jr. (1992). Religious orientations and selective exposure among fundamentalist Christians. Journal for the Scientific Study of Religion, 163–174. doi:10.2307/1387006 Noguchi, K., Albarracín, D., Durantini, M. R., & Glasman, L. R. (2007). Who participates in which health promotion programs? A meta‐analysis of motivations underlying enrollment and retention in HIV‐prevention interventions. Psychological Bulletin, 133, 955–975. doi:10.1037/0033‐ 2909.133.6.955 Penner, L. A., Dovidio, J. F., West, T. V., Gaertner, S. L., Albrecht, T. L., Dailey, R. K., & Markova, T. (2010). Aversive racism and medical interactions with Black patients: A field study. Journal of Experimental Social Psychology, 46(2), 436–440. doi:10.1016/j.jesp.2009.11.004 Stone, P., Richards, M., A’Hern, R., & Hardy, J. (2000). A study to investigate the prevalence, severity and correlates of fatigue among patients with cancer in comparison with a control group of volunteers without cancer. Annals of Oncology, 11(5), 561–567.

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Weinstein, N. D., & Lyon, J. E. (1999). Mindset, optimistic bias about personal risk and health‐protective behaviour. British Journal of Health Psychology, 4(4), 289–300. doi:10.1348/135910799168641 Young, T., Finn, L., Peppard, P. E., Szklo‐Coxe, M., Austin, D., Nieto, F. J., … Hla, K. M. (2008). Sleep disordered breathing and mortality: Eighteen‐year follow‐up of the Wisconsin sleep cohort. Sleep, 31(8), 1071–1078.

Suggested Reading Fischer, P., & Greitemeyer, T. (2010). A new look at selective‐exposure effects: An integrative model. Current Directions in Psychological Science, 19(6), 384–389. doi:10.1177/0963721410391246 Hart, W., Albarracín, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009). Feeling validated versus being correct: A meta‐analysis of selective exposure to information. Psychological Bulletin, 135(4), 555–588. doi:10.1037/a0015701 Knobloch‐Westerwick, S., Johnson, B. K., & Westerwick, A. (2013). To your health: Self‐regulation of health behavior through selective exposure to online health messages. Journal of Communication, 63(5), 807–829. doi:10.1111/jcom.12055

Self‐Affirmation and Health Peter R. Harris, Donna C. Jessop, and Philine S. Harris School of Psychology, University of Sussex, Brighton & Hove, UK

Self‐affirmation theory (Steele, 1988) posits that people are typically highly motivated to protect their “self‐integrity,” which is their global sense of being morally and adaptively adequate. Steele described self‐integrity as the “phenomenal experience” of such things as being “competent, good, coherent, unitary, stable, capable of free choice, [and] capable of controlling important outcomes …” (1988, p. 262). The motive to protect self‐integrity renders people vigilant to threats to self‐integrity. Such threats are subjective, many, and various and include both major life events (e.g., divorce, redundancy, illness) and more mundane, day‐to‐day experiences (e.g., petty insults, disappointing feedback). They have been labeled “psychological threats” and defined as “the perception of environmental challenge to one’s self‐integrity” (Cohen & Sherman, 2014, p. 335). According to self‐affirmation theory, experiencing or anticipating such psychological threats elicits the motivation to protect self‐integrity. Protecting self‐integrity can be achieved in different ways (Sherman & Cohen, 2006). People can reduce the threat by accommodation (i.e., changing their behavior or beliefs in appropriate ways), by assimilation (i.e., rendering the threat consistent with preexisting beliefs and knowledge, even if this involves distorting perceptions about the threat), or by self‐affirmation. For example, from the perspective of self‐affirmation theory, information about the health risks of smoking presents a self‐integrity threat to smokers by challenging self‐perceptions of adequacy; those who value being healthy are knowingly undermining their future health. A smoker might respond to such information by accommodation (e.g., quitting or at least trying to quit or cut down). However, behavior change is not always easy to achieve. Indeed, from the perspective of self‐affirmation theory, change requires acknowledging that one’s behavior has been unwise, which presents a self‐integrity threat. Consequently, for these and other reasons, it may be easier to assimilate the threat. Thus, smokers may instead reject the information or its personal relevance (e.g., by denigrating the evidence or the source of the message) or generate cognitions that enable them to keep smoking while reducing the self‐ integrity threat (e.g., by forming the intention to quit but postponing the date on which

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they will begin). However, self‐affirmation theory posits a third possibility: they may self‐affirm. Self‐affirmation is “an act that manifests one’s adequacy and thus affirms one’s sense of global self‐integrity” (Cohen & Sherman, 2014, p. 337). For example, when faced with information about the health risks of smoking (e.g., exposure to a warning on a cigarette pack), smokers may cope by reminding themselves that they are talented musicians or reliable friends or by bringing to mind a recent act of generosity that typifies important personal values. By reminding themselves that they are morally or adaptively adequate, despite smoking, an effective self‐affirmation will offset the self‐integrity threat entailed in the warning. Thus, self‐affirmation has the potential to preserve self‐integrity. In everyday life self‐affirmation can be realized in many and diverse ways. In experimental work using self‐affirmation theory, however, self‐affirmation is often operationalized as a value affirmation, “an activity that provides the opportunity to assert the importance of core values, often through writing exercises” (Cohen & Sherman, 2014, p. 337). For example, participants may be asked to write a brief essay about their most important personal value and occasions on which they manifest it (e.g., Harris & Napper, 2005). Steele’s critical theoretical insight was that, in responding to psychological threats, people are concerned primarily with their general or global sense of adequacy, rather than with addressing the specific issue that triggered the threat (the focal threat). For instance, although smokers may indeed cope with the threat triggered by a health warning by quitting or by elaborating cognitions that reduce their dissonance without changing behavior, they may also do so by thoughts or deeds that are completely unrelated to smoking, such as through a value affirmation (e.g., by thinking about a recent act of generosity and the personal importance of that value). As a result, self‐affirmation can ameliorate threats in a domain (e.g., health) that is different from the domain in which the self‐affirmation occurs (e.g., generosity). That is, effective affirmations may involve thinking about the self or acting in a way that is unrelated to the focal threat. Self‐affirmations can be used prospectively, to ward off an expected psychological threat, or retrospectively, to ameliorate an experienced threat. To be effective, the self‐affirmation must be at least as important to the self as is the focal threat. It is important to note that self‐affirmation does not eliminate the threat; instead, it serves to contain or contextualize it. That is, a threat that seemed potent seems less so once, through self‐affirmation, individuals have reassured themselves that they are competent or morally worthy. Most of the research on self‐affirmation—especially in the context of health—has focused on a consequence of self‐affirmation that Steele (1988) discussed only relatively briefly, namely, the possibility that self‐affirmation may promote less defensive and more open‐ minded responding to threats. That is, the self‐affirmed person should feel more secure and better able to confront a given threat because self‐integrity has been bolstered by the self‐ affirmation. For example, smokers who have been reassured of their competence or moral worthiness through self‐affirmation should experience less psychological threat when encountering a reminder of the foolhardiness of smoking and may be better able to face up to the need to quit. Thus, self‐affirmation has the potential not only to preserve self‐integrity but also to encourage adaptive change. This element of the theory is of obvious interest to those working on problems such as defensive resistance to potentially useful but psychologically threatening information (e.g., critical feedback, uncongenial risk information), not least because the theory suggests how resistance may be reduced, by providing the opportunity to self‐affirm.

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Self‐Affirmation and Health Two broad strands of research have addressed self‐affirmation and health. One strand applies self‐affirmation theory to understanding and ameliorating defensive resistance to information about health risks and health status. The other strand, which has received less empirical attention, examines whether and how self‐affirmation may ameliorate stress reactions or bolster well‐being.

Self‐Affirmation and Defensive Resistance to Health Information Messages targeting unhealthy behavior and promoting healthier behavior are ubiquitous. They are an integral part of health promotion practice and come in many forms and through various media. However, it is not uncommon to find that members of the targeted audience engage in strategies to avoid, denigrate, or otherwise downplay the information and its implications for their behavior. As outlined above, the assumption underlying the extension of self‐affirmation theory to research on message resistance is that such messages, at least in part, challenge self‐integrity and therefore constitute a psychological threat. The benefits of this analysis in terms of self‐affirmation theory are twofold. First, the analysis extends the account of the processes underlying message resistance to include psychological and physical threat. Second, it offers a practical solution, as self‐affirming should reduce the psychological threat induced by the information and therefore lessen the need to engage in defensive resistance. The research on self‐affirmation and message resistance has been primarily experimental. The typical experiment involves the random assignment of participants to experimental condition (self‐affirmation or a control activity), followed by the presentation of information targeting a specific health‐promoting or health‐damaging behavior and then the completion of an immediate set of outcome (dependent) measures assessing message uptake and readiness to change behavior. Additional elements may include some form of manipulation check, the assessment of potential moderators and mediators, and follow‐up testing for effects on behavior subsequent to the experimental session. Space precludes exhaustive coverage of the many such studies here. Instead we attempt to illustrate the main findings, characteristics, and themes of this literature and to highlight some of the issues facing researchers (see Suggested Reading for more extensive coverage). Overall, evidence suggests that self‐affirmation can influence the cognitive, affective, and behavioral responses to health‐related information, both immediately and at follow‐up, and in the contexts of either health‐damaging (alcohol consumption, cigarette smoking, unsafe sexual activity, consumption of mercury in oily fish, and doping in sport) or health‐promoting (fruit and vegetable consumption, physical activity, dental flossing, sun protection, and screening for diseases) behaviors. Meta‐analyses (see Suggested Reading) have reported small but reliable effects of self‐affirmation manipulations on health‐related outcomes comparable in magnitude to those obtained in meta‐analyses of other health behavior change interventions. The health risk information used in these studies has been predominantly textual and has tended to emulate a typical health leaflet or brochure, but studies have also found positive effects of self‐affirmation manipulations on other types of material (e.g., on‐pack cigarette warning labels; Harris, Mayle, Mabbott, & Napper, 2007; Kessels, Harris, Ruiter, & Klein, 2016). Measures have been predominantly self‐reported and have included measures of brain function (e.g. via fMRI; Falk et al., 2015); attention allocation (e.g. via eye tracking; Kessels et al., 2016); information avoidance (Howell & Shepperd, 2016); objective indices of b ­ ehavior,

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such as weight loss (Logel & Cohen, 2012) and activity recorded on pedometers (Falk et al., 2015); and measures indicating improved treatment adherence in hemodialysis patients (e.g. Wileman et al., 2016). Effects of self‐affirmation manipulations or interventions have been detected at follow‐up months later (Falk et al., 2015; Harris et al., 2014; Harris & Napper, 2005; Logel & Cohen, 2012). However, relatively little theoretical attention has been given to the role that self‐affirmation may play at later stages in the behavior change process (see Suggested Reading), albeit theoretical attention has been paid to explaining how such a relatively brief intervention may have enduring effects in other contexts (see Cohen & Sherman, 2014). Although researchers have proposed numerous potential moderators of self‐affirmation effects, the published meta‐analyses of self‐affirmation and health found limited evidence for moderation by the variables they tested (see Suggested Reading). There is a need for more tests of potential moderators in order to advance understanding of the boundary conditions surrounding the positive effects of self‐affirmation on health‐related outcomes. Theoretically it makes sense for self‐affirmation effects to be moderated by variables that affect the experience of psychological threat, such that those who experience more threat should benefit most. For example, there is evidence that the impact of self‐affirmation on the response to health‐ related information can be moderated by risk level, with those at higher risk of some negative health outcome as a result of their behavior being more responsive to the message after self‐ affirming than those at lower risk (e.g., Harris & Napper, 2005). However, not all studies that have tested for moderation by risk level have found it (see Schüz, Cooke, Schüz, & van Koningsbruggen, 2017). Support for mediating variables has also been mixed. In all likelihood, self‐affirmation is multiply mediated (Cohen & Sherman, 2014). In relation to health, it may be important to differentiate the mediators (and moderators) of message acceptance processes from the mediators (and moderators) of the processes involved in subsequent health behavior change (Harris & Epton, 2010). As the literature matures, negative findings are also being published, notably in the area of binge drinking among students (e.g. Norman & Wrona‐Clarke, 2015). It is also not uncommon to find positive effects of self‐affirmation manipulations on immediate outcomes but not on behavior at follow‐up. It is not clear what is reasonable to expect in terms of the longer‐term, and especially the behavioral, impact of such a relatively brief intervention. However, in the educational context there have been several findings of significant long‐term effects of self‐affirmation interventions, suggesting that the effects can endure (Cohen & Sherman, 2014).

Self‐Affirmation, Stress, and Well‐Being Many stressors in the modern world can be construed as threats to self‐worth or self‐integrity (Sherman & Cohen, 2006). For example, losing one’s job or failing a test may threaten one’s view of the self as valued or capable. Self‐affirmation manipulations may help to remind people of the bigger picture in such situations, reassuring them that they have integrity and broadening their focus beyond the threat (Cohen & Sherman, 2014). Self‐affirmation is thought to boost perceptions of the psychological resources available for coping, thereby allowing individuals to better gain perspective, with the result that potential stressors may have less of an influence on their mental and physical well‐being (Cohen & Sherman, 2014). There is evidence that self‐affirmation can reduce adverse psychological and physiological responses to stressors, both experimental and naturalistic (Cohen & Sherman, 2014). For

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example, participants who were self‐affirmed before giving a speech and completing a mental arithmetic task in front of a hostile audience did not experience increases in cortisol (Creswell et al., 2005); similarly, self‐affirmed university students taking midterm examinations displayed reduced epinephrine responses and reported less worry compared with their non‐affirmed counterparts (Sherman, Bunyan, Creswell, & Jaremka, 2009). Research attention has also been paid to whether self‐affirmation might benefit well‐being. Studies are few, but findings suggest that self‐affirmation may have positive implications for both eudaimonic and hedonic well‐being (see Howell, 2016). However, it should be noted that studies to date have tested for a direct impact of self‐affirmation on well‐being in the absence of an explicit threat. Although there are precedents for this in the broader literature on self‐affirmation and health (e.g. Logel & Cohen, 2012, showed positive effects of self‐­ affirmation on weight loss without explicitly introducing a threat), it is possible that the effects of self‐affirmation on well‐being may differ when an explicit threat is in place.

Practical Developments and Issues Naturally, given the evidence that self‐affirmation can reduce information resistance and avoidance, mitigate the response to stressors, and may even bolster well‐being, researchers and practitioners have been keened to examine its role in interventions (see Schüz et al., 2017), including among various patient groups (e.g., Wileman et al., 2016), and its implications for public policy (Ehret & Sherman, 2014). To date intervention research has produced some encouraging findings. However, caution should be exercised when employing self‐affirmation as an intervention given that its mechanism of action is poorly understood, as are its limits and disadvantages. More generally, there is evidence that self‐affirmation manipulations may produce distinctive and even detrimental effects on responses among those experiencing low or no threat (e.g., Briñol, Petty, Gallardo, & DeMarree, 2007). In mass communications, such individuals will inevitably form part of the audience. Another potential problem for interventions is that being aware of the effects of self‐­ affirmation can attenuate those effects; however, the effect of awareness seems to be eliminated when participants freely choose to self‐affirm (Silverman, Logel, & Cohen, 2013). Setting aside such concerns, the application of self‐affirmation in many contexts outside the laboratory requires the development of reliable ways of self‐affirming that are brief and easily implemented. Few such self‐affirmation manipulations are currently available, and all require further testing. One practical benefit of self‐affirmation is that it can be used with existing health promotion materials. However, whether self‐affirmation will boost persuasion is dependent on the materials themselves. Self‐affirmation is hypothesized to work by promoting less defensiveness and therefore greater readiness to engage with information open‐mindedly. It is not in itself a technique to promote persuasion. So, whether people are persuaded depends on the quality of the material, such as the strength of its arguments.

Individual Differences Although the research to date has mainly used experimental manipulations of self‐affirmation, there has been some research on health testing self‐affirmation as an individual difference. Harris and colleagues (Harris et al., 2019) have developed a measure of individual differences

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in self‐affirmation, items from which have been associated with beneficial outcomes among cancer survivors in a nationally representative US sample (Taber, Klein, Ferrer, Kent, & Harris, 2016) and with reduced information avoidance tendencies in a sample recruited to an NIH genome sequencing study (ClinSeq®) (Taber et al., 2015). A measure developed by Pietersma and Dijkstra (2011) was related positively to perceptions of the negative consequences of smoking in samples of smokers.

Theoretical Developments and Issues Crocker and colleagues (e.g. Crocker, Niiya, & Mischkowski, 2008) have argued that value affirmation reduces defensiveness by inducing positive other‐directed feelings, such as love and connectedness, and thus promotes self‐transcendence rather than self‐integrity. In contrast, Lindsay and Creswell (2014) have proposed that self‐affirmation boosts self‐directed compassion rather than self‐transcendence. Both the self‐transcendence and self‐directed compassion accounts have some empirical support but require further testing. Critcher and Dunning (2015) have claimed (with supportive evidence) that self‐affirmation expands the working self‐concept, producing a broader perspective that reduces the impact of the threat. Other work points to the effects of self‐affirmation on neural reward mechanisms (e.g., Falk et al., 2015), executive function (Harris, Harris, & Miles, 2017), and goal pursuit (e.g., Vohs, Park, & Schmeichel, 2013). Critcher, Dunning, and Armor (2010) have shown that self‐affirmation reduces defensiveness as long as a defensive response to threat has not been initiated, suggesting that self‐affirmation after experiencing a threat may not always be effective. Sherman (2013) has drawn on elements of the above theorizing and findings to develop a three‐component model of the effects of self‐affirmation in which self‐affirmation (a) boosts resources, (b) broadens the perspective with which people view information and events in their lives, and (c) uncouples the threat from the self, thereby reducing its impact and promoting more open‐minded appraisal. Harris and Epton (2010) argue that there are two broad foci for theoretical development in relation to health risk information, explaining the effects of self‐affirmation on message acceptance and on behavior change processes. Although it remains possible that there are features peculiar to health, or even to specific health threats, the application of self‐affirmation theory to message acceptance is potentially a straightforward extension of the theory to a domain in which participants are exposed to information that constitutes a psychological threat, and the mechanisms by which self‐affirmation promotes more open‐minded threat appraisal should be common across domains. The application of self‐affirmation theory to behavior change processes, however, is less straightforward. For example, should the effects of self‐affirmation on motivation to change and subsequent behavior change be fully mediated by changes in known predictors of intentions and behavior—induced by persuasion following exposure to the message—or are there other effects that self‐affirmation induces in parallel, including direct effects on outcomes? Purchase on this question is in part hindered by features of study designs to date, such as the tendency to not include a no‐information control condition, which undermines the ability to differentiate direct from indirect effects of the manipulation. More broadly, it is important to note that most of the research involving self‐affirmation has tested the reflective path to behavior change (e.g., mediated by intentions), and little research attention has been paid to its impact on automatic processes.

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Directions for Future Research Research on self‐affirmation and health, especially as applied to responses to health risk information, is burgeoning. However, there are limitations to the current literature. For example, there is a need to broaden the range of manipulations employed and populations sampled and to increase the sample sizes routinely used. There is also inconsistency from study to study concerning what is measured, how it is measured, and which variables are affected by the manipulation, limiting the ability to integrate findings. More broadly, theoretical development, clarification, and integration with other related ideas would also be welcome. For example, greater attention could be paid to using relevant theories of attitude change, persuasion, and behavior change, when choosing measures and when constructing persuasive materials for self‐affirmation studies. Work also needs to be done to better connect self‐affirmation theory with other theories of self and identity and to clarify and operationalize the central theoretical processes. Nevertheless, as the research reviewed here indicates, self‐affirmation can reduce information resistance and avoidance, foster health behavior change, and mitigate the response to stressors. It may even bolster well‐being. There are many promising avenues, both theoretical and practical, waiting to be explored.

Author Biographies Peter R. Harris is a social and health psychologist and professor of psychology at the University of Sussex, UK. His principal research interests lie in the cognitive, emotional, and behavioral response to risk, especially health risks. A founder member of Sussex University’s Self‐ Affirmation Research Group, to date he has published over 30 papers and articles on self‐affirmation, particularly as applied to health. Donna C. Jessop is a senior lecturer at the University of Sussex. Her research interests broadly lie in the fields of social and health psychology, with a particular focus on the design of interventions to promote health protective behaviors and well‐being. She has conducted research applying self‐affirmation theory to a range of health outcomes, including health‐ related behaviors, stress responses, and well‐being. Donna is a founder member of Sussex University’s Self‐Affirmation Research Group. Philine S. Harris is a research fellow at the University of Southampton, UK, where she develops health interventions aimed at supporting patients’ self‐care. Her PhD research (conducted at the University of Sussex with Peter Harris) investigated the immediate cognitive and emotional effects of self‐affirmation, in particular its effects on the use of executive functioning resources and on mood.

References Briñol, P., Petty, R. E., Gallardo, I., & DeMarree, K. G. (2007). The effect of self‐affirmation in nonthreatening persuasion domains: Timing affects the process. Personality and Social Psychology Bulletin, 33, 1533–1546. doi:10.1177/0146167207306282 Cohen, G. L., & Sherman, D. K. (2014). The psychology of change: Self‐affirmation and social psychological intervention. Annual Review of Psychology, 65, 333–371. doi:10.1146/annurev‐ psych‐010213‐115137

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Creswell, J. D., Welch, W. T., Taylor, S. E., Sherman, D. K., Gruenewald, T. L., & Mann, T. (2005). Affirmation of personal values buffers neuroendocrine and psychological stress responses. Psychological Science, 16, 846–851. doi:10.1111/j.1467‐9280.2005.01624.x Critcher, C. R., & Dunning, D. (2015). Self‐affirmations provide a broader perspective on self‐threat. Personality and Social Psychology Bulletin, 41, 3–18. doi:10.1177/0146167214554956 Critcher, C. R., Dunning, D., & Armor, D. A. (2010). When self‐affirmations reduce defensiveness: Timing is key. Personality and Social Psychology Bulletin, 36, 947–959. doi:10.1177/0146167 210369557 Crocker, J., Niiya, Y., & Mischkowski, D. (2008). Why does writing about important values reduce defensiveness? Self‐affirmation and the role of positive other‐directed feelings. Psychological Science, 19, 740–747. doi:10.1111/j.1467‐9280.2008.02150.x Ehret, P. J., & Sherman, D. K. (2014). Public policy and health: A self‐affirmation perspective. Policy Insights from the Behavioral and Brain Sciences, 1, 222–230. doi:10.1177/2372732214549472 Falk, E. B., O’Donnell, M. B., Cascio, C. N., Tinney, F., Kang, Y., Lieberman, M. D., … Strecher, V. J. (2015). Self‐affirmation alters the brain’s response to health messages and subsequent behavior change. Proceedings of the National Academy of Sciences, 112, 1977–1982. doi:10.1073/ pnas.1500247112 Harris, P. R., Brearley, I., Sheeran, P., Barker, M., Klein, W. M., Creswell, J. D., … Bond, R. (2014). Combining self‐affirmation with implementation intentions to promote fruit and vegetable consumption. Health Psychology, 33, 729–736. doi:10.1037/hea0000065 Harris, P. R., & Epton, T. (2010). The impact of self‐affirmation on health‐related cognition and health behaviour: Issues and prospects. Social and Personality Psychology Compass, 4, 439–454. doi:10.1111/ j.1751‐9004.2010.00270.x Harris, P. R., Griffin, D. W., Napper, L. E., Bond, R., Schüz, B., Stride, C., & Brearley, I. (2019). Individual differences in self‐affirmation: Distinguishing self‐affirmation from positive self‐regard. Self and Identity, 18(6), 589–630. Harris, P. R., Mayle, K., Mabbott, L., & Napper, L. (2007). Self‐affirmation reduces smokers’ defensiveness to graphic on‐pack cigarette warning labels. Health Psychology, 26, 437–446. doi:10.1037/0278‐6133.26.4.437 Harris, P. R., & Napper, L. (2005). Self‐affirmation and the biased processing of threatening health‐risk information. Personality and Social Psychology Bulletin, 31, 1250–1263. doi:10.1177/0146167 205274694 Harris, P. S., Harris, P. R., & Miles, E. (2017). Self‐affirmation improves performance on tasks related to executive functioning. Journal of Experimental Social Psychology, 70, 281–285. doi:10.1016/j. jesp.2016.11.011 Howell, A. J. (2016). Self‐affirmation theory and the science of well‐being. Journal of Happiness Studies. doi:10.1007/s10902‐016‐9713‐5 Howell, J. L., & Shepperd, J. A. (2016). Social exclusion, self‐affirmation, and health information avoidance. Journal of Experimental Social Psychology. doi:10.1016/j.jesp.2016.05.005 Kessels, L. T., Harris, P. R., Ruiter, R. A., & Klein, W. M. (2016). Attentional effects of self‐affirmation in response to graphic antismoking images. Health Psychology, 35, 891–897. doi:10.1037/ hea0000366 Lindsay, E. K., & Creswell, J. D. (2014). Helping the self help others: Self‐affirmation increases self‐ compassion and pro‐social behaviors. Frontiers in Psychology, 5, 421. doi:10.3389/fpsyg. 2014.00421 Logel, C., & Cohen, G. L. (2012). The role of the self in physical health testing the effect of a values‐ affirmation intervention on weight loss. Psychological Science, 23, 53–55. doi:10.1177/09567976 11421936 Norman, P., & Wrona‐Clarke, A. (2015). Combining self‐affirmation and implementation intentions to reduce heavy episodic drinking in university students. Psychology of Addictive Behaviors, 30, 434– 441. doi:10.1037/adb0000144

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Pietersma, S., & Dijkstra, A. (2011). Do behavioural health intentions engender health behaviour change? A study on the moderating role of self‐affirmation on actual fruit intake versus vegetable intake. British Journal of Health Psychology, 16, 815–827. doi:10.1111/j.2044‐8287.2011.02018.x Schüz, B., Cooke, R., Schüz, N., & van Koningsbruggen, G. M. (2017). Self‐affirmation interventions to change health behaviors. In L. Little, E. Sillence, & A. Joinson (Eds.), Behaviour change research and theory: Psychological and technological perspectives (pp. 87–114). San Diego, CA: Elsevier. Sherman, D. K. (2013). Self‐affirmation: Understanding the effects. Social and Personality Psychology Compass, 7, 834–845. doi:10.1111/spc3.12072 Sherman, D. K., Bunyan, D. P., Creswell, J. D., & Jaremka, L. M. (2009). Psychological vulnerability and stress: The effects of self‐affirmation on sympathetic nervous system responses to naturalistic stressors. Health Psychology, 28, 554–562. doi:10.1037/a0014663 Sherman, D. K., & Cohen, G. L. (2006). The psychology of self‐defense: Self‐affirmation theory. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 38, pp. 183–242). Amsterdam, The Netherlands: Elsevier. doi:10.1016/S0065‐2601(06)38004‐5 Silverman, A., Logel, C., & Cohen, G. L. (2013). Self‐affirmation as a deliberate coping strategy: The moderating role of choice. Journal of Experimental Social Psychology, 49, 93–98. doi:10.1016/j. jesp.2012.08.005 Steele, C. M. (1988). The psychology of self‐affirmation: Sustaining the integrity of the self. In L. Berkowitz (Ed.), Advances in experimental social psychology, 21 (pp. 261–302). New York, NY: Academic Press. Taber, J. M., Klein, W. M., Ferrer, R. A., Kent, E. E., & Harris, P. R. (2016). Optimism and spontaneous self‐affirmation are associated with lower likelihood of cognitive impairment and greater positive affect among cancer survivors. Annals of Behavioral Medicine, 50, 198–209. doi:10.1007/ s12160‐015‐9745‐9 Taber, J. M., Klein, W. M., Ferrer, R. A., Lewis, K. L., Harris, P. R., Shepperd, J. A., & Biesecker, L. G. (2015). Information avoidance tendencies, threat management resources, and interest in genetic sequencing feedback. Annals of Behavioral Medicine, 49, 616–621. doi:10.1007/s12160‐014‐9679‐7 Vohs, K. D., Park, J. K., & Schmeichel, B. J. (2013). Self‐affirmation can enable goal disengagement. Journal of Personality and Social Psychology, 104, 14–27. doi:10.1037/a0030478 Wileman, V., Chilcot, J., Armitage, C. J., Farrington, K., Wellsted, D. M., Norton, S., … Almond, M. (2016). Evidence of improved fluid management in patients receiving haemodialysis following a self‐affirmation theory‐based intervention: A randomised controlled trial. Psychology & Health, 31, 100–114. doi:10.1080/08870446.2015.1073729

Suggested Reading Epton, T., Harris, P. R., Kane, R., van Koningsbruggen, G. M., & Sheeran, P. (2015). The impact of self‐affirmation on health‐behavior change: A meta‐analysis. Health Psychology, 34, 187–196. doi:10.1037/hea0000116 Harris, P. R. (2011). Self‐affirmation and the self‐regulation of health behavior change. Self and Identity, 10, 304–314. doi:10.1080/15298868.2010.517963 Sweeney, A. M., & Moyer, A. (2015). Self‐affirmation and responses to health messages: A meta‐analysis on intentions and behavior. Health Psychology, 34, 149–159. doi:10.1037/hea0000110 van Koningsbruggen, G. M., Miles, E., & Harris, P. R. (2018). Self‐affirmation and self‐control: Counteracting defensive processing of health information and facilitating health behavior change. In D. Ridder, M. Adriaanse, & K. Fujita (Eds.), Handbook of self‐control in health and wellbeing (pp. 495–507). Abingdon‐on‐Thames, UK: Routledge/Taylor & Francis Group. van’t Riet, J., & Ruiter, R. A. (2013). Defensive reactions to health‐promoting information: An overview and implications for future research. Health Psychology Review, 7(sup1), S104–S136. doi:10.1080/ 17437199.2011.606782

Self‐Awareness and Health Melissa Spina and Jamie Arndt Psychology Department, University of Missouri System, Columbia, MO, USA

Introduction In the early 1970s, Shelley Duval and Robert Wicklund (1972; see Suggested Reading) formally introduced the modern psychological world to the concept of self‐awareness. With their presentation of objective self‐awareness theory, they explained how people can regard the self as the agent of experience or as the object of attention, and how the latter leads people to reduce or avoid discrepancies between their current state and their attitudes, standards, and goals. This process is now widely recognized as a pivotal aspect of self‐regulation. Thus, although not always credited as such, the theory is the conceptual parent of many self‐regulatory models. This and other contributions mark the concept of self‐awareness as having a profound influence on understanding of human social behavior that notably extends into the field of health psychology. This entry offers a brief overview of conceptualizations of self‐ awareness and their impact on understanding health and health risk behavior.

Objective Self‐Awareness Theory and Self‐Regulation According to objective self‐awareness theory (Duval & Wicklund, 1972), an individual’s conscious attention can be either focused outward on the environment where the self is the subjective agent of experience, or it can be focused inward with the self regarded as the object of attention. Holding the self as an object of attention initiates self‐evaluative processes that help people navigate their social environments with desired behavior. The process of self‐evaluation is such that when self‐aware, people are motivated to match their behavior and attitudes with the standards they maintain for themselves. When an individual observes that she/he is meeting these standards, the individual experiences positive affect and a general tendency to continue this self‐focused state. However, when a discrepancy is detected, this self‐reflective process has the potential to arouse negative affect. These negative feelings then motivate

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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efforts to reduce the discrepancy between the individual’s current state and the standard the individual holds for the self (e.g., changing the standard or changing behavior to meet said standard) or to avoid the self‐focused state (Gibbons & Wicklund, 1976). To illustrate how these ideas might work, consider a person who wants to maintain a healthy diet and is approaching the drive‐through window at a fast‐food restaurant. Will the person order the mega‐burger or the grilled chicken? The theory suggests that people are more likely to act in accordance with their value of maintaining a healthy diet by ordering the chicken sandwich if they are self‐aware. Further, if they do so, they will experience positive feelings as a result of matching their behavior of ordering grilled chicken with their goal of eating well. But if they order the mega‐burger and are then made self‐aware, the awareness of this discrepancy will increase negative affect. Faced with this situation, people may adjust their standard (e.g., “it is not that important to eat well”), their behavior (e.g., “I will exchange the burger for the chicken”), or try to avoid being self‐aware and thus cognizant of the discrepancy. Over the years, research has supported the core hypotheses of self‐awareness induced discrepancy reduction and avoidance of self‐focused attention in a variety of ways (see Silvia & Duval, 2001; see Suggested Reading). Research has also shed light on the conditions that motivate people to reduce discrepancies or avoid self‐focused attention. For example, people will try to reduce a discrepancy to the extent that their rate of progress in reducing said discrepancy is sufficient relative to the degree of the problem (e.g., if our health conscious friend thinks eating well is important and feels like she/he is making progress toward maintaining a healthy diet); otherwise they will avoid self‐focused attention. The original theory has also inspired a number of extensions, refinements, and alternatives. The most generative has been Carver and Scheier’s (1981) self‐regulatory theory. From this perspective, if people perceive their ability to reduce the discrepancy as high (e.g., our friend thinks she/he can buy, cook, and eat healthy food), they engage in discrepancy reduction behavior. However, if people perceive a low likelihood of reducing the discrepancy (e.g., our friend does not think she/he will be able to actually eat healthy food), they experience negative affect which then propels them to avoid self‐focused attention. Carver and Scheier’s (1981) theory also distinguished between focusing on “private” or “public” aspects of the self and, importantly, more explicitly considered the self‐evaluative process to exist within a hierarchy of goal‐directed self‐regulatory systems. For example, the goal of eating well may operate at an intermediate level, serving both the broader more abstract goal of being a healthy person and prescribing the more specific goal of eating more fruits and vegetables. This conceptualization led to further insights into the dynamics of self‐regulation. This includes findings indicating that when people do not meet the standards of a certain representation (e.g., “I want to eat well”), they might either shift down to a lower level representation of the goal (e.g., “In the future I will eat more vegetables”) on the “behavioral representation ladder” or might disengage and avoid self‐focused attention (Carver & Scheier, 1981). While other models of self‐awareness have been proposed over the years (see Silvia & Gendolla, 2001), the conceptualization of self‐awareness as initiating a self‐regulatory process by motivating either discrepancy reducing behavior or self‐focused avoidance is the most well‐ supported and widely accepted view. Across the literature, researchers have often used the terms self‐awareness and self‐focus interchangeably. The first experimental approach to inducing such a state was to present participants with a (unexpected) mirror in the research environment, thereby literally rendering the self the object of attention (see Duval & Wicklund, 1972). Over the years, a number of other methods have been used (e.g., listening to a tape ‐recording of one’s voice; see Silvia & Duval, 2001). Research has also developed measures of self‐consciousness, often used to describe a

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dispositional state of self‐awareness to which certain individuals might be more or less disposed (Fenigstein, Scheier, & Buss, 1975). Fenigstein and colleagues also added the terms private and public self‐consciousness to the lexicon. Private self‐consciousness connotes an inclination to be highly aware of one’s personal values and behavior, whereas public self‐consciousness describes a tendency to be concerned with the opinions of others and thus to seek approval and avoid rejection from others. These concepts have implications for explaining when an individual might be more inclined to experience discrepancies between their own behavior and their self‐­standards and how concerned one might be about others’ perceptions of the discrepancy.

Research on Self‐Awareness and Health Self‐Awareness and Perceptions of Physical Symptoms The original objective self‐awareness theory makes no claims about the effect of self‐focused attention on how accurately people perceive themselves. Yet such an effect has been implicated to varying degrees by different theories of self‐focused attention (see Silvia & Gendolla, 2001). Of relevance to health, this idea inspired research on the effects of self‐awareness on people’s perceptions of their physical experiences and symptoms. For example, in a series of studies, Pennebaker and Lightner (1980) examined the impact of competing sources of attentional cues on physical performance and perceptions. This research discovered that individuals who engaged in physical exercise in environments containing little external distraction were more aware of their internal states (e.g., enhanced volume of breathing) than were individuals exercising in more stimulating environments (e.g., with distracting sounds). Lacking external distraction also led to a greater awareness of the physical symptoms of exercising (e.g., fatigue). Additional studies found that individuals who ran cross country as opposed to on a track typically ran faster. Presumably this occurred because less external stimuli were present on the track, and thus participants were more focused on the performance impairing awareness of their own physical experiences. This work has implications for understanding psychological factors that can influence both the engagement in health‐relevant behavior and for self‐perceptions of health‐relevant symptoms. With regard to the former, one possibility is that under certain conditions self‐awareness might interfere with people’s motivations to engage in a healthy activity (e.g., exercising). If the activity in question has the potential to bring about negative physical experiences (e.g., soreness and fatigue), especially when there is limited experiential stimulation and thus greater internal awareness, people may be less motivated to engage in the activity even if the activity is beneficial to physical well‐being. The effect of self‐awareness on perceptions of physical states may also be helpful for understanding symptom reporting. Over the years research has suggested that interoception, or the perception of bodily cues, and the source to which these cues are attributed are important factors in hypochondriasis (e.g., Mechanic, 1972). The general idea is that heightened levels of interoception, as a facet of elevated self‐awareness, lead people to amplify and misinterpret bodily sensations as reflecting symptoms of physical disease. This amplification and misinterpretation may be especially likely when heightened self‐awareness is combined with negative feelings. In support of this joint impact hypothesis, Gendolla, Abele, Andrei, Spurk, and Richter (2005) found that elevated levels of negative affect coupled with high self‐focused attention increased reporting of greater intensity somatic symptoms. Thus, overreporting of physical ailments as a result of enhanced self‐awareness and negative mood could have

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i­mplications for inaccurate symptomatic self‐report, which in turn might contribute to inaccurate diagnoses and other health problems. For example, more recent research has focused on the role of interoceptive sensitivity in anxiety disorders (Domschke, Stevens, Pfleiderer, & Gerlach, 2010) and medication nonadherence (Alcántara et al., 2014).

Risky Health Behavior as Escape From Self‐Awareness Recall that different perspectives converge to posit that self‐awareness initiates a self‐­regulatory process of comparing one’s behavior with one’s self‐standards. When these standards are not being met, the individual is motivated to reduce this discrepancy between their behavior and self‐standards or to avoid self‐focused attention. But what happens when the need to avoid self‐focused attention becomes chronic and is difficult to accomplish? This question has given rise to the general hypothesis that in these instances people might engage in maladaptive health behaviors that facilitate the avoidance of self‐focused attention. This hypothesis was initially and perhaps most extensively examined in the context of alcohol consumption and binge eating, though conceptually similar analyses have also been directed to such domains as risky sex and drug use (McKirnan, Ostrow, & Hope, 1996) and self‐harm (Chapman, Gratz, & Brown, 2006).

Alcohol Consumption Excessive alcohol consumption as a mechanism to escape self‐awareness has been proposed in several models. Hull (1981) suggested that alcohol use helps to disrupt the cognitive processes that contribute to stress when people are self‐aware and confronted with an unfavorable self‐evaluation. A number of studies support this hypothesis. For example, Hull, Levenson, Young, and Sher (1983) demonstrated in a series of experiments that alcohol reduced self‐awareness and that this effect was especially strong in individuals high in private self‐consciousness. Additional studies showed that individuals high in private self‐consciousness consumed more alcohol when given failure feedback than individuals high in private self‐consciousness who were given success feedback, whereas people low in private self‐­ consciousness did not change their level of alcohol consumption as a function of the feedback given. Such studies suggest that for highly self‐conscious individuals, alcohol use can be motivated by a desire to reduce self‐focused attention in situations that reflect negatively on the self. The attention‐allocation model (an extension of alcohol myopia theory) offers a different perspective and argues that alcohol leads to a narrowing of attention to informational cues (e.g., “myopia”) that enable the individual to avoid anxiety and depressive affect (Steele & Josephs, 1990). In accord with these hypotheses, individuals who are intoxicated and experience an anxiety‐inducing situation are less likely to report anxiety when given a distracting cognitive task than intoxicated individuals who were not given the task (Steele & Josephs, 1988). This implies that for people who were performing a distracting task, alcohol facilitated a reduction in anxiety by narrowing the focus of their attention to the task at hand rather than on the potential for anxiety. However, individuals who were intoxicated but did not engage in a distracting task reported more anxiety than people who had consumed a placebo beverage, suggesting that without a distracting task, alcohol enhanced their focus on the anxiety experienced. Thus, the attention‐allocation model suggests that using alcohol as a means to escape self‐focused attention is primarily effective to the extent it enables one to narrow focus onto some other external cue unrelated to the self.

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Eating Disorders and Appearance Concerns A number of models have applied the general principle of escaping self‐awareness to understanding eating disorders. Heatherton and Baumeister (1991) begin by noting that individuals who overeat seem to suffer from high expectations for the self as well as sensitivity to the approval of others (i.e., private and public self‐consciousness). For individuals who maintain high standards for their physical appearance, failure to meet the standards they set for themselves can lead to the development of heightened levels of self‐awareness and a chronic state of negative affect (i.e., depression and anxiety). The escape model proposes that to escape these negative emotions related to the discrepancy between self‐standards and the reality of one’s physical appearance, people might (ironically) overeat in an attempt to narrow their attentional focus on the immediate environment and avoid thoughts about their own self‐values (Heatherton & Baumeister, 1991; see also Bardone‐Cone, Abramson, Vohs, Heatherton, & Joiner, 2006). Further, individuals with eating disorders are suggested to be plagued by concern for and preoccupation with others’ evaluations of themselves. As a result, they construct a socially appealing “false self” (Striegel‐Moore, Silberstein, & Rodin, 1993). Indeed, among individuals with bulimia nervosa, reported body dissatisfaction positively relates to their social anxiety, perceived fraudulence (or lack of true self), and public self‐consciousness (but not private; Striegel‐ Moore et al., 1993). Thus, for those who are consumed by thoughts of others’ perceptions of themselves, maintenance of a socially desirable “false self” to attain the approval of others could be an indication of a risk factor for an eating disorder (e.g., bulimia nervosa, anorexia). The aforementioned research suggests that when individuals have difficulty with meeting self‐standards (e.g., Heatherton & Baumeister, 1991) or are preoccupied with others’ approval (e.g., Striegel‐Moore et  al., 1993), they can engage in a variety of risky eating behaviors. However, it is important to recognize that the self‐regulatory processes instigated by self‐ awareness are not inherently harmful to health. Recall the example of the person interested in maintaining a healthy diet. The critical issue is whether the behavioral standard is appropriate and facilitates (or undermines) health enhancement or maintenance. Consider, for example, associations between self‐consciousness, concern for oral hygiene, and oral well‐being (Klages, Bruckner, & Zentner, 2004). If high self‐consciousness motivates dental care habits that contribute to oral hygiene, then the self‐awareness induced regulatory processes can be appreciated as having a productive effect on health.

Self‐Regulation and Goal‐Directed Health Behavior Recognizing that people may seek to escape aversive self‐awareness through health risk behavior is one way that understanding self‐awareness processes can contribute to understanding health behavior. But more broadly, the maintenance and change of health behavior often, if not inevitably, requires self‐regulatory steps of goal setting, goal striving, behavioral monitoring, and adjustment (Mann, de Ridder, & Fujita, 2013; see Suggested Reading). All of these steps either directly or indirectly depend on a capacity for self‐focused attention. Thus, self‐ awareness can be noted as playing a key role in various aspects of health behavior maintenance and change. One method of meeting one’s goals, for example, is through effortful inhibition, a process of self‐regulation whereby the individual attempts to elicit self‐control to avoid temptation (e.g., avoiding unhealthy food when it is available). Being successful in this endeavor depends on one’s capacity to consciously fight off temptation, to remain self‐aware of one’s current state relative to the goal one is trying to reach. Thus, when people experience a cognitive

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impairment (e.g., diminished cognitive resources), it is more likely they will act on impulsive processes that derail the pursuit of their goals. Indeed, research has shown that impairments in working memory and executive functioning relate to reduced self‐regulation capacity. For instance, people are more likely to overeat when they are experiencing decrements in working memory capacity (e.g., Hofmann, Gschwendner, Friese, Wiers, & Schmitt, 2008) and a high cognitive burden (e.g., divided attention on multiple tasks; Ward & Mann, 2000). In sum, although classic self‐awareness ideas are not always explicitly used in self‐regulatory research on health goal maintenance, the basic concepts and processes are foundational to theoretical efforts to understand and predict health behavior.

Conclusion Self‐awareness processes arise in people’s everyday involvement with their social environments, and these processes also are pervasively implicated within behavioral health domains. This entry has highlighted a few illustrative connections. These include the involvement of self‐awareness processes in people’s perceptions of physical symptoms, with implications for overreporting of negative bodily sensations. Self‐regulation can also motivate an escape from self‐focused attention through risky health behaviors (e.g., binge drinking, overeating) when people fail to meet self‐relevant standards or are overly vigilant about the evaluations others hold for them. Lastly, self‐awareness plays a general role in the self‐regulatory maintenance of health goals.

Author Biographies Melissa Spina received her BA in psychology from California State University, Fresno, in 2013 and began pursuing her PhD at the University of Missouri, Columbia, in 2013. Her research examines motivational theories generally, with an emphasis in terror management theory, and applies these theories to such areas as health decision making, prosociality, and environmentally friendly behaviors. Jamie Arndt received his BA from Skidmore College and his PhD from the University of Arizona in 1999. He is currently a professor in the Department of Psychological Sciences at the University of Missouri. His work focuses on the self, existential motivation, psychological defense, and health decision making, among other topics, with an increasing interest is moving basic social psychological ideas to translational health domains.

References Alcántara, C., Edmondson, D., Moise, N., Oyola, D., Hiti, D., & Kronish, I. M. (2014). Anxiety sensitivity and medication nonadherence in patients with uncontrolled hypertension. Journal of Psychosomatic Research, 77(4), 283–286. http://dx.doi.org/10.1016/j.jpsychores.2014.07.009 Bardone‐Cone, A. M., Abramson, L. Y., Vohs, K. D., Heatherton, T. F., & Joiner, T. E. (2006). Predicting bulimic symptoms: An interactive model of self‐efficacy, perfectionism, and perceived weight status. Behaviour Research and Therapy, 44(1), 27–42. http://dx.doi.org/10.1016/j. brat.2004.09.009 Carver, C. S., & Scheier, M. F. (1981). Attention and self‐regulation. New York, NY: Springer‐Verlag. http://dx.doi.org/10.1007/978‐1‐4612‐5887‐2

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Chapman, A. L., Gratz, K. L., & Brown, M. Z. (2006). Solving the puzzle of deliberate self‐harm: The experiential avoidance model. Behaviour Research and Therapy, 44(3), 371–394. http://dx.doi. org/10.1016/j.brat.2005.03.005 Domschke, K., Stevens, S., Pfleiderer, B., & Gerlach, A. L. (2010). Interoceptive sensitivity in anxiety and anxiety disorders: An overview and integration of neurobiological findings. Clinical Psychology Review, 30(1), 1–11. http://dx.doi.org/10.1016/j.cpr.2009.08.008 Duval, S., & Wicklund, R. A. (1972). A theory of objective self‐awareness. Oxford, England: Academic Press. Fenigstein, A., Scheier, M. F., & Buss, A. H. (1975). Public and private self‐consciousness: Assessment and theory. Journal of Consulting and Clinical Psychology, 43(4), 522–527. http://dx.doi. org/10.1037/h0076760 Gendolla, G. H., Abele, A. E., Andrei, A., Spurk, D., & Richter, M. (2005). Negative mood, self‐focused attention, and the experience of physical symptoms: The joint impact hypothesis. Emotion, 5(2), 131–144. http://dx.doi.org/10.1037/1528‐3542.5.2.131 Gibbons, F. X., & Wicklund, R. A. (1976). Selective exposure to self. Journal of Research in Personality, 10(1), 98–106. http://dx.doi.org/10.1016/0092‐6566(76)90087‐8 Heatherton, T. F., & Baumeister, R. F. (1991). Binge eating as escape from self‐awareness. Psychological Bulletin, 110(1), 86–108. http://dx.doi.org/10.1037/0033‐2909.110.1.86 Hofmann, W., Gschwendner, T., Friese, M., Wiers, R. W., & Schmitt, M. (2008). Working memory capacity and self‐regulatory behavior: Toward an individual differences perspective on behavior determination by automatic versus controlled processes. Journal of Personality and Social Psychology, 95(4), 962–977. http://dx.doi.org/10.1037/a0012705 Hull, J. G. (1981). A self‐awareness model of the causes and effects of alcohol consumption. Journal of Abnormal Psychology, 90(6), 586–600. http://dx.doi.org/10.1037/0021‐843X.90.6.586 Hull, J. G., Levenson, R. W., Young, R. D., & Sher, K. J. (1983). Self‐awareness‐reducing effects of alcohol consumption. Journal of Personality and Social Psychology, 44(3), 461–473. http://dx.doi. org/10.1037/0022‐3514.44.3.461 Klages, U., Bruckner, A., & Zentner, A. (2004). Dental aesthetics, self‐awareness, and oral health‐related quality of life in young adults. The European Journal of Orthodontics, 26(5), 507–514. http://dx. doi.org/10.1093/ejo/26.5.507 Mann, T., de Ridder, D., & Fujita, K. (2013). Self‐regulation of health behavior: Social psychological approaches to goal setting and goal striving. Health Psychology, 32(5), 487–498. http://dx.doi. org/10.1037/a0028533 McKirnan, D. J., Ostrow, D. G., & Hope, B. (1996). Sex, drugs and escape: A psychological model of HIV‐risk sexual behaviours. AIDS Care, 8(6), 655–670. http://dx.doi.org/10.1080/ 09540129650125371 Mechanic, D. (1972). Social psychological factors affecting the presentation of bodily complaints. New England Journal of Medicine, 286(2), 1132–1139. http://dx.doi.org/10.1056/NEJM 197205252862105 Pennebaker, J. W., & Lightner, J. M. (1980). Competition of internal and external information in an exercise setting. Journal of Personality and Social Psychology, 39(1), 165–174. http://dx.doi. org/10.1037/0022‐3514.39.1.165 Silvia, P. J., & Duval, T. S. (2001). Objective self‐awareness theory: Recent progress and enduring problems. Personality and Social Psychology Review, 5(3), 230–241. http://dx.doi.org/10.1207/ S15327957PSPR0503_4 Silvia, P. J., & Gendolla, G. H. (2001). On introspection and self‐perception: Does self‐focused attention enable accurate self‐knowledge? Review of General Psychology, 5(3), 241–269. http://dx.doi. org/10.1037/1089‐2680.5.3.241 Steele, C., & Josephs, R. (1988). Drinking your troubles away II: An attention‐allocation model of alcohol’s effect on psychological stress. Journal of Abnormal Psychology, 97(2), 196–205. http://dx.doi. org/10.1037/0021‐843X.97.2.196

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Steele, C. M., & Josephs, R. A. (1990). Alcohol myopia: Its prized and dangerous effects. American Psychologist, 45(8), 921–933. http://dx.doi.org/10.1037/0003‐066X.45.8.921 Striegel‐Moore, R. H., Silberstein, L. R., & Rodin, J. (1993). The social self in bulimia nervosa: Public self‐consciousness, social anxiety, and perceived fraudulence. Journal of Abnormal Psychology, 102(2), 297–303. http://dx.doi.org/10.1037/0021‐843X.102.2.297 Ward, A., & Mann, T. (2000). Don’t mind if I do: Disinhibited eating under cognitive load. Journal of Personality and Social Psychology, 78(4), 753–763. http://dx.doi.org/10.1037/0022‐3514.78.4.753

Suggested Reading Carver, C. S., & Scheier, M. F. (1981). Attention and self‐regulation. New York, NY: Springer‐Verlag. http://dx.doi.org/10.1007/978‐1‐4612‐5887‐2 Duval, S., & Wicklund, R. A. (1972). A theory of objective self‐awareness. Oxford, England: Academic Press. Mann, T., de Ridder, D., & Fujita, K. (2013). Self‐regulation of health behavior: Social psychological approaches to goal setting and goal striving. Health Psychology, 32(5), 487–498. http://dx.doi. org/10.1037/a0028533 Silvia, P. J., & Duval, T. S. (2001). Objective self‐awareness theory: Recent progress and enduring problems. Personality and Social Psychology Review, 5(3), 230–241. http://dx.doi.org/10.1207/ S15327957PSPR0503_4

Self‐Efficacy and Health Lisa Marie Warner1 and Ralf Schwarzer1,2 Health Psychology, Freie Universität Berlin, Berlin, Germany Health Psychology, Australian Catholic University, Melbourne, Victoria, Australia 1

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Perceived self‐efficacy has been described as “the conviction that one can successfully execute the behavior required to produce the outcomes” (Bandura, 1977, p. 193) or the “can‐do cognition” (Schwarzer & Warner, 2013, p. 139). This optimistic belief in being capable of succeeding at novel or difficult tasks reflects a sense of control over unforeseen situations as well as challenging environmental demands by means of one’s own behavior (Bandura, 1997). Engaging in a healthy lifestyle is a challenging task for most people. Therefore, self‐efficacy is a construct of great importance to health psychology. Individuals with high self‐efficacy tend to see challenges and opportunities instead of problems and obstacles, choose more ambitious goals, and motivate themselves to attain these goals. They think in a more positive manner and are less stressed prior to challenging situations. If they are confronted with obstacles, people with high self‐efficacy show more perseverance and maintain their efforts over longer periods of time. In contrast, individuals low in self‐efficacy doubt their capabilities to reach their goals. They experience more self‐doubts and anxiety when confronted with situations they perceive as challenging and avoid such situations. Therefore, they cope less effectively and are more vulnerable to stress and depression. Self‐efficacy beliefs hence affect how people feel, think, and act (Bandura, 1997).

Concepts Distinct From Self‐Efficacy Beliefs Self‐efficacy beliefs do not necessarily reflect ability—they are an individual’s subjective impression of being able to succeed. Actual abilities do not need to be high to build self‐efficacy, as self‐efficacy can also generalize if related tasks have been mastered successfully (Bandura, 1977). Self‐efficacy beliefs are distinct from optimism. Optimism is the belief that one’s future will be prosperous and favorable. The causes for this positive outlook into the future need not lie within the person. They can lie, for example, in favorable circumstances, help by others, or

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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pure chance. Self‐efficacious persons, however, believe that they can shape their own future and attain desired outcomes by means of their own actions and decisions. Although the correlation between self‐efficacy and optimism is moderate to high (about.60), optimism can be considered a superordinate construct under which optimistic self‐beliefs, such as self‐efficacy, can be subsumed (Alarcon, Bowling, & Khazon, 2013). Whereas outcome expectancies refer to the perception of consequences of a behavior (e.g., better health, lower weight), self‐efficacy refers to personal control over the execution of a behavior. Both outcome expectancies and self‐efficacy, however, predict health behaviors well and play a major role in Bandura’s (1997) social cognitive theory as clearly distinct constructs. The self‐concept is regarded as being multidimensional, and it subsumes hierarchically organized cognitions people hold about themselves at different levels of concreteness. The self‐ concept is also associated with affective components such as self‐regard or self‐esteem. It is rather normative (developed in comparison to others) and stable, whereas self‐efficacy has been ­conceptualized as situation and task specific and malleable to new experiences. Therefore, self‐efficacy is a cognition that is more proximal to behavior. Whereas autonomy refers to the experience of choice and the ability to pursue activities that are intrinsically motivated, self‐efficacy refers to the belief in being able to pursue those actions that are necessary to successfully accomplish the chosen goals. Some studies find a strong ­association between autonomy and self‐efficacy in specific domains (Vieira & Grantham, 2011), whereas others show that they discriminate well (Warner, Ziegelmann, et al., 2011). Locus of control incorporates external causes for an outcome, such as luck or task difficulty, as well as internal causes, such as ability or effort. Despite the close connection (about.56) between having an internal locus of control and self‐efficacy, the two constructs are clearly distinct (Judge, Erez, Bono, & Thoresen, 2002). Individuals who think that the cause for an outcome is inherent within themselves do not necessarily believe that they can easily change this internal factor. The difference hence lies in the amenability of the internal cause. Self‐­ efficacy is operative, which means that it pertains to one’s future behavior, whereas internal control may refer to one’s personal characteristics in the past (e.g., I caused the accident because I am a poor driver). Whereas some researchers use the terms perceived behavioral control and self‐efficacy interchangeably, others prefer to distinguish them. To understand their conceptual difference, a closer look into typical scale items helps. Perceived behavioral control items usually include a statement about the ease or difficulty of a task, such as “It is easy/difficult for me to…” Self‐ efficacy items mostly incorporate the “can‐do” component, like “I am sure that I can do…” In contrast to related constructs, self‐efficacy hence always implies an internal attribution (one’s own capability is the cause), refers to future actions (prospective), and is proximal to the behavior to be executed (operative).

Self‐Manifesting Mechanism of Self‐Efficacy Through the processes of setting more ambitious goals, acting more determined to attain those goals, and putting more effort into overcoming obstacles, individuals with high levels of self‐efficacy create more opportunities for themselves to experience mastery (Bandura, 1997). Mastery experiences, in turn, increase self‐efficacy beliefs, maintaining a self‐­ manifesting mechanism of both. As individuals with low self‐efficacy believe less in being able

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to achieve what they want, they give up more easily and invest less effort—or as Bandura (1995, p. 4) describes it, “Disbelief in one’s capabilities creates its own behavioral validation.” Also, less self‐efficacious people tend to take more responsibility for their failure than for their success. The issue of attribution—the perceived reasons for success or failure—is very important to consider. Failure only decreases self‐efficacy if attributed to internal causes such as low capabilities. If failure is attributed externally, for instance, to low effort or overwhelming obstacles, it does not affect self‐efficacy. Likewise, success only increases self‐efficacy if attributed internally. If attributed to chance or help from others, successes do not increase self‐efficacy.

Measurement of Self‐Efficacy Dimensionality of Self‐Efficacy Measures Items to assess self‐efficacy can differ on three dimensions: level, strength, and generality. The level dimension describes the difficulty of a task. For tasks that can easily be performed, most people will have similarly high self‐efficacy beliefs, for example, “I am certain that I can eat five portions of vegetables.” To assess variation in levels of self‐efficacy, items need to include tasks with varying difficulty, such as “I am certain that I can eat five portions of vegetables every day,” or additional contextual challenges, such as “I am certain that I can eat five portions of vegetables every day, even on holidays.” For most health behaviors it is the self‐regulatory challenge that determines the level of self‐efficacy that is needed on a regularly basis and in the face of difficulties and temptations. These difficulties or barriers should previously be identified for each target group. The strength dimension refers to how robust the self‐efficacy belief is against failures. If failures can easily diminish a self‐efficacy belief, the belief was low on the strength dimension. Thus, the strength dimension is of special importance for the maintenance of effort and recovery after setbacks. The generality dimension of self‐efficacy spans the narrowness or broadness of a capability belief. If individuals have efficacy beliefs for very specific actions in specific situations but not for similar tasks in comparable situations, their generality dimension is low. Self‐efficacy was originally developed as a task‐ or domain‐specific construct (Bandura, 1997). Badura assumed that individuals hold very different self‐efficacy beliefs for different domains and situations on a more or less general level, like being good at math or at academic tasks in general. This view implies that items to assess self‐efficacy should be formulated at a level of specificity that corresponds with the specificity of the outcome behavior. Specifically assessed self‐efficacy beliefs were also found to predict specific outcomes best (Bandura, 1997). Some researchers, however, argue that experiencing failure and success can generalize to a global perception of one’s ability to deal with various challenges. Hence, the concept of general self‐efficacy was developed (Schwarzer & Jerusalem, 1995a; Sherer et al., 1982). It represents a global confidence in one’s abilities to cope with a wide range of demanding or novel situations. Self‐efficacy should preferably be measured with a psychometric scale consisting of several items. To construct self‐efficacy items, one can apply this scheme: “I am confident that I can perform action XYZ, even if the XYZ barrier occurs.” Sometimes the barrier does not have to be mentioned explicitly, for example, in domains where the execution of a task is quite challenging for an individual without additional barriers, like “I am confident that I can finish a

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marathon.” Detailed guidelines on the construction of self‐efficacy measures can be found in a comprehensive book chapter by Bandura (2006).

Measurement of General Self‐Efficacy Examples of items to measure general self‐efficacy would be “I am confident that I could deal efficiently with unexpected events,” and “If I am in trouble, I can usually think of a solution” (Schwarzer & Jerusalem, 1995a). Assessing a global measure of self‐efficacy can be useful in some research contexts, for example, if multiple health behaviors, multiple chronic diseases, or adjustment to traumatizing experiences are being studied.

Measurement of Domain‐Specific Self‐Efficacy There are measures for more or less specific self‐efficacy beliefs depending on the focus of research. To investigate a healthy diet, self‐efficacy items can, for example, include particular foods or refer to the self‐regulatory effort to resist temptations. For safer sexual behavior, different barriers are named within self‐efficacy items, such as the ability to use condoms correctly or suggest condoms to a new partner. Other examples of specific self‐efficacy scales are diabetes self‐care self‐efficacy, breast feeding self‐efficacy, or work self‐efficacy. Self‐efficacy scales, however, are not without criticism. Williams and Rhodes (2014) recently reactivated a debate about the confoundedness of self‐efficacy measures with outcome expectancies and motivation and suggest including “if I wanted to” at the end of each self‐efficacy items to control for some of the shared variance.

Measurement of Phase‐Specific Self‐Efficacy As the challenge for most health behavior is not the performance of the specific behavior but the initiation and maintenance of that behavior over longer periods of time, Scholz, Sniehotta, and Schwarzer (2005) have developed phase‐specific self‐efficacy beliefs. Motivational self‐ efficacy (task or pre‐action self‐efficacy) refers to the goal‐setting phase and can, for example, be assessed with the stem “I am certain…” followed by items such as “…that I can be physically active on a regular basis, even if it is difficult” (Lippke, Fleig, Pomp, & Schwarzer, 2010). Volitional self‐efficacy refers to the goal‐pursuit phase. It can be subdivided into maintenance self‐efficacy (e.g., “I am capable of strenuous physical exercise on a regular basis even if I need several attempts until I will be successful”) (Lippke et al., 2010) and recovery self‐­ efficacy (e.g., “I am confident that I can resume a physically active lifestyle, even if I have relapsed several times”).

Measurement of Sources of Self‐Efficacy There has not been much research on the systematic assessment of the sources of self‐­ efficacy for specific health behaviors. According to Bandura (1997), mastery experience is the best source, followed by vicarious experience (role modeling), verbal persuasion, and physiological feedback. To assess the sources of self‐efficacy for physical activity, Warner et  al. (2014) validated six scales with items such as “Even if it turned out challenging at times, I have managed to remain active” for mastery experiences; “I feel motivated to be active if I see people my age being active” for vicarious experiences; “Others encourage me

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to be physically active” for verbal persuasion by others; “I motivate myself to be physically active on a regular basis” for self‐persuasion; “Just before I start physical activities, I feel tired” for negative affective states; and “Just before I start physical activities, I feel energetic” for positive affective states.

Self‐Efficacy in Health Psychology: Some Research Findings Self‐Efficacy in Health Behavior Change Theories The concept of self‐efficacy was originally developed by Bandura as the key construct within his social cognitive theory (Bandura, 1977, 1997). Because of the predictive power of self‐­ efficacy beliefs for various health behaviors, other health behavior change theories like the protection motivation theory, the revised health belief model, and the transtheoretical model included self‐efficacy beliefs as well. The related construct of perceived behavioral control was added to the theory of reasoned action, which was then renamed the theory of planned behavior. In the meantime, specific self‐efficacy beliefs were found to be highly predictive of a wide range of health behaviors. There are also several hypotheses addressing how self‐efficacy beliefs interact with other resources such as social support. Luszczynska and Cieslak (2009), for example, found support for the enabling hypothesis (assuming that social support elicits self‐efficacy), as self‐efficacy mediated the association between social support from families and fruit and vegetable consumption in patients after a myocardial infarction. Evidence for the cultivation hypothesis (more self‐­efficacious individuals mobilize more support) was found among patients after prostatectomy, who received more social support to perform pelvic floor exercises if they showed more self‐efficacy previously (Hohl et al., 2015). For the adaptation of survivors of an earthquake, higher levels of family support were found to compensate for lower levels of self‐efficacy (compensation hypothesis; Warner, Gutiérrez‐Doña, Angulo, & Schwarzer, 2015). However, for individuals with high self‐efficacy beliefs, additional support sometimes also shows an interference effect, for example, for autonomy beliefs of older adults (interference hypothesis; Warner, Ziegelmann, et al., 2011).

General Self‐Efficacy A good example of the use of general self‐efficacy is a study on East Germans who migrated to the West after the Berlin Wall had come down. Over a 2‐year period, general self‐efficacy turned out to be the best single predictor of overall adjustment, as assessed by a number of outcomes such as employment status, social integration, physical health, and subjective well‐ being (Schwarzer & Jerusalem, 1995b). A measure of general self‐efficacy proved to be valuable in this study because an assessment of all corresponding domain‐specific self‐efficacy measures for every coping outcome would not have been possible under the circumstances of this particular study. Measures of general self‐efficacy have also been found to relate to a number of mental and physical health outcomes (e.g., depression, stress, physical activity).

Phase‐Specific Self‐Efficacy In an intervention to increase breast self‐examination, task self‐efficacy was associated with intention, whereas maintenance self‐efficacy related to planning and behavior (Luszczynska, 2004). There is also evidence that interventions to increase physical activity affect different

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phase‐specific self‐efficacy beliefs depending on whether they focus on the adoption or ­maintenance of physical activity (Higgins, Middleton, Winner, & Janelle, 2014).

Sources of Self‐Efficacy A study on the sources of self‐efficacy for physical activity in older adults concluded that mastery experiences, vicarious experiences, and subjective perceptions of health were associated with self‐efficacy and physical activity over time (Warner, Schüz, Knittle, Ziegelmann, & Wurm, 2011). A longitudinal study with validated scales showed that mastery experience, self‐persuasion, and negative affective states were the most prominent predictors of self‐ efficacy for physical activity in community‐dwelling older adults (Warner et al., 2014).

How Can Self‐Efficacy Be Increased? From a theoretical perspective, self‐efficacy is built upon four sources: mastery experiences, vicarious experiences, verbal persuasion, and somatic and affective states (Bandura, 1997). Bandura states that mastery experiences are “the most effective source of efficacy information because they provide the most authentic evidence of whether one can master whatever it takes to succeed” (1997, p. 80). Intervention techniques have proven successful for enhancing mastery experiences. However, meta‐analyses have come to the conclusion that graded mastery could also lower self‐efficacy (e.g., Ashford, Edmunds, & French, 2010). To make the most of newly gained mastery experiences, it is also essential to target a positive attributional style. Interventions can prepare for temporal setbacks and lapses to help individuals avoid what Marlatt and Gordon (1985) called the abstinence violation effect. This effect describes that people tend to see lapses as proof for incapability, which makes them drop all effort so they experience a full relapse. When people learn to attribute lapses to external causes, such as the end of a stressful day or highly tempting situations, self‐efficacy can be maintained and trained for future risk situations. Although not always named among the sources of self‐efficacy, Bandura also identifies mental imagery as another possible origin of self‐efficacy beliefs (Bandura, 1977) and proves its use to treat phobias. Health psychological research shows that imagining the outcomes of a behavior (approach imagery) as well as the steps that need to be fulfilled to attain a goal (process imagery) can also be effective to increase physical activity among sedentary adults (Chan & Cameron, 2012). Vicarious experience, like observing others perform a specific behavior with a specific outcome—be it live or symbolic such as in a video or testimonial—increases the belief in being able to master similar tasks and produce comparable results (Bandura, 1977). Vicarious experiences provide the observer with strategies and techniques needed to attain desired goals or to overcome certain stressors. Usually, it is assumed that role models that resemble the observer in attributes such as background, age, gender, or level of expertise show better effects on self‐ efficacy than dissimilar models (Bandura, 1997). Verbal or social persuasion happens if someone expresses faith in the capabilities of another (Bandura, 1997). Verbal persuasion from credible sources such as health professionals is most effective (Perloff, 1993). If, however, someone already has a strong belief in not being capable to achieve their goal, for example, derived from negative experience in the past, it is easier to lower their self‐efficacy than to increase it by attempts of persuasion. Therefore, this source of self‐efficacy needs to be addressed with care and experience to avoid reactance.

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Previous to challenging tasks, most people perceive somatic and affective states such as a fast beating heart or sweaty hands. As the natural tendency is to interpret such physiological symptoms as signs of unpreparedness or anticipation of poor performance, people are more likely to feel competent if they do not experience highly aversive arousal (Bandura, 1997). The optimal level of arousal should, however, lay somewhere in the middle, as too high and too low arousal may impede performance. A meta‐analysis by Prestwich et al. (2014) shows how stress management strategies can be used to prompt self‐efficacy for a healthier diet. Several meta‐analyses have summarized the behavior change techniques that are most effective to increase self‐efficacy in physical activity interventions (e.g., Ashford et  al., 2010; Olander et al., 2013). However, interventionists in this field should keep in mind that there can be “too much of a good thing.” First, evidence shows that very high levels of self‐efficacy can be detrimental to intervention success, especially in the smoking context (e.g., Staring & Breteler, 2004). Self‐efficacy beliefs should hence be realistic to generate motivation and health behavior, as overconfidence could elicit frustration and disappointment along the health behavior change process. To conclude in Bandura’s (2004, p. 162) words, “As you venture forth to promote your own health and that of others, may the efficacy force be with you.”

Author Biographies Lisa Marie Warner is a guest professor at the Division of Health Psychology at the Freie Universität Berlin, Germany. Her research at the interface between health psychology and gerontology focuses on personal and social resources for older adults’ physical activity and social participation, such as their social support and self‐efficacy. Ralf Schwarzer is a professor at the SWPS University of Social Sciences and Humanities in Wroclaw, Poland, and emeritus professor at Freie Universität Berlin, Germany. His research focuses on various aspects of psychology, including stress and anxiety, coping, social support, health behavior change, and self‐efficacy. He is the editor of the journal Applied Psychology: Health and Well‐being. Visit http://my.psyc.de for more information.

References Alarcon, G. M., Bowling, N. A., & Khazon, S. (2013). Great expectations: A meta‐analytic examination of optimism and hope. Personality and Individual Differences, 54(7), 821–827. doi:10.1016/j. paid.2012.12.004 Ashford, S., Edmunds, J., & French, D. P. (2010). What is the best way to change self‐efficacy to promote lifestyle and recreational physical activity? A systematic review with meta‐analysis. British Journal of Health Psychology, 15(2), 265–288. doi:10.1348/135910709X461752 Bandura, A. (1977). Self‐efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. doi:10.1037/0033‐295x.84.2.191 Bandura, A. (1995). Exercise of personal and collective efficacy in changing societies. In A. Bandura (Ed.), Self‐efficacy in changing societies. Cambridge: Cambridge University Press. doi:10.1017/ cbo9780511527692.003 Bandura, A. (1997). Self‐efficacy: The exercise of control. New York, NY: Freeman. doi:10.5860/ choice.35‐1826 Bandura, A. (2004). Health promotion by social cognitive means. Health Education & Behavior, 31(2), 143–164. doi:10.1177/1090198104263660

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Bandura, A. (2006). Guide for constructing self‐efficacy scales. In F. Pajares & T. Urdan (Eds.), Self‐efficacy beliefs of adolescents (Vol. 5, pp. 307–337). Greenwich, CT: Information Age Publishing. Chan, C., & Cameron, L. (2012). Promoting physical activity with goal‐oriented mental imagery: A randomized controlled trial. Journal of Behavioral Medicine, 35(3), 347–363. doi:10.1007/ s10865‐011‐9360‐6 Higgins, T. J., Middleton, K. R., Winner, L., & Janelle, C. M. (2014). Physical activity interventions differentially affect exercise task and barrier self‐efficacy: A meta‐analysis. Health Psychology, 33(8), 891–903. doi:10.1037/a0033864 Hohl, D., Knoll, N., Wiedemann, A., Keller, J., Scholz, U., Schrader, M., & Burkert, S. (2015). Enabling or cultivating? The role of prostate cancer patients’ received partner support and self‐efficacy in the maintenance of pelvic floor exercise following tumor surgery. Annals of Behavioral Medicine, 1–12. doi:10.1007/s12160‐015‐9748‐6 Judge, T. A., Erez, A., Bono, J. E., & Thoresen, C. J. (2002). Are measures of self‐esteem, neuroticism, locus of control, and generalized self‐efficacy indicators of a common core construct? Journal of Personality and Social Psychology, 83(3), 693–710. doi:10.1037/0022‐3514.83.3.693 Lippke, S., Fleig, L., Pomp, S., & Schwarzer, R. (2010). Validity of a stage algorithm for physical activity in participants recruited from orthopedic and cardiac rehabilitation clinics. Rehabilitation Psychology, 55(4), 398–408. doi:10.1037/a0021563 Luszczynska, A. (2004). Change in breast self‐examination behavior: Effects of intervention on enhancing self‐efficacy. International Journal of Behavioral Medicine, 11(2), 95–103. doi:10.1207/ s15327558ijbm1102_5 Luszczynska, A., & Cieslak, R. (2009). Mediated effects of social support for healthy nutrition: Fruit and vegetable intake across 8 months after myocardial infarction. Behavioral Medicine, 35(1), 30–38. doi:10.3200/bmed.35.1.30‐38 Marlatt, G. A., & Gordon, J. R. (1985). Relapse prevention: Maintenance strategies in the treatment of addictive behaviors. New York, NY: Guilford Press. Olander, E. K., Fletcher, H., Williams, S., Atkinson, L., Turner, A., & French, D. P. (2013). What are the most effective techniques in changing obese individuals’ physical activity self‐efficacy and behaviour: A systematic review and meta‐analysis. International Journal of Behavioral Nutrition and Physical Activity, 10(1), 29–29. doi:10.1186/1479‐5868‐10‐29 Perloff, R. M. (1993). The dynamics of persuasion. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Prestwich, A., Kellar, I., Parker, R., MacRae, S., Learmonth, M., Sykes, B., … Castle, H. (2014). How can self‐efficacy be increased? Meta‐analysis of dietary interventions. Health Psychology Review, 8(3), 270–285. doi:10.1080/17437199.2013.813729 Scholz, U., Sniehotta, F. F., & Schwarzer, R. (2005). Predicting physical exercise in cardiac rehabilitation: The role of phase‐specific self‐efficacy beliefs. Journal of Sport & Exercise Psychology, 27(2), 135–151. Schwarzer, R., & Jerusalem, M. (1995a). Generalized self‐efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 35– 37). Windsor: NFER‐Nelson. Schwarzer, R., & Jerusalem, M. (1995b). Optimistic self beliefs as a resource factor in coping with stress. In S. E. Hobfoll & M. W. deVries (Eds.), Extreme stress and communities: Impact and intervention (pp. 159–177). Dordrecht: Kluwer. doi:10.1007/978‐94‐015‐8486‐9_7 Schwarzer, R., & Warner, L. M. (2013). Perceived self‐efficacy and its relationship to resilience. In S. Prince‐Embury & D. H. Saklofske (Eds.), Resilience in children, adolescents, and adults: Translating research into practice (pp. 139–150). New York, NY: Springer Science + Business Media. doi:10.10 07/978‐1‐4614‐4939‐3_10 Sherer, M., Maddux, J. E., Mercandante, B., Prentice‐Dunn, S., Jacobs, B., & Rogers, R. W. (1982). The self‐efficacy scale: Construction and validation. Psychological Reports, 51, 663–671. doi:10.2466/ pr0.1982.51.2.663 Staring, A. B. P., & Breteler, M. H. M. (2004). Decline in smoking cessation rate associated with high self‐efficacy scores. Preventive Medicine, 39(5), 863–868. doi:10.1016/j.ypmed.2004.03.025

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Vieira, E. T., Jr., & Grantham, S. (2011). University students setting goals in the context of autonomy, self‐efficacy and important goal‐related task engagement. Educational Psychology, 31(2), 141–156. doi:10.1080/01443410.2010.536508 Warner, L. M., Gutiérrez‐Doña, B., Angulo, M. V., & Schwarzer, R. (2015). Resource loss, self‐efficacy, and family support predict posttraumatic stress symptoms: A 3‐year study of earthquake survivors. Anxiety, Stress & Coping, 28(3), 239–253. doi:10.1080/10615806.2014.955018 Warner, L. M., Schüz, B., Knittle, K., Ziegelmann, J. P., & Wurm, S. (2011). Sources of perceived self‐ efficacy as predictors of physical activity in older adults. Applied Psychology. Health and Well‐Being, 3(2), 172–192. doi:10.1111/j.1758‐0854.2011.01050.x Warner, L. M., Schüz, B., Wolff, J. K., Parschau, L., Wurm, S., & Schwarzer, R. (2014). Sources of self‐ efficacy for physical activity. Health Psychology, 33(11), 1298–1308. doi:10.1037/hea0000085 Warner, L. M., Ziegelmann, J. P., Schüz, B., Wurm, S., Tesch‐Römer, C., & Schwarzer, R. (2011). Maintaining autonomy despite multimorbidity: Self‐efficacy and the two faces of social support. European Journal of Aging, 8(1), 3–12. doi:10.1007/s10433‐011‐0176‐6 Williams, D. M., & Rhodes, R. E. (2014). The confounded self‐efficacy construct: Conceptual analysis and recommendations for future research. Health Psychology Review, 1–16. doi:10.1080/17437199. 2014.941998

Suggested Reading Bandura, A. (1997). Self‐efficacy: The exercise of control. New York, NY: Freeman. doi:10.5860/ choice.35‐1826 Benight, C. C., & Bandura, A. (2004). Social cognitive theory of posttraumatic recovery: The role of perceived self‐efficacy. Behaviour Research and Therapy, 42(10), 1129–1148. doi:10.1016/j. brat.2003.08.008 Luszczynska, A., & Schwarzer, R. (2015). Social‐cognitive theory. In M. Conner & P. Norman (Eds.), Predicting health behaviours (3rd ed., pp. 225–251). Maidenhead: McGraw Hill Open University Press. Schwarzer, R., & Knoll, N. (2007). Functional roles of social support within the stress and coping process: A theoretical and empirical overview. International Journal of Psychology, 42(4), 243–252. doi:10.1080/00207590701396641 Williams, S. L., & French, D. P. (2011). What are the most effective intervention techniques for changing physical activity self‐efficacy and physical activity behaviour‐and are they the same? Health Education Research, 26(2), 308–322. doi:10.1093/her/cyr005

Self‐Esteem and Health Danu Anthony Stinson and Alexandra N. Fisher Psychology Department, University of Victoria, Victoria, BC, Canada

Self‐esteem has been the subject of intense empirical and theoretical study by psychological scientists for over 50 years, and in the latter decades of the twentieth century, this psychological phenomenon also succeeded in capturing the imagination of popular culture. The notion that one’s feelings of worth as a person matter and have important consequences for emotion, cognition, behavior, and experiences resonated with the general public. People embraced insights from psychological science concerning the origins and nature of self‐esteem, and individuals and governmental organizations alike sought methods for improving self‐esteem and reaping the many benefits associated with positive self‐regard, including career and academic success, and increased life satisfaction. As a result of this academic and public attention, if a student of psychology tells her friends or family that she is studying self‐esteem, her disclosure will often be met with a nod of understanding or a personal anecdote concerning the listener’s own experiences. Yet despite the widespread popularity of self‐esteem and the general acknowledgment of its importance, most students of psychology and the general public remain unaware that self‐ esteem also plays an important role in shaping people’s health. Many people who hear of this connection may wonder, “How can feelings influence something tangible and physical like health?” This question arises from the lingering assumption that the mind and body are distinct entities, an assumption that arose during the dawn of modern Western medicine and continues to dominate Western conceptions of health. But this assumption is erroneous. In fact, the mind and the body are intimately connected, and this connection is abundantly evident in the pervasive links between self‐esteem and the various dimensions of well‐being that characterize human health.

What Is Self‐Esteem? Before it is possible to understand how and why self‐esteem predicts such a seemingly unrelated experience as health, it is essential to first understand how this psychological

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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c­ haracteristic develops and how it relates to other aspects of the self: the collection of traits, qualities, schema, roles, beliefs, and attitudes that form the core of one’s identity. The self resides at the very center of one’s psychological universe, allowing people to make sense of their past experiences, guide their present behavior, and predict their future experiences. Indeed, when the philosopher René Descartes famously opined, “I think, therefore I am,” he was relying on his sense of self to prove his very existence. As one might expect, a psychological system so important takes time to develop. The first sense of self that coheres during infancy is the sense of being an entity distinct from others, who persists over time and across places. Around the age of 3, as children mature and interact with their social and physical worlds, they gradually develop beliefs about their specific traits and abilities (“I am athletic”), learn the social roles that they are expected to fulfill (“I am a girl”), and develop theories, or scripts, to explain and predict their actions (“If I ask Marjan to play with me, she will say yes”). As children approach puberty, they begin to evaluate their worth and value, and it is this global evaluative component of the self that psychologists call self‐esteem. Self‐esteem develops in part through a reflected appraisal process, whereby children observe how other people treat them, and from that treatment infer their worth. Being treated with responsive kindness, warmth, and positivity by social partners, especially family, communicates that the self is worthy and valuable. In contrast, being ignored, invalidated, overlooked, or abused by unresponsive or unavailable social partners, especially family, communicates that the self is not worthy of care or deserving of loving kindness. Over time and through repeated exposure, children internalize these social messages, and thus self‐esteem is born. Of course, people are not passive tabula rasa onto which their social experiences write. Babies are born with a genetic heritage that shapes the ways in which they interact with the world, and these same forces influence the development of self‐esteem. Of particular relevance to the discussion at hand, some people are temperamentally inclined to focus their attention and energy toward seeking rewarding experiences and to experience high levels of positive affect, whereas others are temperamentally inclined to focus their attention and energy toward avoiding punishing experiences and to experience high levels of negative affect. In turn, these higher‐order temperamental factors are also related to self‐esteem. Reward‐focused people who are relatively insensitive to stressors and recover quickly from negative moods will tend to develop higher self‐esteem. In contrast, people who are temperamentally attuned to punishments like criticism, who react strongly to stress and take a long time to recover, and who experience frequent negative moods tend to develop lower self‐esteem. This association between temperament and self‐esteem probably exists because temperament influences the ways in which people experience the world, which in turn informs self‐ evaluations. For example, if someone is very sensitive to rewards, she will be more likely to notice and internalize praise and thus develop higher self‐esteem. Conversely, if someone is very sensitive to criticism, he will feel the sting of social rebuff more deeply and thus will be more likely to develop lower self‐esteem. Thus, temperament is a lens through which self‐ esteem‐relevant experiences and messages must first pass before they are internalized, resulting in self‐esteem that is highly subjective in nature. Two people with different temperaments can have the same experiences and from those experiences infer different conclusions about their social worth and thus develop different levels of self‐esteem. As this example illustrates, self‐ esteem develops from a true symbiotic interaction between nature and nurture. Once self‐esteem crystallizes around the age of 12, it remains remarkably stable across the lifespan. Perhaps due to its high degree of stability, people often rely on their self‐esteem to regulate their interactions with their material and social worlds. The lens of self‐esteem helps

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people to make sense of their past experiences, to determine a present course of action, and to predict future outcomes. In each of these domains, lower self‐esteem individuals tend to adopt a more pessimistic and cautious outlook than higher self‐esteem individuals, who can be quite optimistic and blithe in many circumstances. For example, a lower self‐esteem person may attribute past failures to personal shortcomings and thus avoid situations that call on the skills he believes he does not possess because he anticipates failure. In contrast, a higher self‐esteem person is more likely to dismiss past failures as resulting from external factors and thus pursue opportunities that offer rewards he feels equipped to effectively claim. These differing orientations are evident in school and workplace settings and in social contexts ranging from interactions with strangers to interactions with lovers, and they result in differing outcomes for people with lower and higher self‐esteem. In many life domains, higher self‐esteem individuals tend to experience more positive outcomes than their lower self‐esteem counterparts, and this difference extends to the domain of health.

How Is Self‐Esteem Related to Health? Following the World Health Organization, we consider health to be a multifaceted construct comprising psychological, physical, and social well‐being. Self‐esteem predicts outcomes in each of these varied domains.

Psychological Well‐Being Self‐esteem is negatively associated with a range of mental health concerns, including depression, anxiety, stress, disordered eating, negative body image, and suicidal ideation. Although it is difficult to determine whether self‐esteem is a cause or consequence of these mental health conditions, longitudinal studies that control for third variables and rule out reverse causation can help to tease apart these possibilities. For example, one such study observed that adolescents with low self‐esteem and individuals whose self‐esteem declined during adolescence were more likely to experience depression fully two decades later (Steiger, Allemand, Robins, & Fend, 2014). Another study that examined self‐esteem and mental health outcomes at multiple points in time found that lower self‐esteem predicts depression, which, in turn, predicts heightened rates of stressful life events (Orth, Robins, & Meier, 2009). The consensus among psychological scientists is that lower self‐esteem is an independent risk factor for the development of numerous mental health conditions.

Physical Well‐Being Self‐esteem is also related to a broad range of physical health conditions. For example, lower self‐esteem university students report that they experience worse physical health, visit the doctor more often, and miss more days of school due to illness than their higher self‐esteem counterparts (Stinson et al., 2008). Moreover, self‐esteem is positively associated with people’s ability to recover from physical illness and cope with chronic health conditions, including heart disease, HIV/AIDS, cystic fibrosis, and autoimmune disorders. For example, compared with higher self‐esteem individuals, lower self‐esteem individuals with asthma or rheumatoid arthritis experience more negative affect, less positive affect, greater stress and symptom severity, and more symptom interference and activity restrictions in daily life (Juth, Smyth, & Santuzzi, 2008). Lower self‐esteem is also a risk factor for mortality among the elderly. These and other

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links between self‐esteem and physical health offer a stark reminder of the extensive mind– body connections that characterize the human experience.

Social Well‐Being Self‐esteem is also strongly tied to social well‐being. Lower self‐esteem individuals report that they receive less social support, experience more interpersonal stress, and suffer from loneliness and social isolation to a greater extent than their higher self‐esteem counterparts. Such perceptions may accurately reflect important developmental experiences of acceptance and rejection from family and peers. But with the changing social context of adulthood, appraisals of social well‐being can diverge from objective reality. Lower self‐esteem individuals often underestimate their social partners’ actual regard, whereas higher self‐esteem individuals’ perceptions are often more accurate, or perhaps even overly optimistic. Such individual differences are thought to be motivated by concerns about rejection. Lower self‐esteem people are extremely risk averse and would rather err on the side of caution and overlook some acceptance cues than risk rejection by perceiving acceptance that is not actually present. In contrast, higher self‐esteem people are blithe in the face of rejection and will boldly perceive acceptance from even the most neutral social companions. These differing social orientations can have self‐fulfilling consequences. Lower self‐esteem individuals’ social insecurities and resultant cool, inhibited social behavior can be off‐putting to social partners, whereas higher self‐esteem individuals’ confident and warm social behavior is attractive to others. Thus, self‐esteem and social well‐being are linked throughout the lifespan by a recursive process whereby early social experiences help to forge self‐esteem, which later guides social experiences that further reinforce self‐esteem.

Why Is Self‐Esteem Related to Health? Two dominant models have emerged to explain why self‐esteem and health are so intricately woven together.

A Resource Model of Self‐Esteem and Health Self‐esteem and health may be linked because higher self‐esteem is a psychological resource on which people can rely in times of adversity. In this model, the same temperamental factors and developmental experiences that give rise to higher or lower self‐esteem also give rise to characteristic psychological orientations that benefit, or undermine, health. Specifically, the affective, cognitive, and motivational styles that characterize higher self‐esteem may allow such individuals to cope with and recover from stressors more effortlessly and quickly than their lower self‐ esteem counterparts. For example, higher self‐esteem individuals effectively employ strategies to improve negative moods and prolong positive moods, whereas lower self‐esteem individuals tend to allow negative moods to linger and will actively dampen positive moods (Wood, Heimpel, & Michela, 2003). Higher self‐esteem individuals are also motivationally inclined to overlook criticism, failure, and other negative outcomes in favor of rewarding goal pursuit, whereas lower self‐esteem individuals are decidedly attentive and sensitive to negative stimuli. In turn, these characteristic psychological orientations are thought to influence health. Thus, people with lower self‐esteem exhibit a stronger stress–illness association than higher self‐esteem individuals, such that they are more likely to experience psychological and somatic problems

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both on and following stressful days (DeLongis, Folkman, & Lazarus, 1988). Lower self‐esteem individuals are also more susceptible to illness and mood disturbance when stress levels increase, even if overall stress is low. In light of these and other findings, many psychological scientists consider lower self‐esteem to be a causal risk factor for poor health outcomes.

A Self‐and‐Social‐Bonds Model of Health A second explanatory model posits that self‐esteem predicts health outcomes because self‐ esteem regulates responses to the threat of rejection in a manner that can undermine lower self‐esteem individuals’ health (Stinson et al., 2008). A growing body of evidence suggests that self‐esteem plays an important role in a broader psychobiological regulatory system that services the fundamental human need to belong, which is a drive to connect with and maintain relationships with benevolent social partners, and conversely, avoid the social pain of rejection. In particular, people rely on their self‐esteem to make sense of their past social experiences, to regulate their responses to in‐the‐moment social cues concerning acceptance and rejection, and to predict their future social experiences. As we have already discussed, lower self‐esteem individuals readily perceive rejection from others and react strongly to avoid the rejection they often anticipate, whereas higher self‐esteem individuals eagerly anticipate acceptance from others and pursue rewarding opportunities for social connection with little concern about rejection. In turn, these differing psychological orientations influence physiological responses to the threat of rejection. For everyone, the threat of rejection activates a neuroendocrine system called the hypothalamic–pituitary–adrenal axis (HPA), which triggers a cascade of hormones that characterize the physiological stress response. However, because lower self‐esteem individuals are more sensitive to rejection threats than higher self‐esteem individuals, they are more likely to exhibit this stress response in their daily lives. For example, lower self‐esteem individuals experience a heightened physiological stress response to the threat of rejection, as indicated by increased salivary cortisol levels immediately following rejection and during the early recovery phase (Ford & Collins, 2010). Unfortunately, chronic activation of the HPA is physiologically costly and can undermine health, which may explain why lower self‐esteem individuals experience worse health outcomes than their higher self‐esteem counterparts. Consistent with this account, on the day following an experience of interpersonal rejection, lower self‐esteem individuals report heightened stress, poorer general health, increased physical symptoms of illness, and worse sleep quality relative to both higher self‐esteem individuals’ experiences on similar days and their own experiences following days when they did not experience rejection (Ford & Collins, 2013). Thus, poor outcomes in the domain of social well‐being may be an important mechanism to explain why individuals with lower self‐esteem also experience poor outcomes in psychological and physical health domains.

Conclusions It is tempting to conclude from the preceding discussion that improving self‐esteem could result in a miracle panacea. Unfortunately, attempting to improve self‐esteem is more easily said than done. The stability of important self‐views like self‐esteem is so essential to people’s daily functioning that people will vociferously defend their existing self‐views against the forces of change, even when such defense means maintaining negative evaluations about the self. As a result, attempts to directly improve self‐esteem through positive self‐statements or other

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forms of positive feedback often backfire for lower self‐esteem individuals, resulting in undesirable decrements to well‐being. Thus, we suggest that rather than targeting self‐esteem directly, health interventions should instead attempt to improve the quality of people’s social bonds. Such interventions could directly improve health by improving people’s social well‐being. In turn, because social well‐ being has been causally implicated in both physical and psychological well‐being, such interventions may have spillover benefits for other health domains. Moreover, social well‐being interventions could result in persistent benefits if repeated positive social experiences are ­internalized over time, resulting in increased self‐esteem. Psychological scientists have already ­identified a number of effective social well‐being interventions, including belongingness interventions for stigmatized or marginalized groups. Close relationships researchers have also identified simple interventions that specifically target the social doubts that are characteristic of lower self‐esteem. Currently, psychological scientists are working to determine whether this and other promising social well‐being interventions can serve as “psychological immunizations,” yielding long‐term increments in self‐esteem that also benefit well‐being and health.

Author Biographies Danu Anthony Stinson is an associate professor in the Psychology Department at the University of Victoria, Canada. Most of her research to date has examined how and why self‐esteem influences health and well‐being. More recently, she has begun to investigate and seek ways to alleviate social psychological barriers to health and well‐being that affect negatively stereotyped groups, including higher body‐weight people and women in high‐status social positions. Alexandra N. Fisher is completing her master of science degree in social psychology at the University of Victoria, Canada, under the supervision of Danu Anthony Stinson. She studies the social and interpersonal experiences of female breadwinners and other successful women. Specifically, her research examines the stereotypes and impressions that are often formed and applied to female breadwinners and how these impressions may influence close relationship initiation and maintenance processes.

References DeLongis, A., Folkman, S., & Lazarus, R. S. (1988). The impact of daily stress on health and mood: Psychological and social resources as mediators. Journal of Personality and Social Psychology, 54, 486–495. doi:10.1037/0022‐3514.54.3.486 Ford, M. B., & Collins, N. L. (2010). Self‐esteem moderates neuroendocrine and psychological responses to interpersonal rejection. Journal of Personality and Social Psychology, 98(3), 405–419. doi:10.1037/ a0017345 Ford, M. B., & Collins, N. L. (2013). Self‐esteem moderates the effects of daily rejection on health and well‐being. Self and Identity, 12(1), 16–38. doi:10.1080/15298868.2011.625647 Juth, V., Smyth, J. M., & Santuzzi, A. M. (2008). How do you feel? Self‐esteem predicts affect, stress, social interaction, and symptom severity during daily life in patients with chronic illness. Journal of Health Psychology, 13(7), 884–894. doi:10.1177/1359105308095062 Orth, U., Robins, R. W., & Meier, L. L. (2009). Disentangling the effects of low self‐esteem and stressful events on depression: Findings from three longitudinal studies. Journal of Personality and Social Psychology, 97(2), 307–321. doi:10.1037/a0015645

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Steiger, A. E., Allemand, M., Robins, R. W., & Fend, H. A. (2014). Low and decreasing self‐esteem during adolescence predict adult depression two decades later. Journal of Personality and Social Psychology, 106(2), 325–338. doi:10.1037/a0035133 Stinson, D. A., Logel, C., Zanna, M. P., Holmes, J. G., Cameron, J. J., Wood, J. V., & Spencer, S. J. (2008). The cost of lower self‐esteem: Testing a self‐and‐social‐bonds model of health. Journal of Personality and Social Psychology, 94, 412–428. doi:10.1037/0022‐3514.94.3.412 Wood, J. V., Heimpel, S. A., & Michela, J. L. (2003). Savoring versus dampening: Self‐esteem differences in regulating positive affect. Journal of Personality and Social Psychology, 85, 566–580. doi:10.1037/0022‐3514.85.3.566

Suggested Reading Cohen, S., & Janicki‐Deverts, D. (2009). Can we improve our physical health by altering our social networks? Perspectives on Psychological Science, 4, 375–378. doi:10.1111/j.1745‐6924.2009.01141.x Martens, A., Greenberg, J., & Allen, J. B. (2008). Self‐esteem and autonomic physiology: Parallels between self‐esteem and cardiac vagal tone as buffers of threat. Personality and Social Psychology Review, 12, 370–389. doi:10.1177/1088868308323224 Walton, G. M., Cohen, G. L., Cwir, D., & Spencer, S. J. (2012). Mere belonging: The power of social connections. Journal of Personality and Social Psychology, 102, 513–532. doi:10.1037/a0025731 Wood, J. V., Anthony, D. B., & Foddis, W. F. (2006). Should people with low self‐esteem strive for high self‐esteem? In M. H. Kernis (Ed.), Self‐esteem issues and answers: A source book of current perspectives (pp. 288–296). New York, NY: Psychology Press.

Self‐Regulation Richie L. Lenne and Traci Mann Psychology Department, University of Minnesota Twin Cities, Minneapolis, MN, USA

Self‐regulation is the set of processes through which individuals make efforts to alter or exert control over their behaviors, cognitions, and emotions, or, more simply, the processes used for setting and pursuing goals. Self‐regulation plays an important role in health promotion and illness prevention because many of the most common causes of death in the United States have a behavioral component that requires ongoing management. Poor health may result from failing to regulate smoking, drug and alcohol use, eating, exercise, and sexual activity, and many treatments require individuals to adhere to medical regimens or do exercises to manage pain. Other health problems may result from failing to regulate anger, anxiety, or stress. Self‐regulation can be conceptualized as a feedback or cybernetic system. In this kind of system, there are three main processes. First, individuals have a standard or goal, imposed by themselves or by others, that they wish to obtain. Second, they monitor their current state and compare it to the standard. Third, if their current state does not match the desired standard, they make changes to their behaviors, cognitions, or emotions aimed at moving closer to the standard. A person may have a goal of weighing a certain weight, for example. They may compare their current weight with that goal weight, find that they weigh too much, and therefore change their eating or exercise behavior in an effort to get closer to the goal weight. This conceptualization implies that self‐regulation is conscious and deliberate, but that is not always the case. Self‐regulation can also be automatic, operating through nonconscious processes, for example, when individuals engage in habitual behaviors, or when goals are primed by situational cues. In addition, the real‐life context of self‐regulation is considerably more complicated than implied by a simple feedback system. At any given moment, individuals may have multiple competing goals that they are aiming to fulfill simultaneously. Each goal must not only be attended to but also be protected and shielded from distractions, disturbances, and temptations, many of which come from other goals. For example, the goal of avoiding sugar may conflict with the goal of being a doting parent and baking a cake for one’s child’s birthday.

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Regardless of the overall conceptualization, most concepts that pertain to the topic can be incorporated in one of two components of the self‐regulation process: goal setting and goal striving.

Goal Setting Goal setting is the process of deciding what goals to pursue and by what criteria to judge successful goal attainment. These goals can be as small as deciding to take the stairs instead of the elevator or as large as living a more healthy lifestyle. Individuals adopt health goals for different reasons, and the goals themselves have different characteristics that can affect whether goal pursuit is successful. In addition, people abandon goals for different reasons.

Adopting Health Goals Most people want to live a long and happy life and do not need to be convinced that it is a good idea to take care of their health. However, some form of information is usually the first component to the adoption of a health goal. Accurate health information is not always easy to find in a landscape saturated with often‐conflicting accounts and competing incentives. It can be challenging to know what to believe, which can prevent people from forming health goals. According to theories of health behavior change, once people come to believe that certain behaviors carry health consequences, they are more likely to set goals, particularly if they believe they have the ability to control these behaviors and the behaviors align with what society and significant others deems appropriate (Ajzen, 1991). Another influence on the goal setting process is an individual’s attempts to maintain a consistent self‐concept (Fishbein et al., 2001) and to move toward aspirational selves and away from feared selves (Markus & Nurius, 1986). Whether a person adopts a new health goal will also be determined by the fit of this goal into the person’s larger system of goals (health related or otherwise). Goal systems theory makes predictions about the adoption and successful completion of a goal by accounting for how other goals compete with or support each other (Kruglanski et al., 2002). According to this perspective, goals can be interrelated such that many goals are associated with a common means (multifinal), or a single goal can be achieved through many means (equifinal). The theory postulates that the more goals are interconnected, the stronger motivational value they carry and the more likely people are to adopt and accomplish them.

Goal Characteristics Despite the desire to live a healthy life, people struggle to set goals that will last long enough to make a difference in their health and well‐being. Certain characteristics of goals have been shown to have meaningful implications for successful goal setting and pursuit. These features include motivational aspects of goals, the level of difficulty of the goal, and the type of goal. Goals may differ based on their motivational focus. People can have goals that are approach oriented, which are focused on moving toward a positive end state, as well as goals that are avoidance oriented, which are focused on preventing a negative end state. Goal orientations are not merely linguistic differences, but they have consequences in terms of whether the goal is likely to be met and for whom. Overall, approach‐oriented goals tend to lead to more positive outcomes than avoidance‐oriented goals (Elliott & Dweck, 1988). Approach goals may be

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more effective because it is harder to define success criteria for avoidance goals (i.e., not getting a disease), and to maintain motivation when faced with an obstacle, it is beneficial to have a clear idea of what steps are required for success. There are also individual differences in motivational orientations, with people tending to be oriented toward either approach or avoidance goals, and these differences may have implications for goal pursuit. For example, there is some evidence that it may be beneficial to match health messages to an individual’s motivational orientation (Mann, Sherman, & Updegraff, 2004). An important predictor of successful goal setting and pursuit, according to self‐determination theory (Deci, Koestner, & Ryan, 1999), is whether a person perceives that they have autonomous control over the decision to change their behavior. In fact, social psychologists as early as Lewin (1935) have proposed that self‐imposed goals are more motivating than goals imposed by others. Evidence for the importance of self‐set goals in the health domain is mixed, however. Interventions in which healthcare providers discuss patient values and support the patient’s goal choice instead of recommending a course of action support this hypothesis (Silva et al., 2011). But other research has found similar outcomes for patient and provider set goals (Alexy, 1985), as well as employee and employer set goals (Locke & Latham, 1990). Goals imposed by others may be less motivating because this undermines the intrinsic value of the goal (Deci et al., 1999). Meta‐analytic evidence indicates that extrinsic rewards reduce intrinsic motivation (Deci et al., 1999). In situations where people lack internal motivation, however, extrinsic motivation may be an effective motivator, such as when external reinforcements are added to employee health promotion programs. Having recognized the cost savings that come from investing in the long‐term health of the workforce, many employers have adopted material incentive programs designed to encourage their employees to engage in healthy behaviors, such as exercise and smoking cessation. There is little empirical research on the effectiveness of such programs and what, if any, payment structures are the most effective. Goals also differ in their level of difficulty. Some research suggests that successful goal pursuit requires that the goal is feasible, but other work has found that unrealistic goals may actually inspire goal pursuit (Linde, Jeffery, Finch, Ng, & Rothman, 2004). Research in vocational settings also suggests that high demands (i.e., the goal is challenging) lead to better performance than easier goals as long as self‐efficacy is sufficient (Latham & Locke, 2007). In support of this view, when the goal is highly desirable but unrealistic, there is evidence that people will take actions to better achieve the goal, instead of changing the goal itself. A single goal, such as a weight‐loss goal, can be cast in multiple ways. A person may set a goal to develop the knowledge and cooking skills required to eat a healthy diet (i.e., a “mastery goal”), or they may aim to demonstrate their success by achieving a certain weight (i.e., “performance goal”). There is evidence that mastery goals lead to better outcomes than performance goals (Elliott & Dweck, 1988). For example, mastery goals lead to deeper processing, whereas performance goals trigger superficial processing (for a review, see Covington, 2000). Mastery goals have also been shown to bolster self‐efficacy, increase amount of time spent on task, augment the belief that effort leads to success, and help lead to persistence in the face of obstacles, whereas performance goals have been found to undermine intrinsic motivation and increase avoidance of difficult tasks. While it is reasonable to believe that this distinction applies to health behavior, it has not often been applied.

Goal Abandonment Goal abandonment is common and often happens soon after goal setting. People frequently abandon their goals when other goals compete for time and energy or when there are no

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f­acilitating goals that share a common means of pursuit. Research tends to focus on ways to bolster goal persistence, but this is not always the most advantageous response. Abandoning a goal that is not attainable is an adaptive strategy that can free up time and energy (Latham & Locke, 2007), reduce negative feelings that come from repeated failure to achieve goals, and minimize stress from setting goals without the proper resources to attain them (Schönpflug, 1986). Goal abandonment is also advantageous when goals are beyond personal control, such as the case of those who strive toward the goal of having a “perfect” thin body.

Goal Striving Goal striving encompasses all actions, either deliberate or automatic, to achieve goals. This involves not only carrying out actions that directly promote goal attainment but also protecting those goals from being disrupted by competing goals or temptations. Individuals use many strategies in goal striving, which can be loosely categorized into planning strategies, automatic strategies, cognitive strategies, and effortful inhibition.

Planning Strategies Planning strategies involve making arrangements before one is in a situation in which goal‐ directed behavior will be needed, so that either (a) temptation to engage in behavior counter to the goal can be avoided or (b) the individual is ready to meet the challenge when it occurs. By thinking ahead of time about potential obstacles that may arise, individuals can plan ways to overcome those obstacles. A simple example would be changing one’s route to work so that they do not pass the fast‐food restaurant with a drive‐through window, which they know would dangerously tempt them from their weight‐loss goal. Choosing to keep tempting foods out of one’s home or at a distance are other examples. Mentally rehearsing or simulating a strategy ahead of time is also a form of planning. People may envision themselves engaging in each of the steps necessary to achieve their goal so that they know what to expect and are ready with the appropriate response when the time comes. This strategy has been found effective among students mentally simulating the process of studying and preparing for exams, compared with students imagining the outcome of successful studying (Pham & Taylor, 1999). Focusing on the process rather than the outcome is an important aspect of the strategy, as only a process focus will reveal potential obstacles and challenges that may be encountered.

Automatic Strategies Automatic strategies operate without an individual’s conscious awareness, or with less than full awareness. Automatic strategies are advantageous in many circumstances. They can operate when individuals are not motivated to comply with their goals and when individuals do not have sufficient attentional resources to dedicate to carefully monitoring and altering their behavior. When goal striving is automated, individuals are less likely to consciously talk themselves out of engaging in the relevant goal‐directed behavior and are less susceptible to interference from competing goals. Habits, once formed, operate automatically and can be an effective strategy for reaching a goal. Habits are formed when a behavior is paired with a particular environmental cue many times. After sufficient pairing, the behavior operates automatically whenever that ­environmental

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cue is present. An individual may put on their seat belt when they get into their car so often that this becomes a habit, and they eventually do it without giving it any conscious thought. Behaviors that only happen infrequently are less likely to become habits, as they do not have enough opportunities to be paired with a particular contextual cue. However, they may still be made automatic with the use of implementation intentions. Implementation intentions are if‐then plans that specify a particular action to perform in a particular situation, such as “If I am in the produce section of the store, then I will buy apples.” Forming an implementation intention and repeating it several times has been shown to lead to improved regulation of many health‐related behaviors (for further reading, see Gollwitzer & Sheeran, 2006). Another way in which goal pursuit may become automatic is if alterations are made to one’s environment (planning) so that the goal‐consistent behavior is the more likely response (or the only possible response). This may be possible by changing which behavioral options are available or possible, or by changing how the options are presented. Using “nudge”‐type interventions, for example, keeps all options available but makes certain options more likely to be chosen, by, for example, making certain options more salient, closer, easier, more convenient, or cheaper. The fact that people have behaved based on responding to such changes is generally outside of their conscious awareness.

Cognitive Strategies The category of cognitive strategies includes efforts to change the way one thinks about one’s goals and the obstacles to those goals. There are many ways one might think about the same goal or obstacle, and these different construals affect whether an individual is likely to be successful at attaining the goal. For example, temporal construal theory (Trope & Liberman, 2003) postulates that distant‐future events are evaluated primarily in terms of their desirability, whereas near‐future events are evaluated primarily in terms of their feasibility. When people set a goal, they tend to focus on desirable outcomes in the distant future, but during goal pursuit, these outcomes lose focus, challenges become more apparent, and persistence becomes more difficult, frequently leading to failure to achieve the goal. Individual differences in the extent to which people focus on proximal or distal goals influence goal attainment, with a more distal focus leading to better outcomes. Individuals can be induced to focus on the future by having them think about possible events that could occur, and this serves as an effective self‐regulatory strategy. It should be noted, however, that there is also evidence for the reverse position and that focusing on the means to success rather than on the ends of success leads to more successful goal pursuit and attainment, particularly if the task is difficult. Differences in these perspectives may be explained by the reward value of goal‐directed behavior. A process focus may increase the intrinsic value of a rewarding behavior (e.g., engaging in a desired sport for exercise) but may simply highlight the drudgery of a less rewarding one (e.g., resisting desired foods). Individuals also think about temptations and obstacles differently when those challenges are abstract than when they are specific. Challenges that are more vague and abstract seem less daunting than challenges that are concrete and detailed. For example, when children were trained to think about a tempting marshmallow in an abstract way (as “fluffy clouds”) instead of as a specific yummy treat, they were able to resist eating it for longer (Mischel, Shoda, & Rodriguez, 1989). Merely taking on an overall abstract mindset can work as well. Thinking about general categories of animals instead of specific animals led individuals in one study to think more abstractly, and they were more likely to successfully resist a tempting snack (Fujita & Han, 2009).

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Effortful Inhibition of Impulses The final category of goal striving strategies is effortfully inhibiting an impulse that would interfere with goal pursuit, by fighting off or suppressing the counterproductive thoughts, feelings, or behaviors. This subset of self‐regulation processes is sometimes specifically referred to as self‐control (although in many other cases, the terms self‐control and self‐regulation are used interchangeably), willpower, or delaying gratification. Effortfully inhibiting an impulse is, by definition, not automatic, as it requires conscious effort, and therefore it is subject to derailment or interference when these resources—whether they are cognitive or motivational—are scarce (for further reading, see Inzlicht & Schmeichel, 2012). Dual‐process models suggest that impulsive behaviors may be automatically enacted when there are not sufficient cognitive resources to challenge them. For example, when cognitive resources are scarce, individuals may not notice that they are violating their standards or may not have the ability to consider another option for how to behave. Individuals with impairments to their cognitive functioning, such as impaired working memory or executive function, have less success in regulating their behaviors. In addition, other factors that temporarily occupy these cognitive resources, including divided attention, alcohol intoxication, or fatigue, also reduce self‐regulatory success. Effortful inhibition may also be undermined by a lack of motivation. In particular, an extensive literature on the phenomenon of ego depletion shows that inhibiting one behavior makes it difficult to inhibit another behavior, due to the temporary depletion of a motivational resource. For example, in one study, after resisting eating a tempting cookie, participants performed worse on an unrelated self‐regulation task, persisting in their efforts to solve a difficult puzzle (Baumeister, Bratslavsky, Muraven, & Tice, 1998). Even behaviors that seem to only require a small amount of effort to regulate (such as making a choice between two pens or suppressing one’s facial expression) can still make it difficult to regulate another behavior soon after. Because of the ease and frequency with which both motivational and cognitive resources can become depleted, effortful inhibition is a strategy that is prone to failure. While there is very little evidence that individuals can be taught to increase their effortful inhibition ability, there is some evidence that the depletion effect may be prevented with interventions that provide cash incentives, induce a positive mood, or reduce fatigue. In addition, depletion may be prevented by having participants form an implementation intention beforehand, by reminding them of their core values or even by teaching them that willpower is not limited. Relying solely on effortful inhibition is not likely to be an effective strategy for goal attainment, but making use of multiple strategies may be more successful. For example, cognitive strategies might be used in an effort to help resist temptation, or planning strategies might be used so that individuals are less likely to encounter temptation. Similarly, if automatic strategies have been used to form habits or implementation intentions, effortful inhibition may not be necessary. Future research on self‐regulation might aim to focus on delineating the types of situations in which particular strategies may be effective, as well as the role of partners and family members in aiding an individual’s self‐control. This work may ultimately lead to effective interventions to help people successfully control their health behaviors.

Author Biographies Richie L. Lenne is a PhD student in psychology at the University of Minnesota. He received his BA from Lewis & Clark College in 2011. He spent 3 years conducting research at the

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Oregon Health & Science University before starting his doctorate. His research applies social psychological theory to public health and behavior change interventions with the dual purpose of informing theory and developing effective and efficient health programs. Traci Mann is a professor of psychology at the University of Minnesota. She received her PhD in 1995 from Stanford University and spent ten years on the faculty at UCLA. She is interested in basic science questions about cognitive mechanisms of self‐control and in applying social psychology research to promoting healthy behavior—particularly eating—in individuals’ daily lives. Her research has been funded by NIH, NASA, and the USDA.

References Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. Alexy, B. (1985). Goal setting and health risk reduction. Nursing Research, 34, 283–288. Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology, 74(5), 1252–1265. doi:10.1037/0022‐3514.74.5.1252 Covington, M. V. (2000). Goal theory, motivation, and school achievement: An integrative review. Annual Review of Psychology, 51(1), 171–200. Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta‐analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627–668; discussion 692–700. doi:10.1037/0033‐2909.125.6.627 Elliott, E. S., & Dweck, C. S. (1988). Goals: An approach to motivation and achievement. Journal of Personality and Social Psychology, 54(1), 5–12. doi:10.1037/0022‐3514.54.1.5 Fishbein, M., Triandis, H. C., Kanfer, F. H., Becker, M., Middlestadt, S. E., & Eichler, A. (2001). Factors influencing behavior and behavior change. In T. A. R. A. Baum & J. E. Singer (Eds.), Handbook of health psychology (pp. 3–17). Mahwah, NJ: Lawrence Erlbaum Associates. Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta‐analysis of effects and processes. Advances in Experimental Social Psychology, 38, 69–119. doi:10.1016/ S0065‐2601(06)38002‐1 Inzlicht, M., & Schmeichel, B. J. (2012). What is ego depletion? Toward a mechanistic revision of the resource model of self‐control. Perspectives on Psychological Science, 7(5), 450–463. doi:10.1177/ 1745691612454134 Kruglanski, A. W., Shah, J. Y., Fishbach, A., Friedman, R., Woo Young, C., & Sleeth‐Keppler, D. (2002). A theory of goal systems. Advances in Experimental Social Psychology, 34, 331–378. doi:10.1016/ S0065‐2601(02)80008‐9 Latham, G. P., & Locke, E. A. (2007). New developments in and directions for goal‐setting research. European Psychologist, 12(4), 290–300. doi:10.1027/1016‐9040.12.4.290 Lewin, K. (1935). A dynamic theory of personality: Selected papers of Kurt Lewin. New York, NY: McGraw‐Hill. Linde, J. A., Jeffery, R. W., Finch, E. A., Ng, D. M., & Rothman, A. J. (2004). Are unrealistic weight loss goals associated with outcomes for overweight women? Obesity Research, 12(3), 569–576. doi:10.1038/oby.2004.65 Locke, E. A., & Latham, G. P. (1990). A theory of goal setting & task performance. Englewood Cliffs, NJ: Prentice‐Hall. Mann, T., Sherman, D., & Updegraff, J. (2004). Dispositional motivations and message framing: A test of the congruency hypothesis in college students. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 23(3), 330–334. doi:10.1037/0278‐ 6133.23.3.330

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Markus, H., & Nurius, P. (1986). Possible selves. American Psychologist, 41(9), 954–969. doi:10.1037/0003‐066X.41.9.954 Mischel, W., Shoda, Y., & Rodriguez, M. (1989). Delay of gratification in children. Science, 244(4907), 933–938. doi:10.1126/science.2658056 Pham, L. B., & Taylor, S. E. (1999). From thought to action: Effects of process‐versus outcome‐based mental simulations on performance. Personality and Social Psychology Bulletin, 25(2), 250–260. doi:10.1177/0146167299025002010 Schönpflug, W. (1986). Behavioral economics as an approach to stress theory. In M. H. Appley & R. Trumbull (Eds.), Dynamics of stress: Physiological, psychological, and social perspectives (pp. 81–98). New York, NY: Plenum Press. Silva, M. N., Markland, D., CarraÇa, E. V., Vieira, P. N., Coutinho, S. R., Minderico, C. S., … Teixeira, P. J. (2011). Exercise autonomous motivation predicts 3‐yr weight loss in women. Medicine and Science in Sports and Exercise, 43(4), 728–737. doi:10.1249/MSS.0b013e3181f3818f Trope, Y., & Liberman, N. (2003). Temporal construal. Psychological Review, 110(3), 403–421. doi:10.1037/0033‐295X.110.3.403

Suggested Reading De Ridder, D. T. D., Lensvelt‐Mulders, G., Finkenauer, C., Stok, F. M., & Baumeister, R. F. (2012). Taking stock of self‐control: A meta‐analysis of how trait self‐control relates to a wide range of behaviors. Personality and Social Psychology Review: An Official Journal of the Society for Personality and Social Psychology, Inc, 16(1), 76–99. doi:10.1177/1088868311418749 Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta‐analysis of effects and processes. Advances in Experimental Social Psychology, 38, 69–119. doi:10.1016/ S0065‐2601(06)38002‐1 Inzlicht, M., & Schmeichel, B. J. (2012). What is ego depletion? Toward a mechanistic revision of the resource model of self‐control. Perspectives on Psychological Science, 7(5), 450–463. doi:10.1177/ 1745691612454134 Mann, T., de Ridder, D., & Fujita, K. (2013). Self‐regulation of health behavior: Social psychological approaches to goal setting and goal striving. Health Psychology, 32(5), 487–498. doi:10.1037/ a0028533

Sex Differences and Health Vicki S. Helgeson and Katilyn Mascatelli Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA

One of the seemingly contradictory findings in health psychology is the paradox that women are sicker than men, yet women outlive men. Women report feeling more pain and physical symptoms than men, perceive their health to be worse than men, and are more likely to be depressed than men. Yet men are more likely than women to die from 9 of the 10 leading causes of death in the United States. There are robust sex differences in psychological and physical health. The goal of this encyclopedia entry is first to orient the reader to the nature of these differences and then to offer explanations rooted in both culture and biology for the disparities. We use the phrase “sex differences” rather than “gender differences” when describing these statistics because we are comparing the biological categories rather than the social categories of male and female.

Mortality There are long‐standing sex differences in mortality in the United States. More males than females die at every age, with the ratio of male‐to‐female deaths peaking in adolescence and young adulthood. For example, for every 121 males who die between the ages of 20 and 24, 44 females die, which results in a male‐to‐female ratio of 2.75 (Centers for Disease Control and Prevention, 2015). This phenomenon makes the finding that women have longer life expectancies than men unsurprising. In 2013, the life expectancy was 81.2 years for females and 76.4 years for males (Centers for Disease Control and Prevention, 2015). This sex difference remains even when examining individual races and ethnicities. Life expectancy for Black women is 78.4 and for Black men is 72.3, whereas White women can expect to live to 81.4 and White men to 76.7 years. Latina women have a life expectancy of 83.8, and Latino men have a life expectancy of 79.1. This sex difference in mortality favoring women has been documented for over a century, although the size of the difference has varied over the decades. In 1900, the life expectancy for

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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men was 46 years and for women was 48 years. As lifespans increased for both men and women over the twentieth century due to improvements in medicine and technology, the sex difference in mortality steadily increased, peaking in 1979 when women outlived men by nearly 8 years. This increased sex difference can be attributed to the reduction in women’s mortality during childbirth and the increase in men’s mortality from heart disease and lung cancer, which can be directly linked to smoking behavior. In recent decades, the sex difference in mortality has narrowed. This change has been credited to the relatively greater decrease in mortality from heart disease and cancer among men compared with women, in part due to men’s higher quit rates of smoking compared with women. Females outlive males in countries outside of the United States, but the magnitude of this difference is not consistent. Developing countries have shorter life expectancies than Westernized countries, and the sex difference in mortality is more variable and oftentimes smaller than that in more developed nations. The smaller sex difference in developing countries might be explained in part by the greater status difference between women and men, which contributes to female infanticide, higher maternal mortality, and poverty‐linked mortality.

Morbidity While lifespans have increased over the past century, rates of illness have increased. This trend reflects the fact that people who once would have died from acute illness live longer lives and acquire chronic diseases with which they can often live. In the early part of the twentieth century, it was much more common for people to die from acute illnesses such as influenza, but now they are able to survive these diseases and live long enough to contract illnesses that are more chronic in nature. Although men have higher death rates from the two major chronic diseases and the two leading causes of death (heart disease and cancer), women suffer from more acute and nonfatal chronic illnesses than men. Women suffer more than men from immune‐related disorders, such as lupus, rheumatoid arthritis, and multiple sclerosis, as well as painful disorders, such as migraines, back pain, and carpal tunnel syndrome, and women report more overall disability than men and poorer self‐ratings of health. A primary contributor to the higher rate of morbidity among women compared with men is depression and depressive symptoms. Women experience more depressive symptoms than men in the general population and are also more likely than men to be diagnosed with clinical depression. This finding holds across many different countries, although the size of the difference varies. Interestingly, this difference emerges at the start of adolescence. That is, before age 13, boys and girls are equally likely to be depressed. Thus, any explanation of gender differences in depression must therefore account for this age‐linked phenomenon.

Biology There are a variety of biological explanations for sex differences in health. First, there are clearly some disorders that are linked to chromosomes that may place women or men at a disadvantage. Females are resistant to some X‐linked chromosomal abnormalities because they have a second X chromosome. This may partly explain why women are less likely than men to suffer from hemophilia, meningitis, and muscular dystrophy. There are also disorders that are linked to the XX genotype, such as breast cancer, osteoarthritis, and glaucoma.

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Hormones play a role in health, and men and women have varying levels of different ­hormones. Estrogen is a hormone that is thought to protect women from heart disease until menopause, after which estrogen levels decrease and women’s levels of heart disease dramatically increase (Fetters, Peterson, Shaw, Newby, & Califf, 1996). The relation between estrogen and heart disease specifically and health more generally is not that simple, however. For example, oral contraceptives can increase the risk for heart disease (especially if combined with smoking), and hormone replacement therapy after menopause has not been linked to a reduction in heart disease (Lowe, 2004). Estrogen is thought to play a role in the development of some cancers, such as breast cancer. Thus, although hormones clearly play a role in health, they are likely to interact with genetic and environmental factors to influence health. A third biological explanation for sex differences in health involves the immune system. It has been suggested that the nature of women’s and men’s immune systems differs, but the nature of these differences is not clear. Women are clearly more vulnerable than men to a host of immune‐related disorders, such as lupus and rheumatoid arthritis. Immune factors also are likely to interact with hormones to influence health.

Health Behaviors Smoking There are a variety of health behaviors for which women and men differ that are directly linked to sex differences in morbidity and mortality. One is smoking. Smoking tobacco is one of the leading causes of heart disease and stroke and contributes to over a dozen kinds of cancer. While its ill effects are well known, the link between sex and smoking, as well as its long‐term implications, is best understood within an historical perspective. In the early twentieth century, smoking was socially unacceptable for women. The smoking rate was twice as high among men than women, until the middle of the twentieth century. At that time, social norms relaxed, and advertising agencies focused their sights on selling cigarettes to women as part of a “liberated” lifestyle, a technique that allowed them to sell their product to a previously untapped other half of the population. Thus, smoking among women increased in the 1960s and 1970s as it became associated with women’s fight for equality. In 1970, 31% of women and 44% of men were smokers. Overall smoking rates for both men and women have decreased dramatically as the health hazards of smoking became more widely known, and in 2013 about 15% of women and 20% of men smoked (Jamal et al., 2014). Paralleling the changes in the rates of smoking for women and men, the sex difference in lung cancer has correspondingly shifted. The incidence of lung cancer has decreased for men but increased for women since the 1980s. In 1960, the male–female ratio of lung cancer was 6.7; in 1990, it was 2.3; and in 2015, it was projected to be 1.1 (American Cancer Society, 2015). Because lung cancer develops over the two to three decades following smoking, the changes in men’s and women’s rates of lung cancer can be directly tied to the changes in their rates of smoking. Although both women and men have been quitting smoking, smoking cessation attempts are less successful for women than for men (Piper et al., 2010). Both nicotine replacement therapy and interventions that combine prescription smoking cessation medication with antidepressants are more effective in helping men than women quit smoking. One reason that women have more trouble quitting smoking than men is that women are more likely to be depressed than men, and smoking is more strongly associated with mood enhancement among women than men.

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Other potential explanations are that smoking is more of a social experience for women than men and that women place higher value on the perceived weight‐loss benefits of smoking than men.

Alcohol Men drink more than women and have more alcohol‐related problems than women. Women tend to have an older age of onset of regular drinking, to drink alcohol less frequently and to be more likely to abstain compared with men. Regardless of the level of alcohol intake, the consequences of alcohol are different for women and men. It takes proportionately less alcohol to have the same effect on a woman as a man. For a woman and a man of the same weight who consume the same amount of alcohol, the woman will have a higher blood‐alcohol level than the man due to the greater ratio of fat to water in a woman’s compared with a man’s body. This means that men have more water available in their systems to dilute consumed alcohol. The greater impact of alcohol on women compared with men may be why alcohol is more strongly associated with cirrhosis of the liver in women than in men. The progression from the first drink to an alcohol‐related problem is faster among women than men. Thus, women are more vulnerable than men to both acute and chronic ailments of alcohol. Despite women being more vulnerable to the health consequences of alcohol compared with men, men suffer more alcohol‐related problems than women. The percentage of all deaths attributable to alcohol consumption is greater for males and females of all races: among Whites, 12.4% for males compared with 3.9% for females; among Blacks, 9.9% for males compared with 4.8% for females; among Native Americans, 25.8% for males compared with 11.6% for females; and among Asian/Pacific Islanders, 6.0% for males compared with 2.2% for females (Shield et al., 2013).

Obesity and Exercise The United States has seen a dramatic rise in obesity rates over the past four decades. Men are more likely than women to be overweight, but women are more likely than men to be obese. Overweight is defined as a body mass index that exceeds 25, whereas obesity is defined as a body mass index that exceeds 30. In the majority of countries in the world, women are more likely than men to be obese. In the United States, the rate of obesity is the same for White women and men (33% women, 32% men), whereas the rate of obesity is much higher for females compared with males among Blacks (50 vs. 37%) and Hispanics (45 vs. 36%; Flegal, Carroll, Ogden, & Curtin, 2010). Aside from the fact that obesity is a risk factor for all causes of mortality for both women and men, the social and economic implications of obesity impact women more than men. Women are more likely than men to be obese for two reasons. First, there are life events that are associated with obesity in women, such as having children and menopause. Women are also more likely to gain weight after marriage than men. Second, women are less likely than men to engage in physical activity, which is associated with a reduced risk of obesity. In addition, physical activity is associated with a lower risk of heart disease, hypertension, type 2 diabetes, and depression for both sexes. One reason that women have lower rates of exercise than men is that men tend to be motivated to be active by competition, whereas women are motivated by concerns about appearance and weight control. Unfortunately, body‐shape motives are associated with lower levels of physical activity compared with other motives for exercise (Segar, Spruijt‐Metz, & Nolen‐Hoeksema, 2006).

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Sexuality and Reproductive Health The importance of understanding the impact of sexuality on health was brought to the forefront in recent decades, mostly due to the emergence of the AIDS epidemic. In particular, the idea that people belonging to sexual and gender minorities (i.e., lesbian, gay, bisexual, and transgender individuals, hereafter referred to as LGBT) might have unique healthcare needs gained validation only after the American Psychiatric Association acknowledged that homosexuality was not a psychiatric illness in 2000. LGBT youth are at greater risk for suicide attempts than non‐LGBT youth (Silenzio, Pena, Duberstein, Cerel, & Knox, 2007). Lesbians are more likely than non‐lesbian women to be overweight and obese (Boehmer, Bowen, & Bauer, 2007), putting them at risk for cardiovascular disease, glucose intolerance, and other obesity‐related morbidities. These health problems are exacerbated by unique barriers LGBT persons encounter when attempting to access healthcare, such as reluctance to disclose sexual orientation to healthcare providers and structural barriers that impede access to health insurance for LGBT persons and their partners. Although men tend to have more permissive attitudes about sex than women, this difference has decreased in magnitude over the past several decades, possibly due to an increase in women becoming more permissive. One potential reason for women’s change in attitudes toward sex is the increased safety, efficacy, and availability of birth control. Virtually all women who have ever engaged in sexual intercourse in 2006–2010 have used some form of contraception, compared with about a third of women in the 1970s (Daniels, Mosher, & Jones, 2013). Today, the majority of women who use birth control are on oral contraceptives (the pill), although the proportion of women using other hormonal methods (the patch or shot) has increased from 4.5% in 1995 to 23% in 2006–2010 (Daniels et al., 2013). The proportion of women using long‐acting reversible contraceptives (e.g., the intrauterine device) has also increased. Another tool women in the United States use to control their reproductive health is abortion, rates of which have been steadily declining over the past two decades. The abortion rate in 2010 for women aged 15–44 (14.6 per 1,000) was 32% lower than the rate in 1990 (27.4 per 1,000; Curtin, Abma, Ventura, & Henshaw, 2013). This decrease in the abortion rate has coincided with a national decline in the birth rate (16.7 per 1,000 women in 1990 compared with 13.0 per 1,000 women in 2010), and the most probable cause for these trends is improved or increased contraceptive use. By using birth control, women are able to delay, plan, and space their pregnancies. Unintended pregnancy is not the only potential consequence of sexual activity—the risk of contracting a sexually transmitted infection (STI) is notably present for both males and females. In 2012, nearly 50,000 individuals were newly diagnosed; new cases occurred disproportionately among men who have sex with men, African Americans and people in their 20s (Centers for Disease Control and Prevention, 2014). The rate of HIV infections in the United States has remained stable since the late 1990s due to widespread prevention and testing efforts.

Sociocultural Factors There are also a set of sociocultural factors that are implicated in the relation of gender to health. Here we consider three such factors: gender‐related traits that are socialized in men and women, risky behavior that is socialized in men, and nurturant roles that are socialized in women.

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Gender‐Related Traits As indicated in Janet Hyde’s (2005) summary of 46 meta‐analyses that compare women and men on a host of cognitive and social abilities, there are far more similarities than differences between men and women. Some of these differences may be attributed to the social roles that men and women possess in our society. Men are socialized to be agentic, and women are socialized to be communal. There is a strong body of research that shows the gender‐related traits of agency and communion are more predictive of health than simply the categorical variable of sex (Helgeson, 2012). In fact, there is strong evidence that agency is associated with good mental health outcomes and can account for sex differences in those outcomes (Helgeson, 2012). Agency is also related to good health practices, such as exercise, healthy eating, and good dental hygiene. However, communion is typically unrelated to health and health behaviors. Instead, communion is related to more supportive relationships and can account for sex differences in support. However, as discussed later, relationships are a double‐edged sword for women. Thus, agency but not communion may account for some of men’s lower rates of morbidity compared with that of women. Aside from agency and communion, two more specific gender‐related traits have been examined in their connection with health—unmitigated agency and unmitigated communion (Helgeson, 2012). Unmitigated agency is characterized by an excessive focus on the self and a negative view of others. Unmitigated agency has been linked to reckless driving, substance abuse, poor health behavior, risky behavior, and poor adjustment to disease (Helgeson, 2012). Unmitigated communion reflects a focus on others to the exclusion of the self and is defined by overinvolvement with others and self‐neglect. Unmitigated communion has been linked to depressive symptoms, poor healthcare, and poor adjustment to disease (Helgeson, 2012). Even though some of the relations to health are the same, the reasons differ. For example, unmitigated agency is related to poor health behaviors due to feelings of superiority, beliefs in invulnerability, and possibly psychological reactance—that one does not need to engage in specific behaviors in order to be healthy. By contrast, unmitigated communion is related to poor health behavior due to an involvement with others that detracts from tending to one’s own needs.

Risky Behavior Men are socialized to take risks, whereas women are not. Parents are more encouraging of risk taking in boys than girls. For example, one study had parents watch a video of children on a playground and asked what they would say (Morrongiello & Dawber, 2000). Parents were more likely to intervene and warn of injury when they thought the child was a girl than a boy. Not surprisingly, men report engaging in more risky behaviors than women (Byrnes, Miller, & Schafer, 1999). As evidence of risk‐taking behavior, men work in more hazardous jobs compared with women and have much higher rates of fatal work injuries compared with women; men handle more risky chores around the house compared with women, such as climbing on the roof; men are more aggressive drivers than women; and men engage in riskier leisure activities compared with women, such as skiing, mountain biking, and skydiving. Men are also more likely than women to own guns, and a greater use of guns contributes to a greater number of fatal gun accidents. Thus, risk‐taking behavior may account partly for men’s greater mortality from accidents and injuries compared with women. The male‐to‐female ratio of death from injuries—intentional and unintentional—persists across ethnic groups in the United States but is largest for Hispanic Whites and lowest for Asian/ Pacific Islanders.

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Nurturant Roles Women report more supportive relationships than men, but these relationships have been cast as a double‐edged sword. Relationships are not only sources of support for women but also sources of strain. That is, women not only receive but also provide support. Women are socialized to be caretakers, and caregiving has costs to health. According to the “nurturant role hypothesis,” women’s caretaking leads to increased exposure to communicable diseases, fatigue and vulnerability to illness from taking care of others, and self‐neglect because caring for others takes precedence over caring for the self (Gove, 1984). In the United States, women are more likely than men to engage in volunteer work, women are more likely than men to be the primary caregivers of children, and women are more likely than men to take care of sick family members—spouses, parents, or children. In addition, caregiving is associated with greater burden and greater distress among women than men (Pinquart & Sörensen, 2006).

Conclusion and Future Directions Health—psychological and physical—is one domain for which sex differences persist, although the size of these differences has shifted substantially over the last century. We reviewed several different classes of explanations for these differences—those involving biology, health behaviors, reproduction, and sociocultural variables – but note that these explanations have largely been examined in isolation of one another. It is increasingly recognized that biological, behavioral, and social factors are likely to interact to influence health. Future research aimed to increase our understanding of sex differences in health would benefit from a multidisciplinary approach. Research in this area also is expanding in terms of the methods used to study sex differences in health. Correlational research is being replaced with longitudinal studies, research on middle‐ class White persons is increasingly cross‐cultural, and experimental approaches have been used to understand how we react to men and women as well as how hormones influence behavior. Finally, it is increasingly recognized that the category of being male versus female intersects with multiple other categories, such as race, ethnicity, and social class. Future research on sex differences in health would benefit from incorporating greater intersectionality in its approach.

Author Biographies Vicki S. Helgeson is professor of psychology at Carnegie Mellon University where she studies the intersections of gender, relationships, and health. She studies how people adjust to chronic illness with a focus on the implications of gender‐role socialization and social environmental factors for health. Her most recent work involves couples in which one person has diabetes, with a focus on communal coping. She recently completed the 5th revision of her textbook titled Psychology of Gender. Katilyn Mascatelli is a graduate student in the social/health psychology program at Carnegie Mellon University in Pittsburgh, Pennsylvania. Her research interests include gender, stereotyping, and discrimination based on violations of gender stereotypes.

References American Cancer Society. (2015). Cancer facts & figures 2015. Retrieved from http://www.cancer.org/ acs/groups/content/@editorial/documents/document/acspc‐044552.pdf

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Boehmer, U., Bowen, D. J., & Bauer, G. R. (2007). Overweight and obesity in sexual‐minority women: Evidence from population‐based data. American Journal of Public Health, 97(6), 1134–1140. doi:10.2105/AJPH.2006.088419 Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999). Gender differences in risk taking: A meta‐analysis. Psychological Bulletin, 125, 367–383. doi:10.1037/0033‐2909.125.3.367 Centers for Disease Control and Prevention. (2014). Sexually transmitted disease surveillance 2012. Retrieved from http://www.cdc.gov/std/stats12/surv2012.pdf Centers for Disease Control and Prevention. (2015). Deaths: Final data for 2013 (Vol. 64). Retrieved from http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_02.pdf Curtin, S. C., Abma, J. C., Ventura, S. J., & Henshaw, S. (2013). Pregnancy rates for U.S. women continue to drop. National Center for Health Statistics, 136, 1–8. Daniels, K., Mosher, W. D., & Jones, J. (2013). Contraceptive methods women have ever used: United States, 1982–2010. National Health Statistics Reports, 62, 1–15. Fetters, J. K., Peterson, E. D., Shaw, L. J., Newby, K., & Califf, R. M. (1996). Sex‐specific differences in coronary artery disease risk factors, evaluation, and treatment: Have they been adequately evaluated? American Heart Journal, 131(4), 796–813. doi:10.1016/S0002‐8703(96)90289‐6 Flegal, K. M., Carroll, M. D., Ogden, C. L., & Curtin, L. R. (2010). Prevalence and trends in obesity among U.S. adults, 1999–2008. Journal of the American Medical Association, 303, 235–241. doi:10.1001/jama.2009.2014 Gove, W. R. (1984). Gender differences in mental and physical illness: The effects of fixed roles and nurturant roles. Social Science & Medicine, 19(2), 77–91. doi:10.1016/0277‐9536(84)90273‐9 Helgeson, V. S. (2012). Gender and health: A social psychological perspective. In A. Baum, T. Revenson, & J. Singer (Eds.), Handbook of health psychology (2nd ed., pp. 519–537). New York, NY: Psychology Press. Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60, 581–592. doi:10.1037/ 0003‐066X.60.6.581 Jamal, A., Agaku, I. T., O’Connor, E., King, B. A., Kenemer, J. B., & Neff, L. (2014). Current cigarette smoking among adults—United States, 2005–2013. Morbidity and Mortality Weekly Report, 63(47), 1108–1112. Lowe, G. D. O. (2004). Hormone replacement therapy and cardiovascular disease: Increased risks of venous thromboembolism and stroke, and no protection from coronary heart disease. Journal of Internal Medicine, 256, 361–374. doi:10.1111/j.1365‐2796.2004.01400.x Morrongiello, B. A., & Dawber, T. (2000). Mothers’ responses to sons and daughters engaging in injury‐risk behaviors on a playground: Implications for sex differences in injury rates. Journal of Experimental Child Psychology, 76, 89–103. doi:10.1006/jecp.2000.2572 Pinquart, M., & Sörensen, S. B. (2006). Gender differences in caregiver stressors, social resources, and health: An updated meta‐analysis. Journal of Gerontology, 61B, 33–45. doi:10.1093/geronb/ 61.1.P33 Piper, M. E., Cook, J. W., Schlam, T. R., Jorenby, D. E., Smith, S. S., Bolt, D. M., & Loh, W. Y. (2010). Gender, race, and education differences in abstinence rates among participants in two randomized smoking cessation trials. Nicotine & Tobacco Research, 12, 647–657. doi:10.1093/ntr/ntq067 Segar, M., Spruijt‐Metz, D., & Nolen‐Hoeksema, S. (2006). Go figure? Body‐shape motives are associated with decreased physical activity participation among midlife women. Sex Roles, 54, 175–187. doi:10.1007/s11199‐006‐9336‐5 Shield, K. D., Gmel, G., Kehoe‐Chan, T., Dawson, D. A., Grant, B. F., & Rehm, J. (2013). Mortality and potential years of life lost attributable to alcohol consumption by race and sex in the United States in 2005. PLoS One, 8, e51923. doi:10.1371/journal.pone.0051923 Silenzio, V. M., Pena, J. B., Duberstein, P. R., Cerel, J., & Knox, K. L. (2007). Sexual orientation and risk factors for suicidal ideation and suicide attempts among adolescents and young adults. American Journal of Public Health, 97(11), 2017–2019. doi:10.2105/AJPH.2006.095943

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Suggested Reading Bogart, L. M., Revenson, T. A., Whitfield, K. E., & France, C. R. (2014). Introduction to the special section on lesbian, gay, bisexual, and transgender (LGBT) health disparities: Where we are and where we’re going. Annals of Behavioral Medicine, 47, 1–4. doi:10.1007/s12160‐013‐9574‐7 Case, A., & Paxson, C. (2005). Sex differences in morbidity and mortality. Demography, 42, 189–214. doi:10.1353/dem.2005.0011 Fischer, J., Jung, N., Robinson, N., & Lehmann, C. (2015). Sex differences in immune responses to infectious diseases. Infection, Advance online publication. doi:10.1007/s15010‐015‐0791‐9 Rogers, R. G., Everett, B. G., Onge, J. M. S., & Krueger, P. M. (2010). Social, behavioral, and biological factors, and sex differences in mortality. Demography, 47(3), 555–578. doi:10.1353/dem.0.0119 Sanchez‐Lopez, M. d. P., & Cuellar‐Flores, I. (2012). The impact of gender roles on health. Women & Health, 52, 182–196. doi:10.1080/03630242.2011.652352

Social‐Evaluative Threat Sally S. Dickerson Department of Psychology, Pace University, New York, NY, USA

Have you ever thought that others were negatively judging you while you were giving an important talk or presentation? Have you felt criticized by a relationship partner, friend, or acquaintance or felt that you were judged or rejected by others on the basis of a stigma, deval­ ued characteristic, or an aspect of your identity? These types of situations can be inherently difficult, painful, and threatening—and they all share an underlying core element of social evaluation. Social‐evaluative threat (SET) occurs when an important aspect of the self could be judged by others (Dickerson & Kemeny, 2004). Theoretical and empirical work has docu­ mented how these social‐evaluative situations may be associated with patterns of emotional and physiological reactivity. Why are social‐evaluative situations threatening? Humans have a fundamental need to belong, and maintaining the social acceptance of others is critical to well‐being and survival (Baumeister & Leary, 1995; Holt‐Lunstad, Smith, & Layton, 2010). Situations with the potential for evalu­ ation or rejection therefore jeopardize this fundamental goal. We have proposed that social‐ evaluative situations may elicit a coordinated psychobiological response, characterized by emotional and physiological changes (e.g., Dickerson, Gruenewald, & Kemeny, 2004). Our theoretical and empirical work has documented that SET can lead to increases in self‐conscious cognitions and emotions (e.g., shame, embarrassment) and other negative cognitive and affec­ tive states. Physiologically, SET can elicit increases in cortisol, an important hormone that is involved in energy mobilization, and can help organize the body’s response to stressors; SET can also lead to changes in the cardiovascular and immune systems. If the physiological activation elicited by SET is experienced chronically or persistently, there could be implications for health.

Social‐Evaluative Threat and Cortisol Reactivity A great deal of research has tested whether SET can elicit the hormone cortisol. In a meta‐­ analytic review of 208 acute laboratory stressor studies that assessed cortisol as an outcome

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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(Dickerson & Kemeny, 2004), we found that stressors characterized by SET (e.g., evaluative audience present) were associated with substantially larger cortisol responses than those with­ out this element (e.g., completing difficult math problems alone). The effect size for social‐ evaluative stressors was nearly three times the magnitude of the non‐SET tasks (d = 0.67 vs. d  =  0.15). The increase in cortisol was particularly pronounced when stressors were both social‐evaluative and uncontrollable. Social‐evaluative, uncontrollable stressors were associated with large increases in cortisol (d = 0.92); however, those without either element did not on average lead to increases in this parameter. Taken together, this meta‐analysis demonstrates that social‐evaluative stressors—particularly when uncontrollable—can elicit strong, substan­ tial cortisol reactivity. Subsequent studies have manipulated the social‐evaluative context of the stressor to experi­ mentally test the conditions in which cortisol reactivity is observed. Gruenewald, Kemeny, Aziz, and Fahey (2004) randomly assigned participants to deliver a speech and complete a computerized math task either in front of an evaluative audience (SET) or alone in a room (non‐SET). Participants in both conditions reported that the task was challenging and difficult and required effort (e.g., “stressful”); however, only the SET condition elicited increases in cortisol. Performing the identical task in the absence of social evaluation did not lead to cor­ tisol changes, demonstrating that the social‐evaluative nature of the stressor can shape the physiological response. This SET manipulation has now been used in a number of studies and the primary findings replicated using laboratory performance stressors (e.g., Bosch et al., 2009; Dickerson, Mycek, & Zaldivar, 2008; Het, Rohleder, Schoofs, Kirschbaum, & Wolf, 2009; Taylor et al., 2010) and physical stressors (e.g., cold pressor task; Schwabe, Haddad, & Schachinger, 2008). Other studies have examined whether naturally occurring instances of SET (e.g., real‐life competi­ tions) can also elicit cortisol reactivity. Rohleder, Beulen, Chen, Wolf, and Kirschbaum (2007) collected cortisol from ballroom dancers during both rehearsal and competition days. They found that cortisol levels were much higher on the competition days, when their performances were being judged by an evaluative panel, compared with days in which they were rehearsing (and not evaluated). Further, the dancers who reported feeling more stressed by the judges also showed greater increases in cortisol. Another study examined cortisol responses during a competitive audition for a musical theater production (Boyle, Lawton, Arkbage, Thorell, & Dye 2013). Audition exposure led to increases in cortisol, and cortisol reactivity correlated with subjective ratings of SET. Taken together, these studies demonstrate that naturally occur­ ring performance contexts characterized by social evaluation can also trigger increases in cor­ tisol and that cortisol can be linked to perceptions of social evaluation.

Components of Social‐Evaluative Threat Linked with Cortisol Reactivity We have been interested in unpacking the critical components of the social‐evaluative context that elicit increases in cortisol. For example, is the mere social presence of others enough to trigger cortisol reactivity, or does the social evaluation need to be explicit to lead to increases in this parameter? To test this question, we conducted a study with three experimental groups (Dickerson et al., 2008). Similar to Gruenewald et al. (2004), participants delivered a speech in front of an evaluative audience (SET) or alone in a room (non‐SET). We also included a third experimental condition, in which a research assistant was inattentive (working on a l­ aptop in the room) (PRES). We found that the SET condition elicited a robust increase in cortisol;

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however, the non‐SET and PRES conditions did not lead to increases in this parameter. This suggests that simply having others present during a performance stressor may not be enough to elicit increases in cortisol; instead, the perceived evaluation of others may be critical for activating this physiological system. Others have examined whether SET that occurs remotely can trigger cortisol reactivity; do the evaluative others need to be physically present to elicit a response? Several studies have found that remote evaluation (e.g., participants believe they are being evaluated through a one‐way mirror) is sufficient to increase cortisol levels (e.g., Kelly, Matheson, Martinez, Merali, & Anisman, 2007); evaluation by avatars in an immersive virtual reality environment has also been shown to elicit cortisol reactivity (Fallon, Careaga, Sbarra, & O’Connor, 2016). One study that experimentally manipulated whether the evaluators were physically present or remote (virtual audience or one‐way mirror; Kelly et al., 2007) found having an audience that was physically present elicited about cortisol increases of about three times the magnitude of more remote forms of social evaluation. While future research should test key moderators (e.g., immersive nature or believability of the remote evaluation), it is clear that when percep­ tions of evaluation are evoked (whether the evaluation is in person or virtual), the cortisol system can be activated. The threat inherent in a social‐evaluative context is multidimensional and could lead to concerns of loss of social standing or loss of acceptance. Smith and Jordan (2015) conducted a study to delineate whether status or acceptance concerns are driving cortisol responses to SET. They randomly assigned participants to complete a public speaking task in one of four conditions: a low‐threat condition, in which they were told they would not be evaluated; a high‐acceptance threat condition, in which they would be judged in terms of how likeable, friendly, and interesting they were; a high‐status threat condition, in which they were judged on how competent, skilled, and intelligent they appeared; and a high‐acceptance/high‐status condition, where the instructions were combined. They found that both status and acceptance threats elicited greater increases in cortisol compared with the low‐threat condition and the largest increases were observed for the high‐status/high‐acceptance threat group. This sug­ gests that both status concerns and acceptance concerns can lead to increases in cortisol and contexts that trigger both are potent elicitors of this physiological parameter. The behavior of the evaluators could also be an element that influences cortisol reactivity to SET. Cundiff, Smith, Baron, and Uchino (2016) found that an evaluative interaction with a highly dominant individual (e.g., assertive voice) led to greater cortisol responses compared with interactions with someone low on dominance (e.g., spoke deferentially). This suggests that qualities and behaviors of the evaluative others can also shape responses to SET. Taken together, these experimental studies demonstrate that contexts that heighten the evaluative potential (e.g., explicit evaluation, dominant interaction partner, threaten salient goals) lead to pronounced increases in cortisol. Future research should continue to unpack the critical elements of the social‐evaluative situation that trigger this system and identify impor­ tant moderators of reactivity.

Emotional and Cognitive Effects of Social‐Evaluative Threat SET can elicit robust increases in cortisol, but what are the emotional experiences of individu­ als in these situations? We have been interested in the affective changes that occur under social‐ evaluative contexts. Consistent with theoretical and empirical literature that self‐conscious emotions and affective self‐evaluative states are sensitive to assessments of declining status or

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acceptance (e.g., Gilbert, 2000; Leary, 2007), we have found that the SET conditions are associated with greater increases in self‐conscious cognitions and emotions compared with the non‐SET conditions (Dickerson et al., 2008; Gruenewald et al., 2004). Further, there were no differences between conditions in other emotional states, such as fear or sadness, demonstrat­ ing that the self‐conscious states were most sensitive to the social‐evaluative context of the stressor. In both of these studies, we also found correlations between the self‐conscious emo­ tions and cortisol; those who showed the greatest increases in self‐conscious emotions also showed the greatest increases in cortisol. There was not a relationship between cortisol reactiv­ ity and other negative emotions assessed. Taken together, this suggests that social‐evaluative conditions can elicit both self‐conscious emotions and cortisol reactivity and these systems could be activated in parallel under SET. However, this self‐conscious emotion/cortisol asso­ ciation could vary depending on the type of SET experienced (e.g., status vs. acceptance threats; Smith & Jordan, 2015) or individual difference factors (e.g., subjective social status; Gruenewald, Kemeny & Aziz, 2006); additional research on the emotional correlates of SET, and accompanying physiological changes, is warranted. For some, the social‐evaluative experience may not end when the interaction is over. SET can lead to rumination, or repetitive, unwanted, past‐oriented thoughts. In our experimental studies that manipulate SET, we have assessed post‐task negative thoughts to test if social‐ evaluative contexts are more likely to trigger rumination and if rumination is linked to persis­ tent cortisol activation. Consistent with this premise, we have found that explicit social‐evaluative conditions elicit more negative thoughts (e.g., “I looked stupid”) in the 10 min post‐task compared with other conditions (Zoccola, Dickerson, & Zaldivar, 2008). Additionally, those who reported higher levels of post‐task rumination also had greater cortisol reactivity, and levels remained elevated longer compared with those who reported lower levels of rumination following the stressor. We conducted another study to test whether the SET‐rumination link persists beyond the immediate post‐task period (Zoccola, Dickerson, & Lam, 2012). Replicating our previous finding, post‐task rumination was higher in the SET condition compared with a non‐SET condition 10 min post‐stressor. In this study, we also assessed rumination 40 min post‐task, later that evening and 3–5 days later. At all time points, participants in the social‐evaluative condition reported higher levels of rumination compared with those in the non‐SET condi­ tion. Further, we found this effect was mediated by self‐conscious cognitions and emotions; the SET condition led to greater increases in self‐conscious cognitions and emotions, which in turn was associated with greater rumination. This demonstrates that SET contexts can elicit ruminative thought even days beyond the initial stressor, potentially through the ability to elicit self‐conscious cognitions and emotions.

Social‐Evaluative Threat Activates Other Physiological Systems While much of the research on SET has examined activation of the cortisol system, these situ­ ations can elicit changes in a wide range of physiological parameters. SET can lead to increases in cardiovascular (e.g., systolic blood pressure, diastolic blood pressure, heart rate) and sym­ pathetic (e.g., epinephrine, norepinephrine, alpha‐amylase) activity in both laboratory (Kirschbaum, Pirke, & Hellhammer, 1993; Nater & Rohleder, 2009) and naturally occurring contexts (Lehman & Conley, 2010). Studies that have manipulated the social context of the stressor often observe greater cardiovascular reactivity under conditions that were social evalu­ ative or where the evaluative potential was emphasized (e.g., Bosch et al., 2009).

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SET can also lead to changes in immunologic parameters. For example, we conducted a study in which we randomly assigned participants to a SET or non‐SET laboratory stressor (Dickerson, Gable, Irwin, Aziz, & Kemeny, 2009). We found that the SET condition elicited increases in the production of the pro‐inflammatory cytokine TNF‐α compared with a non‐ SET condition. Further, we found that those who felt the most evaluated during the task showed the greatest increases in TNF‐α production, suggesting a link between perceptions of social evaluation and markers of inflammation. Future research should examine the different systems that can be activated under SET and their interactions.

Implications for Health While acute instances of SET can elicit short‐term increases in cortisol and changes in other systems, experiencing chronic forms of SET could take a physiological toll. For example, a meta‐analysis found that chronic social stressors were associated with higher morning and evening cortisol levels compared with nonsocial stressors (Miller, Chen, & Zhou, 2007). Over‐activation and/or dysregulation in cortisol and other physiological systems is thought to impact health over time and put individuals at greater risk of disease (e.g., McEwen, 1998). Future research should pair acute laboratory reactivity methodologies with longitudinal assess­ ments, to further clarify how patterns of physiological reactivity to SET may be associated with long‐term negative health outcomes.

Author Biography Sally S. Dickerson is the associate provost for research and a professor of psychology at Pace University. Her research integrates psychological and biological levels of analysis by applying theories of social and emotional processes to the context of stress and disease. She examines how social‐evaluative threat and accompanying self‐conscious emotional responses affect neu­ roendocrine and immune outcomes and how individual differences may heighten vulnerability to these psychological and physiological changes.

References Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117, 497–529. http://dx.doi. org/10.1037/0033‐2909.117.3.49 Bosch, J. A., De Gues, E. J. C., Carroll, D., Goedhart, A. D., Anane, L. A., Veldhuizen, et al. (2009). A general enhancement of autonomic and cortisol responses during social‐evaluative threat. Psychosomatic Medicine, 71, 877–885. http://dx.doi.org/10.1097/PSY.0b013e3181baef05 Boyle, N. B., Lawton, C., Arkbage, K., Thorell, L., & Dye, L. (2013). Dreading the boards: Stress response to a competitive audition characterized by social‐evaluative threat. Anxiety, Stress and Coping, 26, 690–699. http://dx.doi.org/10.1080/10615806.2013.766327 Cundiff, J. M., Smith, T. W., Baron, C. E., & Uchino, B. N. (2016). Hierarchy and health: Physiological effects of interpersonal experiences associated with socioeconomic position. Health Psychology, 35, 356–365. http://dx.doi.org/10.1037/hea0000227 Dickerson, S. S., Gable, S. L., Irwin, M. R., Aziz, N., & Kemeny, M. E. (2009). Social‐evaluative threat and proinflammatory cytokien regulation: An experimental laboratory investigation. Psychological Science, 20, 1237–1244. http://dx.doi.org/10.1111/j.1467‐9280.2009.02437.x

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Dickerson, S. S., Gruenewald, T. L., & Kemeny, M. E. (2004). When the social self is threatened: Shame, physiology, and health. Journal of Personality, 72, 1191–1216. http://dx.doi.org/10.1111/ j.1467‐6494.2004.00295.x Dickerson, S. S., & Kemeny, M. E. (2004). Acute stressors and cortisol responses: A theoretical integration and synthesis of laboratory research. Psychological Bulletin, 130, 355–391. http://dx. doi.org/10.1037/0033‐2909.130.3.355 Dickerson, S. S., Mycek, P. M., & Zaldivar, F. P. (2008). Negative social evaluation, but not mere social presence, elicits cortisol responses to a laboratory stressor task. Health Psychology, 27, 116–121. http://dx.doi.org/10.1037/0278‐6133.27.1.116 Fallon, M. A., Careaga, J. S., Sbarra, D. A., & O’Connor, M. F. (2016). Utility of a virtual Trier Social Stress Test: Initial findings and benchmarking comparisons. Psychosomatic Medicine, 78, 835–840. http://dx.doi.org/10.1097/PSY.0000000000000338 Gilbert, P. (2000). Varieties of submissive behavior as forms of social defense: Their evolution and role in depression. In L. Sloman & P. Gilbert (Eds.), Subordination and defeat: An evolutionary approach to mood disorders and their therapy (pp. 3–45). Mahwah, NJ: Erlbaum. Gruenewald, T. L., Kemeny, M. E., & Aziz, N. (2006). Subjective social status moderates cortisol responses to threat. Brain, Behavior and Immunity, 20, 410–419. https://doi.org/10.1016/j. bbi.2005.11.005 Gruenewald, T. L., Kemeny, M. E., Aziz, N., & Fahey, J. L. (2004). Acute threat to the social self: Shame, social self‐esteem, and cortisol activity. Psychosomatic Medicine, 66, 915–924. http://dx. doi.org/10.1097/01.psy.0000143639.61693.ef Het, S., Rohleder, N., Schoofs, D., Kirschbaum, C., & Wolf, O. T. (2009). Neuroendocrine and psycho­ metric evaluation of a placebo version of the “Trier Social Stress Test”. Psychoneuronendocrinology, 34, 1075–1086. http://dx.doi.org/10.1016/j.psyneuen.2009.02.008 Holt‐Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: A meta‐ analytic review. PLoS Medicine, 7, e1000316. https://doi.org/10.1371/journal.pmed.1000316 Kelly, O., Matheson, M., Martinez, A., Merali, Z., & Anisman, H. (2007). Psychosocial stress evoked by a virtual audience: Relation to neuroendocrine activity. Cyberpsychology & Behavior, 10(5), 655– 662. http://dx.doi.org/10.1089/cpb.2007.9973 Kirschbaum, C., Pirke, K.‐M., & Hellhammer, D. H. (1993). The “Trier Social Stress Test”—A tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology, 28, 76– 81. http://dx.doi.org/10.1159/000119004 Leary, M. R. (2007). Motivational and emotional aspects of the self. Annual Review of Psychology, 58, 317–344. http://dx.doi.org/10.1146/annurev.psych.58.110405.085658 Lehman, B. J., & Conley, K. M. (2010). Momentary reports of social‐evaluative threat predict ambu­ latory blood pressure. Social Psychological and Personality Science, 1, 51–56. http://dx.doi. org/10.1177/1948550609354924 McEwen, B. S. (1998). Stress, adaptation, and disease. Allostasis and allostatic load. Annals of the New York Academy of Sciences, 840, 33–44. http://dx.doi.org/10.1111/j.1749‐6632.1998.tb09546.x Miller, G. E., Chen, E., & Zhou, E. (2007). If it goes up, must it come down? Chronic stress and the hypothalamic‐pituitary‐adrenocortical axis in humans. Psychological Bulletin, 133, 25–45. http:// dx.doi.org/10.1037/0033‐2909.133.1.25 Nater, U. M., & Rohleder, N. (2009). Salivary alpha‐amylase as a non‐invasive biomarker for the sympathetic nervous system: Current state of research. Psychoneuroendocrinology, 34, 486–496. http://dx.doi.org/10.1016/j.psyneuen.2009.01.014 Rohleder, N., Beulen, S. E., Chen, E., Wolf, J. M., & Kirschbaum, C. (2007). Stress on the dance floor: The cortisol stress response to social‐evaluative threat in competitive ballroom dancers. Personality and Social Psychology Bulletin, 33, 69–84. http://dx.doi.org/10.1177/0146167206293986 Schwabe, L., Haddad, L., & Schachinger, H. (2008). HPA axis activation by a socially evaluated cold‐ pressor task. Psychoneuroendocrinology, 33, 890–895. http://dx.doi.org/10.1016/j.psyneuen. 2008.03.001

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Smith, T. W., & Jordan, K. D. (2015). Interpersonal motives and social‐evaluative threat: Effects of acceptance and status stressors on cardiovascular reactivity and salivary cortisol response. Psychophysiology, 52, 269–276. http://dx.doi.org/10.1111/psyp.12318 Taylor, S. E., Seeman, T. E., Eisenberger, N. I., Kozanian, T. A., Moore, A. N., & Moons, W. G. (2010). Effects of a supportive or unsupportive audience on biological and psychological responses to stress. Journal of Personality and Social Psychology, 98, 47–56. http://dx.doi.org/10.1037/a0016563 Zoccola, P. M., Dickerson, S. S., & Lam, S. (2012). Eliciting and maintaining ruminative thought: The role of social‐evaluative threat. Emotion, 12, 673–677. http://dx.doi.org/10.1037/a0027349 Zoccola, P. M., Dickerson, S. S., & Zaldivar, F. P. (2008). Rumination and cortisol responses to labora­ tory stressors. Psychosomatic Medicine, 70, 661–667. http://dx.doi.org/10.1097/PSY. 0b013e31817bbc77

Suggested Reading Dickerson, S. S., Gruenewald, T. L., & Kemeny, M. E. (2011). Physiological effects of social threat: Implications for health. In J. Decety & J. Cacioppo (Eds.), Handbook of social neuroscience (pp. 787–803). New York, NY: Oxford University Press. Dickerson, S. S., & Kemeny, M. E. (2004). Acute stressors and cortisol responses: A theoretical integration and synthesis of laboratory research. Psychological Bulletin, 130, 355–391. Miller, G. E., Chen, E., & Cole, S. W. (2009). Health psychology: Developing biological plausible models linking the social world and physical health. Annual Review of Psychology, 60, 501–524. Zoccola, P. M., & Dickerson, S. S. (2012). Assessing the relationship between rumination and cortisol: A review. Journal of Psychosomatic Research, 73, 1–9.

Social Factors in Diet and Obesity Gbolahan O. Olanubi and A. Janet Tomiyama Psychology Department, University of California Los Angeles, Los Angeles, CA, USA

Introduction When attempting to understand factors that influence diet, or food choice, people are quick to assume that people eat when they are hungry and stop when they are full. Hunger, however, turns out to be one of the least important factors in eating. This is because there are a multitude of other social, economic, and psychological factors at play that shape our eating choices, which can over time affect weight and body mass index (BMI). The purpose of this encyclopedia entry is to describe the social factors that are involved in eating, dietary choices, and obesity. We organize our discussion of social factors roughly from the macro‐ to microlevel. The literatures we cover are enormous, but space and reference constraints allow only for highlights of each topic. Throughout the entry, we note when there are seminal review papers in each area. Ultimately, our goal is to highlight the role of social factors in diet and obesity and to position psychology in its rightful place as a key player in diet and obesity research.

Culture Culture has been shaping food choice for millennia. Rozin and Vollmecke (1986) noted that if you want to know as much as possible about a person’s food preference, simply ask, “What is your culture or ethnic group?” Culture, a way of life of a society or an amalgam of shared practices and meanings among a group that guides interactions and is shaped by experience, plays a major role in diet and food choice. Culture influences attitudes toward food, our preferences, and perceptions of what is healthy compared with what is unhealthy. Everyone needs to eat, but not everyone needs to be happy about eating. Among Americans there is an attitude not only that food is “nutrient,” a source of nourishment, but also that it may be dangerous—almost as dangerous as abstaining from food (Rozin, 1996). Americans

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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also endorse attitudes that even trace amounts of salt and fat may be harmful (Rozin, 1996). In contrast to American attitudes, the French have a more positive attitude toward food, viewing it as both nutrient and a source of pleasure. Considering these cultural differences in attitudes, there are also cultural differences in consumption. The French, compared with Americans, eat more high‐fat foods (yet tend to be healthier—a phenomenon known as the French paradox; Ferrières, 2004). Although Americans have more negative attitudes than the French about food, Americans eat larger servings of food. Expanding on this research Rozin, Fischler, Imada, Sarubin, and Wrzesniewski (1999) examined cultural differences in food attitudes and behavior in the United States, France, Belgium, and Japan. Congruent with previous research, they found that Americans had the highest concern about healthiness of food choice, worried about the fattening effects of food as opposed to the savoring of food, and were more likely to make nutritional, as opposed to culinary, associations with food. Americans also had the lowest food‐positive attitudes, considering food less important and drawing little pleasure from food. Finally, despite the worry, concern, and attitudes toward food, Americans also reported viewing themselves as the least healthy eaters compared with the other countries.

Socioeconomic Status Food choice is predicated upon access to foods. Socioeconomic status, comprising income, education, and occupation, plays a focal role in determining purchasing power and food choice. Cost has been one of the most important factors in food choice for low‐income individuals. Ironically, the cost of food is actually more expensive in low‐income neighborhoods compared with high‐income neighborhoods because of a dearth of low‐cost supermarkets and presence of higher‐priced convenience stores (see Morland, Wing, Roux, & Poole, 2002). If low‐income families have a restricted budget but more expensive foods, what are they eating? Low‐income families may be cornered into low‐cost energy dense diets (Darmon, Ferguson, & Briend, 2003). Energy density refers to calories per edible weight of a food item. Energy dense diets consist of eating foods high in energy density (cereals, meats, sweets) more so than foods low in energy density (fruits, vegetables, whole grains). A low‐cost energy dense diet would essentially attempt to maximize calories per dollar while still maintaining sufficient nutrients. Supporting this perspective, Darmon et al. (2003) used a computer simulation to model the food choices of individuals under economic constraint. Under increasing economic constraint the model selected for less fruits and vegetables (FV), meats, fish, and cheese and higher contents of milk, sweets, and starchy foods compared with a normal diet. Fortunately, this barrier appears surmountable. Researchers have intervened to remove financial barriers to increase selection and consumption of FV for low‐income individuals and found that subsidizing FV costs increases consumption for low‐income individuals (Anderson et al., 2001).

Social Norms Norms surrounding food consumption are powerful drivers of eating behavior (see Herman, Roth, & Polivy, 2003). In general, an inhibitory social norm surrounding eating exists in Western countries, based on widespread weight stigma and negative stereotypes of those who

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overeat (Vartanian, Herman, & Polivy, 2007). This inhibitory norm appears to be most ­pronounced in eating among strangers, due to impression management concerns, and true particularly for women, who are seen as more feminine and socially appealing when they eat less (Vartanian et al., 2007). Inhibitory norms are highest with a non‐eating observer, according to a meta‐analysis of studies that experimentally manipulated awareness of one’s eating being observed (Robinson, Hardman, Halford, & Jones, 2015). In contrast, eating in the presence of close others appears to trigger social facilitation of eating (de Castro, 2002). In one illuminating study, Howland, Hunger, and Mann (2012) experimentally manipulated eating norms in existing friend groups. Two members of a friend trio were enlisted to set an eating norm, and the third member’s eating was examined. Participants followed whatever eating norm was set, and these eating norms carried over into situations where participants were then eating alone. A subtype of norms are informational norms, wherein information about eating behavior is communicated (e.g., through visual cues of leftover food). In a meta‐analysis of 15 studies examining experimental manipulations, Robinson, Thomas, Aveyard, and Higgs (2014) found that both high and low informational intake norms had moderate effects on eating behavior. Thus, there appear to be general social norms that affect eating behavior, but these processes are amenable to manipulation, pointing to potential intervention targets. An interesting offshoot related to norms is research that investigates the weight of others in determining an individual’s food intake. Some studies have found that non‐dieters consume more calories when their food server is overweight compared with when she is normal weight (e.g., McFerran, Dahl, Fitzsimons, & Morales, 2010).

Social Support Social support, characterized as instrumental, emotional, or informational, is related to eating behavior and diet quality (for a review, see Vesnaver & Keller, 2011). Social support may be particularly relevant in the context of healthy, as opposed to unhealthy, eating behavior. For example, the national representative National Health and Nutrition Examination Survey found that greater frequency of contact with family, friends, neighbors, coworkers, and others was related to eating at least five servings of FV per day (Ford, Ahluwalia, & Galuska, 2000). A systematic review of environmental determinants of FV intake (Kamphuis et al., 2006) identified social support as a contributor to more intake of FV. One rigorous study included in this review was a brief intervention in low‐income participants, where FV consumption was confirmed via plasma/urine biomarkers (Steptoe, Perkins‐Porras, Rink, Hilton, & Cappuccio, 2004). Baseline social support specifically for dietary change was related to 12‐month increases in FV intake, independent of experimental group or other controls, suggesting the effects of social support are robust to a variety of settings and populations (Kamphuis et al., 2006). Social support is a component of Bandura’s social cognitive theory (SCT; Bandura, 1997), and in a study testing multiple components of SCT, social support was an important correlate of FV intake (Anderson, Winett, & Wojcik, 2007). In the context of an online SCT‐based lifestyle intervention (Anderson‐Bill, Winett, & Wojcik, 2011), perceived social support (from friends and family) was a predictor of better nutrition. The relationship between social support and obesity is best tested in longitudinal studies, and such studies find mixed results. Moreover, positive and negative qualities of social relationships may affect BMI differently. In 4,724 Dutch adults, low positive social support was related concurrently to FV intake, and high negative social support was related concurrently to overweight

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status, but neither was related to future weight change (Croezen et al., 2012). Negative relationships, however, were related to future risk of gains in BMI and waist circumference in another longitudinal study (Kouvonen et al., 2011). To inform this conflicted literature, Kershaw et al. (2014) examined a time frame when obesity tends to develop using 10‐year longitudinal data in 3,074 participants. They found that those with continuously high levels of social support had lower likelihood of gaining (by more than 10%) in BMI or waist circumference than those with continuously low levels of social support. Those who increased in frequency of negative social contacts had higher likelihood of increasing waist circumference, but there was no relationship with BMI, and increasing frequency of positive social contacts showed no effect.

Social Networks One of the most famous findings in the area of modern social network analysis is that obesity spreads across networks. Christakis and Fowler (2007) demonstrated this in the 12,067 participants of the longitudinal Framingham Heart Study (FHS; 1971–2003). Individuals’ risk of becoming obese was higher if their spouses, siblings, and friends previously became obese, with stronger concordance among same‐sex social ties. This phenomenon was not driven merely by similar BMI individuals forming relationships, nor was it driven by neighborhood/ geographic distance effects. Although much of the work examining social network effects on diet have been cross‐sectional, Pachucki, Jacques, and Christakis (2011) used 10‐year longitudinal data from the FHS to examine social influences (spouses, siblings, and friends) on dietary patterns. Using data classification procedures, they extracted dietary patterns based on food frequency questionnaires and found longitudinal social influence was exerted on those such as “alcohol and snacks” and “meat and soda” as well as those such as “healthier” and “caffeine avoidant.” This indicates that both healthy and unhealthy eating patterns are subject to social influence. A study in adolescents in Australia across three waves of data in 1 year found that friends appear to influence low‐nutrient, energy dense food consumption (de la Haye, Robins, Mohr, & Wilson, 2013). This effect, however, was not mediated by beliefs about unhealthy food, indicating that social network influences may be operating implicitly.

Family Relationships and Parenting Parents, caregivers, and families exert enormous influence on eating and diet in children. Food preferences of parents affect children’s eating (Patrick, Nicklas, Hughes, & Morales, 2005), due to simple availability of those foods. Attachment style is also related to eating behavior. In their review, Ventura and Birch (2008) distinguish between parenting styles (attitudes and interaction styles) and parenting practices (behavioral patterns). Moreover, there is likely a bidirectional relationship with children shaping parent’s behavior (e.g., a child rejecting broccoli, causing the parent to avoid serving it again). Authoritative feeding styles are linked to better quality eating (Patrick et  al., 2005), whereas authoritarian (“clean your plate!”), permissive, and neglectful parenting styles are associated with greater likelihood of having an overweight child (see Ventura & Birch, 2008). Using longitudinal design, steeper BMI trajectories and less leveling off of BMI were observed in authoritarian and disengaged parenting styles as opposed to balanced parenting (Fuemmeler et al., 2012). Examples of parenting practices are using food as reward, restricting access to food, or modeling eating (Ventura & Birch, 2008; see Modeling section below). Using food as a reward was

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shown in experimental studies to increase consumption of the target food but decrease liking. Restricting food can backfire, where children work harder to obtain restricted snacks and eat and prefer them more. Families create a social context around mealtimes (for a review, see Patrick et al., 2005). Families eating together is related to better diet quality. When TV watching is part of mealtime, however, diet quality is poorer, likely due to exposure to advertising. Given the importance of parenting for child weight and diet, interventions such as the “Lifestyle Triple P” have targeted parenting. In a randomized controlled trial, this intervention positively affected intermediate outcomes such as soda consumption, but saw no differences at 12 months between intervention and control in terms of weight or body composition (Gerards et al., 2015).

Romantic Relationships Romantic relationships have effects on healthy and unhealthy eating. Women appear to play a larger role than men in terms of controlling eating behaviors of the couple (note: the literature has focused on heterosexual relationships; we know nearly nothing about eating behavior in same‐sex romantic relationships). This is likely due to cultural norms surrounding the idea that women should be the ones who cook, and marriage appears to have differential effects on the eating of men versus women. For example, when wives report poorer marital quality, they also report more unhealthy dieting behaviors, particularly if they have low self‐esteem (Markey, Markey, & Birch, 2001). In this same study, however, no marital quality variables were significantly related to healthy dieting behaviors. However, the literature on marital status and diet/ nutritional quality is very mixed (reviewed in Vesnaver & Keller, 2011), indicating that it may not be marital status per se, but living alone that imparts risk for poor eating. Marriage is associated with greater BMI, and spouses tend to have similar BMI (Jeffery & Rick, 2002). Longitudinal studies indicate that marriage is related to BMI gain, with effects most pronounced for African American women. Spouses’ BMIs also predict one another’s longitudinal BMI change (Jeffery & Rick, 2002). Divorce, in this study, was related to 2‐year weight loss, although other studies have found divorce and other indices of marital dissatisfaction are related to increased BMI trajectories.

Modeling Many food choices are derived from modeling—a behavioral mechanism related to social norms. Nisbett and Storms (1974) were among the first to test the effects of social influence on eating. Initially, they hypothesized that normal weight and overweight participants’ eating behavior would be differentially affected by social suppression or social facilitation cues of eating compared with a no‐cue control condition. Participants completed a task with a confederate where there happened to be crackers on the table. In the suppression condition, the confederate ate one cracker; in the facilitation condition, the confederate ate 20 crackers, and there was no confederate in the control condition. Rather than a differential effect, they found that regardless of participant weight, participants typically mimicked the consumption behavior of the confederate. This finding launched a line of research into the modeling effect and underlying mechanisms of modeling behavior in food consumption (reviewed in Herman et  al., 2003). Researchers have tested if these effects were moderated by participant dieting status, participant satiation,

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and personality characteristics such as extraversion and self‐monitoring and found that the expected main effect of modeling occurs regardless of these moderators. Researchers have even found that participants model behavior when left alone but are given information on others’ behavior (e.g., with a bogus “data sheet” left behind with information on how much the prior participant had ostensibly eaten; see also informational norms section above).

Conclusion People often think of diet and obesity as matters of individual will—individuals exert control over their food choice, and the choices they make directly impact their weight. However, in this entry we covered a wide array of evidence that suggests this is not entirely the case. Culture forms our attitudes around food, socioeconomic status impacts our access to foods, norms guide behavior around food, and social connections with friends, family, and even strangers modify our behavior around food. In addition we showed that weight and obesity are also tied to social factors. Changes in BMI travel through social networks and are affected by social support and the status of our marriages. In considering future directions, it is important to note that these factors we have discussed in isolation are deeply connected and build on one another to create our attitudes and subsequent eating behavior. Future directions should integrate these factors to build better interventions with potentially cascading impacts. For example, a future intervention aiming to change eating norms or attitudes toward food within a community should first examine the community’s social network to identify particular individuals or groups who will have the strongest impact. On a larger scale, another future direction could aim to change eating norms in parents and also to explicitly test whether this also changes norms in children through modeling, with implications for the norms within both parent and child social networks. And, after some time, since culture is shaped by the people within it, these changes in norms in parents could even lead to lasting cultural changes in attitudes toward food and healthier diets.

Author Biographies Gbolahan O. Olanubi, C. Phil, is a doctoral student in the Department of Psychology at the University of California, Los Angeles. His research investigates concealable identities and ­stereotype threat, and of particular relevance to this entry, he is interested in how race affects perceptions of the healthiness and quality of food and the downstream health implications of these perceptions. A. Janet Tomiyama, PhD, is an associate professor in the Department of Psychology at the University of California, Los Angeles. Her research investigates stress, eating, dieting, and weight stigma.

References Anderson, E. S., Winett, R. A., & Wojcik, J. R. (2007). Self‐regulation, self‐efficacy, outcome expectations, and social support: Social cognitive theory and nutrition behavior. Annals of Behavioral Medicine, 34(3), 304–312. doi:10.1007/BF02874555 Anderson, J. V., Bybee, D. I., Brown, R. M., McLean, D. F., Garcia, E. M., Breer, M. L., & Schillo, B. A. (2001). 5 a day fruit and vegetable intervention improves consumption in a low income

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population. Journal of the American Dietetic Association, 101(2), 195–202. doi:10.1016/ S0002‐8223(01)00052‐9 Anderson‐Bill, E. S., Winett, R. A., & Wojcik, J. R. (2011). Social cognitive determinants of nutrition and physical activity among web‐health users enrolling in an online intervention: The influence of social support, self‐efficacy, outcome expectations, and self‐regulation. Journal of Medical Internet Research, 13(1). doi:10.2196/jmir.1551 Bandura, A. (1997). Self‐efficacy: The exercise of control. New York: Macmillan. Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357(4), 370–379. doi:10.1056/NEJMsa066082 Croezen, S., Picavet, H. S. J., Haveman‐Nies, A., Verschuren, W. M., de Groot, L. C., & van’t Veer, P. (2012). Do positive or negative experiences of social support relate to current and future health? Results from the Doetinchem Cohort Study. BMC Public Health, 12(1), 65. doi:10.1186/1471‐2458‐12‐65 Darmon, N., Ferguson, E., & Briend, A. (2003). Do economic constraints encourage the selection of energy dense diets? Appetite, 41(3), 315–322. doi:10.1016/S0195‐6663(03)00113‐2 de Castro, J. M. (2002). Age‐related changes in the social, psychological, and temporal influences on food intake in free‐living, healthy, adult humans. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 57(6), M368–M377. doi:10.1093/gerona/57.6.M368 de la Haye, K., Robins, G., Mohr, P., & Wilson, C. (2013). Adolescents’ intake of junk food: Processes and mechanisms driving consumption similarities among friends. Journal of Research on Adolescence, 23(3), 524–536. doi:10.1111/jora.12045 Ferrières, J. (2004). The French paradox: lessons for other countries. Heart, 90(1), 107–111. Ford, E. S., Ahluwalia, I. B., & Galuska, D. A. (2000). Social relationships and cardiovascular disease risk factors: Findings from the third national health and nutrition examination survey. Preventive Medicine, 30(2), 83–92. doi:10.1006/pmed.1999.0606 Fuemmeler, B. F., Yang, C., Costanzo, P., Hoyle, R. H., Siegler, I. C., Williams, R. B., & Østbye, T. (2012). Parenting styles and body mass index trajectories from adolescence to adulthood. Health Psychology, 31(4), 441. doi:10.1037/a0027927 Gerards, S. M., Dagnelie, P. C., Gubbels, J. S., van Buuren, S., Hamers, F. J., Jansen, M. W., … Kremers, S. P. (2015). The effectiveness of lifestyle triple P in the Netherlands: A randomized controlled trial. PLOS One, 10(4), 1–18. doi:10.1371/journal.pone.0122240 Herman, C. P., Roth, D. A., & Polivy, J. (2003). Effects of the presence of others on food intake: A normative interpretation. Psychological Bulletin, 129(6), 873. doi:10.1037/0033‐2909.129.6.873 Howland, M., Hunger, J. M., & Mann, T. (2012). Friends don’t let friends eat cookies: Effects of restrictive eating norms on consumption among friends. Appetite, 59(2), 505–509. doi:10.1016/j. appet.2012.06.020 Jeffery, R. W., & Rick, A. M. (2002). Cross‐sectional and longitudinal associations between Body Mass Index and marriage‐related factors. Obesity Research, 10(8), 809–815. doi:10.1038/ oby.2002.109 Kamphuis, C., Giskes, K., de Bruijn, G. J., Wendel‐Vos, W., Brug, J., & Van Lenthe, F. J. (2006). Environmental determinants of fruit and vegetable consumption among adults: A systematic review. British Journal of Nutrition, 96(04), 620–635. doi:10.1079/BJN20061896 Kershaw, K. N., Hankinson, A. L., Liu, K., Reis, J. P., Lewis, C. E., Loria, C. M., & Carnethon, M. R. (2014). Social relationships and longitudinal changes in body mass index and waist circumference: The coronary artery risk development in young adults study. American Journal of Epidemiology, kwt311. doi:10.1093/aje/kwt311 Kouvonen, A., Stafford, M., De Vogli, R., Shipley, M. J., Marmot, M. G., Cox, T., … Kivimäki, M. (2011). Negative aspects of close relationships as a predictor of increased body mass index and waist circumference: The Whitehall II study. American Journal of Public Health, 101(8), 1474–1480. doi:10.2105/AJPH.2010.300115 Markey, C. N., Markey, P. M., & Birch, L. L. (2001). Interpersonal predictors of dieting practices among married couples. Journal of Family Psychology, 15(3), 464. doi:10.1037//0893‐3200.15.3.464

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McFerran, B., Dahl, D. W., Fitzsimons, G. J., & Morales, A. C. (2010). Might an overweight waitress make you eat more? How the body type of others is sufficient to alter our food consumption. Journal of Consumer Psychology, 20(2), 146–151. doi:10.1016/j.jcps.2010.03.006 Morland, K., Wing, S., Roux, A. D., & Poole, C. (2002). Neighborhood characteristics associated with the location of food stores and food service places. American Journal of Preventive Medicine, 22(1), 23–29. doi:10.1016/S0749‐3797(01)00403‐2 Nisbett, R. E., & Storms, M. D. (1974). Cognitive and social determinants of food intake. In H. London & R. E. Nisbett (Eds.), Thought and feeling: Cognitive alternation of feeling states (pp. 190–208). Chicago: Aldine. Pachucki, M. A., Jacques, P. F., & Christakis, N. A. (2011). Social network concordance in food choice among spouses, friends, and siblings. American Journal of Public Health, 101(11), 2170–2177. doi:10.2105/AJPH.2011.300282 Patrick, H., Nicklas, T. A., Hughes, S. O., & Morales, M. (2005). The benefits of authoritative feeding style: Caregiver feeding styles and children’s food consumption patterns. Appetite, 44(2), 243–249. doi:10.1016/j.appet.2002.07.001 Robinson, E., Hardman, C. A., Halford, J. C., & Jones, A. (2015). Eating under observation: A systematic review and meta‐analysis of the effect that heightened awareness of observation has on laboratory measured energy intake. The American Journal of Clinical Nutrition, 102(2), 324–337. doi:10.3945/ajcn.115.111195 Robinson, E., Thomas, J., Aveyard, P., & Higgs, S. (2014). What everyone else is eating: A systematic review and meta‐analysis of the effect of informational eating norms on eating behavior. Journal of the Academy of Nutrition and Dietetics, 114(3), 414–429. doi:10.1016/j.jand.2013.11.009 Rozin, P. (1996). Towards a psychology of food and eating: From motivation to module to model to marker, morality, meaning, and metaphor. Current Directions in Psychological Science, 18–24. doi:10.1111/1467‐8721.ep10772690 Rozin, P., Fischler, C., Imada, S., Sarubin, A., & Wrzesniewski, A. (1999). Attitudes to food and the role of food in life in the USA, Japan, Flemish Belgium and France: Possible implications for the diet– health debate. Appetite, 33(2), 163–180. doi:10.1006/appe.1999.0244 Rozin, P., & Vollmecke, T. A. (1986). Food likes and dislikes. Annual Review of Nutrition, 6(1), 433– 456. doi:10.1146/annurev.nu.06.070186.002245 Steptoe, A., Perkins‐Porras, L., Rink, E., Hilton, S., & Cappuccio, F. P. (2004). Psychological and social predictors of changes in fruit and vegetable consumption over 12 months following behavioral and nutrition education counseling. Health Psychology, 23(6), 574. doi:10.1037/0278‐6133.23.6.574 Vartanian, L. R., Herman, C. P., & Polivy, J. (2007). Consumption stereotypes and impression management: How you are what you eat. Appetite, 48(3), 265–277. doi:10.1016/j.appet. 2006.10.008 Ventura, A. K., & Birch, L. L. (2008). Does parenting affect children’s eating and weight status? International Journal of Behavioral Nutrition and Physical Activity, 5(1), 15. doi:10.1186/ 1479‐5868‐5‐15 Vesnaver, E., & Keller, H. H. (2011). Social influences and eating behavior in later life: A review. Journal of Nutrition in Gerontology and Geriatrics, 30(1), 2–23. doi:10.1080/01639366.2011.545038

Suggested Reading Food and Culture Rozin, P. (2007). Food and eating. In S. Kitayama & D. Cohen (Eds.), Handbook of cultural psychology (pp. 391–416). New York: Guilford.

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Norms/Modeling Eating Behaviors Herman, C. P., Roth, D. A., & Polivy, J. (2003). Effects of the presence of others on food intake: A normative interpretation. Psychological Bulletin, 129(6), 873. doi:10.1037/0033‐2909.129.6.873

Socioeconomic Status Morland, K., Wing, S., Roux, A. D., & Poole, C. (2002). Neighborhood characteristics associated with the location of food stores and food service places. American Journal of Preventive Medicine, 22(1), 23–29. doi:10.1016/S0749‐3797(01)00403‐2

Parenting Ventura, A. K., & Birch, L. L. (2008). Does parenting affect children’s eating and weight status? International Journal of Behavioral Nutrition and Physical Activity, 5(1), 15. doi:10.1186/ 1479‐5868‐5‐15

Social Relationships Vesnaver, E., & Keller, H. H. (2011). Social influences and eating behavior in later life: A review. Journal of Nutrition in Gerontology and Geriatrics, 30(1), 2–23. doi:10.1080/01639366.2011.545038

Social Factors in Neuroendocrine Function Theodore F. Robles and Ben W. Shulman Psychology Department, University of California, Los Angeles, CA, USA

Social Neuroendocrinology Social neuroendocrinology is an interdisciplinary field that examines bidirectional links between neuroendocrine function and social behavior. This entry reviews key theory and evidence, focusing on neuroendocrine systems that are key players in responses to stressful events, because such systems are biologically plausible mechanisms through which social relationships can “get under our skin” and impact physical health (Miller, Chen, & Cole, 2009). Specifically, we focus on two main neuroendocrine axes: the autonomic nervous system (ANS) (subdivided into the sympathetic and parasympathetic nervous system [SNS and PSNS], respectively) and the hypothalamic–pituitary–adrenal (HPA) axis. The ANS is critical for mounting rapid bodily responses to help individuals adapt to environmental challenges. For example, the SNS can speed heart rate and increase sweating through norepinephrine and epinephrine signaling, and the PSNS can promote bodily growth and restoration at rest through acetylcholine signaling from parasympathetic nerves to their target organs. The HPA axis is a multi‐hormone cascade that culminates in the adrenal cortex releasing cortisol, which is capable of signaling to virtually all cells in the body. Because of their critical roles in mobilizing stored energy, and the multiple effects their signaling hormones have throughout the body, both axes have received the most attention in research linking social factors to health. We note however that other neuroendocrine axes and hormones are also relevant for social neuroendocrinology, in part by interacting with the ANS and the HPA axis. These include estrogen and the hypothalamic–pituitary–gonadal axis (where prolactin and testosterone are the major signaling hormones), posterior pituitary hormones (including oxytocin and vasopressin), and hormones involved in regulating growth and energy metabolism (including growth hormone, insulin, leptin, and glucagon). We direct interested readers to several reviews and empirical papers (Carter, 2014; Robles & Carroll, 2011). The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Social influences on neuroendocrine function begin in our earliest days of life (Repetti, Robles, & Reynolds, 2011). However, this entry primarily focuses on adulthood, which has received the most empirical attention. In addition, we restrict our discussion to hormone signals that are released by nerves and glands to the rest of the body, as opposed to the effects of those signals on target tissues and cells. For example, we discuss social factors and SNS responses, such as norepinephrine production, but do not discuss the effect of these responses on targets like the heart (in terms of increasing heart rate or blood pressure) or immune cells. That said, this entry will discuss the association between social factors and cardiovascular measures that are considered “pure” indices of sympathetic or parasympathetic activity. For instance, SNS activity is indexed by pre‐ejection period (a time‐based indicator of how quickly the heart contracts, assessed by impedance cardiography), and PSNS activity is indexed by high‐frequency heart rate variability (assessed using electrocardiography and referred to in this entry simply as HRV). This entry is divided into four sections. First, we describe key conceptualizations and measures and examine links between stress‐related neuroendocrine function and structural aspects of social networks, such as the number or frequency of social contacts. We then review links with functional aspects of social networks, both positive (social support) and negative (social strain and conflict). Finally, we conclude by reviewing the neural and psychological mechanisms that link social relationships to stress‐related neuroendocrine activity.

Key Concepts and Research Approaches A key distinction must be made between two different aspects of neuroendocrine function: basal activity and physiological responses to environmental challenges (Robles & Carroll, 2011). Basal activity refers to the steady‐state activity of biological systems, when the organism is not facing environmental challenges, in contrast to physiological responses to such challenges. Each aspect is measured differently. For example, a researcher could examine basal activity by measuring hormone levels at known low points, such as bedtime or at “rest” prior to a laboratory challenge, or measuring hormone levels at consistent times during the day over several days. To measure reactivity to environmental challenges, researchers frequently examine changing hormone levels in response to a laboratory stressor. Models linking social functioning to physical health also distinguish between two types of associations, which stem from broader models linking social functioning to physical health (Cohen, Underwood, & Gottlieb, 2000). Specifically, social functioning may be associated with neuroendocrine function regardless of whether the individual is facing an environmental challenge, which would be consistent with a “main‐effect” model. Alternatively, social functioning may be related only to neuroendocrine responses to an environmental challenge or stressor, which would be consistent with a “buffering” (protective) or “reactivity” (exacerbating) model. Finally, the association between social functioning and neuroendocrine activity can be interrogated in a number of research designs. Correlational methods examine concurrent or prospective associations between neuroendocrine measures and social functioning (self‐ or other‐reported), such as perceived social support or loneliness. Nonexperimental laboratory methods involve assessing social functioning in the laboratory, such as through observational coding of a social interaction paradigm, and correlating measures obtained during the interaction (self‐reported, objectively observed) with neuroendocrine measures. Experimental ­methods involve manipulating social functioning, such as having an individual go through a

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laboratory stressor either with or without the presence of a friend, and testing whether the manipulation changes neuroendocrine activity.

Structural Aspects of Social Networks Structural features of social networks, also described as social integration, refer to the number of social relationships in which a person is involved and the degree of connection between the people they know (Taylor, 2011). For instance, some individuals have more interconnected social networks (in which most people know each other), while others have less interconnected social networks. Most social neuroendocrinology research has focused on number and frequency of social contacts, with few studies of social interconnections. ANS functioning is linked to social integration in prairie voles (a social mammal). When subjected to social isolation, these animals show reduced HRV, which may be due to both increased SNS and reduced PSNS influence on the heart. In humans, the evidence suggests a similar picture. In several large studies, high social integration was related to greater resting HRV and lower basal levels of SNS hormones. In particular, weekly religious attendance was specifically related to less likelihood of having high urinary epinephrine, and being married was related to lower urinary epinephrine. Finally, in a small prospective study, international students were followed from arrival in their host country to 2 and 5 months later. Individuals with low social integration, particularly low social contact with people from other cultures, showed lower HRV over time, an association that grew stronger over the course of the study. Notably, baseline HRV did not predict changes in social integration over time, suggesting the causal direction may be from social structure to parasympathetic function, rather than the reverse. In terms of HPA axis functioning, living alone and reporting less social contact were related to higher cortisol levels at bedtime, when cortisol is expected to be at its lowest levels, and greater cortisol production throughout the day. These findings were observed in middle‐aged and older adults, and work in younger samples shows a similar pattern. In a social network analysis of nursing students, those who were less likely to nominate their classmates as friends had higher daily cortisol levels. The overall pattern of findings across the ANS and HPA axis suggests that lower social connection is related to elevated basal SNS and HPA activity, as well as lower basal PSNS activity. This pattern indicates that neuroendocrine systems designed to respond during stress are upregulated, even when the individual is not facing a challenge. Considerably less is known about links between social structure and neuroendocrine responses during stress, but the literature reviewed suggests plausible neuroendocrine pathways through which low social integration may negatively impact health over time (Holt‐Lunstad, Smith, & Layton, 2010).

Positive Aspects of Social Relationships: Social Support A central benefit of social relationships is the experience of being cared for by others and being valued in a network of mutual dependence and assistance (Taylor, 2011). Social support can be informational (advice), instrumental (tangible assistance), or emotional (validation, comforting). Moreover, the way support is measured varies, and this entry focuses on two broad distinctions: perceived available support (often described as perceived support), which is the expectation of being able to call on others for support when needed, and received support,

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which refers an individuals’ self‐report of being the beneficiary of helpful behavior. Perceived and received support are only moderately correlated, and greater perceived support is more consistently related to psychological and physical well‐being compared with greater received support (Uchino, 2009).

Perceived Support Research on perceptions of support availability is mostly correlational and focused on basal functioning. Perceived support is generally associated with lower SNS and higher PSNS activation. For instance, among people living with chronic stress, those with higher perceived support have lower catecholamine levels. For HPA axis function, the results are mixed. Some studies have found no association between 24‐hr cortisol output and perceived support, both at work and at home (although greater perceived work support was related to higher cortisol output on weekends). Other studies have found that perceived support at work is linked to lower daily cortisol.

Received Support While observational studies assess received support by self‐report, experimental studies manipulate the social conditions of individuals facing laboratory stressors. In such experiments, received support consistently reduces blood pressure and heart rate responses to stress (Thorsteinsson & James, 1999). However, these outcomes are not pure indices of SNS or PSNS function. Received support is related to increased activity in pure markers of PSNS function. For instance, received support was related to elevated HRV when facing a laboratory stress task (particularly for individuals high in dispositional compassion). Similarly, received support is related to decreased activity in pure markers of SNS function. For example, receiving support from a member of one’s self‐identified social group was related to lower galvanic skin response. However, the benefits of received support may be offset by the added evaluative threat of another person’s presence. Even in the presence of a supportive companion, people performing a speech task in an evaluative context had increased SNS activity compared with performing the speech alone, as indicated by larger decreases in cardiovascular pre‐ejection period. One of the earliest and most cited examples of received support and HPA reactivity to stress involved participants either receiving support from an opposite‐sex confederate, receiving support from an opposite‐sex romantic partner, or being alone (no support) prior to going through a speech and arithmetic task. Interestingly, men who received support from their female partners showed a smaller cortisol response during the stressor compared with when alone or receiving support from a stranger. In contrast, women receiving support from their male romantic partners did not show smaller cortisol responses compared with the other conditions. In other studies, men, but not women, receiving verbal support from their best friends showed reduced cortisol responses to a laboratory stress task. Women, in contrast, showed an increase in cortisol responses when receiving verbal support from their male partners but showed buffered responses after a partner’s massage. Finally, men receiving support from a stranger, with whom they had a brief closeness manipulation, showed higher cortisol responses, but women showed no difference. Similar to the literature on autonomic responses, evaluative threat influences HPA axis responses and may explain the mixed pattern of results. Indeed, one study found that performing speech and mental arithmetic tasks in front of a supportive audience showed higher cortisol responses than those performing the task alone. Moreover, the

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benefits of social support in attenuating cortisol responses to stress may depend on oxytocin. A specific polymorphism (rs53576) in the oxytocin receptor gene moderates the benefits of social support on cortisol response to stress, and oxytocin administration can improve the stress‐buffering cortisol reduction of social support.

Summary Research on perceived support and stress‐related neuroendocrine function is largely correlational, with a focus on basal activity. In contrast, research on received support is largely experimental, focusing on reactivity to stress. Interestingly, while findings linking perceived support and health are more consistent than those linking received support and health (Uchino, 2009), the opposite seems to be the case for stress‐related neuroendocrine activity. Received support is robustly related to ANS and HPA responses to stress. However, factors like gender of the support provider, the degree of evaluation potential, and other neuroendocrine systems (i.e., oxytocin) can modify those links. A smaller literature suggests that greater perceived support is related to lower basal SNS and higher PSNS activity, while research on basal HPA axis activity is mixed.

Negative Aspects of Social Relationships: Social Negativity and Loneliness Social Negativity Relationships can be sources of negativity when people face aversive or unwanted behavior from another person (Brooks & Dunkel Schetter, 2011). Such negativity can involve provoking conflict, being insensitive to an individual’s needs, and disrupting an individual’s ability to pursue goals. Most research to date has focused on conflict, and even research examining insensitivity to an individual’s needs has done so in the context of conflict. Most research on social conflict has focused on cardiovascular reactivity in close, romantic relationships. However, a few studies have measured pure indicators of autonomic activity (Robles, Slatcher, Trombello, & McGinn, 2014). In newlywed couples, greater hostile and negative behavior during problem discussions was related to elevated epinephrine and norepinephrine responses during the discussion. Older couples showed similar effects, with negative behavior related to increased epinephrine levels. In terms of PSNS activity, among young married couples, marital conflict was associated with lower HRV, but only for women. Conflict in many different kinds of relationships has been linked to HPA activity (reviewed in Robles et al., 2014). People reporting higher levels of negativity from friends, family, and romantic partners had lower morning cortisol levels and flatter cortisol slopes across the day. In newlywed couples, greater hostile behavior while discussing problems in the relationship was related to elevated adrenocorticotropic hormone levels (ACTH, which stimulates cortisol release by the adrenal cortex) during and after the discussion. Similarly, for these newlyweds, the combination of low supportive behavior and high hostile behavior was related to elevated cortisol levels. Older couples showed similar effects, but only among women, whose levels of cortisol and ACTH increased following more conflictual discussions. Moreover, conflicts in which one spouse withdraws from the other’s demands (e.g., avoids the topic, a notable example of social insensitivity) are viewed as particularly destructive for relationships. Accordingly,

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in newlywed couples, greater probability of husbands withdrawing from their wives’ demands was related to elevated wives’ cortisol over a 24‐hr period.

Loneliness Loneliness is distinct from low social integration. People may live contently in solitude or have active social lives and still feel lonely (Hawkley & Cacioppo, 2010). Few studies have examined links between loneliness and pure indicators of autonomic function, although thus far research suggests that loneliness is not related to SNS or PSNS influences on the heart. However, loneliness was associated with elevated epinephrine in overnight urine samples in middle‐aged and older adults. Thus, the vasculature (arteries), but not the heart, may be targets for loneliness‐related elevations in SNS activity. This pattern could explain the consistent elevations in blood pressure related to loneliness. Regarding HPA functioning, loneliness is related to elevated basal cortisol levels in a number of studies. No studies have examined HPA reactivity to stress, but several studies have found that greater loneliness is related to larger cortisol responses to awakening.

Neural and Psychological Mechanisms We conclude this entry by discussing the mechanisms that may explain how social functioning can impact stress‐related neuroendocrine function and linking those mechanisms to models of how social relationships influence health. To directly influence neuroendocrine functioning, the neural circuits involved in social information processing must interface with circuits that regulate the HPA axis and the ANS. Over the past decade, neuroimaging research in humans has identified the circuitry involved in regulating neuroendocrine function, which had been previously outlined in animal models. Specifically, a “medial prefrontal–brain stem axis” involves evolutionarily newer brain structures processing information about the social environment (e.g., prefrontal cortex, anterior cingulate; Eisenberger, 2013) and then sending signals to evolutionarily older structures that modulate HPA and ANS activity (e.g., amygdala, hypothalamus). More recent work is now identifying links between social relationships and neural structure and function. In terms of neural structure, most work has focused on social integration (Platt, Seyfarth, & Cheney, 2016). Early studies found that greater social integration was specifically associated with greater amygdala volume. This pattern parallels work in macaques, where living in larger social groups (2–7 per group) vs. alone was related to increased gray matter in the amygdala. Living in larger social groups was also related to more gray matter in regions that track attention and process social information and face and body movement. Similarly, in humans, larger social networks were related to greater orbitofrontal cortex volume, a region specifically implicated in social cognition and affective processing, but were not related to dorsal prefrontal cortex volume, a region involved in planning and executive function. In terms of neural connectivity, greater social integration was related to stronger connections between the amygdala and brain regions involved in social perception and affiliation. In addition, greater social integration was related to stronger interconnections (greater white matter integrity) in a frontal region involved in interconnecting other regions involved in social information processing and reward processing (Molesworth, Sheu, Cohen, Gianaros, & Verstynen, 2015). Studies linking the quality of social interactions to neural mechanisms implicate the same medial prefrontal axis structures. For example, Eisenberger, Taylor, Gable, Hilmert, and Lieberman (2007) had participants complete a brief laboratory stressor, 10 days of repeated

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measures on the degree of supportiveness in their daily social interactions, and finally a neuroimaging task assessing neural responses to social rejection. Greater daily reports of feeling supported during social interactions were related to lower neural activity in several regions of the medial prefrontal axis during social rejection. Moreover, activity in several medial prefrontal axis regions explained associations between daily social support and cortisol reactivity during the laboratory stressor. Main effects models suggest that a network of high‐quality relationships provides more opportunities for social influence from others, support provision, and benefits for mood that then influence neuroendocrine responses (Cohen et  al., 2000). However, some researchers have argued that social relationships are so intrinsic to human experience that the presence of other people should be regarded as a default or baseline state (Coan & Sbarra, 2015). Because people adapted to living in close proximity and connection to other people, the brain expects to assess social resources and functions best under such conditions. Deviations from this social baseline (perceiving fewer social resources) are perceived as threats to well‐being, and the individual must be prepared to respond. In the context of social neuroendocrinology, mounting ANS and HPA responses, even at rest, prepares the organism to deal with what it perceives as a threatening environment (mainly by increasing energy available for use by the brain and body). Stress‐buffering models suggest that perceiving greater availability of social resources, or actual receipt of social resources, should affect neuroendocrine mechanisms through mitigating negative cognitive (e.g., reducing threat appraisals) and emotional responses (e.g., reducing negative mood) (Cohen et  al., 2000). However, to date, the literature is mixed as to whether positive aspects of social functioning are linked to neuroendocrine function via psychological mechanisms (Uchino, Bowen, Carlisle, & Birmingham, 2012). Fortunately, a number of potential ways forward have been suggested, including improving research designs, rethinking how social relationships are conceptualized and measured, and including a focus on automatic social cognitive processes (Uchino et al., 2012). Increased attention to these issues will hopefully yield advances in research on social neuroendocrinology.

Author Biographies Theodore F. Robles is an associate professor in the Department of Psychology at the University of California, Los Angeles. His research examines the biological mechanisms that explain how close relationships impact health. His empirical work includes both allostatic and restorative processes, such as HPA responses, inflammation, and skin barrier recovery, and covers the life course, including childhood, young adulthood, parenting age, and older ages. Ben W. Shulman is a graduate student in health psychology at the University of California, Los Angeles, where he studies how challenging self‐regulation affects conflict in romantic relationships. He was a research assistant in the Culture Cognition and Coevolution Lab at the University of British Columbia and at the Center for Health Coping Studies. There, he studied the development of human culture and the ways spouses support each other through the stress of chronic disease.

References Brooks, K. P., & Dunkel Schetter, C. (2011). Social negativity and health: Conceptual and measurement issues. Social and Personality Psychology Compass, 5, 904–918. doi:10.1111/j.1751‐9004.2011.00395.x

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Carter, C. S. (2014). Oxytocin pathways and the evolution of human behavior. Annual Review of Psychology, 65, 17–39. doi:10.1146/annurev‐psych‐010213‐115110 Coan, J. A., & Sbarra, D. A. (2015). Social baseline theory: The social regulation of risk and effort. Current Opinion in Psychology, 1, 87–91. doi:10.1016/j.copsyc.2014.12.021 Cohen, S., Underwood, L., & Gottlieb, B. H. (2000). Social relationships and health. In S. Cohen, L. Underwood, & B. H. Gottlieb (Eds.), Social support measurement and intervention (pp. 3–25). New York, NY: Oxford University Press. Eisenberger, N. I. (2013). An empirical review of the neural underpinnings of receiving and giving social support: Implications for health. Psychosomatic Medicine, 75(6), 545–556. doi:10.1097/PSY. 0b013e31829de2e7 Eisenberger, N. I., Taylor, S. E., Gable, S. L., Hilmert, C. J., & Lieberman, M. D. (2007). Neural pathways link social support to attenuated neuroendocrine stress responses. Neuroimage, 35, 1601–1612. Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: A theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine, 40(2), 218–227. doi:10.1007/ s12160‐010‐9210‐8 Holt‐Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: A meta‐ analytic review. PLoS Medicine, 7(7), e1000316. doi:10.1371/journal.pmed.1000316 Miller, G. E., Chen, E., & Cole, S. W. (2009). Health psychology: Developing biologically plausible models linking the social world and physical health. Annual Review of Psychology, 60, 501–524. doi:10.1146/annurev.psych.60.110707.163551 Molesworth, T., Sheu, L. K., Cohen, S., Gianaros, P. J., & Verstynen, T. D. (2015). Social network diversity and white matter microstructural integrity in humans. Social Cognitive and Affective Neuroscience, 10(9), 1169–1176. doi:10.1093/scan/nsv001 Platt, M. L., Seyfarth, R. M., & Cheney, D. L. (2016). Adaptations for social cognition in the primate brain. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 371, 20150096. http://dx.doi.org/10.1098/rstb.2015.0096 Repetti, R. L., Robles, T. F., & Reynolds, B. (2011). Allostatic processes in the family. Development and Psychopathology, 23, 921–938. doi:10.1017/S095457941100040X Robles, T. F., & Carroll, J. E. (2011). Restorative processes and health. Social and Personality Psychology Compass, 5, 518–537. doi:10.1111/j.1751‐9004.2011.00368.x Robles, T. F., Slatcher, R. B., Trombello, J. M., & McGinn, M. M. (2014). Marital quality and health: A meta‐analytic review. Psychological Bulletin, 140, 140–187. doi:10.1037/a0031859 Taylor, S. E. (2011). Social support: A review. In H. S. Friedman (Ed.), Oxford handbook of health psychology (pp. 189–214). New York, NY: Oxford University Press. Thorsteinsson, E. B., & James, J. E. (1999). A meta‐analysis of the effects of experimental manipulations of social support during laboratory stress. Psychology & Health, 14(5), 869–886. doi:10.1080/08870449908407353 Uchino, B. N. (2009). Understanding the links between social support and physical health: A life‐span perspective with emphasis on the separability of perceived and received support. Perspectives on Psychological Science, 4, 236–255. doi:10.1111/j.1745‐6924.2009.01122.x Uchino, B. N., Bowen, K., Carlisle, M., & Birmingham, W. (2012). Psychological pathways linking social support to health outcomes: A visit with the “ghosts” of research past, present, and future. Social Science & Medicine, 74(7), 949–957. doi:10.1016/j.socscimed.2011.11.023

Suggested Reading Miller, G. E., Chen, E., & Cole, S. W. (2009). Health psychology: Developing biologically plausible models linking the social world and physical health. Annual Review of Psychology, 60, 501–524. doi:10.1146/annurev.psych.60.110707.163551

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Robles, T. F., Slatcher, R. B., Trombello, J. M., & McGinn, M. M. (2014). Marital quality and health: A meta‐analytic review. Psychological Bulletin, 140, 140–187. doi:10.1037/a0031859 Taylor, S. E. (2011). Social support: A review. In H. S. Friedman (Ed.), Oxford handbook of health psychology (pp. 189–214). New York, NY: Oxford University Press.

Social Factors in Sleep Zlatan Krizan and Garrett Hisler Department of Psychology, Iowa State University, Armes, IA, USA

Humans spend about a third of their life sleeping, typically once every night for 8 hr. Sleep is behaviorally defined as a reversible state of perceptual disengagement from—and unresponsiveness to—the environment. In an adult, sleep proceeds in alternating cycles of slow‐wave sleep (non‐rapid eye movement, NREM) and more active rapid eye movement (REM) sleep associated with emotional dreams (Carskadon & Dement, 2011). Modern neuropsychology reveals that circadian (daily) rhythms in wakefulness and sleep are controlled by an internal biological clock located in the suprachiasmatic nuclei of the hypothalamus. This mechanism controls most physiological activity, including daily (i.e., circadian) rhythms in body temperature, hormone levels, psychomotor vigilance, and affect (Van Dongen & Dinges, 2003). Although these rhythms are internally generated, they are synchronized with external time cues, the most important of which is the light‐dark cycle. Critically, sleep plays a vital role in health given its many key functions such as toxin disposal from the brain, physical rest and physiological regeneration, and encoding and integration of newly learned information and emotional experiences (Walker, 2010). Any significant deviation in sleep quantity or timing relative to the optimal sleep needed by a given individual (typically between 7 and 9 hr of uninterrupted sleep during the night) is deemed sleep disruption. Critically, various forms of sleep disruption (i.e., total deprivation, partial restriction, or fragmentation) can produce similarly deleterious neurobehavioral effects. These include reduced attention, lowered energy and enthusiasm, compromised executive functioning, and poor self‐control (Reynolds & Banks, 2010; Van Dongen & Dinges, 2003). In this vein, sleep problems often present themselves alongside anxiety, mood, impulse control, and substance abuse disorders. Conversely, improving sleep problems can alleviate emotional problems such as depression. Sleep problems are also a major risk factor for physical conditions such as obesity, diabetes, and cardiovascular diseases. Sleep problems are even associated with increased all‐cause mortality (Hublin, Partinen, Koskenvuo, & Kaprio, 2007). Although the exact causal mechanisms between sleep and health problems are yet to be elucidated, sleep plays an essential role in maintaining good health, earning its status as the third “pillar of health” alongside diet and exercise. The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Social Behavior and the Sleep–Wake Cycle Are Mutually Dependent Although sleep is a biological phenomenon, it is intrinsically connected to social behavior. The organization of human social and cultural activities has always been constrained by the rhythm of the biologically mandated sleep–wake cycle. Humans need to be awake and alert in order to meaningfully interact with others, so most social interaction is confined to daylight hours (although the need to protect the community from threats also meant some individuals needed to be awake at night). Throughout the day, social behavior such as work and recreation tends to be further concentrated around times of circadian peaks in alertness (i.e., late morning and early evening). Indeed, both the drive to engage socially and the ability to successfully interact with others reflect the endogenous circadian rhythm (Hasler, Mehl, Bootzin, & Vazire, 2008). Social behavior is thus heavily dependent on daily rhythms in biological capacities for psychological and motor engagement. However, any individual’s level of alertness or sleep timing is also highly flexible, showing sensitivity to a variety of internal (e.g., motivation, drug effects) and external modifiers (e.g., threats, social incentives). As illustrated in the next section, many of the most important modifiers of human sleep behavior are social in nature.

The Impact of Social Behavior on Sleep Social Behavior as a Time Cue The general propensity toward social behavior is constrained by the daily rhythm of alertness, yet this rhythm can also be entrained to the regular timing of social interactions. Although light exposure plays the most important role (Czeisler et al., 1999), socially influenced behaviors such as collectively determined sleep–wake schedules, exercise, or eating food can shift the internal clock (Mistlberger & Skene, 2004). Moreover, regularity of social activities may be an important support to a regular sleep–wake cycle and optimal sleep quality (Monk, Petrie, Hayes, & Kupfer, 1994). The social zeitgeber theory goes so far as to suggest that stressful life events lead to depression because they disrupt social rhythms needed for a stable cycle in mood and motivation (Ehlers, Frank, & Kupfer, 1988). Fatigue, negative affect, and impaired processing resulting from sleep disruption may then further perpetuate depressive symptoms. Given the extensive confounding of social activity with light exposure and food intake (e.g., most interactions occur under light, and most eating involves socializing), social factors exert considerable impact on our biological rhythms by affecting when we engage in arousing activities (e.g., going for a run when it fits others’ schedules), go outside into natural light (e.g., leaving the house to go to work), or eat (e.g., having dinner when the family is available). In short, socializing modulates human biological rhythms through other variables, even if not in itself an essential time cue.

Social Interactions as Disruptors of the Sleep–Wake Cycle Although not the chief entrainment signal for our biological rhythms, social factors play a major role in how and when individuals sleep. First, social experiences are often the source of stressors that create insomnia (problems with initiating or maintaining sleep) by undermining onset, subjective quality, or consolidation of sleep. The most stressful events in life are social

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in nature; losing one’s parent or spouse, being fired, being romantically rejected, and failing to be selected for a promotion are all essentially social experiences that draw their power from impact on social identity and connections (Almeida, 2005). Such stressful experiences carry with them physiological arousal, anxiety, and ruminative thinking, which disrupt sleep as individuals aim to cope with the stressors. Critically, these markers of stress play a key role in the formation and development of insomnia, with negative social events often being a precipitating or perpetuating factor (e.g., work and home conflict; Riemann et al., 2010). For example, embroilment in aggression and interpersonal conflict can fuel worry and rumination about one’s safety or guilt about one’s own behavior and its consequences on others; all these can undermine sleep over time, especially among romantic partners (Krizan & Herlache, 2016). Conversely, having trust and intimate relationships with others who can provide social support during a time of crisis may reduce the impact of stressful events on one’s sleep. For people who live and sleep with romantic partners, there seems to be an especially close tie between social interactions and sleep—one study found that when women reported less negatively toned social interactions with their male partner, both individuals slept more efficiently the next night (Hasler & Troxel, 2010). As a result, social experiences not only play a major role in development and perpetuation of sleep problems such as insomnia but also can provide a buffer against them in the form of social support (Maume, 2013). Such processes also contribute to why sociocultural factors such as personal socioeconomic status or neighborhood quality negatively impact sleep and health among disadvantaged populations. Second, conformity to social norms (going out late) and pursuit of personal social goals (getting up early to help an ill partner) exert a large impact on behavioral choices that impact sleep. Correspondingly, perceived norms have been found to predict intentions toward behaviors that promote optimal sleep, referred to as sleep hygiene. Standard recommendations to aid proper sleep include avoiding psychoactive substances that interfere with sleep (e.g., caffeine, alcohol), exercising regularly (although not before bed), maintaining a regular sleep–wake schedule, reducing stimulation in the sleeping environment (e.g., bedroom noise) that is only used for sleeping, and avoiding excessive daytime napping. All of these factors have been linked to optimal sleep in some way, although their potential as public health interventions is still being evaluated (Irish, Kline, Gunn, Buysse, & Hall, 2015). Despite the fact that sleep hygiene recommendations are made to individuals, whether any given individual actually follows good sleep practices often comes down to the social influence of others, be it because of concerns about what others will think or because of others’ pleas and persuasive attempts. For example, the pressure to be constantly available on social media and respond quickly has pushed teenagers to stay up late, use electronic devices in bed, and resultantly displace or harm their sleep (Woods & Scott, 2016). Even more disconcerting is the rise of “sleep texting,” wherein individuals wake up to respond to text massages with little or no memory of the interaction the next morning. Unfortunately, sleep practices can spread through social networks such that one’s friend adopt similarly bad sleep habits. In a similar vein, the use of psychoactive drugs that interfere with sleep typically occurs within social milieus. The impact of social ties on sleep‐relevant behavior is especially strong in adolescence given increased concerns about acceptance (Maume, 2013). In fact, teenagers are more likely to use substances such as tobacco and alcohol (both disruptive to sleep) as a result of both active influence by others (e.g., being offered a cigarette) and more passive modeling of others’ behavior or alignment with social norms (e.g., drinking because others are drinking). In short, understanding individuals’ social ties is critical for understanding why people engage in poor sleep hygiene such as substance use and bedtime delays.

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Third and finally, whether people lie down in bed regularly and keep the bedroom free of distractions strongly depends on people with whom they live, be they family members, roommates, or romantic partners. Romantic partners are likely to play the most significant role given the sharing of both evening activities and the bedroom; for example, couples are more concordant in their behaviorally measured sleep than are random pairs of individuals (Gunn, Buysse, Hasler, Begley, & Troxel, 2014). Moreover, bed times and rise times are typically a function of preferences and responsibilities of both partners, requiring frequent compromise. How people balance work and family demands is constantly shaped by ­interactions with one’s partner, so understanding the nature of intimate relationships is critical for understanding sleep.

The Impact of Sleep on Social Behavior Social processes undoubtedly play an important role in shaping human sleep, and this role represents one pathway by which social behavior affects health. At the same time, sleep itself molds social and health behavior. Although scientific evidence on this score is only emerging, accumulating evidence converges on the conclusion that when and how humans sleep carries considerable consequences for what people do, how they think and feel about others, and how they interact with them.

How Optimal Sleep Timing (Chronotype) Shapes Social Experience A typical person usually sleeps during the night, but individuals’ ideal times for going to sleep and waking up vary considerably. A key reason for this variability stems from individual differences in timing of biological rhythms, namely, circadian phase or chronotype. Although behavior of every individual follows his or her internal biological clock, this clock is often slightly ahead or slightly behind the earth’s 24‐hr light‐dark cycle. As a result, some individuals’ internal cycle starts and ends earlier than the 24‐hr day; these “larks” are said to have a morning chronotype as they rise earlier, feel more energetic in the morning, and have difficulties extending wakefulness late into the night. Conversely, others’ internal cycle starts and ends later than the 24‐hr day; these “owls” have an evening chronotype as they rise later, feel most energetic in the evening, and find it easy to stay up late (Roenneberg, Wirz‐Justice, & Merrow, 2003). Most individuals (“hummingbirds”) fall somewhere in between these extremes along a continuum. As we outline next, individuals’ chronotype exerts a powerful impact on the social environments people inhabit, their psychophysiological state during social interactions at a particular time of day, and weekly patterns of rising and falling asleep that impact socializing. First, because people’s chronotypes bias sleep propensity toward the most optimal time for the individuals’ biological clock, they partially determine when people are awake and participating in social activities. Critically, the nature of social environments accessible to a person throughout the day varies, as early morning affords chatting over a cup of coffee (but not grabbing a beer), while later evening or night affords socializing in entertainment establishments that serve alcohol or provide adult entertainment such as bars and clubs. Because chronotypes partially determine the time of day people have the energy and interest to participate in (social) activities, they also constrain available social opportunities and resultantly the likelihood of participating in them. As a result, evening‐oriented individuals should be more likely to come in contact with vices such as drugs, alcohol, or sex (more available at night) than

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are more morning‐oriented individuals. Consistent with this premise, evening‐oriented ­individuals are more likely to smoke, drink, and behave promiscuously (Wittmann, Dinich, Merrow, & Roenneberg, 2006). This pattern may partially result from individuals’ self‐­ selection into these nighttime environments, but persistent exposure to them may promote the development of substance use problems, impulsive behavior, and social conflicts associated with nighttime environments and intoxication. Second, chronotype also strongly impacts individuals’ psychophysiological state during a given time of day. This resultantly affects both the momentary ability and motivation of an individual to cope with situational and social demands occurring at a particular time of day. Specifically, “larks” are more likely to feel alert and interested, be vigilant, perform better physically, and think carefully earlier rather than later in the day, whereas the opposite is true for “owls.” These interactive patterns between the time of day and chronotype have significant consequences for numerous social variables such as engagement with others, group performance, perspective taking, and impression formation. For example, morning‐oriented individuals maintain exercise more effectively when such activity occurs during morning hours than evening hours (Hisler, Phillips, & Krizan, 2017). Third and finally, because the “social” clock (i.e., timing of socially formalized activities such as work) is more aligned with the “biological” clock of morning‐oriented – rather than evening‐oriented  –  individuals, the latter experience “social jet lag,” that is, a misalignment of biological and social time. As normal work schedules are best suited for morning chronotypes, evening individuals who fall asleep later accrue a sleep debt during the week, for which they compensate by sleeping in on the weekends (Wittmann et al., 2006). This dilemma also leads to greater daytime tiredness, more psychosomatic disturbances, and more emotion dysregulation for evening‐oriented individuals during a typical weekday. Given that many social behaviors (e.g., drinking, smoking, exercising) have important implications for health, these findings point to the essential role that chronotypes play in social behavior and especially to the importance of match between people’s biological clocks and the social opportunities and demands that fluctuate with time of day.

The Effects of Sleep Disruption on Social Behavior Losing sleep has far‐reaching consequences on people’s health and well‐being, yet chronic sleep restriction seems to be prevalent in the US population, with 30% of American adults reporting less than 6 hr of sleep a night (the National Sleep Foundation recommends 7–9 hr of sleep a night). Furthermore, this population has increased over the past two decades. Because sleep impacts virtually all aspects of cognitive and emotional functioning, existing evidence suggests that it creates downstream consequences for how we perceive and interact with the social world. First, sleep disruption leads to feelings of fatigue, loss of energy, and lack of motivation. Specifically, sleep loss leads students to prefer easier tasks, to spend less time on activities such as dressing neatly and fashionably, and to engage in more time wasting during work (Engle‐ Friedman et al., 2003). These choices seem logical because they dovetail with subjective perceptions of increased effort following lost sleep, evident in higher perceptions of difficulty for athletic challenges, physical tasks, and intellectual problems (Engle‐Friedman et  al., 2003). These consequences carry direct implications for people’s motivation and ability to pursue numerous social goals. Dressing less neatly and fashionably lowers perceptions of status and competence, while loafing or avoidance of taxing tasks such as exercise creates opportunities

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for sedentary social behaviors such as texting or use of social media. In short, the reduced effort and amotivation following sleep loss impacts how individuals allocate their time, which can produce a wide variety of social and health consequences. Second, in concert with feelings of fatigue and lack of drive, many of the cognitive and emotional effects of sleep loss broadly impair self‐regulation, namely, the ability to select, enact, and monitor progress toward important goals (Krizan & Hisler, 2016). Sleep loss not only saps energy and motivation needed to enact goal‐directed behavior but also impairs vigilance and executive functions (i.e., selective attention, working memory, task switching) needed to appropriately select and monitor goals (Killgore, 2010). For instance, poorly slept individuals are less aware of abstract goals or standards such as morals, perceive less value in rewards that require effort, fail to incorporate feedback into ongoing goal pursuit, and prefer riskier courses of action. A key aspect of how sleep loss hinders optimal goal selection and pursuit involves blunted positive emotions necessary for goal pursuit (e.g., desire and enthusiasm) and heightened negative emotions that often impede goal striving (e.g., anxiety). Specifically, those who are sleep deprived tend to report more negative affect and less positive affect, perceive neutral stimuli as having negative connotations, have more intense emotional reactions, and show an impaired capacity to reappraise and inhibit these emotions (Tempesta et al., 2010). The importance of sleep for effective self‐regulation is further suggested by broader links between sleep and interpersonal aggression, relationship satisfaction and conflict, and unethical behavior (Barnes, Schaubroeck, Huth, & Ghumman, 2011; Krizan & Herlache, 2016; Troxel, 2010). As many health behaviors require self‐regulation because they are burdensome (e.g., exercising, getting a mammography) or require restraint (e.g., not smoking, not eating fast food), how sleep relates to self‐regulation is key for understanding initiation and maintenance of health behaviors. Third and finally, all these findings underscore that losing sleep (or experiencing fragmented sleep) impairs vigilance, working memory, monitoring of changes in oneself and the environment, and the ability to change thinking strategies or inhibit automatic responses. At a more basic level, then, disrupting an individual’s sleep seems to compromise the person’s ability to engage in reflexive, rule‐based processing of social information that requires effort and is dependent on motivation and cognitive capacity (Kahneman & Frederick, 2005). As a result, individuals may be more likely to engage in low‐effort intuitive and associative processing of social information while pursuing social goals. Because processing of social information central to understanding most social phenomena (e.g., social judgment, attribution, persuasion, aggression, emotions) involves a more intuitive, associative system (impulsive; System 1) and a more rational, rule‐based system (reflexive; System 2), sleep may be one key factor in how these systems are deployed in service of social cognition (Kahneman & Frederick, 2005). Although these implications of sleep for social processing are largely unexplored, existing evidence does suggest that losing sleep due to daylight savings (i.e., moving the clock forward in the spring) increases police officer’s reliance on racial stereotypes when making traffic stop decisions (Wagner, Barnes, & Guarana, 2016), while a night of poor sleep impairs individuals’ stop decisions and undermines taking the perspective of one’s romantic partner (Gordon & Chen, 2014). How sleeplessness impacts elaboration of persuasive messages, attitude change, activation of stereotypic knowledge representations, and social perceptions of warmth and competence remains to be investigated. How sleep affects these social cognitive processes is of particular importance to health intervention endeavors as the cognitive and affective repercussions of sleep loss during exposure to health interventions likely represent an often overlooked barrier to health change.

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Conclusion Sleep is intrinsically tied to social behavior—socializing shapes chronic and acute sleep p ­ atterns, and how and when people sleep impacts their ability to regulate social behavior. Because sleep is an essential health outcome in itself while influencing many other health outcomes and behaviors, the dynamics between sleep and social behavior represent key considerations in theories of health behavior and their application toward interventions.

Author Biographies Zlatan Krizan, PhD, is an associate professor of psychology and an affiliate for the Center for Study of Violence at Iowa State University in Ames, Iowa. He directs the Sleep, Self, and Personality Laboratory that investigates how sleep and dreams intersect with social behavior and individual differences in personality. A key focus of the research is how sleep intersects with self‐control processes, personality, and health behaviors. Garrett Hisler, MS, is a McNair scholar who is currently completing his doctoral training as a graduate assistant in Dr. Zlatan Krizan’s Sleep, Self, and Personality Laboratory at Iowa State University’s Department of Psychology. His research focuses on the bidirectional relationship of sleep and self‐regulation and the role of personality in these dynamics.

References Almeida, D. M. (2005). Resilience and vulnerability to daily stressors assessed via diary methods. Current Directions in Psychological Science, 14(2), 64–68. Barnes, C. M., Schaubroeck, J., Huth, M., & Ghumman, S. (2011). Lack of sleep and unethical conduct. Organizational Behavior and Human Decision Processes, 115(2), 169–180. Carskadon, M. A., & Dement, W. C. (2011). Monitoring and staging human sleep. In M. H. Kryger, T. Roth, & W. C. Dement (Eds.), Principles and practice of sleep medicine (5th ed., pp. 16–26). St. Louis, MO: Elsevier Saunders. Czeisler, C. A., Duffy, J. F., Shanahan, T. L., Brown, E. N., Mitchell, J. F., Rimmer, D. W., … Dijk, D. J. (1999). Stability, precision, and near‐24‐hour period of the human circadian pacemaker. Science, 284(5423), 2177–2181. Ehlers, C. L., Frank, E., & Kupfer, D. J. (1988). Social zeitgebers and biological rhythms: A unified approach to understanding the etiology of depression. Archives of General Psychiatry, 45(10), 948–952. Engle‐Friedman, M., Riela, S., Golan, R., Ventuneac, A. M., Davis, C. M., Jefferson, A. D., & Major, D. (2003). The effect of sleep loss on next day effort. Journal of Sleep Research, 12(2), 113–124. Gordon, A. M., & Chen, S. (2014). The role of sleep in interpersonal conflict: Do sleepless nights mean worse fights? Social Psychological and Personality Science, 5(2), 168–175. Gunn, H. E., Buysse, D. J., Hasler, B. P., Begley, A., & Troxel, W. M. (2014). Sleep concordance in couples is associated with relationship characteristics. Sleep, 38(6), 933–939. Hasler, B. P., Mehl, M. R., Bootzin, R. R., & Vazire, S. (2008). Preliminary evidence of diurnal rhythms in everyday behaviors associated with positive affect. Journal of Research in Personality, 42(6), 1537–1546. Hasler, B. P., & Troxel, W. M. (2010). Couples’ nighttime sleep efficiency and concordance: Evidence for bidirectional associations with daytime relationship functioning. Psychosomatic Medicine, 72(8), 794–801. Hisler, G., Phillips, A. L., & Krizan, Z. (2017). Individual differences in chronotype and time‐of‐exercise interact to predict physical activity. Annals of Behavioral Medicine, 51, 391–401. doi:10.1007/ s12160‐016‐9862‐0

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Hublin, C., Partinen, M., Koskenvuo, M., & Kaprio, J. (2007). Sleep and mortality: A population‐based 22‐year follow‐up study. Sleep, 30(10), 1245. Irish, L. A., Kline, C. E., Gunn, H. E., Buysse, D. J., & Hall, M. H. (2015). The role of sleep hygiene in promoting public health: A review of empirical evidence. Sleep Medicine Reviews, 22, 23–36. Kahneman, D., & Frederick, S. (2005). A model of intuitive judgment. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 267–293). New York, NY: Cambridge University Press. Killgore, W. D. S. (2010). Effects of sleep deprivation on cognition. Progress in Brain Research, 185, 105–129. Krizan, Z., & Herlache, A. D. (2016). Sleep disruption and aggression: Implications for violence and its prevention. Psychology of Violence, 6(4), 542. Krizan, Z., & Hisler, G. (2016). The essential role of sleep in self‐regulation. In K. Vohs & R. Baumeister (Eds.), The handbook of self‐regulation (3rd ed., pp. 182–202). New York, NY: Guilford. Maume, D. J. (2013). Social ties and adolescent sleep disruption. Journal of Health and Social Behavior, 54(4), 498–515. Mistlberger, R. E., & Skene, D. J. (2004). Social influences on mammalian circadian rhythms: Animal and human studies. Biological Reviews, 79(3), 533–556. Monk, T. H., Petrie, S. R., Hayes, A. J., & Kupfer, D. J. (1994). Regularity of daily life in relation to personality, age, gender, sleep quality and circadian rhythms. Journal of Sleep Research, 3(4), 196–205. Reynolds, A. C., & Banks, S. (2010). Total sleep deprivation, chronic sleep restriction and sleep disruption. Progress in Brain Research, 185, 91–103. Riemann, D., Spiegelhalder, K., Feige, B., Voderholzer, U., Berger, M., Perlis, M., & Nissen, C. (2010). The hyperarousal model of insomnia: A review of the concept and its evidence. Sleep Medicine Reviews, 14(1), 19–31. Roenneberg, T., Wirz‐Justice, A., & Merrow, M. (2003). Life between clocks: Daily temporal patterns of human chronotypes. Journal of Biological Rhythms, 18(1), 80–90. Tempesta, D., Couyoumdjian, A., Curcio, G., Moroni, F., Marzano, C., De Gennaro, L., et al. (2010). Lack of sleep affects the evaluation of emotional stimuli. Brain Research Bulletin, 82, 104–108. Troxel, W. M. (2010). It’s more than sex: Exploring the dyadic nature of sleep and implications for health. Psychosomatic Medicine, 72(6), 578–586. Van Dongen, H. P. A., & Dinges, D. F. (2003). Investigating the interaction between the homeostatic and circadian processes of sleep‐wake regulation for the prediction of waking neurobehavioural performance. Journal of Sleep Research, 12(3), 181–187. Wagner, D. T., Barnes, C. M., & Guarana, C. L. (2016). Law and error: Daylight saving time, race and police harassment. American Economic Review, Manuscript in preparation. Walker, M. P. (2010). Sleep, memory and emotion. Progress in Brain Research, 185, 49–68. Wittmann, M., Dinich, J., Merrow, M., & Roenneberg, T. (2006). Social jetlag: Misalignment of biological and social time. Chronobiology International, 23, 497–509. Woods, H. C., & Scott, H. (2016). #Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self‐esteem. Journal of Adolescence, 51, 41–49.

Suggested Reading Hasler, B. P., Mehl, M. R., Bootzin, R. R., & Vazire, S. (2008). Preliminary evidence of diurnal rhythms in everyday behaviors associated with positive affect. Journal of Research in Personality, 42(6), 1537–1546. Irish, L. A., Kline, C. E., Gunn, H. E., Buysse, D. J., & Hall, M. H. (2015). The role of sleep hygiene in promoting public health: A review of empirical evidence. Sleep Medicine Reviews, 22, 23–36.

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Krizan, Z., & Hisler, G. (2016). The essential role of sleep in self‐regulation. In K. Vohs & R. Baumeister (Eds.), The handbook of self‐regulation (3rd ed., pp. 182–202). New York, NY: Guilford. Troxel, W. M. (2010). It’s more than sex: Exploring the dyadic nature of sleep and implications for health. Psychosomatic Medicine, 72(6), 578–586. Wittmann, M., Dinich, J., Merrow, M., & Roenneberg, T. (2006). Social jetlag: Misalignment of biological and social time. Chronobiology International, 23, 497–509.

Social Identity S. Alexander Haslam, Catherine Haslam, Jolanda Jetten, Tegan Cruwys, and Genevieve A. Dingle Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia

People’s social lives and the social worlds they inhabit have a critical bearing on their health. For most health psychologists this is an uncontroversial observation, as there is abundant evidence both (a) that social contact, social connections, and a rich social life are good for one’s well‐being and (b) that those who are impoverished in this respect (e.g., because they are socially isolated as a result of poverty, prejudice, or other forms of social exclusion) suffer from relatively poor health outcomes. However, the social identity approach to health provides a more nuanced analysis of such patterns in suggesting that particular forms of social interaction have an especially important role to play in health dynamics. These are those that are grounded in shared social identity—a person’s internalized sense of shared group membership and an associated sense that they are part of an “us” that is bigger than “me” alone. This argument lies at the heart of the social identity approach to health: a theoretical perspective on health and well‐being that has become increasingly influential in recent years. In what follows, we set out the core tenets of this approach and explain why its ideas are so central to a range of issues that are central to health psychology.

Social Identity as a Basis for Group Behavior When people refer to “the self,” they are typically referring to something singular about a person. A person who is self‐confident, for example, is understood to be assured of their personal place in the world. Importantly, though, we also talk about the self in the first‐person plural, in terms of our social identities as “us” and “we.” Accordingly, we can define ourselves as “us Australians,” “us psychologists,” “us Democrats,” and so on and be self‐confident as members of these groups. The fact that we do this speaks to the capacity for the self to be

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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defined not only in unique terms but also in terms of attributes and qualities we share with other people. The significance of this point for psychology was first illustrated in a famous series of experiments conducted by Henri Tajfel and his colleagues that came to be known as the minimal group studies. In these, schoolboys were assigned to essentially meaningless groups (e.g., supposedly on the basis of their liking for abstract paintings by Klee or Kandinsky) and had the task of assigning points (signifying small amounts of money) to other members of both their own group (their ingroup) and another group (the outgroup). What the researchers found was that even these most minimal of conditions were sufficient to produce group behavior— with many of the boys adopting a strategy in which they awarded points preferentially to members of their ingroup than to those in the outgroup. Moreover, they did so even if this meant their group would end up with worse outcomes than it might have had otherwise (e.g., if the participants had adopted a strategy in which both groups received a high number of points). Seeking to make sense of these findings, researchers came to understand that they reflected the fact that for participants in the studies, the process of acting in terms of their group membership was a way of making an otherwise meaningless situation meaningful. In particular, the actions they engaged in were part of a strategy to attain and maintain a positive and distinctive social identity (as “us in the Klee group” as opposed to “them in the Kandinsky group”). Building upon this analysis, John Turner subsequently noted that it was the participants’ capacity to act in terms of social identity that made this group behavior possible (Turner, 1982). In other words, it was only because the boys could categorize themselves as “us in the Klee group” that they were able to act as members of the Klee group. This, he argued, is true of all other groups too. So it is only because (and to the extent that) Australian people can define themselves in terms of a social identity as Australians that they are able to behave as an organized, coordinated national entity. And the same is true for all other groups—teams, clubs, societies, communities, unions, churches, and so on.

Social Identity as a Basis for Health The foregoing observation is clearly important when it comes to understanding the psychology of group behavior—for example, if we want to explain why people sometimes engage in social competition and conflict. But why is social identity important for health? The core reason is that humans are social animals who live, and have evolved to live, in social groups. In short, group life makes us human and is a key source of personal meaning, purpose, and worth. Among other things, this means that, like hunger and thirst, physical and psychological isolation are inimical to our makeup and design. And in this regard, the fundamental significance of social identity is that, without it, as Turner (1982) argued, group behavior is impossible. If one considers a football team, for example, it is the fact that its members share social identity that allows them to coordinate their play and function as a meaningful social and psychological entity. Shared social identity (as “us members of Team X”) thus allows team members to harness the power and access the benefits of the group, and this at least partly explains why feeling part of a team is generally good for health and well‐being (Steffens, Haslam, Schuh, Jetten, & van Dick, 2016). Moreover, the more that team members define themselves in terms of shared identity—that is, the higher their social identification—the more true this will tend to be.

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Of course this point is not restricted to football teams, but is true of all collectives (e.g., recreational, religious, occupational, political, and community groups). The general point here, then, is that social identities and social identifications allow us to fulfill our potential as humans and hence will have important—and generally positive—implications for health. Consistent with this point, recent meta‐analyses have found (a) that people’s social identification with a broad range of groups is negatively and reliably associated with depression (r = −.25; Cruwys, Haslam, Dingle, Haslam, & Jetten, 2014) and (b) that employees’ social identification with work teams is positively and reliably associated with both psychological well‐being and the absence of stress (rs =.27, .18, respectively; Steffens,Haslam, et al., 2016). Further support for this proposition comes from research that used data from the English Longitudinal Survey on Ageing to examine the power of different types of social connection to predict the cognitive health of more than 3,000 English adults over time (Haslam, Cruwys, & Haslam, 2014). Contrary to the idea that all forms of social connection are equally protective of health, this analysis found that social group ties proved far more beneficial than individual ties. Indeed, only group engagement made a significant and sustained contribution to subsequent cognitive function, and this contribution became more pronounced as people became more vulnerable (i.e., with increasing age). Similarly, a study of over 800 Australian high school students showed that psychological health (in the form of personal self‐esteem) was predicted not by students’ interpersonal social network ties, but only by the quality of the students’ group‐based ties (Jetten et al., 2015). Stated slightly differently, the social identity approach to health suggests that social identities are beneficial to health because they are the basis for meaningful forms of group life and hence give people access to important social and psychological resources (Jetten, Haslam, Haslam, Dingle, & Jones, 2014). Consistent with this idea, evidence from a large number of studies supports the argument that the more social identities a person has access to (i.e., the more group memberships they have internalized as part of their self), the better their physical, mental, and cognitive health. This prediction is confirmed by research with multiple populations including high school students (Jetten et al., 2015), retirees (Steffens, Cruwys, Haslam, Jetten, & Haslam, 2016), people recovering from a stroke (Haslam et al., 2008), or people staving off depression (Cruwys et al., 2013). However, it also follows from this analysis that if our capacity to form meaningful social connections is compromised (e.g., because we lose or lack social identities or because our social identities are stigmatized), then this will limit our access to these same social and psychological resources and hence will threaten psychological and social functioning (e.g., Cruwys et al., 2014). This can also be true if, as a function of their particular nature, groups exert a negative influence on our lives because the content of a given social identity (e.g., “us depressives,” “us heroin users”) is harmful in some way (e.g., because it has negative connotations or prescribes toxic behavior; Dingle, Stark, Cruwys, & Best, 2015). A key point here, then, is that much of the power of the social identity approach to health derives from its capacity to understand the ways in which group and identity processes are implicated in negative health‐related dynamics as well as those that are more positive (Haslam, Jetten, Cruwys, Dingle, & Haslam, 2018).

Social Identity as a Health‐Enhancing Resource Social identity theory (Tajfel & Turner, 1979) and self‐categorization theory (Turner, 1982; Turner, Hogg, Oakes, Reicher, & Wetherell, 1987) together comprise the social identity approach.

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Work informed by this approach is generally concerned with exploring the determinants of social identity (and social identification) as well as its consequences for social behavior (e.g., Haslam et al., 2018; Jetten et al., 2017). As well as having broad implications for the analysis of social behavior, this work has particular relevance for clinical and health psychology in allowing us to understand both the origins and the impact of health‐related social identity resources.

Social Connection One of the primary consequences of coming to see ourselves as sharing social identity with others is that this draws them closer to us psychologically. As spelled out within self‐categorization theory, a key reason for this is that shared social identity transforms people who, as individuals, are different from the self (i.e., “others”) into people who, as group members, are part of the self (Turner et al., 1987). By way of illustration, as individuals, passengers on the London Underground have no connection to each other and treat each other as strangers—with indifference. However, if circumstances were to lead them to see themselves as sharing social identity, then passengers would tend to see themselves as having much more in common, and their psychological orientation to each other would become much more positive as a result. This point was supported in research that documented the sense of solidarity and shared resolve that emerged among passengers who were victims of the London bombings in 2005. It has also been documented in work that examines passengers’ experiences of sharing train carriages with football supporters when those passengers are supporters of the same team, a different team, or no team at all. More prosaically, a range of controlled experimental studies—including those that involve minimal groups—show that manipulations that make salient a social identity that is shared with another person (e.g., as Australians, team members, or Klee likers) serve to make that person appear more similar to the perceiver. Furthermore, as well as changing abstract perceptions of similarity and connectedness, such manipulations also change our openness and receptivity to others, as evidenced in perceptions of liking and trust. Not only do we understand others to be more yoked to us in such circumstances, but once we relate to them as ingroup members we also embrace them more enthusiastically. Consequently, we also experience a greater sense of belonging and feel more comfortable in their company. Indeed, one key reason why social identity (and social identification) tends to have positive implications for health is that it serves to counteract—and indeed is inherently antithetical to—a sense of psychological isolation (Cruwys et al., 2014).

Effective Social Support As people come to define themselves in terms of shared group membership, it is not simply their perceptions of each other that change. So too does their behavior. Not least, this is because social identity provides group members with the motivation to engage with each other in ways that advance the identity they share—so that if a person sees themselves as part of a given group (e.g., a family, a team, an organization), then they are motivated to “do what it takes” to help the group thrive. One obvious way they can do this is by helping each other out by providing social support when it is perceived to be needed (e.g., if a member of the group is in difficulty). Such support can take an emotional, intellectual, or material form and again flows from the fact that shared social identity transforms “others” into “self.” Another large body of empirical work supports this claim (for a review see Haslam, Reicher, & Levine, 2012). For example, studies show that when a football fan trips and falls, a passerby

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is far more likely to stop and help them if the victim appears to be a supporter of the same team rather than of a rival one. However, if the observer’s more general social identity as a football fan is made salient (rather than their identity as a supporter of a specific team), then they are more likely to help the supporter of the rival team than someone who appears not to support any team. On a larger scale, studies of international aid show that charitable giving also follows the contours of shared identity. However, a person’s status as an ingroup or outgroup member (and hence the amount of support they receive) varies as a function of the specific identity lens through which they are viewed. Studies have shown, for example, that British people donate more money to Italian earthquake victims if they are encouraged to define themselves as European (in which case they share identity with Italians) rather than as British (where they do not). Importantly, though, effective support is not just a matter of aid provision; it also depends on recipients being receptive to such charity. And this receptiveness too is structured by shared social identity. Studies thus show that people are more positively oriented to support when it is offered by ingroup rather than outgroup members—in large part because they are more trusting of the motives that lie behind it (Haslam et al., 2012). Together then, these two sides of the support equation combine to mean that support tends both to be more forthcoming and to be more effective when it occurs within, rather than across, the boundaries of social identity. The same is true for the process of social influence that is implicated in most forms of support (and in all forms of leadership; Haslam, Reicher, & Platow, 2011). In particular, whether people give useful advice, and whether others take it on board, depends very much on the degree to which they see themselves as sharing social identity. This is another point that has broad relevance for health psychology. For example, it helps us understand the therapeutic alliance between practitioners and clients as something that is predicated on social identity. And it also helps us understand why interventions tend to be less successful when they occur across identity fault lines (e.g., of ethnicity, culture, and social class).

Meaning and Worth We noted above that within the minimal groups studies, Tajfel observed that it was the process of acting as group members (in terms of social identity) that allowed participants to make an otherwise meaningless task meaningful. At the time he did not make too much of this, but it turns out that this is largely true of most other forms of social behavior. Looked at from the outside, many (if not most) social activities have the capacity to appear meaningless—whether they involve kicking a football, singing an operatic aria, or playing board games. What makes them meaningful (and enjoyable) is the process of identifying with the groups that engage in these activities. In this regard too, it is obviously the case that what people do in their groups (and as individual group members) is largely informed by the content of their identity and what it means to be part of that group. Football teams play football, choir members sing, and board gamers play board games. The significance of this observation for health and well‐being is that in this way, social identities lend meaning, direction, and purpose to actions that would otherwise be meaningless. This point was brought home in Durkheim’s groundbreaking study of suicide in which he noted that people are far less likely to kill themselves during wartime—a pattern that he attributed to the fact that war had the power to bring people together and focus their energies on a common goal. At the same time, the process of contributing to such goals (e.g., by exerting oneself in a football match, by singing enthusiastically in a concert, by taking a board

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game seriously) tends to be valued and valorized by those with whom one shares social identity, and hence it instills in group members a sense that they and their efforts are worthwhile.

Control, Efficacy, and Power Finally, because it provides a basis for people to participate in, and promote, collective projects, it is also the case that shared social identity tends to give people a sense of agency and control. As social identity theory suggests, at a group level, this should furnish them with a sense of being in charge of their own destiny and of having power in the world (Haslam et al., 2008, 2011). This is seen in a range of studies that show that as people’s sense of social identity increases, so too does their sense of agency and power. Moreover, as studies of protest groups have shown (and as fans of perennially underperforming sports teams understand), it is not necessary for a group to achieve its aims in order for these benefits to accrue. At an individual level too, shared social identity also furnishes group members with a sense of efficacy and of personal control over their lives. Research thus shows that people report a greater sense of personal control to the extent that they identify highly with their community, with a political party, with their nation, or with humanity as a whole (Greenaway et al., 2015). Again, this pattern is not contingent on group success—being apparent, for example, among highly identified Republicans immediately after their party had failed to win the 2012 presidential election. Here, then, internalized social identities help group members to interpret, understand, and recover from such failure. An abundance of work on the importance of a sense of control for health underlines the importance of this observation. Indeed, the World Happiness Report identifies control as one of the “six pillars” of life satisfaction. Yet while perceived control is generally thought to have personal origins, social identity research suggests that it often has its basis in group identities and identifications.

Intervention: Groups 4 Health A key message of the social identity approach to health is that social connectedness, and group connectedness especially, is central to health behavior and health outcomes. But how might we translate knowledge of group and social identity processes into applied interventions? Evidence from group‐based interventions points to three features that appear to be shared by those that are the most effective. First, interventions seem to be more effective to the extent that they have an integrated focus on issues of social process, cognition, and behavior—rather than looking at any one of these things in isolation. Second, interventions tend to be more effective to the extent that the groups at their heart are meaningful to their members and involve activities they value, rather than having an arbitrary or trivial basis. Third, more effective interventions create a sense of shared purpose that binds people together and enables them to work collectively toward common goals. Building on these insights, researchers have used social identity theorizing as a basis for developing a manualized intervention—Groups 4 Health (G4H)—that seeks to improve health by enhancing people’s group‐based social connectedness in the context of a non‐stigmatizing in vivo group experience (Haslam, Cruwys, Haslam, Dingle, & Chang, 2016). G4H is composed of five modules—the 5Ss—as shown in Figure  1. Together, these aim to give people the knowledge and skills not only to understand the nature and importance of their social identity resources but also to manage them effectively in the long term.

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Raising awareness of the value of groups for health and of ways to harness this Developing social maps to identify existing connections and areas for social growth Training skills to maintain and utilise existing networks and reconnect with valued groups Using group as platform for new social connections and to train effective engagement Reinforcing key messages and troubleshooting (held one month later as a booster session)

Figure 1  The modules in Groups 4 Health: the 5Ss (Haslam et al., 2016; see also Haslam et al., 2018).

A proof‐of‐concept trial with participants experiencing social isolation and affective disturbance indicated that G4H reduced clinical symptoms of depression as well as anxiety and stress (Haslam et al., 2016). In addition, it proved enjoyable for both participants and facilitators and led to significant improvements in social anxiety, social functioning, self‐esteem, and life satisfaction. Importantly too, follow‐up analysis indicated that most of these improvements were sustained—or even enhanced—6 months after the program had ended (see Haslam et al., 2016). The reason for this, we suggest, is that, because they are foundational to adaptive social functioning, groups are an especially sustainable source of psychological health.

Future Directions Because the social identity approach to health is relatively new, it is clearly the case that more work needs to be done to test and validate its core assumptions and to establish their practical relevance. Work of this form is currently underway in relation to a range of health topics including social disadvantage, stigma, stress, trauma, aging, depression, chronic mental health, eating behavior, brain injury, acute pain, addiction, and physical health (see Haslam et  al., 2018). In order to establish the generalizability and robustness of G4H, further validation studies are also currently in progress to establish its applicability to a range of populations (e.g., students, new mothers, people recovering from addiction, retirees).

Conclusion The theorizing and evidence that supports the social identity approach has been built up over nearly half a century. However, it is only in the past decade that the approach has been applied specifically to the domain of health (e.g., see Haslam et al., 2018; Haslam, Jetten, Postmes, & Haslam, 2009; Jetten, Haslam, & Haslam, 2012). Nevertheless, in this short time, the evidence base and impact of the approach has increased exponentially, with the result that there are now well over 500 publications that speak to its utility. The model that this work supports is represented schematically in Figure 2. This points to the fact that there are important (if complex) interconnections between social identity, group

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Figure 2  Schematic representation of the social identity approach to health.

behavior, and health and that the capacity for social identity to deliver positive health outcomes rests on its capacity to give individuals access to important social and psychological resources. However, it also suggests that health benefits can only be reaped from groups that are psychologically important for their members and, more generally, that the extent to which groups influence health—whether for good or for ill—depends on individuals’ strength of identification with them. Importantly, the resources that social identities provide are not only psychological but also material. They thus relate not only to people’s sense of connection, support, meaning, and control but also to the social reality of these things. This is because, as social identity theorists grasped from the outset, groups are fundamental not only to our sense of self but also to our capacity to do things with others in the world and by this means to contribute to both social change and social progress (Tajfel & Turner, 1979). It is for this reason, then, that social identities are vital not just for the health of individuals but for that of society as a whole.

Acknowledgment Work on this entry was supported by a fellowship from the Australian Research Council (FL110100199).

Author Biographies S. Alexander Haslam is a professor of psychology and Australian Research Council Laureate Fellow at the University of Queensland. His research focuses on the study of group and identity processes in social, organizational, and health contexts. Together with colleagues he has written and edited 11 books and over 200 peer‐reviewed articles on these topics including, most recently, The New Psychology of Leadership: Identity, Influence and Power (Psychology Press, 2011, with S. Reicher & M. J. Platow). Catherine Haslam is professor of clinical psychology at the University of Queensland. Her research focuses on the social and cognitive consequences of identity‐changing life transitions (e.g., trauma, disease, aging). She is an associate editor of the British Journal of Psychology and co‐author of The New Psychology of Health: Unlocking the Social Cure (Routledge, in press; with J. Jetten, C. Cruwys, G. A. Dingle, and S. A. Haslam).

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Jolanda Jetten is a professor of social psychology and research fellow at the University of Queensland. She is a former editor of the British Journal of Social Psychology. She has published over 140 peer‐reviewed articles on topics related to social identity, group processes, and intergroup relations and previously co‐edited The Social Cure: Identity, Health and Well‐Being (Psychology Press, 2012; with C. Haslam & S. A. Haslam). Tegan Cruwys is a lecturer in the School of Psychology, University of Queensland, a practicing clinical psychologist, and a recipient of an Australian Research Council Discovery Early Career Research Award. Her research investigates the social‐psychological determinants of health, with a particular focus on health behaviors, mental health, and vulnerable populations. Genevieve A. Dingle is a senior lecturer in the School of Psychology, University of Queensland. Her research focuses on social factors in addiction, depression, and chronic mental health problems, people experiencing homelessness, and the recovery process. She is on the editorial board of the British Journal of Clinical Psychology.

References Cruwys, T., Dingle, G., Haslam, S. A., Haslam, C., Jetten, J., & Morton, T. (2013). Social group memberships protect against future depression, alleviate depression symptoms, and prevent depression relapse. Social Science and Medicine, 98, 179–186. doi.org/10.1016/j.socscimed.2013.09.013 Cruwys, T., Haslam, S. A., Dingle, G. A., Haslam, C., & Jetten, J. (2014). Depression and social identity: An integrative review. Personality and Social Psychology Review, 18, 215–238. doi:10.1177/ 1088868314523839 Dingle, G. A., Stark, C., Cruwys, T., & Best, D. (2015). Breaking good: Breaking ties with social groups may be good for recovery from substance misuse. British Journal of Social Psychology, 54, 236–254. Greenaway, K., Haslam, S. A., Branscombe, N. R., Cruwys, T., Ysseldyk, R., & Heldreth, C. (2015). From “we” to “me”: Group identification enhances perceived personal control with consequences for health and well‐being. Journal of Personality and Social Psychology, 109, 53–74. doi:10.1037/ pspi0000019 Haslam, C., Cruwys, T., & Haslam, S. A. (2014). “The we’s have it”: Evidence for the distinctive benefits of group engagement in enhancing cognitive health in aging. Social Science and Medicine, 120, 57–66. doi:10.1016/j.socscimed.2014.08.037 Haslam, C., Cruwys, T., Haslam, S. A., Dingle, G., & Chang, M. X.‐L. (2016). Groups 4 Health: Evidence that a social‐identity intervention that builds and strengthens social group membership improves mental health. Journal of Affective Disorders, 194, 188–195. doi:10.1016/j.jad.2016. 01.010 Haslam, C., Holme, A., Haslam, S. A., Iyer, A., Jetten, J., & Williams, W. H. (2008). Maintaining group memberships: Social identity continuity predicts well‐being after stroke. Neuropsychological Rehabilitation, 18, 671–691. doi:10.1080/09602010701643449 Haslam, C., Jetten, J., Cruwys, C., Dingle, G. D., & Haslam, S. A. (2018). The new psychology of health: Unlocking the social cure. London: Routledge. Haslam, S. A., Jetten, J., Postmes, T., & Haslam, C. (2009). Social identity, health and well‐being: An emerging agenda for applied psychology. Applied Psychology, 58, 1–23. doi:10.1111/j.1464‐ 0597.2008.00379.x Haslam, S. A., Reicher, S. D., & Levine, M. (2012). When other people are heaven, when other people are hell: How social identity determines the nature and impact of social support. In J. Jetten, C. Haslam, & S. A. Haslam (Eds.), The social cure: Identity, health, and well‐being (pp. 157–174). Hove, UK: Psychology Press. Haslam, S. A., Reicher, S. D., & Platow, M. J. (2011). The new psychology of leadership: Identity, influence and power. Hove, UK: Psychology Press.

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Jetten, J., Branscombe, N. R., Haslam, S. A., Haslam, C., Cruwys, T., Jones, J. M., … Zhang, A. (2015). Having a lot of a good thing: Multiple important group memberships as a source of self‐esteem. PLoS One, 10(6), e0131035. doi:10.1371/journal.pone.0131035 Jetten, J., Haslam, C., & Haslam, S. A. (Eds.) (2012). The social cure: Identity, health and well‐being. Hove, UK: Psychology Press. Jetten, J., Haslam, C., Haslam, S. A., Dingle, G., & Jones, J. M. (2014). How groups affect our health and well‐being: The path from theory to policy. Social Issues and Policy Review, 8, 103–130. doi:10.1111/sipr.12003 Jetten, J., Haslam, S. A., Cruwys, T., Greenaway, K., Haslam, S. A., & Steffens, N. R. (2017). Advancing the social identity approach to health and well‐being: Progressing the social cure research agenda. European Journal of Social Psychology, 47(7), 789–802. Steffens, N. K., Cruwys, T., Haslam, C., Jetten, J., & Haslam, S. A. (2016). Social group memberships in retirement are associated with reduced risk of premature death: evidence from a longitudinal cohort study. BMJ Open, 6(2), e010164. Steffens, N. K., Haslam, S. A., Schuh, S., Jetten, J., & van Dick, R. (2016). A meta‐analytic review of social identification and health in organizational contexts. Personality and Social Psychology Review, 21(4), 303–335. doi:10.1177/1088868316656701 Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33–47). Monterey, CA: Brooks/ Cole. Turner, J. C. (1982). Towards a redefinition of the social group. In H. Tajfel (Ed.), Social identity and intergroup relations (pp. 15–40). Cambridge, UK: Cambridge University Press. Turner, J. C., Hogg, M. A., Oakes, P. J., Reicher, S. D., & Wetherell, M. S. (1987). Rediscovering the social group: A self‐categorization theory. Cambridge, MA: Basil Blackwell.

Suggested Reading Cruwys, T., Haslam, S. A., Dingle, G. A., Haslam, C., & Jetten, J. (2014). Depression and social identity: An integrative review. Personality and Social Psychology Review, 18, 215–238. doi:10.1177/ 1088868314523839 Haslam, C., Jetten, J., Cruwys, C., Dingle, G. D., & Haslam, S. A. (2018). The new psychology of health: Unlocking the social cure. London, UK: Routledge. Haslam, S. A., Jetten, J., Postmes, T., & Haslam, C. (2009). Social identity, health and well‐being: An emerging agenda for applied psychology. Applied Psychology. An International Review, 58, 1–23. doi:10.1111/j.1464‐0597.2008.00379.x Jetten, J., Haslam, C., & Haslam, S. A. (Eds.) (2012). The social cure: Identity, health and well‐being. Hove, UK: Psychology Press. Jetten, J., Haslam, S. A., Cruwys, T., Greenaway, K., Haslam, S. A., & Steffens, N. R. (2017). Advancing the social identity approach to health and well‐being: Progressing the social cure research agenda. European Journal of Social Psychology.

Social Influence Karen S. Rook, Dara H. Sorkin, and Kelly Biegler Department of Psychological Science, University of California, Irvine, CA, USA

People who have more social ties are known to have a significantly lower risk of mortality and morbidity, and this lower risk is usually attributed to the health‐protective benefits of social support provided by others (Cohen, 2004). Social support refers to the aid and care that social network members often provide in times of need, and it is important in helping to buffer people from the harmful effects of life stress. Providing support is not the only process, however, by which social network members may contribute to health. They can also be a source of social influence, as people monitor each other’s health behavior and seek to foster change when a health behavior (or set of behaviors) is perceived to be health damaging. A wife who is concerned about her husband’s smoking, for example, might tell him how upset she feels when he smokes, might warn him of the long‐term negative consequences of smoking, or might otherwise try to convince him to discontinue smoking. Researchers use different terms to describe such attempts to exert social influence, but a commonly used term is social control. Social control refers to efforts by social network members to monitor each other’s health behavior and to intervene to discourage health‐damaging behavior (Lewis & Rook, 1999; Umberson, 1987). Social control, like social support, could be an important mechanism that explains why having close relationships is associated with better health and greater longevity. The two kinds of interpersonal processes differ, however, in other respects. By serving as a source of aid and care in time of need, social support is typically experienced as welcome and affirming. Social control, in contrast, involves efforts to change or constrain a person’s health behavior, which can be experienced as unwelcome and critical, rather than affirming (Rook, 2015). Yet theorists have argued that social control attempts have the potential to benefit health, even when they are not affirming. Pressuring a family member to follow a prescribed treatment regimen more closely may help to prevent serious illness complications, even if the pressure is frustrating or irritating. The idea that social control may contribute to improved health behavior even while it arouses psychological distress has come to be known as the dual effects hypothesis (Lewis & Rook, 1999). Researchers have recently extended this reasoning to suggest that social control might detract from a recipient’s feelings of self‐efficacy,

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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even though it is intended to benefit the recipient’s health by fostering improved health behavior. Specifically, others’ social control attempts may convey implicitly (or sometimes explicitly) the message that the recipient’s self‐control is poor, which could erode his or her feelings of self‐efficacy (e.g., Franks et al., 2006) and could kindle feelings of guilt or embarrassment. Does social control have these hypothesized dual effects? Existing evidence is somewhat mixed, but it currently suggests three conclusions. First, social control is sometimes associated with better health behavior, but it is often associated with worse health behavior. For example, more frequent social control has been related to less health‐damaging behavior (such as smoking) and more health‐enhancing behavior (such as exercise, sleep, sound dietary practices) in some studies, but it has been related to worse, rather than better, health behavior in numerous studies. Moreover, more frequent social control has been found to be related to hiding of unsound health behavior and pretended adoption of sound health behavior in some studies (Lewis & Rook, 1999; see review by Craddock, van Dellen, Novak, & Ranby, 2015). Such evidence casts doubt on one component of the dual effects hypothesis—the idea that others’ social control attempts tend to encourage better health behavior. Much of the evidence that casts doubt on this conclusion is derived from cross‐sectional studies, however, and cross‐ sectional designs introduce ambiguity with respect to temporal order. Do unsound health behaviors encourage others to engage in social control attempts, or do others’ social control attempts encourage people to engage in unsound health behaviors (e.g., by undermining self‐ efficacy or provoking behavioral reactance)? Longitudinal studies generally yield less ambiguous evidence, and some (though not all) longitudinal studies do suggest that greater social control is associated with improved health behavior over time (e.g., Knoll, Burkert, Scholz, Roigas, & Gralla, 2012; Stephens et al., 2009). A second conclusion suggested by existing research is that social control attempts are often associated with psychological distress, including not only feelings of resentment and irritation but also feelings of guilt and shame (e.g., Rook, August, Stephens, & Franks, 2011). Links between greater social control and lower feelings of self‐efficacy have also been reported (e.g., Berg et al., 2013). It is worth noting, however, that participants in some studies express appreciation for others’ efforts to monitor their health behavior and nudge improvements (Rook et al., 2011). Individual differences exist in how people react to others’ social control attempts, with evidence suggesting that factors such as gender (e.g., August & Sorkin, 2011; Martire et al., 2013), ethnicity (e.g., August & Sorkin, 2011), and expectations about the appropriateness of others’ involvement in one’s health (e.g., Rook et al., 2011) influence whether people react favorably or unfavorably to social control attempts. Similarly, features of the broader context in which social control attempts occur may also influence how people react to them. People in more satisfying relationships appear to respond more favorably to their partner’s social control attempts, exhibiting greater health behavior change and little psychological distress (e.g., Knoll et al., 2012; Tucker, 2002). The specific health context in which social control occurs also appears to influence how people react. Individuals with a serious chronic illness who face the task of adhering to a complex set of health behaviors as part of their day‐to‐day illness management may understand that their partners’ efforts to prompt or urge adherence, even if unwelcome, are well‐intentioned (e.g., Badr, Yeung, Lewis, Milbury, & Redd, 2015). Similarly, if couples regard the day‐to‐day tasks of managing a chronic illness as a collaborative rather than an independent endeavor, patients may react more favorably to their partners’ involvement in their health (Berg et al., 2008). A third conclusion suggested by existing research is that the nature of the social control attempts themselves influence how people respond, with positive and negative strategies of social control eliciting markedly different responses. Positive control strategies refer to others’

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attempts to persuade or motivate a person to improve his or her health behavior, whereas ­negative control strategies refer to others’ attempts to pressure or coerce a person to make improvements (Stephens et al., 2009). Examples of positive strategies include reminding, persuading, bargaining, modeling, and providing contingent positive feedback. Examples of negative strategies include criticism, guilt induction, nagging, pressure, and withdrawal (Lewis & Butterfield, 2005). For example, a wife might try to persuade her husband to improve his adherence to a prescribed diet by reminding him of the health benefits of making better dietary choices, or, on the other hand, she might subtly criticize his food choices (“Are you really going to eat that?”) or express exasperation (“I just do not understand how you can keep eating food that you know is bad for you.”) Some researchers favor the terms persuasion and pressure to refer to these two different categories of social control tactics to avoid potential confusion between the nature of the influence attempt and its presumed effect on the recipient (Stephens et al., 2013). Across many studies, positive strategies have been found to be more likely to elicit improved health behavior and to avoid arousing psychological distress compared with negative strategies (Craddock et al., 2015). One challenge confronting researchers, however, in their efforts to identify distinctive effects of specific tactics of social control is that social network members frequently use multiple strategies of influence (Lewis & Butterfield, 2005). The dual effects model of social control has continued to be elaborated in recent studies, with researchers investigating moderators that may indicate when the hypothesized effects are most likely to occur and mediating mechanisms that may explain why observed effects occur. Relationship characteristics and the health context of social control attempts, as noted earlier, are examples of potentially important moderators. The emotions aroused by social control attempts are examples of potentially important mediators, with social control expected to be more effective to the extent that the tactics used (such as reminders and explanations) elicit positive emotions and less effective, in contrast, to the extent that the tactics used (such as criticism or coercion) elicit negative emotions (e.g., Okun, Huff, August, & Rook, 2007; Tucker, Orlando, Elliott, & Klein, 2006). In the context of a serious chronic illness, however, even social control that arouses frustration or resentment may, nonetheless, help to foster adherence to a demanding self‐care regimen (Badr et al., 2015). Thus, the specific health context in which social control attempts occur may influence the emotional and behavioral responses they elicit. Researchers have also begun to investigate the joint effects of social support and social control, positing, for example, that social control attempts may elicit more favorable responses in social relationships that also serve as a source of support (e.g., Khan, Stephens, Franks, Rook, & Salem, 2013). Researchers have recently extended research on health‐related social control to examine potential effects on the individuals engaged in social control attempts, such as spouses. It is plausible, for example, that having to monitor and seek to influence a partner’s health behavior can be stressful for spouses, perhaps especially in the context of a chronic illness. Evidence consistent with this idea has emerged in studies of couples in which one individual is coping with type 2 diabetes (e.g., August, Rook, Franks, & Stephens, 2013; August, Rook, Stephens, & Franks, 2011). Whether negative effects of engaging in persistent health‐related social control attempts would emerge in other health contexts is a question that warrants investigation in future studies. Although most work on health‐related social influence has emphasized actions by social network members that are intended to discourage health‐compromising behavior, it is also important to note that others’ social influence sometimes serves to encourage health‐compromising behavior. In adolescence and young adulthood, for example, peer influence is known to play a role in the initiation of substance use (e.g., Wills, McNamara, Vaccaro, & Hirky, 1996). Even in the context of illness management, friends and family members sometimes

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undermine rather than bolster adherence to a treatment regimen. For example, spouses of individuals with type 2 diabetes are known to tempt their partners occasionally to eat unhealthy food, thereby undermining the partner’s adherence to a recommended dietary regimen (Henry, Rook, Stephens, & Franks, 2013). The particular circumstances that prompt such undermining remain poorly understood and represent a valuable focus of future research. Social influence, like social support, is a common element of many close relationships. Health‐related social control and social support often co‐occur in close relationships, but these interpersonal processes differ conceptually and empirically and exhibit distinctive associations with health behavior (Rook, 2015). Social support has been investigated more extensively than has social control. Yet across different community samples, as many as 70–86% of participants report experiencing health‐related social control attempts from their social network members (e.g., August & Sorkin, 2011; Lewis & Rook, 1999). Given how common this form of social influence is in close relationships, it warrants greater attention from researchers in future studies. It is particularly important to gain an understanding of the dynamics of health‐ related support and control as they unfold over the course of a chronic illness or other significant health transition. Initial expressions of social support may give way over time to increasingly insistent, and even caustic, forms of social control when individuals experiencing a serious health challenge exhibit limited interest or history of success in initiating and sustaining health behavior changes that are crucial to their health. Future research will hopefully shed light on ways that social network members can avoid engaging in caustic or coercive social influence attempts and, instead, can foster successful health behavior change and maintenance while preserving self‐efficacy in the recipient and good will in the relationship.

Author Biographies Karen S. Rook is a professor of psychology and social behavior at the University of California, Irvine. Her research investigates the role of social relationships in fostering or interfering with health‐enhancing behavior, particularly in the context of chronic illness. Other research examines the effects of interventions designed to strengthen social relationships as a resource for successful health behavior change. Dara H. Sorkin is an associate professor of medicine at the University of California, Irvine. Her research interests focus on social relationships and health, particularly as they may play a role in health disparities. She also conducts and evaluates behavioral health interventions in community settings that seek to reduce health risks and poor health outcomes among under‐ resourced populations. Kelly Biegler is a research scientist at the University of California, Irvine. Her research focuses on examining the impact of health behaviors, quality of life, and chronic stress on disease management, clinical outcomes, and neurophysiologic and immunologic processes in patient populations. She also investigates the effects of community interventions on the health and well‐being of at‐risk, low‐income populations.

References August, K. J., Rook, K. S., Franks, M. M., & Stephens, M. A. P. (2013). Spouses’ involvement in their partners’ diabetes management: Associations with spouse stress and perceived marital quality. Journal of Family Psychology, 27(5), 712–721. doi:10.1037/a0034181

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August, K. J., Rook, K. S., Stephens, M. A., & Franks, M. M. (2011). Are spouses of chronically ill partners burdened by exerting health‐related social control? Journal of Health Psychology, 16(7), 1109– 1119. doi:10.1177/1359105311401670 August, K. J., & Sorkin, D. H. (2011). Support and influence in the context of diabetes management: Do racial/ethnic differences exist? Journal of Health Psychology, 15(5), 711–721. doi:10.1177/ 1359105310388320 Badr, H., Yeung, C., Lewis, M. A., Milbury, K., & Redd, W. H. (2015). An observational study of social control, mood, and self‐efficacy in couples during treatment for head and neck cancer. Psychology & Health, 30(7), 783–802. doi:10.1080/08870446.2014.994633 Berg, C. A., Butner, J. E., Butler, J. M., King, P. S., Hughes, A. E., & Wiebe, D. J. (2013). Parental persuasive strategies in the face of daily problems in adolescent type 1 diabetes management. Health Psychology, 32(7), 719–728. doi:10.1037/a0029427 Berg, C. A., Wiebe, D. J., Butner, J., Bloor, L., Bradstreet, C., Upchurch, R., … Patton, G. (2008). Collaborative coping and daily mood in couples dealing with prostate cancer. Psychology and Aging, 23(3), 505–516. doi:10.1037/a0012687 Cohen, S. (2004). Social relationships and health. American Psychologist, 59, 676–684. doi:10.1037/0003‐066X.59.8.676 Craddock, E., vanDellen, M. R., Novak, S. A., & Ranby, K. W. (2015). Influence in relationships: A meta‐analysis on health‐related social control. Basic and Applied Social Psychology, 37(2), 118–130. doi:10.1080/01973533.2015.1011271 Franks, M. M., Stephens, M. A. P., Rook, K. S., Franklin, B. A., Keteyian, S. J., & Artinian, N. T. (2006). Spouses’ provision of health‐related social support and control to patients participating in cardiac rehabilitation. Journal of Family Psychology, 20, 311–318. doi:10.1037/0893‐3200.20.2.311 Henry, S. L., Rook, K. S., Stephens, M. A. P., & Franks, M. M. (2013). Spousal undermining of older diabetic patients’ disease management. Journal of Health Psychology, 18, 1550–1561. doi: 10.1177/1359105312465913 Khan, C. M., Stephens, M. A. P., Franks, M. M., Rook, K. S., & Salem, J. K. (2013). Influences of spousal support and control on diabetes management through physical activity. Health Psychology, 32, 739–747. doi:10.1037/a0028609 Knoll, N., Burkert, S., Scholz, U., Roigas, J., & Gralla, O. (2012). Anxiety. Stress & Coping, 25(3), 291–307. doi:10.1080/10615806.2011.584188 Lewis, M. A., & Butterfield, R. M. (2005). Antecedents and reactions to health‐related social control. Personality and Social Psychology Bulletin, 31(3), 416–427. doi:10.1177/0146167204271600 Lewis, M. A., & Rook, K. S. (1999). Social control in personal relationships: Impact on health behaviors and psychological distress. Health Psychology, 18, 63–71. doi:10.1037//0278‐6133.18.1.63 Martire, L. M., Stephens, M. A. P., Mogle, J., Schulz, R., Brach, J., & Keefe, F. J. (2013). Daily spousal influence on physical activity in knee osteoarthritis. Annals of Behavioral Medicine, 45(2), 213–223. doi:10.1007/s12160‐012‐9442‐x Okun, M. A., Huff, B. P., August, K. J., & Rook, K. S. (2007). Testing hypotheses distilled from four models of the effects of health‐related social control. Basic and Applied Social Psychology, 29(2), 185–193. doi:10.1080/01973530701332245 Rook, K. S. (2015). Social networks in later life: Weighing positive and negative effects on health and well‐being. Current Directions in Psychological Science, 24, 45–51. doi:10.1177/0963721414551364 Rook, K. S., August, K. J., Stephens, M. A. P., & Franks, M. F. (2011). When does spousal social control provoke negative reactions in the context chronic illness? The pivotal role of patients’ expectations. Journal of Social and Personal Relationships, 28, 772–789. doi:10.1177/0265407510391335 Stephens, M. A. P., Fekete, E. M., Franks, M. M., Rook, K. S., Druley, J. A., & Greene, K. (2009). Spouses’ use of pressure and persuasion to promote osteoarthritis patients’ medical adherence after orthopedic surgery. Health Psychology, 28, 48–55. doi:10.1037/a0012385 Stephens, M. A. P., Franks, M. F., Rook, K. S., Iida, M., Hempill, R., & Salem, J. K. (2013). Spouses’ attempts to regulate day‐to‐day dietary adherence among patients with type 2 diabetes. Health Psychology, 32, 1029–1037. doi:10.1037/a0030018

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Tucker, J. S. (2002). Health‐related social control within older adults’ relationships. Journal of Gerontology: Psychological Sciences, 57, 387–395. doi:10.1093/geronb/57.5.P387 Tucker, J. S., Orlando, M., Elliott, M. N., & Klein, D. J. (2006). Affective and behavioral responses to health‐related social control. Health Psychology, 25(6), 715–722. doi:10.1037/0278‐6133.25.6.715 Umberson, D. (1987). Family status and health behaviors: Social control as a dimension of social integration. Journal of Health and Social Behavior, 28, 306–319. Wills, T. A., McNamara, G., Vaccaro, D., & Hirky, A. E. (1996). Escalated substance use: A longitudinal grouping analysis from early to middle adolescence. Journal of Abnormal Psychology, 105(2), 166– 180. doi:10.1037/0021‐843X.105.2.166

Suggested Reading August, K. J., & Sorkin, D. H. (2010). Marital status and gender differences in managing a chronic illness: The function of health‐related social control. Social Science & Medicine, 71, 1831–1838. doi:10.1016/j.socscimed.2010.08.022 Lewis, M. A., Butterfield, R. M., Darbes, L. A., & Johnston‐Brooks, C. (2004). The conceptualization and assessment of health‐related social control. Journal of Social and Personal Relationships, 21, 669–687. doi:10.1177/0265407504045893 Rook, K. S., August, K. J., & Sorkin, D. H. (2011). Social network functions and health. In R. Contrada & A. Baum (Eds.), Handbook of stress science: Biology, psychology, and health (pp. 123–135). New York: Springer. Stephens, M. A. P., Hemphill, R. C., Rook, K. S., & Franks, M. M. (2014). It sometimes takes two: Marriage as a mechanism for managing chronic illness. In C. R. Agnew & S. C. South (Eds.), Interpersonal relationships and health: Social and clinical psychological mechanisms (pp. 109–132). New York: Oxford University Press.

Social Isolation and Health Mona Moieni and Naomi I. Eisenberger Psychology Department, University of California Los Angeles, Los Angeles, CA, USA

I don’t know if anyone has noticed, but I only ever write about one thing: being alone. The fear of being alone, the desire to not be alone… the devastation of being left alone. The need to hear the words: You are not alone. —Shonda Rhimes, television writer and producer

What drives a compelling story? What pushes people to tune in each week to watch a story unfold on their televisions? Shonda Rhimes, an award‐winning writer, producer, and creator of multiple widely successful television shows such as Grey’s Anatomy and Scandal, revealed her secret for creating a narrative that millions of people around the world want to follow each week: writing about loneliness. Her answer also tapped into the idea that humans have a “need to belong”—a fundamental need or drive to seek out and maintain social relationships. It has been suggested that this desire to connect with others is as essential to our existence as other basic needs such as food, water, and shelter, providing us with advantages for survival (Baumeister & Leary, 1995). If being social is such a crucial aspect of being human, then we would expect that not having social relationships would be associated with disadvantages and detrimental outcomes for our health and survival. Indeed, decades of research support the notion that lacking social connections has consequences for physical and mental health, including a significantly higher risk for all‐cause mortality. In this chapter, we first briefly discuss measurement issues relevant to social isolation and then highlight health outcomes associated with social isolation. We follow this by a discussion of mechanisms that have been proposed to explain the relationship between social isolation and health, as well as consideration of interventions to reduce social isolation.

Measuring Social Isolation Social isolation can be conceptualized in many ways. To assess whether an individual is socially isolated, we could ask whether she is living with other people, how often she gets together The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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with or talks to people, or in how many organizations or social activities (e.g., work, volunteering) she participates. These kinds of measures that tap into an individual’s social network size are typically referred to as measures of objective social isolation. Thus, someone who lives alone, has few social ties, participates in few social activities, and/or has minimal contact with others would be considered objectively socially isolated. Rather than asking how many people comprise a person’s social network, we could also ask about how satisfied the person is with their network by asking them how connected they feel. We could ask them things like “How often do you feel alone?” or “How often do you feel that your relationships with others are not meaningful?” (Russell, 1996). These subjective feelings of social disconnection, or loneliness, are measures of perceived or subjective social isolation. Loneliness is distress resulting from the discrepancy between what an individual wants out of social relationships and what he or she is receiving. For example, people who say they often lack companionship, feel isolated from others, and feel there is no one they can turn to would be considered lonely. Although these variables are related, they are separable and distinct. For example, it is possible to imagine someone who has only one confidant (and thus may appear socially isolated on objective measures) but feels perfectly content and socially satiated. Conversely, someone may be surrounded by people and participate in numerous social activities but still feel isolated or unsatisfied with these social relationships and thus feel lonely. While it is important to note that social isolation can be measured in numerous ways, this chapter covers the effects of isolation on health regardless of the way it was measured.

Impact of Social Isolation on Health Starting with Berkman and Syme (1979) and House, Robbins, and Metzner (1982) and continuing through today, decades of work have documented the impact of social isolation on morbidity and mortality. In fact, social isolation is a mortality risk factor comparable with traditional risk factors such as smoking or obesity (see Holt‐Lunstad, Smith, Baker, Harris, & Stephenson, 2015). Interestingly, a recent meta‐analysis also found that there was no difference between objective and subjective measures of social isolation in terms of predicting mortality risk (see Holt‐Lunstad et al., 2015), suggesting that social isolation is a powerful predictor of mortality regardless of how it is measured. In addition to mortality, social isolation is related to lower levels of self‐rated physical health, risk of cardiovascular disease, elevated blood pressure, risk of poor outcomes post‐stroke, metabolic syndrome, increased frequency of doctor visits, and functional decline and disability (see Cacioppo, Grippo, London, Goossens, & Cacioppo, 2015). There are also many associations between social isolation, in particular subjective social isolation or loneliness, and mental health. Loneliness is associated with depressive symptoms, suicidal ideation, social anxiety, substance abuse, eating disorders, and psychosis. Loneliness is also related to poorer cognitive performance, cognitive decline, and increased risk of Alzheimer’s disease (see Hawkley & Cacioppo, 2010).

Potential Mechanisms Overall, social isolation is associated with a wide array of mental and physical health issues, as well as mortality. Many mechanisms have been proposed to explain the relationship between social isolation and adverse health outcomes. We briefly outline some of the theorized

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­ ehavioral, psychological, and biological mechanisms below. Although we discuss these mechb anisms separately for ease of presentation, in reality one mechanism is unlikely to fully explain the complex relationships between social isolation and health. These proposed pathways, along with other mechanisms not mentioned here, likely interact with and build off each other.

Behavioral Health behaviors have been discussed as potential pathways through which social isolation can impact health (see Hawkley & Cacioppo, 2010; Thoits, 2011). For example, friends and family can help individuals start and maintain healthy habits, and they can also provide meaning in life, which can have a positive impact on health behaviors. Socially isolated individuals may thus be at risk for poor health outcomes in part because they are lacking a significant source of modeling of good health behaviors and a sense of purpose related to having close relationships. Additionally, loneliness may impair executive control and self‐regulation (see Hawkley & Cacioppo, 2010), which could have a direct impact on health behaviors such as substance use, physical activity, and eating behavior. Indeed, social isolation is related to smoking and drinking more (Broman, 1993), and there also appears to be a relationship between loneliness and alcohol abuse (Åkerlind & Hörnquist, 1992). In addition to substance use, loneliness is related to obesity. Lonely individuals are more likely to be overweight than non‐lonely individuals (Lauder, Mummery, Jones, & Caperchione, 2006). Furthermore, feeling disconnected can also impair self‐control over eating behavior (Baumeister, DeWall, Ciarocco, & Twenge, 2005), and loneliness is predictive of reductions in physical activity (Hawkley, Thisted, & Cacioppo, 2009). Sleep is another behavior that may be a potential mechanism through which social isolation can impact health. Loneliness is related to poor sleep quality throughout the lifespan, and it is also associated with fatigue and low energy (Hawkley, Preacher, & Cacioppo, 2010). Given the importance of sleep for health and mortality, this may be another behavior that may partially explain the social isolation–health association.

Psychological Many potential psychological mediators of the social ties–health link have also been proposed (see Hawkley & Cacioppo, 2010; Thoits, 2011). One psychological mechanism that has been proposed is maladaptive social cognition. That is, lonely individuals may have certain social cognitive biases, such as generally seeing the world as a more socially threatening place, which may set off a vicious, self‐feeding cycle of psychobiological reactions that may ultimately contribute to poor health outcomes. Socially isolated individuals may also lack the sense of purpose or meaning in life that is provided by social connection, which can lead to impairments in psychological well‐being and physical health. Those who are isolated may also lack socially supportive others who may “buffer” the impact of stressful life events on well‐being and health (Cohen & Wills, 1985). Besides these mechanisms, some other proposed psychological mediators explaining the impact of social isolation on health include positive affect, social influence, self‐efficacy, and self‐esteem (see Thoits, 2011). However, some have found that existing models of psychological mechanisms linking social ties to health are generally not supported by evidence, indicating that there is still room for further research to better understand the psychological pathways that may underlie the social isolation–health link.

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Biological Numerous biological pathways have also been proposed to explain the effect of social isolation on health (see Hawkley & Cacioppo, 2010). For example, lonely individuals have immune system impairments (Glaser, Kiecolt‐Glaser, Speicher, & Holliday, 1985), suggesting that immune alterations may be one pathway through which loneliness operates to damage health. Inflammation, the body’s first line of defense against infection or injury, is one potential immunological process through which loneliness may exert its effects. Inflammatory processes are implicated in a wide array of physical and mental health conditions, as well as mortality (Emerging Risk Factors Collaboration, 2010). Lonely individuals have increased pro‐inflammatory reactivity (Jaremka et al., 2013), as well as upregulated pro‐inflammatory gene expression (Cole et al., 2007). This heightened inflammatory activity in lonely individuals indicates that inflammation may be mediating some of the effects of social isolation on health. Interestingly, inflammation can also lead to feelings of social disconnection (Eisenberger, Inagaki, Mashal, & Irwin, 2010; Moieni et al., 2015), suggesting that inflammation and loneliness may engage in a dangerous, mutually reinforcing cycle. Related to inflammation, loneliness may also operate through neuroendocrine pathways. For example, lonely individuals have impairments related to the hypothalamus–pituitary–­adrenal (HPA) axis, including increased levels of cortisol (Adam, Hawkley, Kudielka, & Cacioppo, 2006). Chronic social isolation may also lead to glucocorticoid insensitivity (see Hawkley & Cacioppo, 2010), inhibiting the ability of cortisol to have an anti‐inflammatory effect. Finally, neural mechanisms have also been proposed to mediate the social isolation–health link. A set of neural regions which are involved in threat detection, including the amygdala, dorsal anterior cingulate cortex (dACC), anterior insula, and periaqueductal gray, are thought to play a role in the relationship between social isolation and health (Eisenberger & Cole, 2012). These regions may serve as a neural alarm system to detect social threat (e.g., social isolation, social exclusion), and in turn, activation of these regions may have an impact on HPA and sympathetic nervous system activity, ultimately impacting health. For example, social exclusion or rejection activates the dACC and anterior insula (Eisenberger, 2015; Eisenberger, Lieberman, & Williams, 2003). Activation of these neural regions is related to both heightened inflammation, which is involved in many physical and health disorders, and cortisol, a hormone involved in the body’s stress response. While the existing evidence lends support to these particular biological mechanisms, more work is needed to flesh out the details of these pathways, as well as how they may interact with behavioral and psychological processes, to impact health.

Interventions Given the influence of social isolation on health, it is not surprising that multiple types of interventions targeted at reducing social isolation have been conducted. Interventions have included offering social skills training, social cognitive training, mind–body interventions, opportunities for social contact or activities, or social support programs. The mode of delivery has ranged from group interventions to one‐on‐one interventions, from in person to online, and lasting from a few weeks to multiple years. There have been multiple reviews on this topic (e.g., Dickens, Richards, Greaves, & Campbell, 2011; Masi, Chen, Hawkley, & Cacioppo, 2010), so we will highlight a few examples of effective interventions that have been tested using randomized trials.

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One type of intervention that has shown positive effects on social isolation are those promoting social support and social activities. In one study, older adults who were randomly assigned to an intervention intended to increase peer support and social activity showed decreases in social isolation (Routasalo, Tilvis, Kautiainen, & Pitkala, 2009); those in the intervention group became more socially active and found new friends significantly more often than the control group. However, the intervention did not lead to decreases in loneliness or increases in social network size. Other interventions have examined the effect of animal‐assisted therapy to increase socialization and decrease loneliness (Banks & Banks, 2002; Banks, Willoughby, & Banks, 2008). Participants who were randomly assigned to the animal‐assisted therapy group (i.e., regular visits from a dog) showed decreases in loneliness (Banks & Banks, 2002). Another type of intervention that has shown promise for reducing loneliness is social cognitive training (Masi et al., 2010). For example, one study examined the effect of “reminiscence therapy” (vs. a wait‐list control) on loneliness in nursing home patients (Chiang et al., 2010). Over the course of 8 weeks, those in the therapy condition participated in activities such as discussing how to be more aware and expressive of feelings, identifying positive social relationships, and recalling family history and life stories; those in this condition showed improvements in loneliness. Although these interventions have shown promise in reducing feelings of isolation, additional studies examining the effect of this type of intervention, particularly in different samples (e.g., isolated adolescents), are still needed. Creswell et al. (2012) took yet another approach, examining the effect of a mindfulness‐ based stress reduction (MBSR) training on loneliness in older adults. They found that an 8‐ week MBSR program, compared with a wait‐list control, led to reductions in loneliness, as well as reductions in pro‐inflammatory gene expression. This finding suggests that other mind–body interventions may be successful in reducing loneliness, as well as biological mechanisms such as inflammation that may be driving some of the health effects associated with social isolation. Indeed, there is certainly room to develop additional targeted interventions addressing social isolation based on these kinds of successful interventions. Some have suggested that interventions that incorporate some sort of social cognitive training are likely to be the most effective (Masi et al., 2010), while others have found that interventions with a social activity or social support component, and delivered at a group level (compared with one‐on‐one), may be the most effective (Dickens et al., 2011). Still others have suggested tailoring new interventions for particular developmental stages and thus focusing interventions on aspects of social isolation that are most important across different times of the lifespan (Qualter et al., 2015). Overall, it appears that testing new evidence‐based interventions, particularly using randomized trials, is an important area for future research in this field.

Conclusion In summary, social isolation has detrimental effects on a wide range of physical and mental health problems, as well as mortality. Numerous mechanisms have been proposed for the relationship between social isolation and health, but future work is needed to more fully flesh out these pathways. Finally, some interventions aimed at decreasing social isolation have been successful. However, given that social isolation can have such a powerful influence on our physical health and well‐being, developing new low‐cost, effective, and easy‐to‐implement interventions is an important area for further research that could ultimately have significant public health implications.

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Author Biographies Mona Moieni is a postdoctoral scholar in the Department of Psychology at the University of California, Los Angeles (UCLA). Her research focuses on the relationships between biological and social psychological processes, as well as how these relationships may be relevant for physical and mental health. Naomi I. Eisenberger is an associate professor of psychology at the University of California, Los Angeles (UCLA). She specializes in social neurocognitive aspects of emotion and behavior and how these processes relate to health‐relevant biomarkers.

References Adam, E. K., Hawkley, L. C., Kudielka, B. M., & Cacioppo, J. T. (2006). Day‐to‐day dynamics of experience–cortisol associations in a population‐based sample of older adults. Proceedings of the National Academy of Sciences, 103(45), 17058–17063. doi:10.1073/pnas.0605053103 Åkerlind, I., & Hörnquist, J. O. (1992). Loneliness and alcohol abuse: A review of evidences of an interplay. Social Science & Medicine, 34(4), 405–414. doi:10.1016/0277‐9536(92)90300‐F Banks, M. R., & Banks, W. A. (2002). The effects of animal‐assisted therapy on loneliness in an elderly population in long‐term care facilities. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 57(7), M428–M432. doi:10.1093/gerona/57.7.M428 Banks, M. R., Willoughby, L. M., & Banks, W. A. (2008). Animal‐assisted therapy and loneliness in nursing homes: Use of robotic versus living dogs. Journal of the American Medical Directors Association, 9(3), 173–177. doi:10.1016/j.jamda.2007.11.007 Baumeister, R. F., DeWall, C. N., Ciarocco, N. J., & Twenge, J. M. (2005). Social exclusion impairs self‐regulation. Journal of Personality and Social Psychology, 88(4), 589. doi:10.1037/0022‐3514. 88.4.589 Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117(3), 497. doi:10.1037/0033‐2909. 117.3.497 Berkman, L. F., & Syme, S. L. (1979). Social networks, host resistance, and mortality: A nine‐year follow‐up study of Alameda County residents. American Journal of Epidemiology, 109(2), 186–204. Broman, C. L. (1993). Social relationships and health‐related behavior. Journal of Behavioral Medicine, 16(4), 335–350. doi:10.1007/BF00844776 Cacioppo, S., Grippo, A. J., London, S., Goossens, L., & Cacioppo, J. T. (2015). Loneliness clinical import and interventions. Perspectives on Psychological Science, 10(2), 238–249. doi:10.1177/ 1745691615570616 Chiang, K.‐J., Chu, H., Chang, H.‐J., Chung, M.‐H., Chen, C.‐H., Chiou, H.‐Y., & Chou, K.‐R. (2010). The effects of reminiscence therapy on psychological well‐being, depression, and loneliness among the institutionalized aged. International Journal of Geriatric Psychiatry, 25(4), 380. doi:10.1002/gps.2350 Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2), 310. doi:10.1037/0033‐2909.98.2.310 Cole, S. W., Hawkley, L. C., Arevalo, J. M., Sung, C. Y., Rose, R. M., & Cacioppo, J. T. (2007). Social regulation of gene expression in human leukocytes. Genome Biology, 8(9), R189. doi:10.1186/ gb‐2007‐8‐9‐r189 Creswell, J. D., Irwin, M. R., Burklund, L. J., Lieberman, M. D., Arevalo, J. M., Ma, J., … Cole, S. W. (2012). Mindfulness‐based stress reduction training reduces loneliness and pro‐inflammatory gene expression in older adults: A small randomized controlled trial. Brain, Behavior, and Immunity, 26(7), 1095–1101. doi:10.1016/j.bbi.2012.07.006

Social Isolation and Health

701

Dickens, A. P., Richards, S. H., Greaves, C. J., & Campbell, J. L. (2011). Interventions targeting social isolation in older people: A systematic review. BMC Public Health, 11(1), 647. doi:10.1186/ 1471‐2458‐11‐647 Eisenberger, N. I. (2015). Meta‐analytic evidence for the role of the anterior cingulate cortex in social pain. Social Cognitive and Affective Neuroscience, 10(1), 1–2. doi:10.1093/scan/nsu120 Eisenberger, N. I., & Cole, S. W. (2012). Social neuroscience and health: Neurophysiological mechanisms linking social ties with physical health. Nature Neuroscience, 15(5), 669–674. doi:10.1038/ nn.3086 Eisenberger, N. I., Inagaki, T. K., Mashal, N. M., & Irwin, M. R. (2010). Inflammation and social experience: An inflammatory challenge induces feelings of social disconnection in addition to depressed mood. Brain, Behavior, and Immunity, 24(4), 558–563. doi:10.1016/j.bbi.2009.12.009 Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003). Does rejection hurt? An fMRI study of social exclusion. Science, 302(5643), 290–292. doi:10.1126/science.1089134 Emerging Risk Factors Collaboration. (2010). C‐reactive protein concentration and risk of coronary heart disease, stroke, and mortality: An individual participant meta‐analysis. The Lancet, 375(9709), 132–140. doi:10.1016/S0140‐6736(09)61717‐7 Glaser, R., Kiecolt‐Glaser, J. K., Speicher, C. E., & Holliday, J. E. (1985). Stress, loneliness, and changes in herpesvirus latency. Journal of Behavioral Medicine, 8(3), 249–260. doi:10.1007/BF00870312 Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: A theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine, 40(2), 218–227. doi:10.1007/ s12160‐010‐9210‐8 Hawkley, L. C., Preacher, K. J., & Cacioppo, J. T. (2010). Loneliness impairs daytime functioning but not sleep duration. Health Psychology, 29(2), 124. doi:10.1037/a0018646 Hawkley, L. C., Thisted, R. A., & Cacioppo, J. T. (2009). Loneliness predicts reduced physical activity: Cross‐sectional & longitudinal analyses. Health Psychology, 28(3), 354. doi:10.1037/a0014400 Holt‐Lunstad, J., Smith, T. B., Baker, M., Harris, T., & Stephenson, D. (2015). Loneliness and social isolation as risk factors for mortality a meta‐analytic review. Perspectives on Psychological Science, 10(2), 227–237. doi:10.1177/1745691614568352 House, J. S., Robbins, C., & Metzner, H. L. (1982). The association of social relationships and activities with mortality: Prospective evidence from the Tecumseh Community Health Study. American Journal of Epidemiology, 116(1), 123–140. Jaremka, L. M., Fagundes, C. P., Peng, J., Bennett, J. M., Glaser, R., Malarkey, W. B., & Kiecolt‐Glaser, J. K. (2013). Loneliness promotes inflammation during acute stress. Psychological Science, 24(7), 1089–1097. doi:10.1177/0956797612464059 Lauder, W., Mummery, K., Jones, M., & Caperchione, C. (2006). A comparison of health behaviours in lonely and non‐lonely populations. Psychology, Health & Medicine, 11(2), 233–245. doi:10.1080/ 13548500500266607 Masi, C. M., Chen, H.‐Y., Hawkley, L. C., & Cacioppo, J. T. (2010). A meta‐analysis of interventions to reduce loneliness. Personality and Social Psychology Review. doi:10.1177/1088868310377394 Moieni, M., Irwin, M. R., Jevtic, I., Olmstead, R., Breen, E. C., & Eisenberger, N. I. (2015). Sex differences in depressive and socioemotional responses to an inflammatory challenge: Implications for sex differences in depression. Neuropsychopharmacology, 40(7), 1709–1716. doi:10.1038/ npp.2015.17 Qualter, P., Vanhalst, J., Harris, R., Van Roekel, E., Lodder, G., Bangee, M., … Verhagen, M. (2015). Loneliness across the life span. Perspectives on Psychological Science, 10(2), 250–264. doi:10.1177/1745691615568999 Routasalo, P. E., Tilvis, R. S., Kautiainen, H., & Pitkala, K. H. (2009). Effects of psychosocial group rehabilitation on social functioning, loneliness and well‐being of lonely, older people: Randomized controlled trial. Journal of Advanced Nursing, 65(2), 297–305. doi:10.1111/j.1365‐2648. 2008.04837.x Russell, D. W. (1996). UCLA loneliness scale (version 3): Reliability, validity, and factor structure. Journal of Personality Assessment, 66(1), 20–40. doi:10.1207/s15327752jpa6601_2

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Thoits, P. A. (2011). Mechanisms linking social ties and support to physical and mental health. Journal of Health and Social Behavior, 52(2), 145–161. doi:10.1177/0022146510395592

Suggested Reading Cacioppo, S., Grippo, A. J., London, S., Goossens, L., & Cacioppo, J. T. (2015). Loneliness clinical import and interventions. Perspectives on Psychological Science, 10(2), 238–249. doi:10.1177/ 1745691615570616 Eisenberger, N. I., & Cole, S. W. (2012). Social neuroscience and health: Neurophysiological mechanisms linking social ties with physical health. Nature Neuroscience, 15(5), 669–674. doi:10.1038/ nn.3086 Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: A theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine, 40(2), 218–227. doi:10.1007/ s12160‐010‐9210‐8 Holt‐Lunstad, J., Smith, T. B., Baker, M., Harris, T., & Stephenson, D. (2015). Loneliness and social isolation as risk factors for mortality a meta‐analytic review. Perspectives on Psychological Science, 10(2), 227–237. doi:10.1177/1745691614568352 Thoits, P. A. (2011). Mechanisms linking social ties and support to physical and mental health. Journal of Health and Social Behavior, 52(2), 145–161. doi:10.1177/0022146510395592

Social Support and Health Marci E. J. Gleason and Jerica X. Bornstein Department of Human Development and Family Sciences, University of Texas at Austin, Austin, TX, USA

Social support has been consistently shown to be beneficial for both physiological and psychological health. A meta‐analysis of 148 studies investigating the influence of social relationships on mortality found that social support was highly protective (Holt‐Lunstad, Smith, & Layton, 2010). Those who reported low levels of social support had an approximately 50% increase in mortality risk compared with those high in social support. These findings investigated individuals across the lifespan (ages ranged from 6 to 92) and were robust regardless of the sex or initial health status of the participants. However, as with most findings, the effect of social support on health is complicated and can range from very positive to neutral and even become negative depending on how social support is measured. Social support has been measured in a myriad of ways and studied in various disciplines across multiple decades. Perhaps the earliest work that identified a type of social support as beneficial to health was Durkheim’s (1897/1951) report that suicide risk was reduced among those who were socially integrated. He argued that individuals who were more embedded in social networks were more tied into society and were therefore less likely to kill themselves. He cited the higher suicide rate among single compared with married individuals as one example of the power of social ties. In line with this research, many studies of social support have focused on the size of individuals’ social networks and the presence and number of close friends and family members in an individual’s life, often referred to as “close ties.” The size of an individual’s social network is determined by a count of all the people that an individual typically interacts with and can include everyone from a spouse to the mailman. Although social network size has been linked to beneficial health outcomes, those linkages become stronger when one refines this measurement to include information on the strength of the ties between the focal individual and their social network (Holt‐Lunstad et al., 2010). Researchers examining “close ties” gather information on the number of people in an individual’s social network and will often measure the frequency of interaction with network members, as well as the depth and closeness of those relationships. Importantly, the quality of an individual’s

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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r­ elationships is a key enhancer of the gains associated with network ties: having close friends is more protective than having many friends. The use of network ties as a proxy for social support has become less prevalent in the research literature and replaced with the idea that social support is composed of specific support behaviors, such as giving advice, listening, and providing monetary aid. These behaviors could be ones that have occurred within an individual’s social relationship in the past or that the individual believes would be available to them in the future. This characterization of social support is often referred to as perceived social support or the availability of social support. Individuals who report high levels of perceived support, regardless of the number of actual social ties individuals have, report better mental and physical health. Thus the benefits of social support and more specifically perceived social support are well documented, and several mechanisms to explain the association between social support and health have been posited. The dominant explanation is the stress‐buffering hypothesis that contends that social support protects recipients from the negative effects of stress by helping the recipient cope more effectively with that stress—essentially providing extra resources for coping through practical means, such as lending money, or emotional means, by listening or offering comfort. Although numerous studies have shown that the effect of stress on health is buffered by perceived support, few studies have demonstrated that this buffering is the only or even the primary way in which perceived support is protective. In other words, it is not only that those who are high in perceived support seem to be less affected by the stress in their lives but also that regardless of stress level, those high in perceived support report better mental and physical health. Such findings have led others to argue that support, specifically perceived support, reflects an individual difference variable or personality trait. Levels of perceived support are not strongly related to the number of specific support events individuals report receiving, suggesting that people differ in the amount they believe people will be there for them regardless of their actual support experiences. However, studies have failed to find evidence that perceived support operates as a personality characteristic. Instead, research on the source of perceived support has demonstrated that specific relationships create a sense of supportiveness, rather than certain individuals having a tendency to view others as supportive. This idea is explored in relational regulation theory (RRT) that posits that the extent to which someone perceives support is dependent on interactions with individuals that are specific to that particular recipient (Lakey & Orehek, 2011). A sense of perceived support arises when an individual has access to supportive relationships, and that sense of perceived support will not exist in the absence of those specific relationships. Unlike a personality trait that would be fairly consistent across time, RRT predicts that one’s sense of perceived support will vary depending on one’s current social context and relationships. These findings suggest that regardless of how social support promotes positive health and how the perception of social support is formed, increasing social support in individuals’ lives should lead to both mental and physical health benefits. However, there is a clear and established paradox in the social support literature. The perception of social support is positive, but when individual instances of support are investigated, receiving support is often associated with negative outcomes including emotional distress, poorer physical health, and increased mortality risk. This link between single instances of social support or enacted support and negative outcomes has been demonstrated in observational, experimental, and correlational studies. Work in the helping behavior literature corroborates this finding; being helped is associated with decreased self‐esteem and increased depressed mood in recipients. Moreover, these negative outcomes are more pronounced when help is received from a close other such as a relationship partner.

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Social support researchers have suggested many reasons for why enacted support is often associated with negative outcomes. Perhaps the explanation that has received the most attention both in the social support and helping behavior literature is that the receipt of support may undermine the recipient’s sense of efficacy and/or control. Receiving support itself may signal to the recipient that the provider perceives them to be struggling or even incapable of coping with the stressor, therefore exacerbating the recipient’s anxiety rather than alleviating it (Bolger & Amarel, 2007). Despite this general tendency for an instance of social support to be ineffective, research on enacted social support has identified specific types of support, referred to as skillful support, that are often associated with positive outcomes. Four types of skillful support—matched support, invisible support, supportive reciprocity, and capitalization—have been well documented in the literature, and we briefly describe these below. Stressors can be categorized as controllable or uncontrollable, and this distinction is important for determining what type of enacted support is likely to promote positive health outcomes for the recipient. Controllable stressors are likely to benefit from informational support. For example, an individual who is trying to obtain a new passport is likely to find it helpful when a friend who recently renewed a passport tells them the various steps needed to obtain one. Alternatively, uncontrollable stressors, such as the stress associated with waiting to find out whether one has been promoted, are more likely to be alleviated by a friend providing a distraction or reassurance that the recipient is worthy of the promotion. Unfortunately, studies of support matching suggest that people struggle to identify the types of support their relationship partners actually desire. This results in high levels of unmatched support (Cutrona, 1996), rendering those support attempts ineffective and even detrimental to the receiver. Overly visible or directive support has also been linked to negative outcomes, perhaps because these types of overt support decrease recipients’ sense of control and increase their feelings of indebtedness to their providers as well as draw attention to the struggle an individual is undergoing. For instance, in an experimental study, participants were asked to give a difficult and impromptu speech. Those individuals who reported that they were given advice from a confederate during this task reported higher levels of anxiety than those who heard the same advice, but did not think it was directed at them (Bolger & Amarel, 2007). This latter type of support, known as invisible support, has been shown to ameliorate daily anxiety, increase goal attainment, and increase feelings of self‐efficacy in the recipient. Findings from studies examining equity and reciprocity in support, as well as ones examining the effects of provision on the provider, indicate that the costs of receiving support can also be lessened when there is an opportunity to reciprocate by providing support to one’s partner. Interestingly, the provision of social support has generally been found to be highly beneficial to the provider and has been associated with positive outcomes. Further, whereas most of the literature addressing partner support focuses on support provided in response to stressors or hardships, recent research has demonstrated the importance of examining support provided in response to positive events or uplifts. Sharing positive experiences in the hope of getting a receptive, supportive audience is called a capitalization attempt. When the support that follows such attempts (i.e., capitalization support) is both active and positive, it yields strong personal and relational benefits (Gable, Gosnell, Maisel, & Strachman, 2012). The field continues to explore other ways in which support can be both received and perceived positively. One promising approach is that of affectionate and physical touch in adult close relationships, which has been shown to help promote high quality relationships, physical well‐being, and psychological outcomes in adulthood (Jakubiak & Feeney, 2016). Recent research has also suggested that the link between actual interactions and perceived support may come from common everyday conversation rather than actual support attempts. Perhaps

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it is those we can speak comfortably and freely with about everyday matters, regardless of whether those individuals have actually provided support, that create a sense of perceived support in individuals (Lakey & Orehek, 2011). The majority of the current social support literature has focused on in person interactions. However, the ways with which we communicate are changing rapidly, and many individuals now communicate with their closest social ties in part or even largely through email, text, and social networks. Physical proximity to our social networks may become both less common and potentially less important as online communication provides a new avenue through which to receive support. In addition, social networking platforms allow individuals to routinely request and receive support not only from current social network members but also from people they don’t know outside of on online communication. Such communication has the potential to be positive: research has shown that those who engage in more supportive interactions on social networking sites have greater levels of positive affect (Oh, Ozkaya, & LaRose, 2014). However similar to earlier work on social networks, the quality of online support interactions matters. As society’s communication habits and norms shift, so will the experience of social support. Whether the current shift to online communication creates an opportunity for individuals to gain access to better and more frequent supportive interactions remains to be seen. However, it is clear that social support researchers will have to address the implications of the online world on mental and physical well‐being. Social support has been repeatedly and strongly linked to positive health outcomes, but exactly why social support is beneficial is still being explored. In particular, the paradox that while perceived support is almost universally positive, enacted support is often negative has complicated the field’s understanding of social support processes. Researchers continue to explore new mechanisms and types of social support and continue to gain new insights into the importance of social ties to individuals’ health outcomes.

Author Biographies Marci E. J. Gleason is an associate professor at the University of Texas at Austin in the Department of Human Development and Family Sciences. She received her PhD in social psychology at New York University. Her research focuses on how individuals cope with life transitions and stressors in the context of their social relationships. Jerica X. Bornstein is a graduate student at the University of Texas at Austin in the Department of Human Development and Family Sciences. She is particularly interested in how various types of support exchanges within close relationships may help or hinder health behavior change and health outcomes.

References Bolger, N., & Amarel, D. (2007). Effects of social support visibility on adjustment to stress: Experimental evidence. Journal of Personality and Social Psychology, 92, 458–475. doi:10.1037/0022‐3514.92.3.458 Cutrona, C. E. (1996). Social support in couples: Marriage as a resource in times of stress. Thousand Oaks, CA: Sage Publications, Inc. Durkheim, E. (1951). Suicide: A study in sociology (JA Spaulding & G. Simpson, trans.). Glencoe, IL: Free Press. (Original work published 1897).

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Gable, S. L., Gosnell, C. L., Maisel, N. C., & Strachman, A. (2012). Safely testing the alarm: Close others’ responses to personal positive events. Journal of Personality and Social Psychology, 103, 963– 981. doi:10.1037/a0029488 Holt‐Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: A meta‐ analytic review. PLoS Medicine, 7, e1000316. Jakubiak, B. K., & Feeney, B. C. (2016). Affectionate touch to promote relational, psychological, and physical well‐being in adulthood: A theoretical model and review of the research. Personality and Social Psychology Review, 21(3), 228–252. doi:10.1177/1088868316650307 Lakey, B., & Orehek, E. (2011). Relational regulation theory: A new approach to explain the link between perceived social support and mental health. Psychological Review, 118, 482–495. doi:10.1037/ a0023477 Oh, H. J., Ozkaya, E., & LaRose, R. (2014). How does online social networking enhance life satisfaction? The relationships among online supportive interaction, affect, perceived social support, sense of community, and life satisfaction. Computers in Human Behavior, 30, 69–78.

Suggested Reading Gleason, M. E. J., & Iida, M. (2015). Social support. In M. Mikulincer, P. R. Shaver, J. A. Simpson, & J. F. Dovidio (Eds.), APA handbook of personality and social psychology, Volume 3: Interpersonal relations (pp. 351–370). Washington, DC: American Psychological Association. Kiecolt‐Glaser, J. K., & Wilson, S. J. (2017). Lovesick: How couples’ relationships influence health. Annual Review of Clinical Psychology, 13(1), 421–443. Uchino, B. N. (2009). Understanding the links between social support and physical health: A lifespan perspective with emphasis on the separability of perceived and received support. Perspectives on Psychological Science, 4, 236–255. doi:10.1111/j.1745‐6924.2009.01122

Spirituality/Religiosity and Health Kevin S. Masters and Stephanie A. Hooker Psychology Department, University of Colorado at Denver –  Anschutz Medical Campus, Aurora, CO, USA

Religiosity and spirituality (R/S) are important to individuals worldwide. It is estimated that 80% of people identify with a religious group and many who are not members of a religious faith still hold to some religious or spiritual teaching. About 60% of Americans report that they regularly feel a deep sense of “spiritual peace and well‐being” (Pew Research Center for Religion in Public Life, 2014). An important issue that confronts empirical investigators is defining religiosity and spirituality and distinguishing between them. This has proven to be a difficult task and one that continues to evolve. Closely related is the problem of measurement: How is one to measure constructs that prove so elusive to consensual definition? Kapuscinski and Masters (2010) recently discussed these issues in detail and provided historical context. They also reviewed measures of spirituality. Though some individuals find their spirituality and religiosity to be inextricably intertwined, the more recent trend is to view them as related, or overlapping, but differentiated concepts. Both share a sense of being concerned with understanding and experiencing the sacred or the ultimate. However, religiosity undertakes this search within the context of an institution or group, whereas spirituality is viewed as a more personal journey, which may or may not take place within an organized religion. Other connotative differences found in the literature include the relative importance of codes of conduct (higher in religion) and the issue of externality (religion) versus internality (spirituality). There also seems to be an inclination among some social scientists to view religion as more likely “bad” and spirituality as more likely “good.” Scholars in the area, however, warn against this use, noting that most of the research to date has been done on religious constructs, and both religiosity and spirituality are complex and multidimensional. Individuals who have a sense of R/S often believe that their spirituality influences their health as a result of the peace or well‐being that their faith affords, religiously based behavioral practices (e.g., refraining from smoking, moderate use of alcohol, vegetarianism based on religious beliefs), or divine intervention. There is reasonably strong empirical evidence, some of it at the population level and other evidence from meta‐analyses, generally suggesting a modest positive relationship between religiosity and health with the suggestion, not yet confirmed, of a beneficial effect of religiosity on health. The most studied variable, with the most consistently positive The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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findings, is religious service attendance. Attendance has been found, after controlling for relevant confounds, to predict a number of beneficial outcomes including decreased mortality (Chida, Steptoe, & Powell, 2009). Research on health and spirituality per se is only beginning. (Spiritually relevant practices such as meditation or yoga have their own bodies of literature.) We refer those interested in comprehensive reviews of the empirical literature investigating the relationships between R/S and various health outcomes to Koenig, King, and Carson (2012). Park et al. (2017) provide a more recent review with specific emphasis on cardiovascular disease, cancer, and substance abuse. As important as these general findings are, however, the question posed to contemporary investigators is this: What aspects of R/S are related to what aspects of health for what types of people? A sophisticated literature at this level of specificity does not yet exist but is clearly an important direction for future work. One aspect of work aimed at greater specificity and understanding, currently underway, concerns investigation of mechanisms or processes that may account for relationships between aspects of R/S and health indicators. We now turn our attention to this literature.

Mechanisms Linking R/S to Health R/S likely influences health through a number of different mechanisms, and given the multidimensional nature of R/S, different facets of R/S may influence these mechanisms differently. Three primary mechanisms discussed by empirical investigators include social support, health behaviors, and psychological processes. We will review the literature in these areas but openly acknowledge that there are likely other plausible mechanisms.

Social Support One of the primary hypothesized mechanisms linking R/S to health is social support. Ample evidence has shown that social support is beneficial for a number of health outcomes. This mechanism is primarily found in the context of a religious community and, to this point, has not been viewed as a strong mechanism linking spirituality to health. For instance, those who regularly attend religious services have larger social networks, are more likely to stay in contact with other people, report greater perceived social support, are more likely to feel valued by their social group, tend to believe their religious congregations are more cohesive, and are more likely to stay married. Interestingly, religious support has also been shown to be related to another hypothesized mediator of the relationship between R/S and health: health behavior. That is, individuals who feel more support from their congregation are more likely to engage in health‐promoting behaviors such as better diet and increased physical activity. However, those who do not perceive this support or have negative interactions with fellow parishioners may engage in less healthy behavioral practices. Though social support may be important in terms of understanding the relationship between R/S and health, it does not account for a large portion of the variance between the two. Consequently, other factors have also been hypothesized to mediate the R/S and health relationship.

Health Behaviors As noted previously, many religious teachings have health implications; therefore, health behaviors have been hypothesized as a mechanism linking R/S to health. Certainly, religious

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leaders have great potential for influencing the healthy behaviors of their congregations. Given the multiple pathways by which R/S could influence health‐promoting and health‐compromising behaviors, it is not surprising that there are many studies linking R/S to health behaviors. Religious service attendance is an often‐studied variable that is related to a number of healthy behaviors and less engagement in health‐compromising behaviors. There is, however, also evidence that religious importance is associated with greater use of preventive healthcare services, including mammograms, flu shots, cholesterol screening, and prostate cancer screening (Benjamins & Brown, 2004). Additionally, R/S variables, including religious involvement, religious service attendance, personal spirituality, and religious community support, are associated with a healthy lifestyle or engaging in many different health‐relevant behaviors (Hooker, Masters, & Carey, 2014). In general, positive R/S variables are related to increased engagement in health‐promoting behaviors and reduced likelihood of engaging in health‐compromising behaviors, whereas negative R/S variables are related to reduced engagement in health‐promoting behaviors and increased engagement in health‐compromising behaviors. In chronic illness patient populations, R/S may be related to treatment adherence. One study of patients with congestive heart failure (CHF) demonstrated that religious commitment predicts greater adherence to CHF‐specific behaviors over 6 months even when controlling for initial levels of adherence (Park, Moehl, Fenster, Suresh, & Bliss, 2008). In cancer survivors, daily spiritual experiences and spiritual well‐being are related to health‐promoting behaviors, including eating five or more servings of fruits and vegetables a day, engaging in moderate to vigorous physical exercise, adhering to doctors’ orders, and making overall positive changes in behavior (e.g., avoiding alcohol, cigarettes, and sun exposure, using sunscreen and sun‐protective clothing; Park, Edmondson, Hale‐Smith, & Blank, 2009). Conversely, religious struggles are related to less adherence to doctor’s advice and less medication adherence (Park, Edmondson, Hale‐Smith, & Blank, 2009). These studies suggest that R/S variables also influence health behaviors in the context of a chronic illness.

Psychological Processes In addition to social support and health behaviors, a variety of psychological processes, including stress reduction and coping, reduced depressive symptoms, and enhanced self‐regulation, have been proposed to mediate the relationship between R/S and health. R/S may be a buffer against the effects of stressful life events. Religious coping, in particular, has been studied as a protective factor for coping with life stressors. Religious coping behaviors may be thought of as positive (e.g., religious purification or forgiveness, collaborative religious coping) and negative strategies (e.g., spiritual discontent, viewing God as punishing) that individuals use to mitigate the effects of stress. One meta‐analysis has shown that positive religious coping behaviors are positively related to better psychological adjustment (e.g., optimism, happiness, hope, life satisfaction) and negatively related to negative psychological adjustment (e.g., depression, anxiety, burden, distress), whereas negative religious coping behaviors are positively related to negative psychological adjustment (Ano & Vasconcelles, 2005). Moreover, negative religious coping is related to greater perceived stress and to less use of problem‐focused coping strategies, which are related to less perceived stress. Recent experimental studies have begun to examine the effects of religious behaviors, such as prayer, on the effects of cardiovascular reactivity to stress. In an experimental study in our lab, we found that devotional prayer reduced stress reactivity to a provocative religious interview in a group of self‐identified Christians; however, more work is needed to understand the pathways between religious behavior and stress reduction.

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Another psychological process linking R/S and health may be a reduction in depressive symptoms. R/S involvement is shown to be positively related to mental health. One very large meta‐analysis of 147 studies shows that R/S variables are negatively correlated with depressive symptoms (Smith, McCullough, & Poll, 2003). Interestingly, prayer has been shown to be related to fewer depressive symptoms and less distress in patients postcoronary artery bypass graft (Ai et al., 2006). Conversely, religious struggles and negative religious coping are related to increases in depressive symptoms in patients with congestive heart failure (Park, Brooks, & Sussman, 2009). In general, positive R/S variables are protective against depressive symptoms, whereas negative R/S variables are related to greater depressive symptoms. A new and particularly interesting hypothesis is that R/S may be related to health through enhanced self‐regulation. McCullough and Willoughby (2009) offer a number of testable hypotheses in this area that are substantive and potentially important in guiding future research. Deciphering temporal precedence and cause and effect relationships will likely prove challenging. It may be that individuals who are better self‐regulated are more likely to engage in regular behaviors (such as attending services and engaging in healthy behaviors) but are also better at controlling their emotions and coping with stress. However, it is also likely that the doctrines, messages, and practices of religious faiths reinforce and normalize self‐regulatory behavior and provide social support for its maintenance. More work is needed in this area, but we find this mechanism to be a particularly exciting advancement of this field.

Special Topics in R/S and Health Research Distant Intercessory Prayer A topic that, perhaps surprisingly, has garnered significant attention in both the popular and scientific press is the possibility that distant intercessory prayer (i.e., prayer said on another’s behalf and without the intended beneficiary present) can bring about healing or relief of symptoms. There is no question that religious individuals, and many nonreligious individuals, are more prone to pray both for themselves and others when there is an immediate health concern, and those prayers are often for healing or relief of disabling symptoms. The topic of prayer and health is important and worthy of investigation on its own. The focus of this section of our entry, however, is on the potential effects of distant intercessory prayer. In a typical study on this topic, investigators recruit “intercessors” who will pray for a group of patients. In almost all studies, the patients did not know whether they were being prayed for or not and in some cases did not know that they were even in a research study. Some early reports suggested beneficial effects for those who were prayed for versus a control group that did not receive prayer, but a careful analysis of these studies suggests that failure to consider multiple comparisons and other concerns render the results less compelling. Subsequently, Masters, Spielmans, and Goodson (2006) conducted a meta‐analysis and concluded that there was no evidence to suggest any beneficial effects for those who received prayer versus non‐prayed‐for controls. A subsequent Cochrane review (Roberts, Ahmed, & Davison, 2009) reached a similar conclusion, and both reviews recommended that further research in this area not be supported largely because of a serious methodological impediment that cannot be overcome. Regardless of whatever one may think about distant intercessory prayer per se, there is simply no way to conduct a meaningful controlled investigation because there is no practical (or ethical) means to ensure that the control group does not receive prayers of its own from family, friends, clergy, or congregations, and there is no plausible

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rationale to suggest that the prayers of unknown intercessors would somehow have greater efficacy than the prayers of those who know the patient. In this regard, it is interesting to note that in one of these studies, one of three experimental groups knew that they were receiving prayer from study intercessors. This group demonstrated a higher incidence of complications than did two other groups who did not know whether they were receiving prayer (one group was, the other was not).

Spirituality in Cancer Patients and Survivors Research in R/S and health, and in particular spirituality and health, has been embraced within the cancer literature, and there has been a surge of findings in this area in recent years. Indeed, one analysis of the literature shows that the number of abstracts regarding R/S and cancer have increased more than 2.5 times over previous decades (Salsman, Fitchett, Merluzzi, Sherman, & Park, 2015). A series of recent meta‐analyses (Jim et  al., 2015; Salsman, Pustejovsky, et al., 2015; Sherman et al., 2015) show that across 44,000 cancer patients, R/S dimensions are positively related to physical, mental, and social health. The strongest relationships are seen in affective dimensions of R/S, or the emotional experience of R/S including “a sense of transcendence, meaning, purpose, or connection larger than oneself” (Salsman, Fitchett, et al., 2015, p. 3). There are small associations for cognitive (e.g., R/S beliefs, religious orientation, R/S problem solving) and other dimensions of R/S (e.g., composite indexes, social support, religious affiliation) in relation to all three dimensions of health. Behavioral factors, including R/S coping and public and private R/S activities, demonstrated a small, positive relationship with social health but were not significantly related to physical or mental health. This literature could be greatly enhanced with studies that are more rigorous in their design (e.g., less reliance on cross‐sectional data) and attend in greater detail to the possible moderators and mediators of these relationships (Park et al., 2017).

Future Directions The topic of R/S and health has experienced significant growth over the past two decades, resulting in greater understanding of the relationships and implications of what the next steps should be. Clearly, much more needs to be done. We offer suggestions for future research in this area and refer interested readers to Park et al. (2017) for a more detailed discussion. As noted above, future work that aims for greater specificity is needed, for example, to specify the particular aspects of R/S that are beneficial, the particular components of health most likely to be associated with R/S, the relevant theoretical mechanisms, and the particular groups of people most likely to benefit. This work must consider both the socioeconomic and cultural context in which R/S takes place, as well as the aspects of R/S that themselves constitute cultural variables. This kind of detailed work can only be properly conducted as part of an ongoing program of research wherein the relationships between R/S and health are the focus of investigations. Though it is not always possible, depending on what aspects of R/S and health are being studied, greater use of experimental studies is encouraged. Nearly all of the existing work on R/S and health is observational. To some degree, observational work will always play a prominent role in this area because it is simply not possible to randomly assign individuals to, for example, certain religious beliefs or groups. However, some aspects of religious practice (e.g., scripture reading or other private religious practices) are amenable to randomized controlled

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study when carried out in sensitive ways among members of proper groups (i.e., those willing to engage in the particular practice). Greater use of large‐scale longitudinal research designs is strongly indicated. Though these studies are not capable of determining causality, they may be able to establish temporal precedence, an important factor in many areas of R/S and health research and a preliminary step toward drawing causal conclusions. Further, longitudinal designs, particularly those that are carried out over the long term, have the potential to shed light on developmental trends in relationships between R/S and health. Finally, we recommend that investigators partner with theologians or become more familiar with theological constructs themselves, as these constructs pertain to the particular groups that are the focus of study. In this way, a deeper understanding of the workings of religion and health is likely to develop, one that may ultimately have practical implications for clergy and their congregants.

Author Biographies Kevin S. Masters, PhD, Professor of Psychology, Program Director, Clinical Health Psychology, University of Colorado Denver, Denver, Colorado, USA. Dr. Masters’ research investigates religious and spiritual factors and processes as they relate to health, in particular cardiovascular responses to psychological stress and healthy behavior change. Recently he has begun investigations of meaning in daily life and its possible effects on health variables. Dr. Masters is past president of the Society for Health Psychology. Stephanie A. Hooker, MS, MPH, Clinical Health Psychology PhD Candidate, University of Colorado Denver, Denver, Colorado, USA. Ms. Hooker’s research investigates positive psychosocial predictors of health behavior change and maintenance. She is interested in translating her basic research findings to develop and evaluate theory‐driven behavioral interventions for obesity and cardiovascular disease prevention and treatment. Her recent research has focused on daily meaning in relation to physical activity.

References Ai, A. L., Peterson, C., Tice, T. N., Rodgers, W., Seymour, E. M., & Bolling, S. F. (2006). Differential effects of faith‐based coping on physical and mental fatigue in middle‐aged and older cardiac patients. The International Journal of Psychiatry in Medicine, 36, 351–365. doi:10.2190/88CC‐W73K‐0TM4‐JX3J Ano, G. G., & Vasconcelles, E. B. (2005). Religious coping and psychological adjustment to stress: A meta‐analysis. Journal of Clinical Psychology, 61, 461–480. doi:10.1002/jclp.20049 Benjamins, M. R., & Brown, C. (2004). Religion and preventative health care utilization among the elderly. Social Science & Medicine, 58, 109–118. doi:10.1016/s/0277‐9536(03)00152‐7 Chida, Y., Steptoe, A., & Powell, L. H. (2009). Religiosity/spirituality and mortality. A systematic quantitative review. Psychotherapy and Psychosomatics, 78, 81–90. doi:10.1159/000190791 Hooker, S. A., Masters, K. S., & Carey, K. B. (2014). Multidimensional assessment of religiousness/ spirituality and health behaviors in college students. The International Journal for the Psychology of Religion, 24, 228–240. doi:10.1080/10408619.2013.808870 Jim, H. S. L., Pustejovsky, J. E., Park, C. L., Danhauer, S. C., Sherman, A. C., Fitchett, G., … Salsman, J. M. (2015). Religion, spirituality, and physical health in cancer patients: A meta‐analysis. Cancer. doi:10.1002/cncr.29353

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Kapuscinski, A. N., & Masters, K. S. (2010). The current status of measures of spirituality: A critical review of scale development. Psychology of Religion and Spirituality, 2, 191–205. doi:10.1037/ a0020498 Koenig, H. G., King, D., & Carson, V. B. (2012). Handbook of religion and health (2nd ed.). New York, NY: Oxford University Press. Masters, K. S., Spielmans, G. I., & Goodson, J. T. (2006). Are there demonstrable effects of distant intercessory prayer? A meta‐analytic review. Annals of Behavioral Medicine, 32, 337–342. doi:10.1207/s15324796abm3201_3 McCullough, M. E., & Willoughby, B. L. B. (2009). Religion, self‐regulation, and self‐control: Associations, explanations, and implications. Psychological Bulletin, 135, 69–93. doi:10.1037/ a0014213 Park, C. L., Brooks, M. A., & Sussman, J. (2009). Dimensions of religion and spirituality in psychological adjustment in older adults living with congestive heart failure. In A. Ai & M. Ardelt (Eds.), The role of faith in the well‐being of older adults: Linking theories with evidence in an interdisciplinary inquiry (pp. 112–134). Hauppauge, NY: Nova Science Publishers. Park, C. L., Edmondson, D., Hale‐Smith, A., & Blank, T. O. (2009). Religiousness/spirituality and health behaviors in younger adult cancer survivors: Does faith promote a healthier lifestyle? Journal of Behavioral Medicine, 32, 582–591. doi:10.1007/s10865‐009‐9223‐6 Park, C. L., Masters, K. S., Salsman, J. M., Wachholtz, A., Clements, A. D., Salmoirago‐Blotcher, E., … Wischenka, D. M. (2017). Advancing our understanding of religion and spirituality in the context of behavioral medicine. Journal of Behavioral Medicine. doi:10.1007/s10865‐016‐9755‐5 Park, C. L., Moehl, B., Fenster, J. R., Suresh, D. P., & Bliss, D. (2008). Religiousness and treatment adherence in congestive heart failure patients. Journal of Religion, Spirituality & Aging, 20, 249– 266. doi:10.1080/15528030802232270 Pew Research Center for Religion in Public Life. (2014). Religious landscape study. Washington, DC: Pew Research Center. http://www.pewforum.org/religious‐landscape‐study/ Roberts, L., Ahmed, I., & Davison, A. (2009). Intercessory prayer for the alleviation of ill health. Cochrane Database of Systematic Reviews, (2), CD000368. doi:10.1002/14651858.CD000368. pub3 Salsman, J. M., Fitchett, G., Merluzzi, T. V., Sherman, A. C., & Park, C. L. (2015). Religion, spirituality, and health outcomes in cancer: A case for a meta‐analytic review. Cancer. doi:10.1002/cncr.29349 Salsman, J. M., Pustejovsky, J. E., Jim, H. S. L., Munoz, A. R., Merluzzi, T. V., George, L., … Fitchett, G. (2015). A meta‐analytic approach to examining the correlation between religion/spirituality and mental health in cancer. Cancer. doi:10.1002/cncr.29350 Sherman, A. C., Merluzzi, T. V., Pustejovsky, J. E., Park, C. L., George, L., Fitchett, G., … Salsman, J. M. (2015). A meta‐analytic review of religious or spiritual involvement and social heath among cancer patients. Cancer. doi:10.1002/cncr.29352 Smith, T. B., McCullough, M. E., & Poll, J. (2003). Religiousness and depression: Evidence for a main effect and the moderating influence of stressful life events. Psychological Bulletin, 129, 614–636.

Further Readings Ai, A. L., Huang, B., Bjorck, J., & Appel, H. B. (2013). Religious attendance and major depression among Asian Americans from a national database: The mediation of social support. Psychology of Religion and Spirituality, 5, 78–89. doi:10.1037/a0030625 Carter, E. C., McCullough, M. E., & Carver, C. S. (2012). The mediating role of monitoring in the association of religion with self‐control. Social Psychological and Personality Science, 3, 691–697. doi:10.1177/1948550612438925 Ellison, C. G., & Hummer, R. A. (Eds.) (2010). Religion, families, and health: Population‐based research in the United States. New Brunswick, NJ: Rutgers University Press.

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Miller, W. R., & Thoresen, C. E. (2003). Spirituality, religion, and health. An emerging research field. American Psychologist, 58, 24–35. doi:10.1037/0003‐066x.58.1.24 Oman, D., & Thoresen, C. E. (2002). Does religion cause health? Differing interpretations and diverse meanings. Journal of Health Psychology, 7, 365–380. doi:10.1177/135910539900400301

Stress and Resilience in Pregnancy Alyssa C. D. Cheadle1, Isabel F. Ramos2, and Christine Dunkel Schetter2 Psychology Department, Hope College, Holland, MI, USA Department of Psychology, University of California, Los Angeles, CA, USA 1

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Pregnancy is the period during which offspring develop in their mother’s womb. In humans, a full‐term pregnancy lasts 40 weeks and is divided into three trimesters. The first trimester begins at conception and goes through 12 weeks, the second trimester is 13–28 weeks, and the third trimester is 29 weeks through the birth of the baby. The normative physiological changes in pregnancy are well established, and most pregnancies result in a healthy infant born at full term, of normal weight. However, there is substantial variability in pregnancy outcomes. Infants born prior to 37 weeks of gestation are preterm or premature, and those born weighing less than 2500 g (5 lb, 8 oz) are low birthweight. These unfavorable birth outcomes occur for 8 and 12% of pregnancies worldwide. These birth outcomes are consequential with respect to infant mortality and developmental problems in offspring and incur high emotional and economic costs for families (Butler & Behrman, 2007). The etiology of adverse birth outcomes is not well determined. However, the life experiences of women during pregnancy have been clearly implicated. Today, scientists across disciplines are working together to increase our understanding of biopsychosocial and sociocultural influences on pregnancy and birth. Pregnancy is stereotyped as a happy time and for many, it is. The image of a mother and her partner preparing to welcome a child they want is prevalent, but it does not reflect that many pregnant women’s lives are affected by socioeconomic disadvantages, life stress, and emotional distress. During pregnancy, women may face a variety of major stressors, like the death of a family member and legal problems including with their own or family members’ immigration status, relationship conflict, and financial hardship. The impact on pregnancy and birth of these surprisingly common stressors cannot be underestimated. For example, most of the studies on major life events including major catastrophes such as earthquakes show an effect on preterm birth. Additionally, in recent years, stress research has broadened its focus to include the reverse—resilience in the face of chronic stress—defined here as the ability to withstand and

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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cope with ongoing or repeated demands (Schetter & Dolbier, 2011). Resilience resources that a woman has may benefit her emotional welfare, coping, and the outcomes of her pregnancy.

Mood Disorders Affective disorders are more prevalent during pregnancy and postpartum than other times in women’s lives. Prenatal and postpartum affective disorders and symptoms are noteworthy because they can have potentially deleterious effects on women, infants, and families. Depression and anxiety during pregnancy have been implicated in poor birth outcomes, namely, preterm birth and low birthweight (Schetter & Tanner, 2012). Pregnancy and the postpartum period are marked by increased risk of negative mood states including depressive and anxiety disorders, partly related to the extensive physiological changes that can adversely affect mood and often due to changes in life circumstances. Additionally, when women discontinue medications for mood disorders in pregnancy to protect their fetus from risk, their mood may become a risk factor.

Pregnancy Anxiety Up to one in four women experience prenatal anxiety, though prevalence depends on the type of anxiety symptoms and disorders studied (Schetter & Tanner, 2012). Pregnancy anxiety refers to specific fears or worries about a current pregnancy, including about the health of one’s baby, labor, delivery, and parenthood. These pregnancy‐specific worries are associated with increased risk of preterm birth. Elevated levels of pregnancy anxiety also predict infant and child developmental outcomes, especially poorer cognitive and motor performance, and more negative temperament in infancy (Blair, Glynn, Sandman, & Davis, 2011). Little research links prenatal anxiety to low birthweight (Accortt, Cheadle, & Schetter, 2015).

Prenatal and Postpartum Depression Conservative estimates of rates of postpartum depression (PPD) range from 7 to 13% (Rich‐ Edwards et al., 2006), making this type of depression as prevalent as major depressive disorder among nonpregnant women. A recent systematic review found prenatal depression to be consistently associated with increased risk of low birthweight possibly as a result of adverse health behaviors that affect growth. The overall evidence on preterm birth is less conclusive with some studies suggesting a link and most suggesting no link (Accortt et al., 2015). PPD is also consequential for women. Women who experience postpartum affective disorders are more likely to have difficulty returning to prepregnancy levels of general well‐being and employment functioning and to experience relationship stress (Abrams & Curran, 2007). PPD is associated with increased risk of poor infant–parent attachment and low rates of breastfeeding that can lead to greater risk of cognitive, psychological, and behavioral problems in childhood and beyond (Paul, Downs, Schaefer, Beiler, & Weisman, 2013). It is important for women and health providers to pay attention to depressive symptoms preceding and following birth—even if they are not clinically significant.

Health Disparities Socioeconomic status (SES) is a clear risk factor for preterm birth and low birthweight. Low SES can lead to life conditions such as unemployment and crowding, which in turn predict poor

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birth outcomes including low birthweight (Borders, Grobman, Amsden, & Holl, 2007). SES also works in combination with other risks, such as race/ethnicity, for adverse birth outcomes. Ethnic/racial disparities are evident in the numbers and types of stressors and the emotional experiences of pregnancy and birth outcomes. Regarding PPD, the majority of evidence suggests that African American and Latina women experience higher rates than non‐Hispanic White women (e.g., Rich‐Edwards et al., 2006). This disparity persists even when SES is taken into account (e.g., McLennan, Kotelchuck, & Cho, 2001). In addition, African American women have higher rates of poor birth outcomes with rates nearly twice as high as those of other groups, even after controlling for SES. Perceived racism and discrimination during pregnancy and over a mother’s lifetime may contribute to this disparity (e.g., Parker‐Dominguez, Schetter, Mancuso, Rini, & Hobel, 2005). Importantly, these effects are independent of other stressors. Mexican American women, or those born in the United States of Mexican descent, have higher levels of perceived social stress, have more exposure to chronic stressors, and may be more likely to be depressed than Mexican immigrant women. However, pregnant Mexican immigrant women have been found to experience higher levels of pregnancy‐related anxiety (Fleuriet & Sunil, 2014). Finally, all Latinas (foreign born and US born) report feeling more pregnancy anxiety than European American women living in California (Campos et al., 2008).

Close Relationships Partners, family, and friends are common sources of support in life especially during pregnancy. Social support refers to perceived available and received, or enacted, support, both of which can have an impact on the emotional experiences and outcomes of pregnancy. Support may be material, task, emotional, or informational (i.e., advice), and it may be effective or ineffective (Rini, Schetter, Hobel, Glynn, & Sandman, 2006). Generally speaking, greater prenatal support appears to predict better birth outcomes including better fetal growth and higher birthweight (e.g., Hedegaard, Henriksen, Secher, Hatch, & Sabroe, 1996). Social support may also moderate, or buffer, the negative influences of stressors on the emotional experience and outcomes of pregnancy. Some support for this exists. For instance, in a study of pregnant women, social support was associated with higher birthweight, but this effect existed only for those women who reported experiencing high numbers of stressful life events (Collins, Schetter, Lobel, & Scrimshaw, 1993). Though relationships can be sources of support, relationships can also be sources of stress in general and for pregnant women specifically. When a woman has few social connections or support from family and friends is inadequate, poorer birth outcomes are more likely including shorter gestational length, smaller size at birth, and preterm birth. Perhaps the most important relationship during pregnancy is that between the mother and the baby’s father. Fathers who provide material and task assistance and who are perceived as emotionally supportive can be key to a healthier pregnancy. On the flip side, negative interactions and, in the extreme, intimate partner violence can negatively impact a mother’s health and that of her infant (e.g., depressive symptoms; Urquia, O’Campo, Heaman, Janssen, & Thiessen, 2011).

Health Behaviors Smoking during pregnancy is associated with increased risk of giving birth to an infant of low birthweight and with premature delivery (Butler & Behrman, 2007). Low maternal education, low

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income, and non‐Hispanic White ethnicity as well as stress are predictors of smoking during pregnancy (Mathews, 2001). The mechanisms by which unhealthy behaviors during pregnancy explain the influence of stress on adverse birth outcomes remain largely unknown. Nonetheless, there is evidence that expecting mothers who experience high levels of pregnancy‐specific stress, e.g., concerns about medical care, physical symptoms, and bodily changes during pregnancy, smoke cigarettes more often during pregnancy and are more likely to deliver a low birthweight infant (Lobel, 2008). Regular and moderately intense physical activity throughout pregnancy is associated with optimal pregnancy and delivery outcomes (Clapp, Kim, Burciu, & Lopez, 2000). For example, women who begin exercising regularly early in pregnancy and continue to delivery have given birth to higher birthweight infants compared with women who did not exercise regularly.

Sociocultural Influences Researchers have explored the extent to which an expectant mother’s cultural background positively influences the support she receives throughout her pregnancy and even affects her pregnancy health and birth outcomes. Of particular interest is familism, a cultural value that is high among Latinos and highlights the importance of emotionally positive, supportive family relationships (Freeberg & Stein, 1996). In a sample of foreign‐born Latina, US‐born Latina, and European American pregnant women, familism was positively associated with social support and negatively associated with stress and pregnancy anxiety (Campos et al., 2008). Interestingly, for foreign‐born Latinas only (not those born in the United States), social support predicted higher infant birthweight. Thus, sociocultural factors as they influence close relationships figure prominently in both maternal mental health and healthy outcomes such as birthweight.

Religiousness and Spirituality Pregnancy and childbirth are times in the lives of women and families when religiousness and spirituality are especially salient. The birth of a child often occasions discussions of religious issues including religious rituals surrounding birth and parental decision making about religious rearing of children. A small literature shows that religious participation and spiritual behaviors and experiences are associated with lower PPD. Recently, work by our group with African American women found that both religiosity and spirituality independently predicted favorable trajectories of depressive symptoms over the 6 months after the birth of their children (Cheadle et al., 2015). These studies and others indicate that religiousness and spirituality have implications for better emotional experiences of pregnancy.

Conclusions In summary, pregnancy and birth outcomes are impacted by many complex factors in the lives of mothers at the individual, family, and sociocultural levels that can be sources of stress and of resilience. During pregnancy, women are at greater risk of mood disorders and experiencing pregnancy‐related anxiety and depressive symptoms with consequences for birth outcomes and, ultimately, for the well‐being of children and families. Low SES and its sequelae also have extremely adverse and sometimes compounding effects on birth and developmental outcomes. There are also ethnic/racial disparities both in terms of mental health during pregnancy and

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birth outcomes with African American and Latina women experiencing worse mental health and poorer birth outcomes, whereas cultural factors such as familism and collectivism are associated with better mental health during and after pregnancy. In general, resilience resources may enable poor and lower educated women to function well despite low SES. Women’s close relationships can also have positive influences on pregnancy and birth outcomes; however, unsupportive or abusive relationships are clearly harmful. Some health behaviors such as smoking, use of other substances, and unhealthy diet are associated with poorer birth outcomes, whereas positive health behaviors like moderate exercise are associated with better outcomes. The physiological changes of pregnancy are relatively well established and normative, yet knowledge on psychological and sociocultural factors and how they interact with biological processes to impact mental and physical health during and after pregnancy and birth outcomes is underdeveloped. Past research clearly demonstrates that stress and resilience matter to the experience and health of a pregnancy, yet there is much we still can learn and use to help women and their families including how to have a healthy preconception period. Future research should focus on the physiological impacts of stress on mothers, pregnancy experiences, and infants and the interaction of these effects with resilience factors.

Author Biographies Alyssa C. D. Cheadle, PhD, is an assistant professor in the Psychology Department at Hope College in Holland, Michigan. She received her PhD in Psychology from the University of California, Los Angeles, in 2016. She conducts research on religiousness, spirituality, and mental and physical health and mechanisms underlying the effects. Her previous research focused on these topics in pregnant and postpartum women, and she has studied the health effects of forgivingness. Isabel F. Ramos, MA, is a doctoral student in Health Psychology at the University of California, Los Angeles. She received her BA in Psychology from UC Riverside. Isabel now investigates stress and anxiety in pregnant women of diverse ethnicities. Her research examines ethnic and racial disparities in maternal health and cultural factors that influence perinatal biopsychosocial processes. Her work has shown that pregnancy anxiety predicts the timing of delivery in Latina, White, and Black women. Christine Dunkel Schetter, PhD, is a professor of psychology and psychiatry at the University of California, Los Angeles. She received her PhD from Northwestern University and completed postdoctoral training at UC Berkeley with Richard Lazarus. Her research expertise is in stress, coping, and social support in a variety of health and mental health contexts with primary focus on stress processes in pregnancy. She is proud of her many lab members, past and present, including Dr. Cheadle and Ms. Ramos.

References Abrams, L. S., & Curran, L. (2007). Not just a middle‐class affliction: Crafting a social work research agenda on postpartum depression. Health & Social Work, 32(4), 289–296. doi:10.1093/ hsw/32.4.289 Accortt, E. E., Cheadle, A. C., & Schetter, C. D. (2015). Prenatal depression and adverse birth outcomes: An updated systematic review. Maternal and Child Health Journal, 19(6), 1306–1337. doi:10.1007/s10995‐014‐1637‐2

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Blair, M. M., Glynn, L. M., Sandman, C. A., & Davis, E. P. (2011). Prenatal maternal anxiety and early childhood temperament. Stress, 14(6), 644–651. doi:0.3109/10253890.2011.594121 Borders, A. E. B., Grobman, W. A., Amsden, L. B., & Holl, J. L. (2007). Chronic stress and low birth weight neonates in a low‐income population of women. Obstetrics & Gynecology, 109(2, Part 1), 331–338. doi:10.1097/01.AOG.0000250535.97920.b5 Butler, A. S., & Behrman, R. E. (Eds.) (2007). Preterm birth: Causes, consequences, and prevention. National Academies Press. doi:10.17226/11622 Campos, B., Schetter, C. D., Abdou, C. M., Hobel, C. J., Glynn, L. M., & Sandman, C. A. (2008). Familialism, social support, and stress: Positive implications for pregnant Latinas. Cultural Diversity and Ethnic Minority Psychology, 14(2), 155. doi:10.1037/1099‐9809.14.2.155 Cheadle, A. C. D., Schetter, C. D., Gaines Lanzi, R., Reed Vance, M., Sahadeo, L. S., Shalowitz, M. U., … CCHN, T. (2015). Spiritual and religious resources in African American women: Protection from depressive symptoms after childbirth. Clinical Psychological Science, 3(2), 283–291. doi:10.1177/2167702614531581 Clapp, J. F., Kim, H., Burciu, B., & Lopez, B. (2000). Beginning regular exercise in early pregnancy: Effect on fetoplacental growth. American Journal of Obstetrics and Gynecology, 183(6), 1484–1488. doi:10.1067/mob.2000.107096 Collins, N. L., Schetter, C. D., Lobel, M., & Scrimshaw, S. C. (1993). Social support in pregnancy: psychosocial correlates of birth outcomes and postpartum depression. Journal of Personality and Social Psychology, 65(6), 1243. doi:10.1037/0022‐3514.65.6.1243 Fleuriet, K. J., & Sunil, T. S. (2014). Perceived social stress, pregnancy‐related anxiety, depression and subjective social status among pregnant Mexican and Mexican American women in south Texas. Journal of Health Care for the Poor and Underserved, 25(2), 546–561. doi:10.1353/hpu.2014.0092 Freeberg, A. L., & Stein, C. H. (1996). Felt obligation towards parents in Mexican‐American and Anglo‐American young adults. Journal of Social and Personal Relationships, 13(3), 457–471. doi:10.1177/0265407596133009 Hedegaard, M., Henriksen, T. B., Secher, N. J., Hatch, M. C., & Sabroe, S. (1996). Do stressful life events affect duration of gestation and risk of preterm delivery? Epidemiology, 7(4), 339–345. doi:1 0.1097/00001648‐199607000‐00001 Lobel, M., Hamilton, J. G., & Cannella, D. T. (2008). Psychosocial perspectives on pregnancy: Prenatal maternal stress and coping. Social and Personality Psychology Compass, 2(4), 1600–1623. doi:10.1111/j.1751‐9004.2008.00119.x Mathews, T. J. (2001). Smoking during pregnancy in the 1990s. National Vital Statistics Report, 49(7), 1–14. doi:10.1037/e558942006‐001 McLennan, J. D., Kotelchuck, M., & Cho, H. (2001). Prevalence, persistence, and correlates of depressive symptoms in a national sample of mothers of toddlers. Journal of the American Academy of Child & Adolescent Psychiatry, 40(11), 1316–1323. doi:10.1097/00004583‐200111000‐00012 Parker‐Dominguez, T., Schetter, C. D., Mancuso, R., Rini, C. M., & Hobel, C. (2005). Stress in African American pregnancies: testing the roles of various stress concepts in prediction of birth outcomes. Annals of Behavioral Medicine, 29(1), 12–21. doi:10.1207/s15324796abm2901_3 Paul, I. M., Downs, D. S., Schaefer, E. W., Beiler, J. S., & Weisman, C. S. (2013). Postpartum anxiety and maternal‐infant health outcomes. Pediatrics, 131(4), e1218–e1224. doi:10.1542/peds.2012‐2147 Rich‐Edwards, J. W., Kleinman, K., Abrams, A., Harlow, B. L., McLaughlin, T. J., Joffe, H., & Gillman, M. W. (2006). Sociodemographic predictors of antenatal and postpartum depressive symptoms among women in a medical group practice. Journal of Epidemiology and Community Health, 60(3), 221–227. doi:10.1136/jech.2005.039370 Rini, C., Schetter, C. D., Hobel, C. J., Glynn, L. M., & Sandman, C. A. (2006). Effective social support: Antecedents and consequences of partner support during pregnancy. Personal Relationships, 13(2), 207–229. doi:10.1111/j.1475‐6811.2006.00114.x Schetter, C. D., & Dolbier, C. (2011). Resilience in the context of chronic stress and health in adults. Social and Personality Psychology Compass, 5(9), 634–652. doi:10.1111/j.1751‐9004.2011.00379.x

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Schetter, C. D., & Tanner, L. (2012). Anxiety, depression and stress in pregnancy: implications for mothers, children, research, and practice. Current Opinion in Psychiatry, 25(2), 141–148. Urquia, M. L., O’Campo, P. J., Heaman, M. I., Janssen, P. A., & Thiessen, K. R. (2011). Experiences of violence before and during pregnancy and adverse pregnancy outcomes: An analysis of the Canadian Maternity Experiences Survey. BMC Pregnancy and Childbirth, 11(1), 1–9. doi:10.1186/ 1471‐2393‐11‐42

Suggested Reading Giscombé, C. L., & Lobel, M. (2005). Explaining disproportionately high rates of adverse birth outcomes among African Americans: the impact of stress, racism, and related factors in pregnancy. Psychological Bulletin, 131(5), 662–683. doi:10.1037/0033‐2909.131.5.662 Lobel, M., Hamilton, J. G., & Cannella, D. T. (2008). Psychosocial perspectives on pregnancy: prenatal maternal stress and coping. Social and Personality Psychology Compass, 2(4), 1600–1623. doi:10.1111/j.1751‐9004.2008.00119.x Schetter, C. D. (2009). Stress processes in pregnancy and preterm birth. Current Directions in Psychological Science, 18(4), 205–209. doi:10.1111/j.1467‐8721.2009.01637.x Schetter, C. D. (2011). Psychological science on pregnancy: Stress processes, biopsychosocial models, and emerging research issues. Annual Review of Psychology, 62, 531–558. doi:10.1146/annurev. psych.031809.130727 Schetter, C. D., & Lobel, M. (2010). Pregnancy and birth: A multilevel analysis of stress and birth weight. In T. A. Revenson, A. Baum, & J. Singer (Eds.), Handbook of Health Psychology (pp. 43– 463). London: Psychology Press. doi:10.4324/9780203804100

Subjective Health Norms Allecia E. Reid1, Molly B. Hodgkins2, Carly A. Taylor3, and Alexandra A. Belzer1 Colby College, Waterville, ME, USA Department of Psychological and Brain Sciences, University of Maine, Orono, ME, USA 3 Boston Medical Center, Boston, MA, USA 1

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Psychologists, sociologists, and others interested in human behavior have long recognized that the attitudes and behaviors of others bear influence on our own attitudes and behaviors. These social norms reflect common standards for behavior, set by and for members of a social group. Although a number of types of social norms have been examined, a large body of research has specifically focused on Cialdini, Reno, and Kallgren (1990) distinction between descriptive and injunctive norms. Descriptive and injunctive norms capture the distinction between the is and the ought of behavior. That is, while descriptive norms reflect perceptions of how most others behave in a given situation, injunctive norms capture perceptions of what is commonly approved or disapproved by most others. For example, a descriptive norm might reflect that most individuals in an environment smoke cigarettes. However, the corresponding injunctive norm might reflect that most individuals disapprove of smoking due to its negative health effects. Much research has also examined subjective norms, a construct proposed as a determinant of intentions to engage in a behavior in the theories of reasoned action and planned behavior. Subjective norms differ from the broader conceptualization of injunctive norms in that they assess whether specific valued individuals (e.g., family members, one’s doctor) approve of a behavior, rather than whether a generalized group of others approve of the behavior. The present entry focuses on the broader conceptualization of injunctive norm, incorporating research that has assessed subjective norms where appropriate. Descriptive and injunctive norms have distinct motivational underpinnings and therefore are likely to operate under different circumstances (Cialdini & Trost, 1998; Jacobson, Mortensen, & Cialdini, 2011). Descriptive norms are most likely to motivate behavior when accurate decision making is of importance and are particularly influential in novel situations or when the appropriate course of action is unclear. Injunctive norms, on the other hand, exist to support the formation and maintenance of social bonds with important groups or individuals. The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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As a result, injunctive norms motivate behavior primarily through the threat of social disapproval for inappropriate behavior or the promise of approval for conformity. Conformity to injunctive norms is therefore most likely when social approval concerns are heightened. Questions have been raised about the distinctiveness of descriptive and injunctive norms. Indeed, the manner in which others behave is often consistent with what is approved of by most others. However, confirmatory factor analyses examining descriptive and injunctive norms for calcium consumption, exercise, and eating behaviors, among others, support that the two are distinct, unitary constructs. Further, as demonstrated in the smoking example above, descriptive and injunctive norms can convey opposing information for the same behavior. This contradiction is often evident in public service announcements and health interventions that depict unhealthy behaviors as regrettably common, conveying a favorable descriptive norm, but widely disapproved, conveying an unfavorable injunctive norm.

Influences of Norms on Health Behaviors Previous research has examined whether descriptive and injunctive norms predict intentions or behavior with respect to health risk, health protective, and health screening behaviors.

Health Risk Behaviors In the context of health risk behaviors, perceptions of norms play particularly prominent roles in use of cigarettes, alcohol, and other drugs among adolescents and young adults. Research has documented associations of descriptive norms with increases in substance use up to 2 years later. Supporting the view that descriptive norms are particularly influential in novel situations, preexisting perceptions of descriptive norms influence alcohol use across transitions into new situations, such as matriculating to college and studying abroad. The literature is mixed regarding the influence of injunctive norms on alcohol and drug use relative to descriptive norms. Prospective research has demonstrated roles for both norms in alcohol and cigarette use, only an influence of descriptive norms on alcohol use, and only an influence of injunctive norms on alcohol use. Although much of the health risk literature focuses on adolescents and young adults, norms also influence adults’ behaviors. For example, descriptive but not injunctive norms longitudinally predict sharing needles and other drug equipment among middle‐aged injection drug users. The trend toward finding associations between substance use outcomes and descriptive but not injunctive norms may reflect that these behaviors often occur with peers in social settings, a situation that likely enhances the salience and therefore the impact of descriptive norms relative to injunctive norms (Cialdini et  al., 1990). Moreover, recognizing and conforming to others’ behaviors requires less cognitive processing and potentially engages a faster motivational system than considering others’ approval of the behavior (Cialdini, 2003; Jacobson et al., 2011). As a result, descriptive norms may generally more strongly influence behaviors that occur primarily in social contexts.

Health Protective Behaviors Descriptive and injunctive norms also both play roles in health protective behaviors. This literature, drawing heavily on the theories of reasoned action and planned behavior, has demonstrated that the influence of norms on behavior is fully mediated by intentions. As a result,

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cross‐sectional examinations of the relationships between norms and intentions are common, with longitudinal examination of the influence of intentions on behavior. Descriptive and injunctive norms have demonstrated associations with intentions and occasionally behavior for a range of health protective behaviors, including sun protection, fruit and vegetable consumption, diet, and physical activity. Meta‐analysis also supports cross‐sectional and longitudinal associations of both norms with condom use. Both norms also relate to intentions for receiving vaccinations to protect against human papillomavirus and swine flu, with injunctive norms predicting later uptake of the HPV vaccine. Among the limited studies that have simultaneously examined both descriptive and injunctive norms, the balance indicates that descriptive norms are more strongly associated with intentions to engage in healthy eating, calcium consumption, exercise, and other health behaviors. The salience of others’ behavior may explain the stronger influence of descriptive norms on outcomes that are publicly visible. Ceiling effects may also mitigate the role of injunctive norms in this domain, with individuals perceiving little, if any, disapproval for protecting one’s health by eating healthily, exercising, etc.

Screening Behaviors Injunctive and descriptive norms are also both related to screening behaviors. However, research has found that injunctive norms bear stronger influence on intentions to obtain a mammogram, a colonoscopy, and prostate cancer screening than descriptive norms. Likewise, only injunctive norms predict intentions to undergo screening for sexually transmitted infections or uptake of prostate or colorectal cancer screening. Of course, alternative findings exist. Nonetheless, the stronger influence of injunctive norms on screenings may reflect lack of confidence in perceptions of the extent to which others engage in these private behaviors. Indeed, lack of confidence in norm perceptions has been found to weaken the impact of norms. On the other hand, we may infer that health screenings are widely approved of by others, as conveyed by public service announcements encouraging monitoring one’s health. The types of behaviors for which injunctive versus descriptive norms are likely to influence intentions and behavior require more careful scrutiny in future research.

Using Norms to Change Behavior Whether norms can be used to change behavior, which typically requires prolonged effort and self‐regulation, is ultimately of interest. Social norms theory (Perkins & Berkowitz, 1986) forms the basis of most research that uses social norms to promote healthier behaviors. From the perspective of this theory, perceptual biases and reliance on heuristics lead to a general tendency to overestimate the extent to which others engage in and approve of risky behaviors but underestimate others’ engagement in and approval of protective behaviors. For example, young adults overestimate the level of sexual activity among peers and underestimate peers’ condom use. Misperceptions have also been documented with respect to alcohol, tobacco, marijuana, and other drug use. Women also incorrectly believe that other women are thinner than they themselves are and that men find overly thin women attractive. Given the existence of these misperceptions, the social norms approach proposes that making individuals aware of the true norms in their environment should recalibrate their perceptions of the norms, producing positive changes in behavior. These norm reeducation approaches have primarily been applied to health risk and health protective behaviors. Most studies have

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taken either a social norms marketing or personalized normative feedback approach, with fewer studies utilizing alternative methods. The social norms marketing approach simply presents participants with information about the norms for behavior among peers, without addressing participants’ own behavior or misperceptions of the norm. For example, this type of message might state, “More than 90 percent of the time, people in this building use the stairs instead of the elevator,” communicating a descriptive norm that favors staircase over elevator use (Burger & Shelton, 2011). Social norms marketing messages are often delivered via advertisements placed in key locations but may also be delivered individually. Descriptive social norms marketing has demonstrated efficacy in increasing occupational physical activity, fruit consumption, salad consumption, and interest in chemotherapy. This approach has also demonstrated efficacy in improving alcohol use, intervening with friends who may be drinking too much, and intentions for condom use. Although descriptive social norms marketing has been applied to encouraging cancer screening, its efficacy in that domain remains unclear. Importantly, social norms marketing has been shown to alter perceptions of the descriptive norms, and in turn, changes in descriptive norms fully mediate or account for intervention‐induced change in behavior. The few studies that have used social norms marketing to address injunctive norms have documented mixed efficacy. Thus, additional research is needed to clarify whether this is a viable means for altering injunctive norms. It should be noted that the studies described above differ from mass media marketing campaigns in that they either delivered interventions to participants individually or displayed messages in conspicuous locations and also observed participants’ behaviors in the same setting. Mass media efforts that employ normative information can be efficacious (Perkins, Linkenbach, Lewis, & Neighbors, 2010) but may also prove ineffective (Wechsler et al., 2003) because the target audience may not sufficiently processes these messages. Personalized normative feedback, a complementary approach for altering normative perceptions, typically compares three pieces of information: (a) the individual’s perceptions of the norms in his or her environment (e.g., perceptions of the extent of alcohol use among peers), (b) the actual descriptive norm in the environment (e.g., actual level of alcohol use among peers derived from surveys), and (c) the individual’s own behavior. A comparative efficacy trial found social norms marketing and personalized normative feedback to be similarly efficacious (Neighbors et al., 2011). However, the use of individually tailored content in personalized feedback should hypothetically enhance the self‐relevance of the message, thereby encouraging both greater depth of processing and better maintenance of behavior change over time. As with the social norms marketing approach, personalized normative feedback studies have primarily focused on descriptive norm information. This type of feedback has been utilized both individually and in a group setting as a stand‐alone intervention to reduce college students’ alcohol use. It is also the primary active ingredient through which multicomponent interventions reduce college students’ alcohol use. Personalized descriptive norm feedback was also recently shown to reduce alcohol‐induced risky sexual behavior. Further, personalized descriptive norm feedback, whether in single or multiple component interventions, is efficacious because it alters perceptions of the descriptive norms, which in turn drive behavior change. Personalized injunctive norm feedback has received little attention but has been shown to improve women’s sun‐protective behaviors. Future research might examine whether personalized injunctive norm feedback reliably alters behavior. The large literature examining the use of personalized normative feedback in the context of college student drinking provides insight into conditions that maximize its impact (Reid &

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Carey, 2015). Specifically, although normative information is highly portable and can be delivered via the web or mail, normative feedback is in fact less effective when delivered remotely, rather than by a counselor or computer in person. Remotely delivered interventions suffer because recipients often engage in other activities (e.g., texting, listening to music) while completing the intervention (Lewis & Neighbors, 2015). In addition, it is important to choose the optimal referent group to serve as the basis for the normative message. In general, normative data referencing peers in the same environment is more effective than norms derived from national data. Gender differences in alcohol consumption necessitate providing gender‐ specific norms, but the utility of this specification likely varies by behavior. It is possible, however, that a referent group might be too specific. LaBrie et al. (2013) found that students held more accurate perceptions of the norms for students of the same gender, ethnicity, and Greek status, relative to norms for “the typical student.” Given that there was little to correct, feedback on the gender–ethnicity–Greek specific norms was associated with decreased likelihood of behavior change. It is therefore important to identify the optimal referent group for each behavior of interest. As noted above, alternative methods exist for altering perceptions of norms in service of behavior change. One unique approach centers on labeling undesirable behaviors as normative among members of an out‐group. Berger and Rand (2008) provided undergraduates with information that high‐fat food consumption and heavy alcohol use were common among out‐ groups with which they did not want to be associated. This increased selection of healthy foods and reduced alcohol use among message recipients as a means to avoid being incorrectly labeled as members of these undesirable groups. A second alternative method focuses on aspirational peers and demonstrates that norms have shifted over time. Jackson and Aiken (2006) improved sun protection among young women through photographs demonstrating that tanned skin was common in the past but was no longer popular among present day actresses. This approach might be extended to address other observable behaviors. A third alternative approach involves facilitating structured interactions with peers in group settings such that healthy norms are elicited and supported. Participating in intervention sessions with peers has been shown to alter perceptions of norms for healthy eating among firefighters, safe injection practices among injection drug users, and thin‐ideal endorsement and dieting among women with eating disorders (e.g., Ranby et al., 2011). Mediation analyses supported that the effects of these peer‐centered interventions on behavior change were explained by changes in normative perceptions.

Moderators of Norm‐Based Interventions Previous research examining individual differences that may moderate the effect of norms on behavior change have fallen into three main categories: level of identification with the referent group, sensitivity to external pressures, and extent of discrepancy between perceptions and the true norms. Consistent with social identity theory (Tajfel & Turner, 1979), research has found descriptive norm interventions to be particularly efficacious for individuals who identify most strongly with the referent group. Similarly, norm interventions show increased efficacy among individuals who are more sensitive to social pressures because they fear being negatively evaluated, drink alcohol primarily for social reasons, or seek out cues in the environment to determine how to behave. Finally, consistent with social norms theory, the more individuals misperceive the norms in their environment, the more likely they are to change their behavior after having their perceptions corrected. Given these moderators, researchers should be

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a­ ttentive when selecting a referent group and might maximize discrepancy by selecting cognitions for feedback that evidence the largest discrepancies at the individual level, rather than at the group level. Additional research is needed to determine whether social norms interventions can be enhanced for those less sensitive to social forces or if an attitudinal appeal is more appropriate for these individuals.

Future Directions and Conclusions As indicated above, few studies have utilized injunctive norms to change behavior. While additional research is needed on injunctive norms, normative information is likely to be most influential when descriptive and injunctive norms correspond with one another (Cialdini, 2003). Accordingly, research that compares a combined norms condition against separate descriptive and injunctive norms conditions may find support for this contention, as in previous research on sun protection. An additional consideration is whether altering normative perceptions affects other psychosocial constructs en route to behavior change. Reid and Aiken (2013) demonstrated that the effect of injunctive norm feedback was mediated by not only change in injunctive norms but also change in attitudes. Exploring whether additional psychological constructs transmit the effects of descriptive and injunctive norms will aid in identifying conditions that are likely to maximize their impact. In sum, correlational and experimental research support that social norms influence health behaviors. Though unresolved issues remain, research confirms descriptive and injunctive norms are indeed strong motivators of behavior that have much to offer in both characterizing and improving health behaviors.

Author Biographies Allecia E. Reid is an assistant professor of psychology at Colby College. Her research examines the influences of social norms and peer social networks on health behaviors. She studies mediators and moderators of the influences of these social factors, the use of social norms information for motivating healthier behavior, and techniques for protecting individuals against the negative influence of peers. Molly B. Hodgkins completed a bachelor’s degree in psychology from Colby College in 2015. She is currently pursuing a master of social work at the University of Maine. Molly’s research has primarily focused on how feelings of social belonging influence well‐being in college students. Carly A. Taylor completed a bachelor’s degree in psychology from Colby College in 2015. She is currently a patient navigator at Boston Medical Center. Carly’s research interests include developing interventions to reduce stigma associated with mental illness. Consistent with her goal of becoming a medical doctor, she is also interested in improving doctor–patient interactions as a means for improving patient outcomes. Alexandra A. Belzer is an undergraduate at Colby College, where she is a research assistant in Dr. Reid’s lab. Alexandra is broadly interested in the connection between psychological factors and health outcomes. Her research interests focus on understanding and reducing the negative influence of social norms on health behaviors.

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References Berger, J., & Rand, L. (2008). Shifting signals to help health: Using identity signaling to reduce risky health behaviors. Journal of Consumer Research, 35, 509–518. Burger, J. M., & Shelton, M. (2011). Changing everyday health behaviors through descriptive norm manipulations. Social Influence, 6, 69–77. Cialdini, R. B. (2003). Crafting normative messages to protect the environment. Current Directions in Psychological Science, 12, 105–109. Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. Journal of Personality and Social Psychology, 58, 1015–1026. Cialdini, R. B., & Trost, M. R. (1998). Social influence: Social norms, conformity and compliance. In D. T. Gilbert, S. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (4th ed., pp. 151–192). New York, NY: McGraw‐Hill. Jackson, K. M., & Aiken, L. S. (2006). Evaluation of a multicomponent appearance‐based sun‐protective intervention for young women: Uncovering the mechanisms of program efficacy. Health Psychology, 25, 34. Jacobson, R. P., Mortensen, C. R., & Cialdini, R. B. (2011). Bodies obliged and unbound: Differentiated response tendencies for injunctive and descriptive social norms. Journal of Personality and Social Psychology, 100, 433–448. LaBrie, J. W., Lewis, M. A., Atkins, D. C., Neighbors, C., Zheng, C., Kenney, S. R., … Hummer, J. F. (2013). RCT of web‐based personalized normative feedback for college drinking prevention: Are typical student norms good enough? Journal of Consulting and Clinical Psychology, 81, 1074–1086. doi:10.1037/a0034087 Lewis, M. A., & Neighbors, C. (2015). An examination of college student activities and attentiveness during a web‐delivered personalized normative feedback intervention. Psychology of Addictive Behaviors, 29, 162–167. doi:10.1037/adb0000003 Neighbors, C., Jensen, M., Tidwell, J., Walter, T., Fossos, N., & Lewis, M. A. (2011). Social‐norms interventions for light and nondrinking students. Group Processes & Intergroup Relations, 14, 651–669. Perkins, H. W., & Berkowitz, A. D. (1986). Perceiving the community norms of alcohol use among students: Some research implications for campus alcohol education programming. Substance Use & Misuse, 21, 961–976. Perkins, H. W., Linkenbach, J. W., Lewis, M. A., & Neighbors, C. (2010). Effectiveness of social norms media marketing in reducing drinking and driving: A statewide campaign. Addictive Behaviors, 35, 866–874. Ranby, K. W., MacKinnon, D. P., Fairchild, A. J., Elliot, D. L., Kuehl, K. S., & Goldberg, L. (2011). The PHLAME (Promoting Healthy Lifestyles: Alternative Models’ Effects) firefighter study: Testing mediating mechanisms. Journal of Occupational Health Psychology, 16, 501–513. Reid, A. E., & Aiken, L. S. (2013). Correcting injunctive norm misperceptions motivates behavior change: A randomized controlled sun protection intervention. Health Psychology, 32, 551–560. doi:10.1037/a0028140 Reid, A. E., & Carey, K. B. (2015). Interventions to reduce college student drinking: State of the evidence for mechanisms of behavior change. Clinical Psychology Review, 40, 213–224. Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33–47). Monterey, CA: Brooks‐Cole. Wechsler, H., Nelson, T. F., Lee, J. E., Seibring, M., Lewis, C., & Keeling, R. P. (2003). Perception and reality: A national evaluation of social norms marketing interventions to reduce college students’ heavy alcohol use. Journal of Studies on Alcohol and Drugs, 64, 484–494.

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Suggested Reading Cialdini, R. B., & Trost, M. R. (1998). Social influence: Social norms, conformity and compliance. In D. T. Gilbert, S. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (4th ed., pp. 151–192). New York, NY: McGraw‐Hill. Neighbors, C., Larimer, M. E., & Lewis, M. A. (2004). Targeting misperceptions of descriptive drinking norms: Efficacy of a computer‐delivered personalized normative feedback intervention. Journal of Consulting and Clinical Psychology, 72, 434–447. Schultz, P. W., Tabanico, J., & Rendón, T. (2008). Normative beliefs as agents of influence: Basic processes and real‐world applications. In R. Prislin & W. Crano (Eds.), Attitudes and attitude change (pp. 385–409). New York, NY: Psychology Press.

Temptation David A. Kalkstein1 and Kentaro Fujita2 Department of Psychology, New York University, New York, NY, USA Department of Psychology, The Ohio State University, Columbus, OH, USA 1

2

Temptations are stimuli in one’s immediate environment that prompt thoughts, feelings, and behavior that are contrary to one’s goals and values. Temptations rest at the heart of self‐control dilemmas—decisions that require choosing between smaller‐immediate and larger‐distal outcomes (e.g., Mischel, Shoda, & Rodriguez, 1989). Motivationally, self‐control can be understood as a conflict between competing motivations—a desire for something now versus a desire for some other outcome of greater magnitude (Fujita, 2011). The ability to secure some small outcome immediately may tempt people to forgo outcomes that are more valuable yet not immediately available. Dieters, for example, might be tempted to abandon their weight‐loss efforts when presented with an opportunity to eat chocolate cake now. Substance abusers may struggle to advance their desire to be sober when their drug of choice is freely available. The end of a long, hard day in the office may tempt the sedentary to watch TV rather than work out at the gym. Successful self‐control requires overcoming temptations, advancing the pursuit of larger‐distal ends over these smaller‐immediate outcomes. The omnipresence of temptations in one’s social environment poses one of the greatest challenges to achieving and maintaining personal health. Although people may value health, securing and maintaining this end requires overcoming numerous temptations—that is, self‐control. It is important to observe that temptations are subjective in nature. What constitutes a temptation depends on idiosyncratic motivations, values, goals, and desires, which vary from person to person. What represents a temptation for one person may not necessarily represent a temptation for another. First, people must find temptations appealing in some way. Chocolate cake may represent a temptation for someone who likes chocolate. This same object, however, would not represent a temptation for someone who dislikes the taste of chocolate. Second, even when the immediate outcome is desirable in some way, it is only a temptation to the extent that it competes with or undermines some more valuable end. For example, although the allure of cake may represent a temptation for someone concerned with weight loss, it would not represent a temptation for someone unconcerned about their weight. Without this The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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conflict, rewarding stimuli are simply rewarding stimuli, not temptations. What makes a stimulus a temptation is that it idiomatically makes people “want their cake and eat it too”—it evokes a situation in which two competing desires are activated, only one of which can be satisfied. Critically, of these two competing desires, one can be satisfied immediately yet is of lesser value, whereas the other cannot be satisfied immediately yet is of greater value. In short, for a stimulus to be considered a temptation, it must both appeal to immediate desires and impede progress toward more distal but more valued ends. Self‐control is not generally viewed as a problem of knowledge. Self‐control failure is defined by acting on a temptation despite knowing in some way that doing so is detrimental to securing more valuable outcomes. To take an extreme example, suppose some dieters erroneously believe that eating donuts will lead to weight loss. It would be inappropriate to label these donuts as temptations for these individuals, just as it would be inappropriate to label their behavior as a self‐control failure, since they did not know that eating donuts would harm their weight‐loss goals (in this example, the dieters actually thought that eating these donuts would help advance those valued goals). Objects are not temptations if individuals acting on them are not aware implicitly or explicitly that acting on them is detrimental to their broader objectives. A final, often overlooked, aspect of temptations that is important to consider is that temptations come in the form of both positive and negative incentives. One can be tempted by a desire to obtain some smaller yet immediate rewards at the expense of larger yet not immediately available rewards. Examples of this type of temptation include the enjoyment of consuming tasty yet unhealthy food, the highs substance abusers experience from taking harmful drugs, or the hedonic pleasure of engaging in risky sexual behaviors. One might also, however, be tempted by a desire to avoid some smaller yet immediate cost. Examples of this type of temptation include wanting to avoid the discomfort associated with strenuous exercise, the inconvenience and anxiety associated with diagnostic medical tests, and the negative minor side effects of drug regimens. In short, temptations can correspond to desires to obtain proximal rewards or to avoid proximal costs, as both forms have the potential to conflict with and derail more valued goals and objectives.

Deleterious Effects of Temptations Much of the work done on self‐control in the field of psychology has focused on the deleterious impacts of temptations on people’s goal‐directed behavior (Kelley, Wagner, & Heatherton, 2015). Generally, this work tends to argue that exposure to temptations leads to self‐control failure because it triggers cravings and urges to act on immediately gratifying motivations. For example, tobacco users and drug addicts report the strongest urges to use drugs during, or immediately following, exposure to temptation cues such as drug paraphernalia, pictures of drugs, or in vivo presentations of drugs or tobacco (see Carter & Tiffany, 1999). This research argues that these cravings and impulses influence thoughts, attitudes, and behaviors in the direction of favoring indulgence in the temptation. Evidence for this idea comes from the finding that exposing dieters to tempting food cues (e.g., the smell of fresh baked pizza) leads dieters to report stronger cravings to indulge, which subsequently predicts greater consumption of unhealthy foods (for review, see Kelley et al., 2015). At the neural level, researchers have shown that increases in activity in regions of the brain that process rewards (the nucleus accumbens) in response to temptation cues predict future indulgence in those temptations (Kelley et  al., 2015). Indeed, a great deal of research across a variety of health domains has documented cases in which exposure to a temptation cue increases the likelihood of indulgence in that temptation and hence self‐control failure.

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Underlying much of this work is an implicit (or sometimes explicit) assumption that exposure to temptations automatically activates positive thoughts, feelings, and behavioral tendencies that promote indulgence (e.g., Baumeister & Heatherton, 1996; Fujita, 2011). These automatic impulses influence behavior unless people inhibit or override them. This inhibition process, however, is believed to be effortful, requiring sufficient cognitive and motivational resources. It follows from these premises that any burden on these resources, such as cognitive load or distraction, reduces the likelihood that people will successfully inhibit these impulses, thus leading to an increased likelihood of indulging in temptation. One especially influential line of research has proposed that this effortful inhibition process not only requires having sufficient cognitive resources but also depletes a motivational resource, much like a battery or muscle (Baumeister & Heatherton, 1996). Proponents of this limited resource model of self‐control suggest that all acts of self‐control draw on the same limited supply of energy and that an initial exertion of self‐control will result in a period of depletion and thus a temporarily diminished capacity to inhibit further automatic impulses that favor temptations. For example, Vohs and Heatherton (2000) showed that dieters who restrained from eating popcorn in an initial task subsequently ate more ice cream on a following task. Similarly, Hofmann, Rauch, and Gawronski (2007) showed that after regulating their emotional responses during an initial movie viewing task, participants’ candy consumption was predicted by their automatic attitudes toward candy (which tended to be positive) rather than their dietary restraint standards. These findings support the idea that when people lack the requisite energy to engage in effortful inhibition and are thus “depleted,” their behavior is more susceptible to automatic temptation impulses and self‐control failure.

Overcoming Temptations Despite the abundance of research demonstrating the deleterious consequences of exposure to temptations, people also possess a remarkable array of psychological mechanisms and behavioral strategies at their disposal for dealing with temptations.

Motivation to Overcome Temptations The question of how to overcome and resist temptations is fundamentally a question about how to maintain the motivation to behave in line with valued ends when confronted with the opportunity to indulge in more immediate rewards. The more motivated people are to achieve these more distal ends, the more likely they will be to initiate, engage, and persist in coping behaviors and strategies to deal with and overcome temptations (Trope & Fishbach, 2000). The role of motivation is particularly evident in situations that research suggests people are most vulnerable to temptations—when their cognitive and motivational resources are taxed. For example, providing monetary incentives to increase people’s motivation to resist temptation has been shown to improve people’s self‐control even following periods of depletion (for review, see Masicampo, Martin, & Anderson, 2014). The nature of people’s motivation also appears to play an important role in self‐control. Research suggests that when people are intrinsically rather than extrinsically motivated—when they are pursuing an end that they are personally relevant and important—they are less susceptible to depletion (see Masicampo et al., 2014). Research, moreover, indicates that people’s beliefs about themselves can impact their motivation to exert the necessary effort to overcome temptations. Motivational theories suggest

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that bolstering people’s beliefs that they are indeed capable of resisting temptation and procuring more desirable end states (i.e., self‐efficacy; Bandura, 1977) should increase their self‐­ control motivation and help them resist temptations. Indeed, it has been shown that increasing people’s self‐efficacy beliefs about their ability to quit smoking and their ability to lose weight results in greater smoking reduction and more weight loss (for review, see Strecher, DeVellis, Becker, & Rosenstock, 1986). Beyond self‐efficacy, research also points to the important role of people’s beliefs about the nature of self‐control in their successful resistance to temptations. Research indicates that some endorse a limited view of self‐control—a belief that one’s ability to resist temptations is finite and can be depleted—whereas others do not (Job, Dweck, & Walton, 2010). Endorsing the view that self‐control is limited appears to lead people to give in to temptation more readily, whereas endorsing a more unlimited view appears to motivate more sustained effort to overcome temptation. For example, those who endorse a limited versus unlimited view of self‐control tend to eat less healthily, achieve poorer grades, and show less persistence in the face of difficulty (Job, Walton, Bernecker, & Dweck, 2015). Research, moreover, indicates that these beliefs can be changed, suggesting the possibility of leveraging these beliefs about the nature of self‐control as a potential point of intervention (Job et al., 2010).

Cognitive Habits Given sufficient motivation, research suggests that people can develop what might be termed “cognitive habits” to enhance resistance to temptation. In other words, although self‐control is often believed to be effortful and involve intensive use of psychological resources, research suggests that people can automate their psychological responses to temptations and be successful in self‐control without such resource‐intensive effort. Those highly committed to dieting goals, for example, evidence a cognitive readiness to evaluate tempting foods negatively (Fishbach & Shah, 2006). These automatically activated attitudes induce a readiness to resist temptations without requiring effortful deliberation and control. Beyond evaluations and attitudes, research suggests that sufficiently motivated individuals also develop cognitive associations that bias their thoughts in favor of valued ends rather than more immediate smaller rewards. Self‐control should be enhanced to the extent that thoughts about temptations cue thoughts about overriding goals (e.g., “cake” cues “weight loss”), whereas thoughts about those overriding goals does not reciprocally cue thoughts about temptations (e.g., “weight loss” does not cue “cake”). This asymmetric pattern of activation should in turn bias more frequent thoughts about overriding goals rather than temptations. Indeed, research suggests that those committed to their goals are those most likely to display these adaptive asymmetric temptation–goal associations (Fishbach, Friedman, & Kruglanski, 2003). Importantly, people display these associations even under suboptimal processing conditions, such as states of cognitive load—suggesting that these processes may enhance self‐ control efficiently and without requiring the intensive effort that is commonly assumed necessary for resisting temptations. It is often assumed that “cognitive habits” to overcome temptations require repeated practice over time, much like a learned skill. However, research suggests that these habits can develop relatively quickly through what is referred to as “implementation intentions” (Gollwitzer & Sheeran, 2006). Implementation intentions are simple “if‐then” action plans that link anticipated situations or cues with goal‐directed behavior (e.g., “If I’m bored and want a snack, then I will eat an apple!”). They are designed to automate goal‐directed behavior such that when a given cue is encountered (e.g., “I am bored”), people will automatically react

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with their stated intention (e.g., “eat an apple”). Research has shown that these simple plans can increase health behaviors such as cancer screenings, exercise and physical activity, and dietary restraint and appears to do so by promoting self‐control in the face of temptations and under suboptimal processing conditions such as resource depletion (for a review of findings, see Gollwitzer & Sheeran, 2006).

Prospective Control The most effective strategies that one can employ to overcome temptation may be those involving prospective control. Prospective control seeks to combat the allure of immediate temptations by developing strategies to overcome them prior to exposure. Classic research on self‐control has shown that decisions that are made for the future and in the absence of temptations tend to be more in line with people’s valued goals and best interests (Ainslie, 1975). Prospective control facilitates the overcoming of temptation by encouraging people to make decisions to pursue their more valued ends and committing to these decisions in advance of encountering temptations. Prospective control strategies can be divided into two classes: strategies that seek to regulate the availability of temptations and strategies that seek proactively to regulate responses to temptations. Regulating the availability of temptations involves modifying behavior or selecting situations that make it less likely that one will be encountering temptations and thus less likely that they will feel impulses or desires to indulge the temptations. For example, a dieter who takes an alternative route to work in the morning to avoid the donut shop is utilizing prospective control. In this example, the dieter is purposefully avoiding the sight and smell of donuts that may serve as cues that trigger cravings to eat the donut. By reducing the availability of temptations and avoiding temptation cues, the dieter decreases the likelihood of breaking his diet by eating a donut. Other examples of regulating the availability of temptations and exposure to temptation cues would be a recovering alcoholic staying at home on New Year’s Eve instead of going out to the bars, or a past drug addict refraining from hanging out with drug‐using friends. Avoiding temptations from the outset is one of the most effective strategies that people can employ to decrease the likelihood of succumbing to temptation (Mischel et  al., 1989). Individuals cannot give in to temptations upon which they never have the opportunity to act. A second prospective self‐control strategy that people can employ to overcome temptation is to regulate potential responses to temptations and precommit oneself to behavior in line with more valued ends. A typical precommitment strategy involves manipulating the contingencies and outcomes associated with acting on the temptation through means such as self‐ imposed penalties and success‐contingent rewards (also referred to as “side bets”; Ainslie, 1975). In one demonstration of self‐imposed punishment, Trope and Fishbach (2000) presented people with a medical screening opportunity that promised to provide accurate and useful health information but entailed uncomfortable procedures. To protect their health goals from the temptation of averting physical discomfort, participants agreed to pay higher monetary fines for failing to meet their screening appointment. People also will structure self‐ control decisions to render rewards contingent on success. In another study by Trope and Fishbach, participants preferred to delay receiving a monetary reward until after (rather than before) they completed the medical screening. By self‐imposing penalties and making rewards contingent on success, these strategies served to precommit people to a course of action that served their more valued health goals. Implementation intentions, which were discussed earlier, can be viewed as a form of prospective control in that they are designed to regulate responses to temptations proactively by

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specifying action plans prior to exposure. So while self‐imposed penalties and success‐­ contingent rewards regulate responses by changing the cost–benefit contingencies of acting on temptation, implementation intentions regulate responses by automating responses to be in line with more valued ends. As reviewed earlier, research suggests that implementation intentions are indeed an effective means of countering negative influence of temptations, particularly in the health domain (e.g., Gollwitzer & Sheeran, 2006).

The Role of Construal Another major factor that influences people’s motivations to overcome or indulge in temptations is their construal of the temptation. It is a truism of modern psychology that people’s understanding and experience of the world is subjective in nature. The same object or situation can be construed and experienced by different people in different ways or by the same person in different ways depending on the context and that person’s motivations. For example, consider a candy bar. A candy bar can be construed as “tasty snack” or as a “fattening overindulgence.” Clearly, these different construals have different evaluative connotations and suggest divergent responses to the same target. Construing the candy bar as a “tasty snack” promotes consumption and may promote self‐control failure among those concerned about weight loss. By contrast, construing the candy bar as a “fattening overindulgence” promotes restraint and may promote self‐control success among those concerned about weight loss. In this way, how people construe temptations may impact their self‐control decisions. Research suggests that changing people’s construals of temptations is a particularly effective strategy for dealing with and overcoming temptations. The less people incorporate the alluring and rewarding features of the temptation into their construals of it, the less likely people are to want and pursue the temptation (Mischel et al., 1989). Similarly, the more people think about the temptation in terms of the more valued ends that it threatens to undermine, the less appealing it is and the less likely they are to succumb to it (Fujita & Carnevale, 2012). However, one of the major challenges of self‐control is that the immediacy of temptations available in the here and now often prompts people to construe them in concrete ways, leading them to be viewed as an isolated opportunity for visceral, tangible, and immediate reward. Such concrete thought tends to lead people to act on these more proximal motivations and indulge in the temptation. Research suggests that key to overcoming this tendency is adopting a more abstract construal (Fujita & Carnevale, 2012). Abstract construals extract the central, stable, and enduring aspects of a target or situation. Consider a choice between a candy bar and an apple. Whereas a concrete construal might focus deliberations on the pros and cons of “this candy” vs. “that apple,” a more abstract construal focuses decision‐making processes instead on the more essential, global features of the choice—namely, “hedonism” vs. “heath.” In general, whereas concrete construals of temptations tend to highlight their rewarding properties, abstract construals relate temptations to a person’s more valued goals and motives. These construals in turn change the experience of temptations by changing what the temptations mean to people. Abstract relative to concrete construals help individuals transcend the pull of the immediate situation to see the bigger picture and thus to evaluate, plan, and act in accordance with more global, superordinate goals and motives. Extensive research has shown that abstract construals help people resist alluring temptations and enhance self‐control (Fujita & Carnevale, 2012). Of note, research suggests that implementation of many of the psychological mechanisms and behavioral strategies discussed earlier (i.e., cognitive habits, prospective control) may be contingent on more abstract rather than concrete construals. Abstract rather than concrete

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construals of temptation, for example, lead people to associate temptations with negativity, which in turn promotes self‐control (Fujita & Han, 2009). Similarly, asymmetric temptation– goal associations are more evident when people adopt more abstract versus concrete construals of temptations (Fujita & Sasota, 2011). Moreover, abstract rather than concrete construals promote the use of prospective self‐control (Fujita & Roberts, 2010). Thus, people’s construal of temptations appears to represent a key variable in promoting resistance to temptations.

Importance The ubiquity of temptations in people’s environments makes maintaining and sustaining personal health and well‐being goals challenging. Although temptations can undermine health goals, people have at their disposal a wide variety of psychological tools to combat them. Indeed, research suggests that those better able to resist temptations are healthier, happier, and more well adjusted (e.g., Mischel et al., 1989). This link would thus suggest that understanding how people react to temptations and how they successfully overcome them is key to promoting and supporting health behavior change.

Author Biographies David A. Kalkstein is a doctoral candidate in social psychology at New York University. David’s research focuses primarily on how cognitive processes of abstraction aid in self‐regulation, self‐control, and social exchange. Kentaro Fujita is an associate professor of psychology at the Ohio State University. His research focuses on why people so often make decisions and behave in ways that they know are contrary to their goals and values. To understand these self‐control failures, Dr. Fujita’s research draws from a number of areas in psychology, including motivation, cognition, self‐ regulation, and judgment and decision making.

References Ainslie, G. (1975). Specious reward: A behavioral theory of impulsiveness and impulse control. Psychological Bulletin, 82, 463–496. http://doi.org/10.1037/h0076860 Bandura, A. (1977). Self‐efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215. http://doi.org/10.1037/0033‐295X.84.2.191 Baumeister, R. F., & Heatherton, T. F. (1996). Self‐regulation failure: An overview. Psychological Inquiry, 7, 1–15. http://doi.org/10.1207/s15327965pli0701_1 Carter, B. L., & Tiffany, S. T. (1999). Meta‐analysis of cue‐reactivity in addiction research. Addiction, 94, 327–340. http://doi.org/10.1046/j.1360‐0443.1999.9433273.x Fishbach, A., Friedman, R. S., & Kruglanski, A. W. (2003). Leading us not into temptation: Momentary allurements elicit overriding goal activation. Journal of Personality and Social Psychology, 84, 296–309. Fishbach, A., & Shah, J. Y. (2006). Self‐control in action: Implicit dispositions toward goals and away from temptations. Journal of Personality and Social Psychology, 90, 820–832. http://doi. org/10.1037/0022‐3514.90.5.820 Fujita, K. (2011). On conceptualizing self‐control as more than the effortful inhibition of impulses. Personality and Social Psychology Review, 15, 352–366. http://doi.org/10.1177/1088868311411165

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Fujita, K., & Carnevale, J. J. (2012). Transcending temptation through abstraction: The role of construal level in self‐control. Current Directions in Psychological Science, 21, 248–252. http://doi. org/10.1177/0963721412449169 Fujita, K., & Han, H. A. (2009). Moving beyond deliberative control of impulses: The effect of construal levels on evaluative associations in self‐control conflicts. Psychological Science, 20, 799–804. http://doi.org/10.1111/j.1467‐9280.2009.02372.x Fujita, K., & Roberts, J. C. (2010). Promoting prospective self‐control through abstraction. Journal of Experimental Social Psychology, 46, 1049–1054. http://doi.org/10.1016/j.jesp.2010.05.013 Fujita, K., & Sasota, J. A. (2011). The effects of construal levels on asymmetric temptation‐goal cognitive associations. Social Cognition, 29, 125–146. http://doi.org/10.1521/soco.2011.29.2.124 Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta‐analysis of effects and processes. Advances in Experimental Social Psychology, 38, 69–119. http://doi. org/10.1016/S0065‐2601(06)38002‐1 Hofmann, W., Rauch, W., & Gawronski, B. (2007). And deplete us not into temptation: Automatic attitudes, dietary restraint, and self‐regulatory resources as determinants of eating behavior. Journal of Experimental Social Psychology, 43, 497–504. http://doi.org/10.1016/j.jesp.2006.05.004 Job, V., Dweck, C. S., & Walton, G. M. (2010). Ego depletion—Is it all in your head? Implicit theories about willpower affect self‐regulation. Psychological Science, 21, 1686–1693. http://doi. org/10.1177/0956797610384745 Job, V., Walton, G. M., Bernecker, K., & Dweck, C. S. (2015). Implicit theories about willpower predict self‐regulation and grades in everyday life. Journal of Personality and Social Psychology, 108, 637– 647. http://doi.org/10.1037/pspp0000014 Kelley, W. M., Wagner, D. D., & Heatherton, T. F. (2015). In search of a human self‐regulation system. Annual Review of Neuroscience, 38, 389–411. http://doi.org/10.1146/annurev‐neuro‐ 071013‐014243 Masicampo, E. J., Martin, S. R., & Anderson, R. A. (2014). Understanding and overcoming self‐control depletion. Social and Personality Psychology Compass, 8, 638–649. http://doi.org/10.1111/ spc3.12139 Mischel, W., Shoda, Y., & Rodriguez, M. I. (1989). Delay of gratification in children. Science, 244, 933–938. http://doi.org/10.1126/science.2658056 Strecher, V. J., DeVellis, B. M., Becker, M. H., & Rosenstock, I. M. (1986). The role of self‐efficacy in achieving health behavior change. Health Education & Behavior, 13(1), 73–92. http://doi. org/10.1177/109019818601300108 Trope, Y., & Fishbach, A. (2000). Counteractive self‐control in overcoming temptation. Journal of Personality and Social Psychology, 79, 493–506. http://doi.org/10.1037/0022‐3514.79.4.493 Vohs, K. D., & Heatherton, T. F. (2000). Self‐regulatory failure: A resource‐depletion approach. Psychological Science, 11, 249–254. http://doi.org/10.1111/1467‐9280.00250

Suggested Reading Fujita, K. (2011). On conceptualizing self‐control as more than the effortful inhibition of impulses. Personality and Social Psychology Review, 15, 352–366. http://dx.doi.org/10.1177/1088868 311411165 Mann, T., de Ridder, D., & Fujita, K. (2013). Self‐regulation of health behavior: Social psychological approaches to goal setting and goal striving. Health Psychology, 32, 487–498. http://dx.doi. org/10.1037/a0028533 Mischel, W. (2014). The marshmallow test: Mastering self‐control. New York: Little, Brown. Mischel, W., Shoda, Y., & Rodriguez, M. I. (1989). Delay of gratification in children. Science, 244, 933–938. http://dx.doi.org/10.1126/science.2658056

Terror Management Health Model Patrick Boyd and Jamie L. Goldenberg Department of Psychology, University of South Florida, Tampa, FL, USA

The terror management health model (TMHM) builds on terror management theory (TMT), a social psychology theory concerning how people manage the psychological awareness of their own mortality, to offer unique insights into how concerns about mortality affect decision making with respect to health. The model assumes that in the context of health decision making, thoughts of death are likely to be activated; before the TMHM was introduced, no theory in health psychology had been offered to account for the unique defensive responses associated with the awareness of mortality.

Origins of the TMHM In the early 1970s, cultural anthropologist Ernest Becker (e.g., 1973) was arguing that cultures provide belief systems that enable individuals to derive meaning and a sense of personal value in the face of existential uncertainty, brought about by the awareness of death. Then, in the late 1980s, a team of social psychologists (Greenberg, Pyszczynski, & Solomon, 1986) extended Becker’s theorizing and developed TMT, providing an empirical framework for Becker’s ideas. The first of two primary hypotheses proposed by this research team was that if culture functions to help individuals cope with death, cultural investments (or derogation of cultural belief systems that threaten one’s own) should increase when death is salient. The second hypothesis concerned self‐esteem striving: because self‐esteem is derived from the cultural system within which one is embedded, attempts to bolster self‐esteem by living up to cultural standards should also increase in response to the awareness of death. After a decade of research supporting TMT, the theory underwent some conceptual fine‐ tuning. The researchers observed that worldview defense and self‐esteem striving occurred reliably when individuals were reminded of their mortality and then distracted from it, or when death was subliminally primed. A dual‐defense model was proposed (Pyszczynski, Greenberg, & Solomon, 1999) to explain the time course of worldview defense and The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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self‐esteem striving, and critically, specifying that symbolic defenses concerning meaning and self‐esteem are most strongly manifested when thoughts of death are activated outside of focal awareness. Specifically, the dual‐defense model explains that when death thoughts are in focal awareness, individuals focus their cognitive resources on suppressing death thoughts. After suppression, worldview defense and self‐esteem striving occur to ameliorate the residue of unconscious death thoughts.

Overview of the TMHM Jamie Goldenberg and Jamie Arndt (2008) developed the TMHM to extend TMT to the health domain—a seemingly obvious but unexplored application of TMT. The TMHM begins with the assumption that health conditions have varying potential to make people think about death. The model then provides a formal framework for explaining, predicting, and ultimately intervening in behavioral health outcomes as a function of the accessibility of death thoughts. Integrating the insights from the dual‐defense model of TMT, the TMHM specifies that when mortality concerns are conscious, health decisions will be guided by the proximal motivational goal of removing death‐related thoughts from focal attention by reducing perceived vulnerability to the health threat. This process can entail efforts to better one’s health, but perceptions of vulnerability can also be reduced through less productive means, such as denial. In contrast, when mortality concerns are active but outside of focal attention, health relevant decisions are guided by the distal motivational goals of bolstering self‐esteem and maintaining one’s symbolic conception of self. Again, the implications can be both good and bad for health (e.g., sun protection or sun tanning)—but here motivations are focused on the value and meaning of the self.

Proximal TMHM Responses To examine responses to conscious death thoughts with experimental methods, participants are typically asked to contemplate their mortality, usually with open‐ended questions (e.g., “Jot down, as specifically as you can, what you think will happen to you as you physically die and once you are physically dead”), and immediately thereafter health responses are assessed. Perhaps not surprisingly, under such conditions people increase their intentions to exercise (Arndt, Schimel, & Goldenberg, 2003) and protect their skin from the sun (Routledge, Arndt, & Goldenberg, 2004) and also deny their vulnerability to health risk factors (Greenberg, Arndt, Simon, Pyszczynski, & Solomon, 2000). Moreover, supporting the assumption that such responses reflect efforts to reduce perceived vulnerability to the health threat, variables relevant to coping with the health threats have been shown to moderate health outcomes when thoughts of death are conscious. This has been demonstrated by measuring perceived vulnerability (Arndt, Cook, Goldenberg, & Cox, 2007), response efficacy (Cooper, Goldenberg, & Arndt, 2010), active coping strategies (Arndt, Routledge, & Goldenberg, 2006), and health optimism (Arndt et al., 2006; Cooper et al., 2010). These studies converge to indicate that when individuals maintain optimism about their health, perceive a health response as effective, or approach health situations with active coping strategies, they respond to conscious reminders of death with health promotion. For example, Cooper et al. (2010) found that when death was in conscious awareness, participants high in health optimism responded with increased intentions to screen for cancer, whereas individuals low in this resource decreased their intentions. Moreover, these moderating effects

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were not found when death thoughts were allowed to recede from consciousness, nor were they observed in a health domain unrelated to death (i.e., cavity protection), further supporting the assumption that these defenses are aimed at managing death‐related thoughts.

Distal TMHM Responses When mortality concerns are active but outside of focal attention, the TMHM predicts that health decisions will be guided by the implications of the behavior for meaning and self‐esteem rather than by the relevance for health. This research has examined the moderating potential of worldview beliefs (e.g., the effects of religious fundamentalism on prayer as a medical substitute; Vess, Arndt, Cox, Routledge, & Goldenberg, 2009) and the potential for physical inspection of the body (e.g., breast self‐exams) to undermine symbolic value (e.g., Goldenberg, Arndt, Hart, & Routledge, 2008). The lion’s share of research, however, has focused on esteem contingencies. For example, in contrast to the immediate increase in sun protection intentions when death thoughts were conscious, Routledge et al. (2004) found that the women in their study (who had previously indicated being tan as relevant to their self‐esteem) increased their intentions to tan when a delay followed the mortality salience prime and thoughts of death were presumably no longer in consciousness. Likewise, Arndt et al. (2003) not only found increased intentions to exercise immediately following the mortality reminder, but after a delay, participants who derived self‐ esteem from exercising also increased their intentions to exercise, whereas those low in fitness‐ contingent self‐esteem did not. Similarly, Hansen, Winzeler, and Topolinski (2010) first identified individuals who derived self‐esteem from smoking and then showed that they had more positive responses toward questions assessing smoking attitudes after viewing anti‐smoking packaging that reminded them of their mortality. In another series of studies, women restricted their consumption of a healthy but fattening food when mortality concerns were activated, a behavioral decision presumably driven by esteem concerns rather than health (men’s eating was unaffected; Goldenberg, Arndt, Hart, & Brown, 2005).

Interventional Potential of the TMHM The TMHM framework informs what health outcomes can be expected as a function of the consciousness of death thoughts, and it also provides a framework for intervening in such outcomes. One interventional direction is to use conscious death‐related thoughts to bolster the influence of more conventional health cognitions. For example, Cooper, Goldenberg, and Arndt (2014) found that presenting beach patrons with a communication that highlighted risk of death from cancer and simultaneously framed sun protection as effective resulted in increased sun protection intentions compared with when sun protection was framed as ineffective. This relationship was not observed in the non‐death condition, or when a delay followed the cancer prime. Given the finding that responses to nonconscious death thoughts depend on esteem contingencies and cultural beliefs, a complementary and more extensive wave of research targets the malleability of these bases so as to affect more productive health outcomes. In the domain of tanning, for example, Cox et al. (2009) found that participants given a mortality prime followed by a delay, who also read a fashion column touting “bronze is beautiful,” increased tanning intentions, whereas participants primed with “pale is pretty” decreased intentions to tan under the same conditions. These findings were replicated among beach patrons in South

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Florida who, in response to “pale is pretty” and nonconscious death thought activation, indicated that they would prefer sample lotions with higher SPFs. Along similar lines, when thoughts of mortality are accessible but not conscious, smokers have been shown to be more persuaded by health communications highlighting the social disadvantages of being a smoker (Arndt et al., 2009), and women are more persuaded by feedback targeting appearance rather than health consequences of tanning (Morris, Cooper, Goldenberg, Arndt, & Gibbons, 2014). People are also more willing to get a flu shot when it is endorsed by a cultural icon (e.g., a celebrity) than when it is endorsed by a doctor, but only when death thoughts are outside of conscious awareness (McCabe, Vail, Arndt, & Goldenberg, 2014). This pattern is reversed when death thoughts are conscious, with participants showing greater willingness to get a flu shot when a doctor endorses it. In a naturalistic setting, supermarket shoppers were found to make healthier purchases when they were exposed to a questionnaire that nonconsciously primed mortality and then asked them to visualize a prototypical healthy eater (McCabe et  al., 2015). These studies indicate that targeting social norms and bases of self‐ esteem can be effective routes to influencing health decisions and, importantly, that these motivations are most influential when death thoughts are activated but not conscious.

Implications and Conclusion Integrating insights about how people manage existential concerns into a model of health decision making, the TMHM connects previously unconsidered factors influencing health decision making. Research in health psychology has identified important health cognitions (e.g., perceived vulnerability) and concerns about esteem and social norms, but until the TMHM was developed, the impact of mortality awareness on health had not been investigated. The TMHM offers a framework for understanding how conscious and nonconscious death thoughts interact with these other variables and identifies when health risk and health promotion outcomes will occur. In addition, the TMHM offers insights that explain why some health promotion efforts fail. Consider the tactic of fear appeals. Though people may very well heed a cancer warning and put down their cigarette or lather on sunscreen, the TMHM predicts that these effects may be short lived. Once death thoughts have been suppressed, self‐esteem concerns become paramount, and smoking or tanning efforts may increase to the extent that a person derives self‐ esteem from looking cool as a smoker or appearing bronzed and beautiful. Since the inception of the TMHM in 2008, a growing body of support has accumulated. In addition, there is evidence documenting the effectiveness of an interventional approach informed by the TMHM. Going forward it will be important to investigate the durability of TMHM effects, as well as to continue to apply interventions utilizing the TMHM in naturalistic settings.

Author Biographies Patrick Boyd received a BA in psychology from the University of Southern California in 2007 and an MA in social psychology from San Francisco State University in 2012. He began pursuing his PhD at the University of South Florida in 2013. His research generally focuses on terror management theory, and within this paradigm he has explored how self‐worth that is contingent upon being healthful can globally predict behaviors in a variety of health contexts.

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Jamie L. Goldenberg is a professor of psychology at the University of South Florida. Her area of specialization is social psychology with a focus on health behavior and women’s health in particular. She is the developer of the terror management health model (TMHM) along with coauthor Arndt. Research on the TMHM has been funded for 10 years by the National Cancer Institute (NCI) and has resulted in dozens of publications, including a manuscript in Psychological Review depicting the model.

References Arndt, J., Cook, A., Goldenberg, J. L., & Cox, C. R. (2007). Cancer and the threat of death: The cognitive dynamics of death thought suppression and its impact on behavioral health intentions. Journal of Personality and Social Psychology, 92, 12–29. doi:10.1037/0022‐3514.92.1.12 Arndt, J., Cox, C. R., Goldenberg, J. L., Vess, M., Routledge, C., Cooper, D. P., & Cohen, F. (2009). Blowing in the (social) wind: Implications of extrinsic esteem contingencies for terror management and health. Journal of Personality and Social Psychology, 96, 1191–1205. doi:10.1037/a0015182 Arndt, J., Routledge, C., & Goldenberg, J. L. (2006). Predicting proximal health responses to reminders of death: The influence of coping style and health optimism. Psychology and Health, 21, 593–614. doi:10.1080/14768320500537662 Arndt, J., Schimel, J., & Goldenberg, J. L. (2003). Death can be good for your health: Fitness intentions as a proximal and distal defense against mortality salience. Journal of Applied Social Psychology, 33, 1726–1746. doi:10.1111/j.1559‐1816.2003.tb01972.x Becker, E. (1973). The denial of death. New York, NY: Free Press. Cooper, D. P., Goldenberg, J. L., & Arndt, J. (2010). Examining the terror management health model: The interactive effect of conscious death thought and health‐coping variables on decisions in potentially fatal health domains. Personality and Social Psychology Bulletin, 36, 937–946. doi:10.1177/0146167210370694 Cooper, D. P., Goldenberg, J. L., & Arndt, J. (2014). Perceived efficacy, conscious fear of death and intentions to tan: Not all fear appeals are created equal. British Journal of Health Psychology, 19, 1–15. doi:10.1111/bjhp.12019 Cox, C. R., Cooper, D. P., Vess, M., Arndt, J., Goldenberg, J. L., & Routledge, C. (2009). Bronze is beautiful but pale can be pretty: The effects of appearance standards and mortality salience on sun‐ tanning outcomes. Health Psychology, 28, 746–752. doi:10.1037/a0016388 Goldenberg, J. L., & Arndt, J. (2008). The implications of death for health: A terror management health model for behavioral health promotion. Psychological Review, 115, 1032–1053. doi:10.1037/ a0013326 Goldenberg, J. L., Arndt, J., Hart, J., & Brown, M. (2005). Dying to be thin: The effects of mortality salience and body mass index on restricted eating among women. Personality and Social Psychology Bulletin, 31, 1400–1412. doi:10.1177/0146167205277207 Goldenberg, J. L., Arndt, J., Hart, J., & Routledge, C. (2008). Uncovering an existential barrier to breast self‐exam behaviors. Journal of Experimental Social Psychology, 44, 260–274. doi:10.1016/j. jesp.2007.05.002 Greenberg, J., Arndt, J., Simon, L., Pyszczynski, T., & Solomon, S. (2000). Proximal and distal defenses in response to reminders of one’s mortality: Evidence of a temporal sequence. Personality and Social Psychology Bulletin, 26, 91–99. doi:10.1177/0146167200261009 Greenberg, J., Pyszczynski, T., & Solomon, S. (1986). The causes and consequences of a need for self‐ esteem: A terror management theory. In R. F. Baumeister (Ed.), Public self and private self (pp. 189–212). New York, NY: Springer‐Verlag. Hansen, J., Winzeler, S., & Topolinski, S. (2010). When the death makes you smoke: A terror management perspective on the effectiveness of cigarette on‐pack warnings. Journal of Experimental Social Psychology, 46, 226–228. doi:10.1016/j.jesp.2009.09.007

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McCabe, S., Arndt, J., Goldenberg, J. L., Vess, M., Vail, K. I., Gibbons, F. X., & Rogers, R. (2015). The effect of visualizing healthy eaters and mortality reminders on nutritious grocery purchases: An integrative terror management and prototype willingness analysis. Health Psychology, 34(3), 279–282. doi:10.1037/hea0000154 McCabe, S., Vail, K. E. I. I. I., Arndt, J., & Goldenberg, J. L. (2014). Hails from the crypt: A terror management health model investigation of health and celebrity endorsements. Personality and Social Psychology Bulletin, 40, 289–300. doi:10.1177/0146167213510745 Morris, K. L., Cooper, D. P., Goldenberg, J. L., Arndt, J., & Gibbons, F. X. (2014). Improving the efficacy of appearance‐based sun exposure intervention with the terror management health model. Psychology and Health, 29, 1245–1264. doi:10.1080/08870446.2014.922184 Pyszczynski, T., Greenberg, J., & Solomon, S. (1999). A dual‐process model of defense against conscious and unconscious death‐related thoughts: An extension of terror management theory. Psychological Review, 106, 835–845. doi:10.1037/0033‐295X.106.4.835 Routledge, C., Arndt, J., & Goldenberg, J. L. (2004). A time to tan: Proximal and distal effects of mortality salience on sun exposure intentions. Personality and Social Psychology Bulletin, 30, 1347– 1358. doi:10.1177/0146167204264056 Vess, M., Arndt, J., Cox, C. R., Routledge, C., & Goldenberg, J. L. (2009). Exploring the existential function of religion: The effect of religious fundamentalism and mortality salience on faith‐based medical refusals. Journal of Personality and Social Psychology, 97, 334–350. doi:10.1037/a0015545

Suggested Reading Goldenberg, J. L., & Arndt, J. (2008). The implications of death for health: A terror management health model for behavioral health promotion. Psychological Review, 115, 1032–1053. doi:10.1037/ a0013326 Pyszczynski, T., Greenberg, J., & Solomon, S. (1999). A dual‐process model of defense against conscious and unconscious death‐related thoughts: An extension of terror management theory. Psychological Review, 106, 835–845. doi:10.1037/0033‐295X.106.4.835 Routledge, C., Arndt, J., & Goldenberg, J. L. (2004). A time to tan: Proximal and distal effects of mortality salience on sun exposure intentions. Personality and Social Psychology Bulletin, 30, 1347– 1358. doi:10.1177/0146167204264056

The Role of Persuasion in Health‐ Related Attitude and Behavior Change Colin A. Zestcott and Jeff Stone Psychology Department, University of Arizona, Tucson, AZ, USA

Persuasion processes play a central role in the public’s health. Most health professionals, from doctors and nurses in a clinic to public health officials, rely on persuasion to prevent disease and illness and to gain compliance with health recommendations. Achieving these public health goals often requires getting people to change their health‐related attitudes and behaviors. The goal of this entry is to review the major techniques for persuading health attitude and behavior change and the psychological processes that determine how and when people make changes. For the purposes of this entry, we define persuasion as any attempt by an external source, or by the individual, to change a health‐related attitude, behavioral intention, or behavior. Research on persuasion and health tends to target two types of behavioral outcomes: changing behavioral intentions, defined as a person’s deliberate plan to adopt a specific health behavior, and changing the health behavior directly. Thus, the present entry will cover both types of behavioral outcomes. In addition, while many persuasive strategies are designed to change an individual’s behavior, theories in psychology such as the theory of planned behavior illuminate the importance of understanding how attitudes guide future intentions and behavior. Thus, focusing solely on behavior as a main outcome of persuasion techniques in a health context may fail to provide a thorough understanding of the underlying mechanisms, such as the importance of attitude change, when evaluating the effectiveness of health behavior interventions. In line with the broader literature, persuasion processes that target health attitude change tend to define attitudes as an evaluation of an object. According to Eagly and Chaiken (1993), an attitude is “a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor” (p. 1). However, contemporary research in psychology also delineates between explicit, or directly measured, attitudes and implicit, or indirectly measured, attitudes. While explicitly measured attitudes are primarily based on self‐report, most implicit attitudes are measured via reaction time tasks. There remains considerable debate about the psychological nature of implicit attitudes and the reliability and validity of measures

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like the Implicit Association Test. Nevertheless, because research on the role of implicit attitudes in health has greatly expanded in the past decade, we believe this entry would be incomplete if it did not also examine implicit attitude change. Finally, the literature suggests that the source of a persuasive message can stem from external or internal sources. External sources for persuasive messages can include mass media public health campaigns, warning labels on cigarette packs, or a doctor’s advice to get more exercise. In contrast, the individual may serve as the source of persuasion in that individuals may think or act in ways that motivate change in health‐related attitudes or behavior (Aronson, 1999). This entry will address external versus internal sources of persuasion separately.

Persuasion to Change Explicit Health‐Related Attitudes and Behaviors In the early 1950s, the experimental examination of persuasion began to focus on the ways in which external message sources, message recipients, and the content of the message contributed to attitude change. Many of the early studies examined communication strategies designed to change health‐related attitudes and behaviors.

Role of Fear Appeals in Persuasion The formal study of health persuasion in psychology began with research on fear to motivate health‐related attitude and behavior change. When facing a fear appeal, individuals are exposed to a fear‐inducing message that is perceived as a serious threat to one’s health or well‐being. Based on drive model theorizing (Hovland, Janis, & Kelly, 1953), the unpleasant emotional state of experiencing fear motivates individuals to alleviate or eliminate the source of the threat. Meta‐analyses covering a range of health issues such as exercise, sunscreen use, and cigarette smoking suggest that fear appeals can change an individual’s attitudes, behavioral intentions, and behaviors to be more favorable toward the health topic (e.g. Witte & Allen, 2000). According to the drive model, fear appeals effectively reduce defensiveness and reactance to a message if an individual is provided with information that moves them toward the intended outcome. Consequently, fear appeals are most effective at changing health‐related attitudes and behavior when recipients believe the threat of the message is severe and relevant (i.e., recipients believe they are vulnerable), the message provides an effective solution to reduce or eliminate the threat, and recipients perceive that they possess efficacy to mitigate the threat (Witte & Allen, 2000).

The Elaboration Likelihood Model While early health persuasion techniques focused on the role that motivation can play in changing health attitudes and behavior, subsequent research took a broader approach to the concept of motivation and at the same time explored the possibility that people could be persuaded to change their health attitudes and behaviors when their motivation was low. The development of a “dual‐process” approach to persuasion led to novel predictions regarding the factors that affect the persistence of health attitude and behavior change. One of the most influential dual‐process theories of persuasion is the elaboration likelihood model (ELM; Petty & Cacioppo, 1986). Based on the cognitive response approach to attitude change, the ELM stipulates that persuasion occurs along a continuum of elaboration or thinking. On the high end of the elaboration continuum, often referred to as the “central route,”

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persuasion occurs when people generate relatively many positive thoughts after thinking carefully about the content of the message. In order to think carefully about the message, people must be motivated and have the ability to think about the information in the communication. In addition, in order to assure that a highly thoughtful audience generates positive responses to the message, the content of the message must contain strong arguments; otherwise, the audience will counterargue and dismiss the information. Individual difference variables, such as increased involvement with an issue and increased need for cognition, are also associated with more central route processing (Petty & Cacioppo, 1986). Attitude change that follows from careful scrutiny of a strong message tends to endure over time and is more highly predictive of subsequent behavior, which is an important outcome when the goal is to alter future health outcomes. Research in the ELM tradition also shows that people need not think carefully about the message in order for a message to be persuasive. On the lower end of the elaboration continuum, where people are paying attention but not scrutinizing the arguments in the message, factors that work on the periphery of the message can cause attitude change. The “peripheral route” to persuasion occurs when people either do not care about the message content or care but are unable to process the information. Persuasion in the absence of much thinking occurs when the message and its delivery include simple cues (e.g. “he has a nice smile”; “she is really funny”) or heuristics (e.g. “he graduated from Harvard”; “she is a doctor”) that lead to a positive conclusion about the message without considering the arguments. Whereas new attitudes formed through the peripheral route do not last long nor predict future behavior, such outcomes may not be relevant if the goal is to achieve a momentary attitude and behavior shift, like when trying to sign someone up for a new health club membership. The principles stipulated by the ELM are useful for understanding multiple processes by which health attitudes and behavior change. For example, research on AIDS prevention by Bakker (1999) shows that adolescents high in need for cognition report more positive attitudes toward using condoms in the future if they are exposed to a clear and concise written brochure compared with a humorous cartoon brochure with the same information. However, this pattern reversed for those low in need for cognition such that their knowledge and behavioral intentions to use condoms increased when viewing the cartoon brochure rather than the written brochure. The ELM also provides an explanation for why health campaigns show greater success when they tailor and target their messages to specific audience characteristics. Tailoring uses information about an individual to determine the content and delivery that will gain the most attention. Targeting refers to customizing messages for broader population subgroups that share certain characteristics. Consistent with the message‐matching application of the ELM, research shows that tailored messages increase positive thoughts and personal connections about weight‐loss information (Kreuter, Bull, Clark, & Oswald, 1999).

Self‐Affirmation One limitation to using threat, such as fear appeals, to motivate health behavior change is that in some situations, people cope with the threat by denying that the health issue applies to them and avoiding the issue altogether. One way to reduce denial and avoidance responses is by providing a self‐affirming buffer. According to self‐affirmation theory, individuals can bolster the positive integrity of the self‐system by reflecting upon their most cherished positive values, actions, or attributes (Steele, 1988). Consequently, affirming values boosts the psychological resources needed to manage a threat and leads to a separation (i.e. a buffer) of the threat from the self.

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Studies on self‐affirmation and health show that participants who self‐affirm, typically in the form of reflecting on important values, are more likely to change their health‐related attitudes and behavior (see Jessop & Harris, this volume). For example, females who suntan and self‐ affirm report more positive attitudes toward sunscreen and are more likely to acquire a sunscreen sample than those who do not self‐affirm after reading a pamphlet about the risks of sun exposure (Jessop, Simmons, & Sparks, 2009). Other studies show that individuals who self‐ affirm are more likely to accept threatening health risk information and are less likely to derogate a message than those who do not self‐affirm (Jessop et al., 2009).

Persuasion to Change Implicit Health‐Related Attitudes Research over the past two decades in psychology has examined the ways in which implicit, or introspectively inaccessible constructs, can be measured to assess topics such as prejudice, stereotyping, and self‐esteem. While most research in the health domain assesses explicit attitudes, other research suggests that self‐presentational concerns may cause a dissociation between an individual’s implicit and explicit attitudes (e.g. Fazio & Dunton, 1997). For example, an individual who enjoys suntanning may express positive explicit attitudes toward sunscreen to others yet still hold negative implicit attitudes toward sunscreen. Consequently, implicit attitudes may be a better predictor of health‐related attitudes compared with explicit attitudes. The most common way to examine implicit attitudes an individual may hold is through indirect measures that assess reaction times and categorization biases. Using reaction time procedures, research shows that implicit attitudes may be more predictive of behavior than explicit attitudes. For example, researched by Sherman, Chassin, Presson, Seo, and Macy (2009) shows that implicit attitudes toward cigarette smoking are more predictive of later smoking than explicit attitudes. If implicit attitudes are more predictive of health behavior than explicit attitudes, it is important to determine the processes by which implicit attitudes can change. For example, persuasive campaigns that describe the negative health effects of drugs like marijuana and tobacco induce more negative implicit attitudes toward the drugs by creating stronger associations between drug‐related images and negative words (Czyzewska & Ginsburg, 2007). The context in which information is presented may also affect implicit attitudes. Sherman, Rose, Koch, Presson, and Chassin (2003) show that individuals express more negative implicit attitudes toward smoking when the presentation highlights informational aspects of smoking (e.g., pictures of cigarette packs) compared with sensory aspects (e.g., a cigarette burning in an ashtray). Directly changing associative pairings can also affect implicit attitudes. For example, participants in an evaluative conditioning paradigm who paired images of unhealthy snack foods with aversive health consequences were subsequently faster to associate negative words with snack‐related words and positive words with fruit‐related words (Hollands, Prestwich, & Marteau, 2011).

Self‐Persuasion to Change Health‐Related Attitudes and Behavior A parallel tradition in the study of persuasion in health focuses on the processes by which people convince themselves to change their health‐related attitudes and behavior. According to Aronson (1999), self‐persuasion occurs when individuals take a certain course of action and, as a result, change their health‐related attitudes and future behaviors. Thus, the primary source

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of influence to pursue health‐related attitude or behavior change stems from the individual rather than an external source. Researchers interested in the health changes that people make for themselves have primarily focused on the role of cognitive dissonance and self‐perception processes.

Cognitive Dissonance Theory Cognitive dissonance theory stipulates that individuals feel an unpleasant state of tension when they possess two inconsistent cognitions, which motivates them to reduce or eliminate the inconsistency (Festinger, 1957). For example, when people who smoke cigarettes are exposed to information regarding the negative health effects of smoking, the inconsistency between their smoking behavior and knowledge of the health risks can induce a state of psychological discomfort, which they become motivated to reduce. Smokers may alleviate the inconsistency in a number of ways, such as changing the behavioral cognition (e.g., quitting smoking), changing their knowledge about the risks of smoking (e.g., challenging the research on the relationship between smoking and cancer), or by recruiting new cognitions that place the inconsistency in a larger framework of consistency (e.g., “I would quit, but then I will gain weight, and that is worse”). Thus, the key to successful intervention rests in channeling dissonance reduction toward the targeted health attitude or behavior change. One way to use cognitive dissonance to motivate people to change their attitudes toward unhealthy behavior is to have them argue against performing the behavior that they wish to change. A well‐established example of this approach involves women’s perceptions of their bodies and the thin ideal imposed by many societies. Stice and colleagues (for a review, see Stice, Shaw, Becker, & Rohde, 2008) predict that women who voluntarily adopt an anti‐thin position in a group should perceive inconsistency between their positive attitude toward the thin ideal and their present behavior of taking an anti‐thin stance during the small group discussion. Several studies by Stice and colleagues show that following the intervention, women reported more positive body images, reduction in the thin‐ideal internalization and body dissatisfaction, less negative affect, less bulimic symptoms, and less eating disorder risk factors compared with control conditions. Another way to use dissonance to motivate change is through an act of hypocrisy. In the hypocrisy approach, individuals first advocate for a health behavior about which they hold a positive attitude. Dissonance occurs when they are then reminded of times they failed to perform the advocated behavior in the past. The inconsistency between their advocacy about the importance of the target health behavior, and their past failures to perform the target act, makes people aware that they have acted in an insincere fashion. In order to reduce the dissonance and restore their self‐integrity, people become motivated to change their own health behavior so they can bring it into line with the good health advice they were willing to give others. In studies designed to motivate sexually active college students to practice safer sex, individuals who videotaped a speech about the importance of using of condoms, and were then reminded of past failures to use condoms during sexual intercourse, were more likely to purchase condoms at the completion of the study, compared with control conditions (Stone, Aronson, Crain, Winslow, & Fried, 1994).

Self‐Perception Processes Whereas dissonance processes change attitudes and behavior by getting people to act in ways that causes psychological discomfort, it is also possible to promote more healthy attitudes and

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behaviors by getting people to act in ways that induce them to draw a new conclusion about their own behavior. According to self‐perception theory, when people do not already hold an attitude toward an object, they can infer their attitude through an examination of their own behavioral interactions with the object (Bem, 1972). People then develop new positive attitudes toward the topic, which can subsequently guide new behavior. One way in which self‐perception can cause health‐related attitude and behavior change is through the foot‐in‐the‐door (FITD) technique. The FITD predicts that when an individual agrees to a request that is easy to comply with, they are more likely to comply with a second, more difficult request (e.g., Freedman & Fraser, 1966). As a result of complying with the second request, people infer that their behavior must reflect a positive attitude toward the issue or object. In the domain of health, studies show that individuals who complete a questionnaire about organ donation express greater willingness to be an organ donor compared with those that do not complete the initial questionnaire (e.g. Carducci & Deuser, 1984).

Conclusion and Future Considerations The research in this entry indicates that there are a number of mechanisms that effectively persuade people to adopt new attitudes and behaviors that support good health. However, it is important to note that few of the findings above show evidence for changes in health attitudes and behaviors that last beyond the immediate experimental context; with a few notable exceptions (e.g., Stice et al., 2008), most of the studies in the health persuasion literature focus more on questions about basic process than on the critical question of what is necessary to create lasting attitude and behavior change. Thus, at present, the utility of the health persuasion literature for use by public health officials, healthcare providers, and educational institutions is not well understood. The question of when and how health persuasion processes lead people to maintain new changes over time deserves more empirical attention.

Author Biographies Colin A. Zestcott, MA, is a doctoral graduate student in the Department of Psychology at the University of Arizona. His research interests include stereotyping and prejudice, implicit attitudes, and the function of metaphor in social cognition. Jeff Stone, PhD, is a professor of psychology in the College of Science, and professor of psychiatry in the College of Medicine, at the University of Arizona. Dr. Stone’s research investigates mechanisms of health attitude and behavior change, prejudice and stereotyping, stereotype threat processes, and the reduction of intergroup bias. Dr. Stone’s current NIH‐funded research focuses on documenting and reducing the implicit bias processes that contribute directly to ethnic and racial disparities in health.

References Aronson, E. (1999). The power of self‐persuasion. American Psychologist, 54, 875–884. doi:10.1037/ h0088188 Bakker, A. B. (1999). Persuasive communication about AIDS prevention: Need for cognition determines the impact of message format. AIDS Education and Prevention, 11, 150–162.

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Bem, D. J. (1972). Self‐perception theory. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 6, pp. 1–62). New York, NY: Academia. Carducci, B. J., & Deuser, F. S. (1984). The foot‐in‐the donor technique: Initial request and organ donation. Basic and Applied Social Psychology, 5, 75–81. doi:10.1207/s15324834basp0501_S Czyzewska, M., & Ginsburg, H. J. (2007). Explicit and implicit effects of anti‐marijuana and anti‐tobacco TV advertisements. Addictive Behavior, 32, 114–127. doi:10.1016/j.addbeh.2006.03.025 Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Orlando, FL: Harcourt Brace Jovanovich College Publishers. Fazio, R. H., & Dunton, B. C. (1997). Categorization by race: The impact of automatic and controlled components of racial prejudice. Journal of Experimental Social Psychology, 33, 451–470. doi:10.1006/ jesp.1997.1330 Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press. Freedman, J. L., & Fraser, S. C. (1966). Compliance without pressure: The foot‐in‐the‐door technique. Journal of Personality and Social Psychology, 4, 195–202. doi:10.1037/h0023552 Hollands, G. J., Prestwich, A., & Marteau, T. M. (2011). Using aversive images to enhance healthy food choices and implicit attitudes: An experimental test of evaluative conditioning. Health Psychology, 30, 195–203. doi:10.1037/a0022261 Hovland, C., Janis, I., & Kelly, H. (1953). Communication and persuasion. New Haven, CT: Yale University Press. Jessop, D. C., Simmonds, L. V., & Sparks, P. (2009). Motivational and behavioral consequences of self‐affirmation interventions: A study of sunscreen use among women. Psychology and Health, 24, 529–544. doi:10.1080/08870440801930320 Kreuter, M. W., Bull, F. C., Clark, E. M., & Oswald, D. L. (1999). Understanding how people process health information: A comparison of tailored and nontailored weight‐loss materials. Health Psychology, 18, 487–494. doi:10.1037/0278‐6133.18.5.487 Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In L. Berkiwitz (Ed.), Advances in experimental social psychology (Vol. 19, pp. 123–205). New York, NY: Academic Press. Sherman, S. J., Chassin, L., Presson, C., Seo, D., & Macy, J. (2009). The intergenerational transmission of implicit and explicit attitudes toward smoking: Predicting adolescent smoking initiation. Journal of Experimental Social Psychology, 45, 313–319. doi:10.1016/j.jesp.2008.09.012 Sherman, S. J., Rose, J. S., Koch, K., Presson, C. C., & Chassin, L. (2003). Implicit and explicit attitudes toward cigarette smoking: The effects of context and motivation. Journal of Social and Clinical Psychology, 23, 13–39. doi:10.1521/jscp.22.1.13.22/66 Steele, C. M. (1988). The psychology of self‐affirmation: Sustaining the integrity of the self. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 21, pp. 261–302). New York, NY: Academic Press. Stice, E., Shaw, H., Becker, C. B., & Rohde, P. (2008). Dissonance‐based interventions for the prevention of eating disorders: Using persuasion principles to promote health. Prevention Science, 9, 114– 128. doi:10.1007/s11121‐008‐0093‐x Stone, J., Aronson, E., Crain, A. L., Winslow, M. P., & Fried, C. B. (1994). Inducing hypocrisy as a means of encouraging young adults to use condoms. Personality and Social Psychology Bulletin, 20, 116–128. doi:10.1177/0146167294201012 Witte, K., & Allen, M. (2000). A meta‐analysis of fear appeals: Implications for effective public health campaigns. Health Education and Behavior, 27, 591–615. doi:10.1177/109019810002700506

Suggested Reading Mongeau, P. A. (2013). Fear appeals. In J. P. Dillard & L. Shen (Eds.), The SAGE handbook of persuasion (2nd ed., pp. 184–199). Thousan Oaks, CA: SAGE Publications.

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Petty, R. E., Barden, J., & Wheeler, S. C. (2009). The elaboration likelihood model of persuasion: Health promotions that yield sustained behavioral change. In R. J. DiClemente, R. A. Crosby, & M. Kegler (Eds.), Emerging theories in health promotion practice and research (2nd ed., pp. 185–214). San Francisco, CA: Jossey‐Bass. Stone, J., & Focella, E. (2011). Hypocrisy, dissonance and the self‐regulation processes that improve health. Self and Identity, 10, 295–303. doi:10.1080/15298868.2010.538550

The Theories of Reasoned Action and Planned Behavior Christina Nisson and Allison Earl Department of Psychology, University of Michigan, Ann Arbor, MI, USA

The ability to understand the fundamental causes of public health problems and develop interventions to address those problems is important to a range of researchers involved in the study of health psychology. Two of the most widely tested models of this nature are the theory of reasoned action (Ajzen & Fishbein, 2005; Fishbein & Ajzen, 1975) and the theory of planned behavior (Ajzen, 1991; Ajzen & Fishbein, 1980, 2005). The theory of reasoned action posits that behavior is a function of behavioral intentions that are, in turn, a function of attitudes and subjective norms. The theory of planned behavior took the components of the theory of reasoned action but added perceived behavioral control as an additional factor predicting both behavioral intentions and behavior. In recent years, these models have been collapsed under the umbrella of the reasoned action approach.

Description of the Models Theory of Reasoned Action Both the theory of reasoned action and the theory of planned behavior developed out of a theoretical tradition that considered attitudes as a major influence on human behavior (e.g., Smith, 1932; Stagner, 1942). However, other contradictory research emerged suggesting that the link between attitudes and behavior was tenuous at best (e.g., Corey, 1937; LaPiere, 1934), with some researchers even calling for abandonment of the attitude construct altogether (Wicker, 1969). However, Fishbein and Ajzen (1974) noted that the inconsistency between attitudes and behaviors could be improved by measuring attitudes and behaviors at the same level of specificity. Thus, rather than using global attitudes (e.g., attitudes toward condoms) to predict specific behaviors (e.g., condom use during the next sexual encounter), Fishbein and Ajzen (1975) posited that researchers should focus on the specific antecedents of The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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specific behaviors (e.g., attitudes toward condom use during the next sexual encounter predicting condom use during the next sexual encounter). Furthermore, Fishbein and Ajzen (1975) posited that the link between attitudes and behavior might best be explained by an appeal to specific behavioral intentions. That is, attitudes about performing a behavior would predict behavioral intentions to enact the behavior, which would in turn predict behavior. In addition, because one may also take into account how others perceive one’s actions, subjective norms about how to behave were also included as a predictor of behavioral intentions. Behavioral intentions were then identified as the best predictor of behavior (Fishbein & Ajzen, 1975). In this model, attitudes toward the behavior were defined as an aggregate of readily accessible or salient beliefs about the likely outcomes of performing the target behavior, whereas subjective norms were defined as the perceived social pressure to perform or not perform the target behavior and behavioral intentions were defined as the perceived likelihood of performing the target behavior.

Theory of Planned Behavior One factor that may limit the translation of intentions to behavior is one’s ability to enact the desired behavior. As such, the theory of planned behavior updated the theory of reasoned action to include a component of perceived behavioral control, which specifies one’s perceived ability to enact the target behavior. In fact, perceived behavioral control was added to the model to extend its applicability beyond purely volitional behaviors. Prior to this addition, the model was relatively unsuccessful at predicting behaviors that were not mainly under volitional control. Thus, the theory of planned behavior proposed that the primary determinants of behavior are an individual’s behavioral intention and perceived behavioral control. As such, according to the theory of planned behavior, behavioral intentions are framed as the motivational component of the model, or one’s conscious plan or decision to exert effort to perform the target behavior. Behavioral intentions are determined by attitudes toward the behavior (e.g., whether engaging in the behavior is evaluated to be positive or negative), subjective norms surrounding the behavior (e.g., beliefs about whether others think one should engage in the behavior), and perceived behavioral control (e.g., beliefs regarding how easy or difficult performing the behavior is likely to be). In this context, perceived behavioral control reflects both external factors (e.g., availability of time or money, social support) and internal factors (e.g., ability, skill information). In other words, low perceived behavioral control exists in situations in which performance of the target behavior is dependent upon a number of other factors, which may or may not be within an individual’s control. For example, one may experience low perceived behavioral control for the target behavior of eating healthy if constraints such as time, affordability, access, and temptation are viewed as obstacles to engage in the behavior despite strong intentions. As a result, the greater the perceived behavioral control for a target behavior, the stronger the predictive power of behavioral intentions for that behavior.

Reasoned Action Approach In recent years, the theory of reasoned action and the theory of planned behavior have fallen under the umbrella of the reasoned action approach (Ajzen & Albarracín, 2007; Fishbein & Ajzen, 2010). The reasoned action approach encompasses all of the components proposed by earlier models (e.g., attitudes toward the behavior, subjective norms, perceived behavioral control, and intentions) while also including additional factors such as actual control, defined

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as skills, abilities, and environmental factors that influence one’s ability to enact a target behavior. As such, the theory of reasoned action and the theory of planned behavior will be referred to jointly as the reasoned action approach throughout this entry.

Ability to Predict Health Intentions and Behaviors A series of meta‐analyses and reviews examining the application of the reasoned action approach to health behaviors have now been published, including ones focusing on multiple health domains (e.g., Armitage & Connor, 1999; Godin & Kok, 1996; McEachan, Conner, Taylor, & Lawton, 2011; Webb, Joseph, Yardley, & Michie, 2010) and ones focusing on specific behaviors (e.g., exercise; Hagger, Chatzisarantis, & Biddle, 2002; condom use; Albarracín, Johnson, Fishbein, & Muellerleile, 2001). Such reviews have shown the reasoned action approach to be a relatively successful predictor of health intentions and behavior, explaining 32–44% of the variance in intentions and 15–41% of the variance in behavior. One important moderator of the predictive ability of the reasoned action approach is behavior type. For instance, the reasoned action approach appears to be particularly successful in the prediction of diet and exercise behaviors, as well as condom use. In a recent comprehensive meta‐analysis, McEachan et al. (2011) found that the reasoned action approach was able to explain 21 and 24% of the variance in dietary and exercise behaviors, respectively. At the same time, the reasoned action approach appears to be less successful at explaining the variance in addictive and clinical screening/detection behaviors. The same meta‐analysis revealed that the reasoned action approach was able to explain only 15 and 14% of the variance in such behaviors, respectively. It is not surprising that the reasoned action approach is better at predicting some behaviors than others. Looking at the categories in which the reasoned action approach is more versus less successful in predicting behavior, it follows that the model is less predictive of addictive and clinical screening behaviors, as these behaviors are likely to be low in perceived and actual behavior control, affected not only by personal motivation and desire but also other factors (e.g., biological aspects of addiction, access to treatment and health services, financial resources to engage in screening behaviors). Along with behavior type, there are also two important methodological moderators to consider when examining the ability of the reasoned action approach to predict health behavior: length of follow‐up and method of measurement (objective vs. self‐report). The amount of time between measurement of reasoned action approach variables and assessment of behavior is an inherent limiting condition of the reasoned action approach. Ajzen and Fishbein have repeatedly stressed that the measurement of behavior should occur as close as possible to the measurement of the reasoned action approach variables, as the model is only able to predict behavior to the extent that the reasoned action approach variables remain consistent from the time of measurement to the time of assessment of behavior (Ajzen, 1985; Ajzen & Fishbein, 1980). Given that it is less likely for the reasoned action approach variables to remain stable as the length of follow‐up increases, it is not surprising that the overall ability of the model to predict health behavior decreases over time (Albarracín et al., 2001; McEachan et al., 2011).1 For dietary and exercise behaviors, McEachan et al. (2011) found that the reasoned action approach was only able to predict 18% (dietary) and 16% (exercise) of the variance in behavior when follow‐up occurred more than 5 weeks after initial measurement, compared with 23% (dietary) and 32% (exercise) when follow‐up was less than 5 weeks after initial measurement. Like many models of human behavior, a long‐standing concern regarding the ability of the reasoned action approach to predict health behavior, and behavior in general, is its

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frequent reliance on self‐reported measures of behavior. A primary concern regarding reliance on self‐reported behavior is the vulnerability of self‐report measures to social desirability bias, or the tendency to over‐report desirable behavior and underreport undesirable behavior (Edwards, 1953; Schroder, Carey, & Vanable, 2003). This may be particularly problematic for the prediction of health behavior given the tendency for many health behaviors to be viewed as either desirable (e.g., healthy eating, exercise, condom use) or undesirable (e.g., drug and alcohol use). Given the prominent role of attitudes in the reasoned action approach, individuals’ inclination to maintain attitudinal and behavioral consistency (Hessing, Elffers, & Weigel, 1988; Kiesler, 1971) is also of particular concern. Thus, the model may overstate the intention–behavior relation due to individual’s desire to maintain consistency in their reported intentions and behaviors. Two large meta‐analyses support this concern. Armitage and Conner (1999) found that reasoned action approach explained 31% of the variance in self‐reported behavior but only 20% of the variance when behavior was directly observed. Specific to health behavior, McEachan et al. (2011) found that reasoned action approach variables explained 26% of the variance in self‐reported physical activity but only 12% of the variance in objectively measured physical activity. Although the reasoned action approach is able to predict a significant amount of variance in behavior regardless of length of follow‐up or method of measurement, it consistently shows greater efficacy in situations with short follow‐up periods and self‐reported measurement of behavior.

Interventions to Change Behavior Although the reasoned action approach was originally presented as a tool to “understand” and “predict” behavior (Ajzen & Fishbein, 1980), there is growing interest in the theory’s possible utility in designing behavioral interventions. Ajzen and Fishbein (2005) agree that successful modification of predictors specified by the reasoned action approach should lead to a corresponding change in behavior. McEachan et al. (2011) found encouraging evidence for the model’s ability to identify important targets for interventions to change health behaviors. Although their meta‐analysis of health behaviors found that past behavior exhibited the strongest correlation with current behavior (mean ρ  =  0.50),2 intention was also a strong predictor of behavior (mean ρ = 0.43) and remained so after controlling for past behavior. From a behavioral intervention perspective, intentions are more relevant than past behavior as they are susceptible to change, whereas past behavior is not. Thus, it is encouraging that intentions remain a strong predictor of behavior even when controlling for past behavior (McEachan et al., 2011). In 2002, Hardeman and colleagues published a review of behavior change interventions using the reasoned action approach (Hardeman et al., 2002). The review identified 21 interventions targeting health‐related behaviors, including smoking cessation, exercise, and testicular self‐examination. Of the 21 interventions identified, only 10 actually used the reasoned action approach to develop the intervention; the remaining 11 interventions simply used the reasoned action approach for measurement and therefore should not be considered a valid assessment of the theory’s ability to help change behavior. It is important to note that even the 10 theory‐based interventions often focused on selected reasoned action approach components only. Furthermore, the descriptions of the interventions were limited, and it was often difficult to assess the specific manner in which the reasoned action approach informed the intervention design. Nonetheless, among the 10 theory‐driven interventions, 4 (40%) reported

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a positive change in behavioral intentions as a result of the intervention, 3 (30%) reported no difference in behavioral intentions, and 3 (30%) did not measure intentions. In terms of behavior change, three (30%) of the interventions reported a positive change in behavior as a result of the intervention, two (20%) reported no difference in behavior, and five (50%) did not measure behavior. In the domain of condom use, Albarracín and colleagues (Albarracín et al., 2005) examined the efficacy of the reasoned action approach by examining the link between intervention components and change in model constructs, as well as the links with behavior change. In line with the reasoned action approach, attitudinal arguments about condom use did indeed change attitudes toward condom use, which in turn led to changes in condom use. In additional, self‐management training led to changes in perceived behavioral control, which in turn facilitated change in condom use (Albarracín et al., 2005). Although it may be argued that the mixed results of the Hardeman et al. (2002) review suggest that the reasoned action approach is far better suited to predict behavior than to help change it, it is important to remember that there were only 10 studies in which the reasoned action approach was used to develop the intervention, the interventions often focused only on selected reasoned action approach components, the descriptions of the interventions were limited, and evidence about mediation of effects by reasoned action approach components was rare. McEachan and colleagues’ (2011) finding that behavioral intentions remain a significant predictor of behavior even when controlling for past behavior, combined with evidence indicating that a change in individuals’ beliefs can bring about changes in attitudes, intentions, and behavior (Ajzen & Fishbein, 2005), lends support to the potential for the reasoned action approach to inform behavioral change interventions.

Author Biographies Christina Nisson received her BA in psychology and economics from Cornell University in 2009 and her MS and PhD in social psychology from the University of Michigan in 2011 and 2014, respectively. Broadly, Christina is interested in applying social psychological research to health messaging and health behaviors. Her primary line of research examines the role of health message characteristics (e.g., approach vs. avoidance goals; action vs. inaction orientation) on message processing and healthy eating behaviors. Allison Earl received bachelor’s degrees in anthropology and psychology from the University of Florida and a PhD in psychology from the University of Illinois. She is an assistant professor of social psychology at the University of Michigan and director of the Health, Attitudes, and Influence Lab (HAILab). Dr. Earl’s research program focuses on understanding what we pay attention to and why and how to best increase attention to health promotion programs, particularly for high‐risk audiences.

Notes 1 Randall and Wolff (1994) conducted a meta‐analysis of 98 studies and found no significant relationship between length of follow‐up and the strength of the intention–behavior relationship. It is important to note, however, that this meta‐analysis did not focus specifically on health behavior and only looked at the correlation between intentions and behavior, not the overall predictive power of the model. 2 McEachan et al. (2011) used mean true score correlations corrected for sampling and measurement error (mean ρ).

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References Ajzen, I. (1985). From intention to actions: A theory of planned behavior. In J. Kuhl & J. Bechmann (Eds.), Action control: From cognitions to behavior (pp. 11–39). New York, NY: Springer‐Verlag. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice‐Hall. Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracín, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp. 173–222). Mahwah, NJ: Lawrence Erlbaum Associates. Albarracín, D., Gillette, J., Earl, A., Glasman, L. R., Durantini, M. R., & Ho, M. H. (2005). A test of major assumptions about behavior change: A comprehensive look at HIV prevention interventions since the beginning of the epidemic. Psychological Bulletin, 131, 856–897. Albarracín, D., Johnson, B. T., Fishbein, M., & Muellerleile, P. A. (2001). Theories of reasoned action and planned behavior as models of condom use: A meta‐analysis. Psychological Bulletin, 127, 142–161. Armitage, C. J., & Conner, M. (1999). Distinguishing perceptions of control from self‐efficacy: Predicting consumption of a low‐fat diet using the theory of planned behavior. Journal of Applied Social Psychology, 29, 72–90. Corey, S. M. (1937). Professed attitudes and actual behavior. Journal of Educational Psychology, 28, 271–280. Edwards, A. L. (1953). The relationship between the judged desirability of a trait and the probability that the trait will be endorsed. Journal of Applied Psychology, 37(2), 90–93. Fishbein, M., & Ajzen, I. (1974). Attitudes towards objects as predictors of single and multiple behavioral criteria. Psychological Review, 81, 59–74. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison Wesley. Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. New York, NY: Taylor & Francis. Godin, G., & Kok, G. (1996). The theory of planned behavior: A review of its applications to health‐ related behaviors. American Journal of Health Promotion, 11, 87–98. Hagger, M. S., Chatzisarantis, N. L. D., & Biddle, S. J. H. (2002). A meta‐analytic review of the theories of reasoned action and planned behavior in physical activity: Predictive validity and the contribution of additional variables. Journal of Sport and Exercise Psychology, 24, 3–32. Hardeman, W., Johnston, M., Johnston, D., Bonetti, D., Wareham, N., & Kinmonth, A. L. (2002). Application of the theory of planned behavior in behavior change interventions: A systematic review. Psychology & Health, 17, 123–158. Hessing, D. J., Elffers, H., & Weigel, R. H. (1988). Exploring the limits of self‐reports and reasoned action: An investigation of the psychology of tax evasion behavior. Journal of Personality and Social Psychology, 54, 405–413. Kiesler, C. A. (1971). The psychology of commitment: Experiments linking behavior to belief. New York, NY: Academic Press. LaPiere, R. T. (1934). Attitudes vs. actions. Social Forces, 13, 230–237. McEachan, R. R. C., Conner, M., Taylor, N. J., & Lawton, R. J. (2011). Prospective prediction of health‐related behaviours with the theory of planned behaviour: A meta‐analysis. Health Psychology Review, 5(2), 97–144. Randall, D. M., & Wolff, J. A. (1994). The time interval in the intention‐behaviour relationship: Meta‐ analysis. British Journal of Social Psychology, 33(4), 405–418. Schroder, K. E. E., Carey, M. P., & Vanable, P. A. (2003). Methodological challenges in research on sexual risk behavior: II. Accuracy of self‐reports. Annals of Behavioral Medicine, 26, 104–123.

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Smith, H. N. (1932). A scale for measuring attitudes about prohibition. Journal of Abnormal and Social Psychology, 26, 429–437. Stagner, R. (1942). Some factors related to attitude toward war, 1938. Journal of Social Psychology, 16, 131–142. Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010). Using the internet to promote health behavior change: A systematic review and meta‐analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 10, e4. Wicker, A. W. (1969). Attitudes versus actions: The relationship of verbal and overt behavioral responses to attitude objects. Journal of Social Issues, 25, 41–78.

Suggested Reading Ajzen, I., & Albarracín, D. (2007). Predicting and changing behavior: A reasoned action approach. In I. Ajzen, D. Albarracín, & R. Hornik (Eds.), Prediction and change of health behavior: Applying the reasoned action approach (pp. 3–21). Mahwah, NJ: Lawrence Erlbaum Associates. Albarracín, D., Johnson, B. T., Fishbein, M., & Muellerleile, P. A. (2001). Theories of reasoned action and planned behavior as models of condom use: A meta‐analysis. Psychological Bulletin, 127, 142–161. Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. New York, NY: Taylor & Francis. McEachan, R. R. C., Conner, M., Taylor, N. J., & Lawton, R. J. (2011). Prospective prediction of health‐related behaviours with the theory of planned behaviour: A meta‐analysis. Health Psychology Review, 5(2), 97–144.

Thriving in Life Margaret L. Kern1 and Jessie Sun1,2 Center for Positive Psychology, Graduate School of Education, University of Melbourne, Parkville, VIC, Australia 2 Department of Psychology, University of California, Davis, CA, USA 1

Introduction People experience life in a myriad of ways. For some, life is an adventure. They are optimistic, socially engaged, physically healthy, and active participants. For others, life is a daily grind. Work consumes a large portion of time and is simply something to endure each day. And for others, life is a struggle, with physical or psychological disability making it a challenge to function in daily life. Considerable individual and societal resources are spent addressing the needs of those afflicted by physical and mental illness and disability. Medicine has made great strides in restoring normal function for those who suffer. Research and practice within health psychology has successfully identified ways to alleviate distress, helping patients to enjoy the highest quality of life possible, within the constraints of physical or mental disability. But what about the majority of people who are not distressed, but also not functioning as well as they could be? A growing body of literature suggests that individuals who proactively approach life experience lower rates of physical and mental disabilities. It is shortsighted to take action only when symptoms appear. Rather, a proactive approach that builds internal and external resources over time will allow as many people as possible to not only survive but to truly thrive throughout life.

Thriving Defined Thriving can be defined as a state at a single point in time or as a trajectory over time. As a state, thriving refers to current success or prosperity. As a trajectory, thriving refers to successful progression toward a desired goal or outcome, despite obstacles that might occur along the

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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way. The definition of success, prosperity, and desirable outcomes depends on the field of study. The traditional medical perspective defines success in terms of biological function. Thriving can be observed objectively based on organic characteristics. “Failure to thrive” describes infants and children who do not develop as expected and fail to gain and maintain weight. The cause may be physical or emotional, but early diagnosis is important before permanent damage to the child occurs. Failure to thrive is also used in gerontology to define progressive physical and cognitive decline. In contrast, thriving is a normal state of health with no detectable signs of disease. The traditional psychological perspective similarly defines success in terms of the lack of disability and disease but focuses on mental and social health rather than physical conditions. It is more subjective in nature—an organic cause of suffering might not exist, but the individual fails to function well in society. Over the past few decades, there has been growing interest in a positive psychology perspective, which similarly focuses on mental and social health but argues that success is more than the lack of disease and disability. Thriving involves high levels of positive emotions, satisfaction with one’s life, positive social relationships, and the presence of other positive constructs such as a sense of meaning, mastery, personal growth, engagement in life, and accomplishment. From an economic perspective, success is defined in terms of financial outcomes, such as income, gross domestic product, or profit. A thriving business is growing and building financial capital. A thriving economy has low levels of unemployment and performs strongly in the international market. In the 1940s, the World Health Organization defined health as a complete state of physical, mental, and social well‐being, not only the absence of disease and disability. This perhaps best captures the health psychology perspective, which includes physical, mental, and social aspects. Thriving is defined in terms of high quality of life and active engagement in life, despite the possible presence of physical and/or mental disorder. The aim of thriving is to allow a person to live as long as possible with the least physical, psychological, and social disabilities possible, focusing on quality rather than quantity (i.e., length) of life alone. This provides benefit to both the individual (by reducing suffering) and society (by reducing economic burden on the system). Gerontology distinguishes among pathological (where there is accelerated physical, cognitive, and functional decline), usual (general breakdown in the body over time due to cumulative internal and external pressures over time), and successful aging (lack of physical disease and disability, high cognitive functioning, and active engagement in life). Multiple domains— including length of life, biological health, mental health, cognitive function, social competence, productivity, personal control, and life satisfaction—form the foundation of one’s health (Friedman & Kern, 2014). Successful aging involves selecting which elements will optimize life, allowing some domains to compensate for others (Baltes & Baltes, 1990). One’s definition of thriving impacts how it is assessed, the extent to which the construct is continuous versus dichotomous, unidimensional versus multidimensional, subjective versus objective, and whether it can be captured at a single time point or necessitates seeing the person’s trajectory across life as a whole. The common thread across these definitions is that thriving involves success across one or more domains. The traditional medical and psychological models tend to take a diagnostic approach, such that one is either healthy or unhealthy. Other perspectives more directly capture the continuous nature of biopsychosocial functioning, such that there is a broad range of functioning even among the healthy. Thriving refers to the higher end of this spectrum.

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For our purposes here, we define thriving as subjective and objective success across multiple domains (e.g., physical, mental, cognitive, social, functional, economic), and we use the term interchangeably with flourishing. The domains that matter most may vary for different individuals and at different periods of time, depending on one’s personality and social context. From this perspective, thriving is not a single number, but involves feeling and functioning well across multiple valued areas of life. This definition is sufficient if we are only interested in identifying who or how many people are thriving at any given point in time. We suggest that thriving in life goes a step further and involves not only success at a single point in time but also a consistent trajectory of wellness over time. Thriving in life is a dynamic phenomenon that depends on multiple influences that interact and accumulate over the course of years and decades. Sustained states of thriving are rarely the outcome of a one‐time fix or short‐term behaviors that are not maintained; rather, thriving in life depends on the cumulative effects of a constellation of repeated behaviors combined with supportive environments. Just as plants need a regular supply of water and sunlight to flourish, human thriving depends on a sustained combination of salutary supports.

Predictors of Thriving Numerous factors predict one’s level of thriving, including one’s personality, habitual behaviors, social relationships, socioeconomic status (SES), the environment, and culture.

Individual Factors Some of the most consistent predictors of thriving are one’s personality characteristics. Meta‐ analyses spanning thousands of participants find that conscientiousness (the tendency to be self‐controlled, hardworking, dependable, organized, and socially responsible) and intelligence predict length of life on par with well‐established risk factors (such as smoking, alcohol consumption, physical inactivity, and unhealthy diet; Calvin et al., 2011; Kern & Friedman, 2008). Personality accounts for over 40% of the variance in subjective well‐being, with extraversion (the tendency to be sociable, talkative, assertive, enthusiastic, and active) and low neuroticism (the tendency to be irritable, moody, and temperamental) being the strongest predictors (Steel, Schmidt, & Shultz, 2008).

Behavioral Factors Thriving is impacted not only by who we are but also by what we do. Habitual behaviors cumulatively impact one’s health and well‐being over time. Protective behaviors include moderate physical activity, healthy diet and weight control, restful sleep, immunizations, safe driving, wearing sunscreen, and regular medical checkups, whereas risky behaviors include smoking, drug use, alcohol abuse, thrill‐seeking activities, and unsafe sex. For instance, regular physical activity can prevent and assist in the management of chronic illnesses and disease (Pedersen & Saltin, 2006), moderate sleep duration relates to lower mortality risk (Cappuccio, D’Elia, Strazzullo, & Miller, 2010), and the combination of not smoking, physical activity, health diet, and moderate drinking can predict up to a 14‐year difference in length of life (Khaw et al., 2008). Such behaviors are physically healthy and relate to higher levels of psychological well‐being, less mental distress, and better cognitive function.

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Daily Activities A related factor involves how time is spent, including employment, voluntary, home, and leisure activities. The impact of work is most clearly seen in the case of involuntary unemployment, which relates to a greater number of health problems, lower psychological well‐being, and greater psychological distress (McKee‐Ryan, Song, Wanberg, & Kinicki, 2005). Similarly, one of the greatest risk periods for early death is within the first few years after retirement. Beyond being necessary for meeting basic needs, work can provide a sense of identity, connection to like‐minded others, and a source of personal accomplishment. While work provides physical, psychological, social, and economic benefits, too much work can also be problematic. Technological developments have blurred the boundaries between work and home in many occupations. Working beyond contractual hours has been related to increased risk of one or more health problems, greater distress, and perceived work–life imbalance (Arlinghaus & Nachreiner, 2014).

Social Factors The need to belong is one of the most pervasive and powerful fundamental human needs (Baumeister & Leary, 1995). Humans are extremely sensitive to even the slightest hint of social exclusion. Peer rejection and victimization in childhood predicts poor adjustment and psychopathology later in life. Loneliness is one of the strongest risk factors for failure to thrive—including higher risk of mortality, physical disease, and depressive symptoms (Hawkley & Cacioppo, 2010). Social relationships range in quality; in general, positive relationships are protective against poor health outcomes, whereas hostile and negative relationships increase risk. Perceived social support and lower levels of interpersonal conflict may buffer the negative health‐related consequences of stress and relate to lower risk of infection, morbidity, and mortality. It is commonly believed that social relationships are one of the most important contributors to psychological well‐being. However, this is complicated by how thriving is defined and measured. If thriving is defined in part as good social function, then social relationships are a marker of thriving, not a predictor. Associations among both objective and subjective relationship variables and subjective well‐being may also be confounded by shared method variance and underlying variables such as personality traits. To distinguish predictors and outcomes, it may be useful to define factors such as connectedness (i.e., the ability to intimately connect with others) and fulfillment of one’s sense of belonging as predictors and factors such as the quality of social relationships as outcomes.

Socioeconomic Factors There is clear evidence for a social gradient, such that those who have a lower socioeconomic position have a shorter life expectancy, increased risk of mental and physical disease, and lower well‐being. This appears to be a nonlinear relationship, with income making a greater difference at very low levels of income and less of an impact as income increases. Unfortunately, although narrowing income gaps may have the biggest impact on health and well‐being around the world, inequality in most countries is increasing (Pickett & Wilkinson, 2015). Although it is clear that people who have more money tend to be happier and healthier than people with less money, research suggests that how people spend their money can also influence their happiness. People report greater happiness after spending money on other people

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compared with spending money on themselves and derive more satisfaction from purchasing life experiences than purchasing material possessions (Dunn, Aknin, & Norton, 2014).

Environmental Factors Individuals are not isolated, but are embedded within social and environmental contexts. Over the past century, some of the greatest improvements in physical and mental health have occurred through the availability of clean drinking water, proper sewage treatment, and the development of immunizations and antibiotics. Contact with nature relates to greater physical activity, social interactions with others, increased happiness, enhanced cognitive capacity, and less mental distress (Hartig, Mitchell, de Vries, & Frumkin, 2014). There is also emerging discussion about the potential impact of climate change, increased pollution, economic volatility, exposure to violence, and political displacement. The long‐term impact of these external factors is unknown, but it is clear that proactive approaches that create healthy, safe environments are needed.

Cultural Factors At a broader level, culture has a profound influence on individual characteristics, behavior, social relationships, and even the definition of thriving. For example, North Americans tend to associate happiness with personal achievement more closely than with social harmony, whereas the opposite pattern has been observed for Japanese (Uchida & Kitayama, 2009). Thus, pathways to thriving most likely vary across cultures. However, there are also likely to be some common predictors of thriving across cultures. For instance, people who inhabit regions of Italy, Japan, and the United States that have high proportions of centenarians tend to value family and other social relationships, smoke less, have a healthy diet, engage in moderate physical activity, and are generally engaged in life (Buettner, 2012). There is also evidence that there are universal needs (e.g., social support, respect, mastery, autonomy) that predict subjective well‐being across the world, although some cultures might emphasize some needs more than others (Tay & Diener, 2011).

Cultivating Thriving The different predictors of thriving offer a range of areas that can potentially be targeted to help more people thrive. Although intelligence and personality are often viewed as fixed traits that are not readily amenable to change, people can and do change, as part of the natural process of maturation, through intervention, and through goals to change. Such changes in personality longitudinally predict changes in life satisfaction and psychological well‐being (Boyce, Wood, & Powdthavee, 2013). As personality influences behavior, it may be possible for individuals to emulate the behaviors of those who are higher in conscientiousness, extraversion, and intelligence and lower on neuroticism. For example, leading a healthier lifestyle could produce some of the health‐related benefits of being more conscientious. Positive psychology has developed various interventions and activities to build psychological and social well‐being, such as gratitude exercises, discovering and using one’s strengths, random acts of kindness, mindfulness, and active constructive responding. Workplaces and schools have successfully incorporated some of these strategies, providing preliminary support for lower healthcare costs, happier employees, and greater profit.

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The extent to which temporary changes in behavior and short‐term well‐being exercises impact consequential life outcomes, such as long‐term social relationships and length of life, is unknown. Changing a behavior (e.g., overeating) will not reduce mortality risk if it is simply replaced by another unhealthy behavior (e.g., drinking). There are likely to be multiple mechanisms by which a trait exerts its influence on thriving, some pathways may be more malleable than others, and the causal impact of many mechanisms are yet to be determined (Friedman & Kern, 2014). Thus, it may be more effective to change the underlying characteristics than to target multiple mediators. Public health and public policy are well suited to target the external context—creating conditions that support individual and community thriving. Environments can be designed to make healthy behaviors easier to do and more psychologically attractive. For instance, designing parks, communities, transportation systems, schools, and buildings that encourage physical activity may also produce a wide range of environmental, economic, and physical and mental health benefits. Countries where people must opt out of organ donation have higher consent rates (80–100%) than those with an opt‐in policy (5–30%; Johnson & Goldstein, 2003). Policies and legislation can also be used to create social norms and provide checks on behavior. For instance, many countries now levy high taxes on cigarette and tobacco, making it economically unsustainable to smoke. At the same time, policy makers need to tread carefully; not all factors that influence thriving are equally amenable to legislation. For example, screenings for prostate cancer successfully detect many cases, but many of those diagnosed will die of another disease long before the cancer develops. As treatment can cause substantial negative impacts on psychosocial functioning, blindly requiring screenings to target one indicator of thriving (physical health, indicated by lack of cancer) can needlessly undermine functioning in another domain. A systemic consideration of the potential impact that any action will have across multiple domains of thriving is needed. As a whole, different strategies will likely be effective for different people. From a socioecological perspective, interventions that target multiple levels of influence should result in more powerful and sustained changes than interventions only targeting a single level.

Future Directions Although optimal functioning has long been an area of scholarly interest, the rapid expansion and application of positive psychology has made it more visible and is bridging research and applications in the real world. However, many gaps remain. Research often looks for simple, singular, linear causes, whereas life is complex, interconnected, and dynamic. Future research will benefit from incorporating a systems perspective, interdisciplinary scholarship, and closer connections between research and practice. Methodologically, thriving in life is challenging to study. Advances in computational social science provide opportunities for bringing together diverse data sources, which capture both patterns across large populations, and momentary behavioral expressions of thoughts, attitudes, and behaviors. The methodologies, tools, and data that are most useful to understanding thriving are yet to be determined, and as all methodologies have strengths and limitations, multimethod approaches are likely to produce more robust and comprehensive insights. Much of the research and application to date has focused on individuals, but individual thriving is interconnected with the environment in which a person resides. Questions remain around how individual behaviors and choices influence the well‐being of close and distant o ­ thers and how to balance individual well‐being with collective needs. Additional research around person

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and environment interactions could address how individuals relate to, are affected by, and impact upon the environment in which they live, as well as the broader biosphere. Finally, although various interventions have been developed to cultivate thriving, the extent to which these are beneficial across individuals from different backgrounds, different periods of life, and under what circumstances is unclear. It is important to further develop a more nuanced understanding of individual fingerprints of thriving, which may depend on cultural differences, socioeconomic backgrounds, and individual values, needs, and motivations.

Conclusion Thriving involves holistic success across multiple domains of life. Multiple factors impact a person or group’s level of thriving at any given point in time. Although we have reviewed a range of factors that correlate with higher levels of well‐being, the relative importance of different factors may vary between individuals. In this way, just as different plants thrive under different climates, there is no one‐size‐fits‐all intervention or magic bullet for enhancing well‐being; whereas different plants need different amounts of water, individuals may differ in the dose and types of social interaction they need to thrive. We also take the view that optimal well‐being involves having a balanced life, with a mix of daily activities that enable individuals to fulfill a variety of needs. This implies that just as you can overwater a plant, it is also possible to have too much of one factor, at the cost of other domains, which undermines thriving as a whole. Thriving in life not only involves functioning well at a single point in time but also involves one’s trajectory over time. A focus on pathways emphasizes the importance of cultivating personal resources and supportive environments across the lifespan, as well as the need to understand how individual and social factors shift one’s wellness trajectory over time. Harmful trajectories are not immutable, but early influences play a key role in setting a dominant trajectory. It may be relatively more effective to intervene at an early stage to establish healthier pathways in the first place than to try to correct unhealthy pathways in adulthood and old age. We need to know how different factors contribute to thriving across the lifespan, both naturally and through intervention. Thriving in life is a complex, multiply‐determined phenomenon. There are some universal and necessary ingredients, but the specific combination and importance of different factors will differ between individuals and even within a person at different periods of life. Research across a variety of disciplines is focusing on ways to enhance thriving, with potential benefit to both individuals and society as a whole.

Author Biographies Dr. Margaret L. Kern is a senior lecturer at the Centre for Positive Psychology at the University of Melbourne’s Graduate School of Education. She received her undergraduate degree from Arizona State University; a PhD in social/personality psychology from the University of California, Riverside; and postdoctoral training at the University of Pennsylvania. Her research examines questions around who flourishes in life, why, and what enhances and hinders healthy life trajectories. Jessie Sun is a graduate student in the Department of Psychology at the University of California, Davis. She received her bachelor of arts (Hons.) from the University of Melbourne. She is interested in researching the interplay between personality, self‐knowledge, social experiences, and well‐being, especially using methods for studying dynamic processes in daily life.

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References Arlinghaus, A., & Nachreiner, F. (2014). Health effects of supplemental work from home in the European Union. Chronobiology International, 30, 1197–1202. http://dx.doi.org/10.3109/0742 0528.2014.957297 Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful aging: The model of selective optimization with compensation. In P. B. Baltes & M. M. Baltes (Eds.), Successful aging: Perspectives from the behavioral sciences (pp. 1–34). New York, NY: Cambridge University Press. Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117, 497–529. http://dx.doi. org/10.1037/0033‐2909.117.3.497 Boyce, C. J., Wood, A. M., & Powdthavee, N. (2013). Is personality fixed? Personality changes as much as “variable” economic factors and more strongly predicts changes to life satisfaction. Social Indicators Research, 111, 287–305. http://dx.doi.org/10.1007/s11205‐012‐0006‐z Buettner, D. (2012). The blue zones: Lessons for living longer from the people who’ve lived the longest. Washington, DC: National Geographic. Calvin, C. M., Deary, I. J., Fenton, C., Roberts, B. A., Der, G., Leckenby, N., & Batty, G. D. (2011). Intelligence in youth and all‐cause‐mortality: Systematic review with meta‐analysis. International Journal of Epidemiology, 40, 626–644. http://dx.doi.org/10.1093/ije/dyq190 Cappuccio, F. P., D’Elia, L., Strazzullo, P., & Miller, M. A. (2010). Sleep duration and all‐cause mortality: A systematic review and meta‐analysis of prospective studies. Sleep, 33, 585–592. Dunn, E. W., Aknin, L. B., & Norton, M. I. (2014). Prosocial spending and happiness: Using money to benefit others pays off. Current Directions in Psychological Science, 23, 41–47. http://dx.doi. org/10.1177/0963721413512503 Friedman, H. S., & Kern, M. L. (2014). Personality, well‐being, and health. Annual Review of Psychology, 65, 719–742. http://dx.doi.org/doi:10.1146/annurev‐psych‐010213‐115123 Hartig, T., Mitchell, R., de Vries, S., & Frumkin, H. (2014). Nature and health. Annual Review of Public Health, 35, 207–228. http://dx.doi.org/10.1146/annurev‐publhealth‐032013‐182443 Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: A theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine, 40, 218–227. http://dx.doi. org/10.1007/s12160‐010‐9210‐8 Johnson, E. J., & Goldstein, D. (2003). Do defaults save lives? Science, 302, 1338–1339. http://dx.doi. org/10.1126/science.1091721 Kern, M. L., & Friedman, H. S. (2008). Do conscientious individuals live longer? A quantitative review. Health Psychology, 27, 505–512. http://dx.doi.org/10.1037/0278‐6133.27.5.505 Khaw, K.‐T., Wareham, N., Bingham, S., Welch, A., Luben, R., & Day, N. (2008). Combined impact of health behaviours and mortality in men and women: The EPIC‐Norfolk prospective population study. PLoS Medicine, 5(1), e12. http://dx.doi.org/10.1371/journal.pmed.0050012 McKee‐Ryan, F., Song, Z., Wanberg, C. R., & Kinicki, A. J. (2005). Psychological and physical well‐ being during unemployment: A meta‐analytic study. The Journal of Applied Psychology, 90, 53–76. http://dx.doi.org/10.1037/0021‐9010.90.1.53 Pedersen, B. K., & Saltin, B. (2006). Evidence for prescribing exercise as therapy in chronic disease. Scandinavian Journal of Medicine and Science in Sports, 16, 3–63. Pickett, K. E., & Wilkinson, R. G. (2015). Income inequality and health: A causal review. Social Science & Medicine. http://dx.doi.org/10.1016/j.socscimed.2014.12.031 Steel, P., Schmidt, J., & Shultz, J. (2008). Refining the relationship between personality and subjective well‐being. Psychological Bulletin, 134, 138–161. http://dx.doi.org/10.1037/0033‐2909.134.1.138 Tay, L., & Diener, E. (2011). Needs and subjective well‐being around the world. Journal of Personality and Social Psychology, 101, 354–365. http://dx.doi.org/10.1037/a0023779 Uchida, Y., & Kitayama, S. (2009). Happiness and unhappiness in east and west: Themes and variations. Emotion, 9, 441–456. http://dx.doi.org/10.1037/a0015634

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Suggested Reading Friedman, H. S., & Kern, M. L. (2014). Personality, well‐being, and health. Annual Review of Psychology, 65, 719–742. http://dx.doi.org/doi:10.1146/annurev‐psych‐010213‐115123 Huppert, F. A. (2014). The state of wellbeing science: Concepts, measures, interventions, & policies. In F. A. Huppert & C. L. Cooper (Eds.), Wellbeing: A complete reference guide, volume VI, interventions and policies to enhance wellbeing (pp. 1–50). West Sussex: Wiley. http://dx.doi.org/ 10.1002/9781118539415.wbwell01 Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of research on hedonic and eudaimonic well‐being. Annual Review of Psychology, 52(1), 141–166. doi:10.1146/annurev. psych.52.1.141

Unrealistic Optimism and Health James A. Shepperd1 and Jennifer L. Howell2 Psychology Department, University of Florida, Gainesville, FL, USA 2 Psychology Department, Ohio University, Athens, OH, USA

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When asked to predict their future, people are not evenhanded in their prognostications. Rather, they show a strong preference for optimism, believing that favorable outcomes are more likely on their horizon than is realistically possible. This unrealistic optimism occurs for a seemingly endless number of outcomes. For example, college women report that they are less likely than their peers to experience an unwanted pregnancy, smokers believe they are less likely than others smokers to suffer smoking‐related illnesses, drivers report that they are less likely than other drivers to experience an automobile accident, and people generally believe they are less likely than are their peers to experience diseases including HIV, HPV and chlamydia, and cancer (for a review, see Shepperd, Klein, Waters, & Weinstein, 2013). The bias seems remarkably stable across time (Shepperd, Helweg‐Larsen, & Ortega, 2003) and impervious to interventions designed to reduce it (Weinstein & Klein, 1995). At first blush, unrealistic optimism seems problematic in that it could prompt people to take unnecessary risks, fail to take precautions, or otherwise make decisions that increase their odds experiencing harm. However, the relationship between unrealistic optimism and behavior is more complex. Understanding when unrealistic optimism is or is not problematic requires defining unrealistic optimism and understanding why it occurs. We distinguish between two types of unrealistic optimism, review the causes of unrealistic optimism, and discuss the consequences of unrealistic optimism for health.

Defining Unrealistic Optimism Unrealistic optimism refers to the prediction that one’s personal outcomes will be more favorable than can possibly be. On the surface, unrealistic optimism shares some overlap with dispositional optimism, which represents a trait tendency to hold positive expectation about the future. However, unrealistic optimism is empirically and conceptually distinct. Unrealistic

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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optimism is not just a positive expectation (like dispositional optimism); it is a positive expectation that cannot possibly be true. But how do we determine that a person’s predictions are unrealistically optimistic? Researchers have identified two types of unrealistic optimism that differ in the standard used to evaluate the predictions (Shepperd et al., 2013). The first type—unrealistic absolute optimism—occurs when people make overly favorable predictions about their futures relative to an objective, quantitative standard such as base rate data, prior experience, or what is realistically possible. Several studies demonstrate unrealistic absolute optimism: researchers have shown that students overestimate the starting salaries they will receive after graduation from college, financial advisors overestimate future corporate earnings, and taxpayers overestimate how quickly they will file their income tax forms (see Carrol, Sweeny, & Shepperd, 2006 for a review). Other research demonstrates unrealistic optimism relative to base rate or epidemiological evidence. For example, relative to local baseline information, homeowners underestimate the chances that their house has radon gas (Weinstein & Lyon, 1999), and college students underestimate their personal risk for STDs including chlamydia and HPV (Rothman, Klein, & Weinstein, 1996). Other research demonstrates unrealistic optimism by comparing people’s personal estimates with the experiences of a comparable control group. For example, female US Marines in one study estimated on average a 14.5% chance that they would get pregnant in the next 12 months, which was significantly lower than the 12‐month pregnancy rate (22.0%) of a comparable group of female Marines (Gerrard, Gibbons, & Warner, 1991). The second type of unrealistic optimism—unrealistic comparative optimism—occurs when people erroneously conclude that unfavorable outcomes are less likely to happen, and favorable outcomes more likely to happen, to them than to their peers. Researchers measure unrealistic comparative optimism in two ways. The first involves comparing an individual’s comparative estimate with the comparative estimate indicated by an objective standard. To illustrate, a woman may predict that she is less likely than the average woman to get breast cancer. However, if her true risk, based on epidemiological predictors (e.g., family history, age at first menstruation, genetic predisposition), is average or above average, then she is displaying unrealistic comparative optimism. Evidence for this manifestation of unrealistic comparative optimism comes from studies of breast cancer, smoking‐related diseases, and fatal heart attacks (see Shepperd et al., 2013). The second manifestation of unrealistic comparative optimism occurs at the group level and does not identify whether any specific individual is unrealistically optimistic. It occurs when group members on average rate their risk of experiencing an unfavorable event as below average relative to other members of the group. The assumption is that the mean individual risk estimate of the group members for a particular event should be average, with estimates of people whose risk is below average balancing out the estimates of people whose risk is above average. If the group’s mean estimate of experiencing an unfavorable event is below average, then the group is displaying unrealistic comparative optimism. For example, college students overwhelmingly report that they are less likely than other college students to become obese or to have their first marriage end in divorce (Rothman et al., 1996). Because personal estimates should balance out to be “average” rather than “below average,” these students are displaying unrealistic comparative optimism.

Causes of Unrealistic Optimism Researchers have identified three reasons why people are unrealistically optimistic (Shepperd, Waters, Weinstein, & Klein, 2015): (a) people are motivated to expect favorable future events,

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(b) people overrely on personal information when making judgments, and (c) people process information in ways that lead them to be overly optimistic. First, people may be motivated to believe or have others believe that they are unlikely to experience unfavorable future events. The motivation may stem from several causes (Shepperd, Carroll, Grace, & Terry, 2002). For instance, it may arise because it feels good to imagine a positive future and feels bad to imagine a negative future. It also may arise from superstitious thinking, such as the belief that imagining favorable outcomes may somehow increase the likelihood that the favorable outcomes will transpire. Such thinking is not entirely superstitious in that an imagining a favorable outcome may facilitate actions designed to bring about the outcome. Finally, the motivation may reflect a perception of what other people believe they should think. Indeed, people believe that it is better to be unrealistically optimistic in most circumstances than to be accurate or pessimistic. The second reason people may display unrealistic optimism arises from an overreliance on personal information when making judgments. People know their personal history, current actions, and future plans, and they use this information when predicting their personal likelihood of experiencing various outcomes. These future plans are typically plans to bring about desired outcomes and avoid undesired outcomes and may insufficiently weight, or fail to include altogether, possible setbacks, roadblocks, competing demands, and past failures. Consistently, people display greater unrealistic optimism for controllable events than uncontrollable events, presumably because they assume they will take more action to bring about desired outcomes than they actually do (see Shepperd et al., 2015). Conversely, people often neglect or are unaware of other people’s plans, intentions, and goal‐directed behavior (i.e., that other people are employing, or intend to employ, actions to avoid undesired outcomes and achieve desired outcomes). Thus, they underweight other’s experiences when making comparative estimates. In fact, supplying people with information about others’ goal‐directed behavior or intentions diminishes unrealistic optimism (see Shepperd et al., 2015). The reduction may occur because the information prompts people to recognize personal similarity with the comparison target and thus modify their estimates accordingly. Alternatively, the information may make the comparison target seem less abstract and more humanlike. Consistent with the latter possibility are findings that people display less unrealistic comparative optimism when the comparison target is a concrete, specific person as opposed to the amorphous average person (see Shepperd et al., 2015). Together, this evidence suggests that people often overrely on personal information and fail to use information about others when making comparative judgments. The third reason people display unrealistic optimism arises from the way people process information. Three processes are particularly important (Shepperd et al., 2015). The first is that people making likelihood judgments gauge their chances of experiencing an outcome based on how well they match their stereotype of people who experience the outcome. Stereotypes, however, are often inaccurate or exaggerated. For example, the stereotype of the person who gets a sexually transmitted disease may be someone who has frequent unprotected sex with multiple sex partners. In comparison with this stereotype, many people naturally estimate that their risk is lower. The second process is that people making comparative judgments often omit the comparison part of the judgment. Instead, of comparing their chances relative to some standard or to the chances of the average person, people often merely consider their own chances, transforming the comparative judgment into a personal judgment with no clear reference group. Third, people making judgments often weight information more heavily to the extent it suggests that favorable outcomes lie ahead. Indeed, people seem to fail to encode information that optimism is unwarranted (Sharot, Korn, & Dolan, 2011).

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The Consequences of Unrealistic Optimism Examining the consequences of unrealistic optimism is difficult because the research is almost entirely correlational. Unrealistic optimism is challenging to manipulate, making it difficult to assess causal relationships between unrealistic optimism and health outcomes. Although unrealistic optimism may influence behavior, it is also possible that people display unrealistic optimism because of their current or intended behavior or some other third variable. Three types of studies allow researchers to better examine the health consequences associated with unrealistic optimism: (a) longitudinal research, which can show that unrealistic optimism predates health decisions, (b) quasi‐experimental studies that provide research participants the opportunity to engage (or not engage) in health‐related behaviors, and (c) studies that equate participants on pertinent risk factors or past behavior and then examine whether participants high versus low in unrealistically optimism differ in health decisions, behaviors, or outcomes. All of the studies described in the sections that follow use at least one of these methodological approaches. To facilitate our discussion, we first discuss evidence suggesting that unrealistic optimism is beneficial for health followed by evidence that unrealistic optimism is problematic for health.

Health Benefits of Unrealistic Optimism Numerous studies link dispositional optimism to hope, positive affect, and greater goal‐ directed persistence. Given the overlap (albeit a modest overlap) between dispositional optimism and unrealistic optimism, it is reasonable to assume that unrealistic optimism might have benefits. Consistent with this reasoning are the results of two studies. The first examined unrealistic absolute optimism among HIV‐infected gay men, who reported that they were less likely than a comparison group of non‐infected men to develop AIDS (Taylor et al., 1992). More importantly, greater unrealistic optimism among these HIV‐infected men corresponded with reports of health‐promoting behaviors such as a healthy diet, jogging, and getting adequate sleep. The second study examined unrealistic comparative optimism among patients in a cardiac rehabilitation program. Although all study participants experienced some coronary event (myocardial infarction, bypass surgery angina, etc.), some patients displayed unrealistic comparative optimism about their risk of experiencing a future cardiac event, whereas others did not. The patients who reported the greatest unrealistic comparative optimism were less likely to experience a cardiac event (such as readmission to a hospital for chest pain/angina or experience of a heart attack) over the next 12 months, even when controlling for true risk (Hevey, McGee, & Horgan, 2014).

Health Costs of Unrealistic Optimism The two studies just described are quite unique in demonstrating benefits of unrealistic optimism. Most published studies examining the consequences of unrealistic optimism suggest that unrealistic optimism can be problematic for health. Because unrealistic optimism represents an expectation that exceeds what is realistically possible, it can lead people to take unnecessary risks. To understand the health problems linked to unrealistic optimism, we examine the two forms of unrealistic optimism (absolute and comparative) separately.

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Unrealistic Absolute Optimism Unrealistic absolute optimism has a number of problematic consequences for health. For instance, it can lead to misplaced hope, as illustrated by a study of participants in a phase 1 clinical cancer trial. The researchers explained that the purpose of such trials is to establish the feasibility of a future randomized trial and that no participant was likely to experience benefits as a consequence of their participation. Nevertheless, 60% of the participants in the trial reported that they were more likely than the other trial participants to experience health benefits from the trial (Jansen et al., 2011). These estimates reflect unrealistic optimism because the trial participants believed they would experience health benefits when they were explicitly told that they would not. The findings also illustrate unrealistic comparative optimism in that patients believe that they were more likely than fellow clinical trial patients to experience benefits when they were not. Other studies have demonstrated that unrealistic absolute optimism causes disappointment, regret, and other negative emotions when the future does not unfold as expected. For example, students in one study who awaited the results of a classroom examination felt worse about their exam performance if they were unrealistically optimistic about their performance than if they were accurate or unrealistically pessimistic, regardless of their actual exam performance (Sweeny & Shepperd, 2010). Unrealistic absolute optimism can also have behavioral consequences. In a nationally representative sample of smokers, the more unrealistically optimistic smokers were about their lung cancer risk, the less likely they were to list quitting as a means to decrease lung cancer risk, and the more curable they viewed lung cancer to be (Dillard, McCaul, & Klein, 2006). Moreover, despite the fact that quitting smoking is more difficult the longer a person smokes, smokers typically overestimate their ability to quit, which may delay or undermine future attempts to quit. Indeed, greater unrealistic absolute optimism corresponded with lower intentions to quit smoking in the nationally representative sample of smokers just mentioned (Dillard et al., 2006). Other negative consequences also arise from unrealistic absolute optimism. For example, one study examined attention to relevant health information among college students who engaged risky sexual activity. Compared with realists—who accurately perceived themselves to be at high risk for pregnancy and STDs—unrealistically optimistic students perceived an informational pamphlet about STDs and pregnancy as less relevant to them. As a result, their personal risk estimates were less responsive to the information provided in the pamphlet (Wiebe & Black, 1997). Perhaps most sobering is the finding that greater unrealistic optimism about risk for heart disease in a sample of older adults corresponded with the thickness of the carotid artery (Ferrer et al., 2012), a subclinical marker of atherosclerosis that is linked to increased heart attack risk.

Unrealistic Comparative Optimism Research exploring the consequences of unrealistic comparative optimism for health is generally sparse, but several studies have demonstrated that it too can have negative health consequences. In one study, college drinkers who were unrealistically optimistic about their risk of experiencing alcohol‐related problems in the next year reported consuming more alcohol than did realistic participants. More importantly, 6, 12, and 18 months after the initial survey, the drinkers who displayed unrealistic comparative optimism also reported more alcohol‐related negative events (hangover, trouble with the police, missed classes, etc.) than did the realistic participants (Dillard, Midboe, & Klein, 2009). In a second study, older adults who reported

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unrealistic comparative optimism about their risk for heart attack reported less worry about their risk level than did participants who were accurate or unrealistically pessimistic. They also displayed less knowledge of risk factors and poorer recall of an essay they read earlier about heart attack risk factors (Radcliffe & Klein, 2002). Other research finds that people who displayed unrealistic comparative optimism about avoiding the H1N1 virus reported lower intentions to wash their hands and use hand sanitizers (Kim & Niederdeppe, 2013). Other research suggests that the combination of high unrealistic comparative optimism and high dispositional optimism can be particularly problematic. People high in both types of optimism tend to be more defensive and more likely to display behaviors that infirm their health. For example, college students in one study imagined that a dental visit revealed that they needed a root canal. Students who were high in both dispositional and unrealistic comparative optimism were more dismissive of the hypothetical dental threat in the near future than were college students high in dispositional but low in comparative optimism. Moreover, when provided the opportunity to seek additional health information, these students were less likely to do so (Fowler & Geers, 2015).

Summary Unrealistic optimism is remarkably pervasive, stable over time, and resistant to change. It appears in two forms: absolute and comparative unrealistic optimism. Specifically, people can be unrealistically optimistic relative to objective indicators of their risk (unrealistic absolute optimism) or in estimates of how their risk compares with the risk of their peers (unrealistic comparative optimism). Unrealistic optimism can arise from a motivation to believe (or have others believe) that one’s future will be rosy, from disparity in information about oneself versus the objective standard or comparison target, or from cognitive processes that emerge when people are asked to evaluate their risk (e.g., reliance on stereotypes, insufficiently considering others, differential weight of favorable versus unfavorable information). Finally, although unrealistic optimism can have positive health consequences, a number of studies suggest that it can also produce negative health consequences. Perhaps the most challenging task facing researchers studying unrealistic optimism is clarifying when unrealistic optimism is linked to positive versus negative health outcomes.

Author Biographies James A. Shepperd, PhD, is the R. David Thomas endowed professor of psychology at the University of Florida and the research director for the Southeast Center for Research to Disparities in Oral Health. Most of his research examines how people manage threatening information, which includes topics such as optimism, maintaining desired self‐views, and information avoidance. Much of his work involves applying psychological theory to addressing health concerns such as screening for cancer. Jennifer L. Howell, PhD, is an assistant professor at Ohio University. Her research focuses primarily on psychological threat management and health. She seeks to understand the proactive and reactive strategies people use to manage bad news, particularly about their health. This broad umbrella encompasses work on decision making, responses to feedback, defensiveness, physical health behaviors, risk, coping, individual differences, social cognition, attitudes, and communication.

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References Carroll, P., Sweeny, K., & Shepperd, J. A. (2006). Forsaking optimism. Review of general psychology, 10(1), 56–73. Dillard, A. J., McCaul, K. D., & Klein, W. M. P. (2006). Unrealistic optimism in smokers: Implications for smoking myth endorsement and self‐protective motivation. Journal of Health Communication, 11(Suppl 1), 93–102. doi:10.1080/10810730600637343 Dillard, A. J., Midboe, A. M., & Klein, W. M. P. (2009). The dark side of optimism: Unrealistic optimism about problems with alcohol predicts subsequent negative event experiences. Personality and Social Psychology Bulletin, 35(11), 1540–1550. doi:10.1177/0146167209343124 Ferrer, R. A., Klein, W. M. P., Zajac, L. E., Sutton‐Tyrrell, K., Muldoon, M. F., & Kamarck, T. W. (2012). Unrealistic optimism is associated with subclinical atherosclerosis. Health Psychology, 31(6), 815–820. doi:10.1037/a0027675 Fowler, S. L., & Geers, A. L. (2015). Dispositional and comparative optimism interact to predict avoidance of a looming health threat. Psychology & Health, 30(4), 456–474. doi:10.1080/08870446.20 14.977282 Gerrard, M., Gibbons, F. X., & Warner, T. D. (1991). Effects of reviewing risk‐relevant behavior on perceived vulnerability among women marines. Health Psychology, 10(3), 173–179. doi:10.1037/ 0278‐6133.10.3.173 Hevey, D., McGee, H. M., & Horgan, J. H. (2014). Comparative optimism among patients with coronary heart disease (CHD) is associated with fewer adverse clinical events 12 months later. Journal of Behavioral Medicine, 37(2), 300–307. doi:10.1007/s10865‐012‐9487‐0 Jansen, L. A., Appelbaum, P. S., Klein, W. M. P., Weinstein, N. D., Cook, W., Fogel, J. S., & Sulmasy, D. P. (2011). Unrealistic optimism in early‐phase oncology trials. IRB: Ethics & Human Research, 33(1), 1–8. Kim, H. K., & Niederdeppe, J. (2013). Exploring optimistic bias and the integrative model of behavioral prediction in the context of a campus influenza outbreak. Journal of Health Communication, 18(2), 206–222. doi:10.1080/10810730.2012.688247 Radcliffe, N. M., & Klein, W. M. P. (2002). Dispositional, unrealistic and comparative optimism: Differential relations with the knowledge and processing of risk information and beliefs about personal risk. Personality and Social Psychology Bulletin, 28(6), 836–846. doi:10.1177/0146167 202289012 Rothman, A. J., Klein, W. M., & Weinstein, N. D. (1996). Absolute and relative biases in estimations of personal risk. Journal of Applied Social Psychology, 26(14), 1213–1236. doi:10.1111/j.1559‐1816.1996. tb01778.x Sharot, T., Korn, C. W., & Dolan, R. J. (2011). How unrealistic optimism is maintained in the face of reality. Nature Neuroscience, 14(11), 1475–1479. doi:10.1038/nn.2949 Shepperd, J. A., Carroll, P., Grace, J., & Terry, M. (2002). Exploring the causes of comparative optimism. Psychologica Belgica, 42(1‐2), 65–98. Shepperd, J. A., Helweg‐Larsen, M., & Ortega, L. (2003). Are comparative risk judgments consistent across time and events? Personality and Social Psychology Bulletin, 29(9), 1169–1180. doi:10.1177/ 0146167203254598 Shepperd, J. A., Klein, W. M. P., Waters, E. A., & Weinstein, N. D. (2013). Taking stock of unrealistic optimism. Perspectives on Psychological Science, 8(4), 395–411. doi:10.1177/1745691613485247 Shepperd, J. A., Waters, E. A., Weinstein, N. D., & Klein, W. M. P. (2015). A primer on unrealistic optimism. Current Directions in Psychological Science, 24(3), 232–237. doi:10.1177/0963721414568341 Sweeny, K., & Shepperd, J. A. (2010). The costs of optimism and the benefits of pessimism. Emotion, 10(5), 750–753. doi:10.1037/a0019016 Taylor, S. E., Kemeny, M. E., Aspinwall, L. G., Schneider, S. G., Rodriguez, R., & Herbert, M. (1992). Optimism, coping, psychological distress, and high‐risk sexual behavior among men at risk for acquired immunodeficiency syndrome (AIDS). Journal of Personality and Social Psychology, 63(3), 460–473. doi:10.1037/0022‐3514.63.3.460

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Weinstein, N. D., & Klein, W. M. (1995). Resistance of personal risk perceptions to debiasing interventions. Health Psychology, 14(2), 132–140. doi:10.1037/0278‐6133.14.2.132 Weinstein, N. D., & Lyon, J. E. (1999). Mindset, optimistic bias about personal risk and health‐protective behaviour. British Journal of Health Psychology, 4, 289–300. doi:10.1348/135910799168641 Wiebe, D. J., & Black, D. (1997). Illusional beliefs in the context of risky sexual behaviors. Journal of Applied Social Psychology, 27(19), 1727–1749. doi:10.1111/j.1559‐1816.1997.tb01622.x

Suggested Reading Chambers, J. R., & Windschitl, P. D. (2004). Biases in social comparative judgments: The role of nonmotivated factors in above‐average and comparative‐optimism effects. Psychological Bulletin, 130, 813–838. doi:10.1037/0033‐2909.130.5.813 Shepperd, J. A., Klein, W. M. P., Waters, E. A., & Weinstein, N. D. (2013). Taking stock of unrealistic optimism. Perspectives on Psychological Science, 8(4), 395–411. doi:10.1177/1745691613485247 Shepperd, J. A., Waters, E. A., Weinstein, N. D., & Klein, W. M. P. (2015). A primer on unrealistic optimism. Current Directions in Psychological Science, 24, 232–237. doi:10.1177/0963721414568341

Waiting for Health News Angelica Falkenstein, Michael D. Dooley, and Kate Sweeny Department of Psychology, University of California, Riverside, CA, USA

Uncertain waiting periods are a regular part of the healthcare experience. Patients await uncertain news when they undergo diagnostic medical tests, engage in periods of “watchful waiting,” or anticipate learning the efficacy of an administered treatment. Medical waiting periods can range from minutes to months, with patients left in limbo until they are contacted with results. Although research documenting the psychological effects of such waiting periods is relatively sparse, empirical evidence underscores the exceptional distress that patients experience while awaiting news related to their health. For example, waiting for medical results can provoke the same levels of stress as receiving the bad news of a cancer diagnosis (Lang, Berbaum, & Lutgendorf, 2009). One study even found that people awaiting medical news experience anxiety equivalent to patients admitted to psychiatric facilities for generalized anxiety disorder (Scott, 1983), underscoring the significant distress health uncertainty can cause. In this entry, we discuss the nature of uncertain and highly important medical waiting periods as well as how waiting experiences change over time. We then discuss how waiting experiences predict responses to both desirable and undesirable news, ending with suggestions for ways to make medical waiting periods easier for patients to endure.

The Central Role of Anxiety A small but growing body of research documents the distressing nature of both medical and nonmedical waiting periods. Although the experience of waiting for inevitable events (e.g., waiting rooms, queuing) may be frustrating and uncomfortable, people experience particular distress when they await uncertain news. Uncertainty is characterized by heightened anxiety (Sweeny & Falkenstein, 2015), which is often accompanied by rumination (Sweeny & Andrews, 2014). Generally speaking, people experiencing self‐relevant uncertainty feel motivated to seek out information to alleviate uncertainty and associated anxiety. During medical The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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waiting periods, however, additional reliable information is largely unavailable until the test results are in. Because medical waiting periods are characterized by irresolvable uncertainty, anxiety persists as the predominant emotional experience (Sweeny & Cavanaugh, 2012; Sweeny & Falkenstein, 2015). Indeed, patients report anxiety as the most prevalent emotion during diagnostic waiting periods, exceeding levels of anger, confusion, tension, and intrusive thoughts (Montgomery & McCrone, 2010). Research with patients undergoing biopsy procedures similarly documents high levels of anxiety (Northouse, Jeffs, Cracchiolo‐Caraway, Lampman, & Dorris, 1995), surpassing levels among college students, high‐risk controls (Maxwell et al., 2000), and, in some cases, clinical thresholds (Poole et al., 1999). High anxiety during medical waiting periods not only exerts deleterious effects during the waiting period itself, such as an inability to concentrate and plan (Thorne, Harris, Hislop, & Vestrup, 1999), but may also influence patients’ cognitive processing abilities down the line (Scott, 1983). Anxiety experienced during waiting periods surpasses even levels of anxiety in response to bad news, suggesting that in some respects, waiting can be more difficult than receiving bad news (Sweeny & Falkenstein, 2015). To illustrate, a study of women undergoing fertility treatment found that these women experienced high levels of anxiety while awaiting results of in vitro fertilization, contrasted by high levels of disappointment but relatively low levels of anxiety in the face of negative pregnancy test results (Boivin & Lancastle, 2010), indicating that both waiting and receiving bad news are difficult but qualitatively different experiences.

Change Across the Waiting Period Although waiting periods provoke high levels of anxiety and rumination, the intensity of these experiences fluctuates over time. Trends of anxiety and rumination tend to follow a U‐shaped curve, with the highest levels occurring at the beginning of a wait (e.g., directly after a medical test), followed by a respite, only to ramp up again as news becomes imminent (Sweeny & Andrews, 2014). The shape of this pattern likely mirrors patterns of attentional focus and outcome salience and highlights people’s tendency to shirk optimism and embrace pessimism in an attempt to brace for the worst when news is imminent (Shepperd, Ouellette, & Fernandez, 1996; Sweeny & Krizan, 2013). Pessimistic expectations are often accompanied by anxiety (Sweeny & Andrews, 2014; Sweeny, Reynolds, Falkenstein, Andrews, & Dooley, 2016), and people lower their expectations to the greatest extent right before news arrives (Sweeny & Krizan, 2013). The pattern of anxiety during nonmedical waiting periods translates to medical waiting periods. For instance, the majority of patients awaiting an elective cardiac catheterization reported increases in anxiety over time as the procedure approached (Harkness, Morrow, Smith, Kiczula, & Arthur, 2003). Similarly, women awaiting a breast biopsy procedure increasingly sought social support as the biopsy neared, with open‐ended responses revealing that many women experienced increases in anxiety during this period (Lebel et al., 2003). Women who reported relatively low anxiety immediately after a breast biopsy also experienced increases in anxiety while waiting for the biopsy results (no change for women already high in anxiety; Poole et al., 1999). Despite the stress of waiting in the healthcare context, past research has often ignored the diagnostic phase, instead focusing on understanding patients’ experiences after a diagnosis. However, a growing literature illustrates that diagnostic phases are emotionally dynamic and difficult for patients to endure (Montgomery & McCrone, 2010).

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Variability Across Different Types of Waiting Periods Waiting for medical news often has the potential to cause high levels of anxiety, but not all outcomes provoke the same levels of distress. Intuitively, waiting experiences depend on the importance of the news at hand; surely waiting for news of a tumor biopsy is more nerve‐ wracking than awaiting news of a cholesterol test. Situational factors, such as the importance of an outcome or the risk associated with a negative outcome, are indeed one source of variability in people’s waiting experiences. For instance, couples recalled greater distress while trying to conceive their youngest to the extent that they had placed greater importance on parenthood during that time (Sweeny, Andrews, Nelson, & Robbins, 2015). Couples who had a higher risk of complications also recalled experiencing more anxiety and intrusive thoughts as they tried to conceive. Furthermore, the length of the wait influences how people respond to news. When waiting for a potentially desirable outcome, people prefer shorter waiting periods. However, when the news is likely to be undesirable or has potentially detrimental ramifications, people prefer longer waiting periods that ostensibly give them time to prepare for the devastating blow (Montgomery & McCrone, 2010). It is important to note that not all medical waiting periods elicit anxiety and rumination. In a study investigating the wait for genetic risk information, patients did not seem to be particularly distressed (Phelps, Bennett, Iredale, Anstey, & Gray, 2006), an observation that differs dramatically from studies of women awaiting breast biopsy results (e.g., Poole et al., 1999). Additionally, pregnancy may be an inherently unique medical waiting period. Even women who have previously miscarried see the wait for baby as a generally positive experience, although women with recurrent miscarriages experience more stress (Nelson, Robbins, Andrews, & Sweeny, 2015).

Individual Differences Affecting the Waiting Experience In addition to situational factors, research has linked dispositional factors and a broad range of personal resources to experiences of uncertainty. Higher dispositional optimism is often associated with less distress while waiting (Stanton & Snider, 1993; Sweeny et al., 2015). However, dispositional optimists are just as likely as their pessimistic peers to brace for the worst as the moment of truth approaches (Sweeny & Falkenstein, 2017). Chronic anxiety (Novy, Price, Huynh, & Schuetz, 2001), intolerance of uncertainty (Sweeny et  al., 2015), and negative appraisal patterns (Stanton & Snider, 1993) exacerbate waiting distress, making people high in these characteristics especially ill‐suited to wait for health news. Conversely, people with more social, emotional, and cognitive resources demonstrate greater ability to cope with uncertainty‐related distress. For example, people with more personal resources recall less distress during the time when they were trying to conceive (Sweeny et al., 2015), and people high in self‐esteem are buffered against some indicators of waiting distress (Shepperd et  al., 1996; Sweeny & Andrews, 2014). Ample research highlights strong support networks as pivotal to positive health‐related outcomes, which may stem from supportive others helping people reduce or cope with uncertainty (Uchino, 2009). Together, these findings suggest that ensuring the availability of social, emotional, and cognitive resources may be instrumental in improving patients’ ability to navigate medical waiting periods.

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Although some individual differences consistently predict waiting distress or the lack thereof, studies investigating demographic characteristics have yielded inconsistent associations with waiting experiences. For example, some studies have found that age predicts distress during the wait for a potential breast cancer diagnosis (e.g., Novy et al., 2001), but others have failed to find such a link (Montgomery & McCrone, 2010). Some studies also show that family health history (Novy et al., 2001) and previous experience with the issues at hand (e.g., screenings, miscarriages; Maxwell et al., 2000; Schnur et al., 2008) predict heightened distress while waiting, although a quantitative review found that the greatest declines in expectations (i.e., more pessimism) precede news about unfamiliar outcomes (Sweeny & Krizan, 2013). Further research is needed to determine the role that characteristics such as age, previous experience, socioeconomic status, and gender play into how patients wait for medical news.

Effects of Waiting Experiences on Responses to Health News Beyond predictors of waiting experiences, researchers have also examined how waiting experiences shape reactions to news once it arrives. For instance, even pessimists overwhelmingly prescribe optimism to others faced with uncertain, self‐relevant outcomes, revealing a normative belief that there is some inherent benefit in expecting good outcomes (Armor, Massey, & Sackett, 2008). Research exploring expectations during waiting periods supports this idea, linking optimistic expectations with reduced distress (Sweeny & Andrews, 2014; Sweeny et  al., 2016). Optimism, however, seems to backfire when confronted with bad news. Disconfirmed optimistic expectations are met with greater disappointment and shock compared with confirmed pessimistic expectations (Krizan, Miller, & Johar, 2010; Sweeny et al., 2016). Optimism also provides no benefit when the news is good: people with optimistic expectations are less elated by good news than are people who lower their expectations and brace for the worst (Sweeny et al., 2016). In contrast, pessimistic expectations may provide motivational benefits to prepare for bad news. People who expect a bad outcome might take steps to mitigate some of the difficulties that could accompany bad news (Sweeny, Carroll, & Shepperd, 2006). For example, a woman anticipating a breast cancer diagnosis could inquire about her employer’s extended leave policy or arrange for childcare. The literature on defensive pessimism convincingly shows that at least for some people, pessimistic expectations and associated anxiety can serve as a motivating force to take action (Norem & Cantor, 1986). However, pervasive distress can impede daily functioning and sound decision making. Thus, delaying the descent toward pessimism until the end of a wait may allow patients to capitalize on the benefits of both optimism and pessimism, suggesting that well‐timed optimism and well‐timed shifts from optimism may be key to an optimal overall medical waiting experience (Sweeny et al., 2006).

Key Future Directions for Research on Waiting for Health News Because poorly timed pessimism and prevalent distress may reduce patients’ ability to function and thus cope with uncertainty surrounding their health, interventions are needed to target patients’ distress and promote positive strategies to cope with uncertainty. For example, patients may also better cope with a wait if they are able to take their mind off of the news at hand. Although some research points toward distraction as a harmful method of coping (Lebel

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et  al., 2003; Sweeny et  al., 2016), other studies support the efficacy of the strategy (e.g., Boivin & Lancastle, 2010). One possibility is that distraction is only helpful to the extent that people are actually cognitively diverted from their worries and fears, with unsuccessful attempts further compounding the problem. If this is the case, research aimed at understanding activities that effectively distract from anxiety and intrusive thoughts could highlight promising interventions for those experiencing highly stressful medical waiting periods. For instance, research on flow demonstrates that autonomous activities that match one’s skill level can create cognitive immersion in the task as well as an energized focus, such that one loses reflective self‐consciousness and awareness of time. Flow has been associated with motivation to perform and decreased anxiety (Csikszentmihalyi & Rathunde, 1993), indicating it may foster an action orientation in patients and help decrease emotional distress. Other methods of distraction, such as fostering cognitive busyness, may also prove fruitful in helping people cope while waiting for uncertain medical results. Healthcare providers may also be able to meaningfully shape patients’ waiting experiences for the better by communicating effectively with patients. For example, women with abnormal mammogram results experience less anxiety when waiting for follow‐up testing if they are satisfied with the initial information they received from their healthcare provider (Pineault, 2007). Doctors and nurses with the responsibility of communicating such information may be in a unique position to ease patients’ future distress by ensuring that patients not only comprehend the information communicated to them but also are satisfied with the amount of information they receive. Patients may also have an easier time waiting to the extent that they know what to expect. If medical staff and healthcare facilities provide information to patients about typical waiting experiences in the form of counseling or informational pamphlets, patients may be better able to endure the wait for health news. Additionally, word use during medical visits, much like the quality of rapport between physician and patient, is associated with important patient outcomes such as patients’ feelings toward their physician and adherence to treatment recommendations (Falkenstein et  al., 2016). For example, patients liked physicians more when physicians used fewer negative emotion words and reported greater adherence intentions when physicians used less “I” language. Further investigation of word use may illuminate similar relationships with patients’ waiting experiences after a medical consultation, pointing to a relatively simple way to decrease distress while patients wait. Medical waiting periods are unavoidable and generally stressful experiences for patients. As research on extended periods of health‐related uncertainty burgeons, we hope to find ways for patients and healthcare providers to ease the burden of waiting for health news.

Author Biographies Angelica Falkenstein is a doctoral candidate in Social/Personality Psychology program at the University of California, Riverside. Her research focuses on expectation management processes as people anticipate important news in their lives and how expectations for news relate to other key waiting experiences such as emotion regulation and cognition. Angelica is also interested in waiting experiences in the context of healthcare and how such experiences and healthcare interactions predict patient outcomes. Michael D. Dooley is a doctoral candidate in the Social/Personality Psychology program at the University of California, Riverside. Mike’s interest focuses on the provision of social

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support in close relationships, particularly when support needs are uncertain or unclear. How do supportive others recognize when support is desired? How do they decide what support to provide? His research covers supportive behaviors and decision making in a variety of contexts, including waiting for uncertain health news. Kate Sweeny is an associate professor of psychology at the University of California, Riverside. Her work seeks to understand the experience of awaiting uncertain news. What makes waiting so stressful? Do some people have an easier time waiting than others? Are there ways to “wait well?” She addresses these questions in contexts ranging from the wait for an exam grade to the wait for biopsy results, news of pregnancy, and the outcome of medical tests.

References Armor, D. A., Massey, C., & Sackett, A. M. (2008). Prescribed optimism: Is it right to be wrong about the future? Psychological Science, 19(4), 329–331. doi:10.1111/j.1467‐9280.2008.02089.x Boivin, J., & Lancastle, D. (2010). Medical waiting periods: Imminence, emotions and coping. Women’s Health, 6(1), 59–69. doi:10.2217/whe.09.79 Csikszentmihalyi, M., & Rathunde, K. (1993). The measurement of flow in everyday life: Toward a theory of emergent motivation. In J. E. Jacobs (Ed.), Nebraska symposium on motivation, 1992: Developmental perspectives on motivation. Current theory and research in motivation (Vol. 40, pp. 57–97). Lincoln, NE: University of Nebraska Press. Falkenstein, A., Tran, B., Ludi, D., Molkara, A., Nguyen, H., Tabuenca, A., & Sweeny, K. (2016). Characteristics and consequences of word use in physician‐patient communication. Annals of Behavioral Medicine, 50, 664–677. Harkness, K., Morrow, L., Smith, K., Kiczula, M., & Arthur, H. M. (2003). The effect of early education on patient anxiety while waiting for elective cardiac catheterization. European Journal of Cardiovascular Nursing, 2(2), 113–121. doi:10.1016/S1474‐5151(03)00027‐6 Krizan, Z., Miller, J. C., & Johar, O. (2010). Wishful thinking in the 2008 US presidential election. Psychological Science, 21(1), 140–146. doi:10.1177/0956797609356421 Lang, E. V., Berbaum, K. S., & Lutgendorf, S. K. (2009). Large‐core breast biopsy: Abnormal salivary cortisol profiles associated with uncertainty of diagnosis. Radiology, 250(3), 631–637. doi:10.1148/ radiol.2503081087 Lebel, S., Jakubovits, G., Rosberger, Z., Loiselle, C., Seguin, C., Cornaz, C., … Lisbona, A. (2003). Waiting for a breast biopsy: Psychosocial consequences and coping strategies. Journal of Psychosomatic Research, 55(5), 437–443. doi:10.1016/S0022‐3999(03)00512‐9 Maxwell, J. R., Bugbee, M. E., Wellisch, D., Shalmon, A., Sayre, J., & Bassett, L. W. (2000). Imaging‐ guided core needle biopsy of the breast: Study of psychological outcomes. The Breast Journal, 6(1), 53–61. doi:10.1046/j.1524‐4741.2000.98079.x Montgomery, M., & McCrone, S. H. (2010). Psychological distress associated with the diagnostic phase for suspected breast cancer: Systematic review. Journal of Advanced Nursing, 66(11), 2372–2390. doi:10.1111/j.1365‐2648.2010.05439.x Nelson, S. K., Robbins, M. L., Andrews, S. E., & Sweeny, K. (2015). Disrupted transition to parenthood: Gender moderates the association between miscarriage and uncertainty about conception. Sex Roles. doi:10.1007/s11199‐015‐0564‐z Norem, J. K., & Cantor, N. (1986). Defensive pessimism: Harnessing anxiety as motivation. Journal of Personality and Social Psychology, 51(6), 1208–1217. doi:10.1037/0022‐3514.51.6.1208 Northouse, L. L., Jeffs, M., Cracchiolo‐Caraway, A., Lampman, L., & Dorris, G. (1995). Emotional distress reported by women and husbands prior to a breast biopsy. Nursing Research, 44(4), 196–201. Novy, D. M., Price, M., Huynh, P. T., & Schuetz, A. (2001). Percutaneous core biopsy of the breast: Correlates of anxiety. Academic Radiology, 8(6), 467–472. doi:10.1016/S1076‐6332(03)80617‐7

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Phelps, C., Bennett, P., Iredale, R., Anstey, S., & Gray, J. (2006). The development of a distraction‐ based coping intervention for women waiting for genetic risk information: a phase 1 qualitative study. Psycho‐Oncology, 15(2), 169–173. Pineault, P. (2007). Breast cancer screening: Women’s experiences of waiting for further testing. Oncology Nursing Forum, 34(4), 847–853. doi:10.1188/07.onf.847‐853 Poole, K., Hood, K., Davis, B. D., Monypenny, I. J., Sweetland, H., Webster, D. J. T., … Mansel, R. E. (1999). Psychological distress associated with waiting for results of diagnostic investigations for breast disease. The Breast, 8, 334–338. Schnur, J. B., Montgomery, G. H., Hallquist, M. N., Goldfarb, A. B., Silverstein, J. H., Weltz, C. R., … Bovbjerg, D. H. (2008). Anticipatory psychological distress in women scheduled for diagnostic and curative breast cancer surgery. International Journal of Behavioral Medicine, 15(1), 21–28. doi:10.1007/BF03003070 Scott, D. W. (1983). Anxiety, critical thinking and information processing during and after breast biopsy. Nursing Research, 32, 24–28. Shepperd, J. A., Ouellette, J. A., & Fernandez, J. K. (1996). Abandoning unrealistic optimism: Performance estimates and the temporal proximity of self‐relevant feedback. Journal of Personality and Social Psychology, 70(4), 844. doi:10.1037/0022‐3514.70.4.844 Stanton, A. L., & Snider, P. R. (1993). Coping with a breast cancer diagnosis: A prospective study. Health Psychology, 12(1), 16–23. doi:10.1037/0278‐6133.12.1.16 Sweeny, K., & Andrews, S. E. (2014). Mapping individual differences in the experience of a waiting period. Journal of Personality and Social Psychology, 106(6), 1015–1030. doi:10.1037/a0036031 Sweeny, K., Andrews, S. E., Nelson, S. K., & Robbins, M. L. (2015). Waiting for a baby: Navigating uncertainty in recollections of trying to conceive. Social Science & Medicine, 141, 123–132. doi:10.1016/j.socscimed.2015.07.031 Sweeny, K., Carroll, P. J., & Shepperd, J. A. (2006). Is optimism always best? Future outlooks and preparedness. Current Directions in Psychological Science, 15(6), 302–306. doi:10.1111/j.1467‐8721. 2006.00457.x Sweeny, K., & Cavanaugh, A. G. (2012). Waiting is the hardest part: A model of uncertainty navigation in the context of health news. Health Psychology Review, 6(2), 147–164. doi:10.1080/17437199.2 010.520112 Sweeny, K., & Falkenstein, A. (2015). Is waiting the hardest part? Comparing the emotional experiences of awaiting and receiving bad news. Personality and Social Psychology Bulletin, 41(11), 1551–1559. doi:10.1177/0146167215601407 Sweeny, K., & Falkenstein, A. (2017). Even optimists get the blues: Interindividual consistency in the tendency to brace for the worst. Journal of Personality, 85(6), 807–816. Sweeny, K., & Krizan, Z. (2013). Sobering up: A quantitative review of temporal declines in expectations. Psychological Bulletin, 139(3), 702–724. doi:10.1037/a0029951 Sweeny, K., Reynolds, C. A., Falkenstein, A., Andrews, S. E., & Dooley, M. D. (2016). Two definitions of waiting well. Emotion, 16(1), 129–143. doi:10.1037/emo0000117 Thorne, S. E., Harris, S. R., Hislop, T. G., & Vestrup, J. A. (1999). The experience of waiting for diagnosis after an abnormal mammogram. The Breast Journal, 5(1), 42–51. doi:10.1046/j.1524‐4741. 1999.005001042.x Uchino, B. N. (2009). Understanding the links between social support and physical health: A life‐span perspective with emphasis on the separability of perceived and received support. Perspectives on Psychological Science, 4(3), 236–255. doi:10.1111/j.1745‐6924.2009.01122.x

Suggested Reading Montgomery, M., & McCrone, S. H. (2010). Psychological distress associated with the diagnostic phase for suspected breast cancer: Systematic review. Journal of Advanced Nursing, 66(11), 2372–2390. doi:10.1111/j.1365‐2648.2010.05439.x

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Pineault, P. (2007). Breast cancer screening: Women’s experiences of waiting for further testing. Oncology Nursing Forum, 34(4), 847–853. doi:10.1188/07.onf.847‐853 Sweeny, K. (2012). Waiting well: Tips for navigating painful uncertainty. Social and Personality Psychology Compass, 6(3), 258–269. doi:10.1111/j.1751‐9004.2011.00423.x Sweeny, K., & Cavanaugh, A. G. (2012). Waiting is the hardest part: A model of uncertainty navigation in the context of health news. Health Psychology Review, 6(2), 147–164. doi:10.1080/17437199.2 010.520112

Index

23andMe 89 longitudinal study  92 absent‐exempt heuristic  522 accelerometers output (counts per unit time)  448 selection 448 validation studies, results  453 validity outcomes  453 accelerometry application 449 electronic diaries, combination  454–455 accelerosensors, usage  447–448 acceptance (grief stage)  43 occurrence/increase 44 acceptance cues, overlooking  618 acculturation alcohol use, relationship  3 bicultural integration  6–7 cigarette use, relationship  3 conceptual/theoretical considerations  6–7 contemporary models, bidimensional/ multicomponent process  2 cultural stress, separation  4 differential acculturation theory (DAT)  5 illicit drug use, relationship  3 longitudinal/epidemiological studies  9 methodological concerns  8–9 multidimensional construct  7 multidimensional model  6

receiving context, effects (understanding)  8 substance use mechanisms, understanding  4–6 substance use, relationship  1, 2–3 unidimensional studies  5 unidimensional view  2 within‐group diversity  8 accuracy consequences, benefits  14 defining 13–14 downsides 17–18 interpersonal perception, accuracy  13 motivation 582–583 “realistic accuracy” approach  13 acquired immunodeficiency syndrome (AIDS) beliefs, assessment  495 mortality, risk  486 partner loss, coping  415–416 risk 495 action change stage  367 control, top‐down/bottom‐up fashion  333 planning 187–188 plans 315 absence 319 active engagement (relationship‐coping strategy) 66 activity‐based variables  452 acute discrimination  232 acute grief, distress  44 acute infections, death (reduction)  61

The Wiley Encyclopedia of Health Psychology: Volume 2: The Social Bases of Health Behavior, First Edition. General Editor: Lee M. Cohen. Volume Editors: Kate Sweeny and Megan L. Robbins. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

790

Index

acute prototype  318 chronic prototype, contrast  318 adaptation coping, contrast  65–66 core systems, operation  273 protective/vulnerability processes, impact  271 adaptation neglect  22 impact 23 reduction, interventions  23–234 adaptive tasks framework  62 addiction risk  159 additive counterfactuals  72 adequacy, self‐perceptions (challenges)  587 adherence 395. See also patient adherence achievement (factors), information‐motivation‐ strategy model (usage)  399 educational interventions  399–400 electronic monitors, accuracy  398 improvement, interventions  399–400 measurements 398 rates, increase (economic benefits)  397 routines, facilitation  323 adjustment approach‐oriented coping, benefits  65 good adjustment  66 adolescents health risk behavior  517 PWM research  518–519 adrenocorticotropic hormone (ACTH) levels, elevation 663–664 adults life (well‐being challenges), physical/social pain (impact) 459 PWM research  519 Adverse Childhood Experiences (ACE) research project 257–258 adversity exposure  271 affect‐based risk perception, characteristics  552–553 affect heuristic  552–553 consequence/implication 553 affective forecasting  21 health, relationship  21–22 affective states, perception  611 Affordable Care Act, enrollment process (streamlining requirement)  293 African Americans (sickle cell disease)  169 age, disability (relationship)  169 agency 218 Agency for Healthcare Research and Quality (AHRQ) 570 aggressive tendencies, origins  36

agreeableness  412, 423–424 conscientiousness, connection  434 health behaviors, relationship  423–424 tendency 433–434 alcohol consumption, sex differences  634 myopia theory  600 overconsumption 159 use 729 acculturation, connection  3 increase, agreeableness (relationship)  424 Alcohol Attention‐Control Training Program (AACTP) 332 Alcoholics Anonymous (AA), enrollment  219 All Aspects of Health Literacy Scale (self‐report measure) 288 allocations, perception (dispositional tendencies) 227 allostasis 272 allostatic capacities, taxation  273 allostatic load  272 541 Alzheimer’s disease (AD), anxious attachment 340 ambiguity aversion 50–51 meaning 50 ambiguous information  49, 50 ambulatory activity assessment, impact  453 ambulatory activity monitoring  454–455 impact 449 reactivity, findings  454 ambulatory assessment methods  374–375 Electronically Activated Recorder (EAR), contrast 378–379 longitudinal ambulatory assessments  374–375 physiology/experience 375 American College of Sports Medicine (ACSM) intervention techniques  197 American Indians, suicide  170 amygdala, threat detection involvement  698 anchoring‐and‐adjustment heuristic  549 anger (grief stage)  43 anger, trait hostility (association)  530 anorexia nervosa, anxiety/avoidance (report)  34 antecedents 520 anterior insula activity, reduction  460–461 threat detection involvement  698 anterior temporal pole, activity (increase)  35 anticipated discrimination, differentiation  232 anticipatory coping  56

Index anxiety disorders 256 elicitation 783 excessive counterfactual, connection  73 pattern (nonmedical waiting periods)  782 reduction, smoking (usage)  34 role 781–782 appraisal‐coping cycle  64 appreciation feelings, oscillatory systems coherence/ entrainment (relationship)  264 gratitude, feelings  261 approach, avoidance (contrast)  65 approach/avoidance tasks, bias training usage  332 approach bias training  332–333 approach imagery  610 approach‐oriented coping, benefits  65 artificial intelligence (AI) research, usage  51 Ashkenazi Jews, Tay‐Sachs disease  170 Asians, liver cancer  169 asset factors, effects  270–271 asthma (chronic inflammatory process)  322 Asthma (Puerto Ricans)  170 asthmatics, bronchoconstriction (physiological measure) 477 atherosclerosis 256 physiological pathway (optimism)  387 attachment anxiety 542 predictions 36 avoidance dimension 32 physical health, link  32–33 capacity 269 defining 32 differences, health behavior (link)  33–35 dyadic influence  340 health behavior, relationship  340 relationship 339–340 individual differences  31, 36 physical health outcomes, linking mechanisms 33–37 insecurity 339–340 orientation  31, 32 physical health, link  32–33 social support, link  33 physical health associations, examination  38–39 correlates 339–340 problems, emotional regulation skill problems (association) 35

791

processes, health (relationship)  31 theory dyadic framework  37–38 proposal 43 attentional focus, target (inhibition)  558 attention‐allocation model  600 attentional retraining  331–332 repetition, randomized controlled trial (RCT) 332 attentional skills, application  137 attention deficit hyperactivity disorder (ADHD) 449 attention, reduction  669 attitudes 521 definition 747–748 situation, comparison  248–249 subjective norms/attitudes  521 autobiographical narratives collection 218 data, representation  217 quantification 218–219 transformational processing  219 autobiographical reasoning  216 autobiographical scenes, providing  217 autoimmune conditions, childhood trauma (link)  257–258 automatic decisions  162 neuroendocrine axis  659 autonomic nervous system (ANS) activity, modulation  664 oscillatory systems coherence/entrainment, appreciation feelings (relationship)  264 rumination, relationship  560 sleep disturbances, impact  541 sympathetic branch, activation  256 autonomy 206 avoidance. See treatment seeking/avoidance approach, contrast  65 avoidance‐oriented coping, impact  310 demographic predictors  280–281 dimension 32 health behavior risks, association  34 healthcare treatment seeking, relationship  297 negativity 300–301 prediction, education (impact)  281 strategies 413 avoidant attachment characteristics, impact  32–34 immune function, link consistency (reduction)  36 Bandura, Albert  185 bargaining (grief stage)  43

792

Index

baseline comparisons, formation  316–317 Beall, Sandra  149–150 Beecher, Henry  475 behavior assessment 375 attitudes 756 behavior‐related moderators  356–358 counterfactuals, relationship  75–76 effectiveness, belief  213 expectations 520 identification/defining (PAPM step)  502 interference, stress/distress/dysregulated emotion (impact)  135 intervention materials, usage  196–198 process 195–196 target, identification  194–195 maladaptive behaviors, engagement  135 modification 366 moment‐salient behaviors  161 nature 193–194 negative behaviors (increase), counteractuals (impact) 76 one‐way behavior change, occurrence  76–77 prediction, health belief model (usage)  213–214 present codings, absent codings (contrast)  377–378 real‐time capture, EAR (usage)  376–377 types 519 willingness, definition  517–518 behavioral aggression  530 behavioral avoidance  65 behavioral change, health belief model  212 behavioral coping  65 behavioral goals  194 behavioral strategy  186 behavioral willingness  185 behavior change active ingredients  198 difficulty 502 interventions 758–759 development/evaluation, PAPM (usage)  502–503 model 364 norms, usage  727–729 occurrence 76 persuasion, role  747 stage theories, impact (process)  495–496 theories, cataloguing attempt  183 behavior maintenance description 203–204 factors 205–206

importance 203 interventions/research, directions  207 operationalization process  204–205 promotion 206 regulatory focus, effect  206 satisfaction, role  205 self‐determination theory (SDT), application 206 beliefs, meanings (variation)  316 benefit finding  154 bereavement 43 acceptance, occurrence/increase  44 adaptation, trajectories  44 broken‐heart phenomenon  45 persistent complex bereavement disorder (PCBD) 44 physiological changes  45 widowhood effect  45 beta‐band frequency (physiological measure)  477–478 bias bias‐reduction strategies, success  513–514 documentation 512 reduction 105 role 14 training, usage  332 biased treatment  232 bicultural identity integration (BII)  6–7 bicultural integration  6–7 big data collection  381 binge drinking intentions (reduction), counterfactual thinking (impact)  77 biography, cultural concept  216 biological embedding, theory  257–258 biological hyperarousal, reduction  273 biological processes, impact  35–36 biological third variables, involvement  431 biomarkers, measurability  134 bipolar disorders  449 Black patients, narcotic medication (prescription likelihood) 511 blood pressure, spousal support (impact)  341 bodily metaphoric framings, exposure (impact) 123 bodily metaphors  122 experimental evidence  123–124 helpfulness, timing  125–126 implications 124–128 pain 126–128 qualitative evidence  122–123 qualitative linguistic analyses  124

Index body physiological systems, wear and tear  134 stress, impact  256 body mass index (BMI)  649 gains, risk  652 increase, marriage (association)  653 social relationships, impact  651–652 boosters, incorporation  179 breast cancer couples, coping  144 patients (engagement coping), optimism (association) 416 Breast Cancer Literacy Assessment Tool  289 broaden‐and‐build theory  490 broken‐heart phenomenon  45 buffering model  660 bulimia nervosa, anxiety/avoidance (report)  34 cancer adaptation, couple‐relationships  81 adherence rates  396 cancer‐related behaviors/emotions, uncertainty (association) 308–309 functional abilities (decline), care/assistance (couples discussion)  84 partner/family member care  81 patients/partners, QOL challenges  82–84 patients, spirituality (presence)  713 survival, increase  81 treatment, pattern challenges  83–84 war metaphor 123–124 vocabulary 126 “Cancer by the Carton” (Norr)  114 cancer screenings  570 decisions 21 guideline, conflicting information  49 improvement 569 uptake 572 cardiac arrhythmias  256 cardiovascular activity, rumination (association)  560 cardiovascular ailments  257 cardiovascular disease high blood pressure, risk factor  387 optimism, impact  387 cardiovascular health, relationships (impact)  342 cardiovascular pathway, positive relationship interactions 342 cardiovascular reactivity  256 increase, perceived prejudice/discrimination (impact) 233 reduction, friend social support (impact)  240

793

cardiovascular responding  539 cardiovascular smoking‐related diseases, tobacco company refutations  114 cardiovascular system, functioning (health indicator) 338 care seeking  321 car injury (likelihood), seat belt avoidance (relationship) 35 casual sex, willingness (increase)  522 causal geography  168 cause (CSM component)  100 cell‐mediated immunity, optimism (relationship) 388 Center for Epidemiological Studies Depression Scale (CESD)  485–486 central nervous system, nonsteroidal anti‐ inflammatory analgesic (impact)  460–461 central route processing  366 challenge, attitude  440 change. See behavior change barriers 186 stages  186, 367 childhood, peer rejection/victimization  766 childhood trauma, autoimmune conditions (link) 257–258 Choosing Life: Empowerment! Action! Results! (CLEAR) 197 chronic conditions, adherence  321 chronic disease, prevalence (growth)  116–117 chronic illness adaptation 62–64 cognitive adaptation  63–64 coping 61 defining 61–62 management/coping, social comparison (role) 248 stresses 62 timeline, length  62 chronic pain coping, positive affect (impact)  463 social interventions  464 vulnerability/distress, social contributors  461–462 chronic prototype, acute prototype (contrast)  318 chronic stress, resilience  717 chronic trauma, experience  258 chronotype, impact  672–673 cigarettes lethality, modification  193–194 smoking, addictive properties  159 tax, doubling (impact)  116 use, acculturation (relationship)  3

794

Index

circadian phase (chronotype)  672 circulating pro‐inflammatory cytokine levels, optimism (relationship)  388 clinical decision making, stereotyping/prejudice (influence) 510–512 clinical depression, diagnosis  531 clinical disease‐related outcomes, rumination (link) 563 clinical preventive services, USPSTF recommendations 570 clinical recommendations, stereotyping/prejudice link (absence)  512–513 coaching (self‐efficacy belief source)  186 codes of conduct, importance  709 cognitive avoidance  65 cognitive behavioral‐based approaches  156 cognitive closure, requirement  308 cognitive coping  65 cognitive dissonance theory  751 cognitive economy, impact  582 cognitive fatigue  179 cognitive habits  736–737, 738 cognitive mistrustfulness  530 cognitive reasoning, capacity  269 cognitive restructuring  45 emotion‐focused coping activity  413 cognitive space, freeing  177 coherence 264 psychological sense, restoration  273 college student drinking, personalized normative feedback (context)  728–729 colorectal cancer (CRC), perceived risk  573 colorectal/prostate screening, uptake study  572 commitment, attitude  440 commitment, control, and challenge (three Cs)  440–441 commonsense model (CSM)  99, 315 acute prototype  318 baseline comparisons, formation  316–317 chronic conditions, adherence  321 components, assessment (timing)  320–322 descriptions 101 episodic prototype  318–319 framework 100 illness prototypes/representations, multilevel process 318–320 long‐term self‐management, action (automation) 322–323 method/theory, confusion  320 principles/structure 315–316 representations/prototypes 317–318 differentiation, importance  316–317

representations (activation), reasoned processes (involvement) 317 structure/operation 316–323 treatments, prototypes/representations (structure/content similarity)  319 use/misuse 320–323 commonsense modeling (CSM)  315 common sense, representation  321 communication patterns (challenges), cancer treatment (impact)  83–84 communion 218 community structure, geographic differences 173 comparative optimism  249 comparison mode 248 social comparison  247 compensation hypothesis  609 competence  206, 269 compliance stereotyping  511 complicated grief  44 development 45 randomized controlled trials  45 compulsive self‐sufficiency  34 computer‐tailored interventions  365–366 concomitant adversity  271 concrete metaphors, reliance  122 conduct problems, hardiness (negative relationship) 442 confidence issue 385 mechanism 581–582 conflict issue 48–49 physiological correlates  341 conflicting health information  47 conceptual typology  48–49 working definition  48 conflicting information artificial intelligence (AI) research, usage 51 construct, defining  47–48 evidence heterogeneity  49 multiplicity 49 related phenomena  49–50 sources, trustworthiness  52 temporal inconsistency  49 theoretical models  50–52 confrontations, fear  125 confuse, term (defining)  50 confusing information  49, 50 connectedness, perceptions  682

Index conscientiousness  412, 422–423 agreeableness, connection  434 health‐compromising behaviors, avoidance (relationship) 422–423 trait associations 415 predictions 432 conscious control, freedom  178 consciousness raising  184 behavioral strategy  186 conscious processes  178 construals, role  738–739 Consumer Assessment of Health Providers and Systems—Clinician and Group Surveys (CG‐CAHPS) 404 contamination 218 contemplation (change stage)  186, 367 contemplators (behavior)  521 content goals  194 context change interventions  330–331 context‐related moderators  356, 358–359 contexts, defining (considerations)  430 contingency management (behavioral strategy) 186 continuous positive airway pressure (CPAP), treatment 579 continuum models  183–186 effectiveness, comparison  188 target variables, setting  184 control attitude 440 control, life satisfaction pillar  684 locus, external causes  606 patterns (challenges), cancer treatment (impact) 83–84 perceived control, increase  284 controllable stressors  705 coping 413–414 adaptation, contrast  65–66 adjustment 416–417 appraisal 184 behaviors  141, 145 not‐in‐the‐moment coping behavior  142 chronic illness  61 conceptualization 141–142 expansion, transactional models (usage)  55 contrasts 413–414 coping‐relevant facets  144 counterfactuals, relationship  73–74 daily diaries  142–143 daily measurement  142 dyadic coping  66–67

795

ecological momentary assessment  142–143 family coping  67–68 five‐factor model, relationship  415 in‐the‐moment coping strategies, EMA (impact) 143 measurement 141–142 naturalistic observation  143–144 optimism, relationship  415–416 personality, relationship  411, 414–416 perspectives 145 planning 187–188 proactive coping  56, 414 process 56 social influences  58 sibling coping  68 social context  57–68 social factors, positive effects  463 social threat, impact  462 strategies  55–56, 310 categories 65 selection 57 stress and coping paradigm  64–65 style 235 systemic transactional model (STM)  67 theories 64–65 theory building  145 transactional model  309–310 types, contrast  56 coronary artery bypass surgery, short‐term health outcome 386 coronary heart disease (CHD) diagnosis (risk increase), depression (association) 531 diagnosis/mortality, reliability  531 environmental factors  531–533 psychosocial factors  529 social support  532 trait hostility/anger ratings, elevation  530 work stress  531–532 corticotrophin‐releasing hormone (CRH), disruption 256 cortisol increase 642 social‐evaluative threat (SET), impact  643–644 levels, disruption  256 partner levels  338 reactivity, increase  233–234 social‐evaluative threat (SET), impact  641–642 reactivity, social‐evaluative threat (SET) components (link)  642–643

796 cortisol (cont’d) release  36, 337–338 response, exaggeration  540 rumination, relationship  561 Counterfactual Inference Test (CIT)  74 counterfactuals behavior, relationship  75–76 coping, relationship  73–74 counterfactual‐driven behavior change, focus 76 health decisions/health‐relevant behaviors  75–78 helpfulness 75 intentions, relationship  76–77 motivation, relationship  78 trauma survivor generation  73 counterfactuals, types  71–72 counterfactual thinking binge drinking, relationship  77 consequences 77 links 73 reduction Parkinson’s disease, relationship  74 schizophrenia, relationship  74–75 counterfactual thought  71 deficits 74–75 excess 72–73 personal control, chronic deficits (role)  73 prevalence, elevation  73 counterstereotyping 513 couple‐based interventions  84–85 confidence intervals  85 randomized controlled trials, review/meta‐analysis 84 weighted mean effect sizes  85 couple‐relationships, cancer adaptation  81 cravings 163 cue‐behavior implicit associations, strength  179 cultivation hypothesis  609 cultural competency (psychosocial concept) 226 cultural differences exploration 241–242 factors 357 cultural identifications  2 cultural practices  2 cultural stress, acculturation (separation)  4 cultural values  2 culture, impact  424, 650 daily diaries methodologies, benefits  142 usage 142–143

Index daily events (knowledge improvement), EMA (impact) 142–143 data check  451 data reliability, limitations  50 deceased, kinship relationship  44 decisional remorse, avoidance  580–581 decision making, frontal lobes (involvement)  74–75 decision‐making processes, emotion (integration) 75 decision simplification  330–331 deeming rule  116 deep‐level prototypes, disease‐specific prototypes (connection) 318 de facto selective exposure  580 defensive resistance  589–590 demand‐control hypothesis  531 demand‐withdrawal communication patterns, increase 341 demand‐withdraw interactions  377 demographics, DTC genetic testing  90 demographic variables, patient satisfaction (relationship) 406 denial (grief stage)  43 depression CHD diagnosis, risk increase (association)  531 decrease 135 emotion regulation difficulties  35 excessive counterfactual, connection  73 grief stage  43 metaphor 122 metaphorical understanding  124 risk, magnification  271 social exclusion, relationship  460 stress‐reactive rumination, role  558 depressive symptoms  154 HPA functioning, disruption (relationship) 256 reduction, R/S (impact)  712 Descartes, René  616 descriptive norms  572–573 motivational underpinnings  725–726 screening behaviors, relationship  727 destination, metaphor  122 developmental status, consideration  274 diabetes, adherence rates  396 Diabetes Numeracy Test  289 diagnosis (delay), rumination (effect)  563 diagonals (behavior)  520–521 DiClemente, Carlo  186 diet culture, impact  649–650 differences, impact  172

Index family relationships  652–653 Lifestyle Triple P intervention  653 modeling, impact  653–654 parenting 652–653 romantic relationships, effects  653 social factors  649 social networks, effects  652 social norms  650–651 social support  651–652 socioeconomic status (SES)  650 differential acculturation theory (DAT)  5 direct cultural geographic factors  171–173 direct effects  232 model  240, 241 systemic/interpersonal mistreatment  232–233 direction, metaphor  122 direct physical geographical factors  170–171 direct‐to‐consumer (DTC) genetic testing  89 advertising 92 barriers/benefits, perception  91 consequences 92–94 demographics 90 health behaviors, changes  93 healthcare professional consultations  92–93 knowledge/awareness 91 psychological effects  93 psychosocial predictors  90–92 regret, anticipation  92 results comprehension 93 misinterpretation, possibility  91 viewing, avoidance  93–94 risk framing  902 usage/nonusage, self‐reported reasons  90–91 disability, age (relationship)  169 disability paradox  23 discounting, theory  162 discrimination adult reports  234 direct effects  232–233 effect, moderators  235–236 forms 232 health consequences  231 indirect effects  233–235 perception/coping, fairness beliefs (impact) 236 screening, relationship  574–575 stress, coping  235 systemic/interpersonal mistreatment  232–233 terms, defining  231–235 disease burden, health behaviors (relationship)  363 chronic disease, prevalence (growth)  116–117

797

disease‐prone personalities, health trajectories 430 disease‐related variables, impact  99 disease‐specific prototypes, parameters (impact) 318 disease‐specific worry  97 genetic screening, association  573–574 onset 529 pathogenesis, effects  538 perceived risk  571 perceptions 249 proneness 430 recovery, relationship quality (connection)  339 risk, learning (avoidance)  282 rumination, impact  563–564 severity, meta‐analysis  299 social comparison, relationship  248–249 susceptibility 131 worry, degree (determination)  99–101 worry, question  98–99 disengagement avoidant techniques  414 coping, engagement coping (contrast)  414 disengagement‐related strategies  415 dispositional optimism  309 dissonance, reduction  588 distal motivational goals  742 distal TMHM responses  743 distant intercessory prayer  712–713 distress experience 131 minimization 33 expression, minimization  33 mitigation, avoidance‐oriented coping (impact) 310 psychological distress, increase  81 reduction, avoidance (usage)  57 social contributors  461–462 distributive justice beliefs 227 social justice, relationship  224–226 divorce likelihood 15 psychosocial effects, immune system (link)  540 “Do Defaults Save Lives?” (Johnson/ Goldstein) 197 domain‐specific self‐efficacy, measurement  608 dorsal anterior cingulate cortex (dACC) activation levels, increase  459 activity, reduction  460–461 threat detection involvement  698 downward comparisons  63–64 role, evaluation  249

798 downward counterfactuals  71 likelihood 74 downward social comparison, addition (impact) 251 dramatic relief (behavioral strategy)  186 drug use (increase), agreeableness (relationship) 424 dual effects hypothesis  690 dual‐process account  519 dual‐process approaches  162–165 dual processing, evidence  522, 523–524 dual‐process models  520 theme 178 dual‐process theories  160, 161 Duval, Shelley  597 dyadic coping  66–67 partners, benefit  67 promotion 83 dyadic framework  37–38 dyads, focus  58 dysregulation emotion, impact  135 early trauma, experience  258 eating disorders, appearance concerns  601 eating, social facilitation (trigger)  651 Ebola outbreak (2014), American response 348 ecological momentary assessment (EMA)  105, 142–143 advantages 106–107 characteristics 105–106 data collection approaches, extension  110 event‐contingent EMA methods, usage  108 measurement considerations  107–108 methodology, longitudinal nature (advantage) 107 methods daily life capture (ability)  109 development 106 directions 110–111 usage, opportunities/benefits  110 sampling approaches  108–109 studies, limitations  108 success/applications 109–110 time‐based EMA sampling approaches, usage 109 timing, attention  105–106 usage, increase  145 Ecological Momentary Intervention (EMI)  110 education, discrimination  233 efficaciousness (self‐efficacy belief source)  186 effort‐reward imbalance model  531

Index elaboration likelihood model (ELM)  366, 748–749 principles 749 Electronically Activated Recorder (EAR)  143–144, 376–379 ambulatory assessment methods, contrast  378–379 challenges 379 complex/molar psychological construct  377 data, psychometric properties (assessment)  378 iEAR app privacy button  378 randomization feature  377 information capture  378 method, ethical considerations  378–379 methodology, usage  379–380 sampling rate, programming  376–377 sound files, analysis process  377–378 electronic diaries, accelerometry (combination) 454 embodied cognition, perspectives  121 embodied health  121 emergency care setting, care seeking  321 emotional anger  530 emotional approach coping  65 emotional/cognitive processes, cravings/desires (impact) 163 emotional consequences, prediction (accuracy)  27 emotional disclosure  135–136 emotional distress  704 management 64–65 pain‐related emotional distress  459 emotional eating, discrimination (association)  234 emotional experience/response, dynamic features 133 emotional expression, enhancement/suppression  136–137 emotional health, adversity (impact)  271–272 emotional intensity, prediction (accuracy)  26 emotional moments, experience  97 emotional pain, intensity (underestimation)  24 emotional/physiological states  186 emotional stability  424–425 emotional states experience 134 impact 164 emotional worry, transfer  123 emotion‐focused coping categories 413 efforts 55–56 emotional distress management  64–65 problem‐focused coping, contrast  413–414

Index emotion regulation  35, 131–133 differences, intrinsic/extrinsic factors  137 divorce, relationship  542 effects, differences  137 ineffectiveness 135 predictions 133–136 skill, individual/developmental variability  136–137 emotions context‐appropriate regulation, engagement 136 emotion‐related behavior, factors  133 forecast, accuracy  25 management 133 EmotionSense apps, ability  381 emotions, impact overestimation 22–24 underestimation 24–25 empathy gap experience 24–25 possibility 511 employment discrimination  233 enabling hypothesis  609 endocrine dysregulation, sleep disturbances (impact) 541 endocrine pathway, positive relationship interactions 341–342 end‐of‐life arrangements, making  65 endothelin‐1 (vasoconstrictor role)  560 energy/enthusiasm, decrease  669 energy expenditure (EE)  447, 452 expenditure‐based variables  452 energy, loss  673 engagement coping, disengagement coping (contrast) 414 environmental cues, impact  163 environmental variation, adjustment (biological mechanisms) 272 environment, quality (policies)  174 episodic prototype  318–319 epistemic uncertainty, appearance  50 epoch (time interval)  448 escape model  601 esteem contingencies, research  743 estimation strategies  374 ethnic disparities  233 ethnicity, health literacy predictor  290 ethnic minorities, physical activity (increase)  234 evaluative conditioning (EC) tasks, pairings  332 event‐contingent EMA methods, usage  108 events (importance), appraisal (stability)  26 evidence heterogeneity  50

799

evocation, pathway  431 excessive counterfactual depression/anxiety, connections  73 personal control, chronic deficits (role)  73 executive control, loneliness (impact)  697 executive functioning, compromise  669 exercise, sex differences  634 expectancy‐value process models, usage  413–414 expected subjective utility  549–550 experience ambulatory assessment methods  375 momentary data collection  106 openness 412 experienced discrimination, differentiation  232 experience sampling method (ESM)  106 explicit bias  510 explicit health‐related attitudes/behaviors (change), persuasion (impact)  748–750 explicit stereotyping/prejudice  508–509 expressive writing (EW)  149 administration, guidelines  150 demand characteristics  151 effectiveness 150 health benefits  152–153 impact mechanisms 155–156 studies 152 testing 154 individual differences  154 instructions, differences  154–155 intervention 154–155 medical outcomes  152–153 meta‐analysis, usage  150 moderating factors  153–155 physical functioning  152–153 physical health, relationship  151–152 physiological outcomes  150 psychosocial functioning  153 research, early years  149–151 studies, implication  151 usage 542 uses/limitations 152 well‐being, relationship  153 extraversion 412 characteristics 432–433 health‐compromising behaviors, relationship 423 failure feedback, administration  600 fairness, beliefs (impact)  236 fair process effects  225 false self, maintenance  601

800

Index

familial environments (parental psychological health impact), assessment (EAR usage) 380 familial ethnic socialization  5 family coping 67–68 development system  5–6 family systems theory  67 interactions (investigation), EAR (usage)  380 members, social support (effect size)  240 unitary system (family systems theory)  67 Family Smoking and Prevention Tobacco Control Act 116 fatigue, feelings  673, 674 fat‐thin IAT  5109 fear 184 appeals, role  748 emotion, activation  319 excess, problems  127 feedback (self‐efficacy belief source)  186 feelings feeling bad, avoidance  580–581 feeling good  580–581 impact 615 qualitative nature  552–553 fibromyalgia, pain (adaptation)  463 fight‐or‐flight response  134, 257, 429 firearm use, legislation  172 first‐generation immigrants, heritage (reconciliation/integration) 6 Fisher, Ronald  199 five‐factor model  412, 413, 415 flourishing, term (usage)  765 focalism 22 impact 23 results 25 food overconsumption 159 selection, culture (impact)  649–650 Food and Drug Administration (FDA), deeming rule 116 food choices criticism 691 modeling, impact  653–654 foot‐in‐the‐door (FITD) technique  752 forecast accuracy, promotion (process)  23 forecasting affective forecasting  21–22 bias, magnitude/direction (factors)  25–26 errors, recognition  24–25 strengths/weaknesses, identification  25–27 foreign‐born individuals, global south origin  1

Framingham Heart Study (FHS)  652 frequent electrodermal activity and event logging (FEEL) system  145 friendship health correlates/consequences  239 epidemiological evidence  239–240 health link, mechanisms  240–241 individual differences  241–242 interventions friends, usage  242 support groups, usage  242 negative influences  241 frontal lobe, impairment  74–75 fruits and vegetables (FV) consumption 650 intake, environmental determinants  651 fulfillment, cognitive factors  161–162 functional abilities, decline  84 functional social support, longitudinal studies 532 future distress, overestimation  23 future emotion, intensity (judgement)  25–26 future events, emotional impact overestimation 22–24 underestimation 24–25 fuzzy‐trace theory  51 gain‐framed message  355, 360 persuasion 358 responsiveness, increase  357 Galen 429 gambling, legislation  172 gender‐based discrimination  234 general justice beliefs, descriptions  227 general self‐efficacy  609 concept, development  607 measurement 608 genetic polymorphism, presence  539 genetic screening  569–570, 573–574 genetics‐related self‐efficacy, decrease (report)  83 genetic test, completion (simplicity)  89 genetic testing decisions 21 web‐based information, offering  574 genomic technology, advances  574 geography causal geography  168 direct cultural geographic factors  171–173 direct physical geographical factors  170–171 dynamism 174 health, geography  167 indirect geographical factors  169–170

Index geography as demography  167 global self‐reports, concerns  375 glucocorticoid regulation, impact  540 glucose, production  256 goals abandonment 625–626 automatic strategies  626–627 characteristics 624–625 cognitive strategies  627 differences, motivational focus (impact) 624–625 distal motivational goals  742 external imposition  625 goal‐directed health behavior  601–602 health goals, adoption  624 planning strategies  626 pursuit, predictor  625 selection, sleep loss (impact)  674 setting 624–626 predictor 625 striving 626–628 go/no‐go task, bias training usage  332 gratitude causal influence, test  262 defining 261 health behaviors, relationship  263–265 implications 261 link, moderators  265 outcomes, relationship  374 letters, writing (impact)  265 physical health, relationship  263–265 positive affect  262 psychological well‐being, relationship  262–263 sleep, link (importance)  264 social relationships  263 trait gratitude, increase  264–265 gravitational acceleration, measurement  448 gravitational constant  448 grief acute grief, distress  44 complicated grief  44–45 coping 81 medicalization 45 occurrence, reasons  43 stages, theories  43–44 grit, hardiness (connection)  443 grounded cognition, perspectives  121 group behavior basis, social identity (link)  679–680 group identification, influence  235

801

Groups 4 Health (G4H) intervention 684–685 modules  685 habits 177 defining 177–178 formation, process  179 health behavior  178 research 178–179 directions 179–180 theory 178 habitual responses, triggering cue (association) 179 hardiness approach 439 evolution 441–442 assessment, development  440 conduct problems, negative relationship  442 grit 443 levels (males)  442 optimism, relationship  442 practice applications  443 predictors, comparison  442–443 studies (military personnel)  442 training (IBT managers)  443 health. See public health affective forecasting, relationship  21–22 attachment dyadic influence  340 processes, relationship  31 relationship 339–341 beliefs, meta‐analysis  299 causal role (playing), confidence (increase)  168 concerns, worry  98 condition, perceived susceptibility  358 consequences  231, 239 life stories correlates  215 coping, adjustment  416–417 decisions counterfactuals, relationship  75–78 improvement 330–331 defining 261–262 discrimination/prejudice, effect (moderators)  235–236 dyadic framework  37–38 economics/policy, PRO role (increase)  110 embodied health  121 emotion impact 223–224 regulation, prediction  133–136 explicit health‐related attitudes/behaviors (change), persuasion (impact)  748–750

802

Index

health. See public health (cont’d) friendships, link epidemiological evidence  239–240 mechanisms 240–241 friendships, negative influences  241 friends, influence  242 geographic influences  173–174 geography 167 goals, adoption  624 gratitude health implications  261 link, moderators  265 health‐compromising behaviors, harm  421 health‐enhancing behaviors, relationships (impact) 339 health‐promoting behaviors  589–590 analysis 240 health‐protection action, rational thought  102 health‐related attitudes change, self‐persuasion (impact)  750–752 persuasion, role  747 health‐related behaviors change, self‐persuasion (impact)  750–752 research, directions  179–180 health‐related information, understanding/ communication (difficulty)  508 health‐related intentions, associations (meta‐analyses) 185 health‐related policy, effects  113 health‐related propositions  48 health‐related social control, research  691 health‐related social influence  691–692 health‐related uncertainty  305 positive primary appraisal, achievement  310 health‐relevant behaviors counterfactuals, impact  75–78 engagement, psychological factors (influence) 599 health‐relevant intentions, counterfactual thinking (consequences)  77 health‐relevant pathopsychological pathway  539–540 health‐relevant symptoms, self‐perceptions  599 hormones, role  633 implicit health‐related attitudes (change), persuasion (impact)  750 improvement expressive writing (EW), impact/ mechanisms 155–156 PA increase, impact (question)  490–491 policy, impact  113 inequity, social justice framework (connections) 225

information conflict 47 self‐affirmation/defensive resistance  589–590 intentions, prediction (ability)  757–758 latitude 171 maintenance 302 mental models, metaphor shaping  127 messages, framing recommendations  356 methodological themes/issues  485–487 natural disasters, impact  171 news responses, waiting experiences (effects)  784 waiting 781 objective disease‐specific health indicators, assessment 151 pastimes, relationship  172 pathways 430–432 positive affect (good feelings), impact  433 explanation 489–490 problem discrimination/prejudice mechanisms  232 susceptibility, perception  212 traumatic experiences, association  257 traumatic exposures, impact  257 type/intensity 299 promotion campaigns 193 PWM research  519 self‐regulation, role  623 protective behaviors  726–727 psychology findings 132 screening, relationship  570 self‐efficacy, usage  609–610 psychosocial study, ecological momentary assessment 105 regional demographic differences  169–170 relationship dissolution, association  537 research, EAR methodology (usage)  379–380 resilience, health implications  269, 274–275 resource model  618–619 rumination, relationship  559 screening 195 selective exposure  579, 580 self‐affirmation, relationship  587, 589 self‐and‐social‐bonds model  619 self‐awareness relationship 597 research 599–602 self‐efficacy, relationship  605 self‐esteem, relationship  615 process 617–618 reasons 618–619

Index sex differences, relationship  631 social comparison, health correlates/ consequences 274 social identity approach, schematic representation  686 importance 680 social isolation impact 696 relationship 695 social support, relationship  703 spirituality/religiosity (S/R), relationship  709 mechanisms 710–712 subjective health norms  725 symptoms, attachment‐related differences  34 trauma, impact  256 traumatic events, health effects  255 treatment 302 evaluation, PRO role (increase)  110 unrealistic optimism benefits 776 costs 776–778 relationship 773 WHO definition  261–262, 764 health action process approach (HAPA)  187–188, 571 health behavior attachment differences, association  33–35 relationship 340 changes 183 description, prediction equation  496 DTC genetic testing results, impact  93 implicit processes, relationship  329 interventions, development/ implementation/evaluation 199 social cognitive theory applications  196 change theories, self‐efficacy (usage)  609 cognitive/emotional perspectives, integration 162–165 commonsense modeling (CSM)  315 cravings/temptations 163 engagement likelihood 431 study 580 failed control  159 goal‐directed health behavior  601–602 gratitude, relationship  263–265 improvement, social control attempts (doubt) 690 indirect effects  234–235 interventions 193 modality, selection  197–198 success, determination  198–199

803

intrapersonal resources  163–165 involvement 431 maintenance 203 models, behavior maintenance (operationalization process) 204–205 modification 397 norms, influences  726–727 personality relationship 421 traits, association  422–425 prediction ability 757–758 implicit attitudes, impact  750 reasoned action approach, usage  757 process models  204 promotion, conscious monitoring (increase)  331 relationship dissolution  540–541 relationship quality, impact  338 research 178–179 rumination, impact  562–563 sex differences  633–634 social norms, impact  331 stage models  204–205 theories 160 categorization 183–184 health belief model (HBM)  184, 211, 316, 367, 571 behavioral change, health belief model  212 cognitive constructs  212 constructs 212–213 expansion 212–213 history 211–212 interventions, assessment (aspects)  211–212 usage 213–214 healthcare avoidance 297 prejudice/stereotyping 507 occurrence/impact, reduction  513–514 professionals, DTC testing consultations  92–93 setting, prejudice/discrimination  233 treatment seeking  297 treatment seeking/avoidance, context  300 healthcare provider (HCP) recommendation, absence  571–572 trust problems  235 health information avoidance  279 affective concerns  281–282 behavior 279–280 challenge 282 causes 281–283 defining 279–280 demographic predictors  280–281

804

Index

health information avoidance (cont’d) interpersonal motives  282–283 motives contemplation  283 perceived control, increase  284 personality measures  281 predictors 280–281 prevalence 280 problem 280 reduction 283–284 self‐affirmation 283 self‐view/worldview, challenge  282 health literacy  287 adequacy, exception  292 improvement, interventions  291–293 low level, monetary cost  290 measures 288–289 medical decision making, relationship  290–291 NAAL measurement  289 NIH definition  287 physical health outcomes, relationship  291 predictors 289–290 ethnicity, impact  290 rates 289 skills, impact  287 visual aids/images, usage  292 health outcomes behavior maintenance, importance  203 dietary differences, impact  172 emotional impact forecasts, inaccuracy  23 gratitude, relationship  374 personality, relationship  429 prediction 373–374 relationship dissolution, association  542–543 health risk behaviors 726 social comparison, role  247 feedback, avoidance (reduction)  281 PWM research  519 healthy habits, discussion  177 healthy nudges  351–352 heart attack mortality, decrease  167 heart disease, risk (reduction)  634 heart rate variability (HRV)  660 increase 662 reduction 661 RSA index  539 helplessness, feelings (reduction)  66 heteronomy 358 heuristic cues, influence  366 heuristic processing  517 heuristics 749 strategies 374

high blood pressure, long‐term cardiovascular activation (association)  256 hippocampal cell density, reduction  35 Hippocrates 429 Hispanic/Latino populations, nationality (importance) 8 homeostatic function, maintenance  134 homosexuality, APA perspective  635 hormones role 632 signals, release  660 hostility construct 530–531 potential 530 hot/cool system theory  160–161 housing discrimination 233 disparities 232–233 human immunodeficiency virus (HIV) adherence rates  396 beliefs, assessment  495 regimes adherence, health literacy predictor function 291 nonadherence, race differences  290 testing, racial contrast  575 human papillomavirus (HPV) female knowledge, study  308 vaccination, maintenance  204 humor (emotion‐focused coping activity) 413 hybrid third culture, emergence  7 hyperarousal 4256 biological hyperarousal, reduction  273 hypercoagulation 256 hypertension, representations (coherence)  321 hypocrisy approach  751 hyporeactivity 539 hypothalamic‐pituitary‐adrenal (HPA) activation 619 activity impact 257 modulation 664 axis 659 functioning, cortisol (relationship)  661 chronic activation, problem  36 stress‐responsive HPA axis, discrimination activation 233–234 hypothalamic‐pituitary‐adrenal (HPA) axis activation  35–36, 228, 256–258 relationship quality/endocrine function research 337–338

Index identity (illness domain)  99 if‐then plans, formation  333 illicit drug use, acculturation (relationship)  3 Illinois Bell Telephone (IBT) project  440–441 mangers, hardiness training  443 illness adaptation 62–64 sibling coping  68 cause (CSM component)  100 commonsense model (CSM)  99 dynamic process, CSM descriptions  101 emotion, impact  223–224 identity/cause 99 likelihood estimates, connection  99–100 mastery, regaining  63 meaning making  63 pathways 430–432 prototypes/representations, multilevel process 318–320 representation 316 domains 99 impact 320 risk, representations (impact)  101 self‐esteem, restoration  63–64 social comparison, relationship  248–249 susceptibility 131 symptoms, impact  100 timeline likelihood estimates, connection  99–100 perceptions 100 work‐related illness, risk  531 Illness Perception Questionnaire (IPQ/IPQ‐R) 320 immune cell function, decrease  36 immune functioning  539–540 immune function, non‐cardiovascular disease outcomes (relationship)  387–389 immune pathway, positive relationship interactions 342–343 immune response non‐specific immune responses, suppression  561 optimism, negative relationship  388–389 stress, association  540 immune system divorce/separation, psychosocial effects (link) 540 impact 460 rumination, impact  561 impact bias  22 reversal 25 implementation intentions  330, 736 perspective 737–738

805

Implicit Association Test (IAT)  510 fat‐thin IAT  510 implicit bias  512 assessment 509–510 research 224–225 implicit health‐related attitudes (change), persuasion (impact)  750 implicit learning (cognitive processing measure) 477 implicit processes health behavior change, relationship  329 inclusion  330 implicit stereotyping/prejudice  509–510 impulses, effortful inhibition  628 impulsive information processing  329 inactive treatments, effects (alteration)  478 income, geographical differences  173 income inequality, impact  173 indirect effects  232–235 health behaviors  234–235 stress 233–234 indirect geographical factors  169–170 individual‐difference variables/characteristics  363–364 individuation 513 infant bonding  43 infant mortality, analysis  173 infection condition severity, perception  212 risk, increase  538 infectious diseases, marital status (association)  538 inflammation, loneliness (operation)  698 inflammation‐related diseases, reporting (likelihood) 38 information information‐processing modes, types  329 information‐related psychological phenomena  279–280 overload 349 processing, validity (requirement)  308 informational justice  225–226 informational norms  651 information avoidance definition 279 prevalence, variation  280 information‐motivation‐strategy model  399 inhibition process  735 inhibitory skills, application  137 injunctive norms  572 motivational underpinnings  725–726 priming 331 screening behaviors, relationship  727

806

Index

in‐office genetic testing  91 insomnia creation, stressors (impact)  670–671 formation/development, stress markers (impact) 671 risk (elevation), separation/divorce (impact) 541 intellect, impact  424, 434 intent, action (mediation)  187–188 intentional nonadherence  396 intentions counterfactuals, relationship  76–77 strengthening 76–77 willingness comparison 520 relationship 521 interactional justice  225–226 intercessors, recruitment  712 interference effect  609 interference hypothesis  609 interleukin 6 (IL‐6) blood levels, reduction  342 levels, increase  36 international aid, charitable giving (studies)  683 international migrants, flow (increase)  1 interpersonal conflict, personality prediction  431 interpersonal justice  226 interpersonal mistreatment  232–233 interpersonal motives  282–283 interpersonal perception, accuracy  13 bias, role  14 psychological consequences  15–16 relationship/social consequences  14–15 intervention approaches, identification  329–330 computer‐tailored interventions  365–366 framework, implicit processes (inclusion)  330 modality 197–198 social comparison, relationship  250–251 tailored interventions, efficacy (increase)  367 in‐the‐moment coping strategies, EMA (impact) 143 intimate relationships, physical health (relationship) 337 invincibility, capacity  269 ischemic heart disease, mortality proportion  172 isolation, risk factor  462 issue, uncertainty type  306 job outcomes (prediction), dominance (impact) 433 job performance evaluations, availability  440

joint coping  84 joint impact hypothesis, support  599–600 journey, metaphor  122, 123 justice beliefs, descriptions  227 justice‐oriented cognitions  223 multidimensional nature  227 psychosocial perception  226 latitude, seasonal affective disorder (correlation) 171 lesbian, gay, and bisexual (LGB) youth, substance use (increase)  234 lesbian, gay, bisexual, or transgender (LGBT) healthcare needs  635 identification 508 older adults, explicit bias  510 life events biobehavioral responses  538 R/S buffer  711 life stories correlates, health consequences  215 health consequences  219–220 measurement 217–218 representation 216 temporal coherence  216 life stressors, impact  532 lifestyle behaviors, modification  397 changes 366 Lifestyle Triple P intervention  653 life, thriving  763 likelihood estimates, illness identity/cause/ timeline (connection)  99–100 likelihood estimation (risk perception)  548–549 likelihoods, utilities (combination)  550–551 literacy, functional type  287–288 liver cancer (Asians)  169 stimulation, glucose (production)  256 lives, complication  216 living room language, usage  292 locus of control, external causes  606 locus, uncertainty type  306 loneliness 663–664 feelings 461 impact 697 mindfulness‐based stress reduction (MBSR) training 699 longevity, PA effects  487–488 longitudinal ambulatory assessments  374–375

Index long‐term cardiovascular activation, high blood pressure (association)  256 long‐term health goals, theory of discounting 162 long‐term self‐management, action (automation) 322–323 Los Angeles basin, air quality‐control policies (establishment) 174 loss‐framed messages  355 emphasis 357 lung cancer claims, tobacco company refutations 114 main‐effect model  660 maintenance (change stage)  186, 367 maintenance self‐efficacy  608 major depression  449 maladaptation 269 maladaptive behaviors, engagement  135 mapping 121–122 marital conflict, engagement  341 marital dissatisfaction, psychological morbidity (association) 83 marital quality, relationship status (connection) 337–339 marital status, infectious diseases (association)  538 marriage (end), sleep (lifestyle factor)  541 massage (emotion‐focused coping activity)  413 mass customization process, application  368 mass‐media messages, impact  357 Master Settlement Agreement (MSA), implementation 115 mastery experiences 610 regaining 63 meaning making  63, 154 meaning, worth (relationship)  683–684 mediation analyses  509 medical attention (seeking), rumination (negative effect) 563 medical decision making  347 health literacy  290–291 medical decisions difficulty 347 future outcomes, discounting  349–350 strategic behavior  350 trade‐offs 348–349 Medical Interview Satisfaction Scale (MISS‐29) 404 medical jargon/abbreviations avoidance 292 usage 405

807

medical mistrust (psychosocial concept)  226 medical resources, allocation  348 medical treatment, type/source  299–300 medical waiting periods, anxiety/rumination  783 medication labels (misinterpretation), health literacy (impact)  290 meditation (emotion‐focused coping activity) 413 memory construction, bias (reduction)  105–106 MEMScap, usage  398 mental associations, creation  121–122 mental health, well‐being (relationship)  72–75 mental imagery  610 mesolimbic reward system, subcortical areas (activity) 178 message framing  355 application 359–360 behavior‐related moderators  356–357 effectiveness, decrease  359 self‐efficacy, moderator function  358 message framing effects colors, usage  359 mediators 359 moderators 356–359 message resistance, research  589 message tailoring  363 delivery modes, availability  368–369 function 365–366 explanation 366–368 health communication strategy  363 mass customization process, application  368 meta‐analyses 365 process 363–365 metaphoric framing  123 exposure 124 metaphoric language, consequences  127 metaphoric phrases, exposure  125 metaphors bodily metaphors  122 cognitive tool  121–122 conceptual metaphor theory  125 concrete metaphors, reliance  122 images/schemas 126 impact 127–128 implications 124–128 misleading direction  127 method/theory, confusion  320 microelectromechanical systems (MEMS), usage 448 mindfulness‐based stress reduction (MBSR) training 699 mindless behaviors  162

808

Index

Minnesota Multiphasic Personality Inventory (MMPI) 442 misclassification bias  450 miscommunication, metaphors (impact)  127–128 misinformation 49–50 food/nutrition misinformation, ADA position statement 50 mobile sensing methods  380–382 ethical concerns  381 moderate to vigorous physical activity (MVPA) 452 momentary self‐report assessments  375 moment‐salient behaviors  161 mood disorders 256 dysregulation, social problems (risk factors) 462 effects 374 improvement 160 negative mood signals, action  164 morbidity irregular/uneven people, connection  429 PA effects  488–489 sex differences  632 mortality income inequality, impact  173 irregular/uneven people, connection  429 PA effects  487–488 relationship quality, connection  339 risk, increase  704 sex differences  631–632 thoughts, accessibility  744 motivation absence 673–674 beliefs, impact  735–736 counterfactuals, relationship  78 phase 187 motivational focus, impact  624–625 motivational orientation  357 systematic differences  357–358 motivational resource, depletion  735 motivational self‐efficacy  608 motives contemplation  283 movement‐based volume variables  452 multidimensional communication strategy  363–364 Multiple Risk Factor Intervention Trial (MRFIT) 5309 multiplex genetic susceptibility testing (MGST) 574 multiplicity 49

music therapy (emotion‐focused coping activity) 413 mutual dependence, network  661–662 μ‐opioid receptor gene, polymorphisms  460–461 myocardial infarction  256 narratives. See autobiographical narratives National Action Plan to Improve Health Literacy (HHS) 293 National Assessment of Adult Literacy (NAAL), health literacy measurement  289 National Center for PTSD, trauma/treatment information 258 natural disasters, impact  171 naturalistic observation  143–144 natural resources, distribution  171 negative affect (NA) absence 490 PA/morbidity effects, independence  488 role 486–487 negative affectivity  134 negative behaviors (increase), counteractuals (impact) 76 negative emotions increase 35 regulation 136 role 132–133 secretory immunoglobulin A (S‐IgA), release 134 negative feedback, receiving  22 negative mood persistence 164–165 signals, action  164 negative relationship interactions  341 negative social factors, consequences  461 negative state relief, research  164–165 net risk, yield  271 network support, importance  83 network ties, usage  704 neuroendocrine activity, social functioning (association) 660–661 neuroendocrine function concepts/research approaches  660–661 social factors  659 social influences  660 neuroticism  412, 424–425 associations 415 lifespan perspective, importance  435 tendency 434–435 neurotics, health vigilance  435 Newest Vital Sign  288–289 Nicomachean Ethics (Aristotle)  239

Index nicotine, physiological addiction  159 Nietzsche, Friedrich  440 nocebo effects  475, 477 historical context  475–476 neurobiological mechanisms  479 psychological mechanisms  478–479 nonadherence. See intentional nonadherence; unintentional nonadherence adverse health effects  397 consequences 396–397 economic burden  396–397 prediction, patient‐related factors  398 predictors 398–399 rates 396 regimen‐related factors  398–399 non‐cardiovascular disease outcomes, immune function (relationship)  387–389 nonconscious bias  512 nonconscious death thoughts (responses), esteem contingencies/cultural beliefs (dependency) 743–744 nonconscious processes  178 nonfulfillment 395 nonmedical waiting periods, anxiety patterns 782 nonphysical pain, physical pain (shared neural activation) 459–460 non‐rapid eye movement (NREM)  669 non‐specific immune responses, suppression  561 nonsteroidal anti‐inflammatory analgesic, impact 460–461 non‐tailored messages, tailored information (comparison) 365 non‐wear periods  451–452 non‐wear time, measurement  451 normative data referencing  729 normative feedback, effectiveness (reduction) 729 normative statistical rules, following  549 norms 521 descriptive norms  572–573 influences 726–727 informational norms  651 injunctive norms  331, 572 norm‐based interventions, moderators  729–730 subjective health norms  725 subjective norms/attitudes  521 subjective norms, examination  725 usage 727–729 Norr, Roy  114 not‐in‐the‐moment coping behavior  142

809

nudges nudge‐style intervention, benefits (discussion) 197 types 351–352 numeracy  309, 350–351 levels, problem  351 Nyquist principle  448 obesity culture, impact  649–650 family relationships  652–653 modeling, impact  653–654 parenting 652–653 romantic relationships, effects  653 sex differences  634 social factors  649 social networks, effects  652 social norms  650–651 social support  651–652 socioeconomic status (SES)  650 objective assessments, self‐reports (contrast)  449–450 objective disease‐specific health indicators, assessment 151 objective gains, perception  549–550 objective health outcome measures  373–374 objective reality, divergence  618 objective self‐awareness theory  597–599 objective social isolation, measures  696 observational learning/modeling (self‐efficacy belief source)  186 obstacle, metaphor  122 occupational differences, production  171 older adults, loneliness (mindfulness‐based stress reduction training)  699 openness  424, 434 opposite‐sex confederate, support  662 optimism cardiovascular impact  387 circulating pro‐inflammatory cytokine levels, relationship 388 coping, relationship  415–416 defining 385 dimensionality 390 disappointment, vulnerability  388 effects 386–389 epidemiological studies  386–387 expectancy‐value process models, usage  413–414 five‐factor model  413 hardiness, relationship  442

810

Index

optimism (cont’d) immune response, negative relationship  388–389 measurement 386 physical health, relationship  385 psychological/behavioral mechanisms  389–390 research  386 self‐efficacy beliefs, contrast  605–606 ordinary magic process 269 support 273 organizational injustice models  531–532 organizational justice measurement framework 226 oscillatory systems coherence/entrainment, appreciation feelings (relationship)  264 ostracism, feelings  461 others, transformation  682–683 outcomes. See health outcomes defining, considerations  430 evaluation, action plans  315 expectations/evaluations 186 improvement, physician‐patient communication (impact) 470–471 outcome expectancies, reference  606 perception, dispositional tendencies  227 overeating 162 pain adaptation (promotion), social/environmental factors (usage)  462–464 catastrophizing, rumination (examination)  562 experience, social factors (positive effects)  463 management, medication  507 memories 461 PA effect  489 pain‐related emotional distress  459 pain‐related learning, social threat (impact) 462 pain‐related outcomes  562 pain‐relevant difficulties, isolation (risk factor) 462 perception framework  561–562 treatment, pro‐White bias (impact)  511 pair‐bonded monogamous adults, infant (bonding) 43 pair bonding  43 parasympathetic nervous system (PSNS)  659 activity, examination  663 influence, reduction  661 parent‐child communication, increase (reports) 523

parenting practices, food (reward usage)  652–653 Parkinson’s disease counterfactual thinking reduction, association 74 patients, motor performance  477–478 partner QOL, couple‐based interventions (effects) 84–85 pastimes, geographical differences  172 patient adherence  395 definition 395–396 improvement, physician communication skills (impact) 470–471 meta‐analysis 299 physician‐patient communication, association 399 Patient Protection and Affordable Care Act (ACA)  224, 349 patient‐reported outcomes (PROs), usage (increase) 110 patients decisions, emotion (impact)  21–22 interactions, stereotyping/prejudice (influence)  510–512 network support, importance  83 oral communication  292–293 QOL outcomes, couple‐based interventions (effects) 84–85 stereotyping 511 patient satisfaction  403 assessment, concerns  404 assessment/measurement 403–404 correlates 405–406 demographic variables, relationship  406 literature, directions  406 measurement, challenges  404 patient demographics/health status, impacts 405–406 physician‐patient communication skills, importance 405 prediction 405 research findings  404–405 Patient Satisfaction Questionnaire  403–404 Patient Satisfaction Questionnaire‐18 (PSQ‐18) 404 peer pressure, impact/importance  518 Pennebaker, James  149–150 perceived barriers  367–368 perceived behavioral control  756 term, usage  606 perceived benefits  367–368 perceived hopelessness  532 perceived racism (psychosocial concept)  226

Index perceived risk  521–522, 571 perceived self‐efficacy, requirement  187–188 perceived severity  184, 367–368 perceived social support  704 perceived support  662 perceived susceptibility  184 perceivers biological processes  16 psychological consequences  15 perception accuracy biological processes  16–17 downsides 17–18 formation, psychological health (relationship)  15–16 interpersonal/intrapersonal benefits  16 performance action plans  315 job performance evaluations, availability  440 near‐chance levels  349 performance‐enhancing drugs, usage (intention) 520 periaqueductal gray, threat detection involvement 698 peripheral nervous system, parasympathetic branch 134 peripheral route processing  366 persistent complex bereavement disorder (PCBD)  44–45 personal identifiers, personalization/ inclusion 364 personality coping, relationship  411, 414–416 description 421–422 five‐factor model  412 hardiness 439 health behaviors, relationship  421 health outcomes, relationship  429 interventions 435–436 measures 281 prediction, health (impact)  431 psychology 411–413 role 416 traits consistency 704 health behaviors, association  422–425 trajectories 432–435 variables, placebo effects (relationships)  479–480 personalized normative feedback  728 usage 728–729 personal justice beliefs, descriptions  227 personal mastery experiences  186

811

personal persistence, claim  216 person‐related moderators  356, 357–358 perspective taking  513 persuasion cognitive dissonance theory  751 definition 747 elaboration likelihood model (ELM)  748–749 fear appeals, role  748 impact 748–750 role 747 self‐affirmation 749–750 self‐perception processes  751–752 self‐persuasion, impact  750–752 pervasive discrimination  232 phase‐specific self‐efficacy, measurement  608, 609–610 phenylketonuria (PKU), genetic screening  569–570 physical activity accelerosensors 447–448 data check  451 data processing  451–452 decrease/increase, role  449 factors, influence  452–453 increase 234 measurement days, number  451 issues 451–452 principles/technology 447–448 reliability/validity/reactivity 453–454 monitoring 447 national level  195 non‐wear periods  451–452 outcome variables  452 perspectives 450 valid day, identification  452 physical appearance, changes  63–64 physical exercise (emotion‐focused coping activity) 413 physical health defining 262 discrimination/prejudice consequences  232 expressive writing, relationship  151–152 gratitude, relationship  263–265 intimate relationships, connection  337 optimism effects 386–389 relationship 385 outcomes health literacy, relationship  291 individual attachment differences, linking mechanisms 33–37

812

Index

physical health (cont’d) problems 704 relationship breakups, impact  538 rumination, effects (conceptual model)  560 social functioning, link  660 trauma exposure, impact (indirect pathways)  256 indirect/direct links  258 physical health attachment associations, examination  38–39 avoidance, link  32–33 orientation, link  32–33 physical illness, rumination (impact)  563–564 physical pain immune system, impact  460 intervention studies  460–461 nonphysical pain, shared neural activation  459–460 social pain, shared mechanisms  459–461 well‐being challenge  459 physical symptoms, perceptions  599–600 physical well‐being  617–618 QOL challenge  82–83 physician‐patient communication  469 collaboration 471 implications 511–512 outcomes, improvement  470–471 patient adherence, association  399 problems 470 skills importance 405 training 471–472 physicians communication skills impact 470–471 improvement, techniques  471 implicit bias  511 pro‐White bias  511 visits, reduction  154 waiting time, correlation  300 physiological regulation (disruption), personality (impact) 430–431 physiological response systems  35–37 physiological systems (activation), social‐ evaluative threat (impact)  644–645 physiology, ambulatory assessment methods  375 placebo‐controlled RCTs, design  476 placebo effects  475 classification 476–477 clinical translation  480–481 dependent measures  477–478 enhancement, patient‐provider interaction (usage) 481

historical context  475–476 moderators 479–480 neurobiological mechanisms  479 non‐specific effect  476 personality variables, relationships  479–480 psychological mechanisms  478–479 reconceptualization 476–477 research 477–478 planned behavior. See theory of planned behavior Planned behavior model  185 planning‐related cognitions, frontal lobes (involvement) 74–75 pleasure, derivation  160 poor health, pathways (discrimination/prejudice mechanisms) 232 positive affect (PA) (good feelings)  485 active ingredients, issue  486 broaden‐and build theory  490 duration (measurement tool)  486 gratitude, relationship  262 impact 433 explanation 489–490 increase, impact (question)  490–491 longevity/mortality 487–488 main effect model  489 morbidity 488–489 PA‐health findings, generalizability  486 PA‐health review  487–489 pain, relationship  489 self‐related health outcomes, association  489 stress‐buffering model  489–490 study, methodological themes/issues  485–487 survival 488 Positive and Negative Affect Schedule (PANAS) 485 positive emotions perspective 132–133 secretory immunoglobulin A (S‐IgA), release 134 positive information, selective access  163 positive psychology, developments  767–768 positive relationship interactions  341–343 positive strategies, examples  691 postpartum depression (PPD)  718 posttraumatic growth (PTG)  66 post‐traumatic stress disorder (PTSD) emotion regulation difficulties  35 expressive writing, impact  151 supportive, impact  273 symptoms, cardiovascular system activation (association) 256 victims, upward counterfactual generation  73

Index posttraumatic stress (PTS) symptoms, EW effect 153 “Powerful Placebo, The” (Beecher)  475 precaution adoption process model (PAPM)  186–187, 205, 495–500 behavior change, difficulty  502 between‐stage differences  503 description 497–498 inaction, stages  500 research usage  503–504 stage 1 (unaware)  498 stage 2 (unengaged)  499 stage 3 (undecided)  499 stage 4 (decided not to act)  499 stage 5 (decided to act)  499–500 stage 6 (acting)  499–500 stage 7 (maintenance)  500 stage‐based interventions, criteria  500–502 stages  497 assessment 501 justification 498–500 progress, determination (factors)  498 transitions, intervention strategies  503 stage‐targeted messages, delivery  501 unstaged messages, superiority  501 usage 502–503 precontemplation change stage  186, 367 stage 184 pregnancy anxiety 718 close relationships, importance  719 health behaviors  719–720 health disparities  718 mood disorders  718 postpartum depression (PPD)  718 prenatal depression  718 preterm birth/low birthweight, socioeconomic status (risk factor)  718–719 sociocultural influences  720 stereotyping 717–718 stressors, ethnic/racial disparities  719 stress/resilience 717 prejudice clinical recommendations, link (absence)  512–513 direct effects  232–233 effect, moderators  235–236 explicit prejudice  508–509 healthcare, relationship  507 health consequences  231 implicit prejudice  509–510 indirect effects  233–235

813

influence 510–512 likelihood 513 occurrence/impact, reduction  513–514 perception/coping, fairness beliefs (impact) 236 stress, coping  235 systemic/interpersonal mistreatment  232–233 terms, defining  231–235 preparation (change stage)  186, 367 preterm birth/low birthweight, socioeconomic status (risk factor)  718–719 prevention behavior, motivation  123–124 prevention‐focused individual, behavior reinforcement 206 preventive coping  56 preventive health behaviors (promotion), mass‐ media messages (impact)  357 preventive services, usage (increase)  211 primary appraisal (stressors)  64 private/public education, racial/ethnic disparities 233 proactive coping  56, 414, 415 probability estimation, normative rules (disregard) 549 probability neglect, demonstration  551 problem‐focused conversations  144 problem‐focused coping  64–65 emotion‐focused coping, contrast  413–414 facilitation 56 strategies 235 procedural justice beliefs 227 concept 225 instrumental functions, focus  225 relational function  225 process imagery  610 process models  204 Prochaska, James  186 Profile of Mood States (POMS)  485 pro‐inflammatory profiles, exhibition  17 prompts, provision  331 prospective control  738 classes 737 prostate‐specific antigen (PSA) levels, health literacy (impact)  290 protection motivation theory (PMT)  184, 500, 571 protective buffering (relationship‐coping strategy) 66 protective factors, characterization  271 protective processes, influence  271

814

Index

prototypes 520–521 definition 518 development 524 dimensions 520–521 healthy prototype, effects  519 manipulation 522–523 visualization 522–523 prototypes/representations multilevel process  318–320 structure/content, similarity  319 prototype‐willingness model (PWM)  185, 517 adolescents 518–519 adults 519 antecedents 520 components 517–518 dual processing, evidence  522, 523–524 integrative theoretical frameworks  533–534 interventions 523–524 laboratory studies  521–522 manipulations 520 prototypes, manipulation  522–523 research (age/experience)  518–519 safe sex images  522 socioeconomic status (SES)  532–533 effects, meta‐analytic evidence  533 Strong African American Families (SAAF) Program, development  523 ultraviolet (UV) protection lab studies  523 provider‐patient‐spouse triads, coping perspective (application) 58 pro‐White bias, impact  511 proximity‐seeking functions  43 psychic numbing  551 psychoactive drugs, usage/impact  671 psychobiologic systems, demand (excess)  272 psychological appraisal (stressors)  64 psychological consequences  15–16 psychological distress decrease 154–155 increase 81 reduction 542 psychological functioning  13 problems, report  17 psychological health, perception accuracy (relationship) 15–16 psychological immune system (strength), anticipation (failure)  22 psychological justice  226–227 health effects, occurrence  228 psychological morbidity, marital dissatisfaction (association) 83 psychological threats  587

psychological well‐being  617 gratitude, relationship  262–263 increase, gratitude letter writing (impact)  265 QOL challenge  82–83 psychology, ambulatory activity monitoring  454–455 psychomotor energy expenditures, deceleration 460 psychopathological disorders, risk  136 psychophysical numbing  551 psychophysiological state, chronotype (impact) 673 psychosocial functioning, expressive writing (relationship) 153 psychosocial risk factor, defining  529–530 public health improvement 174 weather, acute implications  171 public policy, health (geographic influences)  173–174 Puerto Ricans, asthma  170 pulmonary diseases, adherence rates  396 quality‐adjusted life year (QALY), metric  348 quality of life (QOL) cancer patient/partner challenges  82–84 couple‐based interventions effects 84–85 weighted mean effect sizes/confidence intervals 85t post‐cancer thoughts  81 quitting, action stage  184 race‐based discrimination  234 race stereotyping  511 racial disparities  233 racial minorities medical treatments  225 physical activity, increase  234 Rapid Estimate of Adult Literacy in Medicine (questionnaire) 288 rapid eye movement (REM)  669 reaction time (cognitive processing measure) 477 reactivity model  660 “realistic accuracy” approach  13 reasoned action approach 756–759 theory 185 reasoned pathway  517 recall assessments, EMA advantages  106–107 bias, problem  450

Index construction, bias (reduction)  105–106 retrospective recall (limitations), ESM methods (circumvention) 106–107 recall‐related errors  374 received support  662–663 receiving context, effects (understanding)  8 recovery self‐efficacy  608 Redemption 218 redemptive story, manifestation  218 reflective‐impulsive model (RIM), information‐ processing modes (types)  329 reflective information process  329 reflective system, burden (reduction)  330 rehospitalization, short‐term health outcome  386 rejection, threat (impact)  619 relatedness 206 relational regulation theory (RRT)  704 relationship consequences  14–15 relationship‐coping strategies  66 relationship dissolution allostatic load  541 cardiovascular responding  539 health association 537 behaviors 540–541 outcomes 542–543 health‐relevant responses  538–540 immune functioning  539–540 morbidity/mortality 537–538 psychological processes  542–543 relationship‐focused coping, usage  56 relationship functioning  13 relationship quality disease recovery, connection  339 health behavior, connection  338 mortality, connection  339 physiological health correlates  337–338 relationships. See social relationships gratitude, effects  263 negative relationship interactions  341 positive relationship interactions  341–342 quality, personality prediction  431 social control element  692 status, marital quality (connection)  337–339 relationship‐threatening information  17 relationship well‐being, QOL challenge  83–84 relative deprivation, concept  226 relaxation (emotion‐focused coping activity)  413 religiosity/spirituality (R/S) distant intercessory prayer  712–713 health behaviors 710–711 research 712–713

815

health, relationship  709 mechanisms 710–712 negative R/S variables  711 positive R/S variables  711 psychological processes  711–712 religiousness/spirituality, impact  720 sense, beliefs  709–710 social support  710 topics 712–713 variables 711 religiousness, impact  720 relining policies  232–233 repetitive thinking/thoughts  558 rumination, relationship  563–564 reporting bias, problem  450 representations/prototypes, differentiation (importance) 316–317 representations (activation), reasoned processes (involvement) 317 reproductive health, sexuality (impact)  635 resilience construct 270 developmental model, influence (levels)  274–275 developmental process  270 diagnosis, information (requirement)  269 explanatory mechanisms  272–273 health implications  269, 274–275 influences, consideration  274 mechanisms 273 ordinary magic, support  273 process, influences  270–272 psychological mechanisms, operation  272–273 respiratory sinus arrhythmia (RSA) index  539 response efficacy  125 response styles theory, extension  558 responsibility patterns, (challenges), cancer treatment (impact)  83–84 rest and digest calming response  134 retrospective recall (limitations), ESM methods (circumvention) 106–107 retrospective self‐reports, concerns  375 risk acceptance 552 aversion 356 cognitions, social comparison (moderator function) 250 disease‐related variables, impact  99 estimate, conveyance  307 evaluation, understanding  547, 552 factors, effects  270–271 inequality, perception  553 magnitude 552

816 risk (cont’d) net risk, yield  271 opportunity, openness  517–518 perceived risk  521–522 risk‐taking behavior, evidence  636 uncertainty, relationship  347–348 risk perception  547 affect heuristic  552–553 expected subjective utility  549–550 likelihood estimation  548–549 likelihoods, utilities (combination)  550–551 understanding 547 victims, numbers (perception)  551 risky behaviors peer pressure, impact/importance  518 sex differences  636 social comparison, relationship  250 risky health behavior, usage  600 rumination  557, 783 autonomic nervous system, physiological parameter 560 cardiovascular activity, physiological parameter 560 cortisol, relationship  561 effects, conceptual model  560 goals, role (focus)  448–449 health behaviors 562–563 relationship 559 impact  35, 542 models/definitions 557–559 negative effect  563 neuroendocrine/immune activity  560–561 non‐stressor‐related ruminative thoughts  558 physical illness/disease, relationship  563–564 physiological parameters  560–561 prevention/intervention 564 reduction 564 repetitive thinking  558 sleep, relationship  562 social‐evaluative threat (SET), link  644 somatic complaints  561–562 stress‐reactive rumination, role  558 stress‐related rumination  560 thoughts, integration  561–562 sadness, increase  35 safe sex images  522 salivary cortisol (physiological measure)  477 Sandman, Peter  186 schizophrenia 449 counterfactual thinking reduction, relationship 74–75

Index Schwarzer, Ralf  187 screening attitudes 571 barriers 571–572 behavior 569 injunctive/descriptive norms, relationship 727 discrimination 574–575 health psychology, relationship  570 intraindividual factors, risk/attitudes (perception) 570–571 issues 576 physician recommendation  575–576 racial/ethnic disparities  575 social factors, representation  572–573 social networks, role  573 social norms, relationship  572–573 social stigma  574–575 spousal influence  573 screening varieties/targets  569–570 seasonal affective disorder (SAD) depressive/bipolar episodes  171 latitude, relationship  171 secondary appraisal (stressors)  64 second‐generation immigrants, heritage (reconciliation/integration) 6 secretory immunoglobulin A (S‐IgA), release (positive/negative emotion link) 134 seeking. See treatment seeking/avoidance positivity 300–301 segregation, impact  232–233 selective exposure  579–580 accuracy motivation  582–583 amplification 581 barrier, laziness (impact)  582 cognitive economy, impact  582 confidence mechanism  581–582 de facto selective exposure  580 health, relationship  580 mechanisms/moderators 580–583 self distributive/procedural justice  227 others, transformation  682–683 private/public aspects, focus  598 sense, reliance  616 self‐affirmation  283, 749–750 consequence, focus  588 definition 588 developments/issues  591, 592 health information, link  589–590 relationship  587, 589

Index individual differences  591–592 manipulations/interventions, effects (detection) 590 research 589 stress, link  590–591 theory perspective 587 usage 588 well‐being, link  590–591 self‐awareness appearance concerns  601 core hypotheses, support  598 eating disorders  601 effect 599–600 elevation 599 escape alcohol consumption, usage  600 risky health behavior, usage  600 goal‐directed health behavior  601–602 health, relationship  597 level (elevation), development (problems)  601 objective self‐awareness theory, self‐regulation (link) 597–599 physical symptoms, perceptions  599–600 research 599–602 self‐regulation, usage  601–602 term, usage  598–599 self‐concept changes 63–64 perception 606 self‐control capacity, reduction  179 challenges 738 cognitive factors  161–162 depletion 163 dual‐process theory  161 failure domains 159 emotional factor  164 improvement 735 knowledge perspective  734 motion 160 objectives 161 problem 669 strategies 737 success  733, 738 theories 160–161 self‐determination theory (SDT)  196 application 206 self‐distraction (avoidant strategy)  413 self‐efficacy  184, 736 beliefs concepts, distinction  605–606

817

influence (sources)  186 optimism, contrast  605–606 concept, addition  211–212 defining 185–186 domain‐specific self‐efficacy, measurement  608 enhancement 196 generality dimension  607 general self‐efficacy  607–609 health, relationship  605 increase 196 metaphor, usage  126 methods 610–611 level dimension  607 measurement 607–609 measures, dimensionality  607–608 message frame moderator  358 perceived self‐efficacy, requirement  187–188 phase‐specific self‐efficacy, measurement  608, 609–610 scales, criticism  608 self‐manifesting mechanism  606–607 sources 610 measurement 608–609 strength dimension  607 term, usage  606 usage 608–610 self‐esteem bolstering, distal motivational goals  742 crystallization 616–617 defining 615–617 health, relationship  615 process 617–618 reasons 618–619 increase, downward comparisons  63–64 negative information, disclosure  17 resource model  618–619 restoration 63–64 temperament, association  616 self‐focus, term (usage)  598–599 self‐healing personalities, health trajectory 430 self‐imposed punishment, demonstration  737 self‐improvement motivation, upward counterfactual (relationship)  71 self‐integrity, protection  587 self‐management, health condition requirement 351 self‐monitoring behaviors, impact  187 promoting, prompts (impact)  331 self‐perception processes, usage  751–752 self‐persuasion, impact  750–752

818

Index

self‐regulation  330, 333–334, 623 automatic strategies  626–627 capacity 269 cognitive strategies  627 conceptualization 623 difficulty level, differences  625 goal setting  624–626 importance 623 impulses, effortful inhibition  628 loneliness, impact  697 objective self‐awareness theory, link  597–599 planning strategies  626 real‐life context, complexity  623–624 usage 601–602 self‐reinforcing system, failure  205 self‐reliance 34 self‐reported goals/norms, change (impact)  331 self‐reported health outcomes, PA (association) 489 self‐reported outcomes  150–151 self‐reported physical symptoms, decrease  135 self‐reports instruments, differences (impact)  450 methods, observational methods (combination) 376 momentary self‐report assessments  375 objective assessments  449–450 reliance, limitations  486 scales, impact  375 usage, questions  450 value 373–374 self‐serving intent  50 self‐standards, meeting (difficulty)  601 self‐verification, experiences (enhancement)  16 self‐view, challenge  282 separation (psychosocial effects), immune system (link) 540 serotonin transporter gene (SERT), genetic polymorphism 539 serotonin transporter genotypes, impact  271–272 Set of Brief Screening Questions (SBSQ)  288 severity judgments, CSM framework factors  100 sex, attitudes (gender differences)  635 sex differences alcohol consumption  634 biology 632–633 exercise 634 gender‐related traits  636 health behaviors 633–634 relationship 631

morbidity 632 mortality 631–632 nurturant roles  637 obesity 634 risky behavior  636 smoking 633–634 sociocultural factors  635–637 sexuality, reproductive health (relationship)  635 sexually transmitted infections (STIs), contracting 34 sexual risk behavior reduction (HIV prevention interventions), CDC website list  1907 shared decision making  310–311 shopping (avoidant strategy)  413 Short Assessment of Health Literacy  288 Short Assessment of Health, Newest Vital Sign 288 short‐term thinking (bias production), threatening emotional states (impact)  164 sibling coping  68 sickle cell disease (African Americans)  169 signaling fatigue  460 similarity, perceptions  682 Single‐Item Literacy Screener  288–289 situational justice (state justice)  226 situation selection, pathway  431 skin barrier recovery  36 sleep disorders, adherence rates  396 disruption, effects  673–674 distractions, reduction  672 disturbances, impact  541 gratitude, link (importance)  264 hygiene 671 initiation, problems  670–671 interference, psychoactive drugs (usage/ impact) 671 lifestyle factor  541 loss, cognitive/emotional effects  674 maintenance, problems  670–671 optimal sleep timing (chronotype), impact  672–673 propensity, chronotype bias  672–673 quantity, deviation  669 rumination, relationship  562 sleep‐relevant behavior, social ties (impact)  671 sleep‐wake cycle social behavior, mutual dependence  670 social interactions, disruptor role  670–671 social behavior, impact  670–672 social factors  669 texting 671

Index Sloan Center on Everyday Lives of Families (CELF) study  376 smartphones sensing applications, proof of concept (establishment) 381–382 sensors, usage  380 smoker prototypes  524 smoking addressing, public health professional perspective 117 behavior decrease, cigarette taxes (doubling)  116 problem 36 cessation  161, 213, 524 rates 159 continuation 587–588 denormalization, marketing bans (impact)  115–116 long‐term negative consequences, warnings 689 prevention 524 rates (women/men), sex differences  633 sex differences  633–634 usage 34 youth‐smoking prevalence, decrease  116 social acceptance, maintenance  641 social behavior impact 670–672 sleep disruption, effects  673–674 impact 672–673 sleep‐wake cycle, mutual dependence  670 time cue  670 social clock, biological clock (alignment)  673 social cognitive theories  159–160, 185–186 health behavior change applications  196 social cognitive theory (SCT)  571 social comparison downward/upward social comparisons, addition (impact)  251 functions 249 health correlates/consequences  247 illness/disease, relationship  248–249 intervention, relationship  250–251 moderator function  250 orientation, moderator function  250, 251 processes, operation  249 risky behaviors, relationship  250 role 247–249 theory contributions 247–248 evaluation 250–251

819

social connection  682 social consequences  14–15 social context, role (emphasis)  206 social control  689 attempts 690–691 dual effects  689–690 effects, doubt  690 health‐related social control  691 hypothesis 240 social engineering  224 Social Environment Coding of Sound Inventory (SECSI) 378 social‐evaluative stressors  642 social‐evaluative threat (SET)  641 components 642–643 cortisol reactivity  641–642 emotional/cognitive effects  643–644 experience, continuation  644 impact 644–645 manipulation 642 non‐SET conditions  642–643 PRES conditions  642–643 remote occurrence  643 rumination, link  644 threat, presence  643 social exchange, individual reaction  223 social exclusion, depression (relationship)  460 social experiences creation 616 optimal sleep timing (chronotype), impact  672–673 social facilitation, trigger  651 social factors positive effects  463 screening, relationship  572–573 social functioning  13 frontal lobes, involvement  74–75 neural/psychological mechanisms  664–665 neuroendocrine activity, association  660–661 physical health, link  660 social health equation  373 social identity  679 approach benefits 681 schematic representation  686 control/efficacy/power 684 group behavior basis  679–680 Groups 4 Health (G4H) intervention 684–685 modules  685 health basis  680–681

820

Index

social identity (cont’d) health‐enhancing resource  681–684 importance 680 meaning, worth (relationship)  683–684 social influence  689 health‐related social influence  691–692 social integration, focus  664 social intelligence (SI) intervention, development 464 social interactions disruptor role  670–671 naturalistic observation  373, 376 quality, neural mechanisms (link)  664–665 research, EAR methodology (usage)  379–380 social isolation behavioral mechanism  697 biological mechanism  698 group interventions  698 health, relationship  695 impact 696 interventions 698–699 measurement 695–696 mechanisms 696–698 objective social isolation, measures  696 one‐on‐one interventions  698 psychological mechanism  697 subjective social isolation, measures  696 social justice characteristics 223–224 distributive justice, relationship  224–226 framework, health inequity (connections)  225 health consequences  223, 228–229 health correlates  223 psychological justice  226–227 social negativity  663–664 social networks access/inclusion 273 interactions, quality  464 screening, association  573 structural aspects  661 social neuroendocrinology  659–660 social norms classes, operation  572 conformity 671 diet 650–651 impact 331 marketing approach  728 misperceptions 727–728 obesity 650–651 screening, relationship  572–573 social pain evolutionary value  460 immune system, impact  460

intervention studies  460–461 physical pain, shared mechanisms  459–461 well‐being challenges  459 social persuasion  186, 610 social pressure priming 331 sensitivity 729–730 social problems, risk factors  462 social prototypes  185 social reaction path  517 social relationships gratitude, association  263 impact 651–652 importance 13 consideration 433 negative aspects  663–664 positive aspects  661–663 social stigma, screening (relationship)  574–575 social stressors, effect  461 social support  44, 186 attachment orientations, link  33 availability 704 effectiveness 682–683 effects 463–464 effect size  240 environmental factor  532 health, relationship  703 impact 235 measurement 703–704 negative outcomes  704–705 network ties, usage  704 perceived social support  704 personality prediction  431 person interactions  706 resources 132 social relationships, positive aspects  661–663 social threat, impact  462 social ties‐health link, psychological mediators 697 social well‐being  618 QOL challenge  83–84 socioeconomic status (SES)  532–533, 650, 765 pregnancy risk factor  718–719 socio‐emotional communication  405 somatic complaints  257 rumination, impact  561–562 somatic states, perception  611 source, uncertainty type  306 spillover effects  376 spirituality impact 720 presence 713 spirituality/religiosity. See religiosity/spirituality

Index stage‐based interventions, criteria  500–502 stage‐based models  183–184 stage models  186–188, 204–205 effectiveness, comparison  188 stage‐targeted messages, delivery  501 stage theories elements/assumptions 496 impact, process  495–496 standard behavioral treatment (SBT), usage 242 startle eyeblink reflex (physiological measure) 477 state justice  226 stereotyping clinical recommendations, link (absence)  512–513 counterstereotyping 513 explicit stereotyping  508–509 healthcare, relationship  507 implicit stereotyping  509–510 influence 510–512 likelihood 513 occurrence/impact, reduction  513–514 stimulus attention  558 stimulus control  186 usage 184 stimulus‐response associations  177 stop signal tasks, bias training usage  332 strangers, treatment  682 strategic automaticity  333 strategic behavior  350 stress adjustment, biological mechanisms  272 alleviation 160 cardiovascular reactivity  256 coping, physical activity (increase)  234 emotional factor  164 experience 131 adaptive/maladaptive handling  141–142 geographic variation  172 immune response  540 impact 256 increase 35 indirect effects  233–234 management, social support resources (decrease) 532–533 markers, role  670–671 occurrence/persistence 134 process 134 reactivity 134 recovery, rumination research (association)  559 regulation 136

821

response increase 36 system, activation  134 self‐affirmation, link  590–591 stress‐buffering hypothesis  240 stress‐buffering models  240, 289–290, 665 stress‐reactive rumination  558 role 558 stress‐related rumination  560 stress‐responsive HPA axis, discrimination activation 233–234 transactional model  309–310 vulnerability 160 stress and coping paradigm  64–65 stressors categorization 705 cortisol, increase  642 cultural stress, separation  4 effects 172 geographic differences  172 primary appraisal  64 psychological appraisals  64 psychological/physiological responses (reduction), self‐affirmation (impact)  590–591 recovery 618 resolution 57 secondary appraisal  64 social‐evaluative context, manipulation  642 Strong African American Families (SAAF) Program, development  523 Stroop interference (cognitive processing measure) 477 Stroop probe, paradigm (modification)  331–332 structural social support, longitudinal studies  532 subjective attitudes  521 subjective health norms  725 subjective norms  521, 756 examination 725 subjective social isolation, measures  696 subjective utility, expectation  549–550 substance use  195 lesbian, gay, and bisexual (LGB) youth (increase) 234 measurement 8–9 outcomes, norms (associations)  726 retreat (avoidant strategy)  413 substance use, acculturation mechanisms, understanding  4–6 relationship  1, 2–3 subtractive counterfactuals  72 success, defining  764 suicide (American Indians)  170

822

Index

suicide levels, elevation  172 sun protection (improvement), photographs (usage) 729 super‐adherers, adherence level  322 support groups, impact  242 supportive social connection, impact  273 survival, PA effects  488 survivors, spirituality (presence)  713 sympathetic‐adrenal‐medullary axis, activation  228 sympathetic nervous system (SNS)  659 activity, indexing  660 increase 661 symptom report, PA effect  489 systemic inequality  232 systemic mistreatment  232–233 systemic transactional model (STM)  67 systolic/diastolic blood pressure (increase), pessimists (relationship)  387 tailored information, non‐tailored messages (contrast) 365 tailored interventions efficacy, increase  367 sophistication, increase  368 tailored messaging  364 tailoring. See message tailoring Tajfel, Henri  680 targeted messaging  364 targets biological processes  16–17 psychological consequences  15–16 task, prior success (self‐efficacy belief source)  186 Tay‐Sachs disease (Ashkenazi Jews)  170 teach‐back method  291–292, 471 teach‐to‐goal method  291–292 technological innovations, impact  207 temperament, self‐esteem (association)  616 temporal coherence  216 temporal inconsistency  49 temptations  163, 733 availability, regulation  737 cognitive habits  736–737 construal, role  738–739 deleterious effects  734–735 implementation intentions  736–737 importance 739 overcoming 735–739 motivation 735–736 positive/negative incentives  734 prospective control  737–738 strategies, classes  737 resistance, motivation  735

responses, proactive regulation  737 subjectivity 733–734 termination (change stage)  367 terror management health model (TMHM) 741 distal TMHM responses  743 implications 744 interventional potential  743–744 origins 741–742 overview 742 proximal TMHM responses  742–743 terror management theory (TMT), development 741 Test of Functional Health Literacy (TOFHLA) 288 theory of planned behavior (TPB)  195–196, 755 model, description  756 theory of reason action and planned behavior (TRA/TPB) 368 theory of reasoned action (TRA)  571, 755 model, description  755–756 threat appraisal 184 control, treatments/procedures  315 presence 643 representation 315 rest and digest calming response  134 social‐evaluative threat (SET)  641 threatening emotional states, impact  164 thriving behavioral factors  765 cultivation 767–768 cultural factors  767 daily activities  766 definition 763–765 environmental factors  767 individual factors  765 predictors 765–767 social factors  766 socioeconomic factors  766–767 success, involvement  764–765 thriving in life  763, 765 time‐based amount variables  452 time‐based EMA sampling approaches, usage 109 tobacco cartoon characters, advertising usage  115 fight, education  116–117 marketing regulation  115–116 regulation federal mandate  116 initiation 114–115

Index tobacco use proliferation, contributor  114 role, growth (history)  113–114 training impulse/reflection  330, 331–333 trait hostility anger, association  530 research 530–531 traits gender‐related traits  636 trait gratitude, increase  264–265 trait justice  226–227 trans‐epidermal water loss, wound healing indicator 36 transformational processing  219 transtheoretical model (TTM)  184, 186, 187, 205, 571 usage 367 trauma categories 255 exposure types  255 health problems (prevention), social support/ interactions (usage)  258–259 impact 256 physical health, indirect/direct links  258 survivors, counterfactuals (generation)  73 therapeutic intervention  258 trauma‐related memories  155 traumatic events exposure 461 health effects  255 traumatic experiences, health problems (association) 257 traumatic stressor, experience  256 treatment avoidance, defining  298 regimen, nonadherence (adverse health effects) 397 treatment seeking/avoidance  297 demographic factors  298 factors 298–300 hard factors  298 healthcare context  300 health problem, type/intensity  299 health treatment/maintenance  302 impact 300 incentive programs  301 medical treatment, type/source  299–300 personality, link  299 psychosocial factors  298–299 soft factors  298 wearable technology, usage  301 treatment seeking, defining  297–298

823

triculturalism, complexities  7 triggering cue, habitual response (association)  179179 Turner, John  680 Type A behavior (TAB) pattern, research  429, 530–531 ultraviolet (UV) protection lab studies  523 uncertainty. See health behavioral outcomes  307 cognitive closure, requirement  308 coping 309–310 defining 305–306 intolerance 308 management 309–311 mediational analyses  307 physical outcomes  307–308 preference‐sensitive decisions  311 psychological outcomes  306–307 responses 306–309 individual differences  308–309 risk, relationship  347–348 shared decision making  310–311 types 306 unconscious habits  162 uncontrollable stressors  642, 705 Unequal Treatment: Confronting Racial and Ethnic Health Disparities in Health Care (IOM)  223, 226 unintentional nonadherence  396 unplanned pregnancy, risk (increase)  34 unrealistic absolute optimism  774 health costs  777 unrealistic comparative optimism  774 health costs  777–778 unrealistic optimism causes 774 consequences 776 defining 773–774 display, reasons  774–775 health benefits 776 costs 776–778 relationship 773 types 774 unstaged messages, superiority  501 upward comparison, role (evaluation)  249 upward counterfactuals  71 extreme/excessive responses  72 focus 75 impact 77 PTSD victim generation  73

824

Index

upward identification  248 upward social comparison, addition (impact)  251 US Preventive Services Task Force (USPSTF), clinical preventive services recommendations 570 utilities, likelihoods (combination)  550–551 vaccination, decisions  21 Valentine’s Day, connotations  25 verbal persuasion  610 vicarious experience  186, 610 victims, numbers (perception)  551 violent trauma, experience  258 visual dot probe (VDP) paradigm, modification  331–332 volitional phase, standards awareness  187 volitional self‐efficacy  608 volition phase  187 volume, term (indication)  448 vulnerability 269 factors, characterization  271 processes, impact  271 social contributors  461–462 waiting experiences differences 783–784 effects 784 waiting periods changes 782 types, variability  783 walkable green spaces, creation (impact)  173–174 warm touch intimacy‐building intervention  342 weight‐based discrimination  234 weight loss, health literature focus  194 Weinstein, Neil  186 well‐being challenges 459 emotion regulation prediction  133–136 estimates, reduction  23 expressive writing, relationship  153 mental health, relationship  72–75 PA, impact (explanation)  489–490 self‐affirmation, link  590–591 Western Collaborative Group Study (WCGS)  530

“wet foot, dry foot” law  8 white blood cells, binding ability  540 Wicklund, Robert  597 widowhood effect  45 willingness assessment 518 development 524 increase 522 intention comparison 520 relationship 521 winter SAD, latitude (correlation)  171 wishful thinking (avoidant strategy)  413 within‐group diversity  8 women mortality, sex difference  631–632 nocturnal blood pressure, decrease  241 pregnancy, life experiences  717 word generation (cognitive processing measure) 477 working memory (WM) training  331, 333 work stress (environmental factor)  531–532 World Happiness Report  684 worldview, challenge  282 worldview verification theory  227 worry consequences 101–102 defining 97–98 degree, determination  99–101 disease‐related variables, impact  99 emotional worry, transfer  123 increase, illness symptoms (impact)  100 reduction 564 worth, meaning (relationship)  683–684 wound healing indications, trans‐epidermal water loss (impact) 36 speed, reduction  36 wound infection  386 written diaries, usage  106 X‐linked chromosomal abnormalities, female resistance 632 youth‐smoking prevalence, decrease  116

The Wiley Encyclopedia of Health Psychology Volume 3 Clinical Health Psychology and Behavioral Medicine Edited by

C. Steven Richards Lee M. Cohen

Editor‐in‐Chief

Lee M. Cohen

This edition first published 2021 © 2021 John Wiley & Sons Ltd All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. The right of Lee M. Cohen to be identified as the author of the editorial material in this work has been asserted in accordance with law. Registered Offices John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial Office 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging-in-Publication Data Names: Paul, Robert D., editor. Title: The Wiley encyclopedia of health psychology / edited by Robert Paul, St. Louis MO, USA ; editor-in-chief, Lee Cohen, The University of Mississippi, MS, USA. Description: First edition. | Hoboken : Wiley-Blackwell, 2021. | Includes index. | Contents: volume. 1. Biological bases of health behavior. Identifiers: LCCN 2020005459 (print) | LCCN 2020005460 (ebook) | ISBN 9781119057833 (v. 1 ; cloth) | ISBN 9781119057833 (v. 2 ; cloth) | ISBN 9781119057833 (v. 3 ; cloth) | ISBN 9781119057833 (v. 4 ; cloth) | ISBN 9781119675785 (adobe pdf) | ISBN 9781119675761 (epub) Subjects: LCSH: Clinical health psychology–Encyclopedias. Classification: LCC R726.7 .W554 2021 (print) | LCC R726.7 (ebook) | DDC 616.001/903–dc23 LC record available at https://lccn.loc.gov/2020005459 LC ebook record available at https://lccn.loc.gov/2020005460 Cover image and design: Wiley Set in 10/12.5pt ITC Galliard by SPi Global, Pondicherry, India

10 9 8 7 6 5 4 3 2 1

Contents

List of Contributors

vii

Forewordxxv Prefacexxvii Preface Volume 3: Clinical Health Psychology and Behavioral Medicine 

xxix

Acknowledgmentsxxxi Editor‐in‐Chief Acknowledgments

xxxiii

Body Image Tyler B. Mason, Kathryn E. Smith, Jason M. Lavender, and Stephen A. Wonderlich

1

Caregivers and Clinical Health Psychology Allison Marziliano and Anne Moyer

9

Close Relationships Kieran T. Sullivan

19

Infertility, Miscarriage, and Neonatal Loss Amy Wenzel

27

Prevention and Health Across the Lifespan John L. Romano

35

Self‐Affirmation and Responses to Health Messages: An Example of a Research Advance in Health Psychology Allison M. Sweeney and Anne Moyer Stigma of Disease and Its Impact on Health Lindsay Sheehan and Patrick Corrigan

45 57

iv

Contents

Alcohol Use Disorder: Overview Brittany E. Blanchard, Angela K. Stevens, Molin Shi, and Andrew K. Littlefield

67

Cancer and Psycho‐Oncology Lauren N. Harris and Annette L. Stanton

73

Ischemic Heart Disease, Depression, and Tobacco Smoking: Implications for Health Psychology Allison J. Carroll

83

Neurocognitive Disorders and Health Psychology Claudia Drossel

95

Depression and Comorbidity in Health Contexts C. Steven Richards and Lee M. Cohen

101

Depression and Relapse in Health Contexts C. Steven Richards and Lee M. Cohen

107

Eating Disorders Eliot Dennard

117

Headaches131 Amy Wachholtz, Adam Harris, and Amy Frers Multiple Sclerosis, Walking, and Depression: Implications for Health Psychology Ipek Ensari and Robert W. Motl

143

The Impact of Exposure to Potentially Traumatic Events on Physical Health Candice Presseau, Danielle S. Berke, Julie D. Yeterian, and Brett Litz

153

Schizophrenia161 Thanh Le, Kyle R. Mitchell, Elana Schwartz, Jessica McGovern, and Alex S. Cohen Obesity171 Michael G. Perri, Melissa H. Laitner, and Aviva H. Ariel Pain and Health Psychology Robert J. Gatchel and Don McGeary

183

Perinatal Depression Rachel Vanderkruik, Elizabeth Lemon, Laura River, and Sona Dimidjian

193

Suicide in the Context of Health Psychology Maryke Van Zyl‐Harrison, Tanya Hunt, Rebekah Jazdzewki, Yimi Omofuma, Paola Mendoza‐Rivera, and Bruce Bongar

203

Adherence to Behavioral and Medical Regimens Tricia A. Miller, Summer L. Williams, and M. Robin DiMatteo

217

Contents

v

Tobacco Use Disorder and Its Treatment Victoria A. Torres, Lee M. Cohen, and Suzy Bird Gulliver

223

Psychological Assessment in Medical Settings: Overview and Practice Implications Kelsey A. Maloney and Adam T. Schmidt

235

Complementary, Alternative, and Integrative Interventions in Health Psychology Jeffrey E. Barnett

245

Hypnosis and Health Psychology Steven Jay Lynn, Craig P. Pollizi, Joseph P. Green, Damla E. Aksen, Ashwin Gautam, and James Evans

257

Motivational Interviewing Thad R. Leffingwell

265

Rehabilitation Psychology Monica F. Kurylo and Kathleen S. Brown

273

Administrative Issues in Primary Care Psychology Esther N. Schwartz and David R. M. Trotter

283

Affective Forecasting in Health Psychology Shannon M. Martin, James I. Gerhart, Catherine Rochefort, Laura Perry, and Michael Hoerger

293

Aging301 Ann Rosenthal, Dara Mickschl, Edith Burns, Elizabeth Neumann, Jay Urbain, Sergey Tarima, and Steven Grindel Trends in Clinical Health Psychology and Behavioral Medicine C. Steven Richards and Lee M. Cohen

311

Biopsychosocial Practice in Health Psychology Timothy P. Melchert

321

Child and Family Health Joaquin Borrego, Jr., Tabitha Fleming, Elizabeth Ortiz‐Gonzalez, and Amber Morrow

327

Childhood Cancer Dianna Boone, Savannah Davidson, Jordan Degelia, and Jason Van Allen

335

Ethical Issues for Psychologists in Medical Settings Gerald P. Koocher and Jeanne S. Hoffman

345

History of Clinical Health Psychology Rachel Postupack and Ronald H. Rozensky

355

vi

Contents

Older Adults and Perspectives for Researchers and Clinicians Working in Health Psychology and Behavioral Medicine Louise Sharpe

365

Pediatric Psychology Dianna Boone, Thomas J. Parkman, and Jason Van Allen

373

Reproductive Health Jennifer L. Brown and Nicole K. Gause

383

Schools, Eating, and Health Psychology Catherine Cook‐Cottone and Rebecca K. Vujnovic

395

Sport and Exercise in Health Psychology Erika D. Van Dyke and Judy L. Van Raalte

407

Sexual Minority Populations and Health Mike C. Parent and Teresa Gobble

417

Ethics Issues in the Integration of Complementary, Alternative, and Integrative Health Techniques into Psychological Treatment Jeffrey E. Barnett

427

Alcohol Use Disorder: Long‐Term Consequences Angela K. Stevens, Brittany E. Blanchard, Molin Shi, and Andrew K. Littlefield

437

Body Image Assessment Kathryn E. Smith, Tyler B. Mason, Jason M. Lavender, and Stephen A. Wonderlich

445

Anxiety and Skin Disease Laura J. Dixon and Sara M. Witcraft

451

Insomnia459 Todd A. Smitherman and Daniel G. Rogers Index 469

List of Contributors

Amanda M. Acevedo, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Zeba Ahmad, Icahn School of Medicine at Mount Sinai, New York, NY, USA Leona S. Aiken, Department of Psychology, Arizona State University, Tempe, AZ, USA Olusola Ajilore, Department of Psychiatry, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA Damla E. Aksen, Department of Psychology, Binghamton University, Binghamton, NY, USA Kristi Alexander, Department of Psychology, University of Central Florida, Orlando, FL, USA Andrea G. Alioto, Palo Alto University, Palo Alto, CA, USA Department of Neurology, University of California, Davis, CA, USA Michael L. Alosco, Boston University Alzheimer’s Disease Center and Boston University CTE Center, Department of Neurology, Boston University School of Medicine, Boston, MA, USA Christina M. Amaro, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA Judith Andersen, Department of Psychology, University of Toronto, Mississauga, ON, Canada Aviva H. Ariel, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Danielle Arigo, Psychology Department, The University of Scranton, Scranton, PA, USA Jamie Arndt, Psychology Department, University of Missouri System, Columbia, MO, USA Robert C. Arnold, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA Melissa V. Auerbach, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Lisa Auster‐Gussman, Department of Psychology, University of Minnesota, Minneapolis, MN, USA Aya Avishai, Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Hoda Badr, Icahn School of Medicine at Mount Sinai, New York, NY, USA David E. Balk, Department of Health and Nutrition Sciences, Brooklyn College of the City University of New York, Brooklyn, NY, USA

viii

List of Contributors

Kirk Ballew, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Department of Communication, University of Illinois at Urbana‐Champaign College of Liberal Arts and Sciences, Urbana, IL, USA Diletta Barbiani, Department of Neuroscience, University of Turin Medical School, Turin, Italy Hypoxia Medicine, Plateau Rosà, Switzerland Jeffrey E. Barnett, College of Arts and Sciences, Loyola University Maryland, Baltimore, MD, USA Petronilla Battista, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Institute of Bari, Italy Brittney Becker, Psychology Department, Texas A&M University System, College Station, TX, USA Alexandra A. Belzer, Colby College, Waterville, ME, USA Fabrizio Benedetti, Department of Neuroscience, University of Turin Medical School, Turin, Italy Hypoxia Medicine, Plateau Rosà, Switzerland Danielle S. Berke, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA Mamta Bhatnagar, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA Kelly Biegler, Department of Psychological Science, University of California, Irvine, CA, USA Susan J. Blalock, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Brittany E. Blanchard, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Jennifer B. Blossom, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA Ryan Bogdan, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA Jacob Bolzenius, Missouri Institute of Mental Health, University of Missouri‐St. Louis, St. Louis, MO, USA Bruce Bongar, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Dianna Boone, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Jerica X. Bornstein, Department of Human Development and Family Sciences, University of Texas at Austin, Austin, TX, USA Joaquín Borrego Jr., Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Kyle J. Bourassa, Department of Psychology, University of Arizona, Tucson, AZ, USA Lara A. Boyd, Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada Patrick Boyd, Department of Psychology, University of South Florida, Tampa, FL, USA Adam M. Brickman, Columbia University Medical Center, New York, NY, USA Athena Brindle, Department of Psychology, College of Science, The University of Texas at Arlington, Arlington, TX, USA

List of Contributors

ix

Jennifer L. Brown, Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA Kathleen S. Brown, Private Practice, Fort Myers, FL, USA Wilson J. Brown, Pennsylvania State University, The Behrend College, Erie, PA, USA Steven E. Bruce, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA Angela D. Bryan, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Katherine R. Buchholz, VA Boston Healthcare System, Boston, MA, USA National Center for Post Traumatic Stress Disorder, White River Junction, VT, USA Claire Burgess, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA Edith Burns, Medical College of Wisconsin, Milwaukee, WI, USA Rachel J. Burns, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada Johannes B.J. Bussmann, Erasmus MC University Medical Center Rotterdam, CA Rotterdam, South Holland, The Netherlands Fawn C. Caplandies, Department of Psychology, University of Toledo, Toledo, OH, USA Angela L. Carey, Psychology Department, University of Arizona, Tucson, AZ, USA Brandon L. Carlisle, Department of Psychiatry, University of California, San Diego, CA, USA Delesha M. Carpenter, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Allison J. Carroll, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Charles S. Carver, Department of Psychology, University of Miami, Coral Gables, FL, USA Center for Advanced Study in the Behavioral Sciences, Stanford University, Stanford, CA, USA Isabella Castiglioni, Dipartimento di Fisica “G. Occhialini”, Università degli Studi di Milano‐ Bicocca – Piazza della Scienza, Milan, Italy Gretchen B. Chapman, Department of Psychology, Rutgers University–New Brunswick, New Brunswick, NJ, USA Alyssa C.D. Cheadle, Psychology Department, Hope College, Holland, MI, USA Rebecca E. Chesher, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA William J. Chopik, Psychology Department, Michigan State University, East Lansing, MI, USA Nicholas J.S. Christenfeld, Department of Psychology, University of California San Diego, La Jolla, CA, USA L.E. Chun, University of Colorado, Boulder, CO, USA Briana Cobos, Department of Psychology,Texas State University, San Marcos, TX, USA Alex S. Cohen, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Jenna Herold Cohen, Department of Psychology, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA Lee M. Cohen, Department of Psychology, College of Liberal Arts, The University of Mississippi, Oxford, MS, USA Catherine Cook‐Cottone, Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, USA

x

List of Contributors

Sarah A. Cooley, Department of Neurology, School of Medicine, Washington University in Saint Louis, St. Louis, MO, USA Stephen Correia, Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI, USA Patrick Corrigan, Department of Psychology, Illinois Institute of Technology College of Science, Chicago, IL, USA Juliann Cortese, School of Information, Florida State University, Tallahassee, FL, USA Riley Cropper, Stanford University Counseling and Psychological Services, Stanford Hospital and Clinics, Stanford, CA, USA Robert T. Croyle, National Cancer Institute, Bethesda, MD, USA Tegan Cruwys, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Sasha Cukier, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA Gregory Dahl, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Beth D. Darnall, Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA Savannah Davidson, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Elizabeth L. Davis, Department of Psychology, University of California, Riverside, CA,USA Rachael L. Deardorff, Center for Biomedical Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA Janet Deatrick, Department of Family & Community Health, University of Pennsylvania School of Nursing, Philadelphia, PA, USA Lauren Decaporale‐Ryan, Departments of Psychiatry, Medicine, & Surgery, University of Rochester Medical Center, Rochester, NY, USA Jordan Degelia, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Anita Delongis, Department of Psychology, University of British Columbia, Vancouver, BC, Canada Naiara Demnitz, Department of Psychiatry, University of Oxford, Oxford, UK Eliot Dennard, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Kristine M. Diaz, Walter Reed National Military Medical Center, Bethesda, MD, USA Sally S. Dickerson, Department of Psychology, Pace University, New York, NY, USA M. Robin DiMatteo, Department of Psychology, University of California Riverside, Riverside, CA, USA Sona Dimidjian, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Genevieve A. Dingle, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Laura J. Dixon, Department of Psychology, University of Mississippi, Oxford, MS, USA Michelle R. Dixon, Department of Psychology, Rutgers University Camden, Camden, NJ, USA Tiffany S. Doherty, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA Michael D. Dooley, Department of Psychology, University of California, Riverside, Riverside, CA, USA

List of Contributors

xi

Tessa L. Dover, Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA Colleen Doyle, Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, USA Ira Driscoll, Department of Psychology, University of Wisconsin‐Milwaukee, Milwaukee, WI, USA Claudia Drossel, Department of Psychology, Eastern Michigan University College of Arts and Sciences, Ypsilanti, MI, USA Sean P.A. Drummond, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Christine Dunkel Schetter, Department of Psychology, University of California, Los Angeles, CA, USA William L. Dunlop, Psychology Department, University of California, Riverside, Riverside, CA, USA Jacqueline C. Duperrouzel, Florida International University, Miami, FL, USA Allison Earl, Department of Psychology, University of Michigan, Ann Arbor, MI, USA Amanda C. Eaton, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Ulrich W. Ebner‐Priemer, Karlsruhe Institute of Technology, Karlsruhe, Germany Robin S. Edelstein, Psychology Department, University of Michigan, Ann Arbor, MI, USA Jamie Edgin, Department of Psychology, The University of Arizona, Tucson, AZ, USA Bradley A. Edwards, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Sleep and Circadian Medicine Laboratory, Department of Physiology, School of Biomedical Sciences, Monash University, Melbourne, VIC, Australia Naomi I. Eisenberger, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Amber S. Emanuel, Psychology Department, University of Florida, Gainesville, FL, USA Patrick England, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA Ipek Ensari, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY, USA Emily Evans, Neurology Resident, Washington University in St. Louis, St. Louis, MO, USA James Evans, Department of Psychology, Binghamton University, Binghamton, NY, USA Anne M. Fairlie, Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Angelica Falkenstein, Department of Psychology, University of California, Riverside, Riverside, CA, USA Zachary Fetterman, Psychology Department, University of Alabama, Tuscaloosa, AL, USA Christopher Fink, Durham Veterans Affairs Medical Center, Durham, NC, USA North Carolina State University, Raleigh, NC, USA Alexandra N. Fisher, Psychology Department, University of Victoria, Victoria, BC, Canada Tabitha Fleming, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Tasheia Floyd, Washington University School of Medicine, St. Louis, MO, USA Lauren A. Fowler, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA

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List of Contributors

Elinor Flynn, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Heather Walton Flynn, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA Elizabeth S. Focella, Department of Psychology, University of Wisconsin Oshkosh, Oshkosh, WI, USA Amy Frers, Department of Psychology and University Health Sciences Center, University of Colorado Denver, Denver, CO, USA Howard S. Friedman, Psychology Department, University of California Riverside, Riverside, CA, USA Paul T. Fuglestad, Department of Psychology, University of North Florida, Jacksonville, FL, USA Kentaro Fujita, Department of Psychology, The Ohio State University, Columbus, OH, USA Kristel M. Gallagher, Thiel College, Greenville, PA, USA Andrea M. Garcia, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA Paola García‐Egan, Department of Psychological Sciences, University of Missouri‐St. Louis, St. Louis, MO, USA Robert García, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA Casey K. Gardiner, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Robert J. Gatchel, Endowment Professor of Clinical Health Psychology Research, Department of Psychology, College of Science, The University of Texas at Arlington, Arlington, TX, USA Justine Megan Gatt, Neuroscience Research Australia, Sydney, NSW, Australia School of Psychology, University of New South Wales, Sydney, NSW, Australia Nicole K. Gause, Department of Psychology, University of Cincinnati, Cincinnati, OH, USA Ashwin Gautam, Department of Psychology, Binghamton University, Binghamton, NY, USA Fiona Ge, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA Andrew L. Geers, Department of Psychology, University of Toledo, Toledo, OH, USA Margaux C. Genoff, Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA James I. Gerhart, Department of Psychosocial Oncology, Cancer Integrative Medicine Program, Rush University Medical Center, Chicago, IL, USA Meg Gerrard, Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Frederick X. Gibbons, Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Arielle S. Gillman, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Marci E.J. Gleason, Department of Human Development and Family Sciences, University of Texas at Austin, Austin, TX, USA Teresa Gobble, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Department of Educational Psychology, University of Texas at Austin, Austin, TX, USA

List of Contributors

xiii

Katharina Sophia Goerlich, Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany Jamie L. Goldenberg, Department of Psychology, University of South Florida, Tampa, FL, USA Cesar A. Gonzalez, Mayo Clinic Minnesota, Rochester, MN, USA Raul Gonzalez, Florida International University, Miami, FL, USA Brittany F. Goodman, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA Janna R. Gordon, San Diego State University and SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA Edward C. Green, Department of Anthropology, The George Washington University, Washington, DC, USA Joseph P. Green, Department of Psychology, Ohio State University at Lima, Lima, OH, USA Michael G. Griffin, Department of Psychological Sciences and Center for Trauma Recovery, University of Missouri–St. Louis, St. Louis, MO, USA Steven Grindel, Medical College of Wisconsin, Milwaukee, WI, USA Kristina Gryboski, Project Hope, Millwood, VA, USA Suzy Bird Gulliver, Baylor and Scott and White Hospitals and Clinics, Waco, TX, USA School of Public Health, Texas A&M University, College Station, TX, USA Megan R. Gunnar, Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, USA Jenna Haddock, Department of Psychological Sciences, University of Missouri‐St. Louis, St. Louis, MO, USA Martin S. Hagger, Health Psychology and Behavioral Medicine Research Group, School of Psychology and Speech Pathology, Faculty of Health Sciences, Curtin University, Perth, WA, Australia Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland School of Applied Psychology and Menzies Health Institute Queensland, Behavioural Bases for Health, Griffith University, Brisbane, QLD, Australia Centre for Physical Activity Studies, School of Human, Health and Social Sciences, Central Queensland University, Rockhampton, QLD, Australia Jada G. Hamilton, Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA Paul K.J. Han, Center for Outcomes Research and Evaluation, Maine Medical Center Research Institute, Portland, ME, USA Grace E. Hanley, Psychology Department, University of California, Riverside, Riverside, CA, USA Madeline B. Harms, Department of Psychology, University of Wisconsin‐Madison, Madison, WI, USA Adam Harris, Department of Psychology and University Health Sciences Center, University of Colorado Denver, Denver, CO, USA Lauren N. Harris, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Peter R. Harris, School of Psychology, University of Sussex, Brighton & Hove, UK Philine S. Harris, School of Psychology, University of Sussex, Brighton & Hove, UK William Hart, Psychology Department, University of Alabama, Tuscaloosa, AL, USA M.J. Hartsock, University of Colorado, Boulder, CO, USA

xiv

List of Contributors

Kelly B. Haskard‐Zolnierek, Department of Psychology, Texas State University, San Marcos, TX, USA S. Alexandar Haslam, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Catherine Haslam, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Karen Hasselmo, Department of Psychology, University of Arizona, Tucson, AZ, USA Kathryn S. Hayward, Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada Stroke Division, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia NHMRC Centre for Research Excellence in Stroke Rehabilitation and Recovery, Parkville, VIC, Australia Jodi M. Heaps‐Woodruff, University of Missouri–St. Louis, St. Louis, MO, USA Missouri Institute of Mental Health, St. Louis, MO, USA Dietlinde Heilmayr, Psychology Department, Moravian College, Bethlehem, PA, USA Vicki S. Helgeson, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Joseph A. Helpern, Center for Biomedical Imaging, Department of Neuroscience, Department of Neurology, and Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA Garrett Hisler, Department of Psychology, Iowa State University, Armes, IA, USA Molly B. Hodgkins, Department of Psychological and Brain Sciences, University of Maine, Orono, ME, USA Michael Hoerger, Department of Psychology, Tulane University, New Orleans, LA, USA Jeanne S. Hoffman, US Department of the Army, Washington, DC, USA Stephanie A. Hooker, Psychology Department,University of Colorado at Denver – Anschutz Medical Campus, Aurora, CO, USA Vera Hoorens, Center for Social and Cultural Psychology, Katholieke Universiteit Leuven, Leuven, Belgium Arpine Hovasapian, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Jennifer L. Howell, Psychology Department, Ohio University, Athens, OH, USA Robert M. Huff, Department of Health Sciences, California State University, Northridge, Morgan Hill, CA, USA Jacob Huffman, Behavioral Neuroscience Program, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO, USA Lauren J. Human, Department of Psychology, McGill University, Montreal, QC, Canada Jeffrey M. Hunger, Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA Tanya Hunt, Department of Clinical Psychology, Palo Alto University, Palo Alto, California, USA Steven Hyde, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA Christina Di Iorio, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA

List of Contributors

xv

Jamie M. Jacobs, Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA Lauren James, Department of Psychology, University of North Florida, Jacksonville, FL, USA Rebekah Jazdzewki, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Brooke N. Jenkins, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Jens H. Jensen, Center for Biomedical Imaging, Department of Neuroscience, and Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA Donna C. Jessop, School of Psychology, University of Sussex, Brighton & Hove, UK Jolanda Jetten, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Kimberly Johnson, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA Doerte U. Junghaenel, USC Dornsife Center for Self‐Report Science, University of Southern California, Los Angeles, CA, USA Vanessa Juth, The Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA Samantha Kahn, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA David A. Kalkstein, Department of Psychology, New York University, New York, NY, USA Alexander Karan, Department of Psychology, University of California, Riverside, CA, USA Natalia M. Kecala, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri–St. Louis, St. Louis, MO, USA Robert G. Kent de Grey, Department of Psychology, University of Utah, Salt Lake City, UT, USA Margaret L. Kern, Center for Positive Psychology, Graduate School of Education, University of Melbourne, Parkville, VIC, Australia Nathan A. Kimbrel, Department of Veterans Affairs’ VISN 6 Mid‐Atlantic MIRECC, Durham, NC, USA Durham Veterans Affairs Medical Center, Durham, NC, USA Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA Tricia Z. King, The Neuroscience Institute, Georgia State University, Atlanta, GA, USA Nancy Kishino, West Coast Spine Restoration, Riverside, CA, USA Ekaterina Klyachko, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA Brian Konecky, Boise VAMC Virtual Integrated Multisite Patient Aligned Care Team Hub, Boise, ID, USA Gerald P. Koocher, Department of Psychology, DePaul University, Chicago, IL, USA Laura E. Korthauer, Department of Psychology, University of Wisconsin‐Milwaukee, Milwaukee, WI, USA Abigail O. Kramer, Palo Alto University, Palo Alto, CA, USA Weill Institute for Neurosciences, University of California, San Francisco Memory and Aging Center, San Francisco, CA, USA Joel H. Kramer, Weill Institute for Neurosciences, University of California, San Francisco Memory and Aging Center, San Francisco, CA, USA Zlatan Krizan, Department of Psychology, Iowa State University, Armes, IA, USA

xvi

List of Contributors

Monica F. Kurylo, Departments of Medicine and Public Health, University of Kansas Medical Center, Kansas City, KS, USA Onawa P. LaBelle, Psychology Department, University of Michigan, Ann Arbor, MI, USA Melissa H. Laitner, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Mark J. Landau, Department of Psychology, University of Kansas, Lawrence, KS, USA Shane Landry, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Sleep and Circadian Medicine Laboratory, Department of Physiology, School of Biomedical Sciences, Monash University, Melbourne, VIC, Australia Kevin T. Larkin, Department of Psychology, West Virginia University, Morgantown, WV, USA Jason M. Lavender, Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA Kristin Layous, Department of Psychology, California State University East Bay, Hayward, CA, USA Thanh Le, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Thad R. Leffingwell, Department of Psychology, Oklahoma State University, Stillwater, OK, USA Angela M. Legg, Department of Psychology, Pace University, New York, NY, USA Jana Lembke, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA Elizabeth Lemon, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Richie L. Lenne, Psychology Department, University of Minnesota Twin Cities, Minneapolis, MN, USA Alex Leow, Department of Psychiatry, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA Department of Bioengineering, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA Howard Leventhal, Department of Psychology, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA Linda J. Levine, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Melissa A. Lewis, Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Meng Li, Department of Health and Behavior Sciences, University of Colorado Denver, Denver, CO, USA Huijun Liao, Department of Radiology, Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Alexander P. Lin, Department of Radiology, Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Wendy P. Linda, School of Psychology, Fairleigh Dickinson University, Teaneck, NJ, USA Nikolette P. Lipsey, Psychology Department,University of Florida, Gainesville, FL, USA Dana M. Litt, Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA

List of Contributors

xvii

Andrew K. Littlefield, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Brett Litz, Department of Psychology, Boston University & VA Boston Healthcare System, Boston, MA, USA Marci Lobel, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Todd Lucas, C. S. Mott Endowed Professor of Public Health Associate Professor of Epidemiology and Biostatistics Division of Public Health College of Human Medicine Michigan State University, Wayne State University, Detroit, MI, USA Mia Liza A. Lustria, School of Information, Florida State University, Tallahassee, FL, USA Steven Jay Lynn, Department of Psychology, Binghamton University, Binghamton, NY, USA Salvatore R. Maddi, Department of Psychological Science, University of California, Irvine, Irvine, CA, USA Renee E. Magnan, Washington State University Vancouver, Vancouver, WA, USA Brenda Major, Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA Vanessa L. Malcarne, San Diego State University, UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA UC San Diego Moores Cancer Center, San Diego, CA, USA Department of Psychology, San Diego State University, San Diego, CA, USA Kelsey A. Maloney, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Andrew W. Manigault, Psychology Department, Ohio University, Athens, OH, USA Traci Mann, Psychology Department, University of Minnesota Twin Cities, Minneapolis, MN, USA Soe Mar, Neurology and Pediatrics, Division of Pediatric Neurology, Washington University in St. Louis, St. Louis, MO, USA Shannon M. Martin, Department of Psychology, Central Michigan University, Mount Pleasant, MI, USA Allison Marziliano, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Katilyn Mascatelli, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Tyler B. Mason, Department of Psychology, University of Southern California, Los Angeles, CA, USA Neuropsychiatric Research Institute, Fargo, ND, USA Kevin S. Masters, Psychology Department, University of Colorado at Denver  –  Anschutz Medical Campus, Aurora, CO, USA Kevin D. McCaul, North Dakota State University, Fargo, ND, USA Tara P. McCoy, Psychology Department, University of California, Riverside, Riverside, CA, USA Michael McCrea, Brain Injury Research Program, Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA Scout N. McCully, Department of Psychological Sciences, Kent State University, Kent OH, USA Don McGeary, Department of Psychiatry, Department of Family and Community Medicine, The University of Texas Health Science Center San Antonio, San Antonio, TX, USA

xviii

List of Contributors

Jessica McGovern, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Robert E. McGrath, School of Psychology, Farleigh Dickinson University, Teaneck, NJ, USA Michael P. Mead, North Dakota State University, Fargo, ND, USA Alan Meca, Department of Psychology, Old Dominion University, Norfolk, VA, USA Matthias R. Mehl, Psychology Department, University of Arizona, Tucson, AZ, USA Timothy P. Melchert, Department of Psychology, Marquette University, Milwaukee, WI, USA Paola Mendoza‐Rivera, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Maria G. Mens, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Sai Merugumala, Department of Radiology, Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Rebecca A. Meza, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Dara Mickschl, Medical College of Wisconsin, Milwaukee, WI, USA Tricia A. Miller, Santa Barbara Cottage Hospital, Santa Barbara, CA, USA Department of Psychology, University of California Riverside, Riverside, CA, USA Kyle R. Mitchell, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Mona Moieni, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Phillip J. Moore, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA DeAnna L. Mori, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA Sandra B. Morissette, Department of Psychology, The University of Texas at San Antonio College of Sciences, San Antonio, TX, USA Amber Morrow, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Ariel J. Mosley, Department of Psychology, University of Kansas, Lawrence, KS, USA Robert W. Motl, Department of Physical Therapy, University of Alabama at Birmingham, Birmingham, AL, USA Laurent Mottron, Department of Psychiatry, Faculty of Medicine, Universite de Montreal, Montreal, QC, Canada Anne Moyer, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Elaine M. Murphy, Population Reference Bureau, Washington, DC, USA Carolyn B. Murray, Psychology, University of California, Riverside, CA, USA Christopher S. Nave, Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA Department of Psychology, Rutgers University Camden, Camden, NJ, USA Eve‐Lynn Nelson, KU Center for Telemedicine & Telehealth, University of Kansas Medical Center, University of Kansas, Kansas City, KS, USA Steven M. Nelson, VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX, USA Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA

List of Contributors

xix

Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA Elizabeth Neumann, Medical College of Wisconsin, Milwaukee, WI, USA Jason L. Neva, Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada Jody S. Nicholson, Department of Psychology, University of North Florida, Jacksonville, FL, USA Christina Nisson, Department of Psychology, University of Michigan, Ann Arbor, MI, USA Mary‐Frances O’Connor, Psychology Department, University of Arizona, Tucson, AZ, USA Laura O’Hara, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Gbolahan O. Olanubi, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Nils Olsen, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA Yimi Omofuma, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Elizabeth Ortiz‐Gonzalez, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Ileana Pacheco‐Colón, Florida International University, Miami, FL, USA Aliza A. Panjwani, Graduate School and University Center, City University of New York, New York, NY, USA Mike C. Parent, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Department of Educational Psychology, University of Texas at Austin, Austin, TX, USA Thomas J. Parkman, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Robert H. Paul, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Missouri Institute of Mental Health, St. Louis, MO, USA Stephanie A. Peak, Battelle Memorial Institute, Columbus, OH, USA Michael G. Perri, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Laura Perry, Department of Psychology, Tulane University, New Orleans, LA, USA Alyssa Phelps, Boston University Alzheimer’s Disease Center and Boston University CTE Center, Department of Neurology, Boston University School of Medicine, Boston, MA, USA Carissa L. Philippi, Department of Psychological Sciences, University of Missouri‐St Louis, St Louis, MO, USA L. Alison Phillips, Iowa State University, Ames, IA, USA Paula R. Pietromonaco, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA Craig P. Pollizi, Department of Psychology, Binghamton University, Binghamton, NY, USA Seth D. Pollak, Department of Psychology, University of Wisconsin‐Madison, Madison, WI, USA David B. Portnoy, US Food and Drug Administration, Silver Spring, MD, USA Rachel Postupack, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA

xx

List of Contributors

Matthew R. Powell, Division of Neurocognitive Disorders, Department of Psychiatry & Psychology, Mayo Clinic Minnesota, Rochester, MN, USA Candice Presseau, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA Sarah D. Pressman, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Rebecca N. Preston‐Campbell, Missouri Institute of Mental Health, St. Louis, MO, USA Isabel F. Ramos, Department of Psychology, University of California, Los Angeles, CA, USA Christopher T. Ray, College of Health Sciences, Texas Woman’s University, Denton, TX, USA Amanda L. Rebar, Centre for Physical Activity Studies, School of Human, Health and Social Sciences, Central Queensland University, Rockhampton, QLD, Australia Allecia E. Reid, Colby College, Waterville, ME, USA May Reinert, Department of Psychology, Pace University, New York, NY, USA Kelly E. Rentscher, Psychology Department, University of Arizona, Tucson, AZ, USA Tracey A. Revenson, Department of Psychology, Hunter College, New York, NY, USA C. Steven Richards, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Laura River, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Megan L. Robbins, Department of Psychology, University of California, Riverside, CA, USA Michael C. Roberts, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA John D. Robinson, Department of Surgery and Department of Psychiatry and Behavioral Sciences, Howard University College of Medicine/Hospital, Washington, DC, USA Theodore F. Robles, Psychology Department, University of California Los Angeles, CA, USA Catherine Rochefort, Department of Psychology, Southern Methodist University, Dallas, TX, USA Daniel G. Rogers, Department of Psychology, University of Mississippi, MS, USA John L. Romano, Department of Educational Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA Karen S. Rook, Department of Psychological Science, University of California, Irvine, CA, USA Ann Rosenthal, Medical College of Wisconsin, Milwaukee, WI, USA J. Megan Ross, Florida International University, Miami, FL, USA Tania L. Roth, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA Alexander J. Rothman, Department of Psychology, University of Minnesota, Minneapolis, MN, USA Ronald H. Rozensky, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Kristen L. Rudd, Department of Psychology, University of California, Riverside, Riverside, CA, USA Kirstie K. Russell, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada Arseny A. Ryazanov, Department of Psychology, University of California San Diego, La Jolla, CA, USA

List of Contributors

xxi

Napapon Sailasuta, Centre for Addiction and Mental Health, Toronto, ON, Canada Stella Sakhon, Department of Psychology, The University of Arizona, Tucson, AZ, USA Lauren E. Salminen, Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA Christian Salvatore, Scuola Universitaria Superiore IUSS Pavia, Piazza della Vittoria, Pavia, Italy DeepTrace Technologies S.R.L., Via Conservatorio, Milan, Italy Peter M. Sandman, Department of Human Ecology, Rutgers University New Brunswick, New Brunswick, NJ, USA Keith Sanford, Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA David A. Sbarra, Department of Psychology, University of Arizona, Tucson, AZ, USA Michael F. Scheier, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Adam T. Schmidt, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Elana Schwartz, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Esther N. Schwartz, Department of Family and Community Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA Seth J. Schwartz, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA Ralf Schwarzer, Health Psychology, Freie Universität Berlin, Berlin, Germany Health Psychology, Australian Catholic University, Melbourne, VIC, Australia Richard J. Seime, Mayo Clinic Minnesota, Rochester, MN, USA Claire E. Sexton, Department of Psychiatry, University of Oxford, Oxford, UK Louise Sharpe, School of Psychology, Faculty of Science, The University of Sydney, Sydney, NSW, Australia Lindsay Sheehan, Department of Psychology, Illinois Institute of Technology College of Science, Chicago, IL, USA Paschal Sheeran, Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Daniel Sheinbein, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA James A. Shepperd, Psychology Department, University of Florida, Gainesville, FL, USA Brittany N. Sherrill, Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA Molin Shi, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Emily W. Shih, Department of Psychology, University of California, Riverside, CA, USA Ben W. Shulman, Psychology Department, University of California, Los Angles, CA, USA Elizabeth Silbermann, Neurology Resident, Washington University in St. Louis, St. Louis, MO, USA Department of Neurology, Oregon Health and Science University, Portland, OR, USA Roxane Cohen Silver, Department of Psychological Science, University of California, Irvine, Irvine, CA, USA Colleen A. Sloan, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA

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List of Contributors

Rachel Smallman, Psychology Department, Texas A&M University System, College Station, TX, USA Kathryn E. Smith, Neuropsychiatric Research Institute, Fargo, ND, USA Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA Patrick J. Smith, Duke University Medical Center, Durham, NC, USA Todd A. Smitherman, Department of Psychology, University of Mississippi, University, MS, USA Joshua M. Smyth, The Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA Dara H. Sorkin, Department of Psychological Science, University of California, Irvine, CA, USA R. L. Spencer, University of Colorado, Boulder, CO, USA Melissa Spina, Psychology Department, University of Missouri System, Columbia, MO, USA Bonnie Spring, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA Annette L. Stanton, Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA Ellen Stephenson, Department of Psychology, University of British Columbia, Vancouver, BC, Canada Robert A. Stern, Boston University Alzheimer’s Disease Center and Boston University CTE Center, Department of Neurology, Boston University School of Medicine, Boston, MA, USA Department of Neurosurgery and Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, USA Angela K. Stevens, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Courtney J. Stevens, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Elizabeth M. Stewart, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Danu Anthony Stinson, Psychology Department, University of Victoria, Victoria, BC, Canada Michelle L. Stock, Department of Psychological and Brain Sciences, George Washington University, Washington, DC, USA Arthur A. Stone, USC Dornsife Center for Self‐Report Science, University of Southern California, Los Angeles, CA, USA Department of Psychology, Dana and David Dornsife College of Letters Arts and Sciences, University of Southern California, Los Angeles, CA, USA Jeff Stone, Psychology Department, University of Arizona, Tucson, AZ, USA John A. Sturgeon, Center for Pain Relief, University of Washington, Seattle, WA, USA Kieran T. Sullivan, Department of Psychology, Santa Clara University, Santa Clara, CA, USA Amy Summerville, Psychology Department, Miami University, Oxford, OH, USA Jessie Sun, Center for Positive Psychology, Graduate School of Education, University of Melbourne, Parkville, VIC, Australia Department of Psychology, University of California, Davis, CA, USA Steve Sussman, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

List of Contributors

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Trevor James Swanson, Department of Psychology, University of Kansas, Lawrence, KS, USA Allison M. Sweeney, Department of Psychology, University of South Carolina, Columbia, SC, USA Kate Sweeny, Department of Psychology, University of California, Riverside, Riverside, CA, USA Sergey Tarima, Medical College of Wisconsin, Milwaukee, WI, USA Carly A. Taylor, Boston Medical Center, Boston, MA, USA George T. Taylor, Behavioral Neuroscience Program, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Interfakultäre Biomedizinische Forschungseinrichtung (IBF) der Universität Heidelberg, Heidelberg, Germany A. Janet Tomiyama, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Victoria A. Torres, Department of Psychology, College of Liberal Arts, University of Mississippi, Oxford, MS, USA David R.M. Trotter, Department of Family and Community Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA Bert N. Uchino, Department of Psychology, University of Utah, Salt Lake City, UT, USA John A. Updegraff, Department of Psychological Sciences, Kent State University, Kent, OH, USA Jay Urbain, Milwaukee School of Engineering, Milwaukee, WI, USA Amy E. Ustjanauskas, San Diego State University, UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA UC San Diego Moores Cancer Center, San Diego, CA, USA Jason Van Allen, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Erika D. Van Dyke, Department of Sport Sciences, College of Physical Activity and Sport Sciences, West Virginia University, Morgantown, WV, USA Department of Psychology, Springfield College, Springfield, MA, USA Judy L. Van Raalte, Department of Psychology, Springfield College, Springfield, MA, USA Maryke Van Zyl‐Harrison, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Rachel Vanderkruik, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Elizabeth J. Vella, Department of Psychology, University of Southern Maine, Portland, ME, USA Birte von Haaren‐Mack, German Sport University Cologne, Köln, Nordrhein‐Westfalen, Germany David Von Nordheim, University of Missouri–St. Louis, St. Louis, MO, USA Missouri Institute of Mental Health, St. Louis, MO, USA Mikhail Votinov, Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany Institute of Neuroscience and Medicine (INM‐10), Jülich, Germany Rebecca K. Vujnovic, Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, USA Amy Wachholtz, Department of Psychology and University Health Sciences Center, University of Colorado Denver, Denver, CO, USA

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List of Contributors

Eric F. Wagner, Department of Epidemiology, Florida International University, Miami, FL, USA Ryan J. Walker, Psychology Department, Miami University, Oxford, Ohio, USA Rachel A. Wamser-Nanney, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA Britney M. Wardecker, Psychology Department, University of Michigan, Ann Arbor, MI, USA Melissa Ward‐Peterson, Department of Epidemiology, Florida International University, Miami, FL, USA Lisa Marie Warner, Health Psychology, Freie Universitat Berlin, Berlin, Germany Neil D. Weinstein, Department of Human Ecology, Rutgers University New Brunswick, New Brunswick, NJ, USA Suzanne E. Welcome, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Kristen J. Wells, San Diego State University and SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA Amy Wenzel, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA Private Practice, Rosemont, PA, USA Summer L. Williams, Department of Psychology, Westfield State University, Westfield, MA, USA Cynthia J. Willmon, Reno VA Sierra Nevada Health Care System, Reno, NV, USA Sara M. Witcraft, Department of Psychology, University of Mississippi, Oxford, MS, USA Stephen A. Wonderlich, Neuropsychiatric Research Institute, Fargo, ND, USA Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA E.R. Woodruff, University of Colorado, Boulder, CO, USA Patrick W. Wright, Neurology, Washington University in St. Louis, St. Louis, MO, USA Robert C. Wright, Department of Psychology, University of California, Riverside, CA, USA Shawna Wright, KU Center for Telemedicine & Telehealth, University of Kansas Medical Center, University of Kansas, Kansas City, KS, USA Tuppett M. Yates, Department of Psychology, University of California, Riverside, Riverside, CA, USA Julie D. Yeterian, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA Lok‐Kin Yeung, Columbia University Medical Center, New York, NY, USA Alex J. Zautra, Psychology Department, Arizona State University, Tempe, AZ, USA Colin A. Zestcott, Psychology Department, University of Arizona, Tucson, AZ, USA Peggy M. Zoccola, Psychology Department, Ohio University, Athens, OH, USA

Foreword

Until the 1970s, there were no books, journals, or university courses on health psychology. Although the field’s intellectual roots stretch back to the beginnings of psychology more than a century ago, its formal emergence depended on a convergence of influences (Friedman & Silver, 2007), including psychosomatic medicine, social‐psychological and socio‐­anthropological perspectives on medicine, epidemiology, and medical and clinical psychology. Today, health psychology is a principal area of significant social science research and practice, with vital implications for the health and well‐being of individuals and societies. Understanding the explosive trajectory of health psychology is useful to appreciating the strengths of the field and to approaching this new encyclopedia, the Wiley Encyclopedia of Health Psychology. What is the nature of health? That is, what does it mean to be healthy? The way that this question is answered affects the behaviors, treatments, and resource allocations of individuals, families, health practitioners, governments, and societies. For example, if it is thought that you are healthy unless and until you contract a disease or suffer an injury, then attention and resources are primarily allocated toward “fixing” the problem through medications or surgical repairs. This is the traditional biomedical model of disease (sometimes called the “disease model”). Indeed, in the United States, the overwhelming allocation of attention and resources is to physicians (doing treatments) and to pharmaceuticals (prescription drugs and their development). In contrast, health psychology developed around a much broader and more interdisciplinary approach to health, one that is often termed the biopsychosocial model. The biopsychosocial model (a term first formally proposed by George Engel in 1968) brings together core elements of staying healthy and recovering well from injury or disease (Stone et al., 1987). Each individual—due to a combination of biological influences and psychosocial experiences—is more or less likely to thrive. Some of this variation is due to genetics and early life development; some depends on the availability and appropriate applications of medical treatments; some involves nutrition and physical activity; some depends on preparations for, perceptions of, and reactions to life’s challenges; and some involves exposure to or seeking out of healthier or unhealthier environments, both physically and socially. When presented in this way, it might seem obvious that health should certainly be viewed in this broader interdisciplinary way. However, by misdirecting its vast expenditures on health care, the United States gives its residents mediocre health at high cost (Kaplan, 2019). To approach these matters in a thorough manner, this encyclopedia includes four volumes, with Volume 1 focusing on the biological bases of health and health behavior, Volume 2 concentrating on the social bases, Volume 3 centering around the psychological and clinical aspects, and Volume 4 focused more broadly on crosscutting and applied matters.

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Key parts of health depend on biological characteristics and how they interact with our experiences and environments. So, for example, in Volume 1, there are articles on injury to the brain, alcohol effects on the brain, nutrition, drug abuse, psychophysiology, and the tools and key findings of neuroscience. Note that even individuals with the same genes (identical twins) can and do have different health and recovery outcomes, and the articles delve into such ­complexities of health. Of course humans are also social creatures, and people’s growth, development, and health behaviors take place in social contexts. So, in Volume 2, there are articles on such fundamental matters as social support, coping, spirituality, emotion, discrimination, communication, psychosocial stress, and bereavement. Because our approaches to and conceptions of health are heavily influenced by society’s institutions and structures revolving around medical care, much of health psychology derives from or intersects with clinical psychology and applied behavioral medicine. Volume 3 covers such clinical topics as psycho‐oncology, depression, drug abuse, chronic disease, eating disorders, and the psychosocial aspects of coronary heart disease. Finally and importantly, there are a number of special and cross‐cutting matters that are considered in Volume 4, ranging from relevant laws and regulations to telehealth and health disparities. Taken together, the articles triangulate on what it truly means to be healthy. What are the most promising directions for the future that emerge from a broad and deep approach to health? That is, to where do these encyclopedia articles point? One key clue arises from a unique opportunity to enhance, extend, and analyze the classic Terman study of children who were followed and studied throughout their lives (Friedman & Martin, 2012). These studies revealed that there are lifelong trajectories to health, thriving, and longevity. Although ­anyone can encounter bad luck, a number of basic patterns emerged that are more likely to lead to good health. That is, for some individuals, certain earlier life characteristics and circumstances help propel them on pathways of healthier and healthier behaviors, reactions, relationships, and experiences, while others instead face a series of contingent stumbling blocks. There are multipart but nonrandom pathways across time linking personalities, health behaviors, social groups, education, work environments, and health and longevity. The present encyclopedia necessarily is a compendium of summaries of the relevant elements of health and thriving, but one that would and can profitably be used as a base to synthesize the long‐term interdependent aspects of health. In sum, this encyclopedia is distinctive in its explicit embrace of the biopsychosocial approach to health, not through lip service or hand‐waving but rather through highly detailed and extensive consideration of the many dozens of topics crucial to this core interdisciplinary understanding.

References Friedman, H. S., & Martin, L. R. (2012). The longevity project: Surprising discoveries for health and long life from the landmark eight‐decade study. New York: Penguin Plume Press. Friedman, H. S., & Silver, R. C. (Eds.) (2007). Foundations of health psychology. New York: Oxford University Press. Kaplan, R. M. (2019). More than medicine: The broken promise of American health. Cambridge, MA: Harvard University Press. Stone, G., Weiss, M., Matarazzo, J. D., Miller, N. E., Rodin, J., Belar, C. D., … Singer, J. E. (Eds.) (1987). Health psychology: A discipline and a profession. University of Chicago Press.

Howard S. Friedman University of California, Riverside August 15, 2019

Preface

The field of health psychology is a specialty area that draws on how biology, psychology, behavior, and social factors influence health and illness. Despite the fact that the formal recognition of the field is only about a half of a century old, it has established itself as a major scientific and clinical discipline. The primary reason for this is that there have been a number of significant advances in psychological, medical, and physiological research that have changed the way we think about health, wellness, and illness. However, this is only the tip of the iceberg. We have the field of health psychology to thank for much of the progress seen across our ­current healthcare system. Let me provide some examples that illustrate the diversity of these important contributions. Starting with a broad public health perspective, health psychologists have been involved in how our communities are planned and how urban development has a significant and direct impact on our health behaviors. That is, if we live close to where we work, play, and shop, we are more likely to walk or bike to our destination rather than drive or take public transportation. Focusing on a smaller, but no less important, source of data, health psychologists are involved in using information obtained from our genes to help counsel individuals to make good, well‐informed health decisions that could have an impact on the individual immediately or in the future. Being well‐informed can direct a person toward the best possible path toward wellness, whether it be measures aimed at prevention, further monitoring, or intervention. Of course, there are many other contributions. For instance, data indicates that several of the leading causes of death in our society can be prevented or delayed (e.g., heart disease, respiratory disease, cerebrovascular disease, and diabetes) via active participation in psychological interventions. Knowing that we can improve health status by changing our behaviors seems like an easy “fix,” but we know that behavior change is tough. As such, it is not surprising that health psychologists have been involved in trying to improve upon treatment success by examining patient compliance. When we better understand what motivates and discourages people from engaging in treatment or pro‐health behaviors, we can improve upon compliance and help individuals adopt more healthy lifestyles. Health psychologists have also played a role  in shaping healthcare policy via identifying evidence‐based treatments. This work has direct effects on how individuals receive healthcare as well as what treatments are available/ reimbursable by insurance companies. Finally, there are also factors beyond medical care to consider (e.g., economic, educational) that can lead to differential health outcomes. Thus, health psychologists likewise examine ways to reduce health disparities, ensuring that the ­public and government officials are made aware of the impact of the social determinants of health.

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I could go on and on, however, examples like this illustrate the significant impact this broad and exciting field has had (and will continue to have) on our understanding of health and wellness. Given the constantly changing nature of the field, it is not possible to be all inclusive; however, the aim of these four volumes is to provide readers an up‐to‐date overview of the field. Each entry is written to stand alone for those who wish to learn about a specific topic, and if the reader is left wanting more, suggested readings are provided to expand one’s knowledge. Volume I, Biological Bases of Health Behavior, includes entries that cover topics in the broad areas of neuroscience and biopsychology relevant to health behavior. General topics include degenerative and developmental conditions, emerging methodologies available in clinical research, functional anatomy and imaging, and gene × environment interactions. Volume II, Social Bases of Health Behavior, addresses topics related to theories and concepts derived from social psychology. Specifically, topics related to the self, social cognition, social perception, attitudes and attitude change, perception, framing, and pro‐health behaviors are included. Volume III, Clinical Health Psychology and Behavioral Medicine, covers the applied aspects of the field of health psychology including practical topics that clinical health psychologists face in the workplace, behavioral aspects of medical conditions, the impact of unhealthy behaviors, and issues related to the comorbidity of psychiatric disorders and chronic health concerns. Finally, Volume IV, Special Issues in Health Psychology, contains a wide array of topics that are worthy of special consideration in the field. Philosophical and conceptual issues are discussed, along with new approaches in delivering treatment and matters to c­ onsider when working with diverse and protected populations. It is my sincere hope that the Wiley Encyclopedia of Health Psychology will serve as a comprehensive resource for academic and applied psychologists, other health care professionals interested in the relationship of psychological and physical well‐being, and students across the health professions. I would very much like to thank and acknowledge all those who have made this work possible including Michelle McFadden, the Wiley editorial and production teams, the volume editors, and of course all of the authors who contributed their outstanding work. Lee M. Cohen, PhD

Preface Volume 3: Clinical Health Psychology and Behavioral Medicine

The theory developments, research outcomes, and practice guidelines for the fields of clinical health psychology and behavioral medicine have expanded dramatically during the last 30 years. Thousands of interesting journal articles and hundreds of relevant books have been published during that time interval. Numerous well‐regarded journals focus entirely or in part on these clinical health psychology topics, such as the journals Health Psychology, Annals of Behavioral Medicine, Journal of Consulting and Clinical Psychology, Journal of the American Medical Association, New England Journal of Medicine, and over 100 specialty journals on focused topics such as specific diseases, psychological disorders comorbid with chronic health problems, translational issues in health care, psychosocial interventions in health psychology and behavioral medicine, and so forth. The topics covered in this third volume of the Wiley Encyclopedia of Health Psychology is focused on some of the recent theory, research, and p ­ ractice issues relevant to the field of clinical health psychology. This volume covers some exciting and important issues in the area. Specifically, entries touch on both broad and specific topics covering (a) methodological sophistication, (b) chronic diseases, (c) diversity and inclusiveness, (d) biological issues, (e) translation of health psychology knowledge, (f) chronic psychopathology that is comorbid with health problems and diseases, and (g) social justice. In sum, we have attempted to include a wide array of entries on what we consider to be the most relevant topics and trends, although we could not include all possible relevant topics and trends. Nevertheless, we believe that readers will find a wide range of interesting and important entries in this volume. We should say something about our recruitment and editing guidelines for Volume 3. We tried to recruit well‐regarded experts—in some cases with national and international ­reputations—in each of the entry topic areas. Many of the entries are multiauthored, so most of these experts collaborated with other experts in their specific areas. We also tried to recruit a broad sampling across the numerous trends and topic areas in the field, rather than having most or all of the entries focused on just a few topics. Further, we worked diligently to provide the authors feedback that we believed would make the entries stronger in terms of standard scholarship goals such as accuracy, clarity, thoroughness, and adherence to standard professional writing styles in clinical health psychology. It should be noted, however, that virtually all of the entries were already in excellent shape, when we first received them, which dovetails with the outstanding reputations of our authors. We believe that Volume 3, Clinical Health Psychology and Behavioral Medicine, will be a helpful resource book on an important topic. We also believe that readers will find this volume

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an excellent addition to their professional library, along with the other volumes in the Wiley Encyclopedia of Health Psychology. In closing, we would like to acknowledge the excellent work of our authors, the helpful expertise of the staff at Wiley, the ongoing help of our home ­universities, and the wonderful support from our families. C. Steven Richards, PhD Lee M. Cohen, PhD

Acknowledgments

We would like to acknowledge the excellent work of our authors, the helpful expertise of Michelle McFadden and the staff at Wiley, the ongoing support of our home institutions, Texas Tech University and the University of Mississippi, and the wonderful support from our families—Carol, Dawn, Dave, and Jill (Steve) and Michelle, Ross, Rachel, and Becca (Lee). C. Steven Richards and Lee M. Cohen Editors

Editor‐in‐Chief Acknowledgments

I would like to publicly recognize and thank my family, Michelle, Ross, Rachel, and Becca for being supportive of my work life while also providing me with a family life beyond my wildest dreams. I love you all very much. I would also like to note my great appreciation and thanks to the faculty and staff of the Doctoral Training Program in Clinical Psychology at Oklahoma State University for providing me with an opportunity to expand my education and for providing extraordinary training. In particular, I would like to thank my late mentor Dr. Frank L. Collins Jr., Dr. Larry Mullins, Dr. John Chaney, and Patricia Diaz Alexander. Further, I am grateful to the University of Mississippi and Texas Tech University (and the colleagues and students I have had the privilege to work alongside) for affording me excellent working environments and the support to do the work I am honored to be a part of. Finally, I would like to recognize and thank the volume editors for their vision and perseverance to this project as well as to each of the contributors for their excellent entries and their dedication to this very important field. Lee M. Cohen, Ph.D. Editor‐in‐Chief

Body Image Tyler B. Mason1,2, Kathryn E. Smith2,3, Jason M. Lavender4, and Stephen A. Wonderlich2,3 Department of Psychology, University of Southern California, Los Angeles, CA, USA 2 Neuropsychiatric Research Institute, Fargo, ND, USA 3 Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA 4 Department of Psychiatry, University of California, San Diego, CA, USA 1

Body image involves a person’s subjective appraisal of his or her own physical characteristics and qualities (e.g., Pruzinsky, 2004; Sarwer & Steffen, 2015). Body image concerns are highly prevalent in Western societies and are more prominent among women (Fiske, Fallon, Blissmer, & Redding, 2014), although they occur among both men and women. Additionally, body image concerns appear to be common across the lifespan (Fiske et al., 2014). Evidence s­ uggests that poor body image can negatively affect psychosocial functioning (Patalay, Sharpe, & Wolpert, 2015), health behaviors (Neumark‐Sztainer, Paxton, Hannan, Haines, & Story, 2006), and overall quality of life (Mond et  al., 2013). Conversely, positive body image is ­protective against numerous mental health problems and serves to increase overall psychological well‐being (Gillen, 2015). Further, several mental disorders associated with mortality and reduced quality of life are marked by disturbances in body image (e.g., body dysmorphic ­disorder, eating disorders). Of particular relevance to the field of clinical health psychology, poor body image occurs in a variety of medical conditions and clinical populations (e.g., individuals with obesity, patients with cancer). Understanding and routinely assessing body image concerns may have utility in alleviating health problems and reducing further physical and mental health complications. In this entry, we describe body image in the field of clinical health psychology including risk factors and correlates of body image concerns.

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Body Image and Clinical Health Psychology Body image concerns are important health symptoms to consider in clinical health ­psychology and occur in a myriad of medical conditions and diseases. The most prominent medical ­condition associated with poor body image is obesity. This relationship is in part propagated by a pervasive cultural emphasis on thinness and the associated stigmatization of individuals who are overweight or obese (Puhl & Latner, 2007). Thus, body image concerns may be a problem among any medical and psychiatric patients who present with co‐occurring obesity. Further, while weight loss usually improves overall body image, body image concerns, specifically related to excess skin, may be present among individuals who lose a great deal of weight, notably those who receive bariatric surgery (Song et al., 2006). However, body contouring after large weight loss has been shown to be related to improvements in body image (Song et al., 2006). Body image concerns are also relatively common in patients with various types of cancer and other medical conditions. For example, disturbances in body image have been observed in patients with breast cancer (Lasry et al., 1987), cervical cancer (Hawighorst‐Knapstein et al., 2004), and head and neck cancers (Fingeret et al., 2013). Further, side effects of cancer treatment such as alopecia have also been associated with increases in body image concerns (Münstedt, Manthey, Sachsse, & Vahrson, 1997). Other medical conditions and diseases that may involve body image concerns are inflammatory bowel disease and Crohn’s disease (Saha et al., 2015), endometriosis (Melis et al., 2015), lupus (Jolly et al., 2012), and systemic sclerosis (Ennis, Herrick, Cassidy, Griffiths, & Richards, 2012). Children and adolescents with various medical conditions may experience a similar pattern of concerns. For instance, in a meta‐analysis examining relationships between body image and chronic illness among children and adolescents, Pinquart (2013) found that children and adolescents with obesity, cystic fibrosis, scoliosis, asthma, growth hormone deficits, spina bifida, cancer, and diabetes rated their bodies less positively than healthy comparison groups. While body image concerns may be associated with particular diseases, certain characteristics of diseases and associated treatments may also be related to the manifestation of body image concerns. For example, disease activity and chronicity, treatment type, treatment ­complications, and presenting symptoms in various medical conditions may affect individuals’ body image in various patient populations (e.g., Bullen et  al., 2012; Ennis et  al., 2012; Fingeret et al., 2013; Rhondali et al., 2013). Of note, treatment and improvement in disease activity are not always associated with improvements in body image (Saha et al., 2015). In addition, treatment for medical conditions may worsen body image—for example, increased body dissatisfaction has been reported after mastectomy in breast cancer patients (Lasry et al., 1987). On the other hand, some medical treatments have been shown to reduce body image concerns more so than others (e.g., ileal pouch–anal anastomosis in ulcerative colitis patients; Larson et  al., 2008; and robotic thyroidectomy among patients with papillary thyroid ­carcinoma; Lee et al., 2014).

Risk Factors for Body Image Concerns A host of risk factors for body image concerns has been identified. Demographic, ­sociocultural, and psychosocial variables are among the most widely studied and will be the focus of this ­section of the chapter. First, demographic characteristics including gender, race/ethnicity, and sexual orientation are associated with body image concerns. Women, Caucasian individuals,

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and men who identify as gay or bisexual are generally are more at risk for body image concerns; however, differences in these groups appear to be narrowing in some respects (Field et al., 2014; Morrison, Morrison, & Sager, 2004; Roberts, Cash, Feingold, & Johnson, 2006). Second, numerous sociocultural variables (i.e., characteristics of society that includes cultural norms for beauty and attractiveness) have been linked to body image concerns. For instance, greater media consumption and exposure (e.g., magazines, television, social media, Internet‐ based media) has been found to be associated with increased body image concerns (Grabe, Ward, & Hyde, 2008). Relatedly, internalization of societal norms regarding thinness and perceived sociocultural pressure for thinness are strongly related to increased body image ­concerns (Stice & Whitenton, 2002). Further, social comparisons, which involve tendencies to compare oneself with other people, are also highly predictive of body image concerns, ­especially in response to comparisons in which the other person is perceived as more attractive (Myers & Crowther, 2009). Various psychosocial aspects also have been found to be related to elevated body image concerns. For example, certain personality characteristics are related to poor body image, including neuroticism and other forms of trait negative affectivity as well as perfectionism (Pennesi & Wade, 2016). Lifetime and recent stressful events including weight teasing and discrimination are also related to body image concerns (Eisenberg, Neumark‐Sztainer, Haines, & Wall, 2006). Furthermore, greater internalizing problems, notably anxiety, depression, and low self‐esteem, are related to increased body image concerns (Patalay et al., 2015; van den Berg, Mond, Eisenberg, Ackard, & Neumark‐Sztainer, 2010). Finally, interpersonal and social deficits including attachment insecurity (Abbate‐Daga, Gramaglia, Amianto, Marzola, & Fassino, 2010), poor peer relationships (Schutz & Paxton, 2007), and decreased social support (Stice & Whitenton, 2002) are also associated with increased body image concerns.

Correlates and Consequences of Body Image Concerns Studies of clinical populations have primarily shown body image concerns to be related to reduced disease‐specific and overall quality of life, as well overall psychological well‐being (Rhondali et al., 2013; Yagil et al., 2015). Furthermore, Bullen et al. (2012) reported that preexisting disturbances in body image predicted later psychopathology in patients with colorectal disease. Thus, body image concerns related to disease and medical problems, as well as preexisting body image concerns, may impact mental and physical health outcomes. Additionally, in the broader body image literature, poor body image has been found to be related to numerous domains of mental and physical health, maladaptive behavior, and overall functioning. Below, we briefly review relationships between body image and each of these domains.

Psychological Functioning and Mental Health Poor body image is broadly associated with poorer overall mental health (Mond et al., 2013), as well as lower self‐esteem and increased internalizing symptoms among men and women (Field et al., 2014; Patalay et al., 2015). In addition, body image concerns are related to interpersonal and social problems and difficulties (Gupta & Gupta, 2013; Mond et  al., 2013). Other research has also found increased body image concerns to be associated with suicidal ideation (Gupta & Gupta, 2013) and sleep problems (Gupta, Gupta, & Knapp, 2015).

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Eating‐ and Weight‐Related Behaviors Body image concerns are related to a number of eating‐ and weight‐related behaviors, ­including dietary restriction, use of diet pills, diuretics, and laxatives, and self‐induced v­ omiting (Andrew, Tiggemann, & Clark, 2016; Neumark‐Sztainer et al., 2006). Additionally, among men in particular, body image concerns in the form of a desire for greater muscularity are associated with use of steroids and other appearance‐ and performance‐enhancing drugs (Murray, Griffiths, Mond, Kean, & Blashill, 2016). In turn, maladaptive eating patterns, such as binge eating and overeating, have been shown to be related to increased body image ­concerns (Neumark‐Sztainer et al., 2006).

Psychiatric Conditions In addition to being broadly associated with eating‐ and weight‐control behaviors, body image concerns are a central feature in certain psychiatric disorders and commonly occur in others. For example, the experience of persistent, intrusive thoughts about one or more perceived flaws in one’s appearance is the hallmark feature of body dysmorphic disorder. In addition, body image concerns are most notably associated with eating disorders and are included in nearly all models of disordered eating (Pennesi & Wade, 2016). Specifically, the overvaluation of body shape and weight is a defining characteristic of anorexia nervosa (AN) and bulimia nervosa (BN) and is also commonly observed in subclinical eating disorders and binge eating disorder (BED), the latter of which is characterized by a high prevalence of overweight and obesity.

Other Health Behaviors While research has typically assessed relationships between body image concerns and eating‐ and weight‐related behaviors, there is evidence that body image concerns are associated with other health‐related behaviors as well. For example, studies have found associations between lower body satisfaction and lower physical activity, increased screen‐based media use, and increased smoking among males and females (Farhat, Iannotti, & Caccavale, 2014; Neumark‐ Sztainer et al., 2006). In addition, in an online sample of women, body dissatisfaction was related to less skin screening behaviors and more alcohol consumption, whereas body appreciation was related to more sun protection behaviors (Andrew et al., 2016). Among males, elevated concerns over muscularity among males have been associated with a number of problematic behaviors, including binge drinking and drug use (Field et al., 2014; Jampel, Safren, & Blashill, 2015). Finally, one study of body image concerns and physical health found that chronic weight dissatisfaction predicted elevated risk for type 2 diabetes, over and above weight status (Wirth, Blake, Hébert, Sui, & Blair, 2014).

Sexual Health Body image concerns have also been shown to be related to multiple domains of sexual ­functioning (Woertman & van den Brink, 2012). For example, women with body image ­concerns report more appearance concerns during sexual interactions, lower levels of desire and arousal, and decreased pleasure and sexual satisfaction (Woertman & van den Brink, 2012). In addition, a recent meta‐analysis showed that increased body dissatisfaction was related to less condom use self‐efficacy (Blashill & Safren, 2015). Further, body image c­ oncerns

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were associated with risky sexual behavior among women (Merianos, King, & Vidourek, 2013). Conversely, body appreciation was related to better sexual functioning among women (Satinsky, Reece, Dennis, Sanders, & Bardzell, 2012).

Conclusions Body image concerns are prominent in numerous clinical populations, both medical and ­psychiatric. Body image concerns are generally associated with reduced quality of life in patients and may lead to the development of additional mental and physical health problems. In addition, poor body image may be associated with engaging in numerous maladaptive behaviors including unhealthy eating‐ and weight‐related behaviors, alcohol and drug use, and risky sexual behaviors. Poor body image may also be associated with less positive health behaviors, notably cancer screening behaviors (Andrew et al., 2016) and reduced physical activity. Due to the wide range of negative outcomes related to body image concerns, screening for body image concerns in clinical health settings is imperative in order to provide optimal ­medical care and reduce disease burden.

Author Biographies Tyler B. Mason, PhD, is an assistant professor in the Department of Preventative Medicine at the University of Southern California. He received his PhD in applied psychological science in 2015 and BS in psychology in 2010 from Old Dominion University. His primary research interests include etiology and treatment of binge eating and obesity. Kathryn E. Smith, PhD, is a T32 postdoctoral research fellow at the Neuropsychiatric Research Institute. She received her BA in psychology from Macalester College and her PhD in clinical psychology in from Kent State University. Her primary interests include emotion regulation and co‐occurring psychopathology in eating disorders and obesity. Jason M. Lavender, PhD, is an assistant research scientist in the Department of Psychiatry at the University of California, San Diego. He completed his undergraduate education at Duke University and his PhD in clinical psychology at the University at Albany, SUNY. His research addresses emotion, neurocognition, and personality in eating disorders. Stephen A. Wonderlich, PhD, is the Chester Fritz distinguished professor and associate chairperson in the Department of Psychiatry and Behavioral Science at the University of North Dakota, chair of eating disorders and co‐director of the Eating Disorder and Weight Management Center at Sanford, and president and scientific director of the Neuropsychiatric Research Institute.

References Abbate‐Daga, G., Gramaglia, C., Amianto, F., Marzola, E., & Fassino, S. (2010). Attachment insecurity, personality, and body dissatisfaction in eating disorders. The Journal of Nervous and Mental Disease, 198, 520–524. Andrew, R., Tiggemann, M., & Clark, L. (2016). Positive body image and young women’s health: Implications for sun protection, cancer screening, weight loss and alcohol consumption behaviours. Journal of Health Psychology, 21, 28–39.

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Blashill, A. J., & Safren, S. A. (2015). Body dissatisfaction and condom use self‐efficacy: A meta‐analysis. Body Image, 12, 73–77. doi:10.1016/j.bodyim.2014.10.002 Bullen, T. L., Sharpe, L., Lawsin, C., Patel, D. C., Clarke, S., & Bokey, L. (2012). Body image as a ­predictor of psychopathology in surgical patients with colorectal disease. Journal of Psychosomatic Research, 73, 459–463. doi:10.1016/j.jpsychores.2012.08.010 Eisenberg, M. E., Neumark‐Sztainer, D., Haines, J., & Wall, M. (2006). Weight‐teasing and emotional well‐being in adolescents: Longitudinal findings from Project EAT. Journal of Adolescent Health, 38, 675–683. doi:10.1016/j.jadohealth.2005.07.002 Ennis, H., Herrick, A. L., Cassidy, C., Griffiths, C. E., & Richards, H. L. (2012). A pilot study of body image dissatisfaction and the psychological impact of systemic sclerosis‐related telangiectases. Clinical and Experimental Rheumatology, 31, 12–17. Farhat, T., Iannotti, R. J., & Caccavale, L. J. (2014). Adolescent overweight, obesity and chronic d ­ isease‐ related health practices: Mediation by body image. Obesity Facts, 7, 1–14. Field, A. E., Sonneville, K. R., Crosby, R. D., Swanson, S. A., Eddy, K. T., Camargo, C. A., … Micali, N. (2014). Prospective associations of concerns about physique and the development of obesity, binge drinking, and drug use among adolescent boys and young adult men. JAMA Pediatrics, 168, 34–39. doi:10.1001/jamapediatrics.2013.2915 Fingeret, M. C., Hutcheson, K. A., Jensen, K., Yuan, Y., Urbauer, D., & Lewin, J. S. (2013). Associations among speech, eating, and body image concerns for surgical patients with head and neck cancer. Head & Neck, 35, 354–360. doi:10.1002/hed.22980 Fiske, L., Fallon, E. A., Blissmer, B., & Redding, C. A. (2014). Prevalence of body dissatisfaction among United States adults: Review and recommendations for future research. Eating Behaviors, 15, 357– 365. doi:10.1016/j.eatbeh.2014.04.010 Gillen, M. M. (2015). Associations between positive body image and indicators of men’s and women’s mental and physical health. Body Image, 13, 67–74. Grabe, S., Ward, L. M., & Hyde, J. S. (2008). The role of the media in body image concerns among women: A meta‐analysis of experimental and correlational studies. Psychological Bulletin, 134, 460– 476. doi:10.1037/0033‐2909.134.3.460 Gupta, M. A., & Gupta, A. K. (2013). Cutaneous body image dissatisfaction and suicidal ideation: Mediation by interpersonal sensitivity. Journal of Psychosomatic Research, 75, 55–59. Gupta, M. A., Gupta, A. K., & Knapp, K. (2015). Dissatisfaction with cutaneous body image is directly correlated with insomnia severity: A prospective study in a non‐clinical sample. Journal of Dermatological Treatment, 26, 193–197. Hawighorst‐Knapstein, S., Fusshoeller, C., Franz, C., Trautmann, K., Schmidt, M., Pilch, H., … Vaupel, P. (2004). The impact of treatment for genital cancer on quality of life and body image—Results of a prospective longitudinal 10‐year study. Gynecologic Oncology, 94, 398–403. doi:10.1016/ j.ygyno.2004.04.025 Jampel, J. D., Safren, S. A., & Blashill, A. J. (2015). Muscularity disturbance and methamphetamine use among HIV‐infected men who have sex with men. Psychology of Men & Masculinity, 16, 474. Jolly, M., Pickard, A. S., Mikolaitis, R. A., Cornejo, J., Sequeira, W., Cash, T. F., & Block, J. A. (2012). Body image in patients with systemic lupus erythematosus. International Journal of Behavioral Medicine, 19, 157–164. doi:10.1007/s12529‐011‐9154‐9 Larson, D. W., Davies, M. M., Dozois, E. J., Cima, R. R., Piotrowicz, K., Anderson, K., … Pemberton, J. H. (2008). Sexual function, body image, and quality of life after laparoscopic and open ileal pouch‐anal anastomosis. Diseases of the Colon & Rectum, 51, 392–396. doi:10.1007/s10350‐007‐9180‐5 Lasry, J. C. M., Margolese, R. G., Poisson, R., Shibata, H., Fleischer, D., Lafleur, D., … Taillefer, S. (1987). Depression and body image following mastectomy and lumpectomy. Journal of Chronic Diseases, 40, 529–534. Lee, S., Kim, H. Y., Lee, C. R., Park, S., Son, H., Kang, S. W., … Park, C. S. (2014). A prospective comparison of patient body image after robotic thyroidectomy and conventional open thyroidectomy in patients with papillary thyroid carcinoma. Surgery, 156, 117–125.

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Melis, I., Litta, P., Nappi, L., Agus, M., Melis, G. B., & Angioni, S. (2015). Sexual function in women with deep endometriosis: Correlation with quality of life, intensity of pain, depression, anxiety, and body image. International Journal of Sexual Health, 27, 175–185. doi:10.1080/19317611.2014.952394 Merianos, A. L., King, K. A., & Vidourek, R. A. (2013). Body image satisfaction and involvement in risky sexual behaviors among university students. Sexuality & Culture, 17, 617–630. doi:10.1007/ s12119‐013‐9165‐6 Mond, J., Mitchison, D., Latner, J., Hay, P., Owen, C., & Rodgers, B. (2013). Quality of life impairment associated with body dissatisfaction in a general population sample of women. BMC Public Health, 13, 920. doi:10.1186/1471‐2458‐13‐920 Morrison, M. A., Morrison, T. G., & Sager, C. L. (2004). Does body satisfaction differ between gay men and lesbian women and heterosexual men and women?: A meta‐analytic review. Body Image, 1, 127–138. doi:10.1016/j.bodyim.2004.01.002 Münstedt, K., Manthey, N., Sachsse, S., & Vahrson, H. (1997). Changes in self‐concept and body image during alopecia induced cancer chemotherapy. Supportive Care in Cancer, 5, 139–143. Murray, S. B., Griffiths, S., Mond, J. M., Kean, J., & Blashill, A. J. (2016). Anabolic steroid use and body image psychopathology in men: Delineating between appearance‐versus performance‐driven motivations. Drug and Alcohol Dependence, 165, 198–202. Myers, T. A., & Crowther, J. H. (2009). Social comparison as a predictor of body dissatisfaction: A meta‐ analytic review. Journal of Abnormal Psychology, 118, 683–698. Neumark‐Sztainer, D., Paxton, S. J., Hannan, P. J., Haines, J., & Story, M. (2006). Does body satisfaction matter? Five‐year longitudinal associations between body satisfaction and health behaviors in adolescent females and males. Journal of Adolescent Health, 39, 244–251. doi:10.1016/j.jadohealth. 2005.12.001 Patalay, P., Sharpe, H., & Wolpert, M. (2015). Internalising symptoms and body dissatisfaction: Untangling temporal precedence using cross‐lagged models in two cohorts. Journal of Child Psychology and Psychiatry, 56, 1223–1230. doi:10.1111/jcpp.12415 Pennesi, J. L., & Wade, T. D. (2016). A systematic review of the existing models of disordered eating: Do they inform the development of effective interventions? Clinical Psychology Review, 43, 175–192. doi:10.1016/j.cpr.2015.12.004 Pinquart, M. (2013). Body image of children and adolescents with chronic illness: A meta‐analytic comparison with healthy peers. Body Image, 10, 141–148. Pruzinsky, T. (2004). Enhancing quality of life in medical populations: A vision for body image assessment and rehabilitation as standards of care. Body Image, 1, 71–81. Puhl, R. M., & Latner, J. D. (2007). Stigma, obesity, and the health of the nation’s children. Psychological Bulletin, 133, 557–580. Rhondali, W., Chisholm, G. B., Daneshmand, M., Allo, J., Kang, D. H., Filbet, M., … Bruera, E. (2013). Association between body image dissatisfaction and weight loss among patients with advanced cancer and their caregivers: A preliminary report. Journal of Pain and Symptom Management, 45, 1039–1049. doi:10.1016/j.jpainsymman.2012.06.013 Roberts, A., Cash, T. F., Feingold, A., & Johnson, B. T. (2006). Are black‐white differences in females’ body dissatisfaction decreasing? A meta‐analytic review. Journal of Consulting and Clinical Psychology, 74, 1121–1131. doi:10.1037/0022‐006X.74.6.1121 Saha, S., Zhao, Y. Q., Shah, S. A., Degli Esposti, S., Lidofsky, S., Shapiro, J., … Samad, Z. (2015). Body image dissatisfaction in patients with inflammatory bowel disease. Inflammatory Bowel Diseases, 21, 345–352. doi:10.1097/MIB.0000000000000270 Sarwer, D. B., & Steffen, K. J. (2015). Quality of life, body image and sexual functioning in bariatric surgery patients. European Eating Disorders Review, 23, 504–508. Satinsky, S., Reece, M., Dennis, B., Sanders, S., & Bardzell, S. (2012). An assessment of body appreciation and its relationship to sexual function in women. Body Image, 9, 137–144. Schutz, H. K., & Paxton, S. J. (2007). Friendship quality, body dissatisfaction, dieting and disordered eating in adolescent girls. British Journal of Clinical Psychology, 46, 67–83.

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Song, A. Y., Rubin, J. P., Thomas, V., Dudas, J. R., Marra, K. G., & Fernstrom, M. H. (2006). Body image and quality of life in post massive weight loss body contouring patients. Obesity, 14, 1626–1636. doi:10.1038/oby.2006.187 Stice, E., & Whitenton, K. (2002). Risk factors for body dissatisfaction in adolescent girls: A longitudinal investigation. Developmental Psychology, 38, 669–678. van den Berg, P. A., Mond, J., Eisenberg, M., Ackard, D., & Neumark‐Sztainer, D. (2010). The link between body dissatisfaction and self‐esteem in adolescents: Similarities across gender, age, weight status, race/ethnicity, and socioeconomic status. Journal of Adolescent Health, 47, 290–296. doi:10.1016/j.jadohealth.2010.02.004 Wirth, M. D., Blake, C. E., Hébert, J. R., Sui, X., & Blair, S. N. (2014). Chronic weight dissatisfaction predicts type 2 diabetes risk: Aerobic center longitudinal study. Health Psychology, 33, 912–919. doi:10.1037/hea0000058 Woertman, L., & van den Brink, F. (2012). Body image and female sexual functioning and behavior: A review. Journal of Sex Research, 49, 184–211. Yagil, Y., Geller, S., Sidi, Y., Tirosh, Y., Katz, P., & Nakache, R. (2015). The implications of body‐image dissatisfaction among kidney‐transplant recipients. Psychology, Health & Medicine, 20, 955–962.

Suggested Reading Grogan, S. (2006). Body image and health contemporary perspectives. Journal of Health Psychology, 11, 523–530. doi:10.1177/1359105306065013 Pruzinsky, T. (2004). Enhancing quality of life in medical populations: A vision for body image assessment and rehabilitation as standards of care. Body Image, 1, 71–81.

Caregivers and Clinical Health Psychology Allison Marziliano and Anne Moyer Department of Psychology, Stony Brook University, Stony Brook, NY, USA

An informal caregiver is defined as “any relative, partner, friend, or neighbor who has a ­significant personal relationship with, and provides a broad range of assistance for an older person or an adult with a chronic or disabling condition” (Family Caregiver Alliance, 2016). The American Association of Retired Persons (AARP) and the National Alliance for Caregiving (NAC), a nonprofit coalition of national organizations, collaborate every 5 years to produce a research report on unpaid family caregivers in the United States, the most recent of which was published in June of 2015 (National Alliance for Caregiving, 2016a, 2016b). According to this publication, Caregiving in the U.S. 2015, it is estimated that 34.2 million adults in the United States have provided care to another adult or adults over the age of 50 years old within the last 12 months, representing approximately 14.3% of the American adult population. The majority of caregivers are relatives of the care recipient (85%) and female (60%), and most (82%) care for one other individual rather than multiple individuals. The average age of ­caregivers in general is 49.2 years, compared with the average age when limiting the sample to spousal caregivers, 62.3 years old. In addition to providing useful information about caregivers, the research report, Caregiving in the U.S. 2015, also provides descriptions of the individuals for whom they provide care, or care recipients (National Alliance for Caregiving, 2016a, 2016b). Care recipients are often older individuals with functional disabilities who had a prior relationship with the person who has come to assume the role as their unpaid caregiver. Nearly half (47%) of care recipients live in their own homes, about 1/3 (35%) live with their caregivers, and 18% reside in long‐term care or senior housing. The average age of those individuals receiving care is 69.4 years old, with nearly half (47%) of care recipients above the age of 74 years old.

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Caregiver Burden Caregiver burden is multifaceted. In a systematic review of the literature on assessment tools to measure the impact of family caregiving, caregiver burden is described as a negative response to the impact of providing care on the caregiver’s physical, psychological, emotional, social, and financial condition. Burden is often the result of the caregivers’ belief that demands placed upon them surpass their own physical or mental capacity. Some research has addressed the financial burden associated with caregiving. Providing care to a loved one is costly in and of itself; however, caregiving may interfere with time previously devoted to employment for both the patient and caregiver, which may lead to reductions in the flow of income, further compounding financial strain. Caregivers often report less time for themselves and competing financial demands as a result of their role. In one study, schedule burden was cited as the most prevalent form of burden experienced by caregivers, reported by 58% of end‐of‐life caregivers and 32% of caregivers of patients with chronic illness (Sautter et al., 2014). In one study, 60–80% of caregivers of patients with Parkinson’s disease reported disruptions in their financial status (Sanyal et al., 2015). In a sample of caregivers of patients with head and neck cancer, financial stress was significantly related to high levels of unmet needs (Balfe et  al., 2016). In caregivers of individuals suffering from schizophrenia, only approximately 1/3 (31.9%) endorsed “agree” or “strongly agree” when responding to whether they believed they have the financial capacity to provide adequate care to their loved one (Gupta, Isherwood, Jones, & Van Impe, 2015). Particular attention has been devoted to financial burden in the context of female caregivers. Research using data from 2,093 women over the age of 52 with at least one living parent or parent‐in‐law participating in the Health and Retirement Surveys in 2006, 2008, and 2010 supported a reciprocal relationship between parental caregiving and lower household income (Lee, Tang, Kim, & Albert, 2014). In a study of female caregivers of patients with Alzheimer’s disease, the authors suggested that the greatest financial challenge was the high cost of the care they deliver, with females bearing six times the cost of care than men do (Yang & Levey, 2015). In addition to financial burden, physical and health‐related burden has been reported by caregivers. In a cross‐sectional study of family caregivers of individuals with schizophrenia, approximately 2/3 of the sample (65.5%) believed themselves to be in poor physical health (Thunyadee, Sitthimongkol, Sangon, Chai‐Aroon, & Hegadoren, 2015). Not only do ­caregivers often perceive themselves as being in ill‐health, but also there is some research to suggest that their health is indeed affected by their caregiving role. Spousal caregivers are at a 600% increased risk of developing dementia (Wennberg, Dye, Streetman‐Loy, & Pham, 2015), and caregivers more generally are at risk of experiencing sleep trouble, fatigue, insomnia, weight gain, pain, headaches, and heartburn (Gupta et al., 2015; Skalla, Lavoie Smith, Li, & Gates, 2013). Research has identified correlates and predictors of burden in caregivers. A large body of literature suggests that caregiver burden is related to certain patient‐related factors such as higher levels of patient cognitive impairment (Corallo et al., 2016; Vaingankar et al., 2016) and patient motor disability (Sanyal et al., 2015). Other correlates and predictors of caregiver burden include less use of hospice care, female gender, student status, spousal relationship to care recipient, the use of emotion‐focused coping strategies, duration of caregiving, hours spent caregiving, social isolation, and lack of choice in assuming the caregiving role (Adelman, Tmanova, Delgado, Dion, & Lachs, 2014; Duggleby et al., 2016; Gupta et al., 2015; Sanyal et al., 2015; Thunyadee et al., 2015).

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Negative Psychosocial Sequelae Although caregiver burden is not a classifiable syndrome, such burdens can result in clinical diagnoses such as depression and anxiety and even lead to suicide (Adelman et al., 2014). The prevalence of depressive disorders has been found to be as high as 22% among caregivers of dementia patients (Cuipers, 2005). The prevalence of depressive symptoms for ­caregivers of critically ill patients is similar but is reported to be even higher during the critical care period (Haines, Denehy, Skinner, Warrillow, & Berney, 2015). Although the evidence regarding this is more sparse, clinically significant anxiety is estimated to occur in about a quarter of caregivers for people with dementia (Cooper, Balamurali, & Livingston, 2007). In the context of life‐threatening diseases such as cancer, one of the most common physical conditions for which patients receive informal caregiving (Girgis, Lambert, Johnson, Waller, & Currow, 2013), a reciprocal relationship has been found between ­caregiver and patient distress (Northouse, Katapodi, Schafenacker, & Weiss, 2012). In addition, cancer diagnosis and treatment follow a dynamic course; as a cancer patient’s condition declines or nears death, caregivers’ depression, anxiety, and distress worsen, and a caregiver’s depression and anxiety may also have consequences for the patient (Williams & McCorkle, 2011). Estimates of the prevalence of anxiety in partners and caregivers of people with cancer have been found to range from 16 to 56%, and even PTSD has been documented in 4% (Girgis et al., 2013).

Positive Psychosocial Sequelae The picture is not all bleak, however. Evidence suggests that the capacity exists for ­caregivers to experience, as a result of their role, positive psychosocial sequelae (Moore et al., 2011), such as posttraumatic growth (PTG), the cognitive process through which those who are faced with a traumatic event positively reinterpret and find meaning in the event (Tedeschi & Calhoun, 1996), and benefit finding (BF), one’s perception that positive changes have occurred as a result of experiencing difficult life events. Furthermore, in some studies, ­caregivers score higher in certain facets of PTG, such as personal strength, than the patients for whom they provide care (Cormio et  al., 2014). Research has identified common ­correlates of increased caregiver PTG and benefit finding as increased social support, self‐ affirmation, having a positive outlook on life, positive affect, and the use of positive ­religious coping strategies (Balfe et  al., 2016; Thombre, Sherman, & Simonton, 2010). Some research has focused on the relationship between caregiver PTG and patient‐related factors, indicating a positive relationship between cancer caregivers’ report of patients’ quality of life at end of life and caregiver PTG (Hatano, Fujimoto, Hosokawa, & Fukui, 2015). While BF is an important construct in and of itself, its importance also lays in that some research has found caregiver BF to positively predict caregiver quality of life (Navarta‐Sanchez et al., 2016). Albeit less of a focus than PTG and BF, other research has examined the positive constructs of resilience and accomplishment in relation to caregiving. In a study of caregivers of children with cancer, correlates of parent resilience include coping strategies, social support, positive provider interactions, and lack of psychosocial distress (Rosenberg, Baker, Syrjala, Back, & Wolfe, 2013). In another study of caregivers of individual with multiple sclerosis (MS), ­positive perception of accomplishment was related to older age of the patient and greater number of

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hours per week the caregiver provided care, while negative perception of accomplishment was related to caregiver perception that caregiving is an emotionally draining role, higher levels of caregiver education, and a spousal relationship between patient and caregiver (Buchanan & Huang, 2012). Although a great deal of literature has demonstrated the negative effects of caregiving, fortunately, recent research has focused on routes to amplifying the positive ­psychosocial sequelae associated with caring for a loved one.

Support for Caregivers The challenges associated with caregiving mean that assistance, including finding ways to take care of themselves, is important for caregivers. These types of assistance include support in the legal, financial, emotional, social, educational, physical, and mental health areas of functioning. National organizations provide supportive resources in the form of information and ­education for both caregivers and healthcare and policy professionals, online and in‐person support groups, first‐person stories, connections to clinical trials and research studies, and advocacy. For instance, the Family Caregiver Alliance provides a national registry to assist ­caregivers of people who are living at home or in a residential facility to locate local public, nonprofit, and private programs, such as government health and disability programs, legal resources, and disease‐specific organizations (Family Caregiver Alliance, 2016). Examples of local resources include information, assistance, individual counseling, support groups, c­ aregiver training, respite care, and supplemental services, such as transportation and home modifications (New York State Office of the Aging, 2016). The NAC conducts authoritative research on trends in the needs of caregivers with a focus on reaching a broad, racially and ethnically diverse population sample and works to increase public awareness of family caregiving issues (National Alliance for Caregiving, 2016a, 2016b). Practical and tangible forms of support such as respite, or time away from the duties of ­caregiving, are considered particularly valuable by caregivers, although the influence of social norms (e.g., related to a sense of familial duty) may make caregivers reluctant to let go of their caregiving responsibilities. Making use of respite services can allow caregivers to continue their employment or education, social activities, or hobbies integral to their identity; engage in self‐care activities like recreation, exercise, and good nutrition; and seek legal and financial counsel. To be effective, however, respite needs to be regular or substantial (i.e., at a frequency of at least twice a week) and involves large blocks of time. In response to evidence that caregivers often feel their respite time goes to waste or is unfulfilling, frustrating, or even increased their stress, some research has begun to investigate how to improve the ways that respite time can best meet individual needs, through goal setting that reflects personal priorities and forming strategies to optimize goal attainment (Lund et al., 2014). Practical ways that healthcare providers can support the caregivers of the patients they treat are to assess what types of ­difficulty they might be having, encourage them to engage in self‐care, provide information about their care recipients’ illness, and provide referrals to assistance available to them (Adelman et al., 2014). Technological forms of assistance can also be useful. These include emergency response systems, webcams or mobility trackers for monitoring, lifts, medication reminder systems, and socialization via Skype (Adelman et  al., 2014). Education and counseling can be readily ­provided by web‐ and video‐based platforms, and decision‐making support can be facilitated via telephone and videoconferencing.

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Psychosocial Interventions for Caregivers A number of interventions have been developed for caregivers of individuals with dementia. A  large (N  =  1,222) multisite study (the Resources for Enhancing Alzheimer’s Caregiver Health [REACH] initiative) tested a group of site‐specific, theory‐driven interventions that were tailored for the ethnic groups served (Wisniewski et al., 2003). The interventions were diverse and based on different theoretical frameworks, but they were similar in being based on a basic stress–health model that addressed stressors themselves, their appraisal, and/or care­givers’ response to them. These included care‐recipient‐focused behavior management skill training and caregiver‐focused, problem‐solving training (involving didactic instruction, role‐playing, modeling, feedback, manuals, and videotapes, home visits, and therapeutic telephone calls), a 24‐hr/day telephone‐linked computer intervention (involving a voicemail support network, information on specific care recipients’ behavioral problems, access to a geriatric nurse specialist, and providing distracting conversation), information and referral delivered within primary care clinics (involving commercially available pamphlets regarding dementia), behavioral care (involving targeted one‐on‐one information and counseling sessions related to care recipients’ behavioral problems supplemented by telephone calls), enhanced care (involving specific ­cognitive and behavioral strategies to reduce caregiver distress taught in person and supported by telephone calls), a family‐based structural multisystem in‐home intervention (involving in‐ home family therapy sessions to enhance family functioning and reduce caregiver distress) with and without a computerized telephone integration system, a coping with caregiving class (involving mood‐management skills to reduce stress and distress), an enhanced support group (involving professional leadership and a commitment to meet regularly for 1 year), and an environmental skill building program (involving in‐home individualized environmental strategies delivered by an occupational therapist). Across all interventions, burden and depression scores were lower for active conditions compared with control conditions at 6 months, but this was only statistically significant for burden and the effect was small (Gitlin et  al., 2003). Nonetheless, the interventions were rated as beneficial, helpful, and valuable by participants, and there were significant subgroup effects for those who were female and had lower education for burden and for those who were Hispanic, non‐spouses, and had lower education for depression (Gitlin et al., 2003). As alluded to by the design of some of the interventions in the REACH study, because there is a potentially synergistic relationship between the behavior of the person with dementia and their informal caregiver’s level of burden and the strategies they use to manage the behavior of the person with dementia, interventions are often directed at both individuals in the dyad. For instance, for the person with dementia, such things as behavioral problems, cognitive functioning, mood, independence in daily activities, sleep, and quality of life may be the focus, and for the caregiver, mood, burden, competence, and quality of life may be the focus. These dyadic interventions include components such as information, training in daily life activities, walking or exercise, and adaptations to the physical environment for the person with dementia and information, psychoeducation, skills training, and coping strategies for the caregiver. A systematic review of 20 of these programs showed them to be promising in terms of effectiveness for one or both members of the dyad (Van’t Leven et al., 2013). An approach that differs from psychosocial interventions is meditative‐based interventions that involve mindfulness or concentration‐based techniques, some of which are designed to be undertaken at home. A systematic review of eight studies suggests that they hold some ­promise in alleviating burden and depression in dementia caregivers (Hurley, Patterson, & Cooley,

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2014). Interventions involving relaxation and yoga have been found to be most likely to be effective specifically for anxiety in caregivers, an often untargeted outcome (Cooper, Balamurali, Selwood, & Livingston, 2007).

Barriers to Utilizing Psychosocial Interventions There is evidence to suggest that family caregivers underutilize the mental health services and interventions that may be available to them. In one study (Mosher et al., 2013) of caregivers reporting anxiety and depressive symptoms, only a quarter of participants reported using mental health services, and 39% reported using complementary and alternative medicine in the 3‐month period in which the research took place. These findings are in contrast to those who expressed an interest in support services; 29% of caregivers who did not receive mental health services desired them. Thus, these findings suggest that it may not be a lack of empirically validated interventions for caregivers, but rather the presence of barriers that inhibits caregivers from seeking the psychosocial support they need. A commonly reported barrier to seeking mental health services for both caregivers and patients alike is the fear of stigmatization (Gulliver, Griffiths, & Christensen, 2010). In one study, caregivers of individuals with mental health illnesses indicated their concerns for their loved one seeking treatment as hindering their employment opportunities, being viewed as weak or crazy, being embarrassed or ashamed, or being unable to afford services (Dockery et  al., 2015). Correlates of reporting stigma related to patients’ seeking of mental health ­services included being a female caregiver and being an African American caregiver. Caregivers also cite stigma as a concern preventing them from seeking their own mental health support services (Boots, Wolfs, Verhey, Kempen, & de Vugt, 2015), which may negatively affect their capability to care for not only themselves but also the care recipient. In addition to stigma, research on caregivers has identified other barriers preventing them from utilizing mental health services and interventions. Commonly cited barriers reported by caregivers of patients with various diagnoses also include struggling with acknowledging their own needs, low acceptance of their new role, negative view of mental health professionals, desire for independent management of their issues, perceived conflict between their role as a caregiver and obtaining treatment for themselves, anticipated negative self‐perception, feeling unequipped to manage their situation, and reluctance to allow help (Boots et al., 2015; Mosher et  al., 2013). It is critical to consider these barriers during intervention design in order to maximize the efficacy of mental health treatment for caregivers.

Post‐caregiving Some research refers to the period following the cessation of care, post‐caregiving, as a stage well within the caregiving life course that requires, but often lacks, unique attention (Hash, 2006). Research demonstrates the potential for cognitive and psychological changes that may begin during caregiving but last into the post‐caregiving stage. For example, in a study of caregivers of palliative care patients who completed neuropsychological assessments as caregivers and at least 6 months from the death of the care recipient, results showed an improvement in self‐monitoring and regulating attentional resources and a decline in spatial working ­memory and delayed episodic memory (Mackenzie, Smith, Hasher, Leach, & Behl, 2007). In another study, individuals who care or cared for chronically ill same‐sex partners commonly

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reported loneliness and depression in the post‐caregiving period (Hash, 2006). During this stage, then, caregivers continuously encounter difficult transitions such as coping with grief and mourning, rebuilding, or returning to their pre‐caregiving life.

Conclusion In sum, informal caregivers report financial, schedule, and health‐related burden, as well as experience depressive disorders, anxiety, and distress. Fortunately, research on growth and finding benefit in the caregiving experience is increasing and demonstrates the potential for positive psychosocial sequelae associated with this role. Although support for caregivers comes in various forms, from promotion of respite to psychosocial and meditative interventions, research suggests that mental health services and interventions are underutilized due to ­specific barriers such as fear of stigmatization. Future research would be wise to focus on overcoming the barriers to accessing mental health services that caregivers often face and providing such support into the post‐caregiving period.

Author Biographies Allison Marziliano is a doctoral candidate in social and health psychology at Stony Brook University and a predoctoral fellow in the Department of Psychiatry and Behavioral Sciences at Memorial Sloan Kettering Cancer Center. Her research interests include the study of ­psychosocial issues surrounding cancer patients, their families, and their caregivers, as well as grief and bereavement. Anne Moyer is an associate professor of social and health psychology at Stony Brook University. Her research focuses on psychosocial factors surrounding cancer and cancer risk, medical decision making, research methodology and research synthesis, and the psychological aspects of human research participation.

References Adelman, R. D., Tmanova, L. L., Delgado, D., Dion, S., & Lachs, M. S. (2014). Caregiver burden: A clinical review. JAMA, 113, 1052–1059. Balfe, M., O, Brien, K., Timmons, A., Butow, P., O, Sullivan, E., Gooberman‐Hill, R., & Sharp, L. (2016). What factors are associated with posttraumatic growth in head and neck cancer carers? European Journal of Oncology Nursing, 21, 31–37. Boots, L. M., Wolfs, C. A., Verhey, F. R., Kempen, G. I., & de Vugt, M. E. (2015). Qualitative study on needs and wishes of early‐stage dementia caregivers: The paradox between needing and accepting help. International Psychogeriatrics, 27, 927–936. Buchanan, R. J., & Huang, C. (2012). Caregiver perceptions of accomplishment from assisting people with multiple sclerosis. Disability and Rehabilitation, 34, 53–61. Cooper, C., Balamurali, T. B., & Livingston, G. (2007). A systematic review of the prevalence and covariates of anxiety in caregivers of people with dementia. International Psychogeriatrics, 19, 175–195. Cooper, C., Balamurali, T. B., Selwood, A., & Livingston, G. (2007). A systematic review of intervention studies about anxiety in caregivers of people with dementia. International Journal of Geriatric Psychiatry, 22, 181–188.

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Corallo, F., DeCola, M. C., Buono, V. L., Di Lorenzo, G., Bramanti, P., & Marino, S. (2016). Observational study of quality of life of Parkinson’s patients and their caregivers. Psychogeriatrics, 17(2), 97–102. Cormio, C., Romito, F., Viscanti, G., Turaccio, M., Lorusso, V., & Mattioli, V. (2014). Psychological well‐being and posttraumatic growth in caregivers of cancer patients. Frontiers in Psychology, 5, 1342–1350. Cuipers, P. (2005). Depressive disorders in caregivers of dementia patients: A systematic review. Aging & Mental Health, 9, 325–330. Dockery, L., Jeffery, D., Schauman, O., Williams, P., Farrelly, S., Bonnington, O., … MIRIAD study group. (2015). Stigma‐ and non‐stigma‐related treatment barriers to mental healthcare reported by service users and caregivers. Psychiatry Research, 228, 612–619. Duggleby, W., Williams, A., Ghosh, S., Moquin, H., Ploeg, J., Markle‐Reid, M., & Peacock, S. (2016). Factors influencing changes in health related quality of life of caregivers of persons with multiple chronic conditions. Health and Quality of Life Outcomes, 27, 81–90. Family Caregiver Alliance. (2016). Definitions. Retrieved from https://www.caregiver.org/definitions‐0. Girgis, A., Lambert, S., Johnson, C., Waller, A., & Currow, D. (2013). Physical, psychosocial, relationship, and economic burden of caring for people with cancer: A review. Journal of Oncology Practice, 9, 197–202. Gitlin, L. N., Burgio, L. D., Mahoney, D., Burns, R., Zhang, S., Schulz, R., … Ory, M. G. (2003). Effect of multicomponent interventions on caregiver burden and depression: The REACH multisite initiative at 6‐month follow‐up. Psychology and Aging, 18, 361–374. Gulliver, A., Griffiths, K. M., & Christensen, H. (2010). Perceived barriers and facilitators to mental health help seeking in young people: A systematic review. BMC Psychiatry, 10, 113–122. Gupta, S., Isherwood, G., Jones, K., & Van Impe, K. (2015). Assessing health status in informal schizophrenia caregivers compared with health status in non‐caregivers and caregivers of other conditions. BMC Psychiatry, 15, 162–173. Haines, K. J., Denehy, L., Skinner, E. H., Warrillow, S., & Berney, S. (2015). Psychosocial outcomes in informal caregivers of the critically ill: A systematic review. Critical Care in Medicine, 43, 1112–1120. Hash, K. (2006). Caregiving and post‐caregiving experiences of midlife and older gay men and lesbians. Journal of Gerontological Social Work, 47, 121–138. Hatano, Y., Fujimoto, S., Hosokawa, T., & Fukui, K. (2015). Association between “Good Death” of cancer patients and post‐traumatic growth in bereaved caregivers. Journal of Pain and Symptom Management, 50, e4–e6. Hurley, R. V. C., Patterson, T. G., & Cooley, S. J. (2014). Meditation‐based interventions for family caregivers of people with dementia: A review of the empirical literature. Aging & Mental Health, 18, 281–288. Lee, Y., Tang, F., Kim, K. H., & Albert, S. M. (2014). The vicious cycle of parental caregiving and ­financial well‐being: A longitudinal study of women. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 70, 425–431. Lund, D. A., Utz, R. L., Caserta, M. S., Wright, S. D., Llanque, S. M., Lindfelt, C., … Montoro‐ Rodriguez, J. (2014). Time for living and caring: An intervention to make respite more effective for caregivers. International Journal of Aging and Human Development, 79, 157–178. Mackenzie, C. S., Smith, M. C., Hasher, L., Leach, L., & Behl, P. (2007). Cognitive functioning under stress: Evidence from informal caregivers of palliative patients. Journal of Palliative Medicine, 10, 749–758. Moore, A. M., Gamblin, T. C., Geller, D. A., Youssef, M. N., Hoffman, K. E., Gemmell, L., … Steel, J. L. (2011). A prospective study of posttraumatic growth as assessed by self‐report and family ­caregiver in the context of advanced cancer. Psychooncology, 20, 479–487. Mosher, C. E., Champion, V. L., Hanna, N., Jalal, S. I., Fakiris, A., Birdas, T. J., … Ostroff, J. S. (2013). Support service use and interest in support services among distressed family caregivers of lung ­cancer patients. Psychooncology, 22, 1549–1556.

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National Alliance for Caregiving. (2016a). About the Alliance. Retrived from http://www.caregiving. org/about/about‐the‐alliance/. National Alliance for Caregiving. (2016b). Caregiving in the United States 2015 Research Report. Retrived from http://www.caregiving.org/wp‐content/uploads/2015/05/2015_Caregivinginthe US_Final‐Report‐June‐4_WEB.pdf. Navarta‐Sanchez, M. V., Senosiain, G., Riverol, M., Ursua, S., Diaz de Cerio, A., Anaut, B., … Portillo, M. C. (2016). Factors influencing psychosocial adjustment and quality of life in Parkinson’s patients and informal caregivers. Quality of Life Research, 25, 1959–1968. New York State Office of the Aging. (2016). Programs and Services. Retrieved from http://www.aging. ny.gov/NYSOFA/Services/Index.cfm?id=CSVCS. Northouse, L. L., Katapodi, M. C., Schafenacker, A. M., & Weiss, D. (2012). The impact of caregiving on the psychological well‐being of family caregivers and cancer patients. Seminars in Oncology Nursing, 28, 236–245. Rosenberg, A. R., Baker, K. S., Syrjala, K. L., Back, A. L., & Wolfe, J. (2013). Promoting resilience among parents and caregivers of children with cancer. Journal of Palliative Medicine, 16, 645–652. Sanyal, J., Das, S., Ghosh, E., Banerjee, T. K., Bhaskar, L. V. K. S., & Raghavendra Rao, V. (2015). Burden among Parkinson’s disease care givers for a community based study from India. Journal of Neurological Sciences, 358, 276–281. Sautter, J. M., Tulsky, J. A., Johnson, K. S., Olsen, M. K., Burton‐Chase, A. M., Hoff Lindquist, J., … Steinhauser, K. E. (2014). Caregiver experience during advanced chronic illness and last year of life. Journal of the American Geriatric Society, 62, 1082–1090. Skalla, K. A., Lavoie Smith, E. M., Li, Z., & Gates, C. (2013). Multidimensional needs of caregivers for patients with cancer. Clinical Journal of Oncology Nursing, 17, 500–506. Tedeschi, R. G., & Calhoun, L. G. (1996). The Posttraumatic Growth Inventory: Measuring the positive legacy of trauma. Journal of Traumatic Stress, 9, 455–471. Thombre, A., Sherman, A. C., & Simonton, S. (2010). Religious coping and posttraumatic growth among family caregivers of cancer patients in India. Journal of Psychosocial Oncology, 28, 173–188. Thunyadee, C., Sitthimongkol, Y., Sangon, S., Chai‐Aroon, T., & Hegadoren, K. M. (2015). Predictors of depressive symptoms and physical health in caregivers of individuals with schizophrenia. Nursing and Health Sciences, 17, 12–19. Vaingankar, J. A., Chong, S. A., Abdin, E., Picco, L., Jeyagurunathan, A., Zhang, Y. J., … Subramaniam, M. (2016). Care participation and burden among informal caregivers of older adults with care needs and associations with dementia. International Psychogeriatrics, 28, 221–231. Van’t Leven, N., Prick, A. E., Groenewoud, J. G., Roelofs, P. D., de Lange, J., & Pot, A. M. (2013). Dyadic interventions for community‐dwelling people with dementia and their family caregivers: A systematic review. International Psychogeriatrics, 25, 1581–1603. Wennberg, A., Dye, C., Streetman‐Loy, B., & Pham, H. (2015). Alzheimer’s patient familial caregivers: A review of burden and interventions. Health and Social Work, 40, e162–e169. Williams, A. L., & McCorkle, R. (2011). Cancer family caregivers during the palliative, hospice, and bereavement phases: A review of the descriptive psychosocial literature. Palliative and Supportive Care, 9, 315–325. Wisniewski, S. R., Belle, S. H., Marcus, S. M., Burgio, L. D., Coon, D. W., Ory, M. G., … Schulz, R. (2003). The resources for enhancing Alzheimer’s caregiver health (REACH): Project design and baseline characteristics. Psychology and Aging, 18, 375–384. Yang, Z., & Levey, A. (2015). Gender differences: A lifetime analysis of the economic burden of Alzheimer’s Disease. Women’s Health Issues, 25‐5, 436–440.

Close Relationships Kieran T. Sullivan Department of Psychology, Santa Clara University, Santa Clara, CA, USA

Close relationships are strongly related to physical health. People who are married live longer and are less likely to experience a host of serious health problems, including heart attacks and chronic illnesses (Johnson, Backlund, Sorlie, & Loveless, 2000). This health advantage holds true when comparing married people with all unmarried groups: divorced, widowed, and never married people (though with recent increases in cohabitation, the gap is narrowing between married and never married people). People in dissatisfying, difficult marriages, however, do not enjoy these health benefits; in fact, they actually fare worse than unmarried p ­ eople; high conflict and having a critical or demanding spouse undermines health as surely as ­supportive interactions protect it (Umberson, Williams, Powers, Liu, & Needham, 2006).

How Relationships Affect Health There are several mechanisms by which intimate relationships influence health. The role and responsibilities of being a long‐term intimate partner entail an internal sense of obligation to remain healthy for partners and any children. Indeed, there is ample evidence that simply being married is a significant deterrent of health‐compromising behaviors. Beyond the mere presence of a partner and related role obligations, spouses, partners, and close others provide social support, which may entail emotional support or practical (instrumental support) or both. In fact, spouses and intimate partners are by far the most likely person to provide social support when needed. Social support positively affects biological processes (e.g., leads to a stronger immune system), affect, psychological functioning, health behavior, and the ability to cope with illness. Conversely, a lack of social support in close relationships is associated with anxiety and depression, which, in addition to affecting physiological processes, are associated with behavioral patterns that increase risk for disease and mortality.

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Social Support The health benefits of social support are due in part to the mitigation of stress reactions and assistance in coping with stress. The stress‐buffering hypothesis suggests that social support provided by intimate partners in the face of stress provides a buffer from the negative effects of stress by positively shaping the stress appraisal response. Individuals who know they have close others who will help them cope with stressors (e.g., provide resources, provide ­emotional support) appraise that stressor differently than those without such close others. Support may also buffer the effects of stress on physiological processing by attenuating or eliminating the stress reaction or by directly influencing physiological processes (Cohen & Wills, 1985). Indeed, people with high levels of social support, such as those in intimate relationships, who experience stress are less likely to succumb to bacterial and viral infections, are less likely to develop mental health problems as a consequence of stress (such as depression), and are less likely to incur damage caused by neuroendocrine, immune, and cardiovascular responses to stress. Matching the type of support with the type of stressor (i.e., a solution for a practical problem or emotional support for an emotional issue) is key for the buffering effect to occur. Many studies investigating social support and health have focused on couples dealing with serious or chronic illnesses, a useful context for capturing the practical and emotional ­support provided by close others. Social support increases patient adherence to medical regimens prescribed by physicians following an illness; the odds of adherence are more than twice as high among patients with greater levels of social support. When distinguishing practical and emotional support, studies indicate that the odds of adherence are more than three and a half times greater for patients receiving practical support (e.g., assistance, reminders, and support for a specific behavior) and almost twice as high for patients receiving emotional support (e.g., nurturance) compared with patients without these types of support (DiMatteo, 2004). Psychosocial interventions with patients and their families designed to increase social support suggest a modestly positive though mixed effect of ­family involvement; overall ­evidence indicates that family involvement leads to decreases in depression and mortality (Martire, Lustig, Schulz, Miller, & Helgeson, 2004). Mixed findings regarding social s­upport and illness may be due to variation in the match between desired and received ­ support and the extent to which received or enacted support is ­perceived as support by the receiver. Intimate partners also strive to prevent future health problems by deliberating attempting to promote health‐enhancing behaviors and inhibit health‐compromising behaviors. Such attempts may take the form of social support (helping close others achieve or maintain health through emotional and practical support) and/or social control (deliberate attempts to influence close others’ health behaviors).

Social Control Like social support, social control can have a positive effect on partner health, but it can also impede health behavior change and have negative effects on partners and relationships. According to the dual‐effects hypothesis of social control, deliberate attempts by close ­others to influence health behavior do sometimes improve health behavior but may also lead to ­psychological distress, including feelings of irritation, hostility, sadness, and guilt (Lewis &

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Rook, 1999). Early research guided by this hypothesis, based on general measures of social control, reported mixed results and contributed, in part, to leading researchers to make a distinction between positive and negative social control. Subsequent studies clarified that negative social control (involving tactics such as disapproval, guilt induction, and pressuring) is much less likely to lead to health behavior change and more likely to lead to psychological distress. Negative social control is also much more likely to backfire—resulting in partners hiding behaviors and negatively affecting the relationship. Positive social control (involving tactics such as reinforcing behavior change, modeling, and persuasion) is more likely to positively influence behavior and less likely to lead to psychological or relationship distress. Further, positive social control may even lead to increases in well‐being (Craddock, vanDellen, Novak, & Ranby, 2015). Types of attempts have been delineated beyond positive and negative categories. Researchers have examined the effects of bilateral attempts, such a discussing the issue with the spouse, versus unilateral attempts, such as throwing out unhealthy food, and the effects of direct attempts, such as trying to persuade a partner to make a change, versus indirect attempts, such as inducing guilt. Bilateral and direct attempts are more likely to be associated with health‐ enhancing behavior compared with unilateral and indirect attempts, though the findings have been mixed (Lewis & Butterfield, 2007). Some evidence suggests that direct attempts do not affect partner behavior immediately, but do affect behavior over time. Conversely, indirect attempts help in the short run, but do not last over time. The differential effect of types of attempts can be accounted for, at least in part, by the ­emotions partners experience in response to social control attempts. Early findings that ­positive and negative aspects of social control are independent gave rise to the domain‐specific model, which posits that positive social control is related to positive affect but not related to negative affect, whereas negative social control is related to negative affect but not positive affect; ­evidence for this model is mixed and a recent meta‐analysis suggests that positive social control is positively related to positive affect and negatively related to negative affect. The same holds for negative social control, that is, that negative social control is positively related to negative affect and negatively related to positive affect (Craddock et al., 2015). According to the meditational model of social control, proposed by Tucker and colleagues, variance in behavioral response to social control strategies is significantly reduced when the affective response to attempts is accounted for, indicating that the way a partner feels about a social control attempt is at least partly responsible for how the partner responds behaviorally (Tucker & Anders, 2001). This model, which has been well supported across many studies, explains why the same attempt (e.g., “Honey, let’s go for a walk”) can have a positive or a negative effect—it all depends on how the partner feels about the suggestion. Positive ­emotional responses to social control attempts are more likely to lead to behavior change, whereas negative emotional responses are more likely to backfire—engendering resistance and/or ­hiding of health‐compromising behavior. The expanded meditational model proposes that the effect of social control on health ­behavior can be better explained if motivation to change the health behavior and reactance (resistance to partner’s attempts to influence health behavior) are taken into account, along with emotional response. Preliminary evidence suggests that health behavior change is more likely when positive social control attempts elicit positive affect and predispose the partner to make the change. Further, reactance has a direct negative effect on the likelihood of making a health behavior change. This effect may be in the form of ignoring the attempts and/or hiding the unhealthy behavior (Logic, Okun, & Pugliese, 2009).

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Cognitions and Readiness to Change Social support and social control are distinct concepts, but they are related to one another. Distinguishing between the two—and when an attempt might be construed as positive and negative—can be challenging, as the same type of attempt to influence health behavior may be considered supportive by one person and controlling by another. Researchers have proposed that the attributions that individuals make about their partners’ change tactics (e.g., concern about health vs. concern about appearance) may account for variance in how change tactics are received (Lewis & Butterfield, 2005). Cognition is also a feature of the readiness model, which proposes that an individual’s readiness to make a change determines whether an attempt is perceived as supportive or controlling and, subsequently, how the individual responds emotionally (Sullivan, Pasch, Bejanyan, & Hanson, 2010). Initial evidence suggests that readiness to make a change causes changes in how an attempt is perceived and the emotional reaction to the attempt and that those perceptions and emotions affect how likely the individual will be to make a change. Perceptions of attempts as supportive and subsequent positive emotional responses lead to greater intentions to make a behavior change, and perceptions as controlling and subsequent negative emotional reactions lead to lower intentions to make a health behavior change.

Predictors and Consequences of Using of Social Control Much of the research in the area of social control and health focuses on the recipients of ­control attempts, how they feel, and whether they respond behaviorally. Less is known about the antecedents of social control attempts and the personal and relationship consequences of attempting to influence a partner’s health behavior. In a seminal study, Butterfield and Lewis (2002) examined characteristics of the partner trying to influence the other (agent) and ­characteristics of the partner being influenced (target), as well as characteristics of the relationship and the situation to clarify predictors of social tactic use. In this and a subsequent study, they found that social control approaches vary based on agents’ cognitions and emotions. That is, agents used all types (i.e., bilateral, direct, etc.) of tactics more frequently when they were more ready for the partner to make a change. Further, agents’ perceptions of how difficult it is for their partners to make the change led to more frequent use of negative and indirect ­tactics. Finally, agents’ learned helplessness regarding their ability to influence partners’ health behavior was positively associated with less frequent use of bilateral tactics and more frequent use of negative tactics. Targets’ characteristics affected agents’ tactic use as well; the more ready a target was to make a change, the more the agent used positive, direct, and bilateral tactics. In terms of consequences of attempts to influence partner behavior, agents experience more stress and more tense interactions when they use social control tactics, but less stress and more enjoyable marital interactions when they employ support tactics.

Context The contextual model emphasizes the importance of including situational variables in models of social support and social control and understanding the process by which contextual variables affect partner influence on health behavior. The contextual variables that have received attention to date include relationship satisfaction, gender, ethnicity, personality, history of control attempts, and type of behavior (i.e., health enhancing vs. health compromising).

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All but the first two variables have received attention only very recently, and the information provided is based on only one or two studies.

Relationship Satisfaction Relationship satisfaction may moderate the effect of social control on health behavior such that partners experiencing social control attempts are much more likely to benefit from those attempts when their relationship satisfaction is high. Partners who are less satisfied with their relationships are less likely to change their behavior in response to social control attempts, have lower levels of positive affect and higher levels of negative affect in response to partner control attempts, and are more likely to hide unhealthy behavior in response to positive and negative social control attempts. In contrast, couples high in satisfaction are much less likely to experience psychological distress and hide unhealthy behaviors only in response to negative social control. Finally, relationship length is positively associated with behavior change in dating relationships. It is important to note, however, that findings regarding relationship satisfaction are mixed, with some studies reporting no effect of relationship satisfaction.

Gender Gender appears to be related to the receipt and provision of social control; men are more likely to be the recipients of social control, and women are more likely to attempt to control the health of their partners. There is also evidence of gender differences in affective reactions to and effectiveness of social control attempts. Attempts to influence health are more likely to lead to positive and negative affective responses for women compared with men. Regarding the effectiveness of social control attempts, there is evidence that female partners’ change attempts are more effective than male partners’ change attempts. Further, male partners’ change attempts are more likely to backfire and have the opposite effect of what was intended than women’s change attempts. As with relationship satisfaction, it is important to note that findings regarding gender effects are mixed, with some studies reporting no gender differences in receipt/provision of social control or the affective reactions to and effectiveness of social control attempts. Gender may also interact with equity in affecting social control attempts, for example, husbands have been shown to use unilateral and positive tactics more frequently when their wives view the relationship as unequal.

Ethnicity Few studies have examined ethnic differences in social support or social control of partner health behaviors. One study of dietary behavior in people with diabetes found that partner social support and social control were associated with good dietary behavior in Mexican Americans, but only social support was associated with good dietary behavior in non‐Hispanic whites.

Personality Again, studies here are sparse, but there is evidence that partners who are high in neuroticism experience more social control attempts than partners lower in neuroticism. Further, those high in neuroticism have more negative affective responses to change attempts and are less likely to change their health behavior.

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History of Control Attempts Based on initial evidence, it appears that a history of control attempts by an intimate partner on an individual is associated with an individual’s increased use of indirect tactics when attempting to influence the intimate partner’s health.

Type of Behavior Partners use negative tactics less frequently when trying to initiate a health‐enhancing ­behavior compared with when they are trying to terminate a health‐compromising behavior. Further, health‐compromising behaviors and behaviors with more severe consequences elicit more social control attempts by intimate partners compared with health‐enhancing behaviors and behaviors with less serious consequences.

Future Directions There are a number of avenues by which the understanding of the relationship between ­intimate relationships and health behavior can be further illuminated: The first is research on the buffering effect of social support and how it may vary based on type of stressors and characteristics of the situation, the support provider, and the recipient. The second is the continued examination of contextual variables and the role they play in the frameworks provided by major models in the field. Because there is so little work on contextual variables, replication is needed for variables presented here, and additional variables need to be examined (e.g., age, previous history of health behavior, whether the health behavior is a shared problem, and health barriers). The third is further research on the characteristics of the partners, including readiness and motivation to change, as well as the attributions regarding control attempts and the affect they have on psychological well‐being and health behavior change.

Author Biography Kieran T. Sullivan is a professor of psychology at Santa Clara University. She has published widely in the area of intimate relationships. She is a consulting editor for the Journal of Family Psychology. Her current research focuses on support, control, and health behavior in intimate relationships.

References Butterfield, R. M., & Lewis, M. A. (2002). Health‐related social influence: A social ecological perspective on tactic use. Journal of Social and Personal Relationships, 19, 505–526. Cohen, S., & Wills, T. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2), 310–357. Craddock, E., vanDellen, M. R., Novak, S. A., & Ranby, K. W. (2015). Influence in relationships: A meta‐analysis on health‐related social control. Basic and Applied Social Psychology, 37, 118–130. DiMatteo, M. R. (2004). Social support and patient adherence to medical treatment: A meta‐analysis. Health Psychology, 23(2), 207–218. Johnson, N. J., Backlund, E., Sorlie, P. D., & Loveless, C. A. (2000). Marital status and mortality: The national longitudinal mortality study. Annals of Epidemiology, 10, 224–238.

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Lewis, M. A., & Butterfield, R. M. (2005). Antecedents and reactions to health‐related social control. Personality and Social Psychology Bulletin, 31, 416–427. Lewis, M. A., & Butterfield, R. M. (2007). Social control in marital relationships: Effect of one’s partner on health behaviors. Journal of Applied Social Psychology, 37, 298–318. Lewis, M. A., & Rook, K. S. (1999). Social control in personal relationships: Impact on health behaviors and psychosocial distress. Health Psychology, 18(1), 63–71. Logic, M., Okun, M. A., & Pugliese, J. A. (2009). Expanding the mediational model of the effects of health‐related social control. Journal of Applied Social Psychology, 39, 1373–1396. Martire, L. M., Lustig, A. P., Schulz, R., Miller, G. E., & Helgeson, V. S. (2004). Is it beneficial to involve a family member? A meta‐analysis of psychosocial interventions for chronic illness. Health Psychology, 23, 599–611. Sullivan, K. T., Pasch, L. A., Bejanyan, K., & Hanson, K. (2010). Social support, social control and health behavior change in spouses. In K. T. Sullivan & J. Davila (Eds.), Support processes in intimate relationships (pp. 219–239). New York, NY: Oxford Press. Tucker, J. S., & Anders, S. L. (2001). Social control of health behaviors in marriage. Journal of Applied Social Psychology, 31(3), 467–485. Umberson, D., Williams, K., Powers, D. A., Liu, H., & Needham, B. (2006). You make me sick: Marital quality and health over the life course. Journal of Health and Social Behavior, 47, 1–16.

Suggested Readings Agnew, C. R., & South, S. C. (Eds.) (2014). Interpersonal relationships and health: Social and clinical psychological mechanisms. New York, NY: Oxford University Press. Loving, T. J., & Sbarra, D. A. (2016). Relationships and health. In M. Mikulincer, P. Shaver, R. Phillip, J. A. Simpson, J. F. Dovidio, & F. John (Eds.), APA handbook of personality and social psychology, vol. 3: Interpersonal relations. Washington, DC: American Psychological Association. Newman, M. L., & Roberts, N. A. (Eds.) (2013). Health and social relationships: The good, the bad, and the complicated. Washington, DC: American Psychological Association.

Infertility, Miscarriage, and Neonatal Loss Amy Wenzel Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA Private Practice, Rosemont, Pennsylvania, PA, USA

For many people, becoming a parent is a life task that carries great significance and meaning. Although the prospect of becoming pregnant and having a child is associated with joy and eager anticipation for many couples, it can be associated with sorrow and despair for couples who experience difficulties. Some couples experience infertility, or the inability to conceive after 12 months of regular sexual activity free of birth control. Other couples conceive but lose the baby in various stages of pregnancy. Miscarriage and neonatal loss occur when the embryo or fetus dies before it can live on its own. The term, miscarriage, is typically used when the loss occurs before the 20th week of gestation (sometimes 24th week of gestation in select studies). In contrast, the term neonatal loss (or stillbirth) is typically used when the loss occurs at or after the 20th week of gestation, including instances in which a fetus is carried to term but dies during childbirth. Together, miscarriage and neonatal loss are understood as perinatal loss. This article describes the phenomenology of, emotional consequences of, and interventions for infertility and perinatal loss.

Infertility Infertility is typically designated in two ways. Primary infertility occurs when a woman has never conceived, and secondary infertility occurs when a woman had been previously pregnant, even if the pregnancy resulted in a loss. Although the definition of infertility presented earlier indicates that it is diagnosed after 12 months of trying unsuccessfully to conceive without using birth control, it is often diagnosed after 6 months of trying unsuccessfully to conceive in women over the age of 35. The rationale underlying the use of a different criterion for women The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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over the age of 35 is that the likelihood of conceiving declines rapidly after the age of 30, so implementing fertility treatment sooner rather than later in women of advanced maternal age increases the likelihood of a successful pregnancy. Research shows that among women who have attempted to achieve pregnancy, the rate of infertility varies between 16 and 28%. In women over age 40, between 3 and 6% are involuntarily childless due to infertility, and an additional 4–6% have fewer children than desired (Schmidt, 2006). The causes of infertility are multifaceted. Approximately 80% of infertility cases can be explained by female factors, male factors, or both, leaving the remaining 20% of cases unexplained by medical causes. Causes of female infertility include factors related to ovulation, the cervix or uterus, and/or the fallopian tubes. Women who infrequently ovulate or do not ovulate are at high risk for infertility; these difficulties can be caused by hormonal factors such as polycystic ovary syndrome (PCOS), altered levels of follicle‐stimulating hormone (FSH), or luteinizing hormone (LH) due to stress or sudden weight gain or loss, or excess prolactin. Uterine and cervical causes of infertility include fibroids, an abnormally shaped uterus, and cervical stenosis (i.e., narrowing of the cervix). Women who have endometriosis, a condition in which the uterine lining extends outside the uterus, can have difficulty conceiving because resultant scarring can block the fallopian tubes. Moreover, the fallopian tubes can also be blocked by pelvic inflammatory disease, which is an infection of the uterus and fallopian tubes caused by sexually transmitted infections. Male infertility is typically caused either by a low sperm count or poor‐quality sperm that are not mobile enough to reach the partner’s egg. Some research demonstrates that anxiety and stress are associated with poorer outcomes in infertility treatment (e.g., Boivin & Schmidt, 2005); however, emotional distress is rarely viewed as the sole cause of infertility. The emotional consequences of infertility can be devastating for those who experience it, as becoming a parent is often central to people’s expectations for the course they believe their lives will follow. It is common for people struggling with infertility to report a sense of failure, defectiveness, and incompetence, as well as jealousy of and isolation from others who seem to achieve parenthood easily. Female infertility is commonly associated with a sense of betrayal by one’s body. Male infertility is commonly associated with a threat to virility. Although the prevalence of mental health diagnoses is not elevated in people who struggle with infertility, they often report elevated levels of emotional distress on self‐report inventories of depression, anxiety, and life satisfaction (Greil, Schmidt, & Peterson, 2016). Women tend to report higher levels of infertility‐related depression, anxiety, and stress than do men. In contrast, people who are characterized by resilience, or the capacity to respond adaptively to negative life events, report comparatively lower levels of infertility‐related stress, higher quality of life, and more adaptive coping strategies (Sexton, Byrd, & von Kluge, 2010). Over the past 30 years, technology has allowed for the development of sophisticated approaches to medically assisted reproduction. Assisted reproductive technology (ART) is a category of medical procedures used to achieve pregnancy. Perhaps the most well‐known ART is in vitro fertilization (IVF), a procedure in which (a) a woman takes hormones to increase the number of eggs; (b) her eggs are removed and placed in a petri dish along with sperm; (c) fertilization occurs in the petri dish; and (d) one or more embryos are placed in her uterus. Some women choose to freeze embryos and later thaw one or more embryos and have them transferred to the uterus, a procedure called frozen/thawed embryo transfer cycle (FET). Fertilization can also occur using donor eggs, such that after the eggs are fertilized, they are transferred into a woman’s uterus. Non‐ART medically assisted reproduction interventions include ovulation induction using pharmacotherapy and intrauterine insemination (IUI) using the husband/partner’s or a donor’s sperm. In developed countries, approximately 56% of people who struggle with infertility seek some form of medically assisted reproduction (Boivin, Bunting, Collins, & Nygren, 2007). The live birth rate in women

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under age 40 after undergoing ART is between 15 and 25%, depending in the specific ­intervention (Schieve, Devine, Boyle, Petrini, & Warner, 2009). Many people experience a great deal of stress when they undergo medically assisted ­reproduction interventions, as these interventions can be unpredictable, ambiguous, and time consuming. In many cases, treatment assumes a central focus in their lives, and it can be difficult to know when to discontinue treatment. Some research suggests that infertility treatment ­confers stress above and beyond that conferred by infertility itself (Greil, Lowry, McQuillan, & Shreffler, 2011). As might be expected, infertility‐related emotional distress decreases with ­successful treatment, and it often persists when treatment is unsuccessful. In addition, infertility and infertility treatment can put great strain on the partner or marital relationship. However, a subset of couples who remain involuntarily childless report that their relationship strengthened because of the shared experience (Peterson, Pirritano, Block, & Schmidt, 2011). When women become pregnant through medically assisted reproduction technology, they view pregnancy as more stressful and report more anxiety than women who did not experience infertility. Moreover, women who became pregnant through infertility treatment report more emotional distress following a pregnancy loss than women who did not become pregnant through infertility treatment. However, there is no evidence that women whose children were born through infertility treatment parent any differently or report differing levels of parenting stress than women whose children were conceived naturally, although parents who had ­children using medically assisted reproduction technologies endorse stronger feelings toward their children and a high level of gratitude (Sundby, Schmidt, Heldaas, Bugge, & Tanbo, 2007). There are few long‐term consequences of infertility‐related emotional distress in ­couples who eventually have a successful pregnancy or who adopt children; however, e­ motional distress persists in a subset of people who remain involuntarily childless. Although the need for infertility‐specific mental health services for people experiencing infertility has been recognized, there is a paucity of research that has examined the efficacy of specific interventions for infertility‐related emotional distress. Interventions based on cognitive behavioral therapy (CBT) typically include components such as relaxation, cognitive restructuring (i.e., a process by which people evaluate the accuracy and helpfulness of negative thoughts associated with emotional distress), stress management, and psychoeducation about infertility and its emotional consequences. Research shows that such CBT approaches are more efficacious than routine care (Domar et  al., 2000) and pharmacotherapy (Faramarzi et al., 2008) in reducing depression, anxiety, and stress in people who struggle with infertility. In contrast, evidence is mixed regarding the degree to which mental health intervention ­targeting people struggling with infertility actually improves pregnancy rates.

Miscarriage and Neonatal Loss Because the achievement of parenthood is an important developmental task for many adults, a loss can be devastating on many levels. According to perinatal loss experts David and Martha Diamond, In addition to the loss of the fetus or baby, which can be a devastating event in and of itself, there are lost opportunities for progress in adult development, for the repair of old wounds, and for the redefinition of relationships with others. The grief, loss, and trauma may re‐evoke unresolved grief from the past. Preexisting psychopathology can be exacerbated, and new disorders can be ­precipitated (Diamond & Diamond, 2016, p. 488).

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The often‐abrupt shift from the expectation that there will be a new addition to the family to the stark realization that this will not occur prompts a profound sense of disappointment and despair in many people who experience it. Perinatal loss is a particularly difficult event for women to endure, given that they are already in a vulnerable state due to fluctuating h ­ ormones, shifting their identity to that of being a parent, and perhaps grappling with hurts from their own parentings of which they were reminded during their pregnancy. However, it is important not to underestimate the toll that perinatal loss takes on men as well. The overall incidence of miscarriage is between 15 and 20%, and it increases substantially with age, with as many as 75% of women experiencing a miscarriage after age 45 (Hemminki & Forssas, 1999). Most miscarriages occur in the first 12 weeks of gestation, which is why many people refrain from sharing news of the pregnancy until they have completed the first trimester. Symptoms of miscarriage are cramping and vaginal bleeding. An ultrasound verifies that the pregnancy is no longer viable. Many women are advised to simply allow the miscarriage to ­proceed naturally; however, in instances in which this does not occur, medical intervention may be necessary. Some women undergo dilation and curettage (often referred to as a D&C), in which a medical team dilates a woman’s cervix and uses surgical intervention to remove the contents of the uterus. Other women are given medicine to induce contractions. The incidence of n ­ eonatal loss—loss that occurs at or after 20 weeks of gestation—is approximately 0.6% (MacDorman & Gregory, 2015). Women whose fetus died in utero typically go through the process of labor and delivery. Other women deliver live infants that die soon after birth. There are many factors that account for perinatal loss. Between 50 and 70% of first trimester miscarriages can be attributed to genetic problems, such as chromosomal abnormalities. Additional causes of miscarriage include infections, maternal medical conditions like diabetes or thyroid disease, hormone problems, immune system responses, uterine abnormalities, and instances in which the egg did not implant properly. Risk factors for miscarriage include advanced maternal age (i.e., age 35 or older), maternal medical conditions like diabetes or thyroid disease, a history of three or more miscarriages, smoking, drinking alcohol, use of illicit drugs, toxins, and obesity. Causes of neonatal loss include placental abruption, premature rupture of membranes (i.e., water breaks prematurely), preeclampsia (i.e., a condition that typically occurs after 20 weeks of gestation, characterized by high blood pressure and potential damage to an organ system like the kidneys), birth defects like structural abnormalities, growth restriction, infection, and umbilical cord abnormalities. Many people experience a pronounced grief reaction in the time following a perinatal loss. However, the grief is focused on what their child would have been like and what life would have been like with the child, rather than on memories. Grieving a perinatal loss is particularly challenging because there are few culturally sanctioned grieving practices. Many women report significant feelings of guilt and shame, a sense of failure, and self‐blame. Women who miscarried in the first trimester often grieve in silence, as they had not yet disclosed the pregnancy to others. Women who were visibly pregnant and suffer a neonatal loss must contend with announcing the news to others and fielding questions from acquaintances and co‐workers who ask pregnancy. Men also report elevated grief after perinatal loss, although it is typically less pronounced than in women and more profound in response to a neonatal loss, rather than to a miscarriage. One explanation for this is that men have not experienced quickening (i.e., the perception of fetal movements) and the bodily changes that women typically experience. It is likely that results from studies finding gender differences in grieving perinatal loss can be explained by gender differences in the general expression of grief and emotional distress, with men being more likely to remain stoic and use problem‐solving strategies to cope with loss and women being more likely to express their emotions openly.

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Complicated grief occurs when the grief reaction becomes chronic and is associated with life interference and significant personal distress. Approximately 10–20% of women who experience perinatal loss struggle with complicated grief, although this range is not appreciably ­different from the percentage of complicated grief reactions in response to other losses (Brier, 2008). Factors that put women at risk for complicated grief reactions to perinatal loss include poor psychological functioning prior to the loss, poor social support, and a history of other perinatal losses. A body of research has examined specific symptoms associated with mental health disorders reported by people who have experienced perinatal loss. Research indicates that elevated symptoms of depression in women persist 6 months to 1 year following a perinatal loss and that the women at greatest risk for depression are those who are childless, are highly invested in the pregnancy, and/or have concerns about infertility (Robinson, 2014). Men also report elevated depressive symptoms in the first few months following a perinatal loss, but in contrast to symptoms reported by women, their symptoms drop significantly after 3 months. Moreover, women who have experienced perinatal loss typically report high levels of anxiety during ­subsequent pregnancies, with the understandable worry that they will experience another loss. In fact, there is evidence that women who have experienced multiple perinatal losses have more emergency department visits and hospitalizations than women without such a history (Kinsey, Baptiste‐Roberts, Zhu, & Kjerulff, 2015). Women also endorse rumination and intrusive thoughts up through at least 6 months’ post‐loss. Experts have been increasingly recognizing the traumatic nature of perinatal loss. The definition of trauma in the Diagnostic and Statistical Manual, Fifth Edition (DSM‐5) indicates that a person must have exposure to actual or threatened death, which indeed is the case with a perinatal loss. Many women who experience perinatal loss contend with a great deal of blood and pain. There is a profound sense that something is going horribly wrong, coupled with a sense of uncontrollability. The woman has literally lost an extension of herself in her child, as well as an integral part of a dream for parenthood. For these reasons, Diamond and Diamond (2016) have described perinatal loss as being one type of reproductive trauma. Rates of posttraumatic stress symptoms in women who have experienced perinatal loss range from 10 to 25% at 1‐month post‐loss (Diamond & Diamond, 2016). In addition, other family members often experience emotional distress associated with the perinatal loss. Children who knew about their mother’s pregnancy can experience grief, along with confusion over what happened to the baby. Even when children do not know about the pregnancy, they can often sense when their parents are upset. Grandparents are also affected tremendously by perinatal loss, both in terms of the loss of a grandchild and concern for what their own child is enduring (Robinson, 2014). Although there is little consensus on the best interventions to deliver to parents who experience perinatal loss, most experts agree that it is important for healthcare providers to acknowledge the meaning associated with the loss, as well as to provide parents with some latitude to choose the best way to proceed with logistical issues (such as how to dispose of the remains, whether to have a memorial service). There is insufficient evidence to support the provision of counseling in the immediate aftermath of a perinatal loss. However, psychotherapy and counseling should be offered as an option when people experience a perinatal loss, with the understanding that it is their choice as to whether they take advantage of it. Psychotherapy or counseling should be recommended at follow‐up visits when it becomes clear that their ­emotional distress is at a level that causes life interference. A topic of debate is whether women who experience advanced neonatal loss should hold their babies. Some parents find that doing so provides a sense of closure. However, some research shows that there is an increase in

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depressive, anxiety, and posttraumatic stress symptoms in parents who held their baby, relative to parents who did not (Hughes, Turton, Hopper, & Evans, 2002). Mental health professionals who work with people who have experienced perinatal loss are encouraged to provide the utmost empathy and compassion. They should create an atmosphere in which the client experiences, rather than avoids, the painful affect associated with the loss. Psychoeducation can be helpful in normalizing grief reactions, and previous unresolved losses that have been activated by the current loss should be addressed (Diamond & Diamond, 2016). CBT interventions have demonstrated efficacy in reducing prolonged grief, d ­ epression, anxiety, and posttraumatic stress in an open trial (Nakano, Akechi, Furukawa, & Sugiura‐Ogasawara, 2013) and a randomized controlled trial (Kersting et al., 2013).

Author Biography Amy Wenzel, PhD, ABPP, is the founder and president of Wenzel Consulting, LLC, a clinical assistant professor of psychology in psychiatry at the University of Pennsylvania School of Medicine, an adjunct faculty member at the Beck Institute of Cognitive Behavior Therapy, and an affiliate of The Postpartum Stress Center. She has authored or edited 20 books, many of topics within perinatal psychology.

References Boivin, J., Bunting, L., Collins, J. A., & Nygren, K. (2007). An international estimate of infertility prevalence and treatment‐seeking: Potential need and demand for infertility care. Human ­ Reproduction, 22, 1506–1512. Boivin, J., & Schmidt, L. (2005). Infertility‐related stress in men and women predicts treatment ­outcome 1 year later. Fertility and Sterility, 83, 1745–1752. Brier, N. (2008). Grief following miscarriage: A comprehensive review of the literature. Journal of Women’s Health, 17, 451–464. Diamond, D. J., & Diamond, M. O. (2016). Understanding and treating the psychosocial consequences of pregnancy loss. In A. Wenzel (Ed.), Oxford Handbook of Perinatal Psychology (pp. 487–523). New York, NY: Oxford University Press. Domar, A. D., Clapp, D., Slawsby, E., Kessel, B., Orav, J., & Freizinger, M. (2000). The impact of group psychological interventions on distress in infertile women. Health Psychology, 19, 568–575. Faramarzi, M., Alipor, A., Esmaelzadeh, S., Kherikhahm, E., Poladi, K., & Pash, H. (2008). Treatment of depression and anxiety in infertile women: Cognitive behavioral therapy versus fluoxetine. Journal of Affective Disorders, 108, 159–164. Greil, A. L., Lowry, M., McQuillan, J., & Shreffler, K. M. (2011). Infertility treatment and fertility‐ specific distress: A longitudinal analysis of a population‐based sample of U.S. women. Social Science & Medicine, 73, 87–94. Greil, A. L., Schmidt, L., & Peterson, B. D. (2016). Understanding and treating the psychosocial consequences of infertility. In A. Wenzel (Ed.), Oxford Handbook of Perinatal Psychology (pp. 524–547). New York, NY: Oxford University Press. Hemminki, E., & Forssas, E. (1999). Epidemiology of miscarriage and its relation to other reproductive events in Finland. American Journal of Obstetrics and Gynecology, 18, 396–401. Hughes, P., Turton, P., Hopper, E., & Evans, C. D. (2002). Assessment of guidelines for good practice in psychosocial care of mothers after stillbirth: A cohort study. The Lancet, 360, 114–118. Kersting, A., Dölemeyer, R., Steinig, J., Walter, F., Kroker, K., … Wanger, B. (2013). Brief Internet‐ based intervention reduces posttraumatic stress in parents after the loss of a child during pregnancy: A randomized controlled trial. Psychotherapy and Psychosomatics, 82, 372–381.

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Kinsey, C. B., Baptiste‐Roberts, K., Zhu, J., & Kjerulff, K. H. (2015). Effect of multiple previous miscarriages on health behaviors and health care utilization during subsequent pregnancy. Women’s Health Issues, 25, 155–161. MacDorman, M. F., & Gregory, E. C. W. (2015). Fetal and perinatal mortality: United States, 2013. Atlanta, GA: Centers for Disease Control and Prevention. Retrieved from https://www.cdc.gov/ nchs/data/nvsr/nvsr64/nvsr64_08.pdf Nakano, Y., Akechi, T., Furukawa, T. A., & Sugiura‐Ogasawara, M. (2013). Cognitive behavior therapy for psychological distress in patients with recurrent miscarriage. Psychology Research and Behavior Management, 6, 37–43. Peterson, B. D., Pirritano, M., Block, J. M., & Schmidt, L. (2011). Marital benefit and coping strategies in men and women undergoing unsuccessful fertility treatments over a 5‐year period. Fertility and Sterility, 95, 1759–1763. Robinson, G. E. (2014). Pregnancy loss. Best Practice & Research Clinical Obstetrics & Gynaecology, 28, 169–178. Schieve, L. A., Devine, O., Boyle, C. A., Petrini, J. R., & Warner, L. (2009). Estimation of contribution of non‐assisted reproductive technology ovulation stimulation fertility treatments to US singleton and multiple births. American Journal of Epidemiology, 170, 1396–1407. Schmidt, L. (2006). Infertility and assisted reproduction in Denmark: Epidemiology and psychosocial consequences. Danish Medical Bulletin, 53, 390–417. Sexton, M. B., Byrd, M. R., & von Kluge, S. (2010). Measuring resilience in women experiencing infertility using the CD‐RISC: Examining infertility‐related stress, general distress, and coping styles. Journal of Psychiatric Research, 44, 236–241. Sundby, J., Schmidt, L., Heldaas, K., Bugge, S., & Tanbo, T. (2007). Consequences of IVF among women: 10 years posttreatment. Journal of Psychosomatic Obstetrics & Gynecology, 28, 115–120.

Suggested Reading Covington, S. D., & Burns, L. H. (Eds.) (2006). Infertility counseling: A comprehensive handbook for clinicians (2nd ed.). New York, NY: Cambridge University Press. Diamond, D. J., & Diamond, M. O. (2016). Understanding and treating the psychosocial consequences of pregnancy loss. In A. Wenzel (Ed.), Oxford Handbook of Perinatal Psychology (pp. 487–523). New York, NY: Oxford University Press. Greil, A. L., Schmidt, L., & Peterson, B. D. (2016). Understanding and treating the psychosocial ­consequences of infertility. In A. Wenzel (Ed.), Oxford Handbook of Perinatal Psychology (pp. 524– 547). New York, NY: Oxford University Press. Robinson, G. E. (2014). Pregnancy loss. Best Practice & Research Clinical Obstetrics & Gynaecology, 28, 169–178. Wenzel, A. (2014). Coping with infertility, miscarriage, and neonatal loss: Finding perspective and ­creating meaning. Washington, DC: APA Books (LifeTools Division).

Prevention and Health Across the Lifespan John L. Romano Department of Educational Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA

Throughout history and across different disciplines, there has been a strong consensus that it is better to prevent a problem than to correct it after it is manifested. This axiom has especially been applied in matters of health, as we are regularly reminded about a host of behaviors and actions that we should take to ward off health problems. These include, among many, eating a healthy diet, being physically active, reducing stress, and getting flu shots. These actions are designed to reduce premature illness and disability and promote the quality of life into older age. However, although there is strong agreement among professionals and the lay public alike about the importance of prevention, there is reluctance and difficulty following preventive actions that science tells us will reduce disease and extend life. In addition to exploring the reluctance and lack of motivation to follow preventive recommendations, this chapter will summarize other areas of prevention as they pertain to matters of health across the lifespan. These areas of prevention include recent definitions of prevention, American Psychological Association (APA) Guidelines for Prevention in Psychology, protective factors to promote health and well‐being, prevention in the US Affordable Care Act (ACA), psychological interventions for prevention, and prevention and health in the twenty‐first century.

Definitions of Prevention While the word prevention has an intuitive meaning to most people, the word has generated discussion and nuances among scholars. Primary, secondary, and tertiary prevention were described by Caplan (1964) as activities and interventions designed as appropriate for an entire population (primary), for those at risk for developing a problem (secondary), and to reduce the impact of an existing problem (tertiary). These terms have been in use for many years. The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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However, Gordon (1987) conceptualized prevention interventions differently, describing ­prevention interventions as universal, selective, and indicated. Gordon argued that once a problem was manifested (tertiary), interventions for them are not prevention. Gordon’s ­ ­universal prevention is similar to primary prevention, while selective and indicated refer to interventions for groups who are at less (selective) and greater (indicated) risk for developing a disorder. The US Institute of Medicine’s Committee on Prevention of Mental Disorders adopted Gordon’s definitional terms for prevention in their seminal volume “Reducing Risks for Mental Disorders” (Mrazek & Haggerty, 1994). Romano and Hage (2000) expanded this definition by incorporating the strengthening of individual protective factors and institutional change strategies as importantcomponents of a comprehensive definition of prevention. In a later volume, the National Research Council and Institute of Medicine (2009) also incorporated reduction of risk and promotion of protective factors in its conceptualization of prevention. In a recent volume, Conyne (2015) described everyday wellness and prevention as actions taken to improve “­personal wellness and to avert significant problems, while contributing to the health and well‐being of others” (p. 19). This definition incorporates individual empowerment and systemic change as important prevention strategies. As the professional specialty of prevention has expanded, definitions of prevention have become more focused and inclusive, not only attending to individuals and groups but also recognizing that systemic and institutional preventive interventions are needed to address major societal problems. As will be d ­ iscussed in this ­chapter, the US ACA: Affordable Care Act (Federal Law, also called “Obama Care,” 2010), while c­ontroversial, is one example of legislation that incorporates prevention services as ­important to overall healthcare, and as described in the following s­ection, APA’s Guidelines for Prevention in Psychology reinforce the importance of ­prevention in psychology.

Guidelines for Prevention in Psychology It has been well documented that historically prevention has not held a very visible or ­prominent position in the field of psychology (Romano & Hage, 2000). Prevention is generally not a core course within psychology graduate programs, and prevention has not been considered as a specific competency or domain in accreditation of graduate training programs, or as an educational requirement for license as a psychologist. While community psychology, with a strong prevention and social justice focus, was organized as a specialty in the mid‐1960s and other organizations such as the Society for Prevention Research and the Prevention Section of APA’s Society of Counseling Psychology (Division 17) were created over the last 25 years, the impact of these organizations on the broader profession of psychology has been modest. To advance the cause of prevention, science and its visibility in psychology, a group of APA‐ member psychologists, began the arduous task (over multi‐years) of developing an APA set of guidelines for prevention in psychology. The guidelines, based on an earlier “Best Practices in Psychology” manuscript (Hage et al., 2007), were vetted by all major psychology stakeholders (e.g., professionals and consumers), revised accordingly, and eventually approved by APA Council and published in the American Psychologist (APA, 2014). The Guidelines for Prevention in Psychology are very relevant to all of psychology, including health psychology. The Guidelines, which are aspirational, consist of nine guidelines of best practices for prevention psychology. The Guidelines recommend the following: (a) prevention interventions that are theory driven and supported by evidence, (b) prevention science that attends to socially and culturally appropriate practices, (c) prevention that addresses risk as well as protective factors to enhance well‐being, (d) prevention activities include research and

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e­ valuation as integral to assess prevention practices, (e) prevention research and practices that include consideration of ethical issues as they pertain to prevention, (e) prevention specialists who attend to social disparities that may inform science and practice, (f) psychologists encouraged to seek out prevention training through various avenues throughout their career, (g) psychologists engaged in systemic change interventions to strengthen organizations and units of society to reduce distress and disability, and (h) psychologists encouraged to contribute to public policy discourses to inform decision makers about topics relevant to prevention science. The APA Prevention Guidelines are important to health psychology as they offer a set of aspirational recommendations to psychologists and other mental health workers to advance the cause of prevention in their clinical and research work. The Guidelines also reinforce the importance of not only stopping and reducing problematic behaviors but also strengthening factors that can be protective to individual persons as well as groups and communities. For example, health psychologists can work with schools and workplaces to promote healthy lifestyles, which serve as protections against future disease. The Guidelines also address the relevance of focusing on institutional and public policies to reinforce prevention of health problems and health promotion at the macro levels of society. In the United States, these macrolevel initiatives have reduced tobacco addiction (although it took many years and needs to be regularly reinforced), have produced dietary guidelines to promote good health, and passed laws to reduce traffic fatalities (e.g., use of seat belts, alcohol use, distracted driver penalties), to name a few examples. Community policies and laws to promote health are often contentious and controversial, and thus, at systemic levels, prevention and health promotion initiatives usually become very much a question about individual and community values, beliefs, and attitudes, as exemplified by efforts to enact legislation prohibiting cigarette smoking in bars and restaurants and laws to control access to guns to reduce crime.

Protective Factors to Promote Health and Well‐Being The promotion of protective factors is important to an overall framework of prevention ­initiatives. Protective factors are characteristics of an individual, small group, institution, or a larger community that can serve as protections against problematic behaviors now or in the future. Scholars have conceptualized protective factors in different ways. For example, Lösel and Farrington (2012) identified six types of protective factors that can serve as buffers to violent behaviors, that is, biological, personality, family, school, peers, and community. While Lösel and Farrington directly applied these factors to the prevention of youth violence, they offer a comprehensive framework for prevention of other problems and health promotion across the lifespan. For example, in healthcare settings, providers can assess protective factors related to health problems of an elder patient, such as support from family to follow prescribed care to reduce the risks of an emerging health problem; in younger adults, providers can assess genetic influences that may be related to a potential health problem. In addition, providers can assess personality characteristics (e.g., conscientiousness) and motivations to make health‐enhancing changes to reduce risk. Too often, patients hear what not to do (e.g., stop smoking, eat better, exercise, etc.), but for many these represent long‐term habits (e.g., addictions) and are very difficult to change without specific direction and guidance from professionals. Therefore, it is important that health providers discuss with patients how they might go about making lifestyle changes to improve health and provide referrals and other recommendations that offer specific guidance for making positive changes in lifestyle behaviors. The change process will be strengthened if protective factors are enhanced as part of the strategy to reduce problem behaviors.

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Others have also address protective factors through specific frameworks. For example, Romano (1997) articulated the importance of promoting student well‐being in schools by focusing on strengthening students’ knowledge, skills, attitudes, and behaviors that promote well‐being in school, at home, and in the community. Building resiliency in youth and young adults has also enriched research and practice to strengthen protective factors (e.g., APA Task Force on Resilience and Strength in Black Children and Adolescents, 2008; Luthar, Cicchetti, & Becker, 2000). Promoting protective factors in older adults has also been examined. These interventions can take the form of providing psycho‐educational programs related to healthy lifestyles, developing social support networks to reduce social isolation, and following medical regiments (Reynolds et  al., 2012). In addition, positive psychology (Seligman & Csikszentmihalyi, 2000), with its emphasis on the study of strengths and well‐being, has received much attention in clinical and research practices, and thus, positive psychology offers another perspective through which to focus on protective factors in prevention science. Finally, at the macro level, it is important to also promote community and societal protections and buffers to problem behaviors to reduce risks. Vera and Shin (2006) refer to the importance of reducing toxic environments, communities, and institutions that allow discrimination and disenfranchisement to exist among particular groups within a locale or larger society. Advocating for protections that provide adequate healthcare, housing, employment, and education for all members of a community is extremely important to support an overall health and wellness agenda across all population groups. However, suggestions on how best to achieve such community‐wide initiatives are usually burdened with political controversies. One notable example is the US ACA, which became the law in 2010, but the ACA merits have been hotly debated ever since passage. However, as discussed in the next section, ACA includes many benefits to support preventive interventions across the lifespan.

Prevention and the Affordable Care Act The US ACA has generated much controversy since it was passed in 2010 as policy for ­delivering and providing healthcare across the United States. Despite the political controversies surrounding the law, ACA provides for many benefits that support prevention, and ­psychologists are well positioned to provide leadership and services. Specifically, ACA provides for the Prevention and Public Health Fund that supports grants and state funding for a range of prevention programming, including school‐based health services, maternal health and early childhood home visit programs, and cultural competence training. In addition, ACA supports preventive patient healthcare across the lifespan, including adolescent alcohol and drug screening and assessment, developmental screening for children under age three with continued surveillance throughout childhood, and adolescent screening and counseling for obesity. In addition, ACA provides for screenings and counseling for adults coping with alcohol misuse, diet counseling for those at risk for chronic disease, and tobacco use screening and interventions. The law also provides for annual wellness visits for seniors through Medicare and ­preventive services for those on Medicaid. Psychologists are encouraged to review the benefits of the ACA law and educate their clients about services that can be received under the law. In addition, psychologists can be an excellent resource for primary care physicians and other health professionals as they are often the first ones who learn that their patients are at risk for mental or physical health problems. (See special issue of the American Psychologist on primary care and psychology [McDaniel & deGruy, 2014] for several papers on this topic.) Working in collaboration, psychologists and primary care providers can form a powerful

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­ artnership to prevent health problems and reduce patient risk. Integrated models of care p between psychologists and physicians have been advanced to strengthen these collaborations (Vogel, Kirkpatrick, Collings, Cederna‐Meko, & Grey, 2012). Psychologists not only can assist in direct patient care but also can serve as partners and consultants in developing public health media campaigns and prevention materials that are usually available in healthcare ­settings. One adolescent survey conducted by Taveras et al. (2007) found that while adolescents reported to health providers during physical exams about their lifestyle behaviors that placed them at risk for different problems, they were not offered counseling to change risk behaviors. With benefits available through ACA, perhaps more patients across the lifespan will receive prevention counseling to change problematic lifestyle behaviors and habits that place them at risk for future disease and disability. The ACA offers opportunities for prevention‐oriented psychologists to engage with healthcare professionals to offer benefits and ­services to improve health across the lifespan.

Psychological‐Based Theoretical Interventions for Prevention Patients are often told to change a habit of lifestyle to improve health and well‐being by healthcare professionals. These directives may include, for example, improve diet, lose weight, stop smoking, consume less alcohol, control anger, etc. However, much less often are patients in primary care settings given the tools to make these behavioral changes, and, in addition, most primary care physicians and other health professionals do not have the time and expertise to engage in counseling and coaching with their patients on how best to make these changes. Therefore, behavioral specialists, for example, counselors, psychologists, or other mental health and behavioral specialists, are the most appropriate professionals to engage patients in the process to make necessary behavioral changes, thus, another important reason to advance integrated models of care (Kelly & Coons, 2012). Mental health and behavioral professionals are most often educated and trained in theories of counseling and psychotherapy that have been popularized through research and practice in settings that focus on crisis intervention and remediation. Common theories taught in applied psychology and counseling graduate programs include psychodynamic theories, cognitive behavioral theory, person‐centered theory (PCT), and behavioral theories. While these theories may also be applied in preventive counseling and coaching situations, there are other psychological theories and perspectives that are neglected in many graduate training programs. However, less commonly taught theories can provide additional theoretical frameworks for the preventive counseling practitioner and researcher. These theories will be briefly summarized in this section. Romano (2015) presented a summary of several theories that have been applied in prevention research and practice. They will briefly be reviewed here, and readers who are unfamiliar with these theories are encouraged to learn more about them.

Transtheoretical Model of Behavior Change (TTM) TTM is a stage model of behavioral change introduced by Prochaska and colleagues (Prochaska, DiClemente, & Norcross, 1992; Prochaska, Johnson, & Lee, 2009). TTM asserts that behavioral changes occur in stages, although the stages are not necessarily linear. The six stages of change are precontemplation, contemplation, preparation, action, maintenance, and termination. This model is useful because it acknowledges that before change can occur, clients must be motivated to make a change, prepare a plan of action and carry out the plan, and provide a

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system for maintaining the change. For many behavioral changes of lifestyle, for example, smoking cessation, improvements in diet, maintenance continues, and termination is not ­relevant. TTM is useful in behavioral change counseling because it acknowledges that clients may not be ready or motivated for change (action stage), and if the counselor begins with preparation and action stages, without considering the client’s readiness for change (precontemplation and contemplation), the counseling effort may go for naught and result in early termination of counseling. Prochaska and colleagues have developed a well‐thought‐out and evidence‐based theory that is useful when assisting clients to make behavioral changes.

Theory of Reasoned Action and Planned Behavior (TRA/PB) The TRA was first proposed by Fishbein (1967) and subsequently extended by Ajzen (1991) into the TRA and PB. TRA offers a framework to understand the relationship between beliefs, attitudes, and behaviors, and Ajzen’s component (theory of PB) addressed the extent that a person believes that a behavior is under his/her control. TRA/PB is a cognitive model of behavior change in which change is influenced by a person’s beliefs and attitudes about the behavior change, and these variables are further influenced by the opinions of others (social norms) about the behavior change and the person’s belief that he/she can make the change. Thus, within a prevention framework and applying this theory, it is important to assess the client’s attitudes and beliefs about making a change and the extent that the person believes that change is possible for him/her. Applying TRA/PB to alcohol use prevention with adolescents, for example, a provider would assess the adolescent’s beliefs and attitudes about not using alcohol and how nonuse would be viewed by others important to him/her (e.g., peers, parents, coaches, etc.). In addition, the provider would discuss the youth’s self‐perception of being able to avoid alcohol use. In this example, the adolescent may believe that avoiding alcohol will make him/her less popular with peers, but abstinence from alcohol would receive strong approval from parents and other adults important to the adolescent. Thus, in the TRA/PB framework, assessing beliefs and attitudes is necessary to bring about behavior change. One contribution of TRA/PB is elicitation research, which is a process to assess perceived beliefs among a group targeted for behavior change prior to initiating prevention activities associated with the desired change. For example, a medical clinic with a large Hmong client population may use elicitation research to initially assess with a pilot group of similar Hmong patients the most important beliefs about a desired behavior change (e.g., regular health screenings) prior to initiating a larger‐scale program to encourage change among the Hmong population. Elicitation research data can be collected in different ways, for example, focused groups, questionnaires. Elicitation research recognizes that beliefs about a behavior change are not universal and that the most effective interventions will be population specific.

Health Belief Model (HBM) HBM was developed by US social psychologists in the 1950s to better understand why some people do not participate in health screenings to prevent illness (Rosenstock, 1974). HBM is a cognitive theory based on a person’s beliefs about the importance of health screenings. HBM is based on four perceived personal beliefs about his/her risk for contracting an illness or disease, and these beliefs influence whether or not a person participates in preventive health screenings and actions. The four perceived beliefs are susceptibility, severity, benefits, and ­barriers. Personal beliefs about susceptibility and severity of an illness as well as ­perceived ­benefits and barriers, according to HBM, are determinants and motivations for participating

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in health screenings. For example, a woman with a family history of breast cancer is likely to be more diligent scheduling regular mammograms, and her motivation to receive the screenings will be increased if the procedure is paid for by health insurance. While HBM was originally developed to better understand the lack of participation in some preventive health screenings, the model can also be considered as a preventive theory to address ­nonmedical problems, for example, assessment of risk associated with drinking and driving. The model may be especially useful with children and adolescents who underestimate their ­susceptibility and risk associated with problem behaviors, for example, experimenting with cigarette smoking will not lead to addiction (“I can quit anytime”). It can also be helpful to practitioners to assess perceived personal beliefs among clientele from different ethnic groups to better understand differences between a provider’s beliefs about engaging in a preventive behavior and beliefs of clients from different ethnic groups.

Motivational Interviewing (MI) MI has received considerable attention in the literature, especially in recent years (Miller & Rollnick, 2002). MI has its foundation in the work of Carl Rogers and his PCT. Although MI is more directive than PCT, MI recognizes the value and importance of the therapeutic relationship in behavior change. Four principles guide MI: (a) express empathy, as the counselor communicates empathetic understanding toward the client; (b) develop discrepancy, in which the counselor confronts the client about motivations to maintain or change behavior; (c) roll with resistance, as the counselor nonjudgmentally accepts client resistance to change; and (d) support self‐efficacy, as the counselor assists the client to gain awareness about skills that he/ she possesses to make behavioral changes. While MI was initially developed to help people stop addictive behaviors, it has been applied across a wide range of behaviors. MI would be most useful to assist individuals who are at risk for problematic behaviors (selective and indicated prevention). For example, MI might be used in a college setting to assist students who have low academic achievement or students who have been cited for problematic behavior related to alcohol use. In a medical setting, MI can be a framework to assist individuals whose lifestyle behaviors place them at risk for developing a future medical disease, such as obesity as a risk for diabetes. MI has been applied in brief counseling interventions with supplemental activities to change behaviors (e.g., Murphy et al., 2012), and thus, MI offers an advantage in medical settings where time may be limited for longer interventions. The theoretical frameworks summarized in this section are examples of models that can form a foundation for health providers to assist people make behavioral changes. There are other theories that can also be helpful to clinicians, and it is also understood that theoretical approaches do not necessarily function in isolation from each other. A practitioner may use more than one theory to facilitate client change during the counseling/coaching process, depending on the nature of the desired behavioral change, client demographics and readiness for change, and the provider’s comfort applying the theoretical framework.

Prevention and Health in the Twenty‐First Century This manuscript has presented background on prevention science and the specialty’s ­contribution to strengthening the overall health of a population. Prevention science can be applied across the lifespan, and as psychologists and other mental health professionals gain increased opportunities to collaborate with medical personnel, the collaboration offers

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t­remendous opportunities to assist people make necessary behavioral changes to improve health. Prevention offers much promise to reduce noncommunicable diseases (contributed to by behaviors of lifestyle) and extend the quality of life, especially with an aging US population. The role of psychologists as members of integrated healthcare teams is increasing, and applying prevention in these settings is critically important (see McDaniel & deGruy, 2014 and recently released APA video [www.apa.org/health/psychologists‐integrated‐care.aspx]). Psychologists are encouraged to become familiar with the US government’s major initiative in setting and monitoring health goals for the nation, Healthy People 2020 (www.healthypeople. gov). Healthy People was first published in 1979 and at subsequent 10‐year intervals, to measure progress, set goals, and make recommendations to improve the overall health and well‐ being of the population. The mission of Healthy People 2020 is to identify national health improvement priorities; increase public awareness and understanding about the determinants of health, disease, and disability; apply measurable objectives and goals to monitor progress; engage multiple sectors of the society to improve health; and identify critical research, evaluation, and data collection needs to assess progress. The goals of Healthy People 2020 are to promote longer life that is free of preventable disease, promote health equity and eliminate health disparities, create social and physical environments that support health, and promote quality of life and healthy development and healthy behaviors across the lifespan. Healthy People 2020 is the single best resource to review and collect information about the health status of the US population and become knowledgeable about health promotion and disease prevention in the United States. Researchers and practitioners can search for research literature, evidence‐based interventions, and professional tools that support health improvements/promotions and disease prevention across many health conditions and within different sectors of the society. In addition, to provide direct health promotion and disease prevention programs and activities with at‐risk patients, psychologists are encouraged to consider prevention interventions that offer assistance to those who are in relationship with people with illnesses. For example, psychologists can provide assistance and support to caregivers of the elderly who have developed cognitive disorders. Attending to caregivers of the elderly will become increasingly important with an aging US population. As the population ages, chronic disease and disability will also increase. Therefore, offering programs and support for those who care for this population, either in institutions or at home, is critically important in the twenty‐first century. For example, personnel in nursing homes and assisted living settings will need support to help them understand the needs of disabled elders and also to prevent burnout and emotional stress that can accompany caring for disabled elderly. In addition, relatives who care for family ­members also need assistance to help them understand the disease and disability of their loved one and also to help them manage their own stress levels related to caregiving. Psychologists and other mental health professionals can also assist with end‐of‐life processes and issues that arise with terminally ill patients and their loved ones. Hospice programs are increasingly ­utilized across the United States to support quality of life at the end of life for the terminally ill. Hospice programs funded through Medicare/Medicaid and most private insurance policies offer psychological support and medical assistance during this difficult time to patients and their caregivers to manage patient pain and to educate and provide support during the dying process. As written in the Medicare legislation, direct mental health and social services in ­hospice programs are usually provided by social workers. However, psychologists may serve as bereavement counselors and can support the programs by applying their expertise through training and education of hospice personnel as well as family members of the dying patient to prevent excessive stress and burnout of professional personnel and family members.

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Summary This paper has highlighted some of the ways that health and prevention science intersect. In a previous volume, Romano (2015) summarized research and literature germane to the work of psychologists that has been conducted in the medical sector such as prevention of depression in geriatric patients and public health campaigns to improve health and prevent disease. There are many opportunities for psychology to apply the theories and practices of the profession to advance the health and well‐being of the population through prevention science research and applications. The time is ripe to apply scientific advances in prevention science to reduce illness and disability and enhance the quality of life across the life cycle.

Author Biographies Dr. John L. Romano is an emeritus professor of educational psychology at the University of Minnesota. Romano received his bachelor, masters, and doctoral degrees from Le Moyne College (Syracuse, NY), Pennsylvania State University, and Arizona State University, respectively. He has authored prevention‐focused journal articles and books including the books Prevention Psychology: Enhancing Personal and Social Well‐Being (APA, 2015) and coedited The Cambridge Handbook of International Prevention Science (Cambridge, 2017). He served as cochair and chair of the work group that produced the APA Guidelines for Prevention in Psychology, and he has been the recipient of several US Department of Education grants to promote safe and drug‐free schools. Romano is a fellow of the American Psychological Association (APA). He has received several awards including the Prevention Section (APA’s Society of Counseling Psychology) Inaugural Lifetime Achievement Award, Distinguished Contributions to Counseling Psychology (APA Society of Counseling Psychology), APA’s International Psychology Division International Mentoring Award, and the American Counseling Association Research Award. He has been a visiting professor, consultant, and external examiner at universities in several countries.

References Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. APA. (2014). Guidelines for prevention in psychology. The American Psychologist, 69, 285–296. APA Task Force on Resilience and Strength in Black Children and Adolescents. (2008). Resilience in African American children and adolescents: A vision for optimal development. Washington, DC: Author. Retrieved from http://www.apa.org/pi/cyf/resilience Caplan, G. (1964). Principles of preventive psychiatry. New York, NY: Basic Books. Conyne, R. K. (2015). Counseling for wellness and prevention: Helping people become empowered in systems and settings (3rd ed.). New York, NY: Routledge. Fishbein, M. (Ed.) (1967). Readings in attitude theory and measurement. New York, NY: Wiley. Gordon, R. (1987). An operational definition of disease prevention. In J. A. Sternberg & M. M. Silverman (Eds.), Preventing mental disorders (pp. 20–26). Rockville, MD: U.S. Department of Health and Human Services. Hage, S. M., Romano, J. L., Conyne, R. K., Kenny, M., Matthews, C., Schwartz, J. P., & Waldo, M. (2007). Best practice guidelines on prevention practice, research, training, and social advocacy. The Counseling Psychologist, 35, 493–566.

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Kelly, J., & Coons, H. L. (2012). Integrated health care and professional psychology: Is the setting right for you? Professional Psychology: Research & Practice, 43, 586–595. Lösel, F., & Farrington, D. P. (2012). Direct protective and buffering protective factors in the development of youth violence. American Journal of Preventive Medicine, 43(Suppl.1), 8–23. Luthar, S. S., Cicchetti, D., & Becker, B. (2000). The construct of resilience: A critical evaluation and guidelines for future work. Child Development, 71, 543–562. McDaniel, S. H., & deGruy III, F. V. (2014). Special issue scholarly lead: An introduction to primary care and psychology. American Psychologist, 69, 325–331. Miller, W. R., & Rollnick, S. (2002). Motivational interviewing (2nd ed.). New York, NY: Guilford Press. Mrazek, P. J., & Haggerty, R. J. (Eds.) (1994). Reducing risks for mental disorders: Frontiers for preventive intervention research. Washington, DC: National Academy Press. Murphy, J. G., Dennhart, A. A., Skidmore, J. R., Borsari, B., Barnett, N. P., Colby, S. M., & Martens, M. P. (2012). A randomized controlled trial of a behavioral economic supplement to brief motivational interventions for college drinking. Journal of Consulting and Clinical Psychology, 80, 876–886. National Research Council and Institute of Medicine. (2009). Preventing mental, emotional, and behavioral disorders among young people: Progress and possibilities. Washington, DC: National Academies Press. Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. (1992). In search of how people change: Applications to addictive behaviors. American Psychologist, 47, 1102–1114. Prochaska, J. O., Johnson, S., & Lee, P. (2009). The transtheoretical model of behavior change. In S. A. Shumaker, J. K. Okcene, & K. A. Riekert (Eds.), The handbook of behavior change (3rd ed., pp. 59–83). New York, NY: Springer. Reynolds, C. F., Cuijpers, P., Patel, V., Cohen, A., Dias, A., Chowdhary, N., … Albert, S. M. (2012). Early intervention to reduce the global health and economic burden of major depression in older adults. Annual Review of Public Health, 33, 123–135. Romano, J. L. (1997). School personnel training for the prevention of tobacco, alcohol, and other drug use: Issues and outcomes. Journal of Drug Education, 27, 245–258. Romano, J. L. (2015). Prevention psychology: Enhancing personal and social well‐being. Washington, DC: American Psychological Association. Romano, J. L., & Hage, S. M. (2000). Prevention and counseling psychology: Revitalizing commitments for the 21st century. The Counseling Psychologist, 28, 733–763. Rosenstock, J. M. (1974). Historical origins of the health belief model. Health Education Monographs, 2, 328–335. Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55, 5–14. Taveras, E. M., Sobol, A. M., Hannon, C., Finkelstein, D., Wiecha, J., & Glottmaker, S. L. (2007). Youths’ perceptions of over‐weight related prevention counseling at a primary care visit. Obesity, 15, 831–836. Vera, E. M., & Shin, R. Q. (2006). Promoting strengths in a socially toxic world: Supporting resilience with systematic interventions. The Counseling Psychologist, 34, 80–89. Inte Vogel, M. E., Kirkpatrick, H. A., Collings, A. S., Cederna‐Meko, C. L., & Grey, M. J. (2012). Integrated care: Maturing the relationship between psychology and primary care. Professional Psychology: Research & Practice, 43, 271–280.

Suggested Reading Israelashvili, M., & Romano, J. L. (Eds.) (2016, September). The Cambridge handbook of international prevention science. Cambridge University Press. McDaniel, S. H., & de Gruy, F. V. 2014, May/JuneSpecial Issue: Primary Care and Psychology. American Psychologist, 69(4). Romano, J. L. (2015). Prevention psychology: Enhancing personal and social well‐being. Washington, DC: American Psychological Association.

Self‐Affirmation and Responses to Health Messages: An Example of a Research Advance in Health Psychology Allison M. Sweeney1 and Anne Moyer2 Department of Psychology, University of South Carolina, Columbia, SC, USA 2 Department of Psychology, Stony Brook University, Stony Brook, NY, USA

1

People, especially those at risk, tend to resist persuasive appeals to change their behavior. When people are uninterested in changing a health behavior, they may try to discredit unwanted health information by forming counterarguments or alternative explanations (Ditto & Lopez, 1992). To the extent that people tend to prefer positive information that reflects well on the self, it is not surprising, then, that people scrutinize unfavorable information more than favorable information (Giner‐Sorolila & Chaiken, 1997). To the extent that health information reminds people of their shortcomings, it can be a potent threat to their positive self‐ views. Ironically, the more personally relevant a health message is, the less likely an individual is to process it objectively (Liberman & Chaiken, 1992). One promising approach to overcoming resistance to health information involves leading people to think about their most important values or actions, a process known as self‐affirmation (Steele, 1988). Self‐affirmation theory proposes that highlighting important sources of self‐worth restores or reinforces an individual’s overall sense of self‐integrity. By making salient aspects of the self that are important to one’s identity, but are unrelated to the threat at hand, self‐affirmation helps to offset any potential self‐threat elicited by health information (Sherman & Cohen, 2006). As a result, self‐affirmation should reduce any motivation to respond defensively to health information. Supporting this prediction, a number of studies have found that when people are self‐affirmed, they are more likely to process health information in an ­objective manner (Reed & Aspinwall, 1998; Sherman, Nelson, & Steele, 2000).

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Much of the early research on self‐affirmation and health focused on the ability of self‐­affirmation to change people’s responses to health information, including message acceptance (Sherman et  al., 2000). Further efforts have been made to clarify whether increasing people’s openness toward health information translates into a change in future health‐related behaviors. Several studies indicate that self‐affirmation facilitates behavior change across a diverse range of behaviors, including physical activity, fruit and vegetable consumption (Epton & Harris, 2008), alcohol consumption (Armitage, Harris, & Arden, 2011), and adherence to medication (Wileman et  al., 2014). Such studies typically examine the influence of self‐­ affirmation over the course of a week or longer, with some studies finding effects extending over several months (Harris et al., 2014; Wileman et al., 2014). Self‐affirmation has a small‐sized effect on health behaviors (Epton, Harris, Kane, van Koningsbruggen, & Sheeran, 2015; Sweeney & Moyer, 2015). Although small, the effect size of self‐affirmation is similar to those obtained in meta‐analyses of other health behavior change interventions (e.g., Gallagher & Updegraff, 2012). Thus, self‐affirmation holds considerable promise as a useful framework for explaining when people accept health information and, in turn, make changes in their behavior. In this chapter we will examine some of the recent developments and discoveries among studies of self‐affirmation, including (a) the incorporation of diverse research methods, (b) the identification of moderating variables, and (c) efforts to combine self‐affirmation with other intervention strategies. We will then highlight two major questions for future research: (a) how does self‐affirmation influence health behaviors and (b) what is the trajectory of self‐affirmation?

Applying Diverse Research Methods to Studies of Self‐Affirmation Many early studies on self‐affirmation and health‐related outcomes consisted of laboratory‐ ­­ based studies that measured immediate responses to health information. After completing a self‐affirmation or a control task, people were presented with health information and reported on their responses to it, such as behavioral intentions and message processing (Reed & Aspinwall, 1998; Sherman et  al., 2000). Within the last decade, however, researchers have begun to examine whether self‐affirmation impacts outcomes beyond people’s self‐reported experiences. An emerging literature encompassing a diverse array of methods suggests that self‐affirmation impacts the body’s immediate and physiological response to threat. For example, relative to non‐affirmed individuals, people who are self‐affirmed have a lower cortisol response when exposed to a stressful situation (Creswell et al., 2005) and take less time for their mean arterial blood pressure to return to baseline after receiving threatening information (Schmeichel & Tang, 2015). To date, two studies have examined the influence of self‐affirmation on neural activity. To examine the effect of self‐affirmation on immediate sensitivity to threat, participants completed a speeded response time task while their neural activity was recorded with electroencephalography (EEG) (Legault, Al‐Khindi, & Inzlicht, 2012). The response time task was a go/no‐go task that involved making or withholding a response depending on the type of stimuli presented. Participants were instructed to respond as quickly as possible and, thus, were prone to errors. The researchers tested whether self‐affirmation would impact people’s sensitivity to making errors by examining their error‐related negativity (ERN) response, an event‐related potential found in past research to index motivational significance. Participants who self‐affirmed prior to the go/no‐go task displayed a greater ERN response, suggesting that they were more sensitive toward making mistakes during the response time task. To the

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extent that errors are experienced as aversive, the authors interpreted this finding as suggesting that self‐affirmation increases people’s openness to processing threat. Adopting a different approach, one recent study used functional magnetic resonance ­imaging (fMRI) to examine neural activity during the processing of health information (Falk et al., 2015). The results indicated that people who were self‐affirmed showed greater a­ ctivation in the ventromedial prefrontal cortex (VMPFC), an area of the brain associated with self‐ related processing when reading the health message. Furthermore, increased activation in the VMPFC was associated with greater health behavior change, suggesting that self‐affirmation may engender health behavior change by allowing people to process otherwise threatening health information as self‐relevant. Taken together, research by Legault et al. (2012) and Falk et  al. (2015) provide initial evidence that applying a neurophysiological approach to self‐­ ­ affirmation may be a useful framework for further understanding the cognitive and ­motivational processes that underlie self‐affirmation. Another notable change among studies of self‐affirmation has been an increased emphasis on measuring behavioral changes arising from self‐affirmation. Attempts to examine the impact of self‐affirmation on health behaviors have relied primarily on self‐reports (e.g., Armitage et al., 2011; Epton & Harris, 2008). Recently, however, a few studies have begun to incorporate more objective measures of health behavior change. For example, one study found that self‐affirmation led to a reduction in sedentary behavior, as assessed with accelerometers worn by participants for 1 month (Falk et al., 2015). Another study examined whether self‐affirmation would encourage hemodialysis patients to adhere to recommendations (Wileman et al., 2014). By measuring patients’ serum phosphate levels across 1 year, the researchers were able to show a positive effect of self‐affirmation on medication adherence.

Moderators of Self‐Affirmation’s Effects: The Content of the Message and Characteristics of the Individual As research on self‐affirmation has accumulated, researchers have identified several factors that moderate the effectiveness of self‐affirmation. For example, it may be worthwhile to consider the content of health messages and the way that this interacts with self‐affirmation. One study examined the strength of a message related to the risk of fibrocystic disease (FBD) in those who consumed caffeinated beverages (Klein, Harris, Ferrer, & Zajac, 2011). The rationale was that if those who are self‐affirmed are more likely to attend to messages and become sensitive to their strength, then self‐affirmation should result in stronger inclinations to change behavior in response to strong messages. Messages of high strength about the link between caffeine consumption and fibrocystic disease cited reputable organizations and sources that supported the link; messages of low strength cited less well‐known organizations and mentioned sources that did not support the link. Significant interactions between message strength and self‐affirmation were found for feelings of vulnerability (i.e., worry) and intentions to reduce caffeine intake, such that these were higher in those who were self‐affirmed and were exposed to the strong message. Furthermore, feelings of vulnerability were found to mediate the effect on behavior change intentions, suggesting that attention to message strength may be facilitated by self‐affirmation’s effect on worry. An additional consideration in the effectiveness of self‐affirmation is the characteristics (i.e.,  disposition, life history, or affect) of those who are exposed to health messages. Importantly, if individual differences do interact with self‐affirmation’s effects, examining only main effects may result in some important effects being obscured, if moderating effects are not

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examined (Düring & Jessop, 2015). For instance, people are continually exposed to ­conflicting health information in their everyday lives, and this can result in confusion and feelings of ­fatalism about such things as the causes of cancer (Niederdeppe & Gurmankin Levy, 2007). For individuals who perceive high levels of ambiguity about a health topic, health messages may be especially threatening because, in addition to the disconcerting nature of the information, any additional information may only increase this sense of ambiguity (Klein, Hamilton, Harris, & Han, 2015). Self‐affirmation may be particularly useful in reducing defensiveness and facilitating receptivity to health messages in people who feel confused by health recommendations. This idea was tested in a study that exposed women with varying levels of risk and perceptions of ambiguity about the causes of cancer to a clear, unambiguous, and authoritative article describing the connection between high alcohol consumption and increased breast cancer risk (Klein et al., 2015). Self‐affirmation increased message acceptance in those who perceived high levels of ambiguity in extant cancer prevention recommendations. Although self‐affirmation can be experimentally induced, people are also thought to exhibit tendencies to spontaneously self‐affirm (Pietersma & Dijkstra, 2012). These tendencies are assessed by items such as “When I feel threatened or anxious, I find myself thinking about my strengths” (Harris, Napper, Griffin, Schuz, & Stride, in press, cited in Ferrer et al., 2015). Such tendencies have been found to moderate the relationship between anticipated (but not current) negative affect and intentions to obtain genetic test results for diseases that did not have any medically actionable precautionary measures, such as Huntington disease (Ferrer et al., 2015). Similarly, spontaneous self‐affirmation and optimism moderated the tendencies of those who tend to avoid information to have lowered intentions to learn results for such diseases (Taber et al., 2015). In addition to individual differences in the tendency to spontaneously self‐affirm, another potential moderating variable of self‐affirmation’s effects is self‐esteem. To the extent that those with low self‐esteem have fewer resources to draw upon when presented with messages that threaten the sense of self, self‐affirmation may prove especially beneficial. Accordingly, those with low self‐esteem who were affirmed had more positive attitudes toward behavioral change (increasing exercise), stronger endorsement of intentions to exercise, and lower levels of message derogation, but were no different in terms of behavioral control or actual behavior change (Düring & Jessop, 2015). So, considering moderating characteristics of individuals or health messages is promising, and looking beyond main effects of self‐affirmation can prove illuminating. Some further ­considerations include whether self‐affirmation’s effects for those who perceive high levels of ambiguity extend to receptivity to, for instance, more ambiguous messages (Klein et al., 2015). Weaker messages may also be more ambiguous; for example, in the study cited above, the strong message read: “The link between FBD and breast cancer is well‐established and FBD has been recognized as a breast cancer risk factor by the Centers for Disease Control and Prevention,” whereas the weak message read: “At a recent meeting of the American Academy of Osteopathic Medicine, a small group of dieticians with links to the fast food industry issued a formal statement supporting the link between FBD and breast cancer as well as the link between caffeine and FBD” (p. 1239). Given the potential effects of perceptions of ambiguity, it could be important to disentangle the effects of message strength and level of ambiguity.

Combining Self‐Affirmation With Other Intervention Strategies Another approach to expanding research on self‐affirmation has been to combine self‐­affirmation with other health behavior change techniques. For example, implementation intentions

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(“if‐then” plans that concretely specify how to reach a particular goal; Gollwitzer, 1999) are thought to be useful in channeling the motivation generated via self‐affirmation into volition and actual behavior change (Jessop, Sparks, Buckland, Harris, & Churchill, 2014; van Dijk & Dijkstra, 2014). Accordingly, some research has begun investigating the effectiveness of ­incorporating this technique to enhance the effectiveness of self‐affirmation. Armitage and colleagues (Armitage et al., 2011; Armitage, Rowe, Arden, & Harris, 2014) developed a self‐affirmation implementation intention intervention to reduce alcohol ­consumption in adolescents. Forming implementation intentions for alcohol consumption may help individuals plan appropriate behavioral responses when critical situations are encountered, such as being offered a drink. A goal in the design of this intervention was to affirm the self without essay writing or questionnaire elaboration, which depends on good verbal fluency, in order to make it more broadly implementable. Accordingly, participants completed the stem, “If I feel threatened or anxious, then I will…” with one of four options that involved thinking about things they valued in themselves, remembering things they have succeeded in, or thinking about things they stood for, before viewing a diagram depicting the body parts and conditions affected by alcohol consumption. In a first study, this briefer intervention was as effective as a more traditional self‐affirmation manipulation and more effective than a control condition in reducing alcohol consumption 1 month later (Armitage et al., 2011), and, in a second study, more effective than a control condition 2 months later (Armitage et al., 2014). Harris et al. (2014) examined the independent and synergistic effects of self‐affirmation and implementation intentions with a 2 × 2 study design. There was a significant interaction between the two techniques at the 7‐day but not the 3‐month follow‐up, such that self‐ affirmed participants who formed implementation intentions prior to being exposed to a message emphasizing the benefits of eating fruits and vegetables consumed significantly more. Conversely, however, in a series of two studies with a similar 2 × 2 design, combining self‐­ affirmation and implementation intentions resulted in a negative effect on exercise behavior at 1 week (Jessop et al., 2014). The authors suggested that the counterintuitive finding may have stemmed from the combined techniques producing conflicting construal levels of the behavior or counteracting types of information processing. Other research has examined whether self‐affirmation interacts with aspects of the health message, such as the framing of health information in terms of gains or losses. One study tested the combined effects of self‐affirmation and message framing on intentions to engage in indoor tanning (Mays & Zhao, 2016). Because indoor tanning, despite being a significant risk factor for skin cancer and melanoma, is believed to be related to the sense of self and feelings of attractiveness, self‐affirmation was thought to be a particularly appropriate technique to prevent defensiveness to loss‐framed messages. Loss‐framed messages (that emphasize the costs of tanning) may be more threatening than gain‐framed messages (that emphasize the benefits of not tanning). After completing a self‐affirmation or a control task, participants viewed an image with an accompanying message that emphasized either the risks of indoor tanning or the benefits of avoiding indoor tanning. For both intentions to indoor tan and intentions to quit indoor tanning, contrary to ­expectations, there was no interaction between the framing of the message and self‐affirmation. In addition, although there were main effects for message framing, with loss‐framed messages being more effective, the main effect for self‐affirmation was only significant for intentions to tan, with those in the self‐affirmation conditions reporting stronger intentions to tan. An explanation for this puzzling finding was suggested by the fact that self‐affirmation led to perceptions of lower argument strength. The authors speculated that because indoor ­tanning is so tied to the self‐concept, the self‐affirmation manipulation could have increased the salience of the importance of indoor tanning and, thus, exacerbated defensiveness to the

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­ essages. This finding also brings attention to the notion that the type of target behavior, and m its relevance to sense of self, may be an important consideration in self‐affirmation research. In a rare example of using self‐affirmation techniques in clinical samples, patients with cardiopulmonary disease (angioplasty, hypertension, and asthma) were exposed to a positive affect and self‐affirmation intervention, in addition to a patient education workbook that the control groups also received, in three randomized trials (Mancuso et  al., 2012; Ogedegbe et  al., 2012; Peterson et  al., 2012). The self‐affirmation induction involved thinking of something participants had done that they were proud of and the suggestion that reflecting on these could help them overcome challenges in engaging in a new health behavior. In this instance, the self‐affirmation intervention was intended, in combination with techniques aimed to increase positive affect, such as thinking about small things that made them feel good, to overcome barriers to behavioral change rather than to reduce defensiveness to a message. Related to the point made earlier, the trials with different populations had somewhat different target behaviors, which also yielded different effects: in the angioplasty patients the intervention resulted in increased physical activity relative to the control group; in the hypertensive patients the intervention resulted in increased medication adherence relative to the control group; and in the asthmatic patients the intervention and control groups were no different in terms of physical activity. It is also important to note that, where the interventions were effective, the extent to which the effects can be attributed to the self‐affirmation manipulation versus the components intended to create positive affect is not clear. In sum, the emerging evidence regarding the combination of self‐affirmation with other intervention techniques is mixed, such that the research on even the most commonly studied added technique, implementation intentions, shows both increases and decreases in effectiveness. However, the literature is diverse, with different target behaviors, study populations, follow‐up time points, and even types of self‐affirmation inductions, which may contribute to this lack of uniformity. Considering aspects of the message itself (e.g., framing) may be especially worthwhile, as these vary in the literature also, and, as most messages are framed either positively or negatively, giving explicit attention to this is worthwhile. Finally, some authors have pointed out that self‐affirmation may play only a small role in the larger behavior change process, among other techniques, such as self‐monitoring and identifying barriers, so continuing to consider its role in combined interventions is useful, despite the mixed evidence that has emerged thus far (van Dijk & Dijkstra, 2014). Having highlighted some of the recent developments among studies of self‐affirmation and health‐related outcomes, we will now turn to discussing some of the major research questions that remain for future research.

How Does Self‐Affirmation Lead to Health Behavior Change? Although numerous studies have indicated that self‐affirmation increases acceptance of health information and facilitates behavior change, surprisingly little is known about how it impacts health outcomes. To date, researchers have focused primarily on people’s self‐reported responses to health information as a source of potential mediators. For example, one possibility is that self‐affirmation changes people’s attitudes toward health information (e.g., by increasing perceptions of risk), which, in turn, leads to changes in health behavior. Although there is some preliminary evidence that an increase in perceived vulnerability mediates the effect of self‐affirmation on behavioral intentions (Klein et al., 2011), other studies have found

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that neither attitudes toward the health behavior nor perceived threat (Armitage et al., 2014) mediates the effect of self‐affirmation on health behavior. Another possibility is that self‐affirmation engenders a change in intentions, which, in turn, leads to a change in behavior. Supporting this prediction, one study found that among people who are at a higher risk for developing type 2 diabetes, intentions to take an online diabetes risk test mediated the effect of self‐affirmation on risk test participation (van Koningsbruggen & Das, 2009). Other studies of self‐affirmation, however, have failed to find a mediating effect of intentions on behavior change (Armitage et al., 2014). Further casting doubt on the viability of behavioral intentions as a mediator, several studies have found that self‐affirmation does not always elicit a positive change in behavioral intentions (Harris et  al., 2014; Reed & Aspinwall, 1998). Among studies of self‐affirmation measuring both health intentions and behavior, one meta‐analytic review found that intention effect sizes did not predict behavior effect sizes (Sweeney & Moyer, 2015), suggesting that a change in behavioral intentions does not translate to a comparable change in behavior. Taken together, limited research supports a causal intention–behavior relation among self‐affirmation studies. Whereas much of the extant research has focused on the role of single potential mediating variables, future research may examine whether self‐affirmation influences health outcomes through multiple variables. Few self‐affirmation studies have adopted a path analysis approach to examine whether a set of variables helps to explain the impact of self‐affirmation on health behavior. One exception is by Armitage, Harris, Hepton, and Napper (2008) who found that health message acceptance mediated the relation between self‐affirmation and intentions and intentions mediated the relation between message acceptance and behavior. Self‐affirmation has been applied to health behaviors that vary across a number of dimensions, including the type of behavior (i.e., health promoting vs. health damaging) and the temporal impact of the behavior (i.e., immediate vs. distant consequences). Given this variability, in addition to the inherent complexities associated with long‐term health behavior change, future research may consider adopting an analytic approach that examines a set of variables and takes into account variables specific to the targeted health behavior. Another avenue for future research may involve testing mediating variables that extend beyond people’s immediate self‐reported responses to health information. Several studies have suggested that self‐affirmation facilitates a broader perspective from which to view information (Critcher & Dunning, 2015; Sherman et  al., 2013). For example, when people are self‐ affirmed, they are more likely to think about actions in terms of their abstract, superordinate aspects (e.g., why to improve one’s health), rather than in terms of their concrete, subordinate aspects (e.g., how to improve one’s health; Sherman et al., 2013). In the context of health behaviors, a broader perspective may be helpful for several reasons. For example, threatening information may be experienced as less aversive when viewed from a distance (Sherman, 2013). Offering preliminary support for this prediction, one study tested whether thinking in broad (vs. narrow) terms would influence tanners’ receptivity toward health information on the risks of tanning (Belding, Naufel, & Fujita, 2015). Tanners led to think that broad, superordinate categories were more motivated to reduce their risk of skin cancer than tanners led to think in narrow, subordinate categories. These authors suggest that thinking in broad terms promotes long‐term self‐change motivation, rather than short‐term self‐protection motivation. Relatedly, when a threat or temptation does not loom quite so largely, a broadened perspective may make it easier for people to exert self‐control. Several studies have indicated that people are better at practicing self‐control when led to think in broad (vs. narrow) terms, as evidenced, for example, by differences in preferences for healthy versus unhealthy foods (Fujita & Han, 2009).

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Another possibility is that a broadened perspective helps people to connect their current actions to their long‐term aspirations. Supporting this prediction, past research has found that construing information in broad (vs. narrow) terms facilitates attention toward one’s long‐ term goals when faced with reminders of goal‐related temptations (Fujita & Sasota, 2011). Such research suggests, for example, that a dieter who adopts a broader perspective may find it easier to bring to mind his or her dieting goal when faced with tempting food. Furthermore, other research has found that a broad (vs. narrow) perspective helps people to recognize the commonalities across their various life goals (Clark & Freitas, 2013). As a result, a broadened perspective may make it easier to connect a targeted health behavior (e.g., eating more fruits and vegetables) with one’s long‐term goals (e.g., living a long and happy life). Having established that self‐affirmation has the potential to be a useful tool for promoting health behavior change, an important next step for researchers is to develop an empirically supported mechanistic account of self‐affirmation. Given that people’s immediate self‐reported responses to health information do not appear to mediate the effect of self‐affirmation, future research may consider other ways in which self‐affirmation impacts the self and the consequences this has for evaluation, motivation, and goal‐directed action. Identifying the mechanisms that underlie self‐affirmation effects may help to further refine self‐affirmation theory as a whole and also offers important practical benefits, such as increasing understanding of the specific conditions under which self‐affirmation is most effective.

What Is the Trajectory of Self‐Affirmation? Much of the research on self‐affirmation and health has focused on the immediate changes that occur after self‐affirming, such as changes in message acceptance. However, measures of people’s deliberate responses to health information after self‐affirming have yielded some inconsistent findings. As noted previously, for example, self‐affirmation does not always lead to a positive change in behavioral intentions (e.g., Harris et al., 2014; Jessop et al., 2014). Similarly, other studies have failed to find a significant effect of self‐affirmation on outcomes such as perceived behavioral control, perceived threat, message derogation, or self‐efficacy (Armitage et  al., 2008, 2014; Jessop et  al., 2014). Although most studies have assessed whether self‐affirmation elicits immediate changes in health‐related cognitions, one recent study measured intentions and attitudes immediately after participants read a health message and again during a 1‐week follow‐up. There were no immediate differences in intentions and attitudes between the self‐affirmation and control group; however, the self‐affirmation group did express greater intentions and attitudes 1 week later. Such findings may lead one to wonder whether self‐affirmation elicits an immediate change or is the effect gradual? In a meta‐analytic review Epton et al. (2015) found that self‐affirmation exerts a smaller effect on outcomes that are measured immediately after encountering health information (i.e., message acceptance) than measures that typically involve some delay (i.e., behavior). Importantly, however, the time of measurement of a health behavior (e.g., days vs. weeks) has not been found to moderate the effect of self‐affirmation on behavior (Epton et al., 2015; Sweeney & Moyer, 2015). Furthermore, as reviewed previously, a recent study using fMRI found that when people were self‐affirmed, they engaged in greater self‐ related processing, as reflected through activity in the VMPFC, while reading health information (Falk et  al., 2015). Additionally, neural activity during message processing predicted changes in health behavior that were distinct from changes predicted from participants’ self‐reports of behavioral intentions and attitudes. These findings suggest that self‐affirmation

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enacts immediate effects on the self (as reflected through participant’s neural activity) that relate to subsequent behavior change. Taken together, self‐affirmation appears to exert some immediate influences, as evidenced, for example, by studies showing that self‐affirmation produces an immediate change in physiological responses to threat (e.g., Creswell et al., 2005) and in neural activity (e.g., Falk et al., 2015). However, the observed heterogeneity in people’s self‐reported responses suggests that self‐affirmation may not elicit a change in health‐related cognitions that can be reliably captured at a conscious level. That is, some of the influence of self‐affirmation may occur outside of people’s conscious awareness. Increased attention is being given to understanding how health‐related decisions are shaped by both conscious and unconscious processes (Sheeran, Gollwitzer, & Bargh, 2013). To date, relatively few studies have examined the implicit effects of self‐affirmation in relation to health outcomes. One exception is Klein and Harris (2009) who used a dot probe task to examine whether self‐affirmation enhances people’s tendency to direct their attention toward threatening health information. After completing a self‐affirmation or control task and reading a ­message about the risks of alcohol consumption, female participants completed a task in which a neutral and a threatening word from the health message were presented simultaneously and followed by a dot. Participants were asked to quickly identify whether the dot appeared on the left or right side of the screen. Self‐affirmed participants were faster to identify the location of the dot when it appeared after a threat‐related word, suggesting that self‐affirmed participants displayed a stronger attentional bias for threat‐related words than non‐affirmed participants. Whereas other studies have used self‐report measures to assess differences in perceived threat, this methodology allowed for a novel test of the extent to which self‐affirmation elicits differences in implicit processing of health information. Another exception is van Koningsbruggen, Das, and Roskos‐Ewoldsen (2009) who used a lexical decision‐making task to examine whether self‐affirmation increases responsiveness toward health information at an implicit level. After completing a self‐affirmation or control task and reading a message about the risks of caffeine, coffee drinkers and non‐coffee drinkers completed a response time task that required them to distinguish between non‐words, neutral words, and threat‐related words (e.g., “heart disease”). Among coffee drinkers, those who self‐affirmed were faster at recognizing threat‐related words than non‐affirmed individuals, suggesting self‐affirmation increased accessibility of threat‐related cognitions among people for whom the health message was most relevant. These two studies provide initial support for the potential usefulness of implicit measures in studies of self‐affirmation and health. In light of recent research suggesting that people may not always be able to reliably report on conscious changes that arise after self‐affirmation, future research may consider adopting a framework that encompasses both conscious and unconscious processes. Increasingly health behavior change models are adopting a dual‐­ systems approach that incorporates both reflective and automatic health‐related processes (Sheeran et al., 2013). Whereas much of the extant research has focused on conscious changes arising from self‐affirmation, future research is needed to further elucidate the nonconscious influences of self‐affirmation.

Conclusions Research on self‐affirmation and health outcomes has grown extensively in recent decades. Building upon studies that highlighted the potential of self‐affirmation as a tool for reducing

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defensive processing, numerous studies have been devoted to understanding the different types of health‐related cognitions that are influenced by self‐affirmation and any implication this may have for health behavior change. In this chapter, we highlighted three recent developments in this area of research including the adoption of a wide array of methodological approaches, emphasis on identifying moderating variables, and efforts to combine self‐affirmation with other behavior change techniques. By drawing attention to the need for developing a mechanistic account of self‐affirmation and the need to further our understanding the trajectory of self‐affirmation, we hope this chapter will encourage future investigations of self‐­ affirmation as a tool for health behavior change.

Author Biographies Allison M. Sweeney is a postdoctoral researcher at the University of South Carolina. Integrating theories and methods across health, social, and cognitive psychology, her research focuses on developing theory‐based interventions for promoting health behavior change. Anne Moyer is an associate professor of social and health psychology at Stony Brook University. Her research focuses on psychosocial factors surrounding cancer and cancer risk, medical decision making, research methodology and research synthesis, and the psychological aspects of human research participation.

References Armitage, C. J., Harris, P. R., & Arden, M. A. (2011). Evidence that self‐affirmation reduces alcohol consumption: Randomized exploratory trail with a new, brief, means of self‐affirming. Health Psychology, 30, 633–641. Armitage, C. J., Harris, P. R., Hepton, G., & Napper, L. (2008). Self‐affirmation increases acceptance of health‐risk information among United Kingdom adult smokers with low socioeconomic status. Psychology of Addictive Behaviors, 22, 88–95. Armitage, C. J., Rowe, R., Arden, M. A., & Harris, P. R. (2014). A brief psychological intervention that reduces adolescent alcohol consumption. Journal of Consulting and Clinical Psychology, 82, 546–550. Belding, J. N., Naufel, K. Z., & Fujita, K. (2015). Using high‐level construal and perceptions of changeability to promote self‐change over self‐protection motives in response to negative feedback. Personality and Social Psychology Bulletin, 41, 822–838. Clark, S., & Freitas, A. (2013). Construing action abstractly and perceiving consonance among goal ­pursuits: Implications for activity substitutability and the accessibility of activity‐goal links. Motivation and Emotion, 37, 537–549. Creswell, J. D., Welch, W. T., Taylor, S. E., Sherman, D. K., Gruenewald, T. L., & Mann, T. (2005). Affirmation of personal values buffers neuroendocrine and psychological stress responses. Psychological Science, 16, 846–851. Critcher, C. R., & Dunning, D. (2015). Self‐affirmations provide a broader perspective on self‐threat. Personality and Social Psychology Bulletin, 41, 3–18. Ditto, P., & Lopez, D. (1992). Motivated skepticism: Use of differential decision criteria for preferred and nonpreferred conclusions. Journal of Personality and Social Psychology, 63, 568–584. Düring, C., & Jessop, D. C. (2015). The moderating impact of self‐esteem on self‐affirmation effects. British Journal of Health Psychology, 20, 274–289. Epton, T., & Harris, P. (2008). Self‐affirmation promotes health behavior change. Health Psychology, 27, 746–752.

Self‐Affirmation and Responses to Health Messages

55

Epton, T., Harris, P. R., Kane, R., van Koningsbruggen, G. M., & Sheeran, P. (2015). The impact of self‐affirmation on health‐behavior change: A meta‐analysis. Health Psychology, 34, 187–196. Falk, E. B., O’Donnell, M. B., Cascio, C. N., Tinney, F., Kang, Y., Lieberman, M. D., … Strecher, V. J. (2015). Self‐affirmation alters the brain’s response to health messages and subsequent behavior change. Proceedings of the National Academy of Sciences, 112, 1977–1982. Ferrer, R. A., Taber, J. M., Klein, W. M. P., Harris, P. R., Lewis, K. L., & Biesecker, L. G. (2015). The role of current affect, anticipated affect and spontaneous self‐affirmation in decisions to receive self‐threatening information. Cognition and Emotion, 29(8), 1456–1465. Fujita, K., & Han, H. A. (2009). Moving beyond deliberative control of impulses the effect of construal levels on evaluative associations in self‐control conflicts. Psychological Science, 20, 799–804. Fujita, K., & Sasota, J. A. (2011). The effects of construal levels on asymmetric temptation‐goal cognitive associations. Social Cognition, 29, 125–146. Gallagher, K. M., & Updegraff, J. A. (2012). Health message framing effects on attitudes, intentions and behavior: A meta‐analytic review. Annals of Behavioral Medicine, 43, 101–116. Giner‐Sorolila, R., & Chaiken, S. (1997). Selective use of heuristic and systematic processing under defense motivation. Personality and Social Psychology Bulletin, 23, 84–97. Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54, 493–503. Harris, P. R., Brearley, I., Sheeran, P., Barker, M., Klein, W. M. P., Creswell, J. D., … Bond, R. (2014). Combining self‐affirmation with implementation intentions to promote fruit and vegetable consumption. Health Psychology, 33, 729–736. Jessop, D. C., Sparks, P., Buckland, N., Harris, P. R., & Churchill, S. (2014). Combining self‐affirmation and implementation intentions: Evidence of detrimental effects on behavioral outcomes. Annals of Behavioral Medicine, 47, 137–147. Klein, W. M. P., Hamilton, J. G., Harris, P. R., & Han, P. K. (2015). Health messaging to individuals who perceive ambiguity in health communications: The promise of self‐affirmation. Journal of Health Communication, 20, 566–572. Klein, W. M. P., & Harris, P. R. (2009). Self‐affirmation enhances attentional bias toward threatening components of a persuasive message. Psychological Science, 20, 1463–1467. Klein, W. M. P., Harris, P. R., Ferrer, R. A., & Zajac, L. E. (2011). Feelings of vulnerability in response to threatening messages: Effects of self‐affirmation. Journal of Experimental Social Psychology, 47, 1237–1242. Legault, L., Al‐Khindi, T., & Inzlicht, M. (2012). Preserving integrity in the face of performance threat self‐affirmation enhances neurophysiological responsiveness to errors. Psychological Science, 23, 1455–1460. Liberman, A., & Chaiken, S. (1992). Defensive processing of personally relevant health messages. Personality and Social Psychology Bulletin, 18, 669–679. Mancuso, C. A., Choi, T. N., Westermann, H., Wenderoth, S., Hollenberg, J. P., Wells, M. T., … Charlson, M. E. (2012). Increasing physical activity in patients with asthma through positive affect and self‐affirmation: A randomized trial. Archives of Internal Medicine, 173, 337–343. Mays, D., & Zhao, X. (2016). The influence of framed messages and self‐affirmation on indoor tanning behavioral intentions in 18‐ to 30‐year‐old women. Health Psychology, 35(2), 123–130. Niederdeppe, J., & Gurmankin Levy, A. (2007). Fatalistic beliefs about cancer prevention and three ­prevention behaviors. Cancer Epidemiology, Biomarkers & Prevention, 16, 998–1003. Ogedegbe, G. O., Boutin‐Foster, C., Wells, M. T., Allegrante, J. P., Isen, A. M., Jobe, J. B., & Charlson, M. E. (2012). A randomized controlled trial of positive‐affect intervention and medication adherence in hypertensive African Americans. Archives of Internal Medicine, 172, 322–326. Peterson, J. C., Charlson, M. E., Hoffman, Z., Wells, M. T., Wong, S. C., Hollenberg, J. P., … Allegrante, J. P. (2012). A randomized controlled trial of positive‐affect induction to promote physical activity after percutaneous coronary intervention. Archives of Internal Medicine, 172, 329–336. Pietersma, S., & Dijkstra, A. (2012). Cognitive self‐affirmation inclination: An individual difference in dealing with self‐threats. British Journal of Social Psychology, 51, 33–51.

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Reed, M. B., & Aspinwall, L. G. (1998). Self‐affirmation reduces biased processing of health‐risk information. Motivation and Emotion, 22, 99–132. Schmeichel, B. J., & Tang, D. (2015). Individual differences in executive functioning and their relationship to emotional processes and responses. Current Directions in Psychological Science, 24, 93–98. Sheeran, P., Gollwitzer, P. M., & Bargh, J. A. (2013). Nonconscious processes and health. Health Psychology, 32, 460–473. Sherman, D., & Cohen, G. (2006). The psychology of self‐defense: Self‐affirmation theory. In M. P. Zanna (Ed.), Advances in experimental social psychology (pp. 183–242). San Diego, CA: Academic Press. Sherman, D., Nelson, L., & Steele, C. (2000). Do messages about health risks threaten the self? Increasing the acceptance of threatening health messages via self‐affirmation. Personality and Social Psychology Bulletin, 26, 1046–1058. Sherman, D. K. (2013). Self‐affirmation: Understanding the effects. Social and Personality Psychology Compass, 11, 834–845. Sherman, D. K., Hartson, K. A., Binning, K. R., Purdie‐Vaughns, V., Garcia, J., Taborsky‐Barba, S., … Cohen, G. L. (2013). Deflecting the trajectory and changing the narrative: How self‐affirmation affects academic performance and motivation under identity threat. Journal of Personality and Social Psychology, 104, 591–618. Steele, C. M. (1988). The psychology of self‐affirmation: Sustaining the integrity of the self. In L. Berkowitz (Ed.), Advances in experimental social psychology (pp. 261–302). San Diego, CA: Academic Press. Sweeney, A. M., & Moyer, A. (2015). Self‐affirmation and responses to health messages: A meta‐analysis on intentions and behavior. Health Psychology, 34, 149–159. Taber, J. M., Klein, W. M. P., Ferrer, R. A., Lewis, K. L., Harris, P. R., Sheppard, J. A., & Biesecker, L. G. (2015). Information avoidance tendencies, threat management resources, and interesting in genetic sequencing feedback. Annals of Behavioral Medicine, 49(4), 616–621. van Dijk, S., & Dijkstra, A. (2014). Adding power or losing strength? Searching for strategies on how self‐affirmation may grow stronger: A comment on Jessop et al. Annals of Behavioral Medicine, 47, 131–132. van Koningsbruggen, G., & Das, E. (2009). Don’t derogate the message! Self‐affirmation promotes online type 2 diabetes risk test taking. Psychology & Health, 24, 635–649. van Koningsbruggen, G., Das, E., & Roskos‐Ewoldsen, D. (2009). How self‐affirmation reduces defensive processing of threatening health information: Evidence at the implicit level. Health Psychology, 28, 563–568. Wileman, V., Farrington, K., Chilcot, J., Norton, S., Wellsted, D. M., Almond, M. K., … Armitage, C. J. (2014). Evidence that self‐affirmation improves phosphate control in hemodialysis patients: A pilot cluster randomized controlled trial. Annals of Behavioral Medicine, 48, 275–281.

Suggested Reading Cohen, G. L., & Sherman, D. K. (2014). The psychology of change: Self‐affirmation and social psychological intervention. Annual Review of Psychology, 65, 333–371. Harris, P. R., & Epton, T. (2009). The impact of self‐affirmation on health cognition, health behaviour and other health‐related responses: A narrative review. Social and Personality Psychology Compass, 3, 962–978.

Stigma of Disease and Its Impact on Health Lindsay Sheehan and Patrick Corrigan Department of Psychology, Illinois Institute of Technology College of Science, Chicago, IL, USA

For people who experience chronic diseases and health conditions, stigma threatens to ­compound the physical and mental manifestations of illness, leading to unfavorable health and life outcomes. Particularly stigmatizing conditions such as HIV/AIDS (Farber, Shahane, Brown, & Campos, 2014), lung cancer (Chambers et  al., 2015), irritable bowel syndrome (IBS; Taft, Ballou, & Keefer, 2013), drug addiction (Barry, McGinty, Pescosolido, & Goldman, 2014), and mental illness (Parcesepe & Cabassa, 2013) have been targets of research on the stigma of disease. This paper defines stigma types, explains various mechanisms by which stigma can impact health, and explores strategies for changing stigma. Erving Goffman (1963) first described stigma as a discrediting mark that leads the stigmatized person to feel shame and hide the condition from others. More recently, four necessary components of stigma are described by Link and Phelan (2001). First, a stigmatized group or individual is recognized and labeled as different from others. Next, society perceives these ­differences as negative. Third, an “us” versus “them” (the stigmatized) mentality is established. Finally, the stigmatized person or group is subject to discrimination and loss of social power. Stigma may include stereotypes (thoughts), prejudice (feelings), and discrimination (unfair behaviors) toward individuals or groups from the stigmatizing condition. For example, a person with HIV may be stereotyped as careless, irresponsible, or morally inferior; may experience prejudice in the form of being feared by others; and may be discriminated against when family members avoid social contact. Stereotypes that comprise stigma are culturally created and disease specific, such that health conditions are connected with unique stereotypes that elicit specific prejudice and discrimination. The stereotype of incompetence in schizophrenia leads to discriminatory behavior such as employers not hiring them. When people with lung cancer are viewed as culpable in causing the illness through smoking, others will be less likely to provide help and support.

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Types of Stigma Several types of stigma have the potential to impact health and are illustrated in Table 1. Thus far, we have described public stigma, the stigma that is perpetrated by members of the public toward the stigmatized individual. This stigma can impact the individual through direct experience (“experienced stigma”) in which the person is treated in unfair ways such as being denied housing (Thornicroft et  al., 2009). Public stigma also impacts individuals through mere knowledge or fear of the stigma that could be perpetrated against them (“anticipated stigma”) (Thornicroft et al., 2009). In anticipated stigma, a person who experiences addiction worries how others will treat him or her if they know about the addiction. Stigmatized individuals may also self‐stigmatize, whereby they internalize the public perceptions and believe that they really are as incompetent as society believes them to be (Corrigan, Watson, & Barr, 2006). The stigmatized individual may cognitively and emotionally give in to the “why try” effect, thinking that efforts toward improving health outcomes are futile (“I’m so stupid, why should I even bother taking my medications”). Not surprisingly, internalized stigma is linked to depression, low self‐esteem, and reduced self‐efficacy (Corrigan, Larson, & Rüsch, 2009). Two other types of stigma are important to examine in the context of health psychology: associative stigma and structural stigma. If Fred’s partner has HIV, Fred’s employer might assume he has HIV as well and limit his career advancement opportunities. Sarah’s mother might be judged as incompetent when Sarah is diagnosed with ADHD. In associative stigma, family members, friends, or other acquaintances are tarnished by stigma through their connections to the stigmatized individual. In response to associative stigma, family or friends may ­distance themselves from their loved one to avoid actual or perceived negative societal reactions. Associative stigma can also apply to health workers who treat stigmatized conditions (Verhaeghe & Bracke, 2012). Mental health workers who provide services to “those crazy people” e­ xperience less respect and social status than those working in other helping professions. Table 1  Stigma types, definitions, and examples. Type of stigma

Definition

Public stigma

Unfair beliefs, attitudes, or behaviors directed at individuals or groups

Example

Jasper finds out his neighbor Bob has schizophrenia. He feels scared and starts avoiding Bob Experienced stigma Stigma experienced by the individual, An employer refuses to hire a person usually through discrimination with HIV Anticipated (“felt”) Stigma felt when stigmatized person Renee is worried about what others stigma fears negative reactions from others will think if she reveals her addiction Internalized stigma/ Stigma that occurs when the stigmatized George feels he is just a worthless self‐stigma person agrees with and applies the overweight person who does not public stigma to themselves deserve a girlfriend Associative stigma Stigma experienced by associates of the Ariel’s sister is shunned by neighbors stigmatized group/individual (family, friends, acquaintances, service providers, etc.) Structural stigma Stigma perpetrated through social Funding for substance abuse systems or institutions, either treatment is substantially less than for other diseases intentionally or unintentionally

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In some cases, whole systems fail a stigmatized group. Institutional policies that restrict opportunities of the stigmatized group result in structural stigma (Angermeyer, Matschinger, Link, & Schomerus, 2014). Structural stigma may be intentional, such as laws that limit ­parental rights of people with mental illness, or unintentional, such as inequalities in research funding for stigmatized conditions (Corrigan et al., 2005). In both instances, stereotypes and prejudices about the group—beliefs that people with mental illness are, for example, dangerous or incompetent—lead to discriminatory policies resulting in exclusion, restriction, or denial of rightful opportunities.

The Impact of Stigma Disease stigma works through numerous mechanisms and has serious consequences for health. First, stigma has direct effects on psychological health: the experience of stigma is stressful. In a meta‐analysis examining the connection between perceptions of discrimination and psychological well‐being, average effect sizes in the small to medium range (overall r = −.23) were found with self‐esteem, depression, anxiety, psychological distress, and life satisfaction (Schmitt, Branscombe, Postmes, & Garcia, 2014). Although chronic health conditions elicit stress and depression on their own, stigma‐related distress and depression have been uncovered in people with HIV (Earnshaw, Smith, Cunningham, & Copenhaver, 2015), lung cancer (Chambers et al., 2015), and IBS (Taft et al., 2013). Second, stigma interferes with care seeking. Affected individuals may engage in a process of label avoidance, whereby they forgo, delay, or minimally engage in treatment in order to elude the label of the condition (“I do not want to be seen walking into an HIV clinic!”) (Corrigan, Druss, & Perlick, 2014). Although there are many reasons that people might not seek treatment, stigma is certainly a factor in preventing 40% of people with serious mental illness from engaging in care (Substance Abuse and Mental Health Services Administration [SAMHSA], 2012) and the 12% of people living with undiagnosed HIV (Valdiserri, 2002). Individuals might avoid entering treatment because of the stigma of diagnosis and in later stages of treatment might avoid visiting healthcare providers or following treatment regimen to prevent being seen by others. Associative stigma experienced by family members or close friends can make social support network unsupportive of treatment efforts and lead to early treatment dropout (Corrigan, Druss, et al., 2014). In addition, health providers can themselves engage in label avoidance. Medical residents, for example, have stigma‐related concerns about addictions, mental health diagnoses, and STDs, such that 18% of residents delayed or avoided ­treatment for themselves (Dunn, Hammond, & Roberts, 2009). Once sick individuals decide to seek treatment, quality of that treatment is also impacted by stigma (Harangozo et al., 2014). Stigma perpetrated by health providers is especially harmful when it creates a non‐nurturing, coercive, or paternalistic environment that drives patients from care or restricts choices for treatment. Structural stigma leads to clinics that fail to train providers in the respectful and culturally competent practices, poor availability of services, lack of access to health insurance, or other hurdles in obtaining care. Taken together, these factors greatly influence decisions of individuals to stay in treatment. Fourth, stigma influences illness, self‐management, and treatment adherence. The distress associated with stigma can make comprehension of medical instructions and follow‐up with healthcare recommendations more challenging. People who are preoccupied with stigma stress will have less cognitive resources available for adhering to treatment regimens (Major, Hunger, Bunyan, & Miller, 2014). When exposed to stigma, individuals have more difficulty

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self‐regulating, including reduced information processing, sustained attention, and persistence on task (Richman & Lattanner, 2014). Also, label avoidance restricts access to self‐­management resources, and a desire to hide illness status can interfere with treatment. Fearing reactions from supervisors or coworkers, a person with HIV might avoid bringing medications to work. A person with mental illness or addiction will not be comfortable requesting time off work for treatment appointments when such requests would necessitate disclosure. Internalized stigma is another barrier to treatment self‐management (Livingston & Boyd, 2010). Depression and lack of self‐efficacy occur when a person self‐stigmatizes, leading to lack of recovery efforts (“Why even go see a counselor? I’m just a worthless addict who will never recover”). In fact, adults with chronic illness who self‐stigmatize or who have experienced stigma in the past from health workers expect future stigma‐laden interactions, avoid care, and have a lower quality of life (Earnshaw & Quinn, 2012). Stigma also harms people through the breakdown of social relationships. Acts of discrimination typically involve social avoidance, exclusions, or microaggressions that serve to damage relationships. Landlords are hesitant to rent to people with schizophrenia, friends avoid inviting the person with IBS out to dinner, or acquaintances use underhanded remarks to people with addiction (“you still cannot kick the habit, huh?”). These interactions chip away at ­personal relationships and social networks. The absence of social support during an already difficult period of illness causes further stress and limits social resources needed to engage in treatment (e.g., help driving to doctor appointment). Internalized or perceived stigma results in the person withdrawing from others due to feelings of worthlessness (“Now that I have lung cancer, I have only myself to blame, and no one should have to help me”). Finally, structural stigma has a broad influence on health outcomes. Disparities in research funding, programmatic funding, and insurance coverage for stigmatizing diseases such as mental illness and addiction create fewer quality treatment options that are less accessible for those that would use the services (Angermeyer et al., 2014). People with chronic illness and disability are too often institutionalized despite their capacity for independent living. Given adequate community supports and financial assistance, some individuals in skilled care facilities could live in more independent settings; however, a dearth of supportive services and transition services have continued the segregation of people with disabilities in nursing homes. Legislation and court decisions such as the Americans with Disabilities Act (1990) and Olmstead v. L.C. (1999) challenge structural stigma by mandating civil rights for people with disabilities to live and work in the community. Likewise, the Mental Health Parity Act (2008) and the Affordable Care Act have expanded insurance coverage for mental illness. However, insurance coverage concerns continue to plague people with mental illness and especially those seeking addiction treatment (Jones, Campopiano, Baldwin, & McCance‐ Katz, 2015).

Moderators of Stigma Several factors moderate disease stigma, including perceived responsibility for illness, fear, and familiarity. Stigma is greater when the person is viewed as having responsibility for the onset of illness (“onset responsibility”) or as having control over improving or ending the illness (“­offset responsibility”) (Weiner, 1995). People with HIV, addiction, and lung cancer are viewed as highly responsible for the onset of the illness, whereas those with addiction are seen as responsible for both onset and offset (Corrigan et al., 2002). Obesity, while not a

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disease in itself, is a highly stigmatizing condition with high perceived responsibility both for onset and offset. Conditions such as heart disease and diabetes that have been connected with obesity and are seen as preventable also have high perceived responsibility on the part of the affected person. Greater stigma results when the public views the particular groups as dangerous. For example, people with mental illness are seen as dangerous and unpredictable, resulting in public fears and discrimination (Janulis, Ferrari, & Fowler, 2013). Fear of contagion is also a factor in stigma related to illness such as in H1N1 influenza and SARS (Williams, Gonzalez‐Medina, & Le, 2011), potentially leading to ostracism and reduced opportunities for treatment. Familiarity may be inversely related to stigma (Broussard, Goulding, Talley, & Compton, 2012). Those who have personally experienced the disease, who have a family member with the disease, or who are otherwise familiar with the disease tend to perpetrate less stigma. When community members or family members have a personal connection, they can see the impacted individual as a person, rather than as a member of the stigmatized group. Stigma is especially harmful however when it is perpetrated by family and close friends, as it reduces the person’s social and economic resources.

Combating Stigma Research linking familiarity with stigma is the basis for anti‐stigma interventions that use education about disease and contact with impacted individuals to change attitudes and ­ ­behaviors toward the stigmatized group. Educational interventions provide information that counteracts stigmatizing attitudes (e.g., Power Point presentation outlining myths about mental illness), whereas a contact intervention involves an interaction between a person with the illness and those in the intervention condition (e.g., person with HIV gives presentation about personal experience with HIV stigma). In adults, anti‐stigma programs that promote meaningful contact between people impacted by the disease and members of the public seem most powerful. A meta‐analysis examining stigma change interventions for people with mental illness found change effect sizes for changes in attitudes and intended behavior ranging from .1 to .3 (Corrigan, Morris, Michaels, Rafacz, & Rüsch, 2012). However, it is important to note that few studies have been able to capture actual changes in behavior that result from anti‐stigma interventions. Not all approaches are effective. In the mental illness literature, biogenetic explanations for illness were introduced to reduce stigma reducing the perceived responsibility of the person (“it’s not the person with schizophrenia’s fault that he’s ill, it’s a brain disorder”). However, this approach risks highlighting the differences between the stigmatized individual and society while downplaying possibilities for recovery. A meta‐analysis found that biogenetic explanations for mental illness were associated with less blame but that perceptions of dangerousness were greater and that the public desired more social distance from people with mental illness (Kvaale, Haslam, & Gottdiener, 2013). Anti‐stigma approaches that target health providers may improve health outcomes. These programs should ideally target all levels of health provider (physicians, nurses, dieticians, ­psychologists, case managers, etc.) and aim to reduce negative reactions as well as promote positive or affirming reactions toward people with a health condition (Sheehan, 2015). Whereas more traditional anti‐stigma messages have attempted to reduce negative attitudes, advocates have called for more affirming messages focused on recovery and empowerment of

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the stigmatized person (Corrigan, Powell, & Michaels, 2014). Research on mental illness stigma interventions aimed at health providers find that those using both education and ­contact are effective (Stubbs, 2014). Efforts to reduce internalized stigma (i.e., self‐stigma) have focused on helping the stigmatized individual identifying the impact stigma has on his/her life and identifying ways to change personal reactions toward stigma. Interventions to enhance resistance to internalized stigma include peer support groups, peer education, and cognitive behavioral type interventions (Taft et al., 2013). Efforts to explicitly address internalized stigma have only recently begun to be tested, showing some potential for reducing the impact of stigma (Conner, McKinnon, Ward, Reynolds, & Brown, 2015). In addition to stigma of the primary health condition, service providers must recognize and address multiple levels and types of stigma. Racial, ethnic, or sexual minority stigma can interact with and compound stigma related to the disease condition. Thus, health providers working in an HIV clinic should be sensitive to both stigma related to HIV and stigma related to sexual orientation. Health providers should understand cultural differences in disease‐related stigma and the impact these have on minority patients. Latinos being treated for addiction have different cultural stigmas than do Blacks or Asians. An individualized approach that is sensitive to the complex needs of the person in treatment will help to better engage those of minority status. Also of concern are co‐occurring disease‐related stigmas such as those of mental illness and obesity. Obesity stigma affects many individuals within the patient population, and stigma perpetrated by health providers can be a barrier to physical healthcare (Teixeira & Budd, 2010). The stress associated with obesity‐related stigma is implicated in a cycle of increasing weight gain (Tomiyama, 2014), and internalized obesity stigma is recognized as an important concern (Ratcliffe & Ellison, 2015). Recognition of various types of stigma (public, internalized, associative, and structural) points healthcare providers to specific ­solutions for addressing each.

Conclusion Primary prevention, early intervention, and prevention of physical deterioration and impact are primary concerns of health psychology (Boyer, 2008). Stigma affects the individuals on all of these levels by increasing vulnerability to psychological problems, creating barriers to care seeking, reducing quality of care, reducing self‐management capabilities of the patient, and compromising social support systems. While interventions to combat stigma exist, these can be more thoroughly and systematically implemented and tested throughout the healthcare system.

Author Biographies Lindsay Sheehan is a senior research associate at the Illinois Institute of Technology where she conducts research on mental illness stigma, suicide stigma, and health disparities for individuals with serious mental illness. She is a managing editor of the American Psychological Association journal, Stigma and Health. Dr. Sheehan is also a licensed counselor and has ­experience working in community mental health. Patrick Corrigan is a distinguished professor of psychology at the Illinois Institute of Technology. Dr. Corrigan leads the National Consortium on Stigma and Empowerment

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(NCSE), which focuses on increasing self‐determination and empowerment for people with psychiatric disability. Dr. Corrigan has written more than 300 peer‐reviewed articles and is an editor of Stigma and Health. Corrigan has authored or edited 15 books, most recently, The Stigma of Disease and Disability.

References Angermeyer, M. C., Matschinger, H., Link, B. G., & Schomerus, G. (2014). Public attitudes regarding individual and structural discrimination: Two sides of the same coin? Social Science & Medicine, 103, 60–66. Barry, C. L., McGinty, E. E., Pescosolido, B. A., & Goldman, H. H. (2014). Stigma, discrimination, treatment effectiveness, and policy: Public views about drug addiction and mental illness. Psychiatric Services, 65(10), 1269–1272. Boyer, B. A. (2008). Theoretical models of health psychology and the model for integrating medicine and psychology. In M. I. Paharia & B. A. Boyer (Eds.), Comprehensive handbook of clinical health psychology (pp. 3–30). Hoboken, NJ: Wiley. Broussard, B., Goulding, S. M., Talley, C. L., & Compton, M. T. (2012). Social distance and stigma toward individuals with schizophrenia: Findings in an urban, African‐American community sample. Journal of Nervous and Mental Disease, 200(11), 935–940. Chambers, S. K., Baade, P., Youl, P., Aitken, J., Occhipinti, S., Vinod, S., … Zorbas, H. (2015). Psychological distress and quality of life in lung cancer: The role of health‐related stigma, illness appraisals and social constraints. Psycho‐Oncology, 24(11), 1569–1577. Conner, K. O., McKinnon, S. A., Ward, C. J., Reynolds, C. F., III, & Brown, C. (2015). Peer education as a strategy for reducing internalized stigma among depressed older adults. Psychiatric Rehabilitation Journal, 38(2), 186–193. Corrigan, P. W., Druss, B. G., & Perlick, D. A. (2014). The impact of mental illness stigma on seeking and participating in mental health care. Psychological Science in the Public Interest, 15(2), 37–70. Corrigan, P. W., Larson, J. E., & Rüsch, N. (2009). Self‐stigma and the “why try” effect: Impact on life goals and evidence‐based practices. World Psychiatry, 8(2), 75–81. Corrigan, P. W., Morris, S. B., Michaels, P. J., Rafacz, J. D., & Rüsch, N. (2012). Challenging the public stigma of mental illness: A meta‐analysis of outcome studies. Psychiatric Services, 63(10), 963–973. Corrigan, P. W., Powell, K. J., & Michaels, P. J. (2014). Brief battery for measurement of stigmatizing versus affirming attitudes about mental illness. Psychiatry Research, 215(2), 466–470. Corrigan, P. W., Rowan, D., Green, A., Lundin, R., River, P., Uphoff‐Wasowski, K., … Kubiak, M. A. (2002). Challenging two mental illness stigmas: Personal responsibility and dangerousness. Schizophrenia Bulletin, 28(2), 293. Corrigan, P. W., Watson, A. C., & Barr, L. (2006). Understanding the self‐stigma of mental illness. Journal of Social and Clinical Psychology, 25(8), 875–884. Corrigan, P. W., Watson, A. C., Heyrman, M. L., Warpinski, A., Gracia, G., Slopen, N., & Hall, L. L. (2005). Structural stigma in state legislation. Psychiatric Services, 56(5), 557–563. Dunn, L. B., Hammond, K. A. G., & Roberts, L. W. (2009). Delaying care, avoiding stigma: Residents’ attitudes toward obtaining personal health care. Academic Medicine, 84(2), 242–250. Earnshaw, V. A., & Quinn, D. M. (2012). The impact of stigma in healthcare on people living with chronic illnesses. Journal of Health Psychology, 17(2), 157–168. Earnshaw, V. A., Smith, L. R., Cunningham, C. O., & Copenhaver, M. M. (2015). Intersectionality of internalized HIV stigma and internalized substance use stigma: Implications for depressive symptoms. Journal of Health Psychology, 20(8), 1083–1089. Farber, E. W., Shahane, A. A., Brown, J. L., & Campos, P. E. (2014). Perceived stigma reductions ­following participation in mental health services integrated within community‐based HIV primary care. AIDS Care, 26(6), 750–753.

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Goffman, E. (1963). Stigma: Notes on the management of spoiled identity. Englewood Cliffs, NJ: Prentice‐Hall. Harangozo, J., Reneses, B., Brohan, E., Sebes, J., Csukly, G., López‐Ibor, J. J., … Thornicroft, G. (2014). Stigma and discrimination against people with schizophrenia related to medical services. International Journal of Social Psychiatry, 60(4), 359–366. Janulis, P., Ferrari, J. R., & Fowler, P. (2013). Understanding public stigma toward substance dependence. Journal of Applied Social Psychology, 43(5), 1065–1072. Jones, C. M., Campopiano, M., Baldwin, G., & McCance‐Katz, E. (2015). National and state treatment need and capacity for opioid agonist medication‐assisted treatment. American Journal of Public Health, 105(8), e55–e63. Kvaale, E. P., Haslam, N., & Gottdiener, W. H. (2013). The ‘side effects’ of medicalization: A meta‐ analytic review of how biogenetic explanations affect stigma. Clinical Psychology Review, 33(6), 782–794. Link, B. G., & Phelan, J. C. (2001). Conceptualizing stigma. Annual Review of Sociology, 27, 363–385. Livingston, J. D., & Boyd, J. E. (2010). Correlates and consequences of internalized stigma for people living with mental illness: A systematic review and meta‐analysis. Social Science & Medicine, 71(12), 2150–2161. Major, B., Hunger, J. M., Bunyan, D. P., & Miller, C. T. (2014). The ironic effects of weight stigma. Journal of Experimental Social Psychology, 51, 74–80. Parcesepe, A. M., & Cabassa, L. J. (2013). Public stigma of mental illness in the United States: A  systematic literature review. Administration and Policy in Mental Health and Mental Health Services Research, 40(5), 384–399. Ratcliffe, D., & Ellison, N. (2015). Obesity and internalized weight stigma: A formulation model for an emerging psychological problem. Behavioural and Cognitive Psychotherapy, 43(2), 239–252. Richman, L. S., & Lattanner, M. R. (2014). Self‐regulatory processes underlying structural stigma and health. Social Science & Medicine, 103, 94–100. Schmitt, M. T., Branscombe, N. R., Postmes, T., & Garcia, A. (2014). The consequences of perceived discrimination for psychological well‐being: A meta‐analytic review. Psychological Bulletin, 140(4), 921. Sheehan, L. (2015). Strategies for changing the stigma of behavioral healthcare. Commissioned paper for the National Academy of Sciences: Committee on the science of changing behavioral health norms. Retrieved from http://sites.nationalacademies.org/DBASSE/BBCSS/DBASSE_170049 Stubbs, A. (2014). Reducing mental illness stigma in health care students and professionals: A review of the literature. Australasian Psychiatry, 22(6), 579–584. Substance Abuse and Mental Health Services Administration. (2012). Results from the 2011 National Survey on Drug Use and Health: Mental health findings (NSDUH Series H‐45, HHS Publication No. [SMA] 12‐4725). Rockville, MD: Author. Taft, T., Ballou, S., & Keefer, L. (2013). A preliminary evaluation of internalized stigma and stigma resistance in inflammatory bowel disease. Journal of Health Psychology, 18(4), 451–460. Teixeira, M. E., & Budd, G. M. (2010). Obesity stigma: A newly recognized barrier to comprehensive and effective type 2 diabetes management. Journal of the American Academy of Nurse Practitioners, 22(10), 527–533. Thornicroft, G., Brohan, E., Rose, D., Sartorius, N., Leese, M., & INDIGO Study Group. (2009). Global pattern of experienced and anticipated discrimination against people with schizophrenia: A cross‐sectional survey. The Lancet, 373(9661), 408–415. Tomiyama, A. J. (2014). Weight stigma is stressful. A review of evidence for the Cyclic Obesity/Weight‐ Based Stigma model. Appetite, 82, 8–15. Valdiserri, R. O. (2002). HIV/AIDS stigma: An impediment to public health. American Journal of Public Health, 92(3), 341. Verhaeghe, M., & Bracke, P. (2012). Associative stigma among mental health professionals: Implications for professional and service user well‐being. Journal of Health and Social Behavior, 53(1), 17–32.

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Weiner, B. (1995). Judgment of responsibility: A foundation for a theory of social conduct. New York, NY: Guilford Press. Williams, J., Gonzalez‐Medina, D., & Le, Q. (2011). Infectious diseases and social stigma. Applied Technologies & Innovations, 4(1), 58–70.

Suggested Reading Corrigan, P. W., Druss, B. G., & Perlick, D. A. (2014). The impact of mental illness stigma on seeking and participating in mental health care. Psychological Science in the Public Interest, 15(2), 37–70. Earnshaw, V. A., & Quinn, D. M. (2012). The impact of stigma in healthcare on people living with chronic illnesses. Journal of Health Psychology, 17(2), 157–168. Valdiserri, R. O. (2002). HIV/AIDS stigma: An impediment to public health. American Journal of Public Health, 92(3), 341.

Alcohol Use Disorder: Overview Brittany E. Blanchard, Angela K. Stevens, Molin Shi, and Andrew K. Littlefield Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA

Definitions of Alcohol Use Disorder Initially, alcohol use disorder (AUD), labeled alcoholism, was classified under the personality and other nonpsychotic disorders sections in the first two editions of the Diagnostic and Statistical Manual of Mental Disorders (DSM), and this personality‐based conceptualization of AUDs remained prominent in mental health until 1980 (American Psychiatric Association [APA], 1980). Although the disease concept of alcoholism, which led to the medicalization of this disorder, appeared in 1956 (see Jellinek, 1960), it was not until the third edition of the DSM that “alcoholism” was replaced with alcohol abuse and dependence and was moved to the substance use disorder section (APA, 1980). These changes, in part, reflected the conceptualization of alcohol dependence syndrome (see Edwards & Gross, 1976). Recently, the fifth edition of the DSM drastically changed the classification of alcohol‐related diagnoses (and other substance use disorders). In addition to abolishing the biaxial system in favor of single AUD, which combines abuse and dependence criteria, critical changes to criteria were made based on research conducted since the publication of DSM‐IV (e.g., removal of legal issues and addition of craving; APA, 2013; see Hasin et al., 2013). Under DSM‐5 criteria, an individual must exhibit at least 2 of the 11 criteria in the past year (i.e., briefly, hazardous use, difficulty quitting, social/occupational problems, tolerance, withdrawal, drinking for larger/longer than intended, great deal of time spent obtaining/using/recovering from alcohol, craving, failure to fulfill role obligations, continued drinking despite interpersonal/social problems, continued drinking despite psychological/physical problems; see APA, 2013), and these symptoms must yield significant distress and/or impairment for diagnosis (APA, 2013, p. 491). A severity specifier allows clinicians to rate severity of the AUD based on a symptom count (APA, 2013). Because only one symptom was necessary to establish an alcohol abuse diagnosis in DSM‐IV (APA, 2000), the latest criteria change (i.e., 2+ symptoms required) may improve diagnostic The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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reliability and validity by reducing the number of diagnostic imposters (Lane & Sher, 2015). Although the newest edition of the DSM has made great improvements in classification of individuals with AUD for clinical and research purposes, several limitations remain (e.g., varying severity among symptoms, disregard for symptom constellations; see Lane & Sher, 2015). One proposal to rectify this issue is to require both harm (e.g., presence of alcohol‐related problems) and dysfunction (e.g., presence of either compulsive use or withdrawal) to make an AUD diagnosis (Wakefield & Schmitz, 2015).

Prevalence of AUDs Based on data from the 2012 to 2013 National Epidemiologic Survey on Alcohol and Related Conditions III, a representative US noninstitutionalized civilian sample of adults 18 years or older, the lifetime and 12‐month prevalence of DSM‐5 AUD were 29.1 and 13.9%, respectively (Grant et  al., 2015). Both lifetime and past‐year rates of AUD were highest among males, White and Native American individuals, and younger, never married, or previously ­married adults (Grant et al., 2015). Normatively, the prevalence of AUD and levels of heavy, problematic alcohol use peak in the early 20s and decline thereafter into later adulthood, although heterogeneity exists in trajectories of alcohol outcomes (see Chassin, Sher, Hussong, & Curran, 2013).

Etiological Influences A myriad of theoretical conceptualizations that attempt to explain the mechanisms responsible for the onset, maintenance, and relapse processes of AUD have been proffered (e.g., Cox & Klinger, 1988; Sher, Grekin, & Williams, 2005). Extant literature highlights multiple etiological pathways for problematic alcohol involvement. Importantly, different pathways are not mutually exclusive, and there is potential for both additive and superadditive effects (see Sher, Martinez, & Littlefield, 2011, for a detailed review). Most widely accepted models in research and clinical work utilize the biopsychosocial framework, which posits that biological (e.g.,  genetics, neuroadaptation, pharmacological vulnerability; see Kendler, Myers, & Prescott, 2007; Sher, Trull, Bartholow, & Vieth, 1999; Volkow, Koob, & McLellan, 2016), psychological (e.g., expectancies, motives, personality; see Jones, Corbin, & Fromme, 2001; Kuntsche, Knibbe, Gmel, & Engels, 2005; Littlefield & Sher, 2014), and social factors (e.g., parental and peer influences; Nash, McQueen, & Bray, 2005) contribute to the onset and maintenance of AUD (Marlatt, Baer, Donovan, & Kivlahan, 1988). For example, the motivational model of alcohol use postulates that historical factors (biochemical reactivity, personality, and sociocultural factors), current factors (current affective state and alcohol availability), and alcohol‐related expectancies interact to affect decision‐making processes regarding a­ lcohol consumption (Cox & Klinger, 1988). Diathesis–stress models (including genetic diathesis; see Dick et al., 2008) propose that individuals have biological and/or psychological vulnerabilities, which trigger the onset of AUD in the face of stressful life events (Goldstein, Abela, Buchanan, & Seligman, 2000). Supporting this model are findings from gene–environment interaction research, which suggests that individuals with specific genetic variants develop AUD only under certain environmental conditions (Caspi & Moffitt, 2006). Social cognitive theories of alcohol use integrate psychological factors (e.g., cognitive expectancies, drinking refusal self‐efficacy, drinking motives) and social environmental influences (e.g., parental and peer alcohol use) to predict alcohol consumption and AUD development

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(Cooper, Russell, & George, 1988; Giovazolias & Themeli, 2014). The deviance proneness model (Sher et al., 1999) suggests that substance use behavior is a facet of a general pattern of deviant behaviors (i.e., heterotypic continuity; Moffitt, 1993). In addition to the genetic ­vulnerability of externalizing behaviors in childhood, these behaviors are also thought to be a result of environmental factors such as inadequate socialization and/or associating with a deviant peer group. In sum, multiple integrative models exist within the literature, the most comprehensive of which include examination biological, psychological, and sociocultural/ environmental factors in considering etiological pathways to AUD, as well as alcohol ­consumption, more broadly.

Definitions of Alcohol Consumption Alcohol consumption is necessary to experience alcohol‐related problems and receives a diagnosis of AUD, yet this is not assessed in the DSM‐5 or the International Classification of Diseases (ICD‐10; World Health Organization, 1992). Despite exclusion from formal criteria for AUDs, alcohol quantity, frequency, and “binge drinking” are typically assessed. Although there are discrepancies in definitions of binge drinking (also called heavy episodic drinking), the current standard is the National Institute on Alcohol Abuse and Alcoholism (NIAAA) definition, which is five or more standard drinks for males and four or more standard drinks for females, over approximately 2 hr. This pattern of consumption increases blood alcohol concentration (BAC) to roughly 0.08 g/dL, the legal limit in the United States (NIAAA, 2004). Other definitions include that of the Substance Abuse and Mental Health Services Administration (SAMHSA), which defines binge drinking as more than five drinks per occasion, with heavy drinking defined as binge drinking on 5 or more days within the past month (Center for Behavioral Health Statistics and Quality, 2015).

Acute Effects of Alcohol Consumption Effects of acute alcohol consumption can range from increased talkativeness and relaxation to clumsiness, to, at higher levels of intoxication, respiratory depression, and even death (see Vonghia et al., 2008). Broadly, effects tend to vary as a function of BAC with more severe effects occurring at higher levels of BAC. For example, relaxation is typically experienced by individuals with a BAC of less than 0.05 g/dL, whereas symptoms like respiratory depression and coma occur at BACs of 0.40 g/dL and higher (Vonghia et al., 2008). Although BAC typically increases with consumption, an individual’s BAC depends on a number of factors affecting bioavailability, such as genetics, biological sex, eating prior to the drinking episode, and alcohol tolerance (e.g., Ramchandani, Bosron, & Li, 2001). Regardless, within a given drinking episode, an individual will typically experience both stimulant‐like and depressant‐like effects, due to alcohol’s biphasic effects (i.e., stimulant‐like effects on the ascending limb of the blood alcohol curve and depressant‐like effects on the descending limb). However, the experience of these effects also varies as a function of individual differences (e.g., alcohol use status; King, De Wit, McNamara, & Cao, 2011). Acute alcohol consumption can impact a number of physiological functions, which can influence health outcomes. For example, evidence suggests that alcohol consumption often yields increases in appetite and food consumption (Yeomans, 2010). Although evidence is mixed regarding the effects of alcohol intake on sexual arousal, research indicates a decrease in genital arousal, including decreased penile tumescence (see George & Stoner, 2000). Contrary to popular belief, consuming alcohol before bed may negatively impact sleep; more specifically, alcohol appears to delay the onset of rapid eye movement (REM) sleep and increase disruption

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in the second half of sleep (see Ebrahim, Shapiro, Williams, & Fenwick, 2013). Many ­individuals also experience hangovers after consuming alcohol, which may include symptoms such as headache, nausea, vomiting, diarrhea, fatigue, and decreased motor and cognitive functioning (Wiese, Shlipak, & Browner, 2000). In some cases hangover may negatively impact or prevent performance of work‐related responsibilities. Higher doses of alcohol (i.e., BAC of approximately 0.10 g/dL or higher) can impair ­reaction times and decision making (Vonghia et al., 2008), resulting in an increased chance of risky sexual behaviors, sexual assault, physical injury, homicidal and suicidal behaviors, and motor vehicle accidents (Cooper, 2002; see George & Stoner, 2000). Indeed, the National Highway Traffic Safety Administration (U.S. Department of Transportation, National Highway Traffic Safety Administration, 2015) estimates that approximately 28 people die each day in the United States in alcohol‐related motor vehicle accidents. In sum, at lower doses, alcohol typically produces rewarding effects, such as relaxation and reductions in anxiety, whereas higher doses yield depressant‐like effects and increase the risk of alcohol‐related consequences. In light of this evidence, to maximize the short‐term effects of alcohol and minimize consequences, it is recommended that individuals consume alcohol in a manner consistent with the 2015–2020 Dietary Guidelines for Americans, which defines moderate consumption as no more than two drinks per day for men and one drink a day for women of the legal drinking age, with some exceptions (e.g., those taking certain medications or with certain c­ onditions, pregnant or breastfeeding women; US Department of Health and Human Services and US Department of Agriculture, 2015).

Author Biographies Brittany E. Blanchard is currently a PhD student in clinical psychology at Texas Tech University in the Department of Psychological Sciences. Her research examines individual differences in alcohol and substance use, including use of protective behavioral strategies. Angela K. Stevens is currently a PhD student in clinical psychology at Texas Tech University in the Department of Psychological Sciences. Broadly, her research interests include individual differences in adult problematic substance use. Molin Shi is currently a PhD student in clinical psychology at Texas Tech University in the Department of Psychological Sciences. Her research focuses on the influences and c­ onsequences of substance‐related behaviors, with an emphasis on the familial, social, and acculturative impact of substance use in minority subgroups. Andrew K. Littlefield is currently an assistant professor at Texas Tech University in the Department of Psychological Sciences. He obtained his predoctoral training in clinical ­psychology at the University of Missouri and completed his clinical internship at the University of Mississippi Medical Center. Dr. Littlefield’s research focuses on addictive behaviors.

References American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.

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Caspi, A., & Moffitt, T. E. (2006). Gene–environment interactions in psychiatry: Joining forces with neuroscience. Nature Reviews Neuroscience, 7(7), 583–590. Chassin, L., Sher, K. J., Hussong, A., & Curran, P. (2013). The developmental psychopathology of alcohol use and alcohol disorders: Research achievements and future directions. Development and Psychopathology, 25, 1567–1584. Cooper, M. L. (2002). Alcohol use and risky sexual behavior among college students and youth: Evaluating the evidence. Journal of Studies on Alcohol, s14, 101–117. Cooper, M. L., Russell, M., & George, W. H. (1988). Coping, expectancies, and alcohol abuse: A test of social learning formulations. Journal of Abnormal Psychology, 92, 218–230. Cox, W. M., & Klinger, E. (1988). A motivational model of alcohol use. Journal of Abnormal Psychology, 97, 168–180. Dick, D. M., Aliev, F., Wang, J. C., Grucza, R. A., Schuckit, M., Kuperman, S., … Hesselbrock, V. (2008). Using dimensional models of externalizing psychopathology to aid in gene identification. Archives of General Psychiatry, 65, 310–318. Ebrahim, I. O., Shapiro, C. M., Williams, A. J., & Fenwick, P. B. (2013). Alcohol and sleep I: Effects on normal sleep. Alcoholism: Clinical and Experimental Research, 37(4), 539–549. Edwards, G., & Gross, M. M. (1976). Alcohol dependence: Provisional description of a clinical ­syndrome. British Medical Journal, 1(6017), 1058–1061. George, W. H., & Stoner, S. A. (2000). Understanding acute alcohol effects on sexual behavior. Annual Review of Sex Research, 11(1), 92–124. Giovazolias, T., & Themeli, O. (2014). Social learning conceptualization for substance abuse: Implications for therapeutic interventions. The European Journal of Counseling Psychology, 3(1), 69–88. Goldstein, B. I., Abela, J. R., Buchanan, G. M., & Seligman, M. E. (2000). Attributional style and life events: A diathesis‐stress theory of alcohol consumption. Psychological Reports, 87(3), 949–955. Grant, B. F., Goldstein, R. B., Saha, T. D., Chou, S. P., Jung, J., Zhang, H., … Hasin, D. S. (2015). Epidemiology of DSM‐5 alcohol use disorder: Results from the National Epidemiologic Survey on Alcohol and Related Conditions III. JAMA Psychiatry, 72(8), 757–766. Hasin, D. S., O’Brien, C. P., Auriacombe, M., Borges, G., Bucholz, K., Budney, A., … Schuckit, M. (2013). DSM‐5 criteria for substance use disorders: Recommendations and rationale. American Journal of Psychiatry, 170(8), 834–851. Jellinek, E. M. (1960). The disease concept of alcoholism. Oxford, England: Hillhouse. Jones, B. T., Corbin, W., & Fromme, K. (2001). A review of expectancy theory and alcohol consumption. Addiction, 96(1), 57–72. Kendler, K. S., Myers, J., & Prescott, C. A. (2007). Specificity of genetic and environmental risk factors for symptoms of cannabis, cocaine, alcohol, caffeine, and nicotine dependence. Archives of General Psychiatry, 64(11), 1313–1320. King, A. C., De Wit, H., McNamara, P. J., & Cao, D. (2011). Rewarding, stimulant, and sedative alcohol responses and relationship to future binge drinking. Archives of General Psychiatry, 68(4), 389–399. Kuntsche, E., Knibbe, R., Gmel, G., & Engels, R. (2005). Why do young people drink? A review of drinking motives. Clinical Psychology Review, 25, 841–861. Lane, S. P., & Sher, K. J. (2015). Limits of current approaches to diagnosis severity based on criterion counts: An example with DSM‐5 alcohol use disorder. Clinical Psychological Science, 3(6), 819–835. Littlefield, A. K., & Sher, K. J. (2014). Personality and substance use disorders. In K. J. Sher (Ed.), Handbook of substance use and substance use disorders. New York, NY: Oxford University Press. Marlatt, G. A., Baer, J. S., Donovan, D. M., & Kivlahan, D. R. (1988). Addictive behaviors: Etiology and treatment. Annual Review of Psychology, 39(1), 223–252. Moffitt, T. E. (1993). Adolescence–limited and life–course–persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100(4), 674–701. Nash, S. G., McQueen, A., & Bray, J. H. (2005). Pathways to adolescent alcohol use: Family environment, peer influence, and parental expectations. Journal of Adolescent Health, 37(1), 19–28.

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National Institute on Alcohol Abuse and Alcoholism. (2004, Winter). Council approve definition of binge drinking. National Institute of Alcohol Abuse and Alcoholism Newsletter, (3), 3. Ramchandani, V. A., Bosron, W. F., & Li, T. K. (2001). Research advances in ethanol metabolism. Pathologie Biologie, 49(9), 676–682. Sher, K. J., Grekin, E. R., & Williams, N. A. (2005). The development of alcohol use disorders. Annual Review of Clinical Psychology, 1, 493–523. Sher, K. J., Martinez, J. A., & Littlefield, A. K. (2011). Alcohol use disorders. In D. H. Barlow (Ed.), Handbook of clinical psychology (pp. 405–445). New York, NY: Oxford University Press. Sher, K. J., Trull, T. J., Bartholow, B., & Vieth, A. (1999). Personality and alcoholism: Issues, methods, and etiological processes. In H. Blane & K. Leonard (Eds.), Psychological theories of drinking and alcoholism (2nd ed., pp. 55–105). New York, NY: Plenum. U.S. Department of Health and Human Services and U.S. Department of Agriculture. (2015). 2015– 2020 Dietary Guidelines for Americans (8th ed.). Retrieved from http://health.gov/ dietaryguidelines/2015/guidelines/ U.S. Department of Transportation, National Highway Traffic Safety Administration. (2015). Drunk driving: Overview. Retrieved from https://www.nhtsa.gov/risky‐driving/drunk‐driving Volkow, N. D., Koob, G. F., & McLellan, A. T. (2016). Neurobiologic advances from the brain disease model of addiction. New England Journal of Medicine, 374(4), 363–371. Vonghia, L., Leggio, L., Ferrulli, A., Bertini, M., Gasbarrini, G., Addolorato, G., & Alcoholism Treatment Study Group. (2008). Acute alcohol intoxication. European Journal of Internal Medicine, 19(8), 561–567. Wakefield, J. C., & Schmitz, M. F. (2015). The harmful dysfunction model of alcohol use disorder: Revised criteria to improve the validity of diagnosis and prevalence estimates. Addiction, 110(6), 931–942. Wiese, J. G., Shlipak, M. G., & Browner, W. S. (2000). The alcohol hangover. Annals of Internal Medicine, 132(11), 897–902. World Health Organization. (1992). The ICD‐10 classification of mental and behavioural disorders: Clinical descriptions and diagnostic guidelines. Geneva: Author. Yeomans, M. R. (2010). Alcohol, appetite and energy balance: Is alcohol intake a risk factor for obesity? Physiology & Behavior, 100(1), 82–89.

Suggested Reading Ramchandani, V. A., Bosron, W. F., & Li, T. K. (2001). Research advances in ethanol metabolism. Pathologie Biologie, 49(9), 676–682. Sher, K. J., Grekin, E. R., & Williams, N. A. (2005). The development of alcohol use disorders. Annual Review of Clinical Psychology, 1, 493–523. Volkow, N. D., Koob, G. F., & McLellan, A. T. (2016). Neurobiologic advances from the brain disease model of addiction. New England Journal of Medicine, 374(4), 363–371.

Cancer and Psycho‐Oncology Lauren N. Harris1 and Annette L. Stanton2 VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Department of Psychology, University of California, Los Angeles, CA, USA 1

2

Introduction Cancer is a collection of diseases characterized by uncontrolled division of abnormal cells. In the United States, cancers of the prostate and breast are the most common cancer sites in men and women, respectively. Lung and colon/rectum cancers are the second and third most ­common cancers in both sexes. More than 15.5 million cancer survivors are living in the United States today; this figure is projected to rise to more than 20 million by 2026 (American Cancer Society, 2017). Treatment for cancer varies but often includes surgery to remove the tumor, chemotherapy, radiation, targeted biologic therapy, and/or hormonal therapy. Treatments can cause immediate and long‐term side effects including pain, nausea, fatigue, sleep disturbance, and infertility. Cancer mortality has declined 25% over the past two decades, primarily as a result of decreased tobacco use and improvements in cancer prevention, early detection, and treatment (American Cancer Society, 2017). Despite this trend, cancer remains the second leading cause of death in the United States, accounting for approximately 25% of all deaths, and lung cancer is the leading cause of cancer mortality in women and men. Furthermore, although cancer survival has improved overall, racial/ethnic and socioeconomic disparities in cancer deaths remain. US cancer death rates are higher in African Americans than any other racial or ethnic group. Among African Americans, rates of death from cancer are 24% higher in men and 14% higher in women compared with non‐Hispanic White men and women (American Cancer Society, 2017). Causes of disparities include a complex combination of economic, sociocultural, biological, environmental, and system‐level factors. Contributors to higher cancer death rates among racial/ethnic minorities and economically disadvantaged individuals include ­inequalities in education, income, wealth, work, housing, and barriers to receiving high‐­quality cancer‐related healthcare services (American Cancer Society, 2017).

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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As people live longer after cancer diagnosis, psychosocial concerns and quality of life among cancer survivors become an increasingly important focus of research and clinical attention. Research in the field of psycho‐oncology examines (a) behavioral and psychosocial factors in the initiation and progression of cancer, (b) factors influencing adherence to cancer screening recommendations for early detection, (c) the biopsychosocial experience and trajectory of psychosocial adjustment to cancer, and (d) ­predictors of psychosocial adjustment to cancer (Holland et  al., 2015). Research in p ­ sycho‐oncology is also dedicated to developing and implementing psychosocial and behavioral interventions to ­promote health and well‐being following cancer diagnosis (see Cancer and Psychosocial Interventions entry). Reviews of the literature suggest that these interventions, which include approaches such as cognitive behavioral therapy, relaxation, coping skills training, and ­supportive‐expressive therapy, are effective in improving psychological functioning, fatigue, pain, and other psychological and physical health outcomes (e.g., Faller et al., 2013). This article focuses on adults with ­cancer; for information regarding the unique needs and concerns of children with cancer, refer to the Childhood Cancer entry.

Behavioral and Psychosocial Factors in the Initiation and Progression of Cancer Behavioral factors are implicated in both cancer initiation (i.e., development of cancer in individuals with no previous tumor) and progression (i.e., cancer growth and/or spread in patients with existing disease). Indeed, more than half of cancer cases worldwide could be prevented by reducing known risk factors, most of which are behavioral (American Association for Cancer Research, 2017). Behavioral risk factors for the development of cancer include tobacco use, obesity, poor‐quality diet, physical inactivity, excessive alcohol intake, risky sexual behavior, and insufficient sun protection (Spring, King, Pagoto, Van Horn, & Fisher, 2015). Although risk factors vary by cancer type, known behavioral factors associated with cancer progression, recurrence, and/or mortality include obesity, physical inactivity, poor‐quality diet, tobacco use, and nonadherence to recommended cancer treatments (Gillison et al., 2012; Hershman et al., 2011; Rock et al., 2012). Cancer survivors are advised to achieve or maintain a normal weight, exercise regularly, stop smoking, and eat a healthy diet, which have the potential to improve prognosis (Brandon, Unrod, & Simmons, 2015; Rock et al., 2012). Researchers in psycho‐oncology seek to develop effective interventions to reduce the risk of cancer initiation and improve outcomes following cancer diagnosis. Interventions can target multiple levels of influences on behavior, from public policy initiatives to changing sociocultural contexts and intervening on an individual level (Spring et  al., 2015). The reduction in tobacco use in the United States over the past several decades, for instance, is largely attributable to public policy and environmental interventions such as anti‐smoking campaigns, clean indoor air laws, and price deterrents. Effective individual‐level interventions for quitting smoking also exist, including behavioral counseling, social support, and pharmacotherapy. Despite effective interventions to reduce smoking prevalence, tobacco use remains responsible for nearly one‐third of cancer deaths in the United States each year, and cancer deaths related to tobacco use continue to climb in low‐income countries (American Cancer Society, 2017). Historically, it was believed that individuals with particular personality types were more likely to develop cancer. However, no psychological factor has been demonstrated to promote

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the initiation of cancer (Lutgendorf & Andersen, 2015). The majority of research examines the role of psychosocial factors in the progression, rather than initiation, of cancer. Factors implicated in cancer progression include depression, distress, trauma history, and social isolation (Lutgendorf & Andersen, 2015). Psychological research has identified plausible mechanisms through which these factors may promote disease progression. For instance, psychosocial stressors can trigger activation of the autonomic nervous system (ANS) and the hypothalamic– pituitary–adrenal (HPA) axis, causing release of stress hormones such as catecholamines and glucocorticoids. Over time, chronic activation of these physiological systems can impair immune functioning and promote inflammation, which has been shown to promote tumor growth, particularly in nonhuman animal models (Lutgendorf & Andersen, 2015). Research is needed to better understand the pathways through which biopsychosocial factors might contribute to tumor initiation, growth, and spread.

Screening for Early Detection of Cancer Early detection of cancer can improve treatment outcomes and survival. Indeed, cancer ­screening has contributed to a major public health advance against cancer over the past three decades. The US Preventive Services Task Force (USPSTF) issues recommendations for cervical, breast, and colorectal cancer screening based on age and gender. In issuing screening recommendations, experts must weigh the clear benefits of cancer screening (i.e., early cancer detection and intervention) against potential risks of screening. Most individuals who are screened for cancer do not have, and will never develop, the ­disease. Risks of screening large swaths of the population include psychological distress and physical health consequences from follow‐up investigations associated with false positives, radiation exposure from screening, and overdiagnosis (i.e., identification of pathology that is nonprogressive but is indistinguishable from progressive disease and therefore treated as ­progressive; Wardle, Robb, Vernon, & Waller, 2015). For instance, many prostate cancers progress so slowly that they are unlikely to cause harm within a man’s lifetime. Early identification of prostate cancer typically prompts treatment, which can cause important untoward side effects. Based on findings from randomized controlled trials, the USPSTF recently recommended against prostate cancer screening through prostate‐specific antigen (PSA) testing after concluding that the risks of screening for prostate cancer through PSA measurement outweigh the benefits. Adherence to cancer screening recommendations is suboptimal, although rates have improved over the past several decades. In 2013, 73% of American women reported ­mammography, and 81% reported undergoing cervical screening within the recommended period. Only 58% of women and men were up to date with colorectal cancer screening (Sabatino, White, Thompson, & Klabunde, 2015). Rates of cancer screening in the United Kingdom, which are based on national databases and therefore not subject to biased reporting, are similar (Health and Social Care Information Centre, 2013a, 2013b). Documented barriers to cancer screening include both contextual and individual factors. Contextual factors include absence of a regular physician and/or health insurance, failure of physicians to recommend screening, language barriers, low education, and low social support for screening (Wardle et al., 2015). For these and other reasons, disparities in rates of cancer screening exist: in the United States, individuals of low socioeconomic status and Hispanic Americans are less likely to be up to date with screening than are more advantaged and non‐Hispanic White Americans.

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Individual‐level factors that predict low rates of cancer screening include lack of knowledge about cancer and cancer screening, low perceived risk of cancer, fatalistic beliefs that cancer is incurable, and perception that screening behavior is outside the social norm (Wardle et al., 2015). Research to examine the relationship between worry about cancer and likelihood of undergoing cancer screening is mixed (Hay, Buckley, & Ostroff, 2005). Some studies demonstrate a link between cancer‐related worry and higher screening behavior, whereas others find that cancer‐related worry predicts lower uptake of screening. The relation between cancer worry and participation in screening may be nonlinear, such that moderate worry facilitates screening, whereas high and low levels of worry inhibit screening (Wardle et al., 2015). To improve adherence to screening recommendations, many interventions have targeted structural and organizational, rather than individual or psychological, barriers. These strategies include improving access to healthcare, facilitating the establishment of regular relationships with physicians, and implementing public health campaigns to promote awareness about the benefits of early detection through screening. Other effective approaches for improving ­participation in screening for breast, cervical, and colorectal cancer include reminders for screening and one‐on‐one education.

Psychosocial Adjustment to Cancer Cancer is a profound stressor that can cause significant declines in physical, emotional, and social functioning. Most people diagnosed with cancer adjust well, however. Although prevalence estimates of depression and anxiety following cancer diagnosis vary across studies, meta‐ analyses (e.g., Mitchell et al., 2011) suggest that more than one‐third of cancer patients meet diagnostic criteria for depression, anxiety, or an adjustment disorder.

Adjustment Across the Cancer Trajectory Longitudinal research reveals commonalities in experiences and adjustment along the continuum of the cancer trajectory, from diagnosis to long‐term survivorship and/or end of life. Shortly after cancer diagnosis, anxiety is common as patients are faced with uncertainty regarding treatment options and must make difficult decisions related to treatment (Stanton & Bower, 2015). Individuals may also face challenges as they arrange for changes in employment and other life roles. During active oncologic treatment, many patients experience significant negative side effects such as pain and fatigue (Jacobsen & Andrykowski, 2015). Other common side effects of cancer treatment include sleep disturbance, nausea, and cognitive impairment. Patients also can face challenges in communicating effectively with their medical team and their interpersonal network. During treatment, patients may feel too tired to keep family and friends updated on their health status or feel hesitant to ask for help. Many patients also must navigate changes in work and other responsibilities as they undergo treatment. During the reentry phase (i.e., the months after primary medical treatment is completed), challenges often include resuming work, familial, and social roles despite persistent or late‐ emerging side effects of treatment (Stanton, Rowland, & Ganz, 2015). Fatigue, for instance, is a long‐term consequence of cancer and its treatment for some. Patients can experience a sense of abandonment from the medical team and other sources of support. Fear of cancer recurrence is also common during the reentry phase. Long‐term survivors may have problems

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with employment, enduring and late‐emerging side effects of treatment, and lasting concerns about future health (Institute of Medicine [IOM], 2005). Cancer recurrence and diagnosis of metastatic disease (i.e., cancer that has spread to distant organs) present a host of challenges for patients and their loved ones (Rainbird, Perkins, Sanson‐Fisher, Rolfe, & Anseline, 2009). Metastatic cancer is usually life limiting. Treatment is often intensive and variable over time. Patients with advanced disease may face compromised role functioning and concerns about losing independence and burdening their loved ones. Patients with metastatic disease must also make difficult treatment‐related decisions, which can include determining when to end treatment and prepare for the end of life.

Characterizing the Experience of Cancer Despite the challenges associated with a diagnosis of cancer, most survivors report finding benefit in the cancer experience (Stanton et al., 2015). Facets of benefit finding, also known as posttraumatic growth, include strengthened interpersonal relationships, enhanced life appreciation, greater focus on life priorities, and a deepened sense of spirituality. Although findings are inconsistent, research suggests that finding benefit can promote improved psychological adjustment among cancer patients, with enduring effects in long‐term survivorship (e.g., Bower et al., 2005). Research also documents the challenges and distress that partners, family members, and friends of cancer patients experience. During the continuum of the cancer experience, caregivers assume varied responsibilities such as assisting patients with activities of daily living, ­coordinating medical care, providing emotional support, and gathering information. Rates of depression and anxiety among caregivers of patients with cancer are comparable with, or higher than, those of cancer patients themselves (e.g., Hagedoorn, Sanderman, Bolks, Tuinstra, & Coyne, 2008; Sklenarova et  al., 2015). A majority of caregivers have unmet needs for ­support, particularly with regard to addressing fears about the patient’s condition, obtaining ­adequate disease‐ and treatment‐related information, and receiving emotional support for themselves (Sklenarova et al., 2015; see Caregivers entry). Although many cancer patients adjust well and do not demonstrate significant deterioration in mental health, wide individual variability exists. Longitudinal studies have identified four distinct trajectories of psychological adjustment to breast cancer diagnosis (e.g., Henselmans et al., 2010). In these studies, about one‐third of individuals experienced no distress throughout diagnosis and treatment. Another third showed a pattern of recovery, wherein patients experienced distress during diagnosis and active treatment, followed by a significant decrease in distress after completing treatment. A smaller group (15%) evidenced an increase in distress shortly after treatment completion (during the reentry phase). Finally, a small but notable group of individuals experienced unremittingly high distress throughout diagnosis, treatment, and beyond.

Predictors of Psychosocial Adjustment to Cancer Specifying risk and protective factors for psychosocial adjustment to cancer builds on our understanding of trajectories of adjustment and can help identify individuals who could ­benefit  from intervention. To this end, longitudinal research has identified demographic, cancer‐related, psychosocial, and interpersonal factors associated with adjustment to cancer.

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Demographic risk factors for poor psychosocial adjustment to cancer include younger age, Latino ethnicity, and low socioeconomic status (Stanton & Yanez, 2013). Chemotherapy receipt is associated with less favorable physical adjustment (Ganz, Kwan, Stanton, Bower, & Belin, 2011). Research examining the association between advanced cancer stage and psychological adjustment has yielded mixed results; research is needed to characterize the relationship between cancer stage and emotional functioning and identify potential moderators of the relationship. Personality attributes such as optimism and mastery (i.e., perceived control) are associated with more favorable adjustment, whereas neuroticism is related to poor adjustment (Henselmans et al., 2010). Negative expectancies about cancer‐related outcomes contribute to increased distress. Cancer‐specific coping processes are important predictors of psychological adjustment to cancer (Stanton & Bower, 2015). Coping through approach‐oriented strategies, which include emotional expression, problem solving, seeking social support, positive reappraisal, and active acceptance, predicts better psychological adjustment. Conversely, coping through avoidance of thoughts and feelings regarding cancer predicts increases in distress. Social factors also influence psychological adjustment to cancer (Michael, Berkman, Colditz, Holmes, & Kawachi, 2002). Social isolation, perceived loneliness, and lack of interpersonal support are associated with compromised adjustment. These risk and protective factors may also influence physical health and functioning (e.g., fatigue, pain, sleep disturbance) during and after cancer. Continued research is needed to examine factors that predict both psychological and physical functioning. Research in this area will inform targeted interventions to prevent depression, anxiety, and other negative sequelae by addressing individual risk factors.

Directions for Future Research Much of the research in psycho‐oncology is composed of studies of well‐educated, non‐Hispanic White women with early‐stage breast cancer. Research is needed using diverse samples with various cancer diagnoses and underserved groups including those with advanced cancer, socioeconomically disadvantaged individuals, and racial/ethnic minorities. Furthermore, although the majority of people with cancer are older adults, research in psycho‐oncology largely focuses on middle‐aged and younger adults. Research findings may not apply to older individuals with unique needs and concerns including multiple comorbid conditions, loss of independence, and pronounced side effects of treatment. Despite the documented psychosocial needs and concerns of cancer patients, psychosocial support is not routinely provided to patients as a component of their oncology care (IOM, 2013). Recognizing the importance of psychosocial care for individuals with cancer and implemented in 2015, the American College of Surgeons Commission on Cancer (CoC) mandated universal psychosocial distress screening for cancer patients as a new patient care standard for CoC‐accredited cancer programs (American College of Surgeons Commission on Cancer, 2012). Psychologists are well positioned to conduct research and make recommendations regarding appropriate screening measures, as well as to develop and disseminate evidence‐ based interventions for patients who screen positive for distress (see Assessment in Health Care Settings entry). It will also be critical for psychological researchers to investigate the potential benefits of integrated oncologic care such that patients’ psychosocial needs are addressed within their existing network of cancer healthcare professionals. Researchers in psychological science have increasing opportunities to form ­multidisciplinary collaborations with investigators in medicine, public health, and public policy to inform

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c­ utting‐edge research and advance knowledge in the field of psycho‐oncology. For instance, psychologists could assess patient‐reported outcomes, including psychosocial and physical functioning, in clinical trials of new medical treatments for cancer. Psycho‐oncology will ­benefit from adopting a biopsychosocial approach to investigate and address proactively the complex interplay of biological, intrapersonal, and interpersonal factors that influence cancer‐ related decision making and adjustment. A recent review regarding fatigue in cancer survivors, for example, exemplifies a biopsychosocial approach as it examines the demographic, medical, psychosocial, behavioral, and biological factors that influence cancer‐related fatigue (Bower, 2014). A recent Institute of Medicine report concluded that the cancer care system is unprepared to care for the growing—and aging—population of cancer survivors in the United States (IOM, 2013). Psychologists will have an important role—as both researchers and clinicians— in reducing the burden of cancer through prevention, early detection, and effective management of the challenges of cancer diagnosis, treatment, and posttreatment survivorship.

Author Biographies Lauren N. Harris received her PhD in clinical psychology from the University of California, Los Angeles. Her research focuses on identifying risk and protective factors in adjustment to cancer and developing targeted interventions to improve psychosocial adjustment for cancer survivors. She is currently a postdoctoral fellow specializing in clinical health psychology at the West Los Angeles VA Medical Center. Annette L. Stanton, PhD, is professor of psychology and psychiatry/biobehavioral sciences at the University of California, Los Angeles, and a member of the Jonsson Comprehensive Cancer Center. Her research centers on identifying contributors to biopsychosocial health in adults undergoing chronic stress, with a focus on the experience of cancer, and to translate findings into evidence‐based interventions.

References American Association for Cancer Research. (2017). AACR cancer progress report 2017. Philadelphia, PA: Author. American Cancer Society. (2017). Cancer facts & figures, 2017. Atlanta, GA: Author. American College of Surgeons Commission on Cancer. (2012). Cancer program standards 2012: Ensuring patient‐centered care. Chicago, IL: American College of Surgeons. Bower, J. E. (2014). Cancer‐related fatigue—Mechanisms, risk factors, and treatments. Nature Reviews. Clinical Oncology, 11(10), 597–609. Bower, J. E., Meyerowitz, B. E., Bernaards, C. A., Rowland, J. H., Ganz, P. A., & Desmond, K. A. (2005). Perceptions of positive meaning and vulnerability following breast cancer: Predictors and outcomes among long‐term breast cancer survivors. Annals of Behavioral Medicine, 29(3), 236–245. Brandon, T. H., Unrod, M., & Simmons, V. N. (2015). Tobacco use and cessation. In J. C. Holland, W. S. Breitbart, P. N. Butow, P. B. Jacobsen, M. J. Loscalzo, & R. McCorkle (Eds.), Psycho‐oncology (3rd ed., pp. 3–7). New York, NY: Oxford University Press. Faller, H., Schuler, M., Richard, M., Heckl, U., Weis, J., & Küffner, R. (2013). Effects of psycho‐­ oncologic interventions on emotional distress and quality of life in adult patients with cancer: Systematic review and meta‐analysis. Journal of Clinical Oncology, 31, 782–793. Ganz, P. A., Kwan, L., Stanton, A. L., Bower, J. E., & Belin, T. R. (2011). Physical and psychosocial recovery in the year after primary treatment of breast cancer. Journal of Clinical Oncology, 29(9), 1101–1109.

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Gillison, M. L., Zhang, Q., Jordan, R., Xiao, W., Westra, W. H., Trotti, A., … Ang, K. K. (2012). Tobacco smoking and increased risk of death and progression for patients with p16‐positive and p16‐negative oropharyngeal cancer. Journal of Clinical Oncology, 30, 1–12. Hagedoorn, M., Sanderman, R., Bolks, H. N., Tuinstra, J., & Coyne, J. C. (2008). Distress in couples coping with cancer: A meta‐analysis and critical review of role and gender effects. Psychological Bulletin, 134(1), 1–30. Hay, J. L., Buckley, T. R., & Ostroff, J. S. (2005). The role of cancer worry in cancer screening: A theoretical and empirical review of the literature. Psycho‐Oncology, 14(7), 517–534. Health and Social Care Information Centre. (2013a). Breast Screening Programme, England 2011–12. Health and Social Care Information Centre. (2013b). Cervical Screening Programme, England 2012–2013. Henselmans, I., Helgeson, V. S., Seltman, H., de Vries, J., Sanderman, R., & Ranchor, A. V. (2010). Identification and prediction of distress trajectories in the first year after a breast cancer diagnosis. Health Psychology, 29(2), 160–168. Hershman, D. L., Shao, T., Kushi, L. H., Buono, D., Tsai, W. Y., Fehrenbacher, L., … Neugut, A. I. (2011). Early discontinuation and non‐adherence to adjuvant hormonal therapy are associated with increased mortality in women with breast cancer. Breast Cancer Research and Treatment, 126(2), 529–537. Holland, J. C., Breitbart, W. S., Butow, P. N., Jacobsen, P. B., Loscalzo, M. J., & McCorkle, R. (2015). Psycho‐oncology (3rd ed.). New York, NY: Oxford University Press. IOM (Institute of Medicine). (2005). From cancer patient to cancer survivor: Lost in transition. Washington, DC: The National Academies Press. IOM (Institute of Medicine). (2013). Delivering high‐quality cancer care: Charting a new course for a system in crisis. Washington, DC: The National Academies Press. Jacobsen, P. B., & Andrykowski, M. A. (2015). Tertiary prevention in cancer care: Understanding and addressing the psychological dimensions of cancer during the active treatment period. American Psychologist, 70(2), 134–145. Lutgendorf, S. K., & Andersen, B. L. (2015). Biobehavioral approaches to cancer progression and survival: Mechanisms and interventions. American Psychologist, 70(2), 186–197. Michael, Y. L., Berkman, L. F., Colditz, G. A., Holmes, M. D., & Kawachi, I. (2002). Social networks and health‐related quality of life in breast cancer survivors: A prospective study. Journal of Psychosomatic Research, 52(5), 285–293. Mitchell, A. J., Chan, M., Bhatti, H., Halton, M., Grassi, L., Johansen, C., & Meader, N. (2011). Prevalence of depression, anxiety, and adjustment disorder in oncological, haematological, and ­palliative‐care settings: A meta‐analysis of 94 interview‐based studies. The Lancet Oncology, 12(2), 160–174. Rainbird, K., Perkins, J., Sanson‐Fisher, R., Rolfe, I., & Anseline, P. (2009). The needs of patients with advanced, incurable cancer. British Journal of Cancer, 101(5), 759–764. Rock, C. L., Doyle, C., Demark‐Wahnefried, W., Meyerhardt, J., Courneya, K. S., Schwartz, A. L., … Byers, T. (2012). Nutrition and physical activity guidelines for cancer survivors. CA: A Cancer Journal for Clinicians, 62(4), 242–274. Sabatino, S. A., White, M. C., Thompson, T. D., & Klabunde, C. N. (2015). Cancer screening test use—United States, 2013. Morbidity and Mortality Weekly Report, 64(17), 464–468. Sklenarova, H., Krümpelmann, A., Haun, M. W., Friederich, H. C., Huber, J., Thomas, M., … Hartmann, M. (2015). When do we need to care about the caregiver? Supportive care needs, anxiety, and depression among informal caregivers of patients with cancer and cancer survivors. Cancer, 121(9), 1513–1519. Spring, B., King, A. C., Pagoto, S. L., Van Horn, L., & Fisher, J. D. (2015). Fostering multiple healthy lifestyle behaviors for primary prevention of cancer. American Psychologist, 70, 75–90. Stanton, A. L., & Bower, J. E. (2015). Psychological adjustment in breast cancer survivors. In Improving outcomes for breast cancer survivors (pp. 231–242). NY, USA: Springer International Publishing.

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Stanton, A. L., Rowland, J. H., & Ganz, P. A. (2015). Life after diagnosis and treatment of cancer in adulthood: Contributions from psychosocial oncology research. American Psychologist, 70(2), 159–174. Stanton, A. L., & Yanez, B. (2013). The experience of cancer in women. In M. V. Spiers, P. A. Geller, & J. D. Kloss (Eds.), Women’s health psychology (pp. 491–513). New York, NY: Wiley. Wardle, J., Robb, K., Vernon, S., & Waller, J. (2015). Screening for prevention and early diagnosis of cancer. American Psychologist, 70(2), 119–133.

Suggested Reading Green McDonald, P., Suls, J., & Glasgow, R. (Eds.) (2015). Special issue on cancer and psychology. American Psychologist, 70(2), 61–224. Holland, J. C., Breitbart, W. S., Butow, P. N., Jacobsen, P. B., Loscalzo, M. J., & McCorkle, R. (2015). Psycho‐Oncology (3rd ed.). New York, NY: Oxford University Press. IOM (Institute of Medicine). (2013). Delivering high‐quality cancer care: Charting a new course for a system in crisis. Washington, DC: The National Academies Press.

Ischemic Heart Disease, Depression, and Tobacco Smoking: Implications for Health Psychology Allison J. Carroll Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

Between the years 2009 and 2012, the prevalence of ischemic heart disease among US adults was estimated to be roughly 6% (Benjamin et al., 2017). High‐quality evidence supports an association between ischemic heart disease and a number of behavioral risk factors, including tobacco smoking, physical inactivity, sedentary behavior, and poor diet. More recently, there has been a focus on how the development and progression of ischemic heart disease may be affected by psychological factors, including depression, anxiety, and hostility (Davidson, 2012; Kubzansky, Davidson, & Rozanski, 2005). Significant debate remains, however, regarding the nature of the association between these psychological factors and primary onset of ischemic heart disease, and many argue that the observed associations between psychological factors or personality traits and ischemic heart disease are purely moderated by one or more of the established risk behaviors for ischemic heart disease. This line of discovery provides an opportunity for health psychologists to assist in advancing the understanding and treatment of comorbid risk factors for both primary and secondary prevention of ischemic heart disease. To illustrate the complex relationships between psychological conditions, behavioral risk ­factors, and ischemic heart disease, this chapter will provide an overview of the literature examining the interplay between depression, tobacco smoking, and ischemic heart disease. First, a brief discussion is provided regarding the measurement of ischemic heart disease. Second, the evidence supporting the association between depression and ischemic heart disease is outlined. Third, a description of the basis for evaluating the role of smoking in the association between depression and ischemic heart disease, and why depression and smoking are hypothesized to be synergistically associated with ischemic heart disease, is provided. The chapter concludes with an overview of the societal impact of this research and recommendations for health psychologists. The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Measuring Ischemic Heart Disease There are many definitions of heart disease, but the focus of this chapter will be on ischemic heart disease (i.e., coronary artery disease or coronary heart disease). Ischemic heart disease most typically develops as a result of atherosclerosis, a buildup or blockage, in the coronary arteries that reduces blood flow to the heart. The reduction in blood flow to the heart may have consequences that range from minor physical discomfort, such as angina (chest pain), to death from myocardial infarction (heart attack). The primary reason for focusing specifically on ischemic heart disease (as opposed to cardiovascular diseases more broadly) is because coronary artery calcification (CAC), a subclinical measure that is largely specific to ischemic heart disease, has been the focus of available research studies assessing risk for primary ischemic heart disease. CAC is the best available predictor of future cardiac events (Greenland et al., 2007). CAC is a measure of atherosclerosis, assessed by computed tomography to assess areas of calcium deposits in the coronary arteries. It is most often reported using Agatston scores, and many research studies simply report the presence of CAC (i.e., “detectable” CAC; Agatston score >0). In a 2007 report from the American College of Cardiology Foundation and the American Heart Association Task Force on Practice Guidelines, data compiled from 6 published studies with a total of 27,622 patients demonstrated that detectable CAC was associated with a 4.3‐fold (95% confidence interval [CI]: 3.5–5.2) increase in the relative risk of a cardiac event over 3–5 years (Greenland et al., 2007). CAC scores are highly correlated with age, sex, and race/ethnicity, where older age, male sex, and White race are at highest risk for detectable CAC. For further information about the probability of detectable CAC by age, sex, and race/ ethnicity, readers are referred to the Multi‐Ethnic Study of Atherosclerosis (MESA) calcium calculator (http://www.mesa‐nhlbi.org/calcium/input.aspx).

The Negative Effects of Depression on Ischemic Heart Disease Depression Predicts Ischemic Heart Disease Incidence and Mortality Though significant available evidence supports depression as a risk factor for poor prognosis among patients with ischemic heart disease (Lichtman et  al., 2014), research supporting depression as a risk factor for incident ischemic heart disease is still developing. Emerging ­evidence indicates that the association between depression and the heart is quite broad. In a study of 1.9 million adults in the UK health system, a documented diagnosis of depression or a prescription for antidepressant medication was predictive of 12 different cardiovascular ­disease diagnoses, with hazard ratios (HRs) ranging from 1.12 (95% CI: 1.01–1.24) for abdominal aortic aneurysm to 1.70 (95% CI: 1.60–1.82) for unstable angina, even after adjusting for traditional risk factors for cardiovascular diseases (Daskalopoulou et  al., 2016). In 2006, Nicholson, Kuper, and Hemingway (2006) conducted a systematic review and meta‐ analysis of 11 etiological cohort studies published between 1966 and 2003 evaluating the association between depression and ischemic heart disease. Nicholson et al. observed that even after studies adjusted for other risk factors, the risk of developing ischemic heart disease among individuals with depression is nearly double (95% CI: 1.5–2.4) the risk compared with those without depression (Nicholson et al., 2006). Prospective cohort studies have provided much of the evidence for an association between a clinical diagnosis of depression and ischemic heart disease. Brown, Stewart, Stump, and

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Callahan (2011) analyzed data from a prospective cohort study of older adults (≥60 years of age) followed for 13–16 years in primary care. The authors observed that compared with older adults without depression, those individuals with a history of depression were 46% more likely to experience either an acute myocardial infarction or death due to ischemic heart disease (31.9 vs. 25.7%); importantly, the two groups did not differ in rates of all‐cause mortality, demonstrating specificity of the association between depression and ischemic heart disease. Similarly, in an 18‐year longitudinal study of randomly selected women in Australia, O’Neil et  al. (2016b) observed that a baseline diagnosis of clinical depression was associated with three times greater odds of a coronary event (cardiac death, myocardial infarction or intervention) over the next 18 years. Furthermore, adding the baseline clinical depression diagnosis to the Framingham Risk Equation, a “risk calculator” for cardiac disease and events commonly used in medical practice (https://www.framinghamheartstudy.org/risk‐functions/index. php), modestly but significantly improved risk prediction of 10‐year coronary events (O’Neil et al., 2016a). Unfortunately, not everyone who suffers from depression will receive a formal diagnosis, and others may have persistent but subthreshold symptomatology. In the Whitehall II study, rather than evaluating a clinical diagnosis, Brunner et al. (2014) evaluated whether depressive symptomatology at up to six exams would predict ischemic heart disease outcomes over 24 years of follow‐up. They found a dose–response relationship between number of exams with elevated depressive symptoms and ischemic heart disease, where participants with ­elevated depressive symptoms at two or more exams had nearly 50% increased risk of developing ischemic heart disease compared with participants without elevated depressive ­symptoms at any exams (HR: 1.47, 95% CI: 1.13–1.91) (Brunner et  al., 2014). Taken together, the available research suggests that researchers and clinicians should consider “nontraditional” factors, particularly affective symptomatology, when evaluating risk for ischemic heart disease.

Mechanisms by Which Depression Is Hypothesized to Increase Risk for Ischemic Heart Disease The pathophysiological mechanisms underlying the association between depression and ischemic heart disease are poorly understood at this time, though several physiological and behavioral mechanisms have been theorized. Physiologically based processes by which depression is hypothesized to promote ischemic heart disease include platelet reactivity, blood coagulation, inflammation, endothelial dysfunction, neuroendocrine dysregulation (i.e., increased sympathetic nervous system activity and decreased parasympathetic nervous system activity), cardiac rhythm disturbances (e.g., heart rate variability), and metabolic disorders (e.g., diabetes) (Davidson, 2012). Data from twin studies support this hypothesis, suggesting that certain genetic pathways that underlie both depression and ischemic heart disease may simultaneously increase risk for both conditions (Mulle & Vaccarino, 2013). Others hypothesize that certain behaviors that are highly comorbid with depression, such as smoking and alcohol use, and those that are symptoms of depression, such as fatigue/lethargy and sleep disturbance, may account for the observed relationships between depression and ischemic heart disease. For example, Appleton et al. (2016) found that ischemic heart disease mortality associated with depression (unadjusted HR: 1.28, 95% CI: 1.02–1.62) was no longer significant after adjusting for behavioral factors (adjusted HR: 1.20, 95% CI: 0.94–1.52), including fruit and vegetable intake, physical activity, current smoking, and current alcohol consumption.

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Depression and Coronary Artery Calcification Due to the amount of time required for adults to develop ischemic heart disease or experience a cardiac event, until these mechanisms are better delineated, research progress will benefit from focusing on subclinical measures of ischemic heart disease for the purposes of evaluating the role of depression and depression treatment in the primary prevention of ischemic heart disease. The relationship between depression and CAC has been evaluated in several forms. Persistent or recurrent depression (e.g., diagnosis of major depressive disorder or multiple episodes of significantly elevated depressive symptoms) has consistently been shown to be predictive of CAC. Women with a history of recurrent major depression, compared with those who reported only a single episode or no depression, are twice as likely to have CAC (Agatisa et al., 2005; Jones, Bromberger, Sutton‐Tyrrell, & Matthews, 2003). Furthermore, the progression of CAC (i.e., the magnitude of change in Agatston scores) is directly related to level of depressive symptoms: Janssen et  al. (2011) observed that the risk of CAC progression in women increases by approximately 25% for each incremental increase in depressive symptoms. In two different studies, researchers attempted to model the longitudinal effect of the ­chronicity of depression on the development of subclinical ischemic heart disease by categorizing participants by the number of depressive episodes according to self‐reported current depressive symptomatology assessed at multiple exams. In the Whitehall II study, Hamer, Kivimaki, Lahiri, Marmot, and Steptoe (2010) observed that individuals with two or three depressive episodes (focused only on the cognitive symptoms of depression) had approximately 2.5 times the odds of detectable CAC compared with individuals without any depressive episodes (OR: 2.56, 95% CI: 1.14–5.78). Janssen et al. (2016) similarly observed that women in the Study of Women’s Health Across the Nation Heart Study with three or more depressive episodes (using a global depression score) had over two times greater odds of CAC compared with women without any depressive episodes (OR: 2.20, 95% CI: 1.13–4.28). Interestingly, in both studies, participants with only one depressive episode did not have ­significantly higher odds of CAC compared with individuals without any depressive episodes (Hamer et al., 2010; Janssen et al., 2016). On the other hand, the majority of available evidence indicates that current depressive symptomatology alone does not predict CAC. Cross‐sectional studies evaluating associations between depression and depressive scores and CAC have not found significant associations (e.g., Devantier et al., 2013). Likewise, when participants were only asked about past‐week depressive symptoms, neither continuous depression scores in an older, multiethnic population (Diez Roux et  al., 2006) nor a categorical diagnosis of depression among women (Matthews, Owens, Edmundowicz, Lee, & Kuller, 2006) was associated with greater likelihood of CAC. Overall, it appears as though chronic exposure to and accumulation of depressive symptoms is required to increase risk for ischemic heart disease, similar to other traditional risk factors, bearing in mind that the majority of studies to date have been conducted in women. While these studies provide evidence for an association between depression and subclinical ischemic heart disease, researchers argue that the presence of depression alone does not fully explain the negative association we see with ischemic heart disease. Therefore, it becomes important to evaluate the potential role of comorbid risk factors in the relationship between depression and ischemic heart disease. To illustrate this concept, we chose to focus on the role of tobacco smoking, because it is highly comorbid with depression and because it is a strong and modifiable risk factor for ischemic heart disease, which has implications for prevention and treatment of ischemic heart disease.

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The Role of Smoking in the Association Between Depression and Ischemic Heart Disease A Brief Overview of the Comorbidity Between Depression and Smoking A strong, bidirectional, and causal association exists between depression and smoking, where these two factors are both predisposing to and maintaining of each other. In the United States, adults with elevated depressive symptoms smoke at over twice the rate of the general population (43 vs. 22% in 2005–2008) (Pratt & Brody, 2010). Chaiton, Cohen, O’Loughlin, and Rehm (2009) conducted a meta‐analysis of prospective studies in adolescents, where they found that smoking increased the odds of depression by 73% (95% CI: 1.3–2.4) and also that depression increased the odds of smoking by 41% (95% CI: 1.2–1.6). Genetic studies in identical twins also lend strong support for a biological basis of this comorbidity (Lyons et al., 2008). Novel theories have been invoked to explain this association. One example is a transdiagnostic vulnerability framework, whereby anhedonia, anxiety sensitivity, and distress tolerance are key traits underlying the association between smoking and emotion (Leventhal & Zvolensky, 2015). A second example is an incentive learning theory, which posits that low mood (e.g., depression) increases the expected value of and therefore desire for a cigarette, which ultimately serves to maintain smoking behavior (Hogarth et al., 2015). Taken together, these theories and studies present not only an underlying genetic basis but also a behavioral or trait vulnerability to and maintenance of comorbid depression and smoking.

Smoking Predicts Ischemic Heart Disease Research unequivocally indicates that cigarette smoking increases risk for premature ischemic heart disease morbidity and mortality in a dose‐dependent manner (USDHHS, 2014). With regard to subclinical ischemic heart disease, smoking is a consistent and dose‐dependent predictor of prevalence and progression of CAC (McEvoy et  al., 2012). Studies have demonstrated that, among individuals with CAC, those who smoke are between four and nine times more likely to suffer cardiac death compared with their nonsmoking counterparts, which equates to an average loss of nearly 5 life years (Shaw, Raggi, Callister, & Berman, 2006). The large list of ingredients and chemicals that compose cigarette smoke complicates the determination of any one specific mechanism by which cigarette smoking causes heart disease; discussion of all of the possibilities is beyond the scope of this chapter. Briefly, cigarette smoking stimulates the sympathetic nervous system and the heart. This stimulus induces several downstream physiological effects, including increased demand for oxygen, faster heart rate, elevated blood pressure, and greater myocardial contractility. Other suggested mechanisms include inflammation, endothelial injury, insulin sensitivity, and lipid abnormalities. Several of these mechanisms overlap with those by which depression is hypothesized to affect ischemic heart disease, providing further basis for assessing the potential synergistic association between these two risk conditions in relation to ischemic heart disease.

How Comorbid Depression and Smoking Have Been Observed to Affect the Heart A representation of the hypothesized association between depression and ischemic heart d ­ isease is presented in Figure 1. Research evaluating the potential interactive effect of ­depression and

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Depression Physiologic change and/or health status change

Ischemic heart disease

Tobacco smoking

Figure 1  Hypothesized association between depression, tobacco smoking, and ischemic heart disease based on available research, where the thickness and direction of the arrows are indicative of the proposed strength and directionality of the associations. Source: This diagram was adapted for this chapter from Joynt, Whellan, and O’Connor (2003), figure 1 p. 249.

smoking on incident ischemic heart disease or cardiac events is limited. However, a s­ ynergistic association between depression and smoking has been observed with physical cardiac ­symptoms that may be indicators of future or subthreshold ischemic heart disease. For example, high heart rate variability (i.e., variability in the time interval between heart beats) is indicative of healthy cardiac function and may serve as a buffer for cardiac stress. Harte et al. (2013) observed that among adults with depression, those who smoked had lower heart rate variability than individuals who did not smoke. This finding suggests that cardiac function may be worst among adults who are both depressed and smoking compared with adults with only one or neither risk factor. As noted above, several of the proposed mechanisms for the association between smoking and ischemic heart disease overlap with those relating depression and ischemic heart disease. Some researchers found evidence for a synergistic association between smoking, depression, and measures of these hypothesized mechanisms. Using inflammation, or the inflammatory response, as an example, Vulser et al. (2015) showed that the observed association between depressive symptoms with absolute neutrophil count was fully mediated by smoking. In another study, Stewart, Rand, Muldoon, and Kamarck (2009) argued that depressed individuals experience a dysfunction in the bodily systems that typically facilitate termination of an acute inflammatory response. They suggest that when a proinflammatory stimulus, such as smoking, is introduced to a person suffering from depression, he or she may exhibit a larger and/or more prolonged response to each exposure (e.g., each time that person smokes). The result is that, over time, these exaggerated inflammatory responses may accelerate the progression of atherosclerosis. Though preliminary, this evidence provides a basis and theory for a synergistic association between depression and smoking in relation to ischemic heart disease.

Depression, Smoking, and Coronary Artery Calcification Only one study to date has looked for a potential interactive association between depression and smoking in relation to ischemic heart disease. Using data from the Coronary Artery ­Risk Development in Young Adults (CARDIA) study, Carroll et al. (2017) evaluated the association between cumulative, 25‐year smoking exposure and depressive symptoms with CAC at year 25 among the 3,188 US adults. After adjusting for relevant sociodemographic, clinical, and behavioral covariates, participants with both high smoking exposure (30 pack‐years) and clinically elevated depressive symptoms (a score of 16 on the Centers for Epidemiologic Studies—Depression [CES‐D] scale) had nearly four times greater odds of moderate‐risk CAC (scores 1–99) and over six times greater odds of higher‐risk CAC (scores ≥100) compared

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with individuals without any depressive symptoms or smoking history (Figure  2) (Carroll et al., 2017). These findings provide preliminary evidence for a synergistic effect of depression and smoking with the onset of ischemic heart disease. Other factors must be considered when evaluating the association between depression, smoking, and subclinical ischemic heart disease. First, subtypes of depression and clusters of depressive symptomatology (e.g., cognitive disturbance, somatic symptoms, anhedonia) may have differential associations in the relationship between depression, smoking, and ischemic (a)

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Figure 2  Odds ratios of lower‐risk (Agatston scores 1–99; Panel A) and higher‐risk (Agatston scores ≥100; Panel B) coronary artery calcification by depressive symptoms (CES‐D score) × smoking (pack‐years) relative to CES‐D score = 0 and pack‐years = 0, N = 3,188. Source: Adapted from Carroll et al. (2017).

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heart disease. Second, the influence of age on ischemic heart disease cannot be ignored, given that the association between measures of subclinical ischemic heart disease and cardiac events is not strictly linear. In other words, younger individuals with detectable subclinical ischemic heart disease have lower relative risk, but higher absolute risk, of suffering a cardiac event. Finally, further study of the complex interplays between additional behavioral (e.g., alcohol use, physical activity) and/or psychological (e.g., anxiety, anger) conditions that contribute to the “Perfect Storm”—that is, the “joint presence of many co‐occurring pathophysiologic ­processes that lead to a clinical event” (Burg et al., 2013)—remains crucial.

Societal Implications and the Role of the Health Psychologist Ischemic heart disease accounts for approximately one in seven deaths among US adults, and the estimated direct and indirect costs of ischemic heart disease exceed $200 billion per year (Benjamin et al., 2017). Understanding the influence of psychological and behavioral factors on CAC is essential because, as of yet, there is no evidence to indicate that development of subclinical ischemic heart disease can be reversed. Therefore, preventing the development or limiting the progression of CAC is of the utmost importance. Much of the preservation of cardiovascular health could be achieved through early intervention on modifiable risk factors. Smoking cessation swiftly and effectively reduces risk for ischemic heart disease and overall mortality (USDHHS, 2014); little is known about treating depression for the prevention of ischemic heart disease. However, one intervention study among older adults (>60 years of age) in primary care evaluated the effect of a collaborative care treatment for depression using antidepressants and pharmacotherapy on ischemic heart disease. Stewart, Perkins, and Callahan (2014) found that participants without an ischemic heart disease diagnosis who underwent treatment for their depression (n = 85), compared with those in usual care (i.e., encouragement to follow up with provider; n = 83), experienced fewer cardiac events (28 vs. 47%) over 8 years (HR: 0.52, 95% CI: 0.31–0.86). Consistent with previous research (Baumeister, Hutter, & Bengel, 2011), the authors did not observe improved mortality outcomes associated with depression treatment among individuals with preexisting ischemic heart disease. As such, an emphasis on ischemic heart disease prevention in those suffering from depression should be included in future clinical and public health efforts. Recognizing and treating the depression–smoking comorbidity has the potential to lead to even greater advances in ischemic heart disease prevention. In an updated systematic review and meta‐analysis comprising 42 smoking cessation studies, Hitsman et al. (2013) found that smokers with a history of depression had 17% (95% CI: 0.72–0.95) and 19% (95% CI: 0.67– 0.97) lower odds of short‐term (≤3 months) and long‐term (≥6 months) abstinence, respectively. Furthermore, smokers who quit smoking experience significant improvements in negative affect (Taylor et al., 2014), contrary to popularly held beliefs. Given the recurrent, reciprocal, and relapsing nature of depression and smoking, leading experts in the field recommend a long‐term and/or continuous care model using integrated evidence‐based treatments for depressed smokers in order to address the high rates of relapse for both depression and smoking (Richards, Cohen, Morrell, Watson, & Low, 2013). A number of national and global initiatives have been set forth to reduce the number of deaths due to noncommunicable diseases, including ischemic heart disease. These initiatives include the American Heart Association’s Strategic Impact Goal of reducing cardiovascular disease mortality by 20% and improving cardiovascular health by 20% by the year 2020 (Lloyd‐ Jones et al., 2010), the United Nations Sustainable Development Goals to reduce mortality

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due to noncommunicable diseases by one‐third by 2030 (http://www.un.org/ sustainabledevelopment/health/), and the World Health Organization target to reduce mortality due to noncommunicable diseases by 25% by the year 2025 (World Health ­ Organization, 2013). These initiatives provide an impetus for health psychologists to get involved in the prevention of ischemic heart disease, given their growing presence in primary care and other medical settings. Health psychologists may be in the best position to identify and treat these at‐risk individuals to prevent development and progression of ischemic heart disease. Furthermore, health psychologists, both clinicians and researchers, can contribute to prevention efforts by developing and implementing novel targeted treatments for ischemic heart disease risk factors among at‐risk patients.

Conclusion Recognition of the role of nontraditional risk factors for ischemic heart disease is growing, especially for psychological conditions such as depression. The evidence supporting an association between depression and ischemic heart disease is tantalizing but mixed, likely related to the confounding effects of risk behaviors, and tobacco smoking in particular. As such, further research in this area is needed to fully elucidate the association between depression and risk for ischemic heart disease. Nonetheless, the available evidence indicates that health psychologists can and should play an important role in ischemic heart disease prevention through interventions designed to target and treat comorbid depression and tobacco smoking.

Acknowledgments I would like to thank Dr. Mark Huffman for his conceptual and editorial suggestions in the development of this chapter.

Author Biography Allison J. Carroll is a doctoral candidate in clinical psychology (behavioral medicine) at Northwestern University Feinberg School of Medicine. Her research interests include understanding the associations between multiple risk factors with chronic disease risk and evaluating clinical interventions for disease prevention and health promotion.

References Agatisa, P. K., Matthews, K. A., Bromberger, J. T., Edmundowicz, D., Chang, Y. F., & Sutton‐Tyrrell, K. (2005). Coronary and aortic calcification in women with a history of major depression. Archives of Internal Medicine, 165(11), 1229–1236. Appleton, K. M., Woodside, J. V., Arveiler, D., Haas, B., Amouyel, P., Montaye, M., … The PRIME Study Group. (2016). A role for behavior in the relationships between depression and hostility and cardiovascular disease incidence, mortality, and all‐cause mortality: The prime study. Annals of Behavioral Medicine, 50(4), 582–591. Baumeister, H., Hutter, N., & Bengel, J. (2011). Psychological and pharmacological interventions for depression in patients with coronary artery disease. Cochrane Database of Systematic Reviews, 7(9).

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Benjamin, E. J., Blaha, M. J., Chiuve, S. E., Cushman, M., Das, S. R., Deo, R., … American Heart Association Statistics Committee and Stroke Statistics Subcommittee. (2017). Heart disease and stroke statistics‐2017 update: A report from the American Heart Association. Circulation, 135(10), e146–e603. Brown, J. M., Stewart, J. C., Stump, T. E., & Callahan, C. M. (2011). Risk of coronary heart disease events over 15 years among older adults with depressive symptoms. American Journal of Geriatric Psychiatry, 19(8), 721–729. Brunner, E. J., Shipley, M. J., Britton, A. R., Stansfeld, S. A., Heuschmann, P. U., Rudd, A. G., … Kivimaki, M. (2014). Depressive disorder, coronary heart disease, and stroke: Dose‐response and reverse causation effects in the Whitehall II cohort study. European Journal of Preventive Cardiology, 21(3), 340–346. Burg, M. M., Edmondson, D., Shimbo, D., Shaffer, J., Kronish, I. M., Whang, W., … Davidson, K. W. (2013). The ‘perfect storm’ and acute coronary syndrome onset: Do psychosocial factors play a role? Progress in Cardiovascular Diseases, 55(6), 601–610. Carroll, A. J., Carnethon, M. R., Liu, K., Jacobs, D. R., Colangelo, L. A., Stewart, J. C., … Hitsman, B. (2017). Interaction between smoking and depressive symptoms with subclinical heart disease in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Health Psychology, 36(2), 101–111. Chaiton, M., Cohen, J., O’Loughlin, J., & Rehm, J. (2009). A systematic review of longitudinal studies on the association between depression and smoking in adolescents. BMC Public Health, 9(1), 356. Daskalopoulou, M., George, J., Walters, K., Osborn, D. P., Batty, G. D., Stogiannis, D., … Hemingway, H. (2016). Depression as a risk factor for the initial presentation of twelve cardiac, cerebrovascular, and peripheral arterial diseases: Data linkage study of 1.9 million women and men. PLoS One, 11(4), e0153838. Davidson, K. W. (2012). Depression and coronary heart disease. ISRN Cardiology, 743813(10), 22. Devantier, T. A., Norgaard, B. L., Sand, N. P., Mols, R. E., Foldager, L., Diederichsen, A. C., … Videbech, P. (2013). Lack of correlation between depression and coronary artery calcification in a non‐selected Danish population. Psychosomatics, 54(5), 458–465. Diez Roux, A. V., Ranjit, N., Powell, L., Jackson, S., Lewis, T. T., Shea, S., & Wu, C. (2006). Psychosocial factors and coronary calcium in adults without clinical cardiovascular disease. Annals of Internal Medicine, 144(11), 822–831. Greenland, P., Bonow, R. O., Brundage, B. H., Budoff, M. J., Eisenberg, M. J., Grundy, S. M., … Society of Cardiovascular Computed Tomography. (2007). ACCF/AHA 2007 clinical expert ­consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: A report of the American College of Cardiology Foundation Clinical Expert Consensus Task Force (ACCF/AHA Writing Committee to update the 2000 expert consensus document on electron beam computed tomography). Circulation, 115(3), 402–426. Hamer, M., Kivimaki, M., Lahiri, A., Marmot, M. G., & Steptoe, A. (2010). Persistent cognitive depressive symptoms are associated with coronary artery calcification. Atherosclerosis, 210(1), 209–213. Harte, C. B., Liverant, G. I., Sloan, D. M., Kamholz, B. W., Rosebrock, L. E., Fava, M., & Kaplan, G. B. (2013). Association between smoking and heart rate variability among individuals with depression. Annals of Behavioral Medicine, 46(1), 73–80. Hitsman, B., Papandonatos, G. D., McChargue, D. E., DeMott, A., Herrera, M. J., Spring, B., … Niaura, R. (2013). Past major depression and smoking cessation outcome: A systematic review and meta‐analysis update. Addiction, 108(2), 294–306. Hogarth, L., He, Z., Chase, H. W., Wills, A. J., Troisi, J., 2nd, Leventhal, A. M., … Hitsman, B. (2015). Negative mood reverses devaluation of goal‐directed drug‐seeking favouring an incentive learning account of drug dependence. Psychopharmacology, 232(17), 3235–3247. Janssen, I., Powell, L. H., Matthews, K. A., Cursio, J. F., Hollenberg, S. M., Sutton‐Tyrrell, K., … SWAN Study. (2011). Depressive symptoms are related to progression of coronary calcium in

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midlife women: The Study of Women’s Health Across the Nation (SWAN) Heart Study. American Heart Journal, 161(6), 1186–1191. Janssen, I., Powell, L. H., Matthews, K. A., Jasielec, M. S., Hollenberg, S. M., Bromberger, J. T., … Everson‐Rose, S. A. (2016). Relation of persistent depressive symptoms to coronary artery calcification in women aged 46 to 59 years. The American Journal of Cardiology, 117, 1884–1889. Jones, D. J., Bromberger, J. T., Sutton‐Tyrrell, K., & Matthews, K. A. (2003). Lifetime history of depression and carotid atherosclerosis in middle‐aged women. Archives of General Psychiatry, 60(2), 153–160. Joynt, K. E., Whellan, D. J., & O’Connor, C. M. (2003). Depression and cardiovascular disease: Mechanisms of interaction. Biological Psychiatry, 54(3), 249. Kubzansky, L. D., Davidson, K. W., & Rozanski, A. (2005). The clinical impact of negative psychological  states: Expanding the spectrum of risk for coronary artery disease. Psychosomatic Medicine, 67(Suppl. 1), S10–S14. Leventhal, A. M., & Zvolensky, M. J. (2015). Anxiety, depression, and cigarette smoking: A transdiagnostic vulnerability framework to understanding emotion‐smoking comorbidity. Psychological Bulletin, 141(1), 176–212. Lichtman, J. H., Froelicher, E. S., Blumenthal, J. A., Carney, R. M., Doering, L. V., Frasure‐Smith, N., … American Heart Association Statistics Committee of the Council on Epidemiology and Prevention and the Council on Cardiovascular and Stroke Nursing. (2014). Depression as a risk factor for poor prognosis among patients with acute coronary syndrome: Systematic review and recommendations: A scientific statement from the American Heart Association. Circulation, 129(12), 1350–1369. Lloyd‐Jones, D. M., Hong, Y., Labarthe, D., Mozaffarian, D., Appel, L. J., Van Horn, L., … American Heart Association Strategic Planning Task Force and Statistics Committee. (2010). Defining and setting national goals for cardiovascular health promotion and disease reduction: The American Heart Association’s strategic impact goal through 2020 and beyond. Circulation, 121(4), 586–613. Lyons, M., Hitsman, B., Xian, H., Panizzon, M. S., Jerskey, B. A., Santangelo, S., … Tsuang, M. T. (2008). A twin study of smoking, nicotine dependence, and major depression in men. Nicotine & Tobacco Research, 10(1), 97–108. Matthews, K. A., Owens, J. F., Edmundowicz, D., Lee, L., & Kuller, L. H. (2006). Positive and negative attributes and risk for coronary and aortic calcification in healthy women. Psychosomatic Medicine, 68(3), 355–361. McEvoy, J. W., Blaha, M. J., Rivera, J. J., Budoff, M. J., Khan, A. N., Shaw, L. J., … Nasir, K. (2012). Mortality rates in smokers and nonsmokers in the presence or absence of coronary artery calcification. JACC: Cardiovascular Imaging, 5(10), 1037–1045. Mulle, J. G., & Vaccarino, V. (2013). Cardiovascular disease, psychosocial factors, and genetics: The case of depression. Progress in Cardiovascular Diseases, 55(6), 557–562. Nicholson, A., Kuper, H., & Hemingway, H. (2006). Depression as an aetiologic and prognostic factor in coronary heart disease: A meta‐analysis of 6362 events among 146 538 participants in 54 observational studies. European Heart Journal, 27(23), 2763–2774. O’Neil, A., Fisher, A. J., Kibbey, K. J., Jacka, F. N., Kotowicz, M. A., Williams, L. J., … Pasco, J. A. (2016a). The addition of depression to the Framingham Risk Equation model for predicting coronary heart disease risk in women. Preventive Medicine, 87, 115–120. O’Neil, A., Fisher, A. J., Kibbey, K. J., Jacka, F. N., Kotowicz, M. A., Williams, L. J., … Pasco, J. A. (2016b). Depression is a risk factor for incident coronary heart disease in women: An 18‐year longitudinal study. Journal of Affective Disorders, 196, 117–124. Pratt, L. A., & Brody, D. J. (2010). Depression and smoking in the U.S. household population aged 20 and over, 2005‐2008. NCHS Data Brief, 34, 1–8. Richards, C. S., Cohen, L. M., Morrell, H. E., Watson, N. L., & Low, B. E. (2013). Treating depressed and anxious smokers in smoking cessation programs. Journal of Consulting and Clinical Psychology, 81(2), 263–273.

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Shaw, L. J., Raggi, P., Callister, T. Q., & Berman, D. S. (2006). Prognostic value of coronary artery calcium screening in asymptomatic smokers and non‐smokers. European Heart Journal, 27(8), 968–975. Stewart, J. C., Perkins, A. J., & Callahan, C. M. (2014). Effect of collaborative care for depression on risk of cardiovascular events: Data from the IMPACT randomized controlled trial. Psychosomatic Medicine, 76(1), 29–37. Stewart, J. C., Rand, K. L., Muldoon, M. F., & Kamarck, T. W. (2009). A prospective evaluation of the directionality of the depression‐inflammation relationship. Brain, Behavior, and Immunity, 23(7), 936–944. Taylor, G., McNeill, A., Girling, A., Farley, A., Lindson‐Hawley, N., & Aveyard, P. (2014). Change in mental health after smoking cessation: Systematic review and meta‐analysis. BMJ, 348, g1151. USDHHS. (2014). The health consequences of smoking—50 years of progress: A report of the surgeon g­ eneral. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. Vulser, H., Wiernik, E., Tartour, E., Thomas, F., Pannier, B., Czernichow, S., … Lemogne, C. (2015). Smoking and the association between depressive symptoms and absolute neutrophil count in the investigations preventives et cliniques cohort study. Psychosomatic Medicine, 77, 955. World Health Organization. (2013). Global action plan for the prevention and control of noncommunicable diseases 2013‐2020. Geneva: Author.

Suggested Reading Goldstein, B. I., Carnethon, M. R., Matthews, K. A., McIntyre, R. S., Miller, G. E., Raghuveer, G., … Hypertension and Obesity in Youth Committee of the Council on Cardiovascular Disease in the Young. (2015). Major depressive disorder and bipolar disorder predispose youth to accelerated atherosclerosis and early cardiovascular disease: A scientific statement from the American Heart Association. Circulation, 132(10), 965–986. Nicassio, P. M., Meyerowitz, B. E., & Kerns, R. D. (2004). The future of health psychology interventions. Health Psychology, 23(2), 132–137. Xian, H., Scherrer, J. F., Franz, C. E., McCaffery, J., Stein, P. K., Lyons, M. J., … Kremen, W. S. (2010). Genetic vulnerability and phenotypic expression of depression and risk for ischemic heart disease in the Vietnam era twin study of aging. Psychosomatic Medicine, 72(4), 370–375.

Neurocognitive Disorders and Health Psychology Claudia Drossel Department of Psychology, Eastern Michigan University College of Arts and Sciences, Ypsilanti, MI, USA

Neurocognitive disorder (NCD) describes a persistent pattern of difficulties remembering, thinking, reasoning, or problem solving that represents a decline from baseline functioning (APA, 2013). This functional decline can be due to a range of underlying conditions, some of which are reversible or stable, while others irreversible or progressive. Heterogeneous etiologies (e.g., NCD due to Alzheimer’s and vascular disease) are common (see The Lancet series, “Dementia—not all about Alzheimer’s,” 2015). In addition to etiological subtype, NCDs are distinguished by their level of severity. When interference with activities of daily living is ­subtle and compensatory strategies allow independent completion of tasks, NCDs are ­classified as mild. Major NCDs—the focus of this encyclopedia entry—are customarily known as ­dementias and denote a significant disruption of activities of daily living secondary to neurodegenerative processes. In the course of neurodegeneration, changes occur in multiple domains vital to everyday functioning, such as perception (e.g., loss of olfaction, diminished visuospatial skills, auditory processing deficits), cognition (e.g., deficits in complex attention, planning and execution of multistep actions, and learning or memory), or language (e.g., restricted conversational topics, difficulties naming common objects). Decreased motor skills and gait disturbances put the individual at risk of falls. In addition, sleep–wake rhythms may shift. While patterns of domain‐ specific strengths and weaknesses depend on the underlying neurological disorder as well as the person’s distinct premorbid repertoire, in general, everyday tasks whose execution did not require effort or problem solving become increasingly puzzling and arduous. Adults younger than 60 years of age, whose most frequent conditions underlying the decline are alcohol‐ related NCD, frontotemporal lobar degeneration, or early‐onset Alzheimer’s disease, and their families also experience loss of employment, income, and social status.

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Regardless of etiology, individuals with major NCD must cope with a progressive and ­insidious loss of skill sets, a narrowing life space, and concomitant changes in relationships that can seem perplexing and worrisome. Because coping repertoires, available social supports, and health status other than the neurological condition vary from person to person, physiology intersects with contextual and potentially modifiable factors to create psychological presentations unique to each individual. In other words, the manner with which major NCD manifests in the life of a particular person and the specific emotional or behavioral changes it produces greatly depend upon the individual’s medical and psychosocial history and his or her current circumstances. Kahn (1965) coined the term “excess disability” to describe functional impairment in excess of what would be expected based upon a person’s neurological status alone. Even after more than half a century of clinical research efforts to better support affected individuals and their family and professional supports, excess disability continues to be common in NCD and indicative of care situations that inadvertently hasten decline. Individuals with major NCD eventually depend on others for virtually all tasks of daily ­living, including the most basic such as eating and toileting. In the United States, responsibility for care rests upon family members (hereinafter termed “family care partners,” to underline the dyadic nature of care situations). The degree to which these family care partners are prepared to understand and effectively compensate for functional loss and idiosyncratic ­ ­emotional or behavioral sequelae, and to establish a 24/7 care system including respite in the form of third‐party assistance, directly affects quality of care. It also affects care partners’ ­physical and emotional health in the long run. Given the prevalence of NCD, excess disability, and the care responsibility of families, the role of health psychology in NCD is threefold: i  To mitigate factors that contribute to excess disability when NCD is present. ii  To promote the health of care partners whose responsibility for part or full‐time care places them at risk for adverse physical and emotional sequelae. iii  To prevent the onset of major NCD through policy initiatives and health behavior interventions that address known risk factors. While age is a known risk factor for many progressive NCDs, such as late‐onset Alzheimer’s disease, health status and physical fitness in midlife are important predictors of later functional status and thus notable points of primary prevention. The general role of health ­psychology related to risk factors for major NCD—exogenous (acquired brain injury via exposure to n ­ eurotoxic substances or trauma), endogenous (vascular, metabolic), and behavioral (e.g., ­substance use, sedentary behavior)—is addressed elsewhere in this volume. For this r­eason, the current entry will focus on (1) and (2) above: the prevention of excess ­disability and the promotion of care partner health when NCD is present.

Prevention of Excess Disability in NCD A subtle loss of verbal skills, including self‐description and self‐expression but also complex reasoning and comprehension, puts individuals with major NCD at risk of unmet physical and emotional needs as well as social withdrawal. Already in 1996, the American Academy of Neurology Ethics and Humanities Subcommittee emphasized that prevention and management of factors aggravating NCD are at the heart of care (Bernat et al., 1996). Accordingly, NCD‐appropriate care includes the following:

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1 Optimizing basic functioning as the foundation for quality of life by establishing: a Eating and hydration routines, (i) to maintain adequate nutrition given altered olfactory and taste perception and (ii) to prevent urinary tract infections, constipation, and increased confusion due to dehydration. b Routines for urinary and bowel functioning and implementation of voiding schedules to prevent incontinence episodes and related agitated or irritable behavior. c Proper detection and correction of sensory loss as decreased ability to hear or see may increase confusion, social isolation, and fear. Because cognitive decline may interfere with the effectiveness of corrective devices (e.g., individuals may develop difficulties using bi‐ or trifocal glasses and sophisticated binaural hearing aids or may lose their ability to switch batteries when indicated), removing barriers to correction of sensory loss is important. Often, simpler solutions are indicated, such as pocket amplifiers or glasses that only correct for myopia. d Access to regular physical activity, to prevent restlessness and facilitate sleep. e Routines that promote restful sleep and maintain the sleep–wake cycle (Cipriani, Lucetti, Danti, & Nuti, 2015). 2 Detecting and managing comorbidities. As medical providers generally rely on patients’ self‐report, diminishing and inadequate self‐descriptive skills place individuals with NCD at risk of undetected acute illnesses and unmanaged chronic conditions. Whereas these conditions commonly reduce the cognitive efficiency of a healthy person, they tend to produce significant cognitive, emotional, and behavioral changes in individuals whose neurological functioning is compromised. Family care partners and providers may misattribute these precipitous changes to the NCD and focus on correlated emotional and behavioral changes to the detriment of detecting medical conditions that—when unmanaged—­ exacerbate cognitive difficulties and associated behavior problems. As an example, individuals with cognitive decline and diabetes may continue to be solely responsible for their diabetes medication administration and their blood glucose monitoring. Alternatively, family care partners may receive the information for diabetes management from the person with NCD. Behavioral and emotional changes may overshadow long‐term hyperglycemia and short‐term but potentially serious hypoglycemic episodes that are more common in individuals with diabetes and NCD. Apathy, irritability, and agitation—together with increased confusion the hallmarks of poor glycemic control in older adults—can function as active barriers to care partners assisting the person with diabetes‐related self‐care and requesting access to preventive medical visits. Indeed, some care partners may discount the need for diabetes care and prioritize management of behavioral and affective changes commonly associated with NCD, yet often due to preventable or manageable medical conditions. Other common comorbid conditions that exacerbate cognitive decline are acute and chronic pain (e.g., from urinary tract infections, toothaches, headaches, or rheumatoid arthritis), hypertension, and chronic lung and heart diseases. Because seemingly neuropsychiatric presentations can hinder the detection of acute or chronic illnesses, including ­cancer, health psychology plays a central role in the advocacy for proper rule‐outs and adherence to chronic disease management at the intersection of physical and neuropsychiatric health. 3 Closely monitoring medication effects. Adverse events related to medications are common and may manifest as emotional or behavioral disturbances, particularly in older adults and those with already compromised central nervous system functioning (Campanelli, 2012). Individuals with NCD due to Lewy body disease, for example, are exceptionally sensitive

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to treatment with antipsychotics. Referral to a geriatric pharmacist for medication review may be indicated when the person with NCD is on more than five medications, when there are multiple prescribers and potentially redundant prescriptions, when the medication ­regimen is suspected to worsen cognitive functioning or includes medications that are inappropriate for older adults or individuals with NCD, and when there have been changes in cognitive, emotional, or behavioral functioning that are uncharacteristic of the person and comorbid medical conditions have been ruled out. Of note, medications prescribed for the management of NCD (e.g., cholinesterase inhibitors) are known to precipitate prescribing cascades, in which the prescription drug’s adverse events are diagnosed as novel conditions treated with additional medications. 4 Preventing and addressing depression and anxiety. Depression and anxiety frequently ­precipitate cognitive decline, potentially signaling subtle physiological changes of yet insufficient severity to disrupt activities of daily living. Once cognitive decline begins to interfere with everyday functioning, task or psychosocial demands exceed the person’s skill set, and self‐protective responses emerge. First among these self‐protective responses are frequently avoidance of activities and general disengagement. Accordingly, depression and anxiety are the most common neuropsychiatric complaints and further exacerbate or accelerate the ­progressive effects of the neurodegenerative disease. Appropriate behavioral activation, that is, arranging opportunities for meaningful activities that match the person’s lifetime interests and current skill level, provides the shared activities that are important to a sense of belonging and produces positive health outcomes in NCD. Inclusion of the person with NCD in daily activities, to the best of his or her abilities, is vital to maintaining quality of life. 5 Detecting and reducing substance use. NCDs are prevalent among older adults with ­substance use disorders, and differential diagnosis of the etiology underlying cognitive decline is difficult. Avoidant coping style, history of substance use, and psychosocial factors such as social isolation or family conflict may further increase vulnerability to substance use when cognitive decline is present (Kuerbis, Sacco, Balzer, & Moore, 2014). 6 Recognizing and managing delirium superimposed on dementia. The presence of an NCD predisposes individuals to delirium, associated with precipitous decline, hospitalization or institutionalization, and increased mortality. The practice of health psychology in inpatient medical settings often involves alerting the medical team to the rule‐out of delirium and implementing non‐pharmacological management strategies (Inouye, Westendorp, & Saczynski, 2014). In summary, NCDs are complex conditions that tend to occur in medical and psychosocial contexts whose combined influence on the progression of NCDs is profound (Mast, 2011). In  the United States, the everyday implementation of recommendations for care and the ­prevention of excess disability falls to family members, mostly women.

Promoting the Health of Family Care Partners As family care partners assume responsibility for monitoring the safety and well‐being of the person with NCD, arranging compensatory and supportive settings, assisting with basic and instrumental activities of daily living, and managing emotional and behavioral reactions to cognitive loss, family care partners’ physical and mental health conditions affect how well they can meet the heavy emotional, organizational, problem‐solving, and physical demands inherent in caring for a person with NCD. Family care partners’ physical status and risk for

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clinical depression have been linked to potentially harmful caregiver behaviors, as have greater care needs and cognitive impairment of the person with NCD (Beach et al., 2005). In addition, situational constraints imposed by the need to provide supervision for safety often lead to loss of income, reduced access to preferred activities, increased social isolation, sleep ­deprivation, and poor health behaviors including inadequate healthcare utilization. In ­general, while also described as meaningful, the provision of care within the family is associated with an elevated risk of negative physical and mental health outcomes for the primary care partner, including cardiovascular disease and depression (e.g., Dassel & Carr, 2016). Most programs to date have been developed to support family care partners in the ­implementation of care recommendations and do not intervene directly with the person with NCD. These programs can have single or multiple components, consisting of psychoeducation about NCD and associated skills training to enhance safety and to compensate for ­cognitive loss, introduction to behavioral management techniques to address behavioral or emotional changes, enhancement of problem solving and coping, or stress management including exercise. Psychotherapeutic strategies, such as cognitive behavioral therapies for depression, dialectical behavior therapy skills training, or mindfulness‐based stress reduction, have also been applied. Outcome measures mostly have focused on factors that affect the ability to provide care, that is, care partner’s mental and physical status. Not surprisingly, intervention effects are domain specific and vary depending on the target of the intervention. Care partners often are asked to report the frequency and the perceived impact of the difficulties exhibited by the person with NCD. It is assumed that reported reductions in frequency and/or impact of problematic behaviors translate to direct benefits of the intervention for the person with NCD. Reviews of the evidence have questioned this assumption and called for further systematic studies of patient functioning (Griffin et al., 2015; see also Schulz et al., 2002). The progression of NCDs and the health of care partners interact with dyad or person‐­ specific modifiable psychosocial, biobehavioral, and environmental factors within the purview of health psychology. In general, effective interventions for care partners that reduce burden and enhance mood and well‐being are multifaceted and tailored to individual needs, and they require active participation (e.g., McCurry & Drossel, 2011). In addition, these interventions remove barriers to soliciting assistance (e.g., from friends or third‐party agencies) and provide long‐term support (Sörensen & Conwell, 2011).

Author Biography Claudia Drossel is an assistant professor at Eastern Michigan University. Her work focuses on developing functional assessments and implementing behavioral interventions for individuals and families to increase quality of life and prevent or reduce emotional and behavioral changes associated with neurocognitive disorders.

References American Psychiatric Association (APA). (2013). Diagnostic and statistical manual of mental disorders (5th edition): DSM‐5. Washington, DC: Author. Beach, S. R., Schulz, R., Williamson, G., Miller, L. S., Weiner, M. F., & Lance, C. E. (2005). Risk factors for potentially harmful informal caregiver behavior. Journal of the American Geriatrics Society, 53(2), 255–261. doi:10.1111/j.1532‐5415.2005.53111.x

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Bernat, J. L., Beresford, H. R., Celesia, G. G., Cranford, R. E., McQuillen, M. P., Pellegrino, T. R., … Wichman, A. (1996). Ethical issues in the management of the demented patient: The American Academy of Neurology Ethics and Humanities Subcommittee. Neurology, 46(4), 1180–1183. doi:10.1212/WNL.46.4.1180 Campanelli, C. M. (2012). American Geriatrics Society updated Beers Criteria for potentially inappropriate medication use in older adults. Journal of the American Geriatrics Society, 60(4), 616–631. doi:10.1111/j.1532‐5415.2012.03923.x Cipriani, G., Lucetti, C., Danti, S., & Nuti, A. (2015). Sleep disturbances and dementia. Psychogeriatrics, 15, 65–74. doi:10.1111/psyg.12069 Dassel, K. B., & Carr, D. C. (2016). Does dementia caregiving accelerate frailty? Findings from the Health and Retirement Study. The Gerontologist, 56(3), 444–450. doi:10.1093/geront/gnu078 Griffin, J. M., Meis, L. A., Greer, N., MacDonald, R., Jensen, A., Rutks, I., … Wilt, T. J. (2015). Effectiveness of caregiver interventions on patient outcomes in adults with dementia or Alzheimer’s disease: A systematic review. Gerontology and Geriatric Medicine, 1, 1–17. doi:10.1177/ 2333721415595789 Inouye, S. K., Westendorp, R. G., & Saczynski, J. S. (2014). Delirium in elderly people. The Lancet, 383, 911–922. doi:10.1016/S0140‐6736(13)60688‐1 Kahn, R. S. (1965). Comments. In Proceedings of the York House Institute on the mentally impaired aged. Philadelphia: Philadelphia Geriatric Center. Kuerbis, A., Sacco, P., Balzer, D. G., & Moore, A. A. (2014). Substance use among older adults. Clinics in Geriatric Medicine, 30(3), 629–654. doi:10.1016/j.cger.2014.04.008 Mast, B. T. (2011). Whole person dementia assessment. Baltimore, MD: Health Professions Press, Inc. McCurry, S., & Drossel, C. (2011). Treating dementia in context: A step‐by‐step guide to working with individuals and families. Washington, DC: American Psychological Association. Orrell, M., & Brayne, C. (Eds.) (2015). Dementia—Not all about Alzheimer’s. The Lancet, 386(10004), 1600. Schulz, R., O’Brien, A., Czaja, S., Ory, M., Norris, R., Martire, L. M., … Stevens, A. (2002). Dementia caregiver intervention research: In search of clinical significance. The Gerontologist, 42(5), 589–602. doi:10.1093/geront/42.5.589 Sörensen, S., & Conwell, Y. (2011). Issues in dementia caregiving: Effects on mental and physical health, intervention strategies, and research needs. American Journal of Geriatric Psychiatry, 19(6), 491–496. doi:10.1097/JGP.0b013e31821c0e6e

Suggested Reading Inouye, S. K., Westendorp, R. G., & Saczynski, J. S. (2014). Delirium in elderly people. The Lancet, 383, 911–922. doi:10.1016/S0140‐6736(13)60688‐1 Mast, B. T. (2011). Whole person dementia assessment. Baltimore, MD: Health Professions Press, Inc. McCurry, S., & Drossel, C. (2011). Treating dementia in context: A step‐by‐step guide to working with individuals and families. Washington, DC: American Psychological Association.

Depression and Comorbidity in Health Contexts C. Steven Richards1 and Lee M. Cohen2 Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Department of Psychology, College of Liberal Arts, The University of Mississippi, University, MS, USA

1

2

Depression is associated with many chronic health conditions and diseases (Richards & O’Hara, 2014a). In fact, depressive comorbidity is observed so frequently in health contexts that it is important for healthcare providers to be aware of the presentation of the symptoms of depression, how to quickly assess if this is a concern with their patients, and how to ­structure treatment for multiple concerns simultaneously. It is also important to have an understanding of how depression can influence the course of a variety of physical illnesses (Richards & O’Hara, 2014b). In this brief entry, examples of the concurrence of symptoms of depression with chronic health conditions are provided, followed by a discussion of the implications of this body of research and scholarship.

Cancer and Depression There is often a strong association between being diagnosed with cancer and experiencing significant symptoms of depression (e.g., see reviews by Miller & Massie, 2010; Nezu, Nezu, Greenberg, & Salber, 2014). In fact, there are large numbers of randomized controlled trials examining multiple approaches to treating depressed cancer patients. For example, Manne, Siegel, Heckman, and Kashy (2016) examined 302 female patients with early‐stage breast cancer, along with their spouses. For the most distressed patients, a couple‐focused and ­supportive version of group therapy appeared to be the most effective intervention. In ­contrast, among less distressed patients, a structured and skill‐based version of group therapy appeared the most effective. This study successfully addressed some of the common research concerns

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in this area, such as including a relatively large sample and a broad array of measures. It also highlights the need for individualized treatment, based on the specific needs of the patient. Another recent study by Desautels, Savard, Ivers, Savard, and Caplette‐Gingras (2018) illustrates an alternative approach to evaluating treatment options among depressed cancer patients. This study compared the efficacy of treating breast cancer patients that had considerable depressive symptoms with either cognitive therapy or bright light therapy. The design was a randomized controlled trial, with 62 breast cancer patients, and it included pre‐, post‐, and 6‐month follow‐up assessments. Although caution is warranted because of the relatively small sample of 62 participants, the cognitive therapy group appeared to be efficacious for this group of cancer patients, compared with a wait‐list control group. The bright light therapy showed some promise for reducing depressive symptoms in these patients, but it was not as consistently efficacious across all depression measures when compared with those who participated in the cognitive therapy group. Extensions of this treatment approach to larger and more diverse cancer patient samples, and with longer follow‐up assessments, should prove interesting. This bright light therapy approach may be particularly helpful in circumstances where the well‐tested approaches of cognitive therapy or cognitive behavior therapy are not available, or not acceptable, to cancer patients. Hopko and his colleagues have conducted several studies suggesting that behavioral activation may be an efficacious intervention for psychosocial variables, such as depression, in samples of depressed breast cancer patients (e.g., Ryba, Lejuez, & Hopko, 2014). For instance, Ryba et  al. (2014) conducted a study suggesting that the level of patient compliance with behavioral activation homework is strongly associated with reduced severity in symptoms of depression among cancer patients. The quality of the completed homework, however, appears to be less strongly associated with outcomes following this behavioral activation intervention. Hopko et  al. have also found support for problem‐solving interventions in this context, as have others using various methodologies among diverse samples of cancer patients (e.g., Nezu, Nezu, Felgoise, McClure, & Houts, 2003). Many additional studies have appeared in elite academic journals, such as the Journal of Consulting and Clinical Psychology, Health Psychology, Annals of Behavioral Medicine, The Journal of the American Medical Association (JAMA), and The New England Journal of Medicine, that focus on depressive comorbidity among a multitude specific diseases such as cancer, heart disease, diabetes, arthritis, and neurological disease. The frequency in which this topic is included across these multidisciplinary journals underlines the importance of the topic and regularity of the concern in healthcare settings. Further, research in this area offers important treatment information and implications. For instance, there is a rapidly expanding literature on interventions to improve several ­psychosocial aspects of being diagnosed with cancer, caring for someone with cancer, or functioning effectively at home and work while a family member is being treated for cancer. The treatment goals often include a reduction in diagnosable clinical depression or depressive symptoms. Thus, there are large studies supporting the efficacy of different forms of treatment among this population. Specifically, cognitive behavioral therapy and relaxation training interventions have been shown to be effective in this area (e.g., Gudenkauf et al., 2015). Mindfulness and hope components may also enhance interventions aimed at the psychosocial components of living with cancer and fears of cancer recurrence (e.g., Thornton et al., 2014). Additionally, there are studies that also focus on the impact of cancer (and cancer treatment) on family, friends, and caregivers of cancer patients. Cancer caregivers have been shown to experience serious depressive symptoms after a loved one’s cancer diagnosis. For instance, Kim, Shaffer, Carver, and Cannady (2014) illustrated that social support and caregiving stress may have implications for several years after the initial cancer diagnosis. Furthermore, these

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investigators continue to find that caregivers may benefit from interventions for better ­adjustment to long‐term caregiving, even 5 years after the initial diagnosis. Finally, a randomized controlled trial by Lewis et  al. (2015) suggests that an intervention of educational counseling sessions may reduce the burden of cancer and the level of negative affect for mothers with cancer. Not only did this study find that treatment helped mothers’ mood, but also it helped with their parenting skills and the behavioral and emotional adjustment of their ­children (range of 8–12 years old). In summary, the few examples provided, selected from a very long list of potential examples, illustrate the important progress that is being made regarding depressive comorbidity and cancer.

Treating Depressed Smokers in Smoking Cessation Programs As another brief example relevant to the topic of this entry, we mention the rapidly growing literature on treating depressed (and anxious) smokers as part of smoking cessation interventions. Smoking and nicotine use are typically listed among the most commonly preventable causes of disease and death (e.g., Richards, Cohen, Morrell, Watson, & Low, 2013). Moreover, high rates of depressive (and anxious) symptoms are quite prevalent among patients seeking smoking cessation treatment and can in fact impede treatment progress. Therefore, research on efficacious interventions for smoking cessation that includes one or more modules on depressive symptom reduction is important (see Richards et al., 2013, for a review of treating depressed and anxious smokers in smoking cessation programs). A recent example of this type of research is a study of the synergistic effect of a change in moderate depressive symptoms (dysphoria) and anxiety sensitivity, on post‐quit tobacco withdrawal symptoms (Bakhshaie et al., 2018). This prospective research trial included 198 treatment‐seeking adult smokers, who were enrolled in a smoking cessation treatment study. This large study included a broad array of measures and a 12‐week post‐quit assessment. The results indicated that reducing participant levels of depressive symptoms (dysphoria) and anxiety sensitivity before quitting is strongly related to the reduction withdrawal symptoms (which are highly associated with relapse to smoking) after a quit attempt. In turn, numerous studies suggest that reducing withdrawal symptoms after a quit attempt among participants in smoking cessation programs is often associated with better long‐term treatment outcomes (e.g., Baker et al., 2007). Furthermore, less depression (and anxiety) post‐quit attempt is also associated with a lesser probability of smoking relapse (Richards et al., 2013). In summary, there is a rapidly developing research literature on depressive comorbidity and health‐compromising behaviors such as smoking, particularly in the context of healthcare settings and treatment programs.

Depressive Comorbidity and Diversity It is also important to discuss a couple examples of research that addresses depressive c­ omorbidity as it relates to ethnic diversity in health contexts. For example, Le, Perry, and Stuart (2011) conducted a randomized controlled trial that illustrates the potential of a culturally sensitive cognitive behavior therapy intervention for a sample of 217 Latinas that were classified as high risk for comorbid challenges of perinatal depression, chronic health problems, and distressed relationships with their children and family. The majority of the participants were low‐income immigrants from Central and South America. The cognitive behavior therapy intervention was

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tailored to the precise needs of this sample. Specifically, the intervention was delivered in Spanish by bilingual and bicultural staff, and it was developed to be culturally sensitive to the ethnic and economic circumstances of these participants. The participants were randomized into the adapted cognitive behavior therapy condition (eight group sessions, plus three booster sessions) or a usual‐care condition. The results suggested that the cognitive behavior therapy intervention significantly reduced depressive symptoms. Major depressive episodes, however, which are more severe and rare in this sample, were not significantly reduced by this brief intervention, compared with usual care. This study illustrates that a culturally sensitive version of cognitive behavior therapy offers promise for reducing depressive symptoms in high‐risk, low‐income Latina women who were at risk for chronic health problems, during their pregnancy. Another relevant example that considers the challenge of depressive comorbidity, among a diverse sample, is a study on physical activity and depressive symptoms after treatment for breast cancer (Brunet, O’Loughlin, Gunnell, & Sabiston, 2018). The early research in this area has been limited to samples of almost entirely Caucasian women, within a small age range, who were relatively well educated and mostly married or living with a partner. In contrast, this large study of 201 women with breast cancer reflected efforts to recruit a more diverse sample across a multitude of demographic variables. The investigators note that while they were somewhat successful, even more diverse samples will be important for future research. One of the major findings from this study is that more physical exercise was associated with less frequent depressive symptoms, measured in a cross‐sectional manner, among women post‐­ treatment for breast cancer. This said, not all of the predicted relationships of depression and exercise held up longitudinally.

Discussion Depressive comorbidity is pervasive in health contexts. This has important implications for ­assessment, treatment, follow‐up care, and long‐term survivorship. Although the fields of health psychology and behavioral medicine have recently embraced the merits of recruiting diverse samples in their studies, it is clear that depressive comorbidity is frequently present in underserved populations, high‐risk groups, and ethnically diverse minority populations. Unfortunately, the implications of depressive comorbidity are serious and negative. These implications include assessment and evaluation complexities, threats to effective treatment planning, poorer adherence to treatment regimens, increased rates of relapse and recurrence after treatment, more ­disruptions and distress in day‐to‐day functioning, and a more negative overall prognosis for long‐term morbidity and mortality. Despite these challenges, considerable progress has been made regarding the theory, research, and practice of effectively dealing with depressive comorbidity in health contexts. Clearly, depression or depressive symptoms present concurrently with a majority of the chronic health problems and diseases that clinicians help patients cope with daily. Included below are a few “take‐home messages” that may be culled from this entry and the much broader research literature on depression and comorbidity in health contexts: • This area struggles with clarifications and distinctions between “depressive symptoms” versus “severe clinical depression,” which is defined as meeting the diagnostic criteria of major depressive disorder in the DSM‐5 (American Psychiatric Association, 2013). • Some wonderfully ambitious and elegant studies have been designed and completed in this area. This said, any scientific, psychological, medical, and health area can be improved. For instance, future studies must include large, more diverse samples to more accurately reflect the world’s population.

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• The specifics of depressive comorbidity in health contexts are complex, as researchers and clinicians move from one type of comorbidity to the next, one problem to the next, and one context to the next. Therefore, extra effort and consideration may be warranted for assessment and treatment. • The challenges of depressive comorbidity in health contexts argue for considering the merits of inter‐ and multidisciplinary efforts. Therefore, this seems to be an area where different disciplines working effectively together is critical. • It appears that depression presents with almost all chronic health problems and diseases in health contexts. Therefore, it will be helpful to screen for depressive comorbidity, plan accordingly, and treat thoroughly.

Author Biographies C. Steven Richards, PhD, is a professor of psychological sciences at Texas Tech University. His research interests include depression, clinical health psychology and behavioral medicine, comorbidity of health problems and psychopathology, self‐control, and relapse prevention for depression. He has held 15 administrative positions during his faculty appointments at Texas Tech University, Syracuse University, and the University of Missouri, Columbia. Dr. Richards earned his PhD in clinical psychology at the State University of New York at Stony Brook (now Stony Brook University). Lee M. Cohen, PhD, is dean of the College of Liberal Arts and professor in the Department of Psychology at the University of Mississippi. Dean Cohen came to UM from Texas Tech University, where he served in a number of administrative roles including the director of the nationally accredited doctoral program in clinical psychology, and the chair of the Department of Psychological Sciences. As a faculty member, he received several university‐wide awards for his teaching and academic achievement. As a researcher, he received more than $1.5 million from funding agencies, including the US Department of Health and Human Services, the National Science Foundation, and the National Institutes of Health/National Institute on Drug Abuse. His research program examines the behavioral and physiological mechanisms that contribute to nicotine use, and he worked to develop optimal smoking cessation treatments. He is a fellow of the American Psychological Association and received his PhD in ­clinical psychology from Oklahoma State University.

References American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed., DSM‐5). Washington, DC: Author. Baker, T. B., Piper, M. E., McCarthy, D. E., Bolt, D. M., Smith, S. S., Kim, S. Y., … Toll, B. A. (2007). Time to first cigarette in the morning as an index of ability to quit smoking: Implications for nicotine dependence. Nicotine & Tobacco Research, 9(Suppl. 4), S555–S570. Bakhshaie, J., Kulesz, P. A., Garey, L., Langdon, K. J., Businelle, M. S., Leventhal, A. M., … Zvolensky, M. J. (2018). A prospective investigation of the synergistic effect of change in anxiety sensitivity and dysphoria on tobacco withdrawal. Journal of Consulting and Clinical Psychology, 86, 69–80. Brunet, J., O’Loughlin, J. L., Gunnell, K. E., & Sabiston, C. M. (2018). Physical activity and depressive symptoms after breast cancer: Cross‐sectional and longitudinal relationships. Health Psychology, 37, 14–23. Desautels, C., Savard, J., Ivers, H., Savard, M.‐H., & Caplette‐Gingras, A. (2018). Treatment of depressive symptoms in patients with breast cancer: A randomized controlled trial comparing cognitive therapy and bright light therapy. Health Psychology, 37, 1–13.

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Gudenkauf, L. M., Antoni, M. H., Stagl, J. M., Lechner, S. C., Jutagir, D. R., Bouchard, L. C., … Carver, C. S. (2015). Brief cognitive‐behavioral and relaxation training interventions for breast cancer: A randomized controlled trial. Journal of Consulting and Clinical Psychology, 83, 677–688. Kim, Y., Shaffer, K. M., Carver, C. S., & Cannady, R. S. (2014). Prevalence and predictors of depressive symptoms among cancer caregivers 5 years after the relative’s cancer diagnosis. Journal of Consulting and Clinical Psychology, 82, 1–8. Le, H. N., Perry, D. F., & Stuart, E. A. (2011). Randomized controlled trial of a preventive intervention for perinatal depression in high‐risk Latinas. Journal of Consulting and Clinical Psychology, 79, 135–141. Lewis, F. M., Brandt, P. A., Cochrane, B. B., Griffith, K. A., Grant, M., Haase, J. E., … Shands, M. E. (2015). The enhancing connections program: A six‐state randomized clinical trial of a cancer ­parenting program. Journal of Consulting and Clinical Psychology, 83, 12–23. Manne, S. L., Siegel, S. D., Heckman, C. J., & Kashy, D. A. (2016). A randomized clinical trial of a ­supportive versus a skill‐based couple‐focused group intervention for breast cancer patients. Journal of Consulting and Clinical Psychology, 84, 668–681. Miller, K., & Massie, M. J. (2010). Depressive disorders. In J. C. Holland, W. S. Breitbart, P. B. Jacobsen, M. S. Lederberg, M. J. Loscalzo, & R. McCorkle (Eds.), Psycho‐oncology (2nd ed., pp. 311–318). New York, NY: Oxford University Press. Nezu, A. M., Nezu, C. M., Felgoise, S. H., McClure, K. S., & Houts, P. S. (2003). Project genesis: Assessing the efficacy of problem‐solving therapy for distressed adult cancer patients. Journal of Consulting and Clinical Psychology, 71, 1036–1048. Nezu, A. M., Nezu, C. M., Greenberg, L. M., & Salber, K. E. (2014). Cancer and depression. In C. S. Richards & M. W. O’Hara (Eds.), The Oxford handbook of depression and comorbidity (pp. 302–318). New York, NY: Oxford University Press. Richards, C. S., Cohen, L. M., Morrell, H. E. R., Watson, N. L., & Low, B. E. (2013). Treating depressed and anxious smokers in smoking cessation programs. Journal of Consulting and Clinical Psychology, 81, 263–273. Richards, C. S., & O’Hara, M. W. (Eds.) (2014a). The Oxford handbook of depression and comorbidity. New York, NY: Oxford University Press. Richards, C. S., & O’Hara, M. W. (Eds.) (2014b). Introduction. In The Oxford handbook of depression and comorbidity (pp. 1–10). New York, NY: Oxford University Press. Ryba, M. M., Lejuez, C. W., & Hopko, D. R. (2014). Behavioral activation for depressed breast cancer patients: The impact of therapeutic compliance and quantity of activities completed on symptom reduction. Journal of Consulting and Clinical Psychology, 82, 325–335. Thornton, L. M., Cheavens, J. S., Heitzmann, C. A., Dorfman, C. S., Wu, S. M., & Anderson, B. L. (2014). Test of mindfulness and hope components in a psychological intervention for women with cancer recurrence. Journal of Consulting and Clinical Psychology, 82, 1087–1100.

Suggested Reading Bakhshaie, J., Kulesz, P. A., Garey, L., Langdon, K. J., Businelle, M. S., Leventhal, A. M., … Zvolensky, M. J. (2018). A prospective investigation of the synergistic effect of change in anxiety sensitivity and dysphoria on tobacco withdrawal. Journal of Consulting and Clinical Psychology, 86, 69–80. Desautels, C., Savard, J., Ivers, H., Savard, M.‐H., & Caplette‐Gingras, A. (2018). Treatment of depressive symptoms in patients with breast cancer: A randomized controlled trial comparing cognitive therapy and bright light therapy. Health Psychology, 37, 1–13. Manne, S. L., Siegel, S. D., Heckman, C. J., & Kashy, D. A. (2016). A randomized clinical trial of a ­supportive versus a skill‐based couple‐focused group intervention for breast cancer patients. Journal of Consulting and Clinical Psychology, 84, 668–681. Richards, C. S., & O’Hara, M. W. (Eds.) (2014a). The Oxford handbook of depression and comorbidity. New York, NY: Oxford University Press.

Depression and Relapse in Health Contexts C. Steven Richards1 and Lee M. Cohen2 Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Department of Psychology, College of Liberal Arts, The University of Mississippi, University, MS, USA

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In 1978, Richards and Perri noted, in the context of a treatment maintenance study, that ­psychosocial treatment effects often do not last (Richards & Perri, 1978). Unfortunately, four decades later, this is still the case. Hence, depression is an excellent example of the complex challenges faced when working with individuals to prevent relapse and recurrence (Richards & Perri, 2010a). Further, it often presents along with many health conditions and diseases (Richards & O’Hara, 2014a). In fact, depressive comorbidity is so frequently observed in healthcare settings that healthcare providers from all backgrounds need to have some understanding of this disorder as well as the impact it has on the primary medical concern for which their patients are seeking treatment (Richards & O’Hara, 2014b). In this entry, examples of depressive relapse and recurrence, concurrent with serious health conditions, are presented. Implications of this research and scholarship, as well as recommendations for preventing and reducing depressive relapse, are also discussed.

Depression: Definition and Prevalence Depression is a chronically relapsing and recurring condition (American Psychiatric Association [APA], 2013). It is also quite prevalent, with approximately 7–10% of the adult population in the United States and other industrialized countries experiencing depression or a closely related mood disorder at a given point in time (Gotlib & Hammen, 2015; Kessler & Ustun, 2008). For the purpose of this entry, the term “depression” will be used as the general ­equivalent for major depressive disorder, as defined in the DSM‐5 (APA, 2013). This said, it is important to note that this use of the term “depression” is not universal. In fact, some ­investigators use The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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“depression” to indicate diagnostically subthreshold symptoms and signs, such as elevated symptom levels on a self‐report measure, rather than the interview‐based observational assessments that are the base of DSM‐5 diagnoses. In research settings, mingling the concepts of self‐reported “subthreshold depressive symptoms,” “depression,” and interview‐based “major depressive disorder” is somewhat controversial (e.g., see discussions in APA, DSM‐5, 2013; Gotlib & Hammen, 2015; Richards & Perri, 2002; Watson & O’Hara, 2017). In summary, depression is a serious and debilitating disorder, with negative implications for day‐to‐day functioning, disruptions in close relationships at work and at home, deterioration in chronic health conditions and diseases, and an overall negative impact on morbidity and mortality (Kessler, Scott, Shahly, & Zaslavsky, 2014). Indeed, most large national and international surveys, such as those conducted by the US National Institute of Mental Health and the World Health Organization, have indicated that serious clinical depression, such as major depressive disorder, is always high on the list of causes for day‐to‐day disruptions in functioning and increased disability (Gotlib & Hammen, 2015; Kessler & Ustun, 2008). Moreover, there is a large literature associating depression, and depressive relapse, with a variety of chronic health conditions and diseases. Therefore, it is important that depressive relapse is assessed over time, and when necessary, treated in health contexts in accordance with approved clinical interventions.

Studies on Relapse Prevention for Depression Frank, Kupfer, Reynolds, and their colleagues have conducted a very ambitious series of ­randomized controlled trials examining relapse prevention for depression (e.g., Frank et  al., 1990; Reynolds et al., 2006). These studies include long‐term follow‐ups and comparison of treatment maintenance interventions. Specifically, after successful completion of treatment (often 12–20 weeks), Frank and her colleagues compared various combinations of a psychosocial intervention and an antidepressant medication. The psychosocial intervention studied during both acute treatment and maintenance therapy was interpersonal psychotherapy (IPT) for depression, which is a brief form of psychotherapy that focuses on an array of interpersonal issues and skills (see Weissman, Markowitz, & Klerman, 2017 for a review of IPT). The antidepressant medications used across these studies were FDA‐approved antidepressant medications (e.g., ­imipramine [Tofranil], a tricyclic antidepressant medication, or a selective serotonin reuptake inhibitor [SSRI] such as paroxetine [Paxil]). A review of this group’s extensive body of research along with other relevant studies on IPT is available in O’Hara, Schiller, and Stuart (2010). Several conclusions can be drawn from this important work, which utilized various combinations of IPT and antidepressant medications as relapse‐prevention strategies for depressed adults. First, the continuation of maintenance treatment for depression following successful acute treatment, with IPT, antidepressant medications, or both, usually leads to reductions in the rate of depressive relapse and recurrence over multiyear follow‐ups (e.g., Frank et  al., 1990, 2007; Reynolds et al., 1999, 2006). For example, the Reynolds et al. study (1999) with depressed older adults found that 90% of the participants relapsed across a 3‐year follow‐up in the maintenance group that received a placebo during follow‐up. However, only 20% of ­participants relapsed in the maintenance group that received IPT and antidepressant medication during follow‐up. Adding to this impressive finding is the fact that it was with older adults, a population that often has chronic health conditions. This is an extremely important finding (however, compare with Reynolds et  al., 2006, with even older‐old adults, over 70 years of age, where the maintenance effect of long‐term IPT was not particularly powerful).

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Second, there is support for maintenance IPT as a stand‐alone relapse‐prevention strategy among adult women, which is important, as some women may be reluctant to take long‐term antidepressants due to pregnancy or postpartum breastfeeding (Frank et  al., 2007). Since depression and depressive relapse are highly prevalent, and frequently comorbid with other health concerns, approaches to reducing depressive relapse are very important. Third, given findings from several randomized controlled trials and/or more naturalistic, low‐cost intervention trials in point‐of‐healthcare clinics with depressed postpartum women, IPT interventions in the acute phase of depression treatment, and/or during maintenance treatment, appear to be helpful for depressive relapse prevention with women caring for young children (e.g., Nylen et al., 2010; O’Hara, Stuart, Gorman, & Wenzel, 2000; Serge, Brock, & O’Hara, 2015). Therefore, even in economically challenged and underserved populations, IPT apparently has potential as both an acute and maintenance treatment for depression (e.g., Serge et al., 2015). In addition to the promising research examining the effectiveness of IPT, there is also an extensive research literature evaluating the benefits of cognitive behavior therapy (CBT), or more cognitively focused cognitive therapy (CT), as a relapse‐prevention strategy in depressed adults. As with the treatment maintenance and depressive relapse‐prevention research with IPT, the studies with CBT and CT have often been quite promising in support of this approach (e.g., for studies see Hollon, DeRubeis, et  al., 2005; Jarrett et  al., 2001; for a review see Hollon, Stewart, & Strunk, 2005). In summary, there are few, if any, evidence‐based psychotherapy for depression approaches with as much evaluation using large randomized controlled trials as CBT and CT. Overall, this well‐researched psychosocial intervention has also shown strong potential as a relapse‐prevention strategy for depressed adults. Finally, there has also been encouraging research investigations of mindfulness‐based ­versions of CT (and CBT) in the area of depressive relapse prevention. Recent meta‐analytic reviews of this research indicate considerable potential for mindfulness‐based therapy for relapse prevention of depression. For example, a meta‐analysis of over 1,200 individuals with recurrent depression, collected across nine research trials, suggested that patients receiving mindfulness‐based interventions (sometimes combined with antidepressant medication) showed less depressive relapse by about 23% compared with patients who were continued on antidepressant medication but who did not receive the mindfulness therapy (Kuyken et al., 2016). Researchers, scholarly reviewers, and clinicians who work with individuals diagnosed with depression typically consider a 23% reduction in depressive relapse to be an important clinical finding (Gotlib & Hammen, 2015; Richards & Perri, 2010a). These studies on mindfulness‐based interventions were also typically conducted considering the constraints of real‐world conditions, such as the use of time‐limited psychological treatment. Specifically, the study designs compared 8 weeks of a mindfulness version of CT with “usual care,” where “usual care” primarily consisted of antidepressant medication treatment and did not include an evidence‐based psychotherapy. Therefore, this research by Kuyken et al. (2016) offers hope that mindfulness‐based therapies, as with the more traditional CT and CBT therapies developed before them, may help depressed patients significantly decrease depressive relapse rates.

Examples of Depressive Relapse in the Health Context There are many potential examples of depression and depressive relapse in the health context. Moreover, this research and clinical literature is expanding rapidly, attracting increased

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a­ ttention from investigators, editors, and clinicians. For example, a recent “special section” of the Journal of Consulting and Clinical Psychology (Davila & Safren, 2017) focused on “sexual and gender minority health.” For instance, an ambitious study presented in this special s­ ection, by Choi, Batchelder, Ehlinger, Safren, and O’Cleirigh (2017), illustrates recent work on depression and depressive relapse in the context of important health behaviors. The investigators used network analysis to investigate the comorbidity of depression, PTSD, and sexual risk behaviors in the context of sexual minority men who had histories of trauma. Their sample included 296 sexual minority men who were HIV negative and living in urban environments. The findings suggested that comorbid depression and PTSD, and relapse for either disorder, were strongly related to increases in sexual risk behaviors, which in turn are often related to significant health problems and diseases (Choi et al., 2017). Another example is a recent study by Desautels, avard, Ivers, Savard, and Caplette‐Gingras (2018), which evaluated treatment options in anticipation of enhancing relapse prevention, among depressed cancer patients. This study compared the efficacy of treating breast cancer patients that had considerable depressive symptoms with either CT or bright light therapy. The design was a randomized controlled trial, with 62 breast cancer patients, and it included pre‐, post‐, and 6‐month follow‐up assessments. Although caution is warranted because of the relatively small sample of 62 participants, CT appeared to be efficacious for this group of ­cancer patients, compared with a wait list control group. The bright light therapy showed some promise for briefly reducing depressive symptoms among these patients, but it was not as ­consistently efficacious as CT across all depression measures or at the follow‐up assessments. Moreover, bright light therapy appeared to be more vulnerable, than CT, to the enrolled patients having experiences with depressive relapse during follow‐up. Extensions of this treatment approach to larger and more diverse cancer patient samples, and with longer follow‐up assessments, could prove interesting. This bright light therapy approach may be particularly helpful in circumstances where the well‐tested approaches of CT or CBT are not available, or not acceptable, to cancer patients. Finally, some of the recent research that is relevant to depressive relapse investigates depression and other important health‐related behaviors in the context of longitudinal research in the natural environment, rather than the randomized controlled trials that were highlighted earlier. An interesting example is the study by Trucco, Villafuerte, Hussong, Burmeister, and Zucker (2018). These investigators explored the longitudinal pathways to alcohol use and abuse among 426 adolescents who had genetic risk factors for addiction (e.g., their biological father had been charged with drunk driving and met diagnostic criteria for alcohol use disorder), which the researchers predicted would be moderated by depression and depressive relapse during the last wave of the study (adolescents aged 15–17 years). Thus, they predicted that depression would be related to more substance use and would essentially facilitate the expression of the genetic risk factors. Trucco et al. (2018) also predicted that ineffective coping methods for stress would be a moderator, related to greater substance use. It was found that ongoing depression (or depressive relapse), along with ineffective coping methods, appears to be a pathway that allows genetic risk factors to influence substance use and abuse. This study also represents some of the research trends toward excellence in clinical health psychology and behavioral medicine, noted in other entries in this volume. These trends include use of a large sample (N = 426, plus a comparison sample), which was followed longitudinally over many years (ages 3–17, in 5 waves), using multimodal and diverse measures (multi‐method and multi‐source) that included biological and psychological variables.

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Discussion Overview Treatment effects often do not last. Patients and their healthcare providers are frequently surprised, and disappointed, that the psychological improvements and associated health ­ ­benefits that may occur during, and soon after, acute psychological treatment will disappear over longer periods of time. Depression and depressive relapse are clear examples of this problem. Without evidence‐based care, along with thorough attention to empirically supported relapse‐prevention strategies, most depressed patients will experience relapse or recurrence within a few years of treatment completion. Clinical depression, such as major depressive ­disorder, is a chronic and relapsing condition. Recent studies suggest that depressive relapse can be reduced, even in high‐risk populations such as older adults, through a combination of evidence‐based therapies such as IPT, CBT, and antidepressant medication, along with maintenance follow‐up care that can last several years. In sum, four implications can be culled from this entry and the much broader research ­literature on depression and relapse in health contexts. Noted below are also a few of the ­specific relapse‐prevention strategies for depression that are supported by empirical research (see Gotlib & Hammen, 2015; Hollon, Stewart, et al., 2005; Richards & Perri, 2010a, for reviews of this literature): • This area struggles with clarifications and distinctions between “depressive symptoms” versus “severe clinical depression” meeting the diagnostic criteria of major depressive ­disorder in the DSM‐5 (APA, 2013; see Watson & O’Hara, 2017, for a recent discussion of assessment and diagnostic issues regarding depression). Patients, clinicians, and researchers will find it helpful to keep these distinctions in mind. • Many of the randomized controlled trials and naturalistic longitudinal studies in this area are models of excellent research in terms of large and diverse samples, psychometrically sophisticated and multi‐method measures, and long‐term follow‐up evaluations. This excellent research is helpful and inspiring. • Research on relapse prevention for depression is growing rapidly. Examples of journals that regularly publish sophisticated randomized controlled trials on this topic include Journal of Consulting and Clinical Psychology, JAMA Psychiatry (formerly Archives of General Psychiatry), American Journal of Psychiatry, British Journal of Psychiatry, and general ­medical journals such as the New England Journal of Medicine, JAMA, and The Lancet. • It appears that depressive relapse is likely. It seems that depression is associated with many health problems, and as such, screening for risk regarding depressive relapse, building in relapse prevention and maintenance treatment, and assessing for patient progress and durable improvement during follow‐up evaluations are warranted.

Five Relapse‐Prevention Strategies for Depression 1 Teach patients effective coping skills. Patients with severe depression will usually find it helpful to think about their personal problems more positively, regulate their emotion in a better manner, and behave more adaptively and strategically. Effective coping skills can accomplish these goals. Moreover, these coping skills may become durable (e.g., see studies by Hollon, DeRubeis, et al., 2005; Reynolds et al., 1999, 2006; for reviews see Hollon, Stewart, et al., 2005; Nezu, Nezu, Greenberg, & Salber, 2014; Richards & Perri, 2010a, 2010b).

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2 Accommodate the patient’s circumstances, diversity, and membership in special populations. As with any treatment, relapse‐prevention strategies for depression will be most effective if they are tailored to the specific circumstances, diversity, and membership in special populations. For instance, a 75‐year‐old widowed Latina grandmother with chronic health problems, who is living in a nursing home but has frequent visits from her Latina daughter and grandchildren, may want a different relapse‐prevention focus than a 29‐year‐old single Caucasian male, in relatively good physical health, but with chronic depression and alcohol abuse challenges. The vast research literatures on depression and health psychology include many examples where diversity, context, and special population membership make a huge difference (e.g., Davila & Safren, 2017, JCCP special issue; for reviews see Gotlib & Hammen, 2015; Smith, 2010). 3 Assess carefully and follow‐up thoroughly for the chronic health problems that are often ­associated with depression and depressive relapse. As mentioned several times throughout this entry, depression is associated with other problems, including chronic health conditions and diseases such as coronary heart disease, cancer, arthritis, chronic pain, and neurological conditions such as multiple sclerosis. Depressive comorbidity with these health conditions usually has a negative impact on the patient, including more complicated treatment regimens, poorer adherence to medications and health promotion strategies, more risk for relapse and recurrence, and negative predictions for morbidity and mortality. Depressive relapse and recurrence will also have many negative impacts. Therefore, the comorbidity of depressive relapse and chronic health problems should be carefully assessed, thoroughly treated, monitored throughout follow‐up, and considered for long‐term continuation interventions, after acute treatment, which may prevent relapse (e.g., for studies see Frank et al., 1990, 2007; Nylen et al., 2010; O’Hara et al., 2000; e.g., for reviews see O’Hara et al., 2010; Richards, Cohen, Morrell, Watson, & Low, 2013; Smith, 2010). 4 Carefully evaluate and intervene, as much as possible, with the depressed patient’s social‐­ support network. Depression is typically a “socially based disorder,” with disruption and dysfunction in close relationships at work and home being quite common in the more severe forms of depression. If a depressed patient’s social world stays chaotic and not reinforcing, then it is almost inevitable that the patient will experience depressive relapse and recurrence. Depression disrupts close relationships, and dysfunctional relationships can lead to symptoms of depression (e.g., for reviews see Richards & O’Hara, 2014a; Richards & Perri, 2010a, 2010b; Stroud, Feinstein, Bhatia, Hershenberg, & Davila, 2014). 5 Consider the patient’s treatment preferences in relapse‐prevention strategies. Some patients prefer antidepressant medication regimens and consider the side effects minor or at a minimum tolerable. However, some patients dislike antidepressant medication and find the side effects very difficult to manage. A similar like–dislike relationship to treatment types can be observed with evidence‐based psychotherapies. For example, many patients appreciate the strong evidence base of CBT, the relatively simple set of principles and goals, and the ease with which it can be combined with additional treatment strategies like activity scheduling, problem solving, skill building, and role‐playing of challenging social interactions. Some patients, however, do not like the frequent homework assignments, the focus on cognitive errors and schema change, and the minimizing of some classic psychotherapeutic issues such as childhood and family psychodynamic issues, insight into personality styles, and transference. An analogous line of pros and cons, with different details, could be given for other evidence‐based therapies, such as IPT. What is clear from the literature is that if a patient does not like their treatment, they are unlikely

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to keep doing it after acute treatment ends (and this assumes they are engaged during acute treatment, which is often not the case). Thus, depressive relapse is likely to follow (e.g., for reviews see Gotlib & Hammen, 2015; Nezu et  al., 2014; Richards & Perri, 2010a, 2010b).

Author Biographies C. Steven Richards, PhD, is a professor of psychological sciences at Texas Tech University. His research interests include depression, clinical health psychology and behavioral medicine, comorbidity of health problems and psychopathology, self‐control, and relapse prevention for depression. He has held 15 administrative positions during his faculty appointments at Texas Tech University, Syracuse University, and the University of Missouri–Columbia. Dr. Richards earned his PhD in clinical psychology at the State University of New York at Stony Brook (now Stony Brook University). Lee M. Cohen, PhD, is dean of the College of Liberal Arts and professor in the Department of Psychology at the University of Mississippi. Dr. Cohen came to the University of Mississippi from Texas Tech University, where he served in a number of administrative roles including the director of the nationally accredited doctoral program in clinical psychology and the chair of the Department of Psychological Sciences. As a faculty member, he received several university‐ wide awards for his teaching and academic achievement. As a researcher, he received more than $1.5 million from funding agencies, including the US Department of Health and Human Services, the National Science Foundation, and the National Institutes of Health/National Institute on Drug Abuse. His research program examines the behavioral and physiological mechanisms that contribute to nicotine use, and he worked to develop optimal smoking cessation treatments. Dr. Cohen is a fellow of the American Psychological Association and the Society of Behavioral Medicine. He received his PhD in clinical psychology from Oklahoma State University.

References American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed., DSM‐5). Washington, DC: Author. Choi, K. W., Batchelder, A. W., Ehlinger, P. P., Safren, S. A., & O’Cleirigh, C. (2017). Applying network analysis to psychological comorbidity and health behavior: Depression, PTSD, and sexual risk in sexual minority men with trauma histories. Journal of Consulting and Clinical Psychology, 85, 1158–1170. Davila, J., & Safren, S. A. (Eds.) (2017). Introduction to the special section on sexual and gender minority health. Journal of Consulting and Clinical Psychology, 85, 1109–1110. (Special section in JCCP, 2017, pp. 1109–1181) Desautels, C., Savard, J., Ivers, H., Savard, M.‐H., & Caplette‐Gingras, A. (2018). Treatment of depressive symptoms in patients with breast cancer: A randomized controlled trial comparing cognitive therapy and bright‐light therapy. Health Psychology, 37, 1–13. Frank, E., Kupfer, D. J., Buysse, D. J., Swartz, H. A., Pilkonis, P. A., Houck, P. R., … Stapf, D. M. (2007). Randomized trial of weekly, twice‐monthly, and monthly interpersonal psychotherapy as maintenance treatment for women with recurrent depression. American Journal of Psychiatry, 64, 761–767.

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Frank, E., Kupfer, D. J., Perel, J. M., Cornes, C., Jarrett, D. B., Mallinger, A. G., … Grochocinski, V. J. (1990). Three‐year outcomes for maintenance therapies in recurrent depression. Archives of General Psychiatry, 47, 1093–1099. Gotlib, I. H., & Hammen, C. L. (Eds.) (2015). Handbook of depression (3rd ed.). New York, NY: Guilford Press. Hollon, S. D., DeRubeis, R. J., Shelton, R. C., Amsterdam, J. D., Salomon, R. M., O’Reardon, J. P., … Gallop, R. (2005). Prevention of relapse following cognitive therapy vs. medications in moderate to severe depression. Archives of General Psychiatry, 62, 417–422. Hollon, S. D., Stewart, M. O., & Strunk, D. (2005). Enduring effects for cognitive behavior therapy in the treatment of depression and anxiety. Annual Review of Psychology, 57, 285–315. Jarrett, R. B., Kraft, D., Doyle, J., Foster, B. M., Eaves, G. G., & Silver, P. C. (2001). Preventing recurrent depression using cognitive therapy with and without a continuation phase. Archives of General Psychiatry, 58, 381–388. Kessler, R. C., Scott, K. M., Shahly, V., & Zaslavsky, A. M. (2014). The big picture. In C. S. Richards & M. W. O’Hara (Eds.), The Oxford handbook of depression and comorbidity (pp. 599–614). New York, NY: Oxford University Press. Kessler, R. C., & Ustun, T. B. (2008). The WHO World Mental Health Surveys: Global perspectives on the epidemiology of mental disorders. New York, NY: Cambridge University Press. Kuyken, W., Warren, F. C., Taylor, R. S., Whalley, B., Crane, C., Bondolfi, G., … Dalgleish, T. (2016). Efficacy of mindfulness‐based cognitive therapy in prevention of depressive relapse: An individual patient data meta‐analysis from randomized trials. JAMA Psychiatry, 73, 565–574. Nezu, A. M., Nezu, C. M., Greenberg, L. M., & Salber, K. E. (2014). Cancer and depression. In C. S. Richards & M. W. O’Hara (Eds.), The Oxford handbook of depression and comorbidity (pp. 302–318). New York, NY: Oxford University Press. Nylen, K. J., O’Hara, M. W., Brock, R., Moel, J., Gorman, L., & Stuart, S. (2010). Predictors of the longitudinal course of post‐partum depression following interpersonal psychotherapy. Journal of Consulting and Clinical Psychology, 78, 757–763. O’Hara, M. W., Schiller, C. E., & Stuart, S. (2010). Interpersonal psychotherapy and relapse prevention for depression. In C. S. Richards & M. G. Perri (Eds.), Relapse prevention for depression (pp. 77–97). Washington, DC: American Psychological Association. O’Hara, M. W., Stuart, S., Gorman, L. L., & Wenzel, A. (2000). Efficacy of interpersonal psychotherapy for postpartum depression. Archives of General Psychiatry, 57, 1039–1045. Reynolds, C. F., Dew, M. A., Pollock, B. G., Mulsant, B. H., Frank, E., Miller, M. D., … Kupfer, D. J. (2006). Maintenance treatment of major depression in old age. New England Journal of Medicine, 354, 1130–1138. Reynolds, C. F., Frank, E., Perel, J. M., Imber, S. D., Cornes, C., Miller, M. D., … Kupfer, D. J. (1999). Nortriptyline and interpersonal psychotherapy as maintenance therapies for recurrent major depression: A randomized controlled trial in patients older than 59 years. JAMA, 281, 39–45. Richards, C. S., Cohen, L. M., Morrell, H. E. R., Watson, N. L., & Low, B. E. (2013). Treating depressed and anxious smokers in smoking cessation programs. Journal of Consulting and Clinical Psychology, 81, 263–273. Richards, C. S., & O’Hara, M. W. (Eds.) (2014a). The Oxford handbook of depression and comorbidity. New York, NY: Oxford University Press. Richards, C. S., & O’Hara, M. W. (2014b). Introduction. In C. S. Richards & M. W. O’Hara (Eds.), The Oxford handbook of depression and comorbidity (pp. 1–10). New York, NY: Oxford University Press. Richards, C. S., & Perri, M. G. (1978). Do self‐control treatments last? An evaluation of behavioral problem solving and faded counselor contact as treatment maintenance strategies. Journal of Counseling Psychology, 25, 376–383. Richards, C. S., & Perri, M. G. (2002). Depression. Thousand Oaks, CA: Sage Publications. Richards, C. S., & Perri, M. G. (Eds.) (2010a). Relapse prevention for depression. Washington, DC: American Psychological Association.

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Richards, C. S., & Perri, M. G. (2010b). Epilogue: Top 10 guidelines for practitioners. In C. S. Richards & M. G. Perri (Eds.), Relapse prevention for depression (pp. 271–281). Washington, DC: American Psychological Association. Serge, L. S., Brock, R. L., & O’Hara, M. W. (2015). Depression treatment for impoverished mothers by point‐of‐care providers: A randomized controlled trail. Journal of Consulting and Clinical Psychology, 83, 314–324. Smith, T. W. (2010). Depression in chronic medical illness: Implications for relapse prevention. In C. S. Richards & M. G. Perri (Eds.), Relapse prevention for depression (pp. 199–225). Washington, DC: American Psychological Association. Stroud, C. B., Feinstein, B. A., Bhatia, V., Hershenberg, R., & Davila, J. (2014). Intimate relationships. In C. S. Richards & M. W. O’Hara (Eds.), The Oxford handbook of depression and comorbidity (pp. 441–459). New York, NY: Oxford University Press. Trucco, E. M., Villafuerte, S., Hussong, A., Burmeister, M., & Zucker, R. A. (2018). Biological underpinnings of an internalizing pathway to alcohol, cigarette, and marijuana use. Journal of Abnormal Psychology, 127, 79–91. Watson, D., & O’Hara, M. W. (2017). Understanding the emotional disorders: A symptom‐level approach based on the IDAS—II. New York, NY: Oxford University Press. Weissman, M. M., Markowitz, J. C., & Klerman, G. L. (2017). The guide to interpersonal psychotherapy (Updated ed.). New York, NY: Oxford University Press.

Suggested Reading Kuyken, W., Warren, F. C., Taylor, R. S., Whalley, B., Crane, C., Bondolfi, G., … Dalgleish, T. (2016). Efficacy of mindfulness‐based cognitive therapy in prevention of depressive relapse: An individual patient data meta‐analysis from randomized trials. JAMA Psychiatry, 73, 565–574. Reynolds, C. F., Frank, E., Perel, J. M., Imber, S. D., Cornes, C., Miller, M. D., … Kupfer, D. J. (1999). Nortriptyline and interpersonal psychotherapy as maintenance therapies for recurrent major depression: A randomized controlled trial in patients older than 59 years. JAMA, 281, 39–45. Richards, C. S., & Perri, M. G. (Eds.) (2010). Relapse prevention for depression. Washington, DC: American Psychological Association.

Eating Disorders Eliot Dennard Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA

Eating disorders (EDs) refer to a group of disorders that are characterized by abnormal thoughts, feelings, and behaviors related to food and weight. These disorders often begin in adolescence and are much more common in females than males (American Psychiatric Association [APA], 2013). Individuals with EDs often struggle with other psychological c­oncerns, including depression, anxiety, and substance use. Furthermore, EDs are also associated with medical complications. These factors make the treatment of EDs more complicated. This chapter includes an overview of EDs, including risk factors, comorbidity, complications, and treatment.

Types of Eating Disorders Anorexia Nervosa Anorexia nervosa (AN) describes a disorder characterized by self‐starvation and body image disturbance. To be diagnosed with AN, an individual must meet the following criteria according to the Diagnostic and Statistical Manual of Mental Disorders‐5 (DSM‐5): restriction of caloric intake leading to a low body weight, a fear of gaining weight, and body image disturbance (APA, 2013). AN is a relatively rare psychiatric disorder. Prevalence estimates of AN vary, but one recent nationally representative survey found the lifetime prevalence rates of AN to be 0.9% for women and 0.3% for men (Hudson, Hiripi, Pope, & Kessler, 2007).

Bulimia Nervosa Bulimia nervosa (BN) is characterized by episodes of binge eating followed by purging. According to the APA (2013), to be diagnosed with BN, an individual must meet the following criteria: frequent episodes of binge eating followed by engaging in “compensatory

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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­ ehaviors,” and self‐evaluation that is heavily dependent on one’s weight and size. To be b ­considered an eating binge, the individual must feel a loss of control over eating, which results in the individual eating an unusually large amount of food in short period of time. Compensatory behaviors are designed to rid the individual of the calories just consumed and can include behaviors such as self‐induced vomiting, laxative and diuretic use, and excessive exercise. These symptoms must have occurred at least once a week over the course of 3 months (APA, 2013). The lifetime prevalence estimates for BN are 1.5% among women and 0.5% among men (Hudson et al., 2007).

Binge Eating Disorder Binge eating disorder (BED) is characterized by eating binges without the use of ­compensatory behaviors. There also must be distress experienced related to the disordered eating (APA, 2013). The disordered eating must have occurred at least twice a week for 6 months to be classified as BED (APA, 2013). Lifetime prevalence estimates of BED are 3.5% for women and 2.0% for men, which is higher than that of AN and BN (Hudson et al., 2007).

Other Feeding and Eating Disorders There are several other feeding and EDs described in the DSM‐5 (APA, 2013). Pica involves the ingestion of “nonnutritive, nonfood substances” (APA, 2013, p. 329). Rumination disorder involves the regurgitation of food that is either rechewed and swallowed or spit out. Avoidant/restrictive food intake disorder is characterized by an avoidance of food, including sensory stimulation related to food and concern about negative consequences of eating food. This often results in malnutrition, weight loss, and the need for nutritional supplementation. Avoidant/restrictive food intake disorder is distinguished from AN in that with avoidant/ restrictive food intake disorder, there is not the same fear of weight gain nor is there body image disturbance (APA, 2013). These other feeding and EDs, particularly Pica, are often comorbid with intellectual disabilities. For the remainder of this chapter, the primary focus will be on AN, BN, and BED.

Subthreshold Levels of Eating Disorders The current method of defining EDs in the DSM‐5 uses a categorical approach, which means that individuals either meet diagnostic criteria or they do not. However, both in research and in clinical practice, individuals presenting with eating concerns often do not meet full diagnostic criteria but still experience significant distress related to food and body image. As such, disordered eating is often conceptualized as a continuum of behaviors and feelings about weight and food, rather than the traditional categorical approach. By taking a continuum approach to understanding disordered eating, EDs can be understood as all of the gray area between normal eating and severe, diagnosable EDs. In the research literature, varying levels of disordered eating presentations are often described as subthreshold EDs and partial EDs. For instance, according to Stice, Marti, Shaw, and Jaconis (2009), a subthreshold ED is defined by meeting all of the criteria for a diagnosable ED, except not at the levels specified by the diagnostic criteria. For example, an individual who engages in binging and purging behaviors less than once a week (as opposed to the once a week required by diagnostic standards) would be considered to have subthreshold BN. Similarly, an individual who only meets a

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s­ ubset of the required symptoms for diagnosis would be considered to have a partial ED (Stice et al., 2009). A substantial portion of individuals that struggle with disordered eating have subthreshold and partial levels of EDs, as compared with the relatively small amount of individuals with diagnosable EDs. For example, Stice et al. (2009) found that 12% of adolescents fall somewhere on the continuum of disordered eating, which is substantially more than the prevalence of full threshold EDs, which is around 2%. Therefore, it is important for clinicians and researchers to consider the full continuum of disordered eating in both research and practice.

Risk Factors The research literature has identified a number of factors that contribute to the development of disordered eating symptoms. Understanding different risk factors can hopefully lead to more targeted prevention efforts, as well as better treatment approaches. Although a plethora of research literature has described different risk factors for developing disordered eating, only a small portion of that is presented here.

Biological and Genetic Risk Factors Like many psychological disorders, EDs also seem to have biological and genetic factors that contribute. In Jacobi, Hayward, de Zwaan, Kraemer, and Stewart Agras’ (2004) review article of risk factors of EDs, several twin studies are cited, almost all of which found a higher concordance rate of EDs in monozygotic twins, as compared with dizygotic twins. These finds suggest that there is a genetic component to EDs (i.e., EDs are not solely caused by environmental factors). Recent advances with gene mapping have found several genes that seem linked with EDs (Jacobi et al., 2004). There will undoubtedly be further advances in understanding EDs from a biological perspective, which may lead to advances in pharmacological treatment of EDs.

Psychological, Social, and Cultural Risk Factors The research literature provides evidence for a number of psychological and social risk factors that contribute to disordered eating. Western culture in particular prizes the female body and strongly values thinness (Striegel‐Moore & Bulik, 2007). In addition, having weight concerns has been found to predict subthreshold and full threshold EDs (Killen et al. as cited by Keel & Forney, 2013). Some models even posit that cultural ideals surrounding thinness lead to having weight concerns and body dissatisfaction, which then leads to disordered eating (e.g., see Striegel‐Moore & Bulik, 2007). However, individuals from other cultural backgrounds, including Latina women, also develop EDs (Marques et  al., 2011). Thus the relationships among cultural variables, body dissatisfaction, weight concerns, and disordered eating are complicated and certainly not fully understood. Several other risk factors have been supported in the literature. Both negative affect/­negative emotionality and perfectionistic tendencies have been found to predict disordered eating (Keel & Forney, 2013). High levels of depressive symptoms have also been found to predict disordered eating (Dennard & Steven Richards, 2013). Trauma has also been found to be a significant risk factor for developing disordered eating. Brewerton’s (2007) review of the literature suggested that childhood sexual abuse, as well as

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other forms of abuse and neglect, is significantly associated with EDs, particularly BN. In addition, trauma increases comorbidity in those with EDs (Brewerton, 2007). There are many more hypothesized risk factors for the development of an ED, ranging from dysfunctional family‐of‐origin experiences to cognitive rigidity (see Jacobi et al., 2004 for a more complete overview). Understanding different risk factors will hopefully lead to more successful prevention and treatment efforts.

Diversity Considerations Gender and cultural factors are an important consideration to explore in their relation to EDs. Several studies have found that the prevalence of disordered eating is similar in women of ­different ethnicities (e.g., Marques et al., 2011). However, the expression and cluster of symptoms may be impacted by gender and/or cultural factors. For example, one study utilized Caucasian, Latina, and African American female undergraduate students (Gordon, Castro, Sitnikov, & Holm‐Denoma, 2010). Participants were asked their perception of the ideal body type for members of their cultural group. Results indicated that Caucasian women perceived that slimmer body types were ideal for their ethnicity than both Latinas and African Americans. Latinas indicated that their cultural group preferred a slimmer body than the African American participants. Participants also indicated their personal ideal body type (as opposed to ideal body types of the culture at large). There were no differences between Caucasian and Latina women on personal ideal body type. African American women had larger personal ideal body types than did the other two groups (Gordon et al., 2010). This study highlights how cultural considerations may affect body image concerns. In addition, acculturation levels and acculturative stress may play a role in EDs in individuals from ethnic minority groups. In a sample of Mexican American women with and without EDs, a stronger orientation toward “Anglo American” culture was predictive of disordered eating, whereas a stronger identification with and orientation toward Mexican culture was not associated with disordered eating (Cachelin, Phinney, Schug, & Striegel‐Moore, 2006). Acculturative stress, which is defined “as the difficulties and psychological toll that arise from the process of adapting to a new cultural context,” is also an important factor to consider (Warren & Rios, 2012, p. 4). In a sample of Latino men, acculturative stress was correlated with body image disturbance, while acculturation was not (Warren & Rios, 2012). Therefore, it may be important to consider how acculturation and acculturative stress may contribute to the development of an ED. Gender differences also seem to impact EDs. One indicator of an ED is an individual’s dissatisfaction with his or her weight and body shape. However, this particular symptom may present somewhat differently in men than it does in women. For example, in a sample of undergraduate men and women with and without EDs, Ousley, Cordero, and White (2008) found that both men and women with EDs showed concern about body shape and tone. The women with EDs showed greater concern with weight and fat, while the men showed greater concern with muscle tone. Gender differences are also evident in individuals hospitalized for AN. Over the period of 16 years, data from both male and female patients admitted to a hospital‐based treatment program were analyzed (Gueguen et al., 2012). The males in this study presented for treatment later, had higher mortality rates, were more likely to have been overweight in the past, and were less likely to have a history of suicidality. The females in the study were more likely to be involved in a romantic relationship and living with a partner. Therefore, there may be differences in the interpersonal functioning between men and women with EDs (Gueguen et al., 2012).

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Among males with EDs, sexual orientation is correlated with disordered eating. One study found that the rates of EDs are higher among men that identify as gay or bisexual than among men that identify as heterosexual (Feldman & Meyer, 2007). Further research will contribute to the understanding of how diversity‐related factors impact both the development and treatment of EDs.

Complications EDs are often associated with significant health consequences, which often necessitates monitoring by a physician. AN, in particular, often has very severe health consequences associated with malnutrition. Cardiac complications, including sinus bradycardia, decreased heart rate, and mitral valve prolapse, are often serious and are associated with at least one‐third of the deaths related to EDs (Katzman, 2005). Issues with bone mass, including osteopenia and osteoporosis, are also associated with AN. The effects of losing bone mass are often permanent (Katzman, 2005). Several endocrine consequences, including amenorrhea, hypoglycemia, and thyroid abnormalities, are also potential health consequences of AN (Mehler & Brown, 2015). AN is also associated with reproductive consequences, including an increased risk of miscarriage, lower weight babies, and infertility (Mehler & Brown, 2015). Finally, in cases of severe AN, changes in brain volume can be seen using MRI technology. These changes are presumed to be a result of malnutrition (Mehler & Brown, 2015). Cardiovascular issues, such as arrhythmias, hypotension, and bradycardia, are also associated with BN (Casiero & Frishman, 2006). Esophageal complications are also associated with BN and can even become life threatening in the case of esophageal rupture (Mehler, 2011). Many methods of purging, including laxative and diuretic use, often produce electrolyte imbalances, which require medical intervention (Mehler, 2011). BED is also associated with health complications. One potential implication of binge eating is an increase in body weight, which in itself is associated with negative health outcomes. In one study, obese women that engaged in binge eating had higher rates of medical disorders (e.g., hypertension) than their obese counterparts that did not engage in binge eating (Bulik, Sullivan, & Kendler, 2002). One longitudinal study found that those with BED are at greater risk of developing components of metabolic syndrome (i.e., type 2 diabetes, hypertension, dyslipidemia) when compared with a comparison group individuals of similar body mass index (Hudson et al., 2010). Thus, BED seems to have health complications above and beyond the health risks of being overweight. Individuals with EDs also have a higher mortality rate as a result of these health consequences and an increased rate of suicide. In fact, AN has the highest mortality rate of any psychiatric disorder. Mortality rates for AN have been found to be around 5% (Steinhausen, 2002). Because of the significant health consequences of EDs, early intervention and medical monitoring are crucial.

Comorbidity EDs are associated with increased rates of comorbidity with other psychiatric diagnoses, including mood disorders, anxiety disorders, and substance use disorders. Understanding and evaluating individuals for comorbid conditions is especially important because it likely impacts both the severity of the ED and the treatment approach used. For instance, one study of

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a­ dolescent females with EDs found that the participants with comorbid depression and anxiety had significantly more hospitalizations, suicide attempts, and higher scores on subscales of self‐reported measures (e.g., eating concerns scores, body dissatisfaction scores, perfectionism, etc.) (Brand‐Gothelf, Leor, Apter, & Fenning, 2014). Rates of anxiety disorders in individuals with EDs are higher than in individuals without EDs. In one study, 63.5% of participants with EDs “were diagnosed with at least one lifetime anxiety disorder” (Kaye, Bulik, Thornton, Barbarich, & Masters, 2004, p. 2217). Obsessive– compulsive disorder was the most frequently diagnosed among the sample (40%), and social anxiety disorder was the next most frequently diagnosed anxiety disorder in the sample (20%). The rates of different anxiety disorders did not vary depending on ED diagnosis (i.e., AN and BN) (Kaye et al., 2004). Many studies have found a strong association between depressive and disordered eating symptoms. The rates of comorbidity between eating and mood disorders, particularly depression, may be as high as 94% (Blinder, Cumella, & Sanathara, 2006). Among high school students, strong positive correlations have also been found between disordered eating and depression (Santos, Steven Richards, & Kathryn Bleckley, 2007). Depressive symptoms have also been found to predict disordered eating in a sample of college females (Dennard & Steven Richards, 2013). Substance use disorders are often common among individuals with EDs, particularly among those with BN. One study found that individuals with BN were three times more likely to have substance use concerns than individuals with AN (Blinder et al., 2006). This comorbidity is particularly important, as alcohol and substance use disorders are a significant predictor of mortality for individuals with AN (Keel et al., 2003).

Treatment Conventional wisdom suggests that utilizing a treatment team approach to treat EDs is the most beneficial. EDs are complex disorders that affect an individual psychologically, nutritionally, and medically. As such, EDs are often best treated by a team of professionals collaborating to provide holistic care. Typical members of a treatment team consist of a therapist or ­psychologist, nutritionist or dietician, and a physician. Therefore, individuals with EDs often receive psychotherapy, nutritional counseling, and medical monitoring. Treatment is generally not only provided on an outpatient basis but can also include partial hospitalization and inpatient/residential treatment.

Psychotherapy The research supporting psychological therapies for the treatment of EDs, particularly AN, is disappointingly sparse. This is likely due to the low prevalence of the disorder, medical ­complications requiring hospitalization that interfere with study completion, extended length of time needed for treatment, and individual’s negative attitudes related to recovery (Wilson, Grilo, & Vitousek, 2007). However, recent studies are beginning to show promise in treating both AN and BN with psychotherapy. Anorexia Nervosa Few studies have examined the efficacy of psychological therapies for AN. Many of the studies on the treatment of AN have found low recovery rates and often inconclusive results. High

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levels of attrition within these studies are another complication of conducting randomized controlled trials with this population (Wilson et al., 2007). Despite the limited research on treating EDs, there are several psychological therapies often used, and some of those therapies have empirical support. One randomized controlled trial compared interpersonal therapy (IPT), cognitive behavior therapy (CBT), and nonspecific supportive clinical management therapy for women with AN. The nonspecific supportive clinical management provided support, praise, advice, and education related to healthy eating behaviors. This treatment also focused on fostering a strong therapeutic alliance between the patient and clinician. The results of the study showed that the group of patients receiving the nonspecific supportive clinical management did as well as, and even better than, those treated with interpersonal or cognitive behavioral therapy (McIntosh et al., 2005). Another randomized controlled trial compared a novel therapy for AN, termed the Maudsley model of AN treatment for adults (MANTRA), with “specialist supportive clinical management” (SSCM) for adults with AN (Schmidt et al., 2015). The MANTRA treatment utilized elements of both CBT and IPT to target traits such as obsessiveness, avoidance, and anxiousness that are often central in AN. The treatment also incorporated aspects of motivational interviewing. The other treatment arm of the study utilized SSCM, which included clinical management (e.g., providing advice and psychoeducation) and supportive therapy. Participants completed outcome measures at baseline, 6, and 12 months. Both groups showed substantial improvement in body weight, mood, and other psychological factors at the completion of treatment. There were no differences in body mass index in either treatment group following treatment. Many of the other study outcomes, including assessments related to shape/weight concern, depression, and anxiety, were similar in both treatment groups. However, those in the MANTRA group reported higher treatment acceptability and credibility than those in the SSCM group. Furthermore, MANTRA seemed more effective in increasing body mass index in the most severely underweight participants. The results of this study suggest that a variety of therapeutic approaches may be helpful in treating AN (Schmidt et al., 2015). Following weight restoration in underweight patients with AN, outpatient CBT has been shown to have some benefits. Pike, Walsh, Vitousek, Wilson, and Bauer (2003) utilized a sample of women who had completed inpatient treatment for AN and achieved a weight gain of at least 90% of an ideal body weight. Participants were randomly assigned to one of two treatment conditions, either CBT or nutritional counseling. Both treatments were administered individually for 50 sessions. The results found that the patients receiving CBT relapsed later in treatment (if at all) than those receiving nutritional counseling. Furthermore, the number of patients prematurely ending treatment before session 10 was higher in the nutritional counseling group. Thus, it appears that CBT is beneficial and is delaying or ­preventing relapse in individuals who previously completed inpatient treatment for AN (Pike et al., 2003). Family therapy is often the treatment of choice for adolescents with AN, and it has also received considerable empirical support. One such study compared family‐based treatment (FBT) with adolescent‐focused therapy for adolescents with AN (Lock et al., 2010). The FBT was conducted in three phases, including a weight restoration phase, followed by a transition phase when decisions related to food and weight were given back to the adolescent, and ending with a phase designed to enhance the relationship between the adolescent and the parent. The adolescent‐focused therapy arm of the study focused on helping adolescents understand, identify, and tolerate emotions, as opposed to using starvation as a means of regulating ­emotion. Rates of symptom remission were similar between the two therapies by the end of

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the study. However, at follow‐up evaluations (6‐ and 12‐month posttreatment), the FBT group had superior outcomes (Lock et al., 2010). Despite the limited empirical support for treating AN, the American Psychiatric Association’s (APA) Practice Guidelines for treating AN include “psychodynamically informed” therapy during the initial refeeding and weight gain process. Therapy should incorporate “empathic understanding, explanations, praise for positive efforts, coaching, support, encouragement, and other positive behavioral reinforcement” (p. 16). Providing these elements in therapy may increase the patient’s comfort in therapy, which could potentially reduce premature termination of therapy. For children and adolescents with AN, the guidelines recommended the Maudsley model of family therapy, which is similar to the FBT previously described (APA, 2006). This approach generally requires around 20 sessions over 12 months. The initial phase of treatment helps restore weight in the patient with AN by having the parents control and regulate their child’s caloric intake. In subsequent phases of treatment, the patient is gradually allowed more autonomy in his/her eating choices (Pike, Gianini, Loeb, & Le Grange, 2015). Following weight restoration, the guidelines recommend CBT to prevent relapse and note that IPT and psychodynamically oriented therapy are also often used with success in clinical practice (APA, 2006). Bulimia Nervosa For BN, both CBT and IPT have empirical support. Generally, CBT for BN requires 16–20 sessions. Aims of the treatment include CBT approaches to increase motivation, modify eating patterns, reduce shape/weight concerns, and reduce likelihood of relapse (Wilson et  al., 2007). CBT for BN eliminates binging and purging behaviors in approximately 30–50% of cases (Wilson et al., 2007). Several interventions are often used in CBT for BN. Initial stages of therapy often focus on increasing patient motivation and engagement in the treatment process. Self‐monitoring of eating behaviors and associated behaviors, thoughts, and feelings is another initial component of CBT for BN. Another essential element of CBT for BN is psychoeducation. During this process, the clinician provides the patient with information about the harmful effects of purging, laxative use, etc., as well as how those behaviors are ineffective weight management strategies. Subsequent stages of treatment focus on addressing shape and weight concerns, dietary rules, low self‐esteem, and interpersonal concerns (Murphy, Straebler, Cooper, & Fairburn, 2010). CBT has also been shown to be superior to psychoanalytic therapy in treating BN. For example, a recent randomized controlled trial assigned 70 patients with BN to receive either 2 years of weekly psychoanalytic psychotherapy or 20 sessions of CBT (Poulsen et al., 2014). Although both treatments resulted in improvement, those receiving CBT fared much better than those receiving psychoanalytic psychotherapy. Abstinence from binging and purging behaviors was achieved after 5 months for 42% of the patients in the CBT group, compared with 6% of the patients in the psychoanalytic group. At the end of the 2‐year study, abstinence of binging and purging was maintained by 44% of patients in the CBT group and only 15% in the psychoanalytic group (Poulsen et al., 2014). The results found in randomized clinical trials using CBT for BN seem to translate to clinical practice as well. For example, Waller et al. (2014) utilized CBT to treat individuals with BN in an outpatient therapy clinic. There were few exclusion criteria, and the implementation of the therapy was less manualized than in typical randomized controlled trials. The results of the study showed a remission rate of close to 50%, which is similar to the results observed in randomized clinical trials (Waller et al., 2014). Thus, CBT seems effective in “real‐world” practice as well.

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Dialectical behavior therapy (DBT), which is often considered a “third‐wave CBT,” also shows some promise in the treatment of BN. For example, in a small study, 30 participants with BN were assigned to either 20 sessions of DBT or a 20‐week wait list group. The DBT used was specifically designed to teach emotion‐regulation skills. Those in the DBT group showed much greater reductions in binging and purging episodes than did their wait list group counterparts (Shafer, Telch, & Agras, 2001). There is also empirical support for the use of IPT in treating BN. IPT was originally developed for treating depressive disorders and focuses on modifying interpersonal patterns that contribute to disordered eating (Wilson et al., 2007). One study compared a group of participants that completed CBT with a group that had completed IPT. The results showed no significant differences between the groups at the 1‐year follow‐up. However, measurements taken at the end of treatment showed greater improvement in the group treated with CBT (Agras, Walsh, Fairburn, Terrance Wilson, & Kraemer, 2000). Thus, IPT seems to have equivalent long‐term results and may also be an effective choice for treating BN, but CBT may show quicker results. The APA’s Practice Guidelines (2006) for treating BN suggest using CBT to address acute symptoms. IPT is also recommended as a possible initial therapy, or for patients that do not respond to CBT. In adolescents, family therapy is suggested, and couple’s therapy is suggested for adults experiencing conflict in romantic relationships (APA, 2006).

Medical Management Medical monitoring by a qualified medical professional with experience treating EDs is another component of treatment. A medical provider’s role includes assessing degree of malnutrition and providing medical supervision and monitoring during the individual’s treatment process (Golden et al., 2015). This includes assessment and management of common physical complications of EDs (e.g., cardiac complications, electrolyte imbalances). More severe EDs often require inpatient medical treatment. Some possible reasons for hospitalization include severe electrolyte disturbances, hypothermia, food refusal, extreme low weight (i.e., weighing less than 75% of an ideal body weight), physiological instability, and suicidal ideation (Golden et al., 2015). In addition to medical monitoring, pharmacotherapy may also be beneficial for individuals with BN. Flament, Bissada, and Spettigue (2012) reviewed randomized controlled trials utilizing monoamine oxidase inhibitors (MAOIs), tricyclic antidepressants (TCAs), and selective serotonin reuptake inhibitors (SSRIs). Results showed that all three classes of medication were more effective than a placebo in reducing binge eating (Flament et al., 2012). Topiramate, an antiepileptic drug, has been shown to reduce binge eating in individuals with BED. When compared with group of participants receiving a placebo, the individuals receiving topiramate showed a significant reduction in several outcome measures, including binge frequency and weight (McElroy et al., 2003).

Nutritional Therapy A registered dietician is another integral part of the treatment team. Registered dieticians can provide nutritional therapy to individuals with EDs and focus on food‐related issues faced by the individual with an ED. The goals of nutritional therapy are to provide the individual with psychoeducation related to nutrition, develop a meal plan, and begin to develop healthier ­eating patterns. Dieticians can also be instrumental in monitoring the possible effects of ­malnutrition (Henry & Ozier, 2006).

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Partial Hospitalization Partial hospitalization programs (PHP) are also an option for the treatment of EDs. In these programs, patients are usually provided with 8–12 hr/day of treatment that usually includes individual therapy, group therapy, nutritional therapy, and medical monitoring. In addition, patients are supervised during meal times while at the program. Patients return home on the evenings and weekends. Alternatively, some programs offer “supportive housing” during the patient’s time in the PHP and while transitioning out of PHP (Tantillo, MacDowell, Anson, Taillie, & Cole, 2009).

Residential Treatment Residential treatment programs offer more intensive treatment in a nonhospital setting. Most treatment centers adopt an integrative approach to treatment, incorporating CBT, IPT, and DBT. Individuals receive therapy in a variety of formats while in residential treatment, which includes both individual and group therapy. Many programs also implement 12‐step work, art therapy, yoga, and meditation. One potential disadvantage of residential treatment is that it often requires a significant financial commitment and is sometimes not covered by health insurance (Frisch, Herzog, & Franko, 2006).

Prognosis Although there are numerous treatment options for EDs, the prognosis is often discouraging. One longitudinal outcome study of individuals with AN found that 47.5% of the individuals in the study still met criteria for AN, BN, or ED‐NOS at 12‐year follow‐up (Fitcher, Quadflieg, & Hedlund, 2006). Early intervention is especially important in treating EDs. As research advances, more knowledge will possibly help facilitate better methods of treating EDs.

Conclusion Although EDs have been described as far back as the dark ages and into the Renaissance, research related to etiology and treatment of EDs is still somewhat lacking. However, research has determined several potential contributors to developing disordered eating, including ­biological, social, cultural, and psychological correlates. As scientific research advances, there will likely be a deeper understanding of what causes EDs, which will likely lead to more efficacious treatments. Currently, several treatment strategies indicate the possibility of a good prognosis for individuals suffering from EDs. Having professionals from multiple fields working with individuals with EDs seems to be the best approach, as these individuals have ­psychological, physical, and nutritional needs that must be addressed.

Author Biography Eliot Dennard, PhD, is a counseling psychologist in private practice. She received her undergraduate degree from the University of Oklahoma and her masters and doctoral degrees from Texas Tech University. Although she primarily works in clinical practice, her previous research focused on predictors of disordered eating and gender and cultural differences in eating disorders.

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References Agras, W. S., Walsh, T., Fairburn, C. G., Terrance Wilson, G., & Kraemer, H. C. (2000). A multicenter comparison of cognitive‐behavioral therapy and interpersonal psychotherapy for bulimia nervosa. Archives of General Psychiatry, 57, 459–466. American Psychiatric Association. (2006). Practice guidelines for the treatment of eating disorders (3rd ed.). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: Author. Blinder, B. J., Cumella, E. J., & Sanathara, V. A. (2006). Psychiatric comorbidities of female inpatients with eating disorders. Psychosomatic Medicine, 68, 454–462. doi:10.1097/01.psy.0000221254. 77675.f5 Brand‐Gothelf, A., Leor, S., Apter, A., & Fenning, S. (2014). The impact of comorbid depressive and anxiety disorders on severity of anorexia nervosa in adolescent girls. The Journal of Nervous and Mental Disease, 202, 759–762. doi:10.1097/NMD. 0000000000000194 Brewerton, T. D. (2007). Eating disorders, trauma, and comorbidity: Focus on PTSD. Eating Disorders, 15, 285–304. doi:10.1080/10640260701454311 Bulik, C. M., Sullivan, P. F., & Kendler, K. S. (2002). Medical and psychiatric morbidity in obese women with and without binge eating. International Journal of Eating Disorders, 32, 72–80. doi:10.1002/eat.10072 Cachelin, F. M., Phinney, J. S., Schug, R. A., & Striegel‐Moore, R. H. (2006). Acculturation and eating disorders in a Mexican American community sample. Psychology of Women Quarterly, 30, 340–347. doi:10.1111/j.1471.6402.2006.00309.x Casiero, D., & Frishman, W. H. (2006). Cardiovascular complications of eating disorders. Cardiology in Review, 14, 227–231. doi:10.1097/01.crd.0000216745.96062.7c Dennard, E. E., & Richards, C. S. (2013). Depression and coping in subthreshold eating disorders. Eating Behaviors, 14, 325–329. doi:10.1016/j.eatbeh.2013.05.011 Feldman, M. B., & Meyer, I. H. (2007). Eating disorders in diverse lesbian, gay, and bisexual populations. International Journal of Eating Disorders, 40, 218–226. doi:10.1002/eat.20360 Fitcher, M. M., Quadflieg, N., & Hedlund, S. (2006). Twelve‐year course and outcome predictors of anorexia nervosa. International Journal of Eating Disorders, 39, 87–100. doi:10.1002/eat.20215 Flament, M. F., Bissada, H., & Spettigue, W. (2012). Evidence‐based pharmacotherapy of eating disorders. International Journal of Neuropsychopharmacology, 15, 189–207. doi:10.1017/S1461145711000381 Frisch, M. J., Herzog, D. B., & Franko, D. L. (2006). Residential treatment for eating disorders. International Journal of Eating Disorders, 39, 434–442. doi:10.1002/eat.20255 Golden, N. H., Katzman, D. K., Sawyer, S. M., Ornstein, R. M., Rome, E. S., Garber, A. K., … Kreipe, R. E. (2015). Update on medical management of eating disorders in adolescents. Journal of Adolescent Health, 56, 370–375. doi:10.1016/j.jadohealth.2014.11.020 Gordon, K. H., Castro, Y., Sitnikov, L., & Holm‐Denoma, J. M. (2010). Cultural body shape ideals and eating disorder symptoms among white, Latina, and black college women. Cultural Diversity and Ethnic Minority Psychology, 16, 135–143. doi:10.1037/a0018671 Gueguen, J., Godart, N., Chambry, J., Brun‐Eberentz, A., Foulon, C., Divac, S. M., … Huas, C. (2012). Severe anorexia nervosa in men: Comparison with severe AN in women and analysis of mortality. International Journal of Eating Disorders, 45(537), 545. doi:10.1002/eat.20987 Henry, B. W., & Ozier, A. D. (2006). Position of the American Dietetic Association: Nutrition intervention in the treatment of anorexia nervosa, bulimia nervosa, and other eating disorders. Journal of the American Dietetic Association, 106, 2073–2082. doi:10.1016/j.jada.2011.06.016 Hudson, J. I., Hiripi, E., Pope, H. G., & Kessler, R. C. (2007). The prevalence and correlates of eating disorders in the National Comorbidity Survey replication. Journal of Biological Psychiatry, 61, 348– 358. doi:10.1016/j.biopsych.2006.03.040 Hudson, J. I., Lalonde, J. K., Coit, C. E., Tsuang, M. T., McElroy, S. L., Crow, S. J., … Pope, H. G. (2010). Longitudinal study of the diagnosis of components of the metabolic syndrome in i­ ndividuals

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with binge eating disorder. The American Journal of Clinical Nutrition, 91, 1568–1573. doi:10.3945/ajcn.2010.29203 Jacobi, C., Hayward, C., de Zwaan, M., Helena, K., & Stewart Agras, W. (2004). Coming to terms with risk factors for eating disorders: Application of risk terminology and suggestions for a general ­taxonomy. Psychological Bulletin, 130, 19–65. doi:10.1037/0033‐2909.130.1.19 Katzman, D. K. (2005). Medical complications in adolescents with anorexia nervosa: A review of the literature. International Journal of Eating Disorders, 37, S52–S59. doi:10.1002/eat.20118 Kaye, W. H., Bulik, C. M., Thornton, L., Barbarich, N., & Masters, K. (2004). Comorbidity of anxiety disorders with anorexia and bulimia nervosa. American Journal of Psychiatry, 161, 2215–2221. doi:10.1176/appi.aip.161.12.2215 Keel, P. K., Dorer, D. J., Eddy, K. T., Franko, D., Charatan, D. L., & Herzog, D. B. (2003). Predictors of mortality in eating disorders. Archives of General Psychiatry, 60, 179–183. doi:10.1001/ archpsyc.60.2.179 Keel, P. K., & Jean Forney, K. (2013). Psychosocial risk factors for eating disorders. International Journal of Eating Disorders, 46, 433–439. doi:10.1002/eat.22094 Lock, J., Le Grange, D., Stewart Agras, W., Moye, A., Bryson, S. W., & Jo, B. (2010). Randomized clinical trial comparing family‐based treatment with adolescent focused individual therapy for ­adolescents with anorexia nervosa. Archives of General Psychiatry, 67, 1025–1032. doi:10.1001/ archgenpsychiatry.2010.128 Marques, L., Algeria, M., Becker, A. E., Chen, C.‐n., Fang, A., Chosak, A., & Diniz, J. B. (2011). Comparative prevalence, correlates of impairment, and service utilization for eating disorders across US ethnic groups: Implications for reducing ethnic disparities in health care access for eating disorders. International Journal of Eating Disorders, 44, 412–420. doi:10.1002/eat.20787 McElroy, S. L., Arnold, L. M., Shapira, N. A., Keck, P. E., Rosenthal, N. R., Rezaul Karim, M., & Hudson, J. I. (2003). Topiramate in the treatment of binge eating disorder associated with obesity: A randomized, placebo‐controlled trial. American Journal of Psychiatry, 160, 255–261. doi:10.1176/ appi.ajp.160.2.255 McIntosh, V. V. W., Jordan, J., Carter, F. A., Luty, S. E., McKenzie, J. M., Bulik, C. M., … Joyce, P. R. (2005). Three psychotherapies for anorexia nervosa: A randomized, controlled trial. American Journal of Psychiatry, 162, 741–747. doi:10.1176/appi.ajp.162.4.741 Mehler, P. S. (2011). Medical complications of bulimia nervosa and their treatments. International Journal of Eating Disorders, 44, 95–104. doi:10.1002/eat.20825 Mehler, P. S., & Brown, C. (2015). Anorexia nervosa‐medical complications. Journal of Eating Disorders, 3, 1–8. doi:10.1186/s40337‐015‐0040‐8 Murphy, R., Straebler, S., Cooper, Z., & Fairburn, C. G. (2010). Cognitive behavioral therapy for eating disorders. Psychiatric Clinics of North America, 33, 611–627. doi:10.1016/j.psc.2010.04.004 Ousley, L., Cordero, E. D., & White, S. (2008). Eating disorders and body image of undergraduate men. Journal of American College Health, 56, 617–621. doi:10.3200/JACH.56.6.617‐622 Pike, K. M., Walsh, B. T., Vitousek, K., Wilson, G. T., & Bauer, J. (2003). Cognitive behavior therapy in posthospitalization treatment of anorexia nervosa. American Journal of Psychiatry, 160, 2046– 2049. doi:10.1176/appi.ajp.160.11.2046 Pike, K. M., Gianini, L. M., Loeb, K. L., & Le Grange, D. (2015). Treatments for eating disorders. In P. E. Nathan & J. M. Gorman (Eds.), A guide to treatments that work (pp. 641–658). New York, NY: Oxford University Press. Poulsen, S., Lunn, S., Daniel, S. I. F., Folke, S., Mathiesen, B. B., Katznelson, H., & Fairburn, C. G. (2014). A randomized controlled trial of psychoanalytic psychotherapy or cognitive‐behavioral therapy for bulimia nervosa. American Journal of Psychiatry, 171, 109–116. doi:10.1176/appi. ajp.2013.12121511 Santos, M., Steven Richards, C., & Kathryn Bleckley, M. (2007). Comorbidity between depression and disordered eating in adolescents. Eating Behaviors, 8, 440–449. doi:10.1016/j.eatbeh.2007.03.005 Schmidt, U., Magill, N., Renwick, B., Keyes, A., Kenyon, M., Dejong, H., … Landau, S. (2015). The Maudsley Outpatient Study of treatments for anorexia nervosa and related conditions (MOSAIC):

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Comparison of the Maudsley Model of Anorexia Nervosa Treatment for Adults (MANTRA) with Specialist Supportive Clinical Management (SSCM) in outpatients with broadly defined anorexia nervosa: A randomized controlled trial. Journal of Consulting and Clinical Psychology, 83, 796–807. doi:10.1037/ccp0000019 Shafer, D. L., Telch, C. F., & Agras, W. S. (2001). Dialectical behavior therapy for bulimia nervosa. American Journal of Psychiatry, 158, 632–634. doi:10.1176/appi.ajp.158.4.632 Steinhausen, H.‐C. (2002). The outcome of anorexia nervosa in the 20th century. American Journal of Psychiatry, 159, 1284–1293. doi:10.1176/appi.ajp.159.8.1284 Stice, E., Nathan Marti, C., Shaw, H., & Jaconis, M. (2009). An 8‐year longitudinal study of the natural history of threshold, subthreshold, and partial eating disorders from a community sample of adolescents. Journal of Abnormal Psychology, 118, 587–597. doi:10.1037/a0016481 Striegel‐Moore, R. H., & Bulik, C. M. (2007). Risk factors for eating disorders. American Psychologist, 62, 181–198. Tantillo, M., MacDowell, S., Anson, E., Taillie, E., & Cole, R. (2009). Combining supported housing and partial hospitalization to improve eating disorder symptoms, perceived health status, and health related quality of life for women with eating disorders. Eating Disorders, 17, 385–399. doi:10.1080/10640260903210172 Waller, G., Gray, E., Hinrichsen, H., Mountford, V., Lawson, R., & Patient, E. (2014). Cogntive‐ behavioral therapy for bulimia nervosa and atypical bulimic nervosa: Effectiveness in clinical settings. International Journal of Eating Disorders, 47, 13–17. doi:10.1002/eat.22181 Warren, C. S., & Rios, R. M. (2012). The relationships among acculturation, acculturative stress, endorsement of the Western media, social comparison, and body image in Hispanic male college students. Psychology of Men & Masculinity, 14(192), 201. doi:10.1037/a0028505 Wilson, G. T., Grilo, C. M., & Vitousek, K. M. (2007). Psychological treatment of eating disorders. American Psychologist, 62, 199–216. doi:10.1037/0003066X.62.3.199

Suggested Reading American Psychiatric Association. (2006). Practice guidelines for the treatment of eating disorders (3rd ed.). Washington, DC: Author. Halmi, K. A. (2013). Salient components of a comprehensive service for eating disorders. World Psychiatry, 8, 150–155. Hay, P. (2013). A systematic review of evidence for psychological treatments of eating disorders: 2005‐2012. International Journal of Eating Disorders, 46, 462–469. Wilson, G. T., Grilo, C. M., & Vitousek, K. M. (2007). Psychological treatment of eating disorders. American Psychologist, 62, 199–216.

Headaches Amy Wachholtz, Adam Harris, and Amy Frers Department of Psychology and University Health Sciences Center, University of Colorado Denver, Denver, CO, USA

Worldwide, headache disorders are a painful and disabling condition. According to the World Health Organization, headache disorders are the third leading cause of worldwide years lost due to disability (Institute for Health Metrics and Evaluation, 2013). The experience of a headache can fall into one of two categories. The first category is called a “primary” headache diagnosis. This is due to the headache as the sole diagnosis (e.g., migraine, tension, cluster). These forms of headaches are painful, and can be very disabling, but generally are benign. In the other category, the headache is “secondary” because rather than being the primary focus of treatment, the headache is a symptom of another illness, injury, or event. There are ­hundreds of reasons why someone may experience a secondary headache, from benign allergy‐related sinus pressure to the more serious condition of stroke.

Tension Headaches Epidemiology Tension‐type headache (TTH) is the second most prevalent of all disorders (Vos et al., 2012). The most commonly used diagnostic criteria (see Table 1 for diagnostic criteria) for TTH is the International Classification of Headache Disorders (ICDH), created by the International Headache Society (Headache Classification Committee of the International Headache Society, 2013).The annual prevalence of episodic tension‐type headache (ETTH) is estimated at 38%, and the annual prevalence of chronic tension‐type headache (CTTH) is 2–3% (Schwartz, Stewart, Simon, & Lipton, 1998). Its peak prevalence is between the ages of 30 and 39, and it is slightly more common among women than men (ratio of 5 : 4) (Chowdhury, 2012). For the 5.4% of workers in the United States who reported losing productive time due to headache, TTH accounted for about 70% of this reduction. This translates to an average of 2.9  hours per week of reduced performance at work and an annual estimated cost of The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Table 1  Tension‐type headache diagnostic criteria.

Frequency Duration Additional criteria

Infrequent episodic TTH

Frequent episodic TTH

10+/year;  85th percentile and  40 accrue costs that are two‐ to threefold greater than normal weight individuals (Arterburn, Maciejewski, & Tsevat, 2005). The social and psychological effects of obesity are profound and include social discrimination, personal distress, decreased emotional well‐being, and overall lower health quality of life (Puhl & Heuer, 2010).

Genetic and Environmental Contributors The determinants of obesity include genetic and environmental factors and their interaction. Several genetic profiles have been identified that suggest body weight is linked to inherited biological factors that regulate energy and expenditure (Albuquerque, Stice, Rodríguez‐ López, Manco, & Nóbrega, 2015). However, few specific genes have been identified that put an individual at increased genetic risk for obesity, and the expression of genetic influences on body weight appears to be mediated by the interaction between inherited genetic profile and environmental exposure (Rosenquist et al., 2015). During the latter half of the twentieth century, a variety of important environmental and cultural changes with the potential to influence the expression of genetic predisposition to weight gain occurred. These included (a) major modifications in industrial mechanization that

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reduced energy expenditure across a broad array of occupations; (b) the adoption of an automobile‐centered way of life and changes to the built environment that discouraged walking; (c) the rise of fast‐food restaurants and the ready availability of low‐cost, energy‐dense foods provided in larger serving sizes than home‐cooked meals; (d) the increased proportion of women in the workforce and the greater number of meals eaten away from home; and (e) the introduction and widespread adoption of screen‐based entertainment and information exchange technologies (e.g., televisions, personal computers, and the Internet). The confluence of these changes created an “obesogenic environment”—an environment that promoted high energy intake and low energy expenditure. Among genetically susceptible individuals in the population, this combination may have produced increased body weights, thereby contributing to the rise in the prevalence of obesity (Swinburn, Egger, & Raza, 1999). Many of these same factors that contributed to the rise in the prevalence of obesity in the United States are now at play in low‐ and middle‐income countries throughout the world, resulting in the growing global prevalence of obesity (WHO, 2014).

Treatment Options In the United States, an array of options is available for the management of obesity. These include self‐help approaches, commercial weight‐loss programs, behavioral or “lifestyle” ­treatments, pharmacotherapy, and bariatric surgery.

Self‐Help Approaches The majority of people attempting to lose weight do so on their own. However, while a­ necdotal reports of success are common, empirical studies show on average the degree of weight loss achieved via self‐help is quite modest. For example, a recent meta‐analysis of 23 studies of self‐help approaches to weight management (Hartmann‐Boyce, Jebb, Fletcher, & Aveyard, 2015) showed a mean loss of 1.8 kg at 6 months and no changes at 1 year. Recent technological advances have made Internet and mobile health (mHealth) programs for weight management widely available to help people lose weight on their own. Indeed, numerous devices and applications are available to assist people in tracking and modifying their physical activity, dietary intake, and body weight. However, very few high‐quality studies exist to provide empirical support regarding the efficacy of Internet and mHealth interventions for self‐management of obesity. Recent reviews suggest that interactive mHealth technologies can enhance the effectiveness of in‐person weight‐loss intervention, boosting mean weight losses by 1 kg, but the effects of Internet and mHealth programs as stand‐alone interventions appear to be minimal (Kodama et al., 2012; Okorodudu, Bosworth, & Corsino, 2015). Despite the limited effectiveness of self‐help programs in general, some individuals achieve and maintain substantial weight reductions. For example, the National Weight Control Registry (NWCR) (Wing & Phelan, 2005) tracks a cohort of obese individuals who have lost 30 pounds or more and maintained that loss for at least 1 year. Understanding factors associated with success among NWCR participants may offer insights into effective self‐help strategies for weight management. Most NWCR members reported using a combination of strategies including decreased energy intake (e.g., reducing fats and sugars, portion control, and counting calories) and increased physical activity (e.g., walking, swimming, and strength training) to achieve their initial weight reductions. To maintain their losses, individuals in the NWCR report p ­ articipating

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in approximately 1 hr/day of moderate‐intensity exercise, consuming fewer than 1,400 calories per day, eating the same foods on weekdays and weekends, consuming a healthy breakfast, exerting high levels of control over their eating, and self‐monitoring through weekly weighing and/or daily tracking of food intake. The NWCR participants appear to be a relatively unique group of people, and it is unclear how representative they are of the obese population. Nonetheless, the approaches they employ for managing their weight may be useful guides for others dealing with obesity.

Commercial Weight‐Loss Programs Although many overweight and obese individuals seek treatment through commercial weight‐loss programs, research regarding the effectiveness of such programs remains relatively limited. In a recent systematic review of 45 studies including 39 randomized controlled trials, Gudzune et  al. (2015) found Jenny Craig® and Weight Watchers® had 12‐month mean body weight reductions that were 4.9 and 2.6%, respectively, greater than controls. Nutrisystem® showed 3.8% greater body weight loss at 3 months compared with controls, but there was limited information available regarding its longer term effectiveness. Very‐low‐ calorie programs ( 30 (or a BMI > 27 with an obesity‐related comorbidity) may be appropriate candidates for pharmacotherapy as an adjunct to a comprehensive lifestyle intervention. Currently, there are five weight‐loss medications approved by the Food and Drug Administration (FDA) for long‐term use. Each medication, its mechanism of action, and weight changes commonly associated with its use in clinical trials are described briefly below. Orlistat (Xenical®) Orlistat inhibits lipase, the enzyme in the gastrointestinal tract responsible for fat digestion, and reduces the absorption of fats by approximately 30%. Pooled data from a meta‐analysis (Rucker, Padwal, Li, Curioni, & Lau, 2007) showed a mean weight reduction with orlistat of approximately 8.6 kg compared with 5.5 kg for dietary counseling plus placebo. This finding

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suggests that the incremental weight‐loss benefit of orlistat is approximately 3.1 kg. Common side effects associated with orlistat include gastrointestinal distress, including fecal urgency and fecal incontinence. Naltrexone/Bupropion (Contrave®) Naltrexone/bupropion is a combination of two FDA‐approved drugs, naltrexone and ­bupropion. Naltrexone is an opioid antagonist used to treat alcohol and opioid dependence. Bupropion is a norepinephrine–dopamine reuptake inhibitor approved as an antidepressant and also used for smoking cessation. Results from a clinical trial that directly examined the effects of behavioral treatment plus placebo versus behavioral treatment plus naltrexone/ bupropion showed that among all randomized participants there were 56‐week mean weight losses of 4.9 and 7.8 kg, respectively, indicating an incremental benefit of naltrexone/­ bupropion of approximately 2.9 kg (Wadden et al., 2011). Nausea, constipation, headache, and vomiting are common side effects of the use of naltrexone/bupropion. Lorcaserin (Belviq®) Lorcaserin selectively stimulates the 5‐HT2C receptor for serotonin and thereby suppresses appetite and increases satiety. In randomized trials (e.g., Smith et al., 2010), lorcaserin has produced mean weight reductions of 6.0–6.6% compared with losses of approximately 3% for diet plus placebo. Hence, the incremental weight‐loss benefit of lorcaserin appears to be 3.0–3.6%. Common side effects include headache, dizziness, fatigue, nausea, dry mouth, and constipation. While chemically similar to fenfluramine (a weight‐loss drug discontinued due to its association with heart valve disease), the use of lorcaserin has not been associated with an increase in heart valve problems. Phentermine/Topiramate (Qsymia®) This compound is a combination of the stimulant phentermine, which is approved for short‐ term use in weight management, and the antiseizure/antimigraine drug topiramate. Phentermine is a centrally acting appetite suppressant that works in a manner similar to amphetamine. Topiramate appears to assist in weight loss by inhibiting orexigenic glutamate signaling and increasing energy utilization. A randomized controlled trial (Gadde et al., 2011) of the standard dose of phentermine/topiramate showed an incremental benefit of a 6.6% weight loss versus placebo. Dizziness, nausea, and fatigue are common side effects associated with the use of phentermine/topiramate. Liraglutide (Saxenda®) Liraglutide, the newest drug to be approved by the FDA for weight loss, is a glucagon‐like peptide‐1 (GLP‐1) receptor agonist that mimics an intestinal hormone that signals satiety. A lower dose of liraglutide (marketed as Victoza®) is used as a treatment for type 2 diabetes. Randomized trial data presented to the FDA suggest that 1 year of treatment with liraglutide produces body weight reductions of 4.0–5.4% above the weight losses accomplished with diet plus placebo (FDA, 2014). Common side effects include nausea, hypoglycemia, and diarrhea, and there is some concern that the use of liraglutide may be associated with acute pancreatitis. In summary, an array of medications are now available that may enhance the effectiveness of lifestyle treatments for obesity. Each of these pharmacological agents increases weight loss, and each is associated with notable side effects. Moreover, the withdrawal of previous weight‐loss

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drugs from the market serves as an object lesson that serious adverse effects may become apparent only when a drug is used in larger populations for longer periods of time than in the FDA approval process.

Bariatric Surgery Surgery may be an appropriate option for adults with a BMI > 40 (or a BMI > 35 with an ­ besity‐related comorbid condition) who have not responded sufficiently to behavioral treato ments with or without pharmacotherapy (Jensen et al., 2014). The four types of operations that are commonly offered in the United States are described below. Adjustable gastric band In the “gastric banding” procedure, the surgeon places a small band around the top of the stomach to create a pouch that limits the quantity of food that can be ingested, which helps the patient feel full after eating a small amount. The feeling of satiety is governed by the size of the opening between the pouch and the remainder of the stomach. The gastric band contains a circular balloon that allows the surgeon to increase or decrease the size of the opening between the upper and lower portions of the stomach according to the needs of the patient. Gastric banding generally produces slow and steady weight losses that are larger than what is commonly achieved with behavioral interventions but smaller than other bariatric surgery procedures. Mean body weight reductions of 14–16% are commonly observed 1–3 years after surgery (Colquitt, Pickett, Loveman, & Frampton, 2014). While gastric banding has fewer complications than other procedures, it has the highest rate of reoperation (>30%), often related to slippage or malfunctioning of the band. Roux‐en‐Y gastric bypass Gastric bypass, long considered the “gold standard” of bariatric surgeries, involves a complex set of procedures that entail the restriction of food intake coupled with changes in the absorption of nutrients. The surgeon first creates a small pouch at the top of the stomach. Next, the small intestine is connected directly to the pouch so that after food leaves the pouch, it bypasses the remainder of the stomach, the duodenum, and a portion of the upper intestine. Transporting food directly from the pouch into the small intestine changes how the digestive tract absorbs food, resulting in reduced absorption of nutrients and calories consumed. Gastric bypass is more complicated to perform than gastric banding, requires a longer hospital stay, and has a greater risk of near‐term surgical complications, but it produces much larger weight losses than gastric banding. Patients commonly lose 30–35% of their body weight within 1 year, and this degree of weight reduction is typically maintained well at follow‐ups of 3–10 years (Colquitt et al., 2014). The metabolic and weight‐loss effects of gastric bypass typically include partial if not full remission of type 2 diabetes. The disadvantages of gastric bypass include ­malabsorption of vitamins and minerals (often requiring supplementation) as well as adverse reactions to the consumption of sugary or starchy foods that can cause rapid heart rate, dizziness, abdominal pain, and vomiting. Vertical sleeve gastrectomy This irreversible procedure (known as the “gastric sleeve”) is performed by removing ­approximately 80% of the stomach. What remains is a tube‐like pouch that holds a c­ onsiderably smaller volume than the normal stomach, thereby decreasing the amount of food that can be

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consumed. The gastric sleeve induces large, rapid weight loss comparable in magnitude (>30% of body weight) at 1 year to the gastric bypass (Colquitt et al., 2014). The gastric sleeve also produces favorable changes in the gut hormones that suppress hunger, increase satiety, and improve blood sugar control. Overall, the gastric sleeve appears to be as effective as gastric bypass with respect to weight loss and its impact on type 2 diabetes. Moreover, the rates of complications associated with the gastric sleeve, while higher than gastric banding, are lower than gastric bypass. Biliopancreatic diversion with a duodenal switch This surgery, commonly referred to as the “duodenal switch,” is a complex procedure that entails (a) removal of a large portion of the stomach (to increase satiety), (b) rerouting food away from much of the small intestine (to limit the absorption of nutrients), and (c) modifying how bile and other digestive juices influence digestion (to decrease further the absorption of calories). Unlike the other procedures described previously, over time the duodenal switch allows patients eventually to resume eating “normal” amounts of food while experiencing long‐term weight losses that are larger than those produced by gastric banding, gastric bypass, and the gastric sleeve procedures (Colquitt et al., 2014). The “duodenal switch” results in beneficial changes to gut hormones (which also occur in the bypass and sleeve procedures) and appears to be the most effective surgical treatment for diabetes. The duodenal switch has higher complication rates and greater risk for mortality than alternative procedures—factors that must be carefully considered by prospective patients. Bariatric surgery offers individuals with severe obesity the opportunity to achieve very large weight losses that are generally well maintained over the long run and are associated with ­dramatic improvements in many of the comorbidities associated with severe obesity. The major disadvantages associated with bariatric surgery include the potential for perioperative and postoperative complications that could extend hospitalizations, require additional surgeries, and, in rare cases, result in death. The significant changes in eating patterns associated with most bariatric procedures pose important challenges to many patients. Behavioral counseling is often recommended before and after surgery to help patients prepare for and deal with the significant lifestyle changes dictated by bariatric surgery.

Clinical Guidelines The American College of Cardiology, the American Heart Association, and the Obesity Society have developed a comprehensive set of guidelines for the treatment of obesity in adults (Jensen et al., 2014). The guidelines, written as advice to providers of medical care, recommend that primary care providers measure height, weight, and waist circumference in overweight and obese adults, at least annually. Counseling to lose 3–5% of body weight is recommended for patients with a BMI > 25 who are at increased risk for cardiovascular disease (i.e., those with elevated blood pressure, prediabetes, or hyperlipidemia). A comprehensive, high‐intensity (i.e., >14 sessions over 6 months) lifestyle program led by trained interventionists is recommended for adults with a BMI > 30. Individuals who are unable to achieve or maintain moderate weight losses after participating in a comprehensive lifestyle intervention may be appropriate candidates for pharmacotherapy (for adults with BMI ≥ 30 or ≥27 with a comorbid condition) or bariatric surgery (for adults with BMI ≥ 40 or ≥35 with comorbid condition). In recommending treatment, clinicians are encouraged to consider an individual’s age, severity of ­obesity, comorbid conditions, behavioral health history, and treatment preference.

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Conclusions The current global epidemic of obesity has focused attention on the need to better ­understand, manage, and control this serious threat to public health. An array of cultural and environmental changes associated with economic success and increasing affluence have promoted higher food consumption and decreased energy expenditure, thereby potentiating a genetic predisposition to obesity in much of the population. In the United States, the rise in the prevalence of obesity has resulted in increasingly larger portions of healthcare expenditures devoted to the treatment of obesity and its comorbidities. At the individual level, most people with obesity prefer to manage their weight on their own. Some subgroups, such as the NWCR participants, show dramatic success in the self‐­ management of obesity, but generally there is scant evidence to support the effectiveness of self‐help approaches for obesity management. Some commercial weight‐loss programs offer resources likely to result in tangible but modest weight reductions. Among professionally delivered interventions, behavioral lifestyle interventions reliably produce weight changes of sufficient magnitude to improve health, and an array of medications are now available that may enhance the effectiveness of lifestyle treatments. For individuals with severe obesity, bariatric surgery offers the opportunity to achieve very large weight losses accompanied by significant improvements in many of the comorbidities associated with severe obesity. Despite progress that has been made in the treatment of obesity, numerous challenges remain. For large segments of the population, access to obesity treatment is limited by governmental regulations and lack of coverage by health insurance policies. Furthermore, little research has addressed the important issues of dissemination and implementation of obesity treatment in community settings. More research is needed to deal with the problem of poor weight‐loss maintenance and how an improved understanding of the interplay between biological and behavioral factors that contribute to obesity might be utilized to develop innovative and more effective treatments. Finally, greater attention needs to be given to the prevention of obesity and how governmental policies might modify the environmental factors responsible for the global rise in the prevalence of obesity.

Author Biographies Michael G. Perri, PhD, is the Robert G. Frank Endowed Professor of Clinical and Health Psychology and dean of the University of Florida College of Public Health and Health Professions. His research focuses on the development, evaluation, and dissemination of cost‐ effective programs for the management of obesity. Findings from the 20+ randomized trials conducted by his team have had a significant impact on theory, research, and clinical care related to the behavioral treatment of obesity. Melissa H. Laitner, MS, is a PhD candidate in the Department of Clinical and Health Psychology at the University of Florida where she is concurrently completing a master’s degree in public health. Her research interests include behavioral treatment for obesity, health disparities, and health policy. Her dissertation research focuses on examining potential disparities between rural and urban communities in regard to obesity, diabetes, and prediabetes. Aviva H. Ariel, MS, is a PhD candidate in the Department of Clinical and Health Psychology at the University of Florida where she is concurrently completing a master’s degree in public health with a concentration in social and behavioral sciences. Her research interests include

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holistic health promotion, body image disturbance, and behavioral interventions for weight management and disordered eating.

References Albuquerque, D., Stice, E., Rodríguez‐López, R., Manco, L., & Nóbrega, C. (2015). Current review of genetics of human obesity: From molecular mechanisms to an evolutionary perspective. Molecular Genetics and Genomics, 290, 1191–1221. Arterburn, D. E., Maciejewski, M. L., & Tsevat, J. (2005). Impact of morbid obesity on medical expenditures in adults. International Journal of Obesity, 29, 334–339. Butryn, M. L., Webb, V., & Wadden, T. A. (2011). Behavioral treatment of obesity. The Psychiatric Clinics of North America, 34, 849–859. Cawley, J., & Meyerhoefer, C. (2012). The medical care costs of obesity: An instrumental variables approach. Journal of Health Economics, 31, 219–230. Colquitt, J. L., Pickett, K., Loveman, E., & Frampton, G. K. (2014). Surgery for weight loss in adults. Cochrane Database of Systematic Reviews, 8. Retrieved from http://onlinelibrary.wiley.com/ doi/10.1002/14651858.CD003641.pub4/epdf Diabetes Prevention Program Research Group. (2002). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. New England Journal of Medicine, 346, 393–403. Diabetes Prevention Program Research Group. (2009). 10‐year follow‐up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. The Lancet, 374, 1677–1686. Federal Drug Administration. (2014, May 11). FDA briefing information: Meeting of the Endocrinologic and Metabolic Drugs Advisory Committee. Washington, DC: Author. Gadde, K. M., Allison, D. B., Ryan, D. H., Peterson, C. A., Troupin, B., Schwiers, M. L., & Day, W. W. (2011). Effects of low‐dose, controlled‐release, phentermine plus topiramate combination on weight and associated comorbidities in overweight and obese adults (CONQUER): A randomised, placebo‐controlled, phase 3 trial. The Lancet, 377, 1341–1352. Gudzune, K. A., Doshi, R. S., Mehta, A. K., Chaudhry, Z. W., Jacobs, D. K., Vakil, R. M., … Clark, J. M. (2015). Efficacy of commercial weight‐loss programs: An updated systematic review. Annals of Internal Medicine, 162, 501–512. Hartmann‐Boyce, J., Jebb, S. A., Fletcher, B. R., & Aveyard, P. (2015). Self‐help for weight loss in overweight and obese adults: Systematic review and meta‐analysis. American Journal of Public Health, 105, e43–e57. Helmchen, L. A., & Henderson, R. M. (2004). Changes in the distribution of body mass index of white US men, 1890‐2000. Annals of Human Biology, 31, 174–181. Jensen, M. D., Ryan, D. H., Apovian, C. M., Ard, J. D., Comuzzie, A. G., Donato, K. A., … Yanovski, S. Z. (2014). 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Journal of the American College of Cardiology, 63, 2985–3023. Kodama, S., Saito, K., Tanaka, S., Horikawa, C., Fujiwara, K., Hirasawa, R., … Sone, H. (2012). Effect of web‐based lifestyle modification on weight control: A meta‐analysis. International Journal of Obesity, 36, 675–685. Komlos, J., & Brabec, M. (2011). The trend of BMI values of US adults by deciles, birth cohorts 1882‐1986 stratified by gender. Economics and Human Biology, 9, 234–250. Kuczmarski, R. J., Ogden, C. L., Grummer‐Strawn, L. M., Flegal, K. M., Guo, S. S., Wei, R., … Johnson, C. L. (2000). CDC growth charts: United States. National Center for Health Statistics. Retrieved from http://www.cdc.gov/nchs/about/major/nhanes/growthcharts/fullreport.htm Look AHEAD Research Group. (2010). Long term effects of a lifestyle intervention on weight and ­cardiovascular risk factors in individuals with type 2 diabetes: Four year results of the Look AHEAD trial. Archives of Internal Medicine, 170, 1566–1575.

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Look AHEAD Research Group. (2013). Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. New England Journal of Medicine, 369, 145–154. Look AHEAD Research Group. (2014). Eight‐year weight losses with an intensive lifestyle intervention: The Look AHEAD study. Obesity, 22, 5–13. Mokdad, A. H., Ford, E. S., Bowman, B. A., Dietz, W. H., Vinicor, F., Bales, V. S., & Marks, J. S. (2003). Prevalence of obesity, diabetes, and obesity‐related health risk factors, 2001. Journal of the American Medical Association, 289, 76–79. Ogden, C. L., Carroll, M. D., Fryar, C. D., & Flegal, K. M. (2015). Prevalence of obesity among adults and youth: United States, 2011–2014. NCHS Data Brief, 219, 1–8. Retrieved from Centers for Disease Control and Prevention website: http://www.cdc.gov/nchs/data/databriefs/db219.htm Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011–2012. Journal of the American Medical Association, 311, 806–814. Okorodudu, D. E., Bosworth, H. B., & Corsino, L. (2015). Innovative interventions to promote behavioral change in overweight or obese individuals: A review of the literature. Annals of Medicine, 47, 179–185. Perri, M. G., Limacher, M. C., Durning, P. E., Janicke, D. M., Lutes, L. D., Bobroff, L. B., … Martin, A. D. (2008). Extended‐care programs for weight management in rural communities: The treatment of obesity in underserved rural settings (TOURS) randomized trial. Archives of Internal Medicine, 168, 2347–2354. Perri, M. G., Limacher, M. C., von Castel‐Roberts, K., Daniels, M. J., Durning, P. E., Janicke, D. M., … Martin, A. D. (2014). Comparative effectiveness of three doses of weight‐loss counseling: Two‐ year findings from the Rural LITE trial. Obesity, 22, 2293–2300. Puhl, R. M., & Heuer, C. A. (2010). Obesity stigma: Important considerations for public health. American Journal of Public Health, 100, 1019–1028. Rosenquist, J. N., Lehrer, S. F., O’Malley, A. J., Zaslavsky, A. M., Smoller, J. W., & Christakis, N. A. (2015). Cohort of birth modifies the association between FTO genotype and BMI. Proceedings of the National Academy of Science, 112, 354–359. Rucker, D., Padwal, R., Li, S. K., Curioni, C., & Lau, D. C. W. (2007). Long term pharmacotherapy for obesity and overweight: Updated meta‐analysis. British Medical Journal, 335, 1194–1199. Smith, S. R., Weissman, N. J., Anderson, C. M., Sanchez, M., Chuang, E., Stubbe, S., … Shanahan, W. R. (2010). Multicenter, placebo‐controlled trial of lorcaserin for weight management. New England Journal of Medicine, 363, 245–256. Swinburn, B., Egger, G., & Raza, F. (1999). Dissecting obesogenic environments: The development and application of a framework for identifying and prioritizing environmental interventions for obesity. Preventive Medicine, 29, 563–570. Wadden, T. A., Foreyt, J. P., Foster, G. D., Hill, J. O., Klein, S., O’Neil, P. M., … Dunayevich, E. (2011). Weight loss with naltrexone SR/bupropion SR combination therapy as an adjunct to behavior modification: The COR‐BMOD Trial. Obesity, 19, 110–120. Wadden, T. A., Webb, V. L., Moran, C. H., & Bailer, B. A. (2012). Lifestyle modification for obesity: New developments in diet, physical activity, and behavior therapy. Circulation, 125, 1157–1170. Wing, R. R., & Phelan, S. (2005). Long‐term weight loss maintenance. The American Journal of Clinical Nutrition, 82, 222S–225S. World Health Organization. (2014). Global status report on noncommunicable diseases 2014. Retrieved from http://apps.who.int/iris/bitstream/10665/148114/1/9789241564854_eng.pdf?ua=1.

Suggested Reading Bray, G. A., & Bouchard, C. (Eds.) (2014). Handbook of obesity (4th ed.). Boca Raton, FL: CRC Press. Bray, G. A., & Wadden, T. A. (2015). Improving long‐term weight loss maintenance: Can we do it? Obesity, 23, 2–3.

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Jensen, M. D., Ryan, D. H., Apovian, C. M., Ard, J. D., Comuzzie, A. G., Donato, K. A., … Yanovski, S. Z. (2014). 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Journal of the American College of Cardiology, 63, 2985–3023. U.S. Department of Health and Human Services. (2014). Healthy people 2020: Nutrition and weight status. Retrieved from http://healthypeople.gov/2020/topicsobjectives2020/overview.aspx?topicid=29

Pain and Health Psychology Robert J. Gatchel1 and Don McGeary2 Clinical Health Psychology Research, Department of Psychology, College of Science, The University of Texas at Arlington, Arlington, TX, USA 2 Department of Psychiatry, Department of Family and Community Medicine, The University of Texas Health Science Center San Antonio, San Antonio, TX, USA 1

Overview The first non‐edited and comprehensive textbook in health psychology was published by Gatchel and Baum (1983). At that time, the field of health psychology was simply an array of basic and sometimes fragmented different areas (e.g., clinical psychology, cognitive psychology, experimental psychology, neuropsychology, physiological psychology, social psychology, etc.). These areas, though, began to touch upon important biobehavioral and medical content topics new to the field, such as adherence to medical regimens, promoting healthy lifestyles, rehabilitation approaches to disability, and the prevention and treatment of chronic pain, just to name a few. At about this time, when the term health psychology was still a relatively new one, a series of five edited volumes were published under the title, Handbook of Psychology and Health (Baum & Singer, 1982, 1987; Baum, Taylor, & Singer, 1984; Gatchel, Baum, & Singer, 1982; Krantz, Baum, & Singer, 1983). The books in this series focused on specific topic areas including pain. As subsequently noted by Baum, Revenson, and Singer (2001) in their handbook, this earlier series was published over several years just as health psychology became firmly established in its own right. There have been numerous other such handbooks published since then. Moreover, the quite diverse areas of how health psychology and medical illnesses are interrelated were perfectly aligned with the very important and influential Institute of Medicine’s (IOM) report on Living Well and Chronic Illness: A Call for Public Health Action (Institute of Medicine, Committee on Living Well with Chronic Disease: Public Action to Reduce Disability, Improve Functioning and Quality of Life, 2012). This major IOM report was written as “… a guide for immediate and precise action to reduce the burden of all forms of chronic illnesses through the development and implementation of cross‐cutting and ­coordinated strategies to help Americans live well…” (A‐1). One such illness category was

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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chronic pain. Indeed, one of the most frequently cited reasons for patients in the United States to seek medical care is due to pain. Recent estimates reveal that 25.3 million American adults (11.2%) suffer from daily (chronic) pain and 23.4 million (10.3%) report “a lot of pain” (Nahin, 2015). Moreover, according to earlier statistics from the National Academy of Sciences (Gaskin & Richard, 2011), the societal costs of chronic pain and related disability in the United States alone is $560–$635 billion each year. Unfortunately, many chronic pain conditions cannot yet be successfully treated. More clinical research is still needed to develop better treatment methods. Thus, social, economic, and biobehavioral research have become increasingly important in medicine and in the general related area of health psychology. The above subsequently led to another very influential IOM report, “Relieving Pain in America” (Institute of Medicine of the National Academy of Science, 2011), which highlighted the urgent need for the development of better methods for pain management because the ever‐increasing costs associated with current treatment approaches cannot be sustained. This important focus has been further reinforced by National Institutes of Health’s (NIH’s) recent “National Pain Strategy: A Comprehensive Population Health‐Level Strategy for Pain” (https://www.federalregister.gov/articles/2015/04/02/2015‐07626/solicitation‐of‐ written‐comments‐on‐draft‐national‐pain‐strategy). The mission of the strategy is to “Enhance pain research efforts and promote collaboration across the government, with the ultimate goals of advancing fundamental understanding of pain and improving pain‐related treatment strategies.” It was also charged to develop “a comprehensive population level health strategy for pain prevention, treatment, management, education, reimbursement, and research that includes specific goals, actions, time frames, and resources” as recommended in the IOM report on “Relieving Pain in America.” The proposed strategy also emphasizes the use of the biopsychosocial model of pain, which is the underpinning of interdisciplinary pain assessment and treatment. The result of the new area of health psychology led to the replacement of the outdated ­biomedical reductionism, or a “dualistic” perspective that the mind and body function separately and independently, to a more heuristic biopsychosocial approach to medicine, initially introduced by Engel (1977) and that further emerged in the 1980s. In the context of pain, the biopsychosocial model views it as the result of a dynamic interaction among biological, psychological, and social factors that perpetuate and may even worsen the clinical presentation. Gatchel and others (Gatchel, 2004, 2005; Gatchel, McGeary, McGeary, & Lippe, 2014; Gatchel & Okifuji, 2006; Gatchel, Peng, Peters, Fuchs, & Turk, 2007; Gatchel & Turk, 1999; Turk & Monarch, 2002), for example, have further advanced this health psychology/biopsychosocial perspective of pain.

The Biopsychosocial Model of Pain As earlier reviewed by Gatchel and Okifuji (2006), traditional interventions for chronic pain had predominantly involved monotherapies, such as surgery, injections, and a wide array of pharmacotherapeutic approaches. However, as Turk and Gatchel (2002) began to highlight, a more comprehensive interdisciplinary approach was needed to address both the physical and psychosocial factors involved in chronic pain. A major outgrowth, in turn, was the development of the biopsychosocial model. As briefly discussed earlier, this model views pain as the result of a dynamic interaction among biological, psychological, and social ­factors that perpetuate and may even worsen the clinical presentation. Each patient experiences pain uniquely, and a range of psychological and socioeconomic factors can interact

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with physical pathology to modulate that patient’s report of symptoms and subsequent pain‐related disability. This model has been very influential in the area of pain, especially with the resultant development of treatment‐ and cost‐effective interdisciplinary pain management programs (Gatchel et al., 2007; Gatchel & Okifuji, 2006), to be reviewed later. Such programs, based upon the biopsychosocial model, have been found to be the most heuristic approach to understanding and assessing chronic pain (Gatchel et  al., 2007). Indeed, the influential IOM report (Institute of Medicine of the National Academy of Science, 2011, p. 35) states that “Today, most researchers and clinicians who specialize in pain issues use  the ‘biopsychosocial model’ [denoting the combination of biological, ­psychological and social/family/cultural contexts of pain to understand and treat chronic pain (Gatchel et al., 2007)].” The biopsychosocial model focuses on both disease and illness, with illness being viewed as the complex interaction among biological, psychological, and social factors (Gatchel, 2005). As succinctly summarized by Turk and Monarch (2002), disease is usually viewed as a pathophysiological event that results in a disruption of some anatomical/physiological structure or organ system functioning. Illness, on the other hand, is usually viewed as a more subjective experience that a specific disease is present. Illness, therefore, basically refers to how a “sick” individual and possibly members of his/her family accommodate and react to the resultant symptoms of possible disability and limited activities of daily living. As Turk and Monarch (2002) further point out, the distinction between disease and illness can basically be viewed as similar to the difference one can make between nociception and pain. Essentially, nociception refers to the stimulation of nerves that transmit signals to the brain about potential tissue damage. In contrast, pain is the subjective perception that results from the sensory input to the brain. Moreover, such input is usually “filtered” through a number of layers (e.g., past learning experiences, psychosocial influences, genetic predispositions, etc.). Therefore, because the biopsychosocial model of chronic pain views each individual as experiencing pain uniquely, it is important to evaluate the different dimensions of this interactive process (Gatchel, 2004). Other comprehensive reviews of the biopsychosocial model of pain can also be found elsewhere (Gatchel, 2004; Gatchel et al., 2007, 2014; Turk & Monarch, 2002).

Interdisciplinary Pain Management The biopsychosocial model of chronic pain was one of the first models designed to ­accommodate the complexity of the chronic pain experience. As the model began to permeate academic and clinical medicine, so, too, did multicomponent models of pain management care. To date, the interdisciplinary pain management approach stands as one of the best and most efficacious examples of comprehensive pain management (Gatchel et al., 2014). Before exploring interdisciplinary care in greater detail, one must first be able to draw a distinction between ­interdisciplinary and multidisciplinary care. In an excellent review of interdisciplinary pain management models, Turk et  al. (2010) carefully explained how interdisciplinary and ­multidisciplinary models differ. They appropriately highlight that, although the two models may both include components that match the dimensions of the biopsychosocial pain conceptualization, a truly interdisciplinary pain management program is more integrated and all treatment components are administered “under one roof” at the same pain management ­facility. In contrast, multidisciplinary care may be delivered by different providers at different facilities, thus decreasing integrative care and constant communication among providers

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“under one roof.” Integration of pain management services is essential and can manifest in multiple ways, although most agree that coordination of care through daily team‐based ­communication “under one roof” and common treatment goals is the key to effective integrative pain management (Gatchel et al., 2014). Interdisciplinary pain management teams will often include pain medicine physicians, nursing staff, clinical health psychology, and physical/ occupational therapy providers who synthesize their care through effective leadership and commonly developed goals to ­create a comprehensive treatment program, with the resultant effectiveness greater than the sum of its parts (Okifuji, 2003). A number of published studies describe the effectiveness of interdisciplinary pain management models in multiple pain populations (Gagnon, Stanos, van der Ende, Rader, & Harden, 2013; Gatchel et  al., 2014). Indeed, a PubMed search for “interdisciplinary pain management” returned 50 published studies between 1995 and 2015, most of which demonstrate significant benefit of interdisciplinary pain management programs for pain of various etiologies. Despite a wealth of supportive research outcomes, interdisciplinary pain management programs are widely unavailable to many pain patients. Numerous barriers prohibit both the implementation of comprehensive pain management programs and the long‐term follow‐up needed to ensure that gains are maintained. First, some have noted that third‐party payer reimbursement policies have played an important role in limiting the availability of more ­comprehensive pain management options (Gagnon et al., 2013). This is quite surprising in light of the staggering cost of chronic pain over time and the substantial socioeconomic gains that are possible through interdisciplinary care (Lippe & Polatin, 2014). Others have noted that interdisciplinary programs may not be utilized as well as they could be because many pain treatment providers prioritize simple palliative pain management (e.g., medications) over interdisciplinary options. Breivik, Eisenberg, O’Brien, and OPENMinds (2013) have noted that palliative pain relief options may be preferred because chronic pain patients and their most oft‐seen medical providers (e.g., non‐specialty physicians) are either not aware of more effective treatment options or are not familiar with the data supporting interdisciplinary pain management. There is some hope that increased US congressional attention to the pain management crisis, and federal policy calling more interdisciplinary healthcare options, will alleviate these concerns in the future (Gatchel et al., 2014). In order to truly improve the availability of interdisciplinary pain programs, there needs to be some agreement on the necessary components. In a “Letter to the Editor” in the journal, Pain, Kaiser, Deckert, Kopkow, Schmitt, and Sabatowski (2014) pointed out that there is no broadly accepted definition of multidisciplinary or interdisciplinary pain programs (including the level of emphasis required for psychosocial intervention). This has resulted in heterogeneity in interdisciplinary pain programs under study, making it difficult to examine the state of the research meta‐analytically with much refinement. Some have stressed a need to establish common outcomes across pain programs that can be used to uniformly examine the important components of pain management (i.e., key ingredients) for translation (Deckert et al., 2015). In fact, the NIH have developed the Patient‐Reported Outcomes Measurement Information System (PROMIS) (http://www.nihpromis.org/) in order to encourage all pain practitioners to use the set of outcome measures. Regardless, most who study interdisciplinary pain management seem to agree that psychosocial intervention is vital if a pain management program wants to holistically address the biopsychosocial model (Gatchel et al., 2014). Numerous studies have uncovered psychosocial constructs of pain (e.g., pain catastrophizing, pain acceptance, fear avoidance, etc.) that contribute to the pain management process (Vowles, McCracken, & Eccleston, 2007), and subsequent studies have found that these concepts may mediate the relationship between pain experience and mood (Weiss et al., 2013). In light of these findings,

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interdisciplinary pain programs must appreciate the value added by the clinical health ­psychologist in order for the treatment team to be successful.

Functional Restoration Programs Despite the heterogeneity of interdisciplinary pain programs, there are a few models under study with more definite composition and a more definite role of clinical health psychology. Of these, functional restoration (FR) programs represent some of the most well researched, with a strong emphasis on return to function and the pursuit of long‐term socioeconomic goals (e.g., reduced medical visits for pain management, increased employment, increased retention of employment). The mechanics of FR pain and disability management typically include an emphasis on both physical capacity and psychosocial functioning (Mayer, McGeary, & Gatchel, 2004). Health psychology is a prominent component of FR. In fact, the recommended FR treatment team should always include a psychologist, along with a pain management physician, nurse, and physical or occupational therapist. These providers must work in an interdisciplinary fashion to quantify function across all relevant rehabilitation domains (e.g., functional capacity, mood, pain coping) and to develop an integrated treatment program that targets these domains synergistically (Gatchel & Okifuji, 2006). Treatment begins with a comprehensive assessment battery (i.e., quantify function), and the resulting data are used to identify treatment targets. As an interdisciplinary program, FR’s goals are determined collaboratively across all providers and are agreed upon by patients who are given regular feedback about their assessment and shown evidence of physical and psychosocial progress over the course of the intensive FR program. Again, the clinical health psychologist plays a key role in the FR treatment process. For example, the psychologist helps mitigate emotional disruption (e.g., depression) that may affect physical function and/or engagement in rehabilitation with other FR team members (Heiskanen, Roine, & Kalso, 2012) and helps the patient learn how to effectively track goals and communicate with other healthcare providers to ensure that their clinical needs are met and that they are progressing toward their goals. They also teach the patient effective pain management strategies that can be used to overcome self‐limiting functional activity, which helps maximize the benefits of physical rehabilitation (Gatchel et al., 2014). Over the past 30 years, numerous studies have confirmed the efficacy and effectiveness of FR pain management models, often showing significant gains beyond those demonstrated in one‐dimensional programs of physical/physiotherapy or biomedical care (Mayer et al., 1987; Steiner et al., 2013). As studies of FR have progressed, research surrounding these interdisciplinary programs has started to expand, with increasing emphasis on the effectiveness of FR in managing orthopedic/musculoskeletal pain and common comorbidities (including sleep, non‐orthopedic pain conditions, and psychiatric disorders) that typically present with chronic pain. For example, Hartzell et al. (2014) found that patients with chronic musculoskeletal pain and comorbid fibromyalgia show a significant improvement in fibromyalgia symptoms after completing an FR program (e.g., 41% of completers no longer met criteria for fibromyalgia after completing treatment). Asih, Neblett, Mayer, and Gatchel (2014) found a significant improvement in insomnia among FR program completers, although the presence of insomnia was a predictor of program completion, and more severe insomnia symptoms persisted beyond treatment. As noted above, interdisciplinary pain management programs (including FR) can contribute to significant reductions in some emotional distress conditions (e.g., depression), but there are fewer data supporting the application of FR to more complex psychosocial

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­ resentations like posttraumatic stress disorder (PTSD) (McGeary, Moore, Vriend, Peterson, p & Gatchel, 2011). This is particularly relevant because of the increasing attention paid to PTSD and pain‐related comorbidity among military members and veterans who served as part of the post‐9/11 war effort. Chronic pain and PTSD have been found to co‐occur quite frequently in some military populations. Specifically, Lew et al. (2009) report that over 58% of veterans assessed for trauma and pain in a VA Polytrauma Network Site presented with comorbid pain and PTSD and the presence of PTSD in the context of pain can have significant and deleterious effects on response to pain management intervention (McGeary et al., 2011). Little has been done to examine treatments for pain and PTSD comorbidity, but there is solid evidence suggesting that FR programs are generally effective for military service members with chronic pain (Gatchel et al., 2009), and some psychosocial interventions work quite well for military PTSD (McLean & Foa, 2014). Thus, it stands to reason that a comprehensive interdisciplinary pain management program (like FR) could incorporate effective psychosocial treatment components for PTSD to develop an efficacious treatment option for this significant problem. Large‐scale studies of interdisciplinary pain and PTSD management are underway, but some smaller studies have already started to show the benefits of combined treatments for this comorbidity (Plagge, Lu, Lovejoy, Karl, & Dobscha, 2013).

Conclusions The field of health psychology has grown significantly over the past several decades, and few subspecialty fields have benefitted more from this growth than chronic pain. As the field of healthcare providers who work with chronic pain increasingly adopts and expands the biopsychosocial model, greater integration of treatment will likely follow. Indeed, recent changes in legislation and the growing body of evidence supporting integrated treatment for pain bode well for the growth of interdisciplinary models. Research on psychosocial variables in the pain experience and pain management has rapidly expanded over the past 30 years, and health ­psychology is now widely acknowledged as a vital component of interdisciplinary pain management teams. This growth is likely to continue as pain management intervention and research start to apply the biopsychosocial model to even more complex chronic pain presentations (e.g., comorbid pain and PTSD).

Author Biographies Dr. Robert J. Gatchel, PhD, ABPP, is the Nancy P. & John G. Penson endowed professor of clinical health psychology at the University of Texas at Arlington. He is internationally known for his research on biopsychosocial pain management, and his scientific contributions have resulted in numerous awards including an NIH Senior Scientist Award and the Gold Medal Award for Life Achievement in the Application of Psychology from the American Psychological Association. Dr. Don McGeary, PhD, ABPP, is an associate professor in the Department of Psychiatry and a ReACH scholar at the University of Texas Health Science Center at San Antonio. He is the principle investigator of several federally funded studies of chronic pain and psychiatric comorbidities with a particular emphasis on managing pain‐related polymorbidities in military service members and veterans.

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References Asih, S., Neblett, R., Mayer, T. G., & Gatchel, R. J. (2014). Does patient‐reported insomnia improve in response to interdisciplinary functional restoration for chronic disabling occupational musculoskeletal disorders? Spine, 39(17), 1384–1392. Baum, A., Revenson, T. A., & Singer, J. A. (2001). Handbook of health psychology. Mahway, NJ: Lawrence Erlbaum Associates. Baum, A., & Singer, J. E. (Eds.) (1982). Issues in child health and adolescent health (Vol. 2). Hillsdale, NJ: Lawrence Erlbaum Associates. Baum, A., & Singer, J. E. (Eds.) (1987). Stress (Vol. 5). Hillsdale, NJ: Lawrence Erlbaum Associates. Baum, A., Taylor, S. E., & Singer, J. E. (Eds.) (1984). Psychological aspects of health (Vol. 4). Hillsdale, MJ: Lawrence Erlbaum Associates. Breivik, H., Eisenberg, E., O’Brien, T., & OPENMinds. (2013). The individual and societal burden of chronic pain in Europe: The case for strategic prioritisation and action to improve knowledge and availability of appropriate care. BMC Public Health, 13, 1229. Deckert, S., Kaiser, U., Kopkow, C., Trautmann, F., Sabatowski, R., & Schmitt, J. (2015). A systematic review of the outcomes reported in multimodal pain therapy for chronic pain. European Journal of Pain, 20(1), 51–63. Engel, G. L. (1977). The need for a new medical model: A challenge for biomedicine. Science, 196(4286), 129–136. Gagnon, C. M., Stanos, S. P., van der Ende, G., Rader, L. R., & Harden, R. N. (2013). Treatment outcomes for workers compensation patients in a US‐based interdisciplinary pain management program. Pain Practice, 13(4), 282–288. Gaskin, D. J., & Richard, P. (2011). The economic costs of pain in the United States. Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research. Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK92521/ Gatchel, R. J. (2004). Comorbidity of chronic mental and physical health disorders: The biopsychosocial perspective. American Psychologist, 59, 792–805. Gatchel, R. J. (2005). Clinical essentials of pain management. Washington, DC: American Psychological Association. Gatchel, R. J., & Baum, A. (1983). An introduction to health psychology. Reading, MA: Addison‐Wesley. Gatchel, R. J., Baum, A., & Singer, J. E. (Eds.) (1982). Clinical psychology and behavioral medicine: Overlapping disciplines (Vol. 1). Hillsdale, NJ: Lawrence Erlbaum Associates. Gatchel, R. J., McGeary, D. D., McGeary, C. A., & Lippe, B. (2014). Interdisciplinary chronic pain management: Past, present and the future. American Psychologist, Special Issue on “Psychology and Chronic Pain”, 69(2), 119–130. Gatchel, R. J., McGeary, D. D., Peterson, A. L., Moore, M. E., LeRoy, K., Isler, W. C., … Edell, T. (2009). Preliminary findings of a randomized controlled trial of an interdisciplinary functional restoration program. Military Medicine, 174, 270–277. Gatchel, R. J., & Okifuji, A. (2006). Evidence‐based scientific data documenting the treatment and cost‐effectiveness of comprehensive pain programs for chronic nonmalignant pain. The Journal of Pain, 7(11), 779–793. Gatchel, R. J., Peng, Y., Peters, M. L., Fuchs, P. N., & Turk, D. C. (2007). The biopsychosocial approach to chronic pain: Scientific advances and future directions. Psychological Bulletin, 133, 581–624. Gatchel, R. J., & Turk, D. C. (Eds.) (1999). Psychological factors in pain: Critical perspectives. New York, NY: Guilford. Hartzell, M. M., Neblett, R., Perez, Y., Brede, E., Mayer, T. G., & Gatchel, R. J. (2014). Do comorbid fibromyalgia diagnoses change after a functional restoration program in patients with chronic ­disabling occupational musculoskeletal disorders? Spine, 39(17), 1393–1400. Heiskanen, T., Roine, R. P., & Kalso, E. (2012). Multidisciplinary pain treatment—Which patients do benefit? Scandinavian Journal of Pain, 3(4), 201–207.

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Institute of Medicine, Committee on Living Well with Chronic Disease: Public Action to Reduce Disability, Improve Functioning and Quality of Life. (2012). Living well with chronic illness: A call for public health action. Washington, DC: The National Academies Press. Retrieved from http://www.nap.edu/catalog.php?record_id=13272 Institute of Medicine of the National Academy of Science. (2011). Relieving pain in America: A blueprint for transforming prevention, care, education, and research (pp. 5–5). Washington, DC: Institute of Medicine. Kaiser, U., Deckert, S., Kopkow, C., Schmitt, J., & Sabatowski, R. (2014). Letter to the editor: Dose or content? Effectiveness of pain rehabilitation programs for patients with chronic low back pain: A systematic review. Pain, 155(9), 1903–1904. Krantz, D. S., Baum, A., & Singer, J. E. (Eds.) (1983). Cardiovascular disorders and behavior (Vol. 3). Hillsdale, NJ: Lawrence Erlbaum Associates. Lew, H. L., Otis, J. D., Tun, C., Kerns, R. D., Clark, M. E., & Cifu, D. X. (2009). Prevalence of chronic pain, posttraumatic stress disorder, and persistent postconcussive symptoms in OIF/OEF veterans: Polytrauma clinical triad2026 Research to improve the lives of veterans: Approaches to traumatic brain injury; screening, treatment, management, and rehabilitation in Arlington, Virginia, April 30 to May 2, 2008. Journal of Rehabilitation Research and Development, 46(6), 697–702. Lippe, B., & Polatin, P. (2014). The interdisciplinary treatment approach for chronic pain management: The key components for success. In R. J. Gatchel & I. Z. Schultz (Eds.), Handbook of musculoskeletal pain and disability disorders in the workplace. New York, NY: Springer. Mayer, T. G., Gatchel, R. J., Mayer, H., Kishino, N. D., Keeley, J., & Mooney, V. (1987). A prospective two‐year study of functional restoration in industrial low back injury: An objective assessment procedure. JAMA, 258(13), 1763–1767. Mayer, T. G., McGeary, D., & Gatchel, R. J. (2004). Chronic pain management through functional ­restoration for spinal disorders. In J. F. Frymoyer & S. Wiesel (Eds.), Adult and pediatric spine (3rd ed.). Philadelphia, PA: Lippincott Williams & Wilkins. McGeary, D., Moore, M., Vriend, C. A., Peterson, A. L., & Gatchel, R. J. (2011). The evaluation and treatment of comorbid pain and PTSD in a military setting: An overview. Journal of Clinical Psychology in Military Settings, 18, 155–163. McLean, C. P., & Foa, E. B. (2014). The use of prolonged exposure therapy to help patients with post‐ traumatic stress disorder. Clinical Practice, 11(2), 233–241. Nahin, R. L. (2015). Estimates of pain prevalence and severity in adults: United States, 2012. The Journal of Pain, 16(8), 769–780. http://dx.doi.org/10.1016/j.jpain.2015.05.002 Okifuji, A. (2003). Interdisciplinary pain management with pain patients: Evidence for its effectiveness. Seminars in Pain Management, 1(2), 110–119. Plagge, J. M., Lu, M. W., Lovejoy, T. I., Karl, A. I., & Dobscha, S. K. (2013). Treatment of comorbid pain and PTSD in returning veterans: A collaborative approach utilizing behavioral activation. Pain Medicine, 14(8), 1164–1172. Steiner, A. S., Sartori, M., Leal, S., Kupper, D., Gallice, J. P., Rentsch, D., … Genevay, S. (2013). Added value of an intensive multidisciplinary functional rehabilitation programme for chronic low back pain patients. Swiss Medical Weekly, 143, w13763. Turk, D. C., & Gatchel, R. J. (Eds.) (2002). Psychological approaches to pain management: A practitioner’s handbook (2nd ed.). New York, NY: Guilford. Turk, D. C., & Monarch, E. S. (2002). Biopsychosocial approaches on chronic pain. In R. J. Gatchel & D. C. Turk (Eds.), Psychological approaches to pain management: A practitioner’s handbook (pp. 3–29). New York, NY: Guilford Press. Turk, D. C., Stanos, S. P., Palermo, T. M., Paice, J. A., Jamison, R. N., Gordon, D. B., & Clark, M. E. (2010). Interdisciplinary pain management. Glenview, IL: American Pain Society. Vowles, K. E., McCracken, L. M., & Eccleston, C. (2007). Processes of change in treatment for chronic pain: The contributions of pain, acceptance, and catastrophizing. European Journal of Pain, 11(7), 779–787.

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Weiss, K. E., Hahn, A., Wallace, D. P., Biggs, B., Bruce, B. K., & Harrison, T. E. (2013). Acceptance of pain: Associations with depression, catastrophizing, and functional disability among children and adolescents in an interdisciplinary chronic pain rehabilitation program. Journal of Pediatric Psychology, 38(7), 756–765.

Suggested Reading Okifuji, A. (2003). Interdisciplinary pain management with pain patients: Evidence for its effectiveness. Seminars in Pain Medicine, 1(2), 110–119. Turk, D. C., & Gatchel, R. J. (2018). Psychological approaches to pain management: A practitioner’s handbook. New York, NY: Guilford Press.

Perinatal Depression Rachel Vanderkruik, Elizabeth Lemon, Laura River, and Sona Dimidjian Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA

The transition to motherhood can be both a risky and rewarding time for women physically, interpersonally, and psychologically. Nearly 20% of mothers worldwide are affected by depression during pregnancy or the postpartum (i.e., the “perinatal” time period) (Gavin et  al., 2005). Perinatal depression represents a significant public health issue for women and families. Here, we will review the conceptualization and classification, correlates and consequences, risk and contributing factors, and detection and management of depression during pregnancy and the postpartum.

Conceptualization and Classification There is considerable variability in the severity and duration of symptoms of depression that may be experienced during the perinatal period. For example, postpartum blues, or “baby blues,” are fleeting feelings of depression and anxiety that many new mothers experience within the first 10 days after giving birth. In contrast, the term “depression” is often used in clinical contexts to refer to an episode of major depression. The diagnostic criteria for major depression are the same during pregnancy and the postpartum as in other times of life; thus, it is important to underscore that terms such as “perinatal depression” do not indicate a distinct clinical disorder but rather the occurrence of depression during this specific life phase. Major depression is indicated by the presence of at least 2 weeks of depressed mood or loss of interest or pleasure in activities, for most of the day, nearly every day, accompanied by at least four additional symptoms (i.e., sleep disruption, change in appetite, psychomotor agitation or retardation, fatigue, feelings of worthlessness or guilt, problems with concentration or ­decision making, or thoughts of death or suicide). Further, these symptoms must cause functional impairment.

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Depression during the perinatal period is captured in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, as a specifier of major depressive disorder. To receive a d­iagnosis of major depressive disorder with “peripartum onset,” a woman must meet all of the criteria for a major depressive episode, and the onset of depressive symptoms must have occurred during pregnancy or within 4 weeks after delivery. There is inconsistency within the field, however, about this time restriction, with many defining postpartum depression as onset up to 12 months following delivery. A recent review of 16 longitudinal studies found that the average rate of antenatal depression is 17% and the average rate of postpartum depression is 13% (Underwood, Waldie, D’Souza, Peterson, & Morton, 2016). The point prevalence of antenatal depression is 11% in the first trimester, and 9% in the rest of pregnancy, using DSM criteria for diagnosis (Gavin et al., 2005). Postpartum depression has a period prevalence of up to 19.2% of women within 3 months of delivery; the point prevalence of postpartum depression has been recorded as highest in the third month postpartum (13%), followed by a point prevalence of about 10% during the fourth through seventh months postpartum, and 7% thereafter, also using DSM criteria (Gavin et al., 2005). There is significant comorbidity between postpartum depression and anxiety. Women who have a history of postpartum depression, or who are experiencing current symptoms of postpartum depression, are also at risk for the onset of mania/hypomania (bipolar disorder), although this has been understudied. While rare, symptoms of psychosis may co‐occur with perinatal depression, particularly in the postpartum, potentially increasing the severity of the disorder.

Correlates and Consequences of Depression During Pregnancy and the Postpartum Perinatal depression is associated with increased risk of negative correlates and consequences for the overall health, well‐being, and developmental trajectories of both mother and o ­ ffspring, as well as a broader impact on society.

Mothers Depression during pregnancy has been associated with maternal physical health problems, compromised immune function, and preeclampsia (Field, 2011). Postpartum depression is linked to increased risk of factors that interfere with parenting, including persistent negative affect, increased self‐focus, and negative perceptions of oneself and others (O’Hara & McCabe, 2013). Many women who experience depression in pregnancy also experience postpartum depression. Perinatal depression also poses risk for future episodes of depression (O’Hara & McCabe, 2013).

Offspring Depression during pregnancy is associated with an increased risk of preterm delivery, low birth weight, reduced breastfeeding initiation rate, heightened infant distress and sleep ­disturbance, and emotional and behavioral problems in childhood (Field, 2011). Depression during the postpartum has been associated with heightened infant distress and sleep p ­ roblems, as well as compromised infant self‐regulation, stress, and arousal, and poor cognitive

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f­unctioning and emotional and behavioral disorders in infancy and childhood (e.g., O’Hara & McCabe, 2013).

Society Beyond the risks to individual women and their families, depression presents challenges from public health and economic perspectives. Lifetime costs of perinatal depression have been estimated at 23,151 British pounds for mothers (US$30,805.56 as of time of writing) and 52,577 British pounds for children (US$70,808.07), based on costs of healthcare, as well as reduced productivity and quality of life (Bauer, Knapp, & Parsonage, 2016).

Risk and Protective Factors Demographic Factors We lack a clear and consistent picture of the demographic factors associated with depression during pregnancy and postpartum. Younger maternal age has been associated with increased depressive symptoms during pregnancy and postpartum, as has older maternal age during pregnancy (e.g., Biaggi, Conroy, Pawlby, & Pariante, 2016); however, this association has not been consistently reported in all of the studies included in these reviews. Similarly, belonging to a minority race/ethnicity may be associated with increased rates of depression during pregnancy (Biaggi et al., 2016), but the association was not consistent across all studies included in this review. Few studies have examined the association of race/ethnicity with depression in the postpartum period, although it does appear that there are racial/ethnic disparities in rates of postpartum depression (Liu & Tronick, 2013). Socioeconomic status (SES) is likely an important factor. There is some evidence that low SES has a small effect on the development of postpartum depression, yet varying criteria used to define “low income” contribute to inconsistent evidence of this relationship. Several reviews of demographic risk factors for antenatal depression have identified unemployment, low income, and low educational attainment to be associated with depression during pregnancy; however these associations were not found in all studies reviewed (Biaggi et al., 2016; Lancaster et al., 2010). Having Medicaid or other public insurance versus private insurance is more consistently associated with increased likelihood of antenatal depressive symptoms (Lancaster et al., 2010). Additionally, there is some evidence that women who live in rural areas may face heightened risk for postpartum depression, perhaps because they have less access to care.

Biological and Health Factors There has been growing interest in identifying the pathophysiology of depression during ­pregnancy and postpartum. Much of the work to date has examined the roles of stress and reproductive hormones as risk and protective factors. The evidence for the role of cortisol in the development of perinatal depression is mixed; however, most studies support an association between elevated cortisol and brief mood fluctuations during pregnancy and postpartum “blues”; in contrast, blunted cortisol is likely related to postpartum depression, particularly when it is more chronic (Yim, Tanner Stapleton, Guardino, Hahn‐Holbrook, & Schetter, 2015). Some data suggest that oxytocin may be an important player in the pathophysiology of  postpartum depression, with evidence of an inverse relationship between oxytocin and

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­ epression severity (Yim et  al., 2015). Several studies have found heightened sensitivity to d estrogen fluctuations in perinatal depression (Yim et  al., 2015). Moreover, genetic factors appear to influence depression differently depending on the point in time in the perinatal period that the symptoms emerge (Yim et al., 2015). Understanding the contribution of hormonal factors to the timing and course of depression during pregnancy and the postpartum will be an important direction for future research. Recent research has begun to explore how the development timing of perinatal depression influences child outcomes, as well as how mechanisms of maternal depression may shape the somatic and behavioral health of the child. Such attention to the sensitive periods of when a woman may be at greatest risk for depression, hormonal factors, or exposure effects of perinatal depression on the offspring has implications for design and administration of interventions. Emerging research is using magnetic resonance imaging to examine the neurobiological p ­ rofile of postpartum depression, which appears to be similar to major depression in the general population, and is also examining the relationship between inflammation and perinatal depression as a promising direction for future research. Finally, several health‐related behaviors may also influence risk for perinatal depression. Physical activity during and after pregnancy may be associated with a reduced risk for postnatal depression, yet more research is needed to identify an optimal dose of physical activity for such risk reduction. The evidence relevant to breastfeeding as a potential risk factor has been equivocal: some data suggest that not breastfeeding is associated with postpartum depression; however, others suggest that this association may be due to other risk factors such as income, stress, and social support. Additionally, postpartum depressive symptoms have been positively associated with unhealthful behaviors such as smoking and poor maternal diet. There is limited evidence to suggest an association between postpartum depression and deficiencies in biopterin, homocysteine, zinc, and vitamin B12; more robust evidence supports an association between low vitamin D levels during pregnancy and increased risk for postpartum depression, and there is insufficient evidence to support a link between postpartum depression and levels of maternal iron, selenium, or omega‐3 fatty acids (DHA and EPA) (e.g., Serati, Redaelli, Buoli, & Altamura, 2016). Finally, sleep disturbance during the postpartum period may be associated with postpartum depression, although the direction of the relationship is unclear.

Psychosocial Factors There is strong evidence supporting an association between stressful life events (e.g., losing a job, moving) and elevated symptoms of depression during pregnancy (Lancaster et al., 2010). Support for an association between experiencing stressful life events during the perinatal period and depression in the postpartum has been mixed, with some studies finding a weak association (Yim et al., 2015) and others finding that the experience of multiple stressful life events from the start of pregnancy to shortly after childbirth is associated with depressive symptom severity and increased risk for postpartum depression (Lancaster et  al., 2010). A more consistent association has been identified between chronic stressors and postpartum depression. Parenting stress, and in particular caring for difficult infants (e.g., because of temperament, crying, and colic), is consistently associated with elevated levels of depression in the postpartum period, as are general life stress, perceived stress, and feelings of overwhelm (Yim et al., 2015). However, daily stressors are not consistently associated with depression during pregnancy (Lancaster et al., 2010). Social support has been identified as a key factor in protecting against depression among pregnant and postpartum women. Women with poor social support may be at greater risk for

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antenatal depressive symptoms or postpartum depression (Lancaster et  al., 2010). Lack of intimate partner support may predict depression during pregnancy, particularly in the context of intimate partner violence, whereas support from an intimate partner and overall relationship satisfaction are associated with lower levels of perinatal depression (e.g., Pilkington, Milne, Cairns, Lewis, & Whelan, 2015). Specific partner factors including emotional closeness, low levels of conflict, effective communication, and sufficient emotional and instrumental support are associated with lower depression during pregnancy and the postpartum (Pilkington et al., 2015). Non‐cohabitation with an intimate partner was associated with increased risk of depressive symptoms during pregnancy (Lancaster et al., 2010), but not all findings included in this review were consistent. There also appears to be a relationship between an unwanted or unplanned pregnancy and postpartum depression, and the prevalence of depression among women with unintended pregnancies may be as much as two times higher (e.g., Abajobir, Maravilla, Alati, & Najman, 2016).

Clinical History and Comorbidity A family history of depression and having experienced a previous episode of depression, especially a past perinatal depressive episode, are predictors of perinatal depression. ­ Experiencing anxiety before or during pregnancy increases the risk of developing depression both during pregnancy and postpartum. Experiencing depression during pregnancy also substantially increases the risk of developing postpartum depression (Biaggi et al., 2016; O’Hara & McCabe, 2013; Underwood et al., 2016). A history of childhood sexual abuse is associated with perinatal depression, although the association is more consistently seen during pregnancy than postpartum (Wosu, Gelaye, & Williams, 2015). Substance abuse during pregnancy and the postpartum is also associated with increased rates of depression, although the direction of the relationship is unclear (e.g., Forray, Gotman, Kershaw, & Yonkers, 2014). Body image and eating concerns also may increase risk for perinatal depression. A recent review reported a consistent but weak association between body image dissatisfaction and the onset of perinatal depression (Silveira, Ertel, Dole, & Chasan‐Taber, 2015). A history of ­eating disorders, particularly bulimia nervosa and binge eating disorder, increases risk for perinatal depression.

Detection and Management Screening and Engagement Recently, there has been increasing attention to the importance of screening for depression during pregnancy and the postpartum period. American Congress of Obstetricians and Gynecologists (ACOG)’s Committee on Obstetric Practice recommends that physicians screen women for depression and anxiety symptoms at least once during the perinatal period with a validated tool. ACOG also stated that the screening should include appropriate follow‐up procedures, including the provision of therapy or a referral to receive treatment. Similarly, the US Preventive Services Task Force (USPSTF) recommended that all adults should be screened for depression, including at‐risk populations of pregnant and postpartum women, again highlighting a call for adequate systems to ensure appropriate follow‐up and treatment (Siu et al., 2016). It has been recommended that practitioners take advantage of women’s routine ­antenatal and infant checkups to screen for elevated depressive symptom severity.

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Recently, a systematic review was conducted for the USPSTF surrounding the effects of depression screening and treatment for pregnant and postpartum women (O’Connor, Rossom, Henninger, Groom, & Burda, 2016). Based on six trials, the authors reported an 18–59% ­relative reduction in the risk of depression at follow‐up after participation in programs involving depression screening, with or without additional treatment components, compared to usual care. These data suggest that screening pregnant and postpartum women for depression may reduce depressive symptoms. There are several instruments that have been validated for use during pregnancy and the postpartum period to screen for depression. A commonly used instrument is the Edinburgh Postnatal Depression Scale (EPDS), which consists of 10 self‐reported items, takes less than 5 min to complete, has been translated into 12 languages, requires a low reading level, and is easy to score. The Patient Health Questionnaire 9 (PHQ‐9) is another brief screening tool often used for perinatal depression. Research has explored common barriers to treatment‐seeking behaviors for perinatal women. Women are more likely to prefer non‐pharmacologic (i.e., psychotherapeutic) treatments for depression, rather than antidepressant medication (Battle, Salisbury, Schofield, & Ortiz‐Hernandez, 2013). Additionally, research has found that depressed perinatal women often experience a high level of decisional conflict, or uncertainty, as to how to treat their depression. Such decisional conflict is associated with a lower likelihood to engage in treatment (Battle et  al., 2013). There have been recent efforts to explore models of screening, support for treatment decision making, and therapeutic care that increase the likelihood that women who may benefit from mental health support will be screened and receive treatment (e.g., medical home models, integration into primary care, engaging patients in treatment decision making, including mental health providers in OB‐GYN clinics).

Prevention and Treatment Services are available that can help to prevent perinatal depression. There is evidence that interventions based in cognitive behavioral therapy (CBT), mindfulness‐based cognitive therapy (MBCT), and interpersonal therapy (IPT) are effective in reducing the risk of depression during the perinatal period. Some women with an elevated risk for perinatal depression (i.e.,  women with a history of depression) may benefit from prophylactic treatment with a selective serotonin reuptake inhibitor (SSRI) antidepressant to prevent perinatal depression. Both pharmacological and non‐pharmacological interventions are effective for treating depressed pregnant and postpartum women. Recent guidelines published by the American Psychiatric Association (APA) and ACOG provide instructions for mental healthcare providers regarding appropriate treatment with pharmacotherapy as well as considerations for safety with respect to the use of pharmacotherapy during pregnancy (Yonkers et al., 2009). A growing body of research describes the efficacy and safety of antidepressant medication for depression in pregnancy; however significant gaps still remain. There may be small increases in the risk of preeclampsia, postpartum hemorrhage, or miscarriage, as well as adverse infant outcomes (e.g., preterm birth, pulmonary hypertension, small for gestational age), associated with second‐generation antidepressant use during pregnancy (O’Connor et al., 2016). Recent guidelines also indicate that some forms of psychotherapy should be considered as a treatment, either in place of, or to augment, pharmacotherapy. CBT, IPT, and behavioral activation (BA) therapy demonstrate promise in ameliorating perinatal depressive symptoms. In their review, O’Connor et al. (2016) pooled results for the benefit of CBT for pregnant and postpartum women with screen‐detected depression and found an increase in the likelihood of

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remission compared with usual care. A variety of promising delivery methods for treating ­perinatal depression exist, including Internet‐based therapy and peer‐delivered psychological interventions in addition to delivery by mental health professionals. Some work has examined complementary and alternative medicine (CAM) interventions for perinatal depression. Numerous CAM treatments have been studied, including omega‐3 fatty acid, folate, and St. John’s wort supplements, exercise, and bright light therapy, as either monotherapies or adjunctive treatment for women during the perinatal period (for a review see Deligiannidis & Freeman, 2014). Further study is needed to examine the efficacy and safety of these CAM treatments for perinatal depression.

Summary It is not uncommon for a woman to experience depression during pregnancy or the p ­ ostpartum, particularly in the presence of risk factors such as a history of depression, low social support, or other stressors in her life. The consequences of untreated perinatal depression are substantial for both mothers and offspring, as well as for society as a whole. Healthcare providers should consider the identified risk and protective factors for women during the perinatal time period to assess the level of a woman’s risk for experiencing perinatal depression and current severity of symptoms or disorder. There has been a strong focus on efforts to screen perinatal women for depression during the perinatal period, yet additional work remains to ensure that women receive the follow‐up support and care that may benefit them after they are screened. There are effective interventions available through a variety of delivery methods for both the prevention and treatment of perinatal depression. Consideration for perinatal mental health should be a top priority in order to promote women’s health, as well as the healthy development of children born to women with depression.

Author Biographies Rachel Vanderkruik is a doctoral student in the clinical psychology program at the University of Colorado Boulder. She received a BA in biology from Bowdoin College and an MSc from Harvard T. H. Chan School of Public Health. Her research interests focus on increasing access to mental health services for women during the perinatal period and disseminating evidenced‐ based interventions for mental and behavioral health issues. Elizabeth Lemon is a doctoral student in the clinical psychology program at the University of Colorado Boulder. She received her BA in psychology from Humboldt State University, California, and her MA in psychology from Boston University. Elizabeth’s research interests focus on mood and anxiety disorders during pregnancy and the postpartum period, as well as developing new ways to access and deliver clinical care to decrease barriers to mental health services. Laura River is currently a doctoral student in the child clinical psychology program at the University of Denver. She received her BA in psychology from Pomona College. Her research interests center on parent–child and romantic attachment during the transition to parenthood, as well as interventions promoting parent and infant well‐being. Dr. Sona Dimidjian is an associate professor in the Department of Psychology and Neuroscience at the University of Colorado Boulder. Her research focuses on cultivating enduring well‐being for women and children. She is a leading expert in the treatment and

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prevention of depression, focusing on the mental health of women during pregnancy and postpartum. She received her BA in psychology from the University of Chicago and her PhD in clinical psychology from the University of Washington.

References Abajobir, A. A., Maravilla, J. C., Alati, R., & Najman, J. M. (2016). A systematic review and meta‐­ analysis of the association between unintended pregnancy and perinatal depression. Journal of Affective Disorders, 192, 56–63. Battle, C. L., Salisbury, A. L., Schofield, C. A., & Ortiz‐Hernandez, S. (2013). Perinatal antidepressant use: Understanding women’s preferences and concerns. Journal of Psychiatric Practice, 19(6), 443–453. Bauer, A., Knapp, M., & Parsonage, M. (2016). Lifetime costs of perinatal anxiety and depression. Journal of Affective Disorders, 192, 83–90. Biaggi, A., Conroy, S., Pawlby, S., & Pariante, C. M. (2016). Identifying the women at risk of antenatal anxiety and depression: A systematic review. Journal of Affective Disorders, 191, 62–77. Deligiannidis, K. M., & Freeman, M. P. (2014). Complementary and alternative medicine therapies for perinatal depression. Best Practice & Research. Clinical Obstetrics & Gynaecology, 28(1), 85–95. Field, T. (2011). Prenatal depression effects on early development: A review. Infant Behavior & Development, 34(1), 1–14. Forray, A., Gotman, N., Kershaw, T., & Yonkers, K. A. (2014). Perinatal smoking and depression in women with concurrent substance use. Addictive Behaviors, 39(4), 749–756. Gavin, N. I., Gaynes, B. N., Lohr, K. N., Meltzer‐Brody, S., Gartlehner, G., & Swinson, T. (2005). Perinatal depression: A systematic review of prevalence and incidence. Obstetrics & Gynecology, 106(5), 1071–1083. Lancaster, C. A., Gold, K. J., Flynn, H. A., Yoo, H., Marcus, S. M., & Davis, M. M. (2010). Risk factors for depressive symptoms during pregnancy: A systematic review. American Journal of Obstetrics and Gynecology, 202(1), 5–14. Liu, C. H., & Tronick, E. (2013). Rates and predictors of postpartum depression by race and ethnicity: Results from the 2004 to 2007 New York City PRAMS survey (Pregnancy Risk Assessment Monitoring System). Maternal and Child Health Journal, 17(9), 1599–1610. O’Connor, E., Rossom, R. C., Henninger, M., Groom, H. C., & Burda, B. U. (2016). Primary care screening for and treatment of depression in pregnant and postpartum women: Evidence report and systematic review for the US Preventive Services Task Force. JAMA, 315(4), 388–406. O’Hara, M. W., & McCabe, J. E. (2013). Postpartum depression: Current status and future directions. Annual Review of Clinical Psychology, 9, 379–407. Pilkington, P. D., Milne, L. C., Cairns, K. E., Lewis, J., & Whelan, T. A. (2015). Modifiable partner factors associated with perinatal depression and anxiety: A systematic review and meta‐analysis. Journal of Affective Disorders, 178, 165–180. Serati, M., Redaelli, M., Buoli, M., & Altamura, A. C. (2016). Perinatal major depression biomarkers: A systematic review. Journal of Affective Disorders, 193, 391–404. Silveira, M. L., Ertel, K. A., Dole, N., & Chasan‐Taber, L. (2015). The role of body image in prenatal and postpartum depression: A critical review of the literature. Archives of Women’s Mental Health, 18(3), 409–421. Siu, A. L., Bibbins‐Domingo, K., Grossman, D. C., Baumann, L. C., Davidson, K. W., Ebell, M., … Pignone, M. P. (2016). Screening for depression in adults: US Preventive Services Task Force recommendation statement. JAMA, 315(4), 830–387. Underwood, L., Waldie, K., D’Souza, S., Peterson, E. R., & Morton, S. (2016). A review of longitudinal studies on antenatal and postnatal depression. Archives of Women’s Mental Health, 19(5), 711–720.

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Wosu, A. C., Gelaye, B., & Williams, M. A. (2015). History of childhood sexual abuse and risk of ­prenatal and postpartum depression or depressive symptoms: An epidemiologic review. Archives of Women’s Mental Health, 18(5), 659–671. Yim, I. S., Tanner Stapleton, L. R., Guardino, C. M., Hahn‐Holbrook, J., & Schetter, C. D. (2015). Biological and psychosocial predictors of postpartum depression: Systematic review and call for integration. Annual Review of Clinical Psychology, 11, 99–137. Yonkers, K. A., Wisner, K. L., Stewart, D. E., Oberlander, T. F., Dell, D. L., Stotland, N., … Lockwood, C. (2009). The management of depression during pregnancy: A report from the American Psychiatric Association and the American College of Obstetricians and Gynecologists. General Hospital Psychiatry, 31(5), 403–413.

Suggested Reading For more information about treatment options for perinatal depression, see the following: Cohen, L. S., Wang, B., Nonacs, R., Viguera, A. C., Lemon, E. L., & Freeman, M. P. (2010). Treatment of mood disorders during pregnancy and postpartum. The Psychiatric Clinics of North America, 33(2), 273–293. Pearlstein, T. (2008). Perinatal depression: treatment options and dilemmas. Journal of Psychiatry & Neuroscience, 33(4), 302–318.

Suicide in the Context of Health Psychology Maryke Van Zyl‐Harrison, Tanya Hunt, Rebekah Jazdzewki, Yimi Omofuma, Paola Mendoza‐Rivera, and Bruce Bongar Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA

Health psychology is a multifaceted subfield of psychology that aims to understand the human experience by combining two traditional theoretical approaches: natural science and human science (Marks, Murray, & Evans, 2005). The natural science approach focuses on studying human behavior through experimental studies, while the human science approach focuses on exploring the underlying meanings of human behavior (Marks et  al., 2005). Through the combination of these two approaches, health psychology is able to study how biology, social conditions, and psychological stress intertwine and shape human behavior (Kaplan, 2009). Health psychology currently covers a broad range of practice within the discipline of ­psychology. Throughout the last several decades, there have been numerous attempts to describe the activity, within the context, of what is now called health psychology. Some of the terms previously used have included behavioral medicine, psychosomatic medicine, and medical psychology. Behavioral medicine is the interdisciplinary field related to the development and combination of behavioral and biomedical science, knowledge, and procedures involving prevention, diagnosis, treatment, and rehabilitation of health and illness (Schwartz & Weiss, 1978). This definition was purposefully intended to not subscribe to any one specific theoretical orientation or discipline. Those involved in this field includes various health professionals like health psychologists, nurses, nutritionists, physicians, and members of other fields who decide to engage in the practice, teaching, development of policy, or research related to the combination of behavioral and biomedical disciplines regarding health and disease. Psychosomatic medicine refers to the interdisciplinary practice of examining the interaction between social, psychological, and behavioral components of physiological processes. Medical psychology refers to both the practice of psychology within a medical school facility and the

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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study of psychological factors associated with all the components of diagnosing and treating physical health illness and disease. The relationship between physical and psychological ­symptoms can be addressed on individual, group, and systematic levels. The American Psychological Association (APA) formally recognized health psychology as a specialty over 20 years ago (Belar & Deardorff, 2009). Health psychology entails the use of scientific knowledge and research regarding psychological factors concerning the interaction of thoughts, emotions, personality, and biopsychosocial factors adaptation to illness and disease, disability, rehabilitation, and health promotion involving individuals, groups, and the healthcare system.

Health Psychology Theory of Suicide The purview of health psychology includes the examination of environment, cultural factors, mental distress, health habits, impulsivity, substance use, coping skills and resources, and chronic stress levels as divisions of mechanisms that may lead to risky or unhealthy behaviors (Taylor, Repetti, & Seeman, 1997). Health psychology evaluation of the underlying factors of risky behavior can assist in identifying factors that can lead to suicidal behaviors. However, reducing suicide behaviors includes not only reducing problematic behaviors but also increasing positive behaviors. It must be mentioned that cultural factors can also impact the acceptance of suicidal behaviors. Therefore, understanding the effect of culture, healthy environments, support systems, resilience, and coping skill can lead to the formulation of protective factors and safety plans that may prevent or reduce suicidal behaviors. The role of psychology in health is quite prominent as the literature suggests that most health models hypothesize that psychosocial stressors negatively impact the physical and mental health of individuals (Watson & Pennebaker, 1989). For example, a review by Leeb, Lewis, and Zolotor (2011) found that severe psychological distress during childhood was positively correlated with future negative physical health outcomes (e.g., chronic pain, respiratory illness, cancer), psychological disorders (e.g., depression, anxiety, PTSD), and increased suicide risk. Considering that suicide is regarded as the tenth leading cause of death in the United States (CDC, 2015), the field of health psychology has heightened its efforts to the understanding and reduction of factors that lead to an increased risk of suicide. Particularly, two health psychology theories, the biopsychosocial perspective and the lifespan perspective, may be used to understand the topic of suicide from a health psychology perspective.

Biopsychosocial Perspective The biopsychosocial perspective is a scientific model that considers the development of ­illness and recovery due to the interaction of biological factors, such as genetics; psychological processes, like mood and personality characteristics; and social factors, including culture and ecology (Engel, 1980). Evaluating all the elements that impact a person both positively and negatively allows for a more comprehensive understanding of the development and maintenance of illness, recovery, and well‐being. The biopsychosocial perspective combines all these previously independent factors of health and disease and recognizes that the nonphysiological elements of a person can change the person’s physiological reaction, resulting in vulnerability to the development of disease. In addition, these nonphysiological elements can impact the course of the illness (Jackson, Antonucci, & Brown, 2003). This perspective

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is of particular value when considering the increasing intersectionality of populations. Hoffman (2000) discussed how the biopsychosocial perspective can potentially explain some trends concerning the issue of suicide. According to Engel’s (1997) biopsychosocial model, physical health is affected by biological, cultural, and psychological variables (Marks et al., 2005). This perspective (which is one of the main influences of health psychology) may help to understand why suicide rates are increasing among patients with chronic illness, as well as why there appears to be an increased awareness about how psychosocial factors play a role in suicide (Hoffman, 2000). Hoffman (2000) suggested that, even though humans seem to be living longer, they are experiencing more chronic illness and a poorer quality of life. Therefore, this decreased quality of life may have led to an increased motivation to commit suicide and to a change in how societies view suicide, which has led to policy changes regarding the legitimacy of physician‐assisted suicide (e.g., Oregon’s Death with Dignity Act) (Hoffman, 2000).

Lifespan Perspective The lifespan perspective relates to the study of the consistency and transformation of ­development that occurs throughout the course of life. This perspective stipulates that no ­particular age period can be considered the primary attribute to the regulation of the developmental process. Essentially the lifespan perspective maintains that during all stages of life, the developmental processes are always at work and can occur at any time during a person’s life from birth to death. The system of changes that occur during development can differ in terms of time, direction, and order (Baltes, 1987). A theoretical aspect of the lifespan perspective is the gain/loss argument, which is a product of the belief that function declines as humans age. However, it has been proposed that any change within the adaptive capacity is a form of development, regardless if the change is positive or negative, suggesting that all growth and change is a c­ ombination of desirable and undesirable effects (Baltes, 1987). Another theoretical foundation of the lifespan perspective is plasticity, meaning an individual’s development is based on testing the environmental limits and experiences, suggesting that humans are molded by the limitations of their cultural environments (Baltes, 1987). The lifespan perspective can also be taken to explain suicide trends (Hoffman, 2000). According to this perspective, people are more likely to be subjected to illness and isolation as they become older; thus, research shows that suicide might be seen as a more acceptable solution to chronic illness and poor quality of life among elderly individuals who have a harder time dealing with the vicissitudes associated with aging (Hoffman, 2000). However, more research is required to address how lifespan issues may affect the views and perceived consequences of suicide.

Suicide in the Context of Primary Care Medicine Since suicide tends to be viewed as a mental health issue, clinicians in primary care medicine are often less prepared to respond to suicidal thoughts or behaviors. There are clear gaps in the training of primary care clinicians when it comes to the assessment and treatment of suicide. It is important to consider possible psychological causes of physical complaints, particularly those that are challenging to explain using medical reasoning. Additionally, physical illness, particularly chronic illness, may lead to a sense of hopelessness and suicidal ideation. Ultimately, a patient‐centered integrative care approach compliments an enhanced awareness of culturally based expressions of psychological distress.

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Training Keeping in mind the trends indicating an increase in suicide rates among chronically ill ­individuals (Hoffman, 2000), one could imagine that the issue of suicide is likely to be prevalent in ­primary care practices. Indeed, a study by Luoma, Martin, and Pearson (2002) found that, while only one in five victims of suicide reached out to mental health providers within 1 month of committing suicide, approximately 45% of suicide victims sought primary care providers within 1 month of committing suicide. The tendency to turn to primary care physicians for mental healthcare could present a potential problem if primary care physicians are not properly trained to identify or treat suicidal individuals. According to Sudak et al. (2007), even though the Accreditation Council for Graduate Medical Education requires training programs to provide students with suicide‐related didactic content, less than 50% of the training directors that participated in their study demonstrated confidence or satisfaction with the adequacy of their programs’ suicide ­prevention training. Another potential problem that stems from the limited suicide prevention training received by primary care physicians is that they are not mandated to obtain suicide‐ related continuing education, yet they have the capacity to prescribe psychotropic medications to depressed patients, which may in turn exacerbate their suicidal ideations (Stuber & Quinnett, 2013). Literature shows that antidepressants like selective serotonin reuptake inhibitors (SSRI’s) are linked to increased suicide attempts, particularly among children and young adults (Fergusson et  al., 2005; Ghaziuddin, Merchant, Dopp, & King, 2014). These findings are particularly important since suicide is the third leading cause of death in the United States for individuals between the ages of 15 and 24 (Swanson, McGinty, Fazel, & Mays, 2015, p. 370).

Common Presentation Since primary care is the first line of defense against suicide, it is important to consider the warning signs and common presentations of risk that primary care physicians may come in contact with. Suicidal ideation may be presented in ways that are not merely psychological, but present as somatic symptoms. Individuals endorsing suicidal thoughts often present comorbid mood and substance use disorders (MacLean, Kinley, Jacobi, Bolton, & Sareen, 2011). Older adults who are suicidal tend to seek out their primary care physicians with complaints of somatic symptoms related to physical illness or functional impairment, rather than depressive symptoms (Conwell et al., 2000). Similarly, Asian American patients tend to present psychological distress as somatic symptoms like headaches or stomach pain. Individuals from different cultures may express suicidality in different ways. It is important for clinicians in primary care to be aware of these atypical presentations of psychological distress, particularly suicide, in order to accurately assess suicidal ideation. Furthermore, an awareness of the differences in suicide rates as well as the commonly used means of suicide among individuals who share a cultural identity could enable the early detection of suicide risk in primary care. It is important to note that suicide is the eighth leading cause of death among American Indian/Alaskan Natives and the tenth leading cause of death among Caucasians in the United States. Elderly Asian/Pacific Islander women over the age of 65 have higher suicide rates than any other ethnic group in the same age range (Chu, Hsieh, & Tokars, 2011). African American adolescent boys and Latina adolescent girls have been shown to have higher suicide rates, suicidal ideation, and attempts than their same‐aged Caucasian peers, and ideation, attempts, and completions among LGBTQ individuals have been consistently higher when compared with nonsexual minority populations (Silenzio, Pena, Duberstein, Cerel, & Knox, 2007).

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Mind–Body Physical pain can not only present negative physical sensations but also emotional distress. Medical and psychological problems can sometimes mask one another. While some patients may report medically unexplained physical symptoms (MUPS) (Weiland et al., 2015), oftentimes categorized under somatic symptom disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM), others can experience medical illnesses that first present as psychological symptoms. Some patients may present with mental symptoms, such as depression and anxiety, which are caused by, or exacerbated by, physical diseases such as pancreatic carcinoma, diabetes, heart disease, arthritis, kidney disease, HIV/AIDS, lupus, multiple sclerosis (MS), and hypothyroidism. As psychologists, it is important to conduct thorough interviews at the start of treatment, which includes gathering a medical history, making a referral for a physical evaluation to rule out medical conditions that may be impacting their symptoms, and being aware of changes in patient presentation in terms of appearance, emotional state, physical state, and symptomatology. Conversely, some patients may present in primary care with MUPS. Research indicates that up to 50% of patients who present in general or specialist care are MUPS patients (Weiland et al., 2015). Many of these patients report not feeling understood or empathized with and can develop symptoms of depression and anxiety as a result (Weiland et al., 2015). Furthermore, medical specialists may subject these patients to different tests, for fear of missing a medical cause, and may find these patients difficult to handle (Weiland et al., 2015). Patient‐centered communication can help improve patient outcome and overall experience. As part of a multidisciplinary team, psychologists can help develop training programs for specialists in other disciplines on how to assess patients who have MUPS (Weiland et al., 2015).

Impact of Suicide on Health Suicidal ideation affects individual health, while acts of suicide affect community health. Suicidal ideation is correlated with an increase of substance use. Past suicidal ideation or ­suicidal attempts increase the likelihood for opioid misuse (Ashrafioun, Bishop, Conner, & Pigeon, 2017). Suicidal attempts are also strongly associated with later alcohol, nicotine, and cannabis dependence (Agrawal et al., 2017). General physical health is also affected by suicidal ideation. Perceived health decreases as suicidal ideation increases (Kim, Hong, Yook, & Kang, 2017). Exposure to a peer suicide in one’s community increases the likelihood of another suicide in the community, a phenomenon called suicide contagion (Randall, Nickel, & Colman, 2015). Suicidal contagion is especially impactful for adolescents and in indigenous communities (Goodwin‐Smith, Hicks, Hawke, Alver, & Raftery, 2013; Randall et  al., 2015). The ­continuous exposure to death by suicide crumbles social well‐being within the community and brings added distress to already vulnerable populations (Goodwin‐Smith et al., 2013).

Health Psychology Preventing Suicide Many general health diagnoses, such as cancer, cardiac disease, chronic pain, diabetes, ­functional bowel disorder, and erectile dysfunction, have been linked to an increase in stress levels, physical and psychological pain, as well as an increase in risk of suicide among patients. Common mental illnesses, such as depression, panic attacks, bipolar disorder, and posttraumatic stress disorder (PTSD), are often associated with a risk of suicide. Furthermore, there is a growing body of research highlighting the direct or indirect correlation between unhealthy

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behavior and diseases. With clinical psychology being a field focused on the study of behavior, and psychologist being trained and equipped to manage suicide crisis, it therefore stands to reason that incorporating mental healthcare in the treatment of individuals diagnosed with general health problems, at an early stage, can help reduce the risk of death by suicide. Individuals diagnosed with terminal or chronic illnesses such as cancer, cardiac diseases, or chronic pain often report experiencing feelings of hopelessness (Ahn et al., 2015; Suarez et al., 2015), which is correlated with an increased risk of suicidal behavior (Hirsch, Visser, Chang, & Jeglic, 2012). Evidence‐based psychological treatment approaches, such as cognitive behavioral therapy (CBT) and dialectical behavior therapy (DBT), have strong research backing as treatments that help reduce suicidal cognitions, behaviors, and feelings of hopelessness (Comtois & Linehan, 2006; Mewton & Andrews, 2016). Having psychologists as part of treatment teams for patients with terminal or chronic illnesses can help reduce the risk of ­suicidal behavior, as psychologists can provide the vital support required to treat suicidal ideation.

Identification of Risk Assessment of suicide risk in patients in primary care occurs similarly to risk assessment in the mental health field. Assessment tools such as the Beck Depression Inventory and Hospital Anxiety and Depression Scale (Aiello‐Laws, 2010) can be used. However, some assessment tools used by psychologists can be cumbersome and lengthy and may not be appropriate for use in the primary care setting. Highlighted as a core knowledge and skill, it is important for clinicians to be competent in selecting appropriate evidence‐based screening measures for primary care settings. Psychologists can assist members of the treatment care team, such as nurses, who have more interactions with the patients, in incorporating brief measures that can be included in routine appointments. These measures may assist the treatment team in monitoring for illnesses like depression, anxiety, or insomnia, which can co‐occur with medical ­diagnoses, as well as risk‐related symptoms like hopelessness. Some medication may lead to impulsive or suicidal behavior or have side effects that increase depressive symptoms. As highlighted above, it is imperative that clinicians be aware of the different side effects of the medications that their patients are taking and that they monitor the patient’s response to their medications frequently throughout treatment.

Integration Integrated care can be defined as unified care experienced by patients, when a team of behavioral health and primary care providers, in collaboration with the patient and their family, using an organized and cost‐effective method, work together for the purpose of ­providing patient‐centered care. The healthcare system is steadily moving toward a more integrated approach to treatment, incorporating biological, psychological, and social factors in patient care. The Joint Principles of the Patient‐Centered Medical Home (PCMH), ­published in 2007, highlighted the need for personal, coordinated, and comprehensive healthcare by a team of clinicians, addressing all of a patient’s needs, further emphasizing the  importance of integrated behavioral healthcare. One of the key aspects addressed by integration is unification of records. Integration allows for the availability of shared medical records, collaborative decision making, and shared responsibility of patient care. Barriers to communication between healthcare professionals are reduced, allowing for improved ­

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t­reatment of the patient as a whole, incorporating different aspects of the patient’s life in their treatment as is applicable. Traditionally, the role of psychologists was to assess, diagnose, and treat psychopathology. However, over previous decades, psychology in healthcare has expanded to be multifaceted, from understanding the development and maintenance of illness and disease to determining the development and impact of individuals’ and communities’ health behaviors (Sarafino & Smith, 2014). Health psychology has provided psychological interventions aimed to address and understand health behavior, provide a basis for clinical care, promote research, and inform program and public policy implementation. The role of psychology within the field of health originates from the premise that individuals’ psychological processes are interrelated to biological processes. The understanding associated with what and how all behaviors, including health‐related ones, are influenced is invaluable to the medical care system. This idea has led to the development and implementation of psychologically driven interventions to promote health. This multisystem approach combining psychological and physical aspects has proven to be successful in improving functioning and well‐being. Though significant progress has been made, there remains a high level of stigma surrounding mental health. Integrated care reduces the focus on mental health and increases the emphasis on the overall well‐being of the patient, thereby reducing the stigma surrounding mental health treatment. Furthermore, the patient‐centered approach helps to empower patients by increasing their involvement in their own care and fostering a sense of control over their treatment. The population of the United States is increasingly growing more diverse and older. Therefore, the field of health psychology must continue to refine and adapt conceptual models, research methods, and resources for application that are influenced by these variations.

Changes over Time Suicide prevention in healthcare has expanded from targeting individuals in crisis to a preventative approach that involves the community in outreach and prevention efforts. ­ Stakeholders in the community are now being trained to identify suicidal community ­members. Social relationships are being utilized in suicide safety plans to facilitate resilience. Media campaigns not only target at‐risk individuals but also are beginning to offer information about how to be an advocate for a suicidal peer. These new developments are utilizing the community’s unique risk and resilience factors to create suicide prevention programs.

WHO Global Goals The Millennium Development Goals (MDGs) were created in 2000 by a collective of 189 countries. The objective was for countries and partners to work together in order to globally reduce poverty, gender inequality, child mortality, and diseases such as HIV/AIDS and to improve maternal health, environmental sustainability, and global partnerships, with targets set for 2015. Many of these goals relate to health, whether directly or indirectly. One of the goals outlined by the MDGs and the World Health Organization (WHO) is the reduction of suicide. In an effort to sustain and expand upon the MDG’s of 2000, the UN presented a list of 17 sustainable development goals (SDGs), to be met by 2030. Specifically, these SDG’s resolve to reduce premature mortality related to noncommunicable diseases by one third to promote mental health and well‐being. The objective is to achieve a 10% reduction of suicide

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by 2020 (World Health Organization, 2013). To reach this goal for suicide, smaller goals to increase mental health were set by the WHO. With this objective in mind, countries are encouraged to use expanded mental health treatments in order to prevent suicide. The inclusion of noncommunicable diseases and the explicit mention of mental health serve as an indication that mental health and well‐being are of increasing importance on the agenda of global development.

Future Directions The inclusion of mental health, particularly suicide, in the WHO recommendations toward improved global health, indicates how significant the issue of suicide is from a health perspective. As a result, several policy changes have been implemented by national and global organizations in an attempt to enhance suicide prevention efforts worldwide. The WHO and the US Joint Commission on Accreditation of Healthcare Organizations (JCAHOs) have employed specific recommendations that aimed to improve suicide risk assessment and treatment. Some of these recommendations included conducting suicide risk assessments on any individual over 10 years old that presented with either a medical or psychological condition, as well as being responsible for deglamorizing suicide in the media (Dua et al., 2011).

WHO Recommendations Over time, recommendations from the WHO for suicide prevention have evolved from an individualistic approach to one that begins with the community and then reaches inward. In 2000, the WHO focused their recommendations on demographic risk factors. These recommendations were focused on those with a high risk of suicide. The recommendations consisted of detecting such individuals and managing individual risk by asking targeted questions and referring them to a psychiatrist or hospital. The WHO expanded suicide prevention in 2001 with the recommendation of increased research, training for mental health workers, and public awareness campaigns. An ecological orientation became the framework in 2003 for suicide prevention. This involves intertwining organizations such as schools, companies, and regions to create health networks (Kickbusch, 2003). The current recommendations consist of surveillance, prevention, and evaluation. The WHO recommends surveillance of patterns of suicide in communities and suicidal behavior in individuals. Additionally, it suggests that community patterns such as suicidal contagion should be observed and recorded. The common behaviors of suicidal individuals should also be ­documented in order to increase knowledge of risk factors. Knowing how communities and individuals react in regard to suicide creates opportunities for more accurate prevention strategies. The WHO has three levels of prevention strategies: universal, selective, and indicated. Universal prevention strategies reach the entire population. This type of strategy is effective for reaching low‐risk individuals and educates non‐suicidal individuals in order to create a ­supportive network. A mental health stigma awareness campaign is an example of a universal prevention strategy. Selective interventions target specific vulnerable populations, such as those who are unemployed, adolescents, and people with mental illnesses. Selective interventions focus on training for stakeholders in communities that are vulnerable to suicide. One such intervention would be training high school teachers how to identify and appropriately support a suicidal student. The general training of teachers is a selective intervention, but this

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specific action of the teacher is an indicated intervention. Indicated interventions directly ­target individuals that have a high risk for suicide. Integrating a public health approach into the prevention plan is recommended by the WHO. This shows a commitment to suicide prevention from the government. Restricting suicide means and increasing access to mental health services are common interventions that governments have implemented successfully to reduce suicide. Finally, the aforementioned prevention strategies need to be evaluated in order to ensure effectiveness. The outcome being measured should be clearly outlined before the intervention is administered. Important evaluation metrics include the rate of suicide, suicide attempts, and suicidal ideation. The population being evaluated should be considered when choosing an outcome to measure. Suicidal ideation should be used instead of suicide in a small population since suicidal ideation is more common and enough data is available to conduct statistically significant evaluations.

Joint Commission Recommendations The Joint Commission developed their own set of recommendations to reduce suicide mortality nationally in the United States, which addressed the provision of suicide screenings, assessing for safety needs, and providing resources like crisis hotline numbers for discharged patients (Czyz et  al., 2016). Recently, the Joint Commission’s recommendations were updated to include “safety planning, discharge, treatment, and follow‐up care” (p. 175), among other things (Czyz et al., 2016) The Joint Commission’s recommendations for preventing suicide are targeted for healthcare professionals. Their approach begins with assessing all patients with an evidence‐based screening tool and observing high‐risk individuals through their history of suicide. Patients in a suicidal crisis should not be left alone. The emergency department or psychiatric unit should be immediately called, and possible suicidal means, such as bandages, elastic tubing, and plastic bags, should be kept away. Lower‐risk individuals should be given referrals to outpatient mental health providers, introduced to the suicide hotline, have lethal means restricted, and develop a safety plan. Even after discharge, suicidal prevention should continue. Safety plans are most effective when friends and family are involved. Therapists also have a direct role after ­discharge. The Joint Commission recommends therapies that directly target suicidal ideation like Cognitive Therapy for Suicide Prevention and Collaborative Assessment and Management of Suicide. The Joint Commission had specific recommendations for suicide in the hospital setting. These were based differences between psychiatric hospitals and medical hospitals. Medically ill patients who committed suicide were older and more likely to be employed and married ­compared with the psychiatric patients. Since medically ill patients are more likely to commit suicide in the hospital, the Joint Commission recommends increased suicide assessment for patients in the medical hospitals. A systemic collection of data on suicides and suicide attempts in hospital settings should also be created to fill the gap of information for this issue. As a recommendation for assessing suicide in primary care, be aware that individuals who are part of ethnic and sexual minority subgroups may be at higher risk for suicide, particularly those with multiple minority identities. Be cognizant that culture affects how suicidal ideation is expressed, which methods for suicidal behavior are chosen and which stressors are more salient. Keep in mind that a mental illness diagnosis is not an adequate indication of risk for suicide and that it may be necessary to consider culturally specific stressors and the social environment within the patient’s cultural context.

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Recommendations for Future Research Successful integration of psychologists in primary care requires an understanding and acceptance of patient‐centered care by healthcare providers. More research focusing on how to optimize the systems to ensure that every aspect of a patient’s life is duly addressed is important. This includes research into and the development of policies that ensure that the practice, training, and financing of behavioral health services are included in benefit or insurance plans for patients. Suicidal behavior can occur at different stages in an individual’s life and in conjunction with different medical illnesses as well. Research has shown that individuals are more likely to visit their primary care providers prior to seeking out professionals for mental health‐related problems (Luoma et al., 2002). It is therefore crucial for primary care providers to be equipped to assess for mental illness and risk‐taking behavior in patients. Future research should look into evaluating treatment models in primary care settings, particularly focusing on risk assessment and crisis intervention for patients.

Integrative Care Although public health organizations like the WHO and the US JCAHO have advanced how healthcare professionals manage patients exhibiting suicidal behavior, there remains plenty of room for improvement. To continue achieving progress, the best option appears to be for mental health providers and healthcare providers to take an integrative approach and collaborate in the management of their shared patients. Though healthcare practitioners are the experts on physical health, they do not always have the necessary training or the time to ­conduct psychological assessments and provide psychotherapy to their patients (Nash, McKay, Vogel, & Masters, 2012). Therefore, psychologists are pivotal members in this contemporary concept of multidisciplinary teams. In integrated primary care, effective psychologists must possess the knowledge and skills to serve as consultants, teachers, and supervisors for primary care providers, as well as properly assess patients, provide psychotherapy, remain up‐to‐date in evidence‐based practices, evaluate progress, and serve as case managers.

Author Biographies Maryke Van Zyl‐Harrison, MA, is a doctoral candidate in clinical psychology with an emphasis in diversity and community mental health at Palo Alto University. Maryke earned her bachelor’s degree from Stellenbosch University in South Africa and her masters in marriage and family therapy from Pepperdine University in Los Angeles. She has worked to address HIV/ AIDS, substance abuse, and suicide risk among underserved communities. Maryke works with children with serious medical diagnoses and adults with chronic pain. Most recently, she serves as coordinator of a special interest group on risk, resilience, and reasons for living within the International Association for Suicide Prevention. Tanya Hunt is currently working on earning her PhD in clinical psychology with an emphasis in diversity and community mental health at Palo Alto University. Her areas of concentration also include culture, trauma, and health psychology. She earned her BBA from the University of Kentucky. Tanya has over 20 years’ experience working with marginalized populations. She has provided services in employment and substance abuse counseling, as well as adjudicating claims based on mental and physical health. Tanya is currently providing mental

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health services including addressing health disparities to a diverse population within an ­integrated healthcare setting. Rebekah Jazdzewski, BA, is currently working on her doctorate in clinical psychology with an emphasis in adult trauma at Palo Alto University. Rebekah earned her bachelor’s degree from Point Loma Nazarene University where she studied coping mechanisms in underserved populations. Currently, she is working with Native American tribes to study suicide prevention and resilience. Rebekah also serves as a student therapist at the Gronowski Center, working as a therapist in community mental health. Yimi Omofuma, MA, is currently pursuing her doctorate in clinical psychology with an emphasis in trauma at Palo Alto University. Yimi earned her bachelor’s degree from Southern Illinois University, Carbondale, Illinois and her masters in forensic psychology from Fairleigh Dickinson University in New Jersey. She currently works at an Early Intervention Clinic, ­providing emergency psychology for recently traumatized individuals. Furthermore, Yimi serves on the board of the Imani Foundation, a nonprofit organization currently dedicated to working with women and children who are survivors of abuse in Nigeria. Paola M. Mendoza‐Rivera, BA, is currently pursuing her PhD in clinical psychology with an emphasis in forensic psychology at Palo Alto University. Paola achieved her bachelor’s degree in general psychology as well as a second bachelor’s degree in criminal justice from the University of New Haven in West Haven, Connecticut. Currently, she is working on building her resume and developing her practical clinical skills at Palo Alto University’s Gronowski Center. Paola is serving as a student therapist in the Gronowski Center’s Clinica Latina, a bilingual specialty clinic that specializes in treating underserved Latinx populations. Bruce Bongar, PhD, ABPP, FAPM, CPsychol, CSci, is currently the Calvin Distinguished Professor of Psychology at Palo Alto University and has previously served as consulting professor of psychiatry and behavioral sciences at Stanford Medical School. Dr. Bongar received his PhD from the University of Southern California and maintained a private practice for 25 years. Dr. Bongar is past president of the section on Clinical Crises and Emergencies of the Division of Clinical Psychology of the American Psychological Association. Dr. Bongar has also been a winner of the Edwin Shneidman Award from the American Association of Suicidology for outstanding early career contributions to suicide research and the Louis I. Dublin Award for lifetime achievement in research on suicidology.

References Agrawal, A., Tillman, R., Grucza, R. A., Nelson, E. C., McCutcheon, V. V., Few, L., … Hesselbrock, V. M. (2017). Reciprocal relationships between substance use and disorders and suicidal ideation and suicide attempts in the Collaborative Study of the Genetics of Alcoholism. Journal of Affective Disorders, 213, 96–104. Ahn, M. H., Park, S., Lee, H. B., Ramsey, C. M., Na, R., Kim, S. O., … Hong, J. P. (2015). Suicide in cancer patients within the first year of diagnosis. Psycho‐Oncology, 24(5), 601–607. Aiello‐Laws, L. B. (2010). Assessing the risk for suicide in patients with cancer. Clinical Journal of Oncology Nursing, 14(6), 687. Ashrafioun, L., Bishop, T. M., Conner, K. R., & Pigeon, W. R. (2017). Frequency of prescription opioid misuse and suicidal ideation, planning, and attempts. Journal of Psychiatric Research, 92, 1–7. Baltes, P. B. (1987). Theoretical propositions of life‐span developmental psychology: On the dynamics between growth and decline. Developmental Psychology, 23(5), 611. Belar, C. D., & Deardorff, W. W. (2009). Introduction to clinical health psychology. In C. D. Belar & W. W. Deardorff (Eds.), Clinical health psychology in medical settings: A practitioner’s guidebook pp. 3–16). Washington, DC: American Psychological Association. doi:10.1037/11852‐001

214

Maryke Van Zyl‐Harrison et al.

Centers for Disease Control. (2015). U.S. Government Reports. Atlanta, Georgia, USA. Chu, J. P., Hsieh, K.‐Y., & Tokars, D. A. (2011). Help‐seeking tendencies in Asian Americans with ­suicidal ideation and attempts. Asian American Journal of Psychology, 2(1), 25. Comtois, K. A., & Linehan, M. M. (2006). Psychosocial treatments of suicidal behaviors: A practice‐ friendly review. Journal of Clinical Psychology, 62(2), 161–170. MEDLINE with Full Text, EBSCOhost (accessed June 29, 2017). Conwell, Y., Lyness, J. M., Duberstein, P., Cox, C., Seidlitz, L., DiGiorgio, A., & Caine, E. D. (2000). Completed suicide among older patients in primary care practices: A controlled study. Journal of the American Geriatrics Society, 48(1), 23–29. Czyz, E. K., Horwitz, A. G., Arango, A., Cole‐Lewis, Y., Berona, J., & King, C. A. (2016). Coping with suicidal urges among youth seen in a psychiatric emergency department. Psychiatry Research, 241, 175–181. Dua, T., Barbui, C., Clark, N., Fleischmann, A., Poznyak, V., van Ommeren, M., … Freeman, M. (2011). Evidence‐based guidelines for mental, neurological, and substance use disorders in low‐and middle‐income countries: Summary of WHO recommendations. PLoS Medicine, 8(11), e1001122. Engel, G. L. (1980). The clinical application of the biopsychosocial model. The American Journal of Psychiatry, 137(5), 535–544. Engel, G. L. (1997). From biomedical to biopsychosocial: Being scientific in the human domain. Psychosomatics, 38(6), 521–528. Fergusson, D., Doucette, S., Glass, K. C., Shapiro, S., Healy, D., Hebert, P., & Hutton, B. (2005). Association between suicide attempts and selective serotonin reuptake inhibitors: Systematic review of randomised controlled trials. BMJ, 330(7488), 396. Ghaziuddin, N., Merchant, C., Dopp, R., & King, C. (2014). A naturalistic study of suicidal adolescents treated with an SSRI: Suicidal ideation and behavior during 3‐month post‐hospitalization period. Asian Journal of Psychiatry, 11, 13–19. Goodwin‐Smith, I., Hicks, N., Hawke, M., Alver, G., & Raftery, P. (2013). Living beyond Aboriginal suicide: Developing a culturally appropriate and accessible suicide postvention service for Aboriginal communities in South Australia. Advances in Mental Health, 11(3), 238–245. doi:10.5172/jamh. 2013.11.3.238 Hirsch, J. K., Visser, P. L., Chang, E. C., & Jeglic, E. L. (2012). Race and ethnic differences in hope and hopelessness as moderators of the association between depressive symptoms and suicidal behavior. Journal of American College Health, 60(2), 115–125. Psychology and Behavioral Sciences Collection, EBSCOhost (accessed June 29, 2017). Hoffman, M. A. (2000). Suicide and hastened death: A biopsychosocial perspective. The Counseling Psychologist, 28(4), 561–572. Jackson, J. S., Antonucci, T. C., & Brown, E. (2003). A cultural lens on biopsychosocial models of aging. Advances in Cell Aging and Gerontology, 15, 221–241. Kaplan, R. M. (2009). Health psychology: Where are we and where do we go from here? Mens Sana Monographs, 7(1), 3. Kickbusch, I. (2003). The contribution of the World Health Organization to a new public health and health promotion. American Journal of Public Health, 93(3), 383–388. Kim, M.‐S., Hong, Y.‐C., Yook, J.‐H., & Kang, M.‐Y. (2017). Effects of perceived job insecurity on depression, suicide ideation, and decline in self‐rated health in Korea: A population‐based panel study. International Archives of Occupational and Environmental Health, 90(7), 663–671. Leeb, R. T., Lewis, T., & Zolotor, A. J. (2011). A review of physical and mental health consequences of child abuse and neglect and implications for practice. American Journal of Lifestyle Medicine, 5(5), 454–468. Luoma, J. B., Martin, C. E., & Pearson, J. L. (2002). Contact with mental health and primary care ­providers before suicide: A review of the evidence. American Journal of Psychiatry, 159(6), 909–916. MacLean, J., Kinley, D. J., Jacobi, F., Bolton, J. M., & Sareen, J. (2011). The relationship between physical conditions and suicidal behavior among those with mood disorders. Journal of Affective Disorders, 130(1), 245–250.

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Marks, D. F., Murray, M., & Evans, B. (2005). Health psychology: Theory, research and practice. Sage. Mewton, L., & Andrews, G. (2016). Cognitive behavioral therapy for suicidal behaviors: Improving patient outcomes. Psychology Research and Behavior Management, 9, 21, PsycINFO, EBSCOhost (accessed June 29, 2017) Nash, J. M., McKay, K. M., Vogel, M. E., & Masters, K. S. (2012). Functional roles and foundational characteristics of psychologists in integrated primary care. Journal of Clinical Psychology in Medical Settings, 19(1), 93–104. Randall, J. R., Nickel, N. C., & Colman, I. (2015). Contagion from peer suicidal behavior in a representative sample of American adolescents. Journal of Affective Disorders, 186, 219–225. doi:10.1016/ j.jad.2015.07.001 Sarafino, E. P., & Smith, T. W. (2014). Health psychology: Biopsychosocial interactions. Wiley. Schwartz, G. E., & Weiss, S. M. (1978). Behavioral medicine revisited: An amended definition. Journal of Behavioral Medicine, 1(3), 249–251. Silenzio, V. M. B., Pena, J. B., Duberstein, P. R., Cerel, J., & Knox, K. L. (2007). Sexual orientation and risk factors for suicidal ideation and suicide attempts among adolescents and young adults. American Journal of Public Health, 97(11), 2017–2019. Stuber, J., & Quinnett, P. (2013). Making the case for primary care and mandated suicide prevention education. Suicide and Life‐Threatening Behavior, 43(2), 117–124. Suarez, L., Beach, S. R., Moore, S. V., Mastromauro, C. A., Januzzi, J. L., Celano, C. M., … Huffman, J. C. (2015). Use of the Patient Health Questionnaire‐9 and a detailed suicide evaluation in determining imminent suicidality in distressed patients with cardiac disease. Psychosomatics, 56(2), 181–189. Sudak, D., Roy, A., Sudak, H., Lipschitz, A., Maltsberger, J., & Hendin, H. (2007). Deficiencies in suicide training in primary care specialties: A survey of training directors. Academic Psychiatry, 31(5), 345–349. Swanson, J. W., McGinty, E. E., Fazel, S., & Mays, V. M. (2015). Mental illness and reduction of gun violence and suicide: Bringing epidemiologic research to policy. Annals of Epidemiology, 25(5), 366–376. Taylor, S. E., Repetti, R. L., & Seeman, T. (1997). Health psychology: What is an unhealthy environment and how does it get under the skin? Annual Review of Psychology, 48(1), 411–447. Watson, D., & Pennebaker, J. W. (1989). Health complaints, stress, and distress: Exploring the central role of negative affectivity. Psychological Review, 96(2), 234. Weiland, A., Blankenstein, A. H., Van Saase, J. L. C. M., Van der Molen, H. T., Jacobs, M. E., Abels, D. C., … Arends, L. R. (2015). Training medical specialists to communicate better with patients with medically unexplained physical symptoms (MUPS). A randomized, controlled trial. PLoS One, 10(9), e0138342. World Health Organization (WHO). (2013). Reports on Suicide. Geneva, Switzerland: Author.

Suggested Reading Marks, D. F., Murray, M., & Evans, B. (2005). Health psychology: Theory, research and practice. Thousand Oaks, CA: Sage, Sage. Morrison, J. (2015). When psychological problems mask medical disorders: A guide for psychotherapists. New York, NY: Guilford Publications. Sarafino, E. P., & Smith, T. W. (2014). Health psychology: Biopsychosocial interactions. Hoboken, NJ: Wiley. Taylor, S. E., & Sirois, F. M. (1995). Health psychology. New York: McGraw‐Hill.

Adherence to Behavioral and Medical Regimens Tricia A. Miller1,2, Summer L. Williams3, and M. Robin DiMatteo2 Santa Barbara Cottage Hospital, Santa Barbara, CA, USA Department of Psychology, University of California Riverside, Riverside, CA, USA 3 Department of Psychology, Westfield State University, Westfield, MA, USA 1

2

Introduction Patient adherence (also called compliance) involves the degree to which patients follow treatment recommendations as directed by their healthcare provider. Although empirical ­ ­evidence demonstrates that quality health outcomes depend heavily upon patients’ adherence to recommended treatments, 25% of patients, on average, are nonadherent to prevention and disease management activities (DiMatteo, 2004). These activities include taking medications as prescribed, keeping medical appointments, engaging in regular disease screening, and ­following specific diet and exercise regimens (DiMatteo, Haskard‐Zolnierek, & Martin, 2012; Martin, Williams, Haskard, & DiMatteo, 2005). For patients with chronic disease, adherence can be as low as 50% or less (Vermeire, Hearnshaw, Royen, & Denekens, 2001). Patient nonadherence can be intentional or unintentional. “Unintentional nonadherence” describes situations in which patients incorrectly believe they are adherent, or try to be adherent but fail, whereas “intentional nonadherence” describes the purposeful choice to dismiss treatment or change the regimen without consulting the provider (Lehane & McCarthy, 2007). Whether purposeful or accidental, nonadherence contributes to suboptimal health outcomes. Research has identified some clinical factors that may influence patient nonadherence, such as the complexity of the regimen prescribed, and patients’ existing beliefs about the ­recommended treatment and the severity or seriousness of the disease condition (DiMatteo, Haskard, & Williams, 2007). Patient nonadherence also places an appreciable economic burden on the US healthcare system. Empirical evidence suggests that as many as $100 billion dollars are wasted annually The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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due to the cost of preventable hospitalizations (Cutler & Everett, 2010). Failure to properly adhere to treatment can also cause unnecessary complications or disease progression, with significant increases in the cost of care (Dunbar‐Jacob, Schlenk, & McCall, 2012). Current research suggests that improving adherence requires patients’ knowledge and understanding of their disease and treatment regimen, a collaborative and trusting therapeutic partnership between patients and health professionals, and satisfaction with care. The purpose of this chapter is to highlight the importance of patient adherence, discuss several factors that affect treatment adherence, and examine the Information–Motivation– Strategy Model (IMS Model) as a way in which clinicians and healthcare professionals can improve their patient’s adherence behaviors.

Factors that Affect Patient Adherence Several factors influence whether or not patients engage in disease management and adherence behaviors. Decades of research have identified both real and perceived doubts about the expected benefits and efficacy of treatment, potential financial constraints, and the lack of resources as factors that significantly affect treatment adherence (DiMatteo, Giordani, Lepper, & Croghan, 2002; Zolnierek & DiMatteo, 2009). Socioeconomic factors, financial constraints (e.g., cost of medications), and patient characteristics can also negatively affect adherence (Peltzer & Pengpid, 2013). Some of the unique demands of patients’ prescribed treatment regimens have been associated with lower rates of adherence. These include the number of medications prescribed, the frequency of dosing, and the specific routines of administering medications for complex treatments. Patient nonadherence can be has high as 70% for treatment regimens that are complex and/or require complex lifestyle changes (Chesney, 2003). Dezii, Kawabata, and Tran (2002) found that for patients with type 2 diabetes, adherence was higher for patients on a one‐medication‐per‐day regimen compared with patients who were on a three‐medication‐per‐day regimen. Forgetting to take (or how to take) prescribed medications also contributes to patient nonadherence. Over 56% of patients forget details of their medical instructions shortly after leaving their physician’s or healthcare provider’s office (Martin et al., 2005). Thus, it is important that healthcare providers explain specific steps of their patient’s treatment regimens, review the most important details of their patient’s treatment, and use written instructions to encourage patients to ask questions and recall important information. Patients’ level of health literacy can also affect treatment adherence (Gazmararian, Williams, Peel, & Baker, 2003; Zaghloul & Goodfield, 2004). Health literacy is defined as a patient’s ability to appropriately obtain, process, and understand basic health information needed to make health decisions (Baker, 2006; Schillinger et al., 2003). The Institute of Medicine (2004) estimates that more than 90 million people in the United States lack the necessary health literacy skills needed to understand and act on health information given by their providers. Patients with low health literacy often use fewer preventive services, incur higher medical costs, have more limited understanding of their treatment regimen, and have poorer health status than those with higher health literacy (Gazmararian et al., 2003; Schillinger et al., 2003; Schillinger, Bindman, Wang, Stewart, & Piette, 2004). Thus, low health literacy increases health disparities and negatively impacts patients’ self‐management and collaborative care (Schillinger et al., 2004). The interpersonal dynamics of the clinician–patient relationship can also influence patterns of adherence behaviors. For ­example, patients who feel that their physicians communicate well with them and actively

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encourage them to be involved in their own care are more motivated to adhere (O’Malley, Forrest, & Mandelblatt, 2002). Such agreement fosters a cohesive partnership and allows physicians and patients to work together toward a mutually agreed upon treatment plan (Jahng, Martin, Golin, & DiMatteo, 2005). Training physicians to be better communicators results in significant improvements in patient adherence. Research by Zolnierek and DiMatteo (2009) found that the odds of patient adherence were 1.62 times higher for physicians who were trained in communication skills compared with the physicians who did not receive communication training. Effective communication also gives patients the ability to build rapport and trust; clinicians who promote trust through collaborative partnerships and who express compassion and “bedside manner” for their patients are more likely to succeed in fostering adherence and cooperation for a variety of treatment regimens (Schillinger et al., 2003).

Improving Adherence Using the Information–Motivation–Strategy (IMS) Model Current empirical research highlights the complexity of establishing adherence‐enhancing interventions and suggests that interventions should be tailored to meet the specific needs of patients for the most optimal effectiveness (DiMatteo et  al., 2012; Martin et  al., 2005). Evaluating and targeting patients’ specific needs can be a difficult task for clinicians and healthcare providers, however, especially given the limited time constraints of the medical visit itself. A well‐organized and simple model known as the Information–Motivation–Strategy Model(c) can be used by clinicians and healthcare teams during medical encounters to help guide and promote patients’ adherence behaviors (DiMatteo et al., 2012). Utilizing these three ­elements, that is, providing patients with information, building motivation, and offering effective strategies, in the context of effective clinician–patient communication can result in substantial and significant improvements in adherence and overall health outcomes. The information element of the IMS Model outlines the importance of empowering patients to become more knowledgeable about their disease or condition by means of effective clinician–patient communication. In a review assessing more than 300 studies, researchers found that patients often do not understand much of the information they receive from their healthcare provider (Kindig, Panzer, & Nielsen‐Bohlman, 2004). Proper understanding can be achieved when clinicians communicate effectively with their patients, however (Zolnierek & DiMatteo, 2009). Empirical research suggests a 19% higher risk of nonadherence among patients whose physician communicated poorly than among patients whose physician ­communicated well (Zolnierek & DiMatteo, 2009). Thus, cohesive partnerships, in which clinicians work together with their patients, make it possible for clinicians to both effectively and thoroughly provide clear information and check the adequacy of their patients’ understanding (DiMatteo et al., 2012). Patients who feel that their clinicians communicate well with them and encourage them to be involved in their own care tend to be more motivated to adhere (Martin et al., 2005). A patients’ level of trust in their physician is also an essential component of the physician– patient relationship and can greatly affect patient adherence. Patients must believe that their physician is someone who can understand their unique experience and who can provide them with reliable and honest advice. Martin et al. (2005) found that adherence rates were almost three times higher in primary care relationships consisting of higher levels of patient trust and physicians’ knowledge of their patient as a person. In fact, patients’ trust in their physician has

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been found to exceed many other variables in promoting both patient adherence and overall satisfaction with care (Martin et al., 2005). The second element of the IMS Model, motivation, emphasizes the importance of shared decision making between patients and their physician in order to develop a treatment plan the patient believes in and one to which the patient can make a commitment (DiMatteo et al., 2012; Zolnierek & DiMatteo, 2009). Helping patients to believe in the potential efficacy of a recommended treatment and the establishment of informed collaborative choice can influence a patients’ motivation to adhere (Zolnierek & DiMatteo, 2009). Some research suggests that when a patient believes in the importance of their recommended treatment, healthcare teams can use behavioral contracts to gain further commitment (Martin et al., 2005). Ultimately, patients are more adherent to treatment if they believe that the consequences of nonadherence are more detrimental to their health than if they take the consequences of nonadherence less seriously. Thus, it is extremely important for health professionals to discuss openly, with their patients, any beliefs and specific perceptions or expectations of treatment that may hinder adherence behaviors. The final element of the IMS Model involves developing workable strategies or resources to help patients overcome any (real or perceived) barriers that may hinder adherence. Members of the healthcare team must be willing to direct patients to resources to overcome such barriers as the financial cost of medication, the disadvantages of complex treatment regimen, or the lack of transportation to scheduled appointments. Patients also need assistance and encouragement to access supportive resources from their social support group (such as spouse, family, or friends) and any other aid that might be available to them to support the regimen (e.g., ­community‐ or work‐based interventions) (Miller & DiMatteo, 2013). Health professionals should routinely assess the levels of social support available to their patients, as research by DiMatteo (2004) has shown that the absence of social support is a significant barrier to adherence. The IMS Model can be used as simple heuristic through which clinicians can target the specific needs of their patients in order to establish the most effective treatment. The IMS Model illustrates that knowledge, commitment, and ability are essential for improving patient adherence (DiMatteo et al., 2012).

Conclusion Patient adherence is essential for the achievement of quality healthcare outcomes. Effective patient communication can increase patient satisfaction, improve overall quality of life, and ultimately reduce the risk of nonadherence. Current empirical research continues to try to understand, assess, and predict the many challenges that patients face in the management of their care. The elements of the Information–Motivation–Strategy (IMS) model can be used by clinicians and health professionals to assist patients in building effective long‐term disease management strategies to improve overall health outcomes and ultimately maximize adherence to treatment.

Author Biographies Tricia A. Miller, PhD, is the research scientist for Cottage Children’s Medical Center. She received her doctorate from the University of California, Riverside, in social‐personality ­psychology. Her research focuses on the social psychological process of health and medical

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care delivery including provider–patient communication and treatment adherence. Dr. Miller is a lead contributor to grant‐funded projects examining Pediatric Care Coordination among medically complex children and improvements in clinical and social outcomes for very low birth weight infants and their families. Summer L. Williams, PhD, received her doctorate in social‐personality psychology from the University of California, Riverside. Her research expertise is in health communication and health outcomes research, where she has spent the last decade examining doctor–patient communication in diverse populations, patient adherence, and patient satisfaction. She is an associate professor at Westfield State University in Massachusetts, currently teaching courses in introductory psychology, health psychology, social psychology, psychology of illness, and health of vulnerable populations. M. Robin DiMatteo, PhD, is a distinguished professor emeritus of psychology at the University of California, Riverside. She received her BS in mathematics and psychology from Tufts University, and her MA and PhD degrees from Harvard University. She has served as a resident consultant in health policy at the RAND Corporation. Dr. DiMatteo has been elected Fellow of the American Association for the Advancement of Science. Dr. DiMatteo received the Distinguished Teaching Award from the University of California, Riverside, and is a ­member of the UCR Academy of Distinguished Teachers.

References Baker, D. W. (2006). The meaning and the measure of health literacy. Journal of General Internal Medicine, 21, 878–883. Chesney, M. (2003). Adherence to HAART regimens. AIDS Patient Care STDS, 17, 168–177. Cutler, D. M., & Everett, W. (2010). Thinking outside the pillbox‐medication adherence as a priority for health care reform. The New England Journal of Medicine, 362, 1553–1555. Dezii, C. M., Kawabata, H., & Tran, M. (2002). Effects of once‐daily and twice‐daily dosing on adherence with prescribed glipizide oral therapy for type 2 diabetes. Southern Medical Journal, 95, 68–71. DiMatteo, M. R. (2004). Variations in patients’ adherence to medical recommendations: A quantitative review of 50 years of research. Medical Care, 43, 200–209. DiMatteo, M. R., Giordani, J., Lepper, S., & Croghan, T. W. (2002). Patient adherence and medical treatment outcomes: A meta‐analysis. Medical Care, 40, 794–811. DiMatteo, M. R., Haskard, K. B., & Williams, S. L. (2007). Health beliefs, disease severity, and patient adherence: A meta‐analysis. Medical Care, 45, 521–528. DiMatteo, M. R., Haskard‐Zolnierek, K. B., & Martin, L. R. (2012). Improving patient adherence: A three factor model to guide practice. Health Psychology Review, 6, 74–91. Dunbar‐Jacob, J., Schlenk, E., & McCall, M. (2012). Patient adherence to treatment regimen. In A. Baum, T. A. Revenson, & J. Singer (Eds.), Handbook of health psychology (pp. 271–283). New York: Psychology Press. Gazmararian, J. A., Williams, M. V., Peel, J., & Baker, D. W. (2003). Health literacy and knowledge of chronic disease. Patient Education and Counseling, 51, 267–275. Institute of Medicine. (2004). Health literacy: A prescription to end confusion. Retrieved from http://www.nap.edu/openbook.php?isbn=0309091179 Jahng, K. H., Martin, L. R., Golin, C. E., & DiMatteo, M. R. (2005). Preferences for medical collaboration: Patient‐physician congruence and patient outcomes. Patient Education and Counseling, 57, 308–314. Kindig, D. A., Panzer, A. M., & Nielsen‐Bohlman, L. (Eds.) (2004). Health literacy: A prescription to end confusion. Washington, DC: National Academies Press.

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Lehane, E., & McCarthy, G. (2007). Intentional and unintentional medication nonadherence: A comprehensive framework for clinical research and practice? A discussion paper. International Journal of Nursing Studies, 44, 1468–1477. Martin, L. R., Williams, S. L., Haskard, K. B., & DiMatteo, M. R. (2005). The challenge of patient adherence. Therapeutics and Clinical Risk Management, 1, 189–199. Miller, T. A., & DiMatteo, M. R. (2013). Importance of family/social support and impact on adherence to diabetes therapy. Journal of Diabetes Metabolic Syndrome and Obesity: Targets and Therapy, 6, 421–426. O’Malley, A. S., Forrest, C. B., & Mandelblatt, J. (2002). Adherence of low‐income women to cancer screening recommendations. Journal of General and Internal Medicine, 17, 144–154. Peltzer, K., & Pengpid, S. (2013). Socioeconomic factors in adherence to HIV therapy in low and middle income countries. Journal of Health, Population and Nutrition, 31, 150–170. Schillinger, D., Bindman, A., Wang, F., Stewart, A., & Piette, J. (2004). Functional health literacy and the quality of physician‐patient communication among diabetes patients. Patient Education and Counseling, 52, 315–323. Schillinger, D., Piette, J., Grumbach, K., Wang, F., Wilson, C., Daher, C., … Bindman, A. B. (2003). Closing the loop physician communication with diabetic patients who have low health literacy. Archives of Internal Medicine, 163, 83–90. Vermeire, E., Hearnshaw, H., Royen, P. V., & Denekens, J. (2001). Patient adherence to treatment: Three decades of research a comprehensive review. Journal of Clinical Pharmacy and Therapeutics, 26, 331–342. Zaghloul, S. S., & Goodfield, M. J. (2004). Objective assessment of compliance with psoriasis treatment. Archives of Dermatology, 140, 408–414. Zolnierek, K. B. H., & DiMatteo, M. R. (2009). Physician communication and patient adherence to treatment: A meta‐analysis. Medical Care, 47, 826–834.

Suggested Reading Riekert, K. A., Ockene, J. K., & Pbert, L. (Eds.) (2014). Handbook of health behavior change (4th ed.). New York: Springer.

Tobacco Use Disorder and Its Treatment Victoria A. Torres1, Lee M. Cohen1, and Suzy Bird Gulliver2,3 Department of Psychology, College of Liberal Arts, University of Mississippi, Oxford, MS, USA 2 Baylor and Scott and White Hospitals and Clinics, Waco, TX, USA 3 School of Public Health, Texas A&M University, College Station, TX, USA

1

Overview The diagnosis of tobacco use disorder is a relatively new name for a long‐standing health problem that was previously referred to as nicotine dependence (Britt, Cohen, Collins, & Cohen, 2001). Though the terms “nicotine” and “tobacco” are often synonymously used, “tobacco” technically refers to a plant, while “nicotine” refers to a primary addictive chemical in the tobacco plant. Although this classification was somewhat recently amended, the use of products containing tobacco has been around for centuries. In fact, the popularity of such products swelled in the years preceding the establishment of the United States, and since that time, the tobacco industry has worked tirelessly to increase their consumer base. More recently, tobacco companies have marketed electronic nicotine delivery systems (ENDS). The popularization of these and other tobacco products have contributed to tobacco’s role as the number one cause of preventable death in our society (World Health Organization [WHO], 2011). It is alarming to consider that tobacco use is currently linked to 6 million deaths annually (WHO, 2016). To put this in perspective, during the twentieth century, tobacco use was linked to more deaths than both world wars combined (Chalabi, 2013). Further, one‐quarter of all deaths among US women and men aged 35–69 are directly linked to a smoking‐ related illness (Jha et al., 2013). In addition to high mortality rates, tobacco use is associated with declines in health status, economic productivity, and environmental conditions (WHO, 2011).

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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History and Landmark Studies Tobacco use has a long history, dating back to the pre‐Columbian period where indigenous peoples chewed, smoked, and inhaled tobacco for medicinal and ritualistic purposes (Kohrman & Benson, 2011). This cash crop spread across Europe in the 1500s after Jean Nicot, a diplomat and scholar, first introduced tobacco to the French court. It was not until the beginning of the twentieth century, however, that researchers began warning the public about the harm caused by the use of tobacco. Specifically, in 1930, researchers in Germany noted a correlation between smoking and cancer. However, tobacco was not publically scrutinized until Reader’s Digest published their 1952 article, Cancer by the Carton. As a result of this article, the public’s attention was piqued and cigarette sales plummeted (CNN, 2000). Twelve years later, the surgeon general of the United States released a landmark report warning Americans of tobacco’s adverse effects for the first time (U.S. Department of Health and Human Services [USDHHS], 1964). At that time, an estimated 42.4% of the US population were classified as current smokers (Wilson, 1967). Since the release of the first surgeon general’s report, tobacco companies and public health advocates have been at odds. Specifically, public health advocates have fought for harsher policies to protect the public, while big tobacco companies have worked to persuade the public that they have designed a “cleaner smoke.” As consumers became more aware of the dangers associated with tobacco use, many attempted to quit. Others sued tobacco companies for failing to warn them of the deadly consequences of tobacco use. In 1996, the US Department of Health and Human Services appointed a panel of experts to create clinical practice guidelines. These guidelines were designed to assist in the delivery and support of evidence‐based treatments for tobacco use and dependence (Abrams et al., 2003). Since that time, the guidelines have undergone several rounds of revisions utilizing an evidence base of over 8,700 research articles. Ten key recommendations, mostly focusing on brief and intensive treatments and system‐wide changes, were put forth in the most recent update. Despite an overall decrease in smoking rates and discovering more effective ways to help people quit, problems associated with the use of tobacco products remain. Although smoking rates declined by 10% between 1997 and 2015, 36.5 million Americans continued to smoke in 2015 despite awareness of the harmful consequences to themselves and others (Centers for Disease Control and Prevention [CDC], 2016). Additionally, since the release of the surgeon general’s report, over 2.5 million nonsmoking Americans have died due to the dangerous effects of secondhand smoke (USDHHS, 2014). Another more recent concern involves the misperception that alternative tobacco products (e.g., e‐cigarettes and smokeless tobacco) are safe. In 2003, ENDS became globally available. Now a multibillion dollar industry, ENDS advertisers promise a “cleaner smoke,” leading many to mistakenly assume that these products are less harmful or represent a viable, safe ­substitute for cigarettes that can be used to aid in cessation (USDHHS, 2016). The available data, however, suggest this is not the case. Specifically, e‐cigarettes contain aerosol, while smokeless tobacco products contain known carcinogens and toxic chemicals (USDHHS, 2016). In addition to this misperception, tobacco companies appear to be targeting youth, adding various flavors that mimic familiar sweet tastes (e.g., cotton candy, gummy bears, and sweet tarts) to these products. Sadly, it appears to be working as e‐cigarette use has increased by 900% among high school students between 2011 and 2015 (USDHHS, 2016). In addition to adolescent smokers, several populations are considered particularly vulnerable to developing tobacco use disorder. Broadly, those experiencing negative affect, women, blue‐collar workers, low‐income populations, ethnic minorities, and those without health insurance have

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a higher risk of ­adopting tobacco use patterns leading to disorder (Cohen, McCarthy, Brown, & Myers, 2002; Orleans et al., 1998).

Classification of Tobacco Use Disorder Researchers and clinicians have historically grouped tobacco use with other substances of abuse. However, tobacco influences the human body in a distinct way as nicotine acts as both a stimulant and a depressant. Therefore, it causes the body to relax while simultaneously causing blood pressure to increase. Accordingly, several diagnostic manuals, namely, the APA’s Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD), began to address nicotine use disorders as distinct from other substance use disorders. Exactly what symptoms constitute tobacco use disorder has changed over time. Most recently, the changes made in the current version of the DSM (5th Edition; DSM‐5; American Psychiatric Association [APA], 2013a) includes increased clarity with regard to smoking patterns and severity of cravings (APA, 2013b). Additionally, the term “tobacco abuse” was added, a severity criterion was implemented (i.e., experiencing 2–3 criteria indicates “mild” severity, 4–5 indicates “moderate” severity, and 6 or more indicates “severe”), new specifiers “in a controlled environment” and “on maintenance therapy” were included, and the “physiological” subtype specifier was removed (APA, 2013a). Further, early remission is defined as “at least 3 but less than 12 months without meeting substance use disorder criteria (except craving),” and sustained remission is defined as “at least 12 months without meeting the criteria (except craving)” (APA, 2013a). The DSM‐5 (APA, 2013a) defines tobacco use disorder as “a problematic pattern of tobacco use leading to clinically significant impairment or distress” where a minimum of two of the following symptoms occur within a 12‐month period: 1  2  3  4  5  6  7  8  9 

Tobacco is used in larger amounts or for a longer period than intended. There is a persistent desire to quit use or there are unsuccessful quit attempts. A great deal of time is spent obtaining or using tobacco. There are cravings or strong urges to use tobacco. There is interference in daily activities that occurs as a result of tobacco use. Use continues despite adverse consequences in personal or social situations. Important activities (social, occupational, recreational) are given up due to tobacco use. Tobacco use persists in situations where it is physically hazardous. Despite having a physical or psychological tobacco‐related problem, tobacco use persists. 10  Tolerance to tobacco is observed. 11  A well‐defined withdrawal syndrome is experienced when use is discontinued (p. 571). Tolerance and withdrawal are other constructs associated with tobacco use disorder. Tolerance is defined by either “a need for markedly increased amounts of tobacco to achieve the desired effect” or “a markedly diminished effect with continued use of the same amount of tobacco.” Withdrawal is recognized by either “the characteristic withdrawal syndrome for tobacco” or use of tobacco “to relieve or avoid withdrawal symptoms.” The withdrawal ­syndrome is characterized by an “abrupt cessation of tobacco use” or a “reduction in the

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amount of tobacco used.” Further, four or more of the symptoms below are experienced within 24 hr of reduced use or cessation (p. 575): 1 2 3 4 5 6 7

Irritability, frustration, or anger Anxiety Difficulty concentrating Increased appetite Restlessness Depressed mood Insomnia

Initiation and Maintenance A common question that has plagued researchers and clinicians alike is, “Why do individuals start smoking in the first place given that we have data to support that it is unhealthy?” While the answer to this question has not yet been identified, much has been learned about the ­process of initiation and maintenance. Generally, tobacco use begins before 18 years of age, and recent research indicates that a person can become addicted to nicotine after just one cigarette (University of Massachusetts Medical School, 2007). Since 20–30% of those who experiment with tobacco will meet lifetime criteria for tobacco use disorder over the course of their lifetime, it is not surprising that many teen smokers who experiment with tobacco several times as a means of fitting in socially will progress to more frequent use (Abrams et  al., 2003; American Academy of Addiction Psychiatry [AAAP], 2015). Further, those who experiment with smoking commonly develop associations between smoking, daily life occurrences (e.g., socializing, drinking, waking up, finishing a meal), emotional states (e.g., anxiety, anger), and sensory aspects of tobacco use. These associations frequently contribute to the maintenance of tobacco use. Since nicotine is highly addictive and tobacco use becomes easily engrained in a person’s daily life, quitting becomes difficult. Lastly, the interaction of nicotine and human physiology also helps to explain why people maintain this unhealthy behavior and find it difficult to quit. Tobacco companies have worked untiringly to design the perfect nicotine delivery system. After a person’s first inhalation, nicotine takes only 8 s to reach the brain, where it targets areas associated with feelings of pleasure and reward.

Benefits to Tobacco Cessation The detrimental health effects of using tobacco products have been well established. Regardless of the manner in which it is used, tobacco harms the human body. It increases heart rate and blood pressure while simultaneously decreasing the health of respiratory and circulatory ­systems. In addition, protracted use of tobacco changes cellular characteristics, which may ­support the development of cancer. This said, research has indicated that vast improvements in health can occur in the absence of tobacco use (CDC, 1990). Specifically, in 2013, Jha et al. used the US National Health Interview Survey to obtain smoking and smoking cessation information from 202,248 men and women between 1997 and 2004. This data set revealed that adults who quit smoking reduced their risk of mortality from smoking‐related illnesses by close to 90%. Researchers have also discovered tremendous health improvements including reduced smoking‐related symptoms such as coughing and shortness of breath and decreased

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risk of smoking‐related illnesses (e.g., heart attack and diabetes), some of which occur within 20 min after a person stops smoking (American Cancer Society, 2016).

Evidence‐Based Treatment Now that it has been established that nicotine is a highly addictive substance, is delivered to the human brain in an optimal way, and becomes part of many aspects of individuals’ lives, it should be clear that most individuals who wish to quit smoking find it difficult. In fact, 44% of smokers’ quit attempts result in relapse within 14 days, and 60–98% of smoking quit attempts lead to relapse (Abrams et al., 2003). In addition to the more common challenges described thus far, people tend to present to treatment with an array of problems or “comorbidities” that may complicate the process of smoking cessation. Accordingly, clinicians should take an individual’s unique barriers to smoking cessation into account when creating a treatment plan. Thankfully, a variety of options are available. Currently, the most effective treatment includes a combination of behavioral therapy and pharmacotherapy (i.e., nicotine replacement therapy [NRT]). Before delving into an overview of these broad treatment modalities (as well as other more specific ones), it is important to mention the importance of assessment, which is a common and important component throughout treatment.

Assessment It is best practice to begin any cessation program with a comprehensive assessment. The goal of assessment is to equip the clinician with a better idea of how to work most effectively with a particular individual. Assessment should take different forms throughout treatment and can be useful in understanding an individual’s barriers that may undermine a quit attempt. Generally, healthcare providers attend to five clinical areas when conducting smoking cessation assessments: (a) motivation, (b) nicotine dependence, (c) past quit attempts and smoking history, (d) substance abuse comorbidity, and (e) psychiatric comorbidity (Abrams et al., 2003). By attending to these five areas, clinicians can tailor smoking cessation treatment to specific clients to give them the best chance of success. Additionally, assessment can inform which type of treatment may be best suited for a particular client given that different types of therapy have led to better treatment outcomes among certain populations (e.g., pregnant women, racial and ethnic minorities, low‐income smokers; Vidrine, Cofta‐Woerpel, Daza, Wright, & Wetter, 2006; Weinberger, Pittman, Mazure, & McKee, 2015). Lastly, given that a person’s level of motivation and other relevant individual factors may change across time, it is important that assessment occur throughout treatment so that the clinician is quantitatively and qualitatively aware of how their clients are adjusting during the various stages of treatment.

Pharmacotherapy Beginning with the development of nicotine gum, pharmacotherapy has been the principal treatment modality in most smoking cessation research studies. Since smoking is perpetuated by habitual behavior and the addictive nature of nicotine, it makes sense that the combination of nicotine replacement and behavioral therapies may be helpful for people attempting to quit. By using pharmacotherapy, smokers can focus more on the behavioral aspects of their habit, while at the same time, receiving help with respect to the severity of withdrawal symptoms that

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they may be experiencing. Currently there are five modalities of nicotine replacement therapy (NTR; i.e., gum, patches, lozenges, inhalers, and nasal spray) and two oral medications that do not contain nicotine [Chantix (varenicline tartrate) and Zyban (bupropion hydrochloride)]. Of these options, nicotine gum, patches, and lozenges are available over the counter, while the others require a prescription. It is important to note that while each option in this category may come with its own side effects, use of such products has evidenced higher rates of smoking cessation success when compared with behavioral therapy alone (Fiore et  al., 2000). Still, for those who are unable or unwilling to use pharmacotherapy, other options such as nicotine fading are available. Finally, while use of Chantix and Zyban can be used before the quit date, the NTRs should not start until one has quit smoking.

Behavioral Therapy: Preparing to Quit Whether a person decides to receive brief or more intensive behavioral treatment, the first phase of such treatment involves preparation. While behavioral treatment may differ somewhat based on individuals’ specific needs, it is generally the case that the early stage of this approach focuses on preparing the individual to quit. This includes the use of psychoeducation (e.g., learning about the effects of nicotine), motivational enhancement techniques (e.g., eliciting reasons for wanting to quit), preparation for overcoming withdrawal symptoms (e.g., brand fading or gradual reduction in number of cigarettes), unlearning a behavior (e.g., reducing the number of places an individual smokes), practicing urge control strategies (e.g., delaying the next cigarette), letting others know that you are working on quitting, and ultimately, setting a definitive quit date (which is a critical step given that many have indefinite plans to quit). During the preparation stage, individuals learn as much as they can about their smoking behavior (i.e., when they smoke, what “triggers” their smoking) by self‐monitoring and recording data relevant to this behavior. This phase enables individuals to plan their quit attempt (with the use of data) so that their chance for success is optimized. During this phase, individuals may be advised to purchase cigarettes in smaller quantities than usual so that they do not have too many cigarettes readily available to them or too many left over once they get to their quit date. This strategy is both economical and creates an inconvenience to their normal routine. Individuals may also wish to slowly reduce the number of cigarettes they smoke per day (or gradually change their brand of cigarettes to a brand with less nicotine) in order to minimize withdrawal when they quit (especially if they prefer to not use one of the available pharmacotherapies). It is during this phase of treatment that healthcare providers can help patients identify what in their environments may lead to failure. For instance, evaluation of an individual’s context may reveal that stress at work leads to smoking. Learning and mastering stress reduction techniques as well as planning for what they can do rather than take a “smoke break,” before the quit date, will likely help that individual increase the chance of a successful quit attempt (Abrams et al., 2003). Additionally, clinicians and smokers should consider their time commitments and availability before beginning treatment. The amount of time a smoker can dedicate to treatment must be considered to avoid gaps. Finally, past quit attempts that ended in relapse should be reframed as learning opportunities prior to beginning anew. Utilizing ­previous quit attempts in a positive way will likely contribute to success in a new quit attempt.

Behavioral Therapy: Quit Day As mentioned above, a quit date is generally set during the preparation phase of treatment. Hopefully by this day, the individual has learned a great deal about their smoking behaviors

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and is ready for any challenges they face. It is recommended that on this day, individuals change their regular routine. The reason for this is to lessen the occurrence of paired associations that will ultimately be too difficult to resist. For example, if a cigarette is paired with a cup of coffee at home in the morning, either drink tea or get the cup of coffee on the way to work and drink it in the car (where there are presumably no cigarettes available). It is also important on this day (and every day that follows) to be on guard for triggers and difficult situations.

Behavioral Therapy: Relapse Prevention Being smoke‐free will get easier and easier as time goes on; however, it is important for ex‐smokers to not get overconfident and always be prepared to deal with the stressors or ­cravings they experience in ways other than smoking. It is important for individuals to utilize any urge control strategies they practiced during the preparation stage. Whereas in the preparation stage, a helpful urge control strategy was to delay the amount of time until the next cigarette, in this stage appropriate strategies include, escaping or avoiding situations that historically led to smoking, using distraction techniques such as staying busy, and substituting smoking with other, healthy behaviors, like eating carrots or chewing gum. It is also important to set up a reward structure for being successful (e.g., buying something with the money being saved from not smoking). Additionally, asking friends and family for support when things get rough may be helpful. Finally, if a cigarette is smoked, it is important to frame the occurrence as a “slip” rather than a failure or excuse to continue smoking regularly. If this is not possible, consider setting a new quit date and use all that has been learned from previous quit attempts.

Complementary and Alternative Treatments Despite the advances in more traditional smoking cessation treatments, some people prefer to use methods that are less mainstream and not typically considered standard practice. While some of the treatments that fall into this category have been carefully evaluated, others have not. Complementary treatments are those that are used in conjunction with more traditional methods, whereas alternative treatments are those that are used instead of other options. Although a growing number of these types of treatment are available, a comprehensive discussion of all that are currently used falls outside the scope of this entry. Below are summaries for three such therapies including, hypnotherapy, acupuncture, and confectionery chewing gum. For a more thorough discussion, refer to the National Center for Complementary and Integrative Health’s Clinical Digest (2014), which can be found online at nccih.nih.gov/ health/providers/digest/smoking.

Hypnotherapy Hypnotherapy is a method that aims to decrease a person’s desire to smoke, strengthen the will to quit, and increase a person’s focus on quitting by acting on underlying impulses. Hypnotherapy treatments differ in regard to hypnotic induction, amount of sessions, duration of treatment, and use in combination with other therapeutic methods. While research in this area is quite varied, hypnotherapy’s effectiveness has been studied alongside a number of other smoking cessation methods. For example, in 2010 Barnes and ­colleagues reviewed literature pertaining to the efficacy of hypnotherapy for smoking cessation

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and found 11 studies with data from 1,120 participants. Overall, Barnes et al. (2010) found conflicting results with regard to hypnotherapy’s efficacy. In comparison to no intervention, hypnotherapy was not demonstrated to be more effective 6 months after the beginning of treatment. Additionally, these studies did not reveal a greater effect when compared with rapid smoking or psychological treatments such as counseling (Barnes et al., 2010). More recently, Hasan and coauthors conducted a randomized controlled trial (RCT) among individuals who were hospitalized for a variety of health concerns including cardiac and pulmonary illnesses. This study revealed that at 12 (43.9 vs. 28.2%; p = 0.14) and 26 weeks post‐hospitalization (36.6 vs. 18.0%; p = 0.06), hypnotherapy patients were more likely than NRT patients to be nonsmokers. These findings indicate that hypnotherapy may aid in smoking cessation; however, more research is needed to determine the extent to which hypnotherapy is efficacious and with which populations it is most useful. For a more complete review of the use of hypnotherapy for the treatment of smoking cessation, refer to the Cochrane report at www.cochrane.org/ CD001008/TOBACCO_does‐hypnotherapy‐help‐people‐who‐are‐trying‐to‐stop‐smoking.

Acupuncture Acupuncture, a traditional Chinese method that uses needles to stimulate energy points, has also been promoted as an aid for smoking cessation. The aim of acupuncture techniques for smoking cessation is to reduce nicotine withdrawal symptoms. Similar to current research on hypnotherapy, studies on the efficacy of acupuncture for smoking cessation are mixed. While some lines of research support the use of acupuncture techniques in the short term, it is likely that these results are attributed to a placebo effect. Overall, acupuncture research has not revealed clear evidence in support of its effectiveness above and beyond other treatments (White, Rampes, & Ernst, 2014). For a more complete review of the use of acupuncture for the treatment of smoking cessation, refer to the Cochrane report at www.cochrane.org/CD000009/ TOBACCO_do‐acupuncture‐and‐related‐therapies‐help‐smokers‐who‐are‐trying‐to‐quit.

Confectionery Chewing Gum One healthy alternative to smoking that has received empirical attention is the use of ­confectionery chewing gum. Previous research has shown that confectionery gum appears to aid in the reduction of withdrawal symptoms, particularly those related to negative affect, among individuals dependent on nicotine. In a series of studies, Cohen, Britt, Collins, al’Absi, and McChargue (2001); Cohen, Britt, Collins, Stott, and Carter (1999); and Cohen, Collins, and Britt (1997) have evaluated the utility of confectionery chewing gum as an aid to help smokers when they are asked to abstain from smoking for brief periods of time in the laboratory. Results from two of these studies (1997 and 2001) revealed that the use of chewing gum reduces the severity of self‐reported withdrawal symptoms following 3–4 hr of abstinence. Results from the other study show that the use of chewing gum is associated with changes in smoking behavior (Cohen et al., 1999). Specifically, participants showed significant decreases in the number of cigarette puffs that were taken as well as increased periods of abstinence. More recently, the use of chewing gum has been shown to be useful in reducing symptoms of withdrawal over 24 (Cortez‐Garland, Cohen, VanderVeen, & Cook, 2010) and 48 (Cohen, Collins, VanderVeen, & Weaver, 2010) hr of abstinence. In conclusion, confectionery chewing gum has demonstrated its utility as an aid for smokers during periods of abstinence ranging from 3 to 48 hr. Chewing gum may be a useful adjunct to existing smoking cessation treatments given that it is easily accessible, inexpensive, and

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s­ imple to administer. Due to the well‐documented risk of smoking and the difficulty i­ ndividuals who smoke face during the cessation process, a behavioral substitute that can positively influence the symptoms of negative affect associated with nicotine withdrawal may improve upon treatment success.

Future Research Directions Many important questions concerning tobacco use have been addressed over the past 50 years. Yet, better treatments for tobacco use disorder are needed as it is estimated that 1.1 billion people smoked tobacco in 2015 alone (WHO, 2017). Five key areas concerning tobacco use disorder treatment remain to be addressed. First, since we know smoking initiation typically begins in adolescence and young adulthood, it may be helpful to understand more about underlying mechanisms unique to these age groups. By pinpointing smoking behavior, prevention and effective treatments should aim to reduce the number of people who attempt to begin smoking in the first place. High‐risk and low‐risk thinking patterns in adolescents can be identified and may aid in the development of critical prevention efforts for youth (Choi, Gilpin, Farkas, & Pierce, 2001). Since those who experiment with smoking are highly likely to become regular users, prevention efforts seem to be most effective during this developmental period. Second, more effective treatments are needed for adult smokers. Particularly, current research exploring the relationship of the ­neurobiology of nicotine addiction to behavioral, pharmacological, and combined treatments is needed. Third, more information about tailoring treatments to smokers with distinct profiles may be helpful. Continued research on how to most successfully match smoker profiles to treatment options may result in more efficacious smoking cessation outcomes. Fourth, although smoking and drinking alcohol seem to go hand in hand, we are still uncertain about how these two addictive behaviors are connected or why they tend to be seen regularly in conjunction with one another. More information about underlying smoking and drinking pathways could be helpful for understanding why people start smoking and drinking and how we can help them stop if they wish to do so. Finally, broad system‐wide considerations must be taken into account to reduce the prevalence of tobacco use disorder. Strengthened collaborative efforts between researchers and practitioners will likely aid in this process. As researchers continue to pioneer new treatments and clinicians implement changes into practice, tobacco use disorder will hopefully become less of an issue in our society.

Conclusion Based on recent data, 60–80% of Americans who currently report smoking cigarettes meet diagnostic criteria for tobacco use disorder (AAAP, 2015). A recent survey by the CDC indicates that while rates of cigarette smoking have been generally trending downward in the United States (CDC, 2016), there is a 900% increase in youth e‐cigarette usage from 2011 to 2015. As such, more work in this area remains. Researchers have asserted that the initiation of tobacco use rates in a population predict future smoking patterns. However, little is currently known about adolescent and young adult smoking patterns. Future efforts must target youth tobacco prevention, more effective treatments, and improved system‐wide cessation efforts. As public health advocates continue to be vigilant about this issue, a reduction or elimination of a widespread, long‐standing problem will hopefully soon be seen.

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Author Biographies Victoria A. Torres, BA, is a doctoral candidate in the clinical psychology program at the University of Mississippi. Her current research focuses primarily on factors that contribute to tobacco use. Before Ms. Torres came to UM, she received her baccalaureate degree in psychology from Baylor University. After graduating from Baylor, she worked for 2 years as a research assistant at Baylor Scott & White as part of the Warriors Research Institute, where she contributed to several federally funded research projects. Lee M. Cohen, PhD, is dean of the College of Liberal Arts and professor in the Department of Psychology at the University of Mississippi. Dean Cohen came to UM from Texas Tech University, where he served in a number of administrative roles including the director of the nationally accredited doctoral program in clinical psychology and the chair of the Department of Psychological Sciences. As a faculty member, he received several university‐wide awards for his teaching and academic achievement. As a researcher, he received more than $1.5 ­million from funding agencies, including the US Department of Health and Human Services, the National Science Foundation, and the National Institutes of Health/National Institute on Drug Abuse. His research program examines the behavioral and physiological mechanisms that contribute to nicotine use, and he worked to develop optimal smoking ­cessation treatments. He is a fellow of the American Psychological Association and received his PhD in clinical psychology from Oklahoma State University. Suzy Bird Gulliver, PhD, is a licensed clinical psychologist and clinical researcher. Currently, she serves as director and chief of the Warriors Research Institute (WRI) and as a professor of psychiatry at Texas A&M Health Science Center. She earned her PhD in clinical psychology from the University of Vermont and went on to work as a National Institute of Alcohol Abuse and Alcoholism‐funded postdoctoral fellow at Brown University. Dr. Gulliver later spent 18 years in a variety of roles within the VA in Boston, Massachusetts. Before founding the WRI within Baylor Scott & White Health in 2013, Dr. Gulliver served as the director of the VA VISN 17 Center of Excellence in Waco, Texas. Dr. Gulliver’s team has been federally funded and has published 88 peer reviewed manuscripts, 2 book chapters, as well as hundreds of ­presentations to academic, medical, and emergency response audiences.

References Abrams, D. B., Niaura, R., Brown, R. A., Emmons, K. M., Goldstein, M. G., & Monti, P. M. (Eds.) (2003). The tobacco dependence treatment handbook: A guide to best practices. New York, USA: Guilford Press. American Academy of Addiction Psychiatry. (2015). Nicotine dependence. Retrieved from http://www.aaap.org/wp‐content/uploads/2015/06/AAAP‐nicotine‐dependence‐FINAL.pdf American Cancer Society. (2016). Benefits of quitting smoking over time. Retrieved from https://www. cancer.org/healthy/stay‐away‐from‐tobacco/benefits‐of‐quitting‐smoking‐over‐time. html#references American Psychiatric Association. (2013a). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association. American Psychiatric Association. (2013b). Highlights of changes from DSM‐IV‐TR to DSM‐5. Retrieved from http://dsm.psychiatryonline.org/doi/abs/10.1176/appi.books.9780890425596.changes Barnes, J., Dong, C. Y., McRobbie, H., Walker, N., Mehta, M., & Stead, L. F. (2010). Does ­hypnotherapy help people who are trying to stop smoking? Cochrane Database of Systematic Reviews, 10.

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Retrieved from http://www.cochrane.org/CD001008/TOBACCO_does‐hypnotherapy‐help‐ people‐who‐are‐trying‐to‐stop‐smoking Britt, D. M., Cohen, L. M., Collins, F. L., & Cohen, M. L. (2001). Cigarette smoking and chewing gum: Response to a laboratory‐induced stressor. Health Psychology, 20, 361–368. CDC. (1990). Centers for Disease Control, Report, Department of Health and Human Services of U.S. Government, Atlanta, Georgia, USA. Centers for Disease Control and Prevention. (2016). Early release of selected estimates based on data from the national health interview survey, 2015. Retrieved from http://www.cdc.gov/nchs/data/ nhis/earlyrelease/earlyrelease201605_08.pdf?version=meter+at+0&module=meterLinks&pgtype= article&contentId=&mediaId=&referrer=&priority=true&action=click&contentCollection=me ter‐links‐click Chalabi, M. (2013). Cigarettes or war: Which is the biggest killer? The Guardian. Retrieved from https://www.theguardian.com/news/reality‐check/2013/dec/18/cigarettes‐or‐war‐which‐ is‐the‐biggest‐killer Choi, W. S., Gilpin, E. A., Farkas, A. J., & Pierce, J. P. (2001). Determining the probability of future smoking among adolescents. Addiction, 96(2), 313–323. CNN. (2000). CNN media announcement via TV broadcast. Cohen, L. M., Britt, D. M., Collins, F. L., al’Absi, M., & McChargue, D. E. (2001). Multimodal assessment of the effect of chewing gum on nicotine withdrawal. Addictive Behaviors, 26, 289–295. Cohen, L. M., Britt, D. M., Collins, F. L., Stott, H., & Carter, L. (1999). Chewing gum affects smoking topography. Experimental and Clinical Psychopharmacology, 7, 444–447. Cohen, L. M., Collins, F. L., & Britt, D. M. (1997). The effect of chewing gum on tobacco withdrawal. Addictive Behaviors, 22, 769–773. Cohen, L. M., Collins, F. L., VanderVeen, J. W., & Weaver, C. C. (2010). The effect of chewing gum flavor on the negative affect associated with tobacco abstinence among dependent cigarette smokers. Addictive Behaviors, 35, 955–960. Cohen, L. M., McCarthy, D. M., Brown, S. A., & Myers, M. G. (2002). Negative affect combines with smoking outcome expectancies to predict smoking behavior over time. Psychology of Addictive Behaviors, 16, 91–97. Cortez‐Garland, M., Cohen, L. M., VanderVeen, J. W., & Cook, K. (2010). The effect of chewing gum on self‐reported nicotine withdrawal: Is it the flavor, the act of chewing, or both? Addictive Behaviors, 35, 224–228. Fiore, M. C., Bailey, W. C., Cohen, S. J., Dorfman, S. F., Goldstein, M. G., Gritz, E. R., … & Mecklenburg, R. E. (2000). Treating tobacco use and dependence: Clinical practice guideline. US Department of Health and Human Services, 00‐0032. Jha, P., Ramasundarahettige, C., Landsman, V., Rostron, B., Thun, M., Anderson, R. N., & Peto, R. (2013). 21st‐century hazards of smoking and benefits of cessation in the United States. The New England Journal of Medicine, 368(4), 341–350. Kohrman, M., & Benson, P. (2011). Tobacco. Annual Review of Anthropology, 40, 329–344. National Center for Complementary and Integrative Health’s Clinical Digest. (2014). Complementary health approaches for smoking cessation. Retrieved from https://nccih.nih.gov/health/providers/ digest/smoking Orleans, C. T., Boyd, N. R., Bingler, R., Sutton, C., Fairclough, D., Heller, D., … Baum, S. (1998). A  self‐help intervention for African American smokers: Tailoring cancer information service counseling for a special population. Preventive Medicine, 27, S61–S70. U.S. Department of Health and Human Services. (1964). Report of the Advisory Committee to the surgeon general of the Public Health Service. Retrieved from https://profiles.nlm.nih.gov/ps/ access/nnbbmq.pdf U.S. Department of Health and Human Services. (2014). The health consequences of smoking—50 years of progress: A report of the surgeon general. Retrieved from https://www.surgeongeneral. gov/library/reports/50‐years‐of‐progress/index.html#execsumm

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U.S. Department of Health and Human Services. (2016). E‐cigarette use among youth and young adults: A report of the surgeon general. Retrieved from https://e‐cigarettes.surgeongeneral.gov/ documents/2016_sgr_full_report_non‐508.pdf University of Massachusetts Medical School. (2007). Inhaling from just one cigarette can lead to nicotine addiction: Kids show signs of addiction almost immediately. Retrieved from www.sciencedaily. com/releases/2007/07/070703171843.htm Vidrine, J. I., Cofta‐Woerpel, L., Daza, P., Wright, K. L., & Wetter, D. W. (2006). Smoking cessation 2: Behavioral treatments. Behavioral Medicine, 32(3), 99–109. Weinberger, A. H., Pittman, B., Mazure, C. M., & McKee, S. A. (2015). A behavioral smoking treatment based on perceived risks of quitting: A preliminary feasibility and acceptability study with female smokers. Addiction Research & Theory, 23(2), 108–114. White, A. R., Rampes, H., Liu, J., Stead, L. F., & Campbell, J. (2014). Acupuncture and related interventions for smoking cessation. Cochrane Database of Systematic Reviews, 1. Retrieved from http:// doi/10.1002/14651858.CD000009.pub4 Wilson, R. W. (1967). Cigarette smoking and health characteristics. Retrieved from http://www.cdc. gov/nchs/data/series/sr_10/sr10_034acc.pdf World Health Organization. (2011). WHO Report on the Global Tobacco Epidemic 2011. Retrieved from http://www.who.int/tobacco/global_report/2011/en/ World Health Organization. (2016). Tobacco [Fact sheet]. Retrieved from http://www.who.int/ mediacentre/factsheets/fs339/en/ World Health Organization. (2017). Prevalence of tobacco smoking. Retrieved from http://www.who. int/gho/tobacco/use/en/

Suggested Reading Baker, T. B., Breslau, N., Covey, L., & Shiffman, S. (2012). DSM criteria for tobacco use disorder and tobacco withdrawal: A critique and proposed revisions for DSM‐5. Addiction, 107(2), 263–275. Holford, T. R., Meza, R., Warner, K. E., Meernik, C., Jeon, J., Moolgavkar, S. H., & Levy, D. T. (2014). Tobacco control and the reduction in smoking‐related premature deaths in the United States, 1964–2012. Journal of the American Medical Association, 311(2), 164–171. Robinson, T. E., & Berridge, K. C. (2003). Addiction. Annual Review of Psychology, 54, 25–53.

Psychological Assessment in Medical Settings: Overview and Practice Implications Kelsey A. Maloney and Adam T. Schmidt Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA

The field of health psychology is a rapidly changing and expanding area of clinical and research focus. Skills of psychologists are increasingly being utilized in medical settings, including ­primary care, rehabilitation, and specialty medical clinics including oncology, cardiology, and organ transplantation. Approximately 60 medical disorders frequently encountered in primary care settings can also cause or exacerbate mental health conditions (Kush, 2001). Many primary care physicians may not have sufficient time to routinely detect, diagnose, or provide treatment for common psychological problems in their patients; moreover, many referrals that are made are not followed‐up on by patients because of constraints of access, insurance coverage, or unfamiliarity with mental health assessment and intervention. Because so many ­common medical disorders impact mental health and vice versa, many physicians are beginning to work collaboratively with psychologists as part of a multidisciplinary treatment team in order to provide holistic, integrated care to their patients. Through the formal assessment of psychological symptoms, psychologists can help turn the focus of treatment either to or away from primary psychological issues and help streamline treatment for patients (Kush, 2001). Anxiety disorders, followed by depressive disorders, are the most common presenting ­problems for mental health professionals practicing across medical settings. Both the occurrence of anxiety and depression can complicate treatment of other health conditions either by interfering with reporting of symptoms, reducing compliance with medically recommended treatments, or by contributing to behaviors that complicate the course of the medical ailment (e.g., a patient smokes to reduce stress caused by anxiety thereby exacerbating their cardiovascular disease). Not only can primary medical concerns be complicated by secondary mental health disorders, but primary mental health concerns can also be complicated with secondary medical disorders. Psychologists can provide a more thorough description of a patient’s The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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­ sychosocial milieu and a better understanding of mental health concerns that may exacerbate p the course of the presenting medical issue or, at the very least, complicate the treatment of this condition. Efficient and thorough psychological assessment is a key portion of the role of ­psychology in a medical setting. In this chapter, our goal is to present a brief overview of the specific roles for psychologists and of psychological assessment in particular within different types of medical settings in which their expertise is routinely employed.

Primary Care Settings “Primary Care Psychologists are experts in: (a) assessment & evaluation of common psychosocial symptoms…[seen in primary care setting]; (b) [able to distinguish between symptoms associated with a medical disease and a mental health condition]; (c) collaborate with primary care teams; (d) have the knowledge to triage appropriately; (e) have an understanding of biomedical conditions commonly seen in primary care and applicable pharmacological interventions (p. 8)” (Frank et al., 2004, in James, 2006). Primary care psychologists can be helpful in screening for common psychological conditions impacting certain age groups such as autism and attention deficit hyperactivity disorder (ADHD) in pediatric settings or mild cognitive impairment in older adults, common psychiatric symptoms including depression and anxiety (Kush, 2001; Thombus et al., 2012), and screening for psychoactive substance use (Kush, 2001). Psychologists working with primary healthcare providers may be called on to facilitate screening for disorders common in specific age groups. For example, a psychologist working with a pediatrician may administer screening measures to assess for developmental disabilities such as autism spectrum disorders in toddlers or ADHD in school‐aged children. These could be direct administration of screening measures by the psychologist or, more commonly, consultation and interpretation of instruments administered by the medical staff. A positive result on one of these measures may trigger consultation with a psychologist to either perform a more in‐depth assessment or to provide additional guidelines or context for the findings. Similarly, physicians working with older adults may refer for additional assessment following a physical exam in which a patient appears confused or somewhat disoriented. This model of working with primary care providers can greatly facilitate the continuum of care and help to ensure that individuals who may not otherwise be evaluated for these, relatively common, disorders are able to be screened and, if necessary, assessed in a timely fashion. Screening for depression can be another important role for psychologists working in ­primary care settings. Screening for depression includes “the use of questionnaires concerning the symptoms of depression or small sets of questions about depression to identify patients who may have depression but who have not sought treatment and whose depression has not already been recognized by health care providers” (Thombus et al., 2012). Using depression rating scales can help physicians identify depression, sometimes increasing their ability to do so by 2.5‐ to 25‐fold (Kush, 2001). Certain medical illnesses have been associated with high risk for depression, including “seizure disorders, diabetes, hypothyroidism, hyperthyroidism, Huntington’s disease, Parkinson’s disease, and cancer” (Kush, 2001). Depression in these populations can complicate treatment and exacerbate the course of the medical disorders. More general patient stressors such as disability resulting from the medical condition and/or significant psychosocial stressors (e.g., the loss of a job, grief from the loss of a loved one, or stress resulting from rigorous treatment regimens) should also trigger evaluations for depressive symptoms. A common and quick depression questionnaire that

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can be used for screening in primary care settings is the Beck Depression Inventory, Second Edition (BDI‐II) (Beck, Steer, & Brown, 1996). The BDI‐II is a 21‐item self‐report measure that can be completed in 5–10 min, can be scored rapidly, and can be used from age 13 to adulthood. Mean BDI‐II scores “have been established for Major Depression (single episode, 28.065, recurrent 29.45), Dysthymic Disorder 24.02, Bipolar‐Depressed 20.59, and Adjustment Disorder with Depressed Mood 17.29” (Kush, 2001). The BDI for Primary Care (BDI‐PC) (Steer, Cavalieri, Leonard, & Beck, 1999), an abridged version, is also an option in primary care settings (Kush, 2001). Although advantages for screening for depression has potential value, “no trials have found that patients who undergo screening have better outcomes than patients who do not when the same treatments are available to both groups” (Thombus et al., 2012). Routine screening of all patients for depression is labor intensive and can lead to misidentification of depression in some individuals, resulting in treatment for depression in patients with subclinical or mild symptoms and resources allocated away from patients who truly have depression (Thombus et  al., 2012). As an alternative to screening, the National Institute for Health and Clinical Excellence 2010 guidelines recommend physicians be alert to possible depressive disorders, especially when there is (a) a previous history of depression in the target patient, (b) when patients have a chronic physical condition, and (c) when the patient has a new or worsening physical impairment that causes functional limitations, and screen for depressive symptoms when there is a specific concern such as the recent loss or change in the individual’s living situation, support network, or quality of life (Thombus et al., 2012). Anxiety disorders have significant comorbidity with medical disorders including heart and lung diseases, fibromyalgia, and diabetes (Kush, 2001). Screening for anxiety disorders in primary care health settings is especially important in patients reporting somatic concerns including shortness of breath and chest pains. If undertaken in an efficient manner so as to not interfere with legitimate medical problems, screening can lead to reduced medical costs and improved outcomes by helping physicians make differential diagnoses and guide the selection of appropriate treatment (Kush, 2001). The Beck Anxiety Inventory (BAI) (Beck & Steer, 1993) is a 21‐item self‐report measure that can be completed and scored quickly. Fourteen items measure biological symptoms of anxiety, and seven items measure psychological anxiety symptoms; no items specifically measure phobias or obsessions and compulsions (Kush, 2001). Mean BAI norms have been established for “Panic Disorder with Agoraphobia (M = 27.27, Panic Disorder without Agoraphobia (M = 28.81), Social Phobia (M = 17.77), Obsessive‐Compulsive Disorder (M  =  21.69), and [Generalized Anxiety Disorder] (M = 18.83)” (Kush, 2001). Another consideration for psychologists practicing in primary care settings where time and resources are commodities is the significant comorbidity between anxiety and depression. In settings where this is of concern, other measures that screen for both types of symptoms may be most appropriate. The Hamilton Rating Scale for Depression (HAM‐D) (Hamilton, 1960) is clinician administered and is used to screen for depression; it also contains three anxiety items and can be used to assess a patient’s overlap in anxiety and depression symptoms. Both the BDI‐II and BAI have shown discrimination between depression and anxiety (Kush, 2001). In addition to internalizing concerns, drug and alcohol abuse are aspects of concern when patients present to primary care settings. According to Kush (2001), approximately 10% of adults experience significant dysfunction from alcohol or drug use at any time, yet most of these adults can present to medical appointments without any outward presentation of their substance use. Substance use and complications arising from this use can complicate the course of chronic health conditions and can attenuate the effectiveness of medical interventions. As a

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result, psychologists need to incorporate screening measures of substance abuse in their ­standard assessment protocols for primary care patients. The Drug Abuse Screening Test (DAST‐20) (Skinner, 1982) is a 20‐item assessment for drug abuse over 12 months, including both prescription drug and illicit drug use, and it can be administered in approximately 5 min via self‐report or interview (Kush, 2001). The Alcohol Dependence Scale (ADS) (Skinner & Horn, 1984) is a 28‐item assessment for alcohol abuse over 12 months, and it can be administered in approximately 10 min via self‐report or interview (Kush, 2001). In cases of suspected drug or alcohol abuse, it is important to establish a timeline of whether drug or alcohol use preceded medical problems or vice versa as this distinction may have implications for etiology, prognosis, and treatment.

Rehabilitation Settings In addition to primary care settings, psychologists also frequently play a role in physical ­rehabilitation settings where patients present with a wide range of physical injuries/disabilities and co‐occurring mental health concerns. Important components of psychological assessment in rehabilitation settings include the measurement of social problem solving that may impact treatment, functional impairments that may influence daily living, measures of disability status, and coping skills/behavioral adjustment (Dreer et  al., 2009; Hall, 1999). Empirically supported measures of social problem solving include “the Means‐End Problem Solving Procedure (MEPS; Platt & Spivack, 1975), the Problem Solving Inventory (PSI; Heppner, 1988), and the Social Problem Solving Inventory‐Revised (SPSI‐R; D’Zurilla, Nezu, & Maydeu‐Olivares, 2002)” (in Dreer et al., 2009). These and similar tools are helpful adjuncts to treatment in rehabilitation because social problem skills can help those living with chronic health conditions or disabilities cope with psychological distress (Dreer et al., 2009). Functional assessment measures assess disability across domains including “self‐care, mobility, and, more variably, cognition, communication, and behavior” (Hall, 1999). Measures of functional disability include the Functional Independence Measure (FIM), which measures motor and cognitive functioning; the Functional Assessment Measure (FAM), which is an addition to the FIM (known together as the FIM + FAM) and adds 12 items on cognitive, behavioral, communication, and community function (Hall, 1999); and the Disability Rating Scale (DRS; Rappaport, Hall, Hopkins, Belleza, & Cope, 1982). Other measures of functional disability include length of inpatient stay, hospital or rehabilitation charges (excluding physician fees), and the intensity and type of treatment (i.e., service utilization; Hall, 1999). Finally, client satisfaction with services is an important target variable for ascertaining the quality of outcomes (Hall, 1999). These measures can help psychologists understand the limitations of their patients and provide a road map for cognitive behavioral interventions targeting specific areas of concern. Moreover, using these screening instruments can augment information gained from traditional psychological assessments by providing context as to the physical and cognitive limitations, patients may face upon returning home or during their stay at the facility. Measures of disability status typically include analyses of community integration, service utilization, and independence. The Community Integration Questionnaire (CIQ) (Willer, Ottenbacher, & Coad, 1994) is a 15‐item self‐administered or interview‐based questionnaire that assesses community integration for individuals with TBI across three dimensions: home integration, social integration, and productivity (Hall, 1999). The Craig Handicap Assessment and Reporting Technique (CHART) (Whiteneck, Charlifue, Gerhart, Overholser, &

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Richardson, 1992) assesses five domains of “physical independence, mobility, occupation, social integration, and economic self‐sufficiency” in individuals with spinal cord injury (Hall, 1999). Other measures of handicap status include employment, which can be measured by the CIQ, CHART, FIM + FAM, DRS, or a monthly employment ratio; living arrangements, which provides an estimation of an individual’s “level of dependence…and cost to society”; and service utilization (Hall, 1999). Measures of handicap status are useful additions to assessments of functional limitations as they illustrate a patient’s level of independence in the community and the extent to which their functional impairments impact their occupational and social functioning all of which may interact with mental health conditions and personality characteristics. It is also important to assess psychosocial and behavioral adjustment in patients in rehabilitative settings (Hall, 1999). The Neurobehavioral Rating Scale (NRS) (Levin et al., 1987) is a 27‐item assessment of cognitive and emotional disturbances and has been “the scale of choice for assessing behavioral changes following TBI in neurosurgical trials,” although it requires substantial training, judgment, and “familiarity with the patient” to administer (Hall, 1999). Additionally, quality of life should be assessed as it is “arguably an ultimate aim of rehabilitation”; quality of life can be assessed using the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985) or the Quality of Life Scale (Chubon, 1987, in Hall, 1999).

Specialty Settings Cardiology Anxiety and depression are the most prevalent classes of psychiatric disorders, and coronary heart disease (CHD) is a highly prevalent cardiovascular issue in the general population. Anxiety is an independent predictor of later CHD events and cardiac mortality, and depression is associated with elevated risk of later cardiac episodes (Compare, Germani, Proietti, & Janeway, 2011). “In 2008, the American Heart Association recommended [and the American Psychiatric Association endorsed] that ‘screening tests for depressive symptoms should be applied to identify cardiology patients who may require further assessment and treatment’ if appropriate referral for further depression assessment and treatment is available” (Compare et al., 2011). As patients who have anxiety or depression are at increased risk for cardiovascular complications, it is important to assess patients with cardiovascular disease, in particular, for emotional conditions that may compound their risk for adverse cardiovascular events (Compare et al., 2011). Although general measures of anxiety and depression mentioned previously (BDI, BAI, Hamilton Anxiety Scale) can be used to assess these symptoms, there are several assessment measures for anxiety and depression that were developed for specific use in cardiovascular patient populations. The Diagnostic Interview with Structures Hamilton (DISH) (Freedland et al., 2002) is used with patients with acute coronary syndrome as a measure conducive to brief hospital visits, whereas the Maastricht Questionnaire (Appels, 1989) specifically assesses exhaustion (Compare et al., 2011). If assessment identifies patients with moderate to severe symptoms of anxiety or depression who have also either failed several trials of psychotropic medication, who have a history of previous psychiatric diagnosis, and/or who possess any one of a variety of risk factors (e.g., history of physical abuse or sexual victimization), the recommendation would be for these individuals to receive a more thorough assessment (Compare et al., 2011).

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Organ Transplantation Patients on the wait list for or who have received a donor organ are vulnerable to a variety of psychological issues. Adaptive and mental health challenges can occur at each stage of illness and treatment, including “organ failure/chronic illness, pretransplant evaluation, waiting for a donor, surgery, recovery, rehabilitation, [and] permanent maintenance (Olbrisch, Benedict, Ashe, & Levenson, 2002).” The selection process to receive a donor organ includes a process for determining a patient’s fitness to receive a donor organ such as psychological functioning and determination of any contraindications to transplantation (Maldonado et al., 2012). As the gap between organ donation and patients awaiting transplant increases, it is becoming more important to identify potential risk factors (i.e., “substance abuse, compliance issues, serious psychopathology”) that could result in greater risk of postoperative noncompliance through the utilization of pretransplant psychological evaluations (Olbrisch et  al., 2002). Psychological evaluations also help “[promote] fairness and equal access to care, [provide] a description of the patient’s neuropsychiatric and cognitive functioning, [serve] as a guide for the clinical management of the patient and [address] the psychological needs of the transplant team with regard to patient care” (Olbrisch et al., 2002). A typical pretransplant psychological evaluation consists of a clinical interview on the patient’s background and functioning, the possible use of standardized instruments, possible brief screenings for cognitive deficits such as mental status evaluations or neuropsychological measures of memory and executive functioning, collateral information (especially in patients with substance abuse history), and assessing for the quality of the patient’s support system (Olbrisch et al., 2002). Standardized measures that can be used to assess patients as candidates for organ transplant include the Psychosocial Assessment of Candidates for Transplantation (PACT) (Olbrisch, Levenson, & Hamer, 1989), the Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT) (Maldonado et  al., 2012), and the Readiness for Transition Questionnaire (RTQ) (Gilleland, Amaral, Mee, & Blount, 2012) (Maldonado et al., 2012; Marchak, Reed‐Knight, Amaral, Mee, & Blount, 2015; Olbrisch et  al., 2002). The PACT consists of eight items on a five‐point Likert scale and includes the rater’s impressions. The SIPAT consists of 18 items across 4 domains: “Patient’s Readiness Level & Illness Management, Social Support System Level of Readiness, Psychosocial Stability and Psychopathology, and Lifestyle and Effect of Substance Use” (Maldonado et al., 2012). The RTQ consists of three parallel versions—the RTQ‐Provider, RTQ‐Teen, and RTQ‐Parent—all designed to be ­completed by healthcare providers (Marchak et al., 2015).

Oncology Cancer is a significant physical health ailment in modern society with approximately 14.5 ­million people currently diagnosed with cancer in the United States (Stanton, Rowland, & Ganz, 2015). Diagnosis and treatment of cancer can cause significant psychological distress in patients, including elevated symptoms of depression and anxiety that can affect more than one in four patients with cancer (Stanton et al., 2015; Thalen‐Lindstrom, Larsson, Glimelius, & Johansson, 2013). Stanton et  al. (2015) proposed three periods during the survivorship phase—reentry (completion of treatment to up to 1 year), early survivorship (approximately 5  years post‐diagnosis), and long‐term survivorship (beyond 5 years post‐diagnosis)—and ­proposed that psychological distress may be more pronounced in certain periods than in ­others. As a result, different assessment questions may predominate depending upon the stage of treatment a patient is in.

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For cancer patients, anxiety can center on a fear of cancer recurrence. Fear of recurrence is common in long‐term survivors and can be triggered by “follow‐up medical visits, symptoms that mimic illness (e.g., pain that may be attributed to disease spread), death of a public figure from cancer, or a family member’s illness” (Stanton et al., 2015). Depression is also present in cancer survivors, and there is a higher risk for depressive symptoms in those with advanced cancer, receiving chemotherapy, and with more physical symptoms. However, it should be noted that at 7 years after diagnosis, cancer survivors’ rates of depression are not significantly different from healthy controls. Nonetheless, cancer treatments themselves can also result in a number of cognitive effects including impairments in attention, working memory, concentration, and executive functioning (Stanton et al., 2015). Similar to organ transplant assessment, assessment in oncology settings can help maximize limited resources and identify patients in the most distress (Vodermaier, Linden, & Siu, 2009). Brief, self‐report measures can be used to screen for oncology patients who are most in need of psychological services, and systematic screening helps promote equal access to resources better than physician‐ or patient‐initiated referrals, which can fail to identify emotionally ­distressed patients (Vodermaier et al., 2009). For a detailed review of ultrashort, short, and long assessment measures that can be used as screeners for emotional distress in cancer patients, see Vodermaier et al. (2009). The Hospital Anxiety and Depression Scale (HADS) (Zigmond & Snaith, 1983) is a 14‐item self‐report measure for anxiety and depression, with scores greater than 10 signifying clinical cases of anxiety and depression. The HADS has been used as a screener for oncology patients in clinical settings and does increase referral rates; those referred using the HADS, however, had no difference in improvement from those in standard care (Thalen‐Lindstrom et al., 2013). Beyond anxiety and depression, cancer survivors are also likely to experience fatigue, cognitive impairment, pain, and sexual and urinary/bowel problems, as well as difficulty with more global issues such as finding benefits in their cancer experience and returning to work (Stanton et al., 2015). Anxiety and depression symptoms can be risk factors for fatigue; other risk factors for fatigue include elevated body mass, catastrophic thinking, loneliness, and early life adversity (Stanton et al., 2015). Cancer survivors often endorse problems in memory, attention, concentration, and executive function abilities; these problems can be exacerbated by risk factors such as older age, lower education, or lower IQ (Stanton et al., 2015). Therefore, in addition to psychosocial stressors and mental health symptoms, evaluations involving oncology patients should also include screening measures for cognitive functioning in order to determine if additional referrals for a comprehensive neuropsychological assessment is appropriate. Quality of life is also an important outcome to consider for cancer survivors. Quality of life measures can include indicators of strength of interpersonal relationships and support ­systems, life appreciation, personal regard, and attention to health behaviors (Stanton et al., 2015; Thalen‐Lindstrom et al., 2013). Although quality of life measurement is not t­ ypically included in standard psychological assessments, it is understandable how these may facilitate treatment of cancer survivors or those patients dealing with continued cancer ­treatment. An  example measure for evaluating quality of life in oncology patients is the European Organization for Research and Treatment of Cancer (EORTC) quality of life questionnaire (QLQ‐C30) (Aaronson et  al., 1993) that includes five functional scales (higher scores reflect better ­ functioning), nine symptom scales (higher scores reflect more severe ­problems), and a global quality‐of‐life (QOL) scale (higher scores reflect better f­ unctioning; Thalen‐Lindstrom et al., 2013).

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Conclusions Having psychologists in medical care settings can provide useful insight into issues that arise when there is comorbidity between medical and psychological disorders in medical health settings. Psychological assessments can be used to screen for common psychological syndromes, such as depression and anxiety, autism spectrum disorders, ADHD, and dementia, and to evaluate conditions that may directly impact medical interventions such as substance abuse. The role of psychology in specialty care settings is less focused on direct diagnosis of psychological disorders (although that may continue to be a focus of screening) but rather on issues that are important for treatment and rehabilitation planning such as psychological distress, contraindications of treatment, functional impairment, behavioral/social adjustment, and coping skills. Psychological evaluations have already demonstrated usefulness in a variety of medical settings, and their use and utility will likely increase as we continue to appreciate the role of mental health in the etiology and maintenance of physical disease and the impact of positive psychological adjustment on long‐term physical health and well‐being.

Author Biographies Kelsey A. Maloney is a doctoral student in clinical psychology at Texas Tech University. She received her MA from Sam Houston State University in 2016 and BS from Mississippi State University in 2014. Her research interests include the intersection of health and forensic ­psychology, such as improving health outcomes in juvenile justice populations. Adam T. Schmidt is an assistant professor of clinical psychology at Texas Tech University. He received his PhD in psychology from the University of Minnesota. His research interests are in the areas of psychological and neuropsychological assessment with at risk populations and in using psychological assessment data to improve treatment outcomes.

References Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., … Kaasa, S. (1993). The European Organization for Research and Treatment of Cancer QLQ‐C30: A quality‐of‐life instrument for use in international clinical trials in oncology. Journal of the National Cancer Institute, 85(5), 365–376. Appels, A. (1989). Loss of control, vital exhaustion and coronary heart disease. In A. Steptoe & A. Appels (Eds.), Stress, personal control and health (pp. 215–235). Oxford, England: John Wiley & Sons. Beck, A. T., & Steer, R. A. (1993). BAI, beck anxiety inventory manual. San Antonio, TX: The Psychological Corporation. Beck, A. T., Steer, R. A., & Brown, G. K. (1996). BDI II: Beck depression inventory—Second edition manual. San Antonio, TX: The Psychological Corporation. Chubon, R. A. (1987). Development of a quality‐of‐life rating scale for use in health‐care evaluation. Evaluation & the Health Professions, 10(2), 186–200. Compare, A., Germani, E., Proietti, R., & Janeway, D. (2011). Clinical psychology and cardiovascular ­disease: An up‐ to‐ date clinical practice review for assessment and treatment of anxiety and depression. Clinical Practice and Epidemiology in Mental Health, 7, 148–156. doi:10.2174/1745017901107010148 Diener, E. D., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment, 49(1), 71–75.

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Dreer, L. E., Berry, J., Rivera, P., Snow, M., Elliott, T. R., Miller, D., & Little, T. D. (2009). Efficient assessment of social problem‐solving abilities in medical and rehabilitation settings: A rasch analysis of the social problem‐solving inventory‐revised. Journal of Clinical Psychology, 65(7), 653–669. doi:10.1002/jclp.20573 Freedland, K. E., Skala, J. A., Carney, R. M., Raczynski, J. M., Taylor, C. B., de Leon, C. F. M., … Veith, R. C. (2002). The depression interview and structures Hamilton (DISH): Rationale, development, characteristics, and clinical validity. Psychosomatic Medicine, 64(6), 897–905. Gilleland, J., Amaral, S., Mee, L. L., & Blount, R. L. (2012). Getting ready to leave: Transition readiness in adolescent kidney recipients. Journal of Pediatric Psychology, 37(1), 85–96. Hall, K. M. (1999). Functional assessment in traumatic brain injury. In R. Rosenthal, E. R. Griffith, J. S. Kreutzer, & B. Pentland (Eds.), Rehabilitation of the adult and child with traumatic brain injury (pp. 131–146). Philadelphia, PA: F. A. Davis Company. Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23, 56–62. James, L. C. (2006). Integrating clinical psychology into primary care settings. Journal of Clinical Psychology, 62(10), 1207–1212. doi:10.1002/jclp.20306 Kush, K. R. (2001). Primary care and clinical psychology: Assessment strategies in medical settings. Journal of Clinical Psychology in Medical Settings, 8(4), 219–228. doi:10.1023/A:1011973027283 Levin, H. S., High, W. M., Goethe, K. E., Sisson, R. A., Overall, J. E., Rhoades, H. M., … Gary, H. E. (1987). The neurobehavioural rating scale: Assessment of the behavioural sequelae of head injury by the clinician. Journal of Neurology, Neurosurgery & Psychiatry, 50(2), 183–193. Maldonado, J. R., Dubois, H. C., David, E. E., Sher, Y., Lolak, S., Dyal, J., & Witten, D. (2012). The Stanford integrated psychosocial assessment for transplantation (SIPAT): A new tool for the psychosocial evaluation of pre‐ transplant candidates. Psychosomatics Journal of Consultation and Liaison Psychiatry, 53(2), 123–132. doi:10.1016/j.psym.2011.12.012 Marchak, J. G., Reed‐Knight, B., Amaral, S., Mee, L., & Blount, R. L. (2015). Providers’ assessment of transition readiness among adolescent and young adult kidney transplant recipients. Pediatric Transplantation, 19, 849–857. doi:10.1111/petr.12615 Olbrisch, M. E., Benedict, S. M., Ashe, K., & Levenson, J. L. (2002). Psychological assessment and care of organ transplant patients. Journal of Consulting and Clinical Psychology, 70(3), 771–783. doi:10.1037//0022‐006X.70.3.771 Olbrisch, M. E., Levenson, J. L., & Hamer, R. (1989). The PACT: A rating scale for the study of clinical decision making in psychosocial screening of organ transplant candidates. Clinical Transplantation, 3, 164–169. Rappaport, M., Hall, K. M., Hopkins, K., Belleza, T., & Cope, D. N. (1982). Disability rating scale for severe head trauma: Coma to community. Archives of Physical Medicine and Rehabilitation, 63(3), 118–123. Skinner, H. A. (1982). The drug abuse screening test. Addictive Behaviors, 7, 363–371. Skinner, H. A., & Horn, J. L. (1984). Alcohol Dependence Scale (ADS) user’s guide. Toronto, OT: Addiction Research Foundation. Stanton, A. L., Rowland, J. H., & Ganz, P. A. (2015). Life after diagnosis and treatment of cancer in adulthood. American Psychologist, 70(2), 159–174. doi:10.1037/a0037875 Steer, R. A., Cavalieri, T. A., Leonard, D. M., & Beck, A. T. (1999). Use of the Beck Depression Inventory for primary care to screen for major depression disorders. General Hospital Psychiatry, 21(2), 106–111. Thalen‐Lindstrom, A., Larsson, G., Glimelius, B., & Johansson, B. (2013). Anxiety and depression in oncology patients; a longitudinal study of a screening, assessment and psychosocial support intervention. Acta Oncologica, 52(1), 118–127. doi:10.3109/0284186X.2012.707785 Thombus, B. D., Coyne, J. C., Cuijpers, P., Jonge, P. D., DPhil, S. G., Loannidis, J. P. A., … Ziegelstein, R. C. (2012). Rethinking recommendations for screening for depression in primary care. Canadian Medical Association Journal, 184(4), 413–418. doi:10.1503/cmaj.111035

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Vodermaier, A., Linden, W., & Siu, C. (2009). Screening for emotional distress in cancer patients: A systematic review of assessment instructions. Journal of the National Cancer Institute, 101(21), 1464–1488. doi:10.1093/jnci/djp336 Whiteneck, G. G., Charlifue, S. W., Gerhart, K. A., Overholser, J. D., & Richardson, G. N. (1992). Quantifying handicap: A new measure of long‐term rehabilitation outcomes. Archives of Physical Medicine and Rehabilitation, 73(6), 519–526. Willer, B., Ottenbacher, K. J., & Coad, M. L. (1994). The community integration questionnaire: A comparative examination. American Journal of Physical Medicine & Rehabilitation, 73(2), 103–111. Zigmond, A. S., & Snaith, R. P. (1983). The hospital anxiety and depression scale. Acta Psychiatrics Scandinavica, 67(6), 361–370.

Suggested Reading Hall, K. M. (1999). Functional assessment in traumatic brain injury. In R. Rosenthal, E. R. Griffith, J. S. Kreutzer, & B. Pentland (Eds.), Rehabilitation of the adult and child with traumatic brain injury (pp. 131–146). Philadelphia, PA: F. A. Davis Company. Kush, K. R. (2001). Primary care and clinical psychology: Assessment strategies in medical settings. Journal of Clinical Psychology in Medical Settings, 8(4), 219–228. doi:10.1023/A:1011973027283 Vodermaier, A., Linden, W., & Siu, C. (2009). Screening for emotional distress in cancer patients: A systematic review of assessment instructions. Journal of the National Cancer Institute, 101(21), 1464–1488. doi:10.1093/jnci/djp336

Complementary, Alternative, and Integrative Interventions in Health Psychology Jeffrey E. Barnett College of Arts and Sciences, Loyola University Maryland, Baltimore, MD, USA

Complementary and alternative treatments and interventions (widely known as CAM or ­complementary and alternative medicine) are composed of a wide range of healthcare ­practices, interventions, systems, and products that are not considered to be a part of conventional medicine and healthcare practices (e.g., National Center for Complementary and Integrative Health [NCCIH], 2015d). Complementary practices are those used along with conventional healthcare practices, and alternative practices are those used instead of conventional healthcare practices. In contrast, integrative practices involve utilizing conventional and complementary healthcare practices in a coordinated manner (NCCIH, 2015a). While CAM interventions have received greater scientific scrutiny in recent years, many forms of CAM have been practiced for thousands of years and have rich histories of use in many cultures. Examples include Traditional Chinese medicine (TCM), which dates back to 800 BCE and Ayurveda, a whole medical system with roots in Hinduism over 10,000 years ago (Duke Center for Integrative Medicine [DCIM], 2006). At present, over 100 different interventions, products, systems, and modalities are included in widely accepted definitions of CAM (Cochrane Reviews, 2015). Despite the historical acceptance and use of some forms of CAM, the level of support for each form of CAM varies, so a careful review of available research is needed before recommending or utilizing any CAM modalities with patients. In addition to searching for recent studies on the efficacy of various CAM modalities for the treatment of specific health conditions as well as for health promotion, a careful reading of relevant meta‐analytic studies available through Cochrane Reviews is recommended (Cochrane Reviews, 2015). Additionally, the NCCIH, an institute of the National Institutes of Health, provides up‐to‐date information on the efficacy of various CAM modalities to include defining “through rigorous scientific The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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i­nvestigation, the usefulness and safety of complementary and integrative health interventions and their roles in improving health and healthcare” (NCCIH, 2015b, para. 2). Various CAM modalities may be used to promote health as well as to treat disease. While traditional explanations for the efficacy of many CAM modalities have been questioned by the scientific community, recent research has demonstrated the effectiveness of a range of CAM interventions for a wide variety of disorders. This research has also shed light on the underlying mechanisms of many CAM modalities, helping to explain scientifically what many individuals have accepted based on religious and cultural beliefs and many years of anecdotal support. CAM has a significant presence in the United States. Nahin, Barnes, Stussman, and Bloom (2009) found that there were over 354 million visits to CAM practitioners in 2007 with over 42 billion dollars spent on these services that year. Additionally, it is estimated that 38.3% of adults and 12% of children in the United States use at least one CAM modality each year (Barnes, Bloom, & Nahin, 2008; NCCIH, 2015a). Further, as research demonstrates the efficacy of various CAM modalities and as individuals embrace lifestyles consistent with the practice of CAM, the number individuals utilizing CAM has steadily increased. In the most recent extensive surveyq of CAM practices in the United States, the National Health Interview Survey (National Institutes of Health [NIH], 2012) found that CAM practices are used to promote health, to help treat chronic conditions, and to alleviate the side effects of medications and other conventional treatments with these interventions largely being used along with conventional healthcare and not in place of it. Dietary supplements have consistently been found to be the most widely used form of CAM with 17.7% of all US adults found to be using them (NIH, 2012). While differences exist within and between groups, overall, CAM is used by individuals across the age span from a wide range of cultural backgrounds, socioeconomic levels, and educational levels. For children and adolescents ages 4–17, the most widely used CAM approaches in order of frequency are dietary supplements and natural products; chiropractic or osteopathic manipulation; yoga, tai chi, or qi gong; deep breathing; homeopathy; meditation; special diets; ­massage; guided imagery; and movement therapy. These are used to promote health and wellness as well as to treat conditions (in order of frequency) such as back or neck pain, other musculoskeletal symptoms, head or chest cold, anxiety or stress, ADHD, and insomnia (NCCIH, 2015c). In general, psychologists tend to hold positive views of CAM, both for their personal use and for their patients’ use. Additionally, many are actively integrating various CAM modalities into their treatment of patients and/or referring patients to CAM practitioners in addition to the psychological treatment they are providing (Barnett & Shale, 2012). Members of the general public also tend to hold positive views of CAM and are open to its use in their lives. Increases in the use of CAM by members of the public in recent years are attributed to factors such as dissatisfaction with conventional medicine to include its increasingly technological focus, a focus on more holistic health promotion and healthcare, and a desire to take a more active role in, and have increased control over, their health and healthcare (Stapleton et al., 2015). The NCCIH (2015b) views CAM as being made up of five domains. These are the following: • Whole medical systems. These include homeopathy, naturopathy, TCM, and Ayurveda. • Mind–body medicine. These include meditation, biofeedback, hypnosis, spirituality and prayer, art therapy, music therapy, dance movement therapy (DMT), and yoga.

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• Biologically based practices. These include dietary supplements, herbal supplements, and aromatherapy. • Manipulative and body‐based practices. These include massage therapy and spinal manipulation such as chiropractic and osteopathic. • Energy therapies. These include qigong, reiki, therapeutic touch, and electromagnetic therapy. While a wide range of CAM modalities exist, not all are commonly used, and not all receive equal support and acceptance in our nation today. Information will be shared on the 14 most widely used CAM modalities in the United States as reported by Barnes et  al. (2008) and NCCIH (2015c). These are, in order of the frequency of their use from greatest to least, dietary supplements; meditation; chiropractic; aromatherapy; massage therapy; yoga; progressive muscle relaxation (PMR); spirituality, religion, and prayer; DMT; acupuncture; reiki; ­biofeedback; hypnosis; and music therapy. For additional information on each of these CAM modalities to include their origins, uses, relevant research, and limitations, readers are referred to the website of NCCIH at www.nccih.nih.gov.

Dietary Supplements Dietary supplements include a wide range of typically orally ingested substances that are used to enhance general health and well‐being or to help treat a wide range of ailments and health concerns. Dietary supplements include one or more of many “dietary ingredients (including vitamins; minerals; herbs or other botanicals; Amino Acids; and other substances) or their constituents” (Office of Dietary Supplements [ODS], 2011 para. 3). While dietary supplements are regulated by the US Food and Drug Administration (FDA), they are regulated quite differently than prescription and over‐the‐counter medicines and vitamins. In fact, as Barnett and Shale (2012) emphasize, “the FDA does not review the safety and/or effectiveness of any supplements prior to them being sold” (p. 577). Additionally, for many supplements, there can be significant differences between brands and even between batches within brands for the potency, purity, type, and quantity of the active ingredients and other substances included in them. Additionally, while many dietary supplements are marketed as “natural products,” the use of some of them, both alone and in combination with other products, can result in harmful effects and side effects. Yet, 17.7% of adults use herbal products, and when including vitamins and minerals, 52% of adults are using dietary supplements (Radimer et al., 2004). Psychologists should not make recommendations to patients about the use of dietary ­supplements and should not integrate their use into psychological treatment. Yet, it is recommended that psychologists ask all patients about their use of dietary supplements (as well as their use of prescription and over‐the‐counter medications). While many of these substances may be quite helpful, there are numerous significant health risks associated with their use and misuse. Additionally, when certain herbal supplements are taken along with widely prescribed medications, serious side effects are common. Therefore, it is recommended that psychologists keep informed about the literature on the use of dietary supplements and educate patients about their possible use and attendant risks. All patients currently using, considering using, or who in the psychologist’s view may benefit from the use of dietary supplements (as well as prescription and over‐the‐counter medications) should be referred to their primary care ­ ­physician to oversee and/or recommend their use to help ensure optimal outcomes are achieved.

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Meditation Meditation is a mind–body practice that has long been integrated into a wide range of r­ eligious and spiritual practices. The most widely used and studied forms of meditation at present are mindfulness meditation and concentrative meditation. Mindfulness meditation involves paying attention in a nonjudgmental way in the present moment (Kabat‐Zinn, 1994). It has formed the basis of integrative treatments such as mindfulness‐based stress reduction (MBSR) and mindfulness‐based cognitive therapy (MBCT) and is a component of acceptance and ­commitment therapy (ACT) and dialectical behavior therapy (DBT). Concentrative meditation involves focusing on a specific word or phrase to alter one’s conscious state and to achieve a deep state of relaxation. In the most commonly used and studied form of concentrative meditation, Transcendental Meditation, it “allows the mind to simply, naturally and effortlessly transcend thinking and to experience a deep state of restfully alert consciousness” (e.g., Nagendra, Maruthai, & Kutty, 2012). With appropriate training and competence, meditation practices may be integrated into the treatment of patients with a wide range of presenting problems. Patients may be referred to various commercially available meditation programs to augment the work they are doing in psychotherapy, meditation practices may be integrated into ongoing psychotherapy, and meditation‐based treatments such as MBSR and MBCT may be offered. Numerous studies have demonstrated the usefulness of meditation practices for the treatment of conditions such as high blood pressure, irritable bowel syndrome, ulcerative colitis, anxiety, depression, insomnia, and acute respiratory illnesses, and initial findings indicate some promise for pain management and for smoking cessation (NCCIH, 2015e). For example, in a randomized controlled study, Barnhofer et al. (2015) demonstrated the effectiveness of mindfulness approaches in reducing depressive and suicidal cognitions. While no  significant contraindications or side effects are reported with the use of meditation ­practices, as with all healthcare interventions, consultation with each patient’s primary care physician before recommending or integrating meditation into psychological treatment is recommended.

Chiropractic Chiropractic is a system of spinal adjustments or manipulations that are performed to realign the spine in a manner that is believed to relieve pain and to promote health and wellness. Chiropractic is most widely used to treat back pain, neck pain, pain in the joints of the arms and legs, and headaches (American Chiropractic Association [ACA], 2015). Chiropractic has a long history dating back to 2700 BCE in ancient China and Greece (ACA, 2015). There exists a range of techniques used by in chiropractic treatment, but their primary aim is to realign the spine to reduce or correct spinal nerve impingement and to improve joint mobility. Chiropractic manipulation may be provided as a stand‐alone treatment or in conjunction with conventional healthcare interventions. Research has been conducted on the use of chiropractic for acute low back pain, chronic low back pain, and headaches with moderate to significant effectiveness being found, depending on the studies reviewed. It has also been studied for the treatment of phobias, ADHD, and nocturnal enuresis, with questionable effectiveness being found (Barnett, Shale, Elkins, & Fisher, 2014).

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Chiropractic is a profession that is regulated by state law. Only licensed doctors of ­chiropractic may offer these treatments in the United States. Thus, if chiropractic care is to be considered for patients, referral to a licensed doctor of chiropractic is essential. But these professionals may use a wide range of techniques, with some offering nutritional counseling and other services, so being clear on their training, areas of competence, and techniques used is essential. Additionally, due to the very forceful physical nature of chiropractic spine manipulations, side effects such as muscular soreness are common (Ernst, 2007).

Aromatherapy Aromatherapy is the use of essential oils from herbs, flowers, and trees to promote physical, emotional, and spiritual health and wellness. Proponents of aromatherapy believe that the inhalation of various essential oils from plants positively influences “mood, behavior, and ­spiritual well‐being by stimulation of the olfactory or somatic senses” (Barnett et al., 2014, p. 181). While aromatherapy has a long history of use dating back to ancient Egypt, China, and India as parts of whole medical systems such as TCM and Ayurveda, its use and effectiveness have only recently begun to be studied. Aromatherapy may be used to treat specific symptoms and disorders such as stress, anxiety, insomnia, and pain (Chalifour & Champagne, 2008). It has also been used holistically to promote overall wellness and for esthetic purposes such as to promote relaxation and to create a pleasing and peaceful home environment. Reviews of controlled studies do not yield significant findings for the treatment of any disorders, yet many individuals report that smelling the aroma of a range of plant extracts helps them to feel more relaxed and peaceful. Aromatherapy should be used with caution as there are serious side effects that may result from applying essential oils in an incorrect manner or with incorrect doses. Additionally, some essential oils are toxic and should be avoided completely. Further, some essential oils are ­contraindicated with pregnant women and children. Thus, the possible use of aromatherapy should be given careful consideration that includes a review of the relevant literature and appropriate oversight for any aromatherapy use.

Massage Therapy Massage therapy is a manipulative and body‐based practice that involves “pressing, rubbing, and moving muscles and other soft tissues of the body, primarily by using the hands and ­fingers” (Barnett et al., 2014, p. 210). It is primarily used as a means of reducing stress and promoting relaxation and to reduce muscular tightness and strain. It has also been used in the rehabilitation of sport‐related injuries and to improve mood and reduce anxiety and depression as well as to promote overall wellness. Research findings indicate strong support for the use of massage therapy in the treatment of anxiety, depression, and stress, with moderate effectiveness in the treatment of low back pain and neck pain. Massage therapy has also been shown to positively impact immune functioning for individuals with breast cancer, HIV, and leukemia (Field, 2006). There are numerous massage therapy techniques that are commonly used. Additionally, there are four main forms of massage presently being practiced in the United States. These are Swedish, deep tissue, sports, and trigger point massage. Massage therapy is regulated in the

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United States, and referrals for massage therapy should only be to those who are licensed or certified. Care should be taken to ensure that when massage is included in patients’ overall treatment, it is done so with appropriate oversight. While few side effects or contraindications are reported in the literature, it is recommended that massage therapy only be used after clearance is received from the patient’s primary care physician. Some patients with musculoskeletal difficulties or injuries should not participate in massage therapy.

Yoga Yoga is a form of CAM with ancient roots that has become increasingly popular in recent years. There are a large number of yoga “schools” with each emphasizing different techniques and goals, with different styles existing within each school. But, overall, yoga involves physical postures, meditation, breathing exercises, and sustained concentration to promote wellness, relaxation, strength, and flexibility. The most widely practiced form of yoga in the United States is Hatha yoga with some of its popular styles including Bikram, Ashtanga, Iyengar, and Kundalini yoga. Yoga is practiced by more than 13 million adults in the United States; 58% practice it to promote overall health and well‐being, 16% to treat specific medical conditions such as low back pain, and 11% to seek relief from musculoskeletal conditions. Yet, only 22% reported that their physician recommended the use of yoga (NCCIH, 2015e). Thus, it is likely that many individuals are seeking out the practice of yoga on their own initiative. Psychologists may recommend yoga for a wide range of health concerns. Strongest support has been found for yoga’s use in the treatment of pain and to promote coping skills, with weak evidence for its use in treating depression, anxiety, and eating disorders. Yet, it is widely used to enhance wellness and quality of life overall, with many individuals reporting significant benefits from its use. While few risks are associated with the use of yoga, not all forms of yoga are suitable for all individuals. Each patient’s needs should be assessed, the different forms of yoga researched, and then an individual decision made. Before engaging in some of the more strenuous forms of yoga, clearance by the patient’s physician is recommended.

Progressive Muscle Relaxation PMR is a widely used CAM modality that is now so widely accepted for use in psychological treatment that it soon may not be considered complementary or alternative. PMR is a technique in which the patient learns to progressively relax different muscle groups throughout the body. Its use may also incorporate guided imagery and deep breathing exercises. Psychologists may easily integrate PMR into ongoing psychological treatment, and its use is quite frequently taught in graduate training for psychologists. No additional licensure or certification is needed to teach patients PMR. Scripts and recordings of PMR instructions are widely available online and may be utilized by psychologists and patients alike. Research indicates that PMR can be used quite effectively to treat anxiety, muscular ­tension, and other stress‐related symptoms to include tension headaches, neck ache, and back pain. PMR is useful for promoting general relaxation and as a component of the treatment of a wide range of stress‐related disorders. PMR may be utilized in conjunction with other forms

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of CAM such as meditation, yoga, massage therapy, and biofeedback in the treatment of ­stress‐related disorders.

Spirituality, Religion, and Prayer While many mental health professionals may not consider religion, spirituality, and prayer as relevant to the provision of psychological services, for many individuals who seek out psychological treatment, these are important parts of their lives. In fact, many patients’ suffering and distress may be related to religious or spiritual issues or concerns. For others, religion, spirituality, and prayer may be important sources of strength and support that can be tapped into to assist them in treatment. Spirituality, religion, and prayer are commonly incorporated into the treatment of addictions, trauma, and depression, but they may be utilized in a wide range of treatments to assist patients to achieve their goals. They may also be useful for helping patients find meaning in their suffering, for enhancing coping ability and resilience, and to promote general well‐being. Research support is found for the use of prayer as a coping mechanism for patients with cancer or other terminal illnesses, to treat pain, and for stress reduction (Barnett et al., 2014). Psychologists should be cautious about imposing personal religious beliefs on patients or not valuing and respecting patients’ beliefs and preferences. Further, for those psychologists who will integrate prayer and other religious or spiritual interventions into psychological treatment, care must be exercised not to take on the role of the clergy. When concerns exist, ­consultation with, and referrals to, members of the clergy are recommended.

Dance Movement Therapy DMT is a mind–body practice that incorporates dance and movement and that views motion and emotion interconnected, using expressive movement to assist patients to address unresolved emotional conflicts. The dance movement therapist analyzes the patient’s movements to better understand the patient’s underlying emotional drives and conflicts and, with this deeper understanding, assists the patient to work through and resolve them. DMT has been used to treat anxiety, distorted body image, depression, and fatigue and to promote psychological adjustment and quality of life. Well‐designed studies are uncommon in the literature, and support for DMT is predominantly anecdotal (Barnett et  al., 2014). Available research on the effectiveness of DMT indicates some support for its use in the treatment of the abovementioned issues, but due to methodological issues, caution should be exercised in recommending its use.

Acupuncture Acupuncture is a component of TCM that has been in use for thousands of years. In TCM it is believed that each individual’s life energy, or qi (pronounced chi), flows through 16 pathways throughout the body called meridians. Acupuncturists place needles in combinations of some of the 360 specific acupoints along these meridians and stimulate them either by manually manipulating the needles or with electrical stimulation, with the goal of rebalancing the life energy and thus ameliorating the symptoms being experienced (DCIM, 2006).

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In recent years acupuncture has been studied extensively with well‐designed studies and has been found to be moderately effective for the treatment of chronic low back pain, cancer‐ related pain, anxiety, migraines and headaches, and insomnia. While it has also been used for the treatment of depression and schizophrenia, support for these uses is quite limited (Barnett et  al., 2014). While some may question traditional beliefs about acupuncture’s underlying mechanisms, recent research provides support for the stimulation of nerve endings that result in changes in the brain’s pain centers (White & Ernst, 2004). Acupuncture is found to generally to be safe with minimal side effects when provided by a trained and competent practitioner, and it has fewer side effects than many conventional treatments for chronic low back pain (Thomas et al., 2005). Acupuncture should only be provided by a trained and credentialed acupuncture professional. In many states licensure and/or certification is required to provide acupuncture, and each practitioner’s credentials should be assessed before making referrals.

Reiki Reiki is a form of energy therapy in which the practitioner transfers energy, or life force, into the patient by placing her or his hands slightly above or lightly on the patient’s body. By passing this energy to the patient at a series of known hand positions, it is believed that this energy transfer promotes healing in the participant by allowing her or his own life force to flow more freely (Barnett et al., 2014). While limited research has been conducted on the use and effectiveness of reiki, evidence exists that demonstrates some benefit in the treatment of depression and anxiety and for relieving stress and promoting general wellness. It has also been shown to reduce pain, fatigue, and insomnia in cancer patients. No known side effects or risks are associated with the use of reiki, but due to the limited research support for it at present, caution should be taken when recommending it, and reiki should only be used as a complementary treatment and not a replacement for well‐researched treatments. Reiki practitioners must be certified, yet at present there are numerous certification agencies with no single standard for training and for demonstrating competence. Thus, the competence of reiki practitioners should be carefully assessed when considering making referrals.

Biofeedback Biofeedback is another CAM modality that has been extensively studied and that has been widely integrated into psychological practice. As such, many practitioners are now considering biofeedback to be a component of conventional psychological practice. Biofeedback involves utilizing one of several possible monitoring devices to provide feedback to the patient on underlying physiologic processes such as respiration, heart rate, muscle tension, skin temperature, and brain wave activity. By use of the ongoing feedback, patients learn to impact and control these underlying physiologic processes to reduce or ameliorate symptoms associated with them. Extensive research has demonstrated biofeedback’s effectiveness in treating tension headaches, migraines, Raynaud’s disease, anxiety, and ADHD. Some support is also found for its use in treating fibromyalgia, phantom limb pain, and depression (Barnett et al., 2014). There are no known side effects of biofeedback, and its practitioners provide it within the scope of

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their professional license when appropriately trained. Optional board certification is available through the Biofeedback Certification International Alliance (see www.bcia.org) that also maintains a list of board certified professionals for use when making referrals. Biofeedback is widely used and regularly integrated into treatment provided by appropriately trained psychologists.

Hypnosis Hypnosis is a mind–body therapy that also has been extensively studied and widely integrated into conventional healthcare by appropriately trained practitioners. Hypnosis, also known as hypnotherapy, involves a hypnotic induction that places the patient into an altered stated of consciousness during which the patient is receptive to suggestions that assist the patient to achieve desired goals and symptom reduction. While some individuals are more hypnotizable than others, most individuals are able to benefit from its use. Hypnosis has been utilized as a stand‐alone treatment as well as integrated with other healthcare practices. There is strong research support for the use of hypnosis to treat acute and chronic pain and as an analgesic during medical and surgical procedures. It has also been shown effective in the treatment of irritable bowel syndrome and in the reduction of the side effects of chemotherapy. While it has been used for smoking cessation and other types of habit control, the evidence for these uses is more limited (Barnett et al., 2014). When offered by competent professionals, side effects from the use of hypnosis are quite rare. Most professionals who utilize hypnosis do so within the scope of their licenses as health professionals. Yet, board certification is available through the American Society of Clinical Hypnosis (www.asch.net), and this should be considered when making referrals. Appropriately trained psychologists may easily integrate the appropriate use of hypnosis into their patients’ treatment.

Music Therapy Music therapy involves the use of music appreciation, creation, and performance for emotional expression. As a mind–body intervention, music therapy may assist patients to explore and express their thoughts and feelings through music, with the goal of this process being improved health. Music therapy has been used in the treatment of depression, anxiety, chronic pain, PTSD, chemotherapy side effects, dementia, and schizophrenia. While mixed results have been seen thus far, music therapy does appear to be at least somewhat helpful in the treatment of depression and anxiety, and for promoting general relaxation in response to various stressors. Thus, it may be a useful when integrated with other treatments. Music therapy practitioners should be certified by the American Music Therapy Association (www.musictherapy.org).

Conclusion A wide range of CAM modalities exist that have received strong scientific support for the ­treatment of a wide range of conditions and disorders either alone or in conjunction with other treatments. Yet, not all CAM modalities are helpful to all patients, and some modalities

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may prove harmful if not applied appropriately. One must possess the education and training necessary to be knowledgeable about CAM to make the best possible treatment decisions for patients. Some CAM modalities may effectively be integrated into conventional treatments by psychologists, and for others, referrals to competent and appropriately credentialed CAM practitioners should be made. With so many individuals utilizing CAM on their own and with so many patients open to, or even requesting, the use of CAM in their psychological treatment, it is important that psychologists educate themselves about the use, benefits, limitations, and contraindications of CAM in psychological practice.

Author Biography Jeffrey E. Barnett, PsyD, ABPP, is a licensed psychologist as well as associate dean and ­professor of psychology at Loyola University Maryland. He is board certified by the American Board of Professional Psychology in clinical psychology and in Clinical Child and Adolescent Psychology, and he is a distinguished practitioner in psychology of the National Academies of Practice.

References American Chiropractic Association. (2015). What is Chiropractic? Retrieved from http://www.acatoday. org/level2_css.cfm?T1ID=13&T2ID=61 Barnes, P. M., Bloom, B., & Nahin, R. (2008). Complementary and alternative medicine use among adults and children: United States, 2007. In National Health Statistics Reports (Vol. 12, pp. 1–23). Atlanta, GA: Centers for Disease Control and Prevention. Barnett, J. E., & Shale, A. J. (2012). The integration of complementary and alternative medicine (CAM) into the practice of psychology: A vision for the future. Professional Psychology: Research and Practice, 43, 576–585. Barnett, J. E., Shale, A. J., Elkins, G., & Fisher, W. I. (2014). Complementary and alternative medicine for psychologists: An essential resource. Washington, DC: American Psychological Association. Barnhofer, T., Crane, C., Brennan, K., Duggan, D. S., Crane, R. S., Eames, C., … Williams, J. M. G. (2015). Mindfulness‐based cognitive therapy (MBCT) reduces the association between depressive symptoms and suicidal cognitions in patients with a history of suicidal depression. Journal of Consulting and Clinical Psychology, 83(6), 1013–1020. Chalifour, M., & Champagne, T. (2008). Aromatherapy. Retrieved from http://www.ot‐innovations. com/content/view/35/46/ Cochrane Reviews. (2015). Complementary and Alternative Medicine Reviews in Cochrane Library through Issue 8, 2014. Retrieved from http://www.compmed.umm.edu/cochrane/reviews.asp Duke Center for Integrative Medicine. (2006). The Duke encyclopedia of new medicine: Conventional and alternative medicine for all ages. London, UK: Rodale Books International. Ernst, E. (2007). Adverse effects of spinal manipulation: A systemic review. Journal of the Royal Society of Medicine, 100, 330–338. Field, T. (2006). Massage therapy research. New York, NY: Elsevier. Kabat‐Zinn, J. (1994). Wherever you go, there you are. New York, NY: Hyperion. Nagendra, R.P., Maruthai, N., & Kutty, B.M. (2012, April 18). Meditation and its regulatory role on sleep. Published online: Frontiers of Neurology, vol. 3, p. 54. PMID: 22529834. Nahin, R. L., Barnes, P. M., Stussman, B. J., & Bloom, B. (2009). Cost of complementary and alternative medicine (CAM) and frequency of visits to CAM practitioners, 2007. In National Health Statistics Reports No. 18 (pp. 1–14 (DHHS Publication No. 2009‐1250)). Hyattsville, MD: National Center for Health Statistics.

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National Center for Complementary and Integrative Health (NCCIH). (2015a). Health Info. Retrieved from https://nccih.nih.gov/health/integrative‐health National Center for Complementary and Integrative Health (NCCIH). (2015b). NCCIH Facts at a Glance. Retrieved from https://nccih.nih.gov/about/ataglance National Center for Complementary and Integrative Health (NCCIH). (2015c). Children and Complementary Health Approaches. Retrieved from https://nccih.nih.gov/health/children National Center for Complementary and Integrative Health (NCCIH). (2015d). Health Topics A‐Z. Retrieved from https://nccih.nih.gov/health/atoz.htm National Center for Complementary and Integrative Health (NCCIH). (2015e). Yoga as a Complementary Health Care Approach. Retrieved from https://nccih.nih.gov/news/multimedia/ infographics/yoga National Institutes of Health. (2012). National Health Interview Survey. Retrieved from https://nccih. nih.gov/research/statistics/NHIS Office of Dietary Supplements. (2011). Dietary supplements: Background information. Retrieved from http://ods.od.nih.gov/factsheets/DietarySupplements‐HealthProfessional/ Radimer, K., Bindwald, B., Hughes, J., Ervin, B., Swanson, C., & Picciano, M. F. (2004). Dietary ­supplement use by US adults: Data from the National Health and Nutritrition Examination Survey, 1999–2000. American Journal of Epidemiology, 160, 339–349. Stapleton, P., Chatwin, H., Boucher, E., Crebbin, S., Scott, S., Smith, D., & Purkis, G. (2015). Use of complementary therapies by registered psychologists: An international study. Professional Psychology: Research and Practice, 46, 190–196. Thomas, K. J., MacPherson, H., Radcliffe, J., Thorpe, L., Brazier, J., Campbell, M., … Nicholl, J. P. (2005). Longer term clinical and economic benefits of offering acupuncture care to patients with chronic low back pain. Health Technology Assessment, 9, 1–109. White, A., & Ernst, E. (2004). A brief history of acupuncture. Rheumatology, 43, 662–663.

Resources NCCIH Clearinghouse nccih.nih.gov PubMed® www.ncbi.nlm.nih.gov/pubmed MedlinePlus www.medlineplus.gov Cochrane Database of Systematic Reviews www.cochranelibrary.com

Hypnosis and Health Psychology Steven Jay Lynn1, Craig P. Pollizi1, Joseph P. Green2, Damla E. Aksen1, Ashwin Gautam1, and James Evans1 Department of Psychology, Binghamton University, Binghamton, NY, USA Department of Psychology, Ohio State University at Lima, Lima, OH, USA

1 2

From Mesmer’s early ventures in using suggestion to alleviate physical and psychological ­distress in the eighteenth century to the use of hypnosis to facilitate surgery prior to effective anesthetics, and extending to the present, hypnosis has played an increasingly prominent role in treating medical conditions and in ameliorating pain and suffering (e.g., Flammer & Bongartz, 2003). As hypnosis has moved into the mainstream of psychological science and clinical practice, there is increasing recognition that hypnosis can be used as a stand‐alone intervention and, more commonly, as an adjunct intervention in the field of health psychology. This review will feature qualitative reviews, meta‐analyses, and randomized controlled trials (RCTs) that have documented the promise of hypnosis in treating many health‐related conditions, ranging from acute and chronic pain and obesity to irritable bowel syndrome. Applications of hypnosis to the field of health psychology have been spurred by research that has debunked many popular myths about hypnosis and has, instead, documented that hypnosis is not a special trance or sleep‐like state in which participants respond in robot‐like fashion to suggestions delivered by a powerful hypnotist, lose awareness of their surroundings, and spontaneously forget what transpired during hypnosis. Rather, participants who experience hypnotic suggestions retain their ability to resist or oppose suggestions; are aware of what occurs before, during, and after hypnosis; and achieve therapeutic gains not because of a trance but because merely defining the context as hypnosis can catalyze motivation to experience suggestions fully and bolster positive treatment expectances relevant to successful clinical outcomes.

An Introduction to Clinical Hypnosis Hypnosis—defined as any set of suggestions that are either administered to participants in a context that is presented as “hypnosis” or participants understand to be “hypnosis”—provides The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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health psychologists with tremendous leverage in crafting suggestions that are individually tailored or can be administered in manualized interventions. Hypnosis traditionally involves a so‐called induction that can be virtually any array of verbal (e.g., suggestions for relaxation) and nonverbal communications (e.g., suggestions delivered in a calm, soothing manner) ­presented by the person in the role of the hypnotist that define or establish the proceedings as “hypnosis.” Inductions can range from highly ritualized methods (e.g., the proverbial “swinging watch” popularized in the media) to more naturalistic conversations to experience an increasingly deep hypnotic state. Hypnotic inductions typically contain imaginative suggestions for relaxation, focusing on suggested events, and deepening experiential involvement in suggestions. The attention individuals receive from the hypnotist, accompanied by the soothing, calming way hypnotists often converse with participants, can promote a close therapeutic alliance, bolster motivation to respond, and diminish anxiety. After the induction, suggestions often hone in on the participant’s problem or condition and can be tailored to address specific patient needs. Given the prevalent views that hypnosis can produce positive outcomes, the very idea of undergoing hypnosis can engender positive expectancies for treatment gains. The context of hypnosis lends itself well to adaptation to a health psychology setting because participants can detach themselves from everyday concerns and attend to therapeutic suggestions and helpers can talk to patients in deeply personal ways and exhort participants to modify deeply ingrained cognitive behavioral patterns and to comply with medical recommendations and treatment regimens. The experience of hypnosis depends much more on the motivation and willingness of ­participants to think and imagine along with suggestions, and the nature of the suggestions themselves, rather than on any special skills of the hypnotist. Accordingly, hypnosis can justifiably be presented to individuals as self‐hypnosis, and participants can learn to “self‐administer” suggestions apart from the hypnotist, which can increase the “portability” of hypnosis and generalize treatment gains to everyday life. Additionally, so‐called posthypnotic suggestions for comfort and pain relief, for example, can extend the benefits of hypnosis well beyond the consulting room. Such suggestions may be reinforced with tapes or DVDs that contain therapeutic suggestions that the patient can use to revivify and reinstate key points and elements of the hypnotic proceedings.

Hypnotic Suggestibility In many health‐related interventions, hypnotic suggestibility does not play a prominent role. Although about 15% of people are low or non‐suggestible and pass no or few suggestions, and a comparable percentage of people are highly suggestible and pass most suggestions, the majority of individuals (approximately 70%) can respond to a variety of suggestions. Many of the suggestions (e.g., relaxation, focused attention) delivered in the context of medical interventions do not require a high level of suggestibility. Even people who score relatively low on standardized scales of hypnotic suggestibility can benefit from hypnotic suggestions. In the case of hypnotic treatment of clinical pain, for example, there is only a weak association of hypnotic suggestibility and pain relief. Although a formal hypnotic induction increases suggestibility by approximately 10–20%, compared with identical waking suggestions administered in a nonhypnotic context, the addition of hypnosis can provide incremental benefits beyond nonhypnotic interventions. Kirsch, Montgomery, and Sapirstein’s (1995) meta‐analysis documented the ability of hypnosis to

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enhance treatment gains of psychotherapeutic approaches that span psychodynamic and ­cognitive behavioral psychotherapies. Another meta‐analysis provided preliminary evidence for the use of hypnosis in treating numerous psychosomatic disorders (e.g., conversion ­disorder, gastrointestinal problems, enuresis, asthma; Flammer & Alladin, 2007).

Applications of Hypnosis in Health Psychology Pain Relief Hypnosis exerts its effects largely via altering subjective experience and therefore impacts ­perceptions of pain. The effects of hypnotic analgesia are reflected in brain and spinal cord functioning and vary as a function of the suggestions administered (Jensen & Patterson, 2014). In addition to suggestions for relaxing and imagining a “favorite place” of comfort, calmness, and soothing, for example, suggestions for easing pain can involve reinterpreting pain sensations (e.g., as pressure), distraction (e.g., watching absorbing scenes on a mental television or movie or reliving positive past events), and transforming sensations and perceptions (e.g., imagining pain as a color and suggesting the patient transform the color). Meta‐analyses (Montgomery, DuHamel, & Redd, 2000) have found that (a) hypnosis produces moderate‐to‐large effects in relieving both clinical pain (e.g., coronary disease, cancer, headache, burns) and experimental pain (e.g., pressure, ischemic pain, cold pressure) and that hypnosis produced salutary effects for 75% of the population and (b) hypnosis is associated with greater pain reductions and better recovery outcomes in adults undergoing surgical or medical procedures compared with standard care alone and attention control interventions (Tefikow et al., 2013). Montgomery, David, Winkel, Silverstein, and Bovbjerg (2002) documented that surgical patients undergoing hypnosis treatment experienced better clinical outcomes (e.g., reduced emotional distress, pain medication, recovery time) than 89% of patients in comparison groups. Moreover, hypnosis has proved useful in reducing pain associated with needle procedures (Birnie et al., 2014) and in treating fibromyalgia, compared with control interventions (e.g., relaxation, pharmacological treatment, usual care, wait list; Bernardy, Klose, Busch, Choy, & Häuser, 2012). In a RCT of a 15‐min hypnosis intervention administered preoperatively to women undergoing ­excisional breast biopsy or lumpectomy, hypnosis was superior to attention control, as reflected in decreased medication use; reports of pain, nausea, fatigue and discomfort; emotional upset at discharge time in surgery; and institutional cost savings ($772.71, on average, per patient). Importantly, hypnosis appears to reduce anxiety associated with medical conditions, as a meta‐analysis has revealed in cancer patients (Chen, Liu, & Chen, 2017). Qualitative reviews (e.g., see Elkins, 2016; Elkins, Jensen, & Patterson, 2007; Jensen & Patterson, 2014) confirm that hypnosis (a) is associated with decreases in chronic pain conditions (e.g., arthritis, sickle cell disease, cancer, low back pain, headaches) and (b) produces salutary effects in reducing nausea and vomiting associated with cancer and (c) that hypnosis is generally more efficacious in reducing chronic pain compared with attention control, physical therapy, and psychoeducation. Moreover, hypnosis can be combined with virtual reality to relieve pain across the age spectrum and that hypnosis outperforms attention placebo in reducing pain in burn wound debridement (see Elkins, 2016). Hypnosis for pain control and modulation is the best established of all applications of hypnosis in the field of behavioral health psychology.

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Irritable Bowel Syndrome (IBS) Meta‐analyses, RCTs, and narrative reviews document the promise of hypnosis in treating IBS, even among medically treated refractory patients, and that the benefits of hypnosis are not limited to short‐term success. Gonsalkorale, Miller, Afzal, and Whorwell (2003) followed over 200 patients consecutively treated with hypnosis for IBS and reported that 71% of patients responded positively to treatment and that over 80% of these patients maintained their treatment gains over 5 years. Nevertheless, a Cochrane Review (Webb, Kukuruzovic, Catto‐Smith, & Sawyer, 2007) tempered these positive conclusions in indicating that the evidence base, while promising, requires additional rigorous studies to permit more definitive conclusions regarding the effectiveness of hypnosis in treating IBS.

Obesity and Weight Loss Hypnosis produces weight loss when combined with cognitive behavioral interventions. Milling, Gover, and Moriarity (2018) reported two meta‐analyses, which revealed that hypnosis is a highly promising treatment for obesity, especially when incorporated into cognitive behavioral therapy for weight loss. The average participant reported greater weight reductions than about 94% of participants in control conditions at the conclusion of treatment and 81% of control participants at follow‐up. The average individual who received CBT combined with hypnosis lost more weight than 60% of participants that received CBT alone at the end of treatment and 79% of individuals that received only CBT at follow‐up. The reviewers suggested that studies of long‐term effects of hypnosis on weight loss are needed, on the order of 1–5 years, to determine the persistence of treatment effects over more lengthy time intervals.

Smoking Cessation Hypnosis shows promise in treating smoking. Meta‐analyses indicate that hypnotic i­ nterventions are associated with quit rates on average of 31% for men and 23% for women (Green, Lynn, & Montgomery, 2008). Nevertheless, a 2010 Cochrane Review (Barnes et al., 2010) reported considerable heterogeneity of findings and that the claims regarding hypnosis for smoking cessation based on case studies extended beyond evidence secured from relatively few RCTs reported in the literature. Clearly, more RCTs that include biochemical measures to verify self‐reports of smoking cessation are warranted.

Other Applications More preliminary yet promising findings indicate that hypnosis can be effective in treating a variety of other medical conditions, such as skin disorders, asthma, insomnia, hot flashes, and hypertension, and be useful in palliative care and in assisting women in labor, as summarized in Elkins (2016). Hypnosis awaits further evaluation before more confident conclusions can be drawn.

Conclusions The evidence for hypnosis for treating pain‐related and medical conditions is promising, although findings across studies are mixed. Additionally, firm conclusions are limited by the lack

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of adequate follow‐up periods and assessments and the difficulty of disentangling h ­ ypnosis‐­specific effects from nonspecific treatment variables, including placebo/expectancy effects, a positive therapeutic alliance, and participating in a symptom‐relief endeavor. It is important to distinguish the effects of “hypnosis” from adjunctive cognitive behavioral strategies and to control for placebo/expectancy effects, naturally occurring changes in symptoms, and relaxation and visualization. Further, the effects of hypnosis remain to be disambiguated from the effects of suggestion alone. Additionally, as the response to hypnosis is variable, researchers would do well to evaluate hypnotic suggestibility and to also ensure that studies use naïve raters of treatment effects and that participants are adequately randomized to conditions in research with samples adequate to discern clinically meaningful treatment outcomes. Finally, the mechanisms by which suggestion exerts salutary effects have yet to be well established. In conclusion, hypnosis is a brief, cost‐effective, and popular intervention that can be easily learned to administer by a medical or mental health professional. A steadily accumulating ­literature, marked by studies of increasing rigor and sophistication, indicates that hypnosis can be employed as a vehicle to administer “direct suggestions” in the context of pain or as an adjunctive intervention to augment the effects of cognitive behavioral and other interventions for diverse medical conditions. Again, the positive effects of hypnosis in a health or medical setting are neither the product of a trance nor unique to hypnosis, but are associated with variables that likely mediate the effectiveness of many nonhypnotic interventions, including positive treatment‐relevant expectations, attitudes, and beliefs; a viable therapeutic alliance; goal‐directed motivation; and suggestions that target processes (e.g., relaxation for stress ­management) that are directly or secondarily related to medical symptoms and conditions.

Author Biographies Steven Jay Lynn is Distinguished Professor of Psychology (SUNY) at Binghamton University. He received his bachelor’s degree in psychology from the University of Michigan and his PhD in psychology (clinical) from Indiana University. He is the editor of Psychology of Consciousness: Theory, Research, and Practice, and he is on the editorial board of 10 other journals, including the Journal of Abnormal Psychology. Dr. Lynn has authored or edited 25 books, and he has published more than 360 articles and chapters on the topics of hypnosis, dissociation, trauma, fantasy, psychotherapy, and scientific thinking in psychology. He is the recipient of the Chancellor’s Award from the State University of New York and numerous awards bestowed by the American Psychological Association and other professional societies. His research has been supported by the National Institute of Mental Health and has garnered substantial media attention. Craig P. Polizzi is a clinical psychology doctoral student at Binghamton University. He has collaborated on randomized controlled trials investigating integrative and complementary interventions for veterans with posttraumatic stress disorder (PTSD). He has also participated in trials examining educational interventions promoting self‐regulation in children. His ­current research focuses on clarifying the relations among acceptance, mindfulness, emotion regulation, and resilience and on elucidating how each functions as a self‐regulation strategy. Recently, he has served as principal investigator on a longitudinal study examining novel ways to facilitate the salutary effects of mindfulness meditation. Joseph P. Green is a professor of psychology at The Ohio State University and works at one of OSUs regional campuses in Lima, Ohio. He has published 70 journal articles and book chapters primarily in the area of hypnosis, imagination, and suggestion‐based approaches to

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psychotherapy. He has authored or edited two volumes on applying clinical hypnosis for health care and tobacco addiction. Dr. Green served two terms as president of the American Psychological Association, Division 30 (Society of Psychological Hypnosis). His work has been recognized with numerous awards, including Early Career, Distinguished Contributions to Scientific Hypnosis and Distinguished Contributions to Professional Hypnosis (APA D30), Clark Hull Award for Scientific Excellence in Writing on Experimental Hypnosis (American Journal of Clinical Hypnosis), Arthur Shapiro Best Book Award (Society for Clinical and Experimental Hypnosis), and the Distinguished Teaching Award (OSU Alumni Association. Damla E. Aksen is a clinical psychology doctoral student at Binghamton University. She has worked in interdisciplinary psychology laboratories that studied perception, development, and evolutionary perspectives. She is interested in studying impulsivity, mindfulness, and emotion dysregulation. Ashwin Gautam is a clinical psychology doctoral candidate at Binghamton University. He has coordinated studies investigating the effect of mindfulness inductions on experimental fear conditioning, inhibition, and extinction. He is currently serving as a principal investigator on a study exploring the association between mindfulness and procrastination. His current research interests involve the application of acceptance and mindfulness‐based therapies for treatment of posttraumatic stress and anxiety related disorders. James Evans is a clinical psychology doctoral candidate at Binghamton University, where he is a research associate in the Laboratory of Consciousness and Cognition. His primary research interests are in mindfulness and states of consciousness, including those reported in hypnosis, mystical, and other unusual experiences. His clinical interests are in disorders of personality and emotion regulation.

References Barnes, J., Dong, C. Y., McRobbie, H., Walker, N., Mehta, M., & Stead, L. F. (2010). Hypnotherapy for smoking cessation. The Cochrane Database of Systematic Reviews, (10), CD001008. Bernardy, K., Klose, P., Busch, A. J., Choy, E. H., & Häuser, W. (2012). Cognitive behavioural therapies for fibromyalgia syndrome. The Cochrane Database of Systematic Reviews, (9), CD009796. Birnie, K. A., Noel, M., Parker, J. A., Chambers, C. T., Uman, L. S., Kisely, S. R., & McGrath, P. J. (2014). Systematic review and meta‐analysis of distraction and hypnosis for needle‐related pain and distress in children and adolescents. Journal of Pediatric Psychology, 39(8), 783–808. Chen, P. Y., Liu, Y. M., & Chen, M. L. (2017). The effect of hypnosis on anxiety in patients with cancer: A meta‐analysis. Worldviews on Evidence‐Based Nursing, 14(3), 223–236. Elkins, G. (Ed.) (2016). Handbook of medical and psychological hypnosis: Foundations, applications, and professional issues. New York, NY: Springer. Elkins, G., Jensen, M. P., & Patterson, D. R. (2007). Hypnotherapy for the management of chronic pain. International Journal of Clinical and Experimental Hypnosis, 55(3), 275–287. Flammer, E., & Alladin, A. (2007). The efficacy of hypnotherapy in the treatment of psychosomatic ­disorders: Meta‐analytic evidence. International Journal of Experimental Hypnosis, 55(3), 251–274. Flammer, E., & Bongartz, W. (2003). On the efficacy of hypnosis: A meta‐analytic study. Contemporary Hypnosis, 20(4), 179–197. Gonsalkorale, W. M., Miller, V., Afzal, A., & Whorwell, P. J. (2003). Long term benefits of hypnotherapy for irritable bowel syndrome. Gut, 52(11), 1623–1629. Green, J. P., Lynn, S. J., & Montgomery, G. H. (2008). Gender‐related differences in hypnosis‐based treatments for smoking: A follow‐up meta‐analysis. American Journal of Clinical Hypnosis, 50(3), 259–271. Jensen, M. P., & Patterson, D. R. (2014). Hypnotic approaches for chronic pain management: Clinical implications of recent research findings. American Psychologist, 69(2), 167–177.

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Kirsch, I., Montgomery, G., & Sapirstein, G. (1995). Hypnosis as an adjunct to cognitive‐behavioral psychotherapy: A meta‐analysis. Journal of Consulting and Clinical Psychology, 63(2), 214–220. Milling, L., Gover, M. C., & Moriarity, C. L. (2018). The effectiveness of hypnosis as an intervention for obesity: A meta‐analytic review. Psychology of Consciousness: Theory, Research and Practice, 5(1), 29–45. Montgomery, G. H., David, D., Winkel, G., Silverstein, J. H., & Bovbjerg, D. H. (2002). The effectiveness of adjunctive hypnosis with surgical patients: A meta‐analysis. Anesthesia & Analgesia, 94(6), 1639–1645. Montgomery, G. H., DuHamel, K. N., & Redd, W. H. (2000). A meta‐analysis of hypnotically induced analgesia: How effective is hypnosis? International Journal of Clinical and Experimental Hypnosis, 48(2), 138–153. Tefikow, S., Barth, J., Maichrowitz, S., Beelmann, A., Strauss, B., & Rosendahl, J. (2013). Efficacy of hypnosis in adults undergoing surgery or medical procedures: A meta‐analysis of randomized ­controlled trials. Clinical Psychology Review, 33(5), 623–636. Webb, A. N., Kukuruzovic, R., Catto‐Smith, A. G., & Sawyer, S. M. (2007). Hypnotherapy for treatment of irritable bowel syndrome. The Cochrane Database of Systematic Reviews, (4), CD002823.

Suggested Reading Jensen, M. P. (2011). Hypnosis for chronic pain management: Therapist guide. Oxford, UK: Oxford University Press. Lynn, S. J., Rhue, J. W., & Kirsch, I. (Eds.) (2010). Handbook of clinical hypnosis (2nd ed.). Washington, DC: American Psychological Association. Patterson, D. R. (2010). Clinical hypnosis for pain control. Washington, DC: American Psychological Association.

Motivational Interviewing Thad R. Leffingwell Department of Psychology, Oklahoma State University, Stillwater, OK, USA

MI’s technical and theoretical roots trace to the client‐centered counseling strategies ­popularized by Carl Rogers and classic social psychological theories of cognitive consistency (Festinger, 1962) and psychological reactance (Brehm, 1966). The first clinical application of the method that would come to be known as MI appeared in the research literature in 1988 in the form of the Drinker’s Check‐up, which utilized nonjudgmental client‐centered listening skills while reviewing assessment feedback (Miller, Sovereign, & Krege, 1988). William Miller continued to develop the method, and, after developing a collaborative partnership with Stephen Rollnick, the first book fully describing the MI approach was published in 1991 (Miller & Rollnick, 1991). For the first few decades of the development, investigation, and dissemination of MI, ­applications of the method were focused almost exclusively upon intervening with misuse of and addiction to psychoactive substances, including alcoholism and drug addiction. Indeed, the subtitle of the first book was Preparing People to Change Addictive Behaviors (Miller & Rollnick, 1991). MI targets ambivalence in the behavior change process, which often manifests as resistance to change and is common among people struggling with addictions. After the initial empirical success and accelerating dissemination of MI among substance use professionals, others began applying and investigating MI in other contexts with other target behaviors and populations including primary healthcare (Rollnick, Miller, & Butler, 2008), health promotion (Resnicow & Rollnick, 2011), and mental health (Arkowitz & Westra, 2004), with both adults and adolescents (Cushing, Jensen, Miller, & Leffingwell, 2014; Jensen et  al., 2011). The growing recognition of the wider applicability of the MI approach was reflected in the second book, subtitled Preparing People for Change (Miller & Rollnick, 2002). Today MI is recognized as a broadly useful tool for helping me make a broad range of behavior changes, especially when ambivalence or resistance to change is present.

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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The Problem of Resistance to Change It is a well‐known problem that habits are difficult to change even when the status quo is ­maladaptive or unhealthy and that people often resist professional attempts to assist with change. According to the transtheoretical model, people may be in the pre‐action stages of precontemplation (not even thinking about change) or contemplation (acknowledging need or desire to change, but not now or soon) for months or years (Prochaska, DiClemente, & Norcross, 1992). The contemplation stage is characterized by ambivalence about change—a part of the person sees the need or wants the change, but another part prefers and defends the status quo. The preference for the status quo can be driven by many factors including, for example, lacking confidence about the likely success of change attempts, valuing other priorities more highly right now, or favoring immediate benefits more than long‐term payoffs that are subject to delay or probability discounting (Bickel, Johnson, Koffarnus, MacKillop, & Murphy, 2014). From the MI perspective, these individuals are viewed as stuck in ambivalence, and the goal of the MI conversation is to help patients get unstuck and move toward attempting and sustaining necessary behavior changes.

Patient Language About Change In conversations about a behavior change, patient language basically takes one of two forms— change talk or sustain talk (referred to as “resistance” in earlier versions of MI). Change talk is defined as any expressions that favor or encourage change, including concerns about the status quo, fears about the future if the behavior does not change, ideas about how change might be accomplished, or intentions to change. Sustain talk, on the other hand, is defined as any expressions from the patient that argue for or defend the status quo, including descriptions of advantages or benefits of the status quo, denigration or barriers to change, or hopelessness about change. The MI approach takes several important positions about patient language in conversations about behavior change. First, the MI model posits that the patient’s language is much more influential on subsequent behavior than the provider’s language. As a result, MI de‐emphasizes motivational strategies that are more provider centered such as education, warning of dangers, direct persuasion, or professional advice. Second, the MI model suggests patients are better persuaded by their own language and that the more they hear themselves expressing change talk in the conversation (and, as a result, less sustain talk), the more subsequent attempts at change are to occur. Miller and Rose (2009) proposed a theory of MI that proposes that when change talk grows and sustain talk falls away during an MI conversation, the patient is more likely to experience and/or express a sense of commitment to change that is supported by a sense of intrinsic motivation, and it is this inner psychological experience that sets the stage for subsequent behavior change. As a result, the primary goal during an MI conversation is to increase the frequency and/or intensity of change talk and decrease the frequency and/or intensity of sustain talk as a way of building intrinsic motivation and evoking commitment to change.

Style and Spirit of MI MI has been described as have both a “style and spirit” as well as a set of proscribed ­conversational strategies and recommended techniques. The style and spirit can be thought of as the attitude, worldview, or stance of the MI provider. Rather than being about what the

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MI ­provider is to “do,” the MI style and spirit is about how the MI provider should “be” during the conversation. The style and spirit have been described as the music or melody of MI, giving it pace and feeling, when used with the “lyrics” of the skills and strategies. In the original formulations of MI (Miller & Rollnick, 1991, 2002), the spirit of MI was described as consisting of three main characteristics. First, MI should have a spirit of collaboration. The interaction between provider and patient should have a sharing of power, as opposed to a “one up, one down” character. The provider is to avoid taking on a role as expert or authority and instead try to develop a sense of equal partnership in exploring the topic of behavior change. Second, MI should be evocative. In MI, the most valued words in the conversation at the patient’s own. If change is to happen, it is mostly likely to be as a result of the patient being persuaded by his or her own words, rather than succumbing to the arguments or persuasion of the provider. For this reason, it is important for the MI provider to act in ways that encourage and evoke the patient’s side of the conversation. Finally, MI should be autonomy supportive. Ideally, in an MI conversation, the patient would experience an expansion of their sense of personal choice and autonomy, rather than experiencing attempts to control or limit choices (e.g., “you must…”). This aspect of MI reduces the influence of psychological reactance and improves the chances that the patient may overcome ambivalence and reactance and choose to make necessary changes. The latest description of the MI technique (Miller & Rollnick, 2013) expanded a bit upon these basic aspects of the spirit of MI. The aspects of collaborative partnership and evocation remain, but autonomy support has been subsumed under a broader concept of acceptance. Autonomy support is viewed as one facet of acceptance, but the concept also includes a fundamental belief in the absolute worth of the individual, accurate empathy, and affirmation, or actively seeking out and elevate an individual’s strengths. Finally, the third edition also adds compassion as an important aspect of the essential spirit of MI. Compassion refers to the intentional application of MI in the service of what is best for the patient, not the provider.

Skills and Strategies of MI MI also includes a number of proscribed or recommended strategies for the competent p ­ ractice of MI. These strategies represent the “microskills” by which the spirit of MI is translated into practice and are in service of enacting the spirit. The skills can be reliably coded, and more consistent use of the strategies in the context of MI conversations has been found to lead to more favorable outcomes (Magill et  al., 2014; Moyers, Martin, Manuel, Hendrickson, & Miller, 2005). The most fundamental skills of the MI conversation involve the strategic use of active listening skills. In MI, the provider is expected to utilize evocative strategies such as open‐ended questions, accurate reflections, and frequent summaries. MI de‐emphasizes the value of ­provider‐centered language of advice, education, or persuasion. The goal of the MI provider is to strategically and intentionally apply the skills of active listening to evoke and grow change talk and quiet sustain talk from the patient. One highly effective MI strategy is the use scaled questions, or what are sometimes referred to as readiness rulers (Rollnick et al., 2008). MI providers may wish to assess readiness to change, confidence in change, or the important of change using a 0–10 scale. For example, “How ready would you estimate you are to quit smoking today, from zero to ten, with zero being ‘no interest at all’ and ten being ‘totally committed?’” The answers provided to such questions provide a very useful and quick assessment of the constructs of interest, but they are especially valuable if follow‐up questions are asked. For example, if patient were to respond to the example question

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with “four,” a useful follow‐up question might be something like “Why did you choose four, and not zero?” The answers to that question would almost certainly take the form of change talk. This strategy reliably evokes valuable change talk into the conversation. Scaled questions are often recommended in brief and specialized adaptations of MI like the “5 A’s” and Screening, Brief Intervention, and Referral to Treatment (SBIRT) models, discussed below. Two other broad MI strategies are essential to the competent practice of MI—rolling with resistance and developing discrepancies. Argumentation and confrontation in response to patient resistance are seen as counterproductive, largely because they tend to increase frequency and intensity of sustain talk. Rolling with resistance is an alternative strategy for responding that is intended to quiet sustain talk or, ideally, evoke the “other side of the coin” of change talk. MI includes many specific strategies rolling with resistance including the use of reflective listening strategies but also reframing or emphasizing choice and autonomy, among others. Developing discrepancy is based upon theories of cognitive consistency and dissonance that posit that people strive for consistency between attitudes and actions (Festinger, 1962). When individuals are confronted with inconsistency, they experience a sense of dissonance that is uncomfortable, and they are thus motivated to act in such a way to reduce the discrepancy. When one’s behavior is experienced as inconsistent with an attitude or belief that is highly valued, that individual may be expected to change their behavior to better fit, or “square,” with the attitude or belief. For this reason, the MI practitioner is constantly listening for ways that the status quo may be inconsistent with a value, goal, or aspect of self‐image of the patient. If such discrepancies are found and made salient, they may help move the patient in the direction of change. For example, an MI provider working with a smoker may at some point in the conversation say something to the patient like “you strike me as the kind of person who really values your independence, but at the same time you find yourself quite dependent on those cigarettes. In a sense, you don’t choose them – they choose you.”

Four Processes In the latest iteration of MI (Miller & Rollnick, 2013), a new concept was introduced of the four processes of MI. The four processes essentially define the beginning, middle, and end of an MI encounter, though they do not necessarily always proceed in a linear fashion. The four processes are engaging, focusing, evoking, and planning. Engaging is the first step into an MI conversation and involves establishing a rapport and relationship as a foundation for proceeding. Focusing is a process of narrowing the agenda for the conversation onto one, or a few, important target behaviors for the conversation. Evoking is the process that most providers may recognize as the “meat” of the MI conversation. It involves an intentional conversation about the target behavior, with a focus of evoking and growing change talk and minimizing resistance. The final process of planning involves the transition from whether change should or will occur (the primary focus of MI) into how a change may occur. This process might involve plans to simply revisit the issue if now is not the right time to change, or might involve a detailed plan for change including the use of professional assistance.

Evidence Base for Motivational Interviewing MI has emerged and developed in the age of evidence‐based practice. As a result, MI has been rigorously studied from its very outset, and today there have been hundreds of randomized

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clinical trials and several meta‐analyses evaluating the efficacy of MI for a broad range of ­outcomes and populations. In general, the data overwhelmingly support the conclusion that MI does have a reliable effect on a broad range of target behaviors including alcohol misuse (Burke, Arkowitz, & Menchola, 2003), tobacco dependence (Heckman, Egleston, & Hoffmann, 2010), obesity (Armstrong et  al., 2011), and gambling (Yakovenko, Quigley, Hemmelgarn, Hodgins, & Ronksley, 2015) and as an adjunct for treatment of psychological disorders (Arkowitz, Miller, & Rollnick, 2015), among both adults and adolescents (Cushing et al., 2014; Jensen et al., 2011). The effect sizes for MI interventions are typically small to moderate, but that may not be surprising given that the durations of the interventions studied are often quite brief, typically ranging from only a few minutes to two brief sessions. As a result, the cost‐effectiveness of MI interventions is quite strong.

Training in MI MI is a complicated clinical intervention approach for which training is necessary. The available evidence suggests that a broad array of health professionals, including both medical providers and social service providers, can be trained to effectively implement MI competently (Madson, Loignon, & Lane, 2009). However, in order for improvements toward the competent practice of MI to be maintained, a single training or workshop is unlikely to be sufficient. Sustained competent practice of MI typically requires intensive training with performance feedback followed by several months of ongoing coaching and supervision (Schwalbe, Oh, & Zweben, 2014).

Applications and Adaptations of Motivational Interviewing MI proper is a principle‐driven approach that relies upon the expertise of the provider to ­identify appropriate targets for MI and to weave MI interventions together with other strategies that suit the needs of the particular client or problem at the moment. However, due to the demands of clinical trials to have replicable interventions and the demands of real‐world settings to have more easily disseminable models for specific target behaviors (e.g., tobacco use) or settings (e.g., hospital emergency rooms), there have been a number of efforts to develop specific MI‐based treatment and intervention models. One example of an MI‐based treatment is motivational enhancement therapy (MET), a treatment designed and tested initially as part of Project MATCH, a large multisite clinical trial of interventions for alcohol abuse and dependence (Miller, Zweben, DiClemente, & Rychtarik, 1992). MET was a brief intervention consisting of four treatment sessions and utilized the basic principles of MI at the time. Like the original Drinker’s Check‐up (Miller et al., 1988) and many early trials of MI, the MI conversation occurred in the context of reviewing the results of a comprehensive clinical assessment, especially in the first two sessions. In the last two sessions, spaced weeks apart, the therapist would use MI principles to encourage motivation and progress. Other adaptations of MI have attempted to create simplified, targeted intervention models that could be more easily disseminated to a variety of providers. For example, the Surgeon General’s influential document treating tobacco dependence promoted a “5 A’s” model of brief intervention designed to be implemented by a variety of healthcare providers at every encounter with patients who use tobacco (Tobacco Use and Dependence Guideline Panel, 2008).

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The five A’s are as follows: (a) assess every patient for tobacco use and for those who are ­current users proceed to (b) advise quitting with brief, personalized advice; (c) assess for readiness to make a quit attempt and motivate those who are not currently ready; (d) assist with a quit attempt by offering guidance, prescriptions, or referrals; and (e) arrange for follow‐up at subsequent visits. The recommendations for assessing readiness and motivated unmotivated patients essentially describe the practice of MI. A similar brief intervention model that incorporates MI is SBIRT (Babor et al., 2007). SBIRT is a model for brief intervention designed initially for use in busy hospital emergency departments to address patients for who alcohol or drug misuse may have contributed to their illness or injury. The brief intervention component of SBIRT is essentially MI.

Conclusions MI is a broadly applicable and essential evidence‐based approach to working with patients to overcome resistance and move toward healthy behavior changes. The approach can be learned and applied by many different types of providers and is applicable to many clinical problems and populations.

Author Biography Thad R. Leffingwell is a clinical health psychologist and is currently a professor and head of the Department of Psychology in the College of Arts and Sciences at Oklahoma State University. His research has explored how people change health risk behaviors and how ­providers can interact with patients to encourage behavior change more effectively. He is a member of the Motivational Interviewing Network of Trainers and has providing training in MI to thousands of providers over the last 20 years.

References Arkowitz, H., Miller, W. R., & Rollnick, S. (2015). Motivational interviewing in the treatment of psychological problems. New York, NY: Guilford. Arkowitz, H., & Westra, H. A. (2004). Integrating motivational interviewing and cognitive behavioral therapy in the treatment of depression and anxiety. Journal of Cognitive Psychotherapy, 18, 337–360. doi:10.1891/jcop.18.4.337.63998 Armstrong, M. J., Mottershead, T. A., Ronksley, P. E., Sigal, R. J., Campbell, T. S., & Hemmelgarn, B. R. (2011). Motivational interviewing to improve weight loss in overweight and/or obese patients: A  systematic review and meta‐analysis of randomized controlled trials. Obesity Reviews, 12, 709– 723. doi:10.1111/j.1467‐789X.2011.00892.x Babor, T. F., McRee, B. G., Kassebaum, P. A., Grimaldi, P. L., Ahmed, K., & Bray, J. (2007). Screening, Brief Intervention, and Referral to Treatment (SBIRT): Toward a public health approach to the management of substance abuse. Substance Abuse, 28, 7–30. Bickel, W. K., Johnson, M. W., Koffarnus, M. N., MacKillop, J., & Murphy, J. G. (2014). The behavioral economics of substance use disorders: Reinforcement pathologies and their repair. Annual Review of Clinical Psychology, 10641–10677. doi:10.1146/annurev‐clinpsy‐032813‐153724 Brehm, J. W. (1966). A theory of psychological reactance. Oxford, England: Academic Press.

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Burke, B. L., Arkowitz, H., & Menchola, M. (2003). The efficacy of motivational interviewing: A meta‐ analysis of controlled clinical trials. Journal of Consulting and Clinical Psychology, 71, 843–861. doi:10.1037/0022‐006X.71.5.843 Cushing, C. C., Jensen, C. D., Miller, M. B., & Leffingwell, T. R. (2014). Meta‐analysis of motivational interviewing for adolescent health behavior: Efficacy beyond substance use. Journal of Consulting and Clinical Psychology, 82, 1212–1218. doi:10.1037/a0036912 Festinger, L. (1962). Cognitive dissonance. Scientific American, 207, 93–107. doi:10.1038/ scientificamerican1062‐93 Heckman, C. J., Egleston, B. L., & Hoffmann, M. T. (2010). Efficacy of motivational interviewing for smoking cessation: A systematic review and meta‐analysis. Tobacco Control, 19, 410–416. doi:10.1136/tc.2009.033175 Jensen, C. D., Cushing, C. C., Aylward, B. S., Craig, J. T., Sorell, D. M., & Steele, R. G. (2011). Effectiveness of motivational interviewing interventions for adolescent substance use behavior change: A meta‐analytic review. Journal of Consulting and Clinical Psychology, 79, 433–440. doi:10.1037/ a0023992 Madson, M. B., Loignon, A. C., & Lane, C. (2009). Training in motivational interviewing: A systematic review. Journal of Substance Abuse Treatment, 36, 101–109. doi:10.1016/j.jsat.2008.05.005 Magill, M., Gaume, J., Apodaca, T. R., Walthers, J., Mastroleo, N. R., Borsari, B., & Longabaugh, R. (2014). The technical hypothesis of motivational interviewing: A meta‐analysis of MI’s key causal model. Journal of Consulting and Clinical Psychology, 82, 973–983. doi:10.1037/a0036833 Miller, W. R., & Rollnick, S. (1991). Motivational interviewing: Preparing people to change addictive behavior. New York, NY: Guilford. Miller, W. R., & Rollnick, S. (2002). Motivational interviewing: Preparing people for change. New York, NY: Guilford. Miller, W. R., & Rollnick, S. (2013). Motivational interviewing: Helping people change (3rd ed.). New York, NY: Guilford. Miller, W. R., & Rose, G. S. (2009). Toward a theory of motivational interviewing. American Psychologist, 64(6), 527–537. doi:10.1037/a0016830 Miller, W. R., Sovereign, R. G., & Krege, B. (1988). Motivational interviewing with problem drinkers: II. The Drinker’s Check‐up as a preventive intervention. Behavioural Psychotherapy, 16, 251–268. Miller, W. R., Zweben, A., DiClemente, C. C., & Rychtarik, R. G. (1992). Motivational enhancement therapy manual: A clinical research guide for therapists treating individuals with alcohol abuse and dependence. (Volume 2, Project MATCH Monograph Series). Rockville, MD: National Institute on Alcohol Abuse and Alcoholism. Moyers, T. B., Martin, T., Manuel, J. K., Hendrickson, S. L., & Miller, W. R. (2005). Assessing competence in the use of motivational interviewing. Journal of Substance Abuse Treatment, 28, 19–26. doi:10.1016/j.jsat.2004.11.001 Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. (1992). In search of how people change: Applications to addictive behaviors. American Psychologist, 47, 1102–1114. doi:10.1037/ 0003‐066X.47.9.1102 Resnicow, K., & Rollnick, S. (2011). Motivational interviewing in health promotion and behavioral medicine. In W. M. Cox & E. Klinger (Eds.), Handbook of motivational counseling: Goal‐based approaches to assessment and intervention with addiction and other problems. Chichester, UK: Wiley. doi:10.1002/9780470979952.ch25 Rollnick, S., Miller, W. R., & Butler, C. C. (2008). Motivational interviewing in health care. New York, NY: Guilford. Schwalbe, C. S., Oh, H. Y., & Zweben, A. (2014). Sustaining motivational interviewing: A meta‐analysis of training studies. Addiction, 109, 1287–1294. doi:10.1111/add.12558 Tobacco Use and Dependence Guideline Panel. (2008). Treating tobacco use and dependence: 2008 update. Rockville, MD: United States Department of Health and Human Services. Retrieved from: https://www.ncbi.nlm.nih.gov/books/NBK63952/

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Yakovenko, I., Quigley, L., Hemmelgarn, B. R., Hodgins, D. C., & Ronksley, P. (2015). The efficacy of motivational interviewing for disordered gambling: Systematic review and meta‐analysis. Addictive Behaviors, 43, 72–82. doi:10.1016/j.addbeh.2014.12.011

Suggested Reading Miller, W. R., & Rollnick, S. (2013). Motivational interviewing: Helping people change. New York, NY: Guilford. Rollnick, S., Miller, W. R., & Butler, C. C. (2008). Motivational interviewing in health care. New York, NY: Guilford. Rosengren, D. B. (2018). Building motivational interviewing skills. New York, NY: Guilford.

Rehabilitation Psychology Monica F. Kurylo1 and Kathleen S. Brown2 Departments of Medicine and Public Health, University of Kansas Medical Center, Kansas City, KS, USA 2 Private Practice, Fort Myers, FL, USA

1

Our wounds are often the openings into the best and beautiful part of us. – David Richo

Rehabilitation psychologists “enhance the lives of people with disabilities and chronic ­illness” (American Psychological Association Division 22 Rehabilitation Psychology and the Rehabilitation Psychology Synarchy, 2015, p. 6) as noted in the petition for recognition of rehabilitation psychology as a specialty approved by the American Psychological Association’s (APA) Council of Representatives in August 2015 (e.g., Dunn & Elliott, 2008). Rehabilitation psychologists recognize the wounds and resilience of the people with whom they work. Since World War II, rehabilitation psychologists have partnered with physicians treating patients with a variety of injuries and/or chronic illness, including amputations, spinal cord injuries, and head injuries (Cox, Hess, Hibbard, Layman, & Stewart, 2010). As the field of rehabilitation psychology matured, the specialty expanded to include working with children with ­disabilities and their families related to the establishment of the National Society of Crippled Children and Adults, working with government vocational rehabilitation organizations funded by state‐federal partnerships, and, more recently, advocating for people with disabilities through the passage of the Americans with Disabilities Act (ADA) in 1990. Rehabilitation psychologists may specialize in working with particular medical diagnoses, such as head injury, stroke, spinal cord injury, amputation, or other neurological illness/injury, or they may work with a more broadly varied group of patients whom they encounter in inpatient or outpatient settings, similar to health psychologists. For example, rehabilitation ­psychologists may work with people who have become debilitated due to treatments for metastatic cancer, patients who are being considered for or have undergone solid organ transplant, and people who have sustained burn or electrical injuries. While the focus of psychological evaluation and intervention may be similar among both rehabilitation and health psychologists,

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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the petition for recognition of rehabilitation psychology as a specialty noted that “Rehabilitation Psychology works with individuals who have acquired a disability or chronic health condition. Clinical Health Psychology uses similar assessment and intervention techniques to identify and enact health behavior change, while Rehabilitation Psychology has a ‘specific focus on adaptation to illness or injury’ (Klonoff et al., 2011), and the interactions between the individual, family, and physical, social, and policy environments in order to enhance social role participation” (APA Division 22 Rehabilitation Psychology and the Rehabilitation Psychology Synarchy, 2015, p. 32). In 1952, rehabilitation psychology was introduced as a new subspecialty within the APA. Rehabilitation psychologists were identified as practitioners, educators, researchers, and advocates for people with disabilities working with interdisciplinary teams. Since that time, the field of rehabilitation psychology has expanded to include expertise as policy makers, healthcare administrators, and program developers. The importance of the role of rehabilitation psychology in the team has also been recognized by regulatory agencies, such as CARF (formerly known as the Commission for Accreditation of Rehabilitation Facilities) that includes psychology in their guidelines for staff inclusion on rehabilitation teams. The purpose of this chapter is to introduce rehabilitation psychology as a subspecialty particularly with regard to practice areas of assessment, intervention, and interdisciplinary team functioning. Although some assessment instruments and interventions are similar to clinical health psychology, readers will understand how rehabilitation psychology contrasts with health psychology in its roles and focus. The role of rehabilitation psychology in research, advocacy, and training is also explained. For further information on rehabilitation psychologists ­performing as program developers, policy makers, and healthcare administrators, readers are encouraged to review Cheak‐Zamora, Reid‐Arndt, Hagglund, and Frank (2012).

Practice of Rehabilitation Psychology: Assessment In rehabilitation psychology, the assessment process is used to develop a multidisciplinary or interdisciplinary treatment plan that addresses the impact of a disability or other chronic health condition. In addition, adjustment and accommodation to the disability along with the necessary social and community supports to optimize functioning are also evaluated. Reasons for referral are many: individuals may be referred for assessment of physical, cognitive, emotional, and/or social adaptation to injury, illness, and disability in the patient and family; cognitive, emotional, and behavioral dysfunction; neuropsychological evaluation to determine ability to function at home, school, and/or work with or without accommodations; evaluation of self‐ care and independent living skills; evaluation of psychosexual functioning, with an emphasis on education regarding disability‐related changes and use of adaptive technology; evaluation of social and recreational participation; assessment of health self‐management and prevention of secondary complications; and assessment of caregiver status and functioning, including caregiver knowledge and skills, social support, and self‐care. Assessment of each of these domains requires knowledge of a broad range of quantitative and qualitative assessment instruments and techniques. Along with instruments for mood, pain, coping, adjustment to disability, and cognitive and physical functioning in health populations, assessment instruments have also been created to be used with diverse rehabilitation populations to address capacity for self‐care, work, and independent living. Instruments specific to rehabilitation populations, for example, may include instruments that assess the psychological assimilation of traumatic events, monitor

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progress throughout inpatient rehabilitation, and assess how people with disabilities function as active members of their communities (Heinemann & Mallison, 2010). Given the high prevalence of brain injury in traumatic physical injuries, cognitive screening measures are vital to determine the presence and extent of potential cognitive processing issues that can negatively impact treatment and recovery (Novack, Sherer, & Penna, 2010). Assessments of s­ pecific environmental or behavioral features relevant to intervention, such as stimulus control or contingency management, often focus on factors to aid integrative assessment for the development of a rationale for a particular intervention, to develop a working relationship with the patient and/or significant others, to address emotional reactions, and to develop expectations and attributions that support the intervention. The importance of assessing and providing psychological services that best meet the ­individual and diverse needs of people with disabilities while maximizing their health and welfare, independence and choice, functional abilities, and social participation was highlighted in the Guidelines for Assessment of and Intervention with Persons with Disabilities (APA, 2012). Multiple issues relevant to diversity must be considered (e.g., age, gender, sexual orientation, socioeconomic status, religion, race, ethnicity) along with disability identity. An individual’s military or veteran status may be an important part of their identity and may differentially affect their adaptation to disability or approach to assessment. Individuals with disabilities need to be included in all aspects of decision making to the best extent ­possible to ensure that their preferences and values are integrated into the conceptualization of assessment results. As part of the comprehensive evaluative process, a rehabilitation psychologist may consult with other professionals, such as attorneys, government agencies, educational institutions, the Department of Vocational Rehabilitation, insurance companies, and case managers to optimize an individual’s community functioning. Such consultations may include evaluation of acquired cognitive deficits with educational or vocational implications, development of ­reasonable accommodations for return to school or work, quantification of accident‐related “loss” for forensic purposes, recommendations related to hospital discharge plans, or recommendations concerning resumption of premorbid activities such as driving.

Practice of Rehabilitation Psychology: Intervention Psychotherapeutic interventions are an important part of the rehabilitation plan for individuals with disabilities to improve emotional distress often produced by changes in physical, task, and social functioning; to address the disruption of prior personal, family, and community roles; and to aid motivation, adherence, or participation in rehabilitation efforts. Interventions in rehabilitation psychology focus on applying the findings of assessment and diagnosis to facilitate functional task performance and social role participation in order to maximize the productive engagement of the individual in all environments. As disability is seen as a problem of exclusion from ordinary life through a “person‐first” perspective (Wright, 1983), interventions are aimed at facilitating adaptation and accommodation to illness or injury while seeking to minimize the attendant limitations. Evidence‐based interventions are modified to address the specific challenges an individual, couple, or family face that limit activities and restrict social role participation. Therapeutic interventions can involve emotional, cognitive, and/or behavioral domains. They can help shift the focus of the individual with a disability from ­premorbid sources of self‐esteem, such as physical prowess, to less impaired areas, such as cognitive and personality strengths (Keany & Glueckauf, 1993). At the family and community

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level, such emotional, cognitive, and behavioral shifts in emphasis can also significantly reduce disability by helping develop accommodations in the physical and social environments. Hart and Ehde (2015) developed a theory‐driven framework for specifying rehabilitation interventions according to its targets, its active ingredients, and the mechanisms of action that connect them. They suggest that rather than describing rehabilitation treatments by the discipline(s) providing them (e.g., physical therapy, vocational retraining program), or by the problems they are intended to solve (e.g., attention training), treatments must specify the desired changes to narrow choices about specifically what one can do to effect change. Hart and Ehde call for a common system of nomenclature for characterizing and quantifying the treatment targets and ingredients used in clinical interventions and research to advance theory development and improve the quality of intervention research. In rehabilitation, the team includes not only direct service providers but also the patient, family and significant others, and available community, school, and work relationships. Family members who provide care for persons with disability are themselves at risk for associated physical, mental, emotional, social, and financial problems and therefore may benefit from caregiver‐oriented interventions (Chwalisz & Dollinger, 2010). Caring for the caregiver improves and sustains the care provided to the person experiencing chronic illness or disability resulting in improvements in outcomes. Rehabilitation psychologists not only help caregivers understand their loved ones’ difficulties but also help to manage problem behaviors, obtain and utilize social supports, and provide interventions to maintain caregiver health and well‐ being. In caring for others, caregivers must take care of themselves in order to avoid burnout and the disintegration of a previously workable care plan. Rehabilitation psychologists also intervene to assist with reducing the morbidity and cost of work injuries. Delivering health and rehabilitation services to injured workers in the work environment, instead of in community care settings, has been demonstrated to significantly reduce days away from work and worker’s compensation costs (Wegener & Stiers, 2010). Negative experiences, including stereotyping and marginalization, discrimination, and disempowerment in the workplace, may also need to be addressed for individuals with disabilities, as with any individuals of minority status.

Practice of Rehabilitation Psychology: Interdisciplinary Team Functioning Rehabilitation includes many disciplines working collectively to serve patients with a wide variety of disabilities and chronic conditions. Rehabilitation psychologists contribute across the full spectrum of care from prevention, diagnosis, consultation, treatment, rehabilitation, and/or end‐of‐life and palliative care. As rehabilitation psychologists practice in these diverse healthcare delivery systems, they have a long tradition of working within multidisciplinary and interdisciplinary teams (Butt & Caplan, 2010). Interdisciplinary typically implies a team that is not only composed of different disciplines working toward a shared set of treatment goals but also have some fluidity of professional boundaries as team members collaborate to best assist the patient to achieve their goals. Functioning within teams requires expertise in communication, behavioral issues and management, patient decision making, and human interaction in systems with attention to the environment, culture, and context in which care is delivered. Rehabilitation psychologists must understand the roles, skills, and contributions of each discipline involved on the team as they educate other disciplines about the potential ­contributions of psychologists to the team. Awareness of areas of overlap in team member’s

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c­ ontributions is aided by clear communication and respect for each team member’s identified role that can change across patients or an individual’s rehabilitation program. Barriers to effective team functioning can include hierarchical attitudes, unhealthy stress reactions, the lack of understanding of the advantages of coordinated team care, fear of change, risk aversion, and the challenge of developing an entrepreneurial spirit. Rehabilitation psychologists operate within rehabilitation teams, systems, and programs and provide expertise in the measurement and understanding of rehabilitation team functioning, rehabilitation outcomes, and facilitation of interdisciplinary rehabilitation team functioning (Butt & Caplan, 2010). Participation in interdisciplinary team meetings, case consultations, and rounds allow for the psychologist to aid the integration of problem definition, goals, observations, and services of different ­providers. As disability results from person–task–environment interactions, a focus on effective and efficient team functioning is necessary to optimize outcomes.

Research in Rehabilitation Psychology Health and rehabilitation psychology research has grown significantly in the past several ­decades. Although pioneers in the field of rehabilitation psychology provided an early framework for conceptualizing the work of rehabilitation psychology (i.e., Meyerson, 1948; Wright, 1983), rehabilitation psychology has generally been more focused on empirical evidence and clinical application than theory development (Dunn & Elliott, 2008). This is due in part to the challenge of applying psychological theories in multidisciplinary settings as well as the focus of rehabilitation on the individual and family, whose circumstances are unique and limit generalization as demanded in science. As scientist‐practitioners, practitioners often rely on single‐subject designs to apply the rigorous methods of science to their daily practice in an effort to determine whether interventions work and to improve patient outcomes. For general and geriatric rehabilitation populations, common areas of rehabilitation research typically focus on the natural history of disability, functional assessment and performance evaluations, intervention issues and outcomes, and rehabilitation service delivery systems. Rehabilitation has been called a black box as its precise ingredients, their mechanisms of action, and their efficacy and effectiveness remain largely unknown. This lack of a systematic way to characterize interventions in rehabilitation has prevented advances in the field of rehabilitation in several ways (Dijkers, Hart, Tsaousides, Whyte, & Zanca, 2014). Rehabilitation research has been disadvantaged by interventions that cannot be readily replicated, tested against one another, or analyzed to try to match specific components to different kinds of patients. Lack of clear evidence about effective treatments and the lack of guidance by which to choose specific therapies for individual patients have hampered progress in clinical applications in rehabilitation. Communication and collaboration across the rehabilitation team, as well as communication with patients and third‐party payers, has been impeded by the lack of a common system for naming and describing treatments. Rehabilitation research needs to apply a systematic approach to treatment definition. When we can clearly define the active ingredients and targets of a treatment, then we can reliably study its effects; assess the fidelity of its implementation; replicate it in the correct doses in further studies, both experimental and observational; specify the changes and refinements that might be made to improve it; and disseminate it to the clinic setting (Hart & Ehde, 2015). Healthcare in the United States suffers from high and rising costs and poor and uneven quality and safety, factors that increase the vulnerability of individuals with disabilities. The increasing prevalence of disability and chronic health conditions across the general population

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is largely driven by behavioral and lifestyle factors that are the primary contributors to ­premature morbidity and mortality and the foundation of the work of rehabilitation psychologists. Theories of health behavior change applied to people with chronic illness and disability help to explain the cognitive mechanisms of behavior change and adherence to treatment in the rehabilitation setting. Research in positive psychology and posttraumatic growth, which focuses on resilience and promotes the concept that individuals can grow positively in response to challenge, stress, and trauma, has been applied to a variety of medical populations. The integration of positive ­psychology and rehabilitation psychology research has only more recently been applied to rehabilitation populations. Peter, Geyh, Ehde, Müller, and Jensen (2015) identified three commonalities between the two areas of study: positive principles, focus on individual strengths and resources, and well‐being, social participation, and growth as key outcomes. Within a population health context, MacLachlan and Mannan (2014) identified areas of potential research that rehabilitation psychology could contribute to in an effort to address many of the challenges identified in the World Report on Disability. Specific targeted contributions include addressing the human resources needed for health crises in rehabilitation, developing prosocial and community‐based interventions and programs, helping to identify and overcome difficulties to accessing healthcare, refining the measurement and classification of disability, and strengthening research, policy, and advocacy for and with people with disabilities. Research in interdisciplinary team science has also grown significantly within the past ­decade. Interdisciplinary research in rehabilitation is performed by individuals or teams that integrate information, data, technique, tools, perspectives, concepts, and/or theories from two or more disciplines or bodies of specialized knowledge to advance fundamental understanding or to solve problems whose solutions are beyond the scope of a single discipline or field of research (Committee on Science, Engineering and Public Policy [COSEPUP], 2004). Elements necessary to facilitate interdisciplinary team science include mutual respect among scientists; regular interactions focused on science; common language, constructs, and cultural norms; and institutional leadership and funding that supports transdisciplinary research. For rehabilitation psychologists practicing in the field, interdisciplinary science must address pragmatic concerns that impact how the psychologists can be an effective partner on the team. Concerns may include how the team’s mission is determined; how the team and its mission is tied to the larger organization and the climate for the team’s functioning; how leadership is determined, if it is shared, and whether and how it may change over time; what are the team’s communication patterns; and how does the team make decisions and review and evaluate its progress and decisions. Each of these aspects of effective interdisciplinary team functioning is crucial to understand within our changing healthcare environment and shift toward integrated care in order to optimize patient outcomes within person–task–­ environment models of care.

Rehabilitation Psychologists as Advocates The essence of advocacy in rehabilitation psychology is captured in Guideline 21 of the Guidelines for Assessment of and Intervention With Persons With Disabilities (APA, 2012), which states that “When working with systems that support, treat, or educate people with ­disabilities, psychologists strive to keep the clients’ perspectives paramount and advocate for client self‐determination, integration, choice, and least restrictive alternatives” (p. 26). Rehabilitation psychologists advocate on treatment teams, within their family structure and

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social network, and in the community for their patients with regard to their physical and ­emotional needs. They advocate for needed environmental changes, such as accessibility to/ from health provider offices (e.g., ramps, curb cuts), for additional time to complete tasks or other accommodations (e.g., at work or at school), or for therapeutic assistance (e.g., obtaining a therapy dog to assist with physical or sensory needs). Rehabilitation psychologists also advocate at local, state, and national levels for legislation to assist people with disabilities, including ensuring access to affordable healthcare, social service, and rehabilitative therapy resources, and inclusion in social roles through the ADA (1990) (Cheak‐Zamora et al., 2012). They do this because they are “mindful of the fact that limitations of functioning may not only be inherent in the disability itself, but are often attributable, in whole or part, to environmental and institutional barriers, and to negative social attitudes” (APA Division 22 Rehabilitation Psychology and the Rehabilitation Psychology Synarchy, 2015, p. 6).

Training in Rehabilitation Psychology Graduate students in clinical and counseling psychology programs may first be introduced to physical rehabilitation in practicum experiences in inpatient and/or outpatient medical rehabilitation settings tied to clinical health psychology programs. For example, at the University of Kansas and the University of Kansas Medical Center, where the first author is located, graduate students in clinical psychology (health track) participate in required health practicum medical center experiences, including a neurorehabilitation psychology rotation (the first author is the supervisor) that allows them to participate in interviews, evaluations, and treatment of patients with new onset or chronic physical and medical issues. Externs also have the opportunity to participate in interdisciplinary team meetings, patient/family education, ­support/education groups, and community outings. The foundation of clinical psychology, learned within the first 2 years of their graduate program, provides a basis from which the students apply their skills in the new setting of inpatient or outpatient physical rehabilitation. Training may also occur during predoctoral internship or in postdoctoral experiences. According to postdoctoral training guidelines first published in 1995 by Patterson and Hanson, training at the postdoctoral level is expected to occur for a minimum of 1 year with a minimum of two supervisors and include supervised practice, seminars, and coursework, a minimum of 2 hr of weekly didactics, a minimum of 2 hr of weekly supervision, and the patient populations and didactics are to be related to disabilities and chronic health conditions (Patterson & Hanson, 1995). In addition, trainees should be funded, written objectives for the training program are to be provided, formal trainee evaluations are to occur at least twice a year, and program evaluations are to occur annually. The petition for rehabilitation psychology as a specialty notes that “Training in Rehabilitation Psychology is based upon a disability‐specific body of theory and research (Cox, Cox, & Caplan, 2013; Dunn & Elliott, 2005) which focuses on the physical, psychological, social, environmental, and policy aspects of disability and rehabilitation, and includes individual, ­psychosocial, and cultural aspects of disability. Knowledge acquisition concentrates on the application of psychology principles to understanding the needs of diverse people with disabilities in rehabilitation settings and their families. Content focuses on understanding the impact of physical, cognitive, and/or mental health disabilities on diverse individuals and their relevance in providing rehabilitation services such as assessment, intervention, vocational rehabilitation, case management, and advocacy. Such training can occur in formal experiences, such as

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didactics, seminars, and journal clubs, in interdisciplinary forums, such as interdisciplinary team‐patient care meetings, case conferences and grand rounds, and in supervision, both individual and group” (APA Division 22 Rehabilitation Psychology and the Rehabilitation Psychology Synarchy, 2015, p. 21). The American Board of Rehabilitation Psychology has also determined specialty competencies for practice in rehabilitation psychology that guide training in progress toward an individual’s attainment of board certification in the specialty (Cox et al., 2013). Such competencies include, but are not limited to, the ability to provide assessment and treatment related to adjustment to disability, cognitive functioning, family/couples functioning, pain, substance abuse, and educational, vocational, and recreational functioning; the ability to participate in interprofessional collaboration and consultation; knowledge of ethical, legal, and professional issues including laws related to persons with disability; knowledge of research and treatment and program evaluation methods as well as the ability to apply research findings to patients; and knowledge of and attention to diversity and cultural issues (Stiers & Nicholson Perry, 2012).

Summary In this chapter, we have introduced rehabilitation psychology as a specialty within ­psychology, simultaneously recognizing its similarities with and differences from clinical health psychology. We have discussed practice aspects including assessment, intervention, and interdisciplinary team functioning that together allow rehabilitation psychologists to assist the team in treating the whole person. Research in rehabilitation psychology was reviewed, and the need for more theory‐driven research was described. Advocacy and training in rehabilitation psychology, both vital engagement areas, were also described. While rehabilitation psychologists are involved to varying degrees in research, practice, advocacy, and training, the Division of Rehabilitation Psychology within the American Psychological Association provides a home for those interested in this discipline of psychology. The approval of rehabilitation psychology by the APA Council of Representatives in August 2015 as a recognized specialty within p ­sychology ­demonstrates the importance of this distinct field, thereby recognizing the ability of rehabilitation psychology to treat the wounds that expose the “best and beautiful” aspects of our patients.

Author Biographies Monica F. Kurylo, PhD, ABPP(Rp), is an associate professor and director of neurorehabilitation psychology in the Departments of Psychiatry and Rehabilitation Medicine at the Kansas University Medical Center. She received her doctorate in clinical psychology (health/­ rehabilitation emphases) and has internship and postdoctoral experience in rehabilitation ­psychology and neuropsychology. She is active in practice, training, advocacy, and research and has served on several local, state, and national professional psychology, rehabilitation psychology, and interdisciplinary groups (http://www.kumc.edu/school‐of‐medicine/psychiatry‐and‐ behavioral‐sciences/faculty/clinical‐psychologists/monica‐kurylo‐phd‐abpp.html). Kathleen S. Brown, PhD, is a rehabilitation psychologist involved in consulting, leadership training, and supervision in her independent practice in Fort Myers, Florida. Dr. Brown’s clinical and research interests include pain management, interdisciplinary team development, leadership training in healthcare, and adjustment/coping responses to medical illness.

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References American Psychological Association. (2012). Guidelines for assessment of and intervention with persons with disabilities. American Psychologist, 67(1), 43–62. American Psychological Association Division 22 Rehabilitation Psychology and the Rehabilitation Psychology Synarchy. (2015). Petition for the recognition of a specialty in professional psychology. Washington, DC: American Psychological Association. Americans With Disabilities Act of 1990, Pub. L. No. 101‐336, 104 Stat. 328 (1990). Butt, L., & Caplan, B. (2010). The rehabilitation team. In R. G. Frank, M. Rosenthal, & B. Caplan (Eds.), Handbook of rehabilitation psychology (2nd ed., pp. 451–458). Washington, DC: American Psychological Association. Cheak‐Zamora, N., Reid‐Arndt, S. A., Hagglund, K. J., & Frank, R. G. (2012). Health legislation and public policies. In P. Kennedy (Ed.), The Oxford handbook of rehabilitation psychology (pp. 511–524). New York, NY: Oxford University Press. Chwalisz, K., & Dollinger, S. C. (2010). Evidence‐based practice with family caregivers: Decision‐­ making strategies based on research and clinical data. In R. G. Frank, M. Rosenthal, & B. Caplan (Eds.), Handbook of rehabilitation psychology (2nd ed., pp. 301–312). Washington, DC: American Psychological Association. Committee on Science, Engineering and Public Policy (COSEPUP). (2004). Facilitating interdisciplinary research. COSEPUP. Washington, DC: National Academies Press. Cox, D. R., Cox, R. H., & Caplan, B. (2013). Specialty competencies in rehabilitation psychology. New York, NY: Oxford University Press. Cox, D. R., Hess, D. W., Hibbard, M. R., Layman, D. E., & Stewart, R. K. (2010). Specialty practice in rehabilitation psychology. Professional Psychology: Research and Practice, 41(1), 82–88. Dijkers, M. P., Hart, T., Tsaousides, T., Whyte, J., & Zanca, J. M. (2014). Treatment taxonomy for rehabilitation: Past, present, and prospects. Archives of Physical Medicine and Rehabilitation, 95(1), S6–S16. Dunn, D. S., & Elliott, T. R. (2005). Revisiting a constructive classic: Wright’s Physical Disability: A Psychosocial Approach. Rehabilitation Psychology, 50(2), 183–189. Dunn, D. S., & Elliott, T. R. (2008). The place and promise of theory in rehabilitation psychology research. Rehabilitation Psychology, 53(3), 254. Hart, T., & Ehde, D. M. (2015). Defining the treatment targets and active ingredients of rehabilitation: Implications for rehabilitation psychology. Rehabilitation Psychology, 60(2), 126. Heinemann, A. W., & Mallison, T. (2010). Functional status and quality‐of‐life measures. In R. G. Frank, M. Rosenthal, & B. Caplan (Eds.), Handbook of rehabilitation psychology (2nd ed., pp. 147–164). Washington, DC: American Psychological Association. Keany, K. C., & Glueckauf, R. L. (1993). Disability and value change: An overview and reanalysis of acceptance of loss theory. Rehabilitation Psychology, 38(3), 199–210. MacLachlan, M., & Mannan, H. (2014). The World Report on Disability and its implications for rehabilitation psychology. Rehabilitation Psychology, 59(2), 117. Meyerson, L. (1948). Physical disability as a social psychological problem. Journal of Social Issues, 4, 2–10. Novack, T. A., Sherer, M., & Penna, S. (2010). Neuropsychological practice in rehabilitation. In R. G. Frank, M. Rosenthal, & B. Caplan (Eds.), Handbook of rehabilitation psychology (2nd ed., pp. 165– 178). Washington, DC: American Psychological Association. Patterson, D., & Hanson, S. (1995). Joint Division 22 and ACRM guidelines for post‐doctoral training in rehabilitation psychology. Rehabilitation Psychology, 40, 299–310. Peter, C., Geyh, S., Ehde, D. M., Müller, R., & Jensen, M. P. (2015). Positive psychology in rehabilitation psychology research and practice. In S. Joseph (Ed.), Positive psychology in practice: Promoting human flourishing in work, health, education, and everyday life (2nd ed., pp. 443–460). Hoboken, NJ: Wiley.

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Stiers, W., & Nicholson Perry, K. (2012). Education and training in rehabilitation psychology. In P. Kennedy (Ed.), The Oxford handbook of rehabilitation psychology (pp. 417–431). New York, NY: Oxford University Press. Wegener, S. T., & Stiers, W. (2010). Prevention, assessment and management of work‐related injury and disability. In R. Frank, M. Rosenthal, & B. Caplan (Eds.), Handbook of rehabilitation psychology (2nd ed., pp. 407–416). Washington, DC: American Psychological Association. Wright, B. A. P. (1983). Physical disability, a psychosocial approach (2nd ed.). New York, NY: Harper Collins Publishers.

Suggested Reading Dunn, D. (2014). The social psychology of disability. Academy of Rehabilitation Psychology Series. New York, NY: Oxford University Press. Kennedy, P. (Ed.) (2012). The Oxford handbook of rehabilitation psychology. New York, NY: Oxford University Press. Kerkhoff, T. R., & Hanson, S. L. (2013). Ethics field guide: Applications to rehabilitation psychology. Washington, DC: American Psychological Association. Rusin, M. J., & Jongsma, A. E. (2001). The rehabilitation psychology treatment planner. New York, NY: Wiley.

Administrative Issues in Primary Care Psychology Esther N. Schwartz and David R. M. Trotter Department of Family and Community Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA

Introduction Psychologists and other behavioral health providers (BHPs) have become increasingly ­integrated into primary care in the United States (Bray, 2016; McDaniel et al. 2014; Miller, Petterson, Burke, Philips, & Green, 2014). The types of administrative issues encountered by BHPs working in primary care will vary by proximity of BHP and medical services locations and the level of integration between medical and behavioral health services. BHPs face administrative challenges that are distinct from providers in traditional mental health settings. In this article, the practicalities of BHPs’ daily function such as their appointment structure, coding and billing for services, documentation, availability of administrative support, and position in the organizational hierarchy are discussed. Additionally, BHPs’ navigation and resolution of ethical quandaries specific to psychological practice in a medical setting are also explored.

A Brief Context: Behavioral Health Providers in Primary Care Settings Primary medical care (PC) is defined as “the provision of integrated accessible health care services by clinicians who are accountable for addressing a large majority of personal health care needs, developing a sustained partnership with patients, and practicing in the context of family and community” (Institute of Medicine, 1994, p. 1). PC clinics, including family medicine, general internal medicine, and general pediatrics practices, are the primary entry point for medical and mental healthcare for most Americans. Specifically, more than half (54.6%) of all healthcare visits occur with a primary care physician (PCP) (Centers for Disease Control [CDC], 2012). Integration of BHPs into PC is important as psychosocial and behavioral The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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f­actors such as diet, activity level, and substance use critically affect morbidity and mortality in  the United States. Additionally, psychological symptoms and disorders are frequently comorbid with medical illnesses (Petterson et  al., 2008). Finally, approximately 50–70% of Americans who receive treatment for mental and behavioral health and substance use problems do so from PC (McDaniel & deGruy, 2014). There is mounting evidence supporting that the integration of PC and behavioral health improves health at individual and population levels (Blount et al., 2007). However, primary care behavioral health integration is still not yet widespread.

Administrative Issues as a Function of Level of Integration The types of administrative issues encountered by BHPs working in PC will vary at least ­partially as a function of the level of integration between medical and behavioral health s­ ervices. In general, integration of services requires a team‐based collaboration between all providers using a biopsychosocial framework (Bray, 2016; Engel, 1977). There are three broad levels of integration: coordinated, colocated, and fully integrated care (Bray, 2016). In coordinated care, the BHP retains a separate practice (i.e., separate physical spaces, electronic health record [EHR] systems, scheduling and billing staff) and collaborates via referrals from the PCP. In a colocated arrangement, the psychologist and PCP are located within the same office space but maintain separate practices (e.g., scheduling and billing staff, EHR) and financial arrangements (e.g.,  ­billing and reimbursement structures). In this arrangement the BHP accepts referrals from PCP and provides consultation (written and face to face). In a fully integrated model, the psychologist is embedded within the primary care system and is an integral part of care delivery and treatment. They share the same space, systems (e.g., EHR), and resources (e.g., scheduling and billing staff). In these settings, psychologists may provide immediate consults to patients during their doctor’s visits as requested by the PCP, as well as see patients for brief interventions. For a full discussion of levels of integration, the reader should see Bray (2016). Deft negotiation of administrative issues requires an awareness of the administrative processes ­operating in each clinical setting and the level of behavioral health service integration.

Basic Practice Management Considerations for BHP Integration General Practice Organization BHPs working in colocated or fully integrated primary care settings typically work in a medical facility. Familiarity with the organizational hierarchy and the numerous players in the medical system is helpful for ensuring coordinated care. Key players in these facilities often include a medical director (a physician who makes decisions on standing orders, supervises “mid‐levels,” and may provide general oversight of medical services), medical staff (e.g., physicians and mid‐level providers like nurse practitioners and physicians assistants), clinic manager (a staff administrator who oversees the day‐to‐day operations of the clinic and provides general supervision of the staff), various department managers (e.g., nurse manager, billing manager, medical records manager, scheduler/call center manager, lab manager; typically report to the clinic manager), care coordinators (may coordinate referrals and/or manage patient care plans), and various departmental staff (e.g., nurses, billers/coders, call center staff). It is critical that a BHP understands where they fit into this hierarchy, including who they report to, who ­provides

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practice oversight, how they interact with the other key players, and what services and support they will receive from the various clinic departments (e.g., scheduling assistance, billing/­ coding). For example, will the BHP report to the medical director and be granted privileges in accordance to their license/certification and in a manner similar to the other providers, or will they report to the clinic manager, be considered general staff, and be afforded fewer ­privileges? Discussions regarding how a BHP will fit into the administrative clinic structure is critical prior to integration.

General Clinic Supports As stated above, BHPs should consider and negotiate how they will interact with, and what services they will receive, from the various clinic departments. For example, who will take charge of the day‐to‐day management of the BHPs schedule? Is the BHP responsible for scheduling of patients, taking their phone calls, and contacting them if they have not showed up, or does the scheduling department complete these tasks as they do for the medical providers? In full integration, the BHP’s appointments are often marked on the same standardized weekly clinic grids utilized by all other providers. With these grids in place, the BHP’s current appointments and total available slots in the future are visible to the entire clinic, increasing transparency and availability for same day access (Kearney, Smith, & Pomerantz, 2015). An additional question is, who does the BHP’s billing and coding? Does this responsibility fall under the purview of the BHP’s responsibilities, or is this handled by the billing/coding department in accordance with the other providers? It is advisable (when possible) for patients to have a generally equivalent experience (e.g., with scheduling, billing) with the clinic regardless of the type of services (i.e., medical or behavioral health) they are receiving.

Office/Clinic Space Space in PC settings is often at a premium, and clinics are typically not designed with ­behavioral health practice in mind. Often, the only available space to see patients is an exam room, and BHPs may be expected to meet with patients in exam rooms. Additionally, it is the norm for many PC practices that providers do not have a private office space for charting and completing other administrative activities (e.g., letter writing, responding to patient messages) and may instead do these activities in a more central provider pod or work hub. While this may take some adjustment on the BHP’s part, these centralized work spaces encourage communication between providers in ways that enhance coordination, builds camaraderie, and conveys accessibility. Even if a psychologist is designated a private office space to see patients, simply leaving the door open and being visible conveys accessibility (Robinson & Reiter, 2016). It is advisable that BHPs advocate for space that is roughly equivalent to that or their medical colleagues, use space judiciously, and be flexible.

Appointment Structure Physician visits are typically scheduled in 15‐min increments (high volume), while traditional mental health visits are typically scheduled for 50 min (comparatively low volume). When BHCs work in a coordinated model, they often retain this 50‐min structure. However, when working in colocated or fully integrated settings, many move to using a 25‐min session structure (a higher volume 16 total appointment slots in a day). When choosing an a­ ppointment s­ tructure (i.e., either 50 or 25 min), a number of administrative issues should be considered. First, what

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are the clinical volume expectations in the clinic? Do the other providers expect you to have a volume more similar to theirs, or is there no expectation of this? If a BHC works in a clinic with many other providers, a 25‐min structure is generally preferable as it allows the professional to be available for more of the PCP patients. Second, what level of staffing is available for patient scheduling and check‐in/check‐out? Are there enough scheduling staff to accommodate an additional 16 patients a day plus the calls they will generate? Finally, will the BHP utilize nursing staff to room patients? While this is not common, some clinical settings will provide nursing support for their BHP to room patients, allowing them to move faster through their clinic without having to make trips to the waiting room or ensure that patients have completed the appropriate paperwork. For a more comprehensive discussion and justification for the use of 25‐min appointment structure, the reader may consult Robinson and Reiter (2016).

Documentation Psychologists’ documentation of patient encounters is an important administrative skill, but the style and processes of documentation differ based on level of BHC integration. In a coordinated model, BHP documentation will look similar to other mental health settings, with the addition of workflows and processes to ensure that the referring PCP is provided some level of feedback on the referral (e.g., that the patient was seen, that progress is being made). BHPs working in colocated models often document in a manner similar to traditional mental health providers (i.e., in a separate system, using traditional mental health note structures), while some document more like a BHP in a fully integrated setting. BHPs in fully integrated settings typically document in the same chart used by the PCP. This kind of communication requires that notes are written in a way that is useful to other healthcare team members. Typically, this means that the notes use a structure similar to other providers (e.g., SOAP style), are concise, do not contain psychological jargon, clearly convey the assessment of the patient including risk factors, include a follow‐up plan, and provide recommendations for other providers (when necessary). When using this style of documentation, BHPs need to discuss the strengths and limitations of a shared health record during the informed consent discussion with the patient. The psychologist should also clarify with the patient what session information will be shared in the chart. When using a shared chart, BHPs need to also consider who should have access to their notes. A PCP’s notes are typically available to all staff and other providers, including other physicians and mid‐levels, nursing staff, scheduling/call center staff, billers/coders, and other general administrative staff. A BHP using a shared EHR should carefully consider which of these groups will have a legitimate need to access to their portion record. For example, it would be important for the BHP to grant access to all referring providers in the clinic, as they will be collaborating on patients. Additionally, it may be important for some of the nursing staff to have access to these notes, as the nursing staff are often in charge of triaging patients who contact the clinic. However, it may not be necessary for scheduling staff and general administrative staff to have access to the notes. The BHP should give careful consideration to this issues and inform patients as to who will have access to their record.

Referral Management In a coordinated practice, a BHP will receive referrals from a PCP’s office, as well as from other sources. However, in colocated and fully integrated clinics, PCPs may be a BHP’s sole

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source of referrals. Ensuring that the referral process is easy, smooth, and rapid is critical in promoting PCPs’ actual placement of the referral and will increase the likelihood that a patient will follow through with the initial appointment. BHPs should consider the process of placing written and in‐person referrals. PCPs will be most familiar with creating written referrals, as they refer patients to a host of services (e.g., preventive care screenings, specialist referrals). As most PC practices use an EHR, BHPs should work closely with their IT departments to ensure that their electronic referral process closely matches those that PCPs use for other services. Additionally, BHPs should ensure that common referral barriers (e.g., requirement of prior authorization for service, delays, wait lists) are removed or managed. While PCPs will be most familiar with written referral processes, in‐person referrals are one of the BHPs most effective tools for achieving coordinated care. The most effective in‐person referral is the “warm hand‐ off.” A warm hand‐off is when a PCP consults the BHP in person and then asks the BHP to meet a patient during the patient’s regularly schedule PCP visit. Warm hand‐offs by the PCPs to BHPs may be completed in just a few minutes and demonstrably increase the likelihood a patient will initiate therapy. In‐person referrals may also occur during a “curbside consultation.” A curbside consult is a brief discussion between a BHP and a PCP about a clinical issue that occurs spontaneously. These can occur in an exam room, PCP common work space, or hallway (being mindful of confidentiality issues). Curbside consults are good opportunities to provide accurate, specific, and actionable information on a behavioral problem and support the providers to be more effective in their interactions with patients. Over time, repeated consultations may provide physicians with a good sense of the biopsychosocial model in action and the range of services a psychologist provides. No less important, collaboration communicates to the PCP that they have a potential ally in caring for their patients (Gunn & Blount, 2009). Regardless of how a referral is placed, once a BHP has completed the initial referral visit, he or she should provide the referring PCP with feedback about what the patient is ­targeting in treatment and how treatment is progressing as a way to close the referral loop.

Billing and Reimbursement BHPs working in either coordinated or colocated systems typically bill and are reimbursed in ways similar to that of traditional mental health settings. However, billing issues continue to be a serious hurdle in integrated primary care practices (Robinson & Reiter, 2016). Clinic administrators may hire a BHP with no plans for that provider to bring in direct revenue. In this model, the BHP is typically salaried hourly and paid as a member of the team. This system can work well in capitated payment structures, like those found in accountable care organizations (ACO). A capitated payment structure is a reimbursement model in which a health ­system receives a yearly flat fee from an insurance carrier to provide all services for a patient. Further, insurers may offer additional monetary incentives if the system can reduce overall healthcare costs for its patients by improving quality of care delivered (e.g., decreasing ER visits and hospital stays, reducing redundancy and waste). In fact, there is some evidence to suggest BHPs can reduce healthcare costs at a system level, making their inclusion in these systems attractive. See Blount et al. (2007) for a review of increased cost‐effectiveness when behavioral health services are integrated into primary care. Most BHPs still work in traditional fee‐for‐services models. BHPs working in these environments are typically either employees of the system who receive payment based on their billing or are contractors who provide services for a contracted fee. In fee‐for‐services systems, BHPs bill using current procedural terminology codes (CPT) to bill for services. There are two

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c­ategories of CPT codes available to BHPs: psychotherapy CPT codes (e.g., 90791) and health and behavior CPT codes (i.e., 96150–96155). The psychotherapy codes are used for billing when a BHP is seeing a patient for a mental health issue (e.g., depressive disorder, anxiety disorder). Available psychotherapy CPT codes include those used for testing and assessment and counseling (based on time; 30, 45, 60 min). The CPT codes are used by those working in traditional mental health settings as well as all levels of PCBH integration. Health and behavior codes allow BHPs to bill for services that address social and behavioral aspects of physical health problems as diagnosed by a medical professional (Robinson & Reiter, 2016). Therefore, a BHP can bill for services to improve the course or outcome of medical issues like obesity, chronic pain, and diabetes. It is important to note that billing for both health and behavior codes and mental health codes in the same encounter is not allowed.

Managing Confidentiality Protecting patient confidentiality and privacy in the primary care setting is an important administrative issue but can be challenging. Physicians and BHPs have different laws, rules, and ethics codes that govern provider–patient confidentiality. For example, medical care is organized around referrals and informal consultation with others in which protected health information is exchanged. This is permissible under the Health Insurance Portability and Accountability Act (HIPAA), which allows HIPAA‐covered healthcare providers to disclose protective health information with another healthcare provider without the patients written consent, if the exchange of information pertains to that provider’s treatment of the patient (see Federal Regulation 45 CFR 164.506). As such, PCPs expect team members to be able to discuss all patient care issues as deemed necessary. However, BHPs may be limited in their ability collaborate in this way as they are not covered under HIPAA and are subject to other privacy and ethical considerations. For example, the American Psychological Association Ethical Standard 4.06 clearly states that when consulting with colleagues, psychologists “do not disclose confidential information that reasonably could lead to the identification of a client/patient.” Therefore, a curbside consult between a PCP and a psychologist BHP in which a patient is referred to by name and medical problem may be permissible under HIPAA, but in violation of APA standards on confidentiality. However, in the primary care culture of openly sharing patient information as a way to coordinate care, a psychologist’s reticence to collaborate may be counterproductive (Gunn & Blount, 2009). BHPs must learn to navigate the complex rules, regulations, and work expectations regarding confidentiality. One of the simplest administrative solutions to this issue is to notify patients in writing during the informed consent process that the BHP may collaborate with other providers in the service of their care. Further, during informed consent, patients can be informed about how their written record will be handled (see above).

Managing Multiple Relationships Medical providers and BHPs may possess different perspectives on the issue of multiple ­relationships. For example, consider the example of a provider providing and/or receiving clinical services to/from another colleague or trainee. The APA Ethical Code states: “A ­psychologist refrains from entering into a multiple relationship if the multiple relationship could reasonably be expected to impair the psychologist’s objectivity, competence or ­effectiveness in performing

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his or her functions as a psychologist, or otherwise risks exploitation or harm to the person with whom the professional relationship exists” (see Standard 3.05 in the “Ethical Principles of Psychologists and Code of Conduct”; American Psychological Association [APA], 2017). While APA’s code does not explicitly forbid treating colleagues or professionals in training under this code, the onus would fall on the psychologist to prove that entering into such a relationship would be reasonable, which would be difficult to do. On the other hand, the American Medical Association’s (AMA) Code of Ethics stipulates that while one should exercise caution (for issues similar to those raised by the APA), “car[ing] for a fellow physician is a privilege” and that “physicians‐in‐training should not be required to provide medical care to fellow trainees, faculty members, or attending physicians if they are reluctant to do so.” Taking these guidelines in combination in a real‐world setting, BHPs and PCPs may have very different opinions about the appropriateness of working with a patient with whom both providers have multiple relationship (e.g., colleagues, family members, relatives of current patients). Managing multiple relationships as a BHP is an important administrative consideration, as it will influence the referrals (and associated referral process) that a BHP will or will not accept. Here we will discuss the two types of multiple relationships BHPs will most often encounter and potential administrative solutions.

Working with Colleagues Psychologists are trained communicators with expertise in emotions, relationships, and ­behavior. They are in an apt position to promote team development and a healthy culture in the workplace (McDaniel & Fogarty, 2009). In primary care, the psychologist may be called on to help staff deal with difficult and divisive patients, identify staff burnout, and improve workflows (McDaniel & deGruy, 2014). In some situations, clinic staff may ask the psychologist for help with a personal problem. In such scenarios, a BHP must adopt a policy(s) that will help them navigate these requests. These policies are likely to fall on a spectrum, ranging from a refusal to see any colleagues as patients to a policy in which the BHP will see anyone for an initial appointment. If a BHP adopts a policy refusing to see colleagues as patients, this is best communicated in the beginning when negotiating the terms of a practice. It is always easier to adopt this policy from the beginning, rather than attempt to implement it later on. This policy should then be clearly communicated to providers and staff. If a BHP adopts a policy on the other end of the spectrum, prior to intervening, it is recommended that the psychologist consider his or her working relationship with the staff member, the severity and sensitivity of the problem, and the likelihood the consultation will negatively impact the working relationship between staff member and BHP and the team (Reiter & Runyan, 2013). Again, if a BHP choses such a policy, this should be communicated to providers and staff in a clear format. Regardless of the policies guiding a BHP’s practice, in the event of crises or problems with elevated risk to the staff ­member, the BHP may advise follow‐up care such as counseling or hospitalization. Additionally, all services rendered to colleagues should be documented in their chart in a fashion similar to documenting other patients’ information.

Non‐Colleague Patients Issues of multiple relationships are also common when working with non‐colleague patients. In primary care, it is not uncommon for members of the same family to receive care in the same clinic and even from the same provider (“whole family care”). PCPs may be skilled at navigating the challenges inherent in these multiple relationships, but such scenarios are far

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less common in specialty mental health settings and may present ethical and administrative ­challenges to psychologists (Reiter & Runyan, 2013). Consider the plausible example of an elderly man referred to a BHP to address suicidal ideation and his adult daughter who is referred to the same BHP for a distinct but related issue, stress‐induced gastritis. In this scenario, if the BHP were to follow the APA Standard 10.02 Therapy Involving Couples of Families stating “When psychologists agree to provide services to several persons who have a relationship (such as spouses, significant others, or parents and children), they take reasonable steps to clarify at the outset (1) which of the individuals are clients/patients and (2) relationship the psychologist will have with each person,” then the BHP would risk violating APA Standard 4.01 Maintaining Confidentiality, which instructs psychologists to “take precautions to protect confidential information.” “Whole family care” presents an ethical dilemma for the BHP as it is impossible to abide by one of these guidelines without violating the other (Reiter & Runyan, 2013). In this case, the BHP may not even be aware that father and daughter are related when they are referred. In the event that the BHP is made aware of this existing relationship, several different administrative actions can be taken. First, if other BHPs are available at the clinic, it would be advisable to refer one of the family members to see another BHP. If there is only one BHP, then they should assess the extent to which the father’s presenting issue and the daughter’s presenting issue are reasonably discrete and can each be conceptualized and treated without undue consideration of the other. Based on this assessment, the BHP will have to prioritize which standard will take precedence and be prepared to provide a rationale for this decision. Thus, even when embedded within the primary care team, psychologists’ administrative challenges are unique from medical providers. Moreover, primary care psychologists’ practice management, reimbursement process, and understanding of confidentiality and multiple relationships differ from their colleagues working in traditional mental health settings.

Author Biographies Esther N. Schwartz, PhD is a postdoctoral fellow in primary care psychology in the Department of Family and Community Medicine at the Texas Tech University Health Sciences Center. Dr. Schwartz received her PhD in counseling psychology from the Department of Psychological Sciences at Texas Tech University and completed an APA‐accredited predoctoral internship in psychology at the Dallas VA Medical Center. David R. M. Trotter, PhD is assistant professor and director of behavioral sciences in the Departments of Family and Community Medicine and Medical Education at the Texas Tech University Health Sciences Center. Dr. Trotter earned his PhD in clinical psychology at Texas Tech University and received postdoctoral training in primary care psychology at the UMass Medical School, Center for Integrated Primary Care (Department of Family Medicine). His areas of expertise include behavioral medicine, primary care behavioral health integration, and medical education.

References American Psychological Association. (2017). Ethical principles of psychologists and code of conduct. Retrieved from www.apa.org/ethics/code Blount, A., Schoenbaum, M., Kathol, R., Rollman, B., Thomas, M., O’Donohue, W., & Peek, C. J. (2007). The economics of behavioral health services in medical settings: A summary of the evidence. Professional Psychology: Review and Practice, 38, 290–297. doi:10.10377/0735‐7028.38.3.290

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Bray, J. H. (2016). Primary care setting. In J. C. Norcross, G. R. VandenBos, & D. K. Freedheim (Eds.), APA handbook of clinical psychology: Vol. 1 roots and branches (pp. 463–475). Washington, DC: American Psychological Association. doi:10.1037/14772‐026 Centers for Disease Control. (2012). National ambulatory medical care survey: 2012 summary tables 1.11.16. Atlanta, GA. Retrieved from https://www.cdc.gov/nchs/fastats/physician‐visits.htm Engel, G. L. (1977). The need for a new medical model: A challenge for biomedicine. Science, 196, 129–136. doi:10.1126/science.847460 Gunn, W. B., & Blount, A. (2009). Primary are mental health: A new frontier for psychology. Journal of Clinical Psychology, 65, 235–252. doi:10.1002/jclp.20499 Institute of Medicine. (1994). Defining primacy care: An interim report. Washington, DC: National Academy Press. Kearney, L. K., Smith, C. A., & Pomerantz, A. (2015). Capturing psychologist’ work in integrated care: Measuring and documenting administrative outcomes. Journal of Clinical Psychology in Medical Settings, 22, 232–242. doi:10.1007/s10880‐015‐9442‐7 McDaniel, S. H., & deGruy, F. V. (2014). An introduction to primary care and psychology. American Psychologist, 69(4), 325–331. McDaniel, S., & Fogarty, C. (2009). What primary care psychology has to offer the patient‐centered medical home. Professional Psychology: Research and Practice, 40, 483–492. doi:10.1037/a0016751 McDaniel, S. H., Grus, C. L., Cubic, B. A., Hunter, C., Kearney, L., Lisa, K., … Johnson, S. B. (2014). Competencies for psychology practice in primary care. American Psychologist, 69, 409–429. doi:10.1037/a0036072 Miller, B. F., Petterson, S., Burke, B. T., Phillips, R. L., Jr., & Green, L. A. (2014). Proximity of ­providers: Colocating behavioral health and primary care and the prospects for an integrated workforce. American Psychologist, 69, 443–451. doi:10.1037/a0036093 Petterson, S. M., Phillips, B. L., Jr., Bazemore, A. W., Dodoo, M. S., Zhang, X., & Green, L. A. (2008). Why there must be room for mental health in the medical home. American Family Physician, 77, 757. Reiter, J., & Runyan, C. (2013). The ethics of complex relationships in primary care behavioral health. Families, Systems & Health, 31, 20–27. doi:10.1037/a0031855 Robinson, P., & Reiter, J. T. (2016). Behavioral consultation and primary care: A guide to integrating services (2nd ed.). Basel, Switzerland: Springer.

Suggested Reading Bray, J. H. (2016). Primary care setting. In J. C. Norcross, G. R. VandenBos, & D. K. Freedheim (Eds.), APA handbook of clinical psychology: Vol. 1 roots and branches (pp. 463–475). Washington, DC: American Psychological Association. doi:10.1037/14772‐026 McDaniel, S. H., Grus, C. L., Cubic, B. A., Hunter, C., Kearney, L., Lisa, K., … Johnson, S. B. (2014). Competencies for psychology practice in primary care. American Psychologist, 69, 409–429. doi:10.1037/a0036072 Robinson, P., & Reiter, J. T. (2016). Behavioral consultation and primary care: A guide to integrating services (2nd ed.). Basel, Switzerland: Springer.

Affective Forecasting in Health Psychology Shannon M. Martin1, James I. Gerhart2, Catherine Rochefort3, Laura Perry4, and Michael Hoerger4 Department of Psychology, Central Michigan University, Mount Pleasant, MI, USA 2 Department of Psychosocial Oncology, Cancer Integrative Medicine Program, Rush University Medical Center, Chicago, IL, USA 3 Department of Psychology, Southern Methodist University, Dallas, TX, USA 4 Department of Psychology, Tulane University, New Orleans, LA, USA

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Affective forecasting, the process of predicting future emotional states, often drives decision making. Individuals routinely make decisions based on what they believe will optimize their happiness or general emotional well‐being, such as whether to undergo or avoid a medical procedure that may cause some degree of distress. Unfortunately, affective forecasts are prone to a number of errors that may bias decision making. For example, in general people tend to overestimate the intensity and duration of future emotional states. Applied to healthcare behaviors, overestimating future negative emotionality surrounding proactive health behaviors may lead individuals to avoid medical treatments that may actually serve to improve their health and well‐being. This entry reviews basic research on affective forecasting and discusses a number of important implications for health. Affective forecasting research grew out of investigations of the disability paradox, or the tendency for individuals to overestimate the negative emotional impact of illness and disability (Albrecht & Devlieger, 1999). Although healthy individuals tend to predict catastrophic reductions in quality of life in response to disability, those actually living with disability often adapt to illness in a manner that affords a meaningful life. This work stemmed in part from late twentieth‐century researchers such as Levine and Antonovsky who were interested in how quality of life could be obtained in the face of adversity. Around this time other researchers such as Albrecht and Higgins observed discrepancies between patient‐rated well‐being and their physical status. In the intervening years, numerous studies documented that healthy individuals overestimate the emotional impact of life‐threatening illness such as cancer and noted a reasonable degree of well‐being is possible at the end of life. The disability paradox The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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may arise from several social psychological processes including a tendency disregard the impact of coping and adaptation while maintaining a hyper‐focus on isolated symptoms and subsequent impairment. The interesting and curious findings from the disability paradox yielded further and more rigorously controlled studies. A number of social and cognitive psychologists attempted to better understand how and why people overpredict the intensity of future emotional states by utilizing basic research methods to build upon the important but sometimes anecdotal evidence for the disability paradox. Understanding affective forecasting errors is an important topic of study for these fields as predictions about how the future will unfold are directly ­relevant to decision making. That is, the degree to which people base immediate choices and actions on predictions about how they believe they will feel highlights the intersection of emotional forecasts and decision making. Further, if an individual’s decision making is driven by current beliefs about future emotional states, these decisions may be based on inaccurate information. Not surprisingly, greater inaccuracy in future expectation leads to biased decision making across a range of day‐to‐day decisions, from how to spend free time to consequential decisions such as who to choose as a romantic partner or whether to pursue a particular medical treatment. For example, if an individual predicts that undergoing a particular medical procedure will lead to increased pain or prolonged stress, he or she is not likely to pursue that option, even if it might be better for their longer‐term well‐being or longevity. In order to systematically study the influence of affective forecasts on decision making, researchers measure at least two major components of experience: predicted and experienced affect. First, participants make predictions about how they expect to feel in response to a future event, whether it be their favored sports team winning or losing a game, an important career event (e.g., a professor earning or being denied tenure), or a major health event (e.g., being diagnosed with HIV). These expectations are assessed in advance of the event, and then researchers measure experienced affect during the event or shortly afterward. The ­predicted and experienced affect ratings are then compared in order to quantify the relative accuracy of the forecasts. Additional correlates of accuracy such as personality traits are often measured, or situational variables may be modified to intentionally influence accuracy. In general, people tend to overestimate the duration and intensity of future emotional states, regardless of whether the events are positive or negative in valence, a phenomenon called the impact bias. The impact bias is especially prevalent when individuals make forecasts about future negative life events. When experiencing negative events, individuals utilize numerous coping mechanisms (e.g., motivated reasoning, rationalization, self‐justification, and other aspects of the “psychological immune system”) that combat negative emotional reactions in order to help restore a sense of well‐being. As people tend to disregard this active healing process when they are making predictions about their responses to a negative future event, they overestimate the effect of that event on their future emotions, a phenomenon called immune neglect (Gilbert, Pinel, Wilson, Blumberg, & Wheatley, 1998). For example, a study looked at voters’ affective forecasts if their favored candidate lost an election, as well as their evaluations of the winning candidate before and after the election. Not only were voters’ predictions about how they would feel if their candidate lost the election more extreme than how they felt when that event actually occurred, but they also evaluated the winning candidate more favorably after the election than they had before the election. Furthermore, the voters were unaware of this change in their views regarding the winning candidate from before the negative event to after it, which may have served as a coping mechanism to reduce their experience of negative emotions. These findings are especially important to consider when making choices regarding future potentially uncomfortable or stressful events, such as difficult medical

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decisions. Although individuals have a tendency to fear the worst, many are later surprised that an unpleasant outcome was less intense or long lived as anticipated. Additionally, when making an affective forecast, people tend to focus solely on the primary event in question, a common affective forecasting bias termed focalism (Wilson, Wheatley, Meyers, Gilbert, & Axsom, 2000). By doing so, individuals fail to acknowledge other simultaneously occurring events that may serve to diminish the impact of any single event on their emotions. For example, when predicting how they would feel if their school’s football team lost or won a game, college students may not consider other day‐to‐day occurrences that could affect their emotional well‐being during the same time span and therefore overestimate their emotional reactions to the outcome of that football game. However, if individuals are asked to list activities they engage in during a typical day (e.g., going to class, eating meals, studying, socializing with friends) prior to making a forecast about the outcome of the game, the prediction tends to be less extreme. Simply stopping and thinking about life events other than the focal event mitigates the influence of focalism and leads people to make more accurate affective forecasts. Researchers have identified additional errors in affective forecasts beyond immune neglect and focalism. For example, affective forecasts are based on one’s present understanding of an event in question. Therefore, inaccurate conceptions of an event that one has never before experienced (misconstrual) or an inaccurate memory of emotional responses to an event that one has experienced before (inaccurate theories) may lead people to make errors in their affective forecasts. A number of individual difference variables can also influence affective forecasts (Hoerger, Quirk, Lucas, & Carr, 2009). Researchers determined that factors such as personality traits, attachment style, or mood at the time of the forecast are important predictors of the intensity of forecasts and/or accuracy. For example, neuroticism, or the relatively enduring tendency to experience negative emotional states across situations, is typically associated with greater predicted and experienced negative affect. Similar findings have been found for those scoring higher in introversion. Recent research has suggested that personality factors may explain about 30% of the correspondence between predicted and experienced affect. Other traits, such as scoring higher in emotional intelligence, particularly in the domain of emotional management, is associated with making more realistic predictions about future emotional states. In addition, affective forecasting accuracy can be influenced by one’s mood state. People who experience negative affect during the time of affective forecasting have been shown to overpredict the extent of their negative reactions to future events (dysphoric forecasting bias). Although there is a large body of research on affective forecasting and impact bias in the general population, researchers know less about how affective forecasting relates to health‐­ specific circumstances. However, understanding how these phenomena manifest in medical settings may be key to understanding how patients make important healthcare decisions that have serious consequences for their health and quality of life. For example, a large portion of the adult population may avoid colorectal cancer screenings, putting themselves at greater risk of developing colorectal cancer, because they overpredict the extent of embarrassment and pain they will experience during the procedure. They may not imagine the event accurately (misconstrual) compared to actual patient reports of the procedure. Additionally, men with prostate cancer may choose an unnecessary aggressive or surgical treatment instead of watchful waiting (a more conservative option appropriate in many cases) because they focus too much on, and thus overpredict, the negative effect of “living with cancer” on their emotional well‐ being and fail to consider the consequences these treatments may have on their day‐to‐day

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functioning (focalism), such as incontinence and impotence. In both of these examples, patients’ inaccurate affective forecasts may lead them to make less satisfying healthcare decisions. This is one reason why affective forecasting research needs to expand beyond the social cognitive perspective, which has historically focused on samples of White, healthy college students, and draw upon the health psychology perspective in order to understand and improve healthcare decision making. Affective forecasting biases, such as focalism and immune neglect, play a significant role in many patients’ health decision‐making processes. Many health decisions can be understood as preference sensitive, meaning that the available options have comparable outcomes, unknown differences in outcomes, or outcomes that vary considerably across individuals. In absence of compelling evidence favoring one option over another, these decisions are often driven by affective forecasting. Next, we will discuss examples of preference‐sensitive decision making across the health continuum, including decisions about health behaviors, illness screening, treatment, disease management, and end‐of‐life care. One example of preference‐sensitive decisions involves the decision to engage in health‐ related behaviors, such as exercise, alcohol consumption, or vaccinations. Using vaccination as an example, preference‐sensitive decision making often influences people’s decisions to get non‐required vaccines, such as flu or HPV vaccines. Affective forecasting errors, such as expectation biases, can lead people to believe that post‐vaccination pain will be greater or more bothersome than what it is in reality. Additionally, anticipating future worry or regret can lead patients to get vaccinations, especially if contracting the flu or HPV is forecasted to be of greater emotional impact. Furthermore, patients focusing on the anticipated regret of increasing their risk of cervical cancer may seek the HPV vaccination to avoid that future regret. Deciding against vaccinations based on forecasting errors can negatively influence a patient’s health. Other health‐related examples include screening children for adult‐onset illnesses and early screening for colorectal cancer. Regarding screening children for adult‐onset illnesses, parents may prefer to delay screening due to immune neglect in which parents think that the child would not be able to cope with screening results. In regard to the age at which regular colorectal cancer screening should begin, patients may prefer to begin screening earlier than recommended due to forecasting errors in which they believe that the emotional impact of an abnormal screen will be greater than in reality. Conversely, patients might avoid colorectal cancer screenings altogether if overestimating the distress evoked by the screening process. Screening is an important aspect of preventative healthcare that can be significantly influenced by affective forecasting. Affective forecasting also plays a role in preference‐sensitive prevention and treatment decisions, such as starting preventative breast cancer medications and treating low‐risk prostate cancer. Preventative breast cancer medications, such as tamoxifen, may reduce patient’s risk of developing breast cancer, but patients often choose against these medications if they believe it will increase their health‐related stress. Additionally, options for treating low‐risk prostate cancer include (a) watchful waiting, in which patients and physicians monitor the cancer’s progression; (b) surgery, after which urinary incontinence may occur; or (c) chemotherapy, in which debilitating side effects may occur. Some patients may choose watchful waiting because of difficulties recognizing their ability to cope with side effects or an overestimation of the intensity and duration of surgery or chemotherapy side effects. Other patients may choose surgery or chemotherapy because of difficulties recognizing their ability to adapt to a watchful waiting lifestyle and the potential anxiety of knowing cancer is present. In both of these ­scenarios, the pivotal factor is that challenges anticipating coping skills under ­varying

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c­ ircumstances can lead people to make decisions they later regret. Clearly, more research is needed to determine how to help patients to make forecasts more accurately. In chronic disease management, preference‐sensitive decisions can also be influenced by affective forecasting errors. For example, patients with kidney problems may need to choose between dialysis and an organ transplant. One patient may choose dialysis due to an underestimation of their ability to adjust to stress and post‐transplant protocols, such as taking immunosuppressant medications, but another patient may choose a kidney transplant because he or she overestimated the extent of future improvements to their health after a transplant. Forecasting errors in disease management can influence which decisions a patient believes are preferable, but very little is known about the relative accuracy of affective forecasts in disease contexts, let alone individual differences or situational factors that may augment accuracy. Affective forecasting also influences preference‐sensitive decisions at the end of life. Patients may decide against palliative care if they overestimate the distress of palliative care consultations that are frequently viewed as “giving up,” rather than empathic conversations aimed at improving quality of life. Conversely, some patients may decide against disease‐directed therapy if they overestimate the emotional toll of aggressive end‐of‐life treatments. In either situation, patients may be underestimating their ability to cope with side effects and/or o ­ verestimating the extent to which they will have to adjust to palliative or disease‐directed care. More attention and investigation is needed to help patients and their families understand and make these difficult decisions. Notably, patients are not the only individuals whose decisions are influenced by affective forecasting errors. Focalism and immune neglect may also influence families of patients and physicians in ways that affect the patient’s decision making. For example, family members may underestimate a patient’s ability to adjust and overestimate the emotional duration of a side effect. In these situations, family members’ difficulties in affective forecasting could sway patients’ healthcare decisions. In the context of medical decision making, individuals often heavily weigh the perceived emotional consequences as they consider between alternative treatment plans. To the extent to which individuals are biased toward overestimating negative emotional consequences, they may be less inclined to follow through with a potentially beneficial treatment. Therefore, basic and applied researchers have explored interventions to help mitigate decisional biased resulting from the disability paradox and affective forecasting errors. For instance, Ubel and colleagues have demonstrated that prompting individuals to reflect on adaptation may lead to increased estimates of quality of life with paraplegia. However, it is unclear how these processes may generalize to medical decision‐making situations where patients must choose between various treatment options. Emotional well‐being plays a pivotal role in motivating health behavior. The perceived enjoyment of health behaviors tends to be more important predictors of future behavior ­compared with rational assessments of risk and benefit (e.g., continuing to smoke for enjoyment despite recognizing the risks of lung disease; Loewenstein, Weber, Hsee, & Welch, 2001). Women at high risk for breast cancer who perceive limited emotional benefit from chemoprevention are less inclined to opt for those therapies. Because emotional outcomes to novel events can be hard to predict, it is possible that patients may benefit from more explicit information on how they may feel following various treatment options. Basic research by Gilbert, Killingsworth, Eyre, and Wilson (1999) found that prediction of response to anticipated events could be enhanced by gathering information from others who had already experienced those events. Similar applied research by Dillard and colleagues suggests that forecasting errors regarding colorectal cancer screening can be reduced by providing individuals with

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patient narratives about their experience with the screening process. The impact of these narratives on participants’ attitudes toward screening was significantly more favorable ­ ­compared with participants who did not receive the narratives. Although anecdotal information and patient narratives may hold some influence over medical decision making, additional research is needed to determine how information could be derived and delivered. Consistent with the goals of the Patient‐Centered Outcomes Research Institute (PCORI), clinical trials are needed to assess the differential impact of medical treatments on emotional outcomes so that this information can be shared with prospective patients and reduce uncertainty about emotional well‐being. There is also a need to further explore how this information would be shared with patients in clinical settings. For instance, routine distress screening is now standard care, but healthcare professionals vary widely in their ­willingness and competence in addressing patient concerns. If comparative efficacy data on ­emotional well‐being can be routinely tracked and providers can be coached to deliver this information with compassion and competence, patients may be guided to make better informed decisions. While much of this discussion has focused on affective forecasts about specific life events, it is also important to emphasize that relatively generalized affective forecasts about the future— that life is getting better or worse—can have important implications for decision making, ­perhaps regardless of the accuracy of these expectations. Affective forecasting studies have often focused on predictions about the relatively immediate emotional consequences of events that are highly discrete, and often controllable, with the assumption that if people could forecast more accurately, they might make different and more satisfying life decisions. In the health arena, some events are relatively discrete (e.g., screening tests, influenza vaccinations, surgery). Other health events are significantly more complex such as the development and treatment of many chronic health conditions, which unfold gradually over time, are multidetermined by factors that vary in controllability, and are potentially influenced by decades of decisions. Thus, while people have specific affective forecasts relevant to relatively discrete events, they also have more generalized affect forecasts about the emotional consequences of aging, changing health trajectories, or the future, more globally. Along these lines, fuzzy trace theory (Reyna, 2008), for example, emphasizes the importance of people’s relatively intuitive, gist‐level understanding of life events (e.g., “the future is terrifying,” “I can’t handle cancer and a divorce,” or “I’m lucky they caught it early”) for decision making. These generalized affective forecasts refer to the more nebulous and long‐term expected trajectories in emotional well‐being, rather than the relatively short‐lived affective reactions often studied under the social cognitive paradigm. When people experience a generalized expectation of improved emotional well‐being, this has often been called “hope,” which contrasts with the generalized negative expectancy, or “dread.” Regardless of accuracy, the valence of these generalized expectations—relative hope or dread about the future—could have serious implications for decision making. The importance of generalized affective forecasts about the future—the experience of hope or dread—has known importance to health and is potentially a fruitful path for further investigation. As summarized by crisis decision theory, which has organized several useful frameworks for understanding emotion and health decision making, people routinely manage negative anticipatory emotions by mentally and behaviorally disengaging. Thus, when people feel more hopeful about the future, they may be more inclined to engage in proactive health behaviors, but when experiencing dread, worry, or hopelessness, people may be more inclined to avoid proactive health behaviors. Several studies have shown, for example, that people who are more hopeful are more likely to have better medication adherence, seek cancer screenings, engage in exercise, and maintain healthy diets. Patients who are more hopeful may also be

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more likely to engage in “shared decision making,” a collaborative process by which patients actively participate in preference‐sensitive decisions. These sorts of affective forecasts may also have important implications for suicide and other escape–avoidance behaviors. Whereas ­suicidal ideation and suicide attempts in young adults often receive considerable attention, older adults are at the greatest risk of completed suicide, with the highest rates among older White males, especially those recently diagnosed with a serious health condition and when in the context of other life stressors, such as divorce and unemployment. The experience of hopelessness is a leading predictor of completed suicide in older adults, suggesting the importance of generalized affective forecasts about changing health circumstances in accounting for ­suicidal behavior. These findings help to illustrate an important unanswered question in affective forecasting research: what constitutes a “good” affective forecast? The social cognitive paradigm has traditionally focused on accuracy, but as emphasized in Funder’s (1995) realistic accuracy model of human judgment, there are other possibilities. One might define the relative goodness of an affective forecast not based on accuracy, but some other criterion, such as whether it is hopeful, benefits current emotional well‐being, fosters particular decisions about health and healthcare, or has a beneficial impact on longevity. The impact bias suggests that in light of declining health circumstances, people may be inaccurately negative in their perceptions of the future; thus, helping patients to forecast more accurately is essentially synonymous with helping them to be more positive and hopeful. In contrast, near the end of life, conversations about prognosis are often extremely overly optimistic, and this increased positivity could represent a departure from accuracy with potentially serious implications. For example, while overoptimism may have immediate emotional benefits, it has been hypothesized to be an important driver of overtreatment, lower quality of death, poorer family bereavement, and medical bankruptcies. Therefore, in the context of end‐of‐life care, there is an important debate over the relative importance of realistic versus optimistic communication, the relative benefits of accuracy versus positivity, and even the meaning of the construct “hope” itself. Many have advocated for approaches that, while difficult, seek to increase accurate perceptions of the future while simultaneously fostering a sense of hope and controllability (variously described as “realistic hope,” “deep hope,” and “blues‐inflicted hope”). Ultimately, there are many important open questions about the ways in which affective forecasting drives decision making and health. In conclusion, this section demonstrates the importance of basic research on affective forecasting for understanding decision making across the healthcare continuum. More research is needed to quantify the significance of affective forecasting in medical populations and examine interventions for improving affective forecasting, decision making, and healthcare outcomes.

Acknowledgments This work was supported by U54GM104940 from the National Institute of General Medical Sciences (MH).

Author Biographies Shannon M. Martin, PhD, is an assistant professor of psychology at Converse College in  Spartanburg, South Carolina, USA. Her research interests include the measurement of maladaptive emotional response and its impact on everyday life.

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James I. Gerhart, PhD, is an assistant professor of behavioral sciences and the director of the Psychosocial Oncology and Cancer Integrative Medicine Program at Rush University Medical Center, Chicago, Illinois, USA. He studies the impacts of anger and traumatic stress on medical decision making and patient–provider communication. Catherine Rochefort, MS, is a clinical psychology doctoral student in the Health Behavior Lab at Southern Methodist University in Dallas, Texas, USA. She studies the influence of affect on health behavior change and adjustment to chronic illness. Laura M. Perry, BS, is a health psychology doctoral student in the Psycho‐Oncology Research Program at Tulane University in New Orleans, Louisiana, USA. Her research interests include how personality, affective, and social factors relate to cancer outcomes. Michael Hoerger, PhD, MSCR, is an assistant professor of psychology, psychiatry, and oncology and director of the Psycho‐Oncology Research Program at Tulane University in New Orleans, Louisiana, USA. His research aims to improve quality of life by anticipating, preventing, and alleviating the physical and emotional burden of cancer.

References Albrecht, G. L., & Devlieger, P. J. (1999). The disability paradox: High quality of life against all odds. Social Science and Medicine, 48, 977–988. Funder, D. C. (1995). On the accuracy of personality judgement: A realistic approach. Psychological Review, 102, 652–670. Gilbert, D. T., Killingsworth, M. A., Eyre, R. N., & Wilson, T. D. (1999). The surprising power of neighborly advice. Science, 323, 1617–1619. Gilbert, D. T., Pinel, E. C., Wilson, T. D., Blumberg, S. J., & Wheatley, T. P. (1998). Immune neglect: A source of durability bias in affective forecasting. Journal of Personality and Social Psychology, 75, 617–638. Hoerger, M., Quirk, S. W., Lucas, R. E., & Carr, T. H. (2009). Immune neglect in affective forecasting. Journal of Research in Personality, 43, 91–94. Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127, 267–286. Reyna, V. F. (2008). A theory of medical decision making and health: Fuzzy trace theory. Medical Decision Making, 28, 820–865. Wilson, T. D., Wheatley, T. P., Meyers, J. M., Gilbert, D. T., & Axsom, D. (2000). Focalism: A source of durability bias in affective forecasting. Journal of Personality and Social Psychology, 78, 821–836.

Suggested Reading Halpern, J., & Arnold, R. M. (2008). Affective forecasting: An unrecognized challenge in making serious health decisions. Journal of General Internal Medicine, 23, 1708–1712. Hoerger, M., Scherer, L. D., & Fagerlin, A. (2016). Affective forecasting and medication decision ­making in breast cancer prevention. Health Psychology, 35, 594–603. Wilson, T. D., & Gilbert, D. T. (2005). Affective forecasting: Knowing what to want. Current Directions in Psychological Science, 14, 131–134.

Aging Ann Rosenthal1, Dara Mickschl1, Edith Burns1, Elizabeth Neumann1, Jay Urbain2, Sergey Tarima1, and Steven Grindel1 Medical College of Wisconsin, Milwaukee, WI, USA Milwaukee School of Engineering, Milwaukee, WI, USA 1

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Background Rotator cuff injuries are one of the most common joint ailments plaguing older adults today (e.g., Raymond, Sluka, McGowan, & American Academy of Orthopaedic Surgeons, 2011). This chapter describes the clinical epidemiology of shoulder impairment in older adults, standard approach to management, and the current ambiguity of the literature on the effectiveness of interventions for optimizing long‐term outcomes. Preliminary data is presented grounded within a theoretical model, supporting the hypothesis that patients use “out‐of‐ date” self‐prototypes to establish expectations for outcomes, which has significant implications for long‐term self‐management behaviors and may explain lack of adherence to therapy. Finally, the authors describe feasibility work on theory‐based interventions that have the potential to address and inform unrealistic patient expectations.

Epidemiology of Shoulder Impairment in Older Adults The prevalence of symptomatic rotator cuff tendinopathy in the general population ranges from 2.8 to 7.6% (Milgrom, Schaffler, Gilbert, & van Holsbeeck, 1995; Reilly, Macleod, Macfarlane, Windley, & Emery, 2006; Senbursa, Baltaci, & Atay, 2011; Silverstein et  al., 2006), while asymptomatic rotator cuff tendinopathy is found in 10–20% (Milgrom et  al., 1995). Rotator cuff tears are even more common, with reported prevalence ranging from 20% in the general population (Yamamoto et  al., 2010), 30% in cadaveric studies (Reilly et  al., 2006; Silverstein et al., 2006), to 40% in adults over the age of 50 (Worland, Lee, Orozco,

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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SozaRex, & Keenan, 2003). Self‐reported prevalence of shoulder pain among all adults is estimated to be between 16 and 26% and is the third most common cause of musculoskeletal consultation in primary care (Luime et al., 2004; Urwin et al., 1998). Symptomatic pathology is increasingly common with age, occurring in up to 80% of adults over the age of 80 (Milgrom et al., 1995). The resultant pain and reduced range of motion (ROM) can significantly impair functional status and independence and negatively affect quality of life (Chambers, Shea, & Carey, 2011; Cigolle, Langa, Kabeto, Tian, & Blaum, 2007; Leveille, Zhang, McMullen, Kelly‐Hayes, & Felson, 2005; Sha et al., 2005). In a prior survey of older adults, the authors found a 31% prevalence of chronic, severe shoulder pain (pain lasting more than 3 months in the past year) and 48% prevalence of impaired ROM. Of those with pain and reduced ROM, 36% had impaired basic activities of upper body function (e.g., grooming) (Burner et  al., 2014).

Approach to Management of Shoulder Impairment The first‐line management for shoulder impairment and pain typically includes referral to physical therapy (PT) and home exercise programs (HEP) (Hawkins & Dunlop, 1995; Lombardi, Magri, Fleury, Da Silva, & Natour, 2008; Ludewig & Borstad, 2003). Conservative management may also include injections, pain medications, and other modalities (e.g., heat, topical analgesia). There is little evidence on which to base optimum management, with clinical guidelines being based on mostly inconclusive evidence and expert consensus (Pedowitz, Hunter, Tracy, & Brennan, 2012; Rechardt et  al., 2010; Rookmoneea et  al., 2010). Once patients undergo surgical repair, they are also referred to PT and instructed in HEP to ­maximize postoperative recovery. There has been an increase in surgical treatment of rotator cuff trauma by 141% over the 10‐year period from 1996 to 2006. When adjusting for population growth, this translates into a 115% increase in rotator cuff repairs over this 10‐year period (Colvin, Egorova, Harrison, Moskowitz, & Flatow, 2012). The best operative results are seen when repairing smaller tears that are not associated with significant muscle atrophy. It remains standard practice to refer patients to PT after surgical repair to optimize recovery of ROM.

Efficacy of Management Conservative treatments such as PT and HEP have been shown to contribute to significant improvement in function in small trials, though it may take many months or even years to reach full impact (Chambers et al., 2011; Fritz, Childs, Wainner, & Flynn, 2012). In usual clinical practice, many older adults have limited contact with PT for their shoulder impairment, and it is unknown how long they continue to maintain HEP. The average number of PT visits per patient for shoulder problems has been reported at about 9 over a period of 4 weeks (Fritz et al., 2012). In a longitudinal assessment of shoulder pain in general practice, 41% of patients with reported shoulder pain showed persistent symptoms after 12 months following conservative therapy (Luime et al., 2004). For those experiencing improvement in pain and function with PT, the degree of improvement, and how long improvements are sustained, is generally not specified (Chambers et al., 2011; Fritz et al., 2012). Surgery may lead to significant improvement in function, but pain and limited mobility are unlikely to resolve ­completely, and re‐tearing is a common long‐term complication (Van der Windt et al., 1996).

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Several factors play into the paucity of high‐quality literature on conservative management for rotator cuff impairment. Quantifying improvement remains a challenge, as no standardized treatment protocols or gold standard outcome measurements exist. Methodologic issues are prevalent, and published studies frequently are uncontrolled, and lack consistency in the outcome scales used to measure pain, ROM, and function (Chambers et al., 2011; Rookmoneea et al., 2010). Despite these limitations, there does seem to be improvement in rotator cuff symptoms without a full‐thickness tear with PT (Bennell et al., 2010; Lombardi et al., 2008) or HEP (Ludewig & Borstad, 2003) and no difference comparing PT with HEP (Walther, Werner, Stahlschmidt, Woelfel, & Gohlke, 2004; Werner, Walther, Ilg, Stahlschmidt, & Gohlke, 2002). Adjunct therapies such as scapular stabilization (Baskurt, Başkurt, Gelecek, & Özkan, 2011) and manual therapy (Bang & Deyle, 2000; Senbursa et al., 2011) may provide additive benefit to PT. The improvements in pain range from 2 to 4 cm on the Visual Analogue Scale maintained at 8–10 weeks’ follow‐up (20–40% decrease in symptoms) (Bang & Deyle, 2000; Lombardi et al., 2008; Ludewig & Borstad, 2003). Improvements in pain, ROM, and function in these studies are maintained at 6–22 weeks of follow‐up, though the magnitude of gain is vague (Baskurt et al., 2011; Bennell et al., 2010; Lombardi et al., 2008; Silverstein et al., 2006; Walther et al., 2004; Werner et al., 2002). Less evidence exists for the efficacy of PT and HEP for full rotator cuff thickness tears, consisting mainly of uncontrolled case series. Supervised PT (Ainsworth, 2006; Baydar et al., 2009; Hawkins & Dunlop, 1995) and HEP improved patients’ function, with improvement maintained at 3 months to 3.8 years of follow‐up (Bennell et al., 2010; Lombardi et al., 2008). Thus, patients with rotator cuff issues are typically referred for PT, both with and without surgery and other treatment modalities. With any approach to treatment, it often takes months to years for full recovery, and though extended PT and HEP (self‐management) may help to attain optimum outcomes, full recovery may never be achieved. If patients have the expectation that treatment should result in complete resolution of all symptoms and restoration of full function, especially in a short amount of time, they have a disincentive to maintain longer‐ term treatment. Because patients often focus on somatic symptoms (e.g., pain) and restricted movement as indicators of ineffectiveness of treatment, this may be particularly likely to happen with shoulder dysfunction and result in less than optimal self‐management.

The Commonsense Model of Self‐Regulation (CSM) as a Framework for Examining Management of Shoulder Impairment CSM is based upon the patient’s representations or mental models of the illness (e.g., ­rotator cuff injury), the procedures for treatment (e.g., surgery, PT), an action plan for implementing treatment, and timeline and expectations for outcomes against which they assess efficacy of a treatment. “Normal” ROM and strength is the underlying “self‐prototype,” which may be conscious and or automatic (e.g., “normal shoulder function will feel like it did when I  was 25”). The creation of representations begins with the awareness, either implicit (minimally conscious) or explicit, of deviations from the normal, “healthy” self: pains (e.g., shoulder pain) and functional changes (trouble reaching, carrying). The location and properties of an experienced deviation (sharp pain when overextending the shoulder), its time frames (rapidity of onset, duration), perceived causes (a fall), and response to self or medical treatment (heat eased the pain, rate of “improvement”) are concrete perceptual factors that “define” the deviations as a shoulder injury. The impact of the disorder on thought, physical activity, social exchanges, and work defines its perceived and anticipated ­consequences.

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The five content areas of an “active” mental model are identity (symptoms and location), timelines, cause, control, and consequences. These areas are generated by the match of the deviations from the self‐prototype to a belief structure or prototype of a specific illness. An active mental representation suggests a range of actions for ameliorating the perceived discrepancies that will be experienced as coherent with the illness/injury representation and repeated as needed. If benefits are temporary (timeline violated) or absent (no alleviation), or insufficient to permit important life activities (consequences), the individual is highly likely to talk to family and/or friends and eventually seek medical care (Cameron, Leventhal, & Leventhal, 1993). Differences in the “changeability” of these processes are unfortunately little understood. Active mental models, that is, representations of an illness, a treatment, and their coherence (the treatment fits the problem, i.e., how it works), are potentially highly flexible, while the underlying prototypes are less so. The two least flexible prototypes are (a) the usual/healthy self (Self as Anchor) and (b) expected outcomes of action, that is, the fundamental “Expectancy that Actions Produce Immediate Outcomes” (EAPIO). The self is a powerful, underlying anchor, as the self is experienced implicitly and sometimes explicitly, every moment of the 24 hr in a day, every day of every year of life. Deviations and outcomes of actions (benefits) are perceived and measured against this anchor, and it shapes life expectancies (Mora, DiBonaventura, Idler, Leventhal, & Leventhal, 2008). The EAPIO is an anchor for action; the acquisition begins at birth, as the infant perceives environmental changes in response to action (the mobile moves when legs are shaken; grasping the object seen brings it to the mouth). When action fails to produce an immediate outcome and mom does not come in response to the cry, the cry morphs into distress, rage, and loss of hope. Violations of EAPIO are experienced daily, for example, when the effort needed to lift an apparently light or heavy object is discrepant with experience (small object is extremely heavy, large object unexpectedly light). The Self as Anchor and EAPIO are critical factors for examining the rehabilitation process. The contrast between Self as Anchor and current function (e.g., rotator cuff injury) creates the representation of injury and the target and/or “hope” for treatment outcome. The model suggests that the representation of injury (perceived difference between pre‐ and post‐injured self) often exceeds reality. The “psycho‐logic” for this is simple; the sense of normal Self as Anchor is overlearned, and the “how I am now” versus “how I am (normally) supposed to be” is magnified. The best‐case scenario for therapy is when the results are perceptible and match or exceed expectations. The worst case is that pain and distress following PT (negative feedback) overwhelms actual improved motion. Such immediate effects need to be seen and understood in terms of an extended time frame or pathway to reach recovery; otherwise maintenance of treatment is unlikely.

Self‐Prototypes: The Case in Shoulder Impairment Individual psychosocial factors such as passive coping style, fear of movement, and general psychological distress may impact the chronicity of symptoms. Patients also have explicit expectations for diagnosis, recovery, and self‐management of their condition. Such “commonsense” perceptions may not accurately reflect the underlying biophysical and medical factors at play. There is often a difference between objective clinical and radiological evidence of musculoskeletal disease activity and a patient’s experience of pain and functional ability (Hanusch, Goodchild, Finn, & Rangan, 2009). If actions are taken, and expected results are not seen within the anticipated time frame then further assessment and intervention may be justified

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(e.g., “I’ve gone to PT for my shoulder for 6 weeks and it doesn’t feel like it’s back to normal, so PT is obviously not working”). For patients, the greatest impact of their disease lies in the effect it has on their ability to continue with “normal” life. All the physician authors have had such perceptions communicated by their patients during the course of routine practice (SG, EB, AR). A series of studies were conducted to explore patients’ underlying self‐prototypes and expectations for outcomes of PT in relationship to impaired shoulder function. In tandem, feasibility data was gathered on a system that will provide patients with clear, objective feedback on changes in ROM of an injured rotator cuff, allowing them to see that PT and HEP do improve shoulder function. We utilized a prototype software application and KinectTM system to generate and record a stick‐figure image, or skeletal avatar, that may be stored and replayed to visualize progress in ROM over time. The feedback is designed to overcome the lack of awareness of the very gradual, slow changes generated by PT and HEP and maintain motivation for long‐term adherence. Semi‐structured interviews were conducted with patients entering PT for shoulder pain for the first time (N = 15) and patients planning to undergo surgical rotator cuff repair (N = 10) to gather preliminary data on underlying self‐prototypes (i.e., self “baseline” against which patients were comparing outcomes of treatment), expectations for outcomes of treatment, and anticipated timelines to reach successful outcomes. Mean age of the PT group was 65.7 + 5.9 years, with an average of 6.3 chronic medical conditions (range 0–16). The presurgical group had an average age of 47.8 + 11.2 years and a mean of 5.7 chronic conditions (range 1–21). About 90% of the entire group of patients reported shoulder pain lasting at least 3 months out of the last year, a decreased ability to complete basic activities of daily living (e.g., grooming, dressing), and that their sleep had been adversely affected by shoulder pain.

Self‐Prototypes, Expectations, and Timelines for Outcomes All patients were asked to give their age when their shoulders last felt “normal,” that is, ­without any pain or symptoms of impairment. PT patients who had never gone for PT before (“PT‐naive”) all gave ages at least 15 years younger as their self‐anchor (range 15–40 years). Those who had previously been to PT for other problems, expected their shoulder to function as it had 1–2 years previously. All save one of the surgical patients expected their postoperative shoulder to feel and function as it had at least 15 years earlier. The only patient who varied from this pattern, giving his current age as “baseline normal,” had undergone rotator cuff repair of the opposite shoulder several years before and so had already experienced the procedure and its outcome. When questioned about expectations for outcomes of treatment, most patients expected to regain 90% or more of normal function, expected steady improvement, and reported they would stop HEP if they experienced any pain or discomfort. The one patient with previous shoulder surgery estimated regaining only 50% of prior normal function. This individual had objectively regained about 90% function in the previously injured shoulder, but reported regaining only 75% of normal function, and expected a worse outcome for the current impairment. This exemplifies the CSM construct of an “overlearned” self‐prototype, with exaggeration of a current deficit and projection of this onto future outcomes. All the participants had unrealistic expectations for timeline of recovery, estimating it would take 6 months or less. Forty percent of the entire group estimated it would take less than 4 months to regain full function, and none thought it would take more than a year.

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The results of these interviews highlight that patient expectations about recovery of ­shoulder ROM may be unrealistic, both in terms of timeline and extent of recovery. The CSM predicts that patients use underlying prototypes or “anchors” of the “healthy self” as a comparison for desired outcomes. These anchors/prototypes may date back many years and not reflect that person’s actual pre‐injury “baseline.” Other factors, such as lack of education on the typical course of musculoskeletal ailments, lack of willingness to experience pain in the context of healing, or unrealistic attributions of one’s injury, may all moderate adherence to treatment choices and self‐management. Because all patients reported expectations for steady improvement and progress without setback (even the “surgically experienced” patient), the experience of pain or other perceived setback may negatively impact a patient’s adherence to PT due to perceived lack of efficacy. In general, patients failed to recognize that there might be overlap of discomfort from both PT and an underlying injury, or variability in symptoms.

Technology for Addressing Unrealistic Timelines and Expectations for Outcomes The described surveys of patients entering PT for treatment of shoulder impairment found that the majority have unrealistic expectations for recovery, have difficulty in accurately assessing improvement, and express frustration about lack of positive feedback during PT. Technology such as the Kinect imaging sensor has the potential to address these misperceptions to improve adherence and outcomes from PT. The best‐case scenario for therapy is when the results, such as improved movement and less pain, match or exceed expectations. The worst case is that negative feedback such as pain and distress during or following PT overwhelms the perception of improved motion. Such immediate effects need to be seen and understood in terms of an extended time frame or pathway to recovery; otherwise adherence to a long‐term therapy regimen is unlikely. Our Kinect application has the capability to record and store successive images of a patient’s ROM representing progress over time; viewing stored images alongside current ROM allows the patient to see how his/her movement has improved from “worst function” to the present, providing concrete evidence of efficacy that overrides momentary doubts about benefit and negative feedback (e.g., pain during movement). Such images also provide a dynamic record of ROM, instead of clinician recall or static measurements recorded during prior PT sessions. The authors have designed a software application (JU) that takes the camera image and translates it into a skeleton or “stick avatar” of the patient that is displayed on a screen in real time. As the patient proceeds through an ROM exercise, the avatar shows this range on the screen along with fiducial limb ROM measurements for each exercise. The system is also capable of simultaneously displaying a prerecorded avatar, that is, prior patient images or exemplar patient. The patient (and clinician) thus receive immediate feedback on patient ROM because the application can record and store successive patient images for later review, allowing them to observe progress over time. Feasibility testing was completed in a proof‐of‐concept trial on the surgical patients at “baseline” (preoperatively) and at 6 weeks and 3 months’ postoperatively. At each visit shoulder ROM was measured by a clinician visually and with the Kinect. Motions assessed for all participants included forward elevation, external rotation with arm abducted and arm at side, and abduction. The angle measurements as recorded by the Kinect showed a strong correlation to the clinician measurements (R  =  0.966, 95% confidence interval 0.940–0.981), with no significant

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difference between the two ways of measurement (t = −0.23, p = 0.81). As anticipated, ROM was impaired at baseline on the side of injury, was even more restricted at 6 weeks’ postoperatively (about the time when patients could start moving their shoulder), and improved significantly at 3 months. These data demonstrate the feasibility and validity of the Kinect system to record angle measurements that are in line with, and not statistically different from, that measured by a clinician. The observed ROM over time showed the pattern that would be expected over the course of surgical management of a rotator cuff injury: decreased ROM at baseline, followed by an even greater decrease after 6 weeks of immobilization following surgery, and improvements over baseline by 3 months. The benefits of this system include the ability of clinician and patient to track their improvements over time with concrete data and measurements to provide the patient with positive feedback to override temporary doubts and lapses in motivation. The system is currently being tested in a randomized trial. All participants are imaged prospectively over the course of PT, but half view their progressive images at each visit. This application will eventually be deployed in the home setting by migrating the application to a tablet or smartphone and will include the ability to send ROM data directly to the clinician for monitoring. This approach could limit unnecessary follow‐up visits and cut healthcare costs while allowing the patient to continue their PT at home with proper visual feedback and clinician updates to instill motivation over time. Similar ROM images and measures are being collected on normal, healthy subjects to get a preliminary sense of interindividual variability. This data may be used to develop “ideal ROM” avatars that can be displayed with individual patient images as target goals for recovery.

Conclusion Moving forward, it is hoped that providing patients with accurate feedback on progress over time (such as that provided by the Kinect imagery) will override subjective perceptions of lack of progress and thus improve long‐term adherence to treatment. Another implication of this research is that clinicians may improve patient adherence by assessing their expectations for recovery as part of their initial evaluation and directly addressing unrealistic expectations. This study did not evaluate whether changing a patient’s expectations leads to improved outcomes, but it does suggest that patients with shoulder pain are underinformed and unrealistic about the usual course of their disease.

Author Biographies Ann Rosenthal, MD, is the Will and Cava Ross Professor of Medicine and Chief of Rheumatology at the Medical College of Wisconsin. Her major research interests are in ­calcium pyrophosphate deposition disease and other metabolic and age‐related musculoskeletal syndrome. Dara Mickschl, PA‐C, is a physician assistant at the Medical College of Wisconsin. She focuses on orthopedic upper extremity of all ages. She has athletic training background. Her research topics include surgical interventions for upper extremity orthopedic conditions, demographic and health factors affecting outcomes, investigating techniques for accuracy and efficacy of steroid injections, and kinematic assessment during postsurgical rehabilitation.

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Edith Burns, MD, is a professor of medicine at the Medical College of Wisconsin. Her ­primary research interests are self‐management of chronic illness (e.g., diabetes, CHF, joint impairment), risks for hospital readmission after elective surgery, multimorbidity in geriatric populations, and assessing patient illness perceptions, expectations, and timelines for outcomes. Elizabeth Neumann, MD, graduated from Medical College of Wisconsin in 2017. Her work with Dr. Burns, on expectations for outcomes of shoulder impairment, was supported by an NIA, T35 grant on aging and injury prevention. She is currently a first‐year obstetrics– gynecology resident at the University of Florida, Sacred Heart System. Jay Urbain, PhD, is a professor in the Electrical Engineering and Computer Science Department at the Milwaukee School of Engineering and codirector of Biomedical Informatics for CTSI of SE Wisconsin at the MCW. His research interests include machine learning, NLP, information retrieval and applications in biomedical informatics, intelligence, and real‐time sensors. Sergey Tarima, PhD, is an associate professor of biostatistics. He was appointed to the faculty at the Medical College of Wisconsin at completion of his doctorate in 2005. His research interests include use of additional information in statistical estimation and sample size estimation and reestimation in the presence of nuisance parameters. Steven Grindel, MD, is professor of orthopedic surgery at the Medical College of Wisconsin.  His practice and research interests focus on arthroscopic and reconstructive ­surgery of the shoulder, elbow, wrist, and hand, with extensive expertise in industrial problems ­affecting the upper extremity.

References Ainsworth, R. (2006). Physiotherapy rehabilitation in patients with massive, irreparable rotator cuff tears. Musculoskeletal Care, 4(3), 140–151. Bang, M. D., & Deyle, G. D. (2000). Comparison of supervised exercise with and without manual physical therapy for patients with shoulder impingement syndrome. Journal of Orthopaedic and Sports Physical Therapy, 30(3), 126–137. Baskurt, Z., Başkurt, F., Gelecek, N., & Özkan, M. H. (2011). The effectiveness of scapular stabilization exercise in the patients with subacromial impingement syndrome. Journal of Back and Musculoskeletal Rehabilitation, 24(3), 173–179. Baydar, M., Akalin, E., El, O., Gulbahar, S., Bircan, C., Akgul, O., … Kizil, R. (2009). The efficacy of conservative treatment in patients with full‐thickness rotator cuff tears. Rheumatology International, 29(6), 623–628. Bennell, K., Wee, E., Coburn, S., Green, S., Harris, A., Staples, M., … Buchbinder, R. (2010). Efficacy of standardized manual therapy and home exercise programme for chronic rotator cuff disease: Randomized placebo controlled trial. British Medical Journal, 340, c2756. Burner, T., Abbott, D., Huber, K., Stout, M., Fleming, R., Wessel, B., … Burns, E. (2014). Shoulder symptoms and function in geriatric patients. Journal of Geriatric Physical Therapy, 37(4), 154–158. Cameron, L., Leventhal, E. A., & Leventhal, H. (1993). Symptom representations and affect as determinants of care seeking in a community‐dwelling, adult sample population. Health Psychology, 12(3), 171–179. Chambers, H. G., Shea, K. G., & Carey, J. L. (2011). American academy of orthopaedic surgeons clinical practice guideline: Diagnosis and treatment of osteochondritis dissecans. Journal of the American Academy of Orthopaedic Surgeons, 19(5), 307–309. Cigolle, C. T., Langa, K. M., Kabeto, M. U., Tian, Z., & Blaum, C. S. (2007). Geriatric conditions and disability: The Health and Retirement Study. Annals of Internal Medicine, 147(3), 156–164.

Aging

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Colvin, A. C., Egorova, N., Harrison, A. K., Moskowitz, A., & Flatow, E. L. (2012). National trends in rotator cuff repair. Journal of Bone and Joint Surgery, American Journal of Orthopedic Surgery, 94(3), 227–233. Fritz, J. M., Childs, J. D., Wainner, R. S., & Flynn, T. W. (2012). Primary care referral of patients with low back pain to physical therapy: Impact on future health care utilization and costs. Spine, 37(25), 2114–2121. Hanusch, B. C., Goodchild, L., Finn, P., & Rangan, A. (2009). Large and massive tears of the rotator cuff; functional outcome and integrity of the repair after a mini‐open procedure. Journal of Bone and Joint Surgery, 91‐B, 201–205. Hawkins, R. H., & Dunlop, R. (1995). Nonoperative treatment of rotator cuff tears. Clinical Orthopaedics and Related Research, 321, 178–188. Leveille, S. G., Zhang, Y., McMullen, W., Kelly‐Hayes, M., & Felson, D. T. (2005). Sex differences in musculoskeletal pain in older adults. Pain, 116(3), 332–338. Lombardi, I., Jr., Magri, A. G., Fleury, A. M., Da Silva, A. C., & Natour, J. (2008). Progressive resistance training in patients with shoulder impingement syndrome: A randomized controlled trial. Arthritis and Rheumatism, 59(5), 615–622. Ludewig, P. M., & Borstad, J. D. (2003). Effects of a home exercise programme on shoulder pain and functional status in construction workers. Occupational and Environmental Medicine, 60, 841–849. Luime, J., Koes, B. W., Hendriksen, I. J., Burdorf, A., Verhagen, A. P., Miedema, H. S., & Verhaar, J. A. (2004). Prevalence and incidence of shoulder pain in the general population; a systematic review. Scandinavian Journal of Rheumatology, 33, 73–81. Milgrom, C., Schaffler, M., Gilbert, S., & van Holsbeeck, M. (1995). Rotator‐cuff changes in asymptomatic adults. The effect of age, hand dominance and gender. Journal of Bone and Joint Surgery. British Volume, 77(2), 296–298. Mora, P. A., DiBonaventura, M. D., Idler, E., Leventhal, E. A., & Leventhal, H. (2008). Psychological factors influencing self‐assessments of health: Toward an understanding of the mechanisms underlying how people rate their own health. Annals of Behavioral Medicine, 36(3), 292–303. Pedowitz, R. A., Hunter, S. J., Tracy, D. M., & Brennan, G. P. (2012). Utilization and clinical outcomes of outpatient physical therapy for medicare beneficiaries with musculoskeletal conditions. Physical Therapy, 91(3), 330–345. Raymond, L., Sluka, P., McGowan, R., & American Academy of Orthopaedic Surgeons. (2011). Optimizing the management of rotator cuff problems. Journal of the American Academy of Orthopaedic Surgery, 19(6), 368–379. Rechardt, M., Shiri, R., Karppinen, J., Jula, A., Heliövaara, M., & Viikari‐Juntura, E. (2010). Lifestyle and metabolic factors in relation to shoulder pain and rotator cuff tendinitis: A population based study. BMC Musculoskeletal Disorders, 20(11), 165. Reilly, P., Macleod, I., Macfarlane, R., Windley, J., & Emery, R. J. (2006). Dead men and radiologists don’t lie: A review of cadaveric and radiological studies of rotator cuff tear prevalence. Annals of the Royal College of Surgeons of England, 88(2), 116–121. Rookmoneea, M., Dennis, L., Brealey, S., Rangan, A., White, B., McDaid, C., & Harden, M. (2010). The effectiveness of interventions in the management of patients with primary frozen shoulder. Journal of Bone and Joint Surgery. British Volume, 92(9), 1267–1272. Senbursa, G., Baltaci, G., & Atay, O. A. (2011). The effectiveness of manual therapy in supraspinatus tendinopathy. Acta Orthopaedica et Traumatologica Turcica, 45(3), 162–167. Sha, M. C., Callahan, C. M., Counsell, S. R., Westmoreland, G. R., Stump, T. E., & Kroenke, K. (2005). Physical symptoms as a predictor of health care use and mortality among older adults. American Journal of Medicine, 118(3), 301–306. Silverstein, B. A., Viikari‐Juntura, E., Fan, Z. J., Bonauto, D. K., Bao, S., & Smith, C. (2006). Natural course of nontraumatic rotator cuff tendinitis and shoulder symptoms in a working population. Scandinavian Journal of Work, Environment & Health, 32(2), 99–108. Urwin, M., Symmons, D., Allison, T., Brammah, T., Busby, H., Roxby, M., … Williams, G. (1998). Estimating the burden of musculoskeletal disorders in the community: The comparative prevalence

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of symptoms at different anatomical sites, and the relation to social deprivation. Annals of the Rheumatic Diseases, 57, 649–655. Van der Windt, D. A., Koes, B. W., Boeke, A. J., Devillé, W., De Jong, B. A., & Bouter, L. M. (1996). Shoulder disorders in general practice: Prognostic indicators of outcome. British Journal of General Practice, 46, 519–523. Walther, M., Werner, A., Stahlschmidt, T., Woelfel, R., & Gohlke, F. (2004). The subacromial impingement syndrome of the shoulder treated by conventional physiotherapy, self‐training, and a shoulder brace: Results of a prospective, randomized study. Journal of Shoulder and Elbow Surgery, 13(4), 417–423. Werner, A., Walther, M., Ilg, A., Stahlschmidt, T., & Gohlke, F. (2002). Self‐training versus conventional physiotherapy in subacromial impingement syndrome. Zeitschrift Fur Orthopadie Und Ihre Grenzgebiete., 140(4), 375–380. Worland, R. L., Lee, D., Orozco, C. G., SozaRex, F., & Keenan, J. (2003). Correlation of age, acromial morphology, and rotator cuff tear pathology diagnosed by ultrasound in asymptomatic patients. Journal of the Southern Orthopaedic Association, 12(1), 23–26. Yamamoto, A., Takagishi, K., Osawa, T., Yanagawa, T., Nakajima, D., Shitara, H., & Kobayashi, T. (2010). Prevalence and risk factors of a rotator cuff tear in the general population. Journal of Shoulder and Elbow Surgery, 19(1), 116–120.

Suggested Reading Greenberg, D. L. (2015). Evaluation and treatment of shoulder pain. Medical Clinics of North America, 98, 487–504. Leventhal, H., Phillips, L. A., & Burns, E. A. (2016). The Common‐sense model of self‐regulation (CSM): A dynamic framework for understanding illness self‐management. Journal of Behavioral Medicine, 39(6), 935–946. Webster, D., & Celik, O. (2014). Systematic review of Kinect applications in elderly care and stroke rehabilitation. Journal of Neuroengineering and Rehabilitation, 11(108), 1–24.

Trends in Clinical Health Psychology and Behavioral Medicine C. Steven Richards1 and Lee M. Cohen2 Department of Psyschological Sciences, Texas Tech University, Lubbock, TX, USA Department of Psychology, College of Liberal Arts, The University of Mississippi, Oxford, MS, USA

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Clinical health psychology and behavioral medicine are interesting and important specialty areas where various healthcare fields intersect. In this entry, we briefly discuss seven trends, that we believe, are shaping these fields, including (a) methodological sophistication, (b)  chronic diseases, (c) diversity and inclusiveness, (d) biological issues, (e) translation of health psychology knowledge, (f) chronic psychopathology that is comorbid with health problems and diseases, and (g) social justice. We conclude this entry with a brief discussion of recommendations regarding theory, research, and practice to consider for the future.

Methodological Sophistication After a review of the research in clinical health psychology and behavioral medicine, it is clear that the intricacy of the studies being published has progressed significantly in multiple ways. For instance, many studies include large and diverse samples of participants. Sampling effectively is one of the cornerstones of sound research and certainly in any area involving human participants. An example of a recent study that includes a large and diverse sample was ­conducted by Polenick, Renn, and Birditt (2018), where they studied the dyadic effects of depression on medical morbidity among 992 middle‐aged and older‐adult couples. One of the major findings of this study was that depressive symptoms in this population have long‐term associations with medical morbidity, such as chronic health conditions. Another indication of the current methodological sophistication in this area is an increased use of multi‐variable, multi‐trait, and multi‐method measures, with sound psychometric ­properties. Such methodology permits the collection of a more thorough picture of the The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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c­ ognitive, emotional, behavioral, biological, and other health outcome variables relevant to the research questions being studied. An interesting example is a study that explored the longitudinal pathways to alcohol use and abuse among 426 adolescents who had genetic risk factors for addiction, by Trucco, Villafuerte, Hussong, Burmeister, and Zucker (2018). These investigators predicted that the pathways under investigation would be moderated by depression and depressive relapse ­during the last wave of the study, which included adolescents aged 15–17 years. More specifically, it was expected that depression would be related to more substance use and would essentially facilitate the expression of the genetic risk factors. Trucco et al. (2018) also predicted that ineffective coping methods for stress would be a moderator, related to greater substance use. The investigators’ predictions were supported: ongoing depression (or depressive relapse) over time, along with ineffective coping methods, appears to be a pathway that allows genetic risk factors to influence substance use and abuse. This study represents multiple research trends toward excellence in clinical health psychology and behavioral medicine, such as including a large sample (N = 426, plus a comparison sample), which was followed longitudinally over many years (ages 3–17, in 5 waves), with diverse measures (multi‐method, multi‐trait, and multi‐source), and attention to both biological and psychological variables. Trucco et  al. (2018) included measures of demographics, family history, temperament, ­coping, depression, aggression, substance use, and biological correlates such as gender, race, and genotyping. Data were obtained from multiple sources for the psychosocial variables. Moreover, this study provides a rich evidence base for future efforts at health promotion and intervention. In summary, methodological sophistication is one of the important trends in research on clinical health psychology and behavioral medicine. Indeed, recent editorials by editors of some of the major journals in the field have boasted about this trend and called for it to continue (e.g., for discussions of methodological issues, see Appelbaum et al., 2018; Freedland, 2017; Lilienfeld, 2017).

Chronic Diseases Another trend in clinical health psychology and behavioral medicine is an emphasis on ­psychosocial variables in the context of chronic diseases. There is tremendous interest and concern regarding highly prevalent chronic diseases such as heart disease, cancer, stroke, ­diabetes, arthritis, flu and other pandemic diseases, and so forth. These diseases are often listed in the top 5 or 10 causes of death from disease among adults across the industrialized world. Furthermore, psychosocial variables have been established as having important mediating, moderating, and health‐promoting effects for the course and outcome of such diseases (e.g., for a readable review, see Taylor, 2018). An example of an excellent study illustrating this trend is a recent study by Manne, Siegel, Heckman, and Kashy (2016). This study included 302 female patients with early‐stage breast cancer, along with their spouses. For the most distressed patients, a couple‐focused and supportive version of group therapy appeared to be the more‐effective intervention for coping with psychosocial variables such as depression and anxiety. In contrast, for patients who were less distressed, a structured and skills‐based version of group therapy appeared to be the most effective for reducing depression and anxiety, and in an associated manner, also improving health‐promoting behaviors such as stronger adherence to medical regimens. This study ­successfully addressed some of the common research concerns among the psycho‐oncology

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research community, such as including a relatively large sample, a longitudinal and randomized controlled design, and use of a broad array of measures. There are also numerous excellent examples of research examining combined psychosocial and medical interventions for post‐heart‐attack patients that are represented by the ENRICHD and CREATE randomized controlled trials (Berkman et al., 2003; Lespérance et al., 2007). These large trials (with beginning sample sizes of 2,481 and 284, respectively) randomized participants to combinations of SSRI antidepressant medications and either cognitive behavior therapy (ENRICHD) or interpersonal psychotherapy (CREATE) or usual care with brief ­supportive counseling from clinic and healthcare staff. In addition to the standard goals of better heart health and medical improvement after a heart event, the treatment goals included improvement in depression and social support (Berkman et al., 2003; Lespérance et al., 2007). Numerous correlational studies have indicated clear relationships between these psychosocial variables and long‐term heart health in cardiac disease patients. Although a comprehensive summary of the results from the ENRICHD and CREATE trials is beyond the scope of this brief entry, a major finding was that the medication plus psychosocial intervention conditions indicated improvement in depression and social support over time. However, these psychosocial improvements were not associated with ­significantly fewer heart events or cardiac fatalities over time. Therefore, while the improvements observed during follow‐up visits on the psychosocial measures was encouraging, there was disappointment that improvements in depression and social support over time did not translate into improvement in physical cardiac health and avoidance of future heart attacks. Nonetheless, these heart disease studies stand as excellent examples of sophisticated research on psychosocial processes associated with disease. In summary, there have been numerous excellent studies regarding clinical health psychology, behavioral medicine, and chronic disease. Chronic diseases are major killers, however, and we need to learn more. Fortunately, there appears to be movement in this direction, although the large randomized controlled trials like those noted above are expensive and thus vulnerable to the funding patterns of major national granting agencies and foundations/societies focused on health, such as the National Institutes of Health (United States), the National Health Service (United Kingdom), the American Heart Association, and the American Cancer Society.

Diversity and Inclusiveness We live in a diverse world and the benefits of inclusiveness are obvious. Therefore, many areas within clinical health psychology and behavioral medicine, as with other fields that engage in theory, research, and practice relevant to human beings, have witnessed a large increase in studies and interventions that focus on diversity and inclusiveness. From our perspective, this is one of the most important trends in clinical health psychology and behavioral medicine (e.g., see the editorial by Davila, 2017; and the special section overview, on sexual and gender minority health, by Davila & Safren, 2017). An example of a determined effort to recruit a diverse sample is a study examining physical activity and depressive symptoms after treatment for breast cancer (Brunet, O’Loughlin, Gunnell, & Sabiston, 2018). The early research in this area was often limited to samples of entirely Caucasian women, within a small age range, who were relatively well educated and mostly married or living with a partner. In contrast, this large study of 201 women with breast cancer reflected efforts to recruit a more diverse sample, including a wider range of age, race, civil status, and education. This study also evidenced considerable diversity regarding medical

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variables such as stage of cancer, treatments received, and time since treatment completion. The investigators acknowledge, of course, that even more diverse samples will be helpful for future research. One of the major findings from this investigation is that more physical exercise was associated with less frequent depressive symptoms among participants after treatment for breast cancer; however, not all of the predicted relationships between depression and exercise were maintained longitudinally. Another recent example of a study that included ambitious efforts to recruit a large and diverse sample is a study that examined the associations between pharmacotherapy for diabetes and immigrant status in the United States; this study was conducted by Hsueh et al. (2018). While a complete discussion regarding the many variables in this study that examined diversity is beyond the scope of this entry, if we focus solely on the race, ethnicity, and education variables used, there were numerous participants across several racial and ethnicity groups, including 28% non‐Hispanic Black, 18% Mexican American, 8% other Hispanic, 6% multiracial, and 40% non‐Hispanic White. Educational level completed was also quite diverse, ranging from ninth grade or lower to college graduate or above. One of the major findings included a clinically significant result that being foreign born was associated with significantly reduced odds of receiving insulin treatment (Hsueh et al., 2018). There were also some differential patterns of associations with treatment offerings and different racial/ethnic group membership. These results suggest that perhaps integrating diversity information about a patient’s immigration status and racial/ethnic identity would allow for a more culturally sensitive care of diabetes. In summary, we live in a diverse world, and additional research like the studies noted above is needed. It is essential that future studies make it a priority to recruit and assess diverse participants and consider relevant diversity variables.

Biological Issues Traditional research on chronic health problems and diseases has characteristically paid ­considerable attention to biological factors. More recently, social scientists are paying increasingly more attention to such variables. Specifically, the fields of clinical health psychology and behavioral medicine appear to be moving toward a stronger biological focus (e.g., see editorials by Freedland, 2017; Lilienfeld, 2017). Furthermore, this increased focus on a biological emphasis is facilitated and enhanced by recent technology, such as MRI, PET, and CT scans. The sophistication and options available for biological assays of blood, urine, hormones, ­neurotransmitters, and so forth have expanded and become more accessible. Moreover, the ability to evaluate, store, and reexamine biological samples has grown tremendously (e.g., see discussions in Taylor, 2018). An interesting example of a health psychology study that incorporates biological variables is the Trucco et al. study (2018), mentioned earlier in this entry. Trucco et al. (2018) included measures of genotyping, focusing on the polymorphisms from four genes, which were ­predicted to be relevant to the substance use variables being studied. A major finding was that depression and ineffective coping methods appear to be a pathway that allows genetic risk factors to influence substance use and abuse. Another example of biological measures being utilized in recent clinical health psychology research is an investigation by Bakhshaie et al. (2018). The authors used a carbon monoxide analysis of breath samples as a method to verify the participants’ smoking status. Similar ­biochemical verification methods have been used in numerous smoking cessation research

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studies, along with other studies on nicotine and tobacco use. One of the investigators’ ­primary findings was that teaching the participants strategies to more effectively manage symptoms of anxiety and depression appeared to be associated with fewer withdrawal symptoms, and thus better outcomes, in a smoking cessation study (Bakhshaie et al., 2018). In summary, the inclusion of biological variables in clinical health psychology and behavioral medicine research is becoming more common, more sophisticated, and more central to the questions being asked.

Translation of Health Psychology Knowledge Research studies conducted in laboratories, with carefully controlled variables, under almost perfect conditions are important. However, it is also critical that findings be applicable in real‐ world situations. These real‐world situations often involve imperfect conditions, limited resources, overworked healthcare providers, underserved patients, and numerous other challenges. Practical and cost‐effective translations have found encouraging effectiveness in real‐ world settings, in part, by using mobile and online technologies, public health education campaigns, social service agencies, charitable organizations, and religious organizations (e.g., see the journal editorial in Translational Behavioral Medicine by Miller‐Halegoua, Bowen, Diefenbach, & Tercyak, 2016). O’Hara and his colleagues have conducted several randomized controlled trials and more naturalistic, low‐cost intervention trials in healthcare clinics with depressed postpartum women. Their findings indicate that interpersonal psychotherapy interventions in the acute phase of depression treatment, and/or during maintenance treatment, are helpful for depressive relapse prevention among women who often had numerous health challenges and who were also caring for young children (e.g., Nylen et  al., 2010; O’Hara, Stuart, Gorman, & Wenzel, 2000; Serge, Brock, & O’Hara, 2015). Moreover, these studies showed a transition themselves, from efficacy‐like studies as seen in O’Hara et  al. (2000) to more translational studies as seen in Serge et al. (2015). Thus, even in economically challenged and underserved populations being treated in real‐world healthcare settings with limited resources, interpersonal psychotherapy has potential as both an acute and maintenance treatment for depression and as a treatment that can be translated into real‐world settings (e.g., see Serge et al., 2015 for the most translational study in this series of investigations). Another excellent translational study explored how to improve adherence to cognitive behavior therapy for insomnia (CBT‐I; Dolsen et al., 2017). In this randomized trial for 188 adults with persistent insomnia, the investigators found strong support for the hypothesis that effective sleep the night before and the night after a treatment session will improve patients’ adherence to many aspects of the treatment regimen. Additionally, it was found that CBT‐I interventions usually improve the patient’s sleep before and after a treatment session. Such sleep improvement, in turn, will improve treatment adherence, based on their findings. Given that adherence to treatment is a huge challenge for most psychosocial and medical interventions, this study is an example of a methodologically sophisticated study that may be generalizable to many healthcare environments, help clinicians and patients to improve treatment adherence, and make a significant difference in public health. In summary, research in the fields of clinical health psychology and behavioral medicine is  continuing to improve upon translating findings into practical methods that have clear ­applications in the real world.

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Chronic Psychopathology That Is Comorbid With Health Problems and Diseases Patients suffering from comorbid chronic psychopathology and physical health concerns ­commonly present in healthcare settings. As such, gaining a better understanding of the relationships between physical and mental health is essential if treatments are to advance. One example of a study that has examined this complicated relationship was conducted by Stice, Gau, Rohde, and Shaw (2017). This research team (as well as their colleagues in numerous related studies) investigated the risk factors that predict the various DSM‐5 eating disorders (American Psychiatric Association, 2013). Stice et al. (2017) evaluated a rich array of variables that might predict the future onset of each DSM‐5 eating disorder. These disorders included anorexia nervosa, bulimia nervosa, binge eating disorder, and purging disorder. With a sample of 1,272 young women (M age of 18.5 years), the investigators conducted diagnostic interviews every year for 3 years of follow‐up assessment, following a series of their own prevention trials. In addition, they collected a vast amount of self‐report and weight, height, and health data. It was found that the strongest predictors leading to the development of eating disorders were related to negative affect, such as depressive symptoms, and interpersonal difficulties. As with much of the research literature on eating disorders, pursuit of a “thin ideal” and the associated body dissatisfaction that often accompanies this goal was strongly correlated with extreme dieting, unhealthy weight control behaviors, and the negative affect and interpersonal difficulties mentioned above. Therefore, the investigators note that incorporating interventions for depression and interpersonal discord into prevention efforts for eating disorders may be an effective strategy. In summary, some types of chronic psychopathology, such as depression, are frequently observed with a large number of physical health concerns and diseases. The best research and clinical work accounts and plans for such comorbidity.

Social Justice Given that health concerns (both physical and mental) can be influenced greatly by the ­distribution of wealth and opportunities for personal activity, it is our opinion that issues related to social justice should be of concern to everyone. Research and practice in the fields of clinical health psychology and behavioral medicine, therefore, should reflect these concerns. Fortunately, much of the recent work in these fields has begun to consider such concerns, and the emphasis is increasing enough to justify calling it a “trend.” A good example is the work of Yang, Chen, and Park (2016), who examined perceived housing discrimination and self‐reported health. With a sample of 9,842 adults in 830 ­ ­neighborhoods across Philadelphia, PA, the investigators were interested in whether certain neighborhood features matter in terms of overall health. Specifically, associations of increased perceived housing discrimination, and more negative self‐reported health, were strengthened in neighborhoods with relatively high housing values; however these associations were weakened in neighborhoods with a broader range of housing values and diversity (i.e., income, parenthood, and ethnic minority status). Another interesting finding of this large study was that the statistical models for illustrating a negative relationship between perceived housing discrimination and self‐reported health generally underestimate the relationship, unless the models also incorporate neighborhood features such as economic level, family diversity, and ethnic minority status. This large, sophisticated study by Yang et al. (2016) is an excellent example of research on the intersection of clinical health psychology, social justice, and multicultural issues.

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In summary, there has been important research on social justice, health psychology, ­behavioral medicine, and a broad range of multicultural and diversity issues. This research area is growing rapidly, and it should help guide our future efforts in assessment, intervention, and follow‐up.

Discussion The trends discussed in this entry (while not all inclusive) appear to be among the most prominent, consistent, and frequently cited developments in the fields of clinical health ­ ­psychology and behavioral medicine. While staying close to the data and what is recently ­published, we offer a few cautious recommendations about these trends. The importance of the trend of sophisticated methodology cannot be exaggerated. The ­history of science suggests that it is only as good as its methods. Therefore, we applaud improvements in methodology such as larger and more diverse samples, multi‐method and multi‐source measures, and longitudinal data collection. This kind of ambitious, large, and sophisticated research, along with translation to the real world, requires skill, resources, and determination. We hope that the research and practice infrastructures will continue to support this important work. The trend of an emphasis on chronic diseases is expected and necessary, especially considering the significant impact behavioral and emotional factors play on overall health status. Some very important research and translational efforts have been accomplished in this area, and we hope that this important work continues. Our world is diverse. Therefore, our research, clinical work, and translational efforts should reflect diversity, broadly defined. In our opinion, the fields of clinical health psychology and behavioral medicine have made a considerable amount of progress on this issue. However, we would like these efforts to continue and expand. Biology has been strongly associated with how we behave (including our personality traits). Moreover, the explosion in biological technology allows for more sophisticated assessments of biological variables. As such, over the past couple of decades, it has become increasingly more common in clinical health psychology and behavioral medicine research to incorporate more biological variables and to include more biological measures in clinical and translational efforts. This trend toward a stronger biological focus can be expensive, and the technical demands can be daunting, but we believe the benefits make it worthwhile. We predict that the incorporation of biological variables in health psychology research will become expected in the foreseeable future, moving the emphasis on biology from a trend to a centerpiece of the field. The translation of health psychology knowledge into the real world of healthcare settings is an important trend in clinical health psychology and behavioral medicine. After all, if the results we read about in academic journals do not apply to actual clinical populations in typical healthcare settings, then the utility of these findings holds less meaning. Clearly, researchers in these fields are interested in improving the health status of as many people as possible, not just the “ideal candidate.” Chronic psychopathology is often comorbid with health problems and diseases. Depression is a good example, as it is associated with almost every health problem and disease on record. Depressive comorbidity is not helpful when it comes to our health, as, for example, the association of long‐term depression with heart disease has many negative implications, including increased disability, disrupted close relationships, poorer adherence to medical and psychological regimens, increased problems at work and at home, further heart events, and increased rates of death from heart disease. Therefore, we can no longer afford to consider psychological and medical issues in isolation.

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Last but not least, we discussed the importance of considering variables related to social justice. While progress has been made regarding the inclusion of variables relevant to social justice concerns as part of more traditional clinical health psychology and behavioral medicine studies, more emphasis regarding social justice variables clearly needs to be done. This work is exciting and important, and we need it if we are going to make progress for everyone.

Author Biographies C. Steven Richards, PhD, is a professor of psychological sciences at Texas Tech University. His research interests include depression, clinical health psychology and behavioral medicine, comorbidity of health problems and psychopathology, self‐control, and relapse prevention for depression. He has held 15 administrative positions during his faculty appointments at Texas Tech University, Syracuse University, and the University of Missouri–Columbia. Dr. Richards earned his PhD in clinical psychology at the State University of New York at Stony Brook (now Stony Brook University). Lee M. Cohen, PhD, is dean of the College of Liberal Arts and professor in the Department of Psychology at the University of Mississippi. Dr. Cohen came to the University of Mississippi from Texas Tech University, where he served in a number of administrative roles including the director of the nationally accredited doctoral program in clinical psychology and the chair of the Department of Psychological Sciences. As a faculty member, he received several university‐wide awards for his teaching and academic achievement. As a researcher, he received more than $1.5  million from funding agencies, including the US Department of Health and Human Services, the National Science Foundation, and the National Institutes of Health/National Institute on Drug Abuse. His research program examines the behavioral and physiological mechanisms that contribute to nicotine use, and he worked to develop optimal smoking cessation treatments. Dr. Cohen is a Fellow of the American Psychological Association and the Society of Behavioral Medicine. He received his PhD in clinical psychology from Oklahoma State University.

References American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed., DSM‐5). Washington, DC: Author. Appelbaum, M., Cooper, H., Kline, R. B., Mayo‐Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board Task Force Report. American Psychologist, 73, 3–25. Bakhshaie, J., Kulesz, P. A., Garey, L., Langdon, K. J., Businelle, M. S., Leventhal, A. M., … Zvolensky, M. J. (2018). A prospective investigation of the synergistic effect of change in anxiety sensitivity and dysphoria on tobacco withdrawal. Journal of Consulting and Clinical Psychology, 86, 69–80. Berkman, L. F., Blumenthal, J., Burg, M., Carney, R. M., Catellier, D., Cowan, M. J., … Schneiderman, N. (2003). Effects of treating depression and low perceived social support on clinical events after myocardial infarction: The Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) randomized trial. JAMA, 289, 3106–3116. Brunet, J., O’Loughlin, J. L., Gunnell, K. E., & Sabiston, C. M. (2018). Physical activity and depressive symptoms after breast cancer: Cross‐sectional and longitudinal relationships. Health Psychology, 37, 14–23. Davila, J. (2017). Editorial. Journal of Consulting and Clinical Psychology, 85, 1–4.

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Davila, J., & Safren, S. A. (Eds.) (2017). Introduction to the special section on sexual and gender minority health. Journal of Consulting and Clinical Psychology, 85, 1109–1110. Dolsen, M. R., Soehner, A. M., Morin, C. M., Belanger, L., Walker, M., & Harvey, A. G. (2017). Sleep the night before and after a treatment session: A critical ingredient for treatment adherence? Journal of Consulting and Clinical Psychology, 85, 647–652. Freedland, K. E. (2017). Editorial: A new era for Health Psychology. Health Psychology, 36, 1–4. Hsueh, L., Vrany, E. A., Patel, J. S., Hollingshead, N. A., Hirsh, A. T., de Groot, M., & Stewart, J. C. (2018). Associations between immigrant status and pharmacological treatments for diabetes in U.S. adults. Health Psychology, 37, 61–69. Lespérance, F., Frasure‐Smith, N., Koszycki, D., Laliberté, M. A., van Zyl, L. T., Baker, B., … the CREATE Investigators. (2007). Effects of citalopram and interpersonal psychotherapy on depression in patients with coronary artery disease: The Canadian Cardiac Randomized Evaluation of Antidepressant and Psychotherapy Efficacy (CREATE) trial. JAMA, 297, 367–379. Lilienfeld, S. O. (2017). Clinical psychological science: Then and now. Clinical Psychological Science, 5, 3–13. Manne, S. L., Siegel, S. D., Heckman, C. J., & Kashy, D. A. (2016). A randomized clinical trial of a ­supportive versus a skill‐based couple‐focused group intervention for breast cancer patients. Journal of Consulting and Clinical Psychology, 84, 668–681. Miller‐Halegoua, S., Bowen, D. J., Diefenbach, M. A., & Tercyak, K. P. (2016). Editorial: Creating the future of Translational Behavioral Medicine. Translational Behavioral Medicine, 6, 167–168. Nylen, K. J., O’Hara, M. W., Brock, R., Moel, J., Gorman, L., … Stuart, S. (2010). Predictors of the longitudinal course of post‐partum depression following interpersonal psychotherapy. Journal of Consulting and Clinical Psychology, 78, 757–763. O’Hara, M. W., Stuart, S., Gorman, L. L., & Wenzel, A. (2000). Efficacy of interpersonal psychotherapy for postpartum depression. Archives of General Psychiatry, 57, 1039–1045. Polenick, C. A., Renn, B. N., & Birditt, K. S. (2018). Dyadic effects of depressive symptoms on medical morbidity in middle‐aged and older couples. Health Psychology, 37, 28–36. Serge, L. S., Brock, R. L., & O’Hara, M. W. (2015). Depression treatment for impoverished mothers by point‐of‐care providers: A randomized controlled trail. Journal of Consulting and Clinical Psychology, 83, 314–324. Stice, E., Gau, J. M., Rohde, P., & Shaw, H. (2017). Risk factors that predict future onset of each DSM‐5 eating disorder: Predictive specificity in high‐risk adolescent females. Journal of Abnormal Psychology, 126, 38–51. Taylor, S. E. (2018). Health psychology (10th ed.). New York, NY: McGraw Hill. Trucco, E. M., Villafuerte, S., Hussong, A., Burmeister, M., & Zucker, R. A. (2018). Biological underpinnings of an internalizing pathway to alcohol, cigarette, and marijuana use. Journal of Abnormal Psychology, 127, 79–91. Yang, T.‐C., Chen, D., & Park, K. (2016). Perceived housing discrimination and self‐reported health: How do neighborhood features matter? Annals of Behavioral Medicine, 50, 789–801.

Suggested Reading Berkman, L. F., Blumenthal, J., Burg, M., Carney, R. M., Catellier, D., Cowan, M. J., … Schneiderman, N. (2003). Effects of treating depression and low perceived social support on clinical events after myocardial infarction: The Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) randomized trial. JAMA, 289, 3106–3116. Polenick, C. A., Renn, B. N., & Birditt, K. S. (2018). Dyadic effects of depressive symptoms on medical morbidity in middle‐aged and older couples. Health Psychology, 37, 28–36. Yang, T.‐C., Chen, D., & Park, K. (2016). Perceived housing discrimination and self‐reported health: How do neighborhood features matter? Annals of Behavioral Medicine, 50, 789–801.

Biopsychosocial Practice in Health Psychology Timothy P. Melchert Department of Psychology, Marquette University, Milwaukee, WI, USA

The biopsychosocial approach has been fundamental to the practice of health psychology across the history of the specialization (e.g., Engel, 1977). Indeed, this perspective became so dominant that Suls and Rothman in 2004 concluded that “The conceptual base for health psychologists in their roles as researchers, practitioners, and policymakers is the biopsychosocial model” (p. 119:1; italics in the original). Despite its rapid and widespread adoption, however, the conceptual nature and precise meaning of the biopsychosocial approach was unclear, and critics viewed it as encouraging an uncritical eclectic approach to practice (e.g., Ghaemi, 2010). But scientific knowledge of health and functioning has advanced dramatically in recent years, and the scientific foundations supporting the practice of health psychology are far stronger as a result. The nature of the biopsychosocial framework underlying health ­psychology practice has consequently also evolved. This chapter will outline the evolution of the biopsychosocial approach for understanding health and healthcare from a general metatheoretical framework that was often used to ­support an eclectic approach to practice to a solidly science‐based approach grounded firmly in current scientific knowledge. It will show how scientific explanations of human health and functioning have advanced dramatically such that we now have a much more thorough understanding of the causes of disease and the behaviors that contribute to illness and health. This knowledge is critical for developing more effective prevention and treatment interventions in health ­psychology and in healthcare generally.

Evolution of the Biopsychosocial Approach to Health Psychology George Engel introduced the “biopsychosocial model” for understanding health and h ­ ealthcare in 1977 to counter what he viewed as the overemphasis on biology in medicine. Engel argued The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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that healthcare needed to take a holistic, integrative approach to understand health, disease, and treatment. This perspective was an excellent fit for the new specialization of health ­psychology, which also focused on interactions between biological, psychological, and sociocultural influences on health and functioning. Engel’s model appeared the year before Division 38, Health Psychology became established as a specialization within the American Psychological Association in 1978. Engel’s biopsychosocial model soon became highly influential throughout healthcare and is now widely regarded as the appropriate framework through which to understand health and healthcare. McLaren (1998) pointed out, however, that Engel misnamed his approach because he did not propose a model in the scientific sense of using observations, rules, and scientific laws to explain a class of phenomena, but instead used the term in its colloquial sense referring to a perspective or framework. Engel’s approach technically refers to a metatheoretical framework that points to the range of factors that need to be considered to understand theory and research in medicine, but it does not refer to a scientific model of a falsifiable theory that attempts to explain a particular class of phenomena (Melchert, 2015). Engel’s very general biopsychosocial framework has also been criticized for encouraging an uncritical eclecticism in behavioral healthcare because it can encompass a wide range of biological, psychological, and social interventions without requiring that they be linked to known mechanisms or processes (Ghaemi, 2010). It takes the same general approach as the various integrative and eclectic frameworks that were introduced in psychology about the same time and point to the range of factors that need to be considered to understand human psychology (e.g., Bronfenbrenner’s, 1979 ecological model; Lazarus, 1976 BASIC‐ID framework). Though these frameworks can be very useful in the early stages of researching a topic, they are not scientific models or theories that explain the behavior of particular mechanisms or systems. There are several reasons why psychology relied on general metatheoretical frameworks to understand human development and functioning at the time the biopsychosocial model was introduced and health psychology became established as a specialization. Perhaps the most important underlying reason was that human development and functioning are so staggeringly complex that science had not yet progressed far enough to explain many of the processes involved (Melchert, 2015). Evolutionary theory still had not developed far enough to explain social behavior until the 1970s, functional magnetic resonance imaging (fMRI) was not used to observe brain function until the 1990s, and the human genome was not sequenced until the early 2000s. Given the limitations of scientific knowledge and technology at the time, all that was really possible was a metatheoretical perspective that focused attention on what appeared to be the correct range of variables that should be considered when attempting to understand many aspects of human behavior and functioning. This situation has fundamentally changed, however, as a result of dramatic scientific ­progress in recent years. Replicated findings from experimental tests of falsifiable hypotheses have resulted in explanations of a steadily increasing number of biopsychosocial processes. Of course, knowledge of many biopsychosocial processes is still very limited, and many experimental findings have not yet been sufficiently tested and verified. As a result, many findings remain tentative and sometimes controversial, which is true at the frontiers of knowledge in any scientific discipline. But the overall trajectory is clear. Over the past couple decades, the behavioral, neurological, and biological sciences have advanced dramatically, and we now have verified explanations of many aspects of human psychology and biopsychosocial functioning. Instead of referring to a general metatheoretical framework, the biopsychosocial approach is now understood to refer to the integrated body of scientific knowledge that explains human development and functioning in a manner entirely consistent with the rest of the natural

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s­ ciences (Melchert, 2015). This approach does not rely on the customary practice of choosing one (or more) of the traditional theoretical orientations for conceptualizing cases in psychology (e.g., psychodynamic, behavioral, humanistic, cognitive), but instead relies on the growing body of scientific knowledge that explains biopsychosocial mechanisms and processes. The “biopsychosocial” term is not necessarily superior to alternative terms (such as Bronfenbrenner’s “ecological” model) though it does have two distinct advantages. First, it is widely known and accepted throughout healthcare; indeed, it is accepted in many healthcare professions at levels similar to the way it is in health psychology. Second, and more importantly, it refers to the levels of natural organization that are integral to the understanding of human behavior and biopsychosocial functioning. All organic life is organized in a hierarchy of levels inherent in the natural world (i.e., from the subatomic to the level of atoms, molecules, organelles, cells, ­tissues, organs and organ systems, individual organisms, and, in the case of humans and many other social animals, families, communities, culture, and, for all organic life, the biosphere). The term “biopsychosocial” effectively captures the three broad, interacting levels of natural organization that are necessary for understanding human development and functioning. The biopsychosocial framework is necessary for understanding physical as well as behavioral health and social functioning. Human health and behavior are fundamentally dependent on underlying biological structures and processes that interact with psychological factors, which in turn interact with social, cultural, and environmental processes. Appreciating the inextricably intertwined BPS domains of functioning is necessary for understanding all aspects of human functioning from the level of the particular mechanisms that control behavior (i.e., proximate explanations of behavior) to the level of explaining why humans are designed the way we are (i.e., the origins and functions of behavior, or ultimate explanations of ­behavior; Tinbergen, 1963).

Illustration: Current Biopsychosocial Perspective on the Primary Causes of Disease and Death The impact of the steadily growing body of scientific knowledge regarding human health and disease can be illustrated by considering the current understanding of the primary causes of disease and death in the United States. The primary causes of morbidity and mortality are of course a main concern for health psychology research and practice. Detailed knowledge of many aspects of these topics, however, were unavailable when health psychology was founded four decades ago, but proximate and ultimate explanations for an increasing number of diseases and conditions are now available. A brief overview of this literature highlights the importance of recent scientific findings to education, practice, research, and prevention in health psychology. Patterns of morbidity and mortality have changed dramatically in the United States and other parts of the industrialized world over the past century. At the end of the nineteenth century, the primary causes of illness and death began shifting from acute and infectious disease to chronic diseases associated with lifestyle and behavior, a shift that has been called the epidemiological transition (Gribble & Preston, 1993). The death rate from infectious disease was approximately 800 per 100,000 individuals in 1900 and fell to roughly 50 per 100,000 by 1950, where it has stayed since (Centers for Disease Control [CDC], 1999). Most of these deaths now involve influenza in elderly individuals, while other infectious diseases have been nearly or completely eliminated. Public health measures, vaccines, antiseptic practices in medicine, and antibiotics became so effective over the course of the twentieth century that the majority of Americans now suffer and die from chronic rather than acute infectious diseases.

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The top five leading causes of death in the United States are now heart disease, cancer, chronic lower respiratory diseases, stroke, and accidents (CDC, 2013). Many of these conditions share a small number of underlying causes. At the very general level, the Institute of Medicine (2004) concluded that roughly 50% of overall morbidity and mortality in the United States is caused by behavior and lifestyle. In terms of more specific factors, smoking is the leading cause, accounting for 18.1% of all deaths (CDC, 2004); obesity is the second leading cause, accounting for 16.6% of all deaths (Mokdad, Marks, Stroup, & Gerberding, 2004); and alcohol consumption is the next most important factor, accounting for 3.5% of deaths. The World Health Organization (2008) arrived at similar conclusions regarding the global population and estimated that up to 80% of heart disease, stroke, and type 2 diabetes and more than one‐third of cancers worldwide could be prevented by eliminating the shared risk factors of tobacco use, unhealthy diet, physical inactivity, and excessive alcohol use. More detailed knowledge regarding both proximate and ultimate explanations of these ­diseases is also now available. Research from a range of biological, social science, and medical disciplines has clarified that the long evolution of hominins over the past 6 million years has resulted in capacities and vulnerabilities that are poorly suited to modern diets and behavioral patterns (Lieberman, 2013). Before the development of agriculture (starting 12,000 years ago in the Middle East and more recently in other areas of the world), humans were hunter‐­ gatherers who ate primarily nuts, fruits, tubers, and some meat. Most of their food contained large amounts of fiber—in fact, chewing these foods actually consumed a large amount of time and energy; a typical meal might require thousands of chews (Lieberman, 2011). Before hybridization, the fruits they ate contained far less sugar than modern fruits (e.g., wild crab apples are similar to carrots in sweetness), the only really sweet food that could be consumed was honey, and the meat they ate from wild animals contained only a fraction of the fat found in modern farm‐raised meat, fish, and poultry. Salt was not consumed in amounts larger than what was naturally present in plants and animals. Hunting and gathering (and even chewing) required large amounts of activity, and people were very lean (Lieberman, 2013). Humans evolved to crave sweets and fats so that any excess calorie intake was quickly and easily converted into fat and people could survive periods of insufficient food intake. In ­contrast, since the agricultural revolution and especially since the industrial revolution, people typically eat large amounts of refined grains that contain little fiber, large amounts of sugar, and large amounts of fatty meats (Cordain et al., 2005). Humans’ bodies evolved over millions of years to consume very different foods and be far more active than modern Americans. This has caused widespread problems related to high cholesterol, high blood pressure, obesity, and conditions that are caused by disuse such as back pain, osteoporosis, and dental problems, all of which are rare in hunter‐gatherers (Lieberman, 2013). Translating this growing knowledge regarding the causes of many of the most common modern medical conditions into effective treatments and prevention strategies has been challenging. The reasons for this are complex, but changing behaviors that contribute to the main causes of disease and death clearly can be difficult. Psychologists have long known that individuals can readily engage in a wide variety of behaviors that are unhealthy, self‐defeating, and addictive even when they are aware of the consequences of their behavior. Knowledge of the psychological mechanisms and processes underlying unhealthy behavior has also grown in recent years. Many issues are involved, but one prominent area of research illustrates the progress being made. It has long been known that automatic, subconscious cognitive processes operate in conjunction with reflective, conscious processes, and together they frequently produce seemingly irrational thoughts and behaviors. These two types of ­cognitive processing are often referred to as System 1 and System 2 thinking (Kahneman, 2012).

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System 1 thinking is fast, automatic, largely unconscious, intuitive, reliant on affect, and ­conditioned emotional associations and produces a “quick and dirty” draft of reality that is often highly useful for responding to threats and opportunities. System 2 thinking, on the other hand, evolved much later, is slow and deliberate, and requires conscious effort to reason analytically and explicitly about the world. This system allows us to reason deductively and is less influenced by past events and feelings. It allows us, for example, to have a craving or a feeling such as anger or depression but not act on it due to awareness of long‐term consequences or because it does not fit with one’s values. System 2 thinking is lazy, however, and often accepts the version of events provided by System 1 (Kahneman, 2012). Clearly, changing unhealthy behavior patterns in the context of these two systems can be complicated.

Implications Suls’ and Rothman’s (2004) statement regarding the conceptual base for health psychology is still accurate if “the biopsychosocial model” is replaced with “the biopsychosocial approach.” When health psychology is approached as a clinical science grounded firmly in scientific knowledge of human health and functioning, then the term “model” needs to be used in its scientific sense. From this perspective, the biopsychosocial approach is a general metatheoretical framework that points to the levels of natural organization involved in human health and behavior and the corresponding body of scientific knowledge that provides proximate and ultimate explanations of health, disease, development, and functioning. This steadily growing body of knowledge provides a solid foundation for practicing evidence‐based health psychology and will almost certainly lead to increasingly effective prevention and treatment interventions that can improve the health and well‐being of the public. When psychologists in the past entered medical units in hospitals to consult regarding psychological aspects of a case, they were sometimes apologetic and hesitant in their interactions with patients and other healthcare professionals. This attitude likely resulted in part from the equivocal support for the validity of the traditional theoretical orientations in psychology and the eclecticism associated with the biopsychosocial approach. Compared with the biological basis for much of medicine, the evidence supporting the biopsychosocial approach seemed weak. This is no longer the case. The scientific basis for understanding behavioral health and biopsychosocial functioning is now solidly based on verified research from across the biological, neurological, and behavioral sciences, and research suggests that psychosocial treatments are highly effective and sometimes more effective than many medical interventions (American Psychological Association [APA], 2012). There is certainly still much work to be done, but the scientific basis underlying health psychology is now solid. Clinicians can now interact with patients and other healthcare professionals with confidence that comes from having firm scientific foundations for practice that are entirely consistent with and supported by the rest of the natural sciences.

Author Biography Timothy P. Melchert is a professor in the Department of Counselor Education and Counseling Psychology at Marquette University. He writes on the need to resolve theoretical and conceptual conflicts and confusion in professional psychology by adopting a unified science‐based biopsychosocial framework for understanding human development, functioning, and behavior change.

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References American Psychological Association. (2012). Resolution of psychotherapy effectiveness. Retrieved from www.apa.org Bronfenbrenner, U. (1979). The ecology of human development. Cambridge, MA: Harvard University Press. Centers for Disease Control. (1999). U.S. Department of Health and Human Services, Atlanta, Georgia, USA. Centers for Disease Control. (2004). The health consequences of smoking: A report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services. Centers for Disease Control and Prevention. (2013). Deaths: Final data for 2013. National Vital Statistics Report, 64(2). Retrieved from www.cdc.gov/nchs/data_access/Vitalstatsonline.htm Cordain, L., Eaton, S. B., Sebastian, A., Mann, N., Lindeberg, S., Watkins, B. A., … Brand‐Miller, J. (2005). Origins and evolution of the Western diet: Health implications for the 21st century. American Journal of Clinical Nutrition, 81(2), 341–354. Engel, G. L. (1977). The need for a new medical model: A challenge for biomedicine. Science, 196, 129–136. doi:10.1126/science.847460 Ghaemi, S. N. (2010). The rise and fall of the biopsychosocial model: Reconciling art and science in psychiatry. Baltimore, MD: Johns Hopkins University Press. Gribble, J. H., & Preston, S. H. (Eds.) (1993). The epidemiological transition: Policy and planning implications for developing countries. Washington, DC: National Academies Press. Institute of Medicine. (2004). Improving medical education: Enhancing the behavioral and social science content of medical school curricula. Washington, DC: National Academies Press. Kahneman, D. (2012). Thinking, fast and slow. New York, NY: Farrar, Straus & Giroux. Lazarus, A. A. (1976). Multimodal behavior therapy. New York, NY: Springer. Lieberman, D. E. (2011). The evolution of the human head. Cambridge, MA: Harvard University Press. Lieberman, D. E. (2013). The story of the human body: Evolution, health, and disease. New York, NY: Pantheon. McLaren, N. (1998). A critical review of the biopsychosocial model. Australian and New Zealand Journal of Psychiatry, 32, 86–92. Melchert, T. P. (2015). Biopsychosocial practice: A science‐based framework for behavioral health care. Washington, DC: American Psychological Association. Mokdad, A. H., Marks, J. S., Stroup, D. F., & Gerberding, J. L. (2004). Actual causes of death in the United States, 2000. Journal of the American Medical Association, 291, 1238–1245. doi:10.1001/ jama.291.10.1238 Suls, J., & Rothman, A. (2004). Evolution of the biopsychosocial model: Prospects and challenges for health psychology. Health Psychology, 23, 119–125. doi:10.1037/0278‐6133.23.2.119 Tinbergen, N. (1963). On the aims and methods of ethology. Zeitschrift für Tierpsychologie, 20, 410–433. World Health Organization. (2008). Primary health care: Now more than ever. Geneva, Switzerland: Author.

Child and Family Health Joaquin Borrego, Jr., Tabitha Fleming, Elizabeth Ortiz‐Gonzalez, and Amber Morrow Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA

The health of a child can have a significant impact on the family. When a child has a medical illness (e.g., type 2 diabetes) or experiences a negative event (e.g., child maltreatment), the family, as well as other systems (e.g., communities), can be negatively impacted. The family is inevitably impacted whether it be an acute or chronic condition. This negative impact is ­especially significant for family members and others close to the child (e.g., friends, extended family members). Given this, it is imperative that inclusion of the family and other systems be taken into account when discussing children’s health. Some major public health concerns impacting children and families include, but not limited to, nutrition (an increase in obesity rates for both pediatric and adult populations, food insecurity for families living in poverty), chronic illness (e.g., asthma, cardiovascular disease, high blood pressure), pediatric and adult cancer, poor sleep hygiene, and child maltreatment (the child as victim and adult as the perpetrator). These, and other conditions, are very common in our society and are associated with additional health complications later in life.

Ecological Framework A useful framework to help understand the complexity of child, family, and social relationships as they relate to health is the ecological framework. This conceptual framework is the work conducted by Urie Bronfenbrenner (1979) on the ecology of child development. Briefly, this conceptual model posits that the child and family operate in systems that are interrelated and reciprocal. Most immediate to the child is what is known as the microsystem. This system is the most immediate and includes those that are closely connected to the child such as siblings, parents, family, peers, and schools. Within this microsystem, there are subsystems that focus on relationships that the child has with peers, siblings, and the parents. The mesosystem involves the interactions and relationships that are developed between the different components of the The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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microsystem. As examples, the mesosystem helps explain interactions and relationships formed between the family and the school, siblings and peers, etc. The exosystem involves the linkage between two settings that may directly or indirectly involve and impact the child. This can include extended family members, neighborhood, etc. The macrosystem is the most distant from the child but still has an influence on the child. Examples include social and cultural changes at a societal level that will inherently impact the child and family. A family’s practices, values, beliefs, attitudes, and behaviors are shaped over time. Bronfenbrenner’s model has been applied to explain the complexities of the child and family within the context of health (Kazak, Alderfer, & Rader, 2017). The social ecology model helps identify the systems that are involved related to a child’s health such as the family, hospitals, and school personnel (Kazak et al., 2017). As noted above, this is a useful conceptualization as it helps explain the intricate systems involved and the relationship between these systems. Examining these systems carefully can help identify contextual factors that may decrease the risk for continued health problems and increase resilience and adjustment for the child and family.

Impact of Children’s Health on the Family Children with medical illnesses can be very stressful for the family, especially siblings and ­parents or other caregivers. Illnesses in children can be stressful and even distressing for ­caregivers whether it be parents, guardians, or other caregivers such as a grandparent or close family friend (Cousino & Hazen, 2013). A child who is ill will miss school, and, depending on available family resources, some parents or guardians may need to miss work with or without pay. The literature from pediatric psychology suggests that a child with an illness can have a negative impact on siblings (Vermaes, van Susante, & van Bakel, 2012) and caregivers (Cousino & Hazen, 2013). There is also research to suggest that children with long, chronic illness experience difficulties across different domains in their life including academic and social problems (Pinquart & Teubert, 2012). In addition, children cannot manage their medical illness themselves, so they have to rely on family members for medical care. The level of parent or family involvement can range from being under‐involved in the management of the child’s illness to over‐involvement such as overprotection of the child (Martire & Helgeson, 2017). From an ecological framework, the child’s negative health status has an impact within and across different systems. Within the family, a child’s illness can impact his/her relationship with siblings and the parent–child relationship. These close family relationships have an impact on health (Chen, Brody, & Miller, 2017). In addition, the child’s health status can also impact the relationship between parents or other caregivers. As noted above, a child with an illness can contribute to parenting stress, and, in turn, this can lead to discord in the marriage or the relationship. This discord can have a negative impact on relationship satisfaction. Parents and caregivers can become stressed and frustrated with the child or their partner in the relationship. A child missing significant amount of time from school or other events (e.g., sports‐ related activities) can also have a negative impact on peer relationships.

Factors Relevant to Healthcare Health disparities can be defined as quantifiable differences in incidence, prevalence, mortality, and health indicators in certain populations or groups (e.g., Latinos, African Americans;

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U.S. Surgeon General, 2001). Health disparities also include differences in access to h ­ ealthcare in groups based on ethnicity/race, socioeconomic status (SES), and area of residence (Flores, 2010). This next section discusses factors that may contribute to health disparities.

Socioeconomic Status Poverty can have a long‐term deleterious impact on a child’s psychological, emotional, p ­ hysical, and behavioral development (Bradley & Corwyn, 2002; Yoshikawa, Aber, & Beardsee, 2012). In addition, years of research have identified profound health disparities associated with poverty (Gallo & Matthews, 2003). Particularly, families with means may provide their children with access to health services, education, and social connections that might not be available or less available to lower SES families (Lescano & Rahill, 2017). Research shows that there is a relationship between SES (an exosystemic factor) and health as SES can impact health outcomes even before birth. Growing evidence suggests that children from low SES families are at risk of suffering from neurodevelopmental abnormalities in utero, premature birth, and low birth weight (Bradley & Corwyn, 2002). Unfortunately, low SES families are less likely to consistently follow an immunization schedule and have adequate access to healthcare and proper nutrition (Bradley & Corwyn, 2002). Nutrition has been identified as an important link between SES and psychological and physical well‐being. Additionally, poor nutrition during childhood can negatively impact cognitive and behavioral development and contribute to an increase in morbidity in children. As previously mentioned, SES can contribute to biological and physiological changes in utero, during infancy, and during childhood. These disturbances create vulnerabilities that result in an overall decrease in health status and health problems that continue or manifest in adulthood. However, the degree of impact caused by low SES varies depending on how many years the child has spent in poverty and how young the child is when the family is living in poverty (Bradley & Corwyn).

Access to Healthcare Lower SES families often have limited access to medical care, which often translates into ­deficient prenatal care during pregnancy and inadequate preventive care during childhood. Children from low SES families often do not have adequate immunizations, may not receive appropriate medical care when experiencing acute or chronic diseases, or may only take their children to the doctor when the disease is at an advanced stage (Bradley & Corwyn, 2002). Many of these families do not have financial means to purchase insurances and tend to rely on emergency rooms more or might avoid visiting the doctor altogether.

Ethnic Minorities and Healthcare A disproportionate amount of families living at or below national poverty levels in the United States are ethnic and racial minorities. According to 2016 data, 22% of Black/African Americans and 19.4% of Hispanics (all races) live in poverty (U.S. Census Bureau, 2017). In contrast, 10.1% of those who identify as Asian and 8.8% of individuals who identify as White, not Hispanic, live in poverty (U.S. Census Bureau, 2017). Social disparities, low educational attainment, low income, and deficient housing contribute to the inequalities experienced by ethnic minority families (Sanders‐Phillips, Settles‐Reaves, Walker, & Brownlow, 2009).

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Furthermore, even when adjusting for factors such as income and insurance coverage, ethnic minorities in the United States receive medical services that are of lower quality than European Americans (U.S. Surgeon General, 2001). It is estimated that by 2020, ethnic minority children will outnumber European American children in the United States (U.S. Census Bureau, 2014). However, even when ethnic minority children are close to representing the numerical majority of the child population, profound disparities in access to healthcare remain. Research suggests that ethnic minorities are less likely to seek medical care (U.S. Surgeon General, 2001). This phenomenon can be explained by differences in beliefs and attitudes toward healthcare. Ethnic minority parents may not trust medical providers, and they may place a different emphasis on health. For example, Latino and African American parents often indicate that their doctors do not understand their children’s needs (Flores, 2010). However, the intersectionality between ethnic minority status, low SES, and limited insurance coverage is a key factor that contributes to the underutilization of health services. Racial disparities are evident when examining mortality rates in the United States. For example, childhood mortality rates tend to be significantly higher for African American children compared with European American children (Flores, 2010). African American children have the lowest immunization rates and present with high rates of obesity. The risk for obesity continues throughout adolescence, with African American female adolescents reporting a higher prevalence of obesity and needing but not getting medical care (Flores). Important disparities can be found in obesity rates, physical activity, and nutrition in Latino children as well. Research shows that Latino children have the highest rate of obesity when compared with other ethnic/ racial groups (Flores).

The Influence of Culture Psychologists and medical professionals alike are recognizing the importance and influence that culture has on different aspects of an individuals’ life. Culture can shape attitudes, behaviors, practices, and values, to name a few. Culture can also influence how health distress is expressed in families, how families adjust, and how family relationships can impact health (Campos & Kim, 2017; Clay, 2017). In addition, culture has an influence on how individuals view the concept of the family. As an example, for some, the concept of who family and who is a caregiver only involves immediate family members (e.g., parents and siblings), and for ­others, the concept of family or caregiver is not restricted to the immediate family. As an example, in African American culture, it may be that a long‐standing neighbor or friend is known as family and is not distinguished between other immediate family relatives. This is important to consider as it highlights the flexibility within which the different systems function for the child. From a cultural lens perspective, these systems are best conceptualized as flexible in parameters from one family to another.

Disparities in Access to Healthcare Research suggests that lack of health insurance is a barrier to accessing quality healthcare (U.S. Surgeon General, 2001). This is particularly relevant for ethnic minority populations as being uninsured is a barrier for Latinos and African Americans attempting to access quality healthcare. For example, African American families are more likely to be uninsured and/or sporadically insured. Uninsured families typically experience delays and problems in accessing healthcare. Latino and African American children have an increased likelihood of not having

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access to a usual source of care or primary care physician (Flores, 2010). Moreover, Latino and African American children are less likely to be referred to see a medical specialist. Research indicates that higher hospitalization rates for ambulatory care and emergency department visits for African American children and African American and Latino children have a higher rate of avoidable hospital admissions (Flores). Overall, African American and Latino children experience disparities in the use of healthcare including less visits to the doctor (spending a year or more since the last physician visit) and less calls to the doctor’s office.

Language Other disparities in access that warrant attention include difficulties in communication. Research shows that Latino families are more likely to report difficulties with doctor–patient communication with their provider (Flores, 2010). This may be due to a language barrier between the family being more proficient and comfortable speaking Spanish and medical personnel not being proficient in Spanish. Children of Spanish‐speaking parents are often placed in a position to interpret for their parents or provide their own information. Developmentally, depending on the child’s age, the child may not be able to provide accurate information, may be embarrassed to convey personal information, or may provide distorted information to medical personnel. This can also be the case for any family whose preferred language is not English.

Discrimination and Health Disparities Some ethnic minority children are exposed from a young age to prejudicial attitudes and racial discrimination, and contact with discrimination may contribute to feelings of hopelessness and lead to a poor self‐concept (Sanders‐Phillips et al., 2009). Moreover, an awareness of minority status, which is typically achieved in adolescence, might lead to an internalized devalued group membership and identity. Adversity and negative social experiences experienced by ethnic minority children may have a long‐standing effect in physiological functioning. Moreover, research shows that chronic exposure to racial discrimination correlates with biological and psychological changes that may persist and might impact health outcomes in childhood. Fortunately, the family and other systems such as churches and schools are in a position to help children cope with prejudice and racism through teaching cultural pride and racial socialization. Research suggests that having a sense of ethnic/racial identity may reduce the risk against developing of internalizing behavior problems (Smith & Trimble, 2016).

Protective and Risk Factors As with protective and risk factors associated with psychological disorders, a host of these factors are also correlated with medical disorders and negative health‐related outcomes (Mash & Barkley, 2014). A risk factor can be defined as “a characteristic at the biological, psychological, family, community, or cultural level that precedes and is associated with a higher likelihood of problem outcomes,” while a protective factor can be defined as “a characteristic at the biological, psychological, family, or community (including peers and culture) level that is associated with a lower likelihood of problem outcomes or that reduces the negative impact of a risk ­factor on problem outcomes” (National Research Council and Institute of Medicine, 2009, pp.  xxvii–xxviii). Across the lifespan, protective and risk factors tend to vary according to developmental stage beginning in early childhood all the way through middle childhood,

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a­ dolescence, and adulthood. However, some protective or risk factors are present in several stages over the course of development. These factors play different roles in shaping a child, and, in turn the family, thus it is important to be cognizant of each and their role in the child’s health as well as in the outcomes associated. In early childhood, secure attachment is crucial for healthy development that has implications later in life for emotion regulation, family relationships, and prosocial behavior (National Research Council and Institute of Medicine, 2009). Through building a foundation of consistent and positive and parent–child interactions, a secure relationship can be formed and solidified early, promoting healthy child development. A positive home environment can also contribute to development of executive functioning skills (e.g., problem‐ solving skills, language acquisition), encourage early childhood and lifelong positive psychological, emotional, and behavioral health (National Research Council and Institute of Medicine, 2009). In middle childhood, it is increasingly important for children to successfully achieve appropriate developmental milestones such as academic achievement, following rules for appropriate behavior and positive peer relations. As noted earlier, academic and social problems can develop for children with chronic illnesses. A child’s ability to master academic skills, along with making friends with peers, can contribute to protecting against poor health or overall negative outcomes. Additionally, the concept of resilience is a key component eliciting positive developmental outcomes in this stage of development (National Research Council and Institute of Medicine, 2009). The resilient ability to persevere despite adversity protects ­children in middle childhood from potentially problematic outcomes. In adolescence, essential protective factors leading to positive development and child health include engagement in positive physical health habits, intellectual development, physiological and emotional development, and social development (National Research Council and Institute of Medicine, 2009). On an individualized level, adolescents who exhibit high sense of self‐worth, emotional regulation, and good coping and problem‐solving skills are more equip to avoid or minimize the impact of negative outcomes (National Research Council and Institute of Medicine, 2009). From a family and school perspective, protective factors in adolescence also include receiving extended family support and engaging in positive peer relationships. Further, different systems can each play a role in helping reduce health‐compromising behaviors and increasing health‐promoting behaviors in youth (Wilson, Coulon, & Huffman, 2017). These protective factors associated with outcomes of children across the development are important to consider in contrast with the risk factors also associated. Across the lifespan, there are many risk factors associated with negative health outcomes that can be detrimental regardless of developmental stage. Such risk factors including child maltreatment, family dysfunction (e.g., poor family communication, avoidance of negative emotions) community and school risk factors (e.g., violence), and poverty can contribute to a host of problematic outcomes in children of all ages (National Research Council and Institute of Medicine, 2009). Further, family dysfunction and disruption including divorce, a poor parent–child relationship, insecure attachment, and parental psychopathology can also elevate risk for negative outcomes in children across all developmental stages (National Research Council and Institute of Medicine, 2009). Additionally, community and school risk factors including victimization, bullying, academic failure, and association with deviant peers contribute to potential negative health outcomes in children (National Research Council and Institute of Medicine, 2009).

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Conclusion The health of the child and family has such an important and profound impact on each other. This reciprocal system, in turn, impacts other systems in the child’s life such as development of peer relationships and academic performance in schools. We have a sufficient scientific knowledge base regarding prevention and early intervention programs that can assist in raising children with prosocial skills and having close family relationships (Biglan, 2015). In addition, we can now point to numerous social determinants of health that can have a negative impact on the child and family (Black, 2017). Having close family relationships can ameliorate some of the negative impact that acute and chronic medical conditions can have on the child, family, and other systems such as peers, schools, and communities. Practitioners and researchers alike are encouraged to closely examine the quality of the parent–child relationship as well as the quality of the sibling relationship and the caregiver’s relationship. In addition to examining family cohesion, the degree to which the child and family have a positive and supportive social network should be taken into account. This can include the child’s peer relationships and the family’s network extended family members and friends through work, the child’s school, and the community. At a broader level, schools and the healthcare system need to be examined in the context of child and family health.

Author Biographies Joaquin Borrego, Jr., PhD, is an associate professor in the Department of Psychological Sciences at Texas Tech University. He is a member of the clinical psychology doctoral program and directs the Parent‐Child Interaction Therapy (PCIT) research lab. His research interests include parent–child relationships, discipline practices, culture, and prevention and early intervention of child physical abuse. He moved to Department of Psychology, Pacific University, Forest Grove, Oregon, USA, in summer 2018, after this chapter was initially written. Tabitha Fleming, MA, is a doctoral student in the Texas Tech University Clinical Psychology doctoral program and a member of the Parent‐Child Interaction Therapy (PCIT) research lab. Her research interests include parent management, parental discipline practices, and prevention of child maltreatment. Elizabeth Ortiz‐Gonzalez, MA, is a doctoral student in the Texas Tech University Clinical Psychology doctoral program and a member of the Parent‐Child Interaction Therapy (PCIT) research lab. Her research interests include parenting practices with ethnic minority families, particularly Latino families. Amber Morrow, BA, is a doctoral student in the Texas Tech University Clinical Psychology doctoral program and a member of the Parent‐Child Interaction Therapy (PCIT) research lab. Her research interests include discipline practices and the prevention of the use of physical punishment and child maltreatment.

References Biglan, A. (2015). The nurture effect: How the science of human behavior can improve our lives and our world. Oakland, CA: New Harbinger. Black, M. (2017). Prevention: A multilevel, biobehavioral, lifespan perspective. In M. C. Roberts & R. G. Steele (Eds.), Handbook of pediatric psychology (pp. 538–549). New York, NY: Guilford Press.

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Bradley, R., & Corwyn, R. (2002). Socioeconomic status and child development. Annual Review of Psychology, 53, 371–399. Bronfenbrenner, U. (1979). The ecology of human development. Cambridge, MA: Harvard University Press. Campos, B., & Kim, H. S. (2017). Incorporating the cultural diversity of family and close relationships into the study of health. American Psychologist, 72, 543–554. Chen, E., Brody, G. H., & Miller, G. E. (2017). Childhood close family relationships and health. American Psychologist, 72, 555–566. Clay, D. L. (2017). Culture and diversity in research and practice. In M. C. Roberts & R. G. Steele (Eds.), Handbook of pediatric psychology (pp. 81–91). New York, NY: Guilford Press. Cousino, M. K., & Hazen, R. A. (2013). Parenting stress among caregivers of children with chronic ­illness: A systematic review. Journal of Pediatric Psychology, 38, 809–828. Flores, G. (2010). Technical report: Racial and ethnic disparities in the health and health care of children. Pediatrics, 125(4), 979–1020. Gallo, L., & Matthews, K. (2003). Understanding the association between socioeconomic status and physical health: Do negative emotions play a role? Psychological Bulletin, 129, 10–51. Kazak, A. E., Alderfer, M. A., & Rader, S. K. (2017). Families and other systems in pediatric psychology. In M. C. Roberts & R. G. Steele (Eds.), Handbook of pediatric psychology (pp. 566–579). New York, NY: Guilford Press. Lescano, C. M., & Rahill, G. J. (2017). Racial and ethnic health disparities. In M. C. Roberts & R. G. Steele (Eds.), Handbook of pediatric psychology (pp. 499–508). New York, NY: Guilford Press. Martire, L. M., & Helgeson, V. S. (2017). Close relationships and the management of chronic illness: Associations and interventions. American Psychologist, 72, 601–612. Mash, E. J., & Barkley, R. A. (2014). Child psychopathology (3rd ed.). New York, NY: Guilford Press. National Research Council and Institute of Medicine. (2009). Preventing mental, emotional, and behavioral disorders among young people progress and possibilities. Washington, DC: National Academic Press. Pinquart, M., & Teubert, D. (2012). Academic, physical, and social functioning of children and adolescents with chronic physical illness: A meta‐analysis. Journal of Pediatric Psychology, 37, 376–389. Sanders‐Phillips, K., Settles‐Reaves, B., Walker, D., & Brownlow, J. (2009). Social inequality and racial discrimination: Risk factors for health disparities in children of color. Pediatrics, 124(3), 176–186. Smith, T. B., & Trimble, J. E. (2016). Foundations of multicultural psychology: Research to inform effective practice. Washington, DC: American Psychological Association. U.S. Census Bureau. (2014). Projections and size of the U.S. population: 2014–2060. Washington, DC: Author. U.S. Census Bureau. (2017). Income and poverty in the United States: 2016. Washington, DC: Author. U.S. Surgeon General. (2001). Mental health: Culture, race, and ethnicity. A supplement to mental health: A report of the Surgeon General. Rockville, MD: Author. Vermaes, I. P. R., van Susante, A. M. J., & van Bakel, H. J. A. (2012). Psychological functioning of ­siblings in families of children with chronic health conditions: A meta‐analysis. Journal of Pediatric Psychology, 37, 166–177. Wilson, D. K., Coulon, S. M., & Huffman, L. E. (2017). Health promotion in children and adolescents: An integration of the biopsychosocial model and ecological approaches to behavior change. In M. C. Roberts & R. G. Steele (Eds.), Handbook of pediatric psychology (pp. 566–579). New York, NY: Guilford Press. Yoshikawa, H., Aber, J. L., & Beardsee, W. R. (2012). The effects of poverty on the mental, emotional, and behavioral health of children and youth. American Psychologist, 67, 272–284.

Suggested Reading Biglan, A. (2015). The nurture effect: How the science of human behavior can improve our lives and our world. Oakland, CA: New Harbinger. Roberts, M. C., & Steele, R. G. (2017). Handbook of pediatric psychology. New York, NY: Guilford Press.

Childhood Cancer Dianna Boone, Savannah Davidson, Jordan Degelia, and Jason Van Allen Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA

Introduction Pediatric cancers are newly diagnosed in approximately 14,000 youth in the United States each year (Howlader et al., 2013) and are the leading cause of death by disease for children (Heron et al., 2010). Acute lymphoblastic leukemia (ALL) is the most common form of pediatric cancer and is responsible for one‐fourth of all childhood cancers and 75% of all cases of childhood leukemia (Belson, Kingsley, & Holmes, 2007). In 2014, nearly 16,000 children and adolescents were diagnosed with cancer in the United States. In addition, more than 80% of those diagnosed with pediatric cancer will survive at least 5 years after their diagnosis (Wessler, 2015). This chapter provides an overview of the short‐ and long‐term effects of ­cancer on children and their families, with special attention paid to psychosocial outcomes and intervention efforts. The importance of medication adherence and the health challenges ­associated with survivorship are also discussed.

Psychosocial Functioning in Pediatric Cancer Many studies have examined the impact of pediatric cancer on the psychological and emotional functioning of children, adolescents, and their families (Germann et al., 2015). The psychological effects of cancer therapy include increased levels of anxiety, depression, and posttraumatic stress (Dietz & Mulrooney, 2011). Studies have also indicated that children diagnosed with cancer have significantly higher internalizing symptoms (Sawyer, Antoniou, Toogood, & Rice, 1997) and a potentially higher risk of suicidal ideation than the general population (Recklitis et al., 2010). Research has illustrated that these psychosocial effects are typically experienced near the time of diagnosis and early in the treatment process (Compas et  al., 2014; Kazak, The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Boeving, Alderfer, Hwang, & Reilly, 2005). However, some studies have also illustrated that children with cancer demonstrate better emotional functioning than comparison groups (Van Schoors, Caes, Verhofstadt, Goubert, & Alderfer, 2015) and have not shown higher rates of depression or posttraumatic stress than population norms (Germann et al., 2015).

Health‐Related Quality of Life A component of psychosocial functioning that may be affected by cancer is health related q­uality of life. Health‐related quality of life (e.g., a broad multidimensional concept that ­usually includes self‐reported measures of physical and mental health; Centers for Disease Control and Prevention, 2012) has also been found to be lower in children undergoing treatment for pediatric cancer when compared with the general population, particularly in the areas of physical complaints, motor functioning, and positive emotional functioning (Fakhry et al., 2013). It has also been found that among children with ALL, females, older children, and those with multiple medical complications were at a higher risk of poor quality of life (Fakhry et al., 2013). Demographic factors such as age, gender, type of cancer, treatment modality/intensity, and progression of illness have all been significantly related to health‐ related quality of life (Fakhry et al., 2013).

Depression and Anxiety In addition to lower health‐related quality of life, research has shown that increased levels of depression and anxiety can result from cancer treatment as well (Dietz & Mulrooney, 2011). Myers et al. (2014) examined the emotional and behavioral functioning of a large sample of children with ALL. Both child and parent reports were obtained on measures related to the child’s behavior, anxiety, hyperactivity, aggression, and depression. These measures were given at three different time points (e.g., 1, 6, and 12 months after being given a diagnosis). The results revealed that on average, all of the child outcome variables were similar to population norms; however, more children scored in the at‐risk range for depression at 1, 6, and 12 months (Myers et al., 2014). Children scored in the at‐risk range for anxiety at 1 month only (Myers et al., 2014). Similarly, Compas et al. (2014) examined anxiety and depression in a sample of children recently diagnosed with cancer. They found that based on reports from children and their caregivers, children in the sample experienced mild to moderate symptoms of anxiety and depression (Compas et al., 2014). Both of these studies reveal that anxiety and depression may be a significant problem for children after receiving a diagnosis of pediatric cancer (Myers et al., 2014).

Resiliency Although pediatric cancer can result in negative psychosocial outcomes (e.g., increased ­depression and anxiety and lower quality of life), research has demonstrated that children can show healthy developmental trajectories despite a cancer diagnosis. This positive adaption, often termed resiliency (e.g., achieving positive outcomes despite exposure to significant levels of adversity), has been found to be significantly related to better adjustment (Germann et al., 2015). Germann et al. (2015) conducted a study to determine the pattern of resiliency and adjustment (measured by hope, anxiety, depression, and quality of life) in children between ages 8 and 18 as well as to examine the relationships between these variables. Overall, results

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demonstrated that pediatric cancer patients are resilient and that one’s level of hope has the potential to maintain and promote adjustment and well‐being in children with cancer (Germann et al., 2015). A systematic review was also conducted to examine family resiliency in the context of pediatric cancer. Van Schoors et al. (2015) found that although a diagnosis of pediatric cancer disrupts family functioning, most families are resilient and adapt well to receiving a pediatric cancer diagnosis. Findings from these studies suggest that pediatric cancer patients may experience anxiety and depression, particularly near the time of diagnosis and early in the treatment process (Kazak et al., 2005; Pinquart & Shen, 2011). However, research also indicates that families demonstrate resiliency when facing pediatric cancer (Van Schoors et al., 2015). Thus, it may be important for researchers to develop interventions that promote positive psychosocial ­outcomes such as resiliency in children undergoing cancer treatment.

Family Adjustment to a Pediatric Cancer Diagnosis Families of children with cancer are also at risk for poor adjustment outcomes, including ­posttraumatic stress symptoms (Rodriguez et al., 2012) and anxious and depressive symptoms (Pai et al., 2007). Research has demonstrated that the way a child’s parents adjust after their child is diagnosed with cancer can be crucial to their child’s adjustment following diagnosis (Rodriguez et al., 2016). Thus, interventions targeting parental adjustment can result in positive outcomes for their children (Fedele et al., 2013; Phipps et al., 2015). Fedele et al. (2013) conducted a study to determine if maternal distress predicted child adjustment outcomes and if a parent‐focused interdisciplinary intervention positively affected children’s adjustment. It was found that maternal distress predicted child internalizing symptoms, which suggests that interventions aiming to improve child adjustment outcomes should consider incorporating parent factors (Fedele et al., 2013). This study also found that the parent‐focused intervention significantly reduced child internalizing symptoms, which indicates that psychological interventions for mothers of children newly diagnosed with cancer can positively influence child adjustment outcomes (Fedele et al., 2013). In addition, research has demonstrated that certain sociodemographic factors can be ­associated with psychological distress in families facing pediatric cancer (Bemis et al., 2015). Bemis et al. (2015) found that factors representing sociodemographic disadvantage (i.e., ­single ­parenthood, lower family income, lower parental education, non‐White race) may influence general perceived stress, cancer‐related stressors, and psychological distress in families experiencing pediatric cancer (Bemis et  al., 2015). The results of this study are important, as researchers will be able to develop effective interventions for families who are more likely to experience psychological distress (Bemis et al., 2015). Overall, it is essential to understand the ways that children and families cope with the effects of cancer to develop optimal interventions for improving coping skills in children (Compas et al., 2014). It is important that interventions continue to focus on not only child psychosocial outcomes but family functioning as well.

Medication Adherence Pediatric psychologists often collaborate with multidisciplinary teams in oncology to improve medication adherence. Despite the importance of taking medications as prescribed, some

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studies have shown that as many as 50% of pediatric cancer patients do not adhere to their maintenance treatment regimen (Kondryn, Edmondson, Hill, & Eden, 2011). Nonadherence can take many forms (e.g., not filling prescriptions, skipping or missing doses) and can be intentional (e.g., deciding not to take a medication because of side effects) or unintentional (e.g., forgetting; Hommel & Baldassano, 2010). Poorer adherence has been documented for regimens that are more time consuming and complex, and rates of nonadherence in pediatric chronic illness populations, especially among adolescents, are even higher than those in adult populations (Rapoff, 2010). Nonadherence to a medication regimen, especially for pediatric cancer patients, can potentially interfere with the efficacy of drug regimens, resulting in difficulties meeting treatment goals, more frequent visits to the emergency room, or increased hospitalizations.

Barriers to Medication Adherence Medication adherence, particularly in adolescents with cancer, can be more challenging because adolescents are generally diagnosed with higher risk cancers than younger children; thus, adolescents have more complicated treatment plans (Hullmann, Brumley, & Schwartz, 2015). Hullmann et al. (2015) investigated parent and adolescent reported rates of, and reasons for, nonadherence, as well as the demographic, treatment and disease‐related, and psychosocial correlates of nonadherence. They found that adolescent‐ and parent‐reported adherence were significantly correlated, with approximately half of the sample reporting ­perfect adherence (Hullmann et al., 2015). The most common reason for missed medication reported by adolescents and caregivers was “just forgot” (Hullmann et al., 2015). Interestingly, for adolescents who endorsed perfect adherence, they reported having greater social support from their families (Hullmann et  al., 2015). These results indicate that children receiving ­family support are more likely to adhere to their medication regimens (Hullmann et al., 2015).

Interventions That Aim to Promote Adherence Due to the importance of medication adherence to positive health outcomes for pediatric cancer patients, interventions promoting medication adherence are necessary. Pai and McGrady (2015) recently conducted a literature review examining interventions that aimed to improve medication adherence in pediatric cancer patients. The results indicate that a multifaceted approach is needed to promote medication adherence in this population (Pai & McGrady, 2015). More specifically, these researchers suggest that multidisciplinary teams should ­combine “adherence‐related assessments, education, anticipatory guidance, and documentation in standard clinical care” (Pai & McGrady, 2015). It was also concluded that medication adherence should be consistently monitored throughout the course of treatment (Pai & McGrady, 2015). Interventions targeting family support and psychosocial functioning with an educational component have also been shown to be more effective in improving medication adherence compared with control conditions (Pai & McGrady, 2015). In sum, proper assessment of medication adherence in pediatric cancer patients is crucial. Nonadherence increases one’s risk of developing complications and can lead to treatment goals not being met. Although there are numerous barriers to medication adherence (e.g., side effects of medication, cost of treatment, poor planning), research has demonstrated that improving family involvement is helpful in encouraging medication adherence. Research on interventions to improve adherence in this population has demonstrated that adherence should be promoted through a multifaceted approach, include an educational component, and be

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monitored routinely and throughout the course of one’s treatment (Pai & McGrady, 2015; Rohan et al., 2015).

Health Challenges Associated With Survivorship After surviving pediatric cancer, the patient may experience other chronic health issues such as delayed growth, endocrinological problems, and neurocognitive impairment (Bhatia, 2005). Research has demonstrated that childhood cancer survivors are at a significantly greater risk of developing secondary cancers, cardiovascular disease, osteoporosis, and diabetes (Greving & Santacroce, 2005). Comorbid conditions can result from cancer treatment, genetic predisposition, or various lifestyle factors (Demark‐Wahnefried, Aziz, Rowland, & Pinto, 2005).

Neurological Functioning After Cancer Treatment Survivors of cancer are also at greater risk for adverse neurocognitive outcomes (Patel et al., 2014). Certain methods of treatment such as cranial radiation therapy put children at risk for “late effects” that emerge after cessation of treatment (Ness & Gurney, 2007). Long‐term effects of treatment can include malignant brain tumors and neurocognitive deficits such as deficits in attention, executive functioning, memory, and processing speed (Butler & Mulhern, 2005; Patel et al., 2014). However, interventions have demonstrated efficacy in treating these adverse neurocognitive effects resulting from cancer treatment (Patel et al., 2014). For example, Butler et al. (2008) designed a cognitive remediation intervention for pediatric cancer survivors with documented attention disturbance. Results indicated that the children showed small to modest improvements in academic achievement and parent‐reported behavioral functioning (Butler et  al., 2008). Similarly, Patel et  al. (2014) conducted a study to evaluate the efficacy of a similar intervention directed toward parents of childhood cancer survivors with neurocognitive late effects, to improve targeted parenting skills, and therefore to improve their children’s educational performance. Results revealed that children who participated in the intervention significantly increased their use of study strategies (Patel et  al., 2014). Additionally, caregivers’ self‐efficacy to promote their child’s school success increased after the intervention (Patel et al., 2014). These studies show that interventions can potentially improve cognitive functioning in child cancer survivors.

Increased Risk for Obesity There is also an increased risk for weight gain and decreased physical activity following cancer treatment (Stern et al., 2015). For example, 5 years’ posttreatment, 21% of all pediatric cancer survivors are classified as obese (BMI ≥ 95th percentile for age and gender) and 20% as overweight (BMI ≥ 85th percentile and ≤94th percentile for age and gender; Chow, Pihoker, Hunt, Wilkinson, & Friedman, 2007). Children with certain types of cancers (e.g., ALL and specific sarcomas) are at an even greater risk for obesity (Chow et al., 2007). Research has illustrated that the association between cancer treatment and future obesity may be due to cranial radiation and exposure to corticosteroids (Chow et al., 2007). Additionally, consumption of high fat foods and limited physical activity could influence posttreatment weight gain. However, researchers have developed interventions that aim to promote healthy lifestyles for children after cancer treatment. Family‐based interventions that treat pediatric obesity by

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promoting healthy lifestyle changes, physical activity, and dietary behaviors are effective, and it is likely that these types of interventions are also effective for child cancer survivors (Stern et al., 2015).

Conclusion In sum, pediatric cancer is a prevalent and potentially life‐threatening illness that affects the entire family (Long & Marsland, 2011; Ward, DeSantis, Robbins, Kohler, & Jemal, 2014). Although many families facing pediatric cancer show resiliency (Van Schoors et  al., 2015), research indicates that these children often experience psychosocial problems including anxiety, depression, and posttraumatic stress (Dietz & Mulrooney, 2011; Seitz et al., 2010). In addition to psychosocial problems, children and adolescents may also experience late effects of cancer including neurocognitive deficits, such as with attention and concentration (Butler & Mulhern, 2005). Research in pediatric cancer indicates that there are often significant health challenges associated with survivorship, which include increased risk for obesity, delayed growth, endocrinological problems and neurocognitive impairment (Krivoy, Jenney, Mahajan, & Peretz Nahum, 2012), and other chronic illnesses (Bottomley & Kassner, 2003; Greving & Santacroce, 2005). Pediatric psychologists will continue to address and develop solutions to these challenges to promote resiliency and quality of life in pediatric cancer patients.

Author Biographies Dianna Boone, MA. Dianna is a second year student in the clinical psychology program at Texas Tech University, under the mentorship of Dr. Jason Van Allen. She received her MA in Rehabilitation and Mental Health Counseling from the University of South Florida. Her research interests include pediatric psychology and childhood obesity. Savannah Davidson, BA. Savannah received her bachelor’s degree in psychology from Texas Tech University and is currently a graduate student in the School Psychology Program at the University of Texas at Austin. Jordan Degelia, BA. Jordan received her bachelor’s degree in psychology from Texas Tech University and is currently a graduate student in the Social Work Program at Texas Tech. Jason Van Allen, PhD. Jason is an associate professor in the Clinical Psychology Program at Texas Tech University. He received his PhD in Clinical Child Psychology from the University of Kansas, and his research focuses on pediatric psychology and childhood obesity.

References Belson, M., Kingsley, B., & Holmes, A. (2007). Risk factors for acute leukemia in children: A review. Environmental Health Perspectives, 115(1), 138–145. doi:10.1289/ehp.9023 Bemis, H., Yarboi, J., Gerhardt, C. A., Vannatta, K., Desjardins, L., Murphy, L. K., … Compas, B. E. (2015). Childhood cancer in context: Sociodemographic factors, stress, and psychological distress among mothers and children. Journal of Pediatric Psychology, 40(8), 733–743. doi:10.1093/jpepsy/ jsv024 Bhatia, S. (2005). Cancer survivorship—Pediatric issues. ASH Education Program Book, 2005(1), 507–515. doi:10.1182/asheducation‐2005.1.507

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Bottomley, S. J., & Kassner, E. (2003). Journal of Pediatric Nursing, 18(2), 126–133. Butler, R. W., Copeland, D. R., Fairclough, D. L., Mulhern, R. K., Katz, E. R., Kazak, A. E., … Sahler, O. J. Z. (2008). A multicenter, randomized clinical trial of a cognitive remediation program for childhood survivors of a pediatric malignancy. Journal of Consulting and Clinical Psychology, 76(3), 367. doi:10.1037/0022‐006X.76.3.367 Butler, R. W., & Mulhern, R. K. (2005). Neurocognitive interventions for children and adolescents ­surviving cancer. Journal of Pediatric Psychology, 30(1), 65–78. doi:10.1093/jpepsy/jsi017 Centers for Disease Control and Prevention. (2012). Retrieved from http://www.cdc.gov/hrqol/ Chow, E. J., Pihoker, C., Hunt, K., Wilkinson, K., & Friedman, D. L. (2007). Obesity and hypertension among children after treatment for acute lymphoblastic leukemia. Cancer, 110(10), 2313–2320. doi:10.1002/cncr.23050 Compas, B. E., Desjardins, L., Vannatta, K., Young‐Saleme, T., Rodriguez, E. M., Dunn, M., … Gerhardt, C. A. (2014). Children and adolescents coping with cancer: Self‐ and parent reports of coping and anxiety/depression. Health Psychology, 33, 853–861. doi:10.1037/hea0000083 Demark‐Wahnefried, W., Aziz, N. M., Rowland, J. H., & Pinto, B. M. (2005). Riding the crest of the teachable moment: Promoting long‐term health after the diagnosis of cancer. Journal of Clinical Oncology, 23(24), 5814–5830. doi:10.1200/JCO.2005.01.230 Dietz, A. C., & Mulrooney, D. A. (2011). Life beyond the disease: Relationships, parenting, and quality of life among survivors of childhood cancer. Haematologica, 96(5), 643–645. doi:10.3324/ haematol.2011.042606 Fakhry, H., Goldenberg, M., Sayer, G., Aye, S. S., Bagot, K., Pi, S., … IsHak, W. W. (2013). Health‐ related quality of life in childhood cancer. Journal of Developmental and Behavioral Pediatrics, 34(6), 419–440. doi:10.1097/DBP.0b013e31828c5fa6 Fedele, D. A., Hullmann, S. E., Chaffin, M., Kenner, C., Fisher, M. J., Kirk, K., … Mullins, L. L. (2013). Impact of a parent‐based interdisciplinary intervention for mothers on adjustment in children newly diagnosed with cancer. Journal of Pediatric Psychology, 38(5), 531–540. ­ doi:10.1093/jpepsy/jst010 Germann, J. N., Leonard, D., Stuenzi, T. J., Pop, R. B., Stewart, S. M., & Leavey, P. J. (2015). Hoping is coping: A guiding theoretical framework for promoting coping and adjustment following pediatric cancer diagnosis. Journal of Pediatric Psychology, 40(9), 846–855. doi:10.1093/jpepsy/jsv027 Greving, D. M., & Santacroce, S. J. (2005). Cardiovascular late effects. Journal of Pediatric Nursing Oncology, 22(1), 38–47. doi:10.1177/1043454204272531 Heron, M., Sutton, P. D., Xu, J., Venture, S. J., Strobino, D. M., & Guyer, B. (2010). Annual review of vital statistics: 2007. Pediatrics, 125(1), 4–15. doi:10.1542/peds.2009‐2416 Hommel, K. A., & Baldassano, R. N. (2010). Brief report: Barriers to treatment adherence in pediatric inflammatory bowel disease. Journal of Pediatric Psychology, 35(9), 1005–1010. doi:10.1093/jpepsy/ jsp126 Howlader, N., Noone, A. M., Krapcho, M., Garshell, J., Neyman, N., Altekruse, S. F., & Cho, H. (2013). SEER cancer statistics review, 1975–2010. [Based on the November 2012 SEER data ­submission, posted to the SEER web site, April 2013.]. Bethesda, MD: National Cancer Institute. Hullmann, S., Brumley, L., & Schwartz, L. (2015). Medical and psychosocial associates of nonadherence in adolescents with cancer. Journal of Pediatric Oncology Nursing, 32(2), 103–113. doi:10.1177/1043454214553707 Kazak, A., Boeving, C., Alderfer, M., Hwang, W., & Reilly, A. (2005). Posttraumatic stress symptoms during treatment in parents of children with cancer. Journal of Clinical Oncology, 23(30), 7405–7410. doi:10.1200/JCO.2005.09.110 Kondryn, H. J., Edmondson, C. L., Hill, J., & Eden, T. O. (2011). Treatment non‐adherence in teenage and young adult patients with cancer. The Lancet Oncology, 12(1), 100–108. doi:10.1016/ S1470‐2045(10)70069‐3 Krivoy, E., Jenney, M. E. M., Mahajan, A. M., & Peretz Nahum, M. (2012). Survivorship in childhood cancer. In S. Kreitler, M. W. Ben Arush, & A. Martin (Eds.), Pediatric psycho‐oncology. Oxford, UK: Wiley‐Blackwell.

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Long, K. A., & Marsland, A. L. (2011). Family adjustment to childhood cancer: A systematic review. Clinical Child and Family Psychology Rreview, 14(1), 57–88. doi:10.1007/s10567‐010‐0082‐z Myers, R. M., Balsamo, L., Lu, X., Devidas, M., Hunger, S. P., Carroll, W. L., … Kadan‐Lottick, N. S. (2014). A prospective study of anxiety, depression, and behavioral changes in the first year after a diagnosis of childhood acute lymphoblastic leukemia. Cancer, 120(9), 1417–1425. doi:10.1002/ cncr.28578 Ness, K. K., & Gurney, J. G. (2007). Adverse late effects of childhood cancer and its treatment on health and performance. Annual Review of Public Health, 28, 279–302. doi:10.1146/annurev. publhealth.28.021406.144049 Pai, A. L., Greenley, R. N., Lewandowski, A., Drotar, D., Youngstrom, E., & Peterson, C. C. (2007). A meta‐analytic review of the influence of pediatric cancer on parent and family functioning. Journal of Family Psychology, 21(3), 407–415. doi:10.1037/0893‐3200.21.3.407 Pai, A. L., & McGrady, M. E. (2015). Assessing medication adherence as a standard of care in pediatric oncology. Pediatric Blood & Cancer, 62(S5), S696–S706. doi:10.1002/pbc.25795 Patel, S. K., Ross, P., Cuevas, M., Turk, A., Kim, H., Lo, T. T., & Bhatia, S. (2014). Parent‐directed intervention for children with cancer‐related neurobehavioral late effects: A randomized pilot study. Journal of Pediatric Psychology, 39(9), 1013–1027. doi:10.1093/jpepsy/jsu045 Phipps, S., Long, A., Willard, V. W., Okado, Y., Hudson, M., Huang, Q., & Noll, R. (2015). Parents of children with cancer: At‐risk or resilient? Journal of Pediatric Psychology, 40(9), 914–925. doi:10.1093/jpepsy/jsv047 Pinquart, M., & Shen, Y. (2011). Behavior problems in children and adolescents with chronic physical illness: A meta‐analysis. Journal of Pediatric Psychology, 36(9), 1003–1016. doi:10.1093/jpepsy/ jsr042 Rapoff, M. (2010). Editorial: Assessing and enhancing clinical significance/social validity of intervention research in pediatric psychology. Journal of Pediatric Psychology, 35(2), 114–119. doi:10.1093/ jpepsy/jsp102 Recklitis, C., Diller, L., Li, X., Najita, J., Robison, L., & Zeltzer, L. (2010). Suicide ideation in adult survivors of childhood cancer: A report from the Childhood Cancer Survivor Study. Journal of Clinical Oncology, 28(4), 655–661. doi:10.1200/JCO.2009.22.8635 Rodriguez, E. M., Dunn, M. J., Zuckerman, T., Vannatta, K., Gerhardt, C. A., & Compas, B. E. (2012). Cancer‐related sources of stress for children with cancer and their parents. Journal of Pediatric Psychology, 37(2), 185–197. doi:10.1093/jpepsy/jsr054 Rodriguez, E. M., Murphy, L., Vannatta, K., Gerhardt, C. A., Young‐Saleme, T., Saylor, M., … Compas, B. E. (2016). Maternal coping and depressive symptoms as predictors of mother–child communication about a child’s cancer. Journal of Pediatric Psychology, 41(3), 329–339. doi:10.1093/jpepsy/ jsv106 Rohan, J., Drotar, D., Alderfer, M., Donewar, C., Ewing, L., Katz, E., & Muriel, A. (2015). Electronic monitoring of medication adherence in early maintenance phase treatment for pediatric leukemia and lymphoma: Identifying patterns of nonadherence. Journal of Pediatric Psychology, 40(1), 75–84. doi:10.1093/jpepsy/jst093 Sawyer, M., Antoniou, G., Toogood, I., & Rice, M. (1997). Childhood cancer: A two‐year prospective study of the psychological adjustment of children and parents. Journal of the American Academy of Child & Adolescent Psychiatry, 36(12), 1736–1743. doi:10.1097/00004583‐199712000‐00022 Seitz, D. C., Besier, T., Debatin, K. M., Grabow, D., Dieluweit, U., Hinz, A., … Goldbeck, L. (2010). Posttraumatic stress, depression and anxiety among adult long‐term survivors of cancer in adolescence. European Journal of Cancer, 46(9), 1596–15606. doi:10.1016/j.ejca.2010.03.001 Stern, M., Ewing, L., Davila, E., Thompson, A. L., Hale, G., & Mazzeo, S. (2015). Design and rationale for NOURISH‐T: A randomized control trial targeting parents of overweight children off cancer treatment. Contemporary Clinical Trials, 41, 227–237. doi:10.1016/j.cct.2014.12.018 Van Schoors, M., Caes, L., Verhofstadt, L. L., Goubert, L., & Alderfer, M. A. (2015). Systematic review: Family resilience after pediatric cancer diagnosis. Journal of Pediatric Psychology, 40(9), 856–868. doi:10.1093/jpepsy/jsv055

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Ward, E., DeSantis, C., Robbins, A., Kohler, B., & Jemal, A. (2014). Childhood and adolescent cancer statistics, 2014. CA: A Cancer Journal for Clinicians, 64(2), 83–103. doi:10.3322/caac.21219 Wessler, J. M. (2015). Leukemia in children getting back to school—Part 1. NASN School Nurse, 30(2), 116–118. doi:10.1177/1942602X15570256

Suggested Reading Compas, B. E., Desjardins, L., Vannatta, K., Young‐Saleme, T., Rodriguez, E. M., Dunn, M., … Gerhardt, C. A. (2014). Children and adolescents coping with cancer: Self‐ and parent reports of coping and anxiety/depression. Health Psychology, 33, 853–861. doi:10.1037/hea0000083 Germann, J. N., Leonard, D., Stuenzi, T. J., Pop, R. B., Stewart, S. M., & Leavey, P. J. (2015). Hoping is coping: A guiding theoretical framework for promoting coping and adjustment following pediatric cancer diagnosis. Journal of Pediatric Psychology, 40(9), 846–855. doi:10.1093/jpepsy/jsv027 Hullmann, S., Brumley, L., & Schwartz, L. (2015). Medical and psychosocial associates of nonadherence in adolescents with cancer. Journal of Pediatric Oncology Nursing, 32(2), 103–113. doi:10.1177/ 1043454214553707

Ethical Issues for Psychologists in Medical Settings Gerald P. Koocher1 and Jeanne S. Hoffman2,* Department of Psychology, DePaul University, Chicago, IL, USA 2 US Department of the Army, Washington, DC, USA

1

Over the past several decades, the numbers of psychologists practicing in health delivery ­systems have expanded as have the types of sites and roles of psychologists in these settings. This result follows the progress of research and practice in health psychology, behavioral medicine, and pediatric psychology but also tracks the evolution of the US healthcare system and specialty care settings. Ethical regulation of the practice of psychology began with the first code of ethics published in 1953 and has evolved as the scope of psychology has changed with the most recent revision adopted in 2010 (American Psychological Association [APA], 2010). The inclusion of psychology as a healthcare profession has resulted in new ethical challenges to practitioners. In response APA developed “Guidelines for Psychological Practices in Health Care Delivery Systems” (APA, 2013). These guidelines are aspirational rather than enforceable standards of care and are “intended to facilitate the continued systematic development of the profession and to help ensure a high level of professional practice” (APA, 2013). They also serve to highlight challenges to practitioners in this specific, but varied, specialty.

Working in the Medical Culture Practicing medical psychology requires intercultural skills. Just as we expected psychologists to become knowledgeable about and sensitive to the culture of their patients, they must also acquire knowledge and sensitivity to the culture of the healthcare system. Medical culture has a different lexicon; requires an expanded knowledge of physical illnesses, their symptoms, and

*The views expressed in this manuscript are those of the authors and do not reflect the official policy or position of the Department of the Army, Department of Defense, or the US Government. The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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treatments; has a powerful hierarchy, often with a physician as leader; requires flexibility with tolerance for multitasking, “stat” consultations, and interventions (from the Latin statum, meaning immediately); and may not understand or recognize the distinct capabilities, services, or ethical responsibilities of psychologists. A degree of willingness to adapt and skill working across professional lines are required for any mental health professional planning to work in such settings. One must, for example, acquire a new lexicon of terminology that may seem paradoxical (e.g., a “progressive disease” is one that gets worse, and “positive findings” are a bad sign when discovered during a physical examination). Familiarity with the symptoms, course, and treatment of physical illnesses as well as an understanding of how medical hospitals and outpatient centers work (as distinct from mental hospitals, community mental health centers, or college counseling services) will prove very important. Mental health practitioners in medical settings also must remain keenly aware of their skills and limitations in the context of healthcare regulations. These include interdisciplinary ­collaboration in outpatient settings, maintaining competencies, and attending to issues in hospital practice related to confidentiality and the Health Insurance Portability and ­ Accountability Act (HIPAA). Many medical conditions can present in ways that suggest ­psychopathology, and having a medical degree does not ensure against diagnostic errors. Some physicians seem too willing at times to see physical complaints as psychological, and some mental health practitioners seem all too eager to go along with them. Similarly, some healthcare practitioners can become so focused of detecting or fighting a serious illness that the psychological needs of the patient get lost in the process.

Professional Integrity Typically, mental health professionals working in medical settings will be employed under the supervision of physicians, historically hired in departments of psychiatry or pediatrics, but increasingly in subspecialty practices such as cardiology, endocrinology, gastroenterology, genetics, nephrology, oncology, or organ transplant services. At other times, they may be administratively organized in separate departments labeled behavioral health, medical psychology, or family services in an effort to avoid the stigma some associate with psychiatry and mental illness. They may provide assessment and intervention services to treat psychopathology but may also be involved in addressing medical nonadherence, symptom control (e.g., pain treatment or anxiety about pending medical procedures), screening for qualification as candidates for complex procedures (e.g., organ transplantation or gastric bypass), genetic counseling, or end‐of‐life care. Wherever they work, psychologists must take care not to surrender their professional integrity or standards (APA, 2013, Guideline 1) within this milieu. The Guidelines acknowledge that this may be challenging due to “constraints or pressures posed by other professions or systemic factors” (APA, 2013). Consider the medical practice in which the office medical clerk asks every patient to ­complete brief depression and anxiety measures, scores them, and hand the scores over to a psychologist who writes a few sentences as an “assessment” of patients they have not personally interviewed. The patients’ insurance is billed for the assessment screening that creates an unnecessary expense with no clear benefit for most patients. Consider the hospital outpatient clinic the administrator routinely overbooks all practitioners to allow for DNK (did not keep) or no‐show appointments. On days when every patient shows up, they may be told, “The doctor

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is running late,” with no mention of overbooking as the cause. This type of scheduling may not qualify as unreasonable in some circumstances where the practitioner’s time with each patient is brief, but would certainly qualify as unreasonable for psychotherapy appointments. These examples may seem like good business practices aimed at increasing billing volume, but they do not meet the ethical test of holding the best interests of the patient paramount. One should not assume that other professionals understand the training, services, standards, or ethics of psychologists. We can and should take steps to expand colleagues’ knowledge of their potential contributions within these settings in accord with acceptable practice. Of course this also requires our profession’s understanding the roles of the myriad professionals in ­complex medical systems. For example, do social workers perform psychotherapy or case ­management, do speech therapists provide behavioral treatment for feeding disorders, do pediatricians evaluate ADHD, do pharmacists address adherence issues, and who makes final decisions on how professional services are coded and billed?

Competence and Credentialing in Healthcare Delivery Our ethics require us to practice only within the limits of our training and competence. As psychology has matured as a specialty in medical settings, specific core competences have evolved. An example of such core competencies was generated by the Interorganizational Work Group on Competencies for Primary Care Practice (McDaniel et al., 2014) and could be considered as a model for core competencies in other medical settings: science, systems, professionalism, interprofessional relationships, application of care, and teaching. At the same time, many of the roles we perform in medical settings have evolved with the rapid pace of change in medical care, requiring continuing learning and creative adaptation by experienced clinicians. Intellectual laziness and conceptual rigidity will lead to rapid failure in medical settings. Healthcare delivery systems must, by law (42 U.S.C.233(h)(2)), require documented ­credentialing and privileging of its providers. Credentialing in this context involves “the process of assessing and confirming the qualifications of a licensed or certified health care ­provider” (Health Resources and Services Administration, 2007). Clinical privileges define the scope of services the individual is deemed qualified to provide within the organization. Staff appointments define the extent of participation in the governing bodies of medical institutions. The type of appointment for psychologists (e.g., full medical staff, allied health professional, consultant) and particular privileges (e.g., admitting, consulting, treating, or conducting ­ research) varies between institutions. Board certification of physicians has become a well‐established standard, often required for their credentialing in medical settings. While the majority of psychologists have not sought board certification, those practicing in healthcare are more likely to have board certification (21.7%) (Robiner, Dixon, Miner, & Hong, 2012). The American Board of Professional Psychology (ABPP), founded in 1949, has become the body predominantly responsible for the granting of board certification with its specialties in clinical psychology, clinical health psychology, clinical child and adolescent psychology, clinical neuropsychology, and rehabilitation psychology the most relevant in healthcare practice. As psychologists increasingly become integrated team members in the evolving healthcare system, and with mounting emphasis on competence and safety within these institutions, the necessity for board certification as a ­commensurate credential with our colleagues is in the future of psychology.

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Confidentiality Confidentiality stands as the cornerstone of the therapeutic relationship, but the duty of ­confidentiality becomes nuanced and complex within medical systems. We owe preservation of confidentiality to individual patients, but medical settings include a community of providers that requires a network of confidentiality (Condie, Grossman, Robinson, & Condie, 2014). The psychologist should recognize that within these networks both formal and informal exchanges of information occur. In addition, differing views of confidentiality may be held by the patient, the family, the psychologist, the various other care providers, and also the medical institution. In certain areas such as pediatrics, the primary care provider has an expectation that all information will be shared. Families often have similar expectations even with adult patients. Limits of confidentiality in medical settings are controlled by HIPAA, state law, or other government regulations that create conflicting demands at times. HIPAA allows providers to share information necessary for treatment and payment operations, but there may be circumstances when patients may rightly request that sensitive information be compartmentalized. For example, suppose that family members agree to screening as possible bone marrow donors for a child and genetic testing reveals that the person who believes he is the child’s biological father turns out to be genetically unrelated. This incidental parentage discovery unrelated to the primary question (i.e., Is the putative father a good donor match?) could cause considerable emotional distress, if revealed. Similarly, consulting psychologist will routinely learn of sensitive emotional issues unrelated to immediate medical treatment needs of the patient. In the community of caregivers, some information should be shared on a need‐ to‐know basis. Electronic health records often make better integrated care possible, but it is important to understand and make appropriate use of role segregation. Once again, need‐to‐know becomes the key principle, but the psychologist needs to understand who will have access to what information and understand the potential consequences. For example, the patient registration clerk and the proctologist most likely should not have access to sensitive psychotherapy notes. Patients also have a right to know and consent to the sharing of their medical and mental health records. At an institutional level this typically occurs at the time of clinic ­registration or hospital admission, when the patient signs a standard HIPAA information document. However, many people do not read, understand, or remember the details. Therefore, the psychologist should repeat the necessary HIPAA cautions to patients when we begin to serve them in these settings.

Informed Consent This process supports the basic ethical principle of autonomy and promotes trust between provider and patient. It also addresses some of the conundrums posed within a multidisciplinary care setting. Informed consent not only provides a patient with the risks, benefits, and alternatives to specific treatments but also delineates with whom a patient’s information will be shared and the nature of the community of providers involved. We must remain mindful that consent is a process, rather than a single conversation or signature on a form. We must refresh the patient’s understanding as changes that might affect their decisions occur. The most recent APA Ethics Code (APA, 2010) acknowledges the need to address these complexities when Standards 3.07 “Third‐Party Requests for Services” and 3.11 “Psychological

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Services Delivered To Or Through Organizations” were modified to include the concept of client definition (Behnke, 2014). When psychologists work with children, families, or couples, this becomes most salient (Behnke, 2014; Koocher & Keith‐Spiegel, 2016; Rae, Brunquell, & Sullivan, 2009) and is addressed in Standard 10.2, which mandates that the provider “take reasonable steps at the outset (1) which of the individuals are clients/patients and (2) the relationship the psychologist will have with each person” (APA, 2010). Consent, assent, permission, and capacity are different concepts that demand parsing. Consent implies three separate aspects—knowledge, voluntariness, and capacity (both legal and cognitive). The person seeking the consent must provide sufficient information for the person granting consent to understand fully what is being asked of them. It is not necessary to disclose every potential aspect of the situation, only those facts a reasonable person might need to formulate a decision. Voluntariness refers to the absence of coercion, duress, misrepresentation, or undue inducement. People can only give consent for themselves. Capacity refers to legal competence to give consent as well as the cognitive ability. Although all adults are deemed competent to give consent unless they are found to be incompetent, children are assumed incompetent to grant consent (Koocher & Keith‐Spiegel, 2016). The concept of capacity involves weighing the right to autonomy of the patient vs. beneficence and non‐maleficence. For persons who are legally incapable of giving informed consent, such as children, adolescents, or adults lacking capacity, psychologists nevertheless must seek assent. This means that they must provide an appropriate explanation of the treatment or assessment, seek the individual’s assent and consider the person’s preferences or best interests, and seek appropriate permission from a legally authorized person, if such substitute consent is permitted or required by law. Children and adolescents are presumed to lack competence to consent, but the provider should determine the minor’s ability to understand information offered about the nature and potential consequences of the pending decision (i.e., the risks and benefits of a specific treatment or action) and invite a clear preference based on the information presented. The terms competency and capacity are often used interchangeably, but competence is a legal concept determined by a court or defined by law as in the case of minors. Capacity is determined by a qualified psychologist or physician based on the circumstances and jurisdiction. Questions of capacity arise in medical settings most often arise with refusals or discontinuation of treatment. If an articulate child, medical providers, or the parent/guardian disagrees on the necessity of medical treatment, a judicial decision may be requited.

Boundaries Psychologists are trained to maintain boundaries, but working in medical settings, especially acute care settings, will present many unexpected challenges. The challenges might be categorized as boundary crossings versus boundary violations. The former is in service of the therapy and must be a considered action taking into account the general ethical principle of beneficence while a boundary violation results in harm to the patient. The context in which boundary crossings occur must be considered (Zur, 2017). The medical context is unique and may impose varying degrees of beneficent exigency. In other settings a psychologist does not make physical contact with a patient, but what if a patient in the final stages of life requests that the psychologist hold her hand? What if a patient requires oral suctioning to continue the therapeutic interaction? What if the therapist is assisting the patient remain calm during a procedure in which the patient’s body is exposed? Does this violate the patient’s right to dignity or

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­ romote coping? Context, contingency, and patient needs and rights must all be considered in p the decision to deviate from standard operating procedures in psychotherapeutic practice.

Challenging Healthcare Contexts In this brief chapter we cannot address all possible complicating contexts that will challenge psychologists in healthcare setting, but we have chosen a few that illustrate the range of ethical challenges that can arise. These include providing services in military medical settings, care of patients seeking organ transplantation, children with gender dysphoria, and patients with cystic fibrosis (CF). We selected this sampling because of the significant ethical issues that come up in providing care with these patients and setting contexts. Military medical care provides some thought‐provoking examples. A recent report by the Defense Health Board acknowledges such ethical challenges in its introduction (Defense Health Board, 2015). Clinical health psychologists practicing in military medical settings also face conflicts between their professional ethics, the challenges of working in a medical setting, and the requirement of following military policies and orders. Military medical settings range from very large medical institutions such as the Walter Reed National Military Medical Center to small embedded units in combat zones where the psychologist is the sole mental health provider. Depending on the site and role, the clinician may be a civilian employee or military personnel caring for active duty personnel or their dependents. While psychologists are required to avoid most multiple relationships, this is not always possible in military medical settings (Dobmeyer, 2013; Johnson, Ralph, & Johnson, 2005; Koocher & Keith‐Spiegel, 2016; Staal & King, 2000). Examples include a psychologist serving on an aircraft carrier who was required to provide a urine sample for random drug testing only to find the only female observer available was one of her patients (Johnson et al., 2005) or a psychologist working in a family medicine clinic who received his medical care from a physician who was also his client (Dobmeyer, 2013). In the military active duty psychologists are also officers and bound by law and policy “to hold subordinates accountable, to promote order and discipline and to place the mission above the interests of the individual service member” (Kennedy & Moore, 2008). What is a clinician to do when in the course of therapy another officer reveals that he is having an extramarital affair, an offense under the Uniform Code of Military Justice (UCMJ), or a pilot reports that he is taking stimulant medication for ADHD that is prohibited and considered a safety risk? Confidentiality also becomes much more complex issue in a system where commanders are entitled to information about a service member’s medical or psychological condition and how its treatment may effect fitness for duty or deployability and all treatment is documented in a system‐wide electronic medical record. Cultural competency also comes into acute focus, as psychologists in military medical ­settings work within both the medical culture and the military culture. The latter may seem quite alien to civilian psychologists. The military culture is highly structured, hierarchical, with specific norms of behavior, specific belief systems, and even unique forms of language. The military subscribes to core values that have in common: duty, honor, and country. Families of service members live within this culture, and veterans often subscribe to military values long after their active service ends. Military psychologists understand this culture, but civilians must seek training or mentoring to ethically practice in these settings. Organ transplantation is another arena in which psychologists provide invaluable services as members of transplant teams by evaluating potential recipients and their support systems,

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making recommendations to these teams, providing both clinical interventions and support to recipients and their families, and evaluation of candidates as living tissue or marrow donors. Each of these potential services comes with a myriad of ethical challenges. Confidentiality issues become very complex as information must be shared, especially the results of assessment of psychological or behavioral factors that could adversely affect the success of the transplant. Will the psychological status of the patient compromise success? How likely is the recipient to adhere to the complex post‐transplant regimen? Of course, informed consent must be obtained for psychological evaluation or treatment, but can the patient truly refuse? Does consent qualify as voluntary if the alternative is death? Can the patient’s support system refuse assessment since this will jeopardize the patient being accepted for transplant? What happens if a screened donor qualifies as a “good match” but has intense ambivalence about donating? How can we valance the ethical principles of beneficence and non‐maleficence in transplant decisions? Conflicting loyalties may lead to ethical dilemmas that force the psychologist to sort through the question of to whom one owes a duty of loyalty and in what hierarchy: the patient, the family, the donor, the medical teams, and the transplant center? Certainly issues of justice are a topic of great debate in transplant medicine. What if the potential recipient has cognitive or emotional impairments that might compromise success? Dual relationships are common. Can the psychologist who provides therapy and support also objectively evaluate the patient? Are other psychologists with the requisite skills and knowledge available to consult? The donor status can trigger other ethical issues. Is this to be a living donor? Are pressures being applied to donate? Is there a conflict between the possible harm to the donor and possible good to the recipient? Can a minor consent to donation of an organ in any event? What if the best donor for a parent is their child? In nonliving donation other issues arise: pressure applied to donor families when time is of the essence in harvesting the organ and psychological impact on families when donors are kept alive by mechanical means to facilitate harvest. Issues can even arise related to how death of the donor is determined: brain death or circulatory death.

Minors With Gender Dysphoria/Transgender Youth Psychology has a unique and essential contribution to the treatment of this population both because of our knowledge set but also in our awareness of the ethical challenges embedded in this area of practice. While adults with these conditions have a more direct route to medical and surgical interventions, psychological assessment is still required. Children also require psychological evaluation, but ethical challenges become prominent when they present for treatment. The core of these challenges arises from the fact that gender identity and gender expression may change over time (Tishelman et al., 2015) and the percentage of gender diverse children decreases with age due to return to conformity with natal sex, or a determination they are gay, lesbian, or bisexual (APA, 2015). Ethical principles of beneficence and non‐maleficence as well as autonomy must be addressed as well as the ethical standards of informed consent, assent, and capacity. The typical course of treatment guided by psychosocial development principles suggests these differences. Young children expressing gender dysphoria are candidates for exploratory psychotherapy for the child, but often, primarily for parents. Prepubertal children are candidates for psychotherapy, assessment, and consideration of puberty suppression to give time for the child to mature cognitively in order to more fully understand the consequences of their decision. Pubertal children may be candidates for addition of use of cross‐sex hormones to the treatment course to begin the transition to another sex. Adults may be prepared to consider

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sexual reassignment surgery. Assent becomes a particularly thorny issue with minors as they may not have the capacity to fully understand the course of treatment, irreversible consequences, or psychosocial challenges that they might face. But an additional issue involves the fact rates of autistic spectrum disorders are higher in the population of minors who express gender dysphoria, raising the fundamental question whether these youth with impaired social skills can fully understand the implications of gender modification. The APA guidelines on practice with transgender patients also caution practitioners to remain mindful of the cognitive processes of adolescents that might focus on immediate gratification to the detriment of a considered decision (APA, 2015). Again, we must adhere to the standards of competence and informed consent in order to prevent harm to these youth (non‐maleficence) even as we strive to help them optimize adjustment as part of the treatment team.

Cystic Fibrosis CF provides an excellent example of a life‐threatening genetically transmitted (autosomal recessive) disease illness previously associated with childhood death. With new treatments CF has become a chronic disease with mean survival into the sixth decade of life. Successful treatment of CF involves a complex and unrelenting treatment regimen that imposes a significant burden on caretakers and patients alike. A burgeoning body of research on CF has begun to identify factors related to prognosis. As one might expect, adherence is an important factor that itself impacts other variables such as lung function and body weight that, in turn, influence long‐term survival (Sawicki & Goss, 2015; Smith, Georgiopoulos, & Quittner, 2016). A recent, and growing, body of literature has found that rates of depression in individuals with CF and their caretakers rank well above comparison samples (Quittner et al., 2014). These higher rates of depression and anxiety link to decreased adherence as well as poorer lung function and lower body weight. As a result, the Cystic Fibrosis Foundation (CFF) Committee on Mental Health has recommended that individuals with CF ages 12 to adulthood as well as caretakers of all children and adolescents with CF be screened annually for depression and anxiety. For all children, adolescents, and adults with CF, storing of such results in their medical records would be expected, although with the same ethical consideration given to all mental health test and screening results. But what of the results of caretaker screenings? Should they be recorded in the caretaker’s medical record or CF patient’s? This becomes an ethical question that is being currently discussed on the CFF Mental Health Group Listserve and at the most recent CFF Conference. In addition, the prevention of CF via genetic screening and pregnancy termination raises sensitive questions for family members who may carry the gene.

Emerging Challenges We have simply scratched the surface of the range of issues and contexts that raise ethical ­challenges for psychologists working in medical settings. Reflecting on the special challenges discussed here, it is clear that most have arisen as medical science has advanced. Inexorably with future advances, new ethical challenges to psychologists will emerge extend beyond psychologists’ current knowledge and thereby increase responsibility to acquire working ­ knowledge of these advances. Neuroimaging is one such area as is genetic modification and enhancement technologies. To these and to as yet unimagined technologies, psychologists must apply our basic ethical tenets but also strive to anticipate quandaries that might arise.

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Solutions to Ethical Challenges Just as working in medical settings is a team sport, solutions to ethical dilemmas require a team approach. Collegial consultation is key. Developing familiarity and routine discussions with colleagues across disciplines, consulting with experts including those involved in direct care, and third‐party operations will be essential. National and state psychological associations will be a resource, but monitoring regulatory changes by licensing boards will also prove critical. Hospitals are required by Joint Commission to have ethics committees that exist to provide consultation. Perfection is not required, but knowledge of ethical principles, thoughtfulness, and efforts to avoid harm are.

Author Biographies Gerald P. Koocher, PhD, ABPP, received his doctorate in clinical psychology from the University of Missouri. He is professor of psychology and dean of the College of Science and Health at DePaul University. He previously served as chief psychologist at Boston Children’s Hospital and the Judge Baker Children’s Center. Jeanne S. Hoffman, PhD, ABPP, received her doctorate in developmental psychology and clinical respecialization from the University of Hawaii with a fellowship in pediatric psychology from Boston Children’s Hospital and Judge Baker Children’s Center. She currently is chief of the pediatric psychology clinic at Tripler Army Medical Center, Honolulu, Hawaii.

References American Psychological Association. (2010). 2010 Amendments to the 2002 “ethical principles of psychologists and code of conduct”. American Psychologist, 65, 493. http://dx.doi.org/10.1037/a0020168 American Psychological Association. (2013). Guidelines for psychological practice in health care delivery systems. American Psychologist, 69(1), 1–6. American Psychological Association. (2015). Guidelines for psychological practice with transgender and gender nonconforming people. American Psychologist, 30(9), 832–864. Behnke, S. (2014). American Psychological Association. Informed consent and the APA Ethics Code. Washington, DC, USA. Condie, L. O., Grossman, L., Robinson, J. D., & Condie, D. B. (2014). Ethics and the law. In C. Hunter, C. L. Hunter, & R. Kessler (Eds.), Handbook of clinical psychology in health settings (pp. 99–124). New York, NY: Springer. Defense Health Board. (2015, March 3). Ethical guidelines and practices for U.S. medical professionals. Retrieved from www.health.mil. Dobmeyer, A. C. (2013). Primary care behavioral health: Ethical issues in military settings. Families, Systems & Health, 31(1), 60–68. Health Resources and Services Administration. (2007). U.S. Department of Health and Human Services, Bethesda, MD, USA. Johnson, W. B., Ralph, J., & Johnson, S. J. (2005). Managing multiple roles in embedded environments: The case of aircraft carrier psychology. Professional Psychology, 36, 73–81. http://dx.doi. org/10.1037/0735‐7028.36.1.73 Kennedy, C. H., & Moore, B. A. (2008). Evolution of military psychology ethics. Military Psychology, 20(1), 1–6. Koocher, G. P., & Keith‐Spiegel, P. C. (2016). Ethics in psychology and the mental health professions: Professional standards and cases (4th ed.). New York, NY: Oxford University Press.

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McDaniel, S. H., Grus, C. L., Cubic, B. A., Hunter, C. L., Kearney, L. K., Schuman, C. C., … Johnson, S. B. (2014). Competencies for psychological practice in primary care. American Psychologist, 69, 409–429. Quittner, A. L., Goldbeck, L., Abbott, J., Duff, A., Lambrecht, P., Solé, A., … Barker, D. (2014). Prevalence of depression and anxiety in patients with cystic fibrosis and parent caregivers. Thorax, 69, 1090–1097. Rae, W. A., Brunquell, D., & Sullivan, J. R. (2009). Ethical and legal issues in pediatric psychology. In M. C. Roberts & R. G. Steele (Eds.), Handbook of pediatric psychology (4th Eded. ed., pp. 19– 34). New York, NY: The Guilford Press. Robiner, W. N., Dixon, K. E., Miner, J. L., & Hong, B. A. (2012). Board certification in psychology: Insights from medicine and hospital psychology. Journal of Clinical Psychology in Medical Settings, 19, 30–40. Sawicki, G. S., & Goss, C. H. (2015). Tackling the increasing complexity of CF care. Pediatric Pulmonology, 50, S74–S79. Smith, B. A., Georgiopoulos, A. M., & Quittner, A. L. (2016). Maintaining mental health and function for the long run in cystic fibrosis. Pediatric Pulmonology, 51, S71–S78. Staal, M. A., & King, R. E. (2000). Managing a multiple relationship environment: The ethics of military psychology. Professional Psychology: Research and Practice, 31(6), 698–705. Tishelman, A. C., Kaufman, R., Edwards‐Leeper, L., Mandel, F. H., Shumer, D. E., & Spack, N. P. (2015). Serving transgender youth: Challenges, dilemmas, and clinical examples. Professional Psychology: Research and Practice, 46(1), 37–45. Zur, O. (2017). Multiple relationships in psychotherapy and counseling: Unavoidable, common, and mandatory dual relations in therapy. New York, NY: Rutledge.

Suggested Reading Johnson, W. B., & Koocher, G. P. (Eds.) (2011). Ethical conundrums, quandaries, and predicaments in mental health practice. New York, NY: Oxford University Press. Koocher, G. P., & Keith‐Spiegel, P. C. (2016). Ethics in psychology and the mental health professions: Professional standards and cases (4th ed.). New York, NY: Oxford University Press. Linton, J. C. (2010). 2020 foresight: Practicing ethically while doing things that don’t yet exist. Journal of Clinical Psychology in Medical Settings, 17, 278–284.

History of Clinical Health Psychology Rachel Postupack and Ronald H. Rozensky Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA

Disease is not the accident of the individual, nor even of the generation, but of life itself. In some form, and to some degree or other, it is one of the permanent conditions of life. Henry David Thoreau

Health is a state of complete physical, mental, and social well‐being and not merely the absence of disease or infirmity (World Health Organization, 1948). This formulation of health, adopted globally immediately after World War II, predates Engel’s (1977) “new” “biopsychosocial” model of healthcare by decades and, as pointed out by Rozensky (2015), is as “new” as 190 CE when Galen postulated the integration of mind and body in medicine during the Roman Empire. Every textbook and article that discusses the history of clinical health psychology (Baum, Perry, & Tarbell, 2004; Belar, Deardorff, & Kelly, 1987; Friedman & Adler, 2007; Stone, Cohen, & Adler, 1979; Sweet, Rozensky, & Tovian, 1991) notes that “psychology has long been in the forefront of the scientific inquiry into the understanding of health” (Resnick & Rozensky, 1996, p. 1). While psychology clearly is a scientifically based discipline that both carries out research concerning health, disease, and treatment and ­provides direct clinical services to those who seek help with disease prevention or treatment for managing a wide range of medical diagnoses and problems, it was not until 2001 that “the American Psychological Association (APA) amended its mission statement to include the term ‘health.’” The American Psychological Association (APA) stated that psychology’s focus is to “advance as a science and a profession, and as a means to promoting health and human welfare” (Rozensky, Johnson, Goodheart, & Hammond, 2004, p. xix). First recognized formally as specialty in 1997 by the APA (2015a), “Clinical Health Psychology applies scientific knowledge of the interrelationships among behavioral, emotional, cognitive, social and biological components in health and disease to: the promotion and maintenance of health; the prevention, treatment and rehabilitation of illness and disability; and the improvement of the health care system. The distinct focus of Clinical Health Psychology

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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(also referred to as behavioral medicine, medical psychology and psychosomatic medicine) is at the juncture of physical and emotional illness, understanding and treating the overlapping challenges.” This definition was built, in turn, on Matarazzo’s (1980) definition as adopted by the APA Division of Health Psychology: “the aggregate of the specific educational, scientific, and professional contributions of the discipline of psychology to the promotion and maintenance of health, the prevention and treatment of illness, and the identification of etiologic and diagnostic correlated of health, illness, and related dysfunction” (p. 815). Given these definitions, Belar (2008, p. 230) described both the venues in which health psychology services take place and the range of clinical issues addressed: Clinical health psychology services may be integrated with primary care, urgent care, tertiary care, and dental care, and occur in clinic, hospital‐based, nursing care, rehabilitation, work site, and hospice settings.

Belar listed the nine problem areas addressed by clinical health psychologists: 1 Psychological factors secondary to disease, injury, or disability—which includes both normal adjustment reactions to posttraumatic stress disorders and all psychological diagnoses in the patient or family members. 2 Somatic presentations of psychological issues, which can include such issues as chest pain with panic attack. 3 Psychophysiological problems (e.g., pain, headache). 4 Physical symptoms or medical conditions responsive to psychological and behavioral health interventions (e.g., urinary and fecal incontinence, anticipatory nausea, asthma). 5 Somatic complications associated with behavioral issues such as poor adherence to healthcare regimens. 6 Psychological presentation of organic depression with hypothyroidism. 7 Psychological and behavioral aspects secondary to stressful medical procedures such as self‐infections, dental procedures, and burn debridement. 8 Behavioral risk factors for disease, injury, or disability related to problematic health behaviors such as smoking, overeating, low exercise, and risk‐taking. 9 Problems encountered by healthcare providers and healthcare systems such as provider– patient relationship issues, staff “burnout,” assuring universal precautions, healthcare team behavior and cooperation, clinical pathway development, and quality assurance and program evaluation activities.

Psychology and Health Paralleling the history of health psychology is the development of the US federal ­government’s Healthy People initiative (Department of Health and Human Services [US], 1979) that ­establishes goals for improving the quality of the nation’s health with objectives focused on health promotion and disease prevention (Healthy People 2020, http://www.healthypeople. gov/). Rozensky (2012a) stated that along with psychology’s traditions of efficacy, effectiveness, and community‐based research and treatment, the profession has engaged in this population‐based approach to the scientific study and treatment of the human condition. Further, Rozensky (2012a) reviewed societal and healthcare trends impacting health service ­psychology including competency‐based education, interprofessionalism, evidence‐based care, and

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team‐based, patient‐centered care and changes in population demographics including a more diverse population that is aging with more chronic illness. As one of the recognized specialties under the broader umbrella of health service psychology, clinical health psychology has been a leader, throughout its history, in anticipating the various scenarios, or interactions of these trends, in preparing each new generation of health service psychologists to successfully function in an evolving research and healthcare environment. With definitions in hand, trends in mind, and current clinical activities and services described, the history of clinical health psychology will be presented from the perspective of how the profession itself has advanced. This will include the scientific and clinical changes in the field as reflected in the evolution of philosophies and organizations that have promulgated a biopsychosocial understanding of health and illness.

Defining Health Psychology and the Formation of Professional Organizations The evolution of clinical health psychology in becoming a recognized specialty is rooted in the development of definitions that include the interaction of cognitive, behavioral, emotional, and psychological processes and physical health and the formation of professional organizations that focus on science and practice in those domains. The ancient Greeks provided the philosophical basis for modern Western medicine by shifting the focus from supernatural causes to recognition of the importance of actual bodily factors in health. They theorized that illness was caused by an imbalance of the four bodily humors: blood, phlegm, choler (yellow bile), and melancholy (black bile). Health could be restored then by bringing balance to these fluids (Friedman & Adler, 2007). Galen, a Greek physician in the Roman Empire, posited that the dominant humor in an individual’s balance determined temperament and vulnerability to illness (Maher & Maher, 1994). This early integration of mind and body was later de‐emphasized in favor of focusing on primarily bodily or physical processes in the biomedical model. In the early twentieth century, the split between mind and body was reflected in the distinction of psychiatry as a medical specialization focused on the influence of the mind to explain symptoms for which there was no biological basis. Interest in the contribution of mental ­disturbance to disease became the domain of the emerging field of psychosomatic medicine (Dunbar, 1943). Flanders Dunbar began the journal of Psychosomatic Medicine in 1938–1939, which is the official journal of the American Psychosomatic Society founded in 1942. Today their mission is to “promote and advance the scientific understanding and multidisciplinary integration of biological, psychological, behavioral, and social factors in human health and disease, and to foster the dissemination and application of this understanding in education and health care” (www.psychosomatic.org). Moving into the 1960s and 1970s, growing insight into biological mechanisms prompted a philosophical shift in psychosomatic medicine to consider the bidirectional interactions between physiological and emotional states. George F. Solomon and Moos (1964) introduced the term psychoneuroimmunology (PNI) to refer to the theoretical connections between ­emotions, immunity, and disease. Research on stress and disease expanded and was based on the “fight or flight response” (Cannon, 1932) and understanding of physiological reactions to stress (Selye, 1956). Engel’s (1977) descriptions of the biopsychosocial model contributed to the ongoing integration of the mind and body in contemporary healthcare science and ­practice. Herbert Benson demonstrated the utility of meditation or relaxation to counteract both acute and chronic stress while impacting health status in conditions like hypertension

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(Benson, 1983). These discoveries among others provided an evidence base for the introduction of behavioral treatments into medical settings. Emphasis on the integration of mind and body became known as behavioral medicine (Birk, 1973). Interdisciplinary from its inception, contributors to the science and practice of behavioral medicine included professionals from a range of professions including physicians, psychologists, bio‐behaviorists, and others. The Society of Behavioral Medicine and Journal of Behavioral Medicine were founded in 1978 to promote a “field concerned with the development of behavioral science knowledge and techniques relevant to the understanding of physical health and illness and the application of this knowledge and these techniques to prevention, diagnosis, treatment, and rehabilitation” (Schwartz & Weiss, 1978, p. 7). In the 1980s behavioral health was distinguished from behavioral medicine as “an interdisciplinary field dedicated to promoting a philosophy of health that stresses individual responsibility in the application of behavioral and biomedical science knowledge and techniques to the maintenance of health and the prevention of illness and dysfunction by a variety of self‐initiated individual or shared activities” (Matarazzo, 1980). It is within this focus on the individual and a personal ability to affect change that impacts health that psychologists found a professional niche to more specifically define their scientific and clinical competencies to encourage health promotion as well as address challenges associated with illness and its treatment. The field of health psychology was defined as promoting the integration of psychological knowledge and biomedical information as it applies to health promotion, prevention, diagnosis, treatment, and rehabilitation (APA, 2015b; Matarazzo, 1980). To assist in the development of a cohesive field of specialization then, George Stone established the Journal of Health Psychology in 1982. Then in 1994, the Journal of Clinical Psychology in Medical Settings (JCPMS) was founded by Ronald Rozensky to further the role of health service psychologists as scientists and practitioners in medical settings as they addressed diseases and medical problems (Rozensky, 2006). Established in 1892 the APA advances the creation, communication, and application of psychological knowledge on the national level (APA, 2015c). Today APA is home to 54 ­divisions, which are special interest groups that represent topic areas germane to the range of clinical, scientific, educational, and public interest foci of psychologists. Movement toward the foundation of the division of health psychology began in 1973 when APA’s Board of Scientific Affairs directed that health research should be reviewed and disseminated at scientific meetings. This decision was influenced by William Schofield’s (1969) paper describing ­psychologists’ roles in health service delivery. A section of interest was added to Division 18, Psychologists in Public Service, in 1975 specifically to increase the application of psychological principles to health research and patient care as well as disseminate the results of this work. Steven Weiss and Joseph Matarazzo advocated for the foundation of an APA division devoted to health psychology. Their efforts were successful when Division 38 Health Psychology was established in 1978. The division continues its mission to promote and disseminate health‐ related research through The Health Psychologist newsletter begun in 1979 and the Health Psychology journal that published its first issue 1980–1981 (Wallston, 1997). In August 2015 the Division of Health Psychology voted to change its name the Society for Health Psychology. It continues to provide an intellectual home to health psychologists and other professionals devoted to the intersection of psychological processes and physical health. One venue that illustrates the robust growth of psychologists engaged in health and healthcare research, education, and clinical practice is number of psychologists in academic health centers and hospitals. Noting that environment as “a true healthcare home” for psychologists (p. 353), Rozensky (2012b) reviewed a series of published surveys from the early 1950s through the first decade of the twenty‐first century carried out by such notables as Matarazzo

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and Mensh. The number of psychologist in medical school was less five per school in the 1950s to 20.7 per school in the late 1970s with at least 3,894 psychologists identified in academic medical settings at the end of the twentieth century (Williams & Wedding, 1999). Rozensky noted that the most recent data finds more than half (54.5%) of psychologists work in institutional settings with 12.4% in hospitals and 4.6% in medical school alone (APA, 2009). In 1991, the American Board of Professional Psychology (ABPP) formally recognized clinical health psychology as a specialty in professional psychology and began granting board ­certification in the field based on peer evaluation (Belar & Jeffrey, 1995). In 1997 the APA (1997) archived a formal definition of the specialty of clinical health psychology, and it remains a recognized specialty to date. Today, contemporary clinical health psychologists are seen as working interprofessionally in clinical practice. They learn side by side with other healthcare professionals during their education and training in the context of the changes in healthcare both predating and promulgated by the implementation of the Patient Protection and Affordable Care Act (Public Law No: 111–148, 2010, March 23). As part of the evolution of health psychology’s place in the healthcare system, the growing acceptance of the use of Health and Behavior (billing) Codes reflects both the system’s integration of mind and body and the integration of psychologists on the healthcare team. Allowing clinical health psychologists to directly bill these Health and Behavior Codes for treating and helping patients manage specific medical problems reflects, financially, the recognition of both psychologists’ healthcare ­competencies and systemic integration (Kessler, 2008) as not just mental health providers but members of the integrated healthcare team. Along with their clinical services and educational activities, health psychologists also participate in team science to ensure research integrates mind and body and ensures interprofessionalism as part of science (Rozensky, 2014).

Education and Training Clinical health psychologists conduct research and practice at the intersection of physical and mental health in a variety of settings addressing diversified patient populations and problems. The formulation of education and training guidelines has been an essential component in the development and maintenance of professional competencies for both current and future ­psychologists (Fouad et al., 2009; Health Service Psychology Education Collaborative, 2013; Rodolfa et al., 2005). In 1983 the Arden House National Working Conference on Education and Training established the first set of education and training guidelines for the field of clinical health psychology (Stone, 1983). The conference recommended that training at the doctoral level remain “broad and general” to develop an integration of theory, research, and practice based on knowledge and understanding of the biopsychosocial model. Predoctoral clinical internships completed in settings where both psychological and physical healthcare services take place were encouraged by the Arden House participants as a part of each student’s professional development. Competencies In general, doctoral programs in health service psychology have maintained their commitment to the integration of science‐ and evidence‐based practice (Belar & Perry, 1992) where ­students acquire foundations in scientific psychology, research, ethics, diversity, theory, and clinical practice (Kaslow, Graves, & Smith, 2012) as core competencies in their broad and general education and training. In that manner, curricula for programs with a specific major area of study in clinical health psychology include knowledge of biological, affective, cognitive, social,

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and cultural bases of health and illness. Additional education may be provided on special topics such as pathophysiology, psychoneuroimmunology, and pharmacology (Belar, 2008). Ideally training is conducted in clinical settings conducive to the development of competencies to ­conduct health‐related clinical assessments and interventions, health‐oriented research, and the competencies to function as a part of healthcare delivery system including consultation services and engagement in interprofessional, team‐based collaboration (Belar, 2008; France et  al., 2008). It was recommended that accreditation for health psychology training programs begin at the postdoctoral level (France et  al., 2008), but it is clear that many accredited d ­ octoral ­programs in health service psychology offer major areas of study in clinical health psychology. France et al. (2008) provided a competency model for clinical health psychology that details knowledge and applied skills in assessment, intervention, consultation, research, training in supervision, and management administration. Kaslow, Dunn, and Smith (2008) detail the core foundational and functional competencies for health service psychologists practicing in academic health centers, while Kerns, Berry, Franstve, and Linton (2009) extend the description of competencies to issues related to developing lifelong competencies in clinical health psychology. Across competency models, the importance of ensuring clinical health psychologists possess the interprofessional competencies to function successfully within a healthcare team (Fouad & Grus, 2014) and understand the characteristics of a truly competent team (Interprofessional Education Collaborative, 2011) is key to the ongoing evolution of the s­pecialty of clinical health psychology. Specialization A specialty is defined as an area of psychological practice that requires advanced knowledge and skills acquired through an organized sequence of education and training (Commission for the Recognition of Specialties and Proficiencies in Professional Psychology, 2015; CRSPPP). Specialization allows professionals to focus on content that they must learn, refresh, and maintain to retain competence in a field as it continues to develop (Kaslow et al., 2012; Rozensky & Kaslow, 2012). The Commission for the Recognition of Specialties and Proficiencies in Professional Psychology (CRSPPP) establishes policies and procedures that are used by APA to review and recognize health service specialties with clinical health psychology being one of those recognized specialties. To identify individual specialists, individuals competent to deliver quality health psychology services, the ABPP (www.abpp.org) offers board certification for practitioners at the postdoctoral level who pass a peer‐review, competency‐based exam. Achieving board certification is particularly helpful for clinical health psychologists who work in integrated medical settings because board certification is considered the standard of practice for our colleagues in medicine and related health professions (Rozensky & Kaslow, 2012). Clinical health psychologists face the ongoing challenge of learning and maintaining a ­current, relevant, evidence‐based perspective to meet the ethical requirements of competent practice within their specialty. To help students and professionals describe their level of education and training in clinical health psychology, the Council of Clinical Health Psychology Training Programs (CCHPTP) developed the Clinical Health Psychology Specialty Taxonomy (http://cospp.org/guidelines) based on the APA policy designed to ensure consistent descriptions of the levels of education and training within specialties across the sequence of learning from graduate school, to internship, and through post‐licensure, lifelong learning (Rozensky, Grus, Nutt, Carlson, Eisman, & Nelson, 2015). That CCHPTP taxonomy lists the expectation for a major area of study in clinical health psychology at each stage of training. Recognizing that each year there is a continuing influx of new scientific and clinical information and that there is a decreasing half‐life of professional knowledge, each psychologist must establish a

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commitment to lifelong learning on the part of the competencies required of being a specialist (Wise et al., 2010). Incorporating the lifelong learning process into the broader context of the competency movement involves the evaluation of professional skills, practices, and outcomes (Neimeyer & Taylor, 2011). In addition to formal structures for continuing education and competency‐based assessments, a self‐study/self‐assessment can be used to develop learning plans for specialization throughout a professional’s career (Belar et al., 2001). Clinical health psychology has embraced a competency‐based model of education and training from the early days of the Arden House Conference through professional psychology’s evolution to a “­culture of competence” (Roberts, Borden, Christiansen, & Lopez, 2005, p. 359).

Clinical Health Psychology, the 2010s, and Beyond Each step in the history of clinical health psychology has been motivated by the spirit of that time—spirit based on politics within and external to the profession, scientific developments in psychology and medicine, technological evolution, vicissitudes in contemporary healthcare and healthcare financing, and changes in educational philosophy. The future of clinical health psychology will be based on contemporary zeitgeist composed of those same motivating forces today and tomorrow. Contemporary clinical health psychology exists within the broader context of healthcare reform as detailed in the Patient Protection and Affordable Care Act (Public Law No: 111–148, 2010, March 23) and its ongoing implementation. That law recognizes the importance of evidence‐based healthcare, quality care including credentialing and specialization, team based, and interprofessionalism in both education and practice (Rozensky, 2011, 2013). And clearly, education and training in all branches of health service psychology, including clinical health ­psychology, must address these issues in its curricula and practical training (Rozensky, 2014). In order to understand where clinical health psychologists will be employed in the future, a clear national workforce analysis philosophy must be operationalized, and data collected and studied in order to inform educators and practitioners alike as to societal need and employment opportunities (Rozensky, Grus, Belar, Nelson, & Kohout, 2007). Questions like how many psychologists will be needed in primary care, in cardiac care, in rehabilitation psychology, in psycho‐oncology, etc. will help the field have a picture of the future and provide direction to students and early career psychologists planning their education and practice focus. Many scholars have described the history and successful evolution of the specialty of clinical health psychology and highlighted the various individuals and organizations that have helped define the education, training, science, and practice of clinical health psychologists. This ­history reflects a robust science whose application is key to helping those who wish to learn to prevent disease, improve their health, or seek to manage their illness or their reaction to that illness. Clinical health psychologists clearly are dedicated to improving the human condition, and each recognizes, in their day‐to‐day work, what Shakespeare said some 400 years ago, “What wound does not heal but by degree?”

Author Biographies Rachel Postupack, MS, completed her graduate education in the Department of Clinical and  Health Psychology at the University of Florida, where she is currently completing her ­predoctoral internship. She is focusing her clinical experiences on providing services to

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­ nderserved medical populations. Her research has focused on pain, distress, cytokines, u ­coping, and quality of life for patients with cancer. Ronald H. Rozensky, PhD, ABPP, is a professor in the Department of Clinical and Health Psychology in the College of Public Health and Health Professions at the University of Florida where he served as department chair and associate dean for International Programs. He is the founding editor of the Journal of Clinical Psychology in Medical Settings and board certified in both clinical and clinical health psychology.

References American Psychological Association. (1997, August). Archival description of clinical health psychology as a specialty in professional psychology. Minutes of the meeting of the American Psychological Association Council of Representatives. American Psychological Association. (2009). 2008 APA survey of psychology health service providers. Retrieved from http://www.apa.org/workforce/publications/08‐hsp/index.aspx. American Psychological Association. (2015a). Clinical health psychology. Retrieved from http://www. apa.org/ed/graduate/specialize/health.aspx American Psychological Association. (2015b). Our mission. About Division, 38. American Psychological Association. (2015c). About APA. Retrieved from http://www.apa.org/about/ index.aspx Baum, A., Perry, N. W., & Tarbell, S. (2004). The development of psychology as a health science. In R. G. Frank, J. L. Wallander, & A. Baum (Eds.), Handbook of clinical health psychology (pp. 9–28). Washington, DC: American Psychological Association. Belar, C. D. (2008). Clinical health psychology: A health care specialty in professional psychology. Professional Psychology: Research and Practice, 39, 229–233. Belar, C. D., Brown, R. A., Hersch, L. E., Hornyak, L. M., Rozensky, R. H., Sheridan, E. P., … Reed, G. W. (2001). Self‐assessment in clinical health psychology: A model for ethical expansion of ­practice. Professional Psychology: Research and Practice, 32, 135–141. Belar, C. D., Deardorff, W. W., & Kelly, K. E. (1987). The practice of clinical health psychology. Elmsford, NY: Pergamon Press. Belar, C. D., & Jeffrey, T. B. (1995). Board certification in health psychology. Journal of Clinical Psychology, 2, 129–132. Belar, C. D., & Perry, N. W. (1992). The National Conference on scientist‐practitioner education and training for the professional practice of psychology. American Psychologist, 47, 71–75. Benson, H. (1983). The relaxation response: Its subjective and objective historical precedents and ­physiology. Trends in Neurosciences, 6, 281–284. Birk, L. (1973). Biofeedback: Behavioral medicine. New York, NY: Grune & Stratton. Cannon, W. B. (1932). The wisdom of the body. New York, NY: Norton. Commission for the Recognition of Specialties and Proficiencies in Professional Psychology. (2015). Retrieved from http://www.apa.org/ed/graduate/specialize/index.aspx. Department of Health and Human Services (US). (1979). Healthy people: A report of the Surgeon General. Washington, DC: Author. Dunbar, F. (1943). Psychosomatic diagnosis. New York, NY: Hoeber. Engel, G. L. (1977). The need for a new medical model: A challenge for biomedicine. Science, 196, 129–136. Fouad, N. A., & Grus, C. L. (2014). Competency‐based education and training in professional ­psychology. In W. B. Johnson & N. J. Kaslow (Eds.), The Oxford handbook of education and training in professional psychology (pp. 105–119). New York, NY: Oxford University Press. Fouad, N. A., Grus, C. L., Hatcher, R. L., Kaslow, N. J., Hutchings, P. S., Madison, M. B., … Crossman, R. E. (2009). Competency benchmarks, a model for understanding and measuring competency in

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professional psychology across training levels. Training and Education in Professional Psychology, 3, S5–S26. France, C. R., Masters, K. S., Belar, C. D., Kerns, R. D., Klonoff, E. A., Larkin, K. T., … Thorn, B. E. (2008). Application of the competency model to clinical health psychology. Professional Psychology: Research and Practice, 39(6), 573. Friedman, H. S., & Adler, N. E. (2007). The history and background of health psychology. In H. S. Friedman & R. Cohen Silver (Eds.), Health psychology. New York, NY: Oxford University Press. Health Service Psychology Education Collaborative. (2013). Professional psychology in health care ­services: A blueprint for education and training. American Psychologist, 68, 411–426. Interprofessional Education Collaborative. (2011, February 16 and 17). Proceedings from team‐based competencies: Building a shared foundation for education and clinical practice. Washington, DC: Author. Kaslow, N. J., Dunn, S. E., & Smith, C. O. (2008). Competencies for psychologists in academic health centers. Journal of Clinical Psychology in Medical Settings, 15, 18–27. Kaslow, N. J., Graves, C. C., & Smith, C. O. (2012). Specialization in psychology and health care reform. Journal of Clinical Psychology in Medical Settings, 19(1), 12–21. Kerns, R. D., Berry, S., Franstve, L. M. E., & Linton, J. C. (2009). Life‐long competency development in clinical health psychology. Training and Education in Professional Psychology, 3, 212–217. Kessler, R. (2008). Integration of care is about money too: The Health and Behavior Codes as an element of a new financial paradigm. Families, Systems & Health, 26, 207–216. Maher, B. A., & Maher, W. B. (1994). Personality and psychopathology: A historical perspective. Journal of Abnormal Psychology, 103(1), 72–77. Matarazzo, J. D. (1980). Behavioral health and behavioral medicine. American Psychologist, 35, 807–817. Neimeyer, G. J., & Taylor, J. M. (2011). Continuing education in psychology. In J. C. Norcross, G. R. Vandebos, & D. K. Freedheim (Eds.), History of psychotherapy: Continuity and change. Washington, DC: American Psychological Association. Public Law No: 111–148, 111th Congress: Patient Protection and Affordable Care Act. (2010). 124 STAT, p. 119. Retrieved from www.gpo.gov/fdsys/pkg/PLAW‐111publ148/pdf/ PLAW‐111publ148.pdf. Resnick, R. J., & Rozensky, R. H. (Eds.) (1996). Health psychology through the life span: Practice and research opportunities. Washington, DC: American Psychological Association. Roberts, M. C., Borden, K. A., Christiansen, M. D., & Lopez, S. J. (2005). Fostering a culture shift: Assessment of competence in the education and careers of professional psychologists. Professional Psychology: Research and Practice, 36, 355–361. Rodolfa, E., Bent, R., Eisman, E., Nelson, P., Rehm, L., & Ritchie, P. (2005). A cube model for competency development: Implications for psychology educators and regulators. Professional Psychology: Research and Practice, 36(4), 347. Rozensky, R. H. (2006). Clinical psychology in medical settings: Celebrating our past, enjoying the present, building our future. Journal of Clinical Psychology in Medical Settings, 13(4), 343–352. Rozensky, R. H. (2011). The institution of the institutional practice of psychology: Health care reform and psychology’s future workforce. American Psychologist, 66, 794–808. Rozensky, R. H. (2012a). Health care reform: Preparing the psychology workforce. Journal of Clinical Psychology in Medical Settings, 19, 5–11. Rozensky, R. H. (2012b). Psychology in academic health centers: A true healthcare home. Journal of Clinical Psychology in Medical Settings, 19, 353–363. Rozensky, R. H. (2013). Quality education in professional psychology: Flowers blooming, Flexner, and the future. American Psychologist, 68, 701–716. Rozensky, R. H. (2014). Implications of the Affordable Care Act for education and training in professional psychology. Training and Education in Professional Psychology, 8, 1–12. Rozensky, R. H. (2015). The biopsychosocial model in health service psychology: Has there really ever been another approach. Psyccritiques, 60, article 3.

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Rozensky, R. H., Grus, C. L., Belar, C. D., Nelson, P. D., & Kohout, J. L. (2007). Using workforce analysis to answer questions related to the internship imbalance and career pipeline in professional psychology. Training and Education in Professional Psychology, 1, 238–248. Rozensky, R. H., Grus, C. L., Nutt, R. L., Carlson, C. I., Eisman, E. J., & Nelson, P. D. (2015). A  ­ taxonomy for education and training in professional psychology health service specialties. American Psychologist, 70, 21–32. Rozensky, R. H., Johnson, N., Goodheart, C., & Hammond, R. (Eds.) (2004). Psychology builds a healthy world. Washington, DC: American Psychological Association. Rozensky, R. H., & Kaslow, N. J. (2012). Specialization and lifelong learning. In G. J. Neimeyer & J. M. Taylor (Eds.), Continuing education: Types, roles, and societal impacts (pp. 345–358). Hauppauge, NY: Nova Publishers. Schofield, W. (1969). The role of psychology in the delivery of health services. American Psychologist, 24(6), 565. Schwartz, G. E., & Weiss, S. M. (1978). Yale conference on behavioral medicine: A proposed definition and statement of goals. Journal of Behavioral Medicine, 1(1), 3–12. Selye, H. (1956). The stress of life. New York, NY: McGraw‐Hill. Solomon, G. F., & Moos, R. H. (1964). Emotions, immunity, and disease: A speculative theoretical integration. Archives of General Psychiatry, 11(6), 657–674. Stone, G. C. (1983). National working conference on education and training in health psychology. Health Psychology, 2(5), 1–153. Stone, G. C., Cohen, F., & Adler, N. E. (Eds.) (1979). Health psychology. San Francisco, CA: Jossey‐Bass. Sweet, J. J., Rozensky, R. H., & Tovian, S. M. (Eds.) (1991). Handbook of clinical psychology in medical settings. New York, NY: Plenum. Wallston, K. A. (1997). A history of Division 38 (Health Psychology): Healthy, wealthy, and Weiss. In D. A. Dewsbury (Ed.), Unification through division: Histories of the divisions of the American Psychological Association (Vol. 2, pp. 239–267). Washington, DC: American Psychological Association. Williams, S., & Wedding, D. (1999). Employment characteristics and salaries of psychologists in United States medical schools: Past and current trends. Journal of Clinical Psychology in Medical Settings, 6, 221–228. Wise, E. H., Sturm, C. A., Nutt, R. L., Rodolfa, E., Schaffer, J. B., & Webb, C. (2010). Life‐long learning for psychologists: Current status and a vision for the future. Professional Psychology: Research and Practice, 41(4), 288. World Health Organization. (1948). Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference, New York, 19–22 June 1946; signed on 22 July 1946 by the representatives of 61 States (Official Records of the World Health Organization, no. 2, p. 100) and entered into force on 7 April 1948.

Suggested Reading Belar, C. D. (2008). Clinical health psychology: A health care specialty in professional psychology. Professional Psychology: Research and Practice, 39, 229–233. Belar, C. D., Brown, R. A., Hersch, L. E., Hornyak, L. M., Rozensky, R. H., Sheridan, E. P., … Reed, G. W. (2001). Self‐assessment in clinical health psychology: A model for ethical expansion of ­practice. Professional Psychology: Research and Practice, 32, 135–141. Matarazzo, J. D. (1980). Behavioral health and behavioral medicine. American Psychologist, 35, 807–817. Rozensky, R. H. (2014). Implications of the Affordable Care Act for education and training in professional psychology. Training and Education in Professional Psychology, 8, 1–12. Stone, G. C. (1983). National working conference on education and training in health psychology. Health Psychology, 2(5), 1–153.

Older Adults and Perspectives for Researchers and Clinicians Working in Health Psychology and Behavioral Medicine Louise Sharpe School of Psychology, Faculty of Science, The University of Sydney, Sydney, NSW, Australia

Background It is well established that there is an aging trend in the population among most Western nations and that this trend is also being seen increasingly in developing nations (e.g., Arean, Hegel, Vannoy, Fan & Unuzter, 2008; Cuijpers, van Straten, & Smit, 2006). As the population ages, people continue to develop chronic illnesses, such as cardiovascular conditions, respiratory problems, diabetes, pain, and cancer. However, the way in which these conditions are medically managed has improved, such that the life expectancy of patients diagnosed with these illnesses is increasing. The consequence of improved medical care for chronic illness is that older adults are still living for longer with the burden of chronic physical illnesses than they previously have. Chronic physical illness impacts on a person’s ability to engage in important activities, and as a result most chronic physical illnesses have been shown to be associated with poorer quality of life. Indeed, the majority of the health psychology or behavioral medicine research has focused on individual illnesses (such as pain or diabetes or cancer) in trying to understand the impact of experience chronic illness on people’s life. However, the major risk factors for disease are common across conditions. For example, smoking, physical inactivity, and obesity confer risk of cardiovascular disease, stroke, respiratory disease, cancer, and diabetes. All of these illnesses are also more common in older adults. Hence, there is an increasing problem that has been labeled “multi‐morbidity” where people are living not with a single medical condition, but with a combination of multiple physical health problems. For example, in a recent population‐based study in the United Kingdom, one‐quarter of people The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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attending primary care settings had multi‐morbidity, and the risk for multi‐morbidity is strongly associated with age. That is, only 7% of those under 45 years of age experienced multi‐morbidity, whereas over half of all elderly participants (51% of those over 65) had multi‐morbidity (Mujica‐Mota et al., 2015). The increase in prevalence of multi‐morbidity notes an important change in the management of older adults in health psychology or behavioral medicine. As previously mentioned, health psychology has followed the lead of medical education and research, whereby studies are funded to examine the relationships between different diseases and psychological constructs. However, if the majority of older adults ­experience multi‐morbidity rather than having a single illness, this poses a significant ­challenge to the way in which we study and treat older adults with multi‐morbidity.

Consequences of Multi‐morbidity Multi‐morbidity in older adults is associated with a number of consequences. Firstly, people who have more than one physical condition are often prescribed medications separately for each of their conditions. Therefore, older adults often have a complex medical regimen to follow, involving a range of medications that frequently have contraindications and produce side effects. Secondly, many chronic physical illnesses are accompanied by symptoms that can be difficult to manage, such as pain or fatigue. These symptoms are often secondary to the underlying pathologies (e.g., neuropathies in diabetes or angina in cardiovascular ­disease), but sometimes are seen as more constraining to the individual. As a result of the illness and/or the symptoms or treatments of that illness, individuals typically will have an increase in disability associated with multi‐morbidity, which can lead to increased social ­isolation and a general reduction in physical activity. In some cases, disability can become sufficiently incapacitating to require long‐term formal or informal care. These consequences compound to increase the risk of the development of psychological disorders. While there is no good evidence to suggest that older adults have higher rates of depression and anxiety overall (indeed anxiety disorders appear to be lower among older people), multi‐morbidity does increase the risk of depressive disorders (Read, Sharpe, Modini, & Dear, 2017). Further, as patients become more disabled and frail, it is increasingly common for them to develop a fear of falling.

Adherence to Medication Adherence to medication is a major problem across all age ranges and conditions, with a large proportion of people not taking their medications as prescribed. Nonadherence to medication most commonly refers to patients either not taking medications that are prescribed or not ­taking them as prescribed, including taking larger doses of medication than recommended. There are a number of reasons why older adults might be particularly at risk for nonadherence, including more complex medication regimens to follow, mild cognitive impairment that is common among older people, and the fact that prescribed polypharmacy can give rise to more unwanted side effects and interactions than taking a single medication. Indeed, a qualitative study of nonadherence among the elderly determined that the barriers toward adherence included concern about taking too much medication, a lack of information about the risks and benefits of medicines, a desire to share decision making in the medical setting that is not ­fulfilled, and the difficulties of trying to coordinate different treatments prescribed by different providers (Haverhals et al., 2011). There have been attempts to use both simple and complex

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interventions to improve adherence specifically in older adults (Higgins & Regan, 2004) and among older adults requiring polypharmacy in particular (Cooper et al., 2015). While both identified small, statistically significant effects on improving adherence, per se, there was little evidence in either review that improving adherence influenced clinically significant outcomes (e.g., hospital admissions), and both noted that the quality of individual studies was poor. Few interventions were noted to be theoretically derived, and as such, this would be an important area of future research.

Management of Pain and Fatigue Among Older People Pain and fatigue are the two of the most common symptoms of illness and both associated with poorer quality of life and increased depression. While the relationship between poorer psychological outcomes (such as depression) and pain and fatigue is likely bidirectional, there are very good chronic disease self‐management approaches that appear to result in improvements in reported symptoms of fatigue and pain experienced in the context of illness. Importantly, however, these self‐management approaches help the patients learn to pace their activities and develop coping skills to better manage their symptoms. There is a long history across individual illnesses of using chronic self‐management approaches to improve health outcomes for patients; however, there is now strong evidence that such approaches can be used rolled out in primary settings with successful outcomes in terms of reducing pain, fatigue, and depression among those with chronic illness (Ory et al., 2013). While this study was not specifically with older adults, per se, the average age of participants was 65. In those studies that have attempted to replicate chronic disease self‐management approaches with older adults, they have generally been effective. Since both pain and fatigue are among the strongest predictors of depression among older adults, offering opportunities for chronic disease self‐management approaches in primary care settings is an evidence‐based way in which clinicians can help patients learn to reduce these symptoms, or at least the impact of these symptoms on their daily lives.

Increasing Disability and Frailty The consequence of the aging process itself is that people’s ability for physical activity declines over time; however, this natural part of the aging process is compounded by the presence of one or more physical health conditions. This can lead to older people giving up their valued activities, which not only further increases their disability but also contributes to secondary effects such as social isolation and depression. As older people become more disabled and more unwell, they can become at risk of developing frailty. Frailty is a condition that is seen predominantly in older adults, which is often associated with a sudden decline in functioning associated with a particular stressor (e.g., an illness, a fall), which sparks a sudden decline in functioning. Typically, the functional decline is seen in a range of indicators, such as increased fatigue, weight loss, weakness, an inability to walk at a usual rate, and a r­ eduction in energy expenditure (see Clegg, Young, Iliffe, Rikkert, & Rockwood, 2013). Frailty typically occurs when there is dysfunction in a range of major organ systems (e.g., inflammatory, hematological, neuromuscular, endocrine, hormonal, adiposity), but it is when abnormalities are detected across multiple systems that patients are at a higher risk of developing frailty (Clegg et al., 2013). Frailty has important implications for older patients because it is strongly associated with increased dependence and the need for care, delirium, falls, and a higher rate of mortality.

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The Need for Formal and Informal Caring When elderly people become frail, the level of care that they need increases, and, as such, the development of frailty can often mark the transition from the bulk of care being informal care to requiring the majority of care provision to be formalized. Informal care refers to care that is provided to a person by someone in their family or community when the recipient of care is no longer able to provide that care for themselves. In most societies, the majority of care is provided informally; some studies estimate that up to 90% of care is informally provided (Adelman, Tmanova, Delgado, Dion, & Lachs, 2014). Although formal and informal care are often described separately, in reality, much informal caring is supplement by organized and formal caring, and indeed, the availability of some formal care can reduce the need for the recipient of care requiring a more permanent formalized care plan, such as transition to a nursing home. The majority of care recipients, where possible, prefer to be cared for in the ­community by informal carers. From a health economics position, informal caring is extremely important as the costs of formal care provision are substantially higher. However, health ­psychology has become increasingly interested in the consequences of informal caregiving in recent years. There is strong evidence from a range of sources that providing care is associated with increased distress, depression, and caregiver burden. Most of the research into caregiver burden has been in the dementia literature, but increasingly it is recognized that caregiver burden is an important problem for those who provide care. It is recommended to reduce the burden that doctors are aware of who is the primary caregiver and allow them to have some role in decision making and care planning, where possible. It is also recommended that ­caregiver burden be assessed and that caregivers be offered both practical support (e.g., ­respite) and help with learning skills for dealing with the caring role, where necessary (Adelman et al., 2014). Where caregiver burden is a problem, there are a number of meta‐analyses that confirm the efficacy of psychoeducational programs for caregivers, although the majority are with ­caregivers of those with dementia. However, effects sizes are modest. Hence, for clinicians, these offer viable alternatives to helping patients struggling with caregiver burden. However, there is a clear gap in the literature to research the efficacy of caregiver interventions for a broader set of caregivers, most notably interventions adapted to the needs of the caregivers of older people with multi‐morbidity are absent from the literature.

Fear of Falling In addition to the physical concerns that accompany illness in later life, and the psychological consequences for those who provide care to older adults, there are a number of psychological issues that also emerge for the elderly themselves. Data strongly support the fact that anxiety disorders are actually less common among older adults. A number of potential explanations for this drop in prevalence have been discussed in the literature. It has been suggested that this could be a cohort effect, such that those who are currently in the elderly age group were encouraged to be more stoic, and hence anxiety has always been lower in this cohort. It has also been suggested that it may be a physiological phenomenon, such that people’s level of arousal generally reduces with aging and so the physical manifestations of anxiety reduce. Another suggestion has been that people learn better methods of coping as they age, through experience, and hence they are less likely to find anxiety overwhelming in later life. However, an alternative suggestion is that it could be that current diagnostic systems are based upon the concerns of younger adults and that the concerns of older adults are different. One unique fear among older people is the fear of falling.

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Fear of falling is very common among older people and for good reason. Particularly in the frail elderly, falls are a leading cause of serious injury, increase the risk that an older person will be discharged from an acute hospital setting to a formal care setting, and have a risk of subsequent mortality. Hence, it is unsurprising that as people age, fears of falling develop. Anxiety is typically manifested when an individual appraises a risk as likely and the consequences as important. Given that estimates suggest that as many as one in three older adults fall each year, then older adults are arguably sensible to be mindful of the risk of falling. Moreover, particularly those with medical conditions that include symptoms of balance disturbance or dizziness, the risk is greater. Similarly, the consequences of falling among the elderly, particularly the frail elderly, are serious, and more so for those with conditions like osteoporosis where the risk of serious injury is heightened. While understandable that older people may be fearful of falling, one of the strongest predictors of actually falling is the fear of falling itself. One likely mechanism for this relationship is the fact that those who are more fearful of falling avoid physical activity and the reduction in physical activity actually leads to greater disability that in turn is thought to increase the risk of falling. As such, the most strongly evidence‐based intervention for fear of falling in the elderly is exercise. A recent meta‐analytic review found that there was an estimated reduction across available studies of 37% for all falls, but importantly even larger reductions of 43% of severe injuries following falls and a 61% reduction in falls resulting in a fracture (El‐Khoury, Cassou, Charles, & Dargent‐Molina, 2013). There is less evidence to support psychological approaches to managing fear of falling, and this is a likely area for future research. There is evidence that more complex, multifaceted programs (e.g., including balance training, home modifications) produce larger results than exercise alone. There are currently relatively few randomized controlled trials of psychosocial interventions as an adjunct to exercise reported in the literature. Those that are available have adopted a cognitive behavioral orientation and shown good results on measures of fear of falling, and some have shown improvements in mobility. However, researchers need to determine whether adding a psychosocial component to a predominantly exercise‐based intervention will enhance the efficacy of these programs on the primary outcome of falls prevention, as well as subjective self‐reports.

Management of Depression in Older Adults with Chronic Physical Health Problems As with anxiety, there is little evidence to suggest that older people are more likely to become depressed than young adults. However, having one or more chronic physical illnesses is a risk factor for the development of late‐life depression. As with younger adults with depression, there is evidence that late life can be treated with the same types of interventions that are efficacious in younger adults, that is, antidepressant medication and psychotherapy. Although there are considerably fewer studies of interventions in the treatment of late‐life depression compared with the number with younger people, there is nonetheless strong meta‐analytic evidence to show that both pharmacotherapy and psychotherapy are efficacious in the treatment of depression among the elderly (Pinquart, Duberstein, & Lyness, 2006). Importantly, cognitive behavioral therapy, problem‐solving therapy, and reminiscence therapy all have sufficient trials demonstrating efficacy that we can consider all three to be strong evidence‐based alternative therapies for late‐life depression (Pinquart, Duberstein, & Lyness, 2007). However, there are some limitations to the evidence base for those patients with chronic physical health problems. Although many of the trials of late‐life depression include patients with physical health problems, some trials specifically exclude patients with complex health needs. Indeed,

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Pinquart et al. (2007) found evidence that the inclusion of participants with physical health problems was associated with smaller effect sizes across studies. Few studies have compared the efficacy of different interventions (e.g., cognitive behavior therapy and problem‐solving ­therapy), and few studies have specifically targeted patients with chronic ill health problems or multi‐morbidity and clinical depression (see Sharpe et al., 2012). As such, there is a pressing need for researchers to investigate the most efficacious ways in which to treat late‐life depression in patients with complex medical needs, who are by virtue of their physical health ­problems more likely to be depressed. There have been some excellent attempts to incorporate collaborative care management (Unützer et al., 2002) or prevention (van’t Veer‐Tazelaar et al., 2009) of late‐life depression. The success of problem‐solving therapy in these trials indicates that problem‐solving therapy is an effective treatment for patients with late‐life depression and complex needs, although we need more comparative research to determine the relative efficacy of problem‐solving therapy and other forms of psychotherapy.

Author Biography Louise Sharpe, PhD, is professor in the School of Psychology at the University of London and associate head for research education. Professor Sharpe completed her undergraduate degree and postgraduate training as a clinical psychologist at the University of Sydney and received her PhD from the University of London. As a researcher, Professor Sharpe has published more than 170 peer‐reviewed papers and received more than $5 million from funding agencies, including the National Health and Medical Research Council and the Australian Research Council. Her research program examines adjustment to illness and involves the development and evaluation of psychosocial interventions for people with chronic physical illness. She has been awarded the Ian M. Campbell award from the Australian Psychological Society for her contributions to clinical psychology and the Distinguished Career Award from the Australian Association of Cognitive Behavioural Therapy for her research and clinical practice of c­ ognitive behavior therapy.

References Adelman, R. D., Tmanova, L. L., Delgado, D., Dion, S., & Lachs, M. S. (2014). Caregiver burden: A clinical review. JAMA, 311(10), 1052–1060. Arean, P., Hegel, M., Vannoy, S., Fan, M. Y., & Unuzter, J. (2008). Effectiveness of problem‐solving therapy for older, primary care patients with depression: Results from the IMPACT project. The Gerontologist, 48(3), 311–323. Clegg, A., Young, J., Iliffe, S., Rikkert, M. O., & Rockwood, K. (2013). Frailty in elderly people. The Lancet, 381(9868), 752–762. Cooper, J. A., Cadogan, C. A., Patterson, S. M., Kerse, N., Bradley, M. C., Ryan, C., & Hughes, C. M. (2015). Interventions to improve the appropriate use of polypharmacy in older people: A Cochrane systematic review. BMJ Open, 5(12), e009235. Cuijpers, P., van Straten, A., & Smit, F. (2006). Psychological treatment of late‐life depression: A meta‐ analysis of randomized controlled trials. International Journal of Geriatric Psychiatry, 21(12), 1139–1149. El‐Khoury, F., Cassou, B., Charles, M. A., & Dargent‐Molina, P. (2013). The effect of fall prevention exercise programmes on fall induced injuries in community dwelling older adults: Systematic review and meta‐analysis of randomised controlled trials. BMJ, 347, f6234.

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Haverhals, L. M., Lee, C. A., Siek, K. A., Darr, C. A., Linnebur, S. A., Ruscin, J. M., & Ross, S. E. (2011). Older adults with multi‐morbidity: Medication management processes and design implications for personal health applications. Journal of Medical Internet Research, 13(2), e44. Higgins, N., & Regan, C. (2004). A systematic review of the effectiveness of interventions to help older people adhere to medication regimes. Age and Ageing, 33(3), 224–229. Mujica‐Mota, R. E., Roberts, M., Abel, G., Elliott, M., Lyratzopoulos, G., Roland, M., & Campbell, J. (2015). Common patterns of morbidity and multi‐morbidity and their impact on health‐related quality of life: Evidence from a national survey. Quality of Life Research, 24(4), 909–918. Ory, M. G., Ahn, S., Jiang, L., Smith, M. L., Ritter, P. L., Whitelaw, N., & Lorig, K. (2013). Successes of a national study of the chronic disease self‐management program: Meeting the triple aim of health care reform. Medical Care, 51(11), 992–998. Pinquart, M., Duberstein, P. R., & Lyness, J. M. (2006). Treatments for later‐life depressive conditions: A meta‐analytic comparison of pharmacotherapy and psychotherapy. American Journal of Psychiatry, 163(9), 1493–1501. Pinquart, M., Duberstein, P. R., & Lyness, J. M. (2007). Effects of psychotherapy and other behavioral interventions on clinically depressed older adults: A meta‐analysis. Aging & Mental Health, 11(6), 645–657. Read, J., Sharpe, L., Modini, M., & Dear, B. F. (2017). The relationship between multi‐morbidity and depression: A meta‐analysis. Journal of Affective Disorders, 221, 36–46. Sharpe, L., Gittins, C. B., Correia, H. M., Meade, T., Nicholas, M. K., Raue, P. J., … Areán, P. A. (2012). Problem‐solving versus cognitive restructuring of medically ill seniors with depression (PROMISE‐D trial): Study protocol and design. BMC Psychiatry, 12(1), 1. Unützer, J., Katon, W., Callahan, C. M., Williams, J. W., Jr., Hunkeler, E., Harpole, L., … Areán, P. A. (2002). Collaborative care management of late‐life depression in the primary care setting: A randomized controlled trial. JAMA, 288(22), 2836–2845. van’t Veer‐Tazelaar, P. J., van Marwijk, H. W., van Oppen, P., van Hout, H. P., van der Horst, H. E., Cuijpers, P., … Beekman, A. T. (2009). Stepped‐care prevention of anxiety and depression in late life: A randomized controlled trial. Archives of General Psychiatry, 66(3), 297–304.

Suggested Reading Alexopoulos, G. S., Borson, S., Cuthbert, B. N., Devanand, D. P., Mulsant, B. H., Olin, J. T., & Oslin, D. W. (2002). Assessment of late life depression. Biological Psychiatry, 52(3), 164–174. Bryant, C., Jackson, H., & Ames, D. (2008). The prevalence of anxiety in older adults: Methodological issues and a review of the literature. Journal of Affective Disorders, 109(3), 233–250. Scheffer, A. C., Schuurmans, M. J., Van Dijk, N., Van Der Hooft, T., & De Rooij, S. E. (2008). Fear of falling: Measurement strategy, prevalence, risk factors and consequences among older persons. Age and Ageing, 37(1), 19–24. Sharpe, L., & Curran, L. (2006). Understanding the process of adjustment to illness. Social Science & Medicine, 62(5), 1153–1166.

Pediatric Psychology Dianna Boone, Thomas J. Parkman, and Jason Van Allen Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA

Introduction Pediatric psychology is defined by the Society of Pediatric Psychology as “an integrated field of science and practice in which the principles of psychology are applied within the context of pediatric health” (American Psychological Association, 2009). Pediatric psychologists can work in a variety of settings with children with a wide range of chronic illnesses including asthma, cancer, diabetes, obesity, pediatric sleep problems, sickle cell disease, and others (Aylward, Bender, Graces, & Roberts, 2009; Roberts, Biggs, Jackson, & Steele, 2011). The scope of practice for a pediatric psychologist can include screening for psychopathology in healthcare settings, developing interventions for children with chronic illnesses, and promoting the health and development of children and adolescents from all disease groups (Spirito et al., 2003). Pediatric psychologists are in great demand due to the prevalence of pediatric chronic conditions in the United States. For example, in the United States, 13,000 children are diagnosed with cancer each year, 13,000 children are diagnosed with type 1 diabetes, and 9 million children suffer from asthma (Compas, Jaser, Dunn, & Rodriguez, 2012). Obesity during childhood and adolescence is also a serious public health problem in the United States (Ogden, Carroll, Kit, & Flegal, 2014), and medical trauma results in many pediatric hospital admissions each year (Compas et al., 2012). This chapter provides an overview of the clinical applications of pediatric health psychology including the importance of adjustment to new diagnoses and medication adherence. This chapter also discusses the various forms of treatment delivery and how the settings and modalities of treatments are changing. Finally, this chapter will discuss cultural and diversity issues in pediatric health psychology.

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Adjustment to Diagnosis Due to advances in the treatment of chronic illnesses, pediatric illnesses that were once fatal can now be treated much more effectively than in previous decades (Mokkink, Van der Lee, Grootenhuis, Offringa, & Heymans, 2008). These improved outcomes are a result of successful early detection and diagnosis, as well as effective treatment of many previously life‐threatening illnesses (Compas et al., 2012). As a result, children and adolescents now have to adjust to living with chronic illnesses and medical conditions. These chronic illnesses present children, adolescents, and their families with significant stress that can contribute to emotional and behavioral problems as well as interfere with adherence to treatment regimens (Compas et al., 2012). Further, many pediatric illnesses are worsened by stress encountered in other aspects of children’s lives. It is therefore essential to understand the ways that children and adolescents adjust to living with a chronic condition in order to develop effective interventions to enhance coping and adjustment.

Adjustment to Chronic Versus Acute Illnesses Adjustment to a pediatric illness can be different for each child, depending on if he or she receives a diagnosis of a chronic illness versus a diagnosis of an acute illness. An acute illness is differentiated from a chronic illness by the speed of symptom onset and the duration of symptoms. An acute illness is a condition of short duration that starts quickly and has severe symptoms. However, a chronic illness is a health problem that lasts 3 months or more, affects a child’s normal activities, and requires frequent hospitalizations, home healthcare, and/or extensive medical care (Mokkink et al., 2008). Research has revealed that chronic conditions may result in greater psychological and physical stress than acute illnesses that resolve quickly (Marin, Chen, Munch, & Miller, 2009). Individuals with a chronic illness may experience more psychological stress, especially during childhood or adolescence, because the illness can threaten one’s identity, affect one’s body image, and disrupt one’s lifestyle (Gignac, Cott, & Badley, 2000). Chronic conditions are also typically characterized by different phases over the course of an individual’s lifetime (e.g., acute periods, stable periods, remissions), in which each phase presents its own physical and psychological problems. In addition, one’s family life could be dramatically altered due to a pediatric chronic illness, resulting in unfilled roles, loss of income, and costs of treatment. As a result, individual and familial adjustment to chronic ­illness is an important predictor of long‐term outcomes. Generally speaking, researchers have examined a variety of stress responses in children and adolescents, emphasizing both controlled responses and automatic responses (Compas et al., 2012). Automatic stress responses include “temperamentally based and conditioned ways of reacting to stress such as emotional and physiological arousal, automatic thoughts, and conditioned behaviors” (Compas et al., 2012). However, in order to effectively cope with stress, controlled responses are typically preferred, which involve things children and adolescents do to manage and adapt to stress (Compas et al., 2012). Overall, the strategies a child or adolescent uses to manage stress can provide an idea as to how they will adjust or cope with a chronic illness. It is important to note, however, that when coping with health‐ and illness‐related stressors, the controllability or perceived controllability of the stressor may be a key dimension in determining the efficacy of particular coping strategies (Osowiecki & Compas, 1998).

Interventions That Improve Adjustment In recent years, researchers have developed interventions that aim to reduce stress and improve coping strategies in children after being diagnosed with a pediatric illness. For example,

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Kassam‐Adams et  al. (2015) assessed the effectiveness and feasibility of a web‐based ­intervention aimed to reduce the stress of children who recently suffered an injury or were diagnosed with a chronic illness. Children ages 8–12 years were randomized to either a coping intervention (e.g., an interactive web‐based game promoting coping strategies) or a 12‐week wait list (Kassam‐Adams et al., 2015). Outcome measures included child PTSD symptoms and pediatric quality of life. This study demonstrated that a novel, interactive, game‐like web‐based intervention for children exposed to medical trauma is feasible to deliver and has the potential to prevent posttraumatic stress after being diagnosed with a medical illness (Kassam‐Adams et al., 2015). Interventions targeting caregivers have also been developed, in light of research demonstrating an association between parental functioning and children’s adjustment to a chronic illness (Palermo, Law, Essner, Jessen‐Fiddick, & Eccleston, 2014). For instance, Palermo et al. (2014) examined the effectiveness and feasibility of a problem‐solving skills training (PSST) intervention to reduce distress in caregivers of children with chronic pain. This study revealed that the parent‐focused PSST intervention was effective in reducing distress among caregivers of children and adults with a variety of medical problems (Palermo et al., 2014). Results suggest that a PSST intervention is a feasible and acceptable intervention for parents of children with chronic pain. The intervention used in this study has the potential to help children with other chronic illnesses develop coping strategies and enable them to function effectively in their daily lives (Palermo et al., 2014). In sum, chronic illnesses present children and their families with significant stress that can contribute to psychosocial problems, such as depression and anxiety, nonadherence to medication regimens, and disruptions to family life (Compas et al., 2012). These interventions have shown that they can help improve coping strategies and reduce stress in children adjusting to life with a chronic illness. Researchers need to continue examining how children and families adjust to pediatric illnesses to further inform and develop interventions.

Medication Adherence The term “adherence” refers to the extent to which a person’s behavior (e.g., taking ­medication, following a diet, and/or implementing lifestyle changes) corresponds with recommendations from a healthcare provider (World Health Organization [WHO], 2006). The assessment and treatment of medication adherence has become essential to improving health outcomes as the prevalence of pediatric chronic illnesses has increased (Quittner, Modi, Lemanek, Levers‐ Landis, & Rapoff, 2008). Research has illustrated that the overall treatment adherence rate is approximately 50% for pediatric populations (Rapoff, 1999). Nonadherence to medical regimes can potentially interfere with the efficacy of medications, resulting in failure to reach treatment goals, increased visits to the emergency room, and increased hospitalizations (Lee et al., 2014). In addition, other negative consequences of nonadherence include inappropriate changes in treatment regimens, decreased quality of life (Fredericks et al., 2008), incorrect medication dosage adjustments, and increased costs, among others (Lee et al., 2014).

Barriers That Affect Medication Adherence Medication adherence in pediatric populations presents unique challenges. Types of barriers that are associated with poor medication adherence in pediatric patients include cognitive ­factors (e.g., forgetting, poor planning), aversive medication properties or difficulties i­ ngesting medication (e.g., hard to swallow, bad taste, vomiting, spitting), high cost of medication, or

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voluntary resistance toward taking the medication (Hommel & Baldassano, 2010; Lee et al., 2014; Simons, McCormick, Mee, & Blount, 2009). Medication adherence can be especially difficult for young pediatric patients who may not understand the purpose of their medication and who depend on their caregivers to administer their prescribed medication (Lee et  al., 2014). Thus, it is extremely important that both children and their caregivers participate in the medication adherence process. In addition, it is also common for adolescents to not regularly follow their medication regimens (Staples & Bravender, 2002). Factors that could negatively impact adolescents’ medication adherence include embarrassment, stigmatization, and inability to self‐regulate (e.g., ability to control one’s behaviors, emotions, and cognitions; Berg et al., 2014). Berg et al. (2014) found that self‐regulation in a number of domains (i.e., general executive functioning, attention, self‐control, and emotion regulation) was associated with adolescents’ adherence behaviors in a sample of youths with type 1 diabetes, such that youths who reported lower self‐regulation abilities demonstrated worse adherence behaviors than those who reported higher self‐regulation abilities. In addition, barriers to adherence have been associated with negative psychosocial outcomes such as less family cohesion, less emotional expression, and greater conflict among family members (Simons & Blount, 2007), which highlights the importance of efforts aimed to reduce barriers and improve pediatric adherence.

Treatment Delivery Recently, Western medicine is recognizing the importance of viewing patient care from the biopsychosocial model, which emphasizes the interplay of biological, psychological, and social factors as they either influence the maintenance of health or acquire the capacity to cause illness (Smith & Nicassio, 1995). The importance of approaching treatment from this holistic standpoint is so salient and necessary that it has been endorsed and adopted by the WHO (2002). Consequently, pediatric psychologists are now delivering treatment for acute and chronic pediatric physical and behavioral health conditions in a number of diverse settings. Such settings include inpatient and outpatient medical or psychiatric facilities and pediatric primary care clinics (Borschuk, Jones, Parker, & Crewe, 2015). In these medical environments, pediatric psychologists are typically an integral component of integrated teams composed of psychologists, doctors, nurses, caseworkers, and even administrators who work collaboratively to determine the best course of treatment for the patient (Butler et  al., 2008). One of the charges of the pediatric psychologist in this setting is to ­provide secondary care via psychodiagnostic assessment. Psychometric tests are often used in this environment in order to identify treatment needs, assist with differential diagnoses, monitor treatment progress, and facilitate risk management (Wahass, 2005). Assessments of intelligence, personality, mental status, motivation, health behavior, and presence of psychopathology are typically conducted by the pediatric psychologist and are useful aids in ensuring children receive the highest standard of care. Another charge of pediatric psychologists in medical settings is to provide primary care via behavioral interventions (e.g., cognitive behavioral therapy) targeted at treating or even preventing behavioral disturbances or psychopathology (e.g., anxiety) that may be exacerbating the child’s presenting concerns or impeding their recovery (Borschuk et al., 2015). Although limited, research does suggest that behavioral health interventions are effective at mitigating

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anxiety (Kolko et  al., 2014), behavioral problems (Berkovits, O’Brien, Carter, & Eyberg, 2010), and depression (Richardson, McCauley, & Katon, 2009). Other crucial roles of pediatric psychologists include facilitating communication among medical professionals, managing difficult interpersonal situations between patients and their families, and providing grief counseling as necessary. Finally, the pediatric health psychologist also facilitates the post‐discharge outpatient follow‐ups in order to ensure the child is adhering to their treatment regimen, there are no residual complications, and, if necessary, the ­psychologist is prepared to provide referrals to community‐based care providers (Kolko & Perrin, 2014). With increasing frequency children are receiving care via community‐based programs and facilities, such as schools (Borschuk et al., 2015). In fact, research suggests that more children are receiving services in community‐based outpatient clinics (satellite healthcare facilities that provide health and wellness services outside of the primary care setting) and schools than they are in primary or specialty care clinics (National Survey on Drug Use & Health, 2009). The utility of the pediatric psychologist in the community can be found in their ability to facilitate support groups for children seeking emotional support due to terminal illnesses or chronic pain, assist in medication/treatment adherence, and administer psychometric tests of functioning (Straub, 2014). Furthermore, pediatric psychologists in the community setting also aim to disseminate public health information and provide psychoeducation to children ­regarding illnesses and disease prevention (Straub, 2014).

The Use of Telemedicine to Treat Pediatric Illnesses Telemedicine can be broadly defined as the use of telecommunications technologies to ­provide medical information and services (Perednia & Allen, 1995). The use of telemedicine can help overcome barriers such as geographic challenges, a lack of healthcare specialists, and social and economic barriers (Marcin, Shaikh, & Steinhorn, 2016; Van Allen, Davis, & Lassen, 2011). In recent years, telemedicine has been increasingly used for pediatric health services, and research supports the utility of interventions incorporating telemedicine for children in rural settings (Van Allen et al., 2011). For example, Davis et al. (2013) demonstrated that a telemedicine intervention was equally as effective as an intervention involving visits from a physician in improving children’s BMI, behavior problems, and dietary and physical activity behaviors. This study also demonstrated that telemedicine or similar methods of telehealth can be a feasible method of treatment delivery of empirically supported treatment interventions (Davis, Sampilo, Gallagher, Landrum, & Malone, 2013). Similarly, a study conducted by Chorianopoulou, Lialiou, Mechili, Mantas, and Diomidous (2015) also revealed that the utilization of telemedicine resulted in improved access to specialized healthcare, reduced hospitalizations, and emergency room visits in a sample of children from a rural population (Chorianopoulou et al., 2015). Izquierdo et al. (2009) found that children with diabetes who received telemedicine had lower hemoglobin A1c levels and reported higher levels of pediatric quality of life, compared with children who received usual care. Children in the telemedicine group also reported a reduction in urgent diabetes‐related calls initiated by the school nurse and fewer hospitalizations and emergency department visits (Izquierdo et al., 2009). In addition, Romano, Hernandez, Gaylor, Howard, and Knox (2001) investigated whether specialist care delivered by telemedicine would result in comparable improvements in quality of life and asthma symptoms, compared with face‐to‐face encounters with

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specialists. The results indicated that patients reported an increase in mean number of ­symptom‐free days, a reduction in mean symptom scores, and improvements in quality of life (Romano et al., 2001). It is likely that more interventions will be delivered through telemedicine technologies in the near future. The use of telemedicine and digital technologies can help children and families overcome numerous geographic and economic barriers as well as increase access to interventions and other forms of treatment.

Cultural/Ethical Disparities, Culturally Competent Care, and Future Directions Disparities within the realm of pediatric psychology are predominantly driven by variables such as race, ethnicity, and socioeconomic status. Unfortunately, children of color, especially those born into low‐income families, lag behind their more affluent, majority peers in health status (Cheng, Dreyer, & Jenkins, 2009). This is of particular concern given the fact that such disparities that arise during childhood positively correlate with chronic illness as an adult (Braveman & Barclay, 2009). Also, the dearth of available pediatric psychologists in underprivileged or rural areas is also a force driving these disparities in health status. These disparities point to the need for pediatric psychology to embrace and practice culturally competent care, care that is sensitive to the needs, health literacy, and health beliefs of pediatric patients and their families (Cheng, Emmanuel, Levy, & Jenkins, 2015). Current research demonstrates that health promotion and prevention interventions may be more effective in improving quality of care and patient safety if they are tailored to the cultural identity of the target population. However, there is a paucity of research as to what constitutes a “culturally sensitive” intervention, and whether such an initiative could be capable of facilitating greater changes in behavior than non‐culturally specific interventions is unclear (Resnicow et al., 2002). With that said, there are a number of measures the field of pediatric psychology can enact in order to ameliorate disparities in health status among children of racial and ethnic minorities and socioeconomic status. Research suggests that almost all children in the United States have annual well‐child visits (Bloom, Cohen, & Freeman, 2011); thus, increasing the involvement of pediatric psychologists in the primary care setting—in the role of gatekeeper to behavioral health services—will have the effect of fostering normative development and decreasing health disparities. Furthermore, given the powerful deleterious effect of socioeconomic status on service availability, the field of pediatric health psychology must seek to make access to its ­services more universally accessible and cost‐effective. This can be done through the proliferation of pediatric psychologists throughout community‐based care providers, perhaps with the future aid of government or community cost subsidization.

Conclusion Pediatric psychology is a rapidly expanding field of psychology. Pediatric psychologists work in a multitude of professional settings in the evaluation and treatment of children who p ­ resent with a wide range of chronic illnesses. It is critical to understand how children and families adjust to pediatric illness to further inform and develop more effective interventions for these individuals. Pediatric psychologists work with children and families, in conjunction with

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­ hysicians, to reinforce the importance of medication adherence to maximize positive health p outcomes. Pediatric psychologists are working with children in schools and community ­settings, not just the primary care setting, to promote medical and mental health. The use of telemedicine and digital technologies has become important delivery systems for providing education and interventions for children and families in the rural setting. Identifying pertinent cultural variables and considerations are critical for effective health promotion and prevention interventions. Pediatric psychologists and researchers must continue to develop evidence‐based treatments and examine resilience factors (e.g., self‐esteem, internal locus of control, motivation, health behaviors, coping strategies, emotional regulation) that prevent children from developing chronic illnesses and a lower quality of life. Through these measures, the field of pediatric psychology can ensure that children grow into healthy and well‐adjusted adults.

Author Biographies Dianna Boone, MA, is a second year student in the Clinical Psychology Program at Texas Tech University, under the mentorship of Dr. Jason Van Allen. She received her MA in Rehabilitation and Mental Health Counseling from the University of South Florida. Her research interests include pediatric psychology and childhood obesity. Thomas J. Parkman, BS. T.J. is a third year clinical psychology doctoral student and research assistant in the ENERGY Lab at Texas Tech University, under the mentorship of Dr. Jason Van Allen, PhD. T.J.’s interests rest within the realms of pediatric health psychology and behavioral medicine. Jason Van Allen, PhD. Jason is an assistant professor in the Clinical Psychology Program at Texas Tech University. He received his PhD in Clinical Child Psychology from the University of Kansas, and his research focuses on pediatric psychology and childhood obesity.

References American Psychological Association. (2009). Society of pediatric psychology. Retrieved from http://www. apa.org/about/division/div54.aspx. Aylward, B. S., Bender, J. A., Graves, M. M., & Roberts, M. C. (2009). Historical developments and trends in pediatric psychology. In M. C. Roberts & R. G. Steele (Eds.), Handbook of pediatric ­psychology (pp. 3–18). New York, NY: Guilford. Berg, C. A., Wiebe, D. J., Suchy, Y., Hughes, A. E., Anderson, J. H., Godbey, E. I., … White, P. C. (2014). Individual differences and day‐to‐day fluctuations in perceived self‐regulation associated with daily adherence in late adolescents with type 1 diabetes. Journal of Pediatric Psychology, 39(9), 1038–1048. Berkovits, M. D., O’Brien, K. A., Carter, C. G., & Eyberg, S. M. (2010). Early identification and intervention for behavior problems in primary care: A comparison of two abbreviated versions of ­parent‐ child interaction therapy. Behavior Therapy, 41(3), 375–387. Bloom, B., Cohen, R. A., & Freeman, G. (2011). Summary health statistics for U.S. children: National Health Interview Survey, 2010. Vital And Health Statistics. Series 10, Data From The National Health Survey, 250, 1–80. Borschuk, A. P., Jones, H. A., Parker, K. M., & Crewe, S. (2015). Delivery of behavioral health services in a pediatric primary care setting: A case illustration with adolescent depression. Clinical Practice In Pediatric Psychology, 3(2), 142–153.

380

Dianna Boone, Thomas J. Parkman, and Jason Van Allen

Braveman, P., & Barclay, C. (2009). Health disparities beginning in childhood: A life‐course perspective. Pediatrics, 124(Supplement 3), S163–S175. Butler, M., Kane, R. L., McAlpine, D., Kathol, R. G., Fu, S. S., Hagedorn, H., & Wilt, T. J. (2008). Integration of mental health/substance abuse and primary care. Agency for Healthcare Research and Quality (AHRQ) Evidence Report/Technology Assessment, 173, 1–362. Cheng, T. L., Dreyer, B. P., & Jenkins, R. R. (2009). Introduction: Child health disparities and health literacy. Pediatrics, 124(Supplement 3), S161–S162. Cheng, T. L., Emmanuel, M. A., Levy, D. J., & Jenkins, R. R. (2015). Child health disparities: What can a clinician do? Pediatrics, 136(5), 961–968. Chorianopoulou, A., Lialiou, P., Mechili, E. A., Mantas, J., & Diomidous, M. (2015). Investigation of the quality and effectiveness of telemedicine in children with diabetes. Studies in Health Technology and Informatics, 210, 105–109. Compas, B. E., Jaser, S. S., Dunn, M. J., & Rodriguez, E. M. (2012). Coping with chronic illness in childhood and adolescence. Annual Review of Clinical Psychology, 8, 455–480. Davis, A. M., Sampilo, M., Gallagher, K. S., Landrum, Y., & Malone, B. (2013). Treating rural pediatric obesity through telemedicine: Outcomes from a small randomized controlled trial. Journal of Pediatric Psychology, 38(9), 932–943. Fredericks, E. M., Magee, J. C., Opipari‐Arrigan, L., Shieck, V., Well, A., & Lopez, M. J. (2008). Adherence and health‐related quality of life in adolescent liver transplant recipients. Pediatric Transplantation, 12(3), 289–299. Gignac, M. A., Cott, C., & Badley, E. M. (2000). Adaptation to chronic illness and disability and its relationship to perceptions of independence and dependence. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 55(6), P362–P372. Hommel, K. A., & Baldassano, R. N. (2010). Brief report: Barriers to treatment adherence in pediatric inflammatory bowel disease. Journal of Pediatric Psychology, 35(9), 1005–1010. Izquierdo, R., Morin, P. C., Bratt, K., Moreau, Z., Meyer, S., Ploutz‐Snyder, R., … Weinstock, R. S. (2009). School‐centered telemedicine for children with type 1 diabetes mellitus. The Journal of Pediatrics, 155(3), 374–379. Kassam‐Adams, N., Marsac, M. L., Kohser, K. L., Kenardy, J., March, S., & Winston, F. K. (2015). Pilot randomized controlled trial of a novel web‐based intervention to prevent posttraumatic stress in children following medical events. Journal of Pediatric Psychology, 41(1), 138–148. Kolko, D. J., Campo, J., Kilbourne, A. M., Hart, J., Sakolsky, D., & Wisniewski, S. (2014). Collaborative care outcomes for pediatric behavioral health problems: A cluster randomized trial. Pediatrics, 133(4), e981–e992. Kolko, D. J., & Perrin, E. (2014). The integration of behavioral health interventions in children’s health care: Services, science, and suggestions. Journal of Clinical Child & Adolescent Psychology, 43(2), 216–228. Lee, J. L., Eaton, C., Gutiérrez‐Colina, A. M., Devine, K., Simons, L., Mee, L., & Blount, R. L. (2014). Longitudinal stability of specific barriers to medication adherence. Journal of Pediatric Psychology, 39(7), 667–676. Marcin, J. P., Shaikh, U., & Steinhorn, R. H. (2016). Addressing health disparities in rural communities using telehealth. Pediatric Research, 79(1‐2), 169–176. Marin, T. J., Chen, E., Munch, J. A., & Miller, G. E. (2009). Double‐exposure to acute stress and chronic family stress is associated with immune changes in children with asthma. Psychosomatic Medicine, 71(4), 378–384. Mokkink, L. B., Van der Lee, J. H., Grootenhuis, M. A., Offringa, M., & Heymans, H. S. (2008). Defining chronic diseases and health conditions in childhood (0–18 years of age): National ­consensus in the Netherlands. European Journal of Pediatrics, 167(12), 1441–1447. National Survey on Drug Use and Health. (2009). Substance abuse and mental health services administration. Rockville, MD: Office of Applied Studies.

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Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011–2012. Journal of the American Medical Association, 311(8), 806–814. Osowiecki, D., & Compas, B. E. (1998). Psychological adjustment to cancer: Control beliefs and coping in adult cancer patients. Cognitive Therapy and Research, 22(5), 483–499. Palermo, T. M., Law, E. F., Essner, B., Jessen‐Fiddick, T., & Eccleston, C. (2014). Adaptation of problem‐solving skills training (PSST) for parent caregivers of youth with chronic pain. Clinical Practice in Pediatric Psychology, 2(3), 212–223. Perednia, D. A., & Allen, A. (1995). Telemedicine technology and clinical applications. Journal of the American Medical Association, 273(6), 483–488. Quittner, A. L., Modi, A. C., Lemanek, K. L., Levers‐Landis, C. E., & Rapoff, M. A. (2008). Evidence‐ based assessment of adherence to medical treatments in pediatric psychology. Journal of Pediatric Psychology, 33(9), 916–936. Rapoff, M. A. (1999). Adherence to pediatric medical regimens. Dordrecht, Netherlands: Kluwer Academic Publishers. Resnicow, K., Jackson, A., Braithwaite, R., DiIorio, C., Blisset, D., Rahotep, S., & Periasamy, S. (2002). Healthy body/healthy spirit: A church‐based nutrition and physical activity intervention. Health Education Research, 17(5), 562–573. Richardson, L., McCauley, E., & Katon, W. (2009). Collaborative care for adolescent depression: A pilot study. General Hospital Psychiatry, 31(1), 36–45. Roberts, M. C., Biggs, B. K., Jackson, Y., & Steele, R. G. (2011). Clinical child psychology: Research and practice applications. In P. Martin, F. Cheung, M. Kyrios, L. Littlefield, M. Knowles, B. Overmier, & J. M. Prieto (Eds.), The IAAP handbook of applied psychology (pp. 3–27). New York, NY: Wiley‐Blackwell. Romano, M. J., Hernandez, J., Gaylor, A., Howard, S., & Knox, R. (2001). Improvement in asthma symptoms and quality of life in pediatric patients through specialty care delivered via telemedicine. Telemedicine Journal and E‐Health, 7(4), 281–286. Simons, L. E., & Blount, R. L. (2007). Identifying barriers to medication adherence in adolescent transplant recipients. Journal of Pediatric Psychology, 32(7), 831–844. Simons, L. E., McCormick, M. L., Mee, L. L., & Blount, R. L. (2009). Parent and patient perspectives on barriers to medication adherence in adolescent transplant recipients. Pediatric Transplantation, 13(3), 338–347. Smith, T. W., & Nicassio, P. M. (1995). Psychological practice: Clinical application of the biopsychosocial model. In P. M. Nicassio, T. W. Smith, P. M. Nicassio, & T. W. Smith (Eds.), Managing chronic illness: A biopsychosocial perspective (pp. 1–31). Washington, DC: American Psychological Association. Spirito, A., Brown, R. T., D’Angelo, E., Delamater, A., Rodrigue, J., & Siegel, L. (2003). Society of Pediatric Psychology Task Force Report: Recommendations for the training of pediatric psychologists. Journal of Pediatric Psychology, 28(2), 85–98. Staples, B., & Bravender, T. (2002). Drug compliance in adolescents: Assessing and managing a modifiable risk factors. Pediatric Drugs, 4(8), 503–513. Straub, R. O. (2014). Health psychology (4th ed., pp. 488–499). New York, NY: Worth Publishers. Van Allen, J., Davis, A. M., & Lassen, S. (2011). The use of telemedicine in pediatric psychology: Research review and current applications. Child and Adolescent Psychiatric Clinics of North America, 20(1), 55–66. Wahass, S. H. (2005). The role of psychologists in health care delivery. Journal of Family & Community Medicine, 12(2), 63–70. World Health Organization. (2002). The world health report 2002: Reducing risks, promoting healthy life. Retrieved from http://www.who.int/whr/2002/en/. World Health Organization. (2006). Adherence to long‐term therapies: Evidence for action. Retrieved from http://www.who.int/chp/knowledge/publications/adherence_introduction.pdf.

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Suggested Reading Borschuk, A. P., Jones, H. A., Parker, K. M., & Crewe, S. (2015). Delivery of behavioral health services in a pediatric primary care setting: A case illustration with adolescent depression. Clinical Practice in Pediatric Psychology, 3(2), 142–153. Compas, B. E., Jaser, S. S., Dunn, M. J., & Rodriguez, E. M. (2012). Coping with chronic illness in childhood and adolescence. Annual Review of Clinical Psychology, 8, 455–480. Kolko, D. J., & Perrin, E. (2014). The integration of behavioral health interventions in children’s health care: Services, science, and suggestions. Journal of Clinical Child & Adolescent Psychology, 43(2), 216–228.

Reproductive Health Jennifer L. Brown1 and Nicole K. Gause2 Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA 2 Department of Psychology, University of Cincinnati, Cincinnati, OH, USA

1

As defined by the World Health Organization (WHO), reproductive health refers to the well‐being of reproductive processes throughout all stages of life; this includes the ability to have satisfying and safe sex, as well as the ability to reproduce (WHO, 2016a). Additionally, reproductive health involves the capability to decide when and how often to engage in sexual and reproductive behaviors (WHO, 2016a). According to the WHO, to maintain reproductive health, men and women should also have access to safe, effective, and affordable contraception, as well as access to healthcare services that facilitate healthy pregnancies and childbirths (WHO, 2016a). This chapter provides an overview of reproductive health issues that may be of clinical ­relevance to a health psychologist. Effective strategies for conducting sexual health histories to assess patients’ sexual behavior engagement and sexual orientation are provided. Sexual behaviors that are associated with increased risk for adverse sexual health consequences such as sexually transmitted infections (STIs) and unintended pregnancies are described. Epidemiological data regarding STI prevalence is provided, as well as an overview of efficacious human immunodeficiency virus (HIV) and STI prevention intervention approaches. Modern contraceptive options that provide safe, effective methods of pregnancy prevention are discussed. Disorders related to patients’ sexual health including sexual dysfunction disorders, hypersexuality, and paraphilic disorders are discussed, as well as relevant diagnostic considerations and efficacious treatment options. A brief summary of sexual behavior engagement from a developmental perspective is provided at the end of the chapter.

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Sexual Behaviors There is a wide variety of activities that individuals engage in to express their sexuality (Crooks & Baur, 2008). Abstinence and celibacy refer to the act of refraining from engaging in certain sexual behaviors or any sexual behaviors. Self‐stimulation of one’s genitals for the purpose of sexual pleasure is a sexual behavior known as masturbation. Sexual behaviors such as kissing and touching stimulate the erogenous zones of oneself and one’s partner. Individuals may also sexually stimulate a partner via oral stimulation of a partner’s genitals. Terms such as oral sex (referring broadly to oral–genital stimulation), cunnilingus (oral stimulation of the vulva), and fellatio (oral stimulation of the penis) describe oral–genital sexual behaviors. Anal stimulation may involve touching around the anus or penile insertion in the anus (often referred to as anal sex). Penile insertion in a female’s vagina is referred to as penile–vaginal intercourse, as well as other terms such as vaginal sex or coitus. There is great variability between individuals in how frequently these and other sexual behaviors occur; this variability may be due to a number of factors, including age and the perceived social acceptability of certain behaviors. Furthermore, reasons for engaging in sexual behaviors may differ among individuals and even within the same individual depending on situational circumstances. People may engage in sexual behaviors in order to experience sexual pleasure, sexual arousal, or orgasm. Motivation for sexual behaviors may also involve a desire to procreate. Some individuals may engage in sexual behaviors to earn money or acquire other goods or services; prostitution is the exchange of a sexual behavior for monetary or other compensation. Unfortunately, there are circumstances under which engagement in sexual behaviors is coerced or nonconsensual (e.g., rape), a form of abuse (e.g., child sexual abuse), or a means of sexual exploitation (e.g., pedophilia). Even when sexual behavior is intended and consensual, it may have unintended consequences (e.g., unplanned pregnancy) or put one at risk for HIV and other STI. A comprehensive general health history should include a sexual history to assess for engagement in risky sexual behaviors, as well as for potential sexual dysfunction (Peck, 2001). Structured patient questionnaires may also be incorporated into sexual histories. The core domains that should be assessed when gathering sexual history information are sexual practices (i.e., which specific behaviors the individual engages in), any concerns related to sexual functioning, information regarding sexual partners (e.g., number of partners, partners’ sex(es), relationship status), use of pregnancy prevention method(s), use of STI/HIV prevention method(s), and STI and/or pregnancy history. Gathering this information requires providers discuss sensitive topics; this may make some providers uneasy or uncomfortable and result in avoidance of performing a comprehensive sexual history assessment (Peck, 2001). Provider discomfort in discussing sexual health may cause patient embarrassment or discomfort, which may in turn have a negative impact on provider–patient rapport and the accuracy of a patient’s self‐reported sexual information. Furthermore, failure to perform a comprehensive sexual history assessment could interfere with patient care and may result in incorrect diagnoses or not providing appropriate referrals and treatments. In order to increase patient comfort, build rapport, and obtain the most accurate information, providers should promote open communication, emphasize confidentiality ­surrounding patient disclosure, employ open‐ended questions, and utilize neutral language when discussing sexual behaviors and sexual health topics (Peck, 2001).

Sexual Orientation Sexual orientation refers to the sex or sexes to which one is attracted. Although an individual may be sexually attracted to a particular sex or sexes, he or she may not necessarily identify with

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the corresponding sexual orientation. Further, an individual may engage in sexual behaviors that are associated with particular sexual orientations (e.g., a man who reports sex with other men), but not identify with that particular sexual orientation; in this example, this man would endorse being heterosexual as opposed to homosexual or bisexual. Some argue that sexual orientation is an aspect of an individual’s broader sense of identity, which is not only based on their sexual attractions but also on group membership with others who share a similar attraction. Sexual orientation is often classified by society according to distinct categories; individuals are either heterosexual (sexually attracted to the opposite sex), homosexual/lesbian/gay (sexually attracted to the same sex), or bisexual (sexually attracted to both sexes). However, some have suggested that sexual orientation may perhaps be better understood as existing along a continuum (Crooks & Baur, 2008). For example, Kinsey, Pomeroy, and Martin (1948) proposed in their early work that sexual orientation be viewed as a range from “exclusively heterosexual with no homosexual” to “exclusively homosexual with no heterosexual” attractions and/or sexual contact. Individuals who identify as lesbian, gay, bisexual, or transgender (LGBT) experience a variety of health disparities, including increased STI prevalence, decreased screening for cervical ­cancer, and experiences of abuse or violence. Knowing a patient’s sexual orientation can help to identify potential areas of concern to be addressed via further assessment or treatment. Using information obtained via a sexual history assessment not only improves patient care at the individual level but also has the potential to reduce health disparities among LGBT individuals. Sexual orientation can be assessed by asking a patient whether he or she (a) identifies with a particular sexual orientation (e.g., “Do you consider yourself (i) lesbian, gay, or homosexual; (ii) straight or heterosexual; (iii) bisexual; (iv) something else; (v) don’t know?”) and whether he or she (b) has any potential concerns related to his/her sexual orientation (e.g., “Do you have concerns related to your sexual orientation?”). In addition, gender identity should be assessed by asking patients about their (a) self‐identified gender identity (e.g., “Do you identify as (i) male, (ii) female, (iii)  female to male/transgender male, (iv) male to female/transgender female, (v) gender queer/neither exclusively male nor female, (vi) additional/other category?”) and asking patients about their (b) sex assigned at birth (e.g., male, female). Given that the LGBT community encounters heightened stigma and discrimination, it is especially important that providers ensure patients’ confidentiality and that assessment of sexual orientation and gender identity are conducted in a nonthreatening, nonjudgmental and appropriate manner.

Sexual Risk Behaviors Estimates of the number of newly acquired HIV infections per year in the United States remain relatively stable, with approximately 50,000 incident cases diagnosed annually (Centers for Disease Control and Prevention [CDC], 2015c). Globally, there were approximately 2.1  million incident HIV cases in 2015 (WHO, 2016b). Furthermore, HIV prevalence ­continues to rise globally, with an estimated 36.7 million people now living with HIV worldwide (WHO, 2016b). In the United States, Black/African Americans and Hispanic/Latinos have higher HIV prevalence rates as compared with other racial or ethnic groups (CDC, 2015b, 2016a). In addition, men who have sex with men (MSM) are disproportionately affected by HIV in the United States (CDC, 2015a). Unlike trends that emerged early in the HIV epidemic, US women (particularly racial/ethnic minority women) who have sex with men have also experienced increased incident HIV infections in recent years (CDC, 2016b). In addition to HIV, there are a number of other STIs including bacterial vaginosis (BV), chlamydia, gonorrhea, viral hepatitis, genital herpes, human papillomavirus, pelvic

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i­nflammatory disease (PID), syphilis, and trichomoniasis. Negative health consequences may result from STI, and untreated STI may result in increased biological susceptibility to HIV. MSM, and racial/ethnic minority MSM in particular, experience elevated rates of STIs including chlamydia, gonorrhea, syphilis, and, as noted in a previous section, HIV (CDC, 2015a). Furthermore, individuals between the ages of 15 and 24 years of age (i.e., emerging adults), and racial/ethnic minority adolescents in particular, experience elevated rates of chlamydia and gonorrhea (CDC, 2014a). Certain sexual behaviors, as well as substance use, may increase STI/HIV risk. For example, engaging in sex without a condom, having multiple sexual partners, or injecting drugs (and needle sharing) may increase likelihood of STI/HIV exposure. Moreover, there are structural factors that affect STI/HIV exposure risk; for example, community STI/HIV prevalence and access to adequate STI/HIV testing or treatment services as well as access to needle exchange programs affect STI and HIV prevalence. A comprehensive assessment of engagement in behaviors that increase STI/HIV risk facilitates and informs appropriate STI/HIV testing and treatment recommendations. Practitioners should assess for engagement in key behaviors that may increase an individual’s STI/HIV risk; these include (a) condom use for vaginal and anal sex, (b) frequency of particular sexual behaviors (e.g., frequency of anal sex), (c) number of sexual partners, and (d) use of injection drugs and related behaviors (e.g., needle sharing). Of course, given the personal and sensitive nature of these behaviors, supplementing a face‐to‐ face patient sexual history assessment interview with paper‐and‐pencil or computerized measures may enhance the accuracy of patients’ self‐reported behaviors. Furthermore, focusing on shorter, more discrete time periods (e.g., “How often in the past month have you had vaginal sex without a condom?”) rather than extended time periods (e.g., “How often in your lifetime have you had vaginal sex without a condom?”) may improve the accuracy with which patients report their behaviors. Providers should deliver appropriate risk reduction counseling (e.g.,  encouraging consistent condom use) to patients who report engaging in sexual risk behaviors and may also want to consider further STI/HIV testing in accordance with national guidelines for high‐risk patients. For example, the US CDC recommends that all sexually active women under the age of 25 be annually screened for chlamydia (CDC, 2015d).

STI/HIV Prevention Intervention Approaches Behavioral intervention strategies focus on decreasing high‐risk practices (e.g., non‐condom‐ protected sexual encounters and sharing of contaminated needles) to reduce and prevent the spread of STI/HIV. Indeed, there are a number of behavioral STI/HIV prevention approaches that have been reviewed, evaluated, and identified as efficacious behavioral HIV prevention interventions by the CDC (2014b). These behavioral intervention approaches utilize a variety of intervention modalities (e.g., one on one, small group) and generally target at‐risk populations (e.g., MSM, African American adolescents; CDC, 2014b). For example, HORIZONS (DiClemente et al., 2009) is an STI/HIV prevention intervention that is gender and culturally tailored for African American young women. This particular intervention includes two group‐ based sessions designed to reduce sexual risk behavior engagement and incident STI among African American adolescents; results from the original randomized controlled trial indicated that HORIZONS increased condom use practices and reduced incident chlamydial infections (DiClemente et al., 2009). When used alone, behavioral interventions may reduce STI/HIV incidence, as well as engagement in risky sexual behaviors, use of contaminated needles, and other behaviors that increase STI/HIV risk.

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The HIV prevention “toolkit” has expanded to include biomedical approaches as adjuncts to efficacious behavioral strategies. Biomedical HIV prevention approaches include a diverse set of strategies at different stages of product development, of varying efficacy, and at various stages of approval, including (a) microbicides that are applied to the vagina or the rectum; (b) pre‐exposure prophylaxis (PrEP) antiretroviral (ARV) medications (ARV medications for high‐risk seronegative persons); (c) post‐exposure prophylaxis (PEP) drugs (use of ARV medications following HIV exposure); (d) HIV vaccination; (e) medical male circumcision; (f) HIV testing, linkage, and retention in HIV care (“test and treat”); and (g) enhanced ARV adherence among HIV‐seropositive individuals (treatment as prevention). While the use of microbicides and an HIV vaccination have not been established as efficacious or approved for widespread use, they present possible avenues to pursue in future research; should efficacious treatments be established, they may offer additional means to curtail the HIV epidemic. Medical male circumcision, test‐and‐treat interventions, and ARV medication adherence strategies have demonstrated promise in reducing HIV transmission risk. In 2012, the US Food and Drug Administration approved Truvada (emtricitabine/tenofovir disoproxil fumarate) as a PrEP medication to be used in conjunction with safer sexual practices to prevent the spread of HIV. Combination HIV prevention approaches that integrate both efficacious behavioral and biomedical strategies may enhance the potential to reduce HIV transmission among ­certain high‐risk populations (Brown, Sales, & DiClemente, 2014).

Contraceptive Methods There are a variety of contraceptive methods available, which may be more or less desirable depending on an individual’s reproductive healthcare needs and preferences. Contraceptive methods vary in the degree of pregnancy prevention efficacy, STI prevention efficacy, and availability, which may also impact an individual’s contraceptive method choices. Male condoms are effective at preventing pregnancy and protect against STI; they are widely available and do not require a prescription or doctor visit. Hormonal contraceptives include oral contraceptive pills, contraceptive patch, vaginal ring, implants, intrauterine devices (IUD), and Depo‐Provera, which consists of monthly injections. According to the American Sexual Health Association (ASHA), oral contraceptives are 92–97% effective in preventing pregnancy (ASHA, 2013); however they do not protect against STI and they require a prescription. The contraceptive patch delivers hormones transdermally; it is 92% effective in preventing pregnancy, does not protect against STI, and requires a prescription. The vaginal ring (e.g., NuvaRing) is 92% effective in preventing pregnancy, does not protect against STI, requires a prescription, and must be inserted and removed by a medical professional. Hormonal implants (e.g., Implanon) are inserted beneath the dermis by a medical professional; they are 99.99% effective in preventing pregnancy, but do not protect against STI (ASHA, 2013). Hormonal IUD (e.g., Mirena) are 99.99% effective in preventing pregnancy, but do not protect against STI; they require a prescription and must be inserted and removed by a medical professional. Depo‐Provera is a monthly injection that is 99.7% effective in preventing pregnancy, but does not protect against STI; it requires a prescription and must be administered by a medical professional (ASHA, 2013). Overall, hormonal contraceptives are highly effective in preventing pregnancy, when used correctly and consistently; however male condoms are the only form of contraception that is also effective in preventing STI/HIV (ASHA, 2013). For over a decade, promoting the use of multiple prevention strategies (i.e., dual p ­ rotection) to reduce the risk of both unintended pregnancy and STI/HIV has been recognized as “best

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practice” by medical practitioners and researchers (Workowski & Berman, 2006). Various protection strategies exist and can be combined to reduce both unintended pregnancy and STI/HIV, including hormonal contraception used in conjunction with correct and consistent condom use, STI/HIV screening of sex partner(s), decreasing number of sex partners, monogamy, and abstinence. These strategies, when used conjointly, enhance both pregnancy and STI/HIV prevention. Moreover, it is important to consider the unique social milieu within which these strategies occur, particularly regarding negotiation between sex partners, cost of the protection methods, potential side effects of the methods, acceptability, and other barriers.

Sexual Dysfunction The Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM‐5; American Psychiatric Association [APA], 2013) outlines a group of disorders that are specifically related to sexual dysfunctions. These disorders are characterized by clinically significant disturbances in an individual’s ability to respond to sexual stimulation or to experience sexual pleasure. The onset and frequency of a particular disturbance are important considerations in classifying a sexual dysfunction, as this information may be helpful in determining the etiology of the ­sexual dysfunction and appropriate treatment interventions. Some sexual dysfunctions are gender specific, whereas others may apply to both males and females (e.g., substance/medication‐induced sexual dysfunction). Male hypoactive sexual desire disorder is a gender‐specific condition marked by a lack (either persistent absence or recurrent absence) of sexual thoughts/fantasies or a deficit in desire for sexual activity. Similarly, female sexual interest/arousal disorder includes a lack of or significantly reduced sexual interest or arousal/pleasure. Male erectile disorder is indicated by the presence of at least one of the following symptoms: difficulty obtaining an erection during sexual activity, difficulty maintaining an erection during sexual activity, or a decrease in erectile rigidity. Premature ejaculation is a persistent or recurrent pattern of early (within 1 min following penetration) ejaculation during partnered sexual activity, whereas delayed ejaculation is either a significant delay in ejaculation or infrequency or absence of ejaculation. Correspondingly, female orgasmic disorder is a  delay in infrequency of or absence of orgasm or reduced orgasm intensity. Genito‐pelvic pain/penetration disorder (a female‐specific disorder) includes one or more of the following symptoms, which generally tend to be co‐occurring: (a) difficulty having intercourse, (b) ­genito‐pelvic pain during intercourse, (c) fear of pain or vaginal penetration, and (d) ­tension or tightening of the pelvic floor muscles during intercourse (APA, 2013). According to the APA (2013), healthcare and mental healthcare providers should consider several important factors when assessing a potential sexual dysfunction: (a) partner factors (e.g., partner’s sexual or health‐related problems), (b) relationship factors (e.g., discord in desires, difficulties with communication), (c) individual vulnerability (e.g., body image issues, history of sexual/emotional abuse), (d) psychiatric comorbidity (e.g., depression, anxiety, PTSD), (e) other stressors (e.g., job loss, bereavement), (f) cultural or religious factors (e.g., inhibitions or prohibitions regarding sexual behaviors, attitudes toward sexuality), (g) aging (normative age‐related reductions in sexual responses and physical activity), and (h) medical factors impacting prognosis, course, or treatment of a sexual dysfunction. Before diagnosing a sexual dysfunction, the possibility that symptoms result from inadequate sexual stimulation must be ruled out. Additionally, symptoms must be present in all or most sexual encounters

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(75–100% of sexual encounters), for at least 6 months, and not otherwise attributable to a nonsexual mental disorder, relationship discord, other stressors, the effects of a substance/ medication, or a medical condition (APA, 2013). Restoration of sexual functioning and the experience of sexual pleasure are the focus of treatment approaches for sexual dysfunctions. Efficacious interventions for sexual dysfunctions include cognitive behavioral therapy (CBT) (e.g., stimulus control, cognitive restructuring), behavioral therapy (e.g., systematic desensitization, relaxation), psychoeducation/ sexual education, and couples therapy. A lack of sexual desire may be addressed by using cognitive restructuring techniques to explore negative attitudes about sex and replace those views with new ways of thinking about sex. Arousal disorders may also be treated with cognitive restructuring (in this case targeting beliefs about sex and sexual abilities), as well as with behavioral interventions such as relaxation, systematic desensitization, and sensate focusing. Sensate focusing involves encouraging a couple to experience pleasure from both sexual and nonsexual forms of touch. Treatments for erectile dysfunction typically target sexual performance anxiety and sexual self‐esteem. Behavioral treatments for premature ejaculation involve having a man learn to withstand stimulation and postpone ejaculation for increasingly longer periods of time; this approach has been shown to be particularly effective. Treatments for female orgasm disorders encourage realistic expectations about sexual pleasure and comfort with one’s body and sexual desires, as well as gradual self‐stimulation and relaxation exercises. Relaxation training is also used to treat painful or unpleasurable intercourse that cannot otherwise be attributed to a medical condition. Pharmacological and medical interventions are also available for sexual dysfunctions; these approaches may be used in combination with psychotherapy.

Hypersexuality In the United States, hypersexual disorder has an estimated prevalence rate of 3–6% and is thought to occur mostly in men (Kuzma & Black, 2009). The specific behaviors that are ­considered hypersexual and the etiology of hypersexuality are debated upon in the literature (Brown, Gause, & Garos, 2020; Moser, 2011; Winters, 2010). Hypersexual disorder was not included in the DSM‐5 (APA, 2013); as such there are no formal clinical guidelines or diagnostic criteria for hypersexuality. However, the proposed ­theories of hypersexuality often include the following symptoms: a subjective loss of control; disinhibited sexual activity, which can involve culturally normative or nonnormative sexual behaviors; the presence of subjective distress; continuation of the behavior(s) in spite of escalating negative consequences; and significant impairment in areas of social, educational, occupational, or relationship functioning (Brown et  al., 2020). An assessment of a potential hypersexual disorder should include a thorough sexual history, psychosocial history, psychiatric history, substance use history, relationship history, and medical history, including assessment for sexually transmitted diseases, unplanned pregnancies, abortions, and other medical conditions. In addition, it is important that the clinician assess for disorders that often co‐occur with hypersexual behaviors, such as bipolar disorder, anxiety, mood disorders (Kafka & Hennen, 2002; Krueger & Kaplan, 2001; Reid & Carpenter, 2009), substance abuse, and ADHD (Krueger & Kaplan, 2001), as well as other characteristics such as impulsive behavior and emotional dysregulation (Reid, Carpenter, Spackman, & Willes, 2008; Reid, Stein, & Carpenter, 2011).

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The treatment of hypersexuality is often multi‐method and typically involves some form of psychotherapy. CBT is often used to identify triggers for hypersexual impulses or behaviors, manage sexual urges, and change negative, self‐defeating internalized beliefs. In addition, mindfulness interventions that teach patients to focus on the present and steer patients away from worrisome or distressing thoughts (Kabat, 1990; Kingston, Dooley, Bates, Lawlor, & Malone, 2007) are efficacious (Reid, Garos, Hook, & Hook, 2014). Treatment approaches should also include a relapse prevention component and determine if the patient’s behavior puts him or her as risk for occupational, legal, health, or other severe consequences. Family and couples therapy may also be part of treatment for hypersexuality, as this disorder can have devastating effects on families and intimate partnerships or marriages. Group therapy and self‐ help groups (e.g., Sex Addicts Anonymous) often augment treatment. Pharmacological medications have been shown to be efficacious in reducing sexual urges and obsessive sex‐related thoughts. Selective serotonin reuptake inhibitors (Bradford, 2001; Coleman, Gratzer, Nesvacil, & Raymond, 2000) and antiandrogens have demonstrated promising effects in reducing sexual drive and desire (Guay, 2009). These drugs may be combined with psychotherapeutic treatments that target hypersexuality.

Paraphilic Disorders As described in the DSM‐5, a paraphilia is “…any intense and persistent sexual interest other than sexual interest in genital stimulation or preparatory fondling with phenotypically ­normal, physically mature, consenting human partners” (APA, 2013, p. 685). In order for a paraphilia to be considered a paraphilic disorder, it must cause an individual either distress or impairment, or be a paraphilia that entails personal harm, or risk of harm to others when or if it is satisfied (APA, 2013). Paraphilic disorders include voyeuristic disorder (sexual pleasure or excitement from spying on others in private activities), exhibitionistic disorder (exposing one’s genitals), frotteuristic disorder (touching or rubbing against a non‐consenting individual), sexual masochism disorder (undergoing personal humiliation, bondage, or suffering), sexual sadism disorder (inflicting humiliation, bondage, or suffering on another), pedophilic disorder (sexual attraction toward children), fetishistic disorder (sexual attraction to nonliving objects or a highly specific sexual focus on nongenital body parts), and transvestic disorder (sexual arousal from cross‐dressing; APA, 2013). The sexual behaviors individuals with a particular paraphilia may engage in to fulfill their sexual urges or desires may pose potential harm or negative consequences to others, and they may even be classified as criminal offenses. Furthermore, it is not uncommon for multiple paraphilic disorders to co‐occur or be comorbid with other psychological diagnoses (e.g., depressive disorders; APA, 2013). When assessing for possible paraphilic disorders, a clinician should investigate of the specific nature of the paraphilia and potential negative consequences of the paraphilia (APA, 2013). It is important to note that individuals who report a paraphilia without negative consequences, or with the potential to harm others if/when the sexual desire is fulfilled, do not meet DSM‐5 criteria for a paraphilic disorder (APA, 2013). Both clinical interviews and self‐report measures may be used by clinicians to assess for possible paraphilic disorders. CBT, intensive community supervision, and adjunctive pharmaceutical treatments (Briken & Kafka, 2007) are among the available efficacious treatments for paraphilic disorders. Pharmaceutical treatments may either lower testosterone levels or treat potential comorbid psychological conditions (e.g., depressive disorders).

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Sexual Behavior Engagement Across the Lifespan Sexual activity changes across the lifespan, and sexual development varies between individuals (Crooks & Baur, 2008). Sexual behaviors that occur during childhood may include self‐stimulation of the genitals or play that may be viewed as sexual in nature (e.g., “playing doctor” with other children). During adolescence and puberty secondary sex characteristics (e.g., breasts, pubic hair) develop and result in dramatic physical changes. Sexual activity typically increases during adolescence, including both self‐stimulation behavior and sexual behaviors with partners. Engagement in sexual behavior as people age throughout adulthood varies. During older adulthood, engagement in sexual activities often correlates with sexual activity levels in earlier adulthood. Sexual activity during older adulthood may be adversely affected by co‐occurring health conditions and physical limitations.

Acknowledgements Support provided by R03DA0377860 from the National Institute on Drug Abuse to the first author.

Author Biographies Jennifer L. Brown is an associate professor in the Department of Psychiatry and Behavioral Neuroscience at the University of Cincinnati. She is a licensed clinical psychologist who specializes in the development and evaluation of HIV prevention interventions. Her research aims to reduce health disparities and improve individuals’ reproductive health. Nicole K. Gause is a clinical psychology doctoral student at the University of Cincinnati. Her research and clinical interests include sexual risk behaviors among populations that are disproportionately affected by HIV/STI, substance use among HIV‐infected populations, and the etiology and treatment of substance use disorders.

References American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th. ed.). Arlington, VA: Author. American Sexual Health Association. (2013). Birth control method comparison chart. Retrieved from http://www.ashasexualhealth.org/pdfs/ContraceptiveOptions.pdf Bradford, J. M. (2001). The neurobiology, neuropharmacology, and pharmacological treatment of the paraphilias and compulsive sexual behaviour. Canadian Journal of Psychiatry, 46(1), 26–34. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=mnh&AN=11221487& site=ehost‐live Briken, P., & Kafka, M. P. (2007). Pharmacological treatments for paraphilic patients and sexual offenders. Current Opinion in Psychiatry, 20(6), 609–613. doi:10.1097/YCO.0b013e3282f0eb0b Brown, J. L., Gause, N. K., & Garos, S. (2020). Sexual behavior. In S. R. Waldstein (Ed.), Behavioral and social sciences in medicine. New York, NY: Springer Publishing Company. Brown, J. L., Sales, J. M., & DiClemente, R. J. (2014). Combination HIV prevention interventions: The potential of integrated behavioral and biomedical approaches. Current HIV/AIDS Reports, 11(4), 363–375. doi:10.1007/s11904‐014‐0228‐6

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Centers for Disease Control and Prevention. (2014a). 2013 sexually transmitted diseases surveillance: STDs in adolescents and young adults. Atlanta, GA: Centers for Disease Control and Prevention. Centers for Disease Control and Prevention. (2014b). Effective interventions: HIV prevention that works. Retrieved from https://effectiveinterventions.cdc.gov/ Centers for Disease Control and Prevention. (2015a). HIV among gay and bisexual men. Retrieved from http://www.cdc.gov/hiv/group/msm/index.html Centers for Disease Control and Prevention. (2015b). HIV among hispanics/latinos. Retrieved from http://www.cdc.gov/hiv/group/racialethnic/hispaniclatinos/index.html Centers for Disease Control and Prevention. (2015c). HIV in the United States: At a glance. Retrieved from http://www.cdc.gov/hiv/statistics/overview/ataglance.html Centers for Disease Control and Prevention. (2015d). Screening recommendations referenced in the 2015 STD treatment guidelines and original recommendation sources. Retrieved from https://www.cdc. gov/std/tg2015/screening‐recs‐2015‐std‐tx‐guidelines.pdf Centers for Disease Control and Prevention. (2016a). HIV among African Americans. Retrieved from http://www.cdc.gov/hiv/group/racialethnic/africanamericans/index.html Centers for Disease Control and Prevention. (2016b). HIV among women. Retrieved from http://www. cdc.gov/hiv/group/gender/women/index.html Coleman, E., Gratzer, T., Nesvacil, L., & Raymond, N. C. (2000). Nefazodone and the treatment of nonparaphilic compulsive sexual behavior: A retrospective study. The Journal of Clinical Psychiatry, 61(4), 282–284. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=mnh& AN=10830149&site=ehost‐live Crooks, R., & Baur, K. (2008). Our sexuality (10th ed.). Pacific Grove, CA: Thomson. DiClemente, R. J., Wingood, G. M., Rose, E. S., Sales, J. M., Lang, D. L., Caliendo, A. M., … Crosby, R. A. (2009). Efficacy of sexually transmitted disease/human immunodeficiency virus sexual risk‐ reduction intervention for african american adolescent females seeking sexual health services: A randomized controlled trial. Archives of Pediatrics & Adolescent Medicine, 163(12), 1112–1121. Retrieved from https://login.proxy.library.emory.edu/login?url=http://search.ebscohost.com/ login.aspx?direct=true&db=cmedm&AN=19996048&site=ehost‐live Guay, D. R. P. (2009). Drug treatment of paraphilic and nonparaphilic sexual disorders. Clinical Therapeutics: The International Peer‐Reviewed Journal of Drug Therapy, 31(1), 1–31. doi:10.1016/ j.clinthera.2009.01.009 Kabat, Z. J. (1990). Full catastrophe living: Using the wisdom of your body and mind to face stress, pain and illness. New York, NY: Bantam Books. Kafka, M. P., & Hennen, J. (2002). A DSM‐IV axis I comorbidity study of males (n = 120) with paraphilias and paraphilia‐related disorders. Sexual Abuse: Journal of Research and Treatment, 14(4), 349–366. doi:10.1023/A:1020007004436 Kingston, T., Dooley, B., Bates, A., Lawlor, E., & Malone, K. (2007). Mindfulness‐based cognitive therapy for residual depressive symptoms. Psychology and Psychotherapy: Theory, Research and Practice, 80(2), 193–203. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&d b=rzh&AN=105992288&site=ehost‐live Kinsey, A., Pomeroy, W., & Martin, C. (1948). Sexual behavior in the human male. Philadelphia, PA: Saunders. Krueger, R. B., & Kaplan, M. S. (2001). The paraphilic and hypersexual disorders: An overview. Journal of Psychiatric Practice, 7(6), 391–403. Retrieved from http://search.ebscohost.com/login.aspx?dir ect=true&db=cmedm&AN=15990552&site=ehost‐live Kuzma, J. M., & Black, D. (2009). Epidemiology, prevalence, and natural history of compulsive sexual behavior. The Psychiatric Clinics of North America, 31(4), 603–611.G. Moser, C. (2011). Hypersexual disorder: Just more muddled thinking. Archives of Sexual Behavior, 40(2), 227–229. doi:10.1007/s10508‐010‐9690‐4 Peck, S. A. (2001). The importance of the sexual health history in the primary care setting. Journal of Obstetric, Gynecologic & Neonatal Nursing, 30(3), 269–274 266 p. doi:10.1111/j.1552‐6909.2001. tb01544.x

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Reid, R. C., & Carpenter, B. N. (2009). Exploring relationships of psychopathology in hypersexual patients using the MMPI‐2 (English). Journal of Sex and Marital Therapy, 35(4), 294–310. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=fcs&AN=21713899&si te=ehost‐live Reid, R. C., Carpenter, B. N., Spackman, M., & Willes, D. L. (2008). Alexithymia, emotional instability, and vulnerability to stress proneness in patients seeking help for hypersexual behavior. Journal of Sex and Marital Therapy, 34(2), 133–149. Retrieved from http://search.ebscohost.com/login.aspx?di rect=true&db=fcs&AN=20137283&site=ehost‐live Reid, R. C., Garos, S., Hook, J. N., & Hook, J. (2014). Hypersexual disorder. In S. W. L. Grossman (Ed.), Translating psychological research into practice (pp. 413–418). New York, NY: Springer. Reid, R. C., Stein, J. A., & Carpenter, B. N. (2011). Understanding the roles of shame and neuroticism in a patient sample of hypersexual men. The Journal of Nervous and Mental Disease, 199(4), 263– 267. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=rzh&AN=104859 672&site=ehost‐live Winters, J. (2010). Hypersexual disorder: A more cautious approach. Archives of Sexual Behavior, 39(3), 594–596. doi:10.1007/s10508‐010‐9607‐2 Workowski, K. A., & Berman, S. M. (2006). Clinical prevention guidance. Sexually transmitted diseases treatment guidelines 2006. Morbidity and Mortality Weekly Report, 55(RR‐11), 2–6. World Health Organization. (2016a). Health topics: Reproductive health. Retrieved from http://www. who.int/topics/reproductive_health/en/ World Health Organization. (2016b). Global summary of the AIDS epidemic 2015. Retrieved from http://www.who.int/hiv/data/epi_core_2016.png?ua=1

Suggested Reading Comprehensive Textbooks Lehmiller, J. J. (2013). The psychology of human sexuality. Oxford, UK: Wiley. Van den Akker, O. B. (2012). Reproductive health psychology. Oxford, UK: Wiley.

STI & HIV Screening Recommendations CDC. (2014). STD & HIV screening recommendations. Atlanta, GA: CDC. Retrieved from http://www.cdc.gov/std/prevention/screeningReccs.htm

Effective HIV Behavioral Interventions CDC. (2014). Effective interventions: HIV prevention that works. Atlanta, GA: CDC. Retrieved from http://www.effectiveinterventions.org/en/Home.aspx

Diagnosis of Sexual Dysfunction and Paraphilic Disorders American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: Author.

Schools, Eating, and Health Psychology Catherine Cook‐Cottone and Rebecca K. Vujnovic Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, USA

Introduction This chapter focuses on the development of the healthy and successful student with specific attention to the development of healthy eating and physical self‐care behaviors. Since the beginning of public education, school personnel have understood that in order to teach students, they must first be physically and emotionally well. It is not possible to fully achieve academic potential without a solid physical and emotional foundation to support learning. Therefore, in addition to the substantial charge of providing a strong academic education, schools must also address the physical and mental health of their students. Further, some suggest that by providing a healthy structure for physical and mental health, schools help students thrive, reaching their fullest potential in both academic and personal life. It is now well accepted that physical and mental health are deeply intertwined within a reciprocal process that either increases risk and pathology or decreases risk and promotes healing and positive development. Hence, consideration and promotion of physical health is now considered a necessary ingredient in psychological and educational interventions. With this understanding the field of health psychology has grown and along with it the notion of the healthy school (Cook‐Cottone, Tribole, & Tylka, 2013). Research showing the negative effects of stress and the role of nutrition and physical self‐care on both physical and mental health continues to grow. Accordingly, school personnel have become increasingly i­ mportant in the delivery of health and health psychology interventions in schools (e.g., Cook‐Cottone et al., 2013). The school setting is the ideal place that impacts all children, delivers effective services, and promotes change from the individual to the systemic levels in a manner that can have a positive

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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impact on academics. Specifically in terms of health promotion, schools can help to address issues critical for enhancing resilience and health, as well as to reduce social class disparities in both physical and mental health, to enhance subjective well‐being (Cook‐Cottone et al., 2013; Shoshani & Steinmetz, 2013; Stephens, Markus, & Fryberg, 2012). A review of the literature suggests that subjective well‐being (i.e., life satisfaction, presence of positive affect, absence of negative emotional experiences, and self‐efficacy) be associated with several positive constructs, including academic achievement (Shoshani & Steinmetz, 2013). Within the school setting, models of change to promote well‐being must address both the individual student characteristics and skills, as well as the larger structural level influences, such as material resources (e.g., access to healthy food, safe places to exercise, and access to quality healthcare; Stephens et al., 2012). Consistent with the focus on system, community, and school‐level factors in the promotion of health, the World Health Organization (WHO, 2015) defines a health‐promoting school as a school that is constantly strengthening its capacity as a health setting, within which students can live, learn, and work. This is done by fostering health and striving to provide a healthy school environment through the provision of school services, curriculum, collaborative projects within the community, and programs for mental health promotion (WHO, 2015). It is critical to engage key stakeholders (e.g., health and education officials, teachers, parents, health providers, and community leaders) in making the school a healthy place to learn (Cook‐Cottone et al., 2013; WHO, 2015). As such, schools and communities must implement policies and practices that respect individual dignity and well‐being to allow many chances for success. The school, and not simply the students or the family, is striving to improve the health of all students, families, school personnel, and community members (WHO, 2015). Specific to eating behaviors, there are several ways that students can move toward risk and disorder. The three primary clinical disorders are anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED) (American Psychiatric Association [APA], 2013). Specifically, AN is marked by a strong drive for and pursuit of thinness, body dissatisfaction, disordered body image, and behavior organized around weight loss (APA, 2013; Cook‐ Cottone et  al., 2013). Although BN also involves dissatisfaction with the body, BN is ­characterized by difficulty with self‐regulation that manifests in a cyclic behavioral pattern of bingeing on food and compensatory behaviors intended to either counter the caloric intake through exercise or purging (e.g., vomiting, use of laxative; APA, 2013; Cook‐Cottone, 2015a). BED involves repeated binge episode in which the individual eats much more than typical in a discrete amount of time (APA, 2013). BED is also believed to involve difficulties with emotion and self‐regulation (Cook‐Cottone, 2015; Cook‐Cottone et  al., 2013). Although not considered to be a clinical eating disorder, but directly associated with weight, obesity results from a chronic disruption of the energy balance (i.e., energy intake and energy expenditure; Cook‐Cottone et al., 2013; Loos & Bouchard, 2003). It is believed that c­ urrent societal conditions (e.g., easy access to energy‐dense foods and increased sedentary activities) among Westernized cultures have led to rapid increases in obesity rates (Cook‐Cottone et al., 2013). Although AN and BN are somewhat rare, affecting less than approximately 1–3% and 5–11% of the population, respectively (APA, 2013), rates suggest that over 20% of students may struggle at some point with clinical‐level disorders associated with eating (Cook‐Cottone et al., 2013). Further, if children and adolescents in the overweight category are included, well over half of the student population could feasibly struggle at some point with food, exercise, and self‐care (Cook‐Cottone, 2013). Thus, collectively, AN, BN, BED, and obesity affect a substantial number of children in schools.

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The Healthy Student The healthy student approach views each student within the context of his or her individual strengths and challenges and takes into account the influences of the family, school, community, and cultural supports and risk factors. The individual student behaviors are not seen in isolation; thus, the student’s struggle for nutritional and physical health and stress management is not viewed as a failure of individual willpower or personal effort. Rather, the student, the school, and the community take ownership of their own responsibility to provide a healthy context from which the individual can make choices that serve their physical and mental health. Accordingly, the self is most effectively conceptualized as a self in which sociocultural context and individual physical and mental health characteristics both play a role in individual positive development, health, and self‐regulation. More recent models of the self‐integrate physical well‐being and balance the role of cognitions and emotions and the role of sociocultural context (Cook‐Cottone, 2006, 2015a; Cook‐Cottone et al., 2013; Stephens, et al., 2012). The attuned representational model of self (Cook‐Cottone, 2006) demonstrates the role of the internal aspects of self such as cognitive, emotional, and the physical self. The physical self is viewed as a base for emotional and cognitive well‐being. For example, daily nutrition, hydration, and exercise provide a stable physiological framework for emotional stability and positive cognitive functioning. See Cook‐Cottone et  al. (2013) and Cook‐Cottone (2015b) for a detailed description of the role of self‐care practices in well‐being. When self‐care is intact, the aspects of the internal system work in an attuned manner to serve the functioning of the self within the sociocultural context (Cook‐Cottone, 2006, 2015a). Basic structural conditions present within the student’s sociocultural contexts (e.g., access to healthy food, school educational practices, school resources and funding, community and larger cultural values and priorities) as well as family influences also play a role in student well‐being and performance (Cook‐Cottone, 2006; Stephens et al., 2012). These processes are viewed as reciprocal, as the student’ ability to engage in active self‐care of his or her physical, emotional, and cognitive needs supports the student’s ability to manage the challenges presented by sociocultural context. Conversely, the practices, supports, and tools provided within and by those in the sociocultural context (e.g., school program and practices) can enhance the students development of self‐care and self‐regulation tools (Cook‐Cottone, 2006; Cook‐Cottone et al., 2013). The self functions to witness and manage the internal aspects of self within the context of the external aspects of self. The self that p ­ resents to the world, school friend, teachers, and family members is an aggregation of the ongoing attunement or aggregated risk manifest by the ongoing dynamics within the self‐­system (Cook‐Cottone, 2006; Cook‐Cottone et al., 2013). See Cook‐Cottone (2006) or Cook‐Cottone (2015) for a full description of the model. In order to increase understanding regarding school health and well‐being promotion within the school setting, the history and foundations of the three‐tier approach to school‐based interventions are described. Next, health promotion approaches, including the promotion of healthy eating and positive mind and body relationships, will be described within the context of the three‐tier approach in schools. To illustrate the approach, mind–body and eating‐related interventions will be presented as examples of service provision within the three‐tier model.

Health Psychology and the History of the Three‐Tier Model The three‐tier model of prevention became a classification system pertinent to the field of prevention and public health, highlighting the importance of promoting and maintaining

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public health, rather than solely focusing on the prevention of disease (Leavell & Clark, 1953, 1958, 1965). The first tier became known as the primary prevention level, intended for the promotion of health and well‐being and to provide specific protection against onset of disease. The primary prevention level included the implementation of procedures (i.e., immunizations, genetic screening) to identify potential risk and to intercept such risk prior to infection of the disease. Subsequently, secondary prevention was intended for early detection and timely treatment to prevent infection from spreading to others or to limit prolonged disability in the individual. Finally, the tertiary level of prevention was considered for the treatment of symptoms, rehabilitation, and limitations of the consequence of the disease to prevent progression or premature death (Leavell & Clark, 1953, 1958, 1965) Since its establishment, the fields of preventative health, medicine, and the social sciences have adopted this framework, although definitions of these three categories have varied across disciplines (Machlem, 2013). By the 1990s, the field of prevention adopted the terminologies endorsed by the Institute of Medicine (IOM): universal, selective, and indicated (Strein, Kuhn‐McKearin, & Finney, 2014). At the same time, the field of education increasingly embraced the prevention model framework for several reasons, including the increased development and research documenting the efficacy of evidence‐based interventions, the promotion of well‐being, and the increasing levels of need among students along with the recognition of the limitation of resources and cost‐effectiveness. Further, such a multitier model accommodated recommendations outlined by the National Association of School Psychologists (NASP, 2010) that recommended a ratio of one school psychologist to every 500–700 students, depending on the need within the student population (NASP, 2010). This tremendously high ratio of students to school ­psychologists highlights the necessity of using a prevention model for the provision of services, as direct service to treat all individuals in need of services would be impossible. Applied within the school framework, the three‐tier model is incredibly effective for providing services to all students, especially as school personnel are frequently the first line of defense against emerging eating disorders and health problems related to obesity (Cook‐Cottone et al., 2013). The first of the three tiers is Tier 1, the universal level, and is conceptualized as the core instructional or social‐behavioral program. Tier 1 programs are research supported and aim to have an impact on the majority of students by meeting the needs of 80–90% of students. Accordingly, the majority of students, including those who may be at risk for concerns, respond sufficiently to this level of programming (Stoiber, 2014). Tier 2 is the targeted level of supports that are intended to serve a smaller group of students who require increased support to enhance the core programs at the Tier 1 level (Stoiber, 2014). Tier 3 is the intensive level and is the highest level of intensity. Tier 3 is designed to be individualized and ­strategically aligned with the needs of the individual student being serviced. This level is intended to be implemented for the smallest percentage of students, 1–5% of students who continue to require supports beyond those in the first two tiers (Stoiber, 2014).

Tier 1: Approaches for the Promotion of Health and Well‐Being Tier 1 approaches for health and well‐being promotion are interventions at the universal level. In general, they are core instructional or social‐behavioral programs that are implemented for all students within the school and likely to be sufficient in meeting the needs of the majority of students (Stoiber, 2014). There is a long‐standing history in the provision of universal programs in education that illustrate the link between physical health and learning. For ­ ­example, school lunch programs were among the first interventions provided within the school

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setting to address a foundational health need that was notably interfering with student l­ earning (Cook‐Cottone et al., 2013). Since first initiated, school‐based programs to promote nutrition and physical health have grown substantially. These programs have developed to include large‐ scale federally funded school lunch programs, integrate physical and health education, and target all levels of well‐being support from counseling services to family support. Current implementation issues hinge on funding, challenges related to scheduling and time constraints, and finding evidence‐based programs that meet school needs. Beyond the provision of school lunches, there is emerging evidence that universal practices designed to promote well‐being and health are effective in schools (Cook‐Cottone et  al., 2013; Shoshani & Steinmetz, 2013). However, research is in the early stages; much of the evidence is based on pilot studies or short‐term controlled trials as opposed to more rigorous randomized trials. At the universal level, there is a significant need for higher‐quality research with randomized controlled trials and assessment of long‐term outcomes (Serwacki & Cook‐ Cottone, 2012; Shoshani & Steinmetz, 2013). A review of available research related to universal programs has identified several key factors across programs that were believed to play a role in student well‐being at the universal level, including the cultivation of positive emotions, gratitude, and hope, goal setting, and development of character strengths (Shoshani & Steinmetz, 2013). These foci can easily be woven into daily curriculum, although some schools integrate such themes more formally through structured social emotional learning (SEL) ­programs. Overall research on SEL programs indicates that they result in an increase in s­ tudent well‐being (Ashdown & Bernard, 2012; Durlack, Weissberg, Dynamicki, Taylor, & Schellinger, 2011). Benefits include increased emotional regulation, self‐awareness, stress management, relationship skills, and social awareness and have demonstrated an impact on learning (Ashdown & Bernard, 2012; Durlack et al., 2011). These larger‐scale, often school‐ and district‐wide approaches provide a foundational context for more specific programs that focus on healthy eating and physical health promotion. Effective implementation of school and district‐wide interventions requires an organization around a set of principles, which facilitates communication and aligns efforts. Cook‐Cottone et al. (2013) present an approach to the healthy student, which includes three essential components: (a) intuitive eating and nutrition, (b) healthy physical activity, and (c) mindfulness, self‐care, and emotion regulation. It is important that the overall focus is on promoting health, rather than avoiding obesity (Cook‐Cottone et al., 2013). To do this schools must adopt a transcontextual approach in which adults within schools, families, and the community work together to provide a healthy environment (Cook‐Cottone et al., 2013). This approach helps the entire community “walk the walk” of healthy eating, physical exercise, and mindful self‐ care. The entire school community is encouraged to embrace a focus on overall health over weight and weight loss. This community must also create a zero‐tolerance policy for teasing and bullying, especially related to weight. Additionally, students, teachers, and families should be provided with opportunities to learn about nutrition. Nutrition education should also be included in health class and other appropriate sections of the curriculum. Farm‐to‐school ­programs and school farm program can enhance student knowledge of food sourcing, agriculture, botany, and nutrition. Further, through collaboration between school lunch providers and food vendors, the school should provide an environment full of healthy food choices with minimal exposure to unhealthy, competing foods (i.e., foods that have little nutritional value and are offered as a purchasing option during meal times, outside of, or as part of the school meal program; Cook‐Cottone et  al., 2013). Finally, opportunities for physical activity and access to water throughout the school day are also critical for supporting and promoting ­overall health (Cook‐Cottone et al., 2013).

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One approach that can be applied at the universal level and is aligned with the positive ­psychology paradigm is the Health at Every Size (HAES) program. The HAES program is based on supporting a fundamental understanding of the body, suggesting that an individual is more likely to treat his or her body with respect and care, as well as provide his or her body with good nutrition and exercise, if an individual likes their body (Mukai, Kambara, & Sasaki, 2013). Research suggests that when the body is experienced with a sense of shame, an individual may be more likely to engage in self‐neglect and self‐destructive eating behaviors and is less likely to engage in physical exercise, nutrition, and self‐care (e.g., Duarte, Pinto‐Gouveia, & Ferreira, 2014). The HAES principles include (a) a respect and acceptance of the body (all body types, shapes, and sizes), (b) a focus on making physical activity enjoyable, (c) a decreased focus on food (i.e., not using food as an external reward for achievement or behavior or as a way of celebrating achievement), and (d) a decreased focus on weight and body shaming. These HAES general principles can be readily applied to the school setting (Cook‐Cottone et al., 2013). For example, there is no need to weigh students in school when they are r­ outinely monitored by their primary healthcare provider or during their individual school physical (Cook‐Cottone et al., 2013). For some students, the school weighing day is the beginning of their eating disorder story. This policy has been criticized not only for the risk it induces but also for the frequent lack of connection with any intervention to address variations in body mass index. Finally, schools should help students focus on being present, active, and living fully in their present body no matter its shape or size (Cook‐Cottone et al., 2013). There are several programs that have been shown effective in promoting healthy eating behavior and a positive relationship with the body: Planet Health (Austin et al., 2012; Gortmaker et al., 1999), New Moves (Neumark‐Sztainer et al., 2010), Healthy Buddies (Stock et al., 2007), and Learning Gardens. It is important to note that mindfulness and mindful self‐care strategies are also central to the healthy student approach and schools are increasingly integrating them into their daily classroom practices and social and emotional learning programs. Mindful practice and self‐care are ways of being present in your mind and body that allows for a shift from externally oriented stimuli and standards to an ongoing presence with your own experience. Specifically, mindfulness includes formal mindful practices such as meditation, yoga, and systematic relaxation, as well as an increase in mindful awareness throughout the day. For example, a student could eat lunch, practice his or her instrument, do math, and read mindfully. Although more rigorous research is needed, mindful practices, yoga, and mindful awareness are believed to reduce risk for eating disorder behavior and increase student well‐being (Cook‐Cottone et  al., 2013; Greenberg & Harris, 2012; Scime & Cook‐Cottone, 2008; Serwacki & Cook‐Cottone, 2012). For an expanded review of school‐wide interventions, see Cook‐Cottone et al. (2013). Overall, within the context of honoring and respecting bodies of all shapes and sizes, the ­district‐wide focus is on respect for the body and the body’s wisdom to know when and what to eat, healthy physical exercise, and mindful practice and self‐care.

Tier 2: Approaches to Facilitate Healthy Eating Behaviors and Self‐Care Tier 2 approaches are targeted level supports that are intended to serve a smaller group of students who require increased support to enhance the core programs at the Tier 1 level ­programs (Stoiber, 2014). Tier 2 programs often target a specific risk group (e.g., elite ­athletes) or students already showing risk factors (e.g., dieting, body dissatisfaction) without m ­ anifesting full diagnostic criteria. There are many different types of programs that range from those that

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focus on physical exercise, or are solely focused on nutrition and eating or mood regulation and distress tolerance, to integrated programs that include both yoga and didactic skills training (e.g., media literacy, assertiveness, emotion regulation; see Cook‐Cottone et al. (2013) for a review of school‐based programs). Here, two examples are provided as illustrations of a Tier 2 approach. First, Martinsen et al. (2014) designed and studied an eating disorder prevention intervention for elite high school athletes that addressed self‐confidence, motivation, growth and development, and sports nutrition as related to health, eating disorders, and performance. Results indicated that those in the intervention group showed no onset of clinical eating disorder, whereas control group members showed significant increases in eating disordered behavior. Other programs have focused on students who are referred to the intervention due to suspect increased risk. Programs such as Girls Growing in Wellness and Balance: Yoga and Life Skills to Empower use yoga, and a risk‐based curriculum can be used as a Tier 2 intervention for girls at risk for eating disorders. This program has been found, in controlled trials, to reduce body dissatisfaction, increase self‐care, and decreased drive for thinness (Cook‐Cottone, Kane, Keddie, & Haugli, 2013; Scime & Cook‐Cottone, 2008; Serwacki & Cook‐Cottone, 2012). However, there is still much work needed before there is a strong evidence base supporting a variety of Tier 2 interventions. Specifically relevant for school settings are issues such as dosage of the intervention may matter. For example, how much yoga, mindfulness, or prevention curriculum is needed to create change (Cook‐Cottone, 2013)? Notably, dissonance‐based programs in which students are led through a process of media literacy, psychoeducation on the thin ideal, activities designed to help students internalize a healthy body weight perspective and counter the thin ideal have also found to be effective (Rohde et al., 2014).

Tier 3: Assessing and Supporting Students With Eating Problems Once a student is showing symptoms of a disorder, there should be considered for a Tier 3 level of intervention. Tier 3 is the highest level of intensity and is designed to be intensive, individualized, and strategically aligned with the student’s individual needs (Stoiber, 2014). Screening and assessment can help identify those students who are in need of more intensive interventions. The SCOFF is a brief five‐item screening instrument that can be very helpful. The questions are the following: (S) “Do you make yourself sick (vomit) because you feel comfortably full?” (C) “Do you worry that you have lost control over how much you eat?” (O) “Have you recently lost more than one stone (i.e., 15 pounds) in a 3‐month period?” (F) “Do you believe yourself to be fat when others say you are thin?” (F) “Would you say the food dominates your life?” (Solmi, Hatch, Hotopf, Treasure, & Micali, 2015) Other behaviors of concern include eating in the absence of hunger, emotional eating, body dissatisfaction, and distortions in or over concern with body image (Cook‐Cottone et  al., 2013). For a more detailed review of screening and assessment tools for disordered eating, refer to Cook‐Cottone et al. (2013), Berg and Peterson (2013), and Palmer (2014). Following screening and assessment, if it is determined that the student is at risk for disordered eating, a referral should be made to an eating disorder specialist. Treatment may be done either on an inpatient or outpatient basis depending on the level of symptomatology and the patient’s health status (Cook‐Cottone et al., 2013). Those with clinical‐level eating disorders require comprehensive and multifaceted care (American Academy of Pediatrics [AAP], 2003). The treatment of disordered eating utilizes a multidisciplinary team that specializes in the treatment of eating issues that includes a medical doctor specializing in eating disorders, a

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mental health specialist, and a nutritionist (Cook‐Cottone et al., 2013). See Cook‐Cottone and Vujnovic (2015) for a review of evidence‐based interventions for children and adolescents with eating disorders. Although treatment is likely to occur outside of the school setting, adequate support in the school setting can be a critical part of the treatment and recovery process. Support of the treatment of disordered eating can be most effectively done within the context of a prevention‐oriented school atmosphere already promoting zero tolerance of in‐school advertising, body teasing, and encouraging healthy nutritional behaviors and opportunities for positive physical activity (e.g., soccer, yoga classes, track, swimming; Cook‐Cottone et al., 2013). If a student requires hospitalization or day treatment, providing support for the student’s transition back to school is vital to recovery maintenance (Manley, Rickson, & Standeven, 2000). Student needs may include supportive counseling, medical monitoring, release from physical education classes, meal monitoring, and communication with the treatment team and family (Cook‐ Cottone et  al., 2013). Special academic accommodations may be necessary, which include adjusted workload, alternative assignments for some physical education requirements, extended time on assignments and tests, tutoring for missed coursework, copies of class notes, and access to quiet study locations (Cook‐Cottone et al., 2013; Manley et al., 2000). Given the medical complications sometimes involved in more chronic or severe cases, the school nurse may play an important role (Cook‐Cottone et al., 2013). In‐school counseling can nicely augment outpatient treatment team efforts via relaxation work, supportive and reflective listening, and short‐term, solution‐focused or problem‐solving approaches for in‐school issues. In addition, the treatment team therapist may have specific objectives with which he or she would like supported within the school setting (Cook‐Cottone et al., 2013). Other areas of school support for those with disordered eating include supporting student during meals, alternative assignments in physical education and health classes, and flexibility and support with academic assignments and attendance (Cook‐Cottone et al., 2013).

Conclusion Physical and mental health provides the foundation for student learning. Today, schools have increasing responsibility for the provision of education beyond academics and are now charged with supporting overall well‐being, not only for their students but also for families, school staff, and members of the larger community. One important deterrent to health is the increasing prevalence of eating disorders and obesity, with over half of the student population potentially struggling with food, exercise, and self‐care. Addressing well‐being and health in the school setting is best conceptualized in a three‐tier approach, focusing not on the avoidance of the disorder itself, but on providing a level of programming to all students to support health and wellness within the school community. At the Tier 1 universal level, programs target all students and are usually implemented at the larger school or district‐wide levels, focusing on healthy eating and physical health promotion. At the Tier 2 selective level, programs are intended to be implemented only to a smaller group of students and often target a specific risk group or students already showing risk factors. At this level, programming is varied and focuses on the students’ needs. Thus, programs for use at Tier 2 might include those that focus on physical exercise, nutrition and eating, or mood regulation and distress tolerance. Tier 2 ­programs with a more broad focus integrate both yoga and didactic skills training to support well‐being. Finally, at the Tier 3 indicated level, treatment is likely to occur outside of the school setting, making coordination and support for treatment in the school setting essential.

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Students may require accommodations to support treatment progress and maintenance, and communication with treatment providers is important. Overall, schools provide an important setting for supporting health and well‐being in the school and community.

Author Biographies Catherine Cook‐Cottone, PhD, is a licensed psychologist, registered yoga teacher, professor at SUNY at Buffalo, and co‐editor in chief of Eating Disorders: The Journal of treatment and Prevention. She is founder and president of Yogis in Service, Inc., a not‐for‐profit organization. Her research specializes in embodied self‐regulation (i.e., yoga, mindfulness, and self‐ care) and psychosocial disorders (e.g., eating disorders, trauma). She has written 6 books and over 60 peer‐reviewed articles and book chapters. Rebecca K. Vujnovic, PhD, is a licensed psychologist and nationally certified school ­psychologist. She is director of the University at Buffalo, State University of New York School Psychology Program. She maintains a private practice specializing in youth with attention and anxiety disorders. She has authored a workbook to support the management of anxiety through mindfulness strategies anxiety and has published 4 book chapters and 12 research articles.

References American Academy of Pediatrics. (2003). Identifying and treating eating disorders: Policy statement by the Committee on Adolescence. Pediatrics, 111, 204–211. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Ashdown, D. M., & Bernard, M. (2012). Can explicit instruction in social emotional learning skills benefit the social emotional learning, well being, and academic achievement of young children? Early Childhood Education, 39, 397–405. Austin, S. B., Spadano‐Gasbarro, J. L., Greaney, M. L., Blood, E. A., Hunt, A. T., Richmond, T. K., … Peterson, K. E. (2012). Peer reviewed: Effect of the planet health intervention on eating disorder symptoms in Massachusetts Middle Schools, 2005–2008. Preventing Chronic Disease, 9, E171. Commission on Chronic Illness: Chronic Illness in the United States, vol. 1. Published for the Commonwealth Fund by Harvard University Press, Cambridge, MA, 1957 Cook‐Cottone, C., & Vujnovic, R. K. (2015). Evidence‐based interventions for eating disorders in ­children and adolescents. In L. Theodore (Ed.), Handbook of applied interventions for children and adolescents. New York, NY: Springer. Cook‐Cottone, C. P. (2006). The attuned representation model for the primary prevention of eating disorders: An overview for school psychologists. Psychology in the Schools, 43, 223–230. Cook‐Cottone, C. P. (2013). Dosage as a critical variable in yoga therapy research. International Journal of Yoga Therapy, 23, 11–12. Cook‐Cottone, C. P. (2015a). Incorporating positive body image into the treatment of eating disorders: A model for attunement and mindful self‐care. Body Image, 14, 158–167. Cook‐Cottone, C. P. (2015b). Mindfulness and yoga for self‐regulation: A primer for mental health ­professionals. New York, NY: Springer. Cook‐Cottone, C. P., Kane, L., Keddie, E., & Haugli, S. (2013). Girls growing in wellness and balance: Yoga and life skills to empower. Stoddard, WI: Schoolhouse Educational Services, LLC. Cook‐Cottone, C. P., Tribole, E., & Tylka, T. (2013). Healthy eating in schools: Evidence‐based interventions to help kids thrive. Washington, DC: American Psychological Association. Duarte, C., Pinto‐Gouveia, J., & Ferreira, C. (2014). Escaping from body image shame and harsh self‐ criticism: Exploration of underlying mechanisms of binge eating. Eating Behaviors, 15, 638–643.

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Durlack, J. A., Weissberg, R. P., Dynamicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta‐analysis of school‐based universal interventions. Child Development, 82, 405–532. Gortmaker, S. L., Peterson, K., Wiecha, J., Sobol, A. M., Dixit, S., Fox, M. K., & Laird, N. (1999). Reducing obesity via a school‐based interdisciplinary intervention among youth: Planet Health. Archives of Pediatrics & Adolescent Medicine, 153, 409–418. Greenberg, M. T., & Harris, A. R. (2012). Nurturing mindfulness in children and youth: Current state of research. Child Development Perspectives, 6, 161–166. Leavell, H. R., & Clark, E. G. (1953). Textbook of preventive medicine. New York, NY: McGraw‐Hill Book Company. Leavell, H. R., & Clark, E. G. (1958). Preventive medicine for the doctor in his community (2nd ed.). New York, NY: McGraw‐Hill Book Company. Leavell, H. R., & Clark, E. G. (1965). Preventive medicine for the doctor in his community (3rd ed.). New York, NY: McGraw‐Hill Book Company. Loos, R., & Bouchard, C. (2003). Obesity–Is it a genetic disorder? Journal of Internal Medicine, 254, 401–425. Machlem, G. L. (2013). Preventative mental health at school: Evidence‐based services for students. New York, NY: Springer. Manley, R. S., Rickson, H., & Standeven, B. (2000). Children and adolescents with eating disorders: Strategies for teachers and school counselors. Intervention in School and Clinic, 35, 228–231. Martinsen, M., Bahr, R., Børresen, R. U. N. I., Holme, I., Pensgaard, A. M., & Sundgot‐Borgen, J. (2014). Preventing eating disorders among young elite athletes: A randomized controlled trial. Medicine & Science in Sports & Exercise, 46, 435–447. Mukai, T., Kambara, A., & Sasaki, Y. (2013). Health at every size approach. Eating Disorders: An Encyclopedia of Causes, Treatment, and Prevention, 231. NASP. (2010). Integrated model for comprehensive and integrated school psychological services. Washington, DC: NASP. Neumark‐Sztainer, D. R., Friend, S. E., Flattum, C. F., Hannan, P. J., Story, M. T., Bauer, K. W., … Petrich, C. A. (2010). New moves—Preventing weight‐related problems in adolescent girls: A group‐randomized study. American Journal of Preventive Medicine, 39, 421–432. Palmer, B. (2014). Helping people with eating disorders: A clinical guide to assessment and treatment. New York, NY: Wiley. Rohde, P., Auslander, B. A., Shaw, H., Raineri, K. M., Gau, J. M., & Stice, E. (2014). Dissonance‐based prevention of eating disorder risk factors in middle school girls: Results from two pilot trials. International Journal of Eating Disorders, 47, 483–494. Scime, M., & Cook‐Cottone, C. (2008). Primary prevention of eating disorders: A constructivist integration of mind and body strategies. International Journal of Eating Disorders, 41, 134–142. Serwacki, M., & Cook‐Cottone, C. (2012). Yoga in the schools: A systematic review of the literature. International Journal of Yoga Therapy, 22, 101–110. Shoshani, A., & Steinmetz, S. (2013). Positive psychology at school: A school‐based intervention to promote adolescents’ mental health and well being. Journal of Happiness Studies, 15, 1289–1311. Solmi, F., Hatch, S. L., Hotopf, M., Treasure, J., & Micali, N. (2015). Validation of the SCOFF questionnaire for eating disorders in a multiethnic general population sample. International Journal of Eating Disorders, 48, 312–316. Stephens, N. M., Markus, H. R., & Fryberg, S. A. (2012). Social class disparities in health and education: Reducing inequality by applying a sociocultural self model of behavior. Psychological Review, 119, 723–744. Stock, S., Miranda, C., Evans, S., Plessis, S., Ridley, J., Yeh, S., & Chanoine, J. P. (2007). Healthy Buddies: A novel, peer‐led health promotion program for the prevention of obesity and eating ­disorders in children in elementary school. Pediatrics, 120, e1059–e1068. Stoiber, K. C. (2014). A comprehensive framework for multitiered system of support in school p ­ sychology. In A. Thomas & J. Grimes (Eds.), Best practices in school psychology (5th ed.). Bethesda, MD: NASP.

Schools, Eating, and Health Psychology

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Strein, W., Kuhn‐McKearin, M., & Finney, M. (2014). Best practices in developing prevention strategies for school psychology practice. World Health Organization (WHO). (2015). What is a health promoting school?. Retrieved from http://www.who.int/school_youth_health/gshi/hps/en/.

Suggested Reading Cook‐Cottone, C. P. (2015). Mindfulness and yoga for self‐regulation: A primer for mental health ­professionals. New York, NY: Springer. Cook‐Cottone, C. P. (2017). Mindfulness and yoga in schools: A guide for teachers and practitioners. New York, NY: Springer. Cook‐Cottone, C. P., Tribole, E., & Tylka, T. (2013). Healthy eating in schools: Evidence‐based ­interventions to help kids thrive. Washington, DC: American Psychological Association.

Sport and Exercise in Health Psychology Erika D. Van Dyke1,2 and Judy L. Van Raalte2 Department of Sport Sciences, College of Physical Activity and Sport Sciences, West Virginia University, Morgantown, WV, USA 2 Department of Psychology, Springfield College, Springfield, MA, USA

1

Sport and exercise psychology is a field devoted to understanding biological, psychological, and social influences on performance and well‐being, as well as the ways in which these forces interact to influence participation in sport and exercise (e.g., Biddle, 2000). Scientific findings and practical applications derived from sport and exercise psychology are of direct relevance to health psychology. That is, sport, exercise, and physical activity contribute to health outcomes. Understanding the factors that influence participation in sport, exercise, and physical activity can facilitate the development of effective interventions. Important health‐related factors may also be considered concurrently with sport, exercise, and physical activity.

Physical Activity, Sport, and Exercise as a Cause Much of the health psychology literature pertaining to sport and exercise is focused on the ways in which participation in physical activity influences aspects of health and well‐being. Particular attention has been paid to the effects of sport and exercise on cognition, mood, personal factors, and psychobiological processes. A body of literature indicates that fit older adults display superior cognitive functioning relative to their less fit counterparts. These results are dependent on the task, with attention‐demanding and rapid tasks showing the most ­pronounced effects (Boutcher, 2000). Meta‐analyses further show that physical activity enhances cognition among both healthy elderly people and those with cognitive impairment and dementia (Colcombe & Kramer, 2003). Such cognitive benefits may accrue to exercisers because engaging in regular aerobic exercise minimizes age‐related declines in brain volume and increases brain volume (Colcombe et  al., 2006). Among the elderly, physical activity is a­ssociated with larger gray The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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­ atter volumes and reduced brain volume losses due to n m ­ eurodegeneration (Raji et al., 2016). Because the specific brain regions positively affected by physical activity are also related to Alzheimer’s disease and mild cognitive impairment, physical activity is believed to reduce the risk of developing such cognitive deficits (Raji et al., 2016). Further research is needed to clarify the dose–response relationship between physical activity and cognition, as well as to discover the underlying mechanisms by which physical activity exerts its effects on cognitive functioning (Boutcher, 2000; Carron, Hausenblas, & Estabrooks, 2003). People who engage in regular physical activity often report that exercise makes them feel good (Leith, 2010). Indeed, research indicates that exercise reduces anxiety, depression, and confusion as measured by the Profile of Mood States (POMS) (McNair, Lorr, & Droppleman, 1971). Given these findings, it is not surprising that there has been a proliferation of research on the influence of physical activity behaviors on mood and affect particularly in the areas of anxiety and depression. The state anxiety‐reducing effects of physical activity are clear (the relationship between physical activity and trait anxiety is addressed below with other personality factors). Review papers report low to moderate reductions in anxiety following bouts of physical activity (Leith, 2010). Exercising for at least 15 min can reduce state anxiety for several hours post‐exercise (Leith, 2010). The anxiety‐reducing effects of physical activity have most commonly been found for aerobic, rhythmic activities including running, walking, cycling, and swimming (Leith, 2010). Both aerobic and non‐aerobic forms of exercise reduce anxiety symptoms (Jayakody, Gunadasa, & Hosker, 2014). Although much research indicates that reductions in anxiety are found following physical activity regardless of the intensity of the exercise (Leith, 2010), some research with competitive athletes and exercisers has shown that high‐intensity physical training is associated with increases in anxiety and negative affective states (Szabo, 2000). Exercise prescription recommendations may therefore be best focused on low‐ to ­moderate‐intensity physical activity for anxiety reduction. Physical activity is as effective as other known treatments (e.g., hypnosis, meditation, and relaxation) in reducing anxiety (Carron et al., 2003). Among individuals with clinically defined anxiety disorders including social phobia and panic disorder, exercise can reduce symptoms of anxiety and offers additional benefits compared with medication or psychological treatment alone (Jayakody et al., 2014). Similar positive effects of physical activity have been found with youth sport athletes who experience lower levels of anxiety in social situations (Schumacher Dimech & Seiler, 2011). Taken together, various sport and exercise behaviors have been found effective in reducing state anxiety. Mild forms of nonclinical depression often abate without intervention in a relatively short amount of time. When treatment is sought, medication and psychotherapy are traditional methods that can alleviate symptoms of depression (Leith, 2010). Several meta‐analyses dating back to the 1990s show that exercise reduces depression in individuals relative to non‐ exercising controls (North, McCullagh, & Tran, 1990), that these benefits accrue from both aerobic and anaerobic exercise, and that exercise is as effective a treatment for depression as other established methods including forms of psychotherapy and relaxation (North et al., 1990). Antidepressant effects have been most widely found in exercise programs of at least 10 weeks in length, with bouts of moderate‐intensity activity performed for 15–30 min three times per week (Leith, 2010). Although high‐intensity exercise has also been shown to effectively reduce symptoms of depression (Leith, 2010), the potential for individuals to experience negative affective responses at high intensities may make moderate‐intensity activity a preferred approach for participants (Ekkekakis, Parfitt, & Petruzzello, 2011). Given the relatively few adverse side effects of exercise relative to the many health benefits,

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these findings have encouraging implications for including exercise in treatment ­prescriptions for depression. Personality is a complex phenomenon—one that is relatively stable and enduring but also dynamic and modifiable (Carron et al., 2003). That is, people’s personalities include characteristics displayed across a broad range of situations, as well as some characteristics that are malleable and subject to change. Physical activity has been found to be related to aspects of personality including trait anxiety, body image, and self‐esteem. Regular participation in sport and aerobic exercises (e.g., jogging, walking, cycling, swimming, and aerobic dance) is related to significant reductions in trait anxiety. People who engage in exercise programs of at least 9 weeks typically experience reductions in trait anxiety regardless of the exercise intensity level tested (Leith, 2010). A minimum of 20 min of exercise ­performed three times per week is necessary to promote a positive influence on trait anxiety (Leith, 2010). Thus, physical activity is associated with favorable effects on both state and trait forms of anxiety. Body image has also been found to be positively related to involvement in sport and exercise (Hausenblas & Fallon, 2006; Hausenblas & Symons Downs, 2001). For instance, exercisers tend to have more favorable body image than non‐exercisers, and body image is thought to improve throughout the course of exercise programs (Hausenblas & Fallon, 2006). When considering the body image of athletes in particular, athletes report more positive body image than non‐athletes, and college athletes’ body image is more positive than club/recreational athletes (Hausenblas & Symons Downs, 2001). However, involvement in sports that emphasize highly trained and lean bodies may contribute to body image disturbances (Carter, 2009). Additional research exploring the relationship between physical activity and body image may provide a strong foundation for the development of evidence‐based interventions designed to reduce body image disturbances (Hausenblas & Symons Downs, 2001). Physical activity has been shown to have a positive influence on self‐esteem via such mechanisms as enhanced body image, body satisfaction, body acceptance, physical competence, and personal control over the body (Fox, 2000). Weight training and aerobic exercise programs of at least 12 weeks have the strongest self‐esteem enhancing benefits (Fox, 2000; Leith, 2010). Physical self‐esteem (Bowker, 2006) and perceived sport competence (Wagnsson, Lindwall, & Gustafsson, 2014) mediate the relationship between adolescent sport participation and self‐esteem. Adult involvement in competitive physical activity can have adverse effects on self‐esteem and self‐concept (Leith, 2010), but such effects may be contingent upon the population in question. Competitive sport athletes report significantly higher self‐ esteem and self‐concept than non‐athletes. Among adolescent competitive sport athletes, competitive orientation is a positive predictor of self‐esteem and self‐concept (Findlay & Bowker, 2009). In sum, sport and exercise have been shown to positively influence self‐ esteem and self‐concept, both directly and via the mediating roles of physical competence and body image‐related constructs. Psychobiological factors such as sleep, reactivity to stress, pain, and injury have all been explored in terms of their relationships to physical activity. Acute exercise has small to moderate effects on slow‐wave sleep, rapid eye movement (REM) sleep, REM latency, and total sleep time (Youngstedt, O’Connor, & Dishman, 1997). With regard to the timing of the exercise bout relative to sleep, the majority of studies are conducted with good sleepers and include exercise bouts that are completed at least four hours before the onset of sleep, thus making it difficult to explore hypotheses related to the disruptive effects of exercise on sleep. It should be noted, however, that some individuals involved in physical activity just 30 min prior to ­bedtime do not experience sleep disruption (Youngstedt et al., 1997). These results suggest

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that evenings may serve as an ideal time for individuals with later preferences to exercise (Youngstedt, 2000). Exercise and physical activity reduce reactivity to psychosocial stressors (Crews & Landers, 1987). Aerobically fit individuals often have reduced reactivity to psychosocial stress relative to their unfit counterparts (Crews & Landers, 1987). Exercise can be an effective way to cope with stress or can serve as an “inoculator” whereby repeated exposure to exercise enhances the ability to handle psychosocial stressors. Students who regularly use sport and exercise as coping strategies tend to have high stress tolerance (Bland, Melton, Bigham, & Welle, 2014). These results highlight the value of physical activity, exercise, and sport as effective coping mechanisms for reducing stress reactivity. Pain is a common, and for some activities an integral, part of engaging in sport and exercise (Carron et al., 2003). Interestingly, exercise also has pain‐reducing effects, although the specific mechanisms by which exercise achieves these analgesic effects are not fully understood (Koltyn, 2000). Aerobic and strengthening exercises reduce musculoskeletal pain tied to labor, menstruation, fibromyalgia, low back issues, and forms of arthritis (Carron et al., 2003). Pain tolerance and pain threshold increase following acute bouts of exercise, and ratings of the intensity of a pain stimulus decrease following exercise (Koltyn, 2000). Elite athletes have higher pain thresholds than non‐athletes and control participants (Koltyn, 2000). Anecdotal evidence suggests that athletes and dancers may continue to perform vigorous exercise while injured and report afterward that they did not experience pain (Koltyn, 2000). Such accounts highlight how pain perception can be altered by engaging in sport and exercise. Parallel ­processing models of pain suggest that sport and exercise activities can serve a distraction function, keeping attention directed toward task‐relevant stimuli and away from pain (Brewer & Redmond, 2017). A common cost associated with participation in sport and exercise is injury. Cognitive emotional and behavioral responses to sport injury have been documented in the literature (Brewer & Redmond, 2017). Cognitive appraisals range from relatively benign (e.g., “my body can use a rest anyway”) to very distressed (e.g., “this is the worst thing that ever happened to me”). Negative emotional responses typically occur immediately post injury, including experiences of anger, anxiety, bitterness, confusion, depression, disappointment, devastation, fear, frustration, helplessness, resentment, and shock. Effective coping behaviors and adherence to sport injury rehabilitation can speed the process of recovery from sport injury (Brewer & Redmond, 2017). Positive emotions as well as mixed feelings of apprehension, excitement, and fear of reinjury often occur as return to sport and physical activity near. Although sport, exercise, and physical activity can lead to injury, physical activity behaviors, particularly forms of strength training, have also been associated with lowered risk of injury in both youth and adults (Leppänen, Aaltonen Parkkari, Heinonen, & Kujala, 2014; Rössler et  al., 2014). Thus, strength and exercise‐based activities can be effective in preventing the occurrence of injury in sport and exercise settings.

Physical Activity, Sport, and Exercise as an Effect Social factors such as group cohesion, leadership, group size, and social support have been studied with a particular focus on their effects on participation in sport, exercise, and physical activity. Group cohesion has been explored as a primary factor in individual adherence to physical activity. Perceptions of group cohesion are associated with individual adherence across several different physical activity behaviors including group fitness classes, recreational team

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sports, and elite team sports (Carron, Widmeyer, & Brawley, 1988). Perceptions of cohesion are higher among regular exercise class attendees than among dropouts in both university ­fitness class and private club fitness class settings (Spink & Carron, 1994). Although the specific aspects of group cohesion and moderators (e.g., belonging, enjoyment) that are related to individual adherence vary across settings, the overall conclusions highlight the value of cohesion in sports teams and exercise groups for fostering adherence to physical activity (Carron et al., 1988). In the sport domain, coaches and peers high in transformational leadership qualities have positive influences on competitive team sport athletes (Price & Weiss, 2013). Specifically, transformational leadership behaviors such as inspirational motivation, idealized influence‐ individualized consideration, and intellectual stimulation are linked to greater team cohesion, enjoyment, confidence, perceived competence, and intrinsic motivation, variables likely to have a positive impact on athletes’ participation in sport (Price & Weiss, 2013). In exercise settings, however, individual intentions to participate in fitness classes are not primarily dependent upon leadership style. Rather, leadership style contributes to group environment, and this interaction favorably influences enjoyment of exercise (Fox, Rejeski, & Gauvin, 2000). Thus, leadership characteristics exert influence at both individual and group levels to impact the experiences people have in physical activity. In the context of recreational sport, increasing team size has been found to decrease perceptions of both enjoyment and cohesion (Widmeyer, Brawley, & Carron, 1990). In exercise settings, there is a curvilinear relationship between group size and attendance/retention such that small and large classes are best attended (Carron, Brawley, & Widmeyer, 1990). Exercisers’ satisfaction is related to class size in a linear fashion such that the larger the class, the less ­satisfaction, perhaps due to reductions in individualized attention, reinforcement, and instruction from group leaders in larger groups (Carron et al., 1990). Exercisers’ perceptions of social support from their families and important others are associated with exercise behavior, cognitions pertaining to exercise participation, and attitudes regarding exercise experiences (Carron, Hausenblas, & Mack, 1996). Structural equation modeling has shown that social support has an indirect influence on exercise behavior that is mediated by self‐efficacy (Duncan & Stoolmiller, 1993). That is, social support enhances self‐ efficacy, which is in turn related to increases in exercise behavior. Social support is also a key influence on the sport‐related behaviors of youth, adolescents, and adults (Beets, Cardinal, & Alderman, 2010; Johnson, Kubik, & McMorris, 2011; Santi, Bruton, Pietrantoni, & Mellalieu, 2014). Direct parental involvement in sport activities with children, parental presence or supervision of sport activities, and parental encouragement and praise of children’s’ physical activity behaviors all foster youth physical activity (Beets et  al., 2010). Social support from friends and school staff are associated with increased participation in sports teams among adolescents (Johnson et al., 2011). Supportive coaches and teammates increase participation in masters teams and nonobligatory training sessions (Santi et al., 2014). There is strong e­ vidence that social support is positively related to physical activity, exercise, and sport involvement across the lifespan.

Physical Activity, Sport, and Exercise and Concurrent Factors Several factors such as exercise dependence, eating disorders, and substance use issues, as well as individual demographic/biological and psychological characteristics, have been found to occur concurrently with sport and exercise behavior. Individuals experiencing exercise

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­ ependence exercise to such an extent that they may sustain injury, feel controlled by their d physical activity, and experience a range of problematic physiological, cognitive, and affective symptoms when deprived of physical activity (Szabo, 2000). Symptoms of exercise deprivation experienced by exercise‐dependent people include confusion, impaired concentration, anxiety, depression, irritability, hostility, anger, tension, guilt, frustration, decreased self‐esteem, m ­ uscle soreness, disturbed sleep, lethargy, fatigue, increased galvanic skin response, gastrointestinal problems, and decreased vigor (Carron et al., 2003). The specific amount of exercise that is “too much” and leads to exercise dependence has yet to be determined. Factors associated with the development of exercise dependence include high levels of strenuous exercise, perfectionism, and self‐efficacy (Hausenblas & Symons Downs, 2002). Clarification of conceptual and definitional issues related to exercise dependence may allow researchers to gain a deeper understanding of this phenomenon. High levels of athleticism and exercise have been found to co‐occur with eating disorder symptoms (Szabo, 2000). Athletes, particularly male athletes, report more eating disorder symptoms than non‐athletes (Hausenblas & Carron, 1999). Male and female athletes in ­aesthetic sports and in sports involving weight categories are at heightened risk of developing eating disorder symptoms (Szabo, 2000). The subjective evaluation inherent to aesthetic sport outcomes and the heightened attention toward maintaining a certain body appearance and/or weight are considered probable explanations for these relationships (Hausenblas & Carron, 1999). Among female collegiate athletes, symptoms of eating disorders are related to sociocultural pressure for thinness, negative appraisals of athletic achievement, and anxiety about athletic performance (Williamson et al., 1995). Physical activity in and of itself is not necessarily related to eating disorders (Szabo, 2000). Indeed, walking exercise regimens are effective in managing binge eating disorders among obese women (Levine, Marcus, & Moulton, 1996). Furthermore, anorexia often leads to fatigue that can necessitate reductions in physical activity (Carron et al., 2003). Only a small percentage of exercisers appear to experience eating disorder symptoms (Szabo, 2000). Exercise is associated with a number of positive health outcomes. Physical activity is often incorporated into alcohol treatment programs, and exercise training is effective in helping to alleviate alcohol dependency (Gutgesell & Canterbury, 1999). Additionally, exercise has been found to significantly increase abstinence rates, ease withdrawal symptoms, and reduce symptoms of anxiety and depression among individuals with substance use disorders (Wang, Wang, Wang, Li, & Zhou, 2014). When exploring substance abuse among high school and college athletes, however, participation on sports teams is associated with higher use of alcohol but lower use of cigarettes and illegal drugs than non‐athletes (Lisha & Sussman, 2010). Thus, sport participation may serve a protective function against the use of cigarettes and illegal drugs. With regard to performance‐enhancing substances, anabolic–androgenic steroid use is ­particularly salient in the context of sport and exercise. Athletes are more likely to use anabolic– androgenic steroids than non‐athletes (Sagoe, Molde, Andreassen, Torsheim, & Pallesen, 2014). Both male gender and involvement in power sports are predictive of future anabolic–androgenic steroid use (Wichstrøm, 2006). Given the known adverse health effects of using anabolic–androgenic steroids (Momaya, Fawal, & Estes, 2015), this is an area within sport and exercise warranting continued attention. Although engaging in sport and exercise may provide a protective function relative to the use of some substances, participation in physical activity is also predictive of substance use, particularly the use of alcohol and anabolic–androgenic steroids. Several noteworthy trends have been identified connecting demographic factors and participation in sport and exercise. Physical activity tends to decline with advancing age across the lifespan (Carron et  al., 2003). From preschool‐age children to older adults, males typically

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engage in more moderate to vigorous physical activity than females. This trend may be p ­ artially due to sociocultural differences. Ethnic differences also exist relative to sedentary lifestyles, with 54% of Hispanic Americans, 52% of Black or African Americans, 46% of American Indians, 42% of Asians or Pacific Islanders, and 36% of White Americans being sedentary (USDHHS, 2000). Regarding occupation trends, blue‐collar workers are less inclined to engage in leisure time physical activity than white‐collar workers, potentially due to the impression that ­job‐related tasks are sufficient for health benefits or to the physical fatigue associated with blue‐collar jobs. Education is positively associated with leisure time physical activity, and levels of parental education are also positively related to the physical activity of their children. Healthy individuals tend to be more active than people with physical and mental health conditions (Carron et al., 2003). Frequently reported barriers to participating in physical activity include lack of time, lack of access to facilities, and lack of safe environments (Carron et al., 2003). People who have more positive attitudes toward exercise and who have strong intentions to exercise are more likely to actually engage in physical activity behaviors. In addition, people tend to do the things that they enjoy. Thus, those who enjoy participating in sport and exercise are likely to be motivated to engage in these activities. Although the majority of people know that physical activity is good for health, many people are still not regularly active. This identified gap between knowledge and behavior indicates that interventions aiming to enhance physical activity levels should focus on more than the provision of knowledge regarding the health benefits of physical activity (Carron et al., 2003). Affective responses to exercise may be an area toward which focus might be appropriately placed given the notion that people are more likely to engage in physical activity if the experience is enjoyable (Ekkekakis et al., 2011). Involvement in physical activity, sport, and exercise is related to biological, psychological, and social factors as well as overall health and well‐being. Biological, psychological, and social factors also interact to influence participation in various physical activity behaviors. Engaging in physical activity, sport, and exercise may therefore be viewed as a cause of health outcomes, as an effect of the context and social environment, and as co‐occurring with other personal health factors. Thus, a mounting body of evidence irrevocably ties sport and exercise to health psychology and highlights the importance of physical activity for maintaining physical and mental health across the lifespan.

Author Biographies Erika D. Van Dyke received her master of science degree in athletic counseling from Springfield College and is currently a doctoral student of sport and exercise psychology at West Virginia University. She is a certified group exercise instructor through the Athletics and Fitness Association of America and is a member of both the Association for Applied Sport Psychology and the American Psychological Association (APA) Society for Sport, Exercise and Performance Psychology (Division 47). Erika has coauthored a book chapter on supervision for applied sport psychology consultants in training and has presented at both national and international conferences. In 2016, she received the APA Division 47 Master’s Thesis Award for her research exploring self‐talk and competitive performance among collegiate gymnasts. During her first year of doctoral study, she was granted a Provost Fellowship based on her scholarship and presently serves as an instructor and success coach for students at the university. Judy L. Van Raalte, PhD, is professor of psychology at Springfield College, is certified consultant, Association for Applied Sport Psychology, and is listed in the US Olympic ­

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Committee Sport Psychology Registry. Dr. Van Raalte has presented at conferences in 18 ­countries and published over 100 articles in peer‐reviewed journals. Dr. Van Raalte served as president of the American Psychological Association’s Division of Exercise and Sport Psychology (Division 47) and as the vice president of the International Society of Sport Psychology. She is a fellow of the American Psychological Association and the Association for Applied Sport Psychology.

References Beets, M. W., Cardinal, B. J., & Alderman, B. L. (2010). Parental social support and the physical activity‐ related behaviors of youth: A review. Health Education & Behavior, 37, 621–644. Biddle, S. J. (2000). Emotion, mood and physical activity. In S. J. Biddle, K. R. Fox, & S. H. Boutcher (Eds.), Physical activity and psychological well‐being (pp. 63–87). London, UK: Routledge. Bland, H. W., Melton, B. F., Bigham, L. E., & Welle, P. D. (2014). Quantifying the impact of physical activity on stress tolerance in college students. College Student Journal, 48, 559–568. Boutcher, S. H. (2000). Cognitive performance, fitness, and ageing. In S. J. Biddle, K. R. Fox, & S. H. Boutcher (Eds.), Physical activity and psychological well‐being (pp. 118–129). London, UK: Routledge. Bowker, A. (2006). The relationship between sports participation and self‐esteem during early adolescence. Canadian Journal of Behavioural Science, 38, 214–229. Brewer, B. W., & Redmond, C. (2017). Psychology of sport injury. Champaign, IL: Human Kinetics. Carron, A. V., Brawley, L. R., & Widmeyer, W. N. (1990). The impact of group size in an exercise setting. Journal of Sport & Exercise Psychology, 12, 376–387. Carron, A. V., Hausenblas, H. A., & Estabrooks, P. A. (2003). The psychology of physical activity. New York, NY: McGraw‐Hill. Carron, A. V., Hausenblas, H. A., & Mack, D. (1996). Social influence and exercise: A meta‐analysis. Journal of Sport & Exercise Psychology, 18, 1–16. Carron, A. V., Widmeyer, W. N., & Brawley, L. R. (1988). Group cohesion and individual adherence to physical activity. Journal of Sport & Exercise Psychology, 10, 127–138. Carter, J. E. (2009). Disordered eating in college student‐athletes. In E. F. Etzel (Ed.), Counseling and psychological services for college student‐athletes (pp. 303–322). Morgantown, WV: Fitness Information Technology. Colcombe, S., & Kramer, A. F. (2003). Fitness effects on the cognitive function of older adults: A meta‐analytic study. Psychological Science, 14, 125–130. Colcombe, S. J., Erickson, K. I., Scalf, P. E., Kim, J. S., Prakash, R., McAuley, E., … Kramer, A. F. (2006). Aerobic exercise training increases brain volume in aging humans. Journal of Gerontology: Medical Sciences, 61A, 1166–1170. Crews, D. J., & Landers, D. M. (1987). A meta‐analytic review of aerobic fitness and reactivity to psychosocial stressors. Medicine & Science in Sports & Exercise, 19, 114–120. Duncan, T. E., & Stoolmiller, M. (1993). Modeling social and psychological determinants of exercise behaviors via structural equation systems. Research Quarterly for Exercise and Sport, 64, 1–16. Ekkekakis, P., Parfitt, G., & Petruzzello, S. I. (2011). The pleasure and displeasure people feel when they exercise at different intensities: Decennial update and progress towards a Tripartite Rationale for Exercise Intensity Prescription. Sports Medicine, 41, 641–671. Findlay, L. C., & Bowker, A. (2009). The link between competitive sport participation and self‐concept in early adolescence: A consideration of gender and sport orientation. Journal of Youth and Adolescence, 38, 29–40. Fox, K. R. (2000). The effects of exercise on self‐perceptions and self‐esteem. In S. J. H. Biddle, K. R. Fox, & S. H. Boutcher (Eds.), Physical activity and psychological well‐being (pp. 88–117). London, UK: Routledge.

Sport and Exercise in Health Psychology

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Fox, L., Rejeski, W., & Gauvin, L. (2000). Effects of leadership style and group dynamics on enjoyment of physical activity. American Journal of Health Promotion, 14, 277–283. Gutgesell, M., & Canterbury, R. (1999). Alcohol usage in sport and exercise. Addiction Biology, 4, 373–383. Hausenblas, H. A., & Carron, A. V. (1999). Eating disorder indices and athletes: An integration. Journal of Sport & Exercise Psychology, 21, 230–258. Hausenblas, H. A., & Fallon, E. A. (2006). Exercise and body image: A meta‐analysis. Psychology & Health, 21, 33–47. Hausenblas, H. A., & Symons Downs, D. (2001). Comparison of body image between athletes and nonathletes: A meta‐analytic review. Journal of Applied Sport Psychology, 13, 323–339. Hausenblas, H. A., & Symons Downs, D. (2002). How much is too much? The development and validation of the Exercise Dependence Scale. Psychology & Health, 17, 387–404. Jayakody, K., Gunadasa, S., & Hosker, C. (2014). Exercise for anxiety disorders: Systematic review. British Journal of Sports Medicine, 48, 187–196. Johnson, K. E., Kubik, M. Y., & McMorris, B. J. (2011). Prevalence and social‐environmental correlates of sports team participation among alternative high school students. Journal of Physical Activity & Health, 8, 606–612. Koltyn, K. (2000). Analgesia following exercise: A review. Sports Medicine, 29, 85–98. Leith, L. M. (2010). Foundations of exercise and mental health (2nd ed.). Morgantown, WV: West Virginia University. Leppänen, M., Aaltonen, S., Parkkari, J., Heinonen, A., & Kujala, U. (2014). Interventions to prevent sports related injuries: A systematic review and meta‐analysis of randomised controlled trials. Sports Medicine, 44, 473–486. Levine, M. D., Marcus, M. D., & Moulton, P. (1996). Exercise in the treatment of binge eating disorder. The International Journal of Eating Disorders, 19, 171–177. Lisha, N. E., & Sussman, S. (2010). Relationship of high school and college sports participation with alcohol, tobacco, and illicit drug use: A review. Addictive Behaviors, 35, 399–407. McNair, D. M., Lorr, M., & Droppleman, L. F. (1971). Manual for the profile of mood states. San Diego, CA: Educational and Industrial Testing Service. Momaya, A., Fawal, M., & Estes, R. (2015). Performance‐enhancing substances in sports: A review of the literature. Sports Medicine, 45, 517–531. North, T., McCullagh, P., & Tran, Z. (1990). Effect of exercise on depression. Exercise & Sport Sciences Reviews, 18, 379–415. Price, M. S., & Weiss, M. R. (2013). Relationships among coach Leadership, peer leadership, and adolescent athletes’ psychosocial and team outcomes: A test of transformational leadership theory. Journal of Applied Sport Psychology, 25, 265–279. Raji, C. A., Merrill, D. A., Eyre, H., Mallam, S., Torosyan, N., Erickson, K. I., … Kuller, L. H. (2016). Longitudinal relationships between caloric expenditure and gray matter in the cardiovascular health study. Journal of Alzheimer’s Disease, 52(2), 719–729. doi:10.3233/JAD‐160057 Rössler, R., Donath, L., Verhagen, E., Junge, A., Schweizer, T., & Faude, O. (2014). Exercise‐based injury prevention in child and adolescent sport: A systematic review and meta‐analysis. Sports Medicine, 44, 1733–1748. Sagoe, D., Molde, H., Andreassen, C. S., Torsheim, T., & Pallesen, S. (2014). The global epidemiology of anabolic‐androgenic steroid use: A meta‐analysis and meta‐regression analysis. Annals of Epidemiology, 24, 383–398. doi:10.1016/j.annepidem.2014.01.009 Santi, G., Bruton, A., Pietrantoni, L., & Mellalieu, S. (2014). Sport commitment and participation in masters swimmers: The influence of coach and teammates. European Journal of Sport Science, 14, 852–860. Schumacher Dimech, A., & Seiler, R. (2011). Extra‐curricular sport participation: A potential buffer against social anxiety symptoms in primary school children. Psychology of Sport & Exercise, 12, 347–354. Spink, K. S., & Carron, A. V. (1994). Group cohesion effects in exercise classes. Small Group Research, 25, 26–42. doi:10.1177/1046496494251003

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Szabo, A. (2000). Physical activity as a source of psychological dysfunction. In S. J. H. Biddle, K. R. Fox, & S. H. Boutcher (Eds.), Physical activity and psychological well‐being (pp. 130–153). London, UK: Routledge. Wagnsson, S., Lindwall, M., & Gustafsson, H. (2014). Participation in organized sport and self‐esteem across adolescence: The mediating role of perceived sport competence. Journal of Sport & Exercise Psychology, 36, 584–594. Wang, D., Wang, Y., Wang, Y., Li, R., & Zhou, C. (2014). Impact of physical exercise on substance use disorders: A meta‐analysis. PLoS One, 9, 1–15. doi:10.1371/journal.pone.0110728 Wichstrøm, L. (2006). Predictors of future anabolic androgenic steroid use. Medicine & Science in Sports & Exercise, 38, 1578–1583. Widmeyer, W. N., Brawley, L. R., & Carron, A. V. (1990). The effects of group size in sport. Journal of Sport & Exercise Psychology, 12, 177–190. Williamson, D. A., Netemeyer, R. G., Jackman, L. P., Anderson, D. A., Funsch, C. L., & Rabalais, J. Y. (1995). Structural equation modeling of risk factors for the development of eating disorder symptoms in female athletes. The International Journal of Eating Disorders, 17, 387–393. Youngstedt, S. D. (2000). The exercise‐sleep mystery. International Journal of Sport Psychology, 31, 241–255. Youngstedt, S. D., O’Connor, P. J., & Dishman, R. K. (1997). The effects of acute exercise on sleep: A quantitative synthesis. Sleep: Journal of Sleep Research & Sleep Medicine, 20, 203–214. USDHHS. (2000). United States Department of Health and Human Services, Washington, DC, USA.

Sexual Minority Populations and Health Mike C. Parent1 and Teresa Gobble2 Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Department of Educational Psychology, University of Texas at Austin, Austin, TX, USA 1

2

Introduction Sexual orientation and gender minority health has been a topic of interest for health r­ esearchers for decades. Sexual orientation minorities are persons whose sexual behaviors or attractions are not exclusively heterosexual. The term LGB (meaning lesbian, gay, and bisexual; the order of letters may vary) has often been used to describe such persons, but there is increasing recognition that this term does not capture a full range of potential manifestations of human sexual expression. For example, some individuals may engage in sexual behaviors with ­individuals of the same gender exclusively, or with both men and women, yet identify as ­heterosexual. Sexual orientation minority is used as a broad term, to include persons with non‐heterosexual behaviors, attractions, and identities. However, it is important to note that differences exist within this group, including differences in health disparities. Gender minorities are persons for whom a gender assigned at birth does not align with a felt sense of gender identity. A commonly used term for individuals in this group is transgender (in contrast to cisgender, or persons for whom gender assigned at birth and felt sense of gender identity match), though the more broad term of gender minority may also include other gender identities that persons may identify as, including non‐binary gender identities. Again, it is important to note that differences exist within the gender minorities group (e.g., between persons who are transitioning from female to male or from male to female and also among persons who desire different degrees of transition). Further, although sexual orientation minority persons and gender minority persons share many of the same concerns, they also have many differences between the groups that are relevant to health (Donatone & Rachlin, 2013; Moradi, Mohr, Worthington, & Fassinger, 2009).

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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The Context of Sexual Minority Health Disparities Research on health disparities among sexual orientation and gender minority persons, until relatively recently, has tended to focus on non‐heterosexual and non‐cisgender identities as themselves reflective of pathology. That is, the locus of health disparities was often placed within sexual orientation and gender minority individuals themselves. In recent decades, awareness has shifted away from viewing sexual orientation and gender minority persons as intrinsically pathologized to understanding the social context of living as a sexual orientation or gender minority individual. In light of this, research has increasingly turned toward understanding person–environment interactions and the ways in which one’s circumstances may exacerbate typical stressors or present unique stressors for sexual orientation and gender minority persons. The leading theory of the influence of social structures on the well‐being of sexual orientation and gender minority persons is the minority stress model (Meyer, 1995, 2003). The minority stress model describes the ways in which psychological distress is perpetrated by unique, chronic, and socially based stressors for sexual orientation and gender minority persons. It was initially developed to describe how heterosexism, or behavior that grants preferential treatment to heterosexual people, reinforces the idea that heterosexuality is preferred or more highly valued over minority sexual orientations. Heterosexism is also associated with homophobia or biphobia, which includes a variety of negative attitudes (e.g., anger, fear, discomfort, resentment, or disgust) that an individual may have toward sexual minority ­persons. Heterosexism or homo/biphobia may include behaviors such as discrimination against sexual orientation minority persons, for example, in employment or housing. Indeed, in much of the world and including in many parts of the United States, sexual orientation and gender minority persons may face hiring or promotion discrimination, in some cases without legal recourse for such discrimination. Heterosexism may also include experiences of violence inflicted upon an individual due to actual or perceived status as a sexual orientation or gender minority. Such experiences of violence can begin early, such as in school bullying, and persist into adulthood, such as in experiences of workplace harassment. Heterosexism and homo/biphobia may also be experienced at a social level. For example, laws may be passed that uniquely impact the well‐being of sexual and gender minority persons. Experiences of ­violence may also take on a range of manifestations but may include experiences such as being assaulted. Consistent exposure to homophobic or biphobic messages can lead to internalization of those messages, meaning that sexual orientation minority persons may feel similar feelings of fear, anger, discomfort, resentment, or disgust for themselves and other sexual orientation or gender minority persons. Although initially developed to understand the experiences of s­ exual orientation minorities, the model was quickly applied to gender minority populations and proved useful in research on the health of gender minorities (Bockting, Miner, Swinburne Romine, Hamilton, & Coleman, 2013). The minority stress model conceptualizes these and related experiences of stress as aspects of minority stress. Stress in general is a chronic engagement of taxing coping mechanisms to the extent that the individual does not have the capacity to endure further, leading to mental health and medical concerns. Social stress in particular describes the effect that social environmental events and conditions, above and beyond personal events, can have a deleterious effect on individuals. The minority stress model further emphasizes that the specific stressors faced by sexual orientation and gender minority persons are unique, chronic, and socially based. The stresses are unique, because they are based upon actual or perceived sexual orientation identity. That is, they are not experienced by persons who are, or who are perceived to be, heterosexual or cisgender. The stressors are chronic because they are not reflective of

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merely random and isolated events, but rather reflect a pervasive negative social attitude that promotes their reoccurrence across time, location, and context. Relatedly, the stressors are socially based because they are based in dominant social views within a society, which are socially constructed and maintained. It is important to note that the minority stress model emphasizes ways in which multiple minority identities can serve as both protective and risk factors for behaviors and experiences related to health disparities. For example, sources of stress and health concerns may differ significantly between gay men and lesbian women, or for sexual orientation and gender minority people of color compared with White sexual ­orientation and gender minority individuals. Indeed, the minority stress model has been applied several times to understand the experiences of ethnically and racially diverse populations since its conception in 1995 (Meyer, 1995). The minority stress framework has been instrumental in helping to understand health ­disparities within their context. Numerous health disparities exist for sexual orientation and gender minority populations. Such disparities are known to begin early, being demonstrated even among adolescents who identify as sexual orientation and gender minorities. Sexual orientation and gender minority youth report higher levels of emotional distress and depressive symptoms than their heterosexual and cisgender counterparts. These symptoms are also ­associated with elevated levels of suicidal ideation and suicide attempts, as well as self‐harm behaviors. Further, these disparities have been replicated across multiple countries, across racial/ethnic groups, and across genders. Transgender youth appear to be at specific and ­elevated risk for self‐harm (McConnell, Birkett, & Mustanski, 2015; Peterson, Matthews, Copps‐Smith, & Conard, 2017). Among adults, these health disparities persist and are linked to other negative outcomes for sexual orientation and gender minority individuals. In minority stress theory, stresses are differentiated based on whether they arise from o ­ utside or within an individual. Outside stresses include circumstances in the environment. Such ­circumstances speak to the intersectional interactions of multiple minority statuses in considering minority stress. One clear example of circumstances in the environment is poverty. Poverty would impact factors such as access to basic needs such as healthcare, access to food (i.e., in a food desert), access to transportation, or safe housing. Unfortunately, limited empirical research has been conducted on the intersection of sexual orientation and gender minority identity and poverty, from within the minority stress framework. Nevertheless, sexual orientation and gender minority persons, especially adolescents, face levels of poverty and homelessness far higher than their heterosexual and cisgender peers (Keuroghlian, Shtasel, & Bassuk, 2014). Other circumstances in the environment may also depend on the specific circumstances of the individuals involved; for example, in considering a same‐sex couple who wish to adopt, specific state laws regarding same‐sex adoption may be an especially relevant circumstance in the environment. Such laws may ebb and flow in their relevance to individuals; for example, if a same‐sex couple has adopted a child in a state in which adoption laws were being called into question, they may worry about the status of their legal rights as adoptive parents. Similarly, such stresses may arise of sexual orientation or gender minority persons when the legality of same‐sex marriage is called into question. In prior research with sexual orientation minority persons, in states in which constitutional amendments to define marriage as between a man and a woman were on the ballot, sexual orientation minority persons reported higher exposure to negative media, higher negative affect, and higher levels of distress compared with persons in states where such laws were not on the ballot (Fingerhut, Riggle, & Rostosky, 2011). Myriad other circumstances in the environment may ultimately impact the well‐being of sexual orientation and gender minority persons, including persons with multiple minority statuses or other relevant personal characteristics.

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Circumstances in the environment are inextricably tied to minority statuses. There are ­innumerable personal characteristics that a minority of persons may possess, but do not represent socially stigmatized identities (e.g., having green eyes or completely attached earlobes). Minority stress theory focuses on stresses that arise due to social influence, and thus categories of stigmatized identities are socially defined and may intersect. For example, recent research has explored ways in which sexual orientation and gender or race may intersect. Such investigations have indicated that there may be some pervasive mental health issues that cut across different groups when faced with minority stress (e.g., psychological distress or depression, substance use) but also some specific concerns that may be especially relevant to specific groups. For example, obesity, obesogenic behaviors, and resultant health conditions (e.g­., cardiovascular disease) appear to be a specific concern relevant to lesbian and bisexual women, use of club drugs or anabolic steroids may be relevant to gay and bisexual men, and risk behaviors for HIV or other sexually transmitted infections (STIs) may be relevant to men who have sex with men. Thus, specific minority identities, and the interactions among identities, may impact both which circumstances in the environment are relevant and potential outcomes of minority stress processes.

Minority Identity Because minority stress theory focuses on socially defined identities, a concept similar to minority status is minority identity and its characteristics. Individuals may differ in the extent to which their identities align with the categories into which researchers or others may desire to put them. For example, the term “men who have sex with men” was coined, in part, to capture the experiences of men who have sex with men but who may not identify as gay or bisexual. Minority identity itself is closely tied to the characteristics of minority identity, such as prominence of that identity or integration of that identity into everyday life. Such processes have implications for the health of sexual orientation and gender minority populations. For example, among men who have sex with men, the desire to be perceived by others as heterosexual has been associated with decreased likelihood of asking for an HIV test when visiting a doctor (Parent, Torrey, & Michaels, 2012). Other investigations have linked integration of sexual minority identity with lower internalized heterosexism and lower psychological distress. At the same time, outness has also been associated with more experiences of heterosexism, which may exacerbate minority stress processes. In addition, outness has not always clearly been linked to health variables, with some studies finding no relationship between outness and instances of domestic violence among lesbian and bisexual women (Balsam & Szymanski, 2005; Velez, Moradi, & Brewster, 2013). Thus, the relationships among outness or other variables related to identity salience are not straightforward. Indeed, it is possible that in many circumstances, not being out may have some costs to personal well‐being but may have ­substantial benefits as well, for example, if coming out meant a risk for being physically assaulted by family members or kicked out of one’s home. Minority identity is also associated with stress processes. In minority stress theory, these processes are conceptualized as distal (arising directly from the behaviors of others, such as harassment or discrimination) or proximal (arising directly from within oneself, such as expectations of rejection or internalized homophobia). These proximal and distal stressors have been reliably associated with myriad health concerns for sexual orientation and gender ­minority persons (Meyer, 2003).

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Finally, the minority stress model posits that coping and social support may moderate health outcomes for sexual orientation and gender minority persons. Notably, however, research has not always supported the posited roles of these variables. Some work has ­supported links between aspects of coping, such as spiritual coping, as a buffer between minority stressors and mental health outcome variables, such as substance use. Other work has demonstrated that emotional regulation mediates the relationship between minority status and depression and anxiety among sexual orientation minority adolescents (but also that sexual orientation minority adolescents demonstrate lower levels of emotional regulation than their heterosexual peers). However, other recent work has called into question the utility of concepts such as “coping” or “resilience” among minority persons who may face social or institutional discrimination, insomuch as those terms may refer to returns to baselines of emotional or cognitive content following experiences of minority stress. By conceptualizing such returns to baseline following experiences of harassment, discrimination, or violence, the impetus for change is placed on an individual experiencing such harassment, discrimination, or violence to deal with the implications of those experiences, rather than emphasizing the need for change in the social or institutional power structures that allow or even encourage those experiences. Experiences of posttraumatic growth, transformative coping, or personal growth following minority stress may be fruitful areas to explore and may lead to a more clear understanding of the relationship between minority stresses and adaptive responses (Meyer, 2015). Regarding social support, a number of studies have supported social support as a buffer of the relationship between experiences of minority stress and negative outcomes. At the same time, involvement with the LGBT community is not always synonymous with social support and has indeed, in some cases, been linked with negative health outcomes for sexual orientation and gender minority persons. For example, more often attending clubs and ­parties has been linked to increased body image concerns and substance use among gay and bisexual men (Mattison, Ross, Wolfson, & Franklin, 2001).

Minority Stressors and Health Disparities Experiences of bullying appear to be ubiquitous among sexual orientation and gender minority youth, with nearly all reporting some level of bullying or harassment. More severe forms of bullying including persistent bullying, threats of violence, or actual violence, are also common. Some of these instances of violence can result in physical harm, and sexual orientation and gender minority youth report that often these instances are either not reported to school officials or law enforcement, and if they are, that nothing is done about the behavior. Both harassment and violence are known to persist into adulthood, with sexual orientation and gender minority youth reporting workplace harassment and discrimination in housing or employment. Some disparities appear to be particularly relevant for specific groups within the sexual and gender minority umbrella. In particular, transgender persons may be especially vulnerable to experiences of violence (Berlan, Corliss, Field, Goodman, & Austin, 2010; Kim & Leventhal, 2011). As well, sexual orientation and gender minority persons who are sex workers are also at elevated risk for exposure to violence, again ­especially including transgender persons and especially male‐to‐female transgender persons (Nemoto, Bödeker, & Iwamoto, 2011). Use of substances is also elevated among sexual orientation and gender minority persons, and this finding has been replicated in several countries. Rates of use of all substances are

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e­levated among gender and sexual orientation minority adolescents, though rates of hard drugs and club drugs are markedly elevated, and rates of smoking tobacco are also markedly higher among sexual orientation and gender minority adolescents compared with their cisgender counterparts. This elevation is occurring concurrently with marked drops in smoking behaviors in the general population. Regarding substance use in general, among adolescents, girls appear to have somewhat higher risk for substances than boys, and bisexual adolescents appear to be at somewhat greater risk than gay/lesbian adolescents. Gay and bisexual adolescent boys also appear to be at specific risk for use of anabolic–androgenic steroids. Among adults, rates of substance use remain elevated, including use of tobacco, alcohol, steroids, recreational drugs, and club drugs. Use of such substances may be related to attempts to cope with stresses such as discrimination and violence but also potentially linked to aspects of community engagement. For example, use of illicit substances is common in clubs and circuit parties (large dance parties). As well, use of steroids may facilitate development of a highly muscular body, which facilitates access to a larger number of potential sexual partners (and, potentially, increases risk for exposure to STIs). Among female‐to‐male transgender persons, use of steroids may also facilitate development of a highly masculinized physique, potentially increasing “passing” as male and decreasing risks associated with being perceived as transgender (Guss, Williams, Reisner, Austin, & Katz‐Wise, 2017; Halkitis, Moeller, & DeRaleau, 2008; Lee, Matthews, McCullen, & Melvin, 2014; Marshal et al., 2008). Sexual risk behaviors are also relevant to sexual orientation and gender minority individuals. Most research on sexual health behaviors has focused on boys and men and potential contraction or spread of STIs. Within this domain, health disparities have been found to emerge early. Adolescent gay and bisexual boys report greater risky sexual behaviors than their heterosexual counterparts, with some studies finding bisexual adolescent boys to be at particular risk. These risks persist into adulthood for gay and bisexual men and appear to be exacerbated by use of substances and engaging in sex while intoxicated. Most research in this domain focuses on risk for HIV infection, and HIV rates remain elevated among gay and bisexual men. Although use of pre‐exposure prophylaxis (PrEP) (i.e., medication taken to drastically lower the risk of HIV infection should exposure occur) is increasingly common among men who have sex with men, use of PrEP does not protect individuals from other STIs, and rates of STIs among men who have sex with men on PrEP remain elevated (Rosario et al., 2013; Scott & Klausner, 2016). Obesity is also a concern among sexual orientation and gender minority persons. This ­disparity appears to be most prevalent among lesbian and bisexual women and appears to arise relatively early in life as adolescent girls who identify as lesbian and bisexual report higher rates of obesity compared with heterosexual peers. Obesity, and engagement in obesogenic behaviors, may interact with other stressors such as unemployment or underemployment, further complicating health status. However, obesity is not markedly elevated among gay and bisexual men or among transgender persons. The precise cause of specific health disparities among sexual orientation minority women is not known. Indeed, predictors of overweight status and obesity among sexual orientation minority women and heterosexual women are similar (Bowen, Balsam, & Ender, 2008; Yancey, Cochran, Corliss, & Mays, 2003). Gender minority persons may also face specific health concerns. Some, though not all, transgender persons may seek to undertake aspects of gender transition. Gender transition may involve numerous surgical and nonsurgical procedures. Gender transition processes may be social and legal changes, such as legal adoption of a new name; nonmedical alterations of physical appearance, such as changes in hair style, body hair management, or clothing choices; medical and not surgical, such as use of exogenous hormones; and medical and surgical.

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Medical and surgical changes also take on a range of manifestations, including breast r­ eductions or breast implants, alterations to facial bone structure, and genital reconfiguration. Importantly, not all transgender persons wish to undergo all aspects of gender transition. Research supports that completing desired aspects of gender transition is associated with improvements in mental health and well‐being. However, numerous barriers exist to transition processes: primarily, the cost of the procedures and access to competent care providers. Due to these barriers, some gender minority individuals may seek to undergo transition processes outside of professional medical supervision. Such processes may include unsupervised use of illicit hormones or injection with silicone that may result in infection (Murad et  al., 2010; Sanchez, Sanchez, & Danoff, 2009; Wilson, Rapues, Jin, & Raymond, 2014).

Discussion Sexual orientation and gender minority persons face a range of health disparities. The study of health disparities within this population has moved beyond views of sexual orientation and gender minority status as intrinsically pathological toward a contextual understanding of social and cultural origins of health disparities within this population. To this end, the minority stress model has been instrumental in framing our understanding of unique, chronic, and socially based stresses that may contribute to health disparities among sexual orientation and gender minority populations. Still, many disparities exist across health behaviors including substance use, obesity, and sexual risk behaviors, and many other disparities affect specific groups within the umbrella of sexual orientation and gender minority, and many opportunities exist for ­system‐ and individual‐level intervention and future research. It is imperative to understand how public policy related to health impacts the well‐being of sexual orientation and gender minority populations. Within minority stress theory, such a focus would aim to reduce the institutional or structural supports for distal causes of health disparities for sexual orientation and gender minority individuals. For example, enactment of anti‐same‐sex marriage laws in the United States (prior to the Supreme Court ruling in favor of marriage quality across the country) has been associated with negative impacts on the m ­ ental health and well‐being of sexual minority persons (Riggle, Rostosky, & Horn, 2010). Similar research has explored how anti‐bullying legislation impacts experiences of bullying among sexual minority youth (Hatzenbuehler, Schwab‐Reese, Ranapurwala, Hertz, & Ramirez, 2015), though research indicates that specific implementations of such laws by teachers and in schools may affect their impact (Van Wormer & McKinney, 2003). It is important to explore ways in which public policy can be used to reduce health disparities, including ways in which individuals who work in the front lines of implementing such policy may be empowered to better advocate for sexual orientation and gender minority persons. The minority stress model also emphasizes the importance of examining proximal, or within‐person, stressors related to health disparities. Such stressors may include expectations of rejection, internalized stigma, and concealment of sexual orientation (Meyer, 1995). Despite the large body of research on minority stress, very limited empirical research on the treatment of stress among sexual orientation and gender minority populations has been ­undertaken. Although some treatment guidelines for sexual orientation and gender minority populations have been developed (Pachankis, Hatzenbuehler, Rendina, Safren, & Parsons, 2015), there have been very few trials of interventions that specifically address constellations of minority stress. Studies that have been conducted have tended to be relatively low quality

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(e.g., including no‐treatment wait list groups as controls, having small sample sizes, and ­having relatively homogeneous samples). It is important to extend extant work on minority stress beyond theoretical investigations of ­relations among constructs, which have generally been well established in the literature, and integrate these constructs with empirically supported interventions to provide quality, p ­ atient‐centered care to sexual orientation and gender minority individuals.

Author Biographies Dr. Mike C. Parent earned his PhD in Counseling Psychology from the University of Florida in 2013. He is now an assistant professor in counseling psychology and counselor education at the University of Texas at Austin. His program of research focuses on gender, sexuality, and behavioral health, as well as professional issues in psychology. He is the author of more than 40 peer‐reviewed publications and has received numerous awards for his research and mentorship of students. Teresa Gobble completed her BS in Psychology at the University of Central Florida. She is a doctoral student in the Counseling Psychology Program at the University of Texas–Austin. Her research focus includes examining risk factors for suicide in sexual and gender minority populations.

References Balsam, K. F., & Szymanski, D. M. (2005). Relationship quality and domestic violence in women’s same‐sex relationships: The role of minority stress. Psychology of Women Quarterly, 29, 258–269. Berlan, E. D., Corliss, H. L., Field, A. E., Goodman, E., & Austin, S. B. (2010). Sexual orientation and bullying among adolescents in the growing up today study. The Journal of Adolescent Health, 46, 366–371. Bockting, W. O., Miner, M. H., Swinburne Romine, R. E., Hamilton, A., & Coleman, E. (2013). Stigma, mental health, and resilience in an online sample of the US transgender population. American Journal of Public Health, 103, 943–951. Bowen, D. J., Balsam, K. F., & Ender, S. R. (2008). A review of obesity issues in sexual minority women. Obesity, 16, 221–228. Donatone, B., & Rachlin, K. (2013). An intake template for transgender, transsexual, genderqueer, gender nonconforming, and gender variant college students seeking mental health services. Journal of College Student Psychotherapy, 27, 200–211. Fingerhut, A. W., Riggle, E. D. B., & Rostosky, S. S. (2011). Same‐sex marriage: The social and psychological implications of policy and debates. Journal of Social Issues, 67, 225–241. Guss, C. E., Williams, D. N., Reisner, S. L., Austin, S. B., & Katz‐Wise, S. L. (2017). Disordered weight management behaviors, nonprescription steroid use, and weight perception in transgender youth. Journal of Adolescent Health, 60, 17–22. Halkitis, P. N., Moeller, R. W., & DeRaleau, L. B. (2008). Steroid use in gay, bisexual, and nonidentified men‐who‐have‐sex‐with‐men: Relations to masculinity, physical, and mental health. Psychology of Men & Masculinity, 9, 106–115. Hatzenbuehler, M. L., Schwab‐Reese, L., Ranapurwala, S. I., Hertz, M. F., & Ramirez, M. R. (2015). Associations between antibullying policies and bullying in 25 states. JAMA Pediatrics, 169, e152411–e152411. Keuroghlian, A. S., Shtasel, D., & Bassuk, E. L. (2014). Out on the street: A public health and policy agenda for lesbian, gay, bisexual, and transgender youth who are homeless. American Journal of Orthopsychiatry, 84, 66–72.

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Kim, Y. S., & Leventhal, B. (2011). Bullying and suicide. A review. International Journal of Adolescent Medicine and Health, 20, 133–154. Lee, J. G. L., Matthews, A. K., McCullen, C. A., & Melvin, C. L. (2014). Promotion of tobacco use cessation for lesbian, gay, bisexual, and transgender people. American Journal of Preventive Medicine, 47, 823–831. Marshal, M. P., Friedman, M. S., Stall, R., King, K. M., Miles, J., Gold, M. A., … Morse, J. Q. (2008). Sexual orientation and adolescent substance use: A meta‐analysis and methodological review. Addiction, 103, 546–556. Mattison, A. M., Ross, M. W., Wolfson, T., & Franklin, D. (2001). Circuit party attendance, club drug use, and unsafe sex in gay men. Journal of Substance Abuse, 13, 119–126. McConnell, E. A., Birkett, M. A., & Mustanski, B. (2015). Typologies of social support and associations with mental health outcomes among LGBT youth. LGBT Health, 2, 55–61. Meyer, I. H. (1995). Minority stress and mental health in gay men. Journal of Health and Social Behavior, 36, 38–56. Meyer, I. H. (2003). Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: Conceptual issues and research evidence. Psychological Bulletin, 129, 674–697. Meyer, I. H. (2015). Resilience in the study of minority stress and health of sexual and gender minorities. Psychology of Sexual Orientation and Gender Diversity, 2, 209–213. Moradi, B., Mohr, J. J., Worthington, R. L., & Fassinger, R. E. (2009). Counseling psychology research on sexual (orientation) minority issues: Conceptual and methodological challenges and opportunities. Journal of Counseling Psychology, 56, 5–22. Murad, M. H., Elamin, M. B., Garcia, M. Z., Mullan, R. J., Murad, A., Erwin, P. J., & Montori, V. M. (2010). Hormonal therapy and sex reassignment: A systematic review and meta‐analysis of quality of life and psychosocial outcomes. Clinical Endocrinology, 72, 214–231. Nemoto, T., Bödeker, B., & Iwamoto, M. (2011). Social support, exposure to violence and transphobia, and correlates of depression among male‐to‐female transgender women with a history of sex work. American Journal of Public Health, 101, 1980–1988. Pachankis, J. E., Hatzenbuehler, M. L., Rendina, H. J., Safren, S. A., & Parsons, J. T. (2015). LGB‐ affirmative cognitive‐behavioral therapy for young adult gay and bisexual men: A randomized ­controlled trial of a transdiagnostic minority stress approach. Journal of Consulting and Clinical Psychology, 83, 875–889. Parent, M. C., Torrey, C., & Michaels, M. S. (2012). “HIV testing is so gay”: The role of masculine gender role conformity in HIV testing among men who have sex with men. Journal of Counseling Psychology, 59, 465–470. Peterson, C. M., Matthews, A., Copps‐Smith, E., & Conard, L. A. (2017). Suicidality, self‐harm, and body dissatisfaction in transgender adolescents and emerging adults with gender dysphoria. Suicide and Life‐threatening Behavior, 47, 475–482. Riggle, E. D., Rostosky, S. S., & Horne, S. G. (2010). Psychological distress, well‐being, and legal ­recognition in same‐sex couple relationships. Journal of Family Psychology, 24, 82–86. Rosario, M., Corliss, H. L., Everett, B. G., Reisner, S. L., Austin, S. B., Buchting, F. O., & Birkett, M. (2013). Sexual orientation disparities in cancer‐related risk behaviors of tobacco, alcohol, sexual behaviors, and diet and physical activity: Pooled Youth Risk Behavior Surveys. American Journal of Public Health, 104, 245–254. Sanchez, N. F., Sanchez, J. P., & Danoff, A. (2009). Health care utilization, barriers to care, and hormone usage among male‐to‐female transgender persons in New York City. American Journal of Public Health, 99, 713–719. Scott, H. M., & Klausner, J. D. (2016). Sexually transmitted infections and pre‐exposure prophylaxis: Challenges and opportunities among men who have sex with men in the US. AIDS Research and Therapy, 13, 1–5. Van Wormer, K., & McKinney, R. (2003). What schools can do to help gay/lesbian/bisexual youth: A harm reduction approach. Adolescence, 38, 409–420. Velez, B. L., Moradi, B., & Brewster, M. E. (2013). Testing the tenets of minority stress theory in ­workplace contexts. Journal of Counseling Psychology, 60, 532–542.

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Wilson, E., Rapues, J., Jin, H., & Raymond, H. F. (2014). The use and correlates of illicit silicone or “fillers” in a population‐based sample of transwomen, San Francisco, 2013. The Journal of Sexual Medicine, 11, 1717–1724. Yancey, A. K., Cochran, S. D., Corliss, H. l., & Mays, V. M. (2003). Correlates of overweight and obesity among lesbian and bisexual women. Preventive Medicine, 36, 676–683.

Suggested Reading Frost, D. M., Lehavot, K., & Meyer, I. H. (2015). Minority stress and physical health among sexual minority individuals. Journal of Behavioral Medicine, 38, 1–8. Meyer, I. H., & Northridge, M. E. (2007). The health of sexual minorities. New York, NY: Springer. Saewyc, E. M. (2011). Research on adolescent sexual orientation: Development, health disparities, stigma, and resilience. Journal of Research on Adolescence, 21(1), 256–272.

Ethics Issues in the Integration of Complementary, Alternative, and Integrative Health Techniques into Psychological Treatment Jeffrey E. Barnett College of Arts and Sciences, Loyola University Maryland, Baltimore, MD, USA

Complementary, alternative, and integrative health techniques are widely used in America today, and their use has increased significantly in recent years. With the wide range of ­complementary and alternative medicine (CAM) practices currently being used in the United States, it is quite reasonable to assume that many psychologists will come in contact with patients who either are presently using one or more CAM techniques or who may benefit from the use of CAM interventions in addition to any mental health treatment to be ­provided. In fact, many individuals who value psychotherapy and seek it out are likely to also value and utilize CAM as well (Knaudt, Connor, Weisler, Churchill, & Davidson, 1999). Elkins, Marcus, Rajab, and Durgam (2005) found that 64% of psychotherapy clients they surveyed reported also using at least one CAM modality. Further, many psychotherapy patients may ask for or be open to the use of various CAM treatments. These circumstances may create both opportunities and ethics challenges for psychologists. Relevant ethics issues to be addressed include clinical competence, informed consent, boundaries and multiple relationships, consultation and cooperation with other health professionals, advertising and public statements, integrating CAM techniques with psychological treatment, and making appropriate and effective referrals.

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Clinical Competence Psychologists are required to provide only those services for which they possess the necessary education, training, and supervised clinical experience to ensure that patients are not harmed and hopefully that patients are provided with the highest possible quality of care (American Psychological Association [APA], 2010). Clinical competence has been defined by Epstein and Hundert (2002) as “the habitual and judicious use of communication, knowledge, technical skills, clinical reasoning, emotions, values, and reflection in daily practice for the benefit of the individual and community served” (p. 226). In essence, the competent psychologist will ­possess the knowledge, skills, attitudes, and values necessary, and will implement them effectively, to provide care that maximally benefits each patient (Haas & Malouf, 2005). Possessing the competence necessary to ensure that patients receive appropriate treatment is consistent with the aspirational general principles of the APA Ethics Code (APA, 2010) to include beneficence (doing good and providing benefit to patients), non‐maleficence (avoiding harm to patients), and fidelity (fulfilling our obligations to our patients). It also is important to note that competence is not a static phenomenon; our knowledge and skills may degrade over time, and there will continually be new developments that may render our knowledge and skills obsolete. Thus, consistent with Standard 2.01, Boundaries of Competence (APA, 2010), psychologists “undertake relevant education, training, supervised experience, consultation or study” on an ongoing basis to ensure they maintain their competence and clinical effectiveness (p. 5). With the great prevalence of CAM in our society today and with the significant percentage of the population that engages in the use of at least one CAM modality, an argument can be made that every practicing psychologist should possess at least general knowledge of CAM and its most widely used modalities to be considered competent and compliant with the APA Ethics Code (APA, 2010). This is true not just for those psychologists who may wish to integrate various CAM modalities into their clinical work with patients, but for all psychologists in clinical practice. Psychologists should be familiar with the current research literature on the clinical effectiveness, uses, contraindications, common side effects and interactions with other treatments, and mechanisms of action of the CAM modalities most likely to be used by our patients. It is possible that many patients who seek out psychological treatment will already be ­utilizing one or more CAM modalities, often without medical supervision and of their own initiative. For many patients, what may seem like a harmless and “natural” practice may in fact bring with it great potential for harm. Many “natural” supplements may be quite dangerous and bring with them great risk if used inappropriately or in combination with prescription medications (Clauson, Santamarina, & Rutledge, 2008). Psychologists who are knowledgeable about CAM and who ask all their patients about the use of any CAM modalities can apply their knowledge to promote their patients’ best interests. This is especially important in light of the finding that upward of 35% of all psychotherapy patients use at least one CAM modality without informing their psychotherapist of this fact (Elkins et al., 2005). Patients may “self‐prescribe” various over‐the‐counter herbal substances that may interact with prescription medications and other over‐the‐counter substances they may be using with untoward effects. Knowledgeable psychologists can educate their patients about various CAM modalities and their appropriate uses as well as their relative risks and benefits. These psychologists may also be familiar with common side effects of CAM treatments that may easily be overlooked or misdiagnosed as mental health difficulties by those who are unfamiliar with

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them. This knowledge can lead the psychologist to provide appropriate treatment to patients and, when indicated, refer these patients to their primary care physician to help ensure that medical conditions and treatments are managed appropriately. Psychologists may also have patients they evaluate and treat who may benefit from the use of one or more CAM modalities. This form of integrative healthcare may be in many patients’ best interest. Examples may include pain patients who may benefit from the use of hypnosis, biofeedback, acupuncture, or massage therapy in addition to ongoing psychotherapy and ­ patients suffering from depression who may also benefit from yoga, meditation, dance movement therapy, aromatherapy, or the integration of spirituality and prayer with their ongoing psychotherapy. Accordingly, psychologists will need to possess two forms of clinical competence to effectively integrate CAM modalities with the psychological treatments they provide. Psychologists will first need to possess knowledge about the various CAM modalities to know their uses, limitations, risks, and benefits. This will help psychologists know if a particular CAM modality may be potentially helpful for treating a patient’s presenting problems. They will also need to complete the training necessary to be a competent practitioner of the CAM modality in question. Some CAM modalities require extensive education and supervised clinical experience as well as certification or licensure for a practitioner to be authorized to use that modality with patients. Examples of this include acupuncture, chiropractic, and massage therapy. Others may require extensive education and training to develop needed competence, but they do not require certification or licensure to practice them (e.g., hypnosis, biofeedback, yoga) but that are available as a means of demonstrating competence to the public. Finally, some forms of CAM, such as progressive muscle relaxation, have been well integrated into psychological practice with many psychologists developing competence in their use in the course of their training as psychologists. Being a competent professional also means knowing the limits of one’s competence. Being able to assess each patient’s treatment needs and when not personally possessing needed clinical competence, making referrals to appropriately trained colleagues is in our patients’ best interest. By being knowledgeable about the various CAM modalities and the training and expertise needed to practice them competently, we can then make referrals to appropriately trained and experienced professionals to best meet our patients’ treatment needs.

Informed Consent Informed consent involves providing patients with the information necessary so that they may make an informed decision about participating in the professional services being offered. This involves sharing information with patients at the outset of treatment about the treatment being proposed, reasonably available options and alternatives and their relative risks and benefits, and other relevant information that is likely to impact their decision to participate (Standard 10.01, Informed Consent to Therapy, APA, 2010). Thus, as has been highlighted, psychologists must be knowledgeable about widely available and empirically supported treatments for patients’ presenting problems. This includes knowledge of CAM and the literature on the effectiveness and limitations of each modality so that this information can be shared with patients so they will have the information necessary to make informed decisions about the options available for their treatment. Valid informed consent necessitates that patients have information provided to them v­ erbally and in writing that it be presented in a manner that renders it “reasonably understandable” to

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the patient, and we must actively ensure their understanding of the information being p ­ resented to them (APA, 2010, p. 6). Merely providing patients with a document to read and sign or asking them if they have any questions after all the information is shared with them is not ­sufficient. Further, consent must be given by the patient voluntarily and without any coercion or undue influence. Finally, the patient must be competent to give consent. This means that patients must have the cognitive ability to understand the information shared and they must have the legal right to give consent (e.g., an adult and not a minor). It is also important to note that informed consent should not be considered a one‐time event. Rather, it is an ongoing process that should be updated through open discussion with the patient anytime changes to the course of treatment are recommended or are under consideration. So, anytime a new treatment modality is suggested to the patient, a collaborative discussion should occur so that the patient may make the most informed decision possible about it. This very appropriately may occur after a patient’s psychological treatment begins, and one or more CAM modalities are considered as means of assisting the patient to achieve agreed upon treatment goals.

Boundaries and Multiple Relationships Boundaries provide ground rules and structure for the professional relationship to help ensure that patients are not exploited or harmed. Examples of boundaries that exist include touch, self‐disclosure, time, space, location, and gifts (Pope & Keith‐Spiegel, 2008). Boundaries may be avoided, crossed, or violated. To avoid a boundary is to never traverse it such as never engaging in touch with a patient, never engaging in any self‐disclosure, and never accepting a gift from a patient under any circumstances. A boundary crossing is a clinically relevant and appropriate boundary incursion that is accepted by the patient and motivated by the patient’s treatment needs and not the psychologist’s personal needs. Examples include extending the time of a treatment session for a patient who is in crisis and holding a treatment session in a patient’s home such as for a homebound patient who might otherwise not be able to receive needed treatment. In contrast, a boundary violation holds great potential for the exploitation of, and harm to, the patient, may not be welcomed by the patient, may not be motivated by and relevant to the patient’s treatment needs, does not meet prevailing community standards, and violates the ethics code (Smith & Fitzpatrick, 1995). A multiple relationship constitutes entering into a secondary relationship with a patient in addition to the treatment relationship. This may include social, personal, and business relationships, among others. Inappropriate multiple relationships typically include a conflict of interest and thus are likely to adversely impact the psychologist’s objectivity and judgment in decision making. Multiple relationships that do not hold a significant potential for exploitation or harm are acceptable (APA, 2010); however when unsure, consultation with an experienced and trusted colleague is recommended. When integrating various CAM modalities into ongoing psychological treatment, it is ­possible to violate boundaries and enter into an inappropriate multiple relationship inadvertently. This may occur when endeavoring to meet the patient’s treatment needs while overlooking how the provision of certain psychological and CAM treatments should not be provided by the same individual. For example, a healthcare professional may be a licensed psychologist and a licensed massage therapist. A patient being treated in psychotherapy may also benefit from massage therapy. But for one individual to provide both services to a patient would likely

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constitute an inappropriate multiple relationship with boundary violations occurring. While the boundary of touch may be appropriately crossed such as with a handshake or perhaps a hug to a grieving patient, to engage in the amount and type of physical contact involved in massage therapy holds great potential to violate the parameters of the psychotherapy relationship and process. While a licensed psychologist who is also a licensed massage therapist may appropriately provide psychotherapy and massage therapy to different patients, to provide both to a patient would be ill advised. Psychologists should keep in mind that multiple relationships are not mandatory. Each situation is different, and we must consider each patient’s treatment needs and the reasonably available options and alternatives for meeting them. While a patient may need a particular treatment service, this does not necessarily mean we are the ones who must provide it. Being knowledgeable of competent and credentialed CAM providers in the local area will be important so that appropriate referrals can be made for needed treatment. Thinking (erroneously) that we are the only ones who can meet a patient’s needs can lead to impaired judgment and faulty decision making. Psychologists should also be mindful of conflict of interest situations such as financial motivations that may lead us to provide services to patients when a referral to another professional would be in their best interest. Again, psychologists should be aware of their motivations and keep their patients’ best interests in mind.

Consultation and Cooperation With Other Professionals As has been mentioned, psychologists should work collaboratively with professional colleagues in an effort to meet each patient’s treatment needs. When we are not able to meet a patient’s treatment needs personally, either due to limits to our clinical competence or the potential for an inappropriate multiple relationship, referrals to appropriately trained colleagues are essential. The APA Ethics Code (APA, 2010) requires that psychologists avoid multiple relationships and conflicts of interests that may impair their objectivity and judgment. Yet, knowing when one’s objectivity and judgment are impaired may be very challenging as individuals in general and health professionals in particular tend to be very ineffective at self‐assessing our own abilities accurately (Dunning, Heath, & Suls, 2004). In these situations it is essential that we ­consult with colleagues who can provide an honest assessment of our judgment, functioning, and decision making. We must be open and honest with these colleagues, neither withholding nor distorting relevant information, to ensure that the consultation can be helpful so that patients receive the most appropriate treatment possible. Consultation with colleagues is also relevant to finding the most appropriate treatment for patients as well as to ensure we personally are providing patients with optimal treatment. It is recommended that psychologists utilize the expertise of colleagues whenever questions arise about which treatment might be best for a patient or if the psychologist is the most appropriate individual to provide that treatment.

Advertising and Public Statements While psychologists are entitled to advertise the clinical services they provide so that members of the public will know of and be able to access needed services, it is essential that all a­ dvertising

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be done accurately and without any false or deceptive statements; only factual information may be shared (APA, 2010). When offering CAM services, as well as any psychological services, it is important to not overstate one’s credentials or the likely benefits of treatment services offered. As it is stated in Standard 5.01, Avoidance of False or Deceptive Statements, ­psychologists should not make false or deceptive statements about “their training experience, or competence,” “their credentials,” “their services,” or “the scientific or clinical basis for, or results or degree of success of, their services” (p. 8). Thus, inappropriate and appropriate ways to advertise, respectively, include: • Compy Tent, PhD, Licensed Psychologist Internationally recognized expert in hypnosis, the number one treatment for pain control. versus • Hum Bul, PhD, ABPP, Licensed Psychologist Diplomate in Health Psychology (American Board of Professional Psychology). Practice limited to the treatment of chronic pain. To list one’s relevant earned degrees and other relevant credentials that are based on demonstrating one’s clinical competence and providing an objective description of the professional services offered meets the requirements of the APA Ethics Code’s standard on advertising and public statements (APA, 2010). In contrast, making statements that reflect opinion instead of providing objective information is less useful to the public and may constitute a deceptive or misleading statement. Standard 5, Advertising and Other Public Statements, of the APA Ethics Code (APA, 2010) requires that psychologists provide accurate and up‐to‐date information on all services ­provided, to include “the scientific or clinical basis for … their services” (p. 8). This necessitates that psychologists maintain knowledge of the relevant literature on the various CAM modalities they utilize in their practices. Also, consistent with Standard 2.04, Bases for Scientific and Professional Judgments, psychologists should only offer services, and use techniques, with sufficient scientific evidence and support for their efficacy and use. Additionally, the APA Ethics Code (APA, 2010) in Standard 5.05, Testimonials, forbids the use of testimonials from current patients or others who may be vulnerable to our undue influence. Testimonial endorsements involve utilizing statements from patients about the psychologist, the services received, and the outcomes achieved, which are used in the psychologist’s advertising such as in an advertisement or on one’s website. The use of testimonials should be considered a conflict of interest situation and an inappropriate multiple relationship. The patient who provides a testimonial becomes engaged in a new relationship with the psychologist in addition to the original treatment relationship. With regard to ethical decision making, one can ask what the motivations are for utilizing a patient’s testimonial in one’s advertising (the motivation is the promotion of one’s practice, not the patient’s treatment needs), whose needs are being met by engaging in this action (clearly, the psychologist’s needs), and how this action is relevant to the patient’s treatment needs and treatment plan (there is no therapeutic benefit from taking this action). Even if the patient suggests providing a testimonial endorsement or if the patient is happy to provide it in response to the psychologist’s request, this is a conflict of interest situation that is motivated by the psychologist’s needs, which exploits the patient’s trust and dependence. It will be very difficult for most patients to refuse such a request by their psychologist and in agreeing the nature of the treatment relationship is changed to the detriment of the patient.

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Integrating CAM into Psychological Practice As is hopefully evident, there are a number of issues and concerns relevant to integrating CAM into one’s psychological practice. It is essential that first and foremost, the psychologist is competent to provide the CAM service in question. This will include possessing the necessary education and training but may also involve possessing any needed certification and/or licensure. Before integrating the use of any CAM modalities into one’s psychology practice, credentialing processes should be researched. Barnett, Shale, Elkins, and Fisher (2014) provide a list of professional organizations, credentialing agencies and requirements, and their website addresses for each of the 14 most widely used forms of CAM. Education and training requirements may be found on many of these websites. Additionally, one may consult with experienced CAM practitioners about the knowledge, skills, and clinical experience needed to be a competent practitioner of that CAM modality. When licensure or certification is required, all relevant laws should be followed to ensure legal requirements are fulfilled. Once clinical competence is established and any required certification or licensure is obtained, it is then important to ensure that CAM modalities are applied appropriately. As with any form of treatment, CAM modalities should only be integrated into a patient’s treatment after a thorough assessment or evaluation is completed to ensure its relevance to the patient’s treatment needs. No one CAM modality can be effective for the treatment of all presenting problems and conditions. Further, CAM modalities should only be applied in areas where credible research has demonstrated their effectiveness. If clear support for the use of a particular CAM modality is not established for a patient’s treatment needs, the experimental nature of the treatment must be made clear to the patient as part of the informed consent process (APA, 2010). Once a patient’s treatment needs have been assessed and assuming that the psychologist possesses the needed competence to administer the treatment, one must consider the appropriateness of having both treatments (psychological and CAM) being offered by the same individual. As has been addressed, the administration of some CAM modalities is inconsistent with the psychotherapy relationship. One must consider boundary issues and the potential for adversely impacting the treatment relationship. Examples include integrating acupuncture, chiropractic, and massage therapy with psychotherapy. While one may be credentialed and competent in one or more of these in addition to psychotherapy, the type and amount of physical contact involved can be seen as inconsistent with prevailing professional standards for the psychotherapy relationship and process. Psychologists integrating CAM modalities into psychological treatment should also be mindful of the risk of shifting professional roles. For example, a psychologist who integrates spirituality and prayer into psychotherapy may provide great benefit to a patient. But being supportive of a patient’s personal use of prayer is very different from recommending specific Bible passages to the patient and praying together during psychotherapy sessions. One must be cautious about taking on the role of the clergy and leaving the psychologist role. Barnett and Johnson (2011) provide a decision‐making model that can be applied to make such decisions. When a patient’s needs exceed the psychologist’s clinical competence or when offering a treatment would risk boundary violations or entering into an inappropriate multiple relationship, referring the patient to a qualified colleague is recommended. Ideally, psychologists will maintain a list of competent CAM practitioners in the local area. Names can be added to or deleted from this list based on interactions with these CAM practitioners, reports from patients on their experiences with them, and from other trusted colleagues. It can be helpful to ask

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colleagues who they refer patients to as well. Additionally, one can meet CAM practitioners though involvement in local professional organizations and by attending CAM conferences, workshops, and trainings.

Conclusion Ultimately, it is each patient’s welfare and best interests that should guide our decision m ­ aking. Psychologists should respect patients’ beliefs and preferences, be cautious about taking on multiple relationships and avoid conflict of interest situations, continually seek to maintain their clinical competence, and provide patients with needed information (truthfully and without deception or misrepresentation) to assist them to make informed decisions about participation in CAM treatments. When ethical dilemmas arise, the use of a decision‐making process that includes self‐reflection, consideration of the APA Ethics Code (both the aspirational ­general principles and the enforceable ethical standards), and consultation with colleagues is recommended to help ensure that patients receive the best care possible and to minimize the risk of exploitation of or harm to them.

Author Biography Jeffrey E. Barnett, PsyD, ABPP, is a licensed psychologist as well as professor of psychology and associate dean at Loyola University Maryland. He is board certified by the American Board of Professional Psychology in clinical psychology and in clinical child and adolescent psychology, and he is a distinguished practitioner in psychology of the National Academies of Practice.

References American Psychological Association. (2010). Ethical principles of psychologists and code of conduct. Retrieved from http://www.apa.org/ethics. Barnett, J. E., & Johnson, W. B. (2011). Integrating spirituality and religion into psychotherapy: Persistent dilemmas, ethical issues, and a proposed decision‐making process. Ethics & Behavior, 21(2), 147–164. Barnett, J. E., Shale, A. J., Elkins, G., & Fisher, W. (2014). Complementary and alternative medicine for psychologists: An essential resource. Washington, DC: American Psychological Association. Clauson, K. A., Santamarina, M. L., & Rutledge, J. C. (2008). Clinically relevant safety issues associated with St. John’s wort product labels. BMC Complementary and Alternative Medicine, 8, 42. Dunning, D., Heath, C., & Suls, J. M. (2004). Flawed self‐assessment: Implications for health, education, and the workplace. Psychological Science in the Public Interest, 5, 69–106. Elkins, G., Marcus, J., Rajab, M. H., & Durgam, S. (2005). Complementary and alternative therapy use by psychotherapy clients. Psychotherapy: Theory, Research, Practice, Training, 42, 232–235. Epstein, R. M., & Hundert, E. M. (2002). Defining and assessing professional competence. Journal of the American Medical Association, 287, 226–235. Haas, L. J., & Malouf, J. L. (2005). Keeping up the good work: A practitioner’s guide to mental health ethics (4th ed.). Sarasota, FL: Professional Resource Press. Knaudt, P. R., Connor, K. M., Weisler, R. H., Churchill, L. E., & Davidson, J. R. (1999). Alternative therapy use by psychiatric outpatients. Journal of Nervous and Mental Disease, 187, 692–695.

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Pope, K. S., & Keith‐Spiegel, P. (2008). A practical approach to boundaries in psychotherapy: Making decisions, bypassing blunders, and mending fences. Journal of Clinical Psychology: In Session, 64(5), 638–652. Smith, D., & Fitzpatrick, M. (1995). Patient‐therapist boundary issues: An integrative review of theory and research. Professional Psychology: Research and Practice, 26(5), 499–506.

Suggested Reading Barnett, J. E., Shale, A. J., Elkins, G., & Fisher, W. (2014). Complementary and alternative medicine for psychologists: An essential resource. Washington, DC: American Psychological Association. Field, T. (2009). Complementary and alternative therapies research. Washington, DC: American Psychological Association. Lake, J. H., & Spiegel, D. (2007). Complementary and alternative treatments in mental health care. Washington, DC: American Psychiatric Publishing. National Center for Complementary and Integrative Health. (2015). Reports. Retrieved from https:// nccih.nih.gov/.

Alcohol Use Disorder: Long‐Term Consequences Angela K. Stevens, Brittany E. Blanchard, Molin Shi, and Andrew K. Littlefield Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA

Long‐Term Negative Health Outcomes from Alcohol Use In addition to the host of short‐term consequences associated with alcohol consumption, chronic use of alcohol, especially chronic misuse, is associated with several detrimental health outcomes. In fact, 25 chronic diseases in the International Classification of Disease (ICD‐10) are completely attributable to long‐term alcohol consumption, and chronic alcohol consumption is a component of more than 200 other diseases in the ICD‐10 (see Shield, Parry, & Rehm, 2014). Notably, alcohol shows a dose–response relation to many chronic diseases, in which the risk of disease onset and death from disease depends on the total volume of alcohol consumed (Shield et al., 2014). Further, between 1990 and 2010, alcohol was the third leading risk factor for global disease burden; however, for individuals between the ages of 15 and 49, it was the top risk factor worldwide (Lim et al., 2013). Chronic, heavy drinking has been implicated as a risk factor for several deleterious health outcomes, including high blood pressure (Fuchs, Chambless, Whelton, Nieto, & Heiss, 2001), hemorrhagic and ischemic stroke (Rehm et al., 2009), Korsakoff’s syndrome (i.e., a chronic memory disorder), and Wernicke’s encephalopathy (i.e., alcohol‐related dementia; see Pitel, Segobin, Ritz, Eustache, & Beaunieux, 2015). Indeed, alcohol‐related Korsakoff’s syndrome results from chronic, heavy alcohol consumption and thiamine (vitamin B1) ­ ­deficiency, which is a consequence of a poor diet characteristic of excessive alcohol use (Pitel et al., 2015). Excessive alcohol consumption is also associated with epilepsy, or a predisposition for epileptic seizures (Shield et  al., 2014). Research in this area has revealed a dose– response relation between alcohol consumption and the risk for epilepsy, such that repeated withdrawals from alcohol consumptions by heavy drinkers may increase risk for inducing an

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epileptic episode (see Shield et al., 2014 for more details). Other neuropsychiatric conditions associated with chronic alcohol consumption include unipolar depression, Alzheimer’s disease, and vascular dementia (Shield et al., 2014). Because chronic alcohol misuse is also linked to several types of cancers, alcohol is considered a carcinogen. Some of the cancers associated with problematic use include those of the prostate, upper digestive track (e.g., oral cavity, esophagus, and larynx), lower digestive track (e.g., colon, rectum, and liver), and breast (Rehm et  al., 2009). Further, chronic alcohol ­consumption is associated with liver diseases (e.g., liver cirrhosis, alcoholic hepatitis); in 2010, an estimated 493,300 deaths were attributed to alcohol‐related cirrhosis of the liver (Rehm, Samokhvalov, & Shield, 2013). Further, a recent meta‐analysis found that alcohol consumption increased the risk of developing pancreatitis (though the dose–response relation for acute pancreatitis differed across men and women; see Samokhvalov, Rehm, & Roerecke, 2015). Moreover, chronic alcohol use increases the likelihood of developing diabetes mellitus, a condition commonly caused by pancreatitis (Rehm et  al., 2009). Alcohol consumption affects blood pressure, and chronic alcohol misuse is predictive of hypertensive heart disease and ischemic heart disease (Rehm et al., 2009). There is also evidence that chronic alcohol use is associated with infectious diseases such as tuberculosis (World Health Organization [WHO], 2014). Although the biological evidence to support the causal link is lacking, observational research has indicated that alcohol consumption may exacerbate the disease progression of psoriasis, which is a chronic inflammatory autoimmune skin disease (Shield et al., 2014). Thus, chronic, heavy alcohol consumption puts an individual at risk for a myriad of negative physical conditions and diseases. Detrimental outcomes of alcohol consumption during pregnancy include low birth weight (Rehm et al., 2009) and the development of fetal alcohol spectrum disorders, such as fetal alcohol syndrome (FAS), alcohol‐related birth defects, and alcohol‐related neurodevelopmental disorders (see Riley, Infante, & Warren, 2011). Specifically, infants with FAS exhibit growth deficiencies, craniofacial abnormalities, and heart defects (Riley et al., 2011). Individuals with FAS experience significant problems in adaptive, academic, and behavioral functioning (Streissguth et al., 2004). More specifically, fetal alcohol effects are associated with lower levels of intelligence, disrupted school experiences, criminal behavior and/or incarceration, inappropriate sexual behavior, and alcohol/drug problems (Streissguth et al., 2004). FAS is the leading preventable (i.e., nongenetic) cause of intellectual development disorder (previously known as mental retardation) and birth defects (O’Leary et  al., 2013). In sum, alcohol ­consumption during pregnancy can lead to debilitating long‐term health‐related consequences for the offspring.

Potential Health Benefits Despite the many potential adverse consequences associated with alcohol consumption, empirical evidence suggests that light‐to‐moderate drinking may provide some health benefits. For example, some data indicated that light‐to‐moderate drinking yields a reduction in risk for coronary heart disease and stroke (Shield et al., 2014). Further, alcohol use may reduce the risk of type 2 diabetes (Shield et al., 2014) and dementia (Peters, Peters, Warner, Beckett, & Bulpitt, 2008). There is also some evidence that alcohol consumption might lower the risk of certain types of cancers, including renal cell carcinoma, Hodgkin’s lymphoma, and non‐Hodgkin’s lymphoma (see Shield et al., 2014). Despite these findings, the health benefits of moderate alcohol use have recently been called into question (see Chikritzhs et al., 2015), and the benefits may not outweigh the consequences.

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Treatment Approaches to Alcohol Use Disorders Numerous treatment approaches to AUDs are available. Though non‐exhaustive, provided below are brief summaries of several relevant, empirically supported treatments for AUDs and related problems.

Brief Interventions Brief interventions are evidence‐based practices designed to motivate individuals to understand, reduce, and/or eliminate alcohol use, usually delivered in a 5‐ to 20‐min session (Center for Substance Abuse Treatment, Substance Abuse and Mental Health Services Administration, 1999). Findings from a meta‐analytic review suggest that brief interventions are as efficacious as extended alcohol treatments (Moyer, Finney, Swearingen, & Vergun, 2002). Common brief interventions typically used on college campuses are variants of brief motivational interventions (Murphy et al., 2001), such as the Brief Alcohol Screening and Intervention for College Students (BASICS) (Dimeff, Baer, Kivlahan, & Marlatt, 1999). Brief interventions have also demonstrated effectiveness in community mental health and primary care settings (O’Donnell et al., 2014).

Motivational Interviewing (MI) Motivational interviewing (MI) is a brief, client‐centered method used in conjunction with individual therapy that has strong research support for its efficacy in treating individuals with alcohol‐related problems (Rollnick, Miller, & Butler, 2008). MI seeks to evoke self‐efficacy and motivation while encouraging individuals to use their own reasons for change to resolve ambivalence, reduce resistance, and progress in their readiness to change (Rollnick et  al., 2008). Similarly, motivational enhancement therapy, which combines MI techniques and personal feedback, has also been used to promote motivation for positive change in individuals who engage in problematic drinking (Miller, Zweben, DiClemente, & Rychtarik, 1992). Research has demonstrated strong support for the integration and use of MI as part of treatment for substance use disorders, including AUDs (see Lundahl & Burke, 2009).

Cognitive Behavioral Approaches Numerous cognitive behavioral (CB) interventions to treat alcohol dependence have been developed and empirically tested (see Witkiewitz, Marlatt, & Walker, 2005). The CB approach targets cognitive, affective, and behavioral factors related to problematic drinking. It is hypothesized that deficits in abilities to cope effectively with stress serve to maintain excessive and subsequent drinking (Morgenstern & Longabaugh, 2000). As such, CB interventions typically include standardized, performance‐based techniques, such as behavioral rehearsal and modeling, which teach individuals to have a sense of mastery in stressful situations without resorting to alcohol consumption.

Behavioral Couples Therapy (BCT) Behavioral couples therapy for AUD (ABCT) is the most extensively studied approach of ­treatment at the couple level and has strong research support. ABCT is an outpatient, conjoint treatment that may be used as an additional component of, or subsequent to, individual

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t­ herapy for those with AUDs and their partners (Epstein & McCrady, 1998). The underlying assumption posits that alcohol‐related problems and intimate relationships are reciprocal and reinforcing of the alcohol use (Powers, Vedel, & Emmelkamp, 2008). A meta‐analysis of 12 randomized controlled trials found that ABCT demonstrated a significantly greater effect compared with individual treatment alone for individuals with alcohol use problems in relationships (Powers et al., 2008).

Support Groups Although support groups such as Alcoholics Anonymous (AA) are commonly utilized in the treatment of AUDs, findings are mixed with regard to effectiveness of AA (Tonigan, Toscova, & Miller, 1996). However, some components of AA are supported by research (e.g. recovering individuals as therapists, peer‐led self‐help groups; Kownacki & Shadish, 1999). Although AA and 12‐step programs may not be effective for individuals with certain traits (e.g., severe mental illness), self‐help groups such as these represent a viable option for many individuals who are motivated to maintain abstinence (Montalto, 2016).

Pharmacotherapy The US Food and Drug Administration (FDA) has approved three pharmacological treatments for AUDs: disulfiram (e.g., Antabuse), oral and extended‐release naltrexone ­ (e.g.,  ReVia, Vivitrol), and acamprosate (e.g., Campral; Mark, Kassed, Vandivort‐Warren, Levit, & Kranzler, 2009). Although disulfiram (an aldehyde dehydrogenase inhibitor) is meant to condition an individual to avoid alcohol intake by inducing nausea when individuals consume alcohol, it appears to reduce consumption but often fails to yield abstinence (Garbutt, West, Carey, Lohr, & Crews, 1999). Naltrexone (ReVia, Vivitrol) is an opioid antagonist, which lessens the positively reinforcing effects of alcohol (Petrakis et al., 2005). Studies have demonstrated its relative effectiveness in reducing overall alcohol consumption (Petrakis et  al., 2005), as well as rate of relapse (Carmen, Angeles, Ana, & María, 2004). Naltrexone has also been shown to enhance treatment effectiveness when used in conjunction with other treatment modalities, such as psychosocial therapies or coping skills therapy (Jarosz, Miernik, Wachal, Walczak, & Krumpl, 2013). Acamprosate (Campral) is thought to enhance the inhibitory effects of the neurotransmitter gamma‐aminobutyric acid (GABA) (Hunt, 1993) and acts primarily as a functional glutamate antagonist. Meta‐analytic reviews suggest that acamprosate is also effective as a complementary treatment with different ­psychosocial interventions in treating alcohol dependence and appears to be as effective as naltrexone (Carmen et al., 2004; Garbutt et al., 1999).

Other Medical Interventions for AUDs and Related Conditions Detoxification from chronic alcohol consumption is an important first step in treatment and often requires medical supervision, given the potential fatality of alcohol withdrawal ­symptoms (i.e., delirium tremens; see Kosten & O’Connor, 2003). Benzodiazepines have demonstrated effectiveness for managing seizures and delirium related to detoxification (Kosten & O’Connor, 2003). Further, anticonvulsants (e.g., carbamazepine) and alpha‐agonists (e.g.,  clonidine) appear to decrease severity of withdrawal symptoms, and beta‐blockers reduce cravings for alcohol (Kosten & O’Connor, 2003). Indeed, medication to manage withdrawal symptoms can be delivered on a fixed schedule or as needed, though the latter

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requires more frequent monitoring yet leads to a more rapid detoxification. Without ­pharmacological intervention withdrawal symptoms are expected to peak within 72 hr after last use, but medications reduce these symptoms within hours of delivery (see Kosten & O’Connor, 2003 for more details). The cornerstone of treatment for alcoholic liver disease is abstinence, as significant improvement in disease progression can be observed in as little as 3 months. However, nutrition therapy also appears to be effective, given that the severity of malnutrition related to chronic alcohol consumption is correlated with disease severity and negative outcomes (see O’Shea, Dasarathy, & McCullough, 2010). Steroid treatment for alcoholic hepatitis has received the most attention in the extant literature, and there is evidence to suggest that a tapered dose of prednisolone over 2 weeks is most effective; however, studies often exclude individuals at an advanced stage of liver disease, which limits the generalizability of these findings (O’Shea et al., 2010). Notably, alcoholic liver disease is the second most common indicator for a liver transplant; however, given the stigma related to alcoholic liver disease (i.e., a “self‐induced” disease), less than 5% of patients with advanced liver disease resulting from chronic alcohol consumption are formally evaluated for candidacy for liver transplantation (O’Shea et al., 2010).

Conclusions AUDs are among the most commonly experienced psychiatric disorders in the United States, with especially high rates among young adults. Although many individuals who experience AUDs during adolescence and young adulthood will subsequently recover, underage alcohol use is more likely to be fatal among young people than all other illegal drugs combined (Grunbaum et  al., 2002). In addition to alcohol‐related injuries, alcohol consumption has been linked to over 200 health conditions (WHO, 2014). Given that alcohol use is normative and problematic use is common, treatment options to increase treatment involvement and retention rates, such as MI and stepped care approaches, may be especially beneficial (see Littlefield & Sher, 2009). Further, in light of data supporting the so‐called prevention paradox (i.e., most alcohol‐related harm in a given population arises within drinkers that are “low risk”; see Rossow & Romelsjo, 2006) and the health perils of acute intoxication regardless of disorder status, preventing alcohol‐related health problems through prevention policy efforts, should also be considered in addition to treatments aimed at individuals with AUDs (see Gruenewald, 2011).

Author Biographies Angela K. Stevens is currently a PhD student in clinical psychology at Texas Tech University in the Department of Psychological Sciences. Broadly, her research interests include individual differences in adult problematic substance use. Brittany E. Blanchard is currently a PhD student in clinical psychology at Texas Tech University in the Department of Psychological Sciences. Her research examines individual ­differences in alcohol and substance use, including use of protective behavioral strategies. Molin Shi is currently a PhD student in clinical psychology at Texas Tech University in the Department of Psychological Sciences. Her research focuses on the influences and c­ onsequences of substance‐related behaviors, with an emphasis on the familial, social, and acculturative impact of substance use in minority subgroups.

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Andrew K. Littlefield is currently an assistant professor at Texas Tech University in the Department of Psychological Sciences. He obtained his predoctoral training in clinical ­psychology at the University of Missouri and completed his clinical internship at the University of Mississippi Medical Center. Dr. Littlefield’s research focuses on addictive behaviors.

References Carmen, B., Angeles, M., Ana, M., & María, A. J. (2004). Efficacy and safety of naltrexone and acamprosate in the treatment of alcohol dependence: A systematic review. Addiction, 99(7), 811–828. Center for Substance Abuse Treatment, Substance Abuse and Mental Health Services Administration. (1999). Brief interventions and brief therapies for substance abuse: Treatment improvement protocol (TIP) series (DHHS Publication No. SMA 99‐3353). Rockville, MD: Author. Chikritzhs, T., Stockwell, T., Naimi, T., Andreasson, S., Dangardt, F., & Liang, W. (2015). Has the leaning tower of presumed health benefits from ‘moderate’ alcohol use finally collapsed? Addiction, 110(5), 726–727. Dimeff, L. A., Baer, J. S., Kivlahan, D. R., & Marlatt, G. A. (Eds.) (1999). Brief alcohol screening and intervention for college students (BASICS): A harm reduction approach. New York, NY: Guilford Press. Epstein, E. E., & McCrady, B. S. (1998). Behavioral couples treatment of alcohol and drug use disorders: Current status and innovations. Clinical Psychology Review, 18(6), 689–711. Fuchs, F. D., Chambless, L. E., Whelton, P. K., Nieto, F. J., & Heiss, G. (2001). Alcohol consumption and the incidence of hypertension: The atherosclerosis risk in communities study. Hypertension, 37(5), 1242–1250. Garbutt, J. C., West, S. L., Carey, T. S., Lohr, K. N., & Crews, F. T. (1999). Pharmacological treatment of alcohol dependence: A review of the evidence. JAMA, 281(14), 1318–1325. Gruenewald, P. J. (2011). Regulating availability: How access to alcohol affects drinking and problems in youth and adults. Alcohol Research and Health, 34, 248–256. Grunbaum, J. A., Kann, L., Kinchen, S. A., Williams, B., Ross, J. G., Lowry, R., & Kolbe, L. (2002). Youth risk behavior surveillance: United States, 2001. Journal of School Health, 72(8), 313–328. Hunt, W. A. (1993). Neuroscience research: how has it contributed to our understanding of alcohol abuse and alcoholism? A review. Alcoholism: Clinical and Experimental Research, 17(5), 1055–1065. Jarosz, J., Miernik, K., Wąchal, M., Walczak, J., & Krumpl, G. (2013). Naltrexone (50 mg) plus psychotherapy in alcohol‐dependent patients: A meta‐analysis of randomized controlled trials. The American Journal of Drug and Alcohol Abuse, 39(3), 144–160. Kosten, T. R., & O’Connor, P. G. (2003). Management of drug and alcohol withdrawal. New England Journal of Medicine, 348(18), 1786–1795. Kownacki, R. J., & Shadish, W. R. (1999). Does Alcoholics Anonymous work? The results from a meta‐ analysis of controlled experiments. Substance Use & Misuse, 34(13), 1897–1916. Lim, S. S., Vos, T., Flaxman, A. D., Danaei, G., Shibuya, K., Adair‐Rohani, H., … Aryee, M. (2013). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2224–2260. Littlefield, A. K., & Sher, K. J. (2009). Alcohol use disorders in young adulthood. In J. E. Grant & M. N. Potenza (Eds.), Young Adult Mental Health (pp. 292–310). New York, NY: Oxford University Press. Lundahl, B., & Burke, B. L. (2009). The effectiveness and applicability of motivational interviewing: A practice‐friendly review of four meta‐analyses. Journal of Clinical Psychology, 65(11), 1232–1245. Mark, T. L., Kassed, C. A., Vandivort‐Warren, R., Levit, K. R., & Kranzler, H. R. (2009). Alcohol and opioid dependence medications: Prescription trends, overall and by physician specialty. Drug and Alcohol Dependence, 99(1), 345–349.

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Miller, W. R., Zweben, A., DiClemente, C. C., & Rychtarik, R. G. (1992). Motivational enhancement therapy manual: A clinical research guide for therapists treating individuals with alcohol abuse and dependence (NIH Publication No. 94‐3723). Rockville, MD: National Institute on Alcohol Abuse and Alcoholism. Montalto, M. (2016). Alcoholics Anonymous: One treatment program to rule them all? Journal of Alcoholism & Drug Dependence, 3(6), 1–5. Morgenstern, J., & Longabaugh, R. (2000). Cognitive–behavioral treatment for alcohol dependence: A review of evidence for its hypothesized mechanisms of action. Addiction, 95(10), 1475–1490. Moyer, A., Finney, J. W., Swearingen, C. E., & Vergun, P. (2002). Brief interventions for alcohol problems: A meta‐analytic review of controlled investigations in treatment‐seeking and non‐ ­ treatment‐seeking populations. Addiction, 97(3), 279–292. Murphy, J. G., Duchnick, J. J., Vuchinich, R. E., Davison, J. W., Karg, R. S., Olson, A. M., … Coffey, T. T. (2001). Relative efficacy of a brief motivational intervention for college student drinkers. Psychology of Addictive Behaviors, 15(4), 373–379. O’Donnell, A., Anderson, P., Newbury‐Birch, D., Schulte, B., Schmidt, C., Reimer, J., & Kaner, E. (2014). The impact of brief alcohol interventions in primary healthcare: A systematic review of reviews. Alcohol and Alcoholism, 49(1), 66–78. O’Leary, C., Leonard, H., Bourke, J., D’Antoine, H., Bartu, A., & Bower, C. (2013). Intellectual disability: Population‐based estimates of the proportion attributable to maternal alcohol use disorder during pregnancy. Developmental Medicine & Child Neurology, 55(3), 271–277. O’Shea, R. S., Dasarathy, S., & McCullough, A. J. (2010). Alcoholic liver disease. Hepatology, 51(1), 307–328. Peters, R., Peters, J., Warner, J., Beckett, N., & Bulpitt, C. (2008). Alcohol, dementia and cognitive decline in the elderly: A systematic review. Age and Ageing, 37(5), 505–512. Petrakis, I. L., Poling, J., Levinson, C., Nich, C., Carroll, K., Rounsaville, B., & VA New England VISN I MIRECC Study Group. (2005). Naltrexone and disulfiram in patients with alcohol dependence and comorbid psychiatric disorders. Biological Psychiatry, 57(10), 1128–1137. Pitel, A. L., Segobin, S. H., Ritz, L., Eustache, F., & Beaunieux, H. (2015). Thalamic abnormalities are a cardinal feature of alcohol‐related brain dysfunction. Neuroscience & Biobehavioral Reviews, 54, 38–45. Powers, M. B., Vedel, E., & Emmelkamp, P. M. (2008). Behavioral couples therapy (BCT) for alcohol and drug use disorders: A meta‐analysis. Clinical Psychology Review, 28(6), 952–962. Rehm, J., Mathers, C., Popova, S., Thavorncharoensap, M., Teerawattananon, Y., & Patra, J. (2009). Global burden of disease and injury and economic cost attributable to alcohol use and alcohol‐use disorders. The Lancet, 373(9682), 2223–2233. Rehm, J., Samokhvalov, A. V., & Shield, K. D. (2013). Global burden of alcoholic liver diseases. Journal of Hepatology, 59(1), 160–168. Riley, E. P., Infante, M. A., & Warren, K. R. (2011). Fetal alcohol spectrum disorders: An overview. Neuropsychology Review, 21(2), 73–80. Rollnick, S., Miller, W. R., & Butler, C. (2008). Motivational Interviewing in health care: Helping patients change behavior. New York, NY: Guilford Press. Rossow, I., & Romelsjo, A. (2006). The extent of the “prevention paradox” in alcohol problem as a function of population drinking patterns. Addiction, 101(1), 84–90. Samokhvalov, A. V., Rehm, J., & Roerecke, M. (2015). Alcohol consumption as a risk factor for acute and chronic pancreatitis: A systematic review and a series of meta‐analyses. eBioMedicine, 2(12), 1996–2002. Shield, K. D., Parry, C., & Rehm, J. (2014). Chronic diseases and conditions related to alcohol use. Alcohol Research: Current Reviews, 35(2), 155–171. Streissguth, A. P., Bookstein, F. L., Barr, H. M., Sampson, P. D., O’Malley, K., & Young, J. K. (2004). Risk factors for adverse life outcomes in fetal alcohol syndrome and fetal alcohol effects. Journal of Developmental & Behavioral Pediatrics, 25(4), 228–238.

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Tonigan, J. S., Toscova, R., & Miller, W. R. (1996). Meta‐analysis of the literature on alcoholics ­anonymous: Sample and study characteristics moderate findings. Journal of Studies on Alcohol, 57(1), 65–72. Witkiewitz, K., Marlatt, G. A., & Walker, D. (2005). Mindfulness‐based relapse prevention for alcohol and substance use disorders. Journal of Cognitive Psychotherapy, 19(3), 211–228. World Health Organization. (2014). Global status report on alcohol and health. Geneva, Switzerland: World Health Organization.

Suggested Reading Li, T. K. (2008). Quantifying the risk for alcohol use and alcohol attributable health disorders: Present findings and future research needs. Journal of Gastroenterology and Hepatology, 23(s1), 2–8. Marlatt, G. A., & Witkiewitz, K. (2002). Harm reduction approaches to alcohol use: Health promotion, prevention, and treatment. Addictive Behaviors, 27(6), 867–886. Rehm, J., Room, R., Graham, K., Monteiro, M., Gmel, G., & Sempos, C. T. (2003). The relationship of average volume of alcohol consumption and patterns of drinking to burden of disease: An overview. Addiction, 98(9), 1209–1228. Room, R., Babor, T., & Rehm, J. (2005). Alcohol and public health. The Lancet, 365(9458), 519–530. Shield, K. D., Parry, C., & Rehm, J. (2014). Chronic diseases and conditions related to alcohol use. Alcohol Research: Current Reviews, 35(2), 155–173.

Body Image Assessment Kathryn E. Smith1,2, Tyler B. Mason1,3, Jason M. Lavender4, and Stephen A. Wonderlich1,2 Neuropsychiatric Research Institute, Fargo, ND, USA Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA 3 Department of Psychology, University of Southern California, Los Angeles, CA, USA 4 Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA 1

2

Body image is a complex, multidimensional construct that includes a number of interrelated facets (e.g., Yagil et al., 2015). As such, a wide range of assessments have been developed to measure these domains. Such measures differ in methodology, specificity and content, and in their targeted populations. It is therefore important to consider the targeted patient or sample, the specific nature of presenting body image issues, and the psychometric properties of measures when selecting a body image assessment. Although a comprehensive list and description of body image measures is beyond the scope of this chapter, below we briefly review and describe a variety of measures that differ in the characteristics noted above.

Methodology While the majority of body image assessments are self‐report measures, subscales related to body image are included in three widely used structured interviews for eating disorders: the Eating Disorder Examination (Fairburn & Cooper, 1993), the Structured Interview for Anorexia and Bulimic Disorders (Fichter, Herpertz, Quadflieg, & Herpertz‐Dahlmann, 1998), and the Interview for Diagnosis of Eating Disorders (Kutlesic, Williamson, Gleaves, Barbin, & Murphy‐Eberenz, 1998). Similarly, self‐report measures of global eating psychopathology include subscales that assess overvaluation of shape and weight or body dissatisfaction (e.g., Eating Disorder Examination Questionnaire, Fairburn & Beglin, 1994; Eating Disorder Inventory, Garner, 2004).

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Although many self‐report questionnaires use items scored in a Likert‐type or multiple‐ choice format, figural or schematic measures can also provide an index of self‐discrepancy related to body image. Figure rating scales require respondents to compare their current figure with an array of figures that differ in body size; the discrepancy between one’s current and ideal size provides an index of body dissatisfaction (e.g., Thomson & Gray, 1995; Williamson, Davis, Bennett, Goreczny, & Gleaves, 1989). More recently, the somatomorphic matrix was developed to assess satisfaction based on dimensions of body fat and muscularity (Gruber, Pope, Borowiecki, & Cohane, 2000) and is thought to be relevant to both males and females. In addition, in vivo methods have been applied to the measurement of body image, particularly with respect to perceptual domains (e.g., Video Distortion Method, Probst, Vandereycken, Van Coppenolle, & Pieters, 1995; Digital Photography Mirror‐Based Technique; Shafran & Fairburn, 2002).

Content Measures of body image also range in content and specificity and have previously been ­categorized based on cognitive, affective, subjective, behavioral, perceptual, and sociocultural focus (Thompson, Roehrig, Cafri, & Heinberg, 2005), although many instruments assess multiple domains. Cognitive assessments evaluate individuals’ attitudes and beliefs about the importance of physical appearance (i.e., overvaluation of shape and weight) and the extent to which they experience appearance‐related cognitions (e.g., Appearance Schemas Inventory, Cash & Labarge, 1996; Beliefs about Appearance Scale, Spangler & Stice, 2001). Affective and subjective measures generally assess emotional states that accompany body dissatisfaction (e.g., Situational Inventory of Body Image Dysphoria, Cash, 1994) or individuals’ degree of satisfaction or concern with their body image, which may be global (e.g., Body Shape Questionnaire; Cooper, Taylor, Cooper, & Fairburn, 1987) or specific (e.g., Body Parts Satisfaction Scale; Petrie, Tripp, & Harvey, 2002). In turn, behavioral indices measure c­ ommon manifestations of body dissatisfaction, such as body avoidance (e.g., Body Image Avoidance Questionnaire; Rosen, Srebnick, Saltzberg, & Wendt, 1991) or body checking (e.g., Body Checking Questionnaire; Reas, Whisenhunt, Netemeyer, & Williamson, 2002). Further, measures of perceptual components of body image (e.g., signal detection methods) provide an index of body size distortion, which is a core feature of body dysmorphic disorder and has also been observed in eating disorders. Other measures evaluate sociocultural and interpersonal factors that have been thought to contribute to body image dissatisfaction and the internalization of thin ideals (e.g., Sociocultural Attitudes Towards Appearance Scale; Thompson, Van den Berg, Roehrig, Guarda, & Heinberg, 2004; Ideal Body Internalization Scale; Stice and Agras, 1998; Physical Appearance Related Teasing Scale; Thompson, Fabian, Moulton, Dunn, & Altabe, 1991).

Specific Populations While body image concerns have been documented in a variety of medical and psychiatric populations, the majority of assessment measures have been developed and validated using adult Caucasian females. Thus, there remains a limited number of assessment measures for which psychometric properties have been evaluated in other samples (e.g., males, children, minority populations). Nevertheless, some advances have been made in the development of

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measures that specifically assess muscular body ideals that are particularly common among males (e.g., Drive for Muscularity Scale; McCreary & Sasse, 2000; Muscle Appearance Satisfaction Scale; Mayville, Williamson, White, Netemeyer, & Drab, 2002) and developmentally appropriate measures for children and adolescents (e.g., Cragun, DeBate, Ata, & Thompson, 2013; Veron‐Guidry & Williamson, 1996). Given that body image is not a static construct and may change in response to medical ­status, some measures have been developed to address body image concerns that may be particularly relevant among individuals with specific health problems, such as cancer (Hopwood, Fletcher, Lee, & Al Ghazal, 2001; Kopel, Eiser, Cool, & Carter, 1998), inflammatory bowel disease (McDermott et al., 2014), and conditions associated with disfigurement (Jewett et al., 2015; Lawrence et al., 1998). While the number of body image assessments for specific medical conditions remains limited, it has been recommended that health providers consider conducting routine screenings of body image concerns, as doing so may (a) initiate conversations about issues that patients may not otherwise voice openly, (b) identify patients currently experiencing or who are at risk for the negative sequelae of body dissatisfaction, and (c) provide insight into possible predictors of health outcomes (Pruzinsky, 2004). Thus, further development of condition‐specific measures, as well as the use of standardized screening processes in healthcare settings, has promising clinical utility. These efforts may offer more precise and idiographic measures of condition‐specific body image concerns, which could serve to inform subsequent prevention and intervention.

Conclusions There are a large number of existing assessments, including many that are freely available for use in research and clinical contexts that may be used to measure various aspects of body image. Brief assessments of overall body image (e.g., eight‐item Body Shape Questionnaire; Pook, Tuschen‐Caffier, & Brähler, 2008) and assessments developed for individuals with ­specific health conditions may be of greatest utility in clinical health settings. In sum, the use of accurate methods to assess and detect body image concerns is necessary to provide health professionals with opportunities to address such concerns and ameliorate risk for the development of related psychopathology.

Author Biographies Kathryn E. Smith, PhD, is a T32 postdoctoral research fellow at the Neuropsychiatric Research Institute. She received her BA in psychology from Macalester College and her PhD in clinical psychology from Kent State University. Her primary interests include emotion regulation and co‐occurring psychopathology in eating disorders and obesity. Tyler B. Mason, PhD, is an assistant professor in the Department of Preventative Medicine at the University of Southern California. He received his PhD in applied psychological science in 2015 and BS in psychology in 2010 from Old Dominion University. His primary research interests include etiology and treatment of binge eating and obesity. Jason M. Lavender, PhD, is an assistant research scientist in the Department of Psychiatry at the University of California, San Diego. He completed his undergraduate education at Duke University and his PhD in clinical psychology at the University at Albany, SUNY. His research addresses emotion, neurocognition, and personality in eating disorders.

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Stephen A. Wonderlich, PhD, is the Chester Fritz distinguished professor and associate chairperson in the Department of Psychiatry and Behavioral Science at the University of North Dakota, chair of Eating Disorders and codirector of the Eating Disorder and Weight Management Center at Sanford, and president and scientific director of the Neuropsychiatric Research Institute.

References Cash, T. F. (1994). The Situational Inventory of Body‐Image Dysphoria: Contextual assessment of a negative body image. Behaviour Therapist, 17, 133–133. Cash, T. F., & Labarge, A. S. (1996). Development of the Appearance Schemas Inventory: A new cognitive body‐image assessment. Cognitive Therapy and Research, 20, 37–50. Cooper, P. J., Taylor, M. J., Cooper, Z., & Fairbum, C. G. (1987). The development and validation of the body shape questionnaire. International Journal of Eating Disorders, 6, 485–494. Cragun, D., DeBate, R. D., Ata, R. N., & Thompson, J. K. (2013). Psychometric properties of the body esteem scale for adolescents and adults in an early adolescent sample. Eating and Weight Disorders, 18, 275–282. doi:10.1007/s40519‐013‐0031‐1 Fairburn, C. G., & Beglin, S. J. (1994). Assessment of eating disorders: Interview or self‐report questionnaire? International Journal of Eating Disorders, 16, 363–370. Fairburn, C. G., & Cooper, Z. (1993). The eating disorder examination. In C. G. Fairburn & G. T. Wilson (Eds.), Binge eating: Nature, assessment and treatment (pp. 317–360). New York, NY: Guilford Press. Fichter, M. M., Herpertz, S., Quadflieg, N., & Herpertz‐Dahlmann, B. (1998). Structured interview for anorexic and bulimic disorders for DSM‐IV and ICD‐10: Updated (third) revision. International Journal of Eating Disorders, 24, 227–249. Garner, D. M. (2004). EDI 3: Eating disorder inventory‐3: Professional manual. Lutz, FL: Psychological Assessment Resources. Gruber, A. J., Pope, H. G., Borowiecki, J. J., & Cohane, G. (2000). The development of the somatomorphic matrix: A bi‐axial instrument for measuring body image in men and women. Kinanthropometry, VI, 217–231. Hopwood, P., Fletcher, I., Lee, A., & Al Ghazal, S. (2001). A body image scale for use with cancer patients. European Journal of Cancer, 37, 189–197. Jewett, L. R., Malcarne, V. L., Kwakkenbos, L., Harcourt, D., Rumsey, N., Körner, A., … Heinberg, L. (2015). Development and validation of the Body Concealment Scale for Scleroderma (BCSS). Arthritis Care and Research. doi:10.1002/acr.22819 Kopel, S. J., Eiser, C., Cool, P., & Carter, S. R. (1998). Brief report: Assessment of body image in ­survivors of childhood cancer. Journal of Pediatric Psychology, 23, 141–147. Kutlesic, V., Williamson, D. A., Gleaves, D. H., Barbin, J. M., & Murphy‐Eberenz, K. P. (1998). The interview for the diagnosis of eating disorders—IV: Application to DSM–IV diagnostic criteria. Psychological Assessment, 10, 41–48. doi:10.1037/1040‐3590.10.1.41 Lawrence, J. W., Heinberg, L. J., Roca, R., Munster, A., Spence, R., & Fauerbach, J. A. (1998). Development and validation of the satisfaction with appearance scale: Assessing body image among burn‐injured patients. Psychological Assessment, 10, 64–70. Mayville, S. B., Williamson, D. A., White, M. A., Netemeyer, R. G., & Drab, D. L. (2002). Development of the muscle appearance satisfaction scale: A self‐report measure for the assessment of muscle ­dysmorphia symptoms. Assessment, 9, 351–360. McCreary, D. R., & Sasse, D. K. (2000). An exploration of the drive for muscularity in adolescent boys and girls. Journal of American College Health, 48, 297–304. McDermott, E., Moloney, J., Rafter, N., Keegan, D., Byrne, K., Doherty, G. A., … Mulcahy, H. E. (2014). The body image scale: A simple and valid tool for assessing body image dissatisfaction in

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inflammatory bowel disease. Inflammatory Bowel Diseases, 20, 286–290. doi:10.1097/01. MIB.0000438246.68476.c4 Petrie, T. A., Tripp, M. M., & Harvey, P. (2002). Factorial and construct validity of the body parts ­satisfaction scale‐revised: An examination of minority and nonminority women. Psychology of Women Quarterly, 26, 213–221. Pook, M., Tuschen‐Caffier, B., & Brähler, E. (2008). Evaluation and comparison of different versions of the body shape questionnaire. Psychiatry Research, 158, 67–73. Probst, M., Vandereycken, W., Van Coppenolle, H., & Pieters, G. (1995). Body size estimation in eating disorder patients: Testing the video distortion method on a life‐size screen. Behaviour Research and Therapy, 33, 985–990. doi:10.1016/0005‐7967(95)00037‐X Pruzinsky, T. (2004). Enhancing quality of life in medical populations: A vision for body image assessment and rehabilitation as standards of care. Body Image, 1, 71–81. Reas, D. L., Whisenhunt, B. L., Netemeyer, R., & Williamson, D. A. (2002). Development of the body checking questionnaire: A self‐report measure of body checking behaviors. International Journal of Eating Disorders, 31, 324–333. Rosen, J. C., Srebnik, D., Saltzberg, E., & Wendt, S. (1991). Development of a body image avoidance questionnaire. Psychological Assessment, 3, 32–37. Shafran, R., & Fairburn, C. G. (2002). A new ecologically valid method to assess body size estimation and body size dissatisfaction. International Journal of Eating Disorders, 32, 458–465. doi:10.1002/ eat.10097 Spangler, D. L., & Stice, E. (2001). Validation of the beliefs about appearance scale. Cognitive Therapy and Research, 25, 813–827. doi:10.1023/A:1012931709434 Stice, E., & Agras, S. (1998). Predicting onset and cessation of bulimic factors during adolescence. Behavior Therapy, 29(2), 257–276. Thompson, J. K., Fabian, L. J., Moulton, D. O., Dunn, M. E., & Altabe, M. N. (1991). Development and validation of the physical appearance related teasing scale. Journal of Personality Assessment, 56(3), 513–521. Thompson, J. K., Roehrig, M., Cafri, G., & Heinberg, L. J. (2005). Assessment of body image disturbance. In J. E. Mitchell & C. B. Peterson (Eds.), Assessment of eating disorders (pp. 175–202). New York, NY: Guilford Press. Thompson, J. K., van den Berg, P., Roehrig, M., Guarda, A. S., & Heinberg, L. J. (2004). The Sociocultural Attitudes Towards Appearance Scale‐3 (SATAQ‐3): Development and validation. International Journal of Eating Disorders, 35, 293–304. Thompson, M. A., & Gray, J. J. (1995). Development and validation of a new body‐image assessment scale. Journal of Personality Assessment, 64, 258–269. Veron‐Guidry, S., & Williamson, D. A. (1996). Development of a body image assessment procedure for children and preadolescents. International Journal of Eating Disorders, 20, 287–293. doi:10.1002/ (SICI)1098‐108X(199611)20:33.0.CO;2‐K Williamson, D. A., Davis, C. J., Bennett, S. M., Goreczny, A. J., & Gleaves, D. H. (1989). Development of a simple procedure for assessing body image disturbances. Behavioral Assessment, 11, 433–446. Yagil, Y., Geller, S., Sidi, Y., Tirosh, Y., Katz, P., & Nakache, R. (2015). The implications of body‐image dissatisfaction among kidney‐transplant recipients. Psychology, Health & Medicine, 20, 955–962.

Anxiety and Skin Disease Laura J. Dixon and Sara M. Witcraft Department of Psychology, University of Mississippi, Oxford, MS, USA

Skin represents the largest organ of the human body, and collectively, skin (dermatology) diseases are among the most widespread illnesses in the world. Skin disease affects all ages and encompasses many conditions, including eczema, psoriasis, acne, alopecia, hyperhidrosis, urticaria (hives), fungal skin diseases, cellulitis, viral warts, skin cancer, and rosacea. Given that dermatology is seldom linked to fatality, the impact of skin disease is often underestimated. However, skin diseases represent the fourth leading global cause of nonfatal burden and are associated with significant psychosocial impairment and high rates of psychological comorbidity (Dalgard et al., 2015; Hay et al., 2014). Perhaps unsurprisingly, given the well‐ known link between the mind and skin, many skin diseases have a psychological component. Albeit simplified, skin and psychological reactions are posited to be connected through ­multifaceted responses from the psychoneuroimmunological systems. Psychodermatology addresses this skin–mind connection, and although it is a relatively new subfield, psychodermatology has received increased attention over the past two decades. Psychodermatological (psychocutaneous) disorders have been classified into three categories—psychophysiologic disorders, primary psychiatric disorders, and secondary ­ psychiatric disorders (Koo & Lee, 2003). Psychophysiologic disorders characterize skin ­ ­conditions (e.g., acne, eczema) that may be triggered or worsened by psychological factors, such as stress and anxiety. In primary psychiatric disorders, psychopathology contributes to the manifestation of skin symptoms. Examples of primary psychiatric disorders include trichotillomania and delusions of parasitosis. Lastly, individuals affected by secondary psychiatric disorders experience emotional problems as a result of the disfigurement and impairment associated with skin disease. For instance, an individual with vitiligo or alopecia areata may experience depression or social anxiety symptoms due to their visible skin symptoms. Psychological conditions are catalogued by the Diagnostic and Statistical Manual of Mental Health Disorders (DSM) (American Psychiatric Association [APA], 2013). Of the mental health disorders, anxiety represents one of the most common mental health problems among dermatology patients (Dalgard et  al., 2015; Picardi, Amerio, et  al., 2004). Anxiety is an The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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a­ daptive component of the normal human experience, but it becomes maladaptive when it is persistent, distressing, pervasive, and interferes with everyday life. According to recent studies, as many as 52–70% of dermatology patients report moderate to severe clinical anxiety ­symptoms (Gascón et al., 2012; Rasoulian, Ebrahimi, Zare, & Taherifar, 2010), and the prevalence rate of anxiety disorders ranges between 15 and 17% in dermatology patients (Dalgard et al., 2015; Picardi, Abeni, et  al., 2004). Furthermore, studies have found significantly higher anxiety symptoms in dermatology samples compared with controls (Fleming et  al., 2017; Pärna, Aluoja, & Kingo, 2015). In addition to worsening symptoms of skin disease, anxiety symptoms have been associated with a number of adverse outcomes for dermatology patients, such as the use of poor coping strategies, low quality of life, loss of life meaning, and poor social functioning (Leibovici et al., 2010; Mazzotti et al., 2012). While it is possible that anxiety symptoms may be separate and unrelated to skin symptoms, it is likely that in many cases, these anxiety symptoms may operate within the framework of psychodermatological disorders. In particular, anxiety may be integral in psychophysiologic disorders, with the cognitive, ­emotional, and physiological symptoms of anxiety exacerbating or triggering skin symptoms, or to secondary psychiatric disorders, such as an individual experiencing social or health anxiety symptoms in response to their dermatological condition. Though anxiety refers to a broad syndrome, the most recent editions of the DSM have ­recognized different types of clinical anxiety including social anxiety disorder (SAD), generalized anxiety disorder (GAD), health anxiety, posttraumatic stress disorder, obsessive–­ compulsive disorder (OCD), and panic disorder/agoraphobia. Given the high prevalence rates and costs of clinical anxiety in dermatology patients, it is worthwhile to further explore these disorders in relation to skin disease.

Social Anxiety Disorder (Social Phobia) The primary feature of SAD (formally known as social phobia) is the fear of being judged, rejected, or negatively evaluated in social situations (APA, 2013). Individuals with SAD experience anxiety in situations or in anticipation of a wide range of situations where they perceive that they may act, speak, or appear foolish, silly, stupid, or incompetent in front of others. SAD is one of the most prevalent and costly mental disorders, affecting 7.4% of the US population each year (Kessler, Petukhova, Sampson, Zaslavsky, & Wittchen, 2012). Given the visible nature of skin conditions and potential disfigurement, dermatology patients may be highly susceptible to social anxiety and fear of negative evaluation. Indeed, 33.4% of dermatology outpatients reported clinically significant social anxiety (Montgomery, Norman, Messenger, & Thompson, 2016), and dermatology patients report significantly greater social anxiety symptoms compared with healthy adults (Salman, Kurt, Topcuoglu, & Demircay, 2016). In addition, symptoms of SAD, such as blushing, sweating, and trembling, overlap with various skin diseases. For instance, Davidson, Foa, Connor, and Churchill (2002) found that 24.8–32.3% of individuals with SAD met criteria for hyperhidrosis (excessive sweating). Social anxiety symptoms may contribute to positive feedback loop among individuals with certain skin diseases, such as rosacea, eczema, acne, or hyperhidrosis. For instance, anxiety in social situations may trigger or aggravate skin symptoms, and then an individual becomes ­anxious or embarrassed about experiencing visible symptoms, which further exacerbates skin symptoms. Conversely, in long‐term skin conditions like vitiligo, the skin changes over time, and situational social anxiety may not have a strong impact on the course of the disease. Nevertheless, individuals with vitiligo may experience embarrassment, shame, and social

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a­nxiety due to the visible distortions of their skin. Studies have found that social anxiety ­symptoms are associated with greater impairment and disability among patients with hyperhidrosis (Lessa Lda et al., 2014), vitiligo (Salman et al., 2016), psoriasis (Schneider, Heuft, & Hockmann, 2013), and acne (Salman et al., 2016). Despite the clear connections between SAD and skin disease, there is remarkably limited research in this area. Further research examining prevalence rates, mechanisms, and treatments for social anxiety among dermatology patients is warranted as this work could improve the identification and treatment of SAD and ultimately, prevent long‐term personal and societal costs of this mental disorder.

Generalized Anxiety Disorder GAD is characterized by persistent, uncontrollable, excessive worry across multiple domains (e.g., health, finances, daily routine; APA, 2013). Individuals with GAD may worry about their health symptoms, but as compared with health anxiety (see below), they also experience pathological worry in other areas in their life. GAD is a relatively common anxiety disorder, presenting in 6.2% of the general population (Kessler et al., 2012). Yet, higher rates of GAD have been observed in dermatology samples. For instance, Woodruff, Higgins, Du Vivier, and Wessely (1997) examined medical records of dermatology patients referred to a psychiatrist and found that 24.8% met criteria for mild GAD and 8.7% met criteria for severe GAD. Although the psychology–dermatology literature is composed of many studies examining “general anxiety” or broadly defined symptoms of distress and anxiety, fewer studies have ­specifically examined the GAD syndrome. In particular, research in this area has primarily focused on the role of GAD and pathological worry in psoriasis. One study demonstrated excessive worry symptoms were evidenced by 38% of patients with psoriasis, while 25% of the entire sample reported symptom levels consistent with a clinical diagnosis of GAD (Fortune, Richards, Main, & Griffiths, 2000). Beyond the high prevalence rates of GAD in psoriasis, pathological worry has been associated with greater burden of physiological and psychological symptoms. Pathological worry has been found to negatively affect the body and, specifically, has been associated with slower clearance of psoriasis symptoms (Fortune, Richards, Kirby, et al., 2003). Finally, in addition to direct associations with disease symptoms, GAD symptoms have been linked to worse illness‐related beliefs, greater disability, and worse coping strategies in psoriasis patients (Fortune et al., 2000; Fortune, Richards, Corrin, et al., 2003). Given the common occurrence of GAD in the general population coupled with the findings revealed by the psoriasis literature, additional research examining GAD and pathological worry in other skin diseases is necessary.

Health Anxiety Health anxiety, also known as illness anxiety disorder and hypochondriasis, is a preoccupation having or acquiring a serious illness. These concerns are accompanied by either avoidance of medical visits altogether or, at the other extreme, excessive medical visits and tests despite being told that there is nothing physically wrong (APA, 2013). A recent epidemiological study revealed the lifetime prevalence rate of illness anxiety disorder was 5.7% (Sunderland, Newby, & Andrews, 2013). Unfortunately, health anxiety is often unidentified in medical settings, and medical providers may ineffectively, albeit unintentionally, respond to patients’ anxieties about their health problems by offering reassurance and conducting unnecessary medical tests (Tyrer,

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Eilenberg, Fink, Hedman, & Tyrer, 2016). Very few studies have investigated health anxiety in dermatology patients, and to obtain more information about this phenomena, Long and Elpern (2017) conducted a study examining health anxiety in dermatology outpatients. They found that 31% of dermatology patients reported that they worried “a lot” about a health problem, 17% reported that they worried the problem was more serious than a doctor described, and a total of 46% expressed some degree health anxiety. The findings from this study suggest that health anxiety is particularly relevant for dermatology patients, and given these staggering numbers, further research is needed to explicate health anxiety in dermatology samples.

Posttraumatic Stress Disorder Posttraumatic stress disorder (PTSD) is characterized by anxiety and distress following a potentially life‐threatening event such as a car wreck, natural disaster, or a physical or sexual assault. Further, the anxiety is associated with recurrent, involuntary, and distressing memories of the trauma; attempts to avoid cues that remind the individual of the trauma and physiological arousal (e.g., hypervigilance, exaggerated startle reaction, panic symptoms); and increased negative mood and cognitions (APA, 2013). Despite the fact that many individuals will experience at least one potentially traumatic event in their lifetime, PTSD has a relatively low lifetime prevalence rate of 8.0% in the general population (Kessler et  al., 2012). Interestingly, skin disease has been found to be higher among individuals with PTSD compared with those without PTSD. A recent review of co‐occurring PTSD and dermatology suggested that skin conditions are common among individuals with PTSD due to the increased autonomic and sympathetic arousal, which activates skin symptoms (e.g., sweating; Gupta, Jarosz, & Gupta, 2017). Notably, in a large case–control trial, patients commonly reported psoriasis, alopecia, skin allergies, and other skin diseases, and individuals with PTSD (versus without PTSD) were twice as likely to have dermatology conditions (Britvić et al., 2015).

Obsessive–Compulsive Disorder OCD is composed of two components—(a) uncontrollable, unwanted, recurrent thoughts that cause distress (obsessions) and (b) mental acts or ritualistic behaviors that are instigated by obsessions and are conducted to reduce anxiety (compulsions; APA, 2013). OCD is a chronic, though relatively rare, disorder, with a lifetime prevalence rate of 2.7% (Kessler et al., 2012). Nevertheless, the prevalence of OCD in dermatology patients is much higher, with studies finding that between 9.1 and 24.7% of patients with skin disease meet criteria for OCD (Demet et  al., 2005; Sheikhmoonesi, Hajheidari, Masoudzadeh, Mohammadpour, & Mozaffari, 2014). Interestingly, whereas most of the empirical work examining anxiety disorders has been conducted within specific dermatology conditions (e.g., psoriasis), studies investigating OCD have done so with an unselected dermatological sample. OCD symptoms can interact with skin symptoms in several ways, and within OCD, obsessions or compulsions may relate to or be in response to skin symptoms. The most common obsessions reported by dermatology patients include somatic obsessions, pathological doubt, and contamination (Demet et al., 2005; Ebrahimi, Salehi, & Tafti, 2007). These obsessions may interfere with management of skin symptoms (e.g., doubt about taking medication) and may increase distress about the status of skin symptoms (e.g., intrusive thoughts about the severity of a skin lesion or

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breakout). The most common compulsions reported by dermatology patients include washing and checking (Demet et al., 2005). Chronic washing behaviors may lead to dry or raw skin and even abrasions, which may directly worsen skin symptoms and contribute to excoriation and skin picking. Similarly, though the study did not specify the type of checking behaviors, repeated checking of skin symptoms may result in decreased memory confidence and greater reactivity to perceived changes in skin status. Given the focus on skin and somatic symptoms, these individuals often present to dermatology or primary care clinics rather than seeking ­psychological or psychiatric services.

Panic and Agoraphobia A panic attack is a sudden surge of anxiety and distress that peaks within 10 min and includes at least four of the following symptoms: palpitations/pounding heart, sweating, trembling or shaking, sensations of shortness of breath or smothering, feeling of choking, chest pain or discomfort, feeling dizzy or faint, nausea or abdominal distress, derealization/depersonalization, fear of losing control or going crazy, numbness or tingling sensations, and chills or hot flashes. Panic attacks may be cued by a specific event or environment (e.g., stressful event, needles, crowded area) or may occur “out of the blue” and for no apparent reason. Epidemiological work has estimated that 28.3% of individuals experience one panic attack at some point in their life (Kessler et al., 2006). Although panic attacks are not inherently tied to pathology, individuals who worry about having another panic attack or change their life to avoid having a panic attack may meet criteria for panic disorder. In addition, individuals with other anxiety disorders may experience panic attacks when they come in contact with a feared situation (e.g., social situations in SAD) and individuals with agoraphobia fear situations (e.g., public transportation, being in a crowd) because they are afraid they may not be able to escape or get help if they develop panic‐like symptoms (APA, 2013). There is little literature exploring panic attacks, panic disorder, and agoraphobia in dermatology patients. One existing study found that patients with chronic skin conditions experienced significantly greater panic and agoraphobia symptoms than individuals in the control group (Pärna et  al., 2015). Future research is required to understand panic and agoraphobia in dermatology. For instance, the avoidance and physiological symptoms characteristic of these disorders may amplify impairment and skin symptoms for individuals with skin disease. Moreover, it is likely that symptoms of these disorders may co‐occur with other anxiety‐related disorders as is commonly observed in the general population.

Future Directions As the empirical literature of and clinical services for psychodermatology continue to evolve, anxiety disorders represent an important area for further development. Although anxiety disorders are common and known to contribute to worse outcomes in skin disease, there is a significant unmet need for treatment for individuals with co‐occurring dermatological and anxiety conditions, as well as large gaps in the empirical literature. Regarding clinical services, psychiatric symptoms are often unrecognized in dermatology patients (Picardi, Abeni, et al., 2004), and even fewer patients receive psychotherapeutic interventions in routine dermatology care (Fritzsche et al., 2001). Several studies have shown promising outcomes for the inclusion of psychosocial treatments in standard dermatology care, and in particular, adjunctive cognitive

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behavioral treatments have been found to reduce the severity of dermatological conditions, enhance quality of life, and decrease anxiety symptoms (Lavda, Webb, & Thompson, 2012). With regard to research, studies investigating the prevalence rates and risk factors for the different anxiety disorders across skin disease would be beneficial for improving the awareness and identification of anxiety disorders in routine care. Moreover, investigating mechanisms contributing to the interaction between anxiety and skin symptoms would enhance the understanding of psychodermatology processes and has the potential to inform the development of effective interventions to prevent and reduce anxiety in dermatology patients.

Cross References Psychoneuroimmunology: Immune markers of psychopathology Generalized Anxiety Disorder (GAD) and health Posttraumatic stress disorder (PTSD) Cognitive Behavior Therapy (CBT), sleep, mood disorders, and health

Author Biographies Laura J. Dixon, PhD, is assistant professor at the University of Mississippi, director of the Health and Anxiety Research and Treatment Laboratory, and licensed clinical psychologist. Dr. Dixon has published numerous scientific articles on the etiology and treatment of a­ nxiety‐ related disorders and received an ADAA early career award. Sara M. Witcraft is a clinical psychology doctoral student at the University of Mississippi. Sara has published several peer‐reviewed articles and book chapters on the treatment, understanding, and underlying factors of anxiety and related disorders. Her current research focuses on transdiagnostic vulnerabilities that underlie anxiety disorders.

References American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM‐5®) (Vol. 5). Washington, DC: American Psychiatric Publishing. Britvić, D., Antičević, V., Kaliterna, M., Lušić, L., Beg, A., Brajević‐Gizdić, I., … Pivac, N. (2015). Comorbidities with Posttraumatic Stress Disorder (PTSD) among combat veterans: 15 years postwar analysis. International Journal of Clinical and Health Psychology, 15, 81–92. Dalgard, F. J., Gieler, U., Tomas‐Aragones, L., Lien, L., Poot, F., Jemec, G. B., … Kupfer, J. (2015). The psychological burden of skin diseases: A cross‐sectional multicenter study among dermatological out‐patients in 13 European countries. Journal of Investigative Dermatology, 135, 984–991. Davidson, J. R., Foa, E. B., Connor, K. M., & Churchill, L. E. (2002). Hyperhidrosis in social anxiety disorder. Progress in Neuro‐Psychopharmacology and Biological Psychiatry, 26, 1327–1331. Demet, M. M., Deveci, A., Taskin, E. O., Ermertcan, A. T., Yurtsever, F., Deniz, F., … Ozturkcan, S. (2005). Obsessive‐compulsive disorder in a dermatology outpatient clinic. General Hospital Psychiatry, 27, 426–430. Ebrahimi, A. A., Salehi, M., & Tafti, K. A. (2007). Obsessive‐compulsive disorder in dermatology ­outpatients. International Journal of Psychiatry in Clinical Practice, 11, 218–221. Fleming, P., Bai, J. W., Pratt, M., Sibbald, C., Lynde, C., & Gulliver, W. P. (2017). The prevalence of anxiety in patients with psoriasis: A systematic review of observational studies and clinical trials. Journal of the European Academy of Dermatology and Venereology: JEADV, 31, 798–807.

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Fortune, D. G., Richards, H. L., Corrin, A., Taylor, R. J., Griffiths, C. E. M., & Main, C. J. (2003). Attentional bias for psoriasis‐specific and psychosocial threat in patients with psoriasis. Journal of Behavioral Medicine, 26, 211–224. Fortune, D. G., Richards, H. L., Kirby, B., McElhone, K., Markham, T., Rogers, S., … Griffiths, C. E. (2003). Psychological distress impairs clearance of psoriasis in patients treated with photochemotherapy. Archives of Dermatology, 139, 752–756. Fortune, D. G., Richards, H. L., Main, C. J., & Griffiths, C. E. (2000). Pathological worrying, illness perceptions and disease severity in patients with psoriasis. British Journal of Health Psychology, 5, 71–82. Fritzsche, K., Ott, J., Zschocke, I., Scheib, P., Burger, T., & Augustin, M. (2001). Psychosomatic liaison service in dermatology. Dermatology, 203, 27–31. Gascón, M. R. P., Ribeiro, C. M., Bueno, L. M. d. A., Benute, G. R. G., Lucia, M. C. S. d., Rivitti, E. A., & Festa Neto, C. (2012). Prevalence of depression and anxiety disorders in hospitalized patients at the dermatology clinical ward of a university hospital. Anais Brasileiros de Dermatologia, 87, 403–407. Gupta, M. A., Jarosz, P., & Gupta, A. K. (2017). Posttraumatic stress disorder (PTSD) and the dermatology patient. Clinics in Dermatology, 35, 260–266. Hay, R. J., Johns, N. E., Williams, H. C., Bolliger, I. W., Dellavalle, R. P., Margolis, D. J., … Wulf, S. K. (2014). The global burden of skin disease in 2010: An analysis of the prevalence and impact of skin conditions. Journal of Investigative Dermatology, 134, 1527–1534. Kessler, R. C., Chiu, W. T., Jin, R., Ruscio, A. M., Shear, K., & Walters, E. E. (2006). The epidemiology of panic attacks, panic disorder, and agoraphobia in the National Comorbidity Survey Replication. Archives of General Psychiatry, 63, 415–424. Kessler, R. C., Petukhova, M., Sampson, N. A., Zaslavsky, A. M., & Wittchen, H. U. (2012). Twelve‐ month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. International Journal of Methods in Psychiatric Research, 21, 169–184. Koo, J. Y., & Lee, C. S. (2003). General approach to evaluating psychodermatological disorders. Basic and Clinical Dermatology, 25, 1–12. Lavda, A. C., Webb, T. L., & Thompson, A. R. (2012). A meta‐analysis of the effectiveness of psychological interventions for adults with skin conditions. British Journal of Dermatology, 167, 970–979. Leibovici, V., Canetti, L., Yahalomi, S., Cooper‐Kazaz, R., Bonne, O., Ingber, A., & Bachar, E. (2010). Well‐being, psychopathology and coping strategies in psoriasis compared with atopic dermatitis: A controlled study. Journal of the European Academy of Dermatology and Venereology: JEADV, 24, 897–903. Lessa Lda, R., Luz, F. B., De Rezende, R. M., Duraes, S. M., Harrison, B. J., De Menezes, G. B., & Fontenelle, L. F. (2014). The psychiatric facet of hyperhidrosis: Demographics, disability, quality of life, and associated psychopathology. Journal of Psychiatric Practice, 20, 316–323. Long, V., & Elpern, D. J. (2017). Health anxiety in dermatology. International Journal of Dermatology, 56, 968–971. Mazzotti, E., Mastroeni, S., Lindau, J., Lombardo, G., Farina, B., & Pasquini, P. (2012). Psychological distress and coping strategies in patients attending a dermatology outpatient clinic. Journal of the European Academy of Dermatology and Venereology: JEADV, 26, 746–754. Montgomery, K., Norman, P., Messenger, A. G., & Thompson, A. R. (2016). The importance of mindfulness in psychosocial distress and quality of life in dermatology patients. British Journal of Dermatology, 175, 930–936. Pärna, E., Aluoja, A., & Kingo, K. (2015). Quality of life and emotional state in chronic skin disease. Acta Dermato‐Venereologica, 95, 312–316. Picardi, A., Abeni, D., Mazzotti, E., Fassone, G., Lega, I., Ramieri, L., … Pasquini, P. (2004). Screening for psychiatric disorders in patients with skin diseases. Journal of Psychosomatic Research, 57, 219–223.

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Picardi, A., Amerio, P., Baliva, G., Barbieri, C., Teofoli, P., Bolli, S., … Abeni, D. (2004). Recognition of depressive and anxiety disorders in dermatological outpatients. Acta Dermato‐Venereologica, 84, 213–217. Rasoulian, M., Ebrahimi, A. A., Zare, M., & Taherifar, Z. (2010). Psychiatric morbidity in dermatological conditions. International Journal of Psychiatry in Clinical Practice, 14, 18–22. Salman, A., Kurt, E., Topcuoglu, V., & Demircay, Z. (2016). Social anxiety and quality of life in vitiligo and acne patients with facial involvement: A cross‐sectional controlled study. American Journal of Clinical Dermatology, 17, 305–311. Schneider, G., Heuft, G., & Hockmann, J. (2013). Determinants of social anxiety and social avoidance in psoriasis outpatients. Journal of the European Academy of Dermatology and Venereology: JEADV, 27, 383–386. Sheikhmoonesi, F., Hajheidari, Z., Masoudzadeh, A., Mohammadpour, R. A., & Mozaffari, M. (2014). Prevalence and severity of obsessive‐compulsive disorder and their relationships with dermatological diseases. Acta Medica Iranica, 52, 511. Sunderland, M., Newby, J. M., & Andrews, G. (2013). Health anxiety in Australia: Prevalence, comorbidity, disability and service use. British Journal of Psychiatry, 202, 56–61. Tyrer, P., Eilenberg, T., Fink, P., Hedman, E., & Tyrer, H. (2016). Health anxiety: The silent, disabling epidemic. British Medical Journal, 353, i2250. Woodruff, P., Higgins, E., Du Vivier, A., & Wessely, S. (1997). Psychiatric illness in patients referred to a dermatology‐psychiatry clinic. General Hospital Psychiatry, 19, 29–35.

Suggested Reading Bewley, A., Taylor, R. E., & Reichenberg, J. S. (2014). In M. Magid (Ed.), Practical psychodermatology. Oxford, UK: Wiley. Gupta, M. A., & Gupta, A. K. (2013). A practical approach to the assessment of psychosocial and ­psychiatric comorbidity in the dermatology patient. Clinics in Dermatology, 31, 57–61. Lavda, A. C., Webb, T. L., & Thompson, A. R. (2012). A meta‐analysis of the effectiveness of psychological interventions for adults with skin conditions. British Journal of Dermatology, 167, 970–979.

Insomnia Todd A. Smitherman and Daniel G. Rogers Department of Psychology, University of Mississippi, Oxford, MS, USA

Diagnosis and Epidemiology Insomnia is sleep disorder broadly characterized by dissatisfaction with sleep amount or quality as manifested by recurrent difficulty initiating or maintaining sleep and causing significant distress or daytime impairment (e.g., fatigue, difficulty concentrating, irritability) (Morin & Benca, 2012; Rybarczyk, Lund, Garroway, & Mack, 2013). Sleep maintenance difficulties include waking frequently during sleep or being unable to quickly return back to sleep after waking. Both the International Classification of Sleep Disorders—Third Edition (ICSD‐3) (American Academy of Sleep Medicine, 2014) and the Diagnostic and Statistical Manual of Mental Disorders—Fifth Edition (DSM‐5) (American Psychiatric Association, 2013) also require that sleep difficulties occur despite ample opportunity for sleep, have a frequency of at least 3 days/week for at least 3 months, and are not attributable to another medical or mental disorder. In research settings, regularly obtaining 30 min while trying to fall or stay asleep are commonly employed as diagnostic thresholds.

Prevalence and Impact Insomnia is one of the most common medical complaints in primary care settings. Over 30% of adults report at least one insomnia‐related sleep complaint, 10–15% report daytime impairment resulting from poor sleep, and 6–10% of adults have symptoms that meet formal diagnostic criteria for insomnia (Morin & Jarrin, 2013). Without intervention, 40–60% of individuals with chronic insomnia report similar symptoms a year later (Pillai, Roth, & Drake, 2015). Complaints of poor sleep are more common in women than men (Zhang & Wing, 2006) and among older adults (Morphy, Dunn, Lewis, Boardman, & Croft, 2007). Older adults experience more fragmented sleep and earlier awakening, awaken to external stimuli more ­easily, and are more prone to disruptions of normal sleep than young adults (Fetveit, 2009). The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Older adults also are more likely to experience comorbid medical conditions or disturbed sleep as a side effect of medication, which obfuscates the role of normal aging in sleep impairment (Vitiello, 2006). Insomnia is associated with worsened cognitive functioning, particularly deficits in working memory, episodic memory, and executive functioning (Fortier‐Brochu, Beaulieu‐Bonneau, Ivers, & Morin, 2012). Insomnia results in tens of billions of dollars of lost productivity in the United States annually, mostly as a result of impaired work performance (Kessler et al., 2011), as well as significant direct and indirect costs to individuals (Daley, Morin, LeBlanc, Grégoire, & Savard, 2009).

Changes in Conceptualization of Insomnia The most recent editions of the DSM and ICSD eliminated nonrestorative sleep as a symptom of insomnia (termed “insomnia disorder” in DSM‐5 and “chronic insomnia” in ICSD‐3) as this symptom is often poorly defined and not specific to insomnia. Classification task forces collaborated to ensure similar diagnostic criteria, including frequency and duration of insomnia, across diagnostic manuals. Epidemiological studies adhering to these updated criteria are needed. Previous editions of insomnia diagnostic manuals made distinctions between “primary” and “secondary” insomnia, the latter of which referred to insomnia comorbid with another medical or psychiatric disorder. This distinction proved problematic in several ways: (a) limited understanding of mechanistic pathways prevents conclusions about the direction of causality, (b) use of the term “secondary insomnia” may contribute to potential for inadequate treatment, and (c) regardless of terminology, various insomnia presentations share numerous behavioral and cognitive characteristics and similar treatment approaches (NIH, 2005). As such, recent nosologies have eliminated this distinction.

Risk Factors and Comorbidities Onset of insomnia is often conceptualized from a diathesis–stress perspective, in which ­inherited predisposing factors interact with precipitating events to disrupt biological processes involved in sleep. The sleep–wake cycle is largely determined by circadian and homeostatic processes, the former being regulated by external light cycles and the latter as a function of a sleep drive that increases the longer an individual remains awake. Diatheses involved in insomnia include genetically transmitted factors that predispose one to physiological hyperarousal throughout the day and night (e.g., family history of sleep disorders, negative affectivity, high susceptibility to stress; Riemann et al., 2010; Vgontzas et al., 2001). Precipitating events among susceptible individuals include a broad range of medical, ­environmental, and psychological factors. Medical conditions such as chronic pain, heart disease, chronic obstructive pulmonary disease (COPD), and movement disorders can interfere with sleep onset or maintenance (Smith & Haythornthwaite, 2004). Environmental factors include those in the immediate sleep environment (e.g., excessive light, noise, uncomfortable ambient temperature) and more distal external variables (e.g., life stressors such as a new job or deadlines, changes in an individual’s sleep schedule such as from jet lag). Psychological ­factors encompass comorbid psychiatric disorders and both behaviors and cognitions that ­contribute to disturbed sleep. Individuals with insomnia are up to five times more likely than those without insomnia to experience symptoms of anxiety and depression, both of which disrupt normal sleep patterns (Pearson, Johnson, & Nahin, 2006). This relationship appears

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bidirectional in nature, such that insomnia may develop before or after the psychiatric ­disorder. Use of various substances can also interfere with sleep (Roth et al., 2006): nicotine and ­caffeine can delay sleep onset, and alcohol can disrupt sleep maintenance and quality. Sleep disturbance may also result from use or withdrawal of controlled substances such as alcohol, anxiolytics, opioids, sedatives, and stimulants. Beyond their role in the onset of acute episodes of insomnia, psychological factors operate also to maintain insomnia, such that an individual’s response to difficulty sleeping may inadvertently perpetuate dysregulated sleep and contribute to development of chronic insomnia (Perlis, Giles, Mendelson, Bootzin, & Wyatt, 1997). Individuals experiencing insomnia often begin to worry about their ability to sleep and the functional consequences of too little sleep, and this preoccupation with sleep contributes to physiological arousal that interferes with sleep. Similarly, compensatory behavioral responses are generally designed to increase opportunity for sleep (e.g., remaining in bed if unable to sleep, sleeping in on weekends, utilizing daytime napping) but inadvertently maintain insomnia by conditioning the individual to be awake at night while attempting to sleep. Ultimately, chronification of insomnia thus often occurs through repeated pairing of cognitive and cortical arousal with sleep‐related stimuli.

Assessment Assessment of insomnia focuses principally on gathering information about sleep‐related behaviors and cognitions, symptoms and functional impairment, and perpetuating factors that may maintain insomnia. In most cases, gathering this information via interview and daily sleep diaries is sufficient for making the diagnosis and developing a treatment plan.

Clinical Interview Existing structured diagnostic interviews for insomnia are few in number, not validated using recent insomnia diagnostic criteria, and time consuming, and thus infrequently used outside of research settings or sleep clinics. The clinical interview focuses principally on gathering detailed information about one’s sleep history and screening for the aforementioned comorbid conditions. In addition to querying specific diagnostic criteria and functional impairment, detailed information is gathered about the history of the sleep problem (onset, course, duration), typical sleep–wake pattern, and perpetuating factors. The latter includes identifying behaviors (e.g., daytime napping, staying in bed when unable to sleep, non‐sleep activities while in bed), cognitions (e.g., preoccupation with sleep, catastrophizing about the effects of poor sleep), and medications/substances that condition physiological arousal when attempting to sleep.

Daily Sleep Diaries Self‐monitoring via use of daily sleep diaries (logs) is invaluable for quantifying one’s sleep schedule at baseline and throughout treatment. At a minimum, sleep diaries should solicit information pertaining to time in bed (TiB), time out of bed, time to fall asleep (sleep latency [SL]), total sleep time (TST), and time spent napping. Some diaries include entries for bedtime, wake time after sleep onset (WASO), medication/alcohol consumption, lights on/off times, and ratings of sleep quality. From these self‐monitoring data, sleep efficiency (SE) is calculated (average TST/average TiB × 100), representing the percentage of time in bed that

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one is asleep. Patients are instructed to complete entries shortly after awakening each morning, and a minimum of 2 weeks of self‐monitoring is recommended for each assessment period.

Other Means of Assessment In most cases a thorough clinical interview and daily sleep diaries provide ample data to inform diagnosis and treatment, but other methods of assessment may be used to supplement the interview and diaries. Questionnaires The most widely researched sleep questionnaire is the Pittsburgh Sleep Quality Index (PSQI) (Buysse, Reynolds, Monk, Berman, & Kupfer, 1998), which inquires about one’s sleep pattern over the last month and solicits Likert‐type ratings of various sleep disturbances. Items are aggregated into 7 component scores and then into a total score that ranges from 0 to 21; scores >5 are indicative of poor sleep quality. However, scoring the PSQI can be difficult, and, as it is not specific to insomnia, elevated scores can reflect difficulties stemming from other sleep disorders. The Insomnia Severity Index (ISI) (Morin, Belleville, Bélanger, & Ivers, 2011) is the most widely used questionnaire specific to insomnia. This seven‐item Likert‐type measure quantifies severity of insomnia as a function of symptoms and impairment experienced over the last month. Total scores range from 0 to 28, with higher scores indicative of greater insomnia. The ISI is sensitive to treatment effects and can be utilized to identify insomnia in population samples. Polysomnography Polysomnography involves overnight recording of physiological variables during sleep (­respiration, electrical activity of the brain) and is usually conducted in a sleep clinic. This form of assessment is not indicated in the overwhelming majority of insomnia presentations. Polysomnography is useful when there is strong reason to suspect breathing (sleep apnea) or movement disorders (e.g., restless leg syndrome) as the cause of poor sleep or when standard treatments fail without a clear reason (Schutte‐Rodin, Broch, Buysse, Dorsey, & Sateia, 2008). Actigraphy Actigraphs are ambulatory digital monitoring devices that are worn on the wrist and quantify sleep as a function of movement (via accelerometry) and ambient light. Actigraphy is often used to supplement data from sleep diaries, as the former provides more objective data, or as an alternative to overnight polysomnography (Morganthaler et al., 2007). Several companies produce actigraphs and use proprietary computer algorithms to derive sleep–wake variables.

Treatment Psychological Interventions Cognitive behavioral therapy for insomnia (CBT‐I) is grounded in principles of learning ­theory and seeks to modify behaviors and cognitions that inadvertently perpetuate conditioned arousal. CBT‐I is a multicomponent intervention that includes some combination of stimulus control, sleep restriction, sleep hygiene education, relaxation training, and cognitive therapy. Stimulus control and sleep restriction appear to be the most potent components of

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CBT‐I (Schutte‐Rodin et al., 2008). Stimulus control involves techniques to reassociate the bedroom with sleep using classical and operant conditioning principles. These include instructions in using the bed only for sleep and sexual activity (e.g., eliminating other activities in bed such as eating or watching TV), eliminating daytime naps, and getting out of bed if unable to fall asleep within 20–30 min. Sleep restriction focuses on consolidating one’s sleep by restricting one’s TiB to the actual time spent sleeping and then gradually increasing TiB as SE reaches 85% or higher as indexed by daily sleep diaries. Sleep hygiene is a psychoeducational intervention focused on general habits that promote sleep (e.g., limiting caffeine/alcohol, keeping a comfortable sleep environment and consistent sleep–wake schedule) but is not an efficacious monotherapy (Edinger et al., 2009; Schutte‐ Rodin et al., 2008). Relaxation training endeavors to reduce contributing physiological arousal and is often used for those who have difficulty relaxing at bedtime or in place of sleep restriction for individuals who have bipolar disorder, in whom sleep restriction is contraindicated due to risk of inducing mania. Finally, cognitive therapy techniques may be included to address dysfunctional cognitions that perpetuate insomnia, such as chronic worries that prevent sleep or unrealistic expectations about sleep. CBT‐I is the most frequently researched and well‐established psychological intervention for insomnia. The most recent comprehensive review of its efficacy in adults was provided in a meta‐analysis conducted by the Agency for Healthcare Research and Quality (AHRQ) (Brasure et al., 2015). Eighteen randomized controlled trials provided data for pooled outcomes regarding CBT‐I versus passive control interventions (e.g., attention control, treatment as usual, wait list, sham control) among the general adult population. Most studies utilized individual CBT‐I, though some included alternative modes of delivery (e.g., group or phone administration). On average, CBT‐I was associated with a tripled rate of remission (61 vs. 18% for control); substantially greater reductions on both the PSQI and ISI; and superior improvements in SL, TST, WASO, SE, and sleep quality. Many treatment gains appeared to be maintained for months after cessation of treatment. CBT‐I also produced favorable outcomes versus control treatments among older adults and among those with comorbid pain conditions, though the evidence was of lower quality as there were fewer trials pertinent to these specific groups. Other recent meta‐analytic reviews have reached similar conclusions regarding the short‐ and long‐term efficacy of CBT‐I for insomnia, including insomnia comorbid with other medical and psychiatric conditions (Geiger‐Brown et al., 2015; Trauer, Qian, Doyle, Rajaratnam, & Cunnington, 2015; Wu, Appleman, Salazar, & Ong, 2015). Advances in treatment delivery and information technology have facilitated efforts to condense CBT‐I into as few as two to four treatment sessions so that it can be more easily integrated into routine medical practice settings (Edinger et al., 2009; Edinger & Sampson, 2003), as well as delivered via the Internet to enhance access and utilization (Zachariae, Lyby, Ritterband, & O’Toole, 2016). There is insufficient evidence for complementary and alternative medicine approaches (e.g., acupuncture, homeopathy, valerian root) to suitably assess their efficacy for treatment of insomnia (Brasure et al., 2015).

Pharmacotherapy Pharmacological therapies for insomnia can be broadly delineated into over‐the‐counter (OTC) medications, prescription medications approved by the Food and Drug Administration (FDA) for treatment of insomnia, and prescription medications with sedating properties that are used “off‐label.” In general, the following medications have shown efficacy for short‐term treatment of insomnia, though few studies on long‐term efficacy or safety exist.

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OTC medications Common OTC medications for insomnia include antihistamines (e.g., hydroxyzine, diphenhydramine, doxylamine succinate) and exogenous melatonin. Antihistamines are frequently used for insomnia despite limited evidence of efficacy, rapid development of tolerance with daily usage, and risk of rebound insomnia following discontinuation. Melatonin has been increasingly used to aid sleep since sedative properties were discovered in the 1970s. Melatonin has demonstrated significant effects in reducing sleep onset latency and increasing TST and sleep quality compared with placebo across several studies (Ferracioli‐Oda, Qawasmi, & Bloch, 2013). However, the clinical significance of these effects appears modest at best, with sleep onset latency and TST improved by less than 10 min on average as compared with placebo. Prescription medications approved for insomnia The most common medications approved for the treatment of insomnia are benzodiazepine (e.g., estazolam) and non‐benzodiazepine hypnotics (e.g., zolpidem [Ambien], eszopiclone [Lunesta]), the latter of which have the strongest evidence among adults in the general population. Both classes of agents are GABA agonists, though the non‐benzodiazepines appear to have more selective action at the GABAA receptor and perhaps better safety profiles. Non‐benzodiazepines have been shown to reduce SL and WASO and to increase SE and sleep quality (Becker & Somiah, 2015; Buscemi et al., 2007). Evidence for benzodiazepines is less robust as a function of trial design limitations and significant improvements evidenced only in sleep onset latency (Brasure et al., 2015). The efficacy of hypnotic agents has been called into question relative to risks associated with both short‐term and long‐term usage. Short‐term side effects include drowsiness and fatigue, cognitive impairment, and motor problems, which can be particularly relevant among older adults (Glass, Lancôt, Herrmann, Sproule & Busto, 2005). Long‐term use is associated with risk for dependence, misuse, and rebound insomnia, as well as higher rates of dementia and fractures in observational studies (Brasure et al., 2015). Other approved medications include the melatonin receptor agonists (MRA) (e.g., ramelteon), orexin receptor antagonists (ORA) (e.g., suvorexant), and tricyclic antidepressants (TCA) (e.g., doxepin). Ramelteon does not appear to produce clinically meaningful sleep improvements, similar to conclusions reached regarding exogenous melatonin (Brasure, Fuchs, & Macdonal, 2016). Both ORAs and TCAs have demonstrated efficacy in improving sleep time and reducing WASO, though evidence for TCAs is weaker (Brasure et al., 2015). Prescription medications not approved for insomnia In addition to FDA‐approved medications for insomnia, several antidepressants (e.g., ­trazodone, mirtazapine), anticonvulsants (e.g., tiagabine, pregabalin), and antipsychotics have sedating properties and thus are commonly prescribed. In general, the evidence for off‐label use of these medications is insufficient as compared with the aforementioned approved ­prescription medications, and risk of adverse events is high. As such, these agents are recommended only when insomnia is comorbid with another condition for which they are indicated (Buysse, John Rush, & Reynolds, 2017).

CBT‐I Versus Pharmacotherapy for Insomnia The AHRQ review concluded that there was insufficient evidence to directly compare CBT‐I and pharmacotherapy but noted that CBT‐I carries considerably less risk for adverse effects and physical harm (Brasure et al., 2015). Accordingly, the American College of Physicians’

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most recent clinical practice guideline recommended CBT‐I as the frontline treatment for adults with insomnia, with medication as a secondary option for those who do not respond to an initial trial of CBT‐I (Qaseem, Kansagara, Forciea, Cooke, & Denberg, 2016). As pharmacotherapy is only recommended for short‐term use, most drug trials have been only 4–6 weeks in duration, and thus serious long‐term adverse effects directly associated with use of pharmacological agents for sleep remain largely unknown.

See Also Adherence to Behavioral and Medical Regimens; Depression and Comorbidity in Health Contexts; Older Adults and Perspectives for Researchers and Clinicians Working in Health Psychology and Behavioral Medicine; Pain; Treatment: Anxiety and Stress with Chronic Diseases

Author Biographies Todd A. Smitherman, PhD, is associate professor and director of Clinical Training at the University of Mississippi in Oxford, Mississippi. His research focuses on psychological factors in and behavioral interventions for chronic medical conditions such as migraine and insomnia. Daniel G. Rogers, BAS, is a graduate student in the University of Mississippi Clinical Psychology Program in Oxford, Mississippi.

References American Academy of Sleep Medicine. (2014). International classification of sleep disorders: Diagnostic and coding manual (3rd ed.). Darien, IL: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: Author. Becker, P. M., & Somiah, M. (2015). Non‐benzodiazepine receptor agonists for insomnia. Sleep Medicine Clinics, 10, 57–76. Brasure, M., Fuchs, E., & Macdonald, R. (2016). Psychological and behavioral interventions for managing insomnia. Annals of Internal Medicine, 117, 1–13. Brasure, M., MacDonald, R., Fuchs, E., Olson, C. M., Carlyle, M., Diem, S., … Wilt, T. J. (2015). Management of insomnia disorder. Comparative effectiveness review 159. (Prepared by the Minnesota evidence‐based practice center under contract 290‐2012‐00016‐I.) AHRQ Publication no.15(16)‐ EHC027‐EF. Rockville, MD: Agency for Healthcare Research and Quality. https://www. effectivehealthcare.ahrq.gov/topics/insomnia/research Buscemi, N., Vandermeer, B., Friesen, C., Bialy, L., Tubman, M., Ospina, M., … Witmans, M. (2007). The efficacy and safety of drug treatments for chronic insomnia in adults: A meta‐analysis of RCTs. Journal of General Internal Medicine, 22, 1335–1350. Buysse, D. J., John Rush, A., & Reynolds, C. F. (2017). Clinical management of insomnia disorder. JAMA, 318, 1973–1974. Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1998). The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research, 28, 193–213.

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Daley, M., Morin, C. M., LeBlanc, M., Grégoire, J.‐P. P., & Savard, J. (2009). The economic burden of insomnia: Direct and indirect costs for individuals with insomnia syndrome, insomnia symptoms, and good sleepers. Sleep, 32, 55–64. Edinger, J. D., Olsen, M. K., Stechuchak, K. M., Means, M. K., Lineberger, M. D., Kirby, A., & Carney, C. E. (2009). Cognitive behavioral therapy for patients with primary insomnia or insomnia associated predominantly with mixed psychiatric disorders: A randomized clinical trial. Sleep, 32, 499–510. Edinger, J. D., & Sampson, W. S. (2003). A primary care “friendly” cognitive behavioral insomnia therapy. Sleep, 26, 177–182. Ferracioli‐Oda, E., Qawasmi, A., & Bloch, M. H. (2013). Meta‐analysis: Melatonin for the treatment of primary sleep disorders. PLoS One, 8, e63773. Fetveit, A. (2009). Late‐life insomnia: A review. Geriatrics and Gerontolology International, 9, 220–234. Fortier‐Brochu, É., Beaulieu‐Bonneau, S., Ivers, H., & Morin, C. M. (2012). Insomnia and daytime cognitive performance: A meta‐analysis. Sleep Medicine Reviews, 16(1), 83–94. Geiger‐Brown, J. M., Rogers, V. E., Liu, W., Ludeman, E. M., Downton, K. D., & Diaz‐Abad, M. (2015). Cognitive‐behavioral therapy in persons with comorbid insomnia: A meta‐analysis. Sleep Medicine Reviews, 23, 54–67. Glass, J., Lancôt, K. L., Herrmann, N., Sproule, B. A., & Busto, U. E. (2005). Sedative hypnotics in older people with insomnia: Meta‐analysis of risks and benefits. BMJ, 331, 1169. Kessler, R. C., Berglund, P. A., Coulouvrat, C., Hajak, G., Roth, T., Shahly, V., … Walsh, J. K. (2011). Insomnia and the performance of US workers: Results from the America Insomnia Survey. Sleep, 34, 1161–1171. Morganthaler, T., Alessi, C., Friedman, L., Owens, J., Kapur, V., Boehlecke, B., … Swick, T. J. (2007). Practice parameters for the use of actigraphy in the assessment of sleep and sleep disorders: An update for 2007. Sleep, 30, 519–529. Morin, C., & Jarrin, D. (2013). Epidemiology of insomnia: Prevalence, course, risk factors, and public health burden. Sleep Medicine Clinics, 8, 281–297. Morin, C. M., Belleville, G., Bélanger, L., & Ivers, H. (2011). The insomnia severity index: Psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep, 34, 601–608. Morin, C. M., & Benca, R. (2012). Chronic insomnia. The Lancet, 379(9821), 1129–1141. Morphy, H., Dunn, K. M., Lewis, M., Boardman, H. F., & Croft, P. R. (2007). Epidemiology of insomnia: A longitudinal study in a UK population. Sleep, 30, 274–280. NIH. (2005). NIH state‐of‐the‐science conference statement on manifestations and management of chronic insomnia in adults. NIH Consensus and State‐of‐the‐Science Statements, 22, 1–30. Pearson, N. J., Johnson, L. L., & Nahin, R. L. (2006). Insomnia, trouble sleeping, and complementary and alternative medicine: Analysis of the 2002 National Health Interview Survey Data. Archives of Internal Medicine, 166, 1775–1782. Perlis, M. L., Giles, D. E., Mendelson, W. B., Bootzin, R. R., & Wyatt, J. K. (1997). Psychophysiological insomnia: The behavioural model and a neurocognitive perspective. Journal of Sleep Research, 6, 179–188. Pillai, V., Roth, T., & Drake, C. L. (2015). The nature of stable insomnia phenotypes. Sleep, 38, 127–138. Qaseem, A., Kansagara, D., Forciea, M. A., Cooke, M., & Denberg, T. D. (2016). Management of chronic insomnia disorder in adults: A clinical practice guideline from the American College of Physicians. Annals of Internal Medicine, 165, 125–133. Riemann, D., Spiegelhalder, K., Feige, B., Voderholzer, U., Berger, M., Perlis, M., & Nissen, C. (2010). The hyperarousal model of insomnia: A review of the concept and its evidence. Sleep Medicine Reviews, 14, 19–34. Roth, T., Jaeger, S., Jin, R., Kalsekar, A., Stang, P. E., & Kessler, R. C. (2006). Sleep problems, comorbid mental disorders, and role functioning in the national comorbidity survey replication. Biological Psychiatry, 60(12), 1364–1371.

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Rybarczyk, B., Lund, H. G., Garroway, A. M., & Mack, L. (2013). Cognitive behavioral therapy for insomnia in older adults: background, evidence, and overview of treatment protocol. Clinical Gerontologist, 36(1), 70–93. Schutte‐Rodin, S., Broch, L., Buysse, D., Dorsey, C., & Sateia, M. (2008). Clinical guideline for the evaluation and management of chronic insomnia in adults. Journal of Clinical Sleep Medicine, 4, 487–504. Smith, M. T., & Haythornthwaite, J. A. (2004). How do sleep disturbance and chronic pain inter‐relate? Insights from the longitudinal and cognitive‐behavioral clinical trials literature. Sleep Medicine Reviews, 8, 119–132. Trauer, J. M., Qian, M. Y., Doyle, J. S., Rajaratnam, S. M. W., & Cunnington, D. (2015). Cognitive behavioral therapy for chronic insomnia: A systematic review and meta‐analysis. Annals of Internal Medicine, 163, 191–204. Vgontzas, A. N., Bixler, E. O., Lin, H.‐M., Prolo, P., Mastorakos, G., Vela‐Bueno, A., … Chrousos, G. P. (2001). Chronic insomnia is associated with nyctohemeral activation of the hypothalamic‐pituitary‐ adrenal axis: Clinical implications. The Journal of Clinical Endocrinology & Metabolism, 86, 3787–3794. Vitiello, M. V. (2006). Sleep in normal aging. Sleep Medicine Clinics, 7, 539–544. Wu, J. Q., Appleman, E. R., Salazar, R. D., & Ong, J. C. (2015). Cognitive behavioral therapy for insomnia comorbid with psychiatric and medical conditions: A meta‐analysis. JAMA Internal Medicine, 175, 1461–1472. Zachariae, R., Lyby, M. S., Ritterband, L. M., & O’Toole, M. S. (2016). Efficacy of internet‐delivered cognitive‐behavioral therapy for insomnia: A systematic review and meta‐analysis of randomized controlled trials. Sleep Medicine Reviews, 30, 1–10. Zhang, B., & Wing, Y.‐K. (2006). Sex differences in insomnia: A meta‐analysis. Sleep, 29, 85–93.

Suggested Reading Baglioni, C., Battagliese, G., Feige, B., Spiegelhalder, K., Nissen, C., Voderholzer, U., … Riemann, D. (2011). Insomnia as a predictor of depression: A meta‐analysis of longitudinal epidemiological studies. Journal of Affective Disorders, 135, 10–19. Edinger, J. D., & Carney, C. E. (2008). Overcoming insomnia: A cognitive‐behavioral therapy approach (therapist guide). New York, NY: Oxford University Press. Riemann, D., Nissen, C., Palagini, L., Otte, A., Perlis, M. L., & Spiegelhalder, K. (2015). The neurobiology, investigation, and treatment of chronic insomnia. The Lancet Neurology, 14, 547–558.

Index

6‐minute walk (6MW) performance  146 compromise 143 acamprosate (Campral), usage  440 acceptance and commitment therapy (ACT)  248 accountable care organization (ACO), coordinated/ colocated systems  287 acetaminophen (paracetamol), usage  134 acne  451, 452 actigraphy, usage  462 active mental model, areas  304 activities of daily living (ADLs), disruption  98 acupoints 251 acupuncture  251–252, 429 efficacy 134 migraine headache usage  137 tobacco use cessation  230 acute exercise, impact  409–410 acute illness adjustment, chronic illness adjustment (contrast) 374 acute stress, counteracting  357–358 addictions, medical resident stigma‐related concerns 59 adherence. See medication adherence; patient adherence defining 375 adolescence, development/health  332 adolescents adolescent‐focused therapy, family‐based treatment (comparison)  123–124 alcohol consumption (reduction), self‐affirmation implementation intention intervention (development) 49

alcohol use/abuse, longitudinal pathways (exploration) 312 anorexia nervosa, family therapy (usage)  123–124 CAM approaches  246 medication regimens, nonadherence  376 sexual orientation/gender minority, identification 419 stress responses  374 adulthood, sexual trauma (negative health consequences) 154 adults, depression screening (USPSTF recommendation) 197–198 Adverse Childhood Experiences Study  154 advertising (psychologists)  431–432 aerobic exercises, participation (impact)  409 affective forecasting  293 bias 295 difference variables, impact  295 errors 295 impact 297 focus 295 generalized affective forecasts, importance 298–299 health‐related examples  296 impact 297 study 294 quality, determination  299 research 295–296 role 296–297 affective reactions, gender differences  23 Affordable Care Act. See Patient Protection and Affordable Care Act

The Wiley Encyclopedia of Health Psychology: Volume 3: Clinical Health Psychology and Behavioral Medicine, First Edition. General Editor: Lee M. Cohen. Volume Editors: C. Steven Richards and Lee M. Cohen. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Index

Agency for Healthcare Research and Quality (AHRQ), CBT‐I efficacy review  463 aging 301 agoraphobia, panic (relationship)  455 alcohol abuse diagnosis, establishment  67–68 alcohol‐related birth defects/neurodevelopmental disorders 438 alcohol‐related diagnoses, classification (DSM change) 67 alcohol‐related Korsakoff’s syndrome, results 437 alcohol‐related motor vehicle accidents, NHTSA estimates 70 alcohol‐related NCD  95 blood alcohol concentration (BAC), increase  69 hepatitis 438 reaction time/decision making impairment  70 alcohol consumption acute alcohol consumption, impact  69–70 acute effects  69–70 breast cancer risk, increase (relationship)  48 chronic alcohol consumption, impact  437 definitions 69 detrimental outcomes  438 excess, impact  437–438 moderation, health benefits  438 prediction 68–69 Alcohol Dependence Scale (ADS)  238 alcoholic liver disease, treatment  441 Alcoholics Anonymous (AA), usage  440 alcoholism 67 alcohol use avoidance 40 longitudinal pathways, exploration  312 long‐term negative health outcomes  437–438 social cognitive theories  68–69 alcohol use disorder (AUD)  67 acamprosate (Campral), usage  440 Alcoholics Anonymous (AA), usage  440 behavioral couples therapy (BCT)  439–440 biopsychosocial framework, usage  68 cognitive behavioral (CB) approaches/ interventions 439 development 68 disulfiram, usage  440 etiological influences  68–70 interventions 439 long‐term consequences  437 maintenance 68 medical interventions  440–441 motivational interviewing (MI), usage  439 naltrexone (ReVia/Vivitrol), usage  440 onset 68 pharmacotherapy 440

prevalence 68 psychological factors, integration  68–69 relapse processes  68 support groups  440 treatment approaches  439–441 withdrawal symptoms, severity (decrease)  440–441 allostatic load, manifestation  156 allostatic mediators, production/dissemination  156 alopecia 451 alternative health techniques, ethics issues  427 American Association of Retired Persons (AARP), family caregiver report  9 American Board of Professional Psychology (ABPP), certification  347, 360 American College of Cardiology Foundation, CAC report 84 American Congress of Obstetricians and Gynecologists (ACOG), Committee on Obstetric Practice (depression screening recommendation) 197 American Heart Association Strategic Impact Goal  90 Task Force on Practice Guidelines, CAC report 84 American Medical Association (AMA), Code of Ethics 289 American Music Therapy Association, music therapy practitioner certification  253 American Psychological Association (APA) Ethics Code  428, 431 Standard 5.05  432 “Guidelines for Psychological Practices in Health Care Delivery Systems”  345 mission statement, amendment  355 Practice Guidelines  124 Prevention Guidelines  37 American Psychosomatic Society, mission  357 American Sexual Health Association (ASHA), oral contraceptive effectiveness data  387 Americans with Disabilities Act (ADA)  60 passage 273 anabolic‐androgenic steroid use, impact  412, 422 anhedonia  87, 163–164 schizophrenia, relationship  164 self‐reported anhedonia, emotion regulation deficits (impact)  164 anorexia nervosa (AN)  4, 117, 396 bone mass issues, association  121 family therapy  123–124 Maudsley model of AN treatment for adults (MANTRA) 123 psychotherapy, usage  122–124 reproductive consequences, association  121 specialist supportive clinical management (SSCM) 123

Index treatment, APA Practice Guidelines  124 weight restoration  123 antiretroviral (ARV) medications  387 adherence, enhancement  387 anxiety aromatherapy, usage  249 disorders medical disorders, comorbidity  237 primary care health settings screening  237 rates, elevation  122 health anxiety  452, 453–454 levels, increase  335 pediatric cancer, relationship  336 physical manifestations, reduction  368 physician screening (ACOG recommendations) 197–198 prevention/addressing 98 reduction 454 physical activity, impact  408 sensitivity 87 skin disease, relationship  451 SSRIs/SNRIs, usage  134 treatment, biofeedback (usage)  252 Appearance Schemas Inventory  446 appetite, change  193 aromatherapy, usage  247, 249 arrhythmias, bulimia nervosa (association)  121 art therapy, usage  246 Ashtanga yoga  250 assent, implication  349 assisted reproductive technology (ART), usage  28 asthma 327 athleticism, impact  412 attentional resources, regulation  14 attention deficit hyperactivity disorder (ADHD) 236 biofeedback, usage  252–253 medication, pilot usage  350 attention, sustaining  60 attention training  276 autism spectrum disorders  236 autonomic nervous system (ANS), activation  75 autonomy supportive characteristic (MI)  267 avolition 164–165 Ayurveda  245, 246 back pain (treatment) acupuncture, usage  252 chiropractic, usage  248 bacterial vaginosis (BV)  385 bariatric surgery, usage  177–178 Beck Anxiety Inventory (BAI)  237, 239 Beck Depression Inventory (BDI)  208, 239 Beck Depression Inventory for Primary Care (BDI‐PC) 237

471

Beck Depression Inventory, Second Edition (BDI‐II) 237 behavior change, transtheoretical model  39–40 proximate/ultimate explanations  323 psychological mechanisms/processes, knowledge 324–325 behavioral activation, impact  102 behavioral adjustment  238 behavioral care  13 behavioral couples therapy for AUD (ABCT)  439–440 behavioral health, behavioral medicine (contrast)  358 behavioral health providers (BHPs) administrative issues, types  284 appointment structure  285–286 billing/reimbursement 287–288 colleagues, interaction  289 confidentiality, management  288 documentation 286 general clinic supports  285 general practice, organization  284–285 integration, management considerations 284–286 multiple relationships, management  288–290 non‐colleague patients, interaction  289–290 office/clinic space  285 primary care integration  283 referral management  286–287 behavioral medicine  358 behavioral medicine, trends  311 biological issues  314–315 chronic diseases  312–313 chronic psychopathology, health problems/ diseases (comorbidity)  316 diversity/inclusiveness 313–314 healthy psychology knowledge, translation  315 methodological sophistication  311–312 social justice  316–317 behavioral regimens, adherence  217 behavioral theories  39 behavioral therapy (tobacco use disorder)  228–229 Beliefs about Appearance Scale  446 benefit finding (BF)  11 Benson, Herbert  357 benzodiazepines, effectiveness  440 bereavement, impact  388 Best Practices in Psychology 36 Bikram yoga  250 biliopancreatic diversion, duodenal switch (usage) 178 binge drinking (SAMHSA definition)  69 binge eating disorder (BED)  4, 118, 396 health complications, association  121

472 binge eating, health risk behaviors  155 biofeedback, usage  137, 252–253 biologically based practices  247 biological measures, examples  314–315 biomedical reductionism  184 biopsychosocial (BPS) approach  184 evolution 321–323 biopsychosocial (BPS) functioning  322 biopsychosocial (BPS) model  147, 184–185, 321, 355 focus 185 biopsychosocial (BPS) perspective (suicide) 204–205 biopsychosocial (BPS) perspectives  323–325 biopsychosocial (BPS) practice (health psychology) 321 biopsychosocial (BPS) term, usage  323 biphobia, behaviors  418 birth defects  30 blood alcohol concentration (BAC), increase  69 body body‐based practices  247 dissatisfaction, risk factor  400 fat/muscularity, dimensions (satisfaction)  446 satisfaction/acceptance 409 Body Checking Questionnaire  446 body image  1 assessment 445 methodology 445–446 changes 447 clinical health psychology, relationship  2 concerns correlates/consequences 3–5 risk factors  2–3 demographic/sociocultural/psychosocial variables, study  2–3 disorder/dissatisfaction 396 eating‐related behaviors  4 health behaviors  4 manifestation 2 measures, content/specificity (range)  446 mental health problems  3 populations, assessment  446–447 psychiatric conditions  4 psychological functioning, problems  3 sexual health, relationship  4–5 sport/exercise, relationship  409 weight‐related behaviors  4 Body Image Avoidance Questionnaire  446 body mass index (BMI) levels 171 obesity definition  172 premature death/disability, association  172 quality of life, reduction  172 Body Parts Satisfaction Scale  446

Index Body Shape Questionnaire  446 bone mass issues, anorexia nervosa (association) 121 boundaries, rules/structures  430–431 bradycardia, bulimia nervosa (association)  121 brain activity, suppression  135 schizophrenia 161 breast cancer diagnosis, psychological adjustment (trajectories) 77 patients, behavioral activation (impact)  102 bright light therapy, usage  102 Bronfenbrenner, Urie  327 bulimia nervosa (BN)  4, 117–118, 396 cardiovascular issues, association  121 dialectical behavior therapy (DBT), usage  125 psychotherapy, usage  124–125 treatment, CBT (superiority)  124 bullying, persistence  421 bupropion, usage  176 burnout, prevention  42 cancer cancer‐specific coping processes, psychological adjustment predictors  78 childhood cancer  335 chronic alcohol misuse, link  438 death rates, contributors  73 depressed cancer patients, treatment options (evaluation) 102 depression, association  101–103, 236 development, personality type impact (belief) 74–75 diagnosis, psychosocial aspects (improvement) 102 early detection, screening  75–76 early‐stage breast cancer, group therapy (impact) 101–102 existence, negative effect  295–296 experience, characterization  77 impact, studies  102–103 initiation/progression, behavioral/psychosocial factors 74–75 initiation, risk (reduction)  74 long‐term survivorship  76 mental health, deterioration  77 mortality, decline  73 outcomes, improvement  74 pediatric cancer  335–340 psycho‐oncology, relationship  73 psychosocial adjustment  76–77 demographic risk factors  78 predictors 77–78 recurrence, fear  241

Index reentry phase  76–77 screening barriers, contextual/individual factors  75 individual‐level factors  76 USPSTF recommendations  75 survivorship periods  240 trajectory, adjustment  76–77 Cancer by the Carton (Reader’s Digest)  224 capacity, implication  349 cardiac disease, Framingham Risk Equation  85 cardiac events Framingham Risk Equation  85 reduction 90 cardiology, primary care setting  239 cardiovascular disease  327 mortality (reduction), Strategic Impact Goal (AHA) 90 cardiovascular issues, bulimia nervosa (association) 121 caregivers assistance, technological forms  12 burden 10 caregiver‐focused, problem‐solving training  13 caregiver‐oriented interventions  276 caregiving class, coping  13 clinical health psychology, relationship  9 dementia caregivers, burden/depression (alleviation) 13–14 duties, break  12 enhanced care  13 enhanced support group  13 environmental skill building program  13 female caregivers, financial burden  10 information/referral, usage  13 interventions  13, 375 meditative‐based interventions  13–14 negative psychosocial sequelae  11 positive psychosocial sequelae  11–12 post‐caregiving 14–15 problem‐solving skills training (PSST)  375 psychosocial interventions  13–14 utilization, barriers  14 PTG, patient‐related factors (relationship)  11 respite 12 stressors, responses  13 support 12 care‐recipient‐focused behavior management skill training 13 cellulitis 451 Centers for Epidemiologic Studies—Depression (CES‐D) scale  88–89, 89 central nervous system (CNS), demyelinating disease 143 cervix, narrowing  28 change

473

fear 277 patient language  266 resistance, problem  266 talk  266, 267 changes, self‐affirmation (impact)  52–53 Chantix (varenicline tartrate)  228 childhood cancer 335 obesity, public health problem  373 sexual behaviors  391 children adolescence, development/health  332 anxiety, at‐risk range  336 asthma, suffering  373 CAM approaches  246 chronic illnesses, interventions (development) 373 community‐based programs, care  377 coping strategies (reduction), interventions (impact) 374–375 culture, impact  330 depression levels, increase  336 discrimination 331 early childhood, attachment security (importance) 332 ethnic minorities, healthcare (relationship) 329–331 exosystem 328 health 327 disparities 331 impact 328 healthcare access, disparities  330–331 hemoglobin A1c levels (reduction), telemedicine (impact) 377 language, disparities  331 macrosystem 328 maltreatment 327 medication adherence  337–339, 375–376 barriers 375–376 mesosystem 327–328 microsystem 327–328 middle childhood, developmental milestones  332 negative health status, impact  328 outcomes, perinatal depression development timing (impact)  196 pediatric cancer  335–340 poverty, long‐term impact  329 protective/risk factors  331–332 sexual attraction (pedophilic disorder)  390 sociodemographic disadvantage  337 socioeconomic status (SES), impact  329 stress reduction, interventions (development) 374–375 stress responses  374 chiropractic therapy, usage  133

474 chiropractic, usage  247, 248–249, 429 chlamydia 385 infections, reduction  386 chronic alcohol consumption, impact  437 chronic alcohol misuse, link  438 chronic disease long‐term alcohol consumption, impact  437 management, preference‐sensitive decisions  297 physical/psychological symptoms  147 psychosocial variables, emphasis  312–313 chronic health problems/diseases, biological factors 314–315 chronic illnesses adjustment, acute illness adjustment (contrast) 374 treatment, advances  374 chronic physical health problems, depression management 369–370 chronic physical illness, impact  365 chronic psychopathology, health problems/diseases (comorbidity) 316–317 chronic stress body system, burden  156 counteracting 357–358 chronic stressors  418–419 chronic tension‐type headache (CTTH)  131 acupuncture, efficacy  134 pain threshold, reduction  132 SSRIs/SNRIs, usage  134 cigarette smoking, impact  87 clinical competence (psychologists)  428–429 clinical health psychology ABPP recognition  359 APA Division of Health Psychology definition 356 application/focus 355–356 body image, relationship  2 caregivers, relationship  9 competencies 359–360 education/training 359–361 future 361 history 355 problem areas, psychologist examination  356 professional organizations, formation  357–361 specialization 360 Clinical Health Psychology Specialty Taxonomy, CCHPTP development  360 clinical health psychology, trends  311 biological issues  314–315 chronic diseases  312–313 chronic psychopathology, health problems/ diseases (comorbidity)  316 diversity/inclusiveness 313–314 health psychology knowledge, translation  315 methodological sophistication  311–312

Index social justice  316–317 clinical hypnosis  257–258 close relationships  19 cluster headaches  138 coaches, transformational leadership qualities  411 Cochrane Reviews  245, 260 Code of Ethics (AMA)  289 cognition, social problem solving/work skills (association) 162 cognitive behavioral therapy (CBT)  112, 198 benefits 109 efficacy 133 interventions 29 superiority 124 third‐wave CBT  125 usage 123 cognitive behavioral therapy for insomnia (CBT‐I) 462–463 AHRQ efficacy review  463 cognitive behavior therapy (CBT)  370 adherence, improvement  315 cognitive decline, impact  98 cognitive deficits  162–163 cognitive expectancies  68 Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) 163 cognitive processing, types  324–325 cognitive remediation (DR)  163 cognitive therapy (CG)  462 benefits 109 mindfulness version  109 Cognitive Therapy for Suicide Prevention (The Joint Commission)  211 Collaborative Assessment and Management of Suicide (The Joint Commission)  211 commonsense model of self‐regulation (CSM) construct 305 framework 303–304 Community Integration Questionnaire (CIQ) 238–239 community, promotion  38 comorbid depression, impact  87–88 comorbidities depressive comorbidity, studies  102 detection/management 97 eating disorders  121–122 health context  101 insomnia 460–461 perinatal depression  197 psychiatric comorbidity  227 complementary and alternative medicine (CAM)  199, 245, 427 approaches (children/adolescents)  246 boundaries, rules/structures  430–431

Index domains, NCCIH perspective  246–247 integration 433–434 modalities efficacy (NCCIH information)  245–246 integration 430–431 psychologist knowledge, necessity  429 usage, benefits  429 presence (US)  246 prevalence 428 services, offering  432 complementary and alternative treatments  245 tobacco use disorder  229–231 complementary health techniques, ethics issues  427 complicated grief, occurrence  31 condom use practices, increase  386 confidentiality APA standards  288 limits, controls  348 psychologist confidentiality  348 confusion, increase  97 dehydration, impact  97 conscientiousness 37 consent, implication  349 Consortium on the Genetics of Schizophrenia (COGS) 163 constipation, prevention  97 contraceptive methods  387–388 co‐occurring disease‐related stigmas, concern  62 coping behaviors 410 concept, utility (question)  421 mechanisms, taxing  418–419 skills  204, 238 strategies (reduction), interventions (development) 374–375 coronary artery calcification (CAC) depression, relationship  86, 88–90 lower‐risk/higher‐risk, odds ratios  89 persistent/recurrent depression prediction ability 86 prevalence/progression, smoking (dose‐ dependent predictor)  87 progression limitation/development prevention 90 smoking, relationship  88–90 subclinical measure  84 Whitehall II study  86 Coronary Artery Risk Development in Young Adults (CARDIA) study  88 coronary heart disease (CHD), prevalence  239 Council of Clinical Health Psychology Training Programs (CCHPTP), Clinical Health Psychology Specialty Taxonomy development 360 couples, serious/chronic illnesses  20

475

Craig Handicap Assessment and Reporting Technique (CHART)  238–239 cramping (miscarriage symptom)  30 CREATE randomized controlled trials  313 cross‐dressing, sexual arousal  390 culture impact 330 medical culture, psychologists (interactions) 345–346 Current Procedural Terminology (CPT) codes, usage 287–288 cystic fibrosis (CF) psychologist treatment  352 services 350 daily caloric intake, goal setting  174 daily sleep diaries, usage  461–462 dance movement therapy (DMT)  246, 247, 251 death causes biopsychosocial (BPS) perspective  323–325 ranking 324 risk (reduction), suicide (impact)  208 thoughts 193 decisional bias (mitigation), disability paradox/ affective forecasting errors (impact)  297 decision making affective forecasts, impact (study)  294 processes, alcohol‐related expectancies (interaction) 58 delayed ejaculation  388 delirium benzodiazepines, usage (effectiveness)  440 recognition/management 98 dementia caregivers, burden/depression (alleviation) 13–14 dementia, recognition/management  98 depressed smokers, treatment  103 depression 83 cancer, association  101–103 chronic health problems, assessment/follow‐up  112 chronicity, longitudinal effect (modeling attempt) 86 comorbid depression, impact  87–88 concepts, combination  108 coping skills, education  111 coronary artery calcification, relationship  86, 88–90 definition 107–108 depression‐smoking comorbidity, recognition/ treatment 90 evidence‐based therapies  112–113 health context  101, 107 impacts, prediction  84–85

476

Index

depression (cont’d) interpersonal psychotherapy (IPT), usage  108–109 ischemic heart disease (relationship), smoking (role) 87–90 levels, increase  335 management (older adults)  369–370 mechanisms, impact (hypothesis)  85 mothers, pregnancy (depression)  194 negative effects  84–86 nonclinical depression, reduction  408–409 patient social network, evaluation/ intervention 112 pediatric cancer, relationship  336 perinatal depression  193 prevalence 107–108 prevention/addressing 98 psychological manifestations  144 psychosocial variables, behavioral activation (usage) 102 relapse, relationship  1097 risk, medical illnesses (association)  236 screening 236–237 value, potential  237 smoking, comorbidity  87 subtypes, differential associations  89–90 term, usage  193 tobacco smoking/ischemic heart disease, relationship  88 tobacco smoking, relationship  83–84 treatment acupuncture, usage  252 acute phase, IPT interventions  109 effects 111 SSRIs/SNRIs, usage  134 walking bidirectional association  144–145 relationship 143 Whitehall II study  86 depression, relapse prevention enhancement 110 mindfulness‐based therapy, potential  109 research, growth  111 strategies 111–113 patient treatment preferences, consideration  113–114 studies 108–109 depressive comorbidity challenge, consideration  104 diversity, relationship  103–104 studies 102 depressive disorders risk (increase), multi‐morbidity (impact) 366 depressive relapse chronic health problems, assessment/ follow‐up 112

examples 109–110 research 110 depressive symptomatology, clusters (differential associations) 89–90 depressive symptomotology risk 148 walking impairment, cyclical associations  144 depressive symptoms behavioral approaches  148 chronic exposure/accumulation, impact  86 clinical elevation  88–89 disordered eating symptoms, association  122 dysphoria, change (synergistic effect)  103 psychological manifestations  144 severe clinical depression, clarifications/ distinctions 111 walking (bidirectional, cyclical association) 145–146 walking impairment, association longitudinal associations, tested model  147 possibility 145 studies 146–147 walking impairment changes, impact  148 diabetes depression, association  236 pharmacotherapy, immigrant status (associations) 314 diabetes mellitus, development (likelihood)  438 Diabetes Prevention Program (DPP) lifestyle treatment effects demonstration  175 Diagnostic Interview with Structures Hamilton (DISH) 239 dialectical behavior therapy (DBT), usage  125, 248 Diamond, David/Martha  29 did not keep (DNK) appointments, allowance  346 dietary supplements, usage  247 dieting, risk factor  400 difference variables, impact  295 dilation and curettage (D&C)  30 disability increase (older adults)  367 obesity, association  172 paradox 293–294 errors, impact  297 findings/studies 294 person‐first perspective  275–276 disabled elderly, care  42 discrepancy, development (MI principle)  41 discrimination, promotion  418 disease causes, biopsychosocial (BPS) perspective 323–325 proximate/ultimate explanations  324 stigma 57 components 57

Index distress tolerance  87 disulfiram, usage  440 diversity, depressive comorbidity (relationship) 103–104 donor sperm, usage  28–29 Drinker’s Check‐up 265 drinking motives 68 refusal self‐efficacy  68 Drive for Muscularity Scale  447 Drug Abuse Screening Test (DAST‐20)  238 Dunbar, Flanders  357 duodenal switch, usage  178 dynamic psychological processes, data  165–166 dysphoria change, synergistic effect  103 gender dysphoria  351–352 dysregulated stress reactions  155–156 early childhood, attachment security (importance) 332 early‐onset Alzheimer’s disease  95 early‐stage breast cancer, group therapy (impact) 101–102 eating behaviors, risk/disorder (approach)  396 eating‐related behaviors, body image (relationship) 4 health psychology  395 problems, student assessment/support (Tier 3)  401–402 Eating Disorder Examination  445 eating disorders (EDs)  117, 118 acculturation levels/acculturative stress, impact 120–121 anxiety disorder rates, elevation  122 behavior, risk (reduction)  400 biological factors  119 comorbidity 121–122 complications 121 cultural risk factors  119–120 defining, method  118 diversity considerations  120–121 gender differences  120 genetic risk factors  119 medical management  125 monoamine oxidase inhibitors (MAOIs), usage 125 mortality rate, elevation  121 nutritional therapy  125 partial hospitalization  126 perfectionistic tendencies  119–120 prevention intervention, design/study  401 prognosis 126 psychological factors  119–120

477

psychotherapy, usage  122–125 residential treatment  126 risk factors  119–121 selective serotonin reuptake inhibitors (SSRIs), usage 125 self‐monitoring 124 sexual orientation, correlation  121 social factors  119–120 substance use disorders, commonness  122 subthreshold levels  118–119 symptoms, athleticism/exercise (impact)  412 treatment 122–126 PHP option  126 tricyclic antidepressants (TCAs)  125 types 117–119 e‐cigarettes, components  224 ecological framework, children (negative health status) 328 ecological model (Bronfenbrenner)  323 ecological momentary assessment (EMA)  165–166 eczema  451, 452 Edinburgh Postnatal Depression Scale (EPDS)  198 education, prevention model framework  398 effort construct, complexity  165 deficits 164–165 effort‐based performance deficits, pattern  165 ego depletion  157 elderly (depression management), pharmacotherapy/psychotherapy (efficacy) 369 electroencephalography (EEG), usage  46–47 electromagnetic therapy  247 electronic health record (EHR) impact 348 systems, usage  284, 287 electronic nicotine delivery systems (ENDS), marketing 223 emotion abnormalities 163–164 paradox 164 regulation 399 deficits, impact  164 emotional intelligence, score (elevation)  295 emotional management  295 emotional pain, distance  157 emotional responses, strategic/intentional withholding 157 emotional states, duration/intensity (overestimation) 294–295 emotional stress, prevention  42 emotional well‐being, role  297–298 empathy, expression (MI principle)  41 Energy Expenditures for Rewards Task (EEfRT) 165

478

Index

energy therapies  247 engaging (MI process)  268 enhanced care  13 Enhancing Recovery in Coronary Heart Disease (ENRICHD) randomized controlled trials 313 environmental skill building program  13 epidemiological transition  323–324 epilepsy, alcohol consumption (impact)  437–438 episodic memory, delay  14 episodic tension‐type headache (ETTH)  131 acupuncture, efficacy  134 erection, obtaining (difficulty)  388 error‐related negativity (ERN) response, examination 46–47 Ethical Code (APA)  288–289 “Ethical Principles of Psychologists and Code of Conduct” (APA)  289 Ethics Code (APA)  428, 431 Standard 5.05  432 ethnic minorities, healthcare (relationship) 329–331 European Organization for Research and Treatment of Cancer (EORTC), quality of life questionnaire (QLC‐C30)  241 evoking (MI process)  268 exercise acute exercise, impact  409–410 aerobic exercises, participation (impact)  409 behavior, self‐affirmation/implementation intention combination (impact)  49 cause 407–410 concurrent factors  411–413 effect 410–411 health psychology context  407 high‐intensity exercise, impact  408–409 impact 412 injury 410 positive health outcomes  412 exhibitionistic disorder  390 exosystem 328 Expanded Disability Status Scale (EDSS)  143 Expectancy that Actions Produce Immediate Outcomes (EAPIO)  304 falling, fear (older adults)  368–369 fallopian tubes, blockage  28 falsifiable hypotheses, experimental tests (findings replication) 322 families adjustment, pediatric cancer diagnosis  337 care partners, health (promotion)  98–99 children, health (impact)  328 ethnic minorities, healthcare (relationship) 329–331

family‐based structural multisystem in‐home intervention 13 health 327 social support, exerciser perceptions  411 family‐based treatment (FBT), adolescent‐focused therapy (comparison)  123–124 Family Caregiver Alliance, national registry (usage)  12 fatigue experience 193 management (older adults)  367 fats, human craving  324 feeding disorders  118 female caregivers, financial burden  10 fetal alcohol syndrome (FAS)  438 fetishistic disorder  390 fibrocystic disease (FBD), risk  47 fight or flight response  357–358 first trimester miscarriages, genetics (impact)  30 focalism  295, 296 focusing (MI process)  268 follicle‐stimulating hormone (FSH) levels, alteration 28 formal caring, need (older adults)  368 frailty, increase (older adults)  367 Framingham Risk Equation, risk calculator  85 frontotemporal lobar degeneration  95 frotteuristic disorder  390 frozen embryo transfer (FET)  28 frozen/thawed embryo transfer cycle  28 Functional Assessment Measure (FAM)  238 functional assessment, measurement ability  238 functional capacity  187 Functional Independence Measure (FIM)  238 functional magnetic resonance imaging (fMRI), usage 47 functional restoration (FR) programs  187–188 fungal skin diseases  451 future expectations, inaccuracy (increase)  294 future ischemic heart disease, indicators  88 gait disturbances  95 gamma‐aminobutyric acid (GABA) antagonists 464 inhibitory effects, enhancement  440 gastric banding procedure  177 gastric bypass  346 gastric sleeve procedure  177–178 gender dysphoria, psychologist treatment  351–352 gender identity, assessment  385 gender minorities individuals, sexual risk behaviors (association)  422 persons obesity, concern  422 substance use, elevation  421–422 term, usage  417

Index general clinic supports  285 generalized affective forecasts, importance 298–299 generalized anxiety disorder (GAD)  452, 453 general practice, organization  284–285 genetic test results, obtaining (intentions)  48 genital herpes  385 genital stimulation, sexual interest  390 glasses, usage (problems)  97 gonorrhea 385 grief complicated grief, occurrence  31 expressions 30 Guidelines for Assessment of and Intervention with Persons with Disabilities (APA)  275, 278 Guidelines for Prevention in Psychology 36–37 “Guidelines for Psychological Practices in Health Care Delivery Systems” (APA)  345 guilt, feelings  193 Hamilton Rating Scale for Depression (HAM‐D)  237, 239 Handbook of Psychology and Health 183 hangovers, experience  70 headaches 131 acetaminophen (paracetamol), usage  134 chiropractic, usage  248 cluster headaches  138 medication overuse headaches  136 migraine headaches  134–137 nonsteroidal anti‐inflammatory drugs (NSAIDs), usage 134 tension headaches  131–134 health anxiety  452, 453–454 biopsychosocial (BPS) model  147 complications, binge eating disorder (association) 121 disease stigma, impact  57 disparities, minority stressors (relationship) 421–423 health‐compromising behavior, termination  24 health‐enhancing behavior, initiation  24 health‐enhancing changes, making  37 health‐promoting school, WHO definition  396 health‐related decisions, conscious/unconscious processes (impact)  53 health‐related examples  296 health‐related quality of life  336 information attitudes (changes), self‐affirmation (impact) 50–51 resistance, overcoming  45 responsiveness (increase), self‐affirmation (impact) 53

479

literacy, patient level  218–219 maintenance 358 messages, self‐affirmation/responses  45 outcomes, structural stigma (influence)  60 prevention, relationship  41–42 problem, emergence  327 problems/diseases, chronic psychopathology (comorbidity) 316 providers, anti‐stigma approaches  61–62 psychologist, role  90–91 relationships, impact  19 research 46 risk behaviors  155 service psychology, societal/healthcare trends (impact) 356–357 sexual minority populations, impact  417 subjective perceptions, PTEs (association)  154 suicide, impact  207 Health at Every Size (HAES) program  400 health behavior change, self‐affirmation process (understanding) 50–52 motivation, emotional well‐being (impact)  297–298 self‐affirmation, impact  46 health belief model (HBM)  40–41 healthcare access 329 child access, disparities  330–331 contexts, challenging  350–351 delivery, psychologist competence/ credentialing 347 ethnic minorities, relationship  329–331 factors 328–329 visits, occurrence  283 health contexts comorbidity 101 depression  101, 107 depressive relapse, examples  109–110 relapse 107 Health Insurance Portability and Accountability Act (HIPAA)  288, 346 confidentiality control  348 Health Management Resources weight‐loss program 174 health promotion CAM modalities, usage  246 health damage, contrast  51 protective factors  37–38 tier 1 (three‐tier model)  398–400 health psychology affective forecasting  293 biopsychosocial (BPS) approach, evolution 321–323 biopsychosocial (BPS) practice  321

480

Index

health psychology (cont’d) clinical health psychology history 355 trends 311 complementary, alternative, and integrative interventions 245 defining  357–361, 358 eating 395 exercise 407 hypnosis applications 259–260 relationship 257 implications  83, 143 knowledge, translation  315 neurocognitive disorders, relationship  95 pain, relationship  1832 relationship 356–357 research advance  45 schools 395 sport 407 suicide 203 prevention 207–208 theory of suicide  204–205 three‐tier model, history  397–402 healthy eating behaviors (facilitation), tier 2 (three‐tier model)  400–401 Healthy People 2020 (publication/mission)  42, 356 healthy physical activity  399 healthy school, notion  395 healthy student  397 approach, components  399 self‐care 397 heart, comorbid depression/smoking (impact) 87–88 hemorrhagic stroke, chronic drinking (impact)  437 herbal supplements, usage  247 heterosexism, experiences  418 hierarchical attitudes, inclusion  277 high blood pressure, chronic drinking (impact)  437 high‐intensity exercise, impact  408–409 home exercise program (HEP)  302–305 homeopathy, usage  246 homophobia, behaviors  418 hopelessness, feelings  208 HORIZONS (STI/HIV prevention intervention) 386 hospice programs, Medicare/Medicaid funding  42 Hospital Anxiety and Depression Scale (HADS)  208, 241 human bodies, evolution  324 human immunodeficiency virus (HIV)  383 antiretroviral (ARV) medications  387 care, test and treat  387 contraceptive methods  387–388 medical male circumcision  387

post‐exposure prophylaxis (PEP) drugs  387 pre‐exposure prophylaxis (PrEP)  387 prevalence 385–386 prevention intervention approaches  386–387 prevention methods  384 prevention toolkit, expansion  387 risk 384 increase 386 stigma, sensitivity  62 test 420 testing/linkage/retention 387 transmission risk, reduction  387 Truvada (emtricitabine/tenofovir disoproxil fumarate), FDA approval  387 vaccination 387 human papillomavirus (HPV)  385 Huntington disease, genetic test results (obtaining) 48 Huntington’s disease, depression (association)  236 hyperhidrosis  451, 452 hypersexual disorder  389 hypersexuality 389–390 clinical guidelines/diagnostic criteria, absence 389 treatment, multi‐method  390 hyperthyroidism, depression (association)  236 hypnosis applications 259–260 clinical hypnosis  257–258 experience, factors  258 health psychology, relationship  257 meta‐analyses 259 portability, increase  258 positive outcomes, perspective  258 qualitative reviews  259 sleep‐like state, contrast  257 usage  246, 253 hypnotherapy, usage  229–230 hypnotic suggestibility  258–259 hypotension, bulimia nervosa (association)  121 hypothalamic‐anterior pituitary‐adrenal (HPA) axis, allostatic load (manifestation)  156 hypothalamic‐pituitary‐adrenal (HPA) axis, trigger 75 hypothyroidism, depression (association)  236 if‐then plans  49 illness biopsychosocial (BPS) model  147 definition 185 prevention 358 stigma, influence  59–60 immune functioning, massage therapy (impact)  249 immune neglect  296 impact bias, research  295–296

Index implementation intentions self‐affirmation implementation intention  49 self‐affirmation, independent/synergistic effects 49 impulsivity 204 individuals (characteristics), self‐affirmation effects (moderators) 47–48 indoor tanning, quitting (intentions)  49–50 infectious disease, death rate (US)  323 infertility 27–29 causes 28 emotional consequences  28 infertility‐specific mental health services  29 types 27–28 informal caring, need (older adults)  368 Information‐Motivation‐Strategy Model (IMS Model) 218 information element, impact  219 motivation, emphasis  220 strategies/resources, development  220 usage 219–220 information processing, reduction  60 informed consent  348–349, 429–430 inhibitory emotion regulatory styles  156–157 insomnia 459 actigraphy, usage  462 aromatherapy, usage  249 assessment 461–462 clinical interview  461 cognitive behavioral therapy for insomnia (CBT‐I) 462–463 pharmacotherapy, contrast  464–465 cognitive behavior therapy (CBT), adherence (improvement) 315 comorbidities 460–461 conceptualization, changes  460 daily sleep diaries, usage  461–462 diagnosis/epidemiology 459–461 impact 459–460 improvement 187–188 melatonin receptor agonists (MRAs)  464 orexin receptor antagonists (ORAs)  464 over‐the‐counter (OTC) medications  464 pharmacotherapy 463–464 polysomnography, usage  462 prescription medications, approval/ nonapproval 464 prevalence 459–460 psychological interventions  462–463 questionnaires, usage  462 risk factors  460–461 treatment 462–465 tricycle antidepressants (TCAs)  464 types 460 Insomnia Severity Index (ISI)  462, 463

481

Institute of Medicine (IOM) Living and Chronic Illness report  183 “Relieving Pain in America” report  184 integrated care definition 208–209 possibility, EHRs (impact)  348 integrative health techniques, ethics issues  427 intentional patient nonadherence  217 intentions (engendering), self‐affirmation (impact) 51 interdisciplinary care, multidisciplinary care (contrast) 185–186 interdisciplinary pain management  185–187 models, effectiveness  186 interdisciplinary pain programs availability, improvement  186 heterogeneity 187 interdisciplinary team functioning, practice  276–277 science, research  278 internal experiences, inhibition/avoidance/ escape 156–157 internalized stigma, reduction (efforts)  62 International Classification of Headache Disorders (ICDH), International Headache Society creation 131 Internet, obesity effects  173 interpersonal psychotherapy (IPT)  108, 112 combinations, usage  108–109 interventions 109 usage 123 interpersonal therapy (IPT)  198 intervention strategies, self‐affirmation (combination) 48–50 Interview for Diagnosis of Eating Disorders  445 intimate partners, health problem prevention  20 intrauterine insemination (IUI)  28 in vitro fertilization (IVF)  28 irritable bowel syndrome (IBS) hypnosis, application  260 invitation avoidance  60 ischemic heart disease  83 depression negative effects  84–86 relationship, smoking (role)  87–90 depressive symptomatology  85 health psychologist, role  90–91 incidence/mortality, depression prediction 84–85 indicators 88 measurement 84 prediction, smoking (impact)  87 prevalence, estimate  83 primary/secondary prevention, comorbid risk factors (understanding/treatment)  83

482

Index

ischemic heart disease (cont’d) promotion 85 psychological conditions/behavioral risk factors, relationship 83–84 risk, increase depression mechanisms, impact (hypothesis)  85 depressive symptoms, chronic exposure/ accumulation (impact)  86 risk (reduction), smoking cessation (impact)  90 societal implications  90–91 ischemic stroke, chronic drinking (impact)  437 Iyengar yoga  250 Jenny Craig weight‐loss program  174 job loss, impact  388 Joint Principles of the Patient‐Centered Medical Home (PCMH)  208–209 Kinect application  306–307 knowledge acquisition, concentrations  279–280 Korsakoff’s syndrome, chronic drinking (impact) 437 Kundalini yoga  250 label avoidance, impact  60 language children, disparities  331 patient language, forms  266 lesbian, gay, and bisexual (LGB), term (usage)  417 lesbian, gay, bisexual, or transgender (LGBT) experience 385 life events, affective forecasts (impact)  298 lifespan, prevention/health  35 lifestyle treatment effects, Diabetes Prevention Program (DPP) demonstration  175 Likert scale  240, 446 Liraglutide (Saxenda), usage  176–177 liver diseases  438 alcoholic liver disease, treatment  441 Living Well and Chronic Illness (IOM report)  183 long‐term aspirations, actions (connections)  52 Look AHEAD weight loss study  175 lorcaserin (Belviq), usage  176 lung disease risks (recognition), smoking (continuation) 297 luteinizing hormone (LH), impact  28 Maastricht Questionnaire  239 macrosystem 328 major depressive disorder, concept (combination) 108 manipulative body practices  247 massage therapy  247, 249–250, 429 usage 133

Matarazzo, Joseph  358 Maudsley model of AN treatment for adults (MANTRA) 123 Means‐End Problem Solving (MEPS) Procedure 238 Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) 163 medical culture, psychologists (interactions) 345–346 medical disorders, anxiety disorders (comorbidity) 237 medically assisted reproduction interventions, stress 29 medically assisted reproduction technology, stress 29 medically unexplained physical symptoms (MUPS) 207 medical male circumcision  387 medical nonadherence, addressing  346 medical providers, relationships (management) 288–290 medical psychology, intercultural skills  345–346 medical regimens, adherence  217 medical residents, stigma‐related concerns  59 medical settings psychological assessment, practice implications 235 psychologists, ethics issues  345 medication effects, monitoring  97–98 medication overuse headaches  136 regimens, adolescent nonadherence  376 medication adherence barriers 338 children  337–339, 375–376 barriers 375–376 older adults  366–367 promotion, interventions (impact)  338–339 Medifast weight‐loss program  174 meditation 400 usage 246–248 utility, demonstration  357–358 melatonin receptor agonists (MRAs)  464 memory encoding 157 episodic memory, delay  14 semantic memory networks, activation (abnormality) 164 spatial working memory, decline  14 men, cancer mortality  73 mental health deterioration 77 diagnoses, medical resident stigma‐related concerns 59

Index problems, risk  38–39 professionals adaptability/skill, importance  346 professional integrity  346–347 services/interventions, caregiver usage (barriers) 14 Mental Health Parity Act, mental illness insurance coverage (expansion)  609 mental illness biogenetic explanations, blame/dangerousness (associations) 61 development, absence  154 insurance coverage expansion, Affordable Care Act/Mental Health Parity Act (impact)  60 suicide risk, association  207–208 men who have sex with men (MSM), HIV (impact) 385 meridians 251 mesosystem 327–328 metatheoretical frameworks, psychology reliance (reasons) 322 mHealth programs, impact  173 microbicides, usage  387 microsystem 327–328 middle childhood, developmental milestones  332 migraine headaches  134–136 acupuncture 137 aura phase  135 biofeedback, usage  137, 252 cognitive behavioral treatment (CBT) focus  137 electrical activity, spread  135 etiology 136 headache phase  135 mindfulness‐based methods  137 pathophysiology 135–136 pharmacological treatment  136 phases 135 process 135 psychotherapy treatments  137 symptomology, reasons (debate)  135 transcranial magnetic stimulation (TMS)  136 treatments 136–137 triggers 135 warning phase  135 military active duty psychologists, impact  350 Millennium Development Goals (MDGs), creation 209–210 mind‐body connection (suicide)  207 mind‐body medicine  246 mindful awareness, usage  400 mindfulness 399 mindfulness‐based therapy, potential  109 mindfulness‐based cognitive therapy (MBCT)  198, 248 mindfulness‐based stress reduction (MBSR)  248

483

minority identity  420–421 stress processes, association  420 minority statuses environmental circumstances, impact  420 intersectional interactions  419 minority stress experiences 421 framework, usefulness  419 model  418, 421 minority stressors, health disparities (relationship) 421–423 minors, gender dysphoria  351–352 miscarriage  27, 29–32 complicated grief, occurrence  31 disappointment/despair 30 first trimester miscarriages, genetics (impact)  30 incidence 30 risk (increase), anorexia nervosa (association) 121 risk factors  30 monoamine oxidase inhibitors (MAOIs), usage 125 mood inconsistent information, processing  157 Profile of Mood States (POMS)  408 rehabilitation domain  187 morbidity multi‐morbidity 365–366 patterns, change (US)  323–324 mortality pattens, change (US)  323–324 mothers, pregnancy (depression)  194 motivational enhancement therapy (MET)  269 motivational interviewing (MI)  265 applications/adaptations 269–270 autonomy supportive characteristic  267 collaboration, spirit  267 evidence base  268–269 evocative characteristic  267 microskills 267 patient language perspective  266 principles 41 processes 268 skills/strategies 267–268 style/spirit 266–267 training 269 usage 439 motivation, construct (complexity)  165 motor skills, decrease  95 multidisciplinary care, interdisciplinary care (contrast) 185–186 Multi‐Ethnic Study of Atherosclerosis (MESA) calcium calculator  84 multi‐morbidity 365–366 consequences 366–370 multiple relationships  430–431

484

Index

multiple sclerosis (MS)  207 behavioral approaches  148 caregiver study  11–12 depression/depressive symptoms, psychological manifestations 144 depressive symptoms, impact  145 functional impairment, physical disease (distinction) 148 relapsing‐remitting multiple sclerosis (RRMS), study 146–147 Theory of Unpleasant Symptoms (TOUS) 145–146 walking/depression, health psychology implications 143 Multiple Sclerosis Walking Scale‐12 (MSWS‐12) 143 Muscle Appearance Satisfaction Scale  447 muscle tightness/strain (reduction), massage therapy (usage)  249 musculoskeletal pain (reduction), aerobic/ strengthening exercises (impact)  410 music therapy, usage  246, 253 naltrexone/bupropion (Contrave), usage  176 National Alliance for Caregiving (NAC), family caregiver report  9 National Association of School Psychologists (NASP), school multitier model  398 National Center for Complementary and Integrative Health, Clinical Digest  229 National Epidemiological Survey on Alcohol and Related Conditions III data  68 National Health and Nutrition Examination Survey (NHANES), obesity data  171–172 National Health Interview Survey  246 National Institute of Mental Health (NIMH), schizophrenia initiatives  163 National Institutes of Health (NIH) “National Pain Strategy”  184 Patient‐Reported Outcomes Measurement Information System (PROMIS)  186–187 “National Pain Strategy” (NIH)  184 National Society of Crippled Children and Adults  273 National Weight Control Registry (NWCR), obesity cohort  173–174 naturopathy, usage  246 neck pain (treatment), chiropractic (usage)  248 negative affect negative emotionality  119 persistence 194 negative attitudes, reduction  61–62 neonatal loss (stillbirth)  27, 29–32 causes 30 complicated grief, occurrence  31 disappointment/despair 30

neural activity excess disability, prevention  96–98 message processing, impact  52–53 self‐affirmation, impact  46–47 neural networks, organization  155–156 Neurobehavioral Rating Scale (NRS)  239 neurocognitive disorders (NCDs) comorbidities, detection/management  97 dementia/delirium, recognition/ management 98 dependencies 96 depression/anxiety, prevention/addressing  98 etiology 95 family care partners, health (promotion)  98–99 health psychology relationship 95 role 96 medication effects, monitoring  97–98 patterns 905 prevention/management (importance), American Academy of Neurology Ethics and Humanities Subcommittee emphasis  96–97 problems, coping  96 progression 99 quality of life, basic functioning (optimization) 97 substance use, detection/reduction  98 neurodegeneration, changes  95 neurodegenerative disease, progressive effects (acceleration) 98 neurons, organization  155 neuropsychological evaluation, usage  274 nicotine dependence 227 electronic nicotine delivery systems (ENDS), marketing 223 gum, development/usage  227–228 term, usage  223 use disorders  225 nicotine replacement therapy (NRT), modalities 228 Nicot, Jean  224 nociception, pain (contrast)  185 nonclinical depression, reduction  408–409 noncommunicable diseases, mortality reduction (United Nations Sustainable Development Goals) 90–91 non‐orthopedic pain conditions  187 nonsteroidal anti‐inflammatory drugs (NSAIDs), usage 134 no‐show appointments, allowance  346 obesity 171 adjustable gastric band, usage  177 bariatric surgery  177–178

Index behavioral lifestyle interventions  174–175 biliopancreatic diversion, duodenal switch (usage) 178 BMI definition  172 clinical guidelines  178 commercial weight‐loss programs  174 consequences 172 daily caloric intake, goal setting  174 environmental contributors  172–173 etiology 171–173 extended care maintenance programs, helpfulness 174 genetic contributors  172–173 hypnosis, application  260 influences 172–173 Internet, effects  173 lifestyle treatment effects, Diabetes Prevention Program (DPP) demonstration  175 liraglutide (Saxenda), usage  176–177 lorcaserin (Belviq), usage  176 medical costs  172 mHealth interventions  173 naltrexone/bupropion (Contrave), usage  176 National Health and Nutrition Examination Survey (NHANES) data  171–172 National Weight Control Registry (NWCR) cohort 173–174 obesity‐related comorbidity  175 orlistat (Xenical), usage  175–176 pharmacotherapy 175–177 phentermine/topiramate (Qsymia), usage  176 prevalence, increase  171 rates, increase  327 risk (increase), pediatric cancer (impact) 339–340 roux‐en‐ϒ gastric bypass  177 scope/seriousness 171–173 self‐help approaches  173–174 treatment options  173–178 vertical sleeve gastrectomy, usage  177–178 weight loss, Look AHEAD study  175 obesogenic environment  173 obsessive‐compulsive disorder (OCD)  452, 454–455 offset responsibility  60–61 offspring, postpartum depression  194–195 older adults behavioral medicine researchers/clinicians, perspectives 365 chronic physical health problems, depression management 369–370 cognitive behavior therapy, usage  370 disability, increase  367 falling, fear  368–369 fatigue, management  367

485

formal/informal caring, need  368 frailty, increase  367 health psychology researchers/clinicians, perspectives 365 medication adherence  366–367 pain, management  367 physical activity, reduction  369 problem‐solving therapy, usage  370 shoulder impairment, epidemiology  301–302 olfactory/taste perception, alteration  97 Olmstead v. L.C.  60 oncology primary care setting  240–241 survivorship, periods  240 onset responsibility  60–61 OPTIFAST weight‐loss program  174 oral contraceptives, effectiveness (ASHA data)  387 orexin receptor antagonists (ORAs)  464 organ transplantation  346 assessment 241 primary care setting  240 psychologists, services  350–351 orlistat (Xenical), usage  175–176 osteopathic 247 osteopenia, anorexia nervosa (association)  121 osteoporosis 369 anorexia nervosa, association  121 oxytocin augmentation therapy (OAT), meta‐analysis 166 pain aromatherapy, usage  249 biopsychosocial (BPS) model  184–185 coping 187 functional restoration (FR) programs  187–188 health psychology, relationship  183 interdisciplinary pain management  185–187 effectiveness 186 measurement 303 nociception, contrast  185 relief, hypnosis (applications)  259 VA Polytrauma Network Site, veteran assessment 188 pain management care, multicomponent models  185 older adults  367 services, integration (importance)  186 panic, agoraphobia (relationship)  455 paracetamol (acetaminophen), usage  134 paraphilic disorders  390–391 assessment, clinician investigation  390 parasympathetic nervous system activity, decrease  85 Parkinson’s disease depression, association  236 patients, caregiver study  10

486 partial hospitalization programs (PHPs), ED treatment option  126 partners influence, characteristics  22 negative tactics, usage  24 pathological worry, impact  453 patient adherence (compliance)  217 factors 218–219 improvement, IMS Model (usage)  219–220 Information‐Motivation‐Strategy Model (IMS Model) 218 Patient‐Centered Outcomes Research Institute (PCORI), clinical trials requirement  298 Patient Health Questionnaire 9 (PHQ‐9)  198 patient nonadherence economic burden  217–218 types 217 Patient Protection and Affordable Care Act (US ACA)  36, 38, 359, 361 insurance coverage expansion  60 prevention, relationship  38–39 Patient‐Reported Outcomes Measurement Information System (PROMIS) (NIH) 186–187 patients informed consent  429–430 patient language, forms  266 self‐prescribing 428–429 pediatric cancer depression/anxiety, relationship  336 diagnosis, family adjustment  337 health‐related quality of life  336 impact 339–340 medication adherence  337–339 negative psychosocial outcomes  336–337 psychosocial functioning  335–337 resiliency 336–337 survivorship, health challenges  339–340 treatment, neurological functioning  339 pediatric illnesses (treatment), telemedicine (usage) 377–378 pediatric psychologists, roles  376–377 pediatric psychology cultural/ethical disparities  378 culturally competent care  378 definition 373 diagnosis, adjustment  374–375 interventions, impact  374–375 treatment delivery  376–377 pedophilic disorder  390 peers, transformational leadership qualities  411 pelvic inflammatory disease (PID)  385–386 perfectionistic tendencies  119–120 performance‐enhancing substances, impact  412 pericranial muscles, tenderness (impact)  132

Index perinatal depression  193 biological factors  195–196 clinical history/comorbidity  197 cognitive behavioral therapy (CBT)  198 complementary and alternative medicine (CAM) 199 conceptualization/classification 193–194 demographic factors  195 detection 197–199 development timing, impact  196 engagement 197–198 health factors  195–196 interpersonal therapy (IPT)  198 management 197–199 mindfulness‐based cognitive therapy (MBCT) 198 prevention 198–199 prophylactic treatment  198 protective factors  195–197 psychological factors  196–197 psychotherapy, usage  198–199 risk 195–197 health‐related behaviors, impact  196 screening 197–198 social support, importance  196–197 socioeconomic status (SES), factor  195 SSRI, usage  198 treatment 198–199 perinatal loss aftermath 31–32 factors 30 grief reaction  30 interventions, consensus (absence)  31–32 mental health disorders  31 psychotherapy/counseling 31 permission, implication  349 personality characteristics, assessment  37 complexity 409 personal wellness, improvement  36 person‐centered theory (PCT)  39 phentermine/topiramate (Qsymia), usage  176 phobias (treatment), chiropractic (usage)  248 physical activity anxiety‐reducing effects  408 cause 407–410 concurrent factors  411–413 effect 410–411 effectiveness 408 feelings 408 healthy physical activity  399 participation, barriers  413 psychobiological factors, relationship  409–410 reduction, older adults  369 physical health

Index potentially traumatic events (PTEs), exposure association 154–155 impact 153 problems, risk  38–39 physical therapy (PT)  276, 302–305 physicians board certification  347 patient trust level, importance  219–220 Pittsburgh Sleep Quality Index (PSQI)  462, 463 placental abruption  30 planned behavior (PB)  40 planning (MI process)  268 pleasure, experience capacity (reduction)  163–164 polycystic ovary syndrome (PCOS)  28 polysomnography, usage  462 positive/negative social control, contrast  21 positive psychology research 278 usage 38 post‐caregiving 14–15 post‐exposure prophylaxis (PEP) drugs  387 postpartum depression correlates/consequences 194–195 deficiencies, association  196 postpartum depressive symptoms, unhealthful behaviors (association)  196 posttraumatic growth (PTG)  11 caregiver, patient‐related factors (relationship)  11 experiences 421 research 278 posttraumatic stress disorder (PTSD)  188, 452, 454 lifetime prevalence  154 PTE exposure, poor health (association)  155 posttraumatic stress, levels (increase)  335 potentially traumatic events (PTEs) biology, negative alterations (association)  153 emotional pain, distance  157 exposure health problems, association  155 impact  153, 156 physical health, association  154–155 health, subjective perceptions (association)  154 inhibitory emotion regulatory styles  156–157 posttraumatic stress disorder (PTSD), relationship 153–154 psychopathological syndromes, development  154 responses 154 poverty, long‐term impact  329 Practice Guidelines (APA)  124 prayer, usage  246, 247, 251 preeclampsia 30 pre‐exposure prophylaxis (HIV)  387 pre‐exposure prophylaxis (PrEP)  387 preference‐sensitive decisions

487

affective forecasting, impact  2907 example 296 influences 296–297 preference‐sensitive prevention/treatment decisions, affective forecasting (role) 296–297 pregnancy achievement, medical procedures  28–29 alcohol consumption, detrimental outcomes  438 depression, correlates/consequences  194–195 prevention methods, usage  384 premature death, obesity (association)  172 premature ejaculation  388 premature ischemic heart disease morbidity/ mortality risk (increase), cigarette smoking (impact) 87 Preparing People for Change 265 Preparing People to Change Addictive Behaviors 265 preterm delivery, risk (increase)  194–195 prevention activities 36–37 Affordable Care Act, relationship  38–39 definitional terms (Gordon), US Institute of Medicine’s Committee on Prevention of Mental Disorders adoption  36 definitions 35–36 guidelines 36–37 health belief model (HBM)  40–41 health, relationship  41–42 intervention, conceptualization  36 motivational interviewing (MI)  41 psychological‐based theoretical interventions 39–41 research/practices 37 science, evidence  36 specialists, impact  37 theory of reasoned action and planned behavior (TTA/PB) 40 three‐tier model, history  397–402 training 37 transtheoretical model of behavior change (TTM)  39–40 types 35–36 primary care health settings, anxiety disorders (screening) 237 primary care medicine, suicide (context)  205–209 primary care physician (PCP) healthcare visits, occurrence  283 referral management  286–287 referral placement, promotion  287 primary care psychology, administrative issues  283 primary care settings  236–238 behavioral health providers, involvement 283–284 drug/alcohol abuse, concerns  237–238

488

Index

primary care settings (cont’d) rehabilitation settings  238–239 specialty settings  239–241 primary infertility, occurrence  27–28 primary insomnia  460 primary medical care (PC) BHP work  284 definition 283 primary prevention  35 primary psychiatric disorders  451 problem behaviors, reduction  37 problems, aversion  36 problem‐solving skills training (PSST)  375 problem‐solving therapy  370 Profile of Mood States (POMS)  408 progressive muscle relaxation (PMR)  247, 250–251 Project MATCH  269 prostate‐specific antigen (PSA) testing, prostate cancer screening (USPSTF recommendations) 75 protective factors considerations 38 types 37 psoriasis 451 psychiatric comorbidity  227, 388 psychiatric disorders  187 psychobiological factors, physical activity (relationship) 409–410 psychodermatological (psychocutaneous) disorders, classification 451 psychodermatology, impact  451 psychological conditions, cataloguing  451–452 psychological disorders development, risk (increase)  366 protective/risk factors, association  331 psychological immune system  294 psychological practice, CAM integration  433–434 psychological services, assessment/provision (importance) 275 psychological stress, coping  238 psychological treatment, complementary/ alternative/integrative health techniques (ethics issues)  427 psychologists advertising 431–432 boundaries 349–350 boundaries, rules/structures  430–431 CAM modality knowledge, necessity  429 clinical competence  428–429 competence/credentialing (healthcare delivery) 347 confidentiality 348 ethics issues (medical settings)  345 gender dysphoria treatment  351–352 growth 358–359

healthcare contexts, challenging  350–351 informed consent  348–349 loyalties, conflicts  351 military active duty psychologists, impact  350 multiple relationships  430–431 primary care integration  283 professional colleagues, consultation/ cooperation 431 professional integrity  346–347 public statements  431–432 services (organ transplantation)  350–351 transgender youth treatment  351–352 psychology health, relationship  356–357 medical psychology, intercultural skills  345–346 pediatric psychology  373 positive psychology, usage  38 prevention, guidelines  36–37 professional organizations, formation  357–361 role 204 psychomotor agitation/retardation  193 psychoneuroimmunology (PNI)  357 psycho‐oncology, cancer (relationship)  73 psychophysiologic disorders  451 psychosexual functioning, evaluation  274 Psychosocial Assessment of Candidates for Transplantation (PACT)  240 psychosocial stressors (reactivity reduction), exercise/physical activity (impact)  410 psychosocial triggers, impact  75 psychotherapy migraine headache treatments  137 usage 198–199 public health, three‐tier model (history)  397–402 public statements (psychologists)  431–432 qigong 247 quality of life basic functioning, optimization  97 health‐related quality of life  336 reduction obesity, association  172 walking impairment, association  145 quality of life questionnaire (QLC‐C30)  241 Quality of Life Scale  239, 241 randomized controlled trials (RCTs)  230, 257, 313, 369 range of motion (ROM) change 305 dynamic record  306 feasibility testing, proof‐of‐concept trial  306 measurement 303 normal ROM  303 recovery, optimization  302

Index shoulder ROM, recovery (patient expectations) 306 rapid eye movement (REM) sleep acute exercise, impact  409–410 onset delay, alcohol (impact)  69–70 Raynaud’s disease, biofeedback (usage)  252 Readiness for Transition Questionnaire (RTQ)  240 readiness rulers  267–268 real‐world situations, limitations  315 recreational sport, team size (negative impact)  411 rehabilitation black box perspective  277 interventions, theory‐driven framework (development) 276 psychologists advocate roles  278–279 roles 273–277 settings 238–239 rehabilitation psychology  273 interdisciplinary team functioning, practice 276–277 intervention, practice  275–276 petition 279–280 practice, assessment  274–275 research 277–279 training 279–280 reiki  247, 252 relapsing‐remitting multiple sclerosis (RRMS), study 146–147 relationships change, cognitions/readiness  22 close relationships  19 gender, impact  23 impact 19 multiple relationships  430–431 satisfaction 23 social relationships (breakdown), stigma (impact) 60 relaxation systematic relaxation  400 training, usage  133, 462–463 utility, demonstration  357–358 “Relieving Pain in America,” IOM report  184 religion, usage  247, 251 reproductive consequences, anorexia nervosa (association) 121 reproductive health  383 reproductive trauma  31 resilience (concept), utility (question)  421 resistance, handling (MI principle)  41 Resources for Enhancing Alzheimer’s Caregiver Health (REACH) initiative/study  13 risk aversion 277 factor, definition  331

489

reduction 38 health‐enhancing changes, making  37 ritualistic behaviors  454 Rogers, Carl  265 rosacea  451, 452 rotator cuff injury  303 roux‐en‐ϒ gastric bypass, usage  177 schizophrenia 161 anhedonia, understanding  163–164 assessment, implications  165–166 avolition, impact  164–165 cognitive deficits  162–163 cognitive impairments, severity  162 dynamic psychological processes, data  165–166 ecological momentary assessment (EMA) 165–166 effort‐based performance deficits, pattern  165 effort deficits  164–165 emotion abnormalities  163–164 motivation 164–165 oxytocin augmentation therapy (OAT), meta‐analysis  166 prodromal (prepsychotic) features  164–165 psychiatric/functional impairment  166 treatment, acupuncture (usage)  252 treatment as usual (TAU)  167 treatment, implications  166–167 Schofield, William  358 schools eating disorder prevention intervention, design/ study 401 education, prevention model framework  398 Health at Every Size (HAES) program  400 health‐promoting school, WHO definition  396 health promotion issues 396 system/community/school‐level factors  396 health psychology  395 healthy school, notion  395 lunches, provision  399 setting, impact  395–396 social emotional learning (SEL) programs  399 well‐being/health, promotion  399 Screening, Brief Intervention, and Referral to Treatment (SBIRT) models  268, 270 secondary infertility, occurrence  27–28 secondary insomnia  460 secondary prevention  35–36 secondary psychiatric disorders  451 seizure disorders, depression (association)  236 management, benzodiazepines (effectiveness) 440

490

Index

selective serotonin reuptake inhibitors (SSRIs), usage  125, 134, 198 self‐affirmation 45 effectiveness, considerations  47–48 effects, moderators  47–48 experimental induction  48 implementation intention combination 50 independent/synergistic effects  49 intervention, development  49 influence 53 studies 46–47 intervention strategies, combination  48–50 message content 47–48 framing, impact  49 process, understanding  50–52 research 46 single potential mediating variables, role  51 studies, research methods (diversity/application)  46–47 techniques, clinical samples  50 trajectory 52–54 Self as Anchor, factor  304 self‐care 399 tier 2 (three‐tier model)  400–401 self‐efficacy, support (MI principle)  41 self‐esteem physical activity, impact  409 premorbid sources  275 self‐focus, increase  194 self‐management resource access (restriction), label avoidance (impact) 60 self‐management, stigma (influence)  59–60 self‐monitoring, improvement  14 meditational model  21 self‐protective responses, emergence  98 self‐prototypes 305–306 deviations 304 self‐related processing, increase  52–53 self‐report‐based measures, usage  165–166 self‐reported anhedonia, emotion regulation deficits (impact) 164 semantic memory networks, activation (abnormality) 164 sensory loss, correction (barrier removal)  97 serotonin‐norepinephrine reuptake inhibitors (SNRIs), usage  134 service utilization  238 severe clinical depression, depressive symptoms (clarifications/distinctions) 111 severe stress, experience  155 sexual arousal, alcohol intake (impact)  69–70

sexual behaviors  384 engagement 391 sexual desire, absence  389 sexual dysfunction  388–389 assessment, factors  388–389 gender specificity  388 sexual functioning, restoration  389 sexual/gender minority health, focus  110 sexuality attitudes 388 hypersexuality 389–390 sexually transmitted diseases (STDs), medical resident stigma‐related concerns  59 sexually transmitted infections (STIs)  383 contraceptive methods  387–388 infection methods  384 prevalence 385–386 prevention intervention approaches  386–387 risk, increase  386 sexual masochism disorder  390 sexual minority health disparities, context  418–420 populations, health (relationship)  417 sexual orientation  384–385 disordered eating, correlation  121 minority persons, research  419 minority, term (usage)  417 persons obesity, concern  422 substance use, elevation  421–422 sexual risk behaviors, association  422 sexual pleasure, experience (restoration)  389 sexual risk behaviors  385–386 sexual sadism disorder  390 sexual trauma, negative health consequences  154 shoulder impairment baseline 306 epidemiology (older adults)  301–302 feasibility testing, proof‐of‐concept trial  306 home exercise program (HEP), usage  302–303 Kinect application  306–307 management approach 302 efficacy 302–303 examination, CSM framework  303–304 outcomes physical therapy, impact  306 self‐prototypes/expectations/ timelines 305–306 timelines/expectations, technology (usage) 306–307 physical therapy (PT), usage  302–303 range of motion, dynamic record  306

Index self‐prototypes 304–305 semi‐structured interviews  305 stick avatar, usage  306 shoulder ROM, recovery (patient expectations)  306 single‐occasion interview‐based measures, usage 165–166 single potential mediating variables, role  51 Situational Inventory of Body Image Dysphoria  446 skin cancer  451 risk, reduction  51 skin disease, anxiety (relationship)  451 skin‐mind connection, psychodermatology (impact) 451 skin symptoms, responses/management  454–455 sleep daily sleep diaries, usage  461–462 disorder (insomnia)  459 disruption 193 alcohol consumption, impact  69–70 hygiene 463 problems 327 maintenance difficulties  459 restriction 462–463 sleep‐wake cycle, maintenance  97 sleep‐wake rhythms, changes  95 total sleep time (TST)  461, 463 improvement 464 wake time after sleep onset (WASO)  461 sleep efficiency (SE)  461–462, 463 sleep latency (SL)  461 smoke break, avoidance  228 smoking cigarette smoking, impact  87 coronary artery calcification, relationship  88–90 depression, comorbidity  87 depression‐smoking comorbidity, recognition/ treatment 90 dose‐dependent predictor  87 emotion, association (traits)  87 health risk behavior  155 impact 87–88 smoking‐related illnesses, mortality risk (increase) 226–227 status, breath samples (carbon monoxide analysis) 314–315 tobacco smoking  83–84 smoking cessation assessment, clinical areas  227 hypnosis, application  260 impact 90 programs, depressed smokers (treatment)  103 social anxiety disorder (SAD) (social phobia)  452–453, 455 social cognition, definition  162

491

social control  20–21 attempts history 24 neuroticism 24 contextual model  22–24 delineation, attempts  21 dual‐effects hypothesis  20–21 ethnic differences  23 gender, impact  23 positive/negative social control, contrast  21 usage, predictors/consequences  22 social emotional learning (SEL) programs  399 social justice  316–317 Social Problem Solving Inventory‐Revised (SPSI‐R) 238 social relationships (breakdown), stigma (impact) 60 social support  20 contextual model  22–24 ethnic differences  23 exerciser perceptions  411 social control, relationship  22 Society for Prevention Research  36 Society of Counseling Psychology (APA)  36 socioeconomic status (SES) impact 329 perinatal depression factor  195 Solomon, George F.  357 spatial working memory, decline  14 specialist supportive clinical management (SSCM) 123 specialty settings  239–241 spinal manipulation  247 spirituality, usage  246, 247, 251 sport activities, direct parental involvement  411 cause 407–410 concurrent factors  411–413 effect 410–411 health psychology context  407 injury rehabilitation, adherence  410 participation, impact  409 recreational sport, team size (negative impact) 411 transformational leadership qualities  411 Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT)  240 stigma combatting 61–62 components 57 co‐occurring disease‐related stigmas, concern  62 definitions/examples  58 harm 60 impact 59–60

492 stigma (cont’d) internalized stigma, reduction (efforts)  62 moderators 60–61 reduction 61 structural stigma, influence  60 types  58, 58–59 stillbirth 27 stimulus control  462–463 stress acculturative stress, impact  120–121 adjustment (improvement), interventions (usage) 374–375 aromatherapy, usage  249 chronic stress, body system (burden)  156 coping, assistance  20 dysregulated stress reactions  155–156 emotional stress, prevention  42 mindfulness‐based stress reduction (MBSR)  248 processes, minority identity (association)  420 reactions, mitigation  20 severe stress, experience  155 stressors chronic stressors  418–419 coping 20 minority stressors, health disparities (relationship) 421–423 psychosocial stressors (reactivity reduction), exercise/physical activity (impact)  410 traumatic classification  153–154 structural stigma, influence  60 Structured Interview for Anorexia and Bulimic Disorders 445 students. See healthy student eating problems, assessment/support (Tier 3)  401–402 healthy student  397 SCOFF screening instrument  401 Study of Women’s Health Across the Nation Heart Study 86 Substance Abuse and Mental Health Services Administration (SAMHSA), binge drinking definition 69 substance use  204 detection/reduction 98 disorders classification, DSM change  67 eating disorders, relationship  122 elevation (sexual orientation/gender minority persons) 421–422 genetic risk factors  312, 314 health risk behavior  155 subthreshold depressive symptoms, concepts (combinations) 108 subthreshold ischemic heart disease, indicators  88 suicidal ideation

Index levels, elevation  419 risk, elevation  335–336 suicide biopsychosocial (BPS) perspective  204–205 cognitions, reduction  208 cognitive behavioral therapy (CBT)  208 contagion 207 dialectical behavior therapy (DBT)  208 evidence‐based psychological treatment approaches 208 health psychology context 203 theory 204–205 impact 207 integrated care  208–209 integrative care  212 The Joint Commission, recommendations  211 lifespan, perspective  205 mind‐body connection  207 prevention changes 209–210 health psychology, impact  207–208 primary care medicine context 205–209 presentation 206 training 206 psychologists, role  209 risk identification 208 mental illnesses, association  207–208 suicidal behavior, occurrence  212 thoughts 193 World Health Organization (WHO) global goals  209–210 recommendations 210–211 survivorship pediatric cancer  339–340 periods 240 sustainable development goals (SDGs), UN presentation 209–210 sustain talk  266, 267 sweets, human craving  324 sympathetic‐adrenal‐medullary (SAM) system, allostatic load (manifestation)  156 sympathetic nervous system activity, increase  85 syphilis 386 System 1 (cognitive processing type)  324–325 System 2 (cognitive processing type)  324–325 systematic relaxation  400 task, persistence  60 telemedicine, usage  377–378 telephone‐linked computer intervention  13 temporomandibular joint dysfunction (TMJD), TTH occurrence (likelihood)  133

Index temporomandibular (jaw) joint, impact  133 tension headaches  131–134 epidemiology 131–132 etiology 132–133 treatments 133–134 biofeedback, usage  252 tension‐type headache (TTH)  131 biofeedback training, treatment  133 cognitive behavioral therapy, efficacy  133 diagnostic criteria  132 etiology 132–133 manual therapy, usage  133 pericranial muscles, tenderness (impact) 132–133 relaxation training, usage  133 temporomandibular (jaw) joint, impact  133 treatment 133–134 tertiary prevention  35–36 The Joint Commission, suicide recommendations 211 theory of reasoned action and planned behavior (TTA/PB) 40 Theory of Unpleasant Symptoms (TOUS)  145–146 therapeutic touch  247 thinness, pursuit  396 threat bodily/physiological response  46 processing, openness  47 threatening information, aversion (reduction)  51 three‐tier model, history  397–402 Timed 25‐Foot Walk (T25FW)  143 time in bed (TiB)  461–462 restriction 463 tobacco abuse, term (usage)  225 companies, youth targeting  224–225 dependence, treatment  269 term, usage  223 tobacco smoking  83 depression, relationship  83–84 role 87–90 tobacco use absence, health improvements  226–227 cessation benefits 226–227 symptoms 226 cleaner smoke, design  224 e‐cigarettes, components  224 history 224–225 initiation/maintenance 226 reduction, symptoms  226 studies 224–225 tobacco use disorder acupuncture 230 assessment 227

493

behavioral therapy  228–229 relapse prevention  229 classification 225–226 complementary and alternative treatments 229–231 confectionary chewing gum  230–231 DSM‐5 definition  225 evidence‐based treatment  227–229 hypnotherapy 229–230 randomized controlled trial (RCT)  230 pharmacotherapy 227–228 symptoms 225 tolerance/withdrawal, constructs (association) 225–226 treatment 223 topiramate, usage  176 total sleep time (TST)  461, 463 improvement 464 Traditional Chinese medicine (TCM)  245, 246, 251 transcranial magnetic stimulation (TMS), effectiveness 136 transformational leadership qualities  411 transgender youth, gender dysphoria (psychologist treatment) 351–352 transtheoretical model (TTM)  39–40 transvestic disorder  390 trauma definition 31 reproductive trauma  31 sexual trauma, negative health consequences  154 trauma‐related emotions/thoughts/memories, coping 156 traumatic events cognitive process  11 potentially traumatic events (PTE), exposure (impact) 153 treatment adherence, stigma (influence)  59–60 quality, stigma (impact)  59 treatment as usual (TAU)  167 trichomoniasis 386 tricycle antidepressants (TCAs)  464 tricyclic antidepressants (TCAs)  125 Truvada (emtricitabine/tenofovir disoproxil fumarate), FDA approval  387 tuberculosis 438 type 2 diabetes, patient adherence  218 Uniform Code of Military Justice (UCMJ), offenses 350 unintentional patient nonadherence  217 United Nations Sustainable Development Goals, mortality reduction  90–91 urinary tract infections (UTIs), prevention  97

494

Index

urticaria (hives)  451 US National Health Interview Survey, smoking/ smoking cessation information  226–227 US Preventive Services Task Force (USPSTF) adult depression screening, recommendation 197–198 cancer screening recommendations  75 vaginal bleeding (miscarriage symptom)  30 VA Polytrauma Network Site, veteran assessment 188 ventromedial prefrontal cortex (VMPFC) activation, increase  47 activity 52–53 vertical sleeve gastrectomy, usage  177–178 violence, threats  421 viral hepatitis  385 viral warts  451 vocational retraining program  276 voyeuristic disorder  390 wake time after sleep onset (WASO)  461, 463 walking depressive symptoms (bidirectional, cyclical association) 145–146 Theory of Unpleasant Symptoms (TOUS) 145–146 walking/depression bidirectional association  144–145 relationship 143 walking impairment  143–144 behavioral approaches  148 changes, impact  148 depressive symptomology, cyclical associations  144 depressive symptoms association, possibility  145 association, studies  146–147 impact, reasons  145 longitudinal associations, tested model  147 quality of life, association  145 Walter Reed National Military Medical Center  350

weight loss behavior, organization  396 hypnosis, application  260 Look AHEAD study  175 medications, FDA approval  175 weight‐related behaviors, body image (relationship) 4 Weight Watchers weight‐loss program  174 Weiss, Steven  358 well‐being enhancement, prevention (impact)  36 promotion, protective factors  37–38 tier 1 (three‐tier model)  398–400 wellness (promotion), reiki (usage)  252 Wernicke’s encephalopathy, chronic drinking (impact) 437 Western societies, body image concerns (prevalence) 1 whole medical systems  246 women anxiety, physician screening (ACOG recommendations) 197–198 cancer mortality  73 depression, physician screening (ACOG recommendations) 197–198 pregnancy, medically assisted reproduction technology (stress)  29 World Health Organization (WHO) global goals (suicide)  209–210 recommendations (suicide)  210–211 World Report on Disability 278 worthlessness, feelings  193 yoga  246, 247, 400 styles 250 youth self‐perception, discussion  40 tobacco company targeting  224–225 transgender youth, gender dysphoria (psychologist treatment)  351–352 Zyban (bupropion hydrochloride)  228

The Wiley Encyclopedia of Health Psychology

The Wiley Encyclopedia of Health Psychology Volume 4 Special Issues in Health Psychology Edited by

Suzy Bird Gulliver

Editor‐in‐Chief

Lee M. Cohen

This edition first published 2021 © 2021 John Wiley & Sons Ltd All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/ permissions. The right of Lee M. Cohen to be identified as the author of the editorial material in this work has been asserted in accordance with law. Registered Offices John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial Office 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging‐in‐Publication Data Names: Paul, Robert D., editor. Title: The Wiley encyclopedia of health psychology / edited by Robert Paul, St. Louis MO, USA ; editor-in-chief, Lee Cohen, The University of Mississippi, MS, USA. Description: First edition. | Hoboken : Wiley-Blackwell, 2021. | Includes index. | Contents: volume. 1. Biological bases of health behavior. Identifiers: LCCN 2020005459 (print) | LCCN 2020005460 (ebook) | ISBN 9781119057833 (v. 1 ; cloth) | ISBN 9781119057833 (v. 2 ; cloth) | ISBN 9781119057833 (v. 3 ; cloth) | ISBN 9781119057833 (v. 4 ; cloth) | ISBN 9781119675785 (adobe pdf) | ISBN 9781119675761 (epub) Subjects: LCSH: Clinical health psychology–Encyclopedias. Classification: LCC R726.7 .W554 2021 (print) | LCC R726.7 (ebook) | DDC 616.001/903–dc23 LC record available at https://lccn.loc.gov/2020005459 LC ebook record available at https://lccn.loc.gov/2020005460 Cover image and design: Wiley Set in 10/12.5pts ITC Galliard by SPi Global, Pondicherry, India 10 9 8 7 6 5 4 3 2 1

Contents

List of Contributors

ix

Forewordxxvii Prefacexxix Preface Volume 4: Special Issues in Health Psychology Editor‐in‐Chief Acknowledgments

xxxi xxxiii

The Biopsychosocial Model Robert J. Gatchel, Christopher T. Ray, Nancy Kishino, and Athena Brindle

1

Death and Dying David E. Balk

9

The Role of Regulation in Health Behavior (Listed in System as “Federal/State Regulation (FDA)”) David B. Portnoy

21

Funding for Health Psychology Research Robert T. Croyle

29

Genetics and Psychology (Health) Nathan A. Kimbrel and Christopher Fink

35

The Role of Cultural Mistrust in Health Disparities Brandon L. Carlisle and Carolyn B. Murray

43

Health Disparities Melissa Ward‐Peterson and Eric F. Wagner

51

vi

Contents

Injury, Accident, and Injury Prevention Kristi Alexander and Riley Cropper

61

Integrated Primary Care Sandra B. Morissette and Brian Konecky

67

Neuroimaging73 Steven M. Nelson Palliative Care Mamta Bhatnagar and Robert C. Arnold

79

Patient Navigation/Community Health Workers Kristen J. Wells and Janna R. Gordon

91

The Patient Protection and Affordable Care Act and Health Psychology Ronald H. Rozensky

101

Pediatric Psychology Christina M. Amaro, Michael C. Roberts, Andrea M. Garcia, and Jennifer B. Blossom

115

Placebo Effect Diletta Barbiani and Fabrizio Benedetti

127

Prescriptive Authority for Psychologists Wendy P. Linda and Robert E. McGrath

139

Health‐Related Quality of Life Amy E. Ustjanauskas and Vanessa L. Malcarne

149

The Roles of Health Psychologists in Surgery Lauren Decaporale‐Ryan and John D. Robinson

155

The Bidirectional Relationship Between Sleep and Health Elizabeth M. Stewart, Shane Landry, Bradley A. Edwards, and Sean P. A. Drummond

165

Telepsychology189 Shawna Wright and Eve‐Lynn Nelson Traditional Healing Practices and Healers Robert M. Huff

199

Training in Health Psychology Kevin T. Larkin

205

Contents

vii

Training Non‐psychologists in the Healthcare Settings Cesar A. Gonzalez, Kristine M. Diaz, and Richard J. Seime

213

Transgender Health DeAnna L. Mori, Colleen A. Sloan, Heather Walton Flynn, and Claire Burgess

219

Veterans’ Health Issues Cynthia J. Willmon

229

Team Science in Health Sasha Cukier, Ekaterina Klyachko, and Bonnie Spring

243

Family and Health Brittany N. Sherrill, Janet Deatrick, and Keith Sanford

253

Index263

List of Contributors

Amanda M. Acevedo, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Zeba Ahmad, Icahn School of Medicine at Mount Sinai, New York, NY, USA Leona S. Aiken, Department of Psychology, Arizona State University, Tempe, AZ, USA Olusola Ajilore, Department of Psychiatry, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA Damla E. Aksen, Department of Psychology, Binghamton University, Binghamton, NY, USA Kristi Alexander, Department of Psychology, University of Central Florida, Orlando, FL, USA Andrea G. Alioto, Palo Alto University, Palo Alto, CA, USA Department of Neurology, University of California, Davis, CA, USA Michael L. Alosco, Boston University Alzheimer’s Disease Center and Boston University CTE Center, Department of Neurology, Boston University School of Medicine, Boston, MA, USA Christina M. Amaro, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA Judith Andersen, Department of Psychology, University of Toronto, Mississauga, ON, Canada Aviva H. Ariel, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Danielle Arigo, Psychology Department, The University of Scranton, Scranton, PA, USA Jamie Arndt, Psychology Department, University of Missouri System, Columbia, MO, USA Robert C. Arnold, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA Melissa V. Auerbach, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Lisa Auster‐Gussman, Department of Psychology, University of Minnesota, Minneapolis, MN, USA Aya Avishai, Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Hoda Badr, Icahn School of Medicine at Mount Sinai, New York, NY, USA David E. Balk, Department of Health and Nutrition Sciences, Brooklyn College of the City University of New York, Brooklyn, NY, USA

x

List of Contributors

Kirk Ballew, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Department of Communication, University of Illinois at Urbana‐Champaign College of Liberal Arts and Sciences, Urbana, IL, USA Diletta Barbiani, Department of Neuroscience, University of Turin Medical School, Turin, Italy Hypoxia Medicine, Plateau Rosà, Switzerland Jeffrey E. Barnett, College of Arts and Sciences, Loyola University Maryland, Baltimore, MD, USA Petronilla Battista, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Institute of Bari, Italy Brittney Becker, Psychology Department, Texas A&M University System, College Station, TX, USA Alexandra A. Belzer, Colby College, Waterville, ME, USA Fabrizio Benedetti, Department of Neuroscience, University of Turin Medical School, Turin, Italy Hypoxia Medicine, Plateau Rosà, Switzerland Danielle S. Berke, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA Mamta Bhatnagar, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA Kelly Biegler, Department of Psychological Science, University of California, Irvine, CA, USA Susan J. Blalock, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Brittany E. Blanchard, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Jennifer B. Blossom, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA Ryan Bogdan, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA Jacob Bolzenius, Missouri Institute of Mental Health, University of Missouri‐St. Louis, St. Louis, MO, USA Bruce Bongar, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Dianna Boone, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Jerica X. Bornstein, Department of Human Development and Family Sciences, University of Texas at Austin, Austin, TX, USA Joaquín Borrego Jr., Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Kyle J. Bourassa, Department of Psychology, University of Arizona, Tucson, AZ, USA Lara A. Boyd, Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada Patrick Boyd, Department of Psychology, University of South Florida, Tampa, FL, USA Adam M. Brickman, Columbia University Medical Center, New York, NY, USA Athena Brindle, Department of Psychology, College of Science, The University of Texas at Arlington, Arlington, TX, USA

List of Contributors

xi

Jennifer L. Brown, Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA Kathleen S. Brown, Private Practice, Fort Myers, FL, USA Wilson J. Brown, Pennsylvania State University, The Behrend College, Erie, PA, USA Steven E. Bruce, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA Angela D. Bryan, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Katherine R. Buchholz, VA Boston Healthcare System, Boston, MA, USA National Center for Post Traumatic Stress Disorder, White River Junction, VT, USA Claire Burgess, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA Edith Burns, Medical College of Wisconsin, Milwaukee, WI, USA Rachel J. Burns, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada Johannes B.J. Bussmann, Erasmus MC University Medical Center Rotterdam, CA Rotterdam, South Holland, The Netherlands Fawn C. Caplandies, Department of Psychology, University of Toledo, Toledo, OH, USA Angela L. Carey, Psychology Department, University of Arizona, Tucson, AZ, USA Brandon L. Carlisle, Department of Psychiatry, University of California, San Diego, CA, USA Delesha M. Carpenter, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Allison J. Carroll, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Charles S. Carver, Department of Psychology, University of Miami, Coral Gables, FL, USA Center for Advanced Study in the Behavioral Sciences, Stanford University, Stanford, CA, USA Isabella Castiglioni, Dipartimento di Fisica “G. Occhialini”, Università degli Studi di Milano‐ Bicocca – Piazza della Scienza, Milan, Italy Gretchen B. Chapman, Department of Psychology, Rutgers University–New Brunswick, New Brunswick, NJ, USA Alyssa C.D. Cheadle, Psychology Department, Hope College, Holland, MI, USA Rebecca E. Chesher, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA William J. Chopik, Psychology Department, Michigan State University, East Lansing, MI, USA Nicholas J.S. Christenfeld, Department of Psychology, University of California San Diego, La Jolla, CA, USA L.E. Chun, University of Colorado, Boulder, CO, USA Briana Cobos, Department of Psychology,Texas State University, San Marcos, TX, USA Alex S. Cohen, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Jenna Herold Cohen, Department of Psychology, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA Lee M. Cohen, Department of Psychology, College of Liberal Arts, The University of Mississippi, Oxford, MS, USA Catherine Cook‐Cottone, Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, USA

xii

List of Contributors

Sarah A. Cooley, Department of Neurology, School of Medicine, Washington University in Saint Louis, St. Louis, MO, USA Stephen Correia, Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI, USA Patrick Corrigan, Department of Psychology, Illinois Institute of Technology College of Science, Chicago, IL, USA Juliann Cortese, School of Information, Florida State University, Tallahassee, FL, USA Riley Cropper, Stanford University Counseling and Psychological Services, Stanford Hospital and Clinics, Stanford, CA, USA Robert T. Croyle, National Cancer Institute, Bethesda, MD, USA Tegan Cruwys, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Sasha Cukier, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA Gregory Dahl, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Beth D. Darnall, Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA Savannah Davidson, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Elizabeth L. Davis, Department of Psychology, University of California, Riverside, CA,USA Rachael L. Deardorff, Center for Biomedical Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA Janet Deatrick, Department of Family & Community Health, University of Pennsylvania School of Nursing, Philadelphia, PA, USA Lauren Decaporale‐Ryan, Departments of Psychiatry, Medicine, & Surgery, University of Rochester Medical Center, Rochester, NY, USA Jordan Degelia, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Anita Delongis, Department of Psychology, University of British Columbia, Vancouver, BC, Canada Naiara Demnitz, Department of Psychiatry, University of Oxford, Oxford, UK Eliot Dennard, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Kristine M. Diaz, Walter Reed National Military Medical Center, Bethesda, MD, USA Sally S. Dickerson, Department of Psychology, Pace University, New York, NY, USA M. Robin DiMatteo, Department of Psychology, University of California Riverside, Riverside, CA, USA Sona Dimidjian, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Genevieve A. Dingle, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Laura J. Dixon, Department of Psychology, University of Mississippi, Oxford, MS, USA Michelle R. Dixon, Department of Psychology, Rutgers University Camden, Camden, NJ, USA Tiffany S. Doherty, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA Michael D. Dooley, Department of Psychology, University of California, Riverside, Riverside, CA, USA

List of Contributors

xiii

Tessa L. Dover, Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA Colleen Doyle, Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, USA Ira Driscoll, Department of Psychology, University of Wisconsin‐Milwaukee, Milwaukee, WI, USA Claudia Drossel, Department of Psychology, Eastern Michigan University College of Arts and Sciences, Ypsilanti, MI, USA Sean P.A. Drummond, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Christine Dunkel Schetter, Department of Psychology, University of California, Los Angeles, CA, USA William L. Dunlop, Psychology Department, University of California, Riverside, Riverside, CA, USA Jacqueline C. Duperrouzel, Florida International University, Miami, FL, USA Allison Earl, Department of Psychology, University of Michigan, Ann Arbor, MI, USA Amanda C. Eaton, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Ulrich W. Ebner‐Priemer, Karlsruhe Institute of Technology, Karlsruhe, Germany Robin S. Edelstein, Psychology Department, University of Michigan, Ann Arbor, MI, USA Jamie Edgin, Department of Psychology, The University of Arizona, Tucson, AZ, USA Bradley A. Edwards, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Sleep and Circadian Medicine Laboratory, Department of Physiology, School of Biomedical Sciences, Monash University, Melbourne, VIC, Australia Naomi I. Eisenberger, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Amber S. Emanuel, Psychology Department, University of Florida, Gainesville, FL, USA Patrick England, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA Ipek Ensari, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY, USA Emily Evans, Neurology Resident, Washington University in St. Louis, St. Louis, MO, USA James Evans, Department of Psychology, Binghamton University, Binghamton, NY, USA Anne M. Fairlie, Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Angelica Falkenstein, Department of Psychology, University of California, Riverside, Riverside, CA, USA Zachary Fetterman, Psychology Department, University of Alabama, Tuscaloosa, AL, USA Christopher Fink, Durham Veterans Affairs Medical Center, Durham, NC, USA North Carolina State University, Raleigh, NC, USA Alexandra N. Fisher, Psychology Department, University of Victoria, Victoria, BC, Canada Tabitha Fleming, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Tasheia Floyd, Washington University School of Medicine, St. Louis, MO, USA

xiv

List of Contributors

Lauren A. Fowler, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA Elinor Flynn, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Heather Walton Flynn, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA Elizabeth S. Focella, Department of Psychology, University of Wisconsin Oshkosh, Oshkosh, WI, USA Amy Frers, Department of Psychology and University Health Sciences Center, University of Colorado Denver, Denver, CO, USA Howard S. Friedman, Psychology Department, University of California Riverside, Riverside, CA, USA Paul T. Fuglestad, Department of Psychology, University of North Florida, Jacksonville, FL, USA Kentaro Fujita, Department of Psychology, The Ohio State University, Columbus, OH, USA Kristel M. Gallagher, Thiel College, Greenville, PA, USA Andrea M. Garcia, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA Paola García‐Egan, Department of Psychological Sciences, University of Missouri‐St. Louis, St. Louis, MO, USA Robert García, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA Casey K. Gardiner, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Robert J. Gatchel, Endowment Professor of Clinical Health Psychology Research, Department of Psychology, College of Science, The University of Texas at Arlington, Arlington, TX, USA Justine Megan Gatt, Neuroscience Research Australia, Sydney, NSW, Australia School of Psychology, University of New South Wales, Sydney, NSW, Australia Nicole K. Gause, Department of Psychology, University of Cincinnati, Cincinnati, OH, USA Ashwin Gautam, Department of Psychology, Binghamton University, Binghamton, NY, USA Fiona Ge, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA Andrew L. Geers, Department of Psychology, University of Toledo, Toledo, OH, USA Margaux C. Genoff, Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA James I. Gerhart, Department of Psychosocial Oncology, Cancer Integrative Medicine Program, Rush University Medical Center, Chicago, IL, USA Meg Gerrard, Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Frederick X. Gibbons, Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA Arielle S. Gillman, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Marci E.J. Gleason, Department of Human Development and Family Sciences, University of Texas at Austin, Austin, TX, USA Teresa Gobble, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA

List of Contributors

xv

Department of Educational Psychology, University of Texas at Austin, Austin, TX, USA Katharina Sophia Goerlich, Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany Jamie L. Goldenberg, Department of Psychology, University of South Florida, Tampa, FL, USA Cesar A. Gonzalez, Mayo Clinic Minnesota, Rochester, MN, USA Raul Gonzalez, Florida International University, Miami, FL, USA Brittany F. Goodman, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA Janna R. Gordon, San Diego State University and SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA Edward C. Green, Department of Anthropology, The George Washington University, Washington, DC, USA Joseph P. Green, Department of Psychology, Ohio State University at Lima, Lima, OH, USA Michael G. Griffin, Department of Psychological Sciences and Center for Trauma Recovery, University of Missouri–St. Louis, St. Louis, MO, USA Steven Grindel, Medical College of Wisconsin, Milwaukee, WI, USA Kristina Gryboski, Project Hope, Millwood, VA, USA Suzy Bird Gulliver, Baylor and Scott and White Hospitals and Clinics, Waco, TX, USA School of Public Health, Texas A&M University, College Station, TX, USA Megan R. Gunnar, Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, USA Jenna Haddock, Department of Psychological Sciences, University of Missouri‐St. Louis, St. Louis, MO, USA Martin S. Hagger, Health Psychology and Behavioral Medicine Research Group, School of Psychology and Speech Pathology, Faculty of Health Sciences, Curtin University, Perth, WA, Australia Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland School of Applied Psychology and Menzies Health Institute Queensland, Behavioural Bases for Health, Griffith University, Brisbane, QLD, Australia Centre for Physical Activity Studies, School of Human, Health and Social Sciences, Central Queensland University, Rockhampton, QLD, Australia Jada G. Hamilton, Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA Paul K.J. Han, Center for Outcomes Research and Evaluation, Maine Medical Center Research Institute, Portland, ME, USA Grace E. Hanley, Psychology Department, University of California, Riverside, Riverside, CA, USA Madeline B. Harms, Department of Psychology, University of Wisconsin‐Madison, Madison, WI, USA Adam Harris, Department of Psychology and University Health Sciences Center, University of Colorado Denver, Denver, CO, USA Lauren N. Harris, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Peter R. Harris, School of Psychology, University of Sussex, Brighton & Hove, UK Philine S. Harris, School of Psychology, University of Sussex, Brighton & Hove, UK William Hart, Psychology Department, University of Alabama, Tuscaloosa, AL, USA M.J. Hartsock, University of Colorado, Boulder, CO, USA

xvi

List of Contributors

Kelly B. Haskard‐Zolnierek, Department of Psychology, Texas State University, San Marcos, TX, USA S. Alexandar Haslam, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Catherine Haslam, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Karen Hasselmo, Department of Psychology, University of Arizona, Tucson, AZ, USA Kathryn S. Hayward, Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada Stroke Division, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia NHMRC Centre for Research Excellence in Stroke Rehabilitation and Recovery, Parkville, VIC, Australia Jodi M. Heaps‐Woodruff, University of Missouri–St. Louis, St. Louis, MO, USA Missouri Institute of Mental Health, St. Louis, MO, USA Dietlinde Heilmayr, Psychology Department, Moravian College, Bethlehem, PA, USA Vicki S. Helgeson, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Joseph A. Helpern, Center for Biomedical Imaging, Department of Neuroscience, Department of Neurology, and Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA Garrett Hisler, Department of Psychology, Iowa State University, Armes, IA, USA Molly B. Hodgkins, Department of Psychological and Brain Sciences, University of Maine, Orono, ME, USA Michael Hoerger, Department of Psychology, Tulane University, New Orleans, LA, USA Jeanne S. Hoffman, US Department of the Army, Washington, DC, USA Stephanie A. Hooker, Psychology Department,University of Colorado at Denver – Anschutz Medical Campus, Aurora, CO, USA Vera Hoorens, Center for Social and Cultural Psychology, Katholieke Universiteit Leuven, Leuven, Belgium Arpine Hovasapian, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Jennifer L. Howell, Psychology Department, Ohio University, Athens, OH, USA Robert M. Huff, Department of Health Sciences, California State University, Northridge, Morgan Hill, CA, USA Jacob Huffman, Behavioral Neuroscience Program, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO, USA Lauren J. Human, Department of Psychology, McGill University, Montreal, QC, Canada Jeffrey M. Hunger, Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA Tanya Hunt, Department of Clinical Psychology, Palo Alto University, Palo Alto, California, USA Steven Hyde, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA Christina Di Iorio, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA

List of Contributors

xvii

Jamie M. Jacobs, Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA Lauren James, Department of Psychology, University of North Florida, Jacksonville, FL, USA Rebekah Jazdzewki, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Brooke N. Jenkins, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Jens H. Jensen, Center for Biomedical Imaging, Department of Neuroscience, and Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA Donna C. Jessop, School of Psychology, University of Sussex, Brighton & Hove, UK Jolanda Jetten, Faculty of Health and Behavioural Sciences, School of Psychology, University of Queensland, St. Lucia, QLD, Australia Kimberly Johnson, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA Doerte U. Junghaenel, USC Dornsife Center for Self‐Report Science, University of Southern California, Los Angeles, CA, USA Vanessa Juth, The Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA Samantha Kahn, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA David A. Kalkstein, Department of Psychology, New York University, New York, NY, USA Alexander Karan, Department of Psychology, University of California, Riverside, CA, USA Natalia M. Kecala, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri–St. Louis, St. Louis, MO, USA Robert G. Kent de Grey, Department of Psychology, University of Utah, Salt Lake City, UT, USA Margaret L. Kern, Center for Positive Psychology, Graduate School of Education, University of Melbourne, Parkville, VIC, Australia Nathan A. Kimbrel, Department of Veterans Affairs’ VISN 6 Mid‐Atlantic MIRECC, Durham, NC, USA Durham Veterans Affairs Medical Center, Durham, NC, USA Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA Tricia Z. King, The Neuroscience Institute, Georgia State University, Atlanta, GA, USA Nancy Kishino, West Coast Spine Restoration, Riverside, CA, USA Ekaterina Klyachko, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA Brian Konecky, Boise VAMC Virtual Integrated Multisite Patient Aligned Care Team Hub, Boise, ID, USA Gerald P. Koocher, Department of Psychology, DePaul University, Chicago, IL, USA Laura E. Korthauer, Department of Psychology, University of Wisconsin‐Milwaukee, Milwaukee, WI, USA Abigail O. Kramer, Palo Alto University, Palo Alto, CA, USA Weill Institute for Neurosciences, University of California, San Francisco Memory and Aging Center, San Francisco, CA, USA Joel H. Kramer, Weill Institute for Neurosciences, University of California, San Francisco Memory and Aging Center, San Francisco, CA, USA Zlatan Krizan, Department of Psychology, Iowa State University, Armes, IA, USA

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List of Contributors

Monica F. Kurylo, Departments of Medicine and Public Health, University of Kansas Medical Center, Kansas City, KS, USA Onawa P. LaBelle, Psychology Department, University of Michigan, Ann Arbor, MI, USA Melissa H. Laitner, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Mark J. Landau, Department of Psychology, University of Kansas, Lawrence, KS, USA Shane Landry, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Sleep and Circadian Medicine Laboratory, Department of Physiology, School of Biomedical Sciences, Monash University, Melbourne, VIC, Australia Kevin T. Larkin, Department of Psychology, West Virginia University, Morgantown, WV, USA Jason M. Lavender, Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA Kristin Layous, Department of Psychology, California State University East Bay, Hayward, CA, USA Thanh Le, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Thad R. Leffingwell, Department of Psychology, Oklahoma State University, Stillwater, OK, USA Angela M. Legg, Department of Psychology, Pace University, New York, NY, USA Jana Lembke, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA Elizabeth Lemon, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Richie L. Lenne, Psychology Department, University of Minnesota Twin Cities, Minneapolis, MN, USA Alex Leow, Department of Psychiatry, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA Department of Bioengineering, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA Howard Leventhal, Department of Psychology, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA Linda J. Levine, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Melissa A. Lewis, Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA Meng Li, Department of Health and Behavior Sciences, University of Colorado Denver, Denver, CO, USA Huijun Liao, Department of Radiology, Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Alexander P. Lin, Department of Radiology, Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Wendy P. Linda, School of Psychology, Fairleigh Dickinson University, Teaneck, NJ, USA Nikolette P. Lipsey, Psychology Department,University of Florida, Gainesville, FL, USA Dana M. Litt, Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA

List of Contributors

xix

Andrew K. Littlefield, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Brett Litz, Department of Psychology, Boston University & VA Boston Healthcare System, Boston, MA, USA Marci Lobel, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Todd Lucas, C. S. Mott Endowed Professor of Public Health Associate Professor of Epidemiology and Biostatistics Division of Public Health College of Human Medicine Michigan State University, Wayne State University, Detroit, MI, USA Mia Liza A. Lustria, School of Information, Florida State University, Tallahassee, FL, USA Steven Jay Lynn, Department of Psychology, Binghamton University, Binghamton, NY, USA Salvatore R. Maddi, Department of Psychological Science, University of California, Irvine, Irvine, CA, USA Renee E. Magnan, Washington State University Vancouver, Vancouver, WA, USA Brenda Major, Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA Vanessa L. Malcarne, San Diego State University, UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA UC San Diego Moores Cancer Center, San Diego, CA, USA Department of Psychology, San Diego State University, San Diego, CA, USA Kelsey A. Maloney, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Andrew W. Manigault, Psychology Department, Ohio University, Athens, OH, USA Traci Mann, Psychology Department, University of Minnesota Twin Cities, Minneapolis, MN, USA Soe Mar, Neurology and Pediatrics, Division of Pediatric Neurology, Washington University in St. Louis, St. Louis, MO, USA Shannon M. Martin, Department of Psychology, Central Michigan University, Mount Pleasant, MI, USA Allison Marziliano, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Katilyn Mascatelli, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Tyler B. Mason, Department of Psychology, University of Southern California, Los Angeles, CA, USA Neuropsychiatric Research Institute, Fargo, ND, USA Kevin S. Masters, Psychology Department, University of Colorado at Denver  –  Anschutz Medical Campus, Aurora, CO, USA Kevin D. McCaul, North Dakota State University, Fargo, ND, USA Tara P. McCoy, Psychology Department, University of California, Riverside, Riverside, CA, USA Michael McCrea, Brain Injury Research Program, Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA Scout N. McCully, Department of Psychological Sciences, Kent State University, Kent OH, USA Don McGeary, Department of Psychiatry, Department of Family and Community Medicine, The University of Texas Health Science Center San Antonio, San Antonio, TX, USA

xx

List of Contributors

Jessica McGovern, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Robert E. McGrath, School of Psychology, Farleigh Dickinson University, Teaneck, NJ, USA Michael P. Mead, North Dakota State University, Fargo, ND, USA Alan Meca, Department of Psychology, Old Dominion University, Norfolk, VA, USA Matthias R. Mehl, Psychology Department, University of Arizona, Tucson, AZ, USA Timothy P. Melchert, Department of Psychology, Marquette University, Milwaukee, WI, USA Paola Mendoza‐Rivera, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Maria G. Mens, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Sai Merugumala, Department of Radiology, Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Rebecca A. Meza, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Dara Mickschl, Medical College of Wisconsin, Milwaukee, WI, USA Tricia A. Miller, Santa Barbara Cottage Hospital, Santa Barbara, CA, USA Department of Psychology, University of California Riverside, Riverside, CA, USA Kyle R. Mitchell, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Mona Moieni, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Phillip J. Moore, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA DeAnna L. Mori, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA Sandra B. Morissette, Department of Psychology, The University of Texas at San Antonio College of Sciences, San Antonio, TX, USA Amber Morrow, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Ariel J. Mosley, Department of Psychology, University of Kansas, Lawrence, KS, USA Robert W. Motl, Department of Physical Therapy, University of Alabama at Birmingham, Birmingham, AL, USA Laurent Mottron, Department of Psychiatry, Faculty of Medicine, Universite de Montreal, Montreal, QC, Canada Anne Moyer, Department of Psychology, Stony Brook University, Stony Brook, NY, USA Elaine M. Murphy, Population Reference Bureau, Washington, DC, USA Carolyn B. Murray, Psychology, University of California, Riverside, CA, USA Christopher S. Nave, Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA Department of Psychology, Rutgers University Camden, Camden, NJ, USA Eve‐Lynn Nelson, KU Center for Telemedicine & Telehealth, University of Kansas Medical Center, University of Kansas, Kansas City, KS, USA Steven M. Nelson, VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX, USA Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA

List of Contributors

xxi

Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA Elizabeth Neumann, Medical College of Wisconsin, Milwaukee, WI, USA Jason L. Neva, Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada Jody S. Nicholson, Department of Psychology, University of North Florida, Jacksonville, FL, USA Christina Nisson, Department of Psychology, University of Michigan, Ann Arbor, MI, USA Mary‐Frances O’Connor, Psychology Department, University of Arizona, Tucson, AZ, USA Laura O’Hara, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Gbolahan O. Olanubi, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Nils Olsen, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, USA Yimi Omofuma, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Elizabeth Ortiz‐Gonzalez, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Ileana Pacheco‐Colón, Florida International University, Miami, FL, USA Aliza A. Panjwani, Graduate School and University Center, City University of New York, New York, NY, USA Mike C. Parent, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Department of Educational Psychology, University of Texas at Austin, Austin, TX, USA Thomas J. Parkman, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Robert H. Paul, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Missouri Institute of Mental Health, St. Louis, MO, USA Stephanie A. Peak, Battelle Memorial Institute, Columbus, OH, USA Michael G. Perri, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Laura Perry, Department of Psychology, Tulane University, New Orleans, LA, USA Alyssa Phelps, Boston University Alzheimer’s Disease Center and Boston University CTE Center, Department of Neurology, Boston University School of Medicine, Boston, MA, USA Carissa L. Philippi, Department of Psychological Sciences, University of Missouri‐St Louis, St Louis, MO, USA L. Alison Phillips, Iowa State University, Ames, IA, USA Paula R. Pietromonaco, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, USA Craig P. Pollizi, Department of Psychology, Binghamton University, Binghamton, NY, USA Seth D. Pollak, Department of Psychology, University of Wisconsin‐Madison, Madison, WI, USA David B. Portnoy, US Food and Drug Administration, Silver Spring, MD, USA Rachel Postupack, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA

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List of Contributors

Matthew R. Powell, Division of Neurocognitive Disorders, Department of Psychiatry & Psychology, Mayo Clinic Minnesota, Rochester, MN, USA Candice Presseau, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA Sarah D. Pressman, Department of Psychological Science, University of California Irvine, Irvine, CA, USA Rebecca N. Preston‐Campbell, Missouri Institute of Mental Health, St. Louis, MO, USA Isabel F. Ramos, Department of Psychology, University of California, Los Angeles, CA, USA Christopher T. Ray, College of Health Sciences, Texas Woman’s University, Denton, TX, USA Amanda L. Rebar, Centre for Physical Activity Studies, School of Human, Health and Social Sciences, Central Queensland University, Rockhampton, QLD, Australia Allecia E. Reid, Colby College, Waterville, ME, USA May Reinert, Department of Psychology, Pace University, New York, NY, USA Kelly E. Rentscher, Psychology Department, University of Arizona, Tucson, AZ, USA Tracey A. Revenson, Department of Psychology, Hunter College, New York, NY, USA C. Steven Richards, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Laura River, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Megan L. Robbins, Department of Psychology, University of California, Riverside, CA, USA Michael C. Roberts, Clinical Child Psychology Program, University of Kansas, Lawrence, KS, USA John D. Robinson, Department of Surgery and Department of Psychiatry and Behavioral Sciences, Howard University College of Medicine/Hospital, Washington, DC, USA Theodore F. Robles, Psychology Department, University of California Los Angeles, CA, USA Catherine Rochefort, Department of Psychology, Southern Methodist University, Dallas, TX, USA Daniel G. Rogers, Department of Psychology, University of Mississippi, MS, USA John L. Romano, Department of Educational Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA Karen S. Rook, Department of Psychological Science, University of California, Irvine, CA, USA Ann Rosenthal, Medical College of Wisconsin, Milwaukee, WI, USA J. Megan Ross, Florida International University, Miami, FL, USA Tania L. Roth, Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA Alexander J. Rothman, Department of Psychology, University of Minnesota, Minneapolis, MN, USA Ronald H. Rozensky, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA Kristen L. Rudd, Department of Psychology, University of California, Riverside, Riverside, CA, USA Kirstie K. Russell, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada Arseny A. Ryazanov, Department of Psychology, University of California San Diego, La Jolla, CA, USA

List of Contributors

xxiii

Napapon Sailasuta, Centre for Addiction and Mental Health, Toronto, ON, Canada Stella Sakhon, Department of Psychology, The University of Arizona, Tucson, AZ, USA Lauren E. Salminen, Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA Christian Salvatore, Scuola Universitaria Superiore IUSS Pavia, Piazza della Vittoria, Pavia, Italy DeepTrace Technologies S.R.L., Via Conservatorio, Milan, Italy Peter M. Sandman, Department of Human Ecology, Rutgers University New Brunswick, New Brunswick, NJ, USA Keith Sanford, Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA David A. Sbarra, Department of Psychology, University of Arizona, Tucson, AZ, USA Michael F. Scheier, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA Adam T. Schmidt, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Elana Schwartz, Department of Psychology, Louisiana State University College of Science, Baton Rouge, LA, USA Esther N. Schwartz, Department of Family and Community Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA Seth J. Schwartz, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA Ralf Schwarzer, Health Psychology, Freie Universität Berlin, Berlin, Germany Health Psychology, Australian Catholic University, Melbourne, VIC, Australia Richard J. Seime, Mayo Clinic Minnesota, Rochester, MN, USA Claire E. Sexton, Department of Psychiatry, University of Oxford, Oxford, UK Louise Sharpe, School of Psychology, Faculty of Science, The University of Sydney, Sydney, NSW, Australia Lindsay Sheehan, Department of Psychology, Illinois Institute of Technology College of Science, Chicago, IL, USA Paschal Sheeran, Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Daniel Sheinbein, BRAIN Lab, Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA James A. Shepperd, Psychology Department, University of Florida, Gainesville, FL, USA Brittany N. Sherrill, Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA Molin Shi, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Emily W. Shih, Department of Psychology, University of California, Riverside, CA, USA Ben W. Shulman, Psychology Department, University of California, Los Angles, CA, USA Elizabeth Silbermann, Neurology Resident, Washington University in St. Louis, St. Louis, MO, USA Department of Neurology, Oregon Health and Science University, Portland, OR, USA Roxane Cohen Silver, Department of Psychological Science, University of California, Irvine, Irvine, CA, USA Colleen A. Sloan, VA Boston Healthcare System, West Roxbury, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA

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List of Contributors

Rachel Smallman, Psychology Department, Texas A&M University System, College Station, TX, USA Kathryn E. Smith, Neuropsychiatric Research Institute, Fargo, ND, USA Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA Patrick J. Smith, Duke University Medical Center, Durham, NC, USA Todd A. Smitherman, Department of Psychology, University of Mississippi, University, MS, USA Joshua M. Smyth, The Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA Dara H. Sorkin, Department of Psychological Science, University of California, Irvine, CA, USA R. L. Spencer, University of Colorado, Boulder, CO, USA Melissa Spina, Psychology Department, University of Missouri System, Columbia, MO, USA Bonnie Spring, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA Annette L. Stanton, Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA Ellen Stephenson, Department of Psychology, University of British Columbia, Vancouver, BC, Canada Robert A. Stern, Boston University Alzheimer’s Disease Center and Boston University CTE Center, Department of Neurology, Boston University School of Medicine, Boston, MA, USA Department of Neurosurgery and Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, USA Angela K. Stevens, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Courtney J. Stevens, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Elizabeth M. Stewart, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia Danu Anthony Stinson, Psychology Department, University of Victoria, Victoria, BC, Canada Michelle L. Stock, Department of Psychological and Brain Sciences, George Washington University, Washington, DC, USA Arthur A. Stone, USC Dornsife Center for Self‐Report Science, University of Southern California, Los Angeles, CA, USA Department of Psychology, Dana and David Dornsife College of Letters Arts and Sciences, University of Southern California, Los Angeles, CA, USA Jeff Stone, Psychology Department, University of Arizona, Tucson, AZ, USA John A. Sturgeon, Center for Pain Relief, University of Washington, Seattle, WA, USA Kieran T. Sullivan, Department of Psychology, Santa Clara University, Santa Clara, CA, USA Amy Summerville, Psychology Department, Miami University, Oxford, OH, USA Jessie Sun, Center for Positive Psychology, Graduate School of Education, University of Melbourne, Parkville, VIC, Australia Department of Psychology, University of California, Davis, CA, USA Steve Sussman, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

List of Contributors

xxv

Trevor James Swanson, Department of Psychology, University of Kansas, Lawrence, KS, USA Allison M. Sweeney, Department of Psychology, University of South Carolina, Columbia, SC, USA Kate Sweeny, Department of Psychology, University of California, Riverside, Riverside, CA, USA Sergey Tarima, Medical College of Wisconsin, Milwaukee, WI, USA Carly A. Taylor, Boston Medical Center, Boston, MA, USA George T. Taylor, Behavioral Neuroscience Program, Department of Psychological Sciences, University of Missouri–St. Louis, St. Louis, MO, USA Interfakultäre Biomedizinische Forschungseinrichtung (IBF) der Universität Heidelberg, Heidelberg, Germany A. Janet Tomiyama, Psychology Department, University of California Los Angeles, Los Angeles, CA, USA Victoria A. Torres, Department of Psychology, College of Liberal Arts, University of Mississippi, Oxford, MS, USA David R.M. Trotter, Department of Family and Community Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA Bert N. Uchino, Department of Psychology, University of Utah, Salt Lake City, UT, USA John A. Updegraff, Department of Psychological Sciences, Kent State University, Kent, OH, USA Jay Urbain, Milwaukee School of Engineering, Milwaukee, WI, USA Amy E. Ustjanauskas, San Diego State University, UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA UC San Diego Moores Cancer Center, San Diego, CA, USA Jason Van Allen, Clinical Psychology Program, Department of Psychological Sciences, Texas Tech University, Lubbock, TX, USA Erika D. Van Dyke, Department of Sport Sciences, College of Physical Activity and Sport Sciences, West Virginia University, Morgantown, WV, USA Department of Psychology, Springfield College, Springfield, MA, USA Judy L. Van Raalte, Department of Psychology, Springfield College, Springfield, MA, USA Maryke Van Zyl‐Harrison, Department of Clinical Psychology, Palo Alto University, Palo Alto, CA, USA Rachel Vanderkruik, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Elizabeth J. Vella, Department of Psychology, University of Southern Maine, Portland, ME, USA Birte von Haaren‐Mack, German Sport University Cologne, Köln, Nordrhein‐Westfalen, Germany David Von Nordheim, University of Missouri–St. Louis, St. Louis, MO, USA Missouri Institute of Mental Health, St. Louis, MO, USA Mikhail Votinov, Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany Institute of Neuroscience and Medicine (INM‐10), Jülich, Germany Rebecca K. Vujnovic, Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, USA Amy Wachholtz, Department of Psychology and University Health Sciences Center, University of Colorado Denver, Denver, CO, USA

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List of Contributors

Eric F. Wagner, Department of Epidemiology, Florida International University, Miami, FL, USA Ryan J. Walker, Psychology Department, Miami University, Oxford, Ohio, USA Rachel A. Wamser-Nanney, Department of Psychological Sciences, Center for Trauma Recovery, University of Missouri‐St. Louis, St. Louis, MO, USA Britney M. Wardecker, Psychology Department, University of Michigan, Ann Arbor, MI, USA Melissa Ward‐Peterson, Department of Epidemiology, Florida International University, Miami, FL, USA Lisa Marie Warner, Health Psychology, Freie Universitat Berlin, Berlin, Germany Neil D. Weinstein, Department of Human Ecology, Rutgers University New Brunswick, New Brunswick, NJ, USA Suzanne E. Welcome, Department of Communication, Fontbonne University, St. Louis, MO, USA Department of Communication, University of Missouri at Saint Louis, St. Louis, MO, USA Kristen J. Wells, San Diego State University and SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA Amy Wenzel, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA Private Practice, Rosemont, PA, USA Summer L. Williams, Department of Psychology, Westfield State University, Westfield, MA, USA Cynthia J. Willmon, Reno VA Sierra Nevada Health Care System, Reno, NV, USA Sara M. Witcraft, Department of Psychology, University of Mississippi, Oxford, MS, USA Stephen A. Wonderlich, Neuropsychiatric Research Institute, Fargo, ND, USA Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA E.R. Woodruff, University of Colorado, Boulder, CO, USA Patrick W. Wright, Neurology, Washington University in St. Louis, St. Louis, MO, USA Robert C. Wright, Department of Psychology, University of California, Riverside, CA, USA Shawna Wright, KU Center for Telemedicine & Telehealth, University of Kansas Medical Center, University of Kansas, Kansas City, KS, USA Tuppett M. Yates, Department of Psychology, University of California, Riverside, Riverside, CA, USA Julie D. Yeterian, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA Lok‐Kin Yeung, Columbia University Medical Center, New York, NY, USA Alex J. Zautra, Psychology Department, Arizona State University, Tempe, AZ, USA Colin A. Zestcott, Psychology Department, University of Arizona, Tucson, AZ, USA Peggy M. Zoccola, Psychology Department, Ohio University, Athens, OH, USA

Foreword

Until the 1970s, there were no books, journals, or university courses on health psychology. Although the field’s intellectual roots stretch back to the beginnings of psychology more than a century ago, its formal emergence depended on a convergence of influences (Friedman & Silver, 2007), including psychosomatic medicine, social‐psychological and socio‐anthropological perspectives on medicine, epidemiology, and medical and clinical psychology. Today, health psychology is a principal area of significant social science research and practice, with vital implications for the health and well‐being of individuals and societies. Understanding the explosive trajectory of health psychology is useful to appreciating the strengths of the field and to approaching this new encyclopedia, the Wiley Encyclopedia of Health Psychology. What is the nature of health? That is, what does it mean to be healthy? The way that this question is answered affects the behaviors, treatments, and resource allocations of individuals, families, health practitioners, governments, and societies. For example, if it is thought that you are healthy unless and until you contract a disease or suffer an injury, then attention and resources are primarily allocated toward “fixing” the problem through medications or surgical repairs. This is the traditional biomedical model of disease (sometimes called the “disease model”). Indeed, in the United States, the overwhelming allocation of attention and resources is to physicians (doing treatments) and to pharmaceuticals (prescription drugs and their development). In contrast, health psychology developed around a much broader and more interdisciplinary approach to health, one that is often termed the biopsychosocial model. The biopsychosocial model (a term first formally proposed by George Engel in 1968) brings together core elements of staying healthy and recovering well from injury or disease (Stone et al., 1987). Each individual—due to a combination of biological influences and psychosocial experiences—is more or less likely to thrive. Some of this variation is due to genetics and early life development; some depends on the availability and appropriate applications of medical treatments; some involves nutrition and physical activity; some depends on preparations for, perceptions of, and reactions to life’s challenges; and some involves exposure to or seeking out of healthier or unhealthier environments, both physically and socially. When presented in this way, it might seem obvious that health should certainly be viewed in this broader interdisciplinary way. However, by misdirecting its vast expenditures on health care, the United States gives its residents mediocre health at high cost (Kaplan, 2019). To approach these matters in a thorough manner, this encyclopedia includes four volumes, with Volume 1 focusing on the biological bases of health and health behavior, Volume 2 concentrating on the social bases, Volume 3 centering around the psychological and clinical aspects, and Volume 4 focused more broadly on crosscutting and applied matters.

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Key parts of health depend on biological characteristics and how they interact with our experiences and environments. So, for example, in Volume 1, there are articles on injury to the brain, alcohol effects on the brain, nutrition, drug abuse, psychophysiology, and the tools and key findings of neuroscience. Note that even individuals with the same genes (identical twins) can and do have different health and recovery outcomes, and the articles delve into such ­complexities of health. Of course humans are also social creatures, and people’s growth, development, and health behaviors take place in social contexts. So, in Volume 2, there are articles on such fundamental matters as social support, coping, spirituality, emotion, discrimination, communication, psychosocial stress, and bereavement. Because our approaches to and conceptions of health are heavily influenced by society’s institutions and structures revolving around medical care, much of health psychology derives from or intersects with clinical psychology and applied behavioral medicine. Volume 3 covers such clinical topics as psycho‐oncology, depression, drug abuse, chronic disease, eating disorders, and the psychosocial aspects of coronary heart disease. Finally and importantly, there are a number of special and cross‐cutting matters that are considered in Volume 4, ranging from relevant laws and regulations to telehealth and health disparities. Taken together, the articles triangulate on what it truly means to be healthy. What are the most promising directions for the future that emerge from a broad and deep approach to health? That is, to where do these encyclopedia articles point? One key clue arises from a unique opportunity to enhance, extend, and analyze the classic Terman study of children who were followed and studied throughout their lives (Friedman & Martin, 2012). These studies revealed that there are lifelong trajectories to health, thriving, and longevity. Although ­anyone can encounter bad luck, a number of basic patterns emerged that are more likely to lead to good health. That is, for some individuals, certain earlier life characteristics and circumstances help propel them on pathways of healthier and healthier behaviors, reactions, relationships, and experiences, while others instead face a series of contingent stumbling blocks. There are multipart but nonrandom pathways across time linking personalities, health behaviors, social groups, education, work environments, and health and longevity. The present encyclopedia necessarily is a compendium of summaries of the relevant elements of health and thriving, but one that would and can profitably be used as a base to synthesize the long‐term interdependent aspects of health. In sum, this encyclopedia is distinctive in its explicit embrace of the biopsychosocial approach to health, not through lip service or hand‐waving but rather through highly detailed and extensive consideration of the many dozens of topics crucial to this core interdisciplinary understanding.

References Friedman, H. S., & Martin, L. R. (2012). The longevity project: Surprising discoveries for health and long life from the landmark eight‐decade study. New York: Penguin Plume Press. Friedman, H. S., & Silver, R. C. (Eds.) (2007). Foundations of health psychology. New York: Oxford University Press. Kaplan, R. M. (2019). More than medicine: The broken promise of American health. Cambridge, MA: Harvard University Press. Stone, G., Weiss, M., Matarazzo, J. D., Miller, N. E., Rodin, J., Belar, C. D., … Singer, J. E. (Eds.) (1987). Health psychology: A discipline and a profession. University of Chicago Press.

Howard S. Friedman University of California, Riverside August 15, 2019

Preface

The field of health psychology is a specialty area that draws on how biology, psychology, behavior, and social factors influence health and illness. Despite the fact that the formal recognition of the field is only about a half of a century old, it has established itself as a major scientific and clinical discipline. The primary reason for this is that there have been a number of significant advances in psychological, medical, and physiological research that have changed the way we think about health, wellness, and illness. However, this is only the tip of the iceberg. We have the field of health psychology to thank for much of the progress seen across our ­current healthcare system. Let me provide some examples that illustrate the diversity of these important contributions. Starting with a broad public health perspective, health psychologists have been involved in how our communities are planned and how urban development has a significant and direct impact on our health behaviors. That is, if we live close to where we work, play, and shop, we are more likely to walk or bike to our destination rather than drive or take public transportation. Focusing on a smaller, but no less important, source of data, health psychologists are involved in using information obtained from our genes to help counsel individuals to make good, well‐informed health decisions that could have an impact on the individual immediately or in the future. Being well‐informed can direct a person toward the best possible path toward wellness, whether it be measures aimed at prevention, further monitoring, or intervention. Of course, there are many other contributions. For instance, data indicates that several of the leading causes of death in our society can be prevented or delayed (e.g., heart disease, respiratory disease, cerebrovascular disease, and diabetes) via active participation in psychological interventions. Knowing that we can improve health status by changing our behaviors seems like an easy “fix,” but we know that behavior change is tough. As such, it is not surprising that health psychologists have been involved in trying to improve upon treatment success by examining patient compliance. When we better understand what motivates and discourages people from engaging in treatment or pro‐health behaviors, we can improve upon compliance and help individuals adopt more healthy lifestyles. Health psychologists have also played a role  in shaping healthcare policy via identifying evidence‐based treatments. This work has direct effects on how individuals receive healthcare as well as what treatments are available/ reimbursable by insurance companies. Finally, there are also factors beyond medical care to consider (e.g., economic, educational) that can lead to differential health outcomes. Thus, health psychologists likewise examine ways to reduce health disparities, ensuring that the ­public and government officials are made aware of the impact of the social determinants of health.

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Preface

I could go on and on, however, examples like this illustrate the significant impact this broad and exciting field has had (and will continue to have) on our understanding of health and wellness. Given the constantly changing nature of the field, it is not possible to be all inclusive; however, the aim of these four volumes is to provide readers an up‐to‐date overview of the field. Each entry is written to stand alone for those who wish to learn about a specific topic, and if the reader is left wanting more, suggested readings are provided to expand one’s knowledge. Volume I, Biological Bases of Health Behavior, includes entries that cover topics in the broad areas of neuroscience and biopsychology relevant to health behavior. General topics include degenerative and developmental conditions, emerging methodologies available in clinical research, functional anatomy and imaging, and gene × environment interactions. Volume II, Social Bases of Health Behavior, addresses topics related to theories and concepts derived from social psychology. Specifically, topics related to the self, social cognition, social perception, attitudes and attitude change, perception, framing, and pro‐health behaviors are included. Volume III, Clinical Health Psychology and Behavioral Medicine, covers the applied aspects of the field of health psychology including practical topics that clinical health psychologists face in the workplace, behavioral aspects of medical conditions, the impact of unhealthy behaviors, and issues related to the comorbidity of psychiatric disorders and chronic health concerns. Finally, Volume IV, Special Issues in Health Psychology, contains a wide array of topics that are worthy of special consideration in the field. Philosophical and conceptual issues are discussed, along with new approaches in delivering treatment and matters to c­ onsider when working with diverse and protected populations. It is my sincere hope that the Wiley Encyclopedia of Health Psychology will serve as a comprehensive resource for academic and applied psychologists, other health care professionals interested in the relationship of psychological and physical well‐being, and students across the health professions. I would very much like to thank and acknowledge all those who have made this work possible including Michelle McFadden, the Wiley editorial and production teams, the volume editors, and of course all of the authors who contributed their outstanding work. Lee M. Cohen, PhD

Preface Volume 4: Special Issues in Health Psychology

Special issues, by definition, are an evolving target, especially in a topic area as fluid and diverse as health psychology. When we began this volume some three summers ago (June 2016), a cursory overview of peer‐reviewed journal articles in health psychology journals reveals multiple topics: assessment techniques, education of providers, populations and subpopulations, new and evolving diseases, new treatments, environmental changes, systemic changes, and of course the repackaging of older, more established interventions to any of the preceding “­special issues.” Where health psychology seeks to understand the role of behavior and ­perhaps cognition and behavior in the health of individuals, groups, nations, and humanity writ large, a volume of special issues could range from the molecular to the near universal. In the present volume, we assembled 26 entries, each drafted by content experts in the ­special issue addressed. Every effort was made to ensure that each entry is evidence‐based yet accessible to the most junior student of health psychology. The goal of this volume is to address at least a bit of each category foreseeably concerning health psychologists in the first two decades of the twenty‐first century. To that end, we have three entries on assessment, two entries regarding education of providers, three entries on subpopulations, three entries on new or evolving diseases, six entries on treatments, and eight entries on environmental and ­systemic/cultural change. No doubt, as we go to press, newer and potentially more expansive knowledge will come to the fore, and each of our authors will wish that they had more time or more space to tell us about the latest information relevant to the health of humanity. To the wishful rewriters, I imagine that another opportunity will present itself. In the meantime, I am indebted to the many authors that took time from busy schedules to draft the entries contained herein. Even for the fortunate few that write as easily as they breathe, these chapters are a labor of love. To synthesize one’s lifework and academic passion to a few cogent paragraphs of use to the novice and the expert alike is no small feat. I am also indebted to Ms. Alexia Maness, whose Herculean effort involved mastering multiple software changes while wrangling time zones and mixed messages across continents. Without her ­consistent good cheer, we would not have a volume for which to write a preface! A resounding thank you to all. Suzy Bird Gulliver

Editor‐in‐Chief Acknowledgments

I would like to publicly recognize and thank my family, Michelle, Ross, Rachel, and Becca for being supportive of my work life while also providing me with a family life beyond my wildest dreams. I love you all very much. I would also like to note my great appreciation and thanks to the faculty and staff of the Doctoral Training Program in Clinical Psychology at Oklahoma State University for providing me with an opportunity to expand my education and for providing extraordinary training. In particular, I would like to thank my late mentor Dr. Frank L. Collins Jr., Dr. Larry Mullins, Dr. John Chaney, and Patricia Diaz Alexander. Further, I am grateful to the University of Mississippi and Texas Tech University (and the colleagues and students I have had the privilege to work alongside) for affording me excellent working environments and the support to do the work I am honored to be a part of. Finally, I would like to recognize and thank the volume editors for their vision and perseverance to this project as well as to each of the contributors for their excellent entries and their dedication to this very important field. Lee M. Cohen, Ph.D. Editor‐in‐Chief

The Biopsychosocial Model Robert J. Gatchel1, Christopher T. Ray2, Nancy Kishino3, and Athena Brindle1 Department of Psychology, College of Science, The University of Texas at Arlington, Arlington, TX, USA 2 College of Health Sciences, Texas Woman’s University, Denton, TX, USA 3 West Coast Spine Restoration, Riverside, CA, USA

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Overview At the outset, it should be noted that the term biopsychosocial model was originally introduced by George Engel (1977, 1980) because he felt that the traditionally embraced biomedical reductionist model in medicine was outdated in light of the newer advances in medicine. He emphasized that modern medicine needed to encompass a more dynamic system approach, which included different levels of organization that included not only factors related to the patient and physician but also other important sociocultural and lifestyle variables in a “­systems theory” manner initially introduced in biology by scientists such as Weiss (1969, 1977) and von Bertalanffy (1968, 1969). In terms of the biomedical model, Engel (1980) succinctly noted that: “…The crippling flaw of the model is that it does not include the patient and his attributes as a person, a human being. Yet in everyday works of the physician the prime object of study is a person, and many of the data necessary for hypothesis development and testing are gathered within the framework of an ongoing human relationship and appear in behavioral and psychological forms, namely, how the patient behaves and what he reports about himself and his life. The biomedical model can make provision neither for the person as a whole nor for data of a psychological or social nature, for the reductionism and mind‐body dualism in which the model is predicated requires that these must first be reduced to physico‐chemical terms before they have any meaning…” (p. 536). It should also be noted that Minotti, Licciardone, Kearns, and Gatchel (2010) have indicated how osteopathic medicine actually preceded traditional allopathic medicine in embracing the biopsychosocial model approach to patient care. Indeed, one important tenet of osteopathic medicine is that a patient is composed of body, mind, and spirit, and one needs to

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treat all three of components of the “whole person” in a holistic manner (Seffinger et  al., 2011). Such a holistic approach involves the integration of physical structure, function, ­emotion, and social factors in order to achieve healing and health. “Knowing the whole ­person” is of paramount importance in the osteopathic approach, as it is in the biopsychosocial model. Thus, as will be reviewed in the next section, physical disorders (such as pain) are the result of the dynamic interaction among physiological (bio), psychological (psycho), and social (social) factors (i.e., biopsychosocial) that often perpetuate and may worsen the clinical symptom presentation of patients, as well as accounting for the individual differences across patients. Unfortunately, this new biopsychosocial model conceptualization of illness was not immediately embraced by the general allopathic medical community. This may have been due to the fact that Engel was a psychiatrist, with psychiatry being at the “bottom of the ladder” in terms of medical specialties at that point in time. Fortunately, however, a synergy of events was occurring in the fields of pain medicine and behavioral medicine/health psychology that made the biopsychosocial model very attractive for many clinical researchers interested in the assessment of treatment of chronic pain. Below is a brief listing of how this synergy developed: • Beecher (1959) highlighted the important role that conditioning played in patients’ experiences of pain and helped to further advances the idea that the biomedical model of pain was too limited. • Melzack and Wall (1965) introduced the gate control theory of pain, which emphasized the important effects of emotion and cognitive factors in the experience of pain. Subsequently, Melzack and Casey (1968) extended the model further by introducing the neuromatrix theory of pain, which again emphasized the need to incorporate cognitive and ­motivational– affective components in order to better understand pain. • Loeser (1982) proposed a general model of pain that included four dimensions: physical nociception, the subjective perception of pain due to the nociception, suffering or the emotional responses that are triggered by nociception or some other negative event associated with it (such as fear and anxiety), and pain behaviors, which are things that pain sufferers do, such as avoiding activities of daily living or work because of fear of reinjury or the further exacerbation of pain. • The pioneering work by Fordyce and colleagues (Fordyce, Fowler, Lehmann, & DeLateur, 1968; Fordyce & Steger, 1979) successfully applied behavioral psychological principles (such as operant conditioning) to the treatment of chronic pain. Ever since the above contributions to the study of pain, the biopsychosocial model has become the most heuristic approach to the assessment, treatment, and prevention of chronic pain (e.g., Gatchel, Peng, Peters, Fuchs & Turk, 2007; Turk & Monarch, 2002), as will be reviewed next.

The Biopsychosocial Model of Chronic Pain Indeed, the acceptance of the biopsychosocial model is increasingly important in order to ­r ecognize the comorbidity of chronic pain and other persistent diseases. Fortunately, this model has consistently led to a better understanding of the considerably complex interactive process among components. In the context of pain, the biopsychosocial model approaches it as a consequence of the dynamic interaction among biological, psychosocial, and social components. This ­interaction perpetuates and may even worsen the clinical

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presentation of pain. Many other clinical researchers have also emphasized the importance of this model (e.g., Feinberg & Brigham, 2013; Gatchel, McGeary, McGeary, & Lippe, 2014; Turk & Monarch, 2002). In addition, Gatchel (2004) examined the amplified measures of emotional suffering and psychopathology that disrupted effective treatment of patients with chronic pain. Furthermore, the degree of pain experienced by a patient depends on genetic factors, prior knowledge of pain, overall psychosocial well‐being, and sociocultural variables (Gatchel et  al., 2007). This resulting well‐balanced biopsychosocial model has shown exceptional utility in chronic illness diagnosis and management. Unlike the framework of the biomedical model, the biopsychosocial model emphasizes the importance of the human experience of illness, in addition to the laboratory documentation that represents disease potential by providing a framework of a hierarchy that illustrates the interrelationships of a patient’s psychosocial environment to their biological systems, as originally suggested by Engel (1980). As noted above, the traditional biomedical reductionist model has been insufficient in ­capturing all of the aspects that are needed in order to appropriately diagnose and treat a patient living with chronic pain. Since the designation of 2001–2010 as being the “Decade of Pain Control and Research” by Congress, there have been other initiatives launched to address the growing problem of chronic pain. For example, chronic pain was highlighted as an important area for future research in the Institute of Medicine’s report titled “Living Well and Chronic Illness: A Call for Action” (Institute of Medicine, 2012). This was because chronic pain is becoming the most prevalent illness among Americans today. Nahin (2015) reported that 25.3 million Americans suffer from consistent daily pain, and 23.4 million Americans report “a lot of pain.” The Center for Disease Control and the National Center for Health Statistics (2015) also reported that 15.6% of Americans over the age of 18 reported experiencing a consistent severe headache or migraine, 29% reported ­consistent low back pain, and 14.9% reported consistent neck pain. In addition, pain accounts for approximately 80% of all doctors’ visits, with medical treatment and lost productivity due to pain costing approximately $625 billion annually (Gatchel, 2004; Institute of Medicine of the National Academy of Science, 2011). Although, up until this point, we have been discussing the great utility of the biopsychosocial model for effectively evaluating and managing chronic pain, it is also a heuristic approach for dealing with chronic illnesses in general. This is because there is a close comorbidity of mental and physical disorders (Gatchel, 2004). Most chronic illnesses are accompanied by psychosocial factors (such as anxiety, depression, and anger), as well as cognitive distortions (such as fear avoidance, dysfunctional attitudes and beliefs, and distorted expectations). Fortunately, there are now psychometrically sound tests to measure these psychosocial factors (e.g., Gatchel, 2005). In addition, medical technology has advanced over the years to better evaluate possible biological or pathophysiological underpinnings of these illnesses (e.g., with the use of functional magnetic resonance imaging). However, despite the many improvements in technology and the better understanding of biopsychosocial processes, there are still future advances that will need to be made (e.g., Suls, Krantz, & Williams, 2013).

Beyond the Biopsychosocial Model With growing research in neuroscience and genetics, the significance of a diathesis–stress ­component to the biopsychosocial model must be acknowledged. This new diathesis–stress biopsychosocial model places an additional emphasis that predispositional factors interact with

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stressors such as health problems/illnesses. Once an illness is established, it becomes a stressor in its own right (Gatchel, 2005). Gatchel (2005) also pointed out that Melzack’s (1999) neuromatrix theory of pain can be viewed as incorporating a diathesis–stress model component to it. Such a diathesis–stress component, originally promoted by Levi (1974), assumed that individuals are predisposed toward a specific disorder and manifest that disorder when negatively affected by stress. In other words, the degree/complexity of the interaction among biological, psychological, and social factors often depends on certain diatheses that people have when exposed to stressors such as an illness (especially if it is chronic in nature). For  example, as illustrated in Figure  1, individuals “bring with them” certain diatheses or predispositions, such as certain candidate genes. In the case of chronic pain, examples of such candidate genes are neurotransmitter transporter, serotonin, solute carrier family (5‐HTT), catechol‐O‐methyltransferase (COMT), interleukin 6 (IL‐6), and μ‐opioid receptor (OPRM). Also, for such candidate genes, the growing field of epigenetics has started to elucidate how these genes can be ultimately expressed as behavioral phenotypes (e.g., Belfer & Diatchenko, 2014; Biemont, 2010). Environmental factors, such as stress, can also alter their phenotypic expression and can have long‐lasting effects on health and behavior (Hunter, 2012). One mechanism involved in the epigenetics process is the shortening of telomeres that help protect chromosomes. If they become too short, or are totally decimated by environmental stress factors, the chromosome becomes more vulnerable to possible mutation (Sibille, Witek‐ Janusek, Mathews, & Fillingim, 2012).

Potential diatheses/Predispositions Genetic predispositions Many (e.g., for pain, genes such as 5-HTT, COMT, IL6, OPRM1)

Generalized biological vulnerabilities (e.g., disregulation of the endocrine, immune, cardiovascular and renal systems)

Generalized psychological vulnerabilities (e.g., psychopathology, such as mood, anxiety, and personality disorders

Social vulnerabilities (e.g., SES, culture, health disparities)

Experience of illness-related pain Efferent feedback

Afferent feedback Presence of potential mediators/moderators (e.g., social support, good coping skills/resilience, perceived control, healthy lifestyle)

Yes

No Significant increase in allostatic load/ Central sensitization

Greater physical and mental health illnesses/symptoms

Less increase in allostatic load/ Central sensitization

Fewer physical and mental health illnesses/symptoms

Resultant configuration of the biopsychosocial assessment-treatment model Biological

Psychological Biological Social

Psychological

Figure 1  The diathesis–stress biopsychosocial model of chronic illness.

Social

Few

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There are additional potential biological factors (e.g., dysregulation of the endocrine, immune, and cardiovascular systems), psychological factors (e.g., psychopathology, such as mood, anxiety, and personality disorders), and social vulnerabilities (e.g., SES, culture and health disparities) that may affect a person’s ultimate experience of an illness. In turn, the probability of this illness to become chronic may be lessened by the presence of potential mediators/moderators (such as social support, coping skills, etc.). If mediators or moderators are absent, then they cannot “buffer” the individual from the full impact of the illness. In the event that pain is associated with the illness, central sensitization (CS) may result (a term introduced by Yunus, 2007 to refer to an abnormal and intensified experience of pain by mechanisms such as hyperexcitability in the central nervous system). This CS is postulated to underpin central sensitivity syndromes (CSSs) such as fibromyalgia, TMJ disorders, irritable bowel ­syndrome, and other persistent pain disorders (Adams & Turk, 2015; Mayer et  al., 2012; Woolf, 2011; Yunus, 2000). In addition, the lack of effective mediators/moderators may lead to significant increase in the allostatic load of the patient (a term originally introduce by McEwen (2000)). Allostatic load refers to the significant decrease in body homeostasis due to the chronic exposure to the stress or trauma of an illness. Neuroendocrine‐immune mechanisms, for example, can be negatively affected and result in greater “wear and tear of the body” (thus accelerating the development of diseases such as cardiovascular disorders, as well as ­psychoneuroendocrinology disorders). The above will have implications for the mental and physical health status of the patient and may affect the resultant configuration of the biopsychosocial response pattern displayed. This type of configuration, in turn, will determine how complex the assessment–treatment process will need to be for each individual patient. The higher confluence of genetic and biological ­predispositions may necessitate greater attention to those components of the biopsychosocial interactions (see bottom left configuration in Figure 1). Without doubt, though, a comprehensive interdisciplinary assessment–treatment approach will need to be administered to patients.

Summary and Conclusions The present chapter provided a comprehensive review of the heuristic value of the biopsychosocial model in better understanding health and illness. George Engel (1977, 1980) was the first to introduce the model because of his dissatisfaction of the traditionally embraced biomedical reductionist approach to illnesses that attempted to reduce all illnesses to only physiological/ biochemical underpinnings, with the complete exclusion of potentially important psychosocial factors. Unfortunately, this new biopsychosocial model was not accepted in the general medical community. However, as we discussed, a synergy of events was occurring in the fields of pain medicine and behavioral medicine/health psychology that made this new model extremely attractive to clinical researchers involved in the assessment and management of chronic pain. A more detailed discussion of the biopsychosocial model of chronic pain was next presented. In doing so, the epidemiology and high costs of this prevalent disorder was presented, as well as the various biopsychosocial components associated with it. It was also noted that the biopsychosocial model is generalizable to chronic illnesses in general. Additional advancements in the field, though, are still needed, especially with the development of new technologies that can more reliably evaluate the bio component of the model. Finally, a diathesis–stress biopsychosocial model was introduced for an even better understanding of health and illness. The diathesis–stress component was originally proposed by Levi (1974). Levi suggested that individuals are predisposed toward specific disorders and will

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manifest those disorders when negatively affected by stress. In the conceptual model we ­propose (see Figure  1), it is assumed that patients “bring with them” specific diatheses or predispositions, such as genetic factors, as well as potential biological and psychosocial vulnerabilities that may affect a patient’s ultimate expression of an illness. Obviously, the construct validity of this new model will need to be determined by future research efforts.

Author Biographies Robert J. Gatchel—Robert J. Gatchel, PhD, ABPP, is a professor in the Department of Psychology, College of Science, at the University of Texas at Arlington and a clinical research program consultant at the Eugene McDermott Center for Pain Management at the University of Texas Southwestern Medical Center at Dallas. He is the director of the Center of Excellence for the Study of Health and Chronic Illness at the University of Texas at Arlington, as well as director of Biopsychosocial Research in Osteopathic Research Center at the University of North Texas Health Science Center. Christopher T. Ray—Ray holds a doctorate degree in movement studies from the University of Georgia and earned both a master’s (in human performance and sport studies) and bachelor’s degree (in exercise science) from the University of Tennessee, Knoxville. His postdoctoral fellowship work was at the Veterans Administration Medical Center in Atlanta, which included a summer institute in epidemiology and biostatistics at the University of Washington. His research involves kinesiology and geriatric health. Nancy Kishino—Nancy Kishino, OTR/L, CVE, is the director and founder of West Coast Spine Restoration Center. She is a licensed and registered occupational therapist. Athena Brindle—Athena attended the University of Texas at Arlington and is now a ­candidate for a Doctor of Pharmacy at Texas Tech University Health Sciences Center.

References Adams, L. M., & Turk, D. C. (2015). Psychosocial factors and central sensitivity syndromes. Current Rheumatology Reviews, 11(2), 96–108. Beecher, H. K. (1959). Measurement of subjective responses: Quantitative effects of drugs. New York, NY: Oxford University Press. Belfer, I., & Diatchenko, L. (2014). Pain genetics: Basic to translational science. New York, NY: Wiley. Biemont, C. (2010). From genotype to phenotype. What do epigenetics and epigenomics tell us[quest]. Heredity, 105(1), 1–3. Engel, G. L. (1977). The need for a new medical model: A challenge for biomedicine. Science, 196(4286), 129–136. Engel, G. L. (1980). The clinical application of the biopsychosocial model. The American Journal of Psychiatry, 137(5), 535–544. Feinberg, S. D., & Brigham, C. R. (2013). Assessing disability in the pain patient. In T. R. Deer, M. S. Leong, A. Buvanendran, V. Gordin, P. S. Kim, S. J. Panchal, & A. L. Ray (Eds.), Comprehensive treatment of chronic pain by medical, interventional, and integrative approaches: The American academy of pain medicine textbook on patient management. New York, NY: Springer. Fordyce, W. E., Fowler, R. S., Lehmann, J. F., & DeLateur, B. J. (1968). Some implications of learning in problems of chronic pain. Journal of Chronic Diseases, 21, 179–190. Fordyce, W. E., & Steger, J. C. (1979). Chronic pain. In O. F. Pomerleau & J. P. Brady (Eds.), Behavioral medicine: Theory and practice. Baltimore, MD: Williams & Wilkins.

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Gatchel, R. J. (2004). Comorbidity of chronic mental and physical health disorders: The biopsychosocial perspective. American Psychologist, 59, 792–805. Gatchel, R. J. (2005). Clinical essentials of pain management. Washington, DC: American Psychological Association. Gatchel, R. J., & McGeary, D. (in press). Pain and health psychology. In C. S. Richards (Ed.), Health psychology. New York, NY: Wiley. Gatchel, R. J., McGeary, D. D., McGeary, C. A., & Lippe, B. (2014). Interdisciplinary chronic pain management: Past, present and the future. American Psychologist, Special Issue on “Psychology and Chronic Pain”, 69(2), 119–130. Gatchel, R. J., Peng, Y., Peters, M. L., Fuchs, P. N., & Turk, D. C. (2007). The biopsychosocial approach to chronic pain: Scientific advances and future directions. Psychological Bulletin, 133, 581–624. Hunter, R. G. (2012). Epigenetic effects of stress and corticosteroids in the brain. Frontiers in Cellular Neuroscience, 6. doi:10.3389/fncel.2012.00018 Institute of Medicine. (2012). Living well with chronic illness: A call for public health action. Washington, DC: The National Academies Press. Institute of Medicine of the National Academy of Science. (2011). Relieving pain in America: A blueprint for transforming prevention, care, education, and research. Washington, DC: Institute of Medicine. Levi, L. (1974). Psychosocial stress and disease: A conceptual model. In E. K. Gunderson & R. H. Rahe (Eds.), Life stress and illness. Springfield, IL: Charles C. Thomas. Loeser, J. D. (1982). Concepts of pain. In J. Stanton‐Hicks & R. Boaz (Eds.), Chronic low back pain. New York, NY: Raven Press. Mayer, T. G., Neblett, R., Cohen, H., Howard, K. J., Choi, Y. H., Williams, M. J., … Gatchel, R. J. (2012). The development and psychometric validation of the central sensitization inventory. Pain Practice, 12, 276–285. McEwen, B. S. (2000). Allostasis and allostatic load: Implications for neuropsychopharmacology. Neuropsychopharmacology, 22, 108–124. Melzack, R. (1999). Pain and stress: A new perspective. In R. J. Gatchel & D. C. Turk (Eds.), Psychosocial factors in pain: Critical perspectives. New York, NY: Guilford Publications, Inc. Melzack, R., & Casey, K. L. (1968). Sensory, motivational, and central control determinants of pain: A  new conceptual model. In D. Kenshalo (Ed.), The skin senses (pp. 423–443). Springfield, IL: Thomas. Melzack, R., & Wall, P. D. (1965). Pain mechanisms: A new theory. Science, 50, 971–979. Minotti, D. E., Licciardone, J. C., Kearns, C., & Gatchel, R. J. (2010). The osteopathic medicine approach to pain management. Practical Pain Management, 10, 28–43. Nahin, R. L. (2015). Estimates of pain prevalence and severity in adults: United States, 2012. The Journal of Pain, 16(8), 769–780. doi:10.1016/j.jpain.2015.05.002 National Center for Health Statistics. (2015). Health, United States, 2014: With special feature on adults aged 55‐64. Hyattsville, MD: U.S. Government Printing Office. Seffinger, M. A., King, H. H., Ward, R. C., Jones, J. M., Rogers, F. J., & Patterson, M. M. (2011). Osteopathic philosophy. In A. G. Chila (Ed.), Foundations of osteopathic medicine. New York, NY: Lippincott Williams & Wilkins. Sibille, K. T., Witek‐Janusek, L., Mathews, H. L., & Fillingim, R. B. (2012). Telomeres and epigenetics: Potential relevance to chronic pain. Pain, 153(9), 1789–1793. doi:10.1016/j.pain.2012.06.003 Suls, J., Krantz, D. S., & Williams, G. C. (2013). Three strategies for bridging different levels of analysis and embracing the biopsychosocial model. Health Psychology, 32(5), 597–601. doi:10.1037/a0031197 Turk, D. C., & Monarch, E. S. (2002). Biopsychosocial perspective on chronic pain. In D. C. Turk & R.  J. Gatchel (Eds.), Psychological approaches to pain management: A practitioner’s handbook (2nd ed.). New York, NY: Guilford. von Bertalanffy, L. (1969). Chance or law. In J. Smythies & A. Koestler (Eds.), Beyond reductionism. New York, NY: Macmillan Publishing Co. von Bertalanffy, L. V. (1968). General systems theories: Foundations, development and applications. New York, NY: G. Braziller.

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Weiss, P. (1969). The living system: Terminism stratified. In J. Smythies & A. Koestler (Eds.), Beyond reductionism. New York, NY: Macmillan Publishing Co. Weiss, P. (1977). The system of nature and the nature of systems: Empirical holism and practical reductionism harmonized. In K. E. Schaefer, H. Hensel, & R. Brody (Eds.), Toward a man‐centered medical science. Mt. Kisco, NY: Futura Publishing Co. Woolf, C. J. (2011). Central sensitization: Implications for the diagnosis and treatment of pain. Pain, 152, 2–15. Yunus, M. B. (2000). Central sensitivity syndromes: A unified concept for fibromyalgia and other similar maladies. Journal of Indian Rheumatism Association, 8, 27–33. Yunus, M. B. (2007). Fibromyalgia and overlapping disorders: The unifying concept of central sensitivity syndromes. Seminars in Arthritis and Rheumatism, 36, 339–356.

Death and Dying David E. Balk Department of Health and Nutrition Sciences, Brooklyn College of the City University of New York, Brooklyn, NY, USA

The dying trajectories and principal causes of death in the United States—and in other ­developed countries, for instance, Canada, Germany, and Japan—have changed dramatically over the past century. Whereas a typical dying trajectory in the early twentieth century was sudden and unexpected death due to a fatal, infectious disease or accident, the process of dying has become principally a lingering, drawn‐out trajectory involving a series of chronic, debilitating conditions. The principal causes of death have become heart disease, malignant neoplasms, and circulatory problems. A major factor that has produced these changes in dying trajectories and causes of death is the triumph of the biomedical model. Other influences have come from advances in the ­production and protection of food, success in insuring clean water, architectural and engineering triumphs (buildings with better heating and cooling, for instance), and public health ­campaigns educating communities about healthy and unhealthy behaviors. The biomedical model became the framework organizing medical school curriculum and defining good medical practices. This model looks for biological and chemical reasons to explain disease. Disease is defined as physical deviations from established norms. The body is seen as a complicated, highly organized set of interrelated physical systems. The fundamental goal is to maintain life, and death is the enemy to resist at all costs. The triumph of the biomedical model is no doubt due to its success in producing new ­knowledge and in overcoming previously fatal diseases. In consort with better hygiene and nutrition, the biomedical model helped increase longevity in the United States from an average of 47 years of age in 1910 to a lifespan exceeding 78 years today; whereas in the early twentieth century over 53% of all deaths in the United States occurred to persons 14 years of age and younger, the conquering of infectious diseases has made childhood mortality very uncommon. As a conceptual framework the biomedical model became the paradigm for medical education and medical practice. The biomedical model has gained widespread cultural ­ ­acceptance in ­developed countries. On the whole, most persons in developed countries in the

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twenty‐first century view life, health, disease, and death from within the assumptive world of the biomedical model. A clear upshot of the success of the biomedical model is that American society now has large numbers of individuals living significantly beyond their 60s and developing the many infirmities endemic to aging bodies and minds. Concomitant with many progressive infirmities is the presence of functional limitation, such as difficulty walking, stooping, or swallowing. Of the nearly 328 million persons in the United States, 14% (nearly 46 million) have both chronic, debilitating conditions and functional limitations, and this 14% accrues over 56% of all healthcare costs incurred in the United States. These figures will only increase as the aging population continues to grow. In many cases, these persons form a new societal phenomenon, the “frail elderly,” persons living into their 80s and 90s. In other eras these frail elderly would have died without the life‐prolonging technologies used today. And what is of considerable concern is when life‐prolonging technologies produce a continuation of life bereft of quality. Increasing apprehension has emerged over medical interventions to keep persons alive but helpless, incontinent, lacking dignity, connected to all sorts of instruments, and in pain. Faced with the strong possibility that heroic efforts will be exerted to keep people alive despite their preference to die rather than remain helpless and very possibly in pain, various kinds of written directives have been devised to enable individuals to decline such efforts ahead of time. These written documents are termed advance directives. An advance directive is a legal document that permits competent adults to state their preferences prior to a medical emergency or some other life‐threatening situation. There are four forms of such directives: do not resuscitate orders (DNR), living wills, healthcare power of attorney (HCPOA), and physician orders for life‐sustaining treatment (POLST). DNRs are medical orders explicitly stating that the patient has rejected use of cardiopulmonary resuscitation should he (she) go into cardiac arrest. DNRs are written for medical staff to follow, are placed prominently in the patient’s chart, and denote the explicit preferences of the patient (or of the patient’s surrogate holding HCPOA). Living wills provide the means for mentally competent adults to express preferences regarding the use of specified medical interventions should the person’s wishes be otherwise impossible to determine; an example would be if the person is in a coma. This type of advance directive is grounded in the right of mentally competent adults to refuse medical treatment. Living wills have gained legal status in each state of the United States and in the District of Columbia. However, specific provisions may differ from state to state regarding what a living will must contain to be legal. Healthcare power of attorney, sometimes called durable power of attorney or healthcare proxy, offers an alternative to a living will. With HCPOA an individual empowers someone else to make healthcare decisions for the individual if the person is incapable to make such decisions. POLST is an advance directive a person writes in collaboration with her (his) physician. The POLST makes individual preferences into actionable medical orders signed by the physician and the patient. Reviews of compliance indicate POLSTs are followed more consistently than living wills. Attitudes toward use of advance directives vary considerably across different ethnic and racial groups in the United States, and these group differences underscore the communication challenges facing health psychologists working with persons from diverse backgrounds. When examining these group differences, it is prudent to recall that individual differences within a group can be as varied as differences between groups.

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Advance directives are grounded in respect for the autonomy of the individual and in belief that promoting individual autonomy is a preferred value. Various ethnic groups place greater importance on familial support and guidance than on individual autonomy, and paternalistic decision making is the norm. Advance care planning is observed more commonly in the United States among European Americans than among African, Hispanic, and Asian American groups. As examples, African Americans on the whole are much less likely than European Americans to complete advance directives. A history of discrimination from the majority White culture and documented cases of medical neglect as well as abuse (consider the infamous Tuskegee syphilis study) lead many African Americans to suspect that signing an advance directive gives the medical establishment a blank check to deny them treatment. Researchers have studied Hispanic beliefs and intentions regarding end‐of‐life matters less than those of other ethnic minority groups. One common phenomenon that has emerged, however, is that the family holds a place of primacy among Hispanics and that the decisions of an elderly male possess prominence. While individual Hispanics have told researchers they would want care focused on relieving pain if seriously ill, they had not discussed these matters with family members. At the same time, despite never having voiced these views with their family, these respondents also indicated they wanted their families to be involved in end‐of‐life decision making. It is not difficult to see the challenges facing health psychologists to find ways to cut through the barriers presented by a hesitancy to discuss wishes and yet wanting family members to be involved in end‐of‐life decision making. Asian Americans value saving face and regulating emotions, and this desire to avoid shame and to protect loved ones from shame may lead family members to insist that information about a terminal illness be kept from the patient. Research with groups of Asian patients suggests they are less likely than African American or Hispanic patients to want to learn the life expectancy facing them. The family, particularly a male figure, holds importance when critical decisions are to be made. The realities that many persons die in a lingering, pain‐filled trajectory filled with intrusive medical procedures—procedures that extend length of life but diminish quality of life—have inspired efforts to allow terminally ill persons to die a good death. The notion of a good death is a difficult concept with differing meanings contingent on the assumptive world(s) dominant in a society. In some societies, such as Japanese and Navajo, an underlying concern is whether the wrong kind of death will expose the living to vengeance from the deceased. In countries debating physician‐assisted suicide, fundamentally opposed answers are given to whether consciously chosen self‐termination to end unremitting pain offers a good death. In countries wedded to the biomedical model, ethicists and physicians are asking whether the healthcare system carries obligations to foster a good death when persons are nonresponsive to treatment and whether resources need to be rationed differently in end‐of‐life situations. In twenty‐first century Westernized secular societies a good death refers to the process of dying. The emphasis is on how a person’s life ends, not on consequences in an afterlife. Emphasis is on several humanistic factors: to be free of distressing symptoms, to be free of pain, to be conscious, to be with loved ones, to be in familiar surroundings, and to be able to take care of oneself. Consistent answers are given when persons are asked to identify their wishes regarding dying. They want to die free of pain, surrounded by loved ones, and preferably in familiar ­surroundings. An overwhelming majority of persons dying from a terminal illness, however, experience distressing symptoms such as nausea and vomiting; feel intense, unremitting pain,

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helpless, and hopeless; are hooked up to machines; undergo invasive, painful procedures; and are surrounded by strangers in a medical or other institutional setting. A counterforce to keeping terminally ill persons alive at all costs has been the hospice movement. Inspired by pioneering work in England, American hospice programs began in 1974 and now are present in every state. These programs take a holistic view of human existence. A holistic view is a thread connecting much contemporary thought about and work with persons facing the end of life. Evaluation evidence has demonstrated noticeable family satisfaction with hospice vis‐à‐vis other forms of care for the dying. Further, evaluators have found significantly decreased costs for persons in hospice in contrast with matched controls; the decreased costs amounted to over $2,300 a year. However, concerns have been raised over the projected costs to be incurred with the increasing numbers of frail elderly who will develop cancer or other debilitating ­terminal illnesses. To receive federal funding, American hospice programs must admit persons only if they are diagnosed as having 6 months or less to live and if they forego curative treatment. Hospice efforts are exerted to prevent and manage all pain so that the individual and his (her) family may experience a good death. The practice and the policy are not to keep the terminally ill person alive at all costs. The hospice movement has been instrumental in holding forth that a good death is possible. First and foremost, hospice understands that the dying person is primarily a living human being. Second, the patient in hospice care is the terminally ill person and the family. Further, hospice promotes quality of life by focusing on six fundamental dimensions of human existence: the physical, the behavioral, the emotional, the cognitive, the interpersonal, and the spiritual/existential. Uncontrollable, unremitting, agonizing pain prevents persons from dying on their own terms, from dying with dignity, and from attending to the many aspects that make existence worthwhile. Two useful frameworks on coping with a terminal illness have been proposed (Corr, 1992; Doka, 2013). One framework reviews various holistic tasks facing a dying person, the family, and caregivers; the other looks at changes that occur over time once a diagnosis of terminal illness has been learned. The holistic tasks address physical, behavioral, cognitive, interpersonal, emotional, and ­spiritual dimensions of existence. Looking at a few of these tasks, consider that a terminally ill person may need help with such physical and behavioral tasks as washing, getting into and out of bed, using the toilet, swallowing, sleeping, and coping with the all‐encompassing issue of excruciating pain that affects all dimensions of being alive. Chemotherapy may impair a ­person’s cognitive capacities. Spiritual tasks center on the virtue of hope and on the viability of the person’s assumptive world in the face of death from a terminal illness. The second framework for coping entails responses of ill individuals, family members, and ­caregivers over time once a diagnosis is learned. Persons move from a pre‐diagnostic phase through treatment and then the possibilities of remission, relapse, recovery, and dying. The h ­ olistic tasks mentioned above are carried out within these various phases once the diagnosis is learned. Adopting a holistic perspective could be interpreted as an act of defiance in the face of the technique‐driven biomedical model. Holism’s roots in existential phenomenology place it in opposition to a positivistic approach to life and to the allure of techniques over mindfulness. Yet, such an interpretation would be incomplete: attending to the cares and the suffering of a terminally ill person and that person’s family may well draw on methods and protocols ­inherent to best practices in biomedical science; a proper dosage of morphine may be exactly what the person needs. Awareness that humans have devised effective means to ameliorate physical

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s­uffering can increase hope. Quality care, yet, also recognizes that the dying person is more than the sum of his (her) physical systems, death is not simply a medical phenomenon, and the person will not be reduced to biological or chemical systems that the biomedical model treats very well. The Institute of Medicine (IOM) took a forceful stand on the place of a good death in medical practice. The IOM wrote (1997, p. 5): People should be able to expect and achieve a decent or good death – one that is free from avoidable distress and suffering for patients, families, and caregivers; (one that is) in general accord with patients’ and families’ wishes; and (one that is) reasonably consistent with clinical, cultural, and ethical standards.

Note the main topics in this IOM statement. 1 Achieving a good death is made a norm of medical practice. 2 There is explicit mention of preventing distress and suffering. 3 The stakeholders include more than the person who is dying. A good death will free patients, family members, and caregivers from distress and suffering. 4 The wishes of the patient and of the family will be followed. 5 Accepted clinical standards will be followed, as will cultural and ethical standards. The IOM document was distributed shortly after publication of the SUPPORT study (1995), a very disappointing longitudinal intervention to improve end‐of‐life care at five teaching hospitals in the United States. The first year of the study focused on gathering data on staff understanding of patients’ end‐of‐life preferences, medical decision making relevant to terminally ill persons, and medical interventions employed with terminally ill persons. The second year of the study provided each hospital a medical expert trained to consult with the medical teams on four objectives: 1 2 3 4

Staff would know terminally ill patients’ end‐of‐life preferences. Nurses and doctors would employ better pain control procedures. Physician–patient communication would improve. Outcomes would be patient oriented.

None of the objectives was achieved. An alarming finding from the SUPPORT study was that in nearly a third of cases physicians did not know their patients’ DNR preferences and in at best 50% of the cases spouses were aware of these DNR preferences. It is not unreasonable to surmise that the 1997 IOM study, at least in part, was a direct response to the very disappointing outcomes regarding prospects for quality end‐of‐life care that the SUPPORT study had disclosed. Looking back on the SUPPORT objectives, one can readily infer—based on the success of hospice—steps that may be taken within hospitals to accomplish what the SUPPORT intervention set out to do. Efforts to implement more circumscribed pain management systems have met with resistance in some US hospitals, so rosy‐colored expectations of easy success to accomplish the SUPPORT objectives are naïve. However, given those qualifiers about resistance to change, there are some objectives to consider. As examples, in order for staff to know the end‐of‐life preferences of terminally ill persons, education about working with the dying needs to become holistic, not merely biomedical. Further, integrating hospital teams with

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professionals alert to the social, emotional, and spiritual realities of dying persons would be a means to address the SUPPORT objective of attending to better pain procedures and of improving patient–staff communication. The 1997 IOM report made several recommendations in order that a good death would become the norm of medical practice. Three of the recommendations focused on (a) expectations about excellent care at the end of life, (b) commitment from all healthcare professionals to gain and use the best practices and knowledge to care for the dying, and (c) education of health psychologists that would give importance to care for the dying. In 2014 the IOM reviewed their recommendations and concluded that gains had been made and gaps remain (Institute of Medicine, 2014): 1 Expectations about excellent care. Among gains have been noticeable increases in the ­percentage of terminally ill persons who received at least 3 days of hospice care; an ideal would be to increase involvement with hospice to at least 2 months given the evidence of the benefits to the terminally ill person and long‐term benefits to the family following the death. In addition, numerous pilot programs implemented to demonstrate quality end‐of‐ life care produced not only new care delivery models but also several pilot programs made hospice care accessible in rural areas previously underserved or not served at all. Gaps identified include the lack of palliative care services in at least one‐third of US ­hospitals. While palliative medicine has become a board‐certified specialty, accreditation standards for hospitals and nursing homes in the United States do not require the provision of quality palliative care. 2 Commitment from all healthcare professionals to gain and use the best practices and knowledge for caring for the dying. The chief success reported is the significant increase in palliative care teams within hospital and medical centers. Between 2000 and 2012 the number of these teams increased dramatically from 600 to 1,600. 3 Gaps still needing attention include a continuing neglect in preventing and managing pain, particularly for patients nearing death. More attention needs to be paid to what healthcare professionals know about taking care of persons near the end of life. Education of healthcare professionals must give importance to caring for the dying. Several training programs are now producing annually about 230 new physicians specializing in palliative medicine. Nursing programs are required to prepare students for end‐of‐life and palliative care situations. More than 2,000 individuals have been educated via the Education on Palliative and End‐of‐Life Care Project; graduates work to educate physicians and other healthcare professionals on providing quality palliative care. Gaps still needing attention include the sparse attention given end‐of‐life care and virtual neglect of death and dying in medical school curricula. Further, of the 49 accredited schools of public health, only 3 have even one course on end‐of‐life policy. Analogous to the two IOM reports calling for radical changes in medical care at the end of life, a monograph recently appeared calling for a public health perspective on the end of life. The authors accept that quality care at the end of life is a public health imperative, and they focus on such issues as healthcare inequalities in this country and on the imperative to establish a public health agenda on policy insuring quality end‐of‐life care. Seemingly in tandem with the IOM document on a good death, two nonprofit organizations in New York City sponsored a project to examine what is meant by a good death. These organizations were the Commonwealth Fund, a nonprofit interested in health policy, and the Nathan Cummings Foundation, which is devoted to social justice, democracy, and healthcare.

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The authors of the Commonwealth–Cummings project (Emanuel & Emanuel, 1998) accept that multidimensionality marks a good death and that a holistic framework needs to be adopted to examine the multidimensional aspects of dying. Their holistic framework differs slightly with the framework used by hospice. The Commonwealth–Cummings framework focuses on six dimensions to human existence, namely, physical symptoms, psychological symptoms (hospice refers to emotional and cognitive), social relationships and support (­ hospice refers to the interpersonal dimension), economic demands and caregiving (not addressed in the hospice framework), spiritual/existential beliefs, and hopes and expectations (subsumed in the hospice framework under spiritual/existential). Cicely Saunders, the founder of the modern hospice movement, coined the term “total pain,” an idea based on a holistic understanding of human experience and emphasizing distress in such existential dimensions as the physical, the cognitive, the interpersonal, and the spiritual and including refusal to accept one’s terminal condition. As seen directly above, the Commonwealth–Cummings project built on this seminal idea of holism and identified six dimensions that come into play when human pain is considered. The Commonwealth–Cummings authors refer to “total pain” to depict the suffering that blocks a good death. Suffering is considered to consist of two situations: (a) poor conditions in all, or nearly all, of the six holistic dimensions or (b) an overwhelmingly bad experience in two of the dimensions. The suffering in each dimension is examined: 1 Physical suffering has been studied extensively. By the end of the twentieth century, reliable, valid pain assessment tools were available. Physical suffering also includes such ­distressing symptoms as fatigue, insomnia, vomiting, and nausea. Pain management experts estimate that in 95% of cases, opioid or adjuvant drugs produce pain relief; however, these experts also estimate that pain remains poorly treated in 20–70% of dying patients. Further, around 5% of cases of physical pain do not respond to drug treatment, and other approaches are tried including acupuncture, hypnosis, and yoga. 2 Psychological symptoms include both cognitions and emotions. The symptoms may include depression, anxiety, and irritability. Some medical staff discount the importance of alleviating psychological symptoms because (a) the staff are not skilled in treating them and/or (b) the staff consider these psychological reactions a normal part of dying. One can see the importance of a multidisciplinary team, as in hospice, to ensure that at the end of life a person’s psychological suffering is attended to. Various evidence‐based practices are available to assist in alleviating psychological symptoms. 3 Maintaining social relationships is a key adaptive task when coping with life crises. Maintaining links with others not only provides important outlets for emotions but also sources of information, solace, and acceptance. Much more work is needed to determine the kinds of social support dying patients rely on and whether support external to the ­family helps the person who is dying. 4 The reality of economic demands and caregiver needs at the end of life is a welcome addition to this holistic framework. There are sufficient research data to conclude that caring for a dying loved one imposes significant burdens on families. There can be staggering losses in income; the demands of the disease may induce physical and psychological fatigue. There is a link between severe physical and psychological pain in a patient at the end of life and increases in the caregiving needs that impose economic burdens that leave the family less capable of coping following the death. The concerns over caregiver burnout and ­complicated grief following the death are well documented. A note of hope has emerged

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regarding caregiver needs when caring for a loved one at the end of life: empirical data indicate that early entrance to hospice—2 months or more prior to the death—proves beneficial for family members when later grieving the death. 5 Spiritual/existential beliefs form the category denoting how people find meaning, ­purpose, and value in their lives. Spirituality is integral to being human whether or not a person is religious. Hospice has recognized the central place of spirituality in the life of a dying ­person and in the lives of others left to grieve someone’s death. Meaning making is at the core of prominent models to explain human responses to bereavement. Persons greatly at risk during times of crisis are those overwhelmed by the conclusion that nothing matters. It is difficult to overestimate the profound difficulty a person experiences when what gave her (his) life meaning has been disclosed to be without substance. One can see this scenario playing out when a death due to a terminal illness—for instance, a young child’s—so challenges a parent’s assumptive world that drifting into unrelenting, chronic grief becomes the only alternative. 6 Some scholars subsume hopes and expectations under the notion of spiritual/existential beliefs. Further, it is well understood that trauma from major life issues can so truncate a person’s hopes and expectations that for all practical purposes the person has no future. For the Commonwealth–Cummings project authors, hope and expectations are a bit more prosaic than the points of view mentioned directly above. Persons at the end of life will remain alive until certain anticipated events have taken place; examples would be anniversaries, birthdays, graduations, and the return of a soldier from a war zone. A concern is that terminally ill patients tend to overestimate their life expectancy and therefore tend to put off advance care planning with family members and physicians. Medical training should make achieving a good death the standard of care. Such training needs to start with making physicians and medical students comfortable with caring for p ­ ersons who are dying and competent in caring for the terminally ill. This sort of curriculum objective would mean relaxing the intent of the biomedical model to keep terminally ill patients alive at all costs for as long as possible. Medical training focused on thanatology could start by educating medical doctors how to talk to persons about dying, just as they are taught to talk with someone about an illness. Some aspects of talking to patients about dying would be how to deliver bad news, talking about spiritual concerns, and giving solace and consolation. Around the same time as the Commonwealth–Cummings project, authors writing in The Journal of the American Medical Association (JAMA) gathered information from physicians, terminally ill patients, family members, and other stakeholders about achieving a good death. Karen Steinhauser et al. (2000) conducted a mixed‐methods study using focus group interviews and a national survey to identify what people consider preferable at the end of life. The numerous focus groups were composed of physicians, nurses, hospice volunteers, chaplains, and terminally ill patients. The surveys were sent to a comparable list of individuals; out of 2,000 surveys mailed, 1,462 were returned, a phenomenal response rate of 73.1%. Three main findings were reported: 1 The respondents overwhelmingly agreed on the general importance of being prepared for the end of life and of knowing one’s family is prepared. Examples of such advance ­preparation included completing funeral arrangements and of having a sense when death was likely to occur. All respondents agreed that having time to prepare gives a greater chance for patients, family members, and medical caregivers to experience a good death.

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2 Respondents wanted to know the treatment options for and the preferences of the dying person. Family members said it helped immensely to know what the dying person wanted to happen if he (she) were unconscious and unable to make wishes known. For instance, does the person want DNR orders carried out? There are obvious implications in this ­second finding for the functioning of healthcare proxies. 3 Dying persons wanted time to say goodbye to important persons in their lives. They wanted the chance to complete unfinished business. They wanted the opportunity to talk about the meaning of death with someone willing to listen attentively to their fears about dying. In this third set of findings, there were some stark differences between seriously ill persons and physicians. For instance, being mentally aware, at peace with God, and not a burden on one’s family were endorsed by nearly all seriously ill persons, but by less than two‐thirds of physicians. The differences in what patients and physicians consider attributes of a good death offer topics for medical education sensitizing physicians about end‐ of‐life communication. Some of the differences may stem from patients’ lack of experience with palliative care, such as the overwhelming majority wishing to be mentally aware up to the end; some end‐stage cancer pain is so excruciating that only heavy dosages of morphine, which basically keep the person asleep, can alleviate the pain. It seems unlikely that physicians will consider it good use of their time to converse with the patient about being at peace with God or to spend time praying with patients. These topics are typically seen as the province of chaplains and thus the presence of ministers on hospice teams. While there were some notable differences uncovered between patients and physicians over characteristics seen as important for a good death, another set of findings showed broad across‐the‐board agreement on the ranking of nine attributes linked to achieving a good death. In these findings, the three sets of respondents (patients, family members, and physicians) agreed on the importance of being free from pain, being at peace with God, and of the family being present at the end. Patients and family members placed more importance than physicians on being mentally aware. Physicians gave more importance than patients or family ­members to whether life was meaningful and whether conflicts had been resolved. Dying at home was ranked last by all the respondents. Legalization of physician‐assisted suicide has occurred in a handful of European countries (the Netherlands, Belgium, and Switzerland, in particular; it has been expressly rejected in Great Britain). Seen as serious issues not to be taken lightly, assisted suicide laws have been written with strict guidelines to govern against abuse; and these guidelines have set the template followed in other places. These guidelines specify age requirements and corroborated diagnoses by licensed physicians; requests must be given voluntarily over time and are not to be prompted by suggestions from the physician. The person must be judged to be acting free of any psychiatric or emotional duress. In order to ensure assisted suicides will not be forced by financial circumstances, Belgium makes palliative care available regardless of ability to pay. Physician‐assisted suicide was legalized in the state of Oregon in the late 1990s, and since then the states of Washington, California, Colorado, and Vermont have adopted protocols with guidelines quite similar to the ones specified in Oregon. In these states, a person must have a corroborated diagnosis of a terminal illness with less than 6 months to live, and the person must be able to ingest the lethal overdose on his (her) own. A New Mexico judge in 2014 ruled that assisted suicide was a matter between physicians and patients, but that ruling was overturned. The Montana Supreme Court ruled that nothing in that state’s constitution prohibited assisted suicide. A voter referendum to adopt physician‐assisted suicide was rejected in the state of Massachusetts.

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The typical assumption is that a terminally ill person’s primary motive for selecting assisted suicide is to avoid pain. While such a motive is present in many cases, other intents have been identified: fears about the future and fears of being a burden on others, loss of independence, lack of dignity, and decreasing ability to engage in activities making life meaningful. Typical life conditions people wish to end with assisted suicide include various forms of cancer, amyotrophic lateral sclerosis, early‐onset Alzheimer’s or some other dementia, and complete paralysis. Belgium and the Netherlands leave open the possibility for a person who is not terminally ill to make use of assisted suicide if in a permanently hopeless situation such as complete paralysis.

Cross‐References Biopsychosocial model; Caregivers and their role in health; Family and health; Older adult health; Palliative care; Quality of life.

Author Biography David E. Balk was Director of Graduate Studies in Thanatology and Chair of the Department of Health and Nutrition Sciences at Brooklyn College, where he is now Professor Emeritus. Among his books is Dealing with Dying, Death, and Grief during Adolescence (2014, New York: Routledge).

References Corr, C. A. (1992). A task‐based approach to coping with dying. Omega, 24, 81–94. http://dx.doi. org/10.2190/CNNF‐CX1P‐BFXU‐GGN4 Doka, K. J. (2013). Historical and contemporary perspectives on dying. In D. K. Meagher & D. E. Balk (Eds.), Handbook of thanatology: The essential body of knowledge for the study of death, dying, and bereavement (pp. 17–23). New York, NY: Routledge. Emanuel, E. J., & Emanuel, L. L. (1998). The promise of a good death. The Lancet, 351, 21–29. https://doi.org/10.1016/S0140‐6736(98)90329‐4 Institute of Medicine. (1997). Approaching death: Care at the end‐of‐life. Washington, DC: National Academies Press. Institute of Medicine. (2014). Dying in America: Improving quality and honoring individual preferences near the end‐of‐life. Washington, DC: National Academies Press. Steinhauser, K. E., Christakis, N. A., Clipp, E. C., McNeilly, M., McIntyre, L., & Tulsky, J. A. (2000). Factors considered important at the end‐of‐life by patients, family, physicians, and other care ­providers. Journal of the American Medical Association, 284, 2476–2482. doi:10.1001/jama. 284.19.2476 SUPPORT Principal Investigators. (1995). A concentrated trial to improve care for seriously ill hospitalized patients. Journal of the American Medical Association, 274, 1591–1598.

Suggested Reading Balk, D. E. (2014). Dealing with dying, death, and grief during adolescence. New York, NY: Routledge. Benoliel, J. Q., & Degner, L. F. (1995). Institutional dying: A convergence of cultural values, t­ echnology, and social organization. In H. Wass & R. A. Neimeyer (Eds.), Handbook of dying: Facing the facts (2nd ed., pp. 117–141). Philadelphia, PA: Taylor & Francis.

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Callahan, D. (1995). Setting limits: Medical goals in an aging society with “a response to my critics”. Washington, DC: Georgetown University Press. Chapple, H. S. (2010). No place for dying: Hospitals and the ideology of rescue. Walnut Creek, CA: Left Coast Press. Clark, D. (2015). Hospice care of the dying. In J. M. Stillion & T. Attig (Eds.), Death, dying and bereavement: Contemporary perspectives, institutions, and practices (pp. 135–149). New York, NY: Springer Publishing. Cohen, J., & Deliens, L. (Eds.) (2012). A public health perspective on end‐of‐life care. New York, NY: Oxford University Press. Connor, S. R. (2009). Hospice and palliative care: The essential guide (2nd ed.). New York, NY: Routledge. Corr, C. A., & Corr, D. M. (2018). Death and dying, life and living (8th ed.). Belmont, CA: Wadsworth/ Cengae Learning. Kastenbaum, R. J. (2011). Death, society, and human experience (11th ed.). Boston, MA: Pearson. Nuland, S. B. (1994). How we die: Reflections on life’s final chapter. New York, NY: Knopf. Saunders, C., Baines, M., & Dunlop, R. (1995). Living with dying: A guide for palliative care (3rd ed.). New York, NY: Oxford University Press.

The Role of Regulation in Health Behavior (Listed in System as “Federal/State Regulation (FDA)”) David B. Portnoy US Food and Drug Administration, Silver Spring, MD, USA

Regulation Defined Regulation includes those activities by a government that allows it to intervene in the private domain. Regulations, or implementation of laws, constitute a “binding legal norm…that intends to shape the conduct of individual and firms” (Orbach, 2013, p. 25). Thus, regulations can be defined as a series of rules established by a government to effect change among both individuals and firms in the private and public sectors. Whereas legislation establishes the authority for government actions and can be enforced by the courts, regulations refer to the much more specific rules developed by the governing body to implement those laws. Despite this distinction, many actions by governments, whether they occur through legislation or rulemaking, are often considered part of regulations. The commonality between these and other definitions of regulation is that they all identify the source of action as a government (or components thereof) and the recipient or intended target of those actions as individuals and organizations in the private sector. However, beyond that broad definition, the meaning of regulation is subject to much variability, the greatest source of which derives from the level of the action. Regulation can occur at federal, state/ territory, county, and many other levels of government. Level‐specific differences in purview, jurisdiction, and power result in different types of regulations; for example, the regulations issued by a city council may greatly differ from those issued by a federal government. However, this power disparity does not mean that regulations at different levels must be distinct in their focus or that federal regulations are always “stronger” than local ones. For example, federal agencies may not have the authority to regulate specifically local issues and vice versa, although federal regulations may sometimes preempt those at lower levels of government. The Wiley Encyclopedia of Health Psychology: Volume 4: Special Issues in Health Psychology, First Edition. General Editor: Lee M. Cohen. Volume Editor: Suzy Bird Gulliver. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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Regulation in Context Ecological models of health behavior recognize that health behaviors are influenced not only by individual factors but also by the larger context in which those behaviors occur and in which the individual lives. These models are most effective when they are organized around a specific health behavior, have multiple levels, and recognize influences across these levels, that is, interventions at one level can affect other levels as well. However, certain frameworks seek to focus on multiple related risk factors concurrently that are intertwined at the community level. Although the specific levels may change from model to model, they generally start at the most proximal (individual) level, progress to the community level that can include workplaces or a place‐based focus, and end with the outermost (i.e., national) level. That outermost level is sometimes referred to as public policy, policy environment, or community/policy. Regardless of the terminology, the most distal level of influence on health behavior is that of the law, public policy, and other large‐scale factors that influence health through the other levels. Interventions at this level are intended to affect entire populations, as compared with interventions that focus solely on individuals or specific groups of individuals. Despite this, interventions at this level are by definition broader and thus must work in concert with local regulations, interventions at the individual level, and within the defined social and cultural context. One such review of tobacco control programs noted that comprehensive programs, such as those that include multiple levels of intervention, reduce tobacco use (Community Preventive Services Task Force, 2014). These types of models have received support and endorsement in guiding strategic plans for public health, such as the US Department of Health and Human Services’ Healthy People 2020 and others.

Support for the Role of Regulation in Health Regulation as a strategy to improve health, both at individual and population levels, has received wide support. The Institute of Medicine report on ensuring the health of the public in the twenty first century (2003) notes that federal, state, local, and tribal governments all play a role in identifying those in need of support and intervention and providing necessary services. The report notes that federal governments can intervene in six main areas, the first of which is policy making; decisions on policy optimally result from “…creation and use of an evidence base, informed by social values, so that public decision makers can shape legislation, regulations, and programs” (2003, p. 113). This emphasizes the need for strong regulatory science (as discussed below) to inform, support, and implement policy. Dr. Thomas Frieden, director of the US Centers for Disease Prevention and Control (CDC) from 2009 to 2017, also notes the many ways in which regulation can contribute to improved public health outcomes. He identifies three main areas in which the government can protect public health and safety (Frieden, 2013). First, the government can require the provision of truthful information, such as nutrition information or calorie counts on menus, in order to empower individuals to make better health decisions; similarly, government‐mandated warnings serve to inform the public about health risks and in certain cases to counter industry efforts to suppress or undermine science, such as the tobacco industry’s history or disputing the science on the harms of smoking and of secondhand smoke (Samet & Burke, 2001). Second, the government can take action to prevent harm caused by others; examples include laws and regulations to reduce drunk driving, occupational hazards, and the inclusion of harmful ingredients in foods (e.g., recent local bans on trans fats and their removal from FDA’s list

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of foods that are “generally recognized as safe”). These specific examples represent regulation, some of which have been accompanied by industry self‐regulation prior to enforcement as well as public education. The effectiveness of industry self‐regulation varies widely from industry to industry, but self‐regulation is often ineffective for public health benefit (Sharma, Teret, & Brownell, 2010). Such self‐regulation is often an attempt forestall stronger and more enforceable local, state, and federal regulations. Finally, Dr. Frieden notes that the government can take actions to encourage healthy behaviors or create obstacles to harmful choices, such as zoning regulations that promote physical activity or that limit community density of retailers of harmful products such as alcohol or tobacco. He notes that a critical but often overlooked aspect of how people respond to such actions is “the degree to which individual actions are influenced by marketing, promotion, and other external factors,” a ripe area for research and intervention by health psychologists. Perhaps the most sweeping regulation that supports changes in health behavior and subsequent population health is the Patient Protection and Affordable Care Act. This Act spawned a wide‐ranging series of regulations, many of which focused on encouraging the use of preventive services (e.g., screening and immunizations), employer incentives for employee health promotion, nutrition labeling at chain restaurants, and other activities. In support of the Act, “the health of the individual is almost inseparable from the health of the larger community. And the health of each community and territory determines the overall health status of the Nation” (Koh, 2010, p. 1656).

Examples A diverse and ever‐growing list of health topics can be addressed through regulation. Many regulations focus on information, warnings, and public education about health risks and informed health decision making or promoting health promotion services. Two examples of this form of regulation are the provision of nutrition information on food products and (more recently) on restaurant menus and the provision of health warnings on tobacco products. As in most health‐related regulation, the industry, rather than the target audience, is being regulated, but the focus of the impact is on the public. Nutrition labeling began in the United States with a White House recommendation, which resulted in FDA issuing a final rule in 1973. These regulations were revised to allow health claims about foods and were again updated in the Nutrition Labeling and Education Act (NLEA) of 1990, which mandated nutritional labeling on most foods in the now‐familiar nutrition facts panel (Institute of Medicine, 2010); an additional update occurred in 1993. At each step in this process, policy makers, scientists, and the public provided significant input regarding the benefits of such labeling as well as optimal labeling formats and content, which further illustrates that regulation is a constantly evolving process. More recent changes made include revising the format of the nutrition facts panel, changing the content of the nutrients, including “added sugars,” and other changes (US Food and Drug Administration, 2016, https://www.federalregister.gov/documents/2016/12/30/2016‐31597/food‐labeling‐ nutrition‐labeling‐of‐standard‐menu‐items‐in‐restaurants‐and‐similar‐retail‐food). In recent years, many states and localities have begun to require calorie counts and other forms of ­nutrition information to be displayed on restaurant menus. In 2014, FDA issued a final rule requiring such information be listed on chain restaurant menus and menu boards, with the following stated purpose: “To help make nutrition information for these foods available to consumers in a direct, accessible, and consistent manner to enable consumers to make informed

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and ­healthful dietary choices” (US Food and Drug Administration, 2014). Both menu and package labeling have been the subject of a wide variety of studies of the basic theory behind, potential impacts of, and best practices for labeling (e.g., Andrews, Burton, & Kees, 2011; Roberto & Kawachi, 2014). The Family Smoking Prevention and Tobacco Control Act (FSPTCA) required that FDA publish a list of the harmful and potentially harmful constituents of tobacco and tobacco smoke “in a format that is understandable and not misleading to a lay person.” However, beyond requiring that FDA conduct periodic consumer research to ensure that the list is “understandable and not misleading,” Congress provided no further instructions as to the purpose of this list, unlike the rule on food menu labeling, which is explicit in its attempt to make nutrition information accessible for consumers. However, some intent regarding public health protection can be inferred given that the overall focus of the FSPTCA is to protect public health and that evidence that consumers were misled about the health impact of “light” and “low tar” cigarettes was cited in support of the law.

What Is Regulatory Science and How Can Health Psychology Contribute? Regulations may be directly required or authorized by statute. In some cases, scientific support may be needed to help justify a particular regulation. This is especially true for regulations that seek to influence health through behavior. However, the science used to support regulation may differ from what is traditionally found in the published literature. For example, regulatory science focuses on “ensuring that scientifically valid techniques, tools, and models are available to evaluate products and, subsequently, to inform regulatory actions that promote optimal health outcomes” (Ashley, Backinger, van Bemmel, & Neveleff, 2014, p. 1046). Thus, not all published literature may be specific enough to inform regulation. For example, if a new technique is published, it may move the science forward and may be innovative, but it is unlikely that a new and unvalidated tool would be used to support a regulation, assuming that other more accepted methods are available. Regulatory agencies generally rely upon scientific data to base their actions, as illustrated by the current public debate over the potential benefits or harms of electronic cigarettes. Considerable efforts are being made to increase the regulatory science that is being ­conducted. The science used to support regulation may need to be more focused than typical scientific research and in general must focus on areas for which regulatory actions may be ­possible. For example, a study that examines the psychological mechanism underlying why youth post on social media about their use of tanning beds may be interesting, may advance scientific knowledge, and may inform the design of interventions. Although this type of study may inform the need for a regulation, it would be unlikely to directly inform the regulation itself. In other words, mechanistic or basic psychological research is critically important to advance the science, to allow for development of interventions, and for many other reasons; however it is unlikely to be useful in direct support of regulation unless it can be tied directly to a ­specific regulatory activity. It may, however, have relevance for advocacy and spur other research on the topic. For example, basic psychological research on judgment and decision biases, health literacy, and presentation format could be used to support regulations on restriction of potentially false or misleading claims made in an advertisement for a prescription drug (e.g., Aikin, O’Donoghue, Swasy, & Sullivan, 2011).

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For health psychology research to be maximally useful in regulatory science, it should ­minimally fulfill the following criteria. First, the research should address a topic of interest to a regulatory agency; agencies typically announce topics of interest or solicit comments on agency websites, in notices in the Federal Register, or in similar official methods of public notice. Second, the research should be tailored to the appropriate study populations to ensure that study findings can be directly applied to the potential regulation. For example, a study conducted in women over age 65 may be less relevant if the regulation of interest focuses on women of reproductive age. Third, the stricter the sampling using, the more controlled the methodology and analytical controls, and ensuring a sufficient sample size will increase the likelihood that the research findings will be useful to help support regulatory actions and that conclusions will withstand challenges. In addition, reporting study details and results using one of the many reporting guidelines is helpful to ensure a full description of the study is ­provided. Complementing quantitative data, strong qualitative data can be useful to info to help explain “why” and “how” of certain issues. Fourth, interpretation of results should not extend beyond what the data portray; interpretations should not generalize beyond what is possible with the study design or suggest that cross‐sectional associations are causal. All of these misinterpretations and statistical errors, which are common in published research in psychology and many other fields (e.g., Ioannidis, Munafo, Fusar‐Poli, Nosek, & David, 2014), will weaken the potential usefulness of the study. Lastly, any conflicts of interest should be clearly disclosed. Research studies that adhere to these criteria are more likely to be used by an agency to inform regulation. Researchers can provide study findings to regulators in a number of ways so that the agency may officially consider these findings in its rulemaking. Researchers may directly submit their research along with a lay explanation directly to local and state agencies, some of which may have processes for submission of public comments; other agencies may accept such findings without public notice. For federal regulation, public comment solicitations are generally announced in the Federal Register, and comments can be submitted online (www.regulations. gov). Public comments summarizing research findings can also be provided at in‐person ­public meetings. Even without such outreach efforts by researchers, high‐quality regulatory s­ cience research published in peer‐reviewed journals is often referenced and used by agencies in designing and implementing regulations.

Summary Regulations are actions taken by a government to implement laws. In the context of health, regulations can focus on individuals, industries, or larger organizations. Seen through the lens of ecological models of health behavior, regulations can influence health behavior changes through multiple pathways, interacting with interventions at the individual and community levels as well as state and local laws. Regulations related to health can be informed by strong and specific regulatory science.

Disclaimer This publication represents the views of the author and does not represent FDA/CTP position or policy.

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See Also EHP0064 EHP0080 EHP0133 EHP0167 EHP0130 EHP0156

Psychosocial: Substance abuse: Tobacco Health behavior change Effects of health‐related policies Health behavior interventions Cigarette smoking and cessation Affordable Care Act (“Obama Care”)

Author Biography Dr. David B. Portnoy is a Social Science Branch Chief in the Office of Science at the US Food and Drug Administration Center for Tobacco Products. Dr. Portnoy received his MA and PhD in Social Psychology from the University of Connecticut, MPH from the Johns Hopkins Bloomberg School of Public Health, and postdoctoral training in cancer prevention and ­control at the National Cancer Institute. His research interests focus on risk perception and communication and the interplay between basic social psychological processes and health behaviors. At FDA, he conducts research on consumer perceptions of tobacco products and health warnings, reviews industry submissions, and works to develop the science base for regulations and health communication efforts. He has published on the topics of tobacco regulatory science, consumer perceptions of tobacco products, factors affecting tobacco use, and the effects of risk and worry on health behaviors.

References Aikin, K. J., O’Donoghue, A. C., Swasy, J. L., & Sullivan, H. W. (2011). Randomized trial of risk information formats in direct‐to‐consumer prescription drug advertisements. Medical Decision Making, 31(6), E23–E33. Andrews, J. C., Burton, S., & Kees, J. (2011). Is simpler always better? Consumer evaluations of front‐ of‐package nutrition symbols. Journal of Public Policy & Marketing, 30(2), 175–190. Ashley, D. L., Backinger, C. L., van Bemmel, D. M., & Neveleff, D. J. (2014). Tobacco regulatory ­science: Research to inform regulatory action at the Food and Drug Administration’s Center for tobacco products. Nicotine & Tobacco Research, 16(8), 1045–1049. Community Preventive Services Task Force. (2014). Guide to Community Preventive Services. Reducing tobacco use and secondhand smoke exposure: comprehensive tobacco control programs.“Retrieved from https://www.thecommunityguide.org/findings/tobacco‐use‐and‐secondhand‐smoke‐exposure‐ comprehensive‐tobacco‐control‐programs. Frieden, T. R. (2013). Government’s role in protecting health and safety. New England Journal of Medicine, 368(20), 1857–1859. Institute of Medicine. (2003). The future of the public’s health in the 21st century. Washington, DC: National Academy Press. Institute of Medicine. (2010). Front‐of‐package nutrition rating systems and symbols: Phase I report. Washington, DC: The National Academies Press. Ioannidis, J. P. A., Munafo, M. R., Fusar‐Poli, P., Nosek, B. A., & David, S. P. (2014). Publication and other reporting biases in cognitive sciences: Detection, prevalence, and prevention. Trends in Cognitive Sciences, 18(5), 235–241. Koh, H. K. (2010). A 2020 vision for healthy people. New England Journal of Medicine, 362(18), 1653–1656.

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Orbach, B. (2013). What is regulation? In R. C. Clark (Ed.), Why and How the state regulates, University Casebook Series. New York, NY: Thompson Reuters/Foundation Press. Roberto, C. A., & Kawachi, I. (2014). Use of psychology and behavioral economics to promote healthy eating. American Journal of Preventive Medicine, 47(6), 832–837. Samet, J. M., & Burke, T. A. (2001). Turning science into junk: The tobacco industry and passive smoking. American Journal of Public Health, 91(11), 1742–1744. Sharma, L. L., Teret, S. P., & Brownell, K. D. (2010). The food industry and self‐regulation: Standards to promote success and to avoid public health failures. American Journal of Public Health, 100(2), 240–246. US Food and Drug Administration. 2014. Food Labeling; Nutrition Labeling of Standard Menu Items in Restaurants and Similar Retail Food Establishments. edited by US Food and Drug Administration. 79 Federal Register.

Suggested Reading Frieden, Thomas R. (2013). Government’s role in protecting health and safety. New England Journal of Medicine 368(20), 1857–1859. Orbach, Barak. (2013). Regulation: How and why the state regulates. Edited by Robert C. Clark, University Casebook Series. New York, NY: Thompson Reuters/Foundation Press. Sallis, James F, Neville Owen, and Edwin B Fisher. (2008). Ecological models of health behavior. In Health behavior: Theory, research, and practice, edited by Barbara K. Karen Glanz and K. Viswanath Rimer, 465–486. San Francisco, CA: John Wiley & Sons, Inc. Uchiyama, Mitsuru. 1995. Regulatory science. PDA Journal of Pharmaceutical Science and Technology, 49, 185–187.

Funding for Health Psychology Research Robert T. Croyle National Cancer Institute, Bethesda, MD, USA

Funders of Health Psychology Research Both the public and private sectors provide funding for health psychology research. As the largest funder of health psychology research, the federal government plays an essential role in maintaining and enhancing the vitality of the field. Although several federal agencies ­provide funding, the largest source is the National Institutes of Health (NIH). Other ­agencies within the Department of Health and Human Services also play a significant role, including the Centers for Disease Control and Prevention and the Substance Abuse and Mental Health Services Administration. Because the latter two agencies are focused on ­support for program services, their research funding tends to focus on the evaluation of those services as well as public health surveillance to determine the scope and distribution of diseases and disease risk factors. The NIH contains 27 institutes and centers. Because the governance structure delegates substantial autonomy and authority to the institutes, support for health psychology research can vary substantially. Institute directors are authorized to make final funding selections in most cases and therefore have a substantial impact on funding priorities and allocations. Health psychologists employed by the institutes can and have made a substantial contribution in ensuring that grant applicants in the field receive a fair review and appropriate consideration in funding decisions. Nevertheless, the selection of a new institute director can have a significant impact on that institute’s support for health psychology. In recent years, the NIH Office of the Director has played a larger role in funding and ­coordinating research across the agency. The NIH Common Fund, which is overseen by the NIH Director, supports trans‐NIH initiatives, although individual grants are managed within the institutes.

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In 1993, Congress established the Office of Behavioral and Social Sciences Research (OBSSR) at NIH. This action reflected the growing recognition of the role of behavior in health as well as the need for better coordination of related programs across the agency. Notably, four of the five directors of the office to date are health psychologists. From an operational perspective, this means that a health psychologist has been a member of the agency’s leadership team ever since, and this has afforded the field as a whole with greater opportunities to shape the priorities of the agency. Two other federal departments that have long histories of health psychology research funding are the Department of Defense and the Department of Veterans Affairs. Both departments also support clinician scientist training and have housed many of the most influential health psychologists in the United States. The nonprofit sector has also played a key role in funding for health psychology research. The Robert Wood Johnson Foundation, the American Cancer Society, and the American Heart Association are among the many organizations that have provided support. Because these organizations operate outside of the constraints of the federal government bureaucracy, they can often rely on more nimble or rapid funding processes. Foundations have proven to be an essential source of support for applied and policy‐related work, especially that focused on underserved and stigmatized populations. For example, in the areas of tobacco control and obesity, foundations supported much of the seminal work detailing the role of industry in product marketing and regulatory policy, including strategies for targeting ethnic minority populations. The Robert Wood Johnson Foundation has provided significant leadership in forging collaborations between scientists, health officials, and ­ ­community organizations to facilitate the dissemination of evidence into clinical and public health practice. The role of prominent individual philanthropists has been less significant in health psychology than in other fields. Large individual donations to universities and cancer centers, for example, have rarely targeted work focused on health behavior. Clinical trials supported by industry have supported health psychologists in a wide variety of research domains but often in a secondary role. Not surprisingly, the scientific role of health psychologists in industry‐supported drug trials has often focused on the issues of recruitment, adherence, and retention. As more trials incorporated measures of quality of life and other psychosocial constructs as secondary endpoints, opportunities for health psychologists have expanded. Industry‐funded trials continue to provide important contributions to the research literature in a variety of behavioral medicine domains, such as smoking cessation, weight loss, diabetes and pain management, and cancer treatment symptom management. The contemporary interest in patient‐reported outcomes and comorbidities among both industry and regulatory agencies such as the Food and Drug Administration are likely to further strengthen support for health psychology research within the context of clinical trials.

The Role of Special Initiatives Special funding initiatives from both government and nonprofit sector organizations have played an important role in the development of health psychology. Although most have not been focused on health psychology per se, health psychology investigators have utilized a wide variety of special funding opportunity announcements to build the field, scale up research, and ­accelerate progress in research methods. The growth in funding for interdisciplinary research concerning

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common health problems has enabled health psychologist to expand their ­participation on large‐scale research consortia, clinical trials, centers of excellence, and cohort studies. Several special initiatives supported by the NIH have been led and coordinated by the OBSSR. Initiatives especially relevant to health psychology include the Behavior Change Consortium (co‐funded by the American Heart Association and the Robert Wood Johnson Foundation), launched in 1999, and a follow‐on initiative, the Health Maintenance Consortium. Individual or subsets of NIH institutes also have led important efforts. The National Cancer Institute (NCI), for example, funded a series of transdisciplinary team science research centers in which health psychologists played key roles as principal investigators, project leaders, and co‐investigators. The first in the series, the Transdisciplinary Tobacco Use Research Centers, was co‐funded by the National Institute on Drug Abuse, the Robert Wood Johnson Foundation, and the National Institute on Alcohol Abuse and Alcoholism. This initiative was followed by similar efforts that supported the Centers for Population Health and Health Disparities, the Centers of Excellence in Cancer Communication Research, and the Transdisciplinary Research on Energetics and Cancer Centers. More recently, the National Heart, Lung, and Blood Institute, in collaboration with NCI, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Child Health and Human Development, and OBSSR, funded the Obesity‐Related Behavioral Intervention Trials (ORBIT) initiative. ORBIT focused on the translation of findings from basic research on human behavior into more effective clinical, community, and population interventions to reduce obesity. The NIH Common Fund has supported the Science of Behavior Change initiative, which focuses on the initiation, personalization, and maintenance of behavior change. The utilization of central NIH funds for this effort is a further reflection of the progress that health psychology has made within the NIH leadership community, given that Common Fund topic ideas compete with all areas of biomedical science. Interestingly, the growing interest in and recognition of the importance of behavioral risk factors has also influenced priority setting within mechanistically oriented biomedical research. The NIH recently launched a Common Fund initiative titled Molecular Transducers of Physical Activity in Humans. At the NIH, high‐priority initiatives are solicited via requests for applications (RFAs), ­published in the NIH Guide for Grants and Contracts, which are developed by program staff and reviewed by institute advisory boards prior to their release. Unlike Program Announcements, which also are used by institutes to communicate their research priorities to the scientific ­community, RFAs are associated with funds that are set aside specifically to support grants submitted in response to the announcement. Applications are reviewed by ad hoc panels that are established specifically for the initiative. Grants awarded using RFA‐associated funds often are the basis for a research network or consortium. In many cases, the consortium is convened on a regular basis to enable work in progress to be shared among the funded investigators. This structure also allows the funder to monitor progress more closely and ensure that ­roadblocks are addressed in a timely fashion. Two of the signature efforts led by the foundation sector are the Healthy Eating Research and Active Living Research initiatives, both supported by the Robert Wood Johnson Foundation. The foundation has also supported interdisciplinary population science research training efforts that have helped integrate health psychologists into other fields and settings. One such effort, the Health and Society Scholars Program, supported institutional postdoctoral fellowship grants, which were complemented with an annual national meeting of p ­ rogram participants. Given the individual‐ and group‐level focus of traditional clinical health p ­ sychology

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training programs, special initiatives such as this one have helped to strengthen the ability of health psychologists to participate fully in population‐level research and programs.

Challenges and Opportunities in Health Psychology Research Funding Support for health psychology research now seems firmly established among major research funders. The challenge to sustained support will lie in the productivity and impact of research supported to date and in the future. The growing visibility of health psychology research ­findings among policy makers and the public should facilitate ongoing support. Compelling findings from highly relevant research such as the Diabetes Prevention Program are perhaps the most essential ingredient for success. The growing importance of cost‐effectiveness a­ nalyses needs to be recognized among investigators and funders alike. Most funders do not target their support to specific disciplines like health psychology. Rather, funding tends to be focused on broad research questions, diseases, or public health problems. As a subdiscipline of behavioral science, health psychology is often painted with the same brush as other social and behavioral sciences. Therefore, it is not surprising that as funders like the NIH have expanded support for social and behavioral science, so too have they expanded support for health psychology research. Because so many of today’s medical and public health challenges relate to behavior, broadly defined, it has been easier to make the case for support among the leaders of funding agencies and organizations. Within the political realm, however, the assignment of moral blame to victims of disease continues to be a c­ hallenge for advocates of health psychology research funding. Another challenge to adequate research funding is the paucity of health psychologists in leadership positions within the key funding organizations. The relative status of psychology to medicine within academia and government is exacerbated by the underrepresentation of ­psychologists among leadership. Health psychologists are needed to serve in funding organizations and in advocacy roles before elected officials who allocate appropriations at the national and local levels. Finally, the ability and willingness of health psychologists to participate in and contribute to large‐scale transdisciplinary team science will predict to a large extent the trajectory of funding for health psychology research. Health psychology must be viewed as one of the essential ­disciplines to be included in broader, integrated efforts to advance knowledge and practice ­concerning the most significant medical and public health problems of our day. The ability to effectively communicate and integrate our work with other fields is therefore essential. Health psychologists also must embrace rigorous scientific standards, including objective self‐­criticism, to enable progress and demonstrate the value of funders’ investments. As applicants for research funding, health psychologists are most successful when they recognize the limits of their expertise and methods and embrace collaboration with scientists from other disciplines. These are among the grant‐writing skills that should be afforded to every young scientist in the field to maximize their likelihood of success.

See Also Major Funders for Health Psychology Research: • National Institutes of Health: www.NIH.gov • Robert Wood Johnson Foundation: www.rwjf.org

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Author Biography Robert T. Croyle, PhD, was appointed director of the Division of Cancer Control and Population Sciences (DCCPS) at the National Cancer Institute (NCI) in July 2003. In this role, he is responsible for overseeing a research portfolio and operating budget of nearly half a billion dollars and serves on NCI’s Scientific Program Leaders governance group. As a ­division, DCCPS covers a wide range of scientific domains and disciplines, including epidemiology, behavioral science, surveillance, cancer survivorship, and health services research. He ­previously served as the division’s associate director for the Behavioral Research Program, leading its development and expansion. Before coming to NCI in 1998, he was professor of psychology and a member of the Huntsman Cancer Institute at the University of Utah in Salt Lake City. Prior to that, he was a visiting investigator at the Fred Hutchinson Cancer Research Center in Seattle, visiting assistant professor of psychology at the University of Washington, and assistant professor of psychology at Williams College in Massachusetts. Dr. Croyle received his PhD in Social Psychology from Princeton University in 1985 and graduated Phi Beta Kappa with a BA in Psychology from the University of Washington in 1978. His research has examined how individuals process, evaluate, and respond to cancer risk information, including tests for inherited mutations in BRCA1 and BRCA2. His research has been published widely in professional journals in behavioral science, public health, and cancer, and he has edited two volumes: Mental Representation in Health and Illness (1991) and Psychosocial Effects of Screening for Disease Prevention and Detection (1995). He is co‐editor of the Handbook of Cancer Control and Behavioral Science (2009) and co‐author of Making Data Talk: Communicating Public Health Data to The Public, Policy Makers, and The Press (2009).

Genetics and Psychology (Health) Nathan A. Kimbrel1,2,3 and Christopher Fink2,4 Department of Veterans Affairs’ VISN 6 Mid‐Atlantic MIRECC, Durham, NC, USA 2 Durham Veterans Affairs Medical Center, Durham, NC, USA 3 Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA 4 North Carolina State University, Raleigh, NC, USA 1

Overview Improved understanding of the genetic basis of human disease and human behavior is rapidly changing the field of health psychology. Recent advances in genetic research and technology are permitting researchers to identify the genetic basis of a wide array of health conditions for the first time. Moreover, improved understanding of the field of epigenetics is rapidly shaping understanding of how environmental factors and behavioral changes can fundamentally alter gene expression. Genetic testing also has great potential to improve assessment, optimize the effectiveness of treatments, reduce side effects, and improve safety; however, it can also reveal vulnerabilities for disease that can be difficult for patients to cope with. As such, it is critical that health psychologists recognize the tremendous impact that genetic and epigenetic ­findings will likely have on their practice, as well as the field of health psychology more broadly, for the foreseeable future.

Central Dogma of Molecular Biology All cellular processes begin with the genetic code written in every cell known as ­deoxyribonucleic acid (DNA), which is composed of four different nucleotides: adenine (A), cytosine (C), guanine (G), and thymine (T). Each is made of a phosphate group, a sugar known as 2′‐deoxyribose, and a unique base that gives each nucleotide its identity. These bases pair with one another (A with T and G with C) to form DNA’s well‐known double

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helix structure. These physical characteristics allow DNA to carry out its intended function, which is described by the central dogma of molecular biology. Simply put, the central dogma states that genes specify the sequence of messenger ribonucleic acid (mRNA) molecules, which, in turn, specify the sequence of proteins. Proteins perform a variety of functions in the cell such as regulatory functions, transport of small molecules, support, and catalysis of important reactions that would not normally occur under the conditions seen in a cell. DNA is transcribed to mRNA, the intermediate between DNA and proteins, by DNA‐dependent RNA polymerases and other relevant proteins. RNA uses the same subunits as DNA, with slight modification of the sugar from deoxyribose to ribose, as well as using uracil (U) rather than thymine. Much of the RNA transcribed from DNA performs a host of various functions in the cell; however, a small selection of RNA, known as mRNA, is translated into proteins by structures known as ribosomes.

Epigenetics There are many different levels at which gene expression can be controlled. One of the most heavily studied areas is that of epigenetics, which involve changes to the genome that do not affect the specific sequence of the nucleotides that comprise genes (Weinhold, 2006). One of the levels at which epigenetic marks can be seen is the level of histones and chromatin. The term chromatin describes the nucleoprotein complex that is seen when cells compact the 2–3 m of DNA present in a given cell into its nucleus. This packing begins with the DNA being wrapped around protein complexes known as histones. A segment of DNA that is 146 base pairs (bp) long is wrapped around the histone and is known as the core particle. By ­adding 20 bp and a linker histone known as H1 creates what is known as the chromatosome. Once the length of DNA is greater than 168 bp long, the structure is known as a nucleosome. As one can imagine, navigating the chromatin can cause problems when the cell begins to transcribe genes. Histones placed over promoters, sequences on the DNA that allow transcription to initiate, can prevent the transcriptional machinery from recognizing the region and initiating transcription. In addition, histones placed throughout a gene can prevent the machinery from traveling the length of the DNA to read and transcribe the gene. Thus, one level at which regulation of gene expression can occur is at that of the histones. Modifications to the chromatin that cause a change in gene expression fit the definition of epigenetic marks because the changes are caused without changing the sequence of the genes affected. Two of the most common forms of epigenetic modification come in the form of histone acetylation and methylation, which involve the addition of two different small molecules (acetate and methane, respectively) to the lysine and arginine tails found on histones after the histone ­proteins are translated. These modifications can have significant effects on levels of gene expression of the DNA that interacts with modified histones with the general pattern of methylation decreasing gene expression and acetylation increasing it (Allfrey, Faulkner, & Mirsky, 1964). DNA can also be methylated at cytosine residues. These methylated cytosines are most often found when they neighbor a guanine in what is known as a CpG dinucleotide (Rose & Klose, 2014). Though the specific nucleotides are being modified, the sequence of the nucleotide bases remains the same. Thus, DNA methylation can also be considered an epigenetic modification. As with histone methylation, increased levels of DNA methylation are associated with decreased levels of gene expression. As a result, DNA methylation also plays an important role in cell differentiation.

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Genetic and Epigenetic Analysis Techniques Researchers employ several different techniques to investigate what DNA looks like, the changes that are made to it, and the effects that it may impose on a person. Numerous research designs and techniques for studying the genetic and epigenetic basis of diseases exist, including candidate gene approaches, genome‐wide association studies (GWAS), DNA methylation chips, and DNA sequencing. GWAS and candidate gene approaches are used to identify associations between genes and a disease of interest. Candidate gene studies are theoretically driven studies that aim to examine associations between diseases and specific genes that are hypothesized to potentially play a role in the pathogenesis of the disease under study (e.g., because of the known function of a gene). In contrast, GWAS is an atheoretical approach designed to identify novel genetic associations with a particular disease. This is accomplished by comparing individuals with and without the condition of interest on millions of different locations throughout their genome. While GWAS requires very large sample sizes, it has the potential of identifying novel associations that might not otherwise be identified. Other genome‐ and epigenome‐wide technologies frequently used by modern‐day researchers include DNA sequencing and DNA methylation chips. These techniques enable researchers to quickly identify differences in the order of bases or functional groups that may be added on to DNA. For example, methylation chips are constructed with synthetic DNA designed to fluoresce when it binds to methylated cytosines in the sequence of DNA being examined. As a result, researchers are now able to quickly find the locations of methyl groups within a given sequence of DNA. Still other approaches enable researchers to study both the transcriptomes (i.e., all expressed mRNA molecules) and proteomes (i.e., all expressed proteins). Together, these and other techniques enable modern researchers to systematically study the genetic and epigenetic basis of a wide array of health conditions.

Health Psychology Issues Through a Molecular Genetics Lens Smoking Tobacco smoking is a global public health problem, as it represents one of the primary ­preventable risk factors for a wide array of health conditions, including respiratory diseases, cardiovascular diseases, and cancer (Gao, Jia, Breitling, & Brenner, 2015). Notably, tobacco initiation, regular tobacco use, and nicotine dependence all have a substantial genetic component, with heritability estimates of 75, 80, and 60%, respectively (Maes et al., 2004). More recently, a large number of studies have demonstrated that tobacco smoking is associated with substantial DNA methylation modifications throughout the genome (Gao et  al., 2015). Indeed, a recent review on this topic found more than 1,000 smoking‐associated CpG sites, the most common of which were cg05575921 (AHRR), cg03636183 (F2RL3), and cg19859270 (GPR15; Gao et  al., 2015). There is also increasing awareness that smoking‐ associated DNA methylation changes may also be a key pathway through which tobacco smoking can lead to smoking‐induced diseases (Breitling, 2013; Breitling, Salzmann, Rothenbacher, Burwinkelly, & Brenner, 2012). Epigenetic alterations are also known to play a role in cardiovascular disease. Moreover, methylation levels in F2RL3—one of the top three genes associated with smoking‐associated methylation changes noted above—have also been associated with higher mortality among patients with stable coronary heart disease (Breitling, 2013; Breitling et al., 2012). Thus, it is possible that epigenetic alterations may be one of the

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mechanisms through which tobacco smoking—an environmental risk factor with a known genetic component—may lead to increased risk for cardiovascular disease.

Diet Diet is a behavioral factor that plays an important role in both obesity and cardiovascular ­disease; however, there is growing evidence that dietary changes, particularly during development, may also affect DNA methylation patterns through biochemical pathways. For example, several nutrients are required for the production of S‐adenosylmethionine, which is the key methyl donor for DNA, RNA, proteins, and lipids (Glier, Green, & Devlin, 2014). Specifically, both vitamins (folate, riboflavin, vitamin B12, vitamin B6, choline) and amino acids (methionine, cysteine, serine, glycine) are needed to produce S‐adenosylmethionine. As a result, imbalances in these nutrients have the potential to impact DNA methylation. While there is growing evidence that DNA changes in nutritional status can lead to methylation changes, this area of research is still in the early stages; however, it is hoped that improved understanding of the association between nutrition and DNA methylation could eventually lead to new ­biomarkers and new treatments aimed at reversing altered DNA methylation patterns associated with disease (Glier et al., 2014).

Understanding the Overlap Between Physical and Mental Health Conditions The overlap between physical and mental health conditions has long been of interest to health psychologists. For example, posttraumatic stress disorder (PTSD)—a mental health disorder that has a clear environmental basis (i.e. a traumatic event)—has been linked to a wide range of cardiometabolic conditions, including heart disease, metabolic syndromic, and diabetes (Dedert, Calhoun, Watkins, Sherwood, & Beckham, 2010; Sumner et al., 2017). A variety of behavioral explanations have been posited to account for this association (e.g., poor diet, tobacco smoking, substance use, physical inactivity); however, it is likely that genetic factors also play a significant role in the pathogenesis of PTSD (Duncan et al., 2018). Notably, a novel study recently conducted by Sumner et al. (2017) has provided the first evidence of molecular genetic overlap between PTSD and coronary artery disease using a genome‐wide design. This study utilized computational methods that allow researchers to estimate genetic correlations between complex phenotypes using GWAS summary statistics. Through this methodology, Sumner et al. (2017) were able to demonstrate that a variety of cardiometabolic traits were associated with each other. Moreover, they were also able to demonstrate for the first time that the molecular genetic basis of coronary artery disease is significantly correlated with the molecular genetic basis of PTSD (r = .41, p = .01).

Impact on Assessment and Treatment Given that the field of genetics is rapidly advancing, it stands to reason that these ­advancements will continue to have major implications on healthcare for the foreseeable future. One such area will be the domain of personalized medicine, where we see can already see the emergence of this type of approach in relation to the treatment of breast cancer. Specifically, BRCA1 and BRCA2 are two genes that are known to be implicated in familial breast cancer. Identification of these genes and their cancer‐causing mutations has begun to revolutionize the way in which cancer treatments and screenings are conducted. Already,

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individuals who are identified as carrying genetic mutations associated with increased risk can undergo additional screenings and/or elect to have preventative mastectomies in order to reduce the risk of breast cancer. BRCA mutation type is also being used to guide treatment decisions in patients, as there is evidence that it may influence risk of relapse and that carriers may be particularly likely to benefit from specific treatment approaches (Li et al., 2016; Soenderstrup et  al., 2018). Thus, genetic testing has great potential to improve assessment, optimize the effectiveness of treatments, reduce side effects, and improve safety; however, it can also reveal vulnerabilities for disease that can be difficult for patients to cope with. Since the advent of BRCA1/2 genetic testing, model communication and counseling protocols have been developed to evaluate the behavioral and psychosocial effects of BRCA1/2 genetic testing on patients (e.g., Botkin et al., 1996; Tercyak, O’Neill, Roter, & McBride, 2012). In general, studies on this topic have found that BRCA1/2 genetic counseling and testing is not harmful for the majority of individuals who are found to carry risk variants, although individuals with high levels of preexisting anxiety do appear to respond more poorly (Tercyak et al., 2012). More recent interventions have begun to examine the role of patient knowledge, preferences, and values regarding genetic testing; however, it is clear that more work on this important topic is needed and that health psychologists are uniquely positioned to conduct such work (Tercyak et al., 2012).

Future Directions It is hoped that advances in our understanding of the genetic and epigenetic basis of cancer and other diseases will eventually lead to a plethora of targeted, evidence‐based behavioral health interventions for individuals who are identified as being at increased genetic risk; however, as noted by McBride, Birmingham and Kinney (2015), a significant challenge that health psychologists frequently encounter concerns how to effectively convey to patients the joint contributions of both genetic and environmental factors to disease risk. In addition, there is presently a great need for additional research aimed at (a) determining the optimal communication approaches for providers to employ to help their patients to effectively interpret genetic testing results, (b) identifying the most effective means of leveraging provider–patient communication about genetic testing to increase the patients’ engagement in risk‐reducing health behaviors (e.g., smoking cessation, weight management) and utilization of appropriate healthcare services, and (c) understanding how best to disseminate risk information to family members in order to further support patients’ actions to reduce risk (McBride et  al., 2015). Health psychologists are uniquely well suited to make significant contributions to each of these important and growing areas of science and practice (Tercyak et al., 2012).

Author Biographies Dr. Nathan A. Kimbrel is the assistant director of the Genetics Research Laboratory at the Department of Veterans Affairs’ Mid‐Atlantic Mental Illness Research, Education, and Clinical Center (VA Mid‐Atlantic MIRECC). He is also the assistant director for Implementation Science and Program Evaluation for the VA Mid‐Atlantic MIRECC Clinical Core as well as an assistant professor in the Department of Psychiatry at the Duke University School of Medicine. His research primarily focuses on psychiatric genetics and the etiology, assessment, and

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t­ reatment of self‐injurious behavior. Christopher Fink is a graduate student in the Department of Physiology at North Carolina State University in Raleigh, NC. He received a BS in Biology from the University of North Carolina in 2016 and has served as a clinical research assistant in the Traumatic Stress Laboratory at the Durham VA Medical Center and the VA Mid‐Atlantic MIRECC since 2017.

References Allfrey, V. G., Faulkner, R., & Mirsky, A. E. (1964). Acetylation and methylation of histones and their possible role in the regulation of RNA synthesis. Proceedings of the National Academy of Sciences of the United States of America, 51, 786–794. Botkin, J. R., Croyle, R. T., Smith, K. R., Baty, B. J., Lerman, C., Goldgar, D. E., … Nash, J. E. (1996). A model protocol for evaluating the behavioral and psychosocial effects of BRCA1 testing. Journal of the National Cancer Institute, 88, 872–882. Breitling, L. P. (2013). Current genetics and epigenetics of smoking/tobacco‐related cardiovascular ­disease. Arteriosclerosis, Thrombosis, and Vascular Biology, 33, 1468–1472. doi:10.1161/ ATVBAHA.112.300157 Breitling, L. P., Salzmann, K., Rothenbacher, D., Burwinkelly, B., & Brenner, H. (2012). Smoking, F2RL3 methlylation, and prognosis in stable coronary heart disease. European Heart Journal, 33, 2841–2848. doi:10.1093/eurheartj/ehs091 Dedert, E. A., Calhoun, P. S., Watkins, L. L., Sherwood, A., & Beckham, J. (2010). Posttraumatic stress disorder, cardiovascular, and metabolic disease: A review of the evidence. Annals of Behavioral Medicine, 39, 61–78. Duncan, L. E., Ratanatharathorn, A., Aiello, A. E., Almli, L. M., Amstadter, A. B., Ashley‐Koch, A. E., … Koenen, K. C. (2018). Largest GWAS of PTSD (N=20,070) yields genetic overlap with schizophrenia and sex differences in heritability. Molecular Psychiatry, 23, 666–673. doi:10.1038/mp.2017.77 Gao, X., Jia, M., Breitling, L. P., & Brenner, H. (2015). DNA methylation changes of whole blood cells in response to active smoking exposure in adults: A systematic review of DNA methylation studies. Clinical Epigenetics, 7, 113. doi:10.1186/s13148‐015‐0148‐3 Glier, M. B., Green, T. J., & Devlin, A. M. (2014). Methyl nutrients, DNA methylation, and cardiovascular disease. Molecular Nutrition & Food Research, 58, 172–182. doi:10.1002/mnfr.201200636 Li, X., You, R., Wang, X., Liu, C., Xu, Z., Zhou, J., … Zou, Q. (2016). Effectiveness of prophylactic surgeries in BRCA1 or BRCA2 mutation carriers: A meta‐analysis and systematic review. Clinical Cancer Research, 22, 3971–3981. doi:10.1158/1078‐0432.CCR‐15‐1465 Maes, H. H., Sullivan, P. F., Bulik, C. M., Neale, M. C., Prescott, C. A., Eaves, L. J., & Kendler, K. S. (2004). A twin study of genetic and environmental influences on tobacco initiation, regular tobacco use and nicotine dependence. Psychological Medicine, 34, 1251–1261. doi:10.1017/ S0033291704002405 McBride, C. M., Birmingham, W. C., & Kinney, A. Y. (2015). Health psychology and translational genomic research. American Psychologist, 70, 91–104. doi:10.1037/a0036568 Rose, N. R., & Klose, R. J. (2014). Understanding the relationship between DNA methylation and ­histone lysine methylation. Biochimica Et Biophysica Acta (BBA)  –  Gene Regulatory Mechanisms, 1839(12), 1362–1372. doi:10.1016/j.bbagrm.2014.02.007 Soenderstrup, I. M. H., Laenkholm, A. V., Jensen, M. B., Eriksen, J. O., Gerdes, A. M., Hansen, T. V. O., … Ejlertsen, B. (2018). Clinical and molecular characterization of BRCA‐associated breast ­ cancer: Results from the DBCG. Acta Oncologica, 57, 95–101. doi:10.1080/ 0284186X.2017.1398415 Sumner, J. A., Duncan, L. E., Wolf, E. J., Amstadter, A. B., Baker, D. G., Beckham, J. C., … Vermetten, E. (2017). Posttraumatic stress disorder has genetic overlap with cardiometabolic traits. Psychological Medicine, 47, 2036–2039. doi:10.1017/S0033291717000733

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Tercyak, K. P., O’Neill, S. C., Roter, D. L., & McBride, C. M. (2012). Bridging the communication divide: A role for health psychology in the genomic era. Professional Psychology Research and Practice, 43, 568–575. doi:10.1037/a0028971 Weinhold, B. (2006). Epigenetics: The science of change. Environmental Health Perspectives, 114, A160–A167.

Suggested Reading Bierut, L. J., & Tyndale, R. F. (2018). Preparing the way: Exploiting genomic medicine to stop smoking. Trends in Molecular Medicine, 24(2), 187–196. doi:10.1016/j.molmed.2017.12.001 Deary, I. J., Harris, S. E., & Hill, W. D. (2018). What genome‐wide association studies reveal about the association between intelligence and physical health, illness, and mortality. Current Opinion in Psychology, 27, 6–12. doi:10.1016/j.copsyc.2018.07.005 Pfeiffer, J. R., Mutesa, L., & Uddin, M. (2018). Traumatic stress epigenetics. Current Behavioral Neuroscience Reports, 5(1), 81–93. doi:10.1007/s40473‐018‐0143‐z Rutter, M. (2006). Genes and behavior: Nature‐Nurture interplay explained. Oxford, UK: Blackwell Publishing.

The Role of Cultural Mistrust in Health Disparities Brandon L. Carlisle1 and Carolyn B. Murray2 Department of Psychiatry, University of California, San Diego, CA, USA 2 Psychology, University of California, Riverside, CA, USA

1

This entry examines the role cultural mistrust plays in health disparities experienced by African Americans, in contrast to Whites. While a multitude of individual and environmental factors contribute to trends in health outcomes, the focus of the present entry is the role of trust and its relationship with healthcare disparities, service utilization, adherence to treatment, and patient satisfaction. There are individual differences in the amount of trust potential patients have in the healthcare system, but it is also the case that variation can be partially explained by accounting for patient race/ethnicity. As a result, researchers have conceptualized mistrust as an important cultural variable when interpreting health trends among racial/ethnic minorities, particularly African Americans.

Health Disparities Health inequality in the United States is a serious problem, and while there has been some improvement in particular areas, the overall picture is bleak. The health status of racial and ethnic minorities continues to lag far behind that of the majority population (Center for Disease Control and Prevention [CDC], 2013), especially for African Americans. In fact, African Americans have much higher disease and death rates than other racial and ethnic groups in this country (Kawachi, Daniels, & Robinson, 2005). The disparity of life expectancy across ethnic groups remains staggering. The gap between the highest and lowest life expectancies for race–county combinations in the United States is currently over 35 years (Murray et al., 2006). In the case of African Americans, disparities in mortality and life expectancy are predicted to increase (Levine et  al., 2001). The overall mortality rate was 31% higher for African Americans than for White Americans. African Americans were 46% more likely to die The Wiley Encyclopedia of Health Psychology: Volume 4: Special Issues in Health Psychology, First Edition. General Editor: Lee M. Cohen. Volume Editor: Suzy Bird Gulliver. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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from a stroke, 32% more likely to die from heart disease, and 23% more likely to die from cancer (CDC, 2013). Even when receiving corrective medical surgery, African American patients are twice as likely as White patients to experience mortality (Nathan, Frederick, Choti, Schulick, & Pawlik, 2008). Numerous studies have been conducted to identify the cause of health disparities between African Americans and Whites. These studies have identified individual and/or group factors such as obesity, low health literacy (Schwartz, Crossley‐May, Vigneau, Brown, & Banerjee, 2003), and lower SES (Mandelblatt, Andrews, Kao, Wallace, & Kerner, 1996), as well as institutional factors, such as health insurance status (Bradley, Given, & Roberts, 2001), patient dumping (Randall, 1993), lack of a primary health provider, less qualified doctors (Bao, Fox, & Escarce, 2007), and other factors that, singly or combined, play a causal role in the creation and persistence of health disparities. However, after controlling for many of these factors, the health disparities across racial groups still remain.

The Role of Cultural Mistrust The construct of cultural mistrust has been defined as a tendency of African Americans to distrust White people, based upon a legacy of direct or vicarious exposure to racism or unfair treatment (Benkert, Peters, Clark, & Keves‐Foster, 2006). Terrell and Terrell (1981) first proposed cultural mistrust to describe the extent to which African Americans may distrust institutional systems in the United States that are dominated by the White majority; some of the relevant institutions include the education system, politics, business, and interpersonal contexts that may coincide with daily interactions. In addition to these domains, the healthcare system has also been identified as an institution that can elicit cultural mistrust (Benkert et al., 2006). Understanding why African Americans tend to report greater distrust of the healthcare ­system requires consideration of a specific historical context within the United States. Often, blame for the disproportionate mistrust of the healthcare system held by African Americans is attributed to the infamous Tuskegee syphilis study. This study, conducted by the US Public Health Service, involved the recruitment of hundreds of African American men in order to document the severity of syphilis and its symptoms when left untreated; the study continued long after penicillin was identified as an effective treatment. For many African Americans, the Tuskegee syphilis study serves as a vivid example of African Americans being deliberately ­victimized by an institution in the healthcare system (Kennedy, Mathis, & Woods, 2007). However, it is important to note that this case of gross mistreatment is “symbolic for the larger problem of African American distrust of the largely White medical establishment which has evolved in the presence of racial discrimination, racial inequalities in quality of care received, and a previous history of medical research misuse” (Shavers, Lynch, & Burmeister, 2000, p. 571). Historically, those who now mistrust the dominant group (Whites) either were once held in captivity (African Americans were legally held in chattel slavery), were deprived of their rights to citizenship (Asian Americans), were exploited for their labor and cheated out of their lands (Mexican Americans), or endured genocide and were forced into concentration camps called reservations (Native Americans). While these were governmental policies and practices, often it was medical scientists and practitioners that legitimized and implemented these activities. Specifically, a substantial number of leading American scientists and medical doctors believed in eugenics, a practice they thought would improve the genetic quality of

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mankind. The US ­government, along with the medical profession, embarked on two such programs—one known as positive eugenics and the other known as negative eugenics. Positive eugenic strategies involved urging individuals deemed particularly “fit” (Whites—in particular, Nordics and Aryans) to marry and procreate. Negative eugenics, on the other hand, was meant to actively limit, or control, the “genetically inferior” (e.g., those with low intelligence scores, those believed to be mentally deficient, criminal, and people of color—in particular, those of African American heritage). Negative eugenic practices included abortion, sterilization, and c­ astration, among others. Segregation, incarceration, and terror were employed to keep the “genetically inferior” in their place, and physically limit their ability to increase in number, until such time as it would be acceptable to exterminate them. By the 1950s, 24 states had anti‐miscegenation laws that forbid Whites to marry non‐ Whites (in particular, African Americans). Additionally, in the United States, the government had compulsory sterilization laws; too often, women of color were sterilized without their knowledge. This strategy was used to control the “genetically inferior” populations, which included immigrants, people of color, poor people, unmarried mothers, the disabled, the criminal, the violent, and the mentally ill. As a matter of fact, government‐funded sterilization programs existed in 32 states throughout much of the twentieth century. Driven by prejudiced attitudes, which fostered pseudoscience and social control, these programs were the driving force that fostered ill‐conceived policies on immigration and segregation. Today, if healthcare practitioners’ treatment of African Americans were to reflect a normal bell curve distribution, one would be led to presume that (a) the overwhelming majority of doctors do their job without consideration of race; (b) some doctors mistreat, or even ­purposefully harm, African Americans; and (c) some doctors make extreme efforts to make sure that African Americans receive treatment consistent with the “gold standard” of medical care (i.e., prescribe the best diagnostic tests or medical treatments available). Unfortunately, evidence indicates that the medical treatment of African Americans is far from a normal distribution but is instead skewed to the low end of treatment (Smedley, Stith, & Nelson, 2003). The evidence indicates that healthcare professionals exhibit the same levels of implicit bias toward African Americans as the wider population. Correlational evidence indicates that biases are likely to influence care in diagnosis, treatment decisions, and levels of medical care given in some circumstances, and these discrepancies need to be further investigated. According to Dr. J. Corey Williams (2017), a psychiatrist, it is “no surprise that Blacks do not trust doctors or hospitals” (p. 2). He goes on to say that medical practice discrimination takes many forms, including differential prescription patterns such that African American patients, in contrast to Whites, are less likely to receive better‐tolerated or more effective medications. African Americans are also more likely to receive an older treatment protocol, with reduced effectiveness and worse (i.e., more severe or deleterious) side effects, rather than the “gold standard” treatment, which has comparatively superior health effects and fewer or milder side effects. In addition, African Americans who go to emergency rooms often wait longer to see a doctor, are permitted less time spent with a doctor, and receive comparably poorer treatment (Williams, 2017). This type of unfair and abhorrent treatment of African American patients has been noted on more than a few occasions. For instance, in 2003, the Institute of Medicine issued a report titled “Unequal Treatment” (Smedley et al., 2003) after reviewing over 100 studies concluding that African Americans do not receive the same quality of treatment as Whites, even when insurance status, patient income, and access to care were held constant. Specifically, African Americans received fewer referrals, more incorrect diagnoses, poorer provider– patient communication, and so on. One of the many egregious findings was the significantly

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higher mortality rate from breast cancer seen in African American women. Despite the lower incidence of breast cancer in African American women and the higher rate of aggressive breast cancer in White women, African American women had a much higher mortality rate even when the number of doctor visits was held constant (Bradley et  al., 2001). When searching for a reason for this anomaly, it became clear that physicians employed less aggressive and less appropriate treatment for African American women than for White women (National Cancer Institute, 2003). Discriminatory treatment of African Americans in healthcare is well recognized, yet the literature to a certain extent “blames the victim,” namely, African American patients. In addition to the influence of historical and present abuses, researchers have also investigated modern perceptions of African Americans in order to understand the role of cultural mistrust in health outcomes. In a series of focus groups among African American patients, themes related to medical mistrust emerged when patients reported feeling that their symptoms were discredited and clinicians did not convey respect during their communications (Cuevas, O’Brien, & Saha, 2016). Earlier research had likewise found that racial/ethnic minorities are significantly more likely to report being treated with disrespect during interactions with their healthcare provider (Blanchard & Lurie, 2004). Valid and reliable instruments that specifically measure cultural mistrust can facilitate investigations designed to assess the perceptions of African Americans. Furthermore, tailored measurement can contribute to the identification of correlates and predictors of important health attitudes, behaviors, and, ­ultimately, health outcomes.

Measuring Cultural Mistrust The most common measure of cultural mistrust is the Cultural Mistrust Inventory (CMI), which was developed by Terrell and Terrell (1981). The CMI is a 48‐item questionnaire assessing the extent to which African American people mistrust White people in societal domains of education (e.g., “If a black student tries, he will get the grade he deserves from a White teacher”), business (e.g., “White storeowners, salesmen, and other White businessmen tend to cheat Blacks whenever they can”), politics and law (e.g., “White politicians will promise Blacks a lot but deliver little”), and interpersonal relations (e.g. “Whites will say one thing and do another”). Researchers have administered the questionnaire in order to determine if responses are ­significantly associated with attitudes (e.g., perceived discrimination) and behaviors (e.g., treatment adherence) in the healthcare setting. Despite the well‐documented utility of the CMI (Whaley, 2001), the questionnaire does not specifically assess mistrust of the healthcare system; this creates a potential limitation when extending research on cultural mistrust to healthcare settings. Decades after Terrell and Terrell (1981), the Group‐Based Medical Mistrust Scale (GBMMS) (Thompson, Valdimarsdottir, Winkel, Jandorf, & Redd, 2004) was developed in order to specifically measure the extent to which respondents exhibit race‐based medical mistrust. Thompson et al. argued that an instrument specifically designed to measure mistrust in healthcare settings was warranted, because attitudes and behaviors in healthcare settings may present a unique area of concern given the legacy of medical disparities and abuses experienced by racial/ethnic minorities. The GBMMS is a 12‐item measure that assesses suspicion of mainstream healthcare systems (e.g., “People of my ethnic group should be suspicious of modern medicine”), suspicion of

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healthcare professionals (e.g., “People of my ethnic group cannot trust doctors and healthcare workers”), and perceptions about the treatment provided to individuals that identify with the respondent’s racial/ethnic group (e.g., “I have personally been treated poorly or unfairly by doctors or healthcare workers because of my ethnicity”). Previous research has produced ­evidence demonstrating the validity of the GBMMS, and the measure has been incorporated into several investigations of health attitudes and behaviors exhibited by African Americans (Shelton et al., 2010).

Perception, Utilization, and Adherence The importance of patient attitudes has been emphasized in research on the perceptions of healthcare providers and patient satisfaction (Hall & Dornan, 1988). Furthermore, researchers have also measured patient attitudes in relation to perceptions about help‐seeking and perceived discrimination. Evidence suggests that cultural mistrust can partially explain attitudes reported by African Americans in healthcare settings. In a study of men diagnosed with prostate cancer, African Americans reported greater medical mistrust than Whites, and medical mistrust was negatively associated with physical and emotional well‐being (Bustillo et  al., 2017). Benkert et al. (2006) collected responses from low‐income African Americans across two primary care clinics, and they found that CMI scores were negatively correlated with trust in a patient’s primary healthcare provider. Furthermore, CMI scores were also negatively correlated with indicators of patient satisfaction with healthcare. Previous research has also revealed racial and ethnic differences in use of healthcare ­services (i.e., frequency, duration; Weinick, Zuvekas, & Cohen, 2000). Consideration of cultural mistrust can improve understanding of behaviors exhibited by African Americans in the healthcare setting. In a study of preventive care utilization among African American women, greater cultural mistrust predicted a significantly lower likelihood of receiving a physical exam in the previous year (Pullen, Perry, & Oser, 2014). Thompson et al. (2004) found that African American and Latina women who reported that their last breast cancer screening was more than 5 years ago, or who reported no previous breast cancer screening, had significantly higher GBMMS scores. In a related study of preventive health behaviors, Shelton et al. (2010) found that higher scores on the GBMMS were negatively associated with favorable attitudes about prostate cancer screening among African American men. A similar pattern emerged in a systematic review of quantitative and qualitative research on cultural mistrust and colorectal screening, wherein African Americans with higher mistrust scores reported lower rates of receiving colorectal screening (Adams, Richmond, Corbie‐ Smith, & Powell, 2017). The importance of patients’ adherence to treatment, and its relationship to positive health outcomes, is well documented in the literature. Furthermore, poor adherence to medical treatment can contribute to racial/ethnic disparities in health outcomes (McQuaid & Landier, 2018). The influence of cultural mistrust is significant because it can potentially impact a patient’s decision to adhere to their medical treatment or follow the recommendations of their physician. For example, in a study of self‐care among African Americans diagnosed with type 2 diabetes, greater cultural mistrust was associated with lower adherence to dietary interventions (deGroot et al., 2003). In conclusion, mistrust of the medical profession has been shown to be a major obstacle to African Americans seeking health treatment and properly adhering to health regiments.

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Promoting Trust Cultural mistrust has a deleterious effect on the quality of the relationship between African Americans patients and their physicians. In order to improve the quality and content of their interactions with patients, physicians and medical staff need to understand that cultural mistrust is primarily derived from suspicions at an organizational level, and they must recognize that this mistrust among African Americans is an adaptive defensive strategy stemming both from historical abuses and modern disparities. Cultural competency initiatives have been implemented in systems of physical and mental healthcare. One approach to enhance cultural competency, and to improve patient interactions, is to increase cultural sensitivity and awareness of cultural mistrust so that it can be directly addressed. Trust can also be fostered through the training and recruitment of racial/ethnic minority care providers. Some African Americans may feel more comfortable in healthcare settings when their provider is African American as well. Doctor–patient race concordance has been associated with an increased likelihood of utilizing health services when needed and a decreased likelihood of delayed help‐seeking (LaVeist, Nuru‐Jeter, & Jones, 2003). While race concordance does not always produce significant differences in health behaviors, the improved ­representation of African Americans in healthcare roles can partially address concerns related to cultural mistrust. Based on the existence of several directories that have compiled lists of African American care providers, there appears to be a strong interest in race concordance within segments of the African American population. Improving trust among African Americans could also involve partnering with the Black church. Among African Americans with strong ties to their community church, disseminating health information in an environment where they feel more comfortable can foster trust. Health campaigns that aim to raise awareness and increase health service utilization can improve outcomes by recognizing the importance of pastors and by allowing them to advise and also play a significant role in the promotion of health behaviors that can potentially ­mitigate disparities.

Conclusion Lacking trust in the healthcare system and its providers can influence both service utilization and treatment adherence and thereby influence health outcomes for African Americans. Additionally, differential treatment and disparate outcomes experienced by African Americans could confirm their potential suspicions and consequently exacerbate existing tendencies to lack trust in the healthcare system. Therefore cultural mistrust of healthcare can serve as a predictor of attitudes and behaviors among African Americans, or it can be conceptualized as an outcome that stems from perceived or actual medical mistreatment. These perspectives underscore the importance of examining cultural mistrust and how it may relate to African American health trends. Improving trust among African Americans requires sustained effort on several fronts. Healthcare organizations, providers of care, researchers, and community advocates should give careful consideration to the historical and modern experiences of African Americans in order to inform the development and implementation of interventions. Establishing (or earning) the trust of African Americans will require various methods at individual, situational, and organizational levels, because “just as there are many reasons for the mistrust, there is no one answer that will solve all of the mistrust issues” (Kennedy et  al., 2007, p. 60).

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Author Biographies Brandon L. Carlisle, PhD, is a senior mental health researcher in the Department of Psychiatry at the University of California, San Diego. He contributes to research focusing on the evaluation and improvement of behavioral health services provided to children, youth, and families. His research interests include identifying barriers to seeking and receiving care and the ­importance of cultural competence when providing care to diverse populations. Carolyn B. Bennett Murray, PhD, is currently a full professor in the Psychology Department at the University of California, Riverside (UCR). She received her PhD from the University of Michigan, Ann Arbor, and has published numerous journal articles and book chapters. Her primary research interest is self‐sabotage. A few of her many awards include the Chancellor’s Award for Excellence in Undergraduate Research, the Association of Black Psychologists’ Distinguished Psychologist Award, and the UCR Distinguished Teaching Award.

References Adams, L. B., Richmond, J., Corbie‐Smith, G., & Powell, W. (2017). Medical mistrust and colorectal cancer screening among African Americans. Journal of Community Health, 42, 1044–1061. Bao, Y., Fox, S., & Escarce, J. (2007). Socioeconomic and racial/ethnic differences in the discussion of cancer screening: “Between‐” versus “within‐” physician differences. Health Services Research, 42(3 Pt. 1), 950–970. Benkert, R., Peters, R. M., Clark, R., & Keves‐Foster, K. (2006). Effects of perceived racism, cultural mistrust and trust in providers on satisfaction with care. Journal of the National Medical Association, 98, 1532–1540. Blanchard, J., & Lurie, N. (2004). R‐E‐S‐P‐E‐C‐T: Patient reports of disrespect in the health care setting and its impact on care. Journal of Family Practice, 53, 721. Bradley, C., Given, C., & Roberts, C. (2001). Disparities in cancer diagnosis and survival. Cancer, 91, 178–188. Bustillo, N. E., McGinty, H. L., Dahn, J. R., Yanez, B., Antoni, M. H., Kava, B. R., & Penedo, F. J. (2017). Fatalism, medical mistrust, and pretreatment health‐related quality of life in ethnically diverse prostate cancer patients. Medical Social Sciences, 26, 323–329. Center for Disease Control and Prevention. (2013). Health disparities and inequalities report—United States. Morbidity and Mortality Weekly Report Supplement, 62(3). Retrieved from https:// reducedisparity.org/site/wp‐content/uploads/2014/09/CDC‐Health‐Disparities‐and‐ Inequalities‐Report_2013.pdf#page=186. Cuevas, A. G., O’Brien, K., & Saha, S. (2016). African American experiences in healthcare: “I always feel like I’m getting skipped over”. Health Psychology, 35, 987–995. deGroot, M., Welch, G., Buckland, G., Fergus, M., Ruggiero, L., & Chipkin, S. (2003). Cultural orientation and diabetes self‐care in low‐income African Americans with type 2 diabetes mellitus. Ethnicity & Disease, 13, 6–14. Hall, J. A., & Dornan, M. C. (1988). What patients like about their medical care and how often they are asked: A meta‐analysis of the satisfaction literature. Social Science & Medicine, 27, 935–939. Kawachi, I., Daniels, N., & Robinson, D. E. (2005). Health disparities by race and class: Why both matter. Health Affairs, 24, 343–352. Kennedy, B. R., Mathis, C. C., & Woods, A. K. (2007). African Americans and their distrust of the health care system: Healthcare for diverse populations. Journal of Cultural Diversity, 14, 56–60. LaVeist, T. A., Nuru‐Jeter, A., & Jones, K. E. (2003). The association of doctor‐patient race concordance with health services utilization. Journal of Public Health Policy, 24, 312–323. Levine, R., Foster, J., Fullilove, R., Griggs, N., Hull, P., Husaini, B., et al. (2001). Black‐white inequalities in mortality and life expectancy, 1933–1999: Implication for Healthy People 2010. Public Health Report, 116, 474–483.

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Mandelblatt, J., Andrews, H., Kao, R., Wallace, R., & Kerner, J. (1996). The late‐stage diagnosis of colorectal cancer: Demographic and socioeconomic factors. American Journal of Public Health, 86, 1794–1797. McQuaid, E. L., & Landier, W. (2018). Cultural issues in medical adherence: Disparities and directions. Journal of General Internal Medicine, 33, 200–206. Murray, C., Kulkarni, S., Michaud, C., Tomijima, N., Bulzacchelli, M., Iandiorio, T., et al. (2006). Eight Americas: Investigating mortality disparities across races, counties, and race‐counties in the United States. PLoS Medicine, 3, 1513–1524. Nathan, H., Frederick, W., Choti, M., Schulick, R., & Pawlik, T. (2008). Racial disparity in surgical mortality after major hepatectomy. Journal of the American College of Surgeons, 207, 312–319. National Cancer Institute. (2003). Surveillance, epidemiology, and end results (SEERS). Retrieved from https://seer.cancer.gov/archive/studies/epidemiology/study16.html. Pullen, E., Perry, B., & Oser, C. (2014). African American women’s preventative care usage: The role of social support and racial experiences and attitudes. Sociology of Health & Illness, 36, 1037–1053. Randall, V. R. (1993). Racist health care: Reforming an unjust health care system to meet the needs of African Americans. Health Matrix: The Journal of Law‐Medicine, 3, 127–194. Schwartz, K., Crossley‐May, H., Vigneau, F., Brown, K., & Banerjee, M. (2003). Race, socioeconomic status and stage at diagnosis for five common malignancies. Cancer Causes Control, 14, 761–766. Shavers, V. L., Lynch, C. F., & Burmeister, L. F. (2000). Knowledge of the Tuskegee Study and its impact on the willingness to participate in medical research studies. Journal of the National Medical Association, 92, 563–572. Shelton, R. C., Winkel, G., Davis, S. N., Roberts, N., Valdimarsdottir, H., Hall, S. J., & Thompson, H. S. (2010). Validation of the group‐based medical mistrust scale among urban Black men. Journal of General Internal Medicine, 25, 549–555. Smedley, B. D., Stith, A. Y., & Nelson, A. R. (2003). Institute of medicine: Unequal treatment: Confronting racial and ethnic disparities in health care. Washington, DC: National Academies Press. Terrell, F., & Terrell, S. L. (1981). An inventory to measure cultural mistrust among Blacks. Western Journal of Black Studies, 5, 180–184. Thompson, H. S., Valdimarsdottir, H., Winkel, G., Jandorf, L., & Redd, W. (2004). The group based medical mistrust scale: Psychometric properties and association with breast cancer screening. Preventive Medicine, 38, 209–218. Weinick, R. M., Zuvekas, S. H., & Cohen, J. W. (2000). Racial and ethnic differences in access to and use of health care services, 1977 to 1996. Medical Care Research and Review, 57, 36–54. Whaley, A. L. (2001). Cultural mistrust: An important psychological construct for diagnosis and treatment of African Americans. Professional Psychology: Research and Practice, 32, 555–562. Williams, J. C. (2017). Black Americans don’t trust our healthcare system—Here’s why. The Hill: Opinion Contributor, 1–4. Retrieved from http://thehill.com/blogs/pundits‐blog/healthcare/ 347780‐black‐americans‐dont‐have‐trust‐in‐our‐healthcare‐system.

Suggested Reading Murray, C. B. (2016). Racism and mental health. In H. S. Friedman (Ed.), Encyclopedia of mental health. Elsevier: United Kingdom. Murray, C. B., Woods, V. D., Khatib, S. M., & McAdoo, H. P. (2009). The psychology of health disparities among African Americans. Journal of Black Psychology, (Special Issue), 35. Smedley, B. D., Stith, A. Y., & Nelson, A. R. (2003). Institute of medicine: Unequal treatment: Confronting racial and ethnic disparities in health care. Washington, DC: National Academies Press. Washington, H. A. (2006). Medical apartheid: The dark history of medical experimentation on Black Americans from colonial times to the present. New York: Doubleday. Williams, D. R., & Mohammed, S. A. (2009). Discrimination and racial disparities in health: Evidence and needed research. Journal of Behavioral Medicine, 32, 20–47.

Health Disparities Melissa Ward‐Peterson and Eric F. Wagner Department of Epidemiology, Florida International University, Miami, FL, USA

Introduction Racial and ethnic minority groups, rural populations, populations with low socioeconomic status (SES), and other marginalized and medically underserved minority populations ­persistently experience higher than average rates of illness, impairment, and death. For ­example, diabetes is more than three times as likely to be a cause of death for Native Americans than for other racial/ethnic groups (66.0 per 100,000 population compared with 20.8 per 100,000) (Indian Health Service, 2018). This health disparity, along with others plaguing Native American communities, has contributed to a life expectancy that is 5.5 years shorter than that of the general US population (Indian Health Service, 2018). These types of disadvantages in health and longevity suffered by minority groups are referred to as health disparities (Braveman, 2014). Compared with non‐Hispanic Whites, minority racial and ethnic populations experience earlier onset of illness, more severe disease, and poorer quality of care (Williams, Mohammed, Leavell, & Collins, 2010; Williams, Priest, & Anderson, 2016). Minority racial and ethnic populations also experience dramatically lower SES (e.g.,  income, education, occupation), which is associated with poorer health and reduced longevity (Williams et al., 2010, 2016). However, the relationship between SES and health varies by race. Research has shown as SES levels increase, the health differences between Blacks and Whites become more pronounced, supporting a diminishing returns hypothesis for SES vis‐à‐vis health among minority groups (Farmer & Ferraro, 2005). Since health disparities adversely affect groups of people who are socially and/or economically disadvantaged, health disparities reflect social injustices. Health equity refers to the ­ideology, motivation, and actions behind correcting these social injustices and eliminating health disparities (Braveman, 2014). A health equity perspective adheres to the human rights principles of nondiscrimination and equality (Braveman, 2014) and presumes that health ­disparities ultimately are avoidable if the underlying social structures and arrangements c­ reating and maintaining social injustices are addressed (Pearlin, Schieman, Fazio, & Meersman, 2005). The Wiley Encyclopedia of Health Psychology: Volume 4: Special Issues in Health Psychology, First Edition. General Editor: Lee M. Cohen. Volume Editor: Suzy Bird Gulliver. © 2021 John Wiley & Sons Ltd. Published 2021 by John Wiley & Sons Ltd.

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While the alleviation of health problems experienced by minority individuals may be a­ ccomplished through the types of direct interventions common in health psychology, the alleviation of health disparities affecting marginalized minority groups can only be ­accomplished through social change.

Conceptualizations of Health Disparities There is consensus among health disparities scholars that contextual exposures, a life‐course approach, and the consequences of discrimination, intersectionality, and residential segregation must be taken into account in efforts to understand and eliminate health inequities. Each of these key conceptualizations of health disparities is described next. Contextual exposures refer to the collection of neighborhood, environmental, socioeconomic, and cultural exposures influencing health (Benn & Goldfeld, 2016; Ruiz & Brondolo, 2016; Williams et  al., 2016). Minority individuals are disproportionately exposed to toxic social and environmental contexts that reduce health and longevity (Ruiz & Brondolo, 2016). Toxic contextual exposures directly disrupt biological functioning by producing alterations in gene expression, causing tissue and organ damage and dysregulating stress response and recovery systems (Williams et al., 2016). At least in theory, and hopefully in practice, contextual exposures can be changed in ways that improve health and promote health equity and thus are an increasing focus of health disparities research (Benn & Goldfeld, 2016). A life‐course approach is essential in efforts to understand and address the drivers of health disparities. Health disparities emerge from exposure to adversity throughout the life course, including before conception, in utero, and throughout infancy, childhood, and adolescence (Williams et al., 2016). A life‐course approach takes into account how risks, resources, and demands linked to race, ethnicity, and SES early in life influence health later in life and systematically considers how these factors combine and accumulate with biological factors to affect health outcomes (Braveman, 2014; Williams et al., 2016). Pearlin et al. (2005) point out that health disparities result from differential exposure across the life course to different demands and hardships. For example, exposure to persistent adversity over time produces greater wear and tear on physiological systems, resulting in physiological dysregulation, with consequent health problems; the various terms used by health disparities scholars for this process include allostatic load, weathering, cumulative physiological dysregulation, and biological risk profile (Peek et al., 2010). Among marginalized minority and low SES groups, exposure to multiple adversities across time dramatically increases these individuals’ allostatic loads (Pearlin et al., 2005). It is well established that higher allostatic loads are associated with increased mortality, morbidity, cognitive decline, and disability (Peek et al., 2010). A unique contributor to the allostatic load of individuals from marginalized and stigmatized groups is discrimination. Discrimination results from the exertion of power by dominant groups to preserve their relative privilege over subordinate groups (Krieger, 2014). Discrimination falls into several categories of justifying ideologies, including racism, sexism, heterosexism, ableism, nativism, ageism, and class bias (Krieger, 2014). There are two overarching types of discrimination: de jure discrimination, which is legally permissible in the societies where it occurs, and de facto discrimination, which is not legally allowed but occurs nonetheless because of social and cultural norms (Krieger, 2014). Some of the most frequently cited examples of de jure discrimination are Jim Crow laws, which legalized discrimination against African Americans in the Southern states from the late nineteenth century through the mid‐twentieth century (Krieger, 2014). Although racial discrimination is now prohibited by

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law, de facto discrimination continues to be a rampant and destructive issue problem for minority groups, due in no small part to the norms de jure discrimination cemented into American society during the Jim Crow era (Krieger, 2014). Studies have found that experiencing discrimination is associated with victimization, poor sexual functioning, less REM stage (deep) sleep, more abdominal fat, elevated hemoglobin A1c, coronary artery calcification, uterine myomas, breast cancer, healthcare underutilization, poor treatment adherence, and alcohol, tobacco, and drug use problems (Williams et  al., 2010). Discrimination represents an abject violation of human rights. The experience of discrimination, and the stereotypes and prejudice that go along with it, may foment negative social psychological processes (e.g., internalized racism, status incongruence, rising expectations), emotional ­dysregulation, and impaired stress reactivity and recovery that pile onto an already burdensome allostatic load (Farmer & Ferraro, 2005; Ruiz & Brondolo, 2016; Williams et al., 2010). Discriminatory experiences have long half‐lives—the psychic pain inflicted by discrimination includes not only the pain of the discriminatory experience itself but also worrisome thoughts and emotions about when it will happen again and why it keeps happening again and again (Pearlin et al., 2005). Moreover, while discrimination is often overt and obvious, there are more subtle discriminatory attitudes and actions that contribute to health disparities experienced by marginalized minorities. Microaggressions are subtle insults and subjective reminders to marginalized groups of their oppressed status in society (Sue, 2010). Similar to overt types of discrimination, experiencing microaggressions erodes self‐esteem and self‐worth and raises the allostatic burden, further compromising the health and longevity of marginalized minority groups. Of course, it is unrealistic to assume all individuals belong to only one socially subordinate group that is exposed to discrimination. While a White woman will encounter challenges posed by sexism, a Black woman will encounter the combined challenges posed by sexism and racism. Worse still will be the lived experience of a Black lesbian, whose life will be impacted not only by sexism and racism but also by heterosexism. First proposed in 1989, intersectionality is a framework for understanding this phenomenon of compounding disadvantage caused by membership in more than one subordinate group (Crenshaw, 1989). Intersectionality has been extended to describe challenges posed by sexual orientation and class as well (Bowleg, 2012). Intersectionality is a critical perspective to take when seeking to understand the causes and effects of health disparities experienced by individuals with more than one identity (Bowleg, 2012). Enduring residential segregation by race/ethnicity and income is a particularly pathogenic example of systemic discrimination and is considered “the most critical distinctive social exposure” driving health disparities (Williams et  al., 2016). As enumerated by Williams et  al. (2010), the pathways through which residential segregation adversely affect health include: 1 socioeconomic immobility by limiting access to quality elementary and high school education, preparation for higher education and employment; 2 suboptimal health practices because of conditions created by concentrated poverty; 3 economic hardship and compound stressors for individuals, households, and communities; 4 difficulties and mistrust among neighbors due to weakened community and neighborhood infrastructure; 5 institutional neglect and disinvestment leading to increased exposure to environmental toxins, poor quality housing, and criminal victimization; and 6 limited access to and low quality of healthcare.

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Historical and Current Examples of Health Inequities The following section provides some historical and current examples of health inequities and the factors driving them. While the examples offered here are by no means exhaustive and focus only on the social context of the United States, they offer an important glimpse into the complex realities facing marginalized communities. The examples illustrate how structural social injustices have dire implications for the health of minority groups. Additionally, they underscore the relevance of the life‐course and intersectionality perspectives and show just how pernicious health disparities are in our society.

Historical Examples A life‐course approach emphasizes the role longitudinal exposure plays in creating health ­disparities. Therefore, scholars interested in health equity must be actively engaged in a broad historical perspective on the social conditions that have produced current circumstances. Historical events can impact the health and well‐being of those who lived through them and of later generations. One example can be found in the historical practice of “redlining,” in which banks refused to provide home loans in neighborhoods deemed undesirable (Aaronson, Hartley, & Mazumder, 2018). For minority populations, this practice eliminated access to the financial capital needed to purchase homes. Aaronson, Hartley, and Mazumder estimate discriminatory lending practices contributed to up to half of the disparities in Black/African American home ownership between 1950 and 1980. The systematic obstruction of this path to wealth had a cumulative effect on future generations by limiting the assets that were passed from parents to their children and grandchildren. A second example can be found in the enduring historical trauma experienced by Native Americans. Beginning with first contact and continuing through subsequent colonization and the expansion of the United States, indigenous communities have suffered through ­genocide, dislocation, and other deplorable patterns of violence inflicted upon them by the colonists and their descendants. The cumulative adverse effects of this history are known as “historical trauma,” which describes the profoundly negative, long‐term, and intergenerational impact of colonization, cultural suppression, and historical oppression on indigenous peoples (Brave Heart, Elkins, Tafoya, Bird, & Salvador, 2012). The manifold health disparities experienced by Native Americans (e.g., significantly greater morbidity and mortality from diabetes, cancer, substance use, obesity, bronchiolitis, and injuries; Indian Health Service, 2018) are at least partially attributable to massive historical trauma imposed across many generations.

Contemporary Examples A life‐course approach is also valuable when examining contemporary examples of health ­disparities. A child belonging to a minority group is more likely to be born into racial residential segregation and underprivileged communities, a remnant of redlining and Jim Crow‐era discriminatory practices. In the United States, Black, Latino, and Native American children disproportionately grow up in neighborhoods with constrained opportunity structures, under‐ resourced schools, compound and unrelenting stress, and toxic social and physical e­ nvironments (Braveman, 2014; Pearlin et al., 2005; Williams et al., 2010).

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Environmental exposures are of critical importance during childhood when cognitive and physical development is vulnerable. Lead exposure, which may occur through poor housing conditions or contaminated water, is an especially nefarious environmental toxin because it causes cognitive deficits that limit children’s academic potential, thereby altering their futures (Zhang et  al., 2013). Racial/ethnic disparities in lead exposure have persisted for decades; low‐income Black and Hispanic children residing in older housing continue to have the highest prevalence of exposure (Jacobs, 2011). The most effective way to address lead exposure is to move into a new residence or neighborhood, but this solution is not feasible for the groups most frequently affected. Despite the known risks of even low levels of lead exposure (Lanphear, Dietrich, Auinger, & Cox, 2000) and evidence supporting the effectiveness of interventions that address these risks (Jacobs, 2011), health disparities due to differences in environmental exposures endure. As a result, environmental justice must remain at the forefront of efforts to achieve health equity. Disparities continue into late adolescence and young adulthood. For minority men in the United States, incarceration becomes an important determinant of health. Incarceration is a “disorderly transitional” stressful event that creates nonnormative, undesired, involuntary, and often irreversible life changes (Williams et al., 2010). Incarceration is associated with subsequent homelessness, employment and financial difficulties, destabilization of relationships, increased health risks, and other impediments to health and well‐being (Iguchi, Bell, Ramchand, & Fain, 2005). In 2016, Black individuals were incarcerated at a rate five times higher than that of White individuals (1,608 per 100,000 people compared with 274 per 100,000), and the number of Black children in juvenile detention far exceeds the number of White children in juvenile detention (Gramlich, 2018; Iguchi et  al., 2005). The preceding speaks to the putative influence of incarceration on health disparities and the importance of measuring incarceration in studies of health and longevity differences between Black and White individuals (Williams et al., 2010). Distinctive health disparities are experienced by minority women. In 2011, among Black and Hispanic women, 64 and 50% of pregnancies were unintended, respectively, compared with 38% of pregnancies among White women (Finer & Zolna, 2016). Once pregnant, Black women can expect drastically increased maternal morbidity and mortality compared with White women (Tucker, Berg, Callaghan, & Hsia, 2007). From 2010 to 2014, Black women died at a rate of 40.0 per 100,000 live births, while White women died at a rate of 10.4 per 100,000 live births (Centers for Disease Control and Prevention [CDC], 2018). Potential mediators such as educational achievement do not seem to improve outcomes for Black mothers; a 2016 study conducted by the New York City Department of Health found the rate of severe maternal morbidity for college‐educated Black mothers was still more than twice the rate for White mothers who had not graduated from high school (New York City Department of Health and Mental Hygiene, 2016). Similar to individuals who are racial/ethnic minorities, individuals who are sexual minorities (LGBTQIA) are also subject to de jure and de facto discrimination throughout their lives. While some states have passed laws that explicitly protect LGBTQIA individuals from employment discrimination and hate crimes, many states have not (Krieger, 2014). In fact, as of 2017, 12 states had passed laws allowing for the denial of services to LGBTQIA individuals, largely based on the strongly held religious beliefs of business owners (Raifman, Moscoe, Austin, Hatzenbuehler, & Galea, 2018). Evidence has shown that LGBTQIA individuals in states with de jure discriminatory practices have higher rates of mental distress than LGBTQIA ­individuals from states without de jure discrimination (Raifman et al., 2018).

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Health Disparities Research Priorities Health disparities are receiving increasing attention in health psychology. Though it has long been recognized that marginalized minority groups experience large disadvantages in health and longevity, only recently have sophisticated models and associated empirical work on health disparities and their alleviation begun to appear in the literature. As the literature has grown, so has extramural funding opportunities for conducting research explicitly intended to understand and eliminate health disparities. The following section enumerates several current research priorities for future research on decreasing health disparities and promoting health equity.

Health Disparities Research Priorities 1 Develop multivariate, multilevel, transdisciplinary, integrative, life‐course models to guide research on contextual exposures, their epigenetic effects, and the onset and course of illness among vulnerable populations (Ruiz & Brondolo, 2016; Williams et al., 2010, 2016). 2 Conduct research focused on modifiable drivers of improved health outcomes among vulnerable populations (Benn & Goldfeld, 2016; Ruiz & Brondolo, 2016). 3 Identify influential contextual exposures and optimal intervention strategies at each specific point of the disease continuum based on research conducted with both healthy and clinical populations (Ruiz & Brondolo, 2016; Williams et al., 2010). 4 Understand resilience and coping in the context of social disadvantage and stress (Ruiz & Brondolo, 2016; Williams et al., 2010). 5 Ascertain the degree to which medical care and genetic factors contribute to racial and SES differences in health (Williams et al., 2010). 6 Explore the intersections of race, ethnicity, SES, sex, gender, immigration status, and other variables alongside experiences of discrimination and health outcomes (Ruiz & Brondolo, 2016; Williams et al., 2010). 7 Estimate the heretofore under‐examined contributions of occupational stress, work environment, community violence, and diet to health disparities (Peek et al., 2010; Williams et al., 2010). 8 Attend to the putative influences of acculturation, assimilation, and biological risk on the Hispanic paradox, the health and longevity advantage of foreign‐born (vs. US‐born) Hispanics (Peek et al., 2010; Williams et al., 2010).

Call to Action Racial and ethnic minority groups, LGBTQIA groups, rural populations, populations with low SES, and other marginalized and medically underserved minority populations persistently experience earlier onset of illness, more severe disease, poorer quality healthcare, and disproportionate morbidity and mortality from disease. These health disparities are long standing and result from historical and contemporary structural and social injustices such as constrained opportunity structures, under‐resourced schools, compound and unrelenting stress, toxic social and physical environments, de jure and de facto discrimination, residential segregation, and the experience of microaggressions. The development and testing of multivariate, multilevel, transdisciplinary, integrative, life‐course models of health disparities is urgently needed, especially in regard to modifiable drivers of health outcomes among

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v­ulnerable populations. Although additional research is critically important, advocacy, ­community engagement, and effective communication of findings to decision makers and the lay public are all indispensable and urgent for advancing health equity and eliminating health disparities. While the health risks and health problems experienced by marginalized minority groups may be modifiable on an individual level through behavior change as per conventional health p ­ sychology practices, the long‐term elimination of health disparities is dependent on social change.

Acknowledgment Preparation of this chapter was supported by the National Institute of Minority Health and Health Disparities grant award U54MD012393 for FIU‐RCMI. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author Biographies Melissa Ward‐Peterson, PhD, MPH, is a postdoctoral associate in the Community‐Based Research Institute at Florida International University in Miami, Florida, where she manages the Research Infrastructure Core for FIU’s Research Center in Minority Institutions (FIU‐ RCMI). A social epidemiologist by training, her research interests include disparities in access to substance abuse treatment and women’s health. She has extensive experience in health equity research and program management and has provided technical support for projects sponsored by a number of federal and international agencies, including HRSA, NIH’s Fogarty International Center, the US Department of State, the World Health Organization, the World Bank, and UNAIDS. Eric F. Wagner, PhD, is the director of Florida International University’s (FIU) Community‐ Based Research Institute, a professor in FIU’s Stempel College of Public Health & Social Work, the principal investigator of FIU’s NIMHD‐funded Health Disparities Research Center in a Minority Institute, and a psychologist licensed to practice in Florida (active) and Rhode Island (inactive). His clinical research expertise is addressing substance use and related ­problems among teenage, young adult, and underrepresented medically underserved populations. His work has been supported by grants from NIAAA, NIDA, NIMHD, HRSA, SAMHSA, the Ware Foundation, the Aetna Foundation, and Consejo Nacional de Ciencia y Tecnología.

References Aaronson, D., Hartley, D., & Mazumder, B. (2018). The Effects of the 1930s HOLC “Redlining” Maps. Federal Reserve Bank of Chicago, WP 2017‐12. Benn, E. K., & Goldfeld, K. S. (2016). Translating context to causality in cardiovascular disparities research. Health Psychology, 35, 403–406. doi:10.1037/hea0000309 Bowleg, L. (2012). The problem with the phrase women and minorities: Intersectionality‐an important theoretical framework for public health. American Journal of Public Health, 102, 1267–1273. doi:10.2105/AJPH.2012.300750

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Brave Heart, M. Y., Elkins, J., Tafoya, G., Bird, D., & Salvador, M. (2012). Wicasa Was’aka: Restoring the traditional strength of American Indian boys and men. American Journal of Public Health, 102, S177–S183. doi:10.2105/AJPH.2011.300511 Braveman, P. (2014). What is health equity: And how does a life‐course approach take us further toward it? Maternal and Child Health Journal, 18, 366–372. Centers for Disease Control and Prevention. (2018). Pregnancy mortality surveillance system. Retrieved from https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pregnancy‐mortality‐ surveillance‐system.htm Crenshaw, K. (1989). Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. The University of Chicago Legal Forum, 1989, 139–167. Farmer, M. M., & Ferraro, K. F. (2005). Are racial disparities in health conditional on socioeconomic status? Social Science and Medicine, 60, 191–204. doi:10.1016/j.socscimed.2004.04.026 Finer, L. B., & Zolna, M. R. (2016). Declines in unintended pregnancy in the United States, 2008– 2011. New England Journal of Medicine, 374, 843–852. doi:10.1056/NEJMsa1506575 Gramlich, J. (2018). The gap between the number of blacks and whites in prison is shrinking. Pew Research Center. Retrieved from http://www.pewresearch.org/fact‐tank/2018/01/12/shrinking‐gap‐ between‐number‐of‐blacks‐and‐whites‐in‐prison/ Iguchi, M. Y., Bell, J., Ramchand, R. N., & Fain, T. (2005). How criminal system racial disparities may translate into health disparities. Journal of Health Care for the Poor and Underserved, 16, 48–56. doi:10.1353/hpu.2005.0114 Indian Health Service. (2018). Disparities. Retrieved from https://www.ihs.gov/newsroom/factsheets/ disparities/ Jacobs, D. E. (2011). Environmental health disparities in housing. American Journal of Public Health, 101, S115–S122. doi:10.2105/AJPH.2010.300058 Krieger, N. (2014). Discrimination and health inequities. In L. F. Berkman, I. Kawachi, & M. M. Glymour (Eds.), Social epidemiology (2nd ed.). New York, NY: Oxford University Press. Lanphear, B. P., Dietrich, K., Auinger, P., & Cox, C. (2000). Cognitive deficits associated with blood lead concentrations