Treatment of Psychosocial Risk Factors in Depression 1433834065, 9781433834066

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Treatment of Psychosocial Risk Factors in Depression Edited by

David J. A. Dozois and Keith S. Dobson

Depression is the most prevalent of the psychological disorders, and many children enter adolescence at elevated risk. To reduce the likelihood of onset or recurrence, this treatise takes the novel approach of addressing those processes that contribute to risk. It should make a major contribution to the field. —Steven D. Hollon, PhD, Gertrude Conaway Vanderbilt Professor of Psychology, Vanderbilt University, Nashville, TN Spanning their career-length engagement with both basic and clinical studies of depression, Dozois and Dobson deliver on the promise of risk research—effective interventions for people both in and out of episode. Their blueprint for designing treatments that engage and modulate risk factors will guide the field for years to come. —Zindel V. Segal, PhD, CPsych, Distinguished Professor of Psychology in Mood Disorders, University of Toronto Scarborough, Toronto, ON, Canada

Treatment of Psychosocial Risk Factors in Depression

Treatment of Psychosocial Risk Factors in Depression Edited by

David J. A. Dozois and Keith S. Dobson

Copyright © 2023 by the American Psychological Association. All rights reserved. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, including, but not limited to, the process of scanning and digitization, or stored in a database or retrieval system, without the prior written permission of the publisher. The opinions and statements published are the responsibility of the authors, and such opinions and statements do not necessarily represent the policies of the American Psychological Association. Published by American Psychological Association 750 First Street, NE Washington, DC 20002 https://www.apa.org Order Department https://www.apa.org/pubs/books [email protected] In the U.K., Europe, Africa, and the Middle East, copies may be ordered from Eurospan https://www.eurospanbookstore.com/apa [email protected] Typeset in Meridien and Ortodoxa by Circle Graphics, Inc., Reisterstown, MD Printer: Gasch Printing, Odenton, MD Cover Designer: Mercury Publishing Services, Rockville, MD Library of Congress Cataloging-in-Publication Data Names: Dozois, David J. A., editor. | Dobson, Keith S., editor. Title: Treatment of psychosocial risk factors in depression / edited by   David J. A. Dozois and Keith S. Dobson. Description: Washington, DC : American Psychological Association, [2023] |   Includes bibliographical references and index. Identifiers: LCCN 2022029847 (print) | LCCN 2022029848 (ebook) |   ISBN 9781433834066 (paperback) | ISBN 9781433834059 (ebook) Subjects: LCSH: Depression, Mental. | Depression, Mental--Treatment. |   Depression, Mental--Social aspects. | BISAC: PSYCHOLOGY /   Psychopathology / Depression | PSYCHOLOGY / Suicide Classification: LCC RC537 .T7386 2023 (print) | LCC RC537 (ebook) |   DDC 616.85/27--dc23/eng/20220824 LC record available at https://lccn.loc.gov/2022029847 LC ebook record available at https://lccn.loc.gov/2022029848 https://doi.org/10.1037/0000332-000 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1

This book is dedicated to Dr. Aaron T. Beck (1921–2021), affectionately known to us, and many of his friends and colleagues, as “Tim.” Tim supported and advanced both of our careers in many ways, for which we are truly appreciative. More importantly, he truly revolutionized the assessment, conceptualization, and treatment of depression and many other psychological disorders, and, in so doing, improved the lives of millions of individuals worldwide.

CONTENTS

Contributors ix Acknowledgments xiii Introduction: Treatment of Psychosocial Risk Factors in Depression

3

David J. A. Dozois and Keith S. Dobson

1. Models of Psychosocial Risk in Depression

7

Keith S. Dobson and David J. A. Dozois

2. Parental Psychopathology and Parenting

27

Abigail E. Pine and Judy Garber

3. Low Social Support and Relational Regulation

55

Brian Lakey

4. Interpersonal Risk Factors

81

Jami F. Young, Molly Davis, and Laura Mufson

5. Childhood Adversity, Stressful Life Events, and Trauma

105

Kate L. Harkness

6. Dependency and Excessive Reassurance Seeking

133

Lisa R. Starr, Angela C. Santee, and Meghan Huang

7. Marriage and Relationship Issues

157

Mark A. Whisman and Anna L. Gilmour

8. Emotion Dysregulation

181

Natasha H. Bailen and Renee J. Thompson

 vii

viii  Contents

9. Negative Thinking: Cognitive Products and Schema Structures

207

David J. A. Dozois and Aaron T. Beck

10. Negative Information Processing

233

Wisteria Deng and Jutta Joormann

11. Optimism and Pessimism

253

Max Genecov and Martin E. P. Seligman

12. Perfectionism

281

Paul L. Hewitt, Martin M. Smith, Sabrina Ge, Marcia Mössler, Gordon L. Flett, and Samuel F. Mikail

13. Rumination

305

Ed Watkins

14. Ineffective Social Problem Solving

333

Arthur M. Nezu, Christine Maguth Nezu, Jenna L. Damico, and Holly R. Gerber

15. Cognitive and Behavioral Avoidance

359

Christopher R. Martell and Ajeng J. Puspitasari

16. Metacognition and Mental Regulation

383

Adrian Wells and Henrik Nordahl

17. Investigating and Treating Psychosocial Risk Factors in Depression: An Integrative Summary

407

David J. A. Dozois and Keith S. Dobson

Index 429 About the Editors 459

CONTRIBUTORS

Natasha H. Bailen, PhD, Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States Aaron T. Beck, MD, Beck Institute for Cognitive Behavior Therapy, Bala Cynwyd, PA; Department of Psychiatry (Emeritus), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States Jenna L. Damico, MS, Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States Molly Davis, PhD, Department of Child and Adolescent Psychiatry and Behavioral Sciences and PolicyLab, Children’s Hospital of Philadelphia; Penn Implementation Science Center at the Leonard Davis Institute of Health Economics (PISCE@LDI), University of Pennsylvania, Philadelphia, PA, United States Wisteria Deng, BA, Department of Psychology, Yale University, New Haven, CT, United States Keith S. Dobson, PhD, Department of Psychology, University of Calgary, Calgary, Alberta, Canada David J. A. Dozois, PhD, Department of Psychology, University of Western Ontario, London, Ontario, Canada Gordon L. Flett, PhD, Department of Psychology, York University, Toronto, Ontario, Canada Judy Garber, PhD, Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, United States Sabrina Ge, BSc, Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada Max Genecov, MA, Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States  ix

x  Contributors

Holly R. Gerber, MSc, Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States Anna L. Gilmour, MA, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States Kate L. Harkness, PhD, Department of Psychology, Queen’s University, Kingston, Ontario, Canada Paul L. Hewitt, PhD, Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada Meghan Huang, MA, Department of Psychology, University of Rochester, Rochester, NY, United States Jutta Joormann, PhD, Department of Psychology, Yale University, New Haven, CT, United States Brian Lakey, PhD, Department of Psychology, Grand Valley State University, Allendale, MI, United States Christopher R. Martell, PhD, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA, United States Samuel F. Mikail, PhD, Mental Health Solutions, Sun Life Financial; private practice, Newmarket, Ontario, Canada Marcia Mössler, BA, Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada Laura Mufson, PhD, Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons; New York State Psychiatric Institute, New York City, NY, United States Arthur M. Nezu, PhD, Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States Christine Maguth Nezu, PhD, Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States Henrik Nordahl, PhD, Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway Abigail E. Pine, BA, Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, United States Ajeng J. Puspitasari, PhD, LP, ABPP, Clinical Director, Rogers Behavioral Health, Minneapolis–St. Paul, MN, United States Angela C. Santee, MA, Department of Psychology, University of Rochester, Rochester, NY, United States Martin E. P. Seligman, PhD, Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States Martin M. Smith, PhD, Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada Lisa R. Starr, PhD, Department of Psychology, University of Rochester, Rochester, NY, United States Renee J. Thompson, PhD, Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States Ed Watkins, PhD, Department of Psychology, University of Exeter, Exeter, United Kingdom

Contributors xi 

Adrian Wells, PhD, Division of Psychology and Mental Health, University of Manchester, Manchester, United Kingdom Mark A. Whisman, PhD, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States Jami F. Young, PhD, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania; Department of Child and Adolescent Psychiatry and Behavioral Sciences and PolicyLab, Children’s Hospital of Philadelphia, Philadelphia, PA, United States

ACKNOWLEDGMENTS

It is always a challenge to write an acknowledgments section for a volume such as this, which has involved so many people. On the one hand, the temptation is to name a long list of funding sources, organizations, collaborators, colleagues, and students, but the risk in such a strategy is to miss someone. On the other hand, a general statement seems sometimes too broad and not specific enough. We have elected to take the latter approach, however, and so for those of you who read this set of acknowledgments and think you should be named, please accept our apologies in advance and believe that we find all our partnerships and affiliations to be important. We would be remiss in this brief acknowledgment statement not to thank the large number of contributors to this volume for their excellent work. When the book was planned, we set a number of objectives, and we directed the authors to meet them as well as they could. At least in the field of depression, the translation of fundamental knowledge into practice has not taken place evenly across all domains. Thus, although some of the authors had more material to draw upon, some of the work represented in this volume is truly creative and original, and therefore deserves special recognition. In all cases, however, the authors not only met our expectations, but exceeded them. We hope that the readers can appreciate what a novel perspective on risk and translational science this volume represents. We also need to acknowledge a number of people at the American Psychological Association publishing office. These include our acquisitions editor, Christopher Kelaher, as well as all of the people involved in the development, copyediting, and production of this book, including Judy Barnes, Kyle Linkous, and Elizabeth Brace.  xiii

xiv  Acknowledgments

An acknowledgment must be paid to our families who have kindly allowed us the luxury to work in the research and practice domain of depression that we both love and enjoy. In fact, one of the two of us (David Dozois) was engaged at the beginning of the production of this volume and was married during it. He would like to recognize his spouse, Andrea Piotrowski, for her love, support, and encouragement. The other of us (Keith Dobson) has enjoyed the benefit of a long and happy marriage, and wants to recognize his partner, Debbie Dobson, for her emotional sustenance over the years. Our children and extended families also provide us with the impetus to do the work that we do, and we wish to acknowledge the importance of these relationships in our lives as well. During the development of this book David Dozois was supported by an Insight Grant from the Social Sciences and Humanities Research Council of Canada. Keith Dobson was supported by a grant from the Canadian Institutes of Health Research. This support is gratefully acknowledged. Finally, we want to acknowledge the unfortunately large number of people around the world who struggle with the signs and symptoms of depression, or who indeed develop a diagnoseable major depressive disorder. This volume is really in your service, as it focuses on the ways in which foundational psychosocial research can be applied to either reduce the risk of depression, in the first instance, or help to remediate the problem of depression if prevention is not successful. We acknowledge the challenges that you face, and hope that this volume will serve some value in the important efforts to reduce depression globally.

Treatment of Psychosocial Risk Factors in Depression

Introduction Treatment of Psychosocial Risk Factors in Depression David J. A. Dozois and Keith S. Dobson

C

linical depression is the single most common mental disorder globally, with an annual prevalence that has been estimated to be between 7% and 20%, depending on the methodology used and the specific country or group that is under consideration (Li et al., 2021; Lim et al., 2018). In addition to the enormous emotional, social, and health-related toll of the condition, the global annual economic toll associated with anxiety and depression is approximately $1 trillion USD (“Global Health Matters,” 2020). Clinical depression has sometimes been referred to as the “common cold” of mental disorders, a term that no doubt understates its potentially devastating nature but does speak to its ubiquity. Given the global impact of clinical depression, it should not be surprising that the condition has garnered extensive attention over time. This includes des­criptive studies of its experience, models of diagnosis and nomenclature, epi­ demiological investigations of its rates, studies of the moderating and mediating variables that intersect with the disorder, reports of commonly co-occurring or comorbid conditions, and reports of its often pernicious and persistent course (e.g., Herrman et al., 2022). There are theoretical accounts of depression from a variety of perspectives, most notably those that encompass biological, psycho­ logical, and/or social or cultural vantage points. Associated with these theories are myriad studies of precursors, correlates, and consequences of depression, which, in turn, often serve as a prelude to studies of prevention, treatment, and rehabilitation. As a quick example, a search of the keyword “depression”

https://doi.org/10.1037/0000332-001 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  3

4  Dozois and Dobson

(with no adjectives or modifiers) in the American Psychological Association’s (APA’s) PsycInfo database yields no fewer than 13,649 articles in the year 2021 alone. Predicated on the extensive focus on depression seen in the literature, remarkable achievements have been made in recent years, both related to the risk factors for depression and the implications of these risk factors for treatment. Some years ago, we endeavored to capture this literature in an edited book titled Risk Factors in Depression (Dobson & Dozois, 2008). That work included information related to biological, psychological, and social risk factors in the field of depression. The current volume, in some respects, is the next logical book in that sequence. However, in the intervening time, the sheer volume of theory and research necessitated that we restrict the current work to psychological and social or cultural risk factors, but we have extended the discussion to include both what is known about risk factors and how these risk factors can be reimagined from the perspective of intervention. As the chapters in this book demonstrate, many of the psychosocial risk factors that have been identified are indeed modifiable risk factors, and, as such, are ideal targets for intervention. Furthermore, even more distal risk factors, such as early life adversity, confer vulnerability to depression in the form of ongoing stress responses, beliefs, and coping strategies that can then be considered from the vantage of intervention. The current volume examines a broad range of evidence-based risk factors for clinical depression and, perhaps more important, considers those risk factors from the perspective of intervention. This is an innovative approach to the field, as few books attempt to connect interventions for depression directly to the putative risk factors. Further, the volumes that do make a direct connection between a risk factor and its associated intervention typically only address one or a limited number of such factors, and none of them do so in a comprehensive manner. As the reader will see, the focus here is on risk factors that are not historical and relatively static, such as early life experience (although we recognize that adverse life experiences can create other risk factors for later depression), but instead on those psychosocial risks factors that are potentially modifiable and that, thus, can become the focus of intervention. In developing this book, we tried to provide enough guidance to the authors that the resulting chapters would have some degree of uniformity, even while we recognized that the amount of research and knowledge varied among the different risk factors that were the focus of each chapter. We invited authors to operationally define the primary and related constructs that they discussed, and to address the measurement of these constructs, as we are deeply aware that any idea or concept can only be discussed with as much precision as it can be measured or evaluated with. Given these directives, authors were then asked to review the risk literature in an extended fashion. Finally, the implications of the risk research were reimagined from the vantage point of intervention. Where existing models and methods of treatment exist for a given factor the literature is reviewed, but we note that in some cases the authors

Introduction 5

have proposed treatment innovations that could be the object of future study. Finally, to keep the entire volume grounded in the actual work that clinicians do with clients with depression, authors were asked to provide a case example of how the interventions they reviewed or proposed could be brought into practice. As the astute reader will appreciate, and is often repeated in this volume (e.g., Chapter 1, Chapter 17), although it is conceptually simpler to consider psychosocial risk factors in their own right, it is apparent that they interact in complex and dynamic fashions. Many of the risk factors in this volume can be conceptualized as both the precursor and consequence of others. As just one example, individuals who develop negative beliefs about themselves, perhaps due to early life bullying, are likely to scan their interpersonal environment for experiences that confirm these beliefs. Even more, they are likely to avoid social circumstances that would challenge their worldview, and, as a result of avoidant coping, engage in rumination about their interactions with others, and further enhance their negative beliefs and potentially pessimism about change. This “vicious spiral” of cause and effect in the psychosocial world of individuals with depression makes it extremely challenging to delineate risk and response. Both of us have worked in the field of depression for many years. We have worked directly with our own clients who struggle with this problem and have consulted with distraught family members who wish to help as they can. As academics, we have conducted laboratory-based research to understand basic principles and processes related to depression, and we have participated in and led clinical research to advance the development of our treatment armamentarium. We are both deeply committed to the use of evidence-based strategies to further understand clinical phenomena such as depression and to provide clinical care to those members of society who struggle with these conditions (Dobson & Dozois, 2019). We have both trained other health professionals in the assessment and deployment of evidence-based interventions for clinical depression. As we hope is apparent, we are passionate about the need to understand people who struggle with mental health problems, to help to conceptualize human suffering, and to provide the very best possible assistance to our fellow humans who struggle with depression themselves, or in those they love. In the spirit of these varied commitments, we hope that this volume will be read and used by others with a similar set of compassions. REFERENCES Dobson, K. S., & Dozois, D. J. A. (Eds.). (2008). Risk factors in depression. Academic Press. Dobson, K. S., & Dozois, D. J. A. (Eds.). (2019). Handbook of cognitive-behavioral therapies (4th ed.). Guilford Press. Global Health Matters. (2020). The Lancet, 8(11), E1352. https://doi.org/10.1016/S2214109X(20)30432-0 Herrman, H., Patel, V., Kieling, C., Berk, M., Buchweitz, C., Cuijpers, P., Furukawa, T. A., Kessler, R. C., Kohrt, B. A., Maj, M., McGorry, P., Reynolds, C. F., III, Weissman, M. M.,

6  Dozois and Dobson

Chibanda, D., Dowrick, C., Howard, L. M., Hoven, C. W., Knapp, M., Mayberg, H. S., . . . Wolpert, M. (2022). Time for united action on depression: A Lancet–World Psychiatric Association Commission. The Lancet, 399(10328), 957–1022. https:// doi.org/10.1016/S0140-6736(21)02141-3 Li, Z., Wei, A., Palanivel, V., & Jackson, J. C. (2021). A data-driven analysis of socio­cultural, ecological, and economic correlates of depression across nations. Journal of CrossCultural Psychology, 52(8–9), 822–843. https://doi.org/10.1177/00220221211040243 Lim, G. Y., Tam, W. W., Lu, Y., Ho, C. S., Zhang, M. W., & Ho, R. C. (2018). Prevalence of depression in the community from 30 countries between 1994 and 2014. Scientific Reports, 8(1), 2861. https://doi.org/10.1038/s41598-018-21243-x

1 Models of Psychosocial Risk in Depression Keith S. Dobson and David J. A. Dozois

T

o set the stage for the discussion of potentially modifiable psychosocial risk factors, we present here some of the critical features of clinical depression and, in particular, those that have implications for models of risk. We then consider different conceptual frameworks for risk and discuss the various potential research designs that can be employed to meaningfully assess risk for depression. Finally, we review a number of the critical issues that exist for establishing risk for clinical depression. These issues include the polythetic nature of depression, comorbidity and the potential for specific risk factors, the dynamic nature of different experiences of depression and the implications for risk assessment, and the relative importance of concepts of risk and resiliency as constructs to understand the genesis and maintenance of depression. Readers of this volume may be primarily interested to understand the literature that supports various risk factors for depression, or the ways in which understanding these risk factors can be used to develop interventions. A review of the current chapter will provide a conceptual base to understand the types and quality of evidence reviewed in the other chapters of this book; it is recommended as an entry point for anyone interested in understanding risk factors for depression.

https://doi.org/10.1037/0000332-002 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  7

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THE NATURE OF CLINICAL DEPRESSION Clinical depression is formally referred to as major depressive disorder (MDD) in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), published by the American Psychiatric Association (2013). DSM-5 is used in North America for diagnosing mental health conditions and is generally utilized globally for research in the field of depression. As is well known, there are nine potential symptoms that need to be considered to establish a diagnosis, of which a minimum of five must be present for a period of at least 2 weeks. In addition, there are two “cardinal” symptoms in this list of potential symptoms, at least one of which (sadness or anhedonia) must be present to enable a diagnosis. Further complicating the diagnostic framework for clinical depression, three of the symptoms included in the diagnostic criteria for MDD are “paradoxical” in the sense that dysregulation of one type or another may be counted as a symptom. For example, it is possible to experience either lack of sleep or excessive sleep as a diagnostic symptom; the underlying symptom, therefore, is sleep dysregulation. As can be quickly intuited, there are hundreds of combinations of the diagnostic criteria that can eventuate in a diagnosis (e.g., Park et al., 2017; Zimmerman et al., 2015). Further complicating the diagnostic heterogeneity of clinical depression is the fact that some people may only meet the minimum number of symptoms for a diagnosis, whereas others may present with all nine of them. These two different presentations will obviously vary in the expressed severity of the condition that is being experienced. Even more, although a minimum time frame for a diagnosis of a major depressive episode is 2 weeks, some individuals, unfortunately, experience depression for a much more extended period, which leads to variation in the chronicity of depression. Moreover, depression is often considered to be an episodic phenomenon, in that individuals may meet diagnostic criteria for a while, but then, due to spontaneous recovery, self-initiated actions, or treatment, go into a period of remission and lose the diagnosis. As has been well demonstrated in epidemio­ logical research, however, such individuals are at elevated risk for relapse or recurrence (Beshai et al., 2011; Buckman et al., 2018; Dozois & Westra, 2004; Monroe, 2010; Richards, 2011). So, while some individuals who present with depression may be in their first episode, others may be in a second or even a highly recurrent episode. One of the great challenges in determining risk for a condition such as depression is that it has different symptom presentations, different levels of severity and chronicity, and the possibility of multiple recurrence. Thus, when samples are created for the study of risk factors, it likely makes a difference as to whether the group being studied has more or less chronic depression or is experiencing a first or recurrent episode. Although the diagnostic heterogeneity of clinical depression is a significant challenge for studying risk, there are yet other considerations that further complicate this research endeavor. For example, depression is often highly comorbid with other mental disorders (e.g., Hasin et al., 2018; Steffen et al.,

Models of Psychosocial Risk in Depression  9

2020). Based on epidemiological studies, it appears that approximately 50% of people who end up with a diagnosis of clinical depression either currently have, or, in their past, have had a diagnosis of an anxiety disorder (Dozois et al., 2020). Cross-sectional studies of depression, therefore, are often indirectly also studies of the concurrent anxiety or negative affectivity in the samples being studied. Given high levels of comorbidity between anxiety and depression, it is conceptually possible that the risk factors for depression are, therefore, not unique but represent transdiagnostic or nonspecific forms of risk (Dozois et al., 2009). Depression is also not an uncommon consequence of suffering from other mental or physical conditions. For example, many individuals with chronic pain develop depression as a secondary problem (e.g., Clarke & Currie, 2009; Zhu et al., 2014). Individuals who experience psychotic disorders also often go on to experience secondary depression (e.g., Romm et al., 2010). Studies of depression in the context of chronic pain or other disorders may well reveal different risk factors than studies of depression when it presents either with no concurrent problem, or with other concomitant conditions such as anxiety. There are many ways to describe the nature of human diversity. Humans vary in terms of race and ethnicity, linguistic and cultural background, sexual orientation, gender identity, age, and physical and mental status. These diversity variables can be conceptualized as potential risk factors themselves for the onset or maintenance of depression. For example, it is generally recognized that women are at an increased risk of clinical depression relative to men, and this pattern appears to hold globally (Albert, 2015; Lim et al., 2018). Evidence also shows that age is related to the likelihood of a person experiencing depression, in that younger adults appear to reliably have somewhat higher rates of depression than other adults (Kessler et al., 2010; Villarroel & Terlizzi, 2020). Given these various parameters of clinical depression, it is quite conceivable that the risk factors that are relevant for one or another subgroup may not generalize to others. A study of risk factors for clinical depression in adolescent girls, for example, may tell us relatively little about the risk factors for clinical depression in older adult men. Risk factors for depression need to be studied across the lifespan (Hammen & Garber, 2001). If the previous discussion was not enough to raise significant concerns about the prospect of validating robust risk factor research for depression, another complicating factor is that a considerable amount of the extant literature does not use samples that are based on the diagnostic criteria. Rather, it identifies individuals with “depression” based on a cutoff on a severity index of depression. Although it is logical that people who score high on a depression severity scale are more likely to also meet diagnostic criteria for clinical depression, these two assessment frameworks are not perfectly synchronous. It is generally recognized that severity scales that use a cutoff score to create groups of individuals considered to be depressed likely create a relatively large number of false positives (i.e., individuals who are deemed to be depressed but who would not meet the formal diagnostic criteria; see Manea et al., 2015;

10  Dobson and Dozois

Moriarty et al., 2015; Pettersson et al., 2015; Vilagut et al., 2016). Given the potential lack of correspondence between severity scales and diagnostic processes, it is conceivable that the risk factors identified in different samples based on different methodologies to assess depression may diverge. This chapter section was not written with the intention of criticizing or undermining the enormous amount of research effort that has been expended to try to establish the risk factors for depression. Rather, we have attempted to represent some of the challenges associated with the enormous heterogeneity of the phenomenon of clinical depression, its high concurrence or comorbidity with other presenting conditions, the fact that humans vary greatly on a wide variety of factors, and that diverse methodologies are employed to identify samples of interest. As such, it quickly becomes obvious that studies that find a specific risk factor for depression may be unique to the studied sample or the adopted methodology for a particular investigation and may not generalize broadly. However, risk factors that are conceptually sound and that do emerge across different studies warrant particular attention, as their generalizability implies that they are indeed foundational risk factors.

THE NATURE OF RISK Within the context of the current volume, risk has been conceptualized as any evidence-based factor that is associated with an increased likelihood that an individual or group will experience depression relative to other individuals or groups who do not possess that characteristic (see also Ingram & Price, 2001; Ingram et al., 2004). From this perspective, risk is a statistical phenomenon, and, in principle, any identifiable individual difference characteristic that can be studied could emerge as a putative risk factor. A recent study of 106 modifiable risk factors in the prediction of depression 6 to 8 years later has revealed a remarkable number of factors that either increase the risk or in some cases are associated with lower risk of later depression (Choi et al., 2020). Psychological science rarely examines potential risk factors in a theoretical vacuum, however. Thus, the chapters in this book are logically related to a variety of theoretically relevant potential risk factors. Risk is not synonymous with vulnerability, and this is an important consideration in the literature. As Ingram and Price (2001) argued, risk represents a statistical association, whereas the concept of vulnerability speaks more directly to causal relationships. In this regard, while a statistical association is likely, causality also requires other characteristics such as relative stability over time, endogeneity (i.e., something within the individual, as opposed to external factors), latency (i.e., a factor that may be dormant for some time, but able to be activated), and “kindling” (i.e., likely to show a pattern of activation if and when the appropriate stressor occurs; see Post, 1992). From this perspective, the concept of vulnerability speaks to the causal mechanisms associated with the development of a given disorder, whereas risk models provide

Models of Psychosocial Risk in Depression  11

a less direct set of variables for consideration, some of which may represent vulnerability factors as well. One of the points that is often made in discussions of the relationship between risk or vulnerability and the onset of a given disorder is that there often needs to be some type of activation of the risk or vulnerability factor, often through a stressful event. Such a model can be traced back to the seminal work of Zubin and Spring (1977) and has generally been expressed in a form akin to what is depicted in Figure 1.1a. According to this model, depression (or other forms of psychopathology) may emerge in individuals high on a vulnerability factor, even in the relative absence of stressors, or in individuals who are low on a given vulnerability factor if the stressful circumstances are FIGURE 1.1.  (a) A Theoretical Linear Relationship Between Vulnerability and Stress in Depression Onset (b) A Theoretical Curvilinear Relationship Between Vulnerability and Stress in Depression Onset

High

4 3.5

Level of Stress

3 2.5 2

res

1.5

Th

1

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(a)

High

ld ho

o

ssi

re ep

High Vulnerability

Low Vulnerability

4 3.5 3

Level of Stress

t

se

n nO

2.5 2 1.5

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ho

s hre

or df

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De

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se

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0.5 0 High Vulnerability

Low Vulnerability

12  Dobson and Dozois

sufficient in magnitude. Furthermore, interactions between risk or vulnerability factors and stressors may occur along the gradient of vulnerability, yielding the onset or presence of the disorder based on their combination. One of the difficulties in this framework is that investigators and theorists rarely know the nature of the vulnerability gradient. Often, the gradient is represented and tested with statistical tools as a linear relationship. Although it should be noted that linear relationships are possible in principle, these “stress × vulnerability” relationships are also potentially curvilinear (see Figure 1.1b) and require non­ linear statistical analyses. Indeed, given what is known about the normal distribution of many variables in human diversity, a curvilinear relationship is more likely than a simple linear one in many instances. As a single example, it is possible that a moderately challenging life stressor such as a negative work evaluation may exert a minimal negative effect on a person who has limited or few negative beliefs about themselves, but a very powerful effect on someone who has beliefs that are directly activated by this life event. In this way, the interaction between these two risk factors may not simply reflect the life event, but may be particularly acute in a person with a modicum of the cognitive vulnerability. In addition to the earlier distinction between risk and vulnerability, the consideration of risk factors has three other essential aspects. First, a distinction has been made between those risk factors that are potentially modifiable versus those that are not. Static risk factors can certainly constitute a vulnerability for the onset of a given disorder such as depression, but their intransigent nature makes them of considerably less value when considering models of inter­ vention. As one example, there is strong evidence that being born into the family of a parent with depression increases the long-term risk of the children becoming depressed themselves (Weissman et al., 2021). One cannot change their birth parents, however, so a much more interesting set of questions involves what happens to children of depressed parents that confers risk or vulnerability, and whether those subsequent issues themselves are amenable to change or intervention. Given this important distinction of static and modi­ fiable risk factors, it is not surprising that much of the extant literature in the area of intervention focuses on modifiable risk factors. It is important to recognize, however, that, from a theoretical and causal perspective, it is conceptually possible that historical, static, and intransigent factors will emerge to be at least as important as modifiable risk factors in the ultimate models of depression that can be anticipated in the future. Somewhat related to the issue of modifiable risk factors is the distinction between distal and proximal risk factors. Distal factors are those that have occurred in the past but that result in ongoing changes to risk and vulnerability. In this regard, a commonly cited set of distal risk factors for many forms of psychopathology are adverse childhood experiences (ACEs; Chapman et al., 2004; Dobson et al., 2020; Miller et al., 2020; see also Chapter 5, this volume). ACEs have been shown to increase the likelihood of many adolescent and adult mental health problems, including depression. Thus, while these experiences are often in the past by the time they are assessed, there is something

Models of Psychosocial Risk in Depression  13

in the history of having experienced these events that appears to confer vulnerability (Brown et al., 2008; Li et al., 2016). Much remains to be known about the precise relationships among different types of childhood adversities and later psychopathology, but this is a highly active area of research. In contrast to the research on distal risk factors, much of the literature in the field of depression has examined recent life events and more proximal stressors that potentially explain some of the variance in the onset and maintenance of depression. Within this literature, a distinction has been made between major life events (e.g., marriage, divorce, employment changes) and what are sometimes referred to as daily hassles, or minor stressors (e.g., interpersonal problems, traffic commuting, daily frustrations). It now appears that both forms of stressors do exert an influence on depression, although their relationship with other potential vulnerability factors remains a matter of study. Importantly for the current discussion, the relative importance of distal and proximal risk factors remains a matter of considerable speculation and study. Likely, both categories of stressors exert an influence on the risk for depression, although their precise into relationship remains to be fully explicated. This discussion fails to directly address what is probably the most important aspect of the nature of risk, which is its actual content. Many contemporary theorists have adopted the biopsychosocial framework to consider risk, which includes formally recognizing biological, psychological, and social risk factors (e.g., Borrell-Carrió et al., 2004; Dobson & Dozois, 2008). The bio­psycho­social framework invites theorists and researchers to examine a broad range of constructs, in both distal and proximal formats, and potentially as related to risk for specific subpopulations (Engel, 1980). This model permits the examination of unique variables in their own right, but it also implicitly encourages studies that examine the relationships and interactions among the range of variables under consideration. It should also be noted that while the terms “biological,” “psychological,” and “social” may connote a relatively finite and circumscribed set of constructs, these terms should be read through the widest lens possible. Thus, biological factors could include a range of genetic, epigenetic, biostructural, biochemical, neurotransmitter, dietary, sleep regulatory, and even behavioral factors related to physiological functions (e.g., exercise). In the same vein, social factors should not be viewed as comprised only of interpersonal interactions but should also include social structure, class, ethnicity, and current and historical aspects of culture that could influence psychopathology. When the range of potential biopsycho­social factors is considered, it becomes apparent that the potential scope of complexity is enormous.

RESEARCH METHODS TO STUDY RISK Conceptual models to study risk require a research methodology for their enactment. The field is fortunate that many research methods exist and that a wide range of statistical tools that correspond with these methods are also available for use. These methods are briefly discussed here in roughly ascending order of

14  Dobson and Dozois

their informative value. Several caveats and limitations of the various methods are also provided. When first approaching a novel area of study, many investigators begin with case research. Indeed, some of the more important areas of investigation began with a serendipitous observation in a research or clinical trial and the generation of a hypothesis that the investigator began to study. For example, the early work of Aaron Beck includes descriptions of case studies of the dreams of patients with depression, and the generation of the idea that negative cognition was a driving force in these dreams (Beck, 1973; Beck & Dozois, 2014). In some instances, individual case studies become more formalized in systematic qualitative research. In these qualitative studies, investigators often have a preliminary set of questions to explore, and they then interview appropriate participants with respect to these questions. For example, Drew et al. (1999) conducted a qualitative study of patients with depression to examine their self-concept. The study utilized a formal method called discourse analysis and revealed that, while many participants with depression struggled with their sense of worth, most believed they had virtue and a positive identity that underlaid their experience of depression. While case studies and qualitative investigations may form the beginning of a line of inquiry, many investigators adopt various correlational methods and statistical analyses. Indeed, the vast majority of risk studies in the field of depression are correlational in nature, as they take advantage of the naturally occurring associations among various putative risk factors and the genesis or maintenance of depression. Within this broad category of correlational studies, two primary strategies can be observed. In the first instance, investigators identify a particular risk factor and use a sample of participants to see whether this factor is more or less common in individuals with depression. The most basic of these studies develop a dimensional measure of the construct of interest, use a convenience sample of individuals, and measure their relative levels of depression, to then see if there is a correlational relationship between the constructs. This type of study can be made more complex by utilizing two or more hypothetical risk factors and observing their relative correlation with participants’ level of depression. It is also possible to use multiple correlation and/or regression methods to observe the comparative correlation among various risk factors and current levels of depression. Another strategy to examine the contemporaneous relations between risk factors and depression is to create different groups of participants based on their depression status (e.g., individuals with current depression versus participants with remitted depression) and then measure the relative severity of the risk factor under investigation. For example, Ottenbreit et al. (2014) compared women with depression, social anxiety or no psychiatric history. Although both of the clinical groups had comparable levels of avoidance, their scores were higher than controls. Such studies can be analyzed statistically using t-tests, analyses of variance, or even multivariate analyses of variance if multiple risk factors are simultaneously studied. In some of the more advanced

Models of Psychosocial Risk in Depression  15

studies of this type, it is even possible to statistically control for potential nuisance variables through covariate adjustments. It is important to note, however, that all of these various strategies are essentially descriptive in nature and can only present an association between a risk factor and depression if it is naturally occurring. In addition to research methods that take advantage of current associations between risk factors and depression as described above, there are a variety of research methods to examine associations among risk factors and depression across time. Longitudinal studies typically assess potential risk factors at an earlier point in time, and then evaluate the likelihood of the individual experiencing depression at a later point in time. In some studies of this type, it is also possible to statistically control for potential nuisance variables at the earlier point in time. Longitudinal research can, of course, vary in multiple ways. The number of risk factors that are studied as predictive of depression, the length of time between the assessment of risk factors and later depression, and the way in which the dependent variable of depression is conceptualized can all vary. For example, it is possible to use quantitative measures of the relative degree of depression using a severity scale as the dependent variable in such research. It is also possible to categorize individuals into diagnostic groups at the later point in time and use statistical strategies such as multiple or logistic regression to determine if there is an association between the risk factors and the outcome of depression status. In addition to using correlational or regression techniques to document the associations among various factors and later depression, the same data sets can be used to estimate the relative likelihood that a person will become depressed at a later point in time based on the risk factor(s) that are studied, or the relative likelihood that one group of people with a given risk factor will become depressed as compared to a reference group. These types of studies often compute either the regression weight associated with the variety of risk factors or the odds ratios of a person’s experiencing later depression, based on the relative presence or absence of the studied risk factors. A recent study of this type examined 106 modifiable risk factors at an earlier point in time and then examined the association of these risk factors with later depression status using odds ratios, at periods of 6 to 8 years later (Choi et al., 2020). The literature examining the associations among a variety of potential risk factors and later depression has in some cases become quite sophisticated. There are now a number of studies that employ structural equation modeling to examine potentially more distal risk factors, intermediate or more proximal risk factors, and later depression as an outcome (e.g., Christensen et al., 1999; Dipnall et al., 2017; Firat et al., 2021). In some such studies, the samples were collected at a single point in time, and so, what was actually being examined was a more complex set of correlational associations than simply bivariate correlations, but these are still only observed associations. Fortunately, authors are generally quite responsible and recognize that the use of a single timepoint for observation limits any conclusions that can be made from the study.

16  Dobson and Dozois

In some of the more advanced research of this type, depression was measured at a later point in time, and such longitudinal studies can evaluate multiple pathways of associations, although, again, these are ultimately correlational in nature. Longitudinal studies can provide considerable information about the relative associations among studied factors and later depression and are sometimes even discussed in terms of potential “causal” associations. It is important to recall, however, that naturalistic studies that examine these types of associations cannot establish causal links. It always remains possible that there are unmeasured “third variables” that account for the observed associations, or even that the observed patterns are causally linked, but in the inverse order to which the investigators may suppose. For example, it is well known that reports of reduced social connection at an earlier point in time are correlated with later increased rates of depression. Reduced social connection is, therefore, a putative risk factor for depression. It is, however, equally plausible that individuals with depression tend to withdraw from social connection and so the causal pathway may actually be from depression to social withdrawal (or these variables may influence each other reciprocally), rather than the model first advanced by the investigator. Researchers who conduct longitudinal studies will ideally measure their set of constructs at both periods (or all, ideally, at multiple assessment time points), so that they can explore both the hypothesized and the reciprocal relationships, to determine the relative strength of different pathways. Although correlational research methods and analyses can document associations among risk factors and depression, causal inferences largely require experimental methods and matching statistical analyses. One of the primary challenges for the use of experimental paradigms within psychopathology research, however, is that it would be unethical to manipulate risk factors, only for the purpose of ascertaining if there would be consequent increases in rates of depression. To circumvent the ethical problems associated with the direct modification of risk factors, two primary strategies have been adopted. The first is the use of quasi-experimental methodologies, such as taking advantage of natural disasters or naturalistic life events, to discern if individuals who are affected by such circumstances have different rates of depression relative to comparable communities or individuals. For example, researchers utilized the Loma Prieta earthquake of 1989 to examine changes in depression as a function of ruminative style that was assessed prior to the earthquake (NolenHoeksema & Morrow, 1991). This study was enabled, however, by the fact that the research participants had been assessed for another study just recently prior to the earthquake. A more purposeful example of a quasi-experimental design was the work of the Temple-Wisconsin Cognitive Vulnerability to Depression Project (e.g., Alloy & Abramson, 1999; Alloy et al., 2000, 2006). This prospective study recruited a large group of university students and followed them for about 2.5 years. As the sample was large, and the evaluation process extensive, the investigators were able to examine a number of associations and found, for example, that

Models of Psychosocial Risk in Depression  17

depression was more likely observed in participants with higher levels of emotional maltreatment (defined using a life events interview) in the period after the initial assessment, relative to participants who had lower levels of maltreatment (Liu et al., 2009). Thus, although emotional maltreatment was not randomly administered to participants, as would have been true in an actual experiment, the observed pattern is a quasi-experiential study that supports the likely causal role of emotional maltreatment in risk for depression onset. True experiments do exist in the research literature related to risk for depression. For example, a number of laboratory analogue studies have been conducted in which participants with varied histories of depression and suspected vulnerability are randomly assigned to receive a short-term depressive induction, or to be studied without this induction. The typical methodology is to use one of several negative mood induction procedures (Gilet, 2008; Gillies & Dozois, 2021; Zhang et al., 2014) and to recruit participants who are either vulnerable to depression or who may have experienced a diagnosable clinical depression episode in the past. In one such study, Soltani et  al. (2015) recruited participants who had a history of clinical depression, no history of clinical depression, or were currently experiencing clinical depression. For the two groups that were not currently depressed, participants were randomly assigned to either receive a negative mood induction or not, and these five groups where then compared on measures related to cognitive vulnerability. The purpose of this type of study is to document that both individuals who are currently depressed and those who have a history of depression and are currently in a sad mood are similar to each other with respect to the risk factor (attentional bias toward sad faces in this study) and different from participants who have never been depressed, even when exposed to a short-term negative mood induction. While short-term mood induction studies can document the susceptibility of vulnerable individuals to such a procedure, one of the criticisms about this literature with respect to its causal value is that the existence of a mood induction itself suggests that the individuals for whom the procedure works are, by definition, vulnerable to depressive experience. It is noted that a sizable proportion of people who undergo mood induction procedures do not successfully change in their reported level of depression; thus, the mood induction procedure itself may be activating something that leads to what looks like a depressive response in those individuals who are susceptible, but not otherwise (see Gillies & Dozois, 2021). It has also been suggested that a short-term mood induction experience that may be, for example, induced through a sad film or listening to sad music, is not at all the same as experiencing major losses or life events from a qualitative perspective. Thus, while experimental paradigms that use mood inductions provide some information related to depressive risk and vulnerability, these methods are not without their critics. A final method that has been used to examine risk and vulnerability for depression is treatment. Many treatment models for depression are predicated on a conceptual model and developed with the view that levels of depression will reduce by minimizing the risk factor through a treatment process. In this

18  Dobson and Dozois

regard, it can be argued that successful treatment using targeted interventions provides corroborative evidence about the importance of the risk factors themselves. As one example of this literature, Watkins (2016) developed a focused model of cognitive behavioral therapy for depression, targeted toward individuals with high rumination (see Chapter 13, this volume). Participants who received this treatment do indeed show decreases in their levels of depression on average, and there is evidence that these decreases are mediated through changes in rumination (Hvenegaard et al., 2015, 2020). Unfortunately, as with all treatment studies, this literature is susceptible to the same logical problems of correlational research in that it is possible that third variables help to explain the observed results, and the causal mechanisms related to risk cannot be determined through treatment studies. In the same way that symptom reduction after taking a medication cannot prove that the disorder was caused by a lack of whatever ingredients are in that medication (e.g., serotonin), the successful use of a psychosocial intervention cannot provide definitive proof that the treatment effect was uniquely due to changes in the hypothesized target of the treatment. Given the wide range of research paradigms that exist to study risk and vulnerability, it is not surprising that this literature has grown immensely. As is generally documented in the chapters that follow, observational and correlational research designs tend to predominate in the field. In evaluating the overall strength of the claim that any particular risk factor is important in the field, readers are encouraged to look across the range of research methods that are being used to study the claim for a given risk factor, the range of participants who have been studied and, to the extent that it is known, the number of times a predicted relationship between a risk factor and depression outcome is not observed. In general, claims for risk factors that are based on stronger research methodologies should be given higher weight than those based on correlational or observational methods.

ISSUES IN ESTABLISHING RISK FOR DEPRESSION As this chapter clearly reveals, many conceptual, methodological, and research issues can arise in the study of risk factors for clinical depression. In this final section, we review the major considerations and cautions that should be taken when reading the literature. With these cautions in mind, we encourage the reader to read and consider the other chapters in this volume with a critical lens. One of the major challenges for individuals who want to study risk and depression is its polythetic nature. As discussed earlier, there are hundreds of combinations and permutations of diagnostic criteria for clinical depression that can yield a diagnosis. The risk factors for depression also may vary in first (or potentially single) as opposed to recurrent episodes, in depressions that occur with comorbid problems, or depressions that occur in specific sociodemographic groups. It cannot be assumed that the risk factors for all these

Models of Psychosocial Risk in Depression  19

various presentations of depression are the same. Rather, the results of any given study need to be replicated in different samples, and, hopefully, in ones that vary on theoretically important dimensions. Embedded in the previous paragraph is the caution that depression is not itself a static outcome. Rather, the experience of depression tends to wax and wane, in what is typically an episodic manner. Further, it is now clear that individuals who suffer a first episode of depression may experience subsequent episodes, but it is possible that the nature of risk for onset is different from risk for relapse or recurrence (Burcusa & Iacono, 2007). Indeed, it is possible that researchers who study recurrent depression are incidentally studying the “scars” of previous episodes (Rohde et al., 1990; Wichers et al., 2010), rather than risk factors in their own right. Researchers working in this area need to be able to replicate results across first versus recurrent episodes of depression to discern whether the risk factors being studied are potentially the consequence of having been depressed in the past. An inherent irony exists in the research on risk for most forms of psychopathology, including depression, in that the easier it is to develop and implement a research study, the less likely the study is to be informative. In this respect, observational and correlational studies are relatively easy to conduct, but yield less valuable information about risk than do longitudinal studies or those studies that implement true experimental designs. At this point in time, several contemporary studies document correlations between depression and multiple potential risk factors. As the chapters in this volume attest, in a number of instances, these risk factors have been able to be converted into modifiable targets of intervention, and the efficacy and effectiveness of these interventions have been evaluated. While the observation of further correlations will no doubt have a benefit in terms of identifying further risk factors for evaluation, our perspective is that the field now needs to move more generally toward more complex and definitive investigations. A specific direction that the field of risk for depression needs is the use of multifactorial models, as opposed to studies of individual risk factors. Although it is comparatively simpler to examine a single factor, the biopsychosocial model of depression strongly implies that vulnerability and risk factors exist in multiple areas of functioning, and that they often correlate among themselves. Thus, while unifactorial studies have value, studies that begin to examine theoretically related constructs within the same sample are likely to yield more usable data in the longer term. It is also useful for researchers to remember that it can be helpful in multifactorial studies to include variables that theoretically should not be related to the construct of interest—depression, in the current instance. Using unrelated variables permits the discrimination between correlated constructs and other constructs that do not relate. For example, the general consensus is that global intelligence is not significantly correlated with risk for depression in its own right (Navrady et al., 2017). As such, researchers who are interested in looking at various aspects of cognition and depression such as attentional or memory biases could employ a brief measure of general intelligence in their designs to strengthen the argument that the constructs of

20  Dobson and Dozois

interest are related to depression, but constructs that should not be related do not demonstrate a positive relationship. It is important that investigators employ both reliable and valid measures of whatever construct they study. Within the area of depression, the field is now replete with validated measures of the construct of depression itself (e.g., Dozois et al., 2020; Nezu et al., 2000). Thus, there are both diagnostic and severity measures of the construct, and these generally correspond well with each other. In addition, depression scales have been developed and validated for specific age groups and populations, and measures have been developed or validated in multiple languages as well. In contrast, measures of risk vary in their psychometric development. In some cases, researchers will use measures that have been studied from a psychometric perspective. For example, there are multiple self-report measures of a variety of constructs that can be used in research. In other instances, however, investigators develop new tasks or implementations of theoretical constructs that receive limited validation. In these latter cases, it therefore becomes possible that observed results are due to error related to the measurement of the construct, rather than the construct that is theoretically being investigated itself. Again, researchers in the field of risk for depression are advised to use well-conceptualized, welldeveloped, and well-validated measures of their constructs whenever possible. This volume is organized with respect to identifying and treating risk factors in depression. As noted earlier in this chapter, risk and vulnerability are related ideas, as both attempt to identify variables that are related to increased rates or severity of the depressive experience. An alternative perspective to focusing on risk, however, is discerning and studying variables that are associated with a decreased risk. This area is now typically considered as the study of resiliency, and its potential preventive influence for depression (Poole et al., 2017; Smith, 2009). Indeed, interventions that promote resilience as a preventive or treatment factor for depression have emerged and shown promising results (e.g., Laird et al., 2019; Waugh & Koster, 2015). The development of these models has not yet reached the point of maturity in which they can be easily categorized or conceptualized. Furthermore, the existing scope of the literature is more limited than that for risk factors. Thus, although future volumes similar to this one may well wish to include chapters related to the promotion of resilience factors to enhance well-being or treat depression, we decided to focus on the more studied and validated constructs related to risk in this book. A final point that we wish to make about the study of risk and depression relates to the ethics of this line of inquiry. The development of a more comprehensive and scientifically sound set of models for the onset and maintenance of depression is a critical global health concern, in part as depression is the mental disorder with the highest burden of disease globally (Liu et al., 2020). Such research needs to be conducted within an ethical framework, however, such as that promoted by the Declaration of Helsinki (World Medical Association, 2013) and the national codes of ethics and/or standards of practice of the various health professionals who study and attempt to treat people with major

Models of Psychosocial Risk in Depression  21

depression. First and foremost, participants in any research study need to be able to provide informed consent and to know what their participation involves. In risk research, participants further need to know if their involvement in a particular study is one of scientific inquiry or if there is any prospect of intervention. If the participant is being offered intervention, it should be further clarified whether the intervention is experimental or already established through other research, and the timing of the intervention should be noted, as some researchers may wish not to provide intervention too quickly and potentially interfere with natural risk processes. In general, researchers are encouraged to employ the highest standards of research ethics (see Jain et al., 2017), particularly if the study is observational in nature (von Elm, 2007), so that the field can have confidence in the published data, and to know that research participants have not been studied inappropriately. The examination of various facets of psychopathology and human distress requires the highest levels of investigator compassion and understanding.

CONVERTING MODELS OF RISK INTO INTERVENTIONS The study of psychopathology has merit in his own right, just as is true for any other area of investigation. Many of these studies related to depression or other areas of psychopathology, however, are done so with the implicit goal of understanding how to potentially ameliorate or eliminate these risk factors, and, therefore, either prevent or reduce the chronicity and severity of the experience of depression. The current volume represents a distillation of decades of research that has examined potentially modifiable risk factors, which can be used either in prevention or treatment models. This chapter has elucidated some of the cautions and caveats that should be adopted when reading the subsequent chapters in this volume. Risk research varies dramatically with respect to both its content and quality. Furthermore, even if a particular risk factor is well substantiated in research, it does not necessarily follow that this factor can be easily modified, and there are a number of additional considerations in the effort to move from risk to intervention. The following chapters provide strong evidence that the process of translating risk research into intervention can be successfully undertaken. In selecting the content of this volume, care was taken to select those psychosocial and modifiable risk factors in which such a literature already existed. In making these selections, we hoped to demonstrate this translational process. As the reader will come to appreciate, there is a large evidence base related to psychosocial risk factors for depression (see also Dobson & Dozois, 2008), and many of these risk factors are potentially modifiable and, therefore, potential aspects of evidence-based interventions. In addition, when one reads the treatment literature for depression, it becomes apparent that many of the risk factors identified in this book are already incorporated in treatment. Unfortunately, many treatment manuals utilize these risk factors without a precise statement of how a given treatment technique has its putative effect on the risk factor.

22  Dobson and Dozois

It is, therefore, hoped that one outcome of the current volume is a better appreciation of these relationships. As this awareness increases, it may become more possible to develop more idiographic or personalized models of care (Fisher & Bosley, 2015), to be able to identify and select intervention(s) for a given client who presents with a given risk factor, and to provide maximally efficient and effective care. REFERENCES Albert, P. R. (2015). Why is depression more prevalent in women? Journal of Psychiatry & Neuroscience, 40(4), 219–221. https://doi.org/10.1503/jpn.150205 Alloy, L. B., & Abramson, L. Y. (1999). The Temple-Wisconsin Cognitive Vulnerability to Depression (CVD) Project: Conceptual background, design and methods. Journal of Cognitive Psychotherapy, 13(3), 227–262. https://doi.org/10.1891/0889-8391.13.3.227 Alloy, L. B., Abramson, L. Y., Hogan, M. E., Whitehouse, W. G., Rose, D. T., Robinson, M. S., Kim, R. S., & Lapkin, J. B. (2000). The Temple-Wisconsin Cognitive Vulnerability to Depression Project: Lifetime history of axis I psychopathology in individuals at high and low cognitive risk for depression. Journal of Abnormal Psychology, 109(3), 403–418. https://doi.org/10.1037/0021-843X.109.3.403 Alloy, L. B., Abramson, L. Y., Whitehouse, W. G., Hogan, M. E., Panzarella, C., & Rose, D. T. (2006). Prospective incidence of first onsets and recurrences of depression in individuals at high and low cognitive risk for depression. Journal of Abnormal Psychology, 115(1), 145–156. https://doi.org/10.1037/0021-843X.115.1.145 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing. Beck, A. T. (1973). The diagnosis and management of depression. University of Pennsylvania Press. Beck, A. T., & Dozois, D. J. A. (2014). Cognitive theory and therapy: Past, present and future. In S. Bloch, S. A. Green, & J. Holmes (Eds.), Psychiatry: Past, present and prospect (pp. 366–382). Oxford University Press. https://doi.org/10.1093/med/9780199638963. 003.0020 Beshai, S., Dobson, K. S., Bockting, C. L., & Quigley, L. (2011). Relapse and recurrence prevention in depression: Current research and future prospects. Clinical Psychology Review, 31(8), 1349–1360. https://doi.org/10.1016/j.cpr.2011.09.003 Borrell-Carrió, F., Suchman, A. L., & Epstein, R. M. (2004). The biopsychosocial model 25 years later: Principles, practice, and scientific inquiry. Annals of Family Medicine, 2(6), 576–582. https://doi.org/10.1370/afm.245 Brown, G. W., Craig, T. K. J., & Harris, T. O. (2008). Parental maltreatment and proximal risk factors using the Childhood Experience of Care & Abuse (CECA) instrument: A life-course study of adult chronic depression—5. Journal of Affective Disorders, 110(3), 222–233. https://doi.org/10.1016/j.jad.2008.01.016 Buckman, J. E. J., Underwood, A., Clarke, K., Saunders, R., Hollon, S. D., Fearon, P., & Pilling, S. (2018). Risk factors for relapse and recurrence of depression in adults and how they operate: A four-phase systematic review and meta-synthesis. Clinical Psychology Review, 64, 13–38. https://doi.org/10.1016/j.cpr.2018.07.005 Burcusa, S. L., & Iacono, W. G. (2007). Risk for recurrence in depression. Clinical Psychology Review, 27(8), 959–985. https://doi.org/10.1016/j.cpr.2007.02.005 Chapman, D. P., Whitfield, C. L., Felitti, V. J., Dube, S. R., Edwards, V. J., & Anda, R. F. (2004). Adverse childhood experiences and the risk of depressive disorders in adulthood. Journal of Affective Disorders, 82(2), 217–225. https://doi.org/10.1016/j.jad. 2003.12.013 Choi, K. W., Stein, M. B., Nishimi, K. M., Ge, T., Coleman, J. R. I., Chen, C.-Y., Ratanatharathorn, A., Zheutlin, A. B., Dunn, E. C., 23andMe Research Team, Major

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2 Parental Psychopathology and Parenting Abigail E. Pine and Judy Garber

O

ne of the most robust and reliable risk factors for the development of depression is having a family member with a history of the disorder (Beardslee et al., 2011; Goodman, 2020). Children of parents who have been diagnosed with a depressive disorder are 3 to 4 times as likely to develop depression and other psychopathology and to have greater academic and social impairment as compared with individuals whose parents have not had depression (Weissman et al., 2016; Weissman, Wickramaratne, et al., 2006). An estimated 15 million children in the United States have a parent who has had one or more depressive episodes (England & Sim, 2009). Depression is significantly more common in individuals from lower income levels as compared with higher ones (Miranda & Green, 1999). Across races and ethnicities, about 10% to 12% of U.S. mothers experience a major depressive episode (MDE) per year, resulting in an estimated 10% of all children being exposed to maternal depression each year (Ertel et al., 2011). Estimates of the global prevalence of postpartum depression among women are as high as 17.7% (HahnHolbrook et al., 2018), with about 13% of women having a MDE during the first year after giving birth (O’Hara & Swain, 1996). Thus, depression in caretakers is common and can affect the health and well-being of their children. The prevalence of depression is more than 2 times greater in women than in men. Most research on the link between parental depression and children’s adjustment has focused on mothers because of their higher rate of depression,

This work was supported in part from grants (R61MH115125; R61MH119270) and a training grant (T32MH18921) from the National Institute of Mental Health. https://doi.org/10.1037/0000332-003 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  27

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the effects of depression exposure during pregnancy, and the greater amount of time mothers typically spend with their young children compared with fathers (Goodman, 2020; Goodman et al., 2011). Nevertheless, depression in fathers also is associated with problems in children and is worthy of study (Connell & Goodman, 2002; Sweeney & MacBeth, 2016). Less is known about the combined relations among maternal depression, paternal depression, and subsequent outcomes on youth. For instance, Mezulis et al. (2004; but not Fredriksen et al., 2019) reported that paternal depression moderated the relation between maternal depression and children’s outcomes, resulting in worse outcomes for youth with both parents having depression. Other work has demonstrated that coparent support from fathers may buffer some of the negative effects of maternal depression on offspring mental health (Collishaw et al., 2016). By contrast, a study of two large population-based cohorts found that both maternal and paternal depressive symptoms were independently and equally associated with their children’s depressive symptoms (Lewis et al., 2017). Knowing how maternal and paternal depression are related to children’s adjustment—independently, additively, or interactively— is important for the development of interventions aimed at improving offspring outcomes. Given the prevalence of depression and the centrality of the relation between parental depression and offspring risk, this chapter focuses on parental psycho­ pathology and parenting within the context of depression. We begin by presenting research on the relations between certain features of parental depression—severity, chronicity, timing—and children’s outcomes. We then discuss specific mechanisms (i.e., parenting) that help to explain the link between parent and offspring depression. Finally, we review the current evidence base of interventions that target parental depression, parenting, and the parent–child relationship. The chapter concludes with a case example and a discussion of future directions.

CLINICAL CHARACTERISTICS OF DEPRESSION Depression has been characterized as both a dimensional set of symptoms that vary along a continuum of severity and a categorical disorder. The specific combination of symptoms that comprise the diagnosis of major depressive disorder (MDD), and the overall severity, duration, chronicity, and age of onset can vary widely, and children’s outcomes associated with these different features also vary. For example, children of mothers with both persistent and severe depression are at the highest risk for developing behavioral problems and depression (Netsi et al., 2018). Chronicity and severity of maternal depression also have been linked to higher levels of youth internalizing problems (O’Connor et al., 2017). A longer duration of a current maternal depressive episode was associated with more internalizing and externalizing symptoms in children (Foster et al., 2008), whereas greater severity of maternal depression was a more important predictor of youth depression diagnoses (Hammen

Parental Psychopathology and Parenting 29

& Brennan, 2003). Dimensional self-report measures of depressive symptoms or anxiety also predict symptom levels in offspring (e.g., Goodman et al., 2011; O’Donnell et al., 2014) and are not specific to a clinical diagnosis. Thus, certain characteristics of depression (e.g., severity and chronicity) are differentially related to psychopathology and cognitive functioning in children (Sutherland et al., 2021) and to lower educational attainment, externalizing psychopathology, and increased negative affect and behavior (Goodman, 2020; Goodman et al., 2011). The age at which a child is exposed to parental depression also may be differentially related to child outcomes. Maternal depression during sensitive developmental time periods may be distinctly related to youth internalizing and behavior problems (Bagner et al., 2010). Maternal depression during the prenatal, postpartum, or toddler periods are associated with higher internalizing symptoms in youth (Hentges et al., 2020). Parental depression as early as during pregnancy can affect fetal and infant development (Aktar et  al., 2019), and early exposure to parental depression makes it more likely that offspring will experience longer and recurrent maternal depressive episodes (Goodman, 2020). Prospective studies that begin early in pregnancy are needed to disentangle the associations among parental depression and children’s adjustment across development. Finally, although we focus here on depression in parents, other types of parental psychopathology have been linked with both internalizing and externalizing problems in offspring (Goodman, 2020). Approximately 75% of individuals with a lifetime MDD have at least one additional lifetime psychiatric disorder (Kessler et al., 2003), including anxiety (59%), impulse control problems (32%), and substance use (24%). Thus, parents with depression likely experience at least one other comorbid condition and thereby expose their children to other psychopathology in addition to depression. In summary, depression is quite heterogeneous regarding its severity, chronicity, recurrence, duration, and comorbidity. The association of these different features of depression with children’s psychopathology and functioning also is quite variable. The effect of these different aspects of depression on parenting behaviors and on parents’ receptivity to interventions aimed at improving their parenting is an important future direction.

INTERGENERATIONAL TRANSMISSION OF DEPRESSION: RISK FACTORS Understanding the mechanisms that underlie the intergenerational transmission of risk will inform theories about the pathways to depression and can guide the construction of interventions aimed at reducing risk and the resulting negative consequences (Goodman & Garber 2017). Several mechanisms have been proposed; the most noteworthy is the developmental model for understanding mechanisms of transmission (Goodman & Gotlib, 1999), which suggests four broad mechanisms: heritability, innate dysfunctional neuroregulation, high

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levels of stress occurring in the context of children’s lives, and exposure to negative maternal cognitions, behaviors, and affects. Over the last 2 decades, evidence has shown that each of these processes likely contributes to the transmission of depression across generations. We highlight here several mechanisms proposed to explain the intergenerational transmission of depression. This list is not exhaustive, nor does it detail the complex relations among these various mechanisms.

Genetics Recent progress in basic molecular and behavioral genetics has increased our understanding of heritability as a significant mechanism in the intergenerational transmission of depression. Various methodologies such as twin studies, children of twin-study designs (Natsuaki et al., 2014), and in vitro fertilization paralleling prenatal cross-fostering designs used in animal research (Rice et al., 2010) have advanced knowledge about the heritability of depression. In addition, an increasingly important mechanism concerns epigenetic changes (e.g., DNA methylation) in children resulting from fetal exposure or early childhood experiences related to depression in mothers (Conradt et al., 2018). Conradt and colleagues (2018) have advocated for epigenetic pathway analyses that target DNA methylation at transcription factor binding sites to study how prenatal exposures influence newborn neurobehavior. Exposure to maternal mood disorder is associated with altered neuroendocrine stress responses in children (Essex et al., 2002) and a more fearful and reactive temperament (Davis et al., 2007). These children are also more likely to exhibit internalizing symptoms in early childhood and adolescence (Goodman et al., 2011). Importantly, this risk may be partially transmitted from mother to child in utero (Monk et al., 2016). This route of risk transmission represents a third pathway beyond shared genes and postnatal environmental exposures by which the next generation inherits familial vulnerability to psychopathology (Monk et al., 2012).

Prenatal Exposure and Children’s Neurodevelopment Risk also may be partially transmitted from mother to child in utero (Monk et al., 2016). Fetal exposure to maternal mood disorders may program fetal and newborn neurobehavior and a vulnerability to the development of psycho­ logical disorders later in life. Exposure to prenatal maternal depression has been linked to increased risk for the development of psychopathology in offspring (Essex et al., 2002), as antenatal maternal emotional well-being predicts the risk for later psychopathology in children (O’Donnell & Meaney 2017). O’Donnell and Meany (2017) argued that “compromised fetal development appears to establish a ‘meta-plastic’ state that increases sensitivity to postnatal influences” (p. 319), some of which may include environmental adversity. Maternal depression may affect the developing neural systems, brain structures, and neuroendocrine systems and pathways such as hypothalamic– pituitary–adrenal axis functioning (Conradt et  al., 2018). The frequently

Parental Psychopathology and Parenting 31

observed association between maternal mental health and children’s neuro­ developmental outcomes may result from both underlying genetic vulnerabil­ ities and poorer health behaviors and inadequate nutrition in mothers. Thus, shared genes, neurodevelopmental dysfunction, and postnatal environmental exposures may partially explain the cross-generational transmission of familial vulnerability to psychopathology (Monk et al., 2012). Exposure to Stress and Adversity There is increasing evidence of a set of empirically supported mechanisms and developmental processes that mediate the associations between early childhood adversities and later psychopathology (e.g., McLaughlin et  al., 2019). Several studies have shown a link between depression in mothers and children’s greater exposure to childhood adversities and stressful events. Growing up in an economically disadvantaged environment is associated with a multitude of stressors such as community violence, marital conflict, domestic abuse, and child maltreatment. Thus, poverty exposes children to a variety of chronic and acute stressors in addition to parental depression (Feurer et al., 2016). For example, the association between mothers’ depression symptom severity when children were 1.5  years old and their internalizing and externalizing disorders at ages 7 to 8 years was largely explained by a greater cumulative risk factor score reflecting mothers’ early age of parenthood, lower educational attainment, and substance use or criminal history within the child’s first 2 years of life (Barker et al., 2012). Mothers’ reports of parenting stress for 1-year-old children mediated the association of mothers’ depressive symptoms in pregnancy and during the postpartum year with children’s dysregulation and externalizing problems at 18 months (Fredriksen et al., 2019). Thus, research on correlates of early adversity further supports the centrality of stress as a mechanism of the intergenerational transmission of depression. Stress can serve as a mechanism for the transmission of depression from parents to children through several different pathways. First, depressed parents might generate stressors such as marital conflict or job loss, which then can affect their children by disruption of the family (e.g., divorce) and economic hardship. Such stressors may occur even without the depressed parent generating them. In either case, parental reactions to adversity may serve as a model for how to respond. Through observation of and interactions with their parent with depression, children likely observe and sometimes model different strategies for coping with adversity. A related process is that parents’ own manner of dealing with stress may affect their parenting behaviors. Thus, exposure to stress and adversity not only may be a proximal mediator of the parent to child transmission of depression, but it also might serve as a more distal factor in the causal chain that affects the more proximal process of parenting behaviors. Randomized controlled trials offer opportunities to test the possible causal role of stress reactivity and its effects on parenting in the intergenerational transmission of mood disorders. For example, home visiting programs have been found to enhance stress management skills and minimize social isolation (Ammerman et  al., 2013). Parents learn problem-solving strategies through

32  Pine and Garber

Head Start programs (Silverstein et al., 2017), and some parenting programs aim to reduce conflict between parents (Sanders et al., 2014). Thus, several of the interventions with depressed parents (see later in this chapter) attempt to manage stress, as well as improve parenting. Child Characteristics Another important risk factor for the development of depression and other internalizing symptoms involves characteristics of the child themself. Individual difference variables have been found to moderate the relation between parent depression and child adaptation. The relation between maternal depression and internalizing symptoms has been found to vary by children’s age and gender; it is stronger for younger children, and for daughters (Goodman et al., 2011). Younger children are more reliant on their caregivers for support and other essentials, spend more time with them, and may be exposed to maternal depression for a longer time compared with older offspring. In adolescence, when rates of depression generally increase, girls are at particularly high risk of experiencing interpersonal stressors and subsequent depression (Hammen et al., 2011), and they may be more affected by maternal depression than are adolescent boys. Parenting behaviors of individuals with depression also differ by offspring gender (Gruhn et al., 2016; Sheeber et al., 2002), which may partially contribute to gender differences in rates of depression in adolescence. Other salient child characteristics include temperament (e.g., low positive affect [PA], high negative affect [NA]), emotion regulation, and intelligence. These vulnerabilities might represent diatheses for psychopathology. Complex models exist regarding individual differences in response to environmental events, including the biological sensitivity to context theory (BSCT) and differential susceptibility theory (DST). These evolutionary perspectives posit that some individuals are more susceptible than others to both negative (riskpromoting) and positive (development-enhancing) environmental circumstances (Ellis et al., 2011), and that organisms vary in their “neurobiological susceptibility to the environment in regulating environmental effects on adaptation, development, and health” (p. 7). The differential susceptibility perspective has important implications for designing interventions aimed at improving parenting behaviors and for how well children and parents benefit from these programs. From a differential susceptibility perspective, optimal parenting behaviors involve first recognizing their own and their child’s specific susceptibility to context. Second, optimal parenting includes parents responding to their children with their child’s distinct vulnerabilities and patterns of responding in mind. Parents likely need assistance identifying their child’s unique susceptibilities and optimizing the response to these features. Thus, parents not only need to recognize and consider their child’s distinctive characteristics, but they also should use this understanding to alter their own behaviors when interacting with their child.

Parental Psychopathology and Parenting 33

PARENTING Parenting is a central mechanism for the transmission of risk for psycho­ pathology in families with a mother or father with depression (Goodman et al., 2020). Both positive—warmth, responsiveness, and engagement—and negative—harsh, critical, and withdrawn—parenting are significantly linked to children’s mental health. A central reason for targeting parenting as a mechanism for understanding and modifying the intergeneration transmission of depression is that parenting behaviors are modifiable (Goodman & Garber 2017). Parents with depression are often characterized by high negative affect, poor emotion regulation, diminished reward processing, and rumination, all of which may affect their ability to engage in optimal parenting behaviors and bond formation (Psychogiou & Parry, 2014). Attachment theory (Bowlby, 1988) highlights the critical influence of responsive, sensitive parenting and the secure parent–infant bond on children’s physical, social, and psychological health and functioning. Parenting consists of learned parent–child inter­actions that are necessary for healthy child development (Sanders, 1999). Such interactions involve a complex interplay between both genes and the context within which a child is raised (Collins et al., 2000). The action–control framework (Dix & Meunier, 2009) suggests five steps that explain how depression may impact parenting competence: (a) parenting goals: increased parent-oriented and decreased child-oriented goals; (b) processing of input from their child reduced attention to the child; (c) appraisals: increased likelihood of making and communicating negative appraisals of children; (d) emotions: lower positive and higher negative emotions with and toward their children; and (e) responsiveness: diminished ability to generate appropriate responses to their children. Additional work is needed to explore other steps applicable to this framework and how these steps may be influenced by offspring behavior (Dix & Meunier, 2009). The action–control theory offers an interesting framework through which to understand how parental depression may negatively affect parenting behaviors. Both longitudinal and intervention studies have provided evidence of parenting as a mechanism that links parental depression and children’s adjustment. For example, observed parenting quality (i.e., autonomy–respective, supportive, nonhostile) in children 10 to 11 years old partially mediated the association between mothers’ persistent depressive symptoms when their children were age 2 and their children’s social skills at age 15 (DeRose et al., 2014). Another longitudinal study showed that more negative parenting when the child was age 7 mediated the association between higher postnatal depressive symptoms and children’s academic attainment at age 16, with their children’s mental health (total problems score) at 10 to 11 years old being intermediate in the pathway (Psychogiou et al., 2017). A meta-analysis of 46 studies of the relation between maternal depression and parenting showed that mothers with depression had higher levels of dis­ engagement and negativity (e.g., coercion, hostility) and lower levels of positive

34  Pine and Garber

parenting behaviors (Lovejoy et al., 2000). The relation between maternal depression and negative parenting behaviors is strongest for current depression compared with past depression (Goodman et al., 2020). Moreover, the relation between maternal depression and positive parenting behaviors was especially salient among low-income families, and in infants more so than toddlers. Similarly, a meta-analysis of the link between paternal depression and parenting behaviors showed that depression in fathers was also associated with less positive and more negative parenting (Wilson & Durbin, 2010). Some parenting behaviors, including decreased warmth, increased control, rejection, withdrawal, aversiveness, and overinvolvement, have been shown to predict depression in adolescents (McLeod et al., 2007; Yap et al., 2014). Thus, even if a parent is not depressed, certain dysfunctional parenting behaviors predict depression in youth. Such findings strengthen the evidence for a pathway from poor parenting to depression in children. Longitudinal studies have demonstrated associations among parental depression, parenting behaviors, and youth outcomes. For example, parent–child conflict was associated with increased odds of a depressive episode in youth over a 3-year period, even after controlling for parent depression history (Griffith et al., 2019). Moreover, among children of mothers with a depression history, decreased authoritative parenting predicted differences in reward processing circuitry (Kujawa et al., 2015). Finally, elevated parental criticism predicted clinical levels of depression and increased symptom trajectories in youth over time (Rapp et al., 2021). Intervention studies that modify parenting behaviors also provide support for parenting as a putative mechanism in the associations among parental depression, parenting behaviors, and children’s adjustment. For example, a home visiting program showed that enhanced maternal sensitivity mediated the association between the intervention and children’s later language outcomes (Neuhauser et al., 2018). A meta-analysis of 37 studies found that problematic parenting significantly mediated the association between maternal depression and children’s functioning (Goodman et al., 2020). Overall, there is strong and consistent evidence that parenting behaviors are disturbed among some parents with current or past depression. Fortunately, parenting behaviors are modifiable, even among mothers with depression. Several evidence-based family interventions exist that target parental depression and/or parenting in the service of altering youth risk trajectories of depression. We next describe these interventions, present a case example, and suggest directions for future research on interventions aimed at reducing the intergenerational transmission of depression.

INTERVENTIONS Interventions that treat parental depression and teach parenting skills are often successful (Goodman & Garber, 2017). Evidence indicates that treating parental depression alone may result in benefits for parenting and offspring’s

Parental Psychopathology and Parenting 35

symptoms and functioning (Cuijpers et  al., 2015; Goodman et  al., 2018; Gunlicks & Weissman, 2008). For example, data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial showed that remission of maternal depression following medication treatment was correlated with reductions in child diagnoses and internalizing symptoms (Weissman, Pilowsky, et al., 2006), and some of these improvements were sustained 1 year following parental remission (Wickramaratne et al., 2011). Similar outcomes were found in a study with infants, whereby treatment with cognitive behavior therapy (CBT) of women with depression postpartum promoted improvements in infant emotion regulation (Krzeczkowski et al., 2021). The absence of an untreated control group, however, precludes concluding that child improvement was due to parental treatment as opposed to other variables such as maturation or some shared third variable (e.g., reduced stress). In contrast, randomized controlled trials (RCTs) allow for causal conclusions. For example, interpersonal psychotherapy for mothers with depression resulted in decreased depressive symptoms in their children who were receiving psychiatric care (Swartz et al., 2008). Nine-month-old infants of mothers treated with a CBT intervention during pregnancy showed positive outcomes for parentreported infant self-regulation and stress reactivity (Milgrom et al., 2015). Findings are mixed, however, as another trial found that treating postpartum depression was not sufficient to produce improvements in offspring (Forman et al., 2007). Parental depression also may impede youths’ responses to intervention. Garber et al. (2009) reported that current parental depression moderated outcomes of a depression prevention program for at-risk adolescents. For youth who received a CBT intervention, onset of a depressive episode was prevented in adolescents whose parents were not currently depressed, but not for youth whose parent was in a depressive episode currently. Integrated interventions that both reduce parental depression and enhance parenting behaviors likely will be more efficacious in preventing depression in at-risk children (Goodman & Garber, 2017). In the next section, we review existing evidence-based interventions that target parenting or the parent–child relationship within the context of maternal depression. To be considered an evidence-based intervention, an RCT must have shown that the target intervention was more efficacious than a control condition. We present the interventions here in order of children’s age, from infancy and toddlerhood to middle childhood and adolescence. Table 2.1 provides additional information about these interventions. Home-Visiting Interventions Home-visiting interventions are an accessible, cost-effective alternative to typical care; they show promising results, particularly for low-income, new mothers with barriers to receiving help (Ammerman et  al., 2010, 2017). In-home CBT (IH-CBT) has been adapted for mothers with depression. Treatment includes standard home visitation with a variety of CBT components,

TABLE 2.1.  Evidence-Based Interventions

Home-Visiting

References

Child’s age

Parents’ sex and age

Treatment targets

Treatment outcomes

Ammerman et al. (2015)

3 months

Mothers, age 16 or older, Mean age = 22 years

Mothers were randomized to either in home CBT with standard home visiting (n = 47) or standard home visiting alone (n = 46).

No group differences found for parenting and child adjustment. Across condi­ tions, improvements in depression were linked to increases in positive parenting.

Van Doesum et al. (2008)

1–12 months

Mothers, Mean age = 30 years

Mothers were randomized to an intervention aimed at improving parent–child relations through videotaped interactions (n = 35) or a control condition (n = 36).

Mothers in the intervention significantly increased in sensitivity (h2 = .28), structuring (h2 = .16), child involvement (h2 = .13), and child responsiveness (h2 = .10). Infants of mothers in the intervention showed greater attach­ ment security and competence at follow-up.

Stein et al. (2018)

4–9 months

Mothers, Mean age = 32 years

Mothers were randomized to either parenting video feedback therapy (n = 72) or progressive muscle relaxation (control; n = 72).

No significant differences on child outcomes. At follow-up, across conditions, child outcomes were like nonclinical norms.

Learning Through Play Plus Program

Husain, Kiran, Fatima, et al. (2021)

0–30 months

Mothers, 18–44 years

Mothers were randomly assigned to an intervention of psycho­ education about child development and CBT skills (n = 402) or routine care (n = 372).

Infants of mothers in the intervention had significantly higher social and emotional development scores, and greater improvements in health than infants of mothers in routine care.

Toddler-Parent Psychotherapy

Guild et al. (2021)

20 months to 9 years

Mothers, Mean age = 31.7 years

Mothers were randomly assigned to an attachment intervention (n = 66) or a control condition (n = 64). Both conditions were compared to a group of healthy controls (n = 68).

Children of mothers in the intervention were significantly more likely to show change to secure attachment than those in both control and healthy conditions. For youth receiving the intervention, change to secure attachment was linked with increased maternal warmth and decreased child problem behaviors at age 9.

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Intervention name

Sanders et al. (2014)

Birth to 18 years Mean child age = 5.85 years

Mothers & fathers

Meta-analysis included 62 RCTs, six cluster randomized trials, five quasi-experimental designs, and 24 uncontrolled trials.

Intervention had significant effects on children’s social, emotional, and behavioral development (d = .473), parenting (d = .578), parental adjustment (d = .340), and the parental relationship (d = .225).

EFFEKT-E

Bühler et al. (2011)

4–7 years

Mothers, Mean age = 45.8 years

375 families received the parenting and child social-cognitive skills training intervention at clinics in Germany.

Compared with treatment as usual, families in the intervention improved in youth emotional and behavioral problems and decreased in negative parenting.

Family Talk & Preventive Intervention Project

Beardslee et al. (2007)

8–15 years

Mothers & fathers, Mean age = 43.1 years

Families were randomized to either a psychoeducation, clinicianfacilitated intervention (n = 55) or a lecture condition (n = 44).

No significant differences by condition at 4- to 5-year follow-up, both interventions showed increases in family functioning and decreases in child internalizing symptoms (ES = .32).

Family Group Cognitive Behavioral Preventive Intervention

Compas et al. (2011)

9–15 years

Mothers & fathers, Mean age = 44.75 years

Families were randomized to either a family, group CBT intervention (n = 56) or a control condition (n = 55).

Youth in the intervention had significantly lower internalizing symptoms at follow-up (18 months; d = .41). Rates of youth MDD diagnoses were significantly lower through the 24-month follow-up (odds ratio = 2.91).

Keeping Families Strong

Valdez et al. (2011)

9–16 years

Mothers & fathers, Mean age = 39.1 years

Nonexperimental pilot study; 10 families received the CBT + psychoeducation intervention.

Youth showed decreases in internalizing symptoms (ES = .38); the intervention was deemed acceptable for both youth and families.

Note. CBT = cognitive behavior therapy; RCT = randomized controlled trial; ES = effect size; MDD = major depressive disorder.

Parental Psychopathology and Parenting 37

Triple P-Positive Parenting Program

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including behavioral activation, identifying automatic thoughts, cognitive restructuring, and relapse prevention, delivered over 15 weekly sessions followed by a booster session 1 month after termination of treatment. IH-CBT also includes adaptations that address concerns specific to low-income, new mothers, such as parenting challenges and stress management. In an RCT compared with standard home visitation alone, mothers receiving IH-CBT reported significantly fewer symptoms of depression, were less likely to meet diagnostic criteria for depression, and received greater ratings of overall functioning at follow-up (Ammerman et al., 2013). Moreover, reductions in maternal depression were significantly associated with parenting improvements in both conditions (Ammerman et al., 2015). Other home-visiting interventions target the parent–child relationship. One RCT revealed improvements in the quality of mother–infant interactions, attachment security, and aspects of youth socioemotional functioning by providing video feedback from taped mother–child interactions in the home (Van Doesum et al., 2008). Another RCT of in-home treatment of mothers with persistent postnatal depression assessed the efficacy of CBT with either a parenting video feedback intervention or control progressive muscle relaxation. Both conditions showed significant improvements in maternal depression, with an overall remission rate of 85%, but no differences in child outcomes. Children’s developmental functioning was comparable to norms for nonclinical samples at 2 years postpartum (Stein et al., 2018). Home-visiting psychotherapy and parenting programs show acceptability and benefit for families from different cultural backgrounds. Early Head Start Latina mothers of young children reported decreases in self-reported depressive symptoms and in their child’s aggressive behavior following an in-home interpersonal psychotherapy intervention (Beeber et al., 2010). Native American teen mothers who received an adapted in-home parenting intervention reported significantly fewer depressive symptoms and greater parenting knowledge, as well as reductions in children’s internalizing problems over 3 years when compared with standard care (Barlow et al., 2015). Thus, home-visiting interventions offer hopeful benefits, although longer term follow-up is needed to determine whether these interventions affect youth depression as they move into adolescence. Learning Through Play Plus Program The Learning Through Play Plus Program (LTP Plus) is a combination parenting and CBT program for mothers with depression and their children (0–3 years) that was developed and tested in Pakistan. LTP Plus involves 10 weekly sessions with education about child development and five modules on the mother’s health, the mother–infant relationship, and social support from others. Mothers learn CBT skills such as targeting negative thinking and receive homework assignments between sessions. It is a low-cost intervention that utilizes items

Parental Psychopathology and Parenting 39

typically found at home (e.g., toys, books). It also can be implemented by trained nonspecialists. When compared with a control group, women randomized to LTP Plus had significant decreases in depressive symptoms (Husain et al., 2017) and increases in parenting competence and behaviors (Husain, Kiran, Fatima, et al., 2021). Children of parents receiving LTP Plus showed significant improvements in health outcomes and social and emotional development (Husain, Kiran, Fatima, et al., 2021). LTP Plus also has been found to be feasible and acceptable for fathers with depression (Husain, Chaudhry, et al., 2021) and mothers of malnourished children (Husain, Kiran, Shah, et al., 2021). Longer term follow-up is needed to determine the impact of LTP Plus of offspring into young adulthood. Toddler-Parent Psychotherapy Toddler-parent psychotherapy (TPP) is an intervention originally based on attachment theory, designed to promote healthy mother–child attachment by targeting communication and interactions between mothers and toddlers (Lieberman, 1992). TPP targets mothers’ abilities to provide their child with appropriate care and toddlers’ sense of security and trust. TPP also focuses on mothers’ and children’s perceptions of each other by observing mother–child interactions during joint therapy sessions. When compared with a control condition, children randomized to TPP had significant increases in attachment security at a 36-month-old follow-up (Cicchetti et al., 1999; Toth et al., 2006), and this change was associated with increased maternal warmth and decreased youth anger and problem behavior when the children were age 9 (Guild et al., 2021). Longer term follow-up is needed to determine outcomes over time later in development. Triple P-Positive Parenting Program The Triple P-Positive Parenting Program (Triple-P) is a multilevel intervention designed to prevent behavioral and emotional problems in preadolescent youth (Sanders, 1999; Sanders et al., 2003) and infants (Tsivos et al., 2015). Triple-P has been found to be acceptable to parents from a wide variety of culturally diverse backgrounds (Morawska et al., 2011). It has been adapted to address challenges unique to mothers with depression into Enhanced Triple-P (Sanders et al., 2000). Enhanced Triple-P provides up to eleven 60- to 90-minute sessions focused on parent training, mood and stress management, CBT, coping, and partner and social support skills. Meta-analyses reveal that Enhanced Triple-P reduces children’s social, emotional, and behavioral problems, and improves parenting practices, parenting satisfaction and efficacy, as well as the coparent relationship (Nowak & Heinrichs, 2008; Sanders et al., 2014). Enhanced Triple-P demonstrates how targeting both parenting and parental depression may be particularly beneficial for the prevention of offspring depression and other problem behaviors.

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EFFEKT-E The Entwicklungsförderung in Familien: Eltern- und Kinder-Training Effektivität nachgewiesen (EFFEKT-E) is a family-based program that targets parenting behaviors and social-cognitive skills in children ages 4 to 7 years old to prevent familial transmission of depression (Bühler et al., 2011). The program is based on the integrative model for the transmission of risk to children of depressed mothers (Goodman & Gotlib, 1999). EFFEKT-E involves six sessions, held twice per week for 3 weeks. Four of the parent sessions target parenting skills (e.g., positive parenting skills, discipline, cooperation), and the remaining sessions discuss challenges unique to depression, such as negative thoughts about parenting and coping with stressful situations. The child training portion targets social problem-solving skills and has one parent–child session focused on role plays and utilizing learned skills in real time. In a quasi-experimental study, families were recruited from 13 mother–child rehabilitation clinics in Germany and enrolled into either EFFEKT-E or a control condition. At the 6-month follow-up, families in EFFEKT-E had increased parenting competence and fewer parentreported youth emotional problems compared with parents and children in the control group (Bühler et al., 2011). An RCT and longer term follow-up are essential to determine the comparative and longer-term efficacy of EFFEKT-E.

Family Talk Family Talk (also called the Preventive Intervention Project) is a family-based prevention program that aims to decrease psychopathology in youth ages 8 to 15 years old who have a parent with a mood disorder (Beardslee et al., 2003). It consists of six to 11 sessions, including individual meetings with parents and children, family meetings, and telephone support. Primary goals of the intervention include psychoeducation regarding mood disorders and vulnerabilities in at-risk children, targeting feelings of guilt and blame in youth, and helping children pursue activities and relationships both within and outside of the family. Family Talk has been compared with a lecture intervention in RCTs. A 4- to 5-year follow-up revealed no significant group differences, as both groups showed increased family functioning and decreased youth internalizing symptoms (Beardslee et al., 2007). Families who received Family Talk had significantly greater improvements in parent behaviors and attitudes and in youth’s understanding of their parent’s disorder than with the lecture-only condition (Beardslee et al., 2007). Let’s Talk About the Children Let’s Talk About the Children (Solantaus et al., 2009, 2010; Solantaus & Toikka, 2006) is a similar, although less extensive psychoeducational intervention to support families with parental depression. It has shown decreases in youths’ emotional problems and anxiety in Finland (Solantaus et  al., 2010), and a

Parental Psychopathology and Parenting 41

decrease in parental depression and youth depression in a trial in Greece (Giannakopoulos et al., 2021). Future work should examine conditions under which Family Talk shows a significant benefit for youth outcomes as compared with a control condition and attempts to identify the mechanisms that account for any significant effects. Family Group Cognitive Behavioral Preventive Intervention The family group cognitive behavioral preventive intervention (FGCB) targets children aged 9 to 15, and parents with a major depressive disorder during the lifetime of their child (Compas et al., 2009). FGCB includes 12 sessions (eight weekly and four monthly) and involves participation of both parents and children. Joint sessions (1–3) include psychoeducation about familial transmission of depression and coping skills for depression. Separate sessions (4–8) involve meetings to teach parenting skills (e.g., praise, positive time together, structure) to parents, and to teach skills for coping with an uncontrollable stressor for youth (e.g., acceptance, distraction, activities, and positive thinking). Monthly booster sessions (9–12) are used to evaluate continuing problems and to provide additional practice and reinforcement of skills. When compared with a self-study written information control condition, FGCB had significant reductions in parent’s depressive symptoms and children’s depressive and internalizing symptoms at the 12-month follow-up (Compas et al., 2009). Youth who participated in FGCB had significantly lower levels of internalizing symptoms and were less likely to meet criteria for a diagnosis of MDD at the 18- and 24-month follow-up, respectively (Compas et al., 2011). Finally, participation in the family intervention was related to changes in observed positive parenting skills. Importantly, changes in positive parenting skills and youth secondary control coping skills, significantly mediated the effect of FGCB on youths’ depressive and internalizing symptoms (Compas et al., 2010). Targeting parenting behaviors and youth coping skills directly improved both parental and offspring outcomes. Future work should assess how outcomes may differ if interventions target parental depression, in addition to parenting, among families of at-risk youth. Keeping Families Strong Keeping Families Strong is a family-based intervention that targets youth aged 10 years and older of mothers with depression (Riley et al., 2008). It involves 10 weekly group sessions in which caregivers and children meet separately. The parent group focuses on psychoeducation about depression and parenting, problem-solving, and CBT skills such as cognitive restructuring. The child group targets coping and communication skills in addition to feelings of guilt and responsibility related to their parents’ depression. In a pilot study of Keeping Families Strong, youth reported decreases in internalizing symptoms and increases in coping and in maternal warmth and

42  Pine and Garber

acceptance (Valdez et al., 2011). An adapted intervention for Latinx families, Fortalezas Familiares, showed that the intervention was associated with improvements in children’s emotional symptoms and in family functioning (Valdez et al., 2013). Larger, randomized controlled trials are needed to evaluate more thoroughly the efficacy of Keeping Families Strong and its variants. Additional Notable Interventions Several other interventions have had a demonstrated impact on depression in children and adolescents despite not being designed to target children’s depression per se. Thus, the interventions described in this section were not designed within the context of parental depression and yet have yielded findings relevant to the prevention of offspring depression. The Family Check-Up targets parenting skills to prevent the development of conduct problems in children at age 2. It includes motivational interviewing methods to promote positive parenting change, with attention to the unique context and problems of each family. A trial showed that the Family Check-Up led to decreases in maternal depressive symptoms, which mediated the intervention and lower externalizing and internalizing problems in children (Shaw et al., 2009). Similar findings were reported at a longer term follow-up, as mothers’ reductions in depressive symptoms predicted subsequent decreases in children’s depressed or withdrawal symptoms at ages 7.5 to 8.5 years (Reuben et al., 2015). The Family Bereavement Program was designed for children ages 8 to 16 who experienced the death of a parent. This intervention involves 12 sessions in which caregivers and children meet separately. Parents learn skills to parent and improve the parent–child relationship, behavioral activation, and problem solving. Youth are taught skills to improve coping, self-esteem, and problemsolving abilities. Compared with families in a control condition, participants in the Family Bereavement Program showed improvements in parenting and caregiver mental health and reductions in internalizing symptoms for girls and those with elevated scores at baseline (Sandler et al., 2003). The Program for Children of Divorce was designed for children ages 9 to 12 and their mothers who have experienced a divorce in the past 2 years. Families were randomly assigned to either the mother-only, mother–child, or self-study conditions. In the mother–child condition, mothers and children met separately in groups for 11 weeks. The mother program involved planning positive family activities, utilizing listening skills, and using consistency and consequences in parenting. The child program taught skills for problem solving, cognitive restructuring, and relaxation techniques. Both groups were encouraged to practice the skills at home. Compared with the self-study program, families who participated in the mother-only program showed increases in the quality of the mother–child relationship and discipline and decreases in youth internalizing problems (Wolchik et al., 2000). At a 6-year follow-up, the percentage of youth with a diagnosed mental health

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disorder was significantly lower for those in the mother–child condition compared with the self-study condition (Wolchik et  al., 2002). Youth who had higher initial mental health problems and whose families were in either the mother–child condition or mother-only condition also had significantly lower externalizing problems and fewer symptoms of mental health dis­orders at the 6-year follow-up than did youth in the self-study control program.

CASE EXAMPLE Sara (cisgender woman) was a 36-year-old single mother of two boys, Chase (15 years old) and Ron (9 years old).1 She had experienced multiple episodes of MDD, as well as anxiety throughout her lifetime, starting in adolescence. Sara worked long hours in a local pharmacy and, as a result, Chase became Ron’s primary caretaker, particularly on weekday afternoons and evenings, which interfered with his time to see friends and participate in afterschool activities. Chase had been experiencing low mood, loss of interest, anxiety, and sleep disturbance. Sara asked their primary care physician for a referral for Chase to be evaluated. During the intake with a clinical psychologist, Sara became tearful as she described the economic strain on her family as a single mother, as well as the demanding nature of her job. She reported that she felt even more depressed and guilty because Chase was experiencing some of the same kinds of difficulties as she was having. Sara had trouble controlling her anxiety about not being available to support her sons. She also noted that, sometimes, she was irritable after a long day at work, and that this state interfered with her being a present and engaged parent. This family was a good candidate for the FGCB (described earlier), which focuses on education about depression, parenting skills, and coping with stress to prevent depression in at-risk youth. In the first three joint sessions, the families learn about depression and how it affects the whole family. At first, Chase was especially reluctant to attend and participate. Ron enjoyed the pizza and the small tokens for prizes he could earn. An important part of the psychoeducation was normalizing the symptoms of depression and relating them to stress. A crucial message was that it was not the teen’s responsibility to be a “caretaker” for their depressed parent. Sara had not been aware of the extent to which Chase felt that he had to be the “man” of the house, which he thought included being responsible for reducing his mother’s depression and taking care of Ron. Chase and Sara talked about his feelings regarding being a caretaker, and he felt better after Sara reassured him that there was no expectation that he was to play that role for either his mother or brother. Sara often returned to this theme in the weekly

Specifics of this case have been changed to protect the identity of the patient.

1

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parenting group, and she received several good suggestions about how to reduce the caretaking burden on Chase. Also, in the early sessions, the group discussed the stressors each person was experiencing. They discovered that feelings of being overwhelmed and out of control were common among the parents and children. Hearing about the stressors that others in the group experienced enhanced the bonding among the participants and reduced some of the perceived stigma associated with the parents’ depression. The parent and children groups met separately in the remaining weeks, except for the last 10  minutes of each session, during which the families reunited to engage in a fun family activity. The parenting group emphasized the importance of spending quality time with each child, positive reinforcement of desirable behaviors and ignoring undesirable behaviors, and active listening. Sara incorporated more positive parenting practices, such as spending at least 5 minutes each day with Chase and Ron separately. Additionally, she created reward charts to encourage Ron to complete his chores and homework, which had the added benefit of reducing the burden on Chase to oversee Ron’s activities. Ron’s reward system also reduced the daily conflict between the brothers, thereby also reducing Sara’s irritability when she got home from work. Chase, Ron, and four other youth learned about secondary control coping and ADAPT (Acceptance, Distraction, Activities, and Positive Thinking) skills in their group sessions. They learned to implement these skills at home without their mother or when she was tired after work. Chase learned to monitor his thoughts about feeling guilty about his mother’s depression and used more positive thinking. Both of Sara’s boys learned to distract themselves when distressed and increased their fun activities, including watching television and playing computer games. Over the 4  months of booster sessions, families practiced the skills and used the meetings to problem solve new stressors. For example, Chase got his driving learner’s permit. He was eager to practice driving to get his full license as soon as he turned 16. Given Sara’s busy work schedule, there often was conflict regarding when she would be able to drive with him. Discussions at the monthly booster session reminded Sara about which parenting skills to use. Sara and Chase jointly devised a driving practice schedule, which diminished Chase’s nagging and the conflict that followed. Sara also asked her sister to drive with Chase one afternoon a week to increase his practice time, which he appreciated. Chase’s scores on a measure of depressive symptoms were reduced significantly by the end of the eight sessions. He learned to use the ADAPT skills to manage his distress and to accept that, although his mother may be depressed at times, he was not responsible to fix it. Sara implemented several of the parenting techniques to increase desirable behaviors. An important feature of the FGCB intervention is that the parenting skills Sara learned were relevant to both sons, who were at quite different developmental levels. Moreover,

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Sara’s depressive and anxiety symptoms diminished over the course of the intervention as she became more confident in her ability to affect meaningful and measurable change in her children.

SUMMARY AND FUTURE DIRECTIONS Parental depression is a robust risk factor for developing depression in children. This relation may be moderated by specific features of parental depression, such as chronicity, severity, and comorbidity, as well as child characteristics, such as age, gender, and temperament. Research has consistently identified strong links among parental depression, parenting behaviors, and youth risk for depression and other psychopathology (Goodman et  al., 2020). Several evidence-based interventions that target parental depression and/or parenting and have promising outcomes for the prevention of depression in children. Important questions remain, however, regarding how best to alter children’s risk trajectory, as it is unclear whether interventions should optimally target the risk factor (parental depression) or the mechanism (parenting). It is also unclear if interventions should involve both the parent and child, or just the parent, or just the child. Although some studies have found improvements in children following remission of parental depression (Cuijpers et al., 2015; Gunlicks & Weissman, 2008), other studies suggested that targeting parental depression alone was not sufficient to impact their children’s well-being (Forman et al., 2007). Interventions that target both parental depression and parenting appear to have the strongest evidence base thus far. An important question for the future is whether the interventions for the depressed parents and their children should be simultaneous or sequential. In other words, would the intervention with the children be more effective if the parents’ depression had remitted (see Garber et al., 2009)? Although parental depression is clearly associated with dysfunction in youth, depression is treatable. Parenting also can be improved, and such improve­ments are linked with better child outcomes. We do not suggest that this work is easy. Raising children is challenging under the best of circumstances, but parents can learn strategies and perspectives to engage in positive behaviors that are known to be effective. Like many other things in life, parenting is an ongoing and dynamic process. Although the specific strategies will change as children develop, the fundamental parenting principles of warmth and structure remain. Many parents know what to do, but many things interfere with their parent­ ing optimally at times, such as their mental health, limited economic and social resources, and various child characteristics. Psychopathology is complex and multidetermined. Should we first reduce the parents’ depression and then improve their parenting or the reverse? Can interventions target both issues simultaneously? Future trials should address the sequencing of interventions as well as how to optimize parental and child participation in these

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interventions. Another critical future direction is to identify moderators that can facilitate the personalization of interventions. Child variables to consider are their age, level of cognitive, neurobiological, and social development, temperament, ability to self-regulate, and exposure to early adverse events. Parent factors include personality, severity and chronicity of their psychopathology, intelligence, social support, social injustice, and economic resources. Experimental designs such as RCTs can yield important and meaningful results at both the individual and societal levels.

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maternal depression and child development in a low-resource setting: Cluster randomized controlled trial. Depression and Anxiety, 38(9), 925–939. https://doi.org/ 10.1002/da.23169 Husain, N., Kiran, T., Shah, S., Rahman, A., Raza-Ur-Rehman., Saeed, Q., Naeem, S., Bassett, P., Husain, M., Haq, S. U., Jaffery, F., Cohen, N., Naeem, F., & Chaudhry, N. (2021). Efficacy of learning through play plus intervention to reduce maternal depression in women with malnourished children: A randomized controlled trial from Pakistan. Journal of Affective Disorders, 278, 78–84. https://doi.org/10.1016/ j.jad.2020.09.001 Husain, N., Zulqernain, F., Carter, L. A., Chaudhry, I. B., Fatima, B., Kiran, T., Chaudhry, N., Naeem, S., Jafri, F., Lunat, F., Haq, S. U., Husain, M., Roberts, C., Naeem, F., & Rahman, A. (2017). Treatment of maternal depression in urban slums of Karachi, Pakistan: A randomized controlled trial (RCT) of an integrated maternal psychological and early child development intervention. Asian Journal of Psychiatry, 29, 63–70. https://doi.org/10.1016/j.ajp.2017.03.010 Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., Rush, A. J., Walters, E. E., & Wang, P. S. (2003). The epidemiology of major depressive disorder: Results from the National Comorbidity Survey Replication (NCS-R). Journal of the American Medical Association, 289(23), 3095–3105. https://doi.org/10.1001/ jama.289.23.3095 Krzeczkowski, J. E., Schmidt, L. A., & Van Lieshout, R. J. (2021). Changes in infant emotion regulation following maternal cognitive behavioral therapy for post­partum depression. Depression and Anxiety, 38(4), 412–421. https://doi.org/10.1002/da.23130 Kujawa, A., Proudfit, G. H., Laptook, R., & Klein, D. N. (2015). Early parenting moderates the association between parental depression and neural reactivity to rewards and losses in offspring. Clinical Psychological Science, 3(4), 503–515. https://doi.org/ 10.1177/2167702614542464 Lewis, G., Neary, M., Polek, E., Flouri, E., & Lewis, G. (2017). The association between paternal and adolescent depressive symptoms: Evidence from two population-based cohorts. The Lancet, 4(12), 920–926. https://doi.org/10.1016/S2215-0366(17)30408-X Lieberman, A. F. (1992). Infant–parent psychotherapy with toddlers. Development and Psychopathology, 4(4), 559–574. https://doi.org/10.1017/S0954579400004879 Lovejoy, M. C., Graczyk, P. A., O’Hare, E., & Neuman, G. (2000). Maternal depression and parenting behavior: A meta-analytic review. Clinical Psychology Review, 20(5), 561–592. https://doi.org/10.1016/S0272-7358(98)00100-7 McLaughlin, K. A., Weissman, D., & Bitrán, D. (2019). Childhood adversity and neural development: A systematic review. Annual Review of Developmental Psychology, 1(1), 277–312. https://doi.org/10.1146/annurev-devpsych-121318-084950 McLeod, B. D., Weisz, J. R., & Wood, J. J. (2007). Examining the association between parenting and childhood depression: A meta-analysis. Clinical Psychology Review, 27(8), 986–1003. https://doi.org/10.1016/j.cpr.2007.03.001 Mezulis, A. H., Hyde, J. S., & Clark, R. (2004). Father involvement moderates the effect of maternal depression during a child’s infancy on child behavior problems in kindergarten. Journal of Family Psychology, 18(4), 575–588. https://doi.org/10.1037/ 0893-3200.18.4.575 Milgrom, J., Holt, C., Holt, C. J., Ross, J., Ericksen, J., & Gemmill, A. W. (2015). Feasibility study and pilot randomised trial of an antenatal depression treatment with infant follow-up. Archives of Women’s Mental Health, 18(5), 717–730. https://doi.org/ 10.1007/s00737-015-0512-5 Miranda, J., & Green, B. L. (1999). The need for mental health services research focusing on poor young women. The Journal of Mental Health Policy and Economics, 2(2), 73–80. https://doi.org/10.1002/(SICI)1099-176X(199906)2:2 3.0.CO;2-3

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Monk, C., Feng, T., Lee, S., Krupska, I., Champagne, F. A., & Tycko, B. (2016). Distress during pregnancy: Epigenetic regulation of placenta glucocorticoid-related genes and fetal neurobehavior. The American Journal of Psychiatry, 173(7), 705–713. https:// doi.org/10.1176/appi.ajp.2015.15091171 Monk, C., Spicer, J., & Champagne, F. A. (2012). Linking prenatal maternal adversity to developmental outcomes in infants: The role of epigenetic pathways. Development and Psychopathology, 24(4), 1361–1376. https://doi.org/10.1017/S0954579412000764 Morawska, A., Sanders, M., Goadby, E., Headley, C., Hodge, L., McAuliffe, C., Pope, S., & Anderson, E. (2011). Is the Triple P-Positive Parenting Program acceptable to parents from culturally diverse backgrounds? Journal of Child and Family Studies, 20(5), 614–622. https://doi.org/10.1007/s10826-010-9436-x Natsuaki, M. N., Shaw, D. S., Neiderhiser, J. M., Ganiban, J. M., Harold, G. T., Reiss, D., & Leve, L. D. (2014). Raised by depressed parents: Is it an environmental risk? Clinical Child and Family Psychology Review, 17(4), 357–367. https://doi.org/10.1007/s10567014-0169-z Netsi, E., Pearson, R. M., Murray, L., Cooper, P., Craske, M. G., & Stein, A. (2018). Association of persistent and severe postnatal depression with child outcomes. JAMA Psychiatry, 75(3), 247–253. https://doi.org/10.1001/jamapsychiatry.2017.4363 Neuhauser, A., Ramseier, E., Schaub, S., Burkhardt, S. C. A., & Lanfranchi, A. (2018). Mediating role of maternal sensitivity: Enhancing language development in at-risk families. Infant Mental Health Journal, 39(5), 522–536. https://doi.org/10.1002/imhj. 21738 Nowak, C., & Heinrichs, N. (2008). A comprehensive meta-analysis of Triple P-Positive Parenting Program using hierarchical linear modeling: Effectiveness and moderating variables. Clinical Child and Family Psychology Review, 11(3), 114–144. https://doi.org/ 10.1007/s10567-008-0033-0 O’Connor, E. E., Langer, D. A., & Tompson, M. C. (2017). Maternal depression and youth internalizing and externalizing symptomatology: Severity and chronicity of past maternal depression and current maternal depressive symptoms. Journal of Abnormal Child Psychology, 45(3), 557–568. https://doi.org/10.1007/s10802-0160185-1 O’Donnell, K. J., Glover, V., Barker, E. D., & O’Connor, T. G. (2014). The persisting effect of maternal mood in pregnancy on childhood psychopathology. Development and Psychopathology, 26(2), 393–403. https://doi.org/10.1017/S0954579414000029 O’Donnell, K. J., & Meaney, M. J. (2017). Fetal origins of mental health: The developmental origins of health and disease hypothesis. The American Journal of Psychiatry, 174(4), 319–328. https://doi.org/10.1176/appi.ajp.2016.16020138 O’Hara, M. W., & Swain, A. M. (1996). Rates and risk of postpartum depression—A metaanalysis. International Review of Psychiatry, 8(1), 37–54. https://doi.org/10.3109/ 09540269609037816 Psychogiou, L., Moberly, N. J., Parry, E., Nath, S., Kallitsoglou, A., & Russell, G. (2017). Parental depressive symptoms, children’s emotional and behavioural problems, and parents’ expressed emotion—Critical and positive comments. PLOS ONE, 12(10), e0183546. https://doi.org/10.1371/journal.pone.0183546 Psychogiou, L., & Parry, E. (2014). Why do depressed individuals have difficulties in their parenting role? Psychological Medicine, 44(7), 1345–1347. https://doi.org/ 10.1017/S0033291713001931 Rapp, A. M., Chavira, D. A., Sugar, C. A., & Asarnow, J. R. (2021). Incorporating family factors into treatment planning for adolescent depression: Perceived parental criticism predicts longitudinal symptom trajectory in the Youth Partners in Care trial. Journal of Affective Disorders, 278, 46–53. https://doi.org/10.1016/j.jad.2020.09.028 Reuben, J. D., Shaw, D. S., Brennan, L. M., Dishion, T. J., & Wilson, M. N. (2015). A family-based intervention for improving children’s emotional problems through

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effects on maternal depressive symptoms. Journal of Consulting and Clinical Psychology, 83(6), 1142–1148. https://doi.org/10.1037/ccp0000049 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(2), 335–345. https://doi.org/10.1017/S0033291709005911 Riley, A. W., Valdez, C. R., Barrueco, S., Mills, C., Beardslee, W., Sandler, I., & Rawal, P. (2008). Development of a family-based program to reduce risk and promote resilience among families affected by maternal depression: Theoretical basis and program description. Clinical Child and Family Psychology Review, 11(1–2), 12–29. https://doi.org/ 10.1007/s10567-008-0030-3 Sanders, M. R. (1999). Triple P-Positive Parenting Program: Towards an empirically validated multilevel parenting and family support strategy for the prevention of behavior and emotional problems in children. Clinical Child and Family Psychology Review, 2(2), 71–90. https://doi.org/10.1023/A:1021843613840 Sanders, M. R., Kirby, J. N., Tellegen, C. L., & Day, J. J. (2014). The Triple P-Positive Parenting Program: A systematic review and meta-analysis of a multi-level system of parenting support. Clinical Psychology Review, 34(4), 337–357. https://doi.org/ 10.1016/j.cpr.2014.04.003 Sanders, M. R., Markie-Dadds, C., Tully, L. A., & Bor, W. (2000). The Triple P-Positive Parenting Program: A comparison of enhanced, standard, and self-directed behavioral family intervention for parents of children with early onset conduct problems. Journal of Consulting and Clinical Psychology, 68(4), 624–640. https://doi.org/10.1037/ 0022-006X.68.4.624 Sanders, M. R., Markie-Dadds, C., & Turner, K. M. T. (2003). Theoretical, scientific and clinical foundations of the Triple P-Positive Parenting Program: A population approach to the promotion of parenting competence. Parenting Research and Practice, 1–21. https://www.researchgate.net/publication/37621619 Sandler, I. N., Ayers, T. S., Wolchik, S. A., Tein, J. Y., Kwok, O. M., Haine, R. A., TwoheyJacobs, J., Suter, J., Lin, K., Padgett-Jones, S., Weyer, J. L., Cole, E., Kriege, G., & Griffin, W. A. (2003). The family bereavement program: Efficacy evaluation of a theory-based prevention program for parentally bereaved children and adolescents. Journal of Consulting and Clinical Psychology, 71(3), 587–600. https://doi.org/10.1037/ 0022-006X.71.3.587 Shaw, D. S., Connell, A., Dishion, T. J., Wilson, M. N., & Gardner, F. (2009). Improvements in maternal depression as a mediator of intervention effects on early childhood problem behavior. Development and Psychopathology, 21(2), 417–439. https:// doi.org/10.1017/S0954579409000236 Sheeber, L., Davis, B., & Hops, H. (2002). Gender-specific vulnerability to depression in children of depressed mothers. In Children of depressed parents: Mechanisms of risk and implications for treatment. In S. H. Goodman & I. H. Gotlib (Eds.), Children of depressed parents: Mechanisms of risk and implications for treatment (pp. 253–274). American Psychological Association. https://doi.org/10.1037/10449-010 Silverstein, M., Diaz-Linhart, Y., Cabral, H., Beardslee, W., Hegel, M., Haile, W., Sander, J., Patts, G., & Feinberg, E. (2017). Efficacy of a maternal depression prevention strategy in Head Start: A randomized clinical trial. JAMA Psychiatry, 74(8), 781–789. https:// doi.org/10.1001/jamapsychiatry.2017.1001 Solantaus, T., Paavonen, E. J., Toikka, S., & Punamäki, R. L. (2010). Preventive interventions in families with parental depression: Children’s psychosocial symptoms and prosocial behaviour. European Child & Adolescent Psychiatry, 19(12), 883–892. https://doi.org/10.1007/s00787-010-0135-3 Solantaus, T., & Toikka, S. (2006). The Effective Family Programme: Preventative services for the children of mentally ill parents in Finland. International Journal of Mental Health Promotion, 8(3), 37–44. https://doi.org/10.1080/14623730.2006.9721744

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Solantaus, T., Toikka, S., Alasuutari, M., Beardslee, W. R., & Paavonen, E. J. (2009). Safety, feasibility and family experiences of preventive interventions for children and families with parental depression. International Journal of Mental Health Promotion, 11(4), 15–24. https://doi.org/10.1080/14623730.2009.9721796 Stein, A., Netsi, E., Lawrence, P. J., Granger, C., Kempton, C., Craske, M. G., Nickless, A., Mollison, J., Stewart, D. A., Rapa, E., West, V., Scerif, G., Cooper, P. J., & Murray, L. (2018). Mitigating the effect of persistent postnatal depression on child outcomes through an intervention to treat depression and improve parenting: A randomised controlled trial. The Lancet Psychiatry, 5(2), 134–144. https://doi.org/10.1016/S22150366(18)30006-3 Sutherland, S., Nestor, B. A., Pine, A. E., & Garber, J. (2021). Characteristics of maternal depression and children’s functioning: A meta-analytic review. Journal of Family Psychology. Advance online publication. https://doi.org/10.1037/fam0000940 Swartz, H. A., Frank, E., Zuckoff, A., Cyranowski, J. M., Houck, P. R., Cheng, Y., Fleming, M. A. D., Grote, N. K., Brent, D. A., & Shear, M. K. (2008). Brief interpersonal psycho­ therapy for depressed mothers whose children are receiving psychiatric treatment. The American Journal of Psychiatry, 165(9), 1155–1162. https://doi.org/10.1176/appi. ajp.2008.07081339 Sweeney, S., & MacBeth, A. (2016). The effects of paternal depression on child and adolescent outcomes: A systematic review. Journal of Affective Disorders, 205, 44–59. https://doi.org/10.1016/j.jad.2016.05.073 Toth, S. L., Rogosch, F. A., Manly, J. T., & Cicchetti, D. (2006). The efficacy of toddlerparent psychotherapy to reorganize attachment in the young offspring of mothers with major depressive disorder: A randomized preventive trial. Journal of Consulting and Clinical Psychology, 74(6), 1006–1016. https://doi.org/10.1037/0022-006X. 74.6.1006 Tsivos, Z. L., Calam, R., Sanders, M. R., & Wittkowski, A. (2015). A pilot randomised controlled trial to evaluate the feasibility and acceptability of the Baby Triple P-Positive Parenting Programme in mothers with postnatal depression. Clinical Child Psychology and Psychiatry, 20(4), 532–554. https://doi.org/10.1177/1359104514531589 Valdez, C. R., Mills, C. L., Barrueco, S., Leis, J., & Riley, A. W. (2011). A pilot study of a family-focused intervention for children and families affected by maternal depression. Journal of Family Therapy, 33(1), 3–19. https://doi.org/10.1111/j.1467-6427. 2010.00529.x Valdez, C. R., Padilla, B., Moore, S. M., & Magaña, S. (2013). Feasibility, acceptability, and preliminary outcomes of the Fortalezas Familiares intervention for Latino families facing maternal depression. Family Process, 52(3), 394–410. https://doi.org/10.1111/ famp.12033 van Doesum, K. T. M., Riksen-Walraven, J. M., Hosman, C. M. H., & Hoefnagels, C. (2008). A randomized controlled trial of a home-visiting intervention aimed at preventing relationship problems in depressed mothers and their infants. Child Development, 79(3), 547–561. https://doi.org/10.1111/j.1467-8624.2008.01142.x Weissman, M. M., Pilowsky, D. J., Wickramaratne, P. J., Talati, A., Wisniewski, S. R., Fava, M., Hughes, C. W., Garber, J., Malloy, E., King, C. A., Cerda, G., Sood, A. B., Alpert, J. E., Trivedi, M. H., & Rush, A. J. (2006). Remissions in maternal depression and child psychopathology: A STAR*D-child report. Journal of the American Medical Association, 295(12), 1389–1398. https://doi.org/10.1001/jama.295.12.1389 Weissman, M. M., Wickramaratne, P., Gameroff, M. J., Warner, V., Pilowsky, D., Kohad, R. G., Verdeli, H., Skipper, J., & Talati, A. (2016). Offspring of depressed parents: 30 years later. The American Journal of Psychiatry, 173(10), 1024–1032. https://doi.org/ 10.1176/appi.ajp.2016.15101327 Weissman, M. M., Wickramaratne, P., Nomura, Y., Warner, V., Pilowsky, D., & Verdeli, H. (2006). Offspring of depressed parents: 20 years later. The American Journal of Psychiatry, 163(6), 1001–1008. https://doi.org/10.1176/ajp.2006.163.6.1001

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Wickramaratne, P., Gameroff, M. J., Pilowsky, D. J., Hughes, C. W., Garber, J., Malloy, E., King, C., Cerda, G., Sood, A. B., Alpert, J. E., Trivedi, M. H., Fava, M., Rush, A. J., Wisniewski, S., & Weissman, M. M. (2011). Children of depressed mothers 1 year after remission of maternal depression: Findings from the STAR*D-child study. The American Journal of Psychiatry, 168(6), 593–602. https://doi.org/10.1176/appi.ajp. 2010.10010032 Wilson, S., & Durbin, C. E. (2010). Effects of paternal depression on fathers’ parenting behaviors: A meta-analytic review. Clinical Psychology Review, 30(2), 167–180. https:// doi.org/10.1016/j.cpr.2009.10.007 Wolchik, S. A., Sandler, I. N., Millsap, R. E., Plummer, B. A., Greene, S. M., Anderson, E. R., Dawson-McClure, S. R., Hipke, K., & Haine, R. A. (2002). Six-year follow-up of preventive interventions for children of divorce: A randomized controlled trial. Journal of the American Medical Association, 288(15), 1874–1881. https://doi.org/10.1001/ jama.288.15.1874 Wolchik, S. A., West, S. G., Sandler, I. N., Tein, J. Y., Coatsworth, D., Lengua, L., Weiss, L., Anderson, E. R., Greene, S. M., & Griffin, W. A. (2000). An experimental evaluation of theory-based mother and mother–child programs for children of divorce. Journal of Consulting and Clinical Psychology, 68(5), 843–856. https://doi.org/10.1037/0022006X.68.5.843 Yap, M. B. H., Pilkington, P. D., Ryan, S. M., & Jorm, A. F. (2014). Parental factors associated with depression and anxiety in young people: A systematic review and meta-analysis. Journal of Affective Disorders, 156, 8–23. https://doi.org/10.1016/j.jad. 2013.11.007

3 Low Social Support and Relational Regulation Brian Lakey

T

his chapter describes research and theory on interventions designed to improve social support. Perceptions of lower social support are consistently linked to current major depressive disorder, and several novel interventions to improve social support and related outcomes have been derived from social support theory. Unfortunately, randomized controlled trials on the two most common interventions (support groups and befriending) have yielded disappointing results. Instead, psychological therapy, including cognitive behavior therapy (CBT), is effective in boosting perceived support. The poor performance of social support interventions requires a revaluation of social support theory related to intervention. Recent research suggests that core assumptions that drove the development of social support interventions are mostly inaccurate. A newer theory of social support (relational regulation theory [RRT]; Lakey & Orehek, 2011) states that low perceived support is not in itself a risk factor for depression. Instead, it is a marker for strong effects whereby specific personal relationships evoke or inhibit in recipients putative risk factors for depression, including subclinical negative affect, low self-esteem, automatic negative thoughts, and dysfunctional attitudes. Who and what regulate recipients mostly reflects dyadic relationships rather than the traitlike characteristics of recipients and relationship partners. RRT predicts that relational regulation occurs in psychological therapy, provides new approaches to patient–therapist and patient–treatment matching, as well as a theoretically agnostic, methodologically rigorous approach to studying interpersonal processes in psychological therapy.

https://doi.org/10.1037/0000332-004 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  55

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SOCIAL SUPPORT AS A RISK FACTOR FOR MAJOR DEPRESSION Research on social support as a risk factor for major depression has been primarily driven by stress and coping social support theory (S. Cohen & Wills, 1985). The theory has been so influential that it will be referred to as the standard model in this chapter. According to the standard model, low social support is a stable vulnerability factor for depression and mental disorders that protects people from the adverse effects of stress (Brown & Harris, 1978; S. Cohen & Wills, 1985; Lakey & Cronin, 2008). As applied to depression, social support decreases the likelihood that stressful life events and other stressors will provoke an episode of major depression because specific supportive actions (e.g., advice, reassurance) provided by friends and family improve the stressed person’s coping with the stressor (S. Cohen & Wills, 1985). Social support is typically measured through self-report questionnaires that require participants to make subjective judgments about the quality of support available from their social networks (i.e., perceived support, such as “My family gives me the moral support I need”). Other types of measures ask participants to report the specific supportive acts received (i.e., enacted or received support) or the size of their social networks. However, it is well established that perceived support has the strongest and most replicable links to psychological distress (Finch et al., 1999; Lakey & Cohen, 2000; Lakey & Orehek, 2011) and clinical depression (Lakey & Cronin, 2008). Low perceived social support,1 as described by the standard model, is a good example of a vulnerability risk factor, as described by Dobson and Dozois (2008). Lower perceived support is an enduring characteristic of the person, derived from past environmental experiences (i.e., a history of inadequate enacted support), that is present even in absence of disorder. Lakey and Cronin (2008) reviewed all empirical studies published in English that included social support and diagnoses of clinical depression (e.g., major depressive disorder). In nearly every study, individuals with clinical depression had lower perceived support than did healthy controls. This correlation was observed across a range of nations, ages, genders, measures, as well as in clinical and epidemiological samples. Although this is an important finding, it does not by itself provide strong evidence that low perceived support acts as a risk factor for major depression. Prospective studies provide more rigorous tests of whether low perceived support acts as a risk factor for depression (Lakey & Cronin, 2008). In such studies, a sample is assessed on social support and depression at Time 1 and at subsequent follow-up(s). The primary outcome is whether participants with low perceived support at Time 1 are more likely to have onset of major depression between Time 1 and follow-up. Because Time 1 continuous measures of subclinical depressive symptoms forecast the onset of clinical depression and There is no agreed-upon threshold that identifies the level of perceived support that places a person at risk for current depression and in research perceived support is nearly always treated as a continuous variable. Thus, the term “low social support” only means that higher depression covaries with lower perceived support.

1

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are also linked to Time 1 lower perceived support, Time 1 depressive symptoms must be controlled statistically. This is to rule out the possibility that low perceived support forecasts onset of depression only because of its link to Time 1 depressive symptoms. In addition, the standard model predicts significant interactions between Time 1 social support and life events occurring between Time 1 and follow-up. The form of the interaction should show evidence for stress buffering in that the link between events and onset of depression is stronger for people with low perceived support. Stronger evidence for the standard model would show that, low perceived support is unrelated to depression in the absence of stress (cf. Brown & Harris, 1978). Main effects, whereby Time 1 low social support forecasts the onset of depression regardless of the level of stress, do not provide evidence for the standard model, as the standard model predicts stress buffering, not main effects. A strict test of the theory also requires a coping × life events interaction that explains the support × events interaction (Lakey & Cohen, 2000). However, coping is rarely assessed in research on social support and major depression (Lakey & Cronin, 2008). Prospective stress buffering and main effects are rarely observed in studies of the onset of depression (Lakey & Cronin, 2008). For example, Wade and Kendler (2000) followed nearly 2,000 community-dwelling women over 5 years. Diagnoses of major depressive disorder were made according to the revised version of the Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DSM-III-R), and participants completed several measures of perceived support. Life events were assessed in structured interviews covering the preceding year at Time 2. Concurrently, participants with depression typically reported lower perceived support. However, there were no significant concurrent or prospective stress buffering effects. There were no significant prospective main effects either, even though the study had abundant statistical power. Lakey and Cronin (2008) concluded that there was little evidence that low social support acted as a stable risk factor for the onset of major depression either as a main effect or by buffering the effects of stress. This null effect is striking because low perceived support does act as a risk factor for mortality (Holt-Lunstad et al., 2010), and genetic risk acts as a stable risk factor for depression (Sullivan et al., 2000). Thus, perceived support acts as a stable risk factor for some health outcomes, and stable risk factors have been identified for major depression. Perceived support simply does not appear to be a stable risk factor for major depression. Nonetheless, the consistent concurrent link between low perceived support and clinical depression indicate that low perceived support might be a marker for a more proximate, temporally unstable cause of major depression. Theory and evidence for such a cause are discussed later in this chapter.

EFFECTIVENESS OF SOCIAL SUPPORT INTERVENTIONS An important emphasis of social support research has always been to develop public health interventions to prevent the onset of mental and physical disorders by harnessing the health promoting functions of family and friends (Heller et al., 1991). Most interventions fall into one of two categories: (a) interventions

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in which a nonprofessional support provider is assigned to at-risk individuals (befriending) and (b) support groups in which people exposed to similar stressors (e.g., cancer) meet in groups to provide support to each other. These interventions have been subjected to a number of randomized controlled trials (RCTs). Two early narrative reviews concluded that RCTs did not provide strong evidence for either type of intervention compared with controls in improving perceived support and related health outcomes (Hogan et  al., 2002; Lakey & Lutz, 1996). In the following section, a few prototypical RCTs are described, followed by more definitive meta-analyses. In an important early study, Heller et al. (1991) tested nearly 300 low-income community-dwelling elderly women with low perceived support. Participants were randomly assigned to a 10-week intervention or to an assessment-only control. Intervention participants received phone calls from project support providers at least weekly for 10 weeks. Surprisingly, there were no differences between groups on perceived support, loneliness, morale, or depressive symptoms. The absence of effects was shocking at the time, and the original article was followed by over 70 pages of invited commentary. The null result from Heller et al. (1991) has been confirmed by meta-analysis. Siette et al. (2017) analyzed the results of seven RCTs and quasi-experiments on befriending interventions that included over 2,000 participants. Study populations included older people, people with severe mental disorders, and those with anxiety or depression. There were no significant intervention effects on perceived support or loneliness. The standardized mean difference (SMD) was 0.08 for perceived support (k = 7) and −.03 for loneliness (k = 5). For perceived support, the meta-analytic estimate included two quasi-experiments that provided two of the three highest estimates. The interventions also had no significant effects on depression, self-esteem, or quality of life. Other RCTs have focused on support groups. In a seminal study, Helgeson et al. (1999) randomly assigned over 300 women with breast cancer to one of four conditions. All participants received standard care. Controls received no group intervention. In the support group condition, professional facilitators led discussions for 8 consecutive weeks among members emphasizing the acceptance of emotion and encouraging its expression. In the education condition, the same facilitators led information sessions in lecture format on topics such as chemotherapy, nutrition, exercise, body image, and sexuality. The combined group included all aspects of both education and support groups. Surprisingly, at postintervention, groups including support (support and combined) did not fare better on any outcome than groups without it (control and education). In fact, support groups had worse outcomes on some measures. Groups with education had significantly better effects than groups without. These findings were mostly stable at 6-month follow-up. Although a standardized social support measure was not administered, participants in groups rated “how close they thought the group was and how close they felt to the group” (Helgeson et al., 1999; p. 343). Support group participants did not rate their groups as closer than did comparison participants. A subsequent meta-analysis (Zimmermann et  al., 2007) confirmed and expanded upon the results of Helgeson et  al. (1999) for women with breast

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cancer. Across all outcomes, education (SMD = .53) was significantly more effective than support groups (SMD = .13). Support groups were more effective than waitlist or assessment-only controls (SMD = .21) but not controls (SMD = .01) that included “active ingredients.” There appear to be no meta-analytic estimates of the effects of support groups on perceived support, as perceived support is commonly not assessed in studies of support groups (Matthews et al., 2017). However, individual RCTs have shown outcomes on social support ranging from qualified positive effects (Classen et al., 2008) to no effects (Gunn et al., 2005) and negative effects (Fobair et al., 2002). In summary, consistent with the conclusions of earlier, narrative reviews (Hogan et al., 2002; Lakey & Lutz, 1996), the two most intensively studied interventions inspired by the standard model have not yielded promising results. In meta-analyses, befriending interventions have not been more effective than waiting list or assessment only controls (Siette et al., 2017). Support groups have been more effective than waiting list or assessment controls, but not more rigorous active controls, and have been less effective than education (Zimmermann et  al., 2007). Regardless of statistical significance, effect sizes from both types of interventions have been small, with SMDs ranging between −.03 and .21. It should be noted that some RCTs have yielded promising results. Barrera et  al. (2002) found that online support groups for people with diabetes was more effective than a self-directed education control. Brand et al. (1995) found that a group intervention focusing on challenging negative thinking about relationships and building social skills improved perceived support from family compared with controls. Nonetheless, the few positive outcomes do not change the conclusions drawn from the larger literature. The weak results for social support interventions are even more striking when compared with the ability of psychological therapy to modify perceived support. Park et  al. (2014) meta-analyzed 11 RCTs of mostly group CBT for depression that included measures of perceived support. Controls were waitlist or standard care. Psychological therapy significantly improved perceived support (SMD = .38) compared with controls. Thus, perceived support is modifiable, but interventions generated by the standard model are not especially effective. Why is this? One possibility is that the core assumptions underlying social support interventions are mostly inaccurate. The next section reveals important mismatches between the core assumptions and basic science on perceived support. Understanding these mismatches might help explain why social support interventions have not been very effective and might help inform the treatment of depression.

CORE ASSUMPTIONS UNDERLYING SOCIAL SUPPORT INTERVENTIONS Nearly all social support interventions are based on the standard model’s two core, but often implicit, assumptions about perceived support: (a) the link between support and disorder reflects social influence, and (b) the social influence is primarily nomothetic.

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The assumption that social support operates through social influence means only that interacting with or thinking about some support providers evokes less depression than other support providers. For example, Maria experiences less depression when interacting with Gail than when interacting with Tom and, thus, Maria sees Gail as more supportive than Tom. Gail has this effect on Maria because Gail does and says more supportive things. Maria, thus, spends more time with Gail; and when Maria rates the supportiveness of their social network, Maria is thinking mostly of Gail. Social influence contrasts with personality processes in which the link between low perceived support and depression reflects recipients’ traitlike cognitive biases to see others as unsupportive (Lakey & Cassady, 1990; Lakey & Drew, 1997). The nomothetic assumption is that people respond similarly to the same providers and the same supportive statements. For example, like Maria, most people respond to Gail with less depression than to Tom, and most would agree that Gail says and does more supportive things than Tom. Befriending interventions reflect the nomothetic assumptions of the standard model. In such interventions, some providers, but not others, are selected by project managers to visit at-risk recipients. Project managers select providers who managers see as supportive, and recipients are assumed to agree with the managers’ judgments. Support groups reflect the nomothetic assumption that group members agree that some statements are more supportive than others and that members can be counted on to say supportive things. However, if these nomothetic assumptions are not met, the interventions might not be effective. Methods for Estimating Nomothetic Social Influences The social relations model (SRM; Kenny, 2020; Malloy, 2018) can estimate the strength of the nomothetic social influences assumed by the standard model. SRM designs are essentially repeated measures, random effects analysis of variance (ANOVA) models in which recipients rate the same providers or statements on supportiveness. Figure 3.1 (panel A) depicts the data structure for a block design. Recipients rate the supportiveness of each provider and might also rate their affect and thought in response to each provider. Each provider serves as a level of the repeated measures provider factor (or target or partner), and each recipient serves as a level of the between-subjects recipient factor (or perceiver or actor). Results are expressed in terms of proportion of variance explained by each factor. Panels A and B of Figure 3.1 depict three types of effects derived from the block design. Provider effects reflect agreement among recipients that Provider 1 is more supportive than Provider 2, who is more supportive than Provider 3, as reflected in the providers’ mean differences on supportiveness, averaged across recipients. Recipient effects reflect that on average, recipients differ in their perceived supportiveness of providers, averaged across providers (Lakey & Cassady, 1990). Relationship effects reflect the extent to which recipients disagree about the supportiveness of providers. In panels A and B, recipients show substantial disagreement about the supportiveness of providers. More formally,

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FIGURE 3.1.  Example Data Structure for a Block Design (Panel A) With Example Effects for Recipient, Provider, and Relationship Effects (Panel B) Panel A Provider 1

Provider 2

Provider 3

Mean

6 11 7 8.0

3 4 9 5.3

7 2 5 4.7

5.3 5.7 7.0

Recipient 1 Recipient 2 Recipient 3 Mean Panel B 12 10 8 6 4 2 0

Provider 1 Recipient 1

Provider 2 Recipient 2

Provider 3 Recipient 3

Note. Adapted from “The Relative Contribution of Trait and Social Influences to the Links Among Perceived Social Support, Affect, and Self-Esteem,” by B. Lakey and A. Scoboria, 2005, Journal of Personality, 73(2), p. 367 (https://doi.org/10.1111/j.14676494.2005.00312.x). Copyright 2005 by Wiley. Adapted with permission.

relationship effects occur when a recipient sees a provider as more (or less) supportive than one would expect from (a) the recipient’s tendencies to see providers as supportive (recipient effects) and (b) the provider’s tendency to be seen as supportive (provider effects). A disadvantage of the design just described is that it is difficult to include a large number of providers because of time required for recipients to rate each provider on each measure. A more efficient design is the round-robin, which is the prototypic SRM design (Kenny, 2020; Malloy, 2018). Within groups, each participant rates every other one and, thus, serves in the roles of both recipients and providers. Recipients, providers, and relationship effects are defined as in the block design, modified to account for dependency within groups among scores. A disadvantage of the block and round-robin designs is that it can be difficult to study people’s most important relationships, as all recipients rate the same providers, but few recipients have the same most important relationships. A one-with-many design (Kenny 2020; Malloy, 2018) addresses this by

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having recipients rate their own most important providers. Providers are, thus, nested within recipients as each recipient rates different providers. Because each provider is rated by only one recipient, provider effects cannot be estimated, and, thus, relationship and provider effects are included in a single dyadic effect. The Relative Strength of Provider, Recipient, and Relationship Effects As just described, befriending and support group interventions assume that social support reflects nomothetic social influences. If social influences predominate, then provider and relationship effects should be larger than recipient effects. If the social influences are nomothetic, then provider effects should be larger than relationship effects. There are consistent estimates of the relative sizes of these effects, and the results are illuminating. In the first study to estimate these effects, investigators used a block design in three samples. Sorority members rated a small subset of fellow members, PhD students rated program faculty, and students rated supportive conversations depicted via video (Lakey et al., 1996). Agreement on the supportiveness of providers explained only 20% of the variance, on average, across the three samples. In a subsequent meta-analysis of five studies in which recipients knew providers well, provider effects explained only 7% of the variance (Lakey, 2010). In a recent large-sample round-robin study of mostly well-acquainted students, provider effects explained 20% of the variance in supportiveness (Lakey et  al., 2022). Two small studies of adolescent inpatients with major depressive disorder (Lakey et al., 1999) and psychotherapy outpatients (Lakey et al., 2008) both estimated provider effects at 11%. Agreement was similarly low when recipients rated the supportiveness of statements in specific contexts, in samples of children, baccalaureate students and graduate students (Tanner et al., 2018). Thus, agreement about the supportiveness of providers and statements ranges from 7% to 20%. This value will surprise many readers. Of course, similar values for interrater agreement for clinical diagnoses or other psychological constructs would be unacceptably low. Befriending and support group interventions reflect an assumption of large provider and statement effects, but this assumption is mostly inaccurate. The obvious question is, how large are recipient and relationship effects? Recipients’ trait-like cognitive biases to see others as unsupportive explained 27% of the variance in Lakey’s (2010) meta-analysis and 18% in Lakey et al.’s (2022) large round-robin sample. In contrast, unique relationships between recipients and providers had the largest influence, accounting for 62% of the variance in both studies. Although provider effects on supportiveness are comparatively small, they might still make a powerful impact on depression-related affect. Such an impact would be revealed in the extent to which providers who are seen as less supportive by recipients also evoked more negative affect in recipients. Negative affect

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is a core emotional feature of depressive and anxiety disorders (Krueger & Markon, 2014). In Lakey et al. (2022), consensually supportive providers evoked more positive, but not less negative, affect. Recipients who characteristically saw others as supportive had high positive, but not low negative, affect. In contrast, when a relationship was perceived as unusually supportive, it evoked unusually high positive (r =.62) and low negative affect (r = -.40). In summary, the social influence assumption of the standard model is mostly accurate when recipients rate specific providers. The sum of provider and relationship effects are much stronger than recipient effects. However, the nomothetic assumption is mostly wrong. Idiographic relationship effects are much larger than nomothetic provider effects. Given this, perhaps it is not surprising that social support interventions based on nomothetic assumptions have not shown strong effects in RCTs. Thus, if perceived support is relevant to the treatment of major depression, it will likely be through understanding relationship effects. The next section describes research and theory on relational support, as well as its potential relevance to and implications for the psychological treatment of depression. Relational Regulation Theory RRT (Lakey & Orehek, 2011) was developed to explain the finding that perceived support is primarily relational. RRT adopts the SRM’s mathematic definition of relationships, described previously as the extent to which a recipient sees a provider as more supportive than expected based on (a) the recipient’s tendency to see providers as supportive and (b) the provider’s tendency to be seen as supportive. Although most RRT research has focused on supportiveness, the relational aspect of any construct can be derived when using the appropriate SRM designs. A quantitative definition of relationships has the important advantage of operational clarity that clearly distinguishes relationships from the effects of recipient and provider personality (Lakey & Orehek, 2011). In addition, the mathematical definition is agnostic regarding theoretical tradition. If a construct can be operationalized, its relational component can be derived. According to RRT, the concurrent correlation between low perceived support and depression primarily reflects the relational regulation of affect, action and thought. People regulate themselves in interaction with important other people and their environments. Most of the time, people regulate each other by talking about such quotidian topics as the weather, events of the day, sports, films, and family. People also share activities such as games, shopping, cooking, and watching TV. Regulation is relational, in that the provider who regulates a recipient’s affect, action and thought will not likely regulate well a second randomly selected recipient. Likewise, conversation topics and shared activities that regulate a recipient well will not likely regulate a second randomly selected recipient. Judgments of the supportiveness of a provider is based primarily on how well the provider regulates the recipient.

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Beyond positive and low negative affect, evidence for relational regulation2 has been observed for a number of constructs relevant to depression, including self-esteem (Barry et al., 2007; Lakey & Scoboria, 2005), self-discrepancies (Barry et al., 2007), dysfunctional attitudes, automatic negative thoughts, perfectionism, hopelessness and optimism (Lakey & Tanner, 2013). For example, Paul evokes many negative thoughts in Ronald, but Paul does not typically evoke negative thought and affect in recipients more generally, and Ronald does not characteristically experience negative thought and affect with most providers. Although many of these studies are based on college students of mostly European ancestry, effects for supportiveness and affect have been found for people with opiate dependence in an inner city (Lakey & Rhodes, 2015), U.S. Marines (Lakey et  al., 2016), Black college students (Hubbard et  al., 2022), Dutch (Branje et al., 2002) and Italian (Lanz et al., 2004) nuclear families, PhD students (Lakey et al., 1996), and medical residents (Giblin & Lakey, 2010). Relationship effects on affect, action, and thought are likely causal, as they are revealed in repeated measures experimental designs in which providers serve as levels of the repeated measures factor. In many studies, the provider is physically present when recipients rate their reactions to the providers (Lakey et al., 2016, 2021), or ratings are made immediately after interacting with the provider (Lakey et al., 2016, 2021; Neely et al., 2006; Veenstra et al., 2011). The specific person who serves as a provider in the designs has the same degree of objective reality as any repeated measures stimulus typically used in psychological science. RRT suggests new strategies for intervention. Like most social support research, RRT has focused on public health or preventive interventions that rely on nonprofessional support providers. RRT’s chief recommendation is to match participants so that mutually supportive relationships will emerge spontaneously without the need for further intervention. Mutually supportive means that dyad members see each other as supportive. According to RRT, supportive matches can be forecasted from a dyad’s similarity in what each member likes to do and talk about. However, recent research indicates that forecasting mutually supportive matches will be practically impossible (Lakey et  al., 2021). The problem is that the agreement/correlation among dyad members (indicators) about each other’s supportiveness is often quite low, undermining the reliability by which the dyad is assessed. As is well known, poor reliability undermines predictive accuracy. Normally, one offsets low reliability by adding indicators. However, this is not possible when forecasting mutually support dyads because the number of dyad members (indicators) is fixed at two. Thus, RRT’s recommendation for public health or preventive interventions are not practically feasible. RRT’s recommendations for psychological Most of the studies cited in this paragraph used one-with-many designs, which combine relationship and provider effects into a single dyadic effect. As provider effects have been consistently small, it is assumed that dyadic effects primarily reflect relationship effects.

2

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therapy might still be viable, as mutually supportive dyads are not expected in psychological therapy. These recommendations are discussed next.

RELATIONAL REGULATION IN PSYCHOLOGICAL THERAPY This section presents hypotheses about relational regulation in psychological therapy along with a brief discussion of supporting evidence. RRT is a general theory of how people’s affect, action, and thought are regulated by specific other people, and it assumes that these basic processes occur whenever two or more people interact, including in psychological therapy. Drawing from basic principles in psychological science is consistent with long-standing assumptions that basic processes in learning and cognition apply to therapy.3 RRT’s potential utility in understanding therapy lays in its focus on relationship effects. Specifically, RRT predicts that there will be comparatively small provider effects and large relationship effects on a range of psychological therapy process and outcome constructs. RRT describes new approaches to patient–therapist and patient–treatment matching. RRT also suggests a new approach to case studies, and two are presented. The chapter closes with an argument for advocates of evidence-based therapies to consider relationship processes in the psychological treatment for depression. Provider effects in the SRM and therapist effects in research on psychological therapy are defined in the same way mathematically and are estimated using similar techniques. Therapist effects are typically estimated in one-with-many designs in which therapists treat different patients and outcomes are assessed. Effects are usually estimated with multilevel modeling. Therapist effects are small, accounting for about 5% of the variance in patient-reported symptoms in a meta-analysis of 20 studies including over 3500 therapists and 125,000 patients (Johns et al., 2019). For example, Wampold and Brown (2005) studied nearly 600 therapists treating over 6,000 patients in a managed care practice that used well-validated, standardized measures of outcomes completed by patients. Controlling for patient severity and range of other constructs, therapist effects accounted for about 5% of the variance. Small provider/therapist effects have also been found for patient ratings of the working alliance. The working alliance reflects the quality of the working relationship between patients and therapists (Horvath & Greenberg, 1989). Working alliance can be rated by patients, therapists, and observers, although client ratings seem to forecast therapeutic outcomes best (Horvath & Symonds, 1991). Some items from the Working Alliance Inventory (Horvath & Greenberg, 1989) are similar to perceived support items (e.g., “I believe [my therapist] is genuinely concerned for my welfare”), whereas others focus on the goals or tasks of therapy (e.g., “I am clear on what my responsibilities are in therapy”). RRT’s hypothesis that people primarily regulate each other through ordinary social interaction does not apply to therapy, as therapy focuses primary on discussions of emotional distress and what to do about it.

3

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In one study, more than 200 outpatients with a range disorders and problems rated their alliance with one of over 60 therapists (Marcus et al., 2009). In another, nearly 400 adolescents in substance abuse treatment rated their alliance with one of 14 therapists (Marcus et al., 2011). In both studies, therapist effects explained about 5% of the variance. Is there better agreement about the effectiveness of therapeutic statements? In Study 3 of Tanner et al. (2018), students, faculty, and alumni of a PhD program in clinical psychology viewed three training videos depicting cognitive therapy, gestalt therapy, and brief dynamic therapy. Participants rated the effectiveness of nine randomly selected statements (three per therapist) presented in context. Agreement among observers accounted for only 12% of the variance. Thus, very little of patient improvement and patient perceptions of the working alliance reflected the effects of therapists. According to the SRM, the remaining lawful variance must reflect recipient and relationship effects, and RRT predicts most of the variance will reflect relationships. Relationship effects in psychological therapy are difficult to estimate because they require that patients have multiple therapists during the same period of time, which violates an important norm held by many therapists. Thus, the few studies estimating relationship effects are analogue studies or studies of group therapy in which group members rate each other. However, each of these suggest strong relationship effects in psychological therapy. The first estimates of relationship effects in a therapy context were provided by Ingraham and Wright (1987). The investigators studied 16 graduate students in clinical psychology who participated in group therapy training groups according to a model emphasizing the development of group cohesion through unstructured interactions among group members (Study 1). Participants rated their anxiety experienced with each group member in a round-robin design. Averaged across four sessions, there were strong relationship effects explaining 41% of the variance, indicating that providers evoked anxiety ideographically in some recipients, but not others. There were also strong recipient (49%) and weak provider effects (10%). In Ingraham and Wright’s Study 2, six outpatients participating in group therapy rated each other on anxiety experienced when with each group member. Of the variance that was stable over time, 100% was relational. Participants in Study 1 also rated the extent to which they felt appreciated by other group members (Wright & Ingraham, 1986). Relationship effects accounted for 53% of the variance, followed by provider (37%) and recipient effects (10%). Marcus and Holahan (1994) studied a larger sample of outpatients with a range of mental disorders participating in group therapy (N = 45 in nine groups). Recipients rated their own dominance, hostility, submissiveness, and friendliness evoked by each provider within groups. On average, across the four dimensions, relationship effects explained 42% of the variance, followed by 33% for recipients and 25% for providers. Finally, Mallinckrodt and Chen (2004) studied 76 graduate students in one of 12 therapy training groups. Recipients rated the reactions evoked by each provider within groups using the same measures as Marcus and Holahan (1994).

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Across dimensions, relationship effects accounted for 35% of the variance, followed by provider (26%) and recipient effects (21%). Thus, in both training groups and outpatients participating in group therapy, strong relationship effects emerged across a range of constructs. However, these studies do not provide estimates of relationship effects for patient–therapist dyads in individual therapy. Preliminary estimates for individual therapy are provided by three analogue studies, including one study of psychotherapy outpatients. In the first study of this kind (Hoyt, 2002), college students viewed four therapists interacting with a patient from four training videos and rated their expected working alliance (Horvath & Greenberg, 1989). Relationship effects accounted for 70% of the variance, followed by 18% for recipient effects and 9% for therapist effects. Lakey et al. (2008) conducted a replication of Hoyt’s (2002) study in samples that included outpatients. In Study 1, 15 therapy patients with a range of diagnoses and 15 college students rated therapists depicted in the classic “Gloria” training videos. Participants rated the therapists on expected working alliance and supportiveness if participants were receiving treatment from the therapists. Patients and students did not differ significantly in their ratings. Across both groups, relationship effects explained for 71% of the variance in expected working alliance, therapist effects accounted for 18%, and patient or recipient effects accounted for 0%. For expected supportiveness, relationship effects accounted for 72% of the variance, followed by 11% for therapists and 0% for patient or recipients. Moreover, the relationship components of working alliance and supportiveness were strongly correlated (r = .71). In other words, when a patient or recipient saw a therapist as likely evoking a good working alliance, they also saw the therapist as likely being supportive. Study 2 replicated these findings with a new sample of 64 students and three new therapists presented via video. Participants rated expected working alliance, as well as positive and negative affect evoked by each therapist. As in Study 1, relationship effects were very large for working alliance (69%). When a therapist evoked unusually high levels of the working alliance, the therapist also evoked high positive and low negative affect. To conclude this section, studies of group therapy participants and analogue studies of individual therapy find strong relationship effects and weaker provider effects, consistent with research showing small therapist effects in psychotherapy. Thus, there is strong suggestive evidence for relationship effects in psychological therapy. Now, the field needs studies in which sets of patients see the same therapists during the same period of time in actual therapy sessions. Such a design violates an important treatment norm for many therapists and extra care would be needed to protect patients from any adverse effects. Nonetheless, there were several such investigations in the 1960s (Houts et al., 1969; Moos & Clemes, 1967; Moos & MacIntosh, 1970; Van der Veen, 1965). The results of these studies are not reviewed here, as they had very small samples yielding somewhat inconsistent results and the contemporary relevance of the constructs investigated is not clear. Nonetheless, they are important for

68  Brian Lakey

showing that is it possible to conduct psychotherapy studies that can detect relationship effects. Perhaps the time is right to estimate relationship effects in individual therapy.

IMPLICATIONS OF RELATIONSHIP EFFECTS IN THE PSYCHOLOGICAL THERAPY OF DEPRESSION If there are strong relationship effects in individual psychological therapy, they would have implications for the nomothetic assumptions of theories of psychological therapy as well as for patient–therapist and patient–treatment matching, in addition to providing a new approach to case studies. Nomothetic Assumptions Nearly all theory and training in psychotherapy assumes that how patients respond to therapists’ statements is nomothetic, in that standardized techniques, modified to fit the patient’s context, are expected to alleviate symptoms, on average across patients. These nomological assumptions are part of what makes manualized treatment possible, permitting RCTs and evidence-based psychotherapy. For example, in CBT for depression, specific procedures are used to identify and challenge the negative thoughts of patients. These procedures are sufficiently clear that they permit the development of rating scales of therapist competency based on observations of therapists’ actions in psychological therapy (Vallis et al., 1986). However, if there were strong relationship effects in psychological therapy, these nomothetic assumptions might need to be relaxed. For example, a given patient with depression might respond very well to one CBT therapist and poorly to another, even though both therapists might be highly competent. A second patient might show the opposite pattern of response. Moreover, such relationship effects might not be based on therapist technique, but on features normally considered to be irrelevant to treatment (e.g., facial structure, tone of voice). In addition, patient responses to specific CBT techniques might be more relational than expected. The strength of nomothetic (therapist or statement effects) and idiographic (relationship effects) effects can be estimated as described in this chapter. Patient–Therapist and Patient–Treatment Matching If there are strong relationship effects in psychological therapy, it will be important to attempt to improve treatment effectiveness by matching specific patients with specific therapists. RRT provides the appropriate prediction model for developing the algorithms for use in clinical practice. The key feature of relational prediction is that both the predictor and the outcome must be represented as relationship effects, which requires constructing each patient’s unique profile of responses across each therapist for both criterion (e.g., reduction

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of symptoms) and predictor variables. As depicted in Figure 3.2, the predictor for the Patient 1–Therapist 1 dyad (P1T1) is matched with patient outcome for P1T1, and so on for each patient–therapist dyad. Predictive accuracy is assessed through interclass correlations (e.g., Pearson correlation) in which the units of observation are patient−therapist relationships. Veenstra et al. (2011) demonstrated the prediction model for forecasting relational support. In two studies, recipients and providers had multiple faceto-face conversations over several weeks to months, depending on the study. The outcome variable was relational support assessed over the last two conversations. The predictor variable was relational support assessed after the first 10- or 20-minute conversation, depending on the study. Recipients’ initial judgment of providers’ relational supportiveness forecasted with respectable accuracy (r = .42 and r = .43) recipients’ final judgments of relational supportiveness. The reader might remember that Lakey et al. (2021) concluded that forecasting mutually supportive dyads was not practically possible because of poor agreement between dyad members. However, Lakey et al.’s (2021) conclusion holds only when mutually supportive dyads are the predicted outcome. In Veenstra et al., only recipients’ perspectives were considered. In psychotherapy, only the patient’s perception of the relationship might matter. The implication of Veenstra et al. (2011) for clinical practice is clear. Patients could have brief conversations with therapists, and patient perceptions of the relationship would be used to forecast treatment outcomes. The practical disadvantages of such a procedure are equally clear. Instead of having therapists

FIGURE 3.2.  The Structure of Relational Forecasting 20 18 16 14

Patient 1 Predictor

12

Patient 1 Criterion

10

Patient 2 Predictor

8

Patient 2 Criterion

6

Patient 3 Predictor

4

Patient 3 Criterion

2 0

Therapist 1

Therapist 2

Therapist 3

Note. Predictor and criterion variables are represented as patient × therapist profiles. Adapted from “Understanding the P × S Aspect of Within Person Variation: A Variance Partitioning Approach,” by B. Lakey, 2016, Frontiers in Psychology, 7, p. 6 (https://doi.org/ 10.3389/fpsyg.2015.02004). Copyright 2016 by B. Lakey. CC BY.

70  Brian Lakey

meet with prospective patients, perhaps standardized information about therapists could be presented to patients. Many practices already provide information about therapists to aid patients in forecasting their own matches. Veenstra et al. (2011) demonstrated how studies of relational forecasting could be conducted with such information. The question is how much information is required to permit accurate forecasting. If a still photo and a brief biographical statement are insufficient, would video presentations of therapists be more effective? Relational forecasting using video has been tested for students’ reactions to professors’ teaching (Gross et al., 2015). Students were presented with 6-minute video highlights of professors’ actual classroom teaching. Later in the semester, students heard 50-minute guest lectures presented by the same professors. The relational components of students’ reactions to professors on video forecasted relational reactions of professors’ actual lectures weeks to months later. This study suggests that video can be used a tool for relational forecasting. Nonetheless, there might be nuances that could complicate the use of video for relational forecasting in therapy. For example, J. L. Cohen (2006) attempted to predict students’ relational reactions to therapy demonstration videos from videos depicting the same therapists talking about their approach to treatment. Unexpectedly, reactions to the therapeutic approach videos did not forecast reactions to the demonstration videos. Thus, talking about one’s approach might be a poor indicator of how one interacts with patients. A simpler approach to achieving more effective patient–therapist matches might be to frequently assess patient outcomes, and if sufficient progress has not been made, invite the patient to see a different therapist. The procedure could be repeated until an optimal patient–therapist match occurred. This is likely how people find optimal supportive relationships in their natural environment: continuously sample providers and spend more time with supportive providers and less time with unsupportive providers. An advantage of this approach to patient–therapist matching is that its implementation does not require the previous development of validated assessment procedures. A disadvantage is its cost, and it might not be appropriate for patients with some types of personality pathology. Similar approaches can be applied to matching patients with treatments or treatment components. In such analyses, treatment components are treated as though they are providers, and patients are exposed to multiple components. For example, Lakey and Ondersma (2008) exposed a clinical sample of women who used illegal drugs during pregnancy to three brief components of a motivational intervention. Women rated their state motivation to use drugs after each component. There were substantial relationship effects whereby patients differed in the component that boosted their state motivation. For example, one patient showed a large increase in motivation to reduce drug use following the goal setting component, but a large decrease in motivation following the pros and cons component. A second patient showed the opposite pattern. Expressing patient–component matches from traditional individual differences variables (e.g., self-efficacy) were not successful. As applied

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in a clinical setting, brief exposures to treatment components might be used to forecast reactions to the full components.

CASE EXAMPLES If relationship effects are large in psychotherapy, it would suggest a new perspective in case studies. Typically, case studies are described from the perspective of a single clinician based on the clinician’s interactions with the patient. The nomothetic assumptions of psychotherapy imply that other clinicians would see the patient in the same way, and how the patient responds to the therapist’s techniques should generalize to other therapists. However, relationship effects in psychotherapy are unobservable by a single clinician unless that clinician observes the patient interacting with other therapists. Thus, case studies in relational regulation would be from the perspective of patients rather than therapists. The following brief case examples demonstrate this shift in perspective. Each patient described experiences with three therapists. Therapists are described in the order in which the patient saw the therapist. As suggested by RRT, the patients respond quite differently to different therapists and techniques. Case Example 1 Patient 1 described treatment with three therapists.4 Two of the therapists were very useful, and one was not. Although one might be tempted to conclude that the second therapist is poor, as reviewed previously, therapist effects are small, and, thus, the poor outcome with Therapist 2 might primarily reflect a poor patient−therapist match. When the therapists were helpful, the patient most valued the quality of the therapeutic relationship rather than the application of specific therapy techniques. Therapist 1 The patient saw the therapist for grief counseling after the patient’s mother died and remained in treatment for 4 years. Their meetings were awkward at first, and the patient thought the therapist might lack self-confidence. However, as their sessions continued, the therapist became an important attachment figure who accepted the patient and validated the patient’s emotional experience. As the therapeutic relationship improved, therapeutic technique was emphasized less, and their interactions became more informal. This was especially valuable to the patient given that the patient’s mother had recently died. The therapist’s acceptance of the patient’s emotional experiences helped the patient understand that there were mental health issues beyond normal grief.

The names of the patients in these case examples are disguised to protect their identities.

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Therapist 2 The patient saw Therapist 2 during a semester at a religious college. The patient experienced a very poor therapeutic relationship with the therapist and did not benefit from treatment. The patient perceived the therapist as cold and distant. The therapist explained the patient’s unhappiness as a deficiency in counting blessings. The therapist asked scripted questions but did not seem interested in the patient’s emotional experience. The patient worried about confidentiality as information seemed to pass between the therapist and the patient’s dormitory resident assistant. Therapy was terminated after the patient left the college after one semester. Therapist 3 Treatment with Therapist 3 started awkwardly but, ultimately, developed into an important therapeutic relationship that lasted over 8 years and was terminated only when the patient moved to different region of the country. The treatment facility was in a small town and the patient initially had concerns that the therapist knew well the patient’s partner’s family. Thus, the patient was worried about boundaries and confidentiality. The patient experienced the partner as controlling and abusive. Normally, the patient interpreted this experience as a reflection of the patient’s own mental health problems. However, because the therapist also knew the patient’s partner well, the therapist helped the patient develop a more nuanced view. This therapist also became an important attachment figure. As with Therapist 1, their interactions during sessions gradually moved from more formal, technique focused interactions to more informal opportunities to vent and receive constructive feedback. This relationship overlapped with the patient’s first manic episodes, and the patient trusted the therapist to know when the patient needed more intensive care. The therapist seemed committed to the patient, as indicated by staying late when the patient needed to be seen and by visiting the patient in a nontherapist capacity during an inpatient stay. The patient’s only complaint was that, occasionally, attempts to challenge cognitive distortions seemed dismissive of realistic concerns. Case Example 2 Patient 2 also responded quite differently to three therapists. A theme found with two of the therapists was the patient’s adverse reaction to a widely used set of evidence-based interventions and the difficulty in getting the therapists to change course. Therapist 1 Patient 2 was in treatment with Therapist 1 for 4  years to address depressive symptoms. The patient saw the therapist as warm and devoted to developing a good therapeutic relationship. As treatment progressed, the therapist relied more extensively on cognitive interventions such as identifying and challenging automatic negative thoughts. However, the patient found this approach irritating.

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The patient saw the cognitive techniques as a kind of debate about logic puzzles that were mostly irrelevant to the patient’s symptoms. However, the patient liked intellectual debate in general and was easily drawn into them in therapy. Nonetheless, the discussions struck the patient as arguing for the sake of arguing and the patient often felt invalidated if the argument was “lost.” The patient tried to express doubts about the utility of the technique, but the therapist did not seem to know what else to do. Ultimately, therapy moved to more informal conversation, not specifically focused on symptoms, which the patient did not find useful. Concurrently, the relationship seemed to lose its warmth, and the patient terminated therapy abruptly. Therapist 2 The patient saw Therapist 2 for only three sessions. The patient perceived the therapist as believing the therapist had detected something deeply disturbing and dangerous about the patient and wanted to convey to the patient the gravity of the concern. However, the patient saw this as aggressive and threatening. The patient paraphrased the therapist’s point of view as “you’re really going to need to take a good long hard look at yourself to get past this.” The patient abruptly terminated. Therapist 3 The patient ultimately derived substantial benefit from Therapist 3, but treatment got off to a rocky start. The patient initially alerted the therapist that the patient found cognitive techniques irritating, but the therapist still spent 2 years pursuing the techniques without success. The patient continued treatment because they were talking about the patient’s concerns, even though not in the way the patient wanted. The patient saw the therapist as extremely empathic and, although the therapist’s use of cognitive techniques was irritating, the therapist never seemed invalidating. The patient trusted that the therapist would ultimately grasp the patient’s reaction to cognitive techniques. Ultimately, the therapist understood and apologized for not seeing it sooner. The apology was meaningful to the patient and advanced the therapeutic relationship. The techniques that were ultimately successful focused on reducing heart rate, improving breathing, relaxing muscles, and learning to accept thoughts and strong emotion without feeling compelled to act on them.

SUMMARY AND FUTURE DIRECTIONS This chapter described interventions intended to improve low perceived support, a putative risk factor for major depression. The two most common interventions, befriending and support groups, have not fared well in randomized controlled trials. Befriending appears to have no effects. Support groups are more effective than waitlist controls, but not active controls, and are less effective than education. The likely reason that these interventions are ineffective is the assumption that there is good agreement among observers about

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which providers and statements are supportive. Recent research indicates that the evidence for this assumption is weak. Instead, who and what is supportive primarily reflects the unique properties of relationships rather than the stable characteristics of support recipients or providers. Recent theory and research on relational processes were described. Relationship effects are defined mathematically, and there are strong, likely causal, effects, whereby specific providers evoke high or low levels of a range of potential risk factors for depression, including negative affect, low self-esteem, automatic negative thoughts, and dysfunctional attitudes. These effects are relational in that the provider who evokes risk factors in one recipient, will not likely evoke them in another recipient. Perceived support is a marker for relationships that evoke emotional well-being rather than a stable risk factor. Perceived support can perhaps best be modified by psychological therapy, and this chapter described implications for improving the psychological treatment of depression if relationship effects are strong in therapy. Studies of group therapy and analogue studies of psychotherapy suggest relationship effects are strong. Finally, the chapter closes with a plea for clinical scientists to consider the potential benefits of incorporating relationships, as defined mathematically by RRT and the SRM, into theory and research on the psychological treatment of depression. Although the superiority of CBT to placebo controls and CBT’s equal effectiveness to antidepressant medication in acute treatment (e.g., DeRubeis et al., 2005) represent a triumph of evidence-based approaches to the treatment of depression, the triumph is insufficient. In absolute terms, many patients do not respond well to any evidence-based treatments. For example, in DeRubeis et al. (2005), only about half of patients achieved remission after 16 weeks of treatment. Further, there is reason to doubt that CBT works according to the hypothesized mechanisms. For example, behavioral activation alone produces equivalent results to combined behavioral activation and cognitive therapy (Mazzucchelli et al., 2009), raising questions about the unique contribution of cognitive techniques. The field appears to need a conceptual breakthrough to dramatically improve the effectiveness of psychological treatment for depression. Advances in the basic science of relational regulation might contribute to such a breakthrough. Many clinical scientists seem resistant to claims about the importance of relationships in psychotherapy. This is understandable. Most research on relationships in psychotherapy is correlational (e.g., the working alliance), which is not as convincing as RCTs. Claims for the importance of relationships are rooted in theories of psychotherapy that often do not have strong footing in psychological science. The therapeutic techniques of relationship focused therapies often seem to fit poorly with the techniques of more evidence-based treatments. The constructs in interpersonal approaches to psychotherapy strike many clinical scientists as excessively vague. Relationship effects from the SRM or RRT perspective might be more amenable to many clinical scientists. Relationship effects are mathematically defined and thus have the operational clarity expected in basic psychological science. Relationship effects are documented in repeated measures experimental

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designs, which can establish causality. Relationship effects are agnostic with regard to theories of psychological therapy and thus can be applied to constructs ranging from dysfunctional attitudes to the therapeutic alliance. To conclude, RRT and SRM approaches offer a novel perspective and suite of conceptual and methodological tools which, although unfamiliar to many clinical scientists, might contribute to improving the effectiveness of psychological treatment for depression. REFERENCES Barrera, M., Jr., Glasgow, R. E., McKay, H. G., Boles, S. M., & Feil, E. G. (2002). Do internet-based support interventions change perceptions of social support?: An experimental trial of approaches for supporting diabetes self-management. American Journal of Community Psychology, 30(5), 637–654. https://doi.org/10.1023/ A:1016369114780 Barry, R., Lakey, B., & Orehek, E. (2007). Links among attachment dimensions, affect, the self, and perceived support for broadly generalized attachment styles and specific bonds. Personality and Social Psychology Bulletin, 33, 340–353. https://doi.org/ 10.1177/0146167206296102 Brand, E. F., Lakey, B., & Berman, S. (1995). A preventive, psychoeducational approach to increase perceived social support. American Journal of Community Psychology, 23(1), 117–135. https://doi.org/10.1007/BF02506925 Branje, S. J. T., van Aken, M. A. G., & van Lieshout, C. F. M. (2002). Relational support in families with adolescents. Journal of Family Psychology, 16(3), 351–362. https:// doi.org/10.1037/0893-3200.16.3.351 Brown, G. W., & Harris, T. (1978). Social origins of depression: A study of psychiatric disorder in women. Free Press. Classen, C. C., Kraemer, H. C., Blasey, C., Giese-Davis, J., Koopman, C., Palesh, O. G., Atkinson, A., Dimiceli, S., Stonisch-Riggs, G., Westendorp, J., Morrow, G. R., & Spiegel, D. (2008). Supportive-expressive group therapy for primary breast cancer patients: A randomized prospective multicenter trial. Psycho-Oncology, 17(5), 438–447. https://doi.org/10.1002/pon.1280 Cohen, J. L. (2006). A new approach to patient-therapist match: Forecasting therapeutic alliance (Order No. AAI3232074) [Doctoral dissertation, Wayne State University]. ProQuest Dissertations Publishing. Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2), 310–357. https://doi.org/10.1037/0033-2909.98.2.310 DeRubeis, R. J., Hollon, S. D., Amsterdam, J. D., Shelton, R. C., Young, P. R., Salomon, R. M., O’Reardon, J. P., Lovett, M. L., Gladis, M. M., Brown, L. L., & Gallop, R. (2005). Cognitive therapy vs medications in the treatment of moderate to severe depression. Archives of General Psychiatry, 62(4), 409–416. https://doi.org/10.1001/ archpsyc.62.4.409 Dobson, K. S., & Dozois, D. J. A. (2008). Introduction: Assessing risk and resilience factors in models of depression. In K. S. Dobson & D. J. A. Dozois (Eds.), Risk factors in depression (pp. 1–16). Academic Press. https://doi.org/10.1016/B978-0-08-0450780.00001-0 Finch, J. F., Okun, M. A., Pool, G. J., & Ruehlman, L. S. (1999). A comparison of the influence of conflictual and supportive social interactions on psychological distress. Journal of Personality, 67(4), 581–621. https://doi.org/10.1111/1467-6494.00066 Fobair, P., Koopman, C., DiMiceli, S., O’Hanlan, K., Butler, L. D., Classen, C., Drooker, N., Davids, H. R., Loulan, J., Wallsten, D., & Spiegel, D. (2002). Psychosocial intervention for lesbians with primary breast cancer. Psycho-Oncology, 11(5), 427–438. https:// doi.org/10.1002/pon.624

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(2021). When forecasting mutually supportive matches will be practically impossible. Psychological Science, 32(5), 780–788. https://doi.org/10.1177/0956797620984460 Lakey, B., & Cassady, P. B. (1990). Cognitive processes in perceived social support. Journal of Personality and Social Psychology, 59(2), 337–343. https://doi.org//10.1037/ 0022-3514.59.2.337. Lakey, B., Cohen, J. L., & Neely, L. C. (2008). Perceived support and relational influences on psychotherapy process constructs. Journal of Counseling Psychology, 55(2), 209–220. https://doi.org/10.1037/0022-0167.55.2.209 Lakey, B., & Cohen, S. (2000). Social support theory and selecting measures of social support. In S. Cohen, L. U. Gordon, & B. H. Gottlieb (Eds.) Social support measurement and interventions: A guide for health and social scientists (pp. 29–52). Oxford University Press. Lakey, B., & Cronin, A. (2008). Low social support and major depression: Research, theory and methodological issues. In K. S. Dobson & D. J. A. Dozois (Eds.), Risk factors in depression (pp. 385–408). Academic Press. https://doi.org/10.1016/B978-0-08045078-0.00017-4 Lakey, B., & Drew, J. B. (1997). A social-cognitive perspective of social support. In G. R. Pierce, B. Lakey, I. G. Sarason, & B. R. Sarason (Eds.), Sourcebook of social support and personality (pp. 107–140). Plenum. https://doi.org/10.1007/978-1-4899-1843-7_6 Lakey, B., Drew, J. B., & Sirl, K. (1999). Clinical depression and perceptions of supportive others: A generalizability analysis. Cognitive Therapy and Research, 23(5), 511–533. https://doi.org/10.1023/A:1018772421589 Lakey, B., Hubbard, S. A., Woods, W. C., Brummans, J., Obreiter, A., Fles, E., Andrews, J., Vander Molen, R. J., Hesse, C., Gildner, B., Lutz, R., & Maley, M. (2022). Supportive people evoke positive affect, but do not reduce negative affect, while supportive groups result from favorable dyadic, not group effects. Anxiety, Stress, and Coping, 35(3), 323–338. https://doi.org/10.1080/10615806.2021.1965995 Lakey, B., & Lutz, C. L. (1996). Increasing social support: Preventive and therapeutic interventions. In G. R. Pierce, B. R. Sarason, & I. G. Sarason (Eds.), Handbook of social support and the family (pp. 435–465). Plenum. https://doi.org/10.1007/978-1-4899-1388-3_18 Lakey, B., McCabe, K. M., Fisicaro, S. A., & Drew, J. B. (1996). Environmental and personal determinants of support perceptions: Three generalizability studies. Journal of Personality and Social Psychology, 70(6), 1270–1280. https://doi.org/10.1037/ 0022-3514.70.6.1270 Lakey, B., & Ondersma, S. (2008). A new approach for detecting patient–treatment matching in psychological therapy. Journal of Social and Clinical Psychology, 27(1), 56–69. https://doi.org/10.1521/jscp.2008.27.1.56 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(3), 482–495. https://doi.org/10.1037/a0023477 Lakey, B., & Rhodes, G. (2015). The social regulation of affect and self-esteem among opiate dependent adults. Personal Relationships, 22(1), 111–121. https://doi.org/10.1111/ pere.12066 Lakey, B., & Scoboria, A. (2005). The relative contribution of trait and social influences to the links among perceived social support, affect, and self-esteem. Journal of Personality, 73(2), 361–388. https://doi.org/10.1111/j.1467-6494.2005.00312.x Lakey, B., & Tanner, S. M. (2013). Social influences in negative thinking and affect. Cognitive Therapy and Research, 37(1), 160–172. https://doi.org/10.1007/s10608-012-9444-9 Lakey, B., Vander Molen, R. J., Fles, E., & Andrews, J. (2016). Ordinary social inter­ action and the main effect between perceived support and affect. Journal of Personality, 84(5), 671–684. https://doi.org/10.1111/jopy.12190 Lanz, M., Tagliabue, S., & Rosnati, R. (2004). Il social relations model nello studio delle relazioni familiar [Social relations model in family relationships’ study]. Testing Psicometria Metodologia, 11, 197–214.

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Mallinckrodt, B., & Chen, E. C. (2004). Attachment and interpersonal impact perceptions of group members: A social relations model analysis of transference. Psychotherapy Research, 14(2), 210–230. https://doi.org/10.1093/ptr/kph018 Malloy, T. E. (2018). Social relations modeling of behavior in dyads and groups. Elsevier. Marcus, D. K., & Holahan, W. (1994). Interpersonal perception in group therapy: A social relations analysis. Journal of Consulting and Clinical Psychology, 62(4), 776–782. https://doi.org/10.1037/0022-006X.62.4.776 Marcus, D. K., Kashy, D. A., & Baldwin, S. A. (2009). Studying psychotherapy using the one-with-many design: The therapeutic alliance as an exemplar. Journal of Counseling Psychology, 56(4), 537–548. https://doi.org/10.1037/a0017291 Marcus, D. K., Kashy, D. A., Wintersteen, M. B., & Diamond, G. S. (2011). The therapeutic alliance in adolescent substance abuse treatment: A one-with-many analysis. Journal of Counseling Psychology, 58(3), 449–455. https://doi.org/10.1037/a0023196 Matthews, H., Grunfeld, E. A., & Turner, A. (2017). The efficacy of interventions to improve psychosocial outcomes following surgical treatment for breast cancer: A systematic review and meta-analysis. Psycho-Oncology, 26(5), 593–607. https://doi.org/ 10.1002/pon.4199 Mazzucchelli, T., Kane, R., & Rees, C. (2009). Behavioral activation treatments for depression in adults: A meta-analysis and review. Clinical Psychology: Science and Practice, 16(4), 383–411. https://doi.org/10.1111/j.1468-2850.2009.01178.x Moos, R. H., & Clemes, S. R. (1967). Multivariate study of the patient–therapist system. Journal of Consulting Psychology, 31(2), 119–130. https://doi.org/10.1037/ h0024435 Moos, R. H., & MacIntosh, S. (1970). Multivariate study of the patient–therapist system: A replication and extension. Journal of Consulting and Clinical Psychology, 35(3), 298–307. https://doi.org/10.1037/h0030109 Neely, L. C., Lakey, B., Cohen, J. L., Barry, R., Orehek, E., Abeare, C. A., & Mayer, W. (2006). Trait and social processes in the link between social support and affect: An experimental, laboratory investigation. Journal of Personality, 74(4), 1015–1046. https://doi.org/10.1111/j.1467-6494.2006.00401.x Park, M., Cuijpers, P., van Straten, A., & Reynolds, C. F., III. (2014). The effects of psychotherapy for adult depression on social support: A meta-analysis. Cognitive Therapy and Research, 38(6), 600–611. https://doi.org/10.1007/s10608-014-9630-z Siette, J., Cassidy, M., & Priebe, S. (2017). Effectiveness of befriending interventions: A systematic review and meta-analysis. BMJ Open, 7(4), e014304. https://doi.org/ 10.1136/bmjopen-2016-014304 Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000). Genetic epidemiology of major depression: Review and meta-analysis. The American Journal of Psychiatry, 157(10), 1552–1562. https://doi.org/10.1176/appi.ajp.157.10.1552 Tanner, S. M., Lakey, B., Cohen, J. L., MacGeorge, E. L., Clark, R. A., Stewart, S., & Robinson, L. (2018). What is the right thing to say? Agreement among perceivers on the supportiveness of statements. Basic and Applied Social Psychology, 40(5), 329–339. https://doi.org/10.1080/01973533.2018.1509341 Vallis, T. M., Shaw, B. F., & Dobson, K. S. (1986). The Cognitive Therapy Scale: Psychometric properties. Journal of Consulting and Clinical Psychology, 54(3), 381–385. https://doi.org/10.1037/0022-006X.54.3.381 Van der Veen, F. (1965). Effects of the therapist and the patient on each other’s therapeutic behavior. Journal of Consulting Psychology, 29(1), 19–26. https://doi.org/ 10.1037/h0021674 Veenstra, A., Lakey, B., Cohen, J. C., Neely, L. C., Orehek, E., Barry, R., & Abeare, C. A. (2011). Forecasting the specific providers that recipients will perceive as unusually supportive. Personal Relationships, 18(4), 677–696. https://doi.org/10.1111/j.14756811.2010.01340.x

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Wade, T. D., & Kendler, K. S. (2000). Absence of interactions between social support and stressful life events in the prediction of major depression and depressive symptomatology in women. Psychological Medicine, 30(4), 965–974. https://doi.org/10.1017/ S0033291799002251 Wampold, B. E., & Brown, G. S. (2005). Estimating variability in outcomes attributable to therapists: A naturalistic study of outcomes in managed care. Journal of Consulting and Clinical Psychology, 73(5), 914–923. https://doi.org/10.1037/0022-006X.73.5.914 Wright, T. L., & Ingraham, L. J. (1986). A social relations model test of the inter­ personal circle. Journal of Personality and Social Psychology, 50(6), 1285–1290. https:// doi.org/10.1037/0022-3514.50.6.1285 Zimmermann, T., Heinrichs, N., & Baucom, D. H. (2007). “Does one size fit all?” moderators in psychosocial interventions for breast cancer patients: A meta-analysis. Annals of Behavioral Medicine, 34(3), 225–239. https://doi.org/10.1007/BF02874548

4 Interpersonal Risk Factors Jami F. Young, Molly Davis, and Laura Mufson

D

epression is the leading cause of disability worldwide (World Health Organization, 2017). Depression symptoms and disorders rise dramatically during adolescence (Hankin et  al., 2015), and adolescent-onset depression increases risk for recurrence of depression later in life (Rutter et al., 2006). Thus, it is essential to identify modifiable psychological risk and vulnerability factors for depression and to provide evidence-based interventions during adolescence that target these risks. In this chapter, we describe interventions for the prevention and treatment of adolescent depression that are based on interpersonal psychotherapy (IPT), a brief, time-limited intervention that was initially developed for the treatment of adult depression (Weissman et al., 2000). Specifically, we focus on interpersonal psychotherapy for depressed adolescents (IPT-A; Mufson, Dorta, Moreau, et al., 2004), which is an evidence-based treatment for adolescent depression that targets interpersonal risks for depression. We also describe Interpersonal Psychotherapy–Adolescent Skills Training (IPT-AST; Young, Mufson, et al., 2016), an evidence-based interpersonal prevention program for adolescent depression. Consistent with interpersonal theories of depression (e.g., Hammen, 1999; Joiner et al., 1999; Rudolph et al., 2008), IPT is based on the premise that the quality of one’s interpersonal relationships can cause, maintain, or buffer against depression. These interpersonal theories argue that some people possess vulnerability factors which make them susceptible to depression, such as maladaptive interpersonal behaviors or impairments in relationships. Depression further contributes to these interpersonal difficulties. Thus, IPT-based

https://doi.org/10.1037/0000332-005 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  81

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interventions for the prevention and treatment of adolescent depression aim to address interpersonal problems as a means to improve depressive symptoms. Interpersonal interventions are developmentally relevant for adolescents who are becoming increasingly focused on their relationships. They address key developmental issues, such as increasing independence from parents, negotiating peer relationships, and developing romantic relationships. IPT-A targets four key interpersonal problem areas that have been linked in the literature to the onset and persistence of adolescent depression: grief, role transitions, interpersonal role disputes, and interpersonal deficits. IPT-AST also targets these key interpersonal issues and focuses on interpersonal skills training that can be applied to current and future relationship difficulties to reduce depressive symptoms and prevent the onset of depression. In this chapter, we provide a summary of the literature linking these problem areas to depression, followed by a description of IPT-A and IPT-AST, brief case examples, a summary of the research supporting the efficacy of these interventions, and suggestions for future directions.

THE INTERPERSONAL RISK FACTOR LITERATURE Several interpersonal events can contribute to the onset, maintenance, and exacerbation of depression in adolescence. Individual-level (e.g., genetic, cognitive) risk and protective factors likely reciprocally relate to and interact with aspects of the social environment (e.g., peer and family relationships) to influence youth depression (Shortt & Spence, 2006). In this section, we review interpersonal risk factors tied to the problem areas that are addressed in IPT to illustrate the rationale for these interventions. Within each problem area, there are many unique interpersonal situations that can confer risk for depression. For brevity, we provide several of the most common ways that these interpersonal risk factors may manifest and highlight how IPT-based interventions help adolescents cope with these interpersonal challenges. Role Transition Adolescence is a period marked by shifts in social roles. Many adolescents want increased autonomy from their parents and to spend more time with their peers. These desires can lead to increased disputes with parents, particularly when parents have different expectations for independence than their adolescent children (Juang et al., 1999). Moreover, some adolescents may struggle to obtain autonomy while also maintaining positive relationships with parents, and difficulties in these areas predict increases in depressive symptoms (Allen et  al., 2006). The desire for increased autonomy can be addressed in IPT-A under the framework of a role transition or as a role dispute. Other role transitions in adolescence include both expected and unexpected life events. Common role transitions include starting a new school, moving, or experiencing parental divorce.

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These role transitions can pose risks for depression (Newman et al., 2007; Simoni & Bauldry, 2020), particularly for adolescents with limited social supports. Over the course of adolescence, the educational transitions youth experience (e.g., the transition to high school or the beginning of college) require adolescents to adjust to new social circumstances, including developing relationships with new peers and teachers and increasing their academic independence. Spikes in depressive symptoms can coincide with these transitions. For instance, increases in depressive symptoms from eighth to ninth grade have been documented (Newman et al., 2007). Studies suggest that social support from peers, teachers, and parents serve a protective function and lack of support contributes to depression risk during these transitions (e.g., Young et al., 2005). Moving is another life event that can lead to a cascade of transitions, including forming new peer networks, all while trying to maintain previous relationships. Moving in adolescence has been associated with higher levels of depressive symptoms, at least partially due to loss of support from caring adults (e.g., Simoni & Bauldry, 2020). Finally, the experience of parental divorce (Sands et al., 2017), in conjunction with the changes that ensue as a result (e.g., stepfamily formation; Shafer et al., 2017), can confer risk for depression. In IPT-based interventions, it is important to identify the specific role transition to understand how the transition has impacted the adolescents’ relationships, and to identify the supports adolescents have in their lives to help them cope with these changes. This process informs the clinician about how to best to intervene. For instance, for some adolescents, a major focus may be finding ways to stay connected to peers from their old school or community. For others, a greater emphasis may be placed on establishing new relationships or developing skills to help the adolescent adjust to their new social role. Finally, providing adolescents with communication strategies to productively negotiate these transitions with parents is often important, particularly when the parent or caregiver has difficulty with the adolescent’s desire for increased autonomy and time with peers. Role Disputes Interpersonal role disputes refer to conflicts in important relationships, particularly when individuals have mismatched expectations about the relationship. A large body of literature links conflict with family and peers to adolescent depression (e.g., Allen et al., 2006; Cohen et al., 2015). For example, conflictual interactions with parents predict future depressive symptoms (Allen et al., 2006), and adolescents with elevated depressive symptoms report more conflictual relationships with their parents (Sheeber et al., 2007). Studies have further found that conflict with parents and adolescent depression reciprocally predict one another (Brière et al., 2013). As described under role transitions, there may be increased conflict with parents specifically around issues of autonomy, spending time with friends, and romantic relationships. Other common areas of conflict between adolescents and parents include chores,

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academic performance, and time spent on technology. Sibling conflict is also common and can contribute to depression. Longitudinal research that has tracked sibling relationships and youth adjustment across middle childhood and adolescence has demonstrated an association between increases in sibling conflict and increases in depression symptoms (Kim et al., 2007). Ongoing and unmanageable conflict can serve as a catalyst for the development of depression, and depression and conflict may reciprocally exacerbate each other. While often discussed in terms of family relationships, role disputes also take place in the context of peer and romantic relationships. Further, conflict with parents and peers often co-occur, and conflict in these relationships predicts increased risk for depression symptoms longitudinally (Cohen et  al., 2015). Arguments with friends have been found to predict increased next-day depressed affect, and higher depressed affect also predicted greater likelihood of arguments with friends the next day (Vannucci et al., 2018). This finding supports the reciprocal relationship between mood and what happens in important relationships. Using an observational task, Allen and colleagues (2006) found that adolescents who displayed anger and hostility in an interaction with their best friend had greater relative increases in future depressive symptoms. Dating is a new social experience for many adolescents; with this experience comes the potential for conflict and relationship dissolution, both of which can contribute to depression. A recent romantic breakup has been found to predict first onset of major depressive disorder in adolescence (Monroe et al., 1999). Taken together, the research suggests that helping adolescents better handle conflicts in their relationships may lead to improvements in depression and prevention of later symptoms. Techniques for reducing conflict, such as effectively communicating in ways that decrease the likelihood of conflict in the first place and pausing conversations when they become too emotionally charged, are important components of IPT-based approaches. Work in IPT-A and IPT-AST may also focus on helping the adolescent communicate their expectations more effectively, as well as assess whether their expectations for a given relationship can be met. Interpersonal Deficits A lack of social skills can interfere with the initiation and maintenance of close relationships with family and peers and may contribute to social isolation and low levels of support. An interpersonal model of youth depression outlined by Rudolph and colleagues (2008) highlighted how social-behavioral deficits (e.g., ineffective interpersonal problem solving, excessive reassurance seeking) can lead to relationships disturbances that then contribute to depression. Once depressed, adolescents may be more likely to respond in ways that contribute to and maintain these relationship difficulties, sub­sequently leading to further social isolation. Social skills deficits among youth have been linked to problems such as loneliness (Lodder et  al., 2016), which can predict depression (Qualter et al., 2010). There is also evidence for bidirectional associations between depressive symptoms and loneliness among adolescents,

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suggesting that loneliness and depressive symptoms may maintain and exacerbate one another (Vanhalst et al., 2012). Additionally, a direct relationship between social skills difficulties and youth depressive symptoms has been observed. For example, Nilsen and colleagues (2013) found that less-developed social skills in early adolescence predicted increased depressive symptoms in late adolescence. Moreover, this relationship was mediated by lower levels of friend support in middle adolescence for girls. Lack of social support from parents, as well as peers, plays a critical role in youth depression (e.g., Hankin et al., 2018; Stice et al., 2004). Adolescents with symptoms of depression report less-supportive relationships with their parents than adolescents without emotional difficulties (Sheeber et al., 2007). Stice and colleagues (2004) found that deficits in parent but not peer support longitudinally predicted increases in depressive symptoms and onset of major depression in adolescent girls. As is the case with many other interpersonal vulnerabilities, the relationship between support and depression is reciprocal. While deficits in parent support predicted increases in depression, initial levels of depressive symptoms, as well as a major depression diagnosis, predicted future decreases in peer but not parental support (Stice et al., 2004). Taken together, bolstering social skills and supports can be an important avenue to reduce negative social experiences and decrease risk for escalation or onset of depressive symptoms. Given the important buffering role of social support against depression in the face of life events, increasing social support is a goal of IPT-A for all adolescents, regardless of problem area; it is the predominant focus for adolescents with interpersonal deficits who have limited supports. Similarly, IPT-AST encourages youth to access their existing supports and develop new supportive relationships, both among group members and in their social networks. Grief The experience of grief following the loss of a loved one such as a parent or sibling can take a toll on the mental health of children and adolescents (e.g., Gray et al., 2011; Harrison & Harrington, 2001). Experiencing the deaths of parents, siblings, grandparents, aunts or uncles, and close friends have all been found to be associated with depressive symptoms (Harrison & Harrington, 2001). The most robust literature linking grief and youth depression has focused on the loss of a parent. For example, children and adolescents who have experienced the death of a parent were more likely to experience a major depressive episode or a subsyndromal depressive episode within several months of the parent’s passing compared to nonbereaved youth (Gray et  al., 2011). Mothers’ warm, sensitive, and engaged communication with their child after the death of the father was associated with lower levels of maladaptive grief (i.e., atypical reaction to a loss in terms of severity, length, or symptoms) and depressive symptoms (Shapiro et  al., 2014). This association suggests that targeting communication with the surviving caregiver may be a key intervention following the loss of a parent.

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Loss of peers and siblings has also been associated with adolescent depression. Much of the literature focuses on depression and complicated grief in the context of traumatic deaths. In particular, exposure to suicide by both siblings and peers has been found to be related to major depression in adolescents (e.g., Brent et al., 1993). Among adolescents exposed to a fatal school bus accident in which several students were killed, a significant portion reported elevated depression symptoms 2 months after the accident, with many reporting continued depression symptoms at 18-month follow-up (Giannopoulou et al., 2021). While the literature typically focuses on traumatic deaths, the death of a peer or sibling to illness or other natural causes may also lead to depression in some adolescents. As described below, grief work in IPT-A helps the adolescent to mourn their loss by discussing the relationship in detail to prevent an idealized view of the deceased, which can make it difficult for the adolescent to move forward. Additionally, the therapist helps the adolescent strengthen existing relationships and develop new relationships to substitute for some of the supports that the adolescent received from their deceased family member or friend. Important intervention targets include identifying trusted adults and peers for the adolescent to confide in and helping adolescents plan conversations with those individuals.

INTERPERSONAL PSYCHOTHERAPY FOR DEPRESSED ADOLESCENTS Next, we provide an overview of IPT-A, which targets the interpersonal risk factors described earlier. IPT-A is a guideline-based outpatient treatment for adolescents with mild to moderate depression (see Mufson, Dorta, Moreau, et al., 2004, for more details). The typical course of treatment is 12 to 15 sessions (depending upon parental involvement) conducted in 12 weeks, although IPT-A can be extended to 16 weeks, especially when delivered in the school setting. IPT-A is divided into three phases of treatment: the initial phase (Sessions 1–4), the middle phase (Sessions 5–9), and the termination phase (Sessions 10–12). Most sessions are conducted individually, but parents are encouraged to be involved in the three phases of treatment to learn about depression and the focus of treatment, and to support the adolescent’s interpersonal work. The therapist and adolescent both play an active role in the sessions, which focus on the interpersonal nature of the depression. Each session begins with a brief assessment of the adolescent’s current depressive symptoms and a weekly mood rating, noting any changes that occurred over the course of the week and linking changes in symptoms to interpersonal events. This enables the therapist to identify links between interpersonal events and mood and to monitor improvements in the adolescent’s symptoms over the course of treatment. Following this symptom review, the session progresses to the tasks associated with each phase of treatment.

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Initial Phase of IPT-A During the first four sessions of IPT-A, the therapist assesses the adolescent’s depressive symptoms, provides education about depression and the focus of treatment, explores the adolescent’s current relationships by conducting an interpersonal inventory, and identifies the problem area that will be the focus of treatment. In the first session, the therapist conducts a more detailed assessment of the adolescent’s depression symptoms using a validated self-report measure or clinical interview. In later sessions, the review of symptoms is briefer, although it is common to have the adolescent complete a depression measure midway through the treatment and in the termination phase to assess progress. Part of the psychoeducation includes assigning the adolescent the limited sick role. The limited sick role acknowledges that, similar to someone with a medical illness, an adolescent with depression may not be functioning as well as they did before they were depressed. The goal of this conceptualization is to shift the blame for poor functioning from the adolescent to the depression and contributing circumstances while encouraging the adolescent to continue to engage in their normal activities. The expectation is that the adolescent’s performance will improve as the depression remits, and this is monitored as part of the weekly symptom check-in at the beginning of each session. It is important to share this concept with the parent so that they can be more supportive and scaffold participation in typical activities until the adolescent is feeling and functioning better. One of the core strategies in the initial phase of IPT-A is the interpersonal inventory, which is an in-depth assessment of the adolescent’s current important relationships. The goal of the inventory is to identify the relationships or interpersonal patterns that are most closely linked to the adolescent’s depression. The adolescent first identifies important people in their life using the closeness circle. Next, the therapist asks detailed questions about each relationship, including positive and negative aspects of the relationship, areas of conflict, things that can and cannot be discussed with the other person, the reciprocal influences of the relationship and the adolescent’s mood, and aspects of the relationship the adolescent would like to change. The inventory culminates in the adolescent and therapist choosing one of the four interpersonal problem areas to focus on in the middle phase of treatment: grief, interpersonal role disputes, role transitions, or interpersonal deficits. Grief is the identified problem area if the adolescent is experiencing a depressive episode in response to a death of a significant other, either recently or in the past. A role dispute is the focus when the inventory uncovers a disagreement over an adolescent’s role within a particular relationship, such as with a parent or peer. Role transitions refer to relationship changes that occur when an adolescent experiences a change in role in the family or peer group such as transitioning to high school, individuating from parents, or adjusting to a change in family status due to a birth, divorce, or an illness in the family. Interpersonal deficits are identified when an adolescent lacks the social skills

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needed to develop and maintain close relationships and thus is socially isolated and unsure of how to initiate new relationships. At the end of the initial phase, the therapist summarizes what they learned in the inventory in an interpersonal formulation. In the formulation, the therapist explains the connection between the depressive symptoms and the specific interpersonal difficulty that seems to be having the greatest impact on the adolescent’s mood. The formulation provides a rationale for focusing treatment on a particular interpersonal problem or a specific relationship to help the adolescent to feel better. In cases in which more than one problem area may be appropriate, the therapist and adolescent decide together which would be best to focus on initially. This decision is guided by the adolescent’s willingness to work on a particular relationship or problem area. For instance, if the inventory uncovers a role transition related to an adolescent’s romantic partner going to college as well as long-standing conflict with a parent, the therapist and adolescent would decide which of these to work on first, starting with the problem area most closely linked to the depression and/or the relationships most amenable to change. Ideally, there will be time to work on both issues over the course of treatment. It is important to ensure that the adolescent agrees with and understands the formulation and can explain it back to the therapist, as the formulation sets the stage for the work that will be conducted in the middle phase of treatment. Middle Phase of IPT-A During the middle phase of IPT-A, the therapist and adolescent discuss the problem area in greater detail, focusing on the identification and implementation of communication and interpersonal problem-solving skills that can improve the adolescent’s relationships. In the case of grief, the IPT-A therapist helps the adolescent mourn the loss of the deceased, while encouraging the adolescent to strengthen and develop other relationships. In the middle phase, the therapist discusses the adolescent’s relationship with the deceased in detail, as well as the actual death. During this process, the therapist encourages the adolescent to identify and express feelings associated with the relationship and the loss to create a more realistic memory of the deceased. As treatment progresses, the therapist encourages the adolescent to develop new relationships or to further strengthen established relationships to help replace the support and roles that were lost. This involves exploring the adolescent’s fears about developing new relationships and rehearsing skills needed to develop relationships or to engage in new activities. In a role dispute, the middle phase of treatment focuses on clarifying the dispute and working with the adolescent and the other person, if possible, to modify maladaptive communication patterns and any unrealistic expectations about the relationship. If the conflict involves a parent, the parent is invited into a middle phase session to try and resolve the problem, with a focus on how the adolescent and parent communicate about important issues in their relationship. If a resolution to the interpersonal role dispute does not

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seem possible, the therapist works with the adolescent to develop strategies to cope with this relationship. This work may involve small changes in the adolescent’s communication or expectations that could result in a decrease in the frequency of conflict, thereby reducing the impact of the conflict on the adolescent’s mood. In the case of a role dispute with a peer or romantic partner where resolution is not possible, the therapist would support the adolescent in the dissolution of this relationship. The therapist would also encourage the adolescent to seek out other positive relationships. For adolescents with a role transition focus, the IPT-A therapist encourages the teen to explore the gains, losses, and demands associated with the life change. In addition, the therapist works with the adolescent to develop skills that are needed to successfully manage the new role. For example, if the adolescent is having difficulty adjusting to the transition to high school and having to interact with multiple different teachers, the therapist may help the teen develop skills to communicate more effectively with these teachers about their expectations. If the role transition involves the adolescent’s parents, they can be invited to a middle phase session to support the transition and address any differences in expectations about this new role. The middle phase treatment for interpersonal deficits involves reviewing current relationships in more detail, with a focus on problematic patterns in these relationships. Some adolescents with interpersonal deficits have limited current relationships, so the middle phase may also include a review of past relationships. The therapist helps the adolescent recognize the link between these interpersonal problems and the depression and introduces new strategies for strengthening interpersonal relationships. For example, a middle-phase session might involve a discussion about how best to strengthen a friendship, having the adolescent practice asking the friend to do something outside of school, and then encouraging the adolescent to try this outside of session and to report back in the following session. Parents may be involved in middle phase sessions, particularly to help support the adolescents as they develop and practice these skills. There are several IPT-A techniques that are used across the various problem areas in the middle phase. These include encouraging the adolescents to express how they feel in their relationships and clarifying their expectations for relationships. For instance, in the case of an interpersonal role dispute, a discussion of whether an adolescent’s current romantic partner is meeting their expectations for what a relationship should look like may take place. It is then possible for the adolescent to assess whether these expectations are realistic and learn how to express their feelings about what is happening in this relationship more effectively. One technique used in the middle phase across the problem areas is communication analysis. In communication analysis, the therapist dissects specific interactions that occurred between an adolescent and another person to help the adolescent understand the impact of their words and nonverbal communication on others. The goal is to identify ways to modify aspects of the communication to change the interaction and its associated feelings. As an

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example, if an adolescent has interpersonal deficits, the therapist might use communication analysis to unpack an interaction that the adolescent had with a classmate to try and identify opportunities for skill development, as well as to highlight moments when the adolescent was able to successfully engage their peer. In a role transition or an interpersonal role dispute, a communication analysis can be helpful if the adolescent reports a recent disagreement or misunderstanding to assess how the conversation was handled and identify opportunities for improving the interaction by using specific communication strategies or problem-solving skills. Communication analysis is often followed by a discussion of specific communication strategies that might be helpful, such as acknowledging the other person’s point of view or using an “I statement” to communicate how the adolescent is feeling. These communication strategies, which are helpful across problem areas, are outlined in a teen tips handout, which can be given to the adolescent. After the therapist and adolescent identify more adaptive communication strategies, it is helpful to role play new interaction patterns using one or more of these identified strategies. Role playing helps the adolescent practice new interpersonal skills and techniques that they can apply to important relationships, and it provides an opportunity for the therapist to give the adolescent feedback. Following the role play practice in session, the adolescent can be assigned interpersonal homework experiments, where they are asked to have the conversation on their own, outside of the session, and to report back on how it went. It can be helpful for some interpersonal problems to conduct a decision analysis to determine the best course of action for an adolescent to pursue. Decision analysis, like other forms of problem solving, includes identifying the specific problem, generating potential solutions, evaluating the pros and cons of each solution, and selecting a solution to try. For a role dispute with a friend, the therapist might use a decision analysis to help the adolescent weigh the pros and cons of ending the friendship, talking to another peer or family member about what is going on to get support, waiting to see if things resolve, or having a conversation with the friend about the dispute. If the adolescent decides on a solution which involves a future conversation, such as talking directly to the friend or seeking support from a peer or family member, the therapist and adolescent can plan and role play that interaction, increasing the likelihood of a positive outcome. Decision analysis helps adolescents develop interpersonal problem-solving skills that they can use within and outside of sessions. Finally, the therapist links improvements in the adolescent’s mood and depressive symptoms to their improved communication and decision making in important relationships. Termination Phase of IPT-A In the termination phase, the therapist helps the adolescent identify warning signs of future depressive episodes and a plan of action if the depression recurs. This plan can include a discussion about whether further treatment is

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needed. The therapist and adolescent review the adolescent’s progress, especially regarding the identified problem area. Changes in interpersonal relationships are linked to improvements in mood and reductions in symptoms, which have been monitored weekly. The therapist and adolescent highlight which specific strategies have been most helpful, and how the adolescent has successfully implemented them during interpersonal experiments. The therapist emphasizes the importance of continued implementation of these strategies after termination, including a discussion about how these skills might be helpful in future situations and for other relationships in the adolescent’s life. IPT-A Case Example Kayla was a 15-year-old cisgender girl, referred for treatment for depression by her mother.1 Her mom (a cisgender woman) reported she was having conflict with her daughter, particularly around the increased amount of time Kayla was spending with her boyfriend. During the interpersonal inventory, Kayla spoke about her relationship with her parents, younger sister, two close friends and her boyfriend Mateo (a cisgender boy). She reported tension in her relationship with her stepfather, the father of her 2-year-old sister, and significant conflict with her mother. She reported feeling close with her friends, although she did not want to burden them too much with her problems. Her main source of support was her boyfriend. Mateo’s mom was very accepting of Kayla, which made her feel more comfortable at his house than her own. This was her first romantic relationship, and she was nervous about how to navigate their relationship, from how much time should they spend together to what was appropriate for their physical relationship. In addition, she was aware that her mother was very nervous about the relationship and thought she was too young to have a boyfriend. Her symptoms of depression and conflict with her mom worsened following an argument when she told Kayla she did not want her spending so much time at Mateo’s home. At the end of the initial phase, the therapist shared the interpersonal problem area formulation with Kayla. She explained that it sounded like the relationship that was causing Kayla the most distress and may be triggering or contributing to her depression was conflict with her mother, which was occurring in the context of many changes, including having a new sister, becoming a teenager, and starting her first romantic relationship. As a result of these many changes, her role in the family shifted, and her expectations for herself and her mother’s expectations were not fully aligned. Additionally, the therapist shared that Kayla seemed to be having trouble communicating about how she felt to her mom and, thus, felt that her mom did not understand what is going on with her. Kayla thought the formulation was accurate and was able to explain it in her own words to the therapist.

The specifics of these case examples have been altered to protect the identities of the patients.

1

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The goals of the middle phase of treatment were to help clarify Kayla’s expectations for herself and her mom, and to prepare Kayla for communicating these expectations and accompanying emotions to her. Kayla was able to share different interactions that involved her mom each week, and the therapist conducted several communication analyses of these interactions. The next step involved helping Kayla plan a conversation to hear her mom’s concerns about her relationship with Mateo and to share what Kayla enjoys about her relationship with him. This facilitated Kayla’s ability to see her mom’s perspective, provided practice expressing herself clearly, and helped both Kayla and her mom talk about their feelings about Kayla having a boyfriend. To help Kayla in her communications, the therapist introduced her to the teen tips handout so that she could practice strategies such as using “I statements” and “give to get” to show understanding of the other’s point of view. Kayla and the therapist role played a conversation in which Kayla could share her feelings with her mother and hear her mom’s concerns about Mateo. Kayla carried out this initial conversation at home with her mother, but she and the therapist thought a dyadic session would be helpful to negotiate the expectations about time spent with her boyfriend since this was an emotionally charged topic for both of them. In the dyadic session, the therapist could coach them to listen and communicate their feelings and to better help Kayla address her mom’s concerns. Part of Session 8 was a dyadic meeting with Kayla and her mom. With coaching, both were able to talk about Kayla’s mom’s fears that Kayla might get pregnant before finishing high school. Kayla shared that she did not want that to happen, validated her mom’s concerns, and told her that she had no plans to be sexually active at this time. Kayla shared what she enjoyed about her time with Mateo and that she would like to be able to bring him to their house so her mom could get to know him and trust her more when she is with him. Her mom also shared her concerns that the relationship would negatively impact Kayla’s school performance. They began a discussion about how to make sure Kayla is prioritizing school and helping at home, while also being able to spend time with Mateo. In Session 9, Kayla reported that she and her mom were talking more rather than arguing and that Kayla was spending less time at Mateo’s house, though her mom still had not agreed for him to come to her house. The therapist encouraged Kayla to continue to work on her interactions with her mom and reflected that these same communication skills would be helpful in her relationship with her stepfather and her boyfriend. During the termination phase, the therapist and Kayla reviewed the significant decrease in her depression symptoms and improvement in her mood ratings during the middle phase of treatment. Kayla was prompted to identify the signs of relapse or recurrence of her depression based on specific symptoms and behaviors. They reviewed the specific interpersonal experiments that she had implemented along with the strategies that had been helpful such as “give to get,” “I statements” and “strike while the iron is cold.” The therapist also asked Kayla to think about upcoming events or situations over the next 6 months and to brainstorm how she might use these and other strategies to

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help her navigate them successfully. One situation that they discussed was her plans for the upcoming summer and her desire to get a job rather than stay home to take care of her sister. Kayla was able to plan how to share her feelings and goals for the job in addition to understanding that her help is needed around the house. Kayla shared some concerns about whether she would be able to be successful in her conversations with her mom after therapy ends. She also was proud of the successes she had communicating with her mom on her own outside of the sessions, which gave her some confidence that she would be able to keep working on their relationship. Her mom joined for part of a termination session where they reviewed Kayla’s progress in treatment, highlighted the work that they should both continue to do to help their relationship, and reviewed options for returning for treatment if needed. Treatment concluded with Kayla feeling like she had some new skills and perspective on how important it is to communicate with her mom and in other relationships rather than avoid interactions when there are disagreements, and to share her feelings rather than expect people to read her mind. She was no longer feeling sad and was hopeful that she and her mom would continue to be more open and communicative. Evidence of Efficacy of IPT-A Several randomized controlled clinical trials of IPT-A have been conducted by different research teams. These trials have demonstrated the efficacy of IPT-A for the treatment of adolescent depression. IPT-A has been included as an efficacious treatment in the Substance Abuse and Mental Health Services Administration’s (SAMHSA) National Registry of Evidence-based Programs and Practices and is one of the recommended first line treatments for adolescent depression in the American Psychological Association’s (APA, 2019) depression guidelines. In the initial efficacy study of IPT-A, significantly more adolescents who received IPT-A met recovery criteria for major depression than adolescents in clinical monitoring. IPT-A adolescents also reported a significant decrease in depressive symptoms and improvements in social functioning relative to the control condition (Mufson et al., 1999). Rosselló and Bernal (1999) and Rosselló et al. (2008) have examined different adaptations of IPT designed specifically for adolescents with depression in Puerto Rico, including both individual and group models of IPT. In these studies, adolescents receiving IPT experienced reductions in depression symptoms and improvements in social functioning. Recently, another research group compared the efficacy of group and individual IPT-A in a small study of adolescents with major depressive disorder (O’Shea et al., 2015). There were significant improvements in depression, anxiety, and global functioning following treatment. There were no significant differences in outcomes between group and individual formats of IPT-A. Secondary data analyses of this study revealed significant improvements in interpersonal functioning and changes in attachment style following treatment (Spence et al., 2016), in line with IPT-A’s focus on improving interpersonal relationships.

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In addition to these efficacy trials, there is also evidence of the effectiveness of IPT-A when delivered by school-based clinicians (Mufson, Dorta, Wickramaratne, et al., 2004). Adolescents who received IPT-A demonstrated greater decreases in depression symptoms and greater improvements in overall functioning and social functioning than adolescents who received usual care in the school-based health clinics. Finally, IPT-A has been tested as part of a stepped care model in primary care. Adolescents received eight weekly sessions of brief IPT-A, followed by either three IPT-A maintenance sessions or IPT-A plus medication. IPT-A was delivered by clinicians embedded in primary care. Adolescents who received IPT-A experienced greater reductions in depression symptoms than youth in enhanced treatment as usual (Mufson et al., 2018), providing initial support for the effectiveness of IPT-A in primary care.

INTERPERSONAL PSYCHOTHERAPY–ADOLESCENT SKILLS TRAINING IPT-AST is a group-based depression prevention program (see Young, Mufson, et al., 2016, for more details). IPT-AST was initially designed as an indicated preventive intervention for adolescents with elevated symptoms of depression. It has also been delivered as a universal prevention program as well as for adolescents who are at risk for depression based on high parent–child conflict or low peer support, two interpersonal risks for depression (Allen et al., 2006; Hankin et al., 2018; Rudolph et al., 2008; Sheeber et al., 2007). The typical course of IPT-AST includes one or two individual pregroup sessions, eight group sessions, and an individual midgroup session. IPT-AST is a relatively active intervention and includes a brief assessment of the adolescents’ depression symptoms over the prior week. Parents are invited to attend part of a pregroup session to learn about the program and part of the midgroup session so teens can practice the interpersonal strategies with their parent. The group component is divided into phases: initial, middle, and termination. Unlike IPT-A, the adolescent is not assigned an interpersonal problem area. Rather, IPT-AST focuses on general interpersonal skill building that can be applied to different relationships to address the interpersonal vulnerabilities outlined earlier in this chapter. IPT-AST Pregroup Sessions Depending on the setting, the pregroup session can be one longer session (75–90 minutes) or two 45-minute sessions. The pregroup sessions provide an opportunity for the IPT-AST leader to get to know the adolescent, review their depression symptoms, explain the purpose of the group, and conduct an abbreviated interpersonal inventory. The adolescent is told that the purpose of the group is to teach them interpersonal strategies to improve their relationships and help prevent them from developing depression. The remainder

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of the pregroup session(s) is spent completing an abbreviated interpersonal inventory with the adolescent, asking the types of questions outlined earlier. The inventory in IPT-AST is briefer and goes into less detail than when conducted in IPT-A. Ideally, the leader and adolescent will discuss four or five of the most important relationships, with an attempt to discuss parents, one or two peers, and an additional relationship or two. After completing the inventory, the leader and adolescent identify two interpersonal goals. These goals may be focused on a particular relationship, such as arguing less with their mother, or may be more general, such as talking more to people when the adolescent feels upset. While some of these goals may align with the inter­ personal problem areas, the IPT-AST leader does not assign a particular problem area, as the group program focuses on interpersonal skill building that applies across different relationships and problems. Initial Phase of IPT-AST The initial phase of IPT-AST includes group Sessions 1 through 3. During these sessions, the primary goal is for the adolescents in the group to get comfortable with each other and to begin to think about the link between relationships and mood. At the beginning of each group session, adolescents independently complete a depression checklist. Similar to IPT-A, time is spent in group Session 1 educating adolescents about the symptoms of depression and talking about the focus of the program. In the first group session, the leader provides hypothetical examples of different adolescents with various levels of depression and asks the group members to identify the symptoms in each case and discuss the differences in severity. Additionally, time is spent discussing the different types of problems that adolescents experience and how the program can help with common interpersonal issues with family members, peers, and others. Group Session 2 focuses on how what we say and how we say it impacts others, both how others think we are feeling and how they respond. This work is done primarily through group activities. In one activity, adolescents are given a piece of paper that asks them to say a statement in a certain way. For instance, they might have to say with their arms crossed, “You always do that. You say we are going to hang out after school and then you don’t show.” Each adolescent is asked to act out their statement, and then the group discusses how the person may be feeling and what conveyed that feeling. This activity leads to a discussion of both verbal and nonverbal cues and how words can mean different things depending on how they are said or the context of the interaction. In the second activity, group members are asked to role play different scenarios to illustrate how communicating in a certain way influences the other person’s response. Scenarios can include having a parent who calls frequently while you are out with a friend or having a peer who is spreading rumors about you. Following each role play, the leader engages the group in a communication analysis of what was said, how each person felt during the discussion, and moments in the conversation that took on a positive

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or negative tone. This activity gets group members comfortable with communication analysis, which will be applied to the adolescents’ real-life interactions during the middle phase of group. During Session 3, the leader introduces group members to the interpersonal strategies that they will be encouraged to use in their own relationships. Examples of the interpersonal strategies include finding the right time for a conversation, letting the other person know that their point of view is understood, and thinking through potential solutions that can be shared with the other person. Adolescents are taught these techniques in Session 3 and then practice them by role playing one or two hypothetical situations. Middle Phase of IPT-AST The middle phase of IPT-AST includes Sessions 4, 5, and 6. The content is similar to the middle phase of IPT-A. Each group member completes a depression checklist on their own, and the adolescents are asked to share with the group if their mood is better, worse, or the same as the week before and to highlight a relationship or interpersonal event that impacted their mood. Typically, each of the middle phase sessions focuses on one or two adolescents who have an interpersonal issue to discuss that is related to their goals for group. For example, if an adolescent reports that they had an argument with their father and another adolescent reports that they want to invite a friend to go to the movies, the leader will focus on either or both of these adolescents in the session. This focus involves having the adolescent describe the interpersonal situation, conducting a communication analysis if this would be helpful, talking about what interpersonal strategies may be particularly relevant to the problem, engaging in decision analysis when needed, and practicing the interaction using a role play. The leader encourages the other group members to help the adolescent identify interpersonal strategies and possible solutions, share advice based on their own interactions, and participate in the role plays. One group member also serves as the coach in the role play, reminding the teen with cue cards to use relevant interpersonal skills. Group members are given homework assignments to try these techniques at home and to report back to the group on how they went. Termination Phase of IPT-AST The termination phase is Sessions 7 and 8. Session 7 is typically a continuation of middle phase work, including reviewing homework from Session 6 and discussing any new interpersonal issues that have arisen. Toward the end of Session 7 and during Session 8, the adolescents review techniques that worked and practice techniques that were more difficult. This includes a discussion of specific communication strategies that were helpful for the different group members and brainstorming ways to overcome barriers to using the communication strategies. The group also discusses how these strategies may be used in future interactions. This discussion helps emphasize that this

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group is the beginning of a new way of communicating and interacting in relationships and the need to continue this work beyond the group. In the last group session, time is spent discussing what adolescents can do in the future if problems arise in their relationships or they notice a return of depression symptoms. The adolescents are encouraged to review their own symptoms of depression and to identify warning signs that would signal to them that they may want to seek out help. In the final activity of the group, adolescents identify characteristics of other group members that made the adolescents feel supported, both to provide positive feedback to the adolescents and create a forum to discuss what characteristics to look for when developing new relationships and/or deciding who to turn to for support. IPT-AST Case Example Sam was a 14-year-old cisgender boy who participated in an IPT-AST group. During his individual pregroup session, he identified several close family and friend relationships while completing his interpersonal inventory. He noted that a couple of his friends were attending a different high school than he is, but that he was hopeful that they would continue to stay in touch. During the inventory, he endorsed hesitation about expressing his feelings to those close to him given concerns about being a burden to others. Sam explained his tendency to internalize his emotions and to work through feelings of sadness and irritability on his own. Thus, when setting goals for IPT-AST group, Sam identified wanting to confide in his close friend when something is bothering him and to ask his mom (a cisgender woman) for support when he is feeling down. In the first few group sessions, Sam became comfortable with the other group members. He participated in the communication activities in Session 2 and was able to note moments in the role plays when the conversation became more heated. He was able to give examples of the communication skills taught in Session 3 and helped his peers apply these skills to the interpersonal situations they brought up in group sessions. During the individual midgroup session, the leader reviewed the interpersonal goals Sam had set at his individual pregroup session. Sam expressed ongoing hesitation about being open with his mom, as he did not want to worry her but recognized that she wanted to know his feelings and that sharing his feelings could be helpful. Sam invited his mother to the latter portion of the midgroup session to have a conversation. He started with a relatively low stress conversation, focused on his disappointment about his recent academic performance. He expressed difficulty with math, despite spending more time than usual studying, and asked his mom for help in problem solving methods to boost his grades. She was receptive to the conversation and verbalized that she knew Sam had been putting forth effort in math and felt badly that she had been snapping at him about his grades. Sam and his mom collaboratively identified solutions for Sam, including asking his math teacher for help after school. Both reported that the conversation was calmer than if they had the discussion at home, and the mother noted

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that she gets upset easily when she sees Sam’s grades are not as good as expected. They discussed that it would be helpful for Sam to “strike while the iron is cold” and speak to his mom before she sees his grades so she is aware of the effort he is making, and, so, they can check in with each other on the afterschool help. In one of the later middle-phase sessions, Sam elected to share with the group that he lost touch with a friend and was debating whether to try to reconnect. After a brief decision analysis, Sam decided he wanted to reach out to this friend. Group members helped Sam brainstorm how he could express to this peer what the friendship means to him and that he wants to find a time to meet up. The group thought that the strategies “I statements” and “have solutions in mind” would be particularly helpful. While Sam felt awkward saying directly that he missed his friend, he was willing to express that he would be excited if they could spend more time together. Sam role played this conversation during the group session, with another group member playing the peer and a different group member coaching Sam as he used the communication skills. The group gave Sam feedback about the role play, and he was encouraged to reach out to his friend. During the group session the following week, Sam reported that he had called the friend and scheduled a time to stay after school and talk. Sam reported he was glad he initiated this conversation with his friend and was hopeful they would be able to rebuild their friendship over time. During the termination phase of group, Sam noted skills that were most helpful to him such as “strike while the iron is cold.” He indicated the need for additional practice using “I statements,” given that it still felt unnatural at times to express his emotions, particularly more negative feelings. He explained that practicing in sessions and listening to his peers share their feelings made him more confident in his ability to be open with his family and friends about his emotions and need for support, although it was still challenging to express his vulnerability. Sam said he planned to keep the skill cards from group on his desk at home to remind himself to continue using the skills in future interpersonal situations. Evidence of Efficacy of IPT-AST There have been several randomized controlled trials of IPT-AST. Most of these studies have focused on adolescents where prevention is indicated due to elevated symptoms of depression, but IPT-AST has also been tested as a universal prevention model (Horowitz et al., 2007) and with adolescents who are at risk for depression based on cognitive or interpersonal risks for depression (Young et al., 2021). Based on this research, IPT-AST is included on several evidence-based prevention registries, including Blueprints for Healthy Youth Development and the California Evidence-Based Clearinghouse. In two school-based studies of adolescents with elevated symptoms of depression, IPT-AST adolescents had lower symptoms of depression and higher levels of overall functioning than youth who received usual school counseling (SC)

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at postintervention and 6-month follow-up. IPT-AST adolescents also experienced fewer depression diagnoses than SC adolescents through the 6-month follow-up in the two studies (3.7% vs. 28.6%; 0% vs. 19.1%; Young et al., 2006, 2010). In analyses that combined data from these two studies, there was evidence of a significant difference in anxiety symptoms between IPT-AST and SC at postintervention and 6-month follow-up (Young, Makover, et al., 2012). Secondary analyses of these studies demonstrated that youth in IPT-AST experienced significant decreases in mother–child conflict (Young et al., 2009) and significant improvements in social functioning (Young, Kranzler, et al., 2012). In a recent large-scale indicated school-based prevention study, we examined the efficacy of IPT-AST in comparison to enhanced group counseling delivered by school counselors in public middle and high schools. IPT-AST resulted in significantly greater reductions in depression symptoms, general internalizing symptoms, and overall functioning compared to group counseling from baseline to the 6-month follow-up (Benas et al., 2019; Young, Benas, et al., 2016). Consistent with the model that underlies IPT-AST, reductions in peer conflict and mother–adolescent conflict, and improvements in family functioning mediated the effects of IPT-AST on overall functioning. Further improvements in romantic functioning mediated the effects of IPT-AST on postintervention depression symptoms and overall functioning (Jones et al., 2021). Finally, in a study that focused on the benefits of personalized prevention, IPT-AST was associated with reduced depression symptoms and diagnoses, particularly for adolescents with high parent–child conflict and/or low peer support (Young et al., 2021).

SUMMARY AND FUTURE DIRECTIONS A growing body of research highlights the interpersonal risks for depression and the reciprocal relationship between depression and interpersonal difficulties. This chapter focuses specifically on interpersonal risks linked to the problem areas that IPT seeks to address, namely, role transition, role disputes, interpersonal deficits, and grief. Interpersonal theories of depression and empirical studies highlight other interpersonal vulnerabilities for depression that may also be amenable to intervention, several of which are addressed in other chapters in this book (e.g., dependency, excessive reassurance seeking; see Chapter 6, this volume). Future work should examine whether there is a benefit of expanding the interpersonal vulnerabilities that are addressed in IPT, as well as the efficacy of other depression interventions which target different interpersonal risks. There is considerable empirical support for the efficacy and effectiveness of IPT-A and the efficacy of IPT-AST in reducing depression symptoms and improving overall functioning. Nonetheless, there is always potential to strengthen the effects of these and other depression interventions and, in the case of IPT-AST, enhance the long-term preventive effects (see Young et al., 2010; Young, Benas, et  al., 2016, for further discussion). There is growing evidence that IPT-A and IPT-AST result in improvements in social functioning

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and interpersonal conflict (e.g., Mufson et al., 1999; Spence et al., 2016; Young et al., 2009; Young, Kranzler, et al., 2012) and preliminary evidence that improvements in these interpersonal factors mediate the effects of IPT-AST (Jones et al., 2021). More research is needed about the effects of IPT-based interventions on theorized mechanisms. This research should include an examination of changes in interpersonal behaviors using direct observational techniques and ecological momentary assessment as a complement to self-report measures. Alternative analytic approaches may also be needed, given the individualized nature of IPT. For instance, if one adolescent in IPT-A focuses on reducing conflict with a parent as in the case of Kayla described earlier, we would expect to see improvements in conflict with her mom, but smaller and possibly no improvements in other interpersonal domains. If another adolescent with interpersonal deficits focuses on building support with peers, we might expect improvements in peer support. The individualized nature of IPT-A makes it difficult to detect mean level effects for any one interpersonal factor. A personcentered approach may help to advance the field. Additionally, we likely need to consider innovative ways to individualize prevention and treatment approaches for adolescent depression (see Young et al., 2021, for one approach) given the large number and range of risk factors and stressors that have been linked with the onset of depression. This might include the use of sequential, multiple assignment, randomized trials to inform treatment decisions based on an adolescent’s individual characteristics. Finally, increasing access to IPT-A and IPT-AST interventions is essential, whether by scaling up delivery in usual care settings (e.g., primary care, schools), embarking on large dissemination projects, or expanding the modalities through which these interventions are offered (e.g., via telehealth). All these strategies will help to maximize the number of adolescents who benefit from these programs, which target vital and salient interpersonal issues that occur in adolescence. REFERENCES Allen, J. P., Insabella, G., Porter, M. R., Smith, F. D., Land, D., & Phillips, N. (2006). A social-interactional model of the development of depressive symptoms in adolescence. Journal of Consulting and Clinical Psychology, 74(1), 55–65. https://doi.org/10.1037/ 0022-006X.74.1.55 American Psychological Association. (2019, February 16). Clinical practice guidelines for the treatment of depression across three age cohorts. https://www.apa.org/depressionguideline Benas, J. S., McCarthy, A. E., Haimm, C. A., Huang, M., Gallop, R., & Young, J. F. (2019). The depression prevention initiative: Impact on adolescent internalizing and externalizing symptoms in a randomized trial. Journal of Clinical Child and Adolescent Psychology, 48(Suppl. 1), S57–S71. https://doi.org/10.1080/15374416.2016. 1197839 Brent, D. A., Perper, J. A., Moritz, G., Liotus, L., Schweers, J., Roth, C., Balach, L., & Allman, C. (1993). Psychiatric impact of the loss of an adolescent sibling to suicide. Journal of Affective Disorders, 28(4), 249–256. https://doi.org/10.1016/01650327(93)90060-W

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Brière, F. N., Archambault, K., & Janosz, M. (2013). Reciprocal prospective associations between depressive symptoms and perceived relationship with parents in early adolescence. Canadian Journal of Psychiatry, 58(3), 169–176. https://doi.org/10.1177/ 070674371305800307 Cohen, J. R., Spiro, C. N., Young, J. F., Gibb, B. E., Hankin, B. L., & Abela, J. R. Z. (2015). Interpersonal risk profiles for youth depression: A person-centered, multiwave, longitudinal study. Journal of Abnormal Child Psychology, 43(8), 1415–1426. https://doi.org/10.1007/s10802-015-0023-x Giannopoulou, I., Richardson, C., & Papadatou, D. (2021). Peer loss: Posttraumatic stress, depression, and grief symptoms in a traumatized adolescent community. Clinical Child Psychology and Psychiatry, 26(2), 556–568. https://doi.org/10.1177/ 1359104520980028 Gray, L. B., Weller, R. A., Fristad, M., & Weller, E. B. (2011). Depression in children and adolescents two months after the death of a parent. Journal of Affective Disorders, 135(1–3), 277–283. https://doi.org/10.1016/j.jad.2011.08.009 Hammen, C. (1999). The emergence of an interpersonal approach to depression. In T. Joiner & J. Coyne (Eds.), The interactional nature of depression: Advances in inter­ personal approaches (pp. 21–35). American Psychological Association. https://doi.org/ 10.1037/10311-001 Hankin, B. L., Young, J. F., Abela, J. R. Z., Smolen, A., Jenness, J. L., Gulley, L. D., Technow, J. R., Gottlieb, A. B., Cohen, J. R., & Oppenheimer, C. W. (2015). Depression from childhood into late adolescence: Influence of gender, development, genetic susceptibility, and peer stress. Journal of Abnormal Psychology, 124(4), 803–816. https://doi.org/10.1037/abn0000089 Hankin, B. L., Young, J. F., Gallop, R., & Garber, J. (2018). Cognitive and interpersonal vulnerabilities to adolescent depression: Classification of risk profiles for a personalized prevention approach. Journal of Abnormal Child Psychology, 46(7), 1521–1533. https://doi.org/10.1007/s10802-018-0401-2 Harrison, L., & Harrington, R. (2001). Adolescents’ bereavement experiences: Prevalence, association with depressive symptoms, and use of services. Journal of Adoles­ cence, 24(2), 159–169. https://doi.org/10.1006/jado.2001.0379 Horowitz, J. L., Garber, J., Ciesla, J. A., Young, J. F., & Mufson, L. (2007). Prevention of depressive symptoms in adolescents: A randomized trial of cognitive-behavioral and interpersonal prevention programs. Journal of Consulting and Clinical Psychology, 75(5), 693–706. https://doi.org/10.1037/0022-006X.75.5.693 Joiner, T., Coyne, J., & Blalock, J. (1999). On the interpersonal nature of depression: Overview and synthesis. In T. Joiner & J. Coyne (Eds.), The interactional nature of depression: Advances in interpersonal approaches (pp. 3–19). American Psychological Association. https://doi.org/10.1037/10311-013 Jones, J. D., Gallop, R., Gillham, J. E., Mufson, L., Farley, A. M., Kanine, R., & Young, J. F. (2021). The depression prevention initiative: Mediators of Interpersonal Psychotherapy–Adolescent Skills Training. Journal of Clinical Child and Adolescent Psychology, 50(2), 202–214. https://doi.org/10.1080/15374416.2019.1644648 Juang, L. P., Lerner, J. V., McKinney, J. P., & von Eye, A. (1999). The goodness of fit in autonomy timetable expectations between Asian-American late adolescents and their parents. International Journal of Behavioral Development, 23(4), 1023–1048. https:// doi.org/10.1080/016502599383658 Kim, J. Y., McHale, S. M., Crouter, A. C., & Osgood, D. W. (2007). Longitudinal linkages between sibling relationships and adjustment from middle childhood through adolescence. Developmental Psychology, 43(4), 960–973. https://doi.org/10.1037/00121649.43.4.960 Lodder, G. M. A., Goossens, L., Scholte, R. H. J., Engels, R. C. M. E., & Verhagen, M. (2016). Adolescent loneliness and social skills: Agreement and discrepancies between self-, meta-, and peer-evaluations. Journal of Youth and Adolescence, 45(12), 2406–2416. https://doi.org/10.1007/s10964-016-0461-y

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Sheeber, L. B., Davis, B., Leve, C., Hops, H., & Tildesley, E. (2007). Adolescents’ relationships with their mothers and fathers: Associations with depressive disorder and subdiagnostic symptomatology. Journal of Abnormal Psychology, 116(1), 144–154. https://doi.org/10.1037/0021-843X.116.1.144 Shortt, A. L., & Spence, S. H. (2006). Risk and protective factors for depression in youth. Behaviour Change, 23(1), 1–30. https://doi.org/10.1375/bech.23.1.1 Simoni, Z. R., & Bauldry, S. (2020). Moving during adolescence and depressive symptoms: The role of social support. Youth & Society, 52(4), 639–660. https://doi.org/ 10.1177/0044118X18757149 Spence, S. H., O’Shea, G., & Donovan, C. L. (2016). Improvements in interpersonal functioning following interpersonal psychotherapy (IPT) with adolescents and their association with change in depression. Behavioural and Cognitive Psychotherapy, 44(3), 257–272. https://doi.org/10.1017/S1352465815000442 Stice, E., Ragan, J., & Randall, P. (2004). Prospective relations between social support and depression: Differential direction of effects for parent and peer support? Journal of Abnormal Psychology, 113(1), 155–159. https://doi.org/10.1037/0021-843X.113.1.155 Vanhalst, J., Klimstra, T. A., Luyckx, K., Scholte, R. H. J., Engels, R. C. M. E., & Goossens, L. (2012). The interplay of loneliness and depressive symptoms across adolescence: Exploring the role of personality traits. Journal of Youth and Adolescence, 41(6), 776–787. https://doi.org/10.1007/s10964-011-9726-7 Vannucci, A., Ohannessian, C. M., Flannery, K. M., De Los Reyes, A., & Liu, S. (2018). Associations between friend conflict and affective states in the daily lives of adolescents. Journal of Adolescence, 65(1), 155–166. https://doi.org/10.1016/ j.adolescence.2018.03.014 Weissman, M. M., Markowitz, J. C., & Klerman, G. L. (2000). Comprehensive guide to interpersonal psychotherapy. Basic Books. World Health Organization. (2017). Depression and other common mental disorders: Global health estimates. https://apps.who.int/iris/bitstream/handle/10665/254610/ WHO-MSD-MER-2017.2-eng.pdf Young, J. F., Benas, J. S., Schueler, C. M., Gallop, R., Gillham, J. E., & Mufson, L. (2016). A randomized depression prevention trial comparing Interpersonal Psychotherapy– Adolescent Skills Training to group counseling in schools. Prevention Science, 17(3), 314–324. https://doi.org/10.1007/s11121-015-0620-5 Young, J. F., Berenson, K., Cohen, P., & Garcia, J. (2005). The role of parent and peer support in predicting adolescent depression: A longitudinal community study. Journal of Research on Adolescence, 15(4), 407–423. https://doi.org/10.1111/j.1532-7795.2005. 00105.x Young, J. F., Gallop, R., & Mufson, L. (2009). Mother–child conflict and its moderating effects on depression outcomes in a preventive intervention for adolescent depression. Journal of Clinical Child and Adolescent Psychology, 38(5), 696–704. https:// doi.org/10.1080/15374410903103577 Young, J. F., Jones, J. D., Gallop, R., Benas, J. S., Schueler, C. M., Garber, J., & Hankin, B. L. (2021). Personalized depression prevention: A randomized controlled trial to optimize effects through risk-informed personalization. Journal of the American Academy of Child & Adolescent Psychiatry, 60(9), 1116–1126.e1. https://doi.org/10.1016/ j.jaac.2020.11.004 Young, J. F., Kranzler, A., Gallop, R., & Mufson, L. (2012). Interpersonal Psychotherapy–Adolescent Skills Training: Effects on school and social functioning. School Mental Health, 4(4), 254–264. https://doi.org/10.1007/s12310-012-9078-9 Young, J. F., Makover, H. B., Cohen, J. R., Mufson, L., Gallop, R. J., & Benas, J. S. (2012). Interpersonal Psychotherapy–Adolescent Skills Training: Anxiety outcomes and impact of comorbidity. Journal of Clinical Child and Adolescent Psychology, 41(5), 640–653. https://doi.org/10.1080/15374416.2012.704843

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Young, J. F., Mufson, L., & Davies, M. (2006). Efficacy of Interpersonal Psychotherapy– Adolescent Skills Training: An indicated preventive intervention for depression. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 47(12), 1254–1262. https://doi.org/10.1111/j.1469-7610.2006.01667.x Young, J. F., Mufson, L., & Gallop, R. (2010). Preventing depression: A randomized trial of Interpersonal Psychotherapy–Adolescent Skills Training. Depression and Anxiety, 27(5), 426–433. https://doi.org/10.1002/da.20664 Young, J. F., Mufson, L., & Schueler, C. M. (2016). Preventing adolescent depression: Inter­ personal Psychotherapy–Adolescent Skills Training. Oxford University Press. https://doi.org/ 10.1093/med:psych/9780190243180.001.0001

5 Childhood Adversity, Stressful Life Events, and Trauma Kate L. Harkness

S

tress is a universal human experience and has been throughout our history. The sophist Gorgias’s (483–376 BCE) Encomium of Helen is believed to be the earliest account in the West of psychological symptoms resulting from exposure to stress. The Roman philosopher Cicero (106–43 BCE) also attributed his recurrent episodes of depression to the stress of his exile from Rome and his daughter Tullia’s death. In the present day, stress features prominently in almost all etiological theories of depression (LeMoult, 2020), and the mechanisms that mediate its effect on depression have been investigated at multiple levels of analysis (Harkness & Hayden, 2020). Although research indicates that major life events (e.g., divorce, job loss) are the strongest proximal triggers of depression onset (Vrshek-Schallhorn et al., 2020), just under 60% of first depressive episodes are actually preceded by such a stressor (Monroe et al., 2019). Similarly, while a history of childhood trauma raises risk for depression in adulthood threefold (Li et al., 2016), just under 60% of individuals with depression have such a history (Vallati et al., 2020). While these are sizable percentages, they nevertheless suggest that stress may not feature at all in the etiology or pathology of depression for a large number of sufferers. An important research question, then, is whether stress-related etiologic or pathologic heterogeneity translates to heterogeneity in response to treatment. Consistently, in trials of both pharmacotherapy and cognitive behavioral interventions, sustained remission rates rest at around 40% to 50% (Trivedi & Daly, 2008). However, there is emerging evidence that these rates can be

https://doi.org/10.1037/0000332-006 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  105

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improved by assigning patients to treatments that have the best chance of working for them, based on individual difference characteristics that are assessed a priori (DeRubeis et al., 2014). Stress exposure is a particularly promising characteristic to inform such a personalized approach. This chapter addresses three interrelated questions regarding the role of stress in depression treatment. First, are proximal and distal (childhood) stress exposures negative prognostic markers in depression? That is, does stress exposure predict a poor response to depression treatment, in general? Second, are proximal and distal stress exposures negative prescriptive markers in depression, predicting a poorer response to particular treatment modalities over others? Third, what is the evidence supporting the efficacy of treatments developed specifically to treat individuals with depression who have been exposed to stress? One could argue that a major goal of all treatments for depression is to improve patients’ ability to cope with, and reduce exposure to, life’s adversities. However, as we will see, very few randomized controlled trials (RCTs) specifically include patients with high levels of stress exposure or assess stress-related outcomes. Therefore, this third question will address the treatments and mechanisms that show the greatest promise in this vulnerable subgroup.

DEFINITIONAL ISSUES The two specific classes of environmental exposures that will form the basis of the current chapter are proximal stressful life events and a history of trauma in childhood, with a particular focus on maltreatment. Examples of proximal life events include getting fired from a job, breaking up with a romantic partner, experiencing the death of a close relative, and so on. Stressful life events can vary substantially in severity, from relatively minor events (e.g., argument with a friend) to major events (e.g., spouse unexpectedly files for divorce) to events that would meet criteria for a “traumatic event” (e.g., sexual assault) according to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013). Childhood maltreatment is a broad category that includes physical, sexual, and/or emotional abuse and/or neglect. Each of these exposures can vary in severity, frequency, and duration or chronicity, and they often co-occur. An important limitation of the literature on childhood maltreatment as a prognostic or prescriptive predictor in treatment trials is its focus on broad indices of exposure that fail to account for heterogeneity in these experiences.

CONCEPTUAL ISSUES A fundamental conceptual issue that has significant implications for research on life stress involves the two distinct definitions of stress. This term can refer to the external environmental stressors to which individuals are exposed

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(i.e., the proximal life events or experiences of childhood maltreatment described earlier). However, the term is also used to refer to individuals’ emotional, behavioral, and/or physiological responses to these exposures (i.e., the feeling state of “stress”). Certainly, stress exposures elicit stress responses, but the relation between exposure and response is not perfect. Stress responses are also influenced by many other factors that vary across individuals (e.g., personality, cognitive style, state symptoms of distress, genetic vulnerability; Harkness & Monroe, 2016). As such, there can be wide differences in responses to the same stress exposure, both across individuals and within individuals over time. For this reason, “stress exposure” and “stress response” should be distinguished, both conceptually and in measurement. Most important, stress exposures should not be assessed with measures that are contaminated by the stress response (Harkness & Monroe, 2016). An example serves to illustrate this concern. A research question pertinent to the current discussion is whether patients with a history of childhood maltreatment are less likely to respond to treatment than those without this history. A common way to assess patients’ exposure to abuse in childhood is to administer a questionnaire asking patients to self-report on how much “stress” they felt following certain childhood experiences, or asking them to indicate the extent to which they agree with statements such as “I believe that I was emotionally abused.” These measures assess participants’ responses to their potential experience of maltreatment or, even more problematically, their beliefs about whether these exposures actually happened. While perceptions of, and responses to, childhood maltreatment are important variables to examine in relation to depression, they are distinct from an assessment of the exposure itself (i.e., what actually happened). Further, responses to the former sets of items will be influenced by factors that, importantly, may be confounded with the investigator’s dependent variable of interest. For example, patients with greater severity of depression show greater depressive autobiographical recollection and reporting biases (Duyser et al., 2020). To the extent that patients with greater severity of depression may also be those least likely to respond to treatment, any relation between “stress” and treatment outcome using measures that conflate exposure and response may be spuriously driven by depression severity. There are two types of measures that provide valid assessments of life stress exposure that minimize contamination by the stress response (see Harkness & Monroe, 2016). The first involves focusing specifically on objective assessment. In the area of childhood maltreatment, particularly in a child or adolescent sample, this could include recruiting youth whose maltreatment history has been documented through official reports. In the area of proximal stress, this could include recruiting patients who have been exposed to a particular, clearly documented stressor (e.g., victims of a natural disaster, patients recovering from a particular surgery, parents of pediatric cancer victims). To be clear, again, there will be large variability in responses to all of these exposures, which can be measured through self-report, but the exposure itself has been documented independently of those responses.

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Often, however, it is not feasible to rely on documented reports. For example, official reports are typically not available (or were never made) in samples of adults with childhood maltreatment histories. Similarly, it is often not desirable to focus on samples who were all exposed to the same stressor, and, instead, a common question in the field is to understand how exposure to the onslaught of life’s adversities (e.g., divorces, job losses, illnesses) predicts mental health outcomes. Assessing these exposures presents a challenge. The investigator must rely on the individual’s retrospective report, while at the same time ensuring that biases related to the stress response or state depression do not contaminate the report of these exposures. The gold standard in the field of retrospective childhood maltreatment and stressful life event assessment is the contextual life event interview. The most commonly used measures include the Childhood Experience of Care and Abuse Interview (CECA; Bifulco et al., 1994), the Life Events and Difficulties Schedule (LEDS; Bifulco et  al., 1989), and the UCLA Life Stress Interview (LSI; Hammen et al., 1992). These contextual measures include two critical methodological features that serve to minimize the influence of response bias. The first feature is detailed information about the exposure and relevant contextual details is solicited from the respondent, but no questions are asked about the respondent’s emotional reaction to, or perception of, the exposure. For example, if a respondent indicated that they were recently fired from their job, the interviewer would ask questions regarding, for example, the circumstances of the firing and financial implications, but would not ask how upset the respondent was about the firing or whether the event had caused the respondent’s depression. The second one is contextual information gathered in the interview is subsequently rated for inclusion and severity by independent judges who are unaware of the respondent’s depression status. These ratings are made with reference to manuals, which provide explicit operational definitions and, sometimes, anchored exemplars to enhance standardization. The contextual interview approach has demonstrated superior reliability and validity, including predictive validity, over self-report life event checklists (Baldwin et al., 2019; Brown et al., 2007; Harkness & Monroe, 2016). In the review that follows, discussion will pay special attention to the strength of stress assessment.

PROXIMAL STRESS AND CHILDHOOD MALTREATMENT AS RISK FACTORS FOR DEPRESSION The etiology of depression is difficult to understand without considering the role of stress. Stress features prominently in almost all major etiological theories of depression focusing on behavioral, cognitive, and biological levels of analysis (LeMoult, 2020). Further, rigorous prospective, longitudinal research has confirmed empirically the strong causal role of stress in the onset of major depression (Kendler et al., 1999).

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Proximal Stress A very large number of studies conducted over the last 45 years have consistently demonstrated that individuals with depression are nearly 3 times more likely to have experienced a stressful life event within the 3-month period prior to onset than are nondepressed individuals in a matched time period (Vrshek-Schallhorn et al., 2020). Even more compelling, evidence from behavioral genetic studies indicates that nonshared environmental factors, primarily life events, contribute a majority of the variance in risk for depression and are causally related to depression onset (meta-analytic estimate = 63%; Sullivan et al., 2000). Studies using contextual interview methods have provided the following conclusions regarding the types of proximal stressors that most strongly predict depression. First, acute “severe” life events (e.g., finding that one’s spouse of 20 years has moved out, or learning that one’s father has died suddenly of a heart attack) are strong predictors of onset, whereas more minor events generally are not (Vrshek-Schallhorn et al., 2020). Second, multiple events increase risk for depression onset over single events, with risk increasing exponentially after three events (Kendler et al., 1999). Third, events involving interpersonal loss (e.g., close confidant moves away) and/or targeted rejection (e.g., spouse leaves for another partner) more strongly predict depression than other types of events (Slavich et al., 2009). Further, there is robust evidence that the onset of depression, and its associated interpersonal and occupational impairment, is associated with the generation of stressful life events (see Hammen, 2020). In other words, not only do stressful life events cause depression, but depression also causes subsequent exposure to stressful life events, particularly in the interpersonal domain (e.g., interpersonal conflict, rejection), which may be the events most likely to impact the treatment process. Certain individuals may be more at risk for sensitivity to, and generation of, stressful life events than others. For example, women are exposed to, and have been found to generate, higher levels of interpersonal life events than men, and are more likely than men to develop depression in response to these events (e.g., Harkness et al., 2010). Further, racial discrimination is a potent stressor. Higher levels of exposure to discrimination among individuals of ethnic minority status are associated with higher levels of depression symptoms (Hunter et al., 2017). However, there is little evidence that individuals of ethnic minority status are more likely to develop depression in the face of acute or chronic stressors, in general, or to generate higher levels of stress, than people who are not minorities (see Vrshek-Schallhorn et al., 2020). Childhood Maltreatment Epidemiological evidence indicates that over 50% of lifetime depression and anxiety disorders in the population are attributable to exposure to childhood maltreatment (Li et al., 2016). Further, depressed individuals with a history

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of childhood maltreatment present with an earlier age of onset, more severe symptoms, a more persistent course, higher rates of comorbidity, and more lifetime suicide attempts than those without this history (Nanni et al., 2012; Vallati et  al., 2020). Population-based studies reveal little difference in the relation of maltreatment to depression by gender or ethnicity (McLaughlin et al., 2012). Childhood maltreatment may cause depression in part by heightening the generation of, and sensitivity to, proximal stressful life events. Studies including rigorous contextual interview assessments of both childhood maltreatment and proximal life events have found that depressed individuals with a history of maltreatment generate significantly greater levels of interpersonal stress over time than those without a maltreatment history (Hammen, 2020). Further, depressed individuals with a history of childhood maltreatment require a lower level of life event severity to trigger the onset of their depression than those without (Harkness et al., 2006). Several complementary mechanisms have been proposed to mediate the relation of childhood maltreatment to heightened stress generation and sensitivity, and these mechanisms relate directly to the theorized mechanisms of action of commonly used depression treatments. Of prime relevance to cognitive therapy, childhood maltreatment is significantly associated with the development of negative cognitive schemas, particularly with themes of emotional deprivation and dependency, and a more tightly consolidated negative schema structure (Lumley & Harkness, 2009). Both negative cognitive content and structure significantly mediate the relation of childhood maltreatment to interpersonal stress generation (Zheng et al., 2022). Childhood maltreatment is also significantly associated with reduced motivation to engage in rewarding activities, and blunted behavioral and neural responses to reward (Cohen et  al., 2019). Behavioral activation treatments for depression focus specifically on enhancing depressed individuals’ motivation to pursue rewarding activities and their experience of positive affect when engaging in those activities (Craske et al., 2016). Further, functioning of the reward system is mediated neurobiologically by the mesolimbic dopamine system, and there is emerging evidence that antidepressant medications with dopaminergic action may be more effective in treating depression that presents with high levels of anhedonia than are typical antidepressant medications that selectively target serotonin (Belujon & Grace, 2017).

PROXIMAL STRESS, CHILDHOOD MALTREATMENT, AND TREATMENT RESPONSE IN DEPRESSION Details of trials examining proximal life events and childhood maltreatment as prognostic and prescriptive factors in treatment trials for depression are summarized in Tables 5.1 and 5.2.

TABLE 5.1.  Characteristics of Studies Examining Relation of Proximal Life Events to Treatment Response Authors Lloyd et al. (1981)

Sample 80 adult outpatients with MDD

Design 8-week RCT

Tx modalities Amitriptyline vs. amoxapine

SLE measure Self-report checklist

Result SLEs in 2- or 12-month period prior to start of treatment were not related to response. SLEs concurrent with treatment were related to a significantly poorer response in both conditions.

Monroe et al. (1992)

91 adult outpatients with recurrent MDD

Single arm, 20-week trial

IPT + imipramine

LEDS contextual interview and rating system

Severe SLEs prior to treatment predicted poor response.

Tomaszewska et al. (1996)

312 adult outpatients with MDD

Double-blind 6-week RCT

Placebo vs. ADM (details not reported)

Self-report checklist

Patients who did not respond to medication had a significantly higher number of SLEs prior to start of treatment than responders.

Kim et al. (2011)

580 adult outpatients with unipolar depression from the CRESCEND study

12-week naturalistic, open-label trial

ADM with type, dosage, and regimen at psychiatrist discretion

Self-report checklist

Number of SLEs reported in the year prior to treatment was not significantly associated with treatment response.

Keers et al. (2010)

728 adult outpatients with MDD from the European GENDEP study

12-week multicenter part-randomized pharmacogenomics trial

Escitalopram vs. nortriptyline

Self-report checklist

Patients who had at least one SLE prior to escitalopram treatment had a significantly greater decline in cognitive symptoms than those who did not have life events prior to treatment. This association was not significant for mood or vegetative symptoms. SLEs were not associated with response of any symptoms in the nortriptyline condition. (continues)

Childhood Adversity, Stressful Life Events, and Trauma 111

Severe and nonsevere SLEs in first 6 weeks of treatment predicted poor response.

Authors Keers et al. (2011)

Sample

Design

674 adult outpatients with MDD from the GENDEP study

12-week multicenter part-randomized pharmacogenomics trial

Tx modalities Escitalopram vs. nortriptyline

SLE measure Self-report checklist

Result Presence of SLEs reported in 6 months prior to treatment was associated with better response to escitalopram. Among patients with a SLE prior to escitalopram treatment, those with the risk (short) allele of the serotonin transporter gene had worse treatment response than those heterozygous for the nonrisk (long) allele. SLEs were not associated with response, either on their own or in interaction with genotype, in the nortriptyline condition.

Fournier et al. (2009)

180 adult outpatients with MDD

16-week RCT

CT vs. paroxetine

Self-report checklist

Greater SLEs at baseline predicted greater number of symptoms at end of treatment in the paroxetine condition, but lower number of symptoms in the CT condition.

Bulmash et al. (2009)

113 adult outpatients with MDD

16-week RCT

CBT/IPT vs. ADM with type, dosage, and regimen at psychiatrist discretion

LEDS contextual interview and rating system

Severe SLEs 16 weeks prior to, and during, treatment significantly predicted lower likelihood of response in the ADM condition, but were not related to response to psychotherapy. Nonsevere SLEs prior to or during treatment did not significantly predict response in either condition.

Vittengl et al. (2020)

276 adult outpatients with recurrent MDD

12-week single-arm trial

CT

Clinician-rated lifetime SLEs based on multiple sources

Greater lifetime SLE exposure predicted a lower likelihood of response, but only for patients with weaker CT skills. Lifetime SLE exposure was not significantly related to response in those with higher CT skills.

Note. ADM = antidepressant medication; CBT = cognitive behavior therapy; CT = cognitive therapy; LEDS = Life Events and Difficulties Schedule; IPT = interpersonal psychotherapy; MDD = major depressive disorder; RCT = randomized controlled trial; SLE = stressful life event; Tx = treatment.

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TABLE 5.1.  Characteristics of Studies Examining Relation of Proximal Life Events to Treatment Response (Continued)

TABLE 5.2.  Characteristics of Studies Examining Relation of Childhood Maltreatment to Treatment Response Authors

Design

Tx modalities

CM measure

Enns & Cox (2005)

171 adult outpatients with MDD

Naturalistic treatment

Various ADMs and psychotherapy

One sexual abuse item

Sexual abuse was significantly associated with lack of response and remission.

L. M. Williams et al. (2016)

1,008 adult outpatients with MDD

8-week RCT

escitalopram-, sertraline-, or venlafaxine-extended release (XR)

Self-report questionnaire

Emotional, physical, and sexual abuse, particularly between age 4 and 7, predicted poorer response. Abuse was a stronger predictor of poor response to sertraline than to escitalopram or venlafaxine.

Quilty et al. (2016)

52 adult outpatients with MDD

26-week open-label trial

ADM with type, dosage, and regimen at psychiatrist discretion

Self-report CTQ

Higher CTQ scores were significantly associated with higher levels of depression severity at end of treatment, but only in those receiving ADMs with a weak affinity for the serotonin transporter.

Klein et al. (2009)

808 adult outpatients with chronic depression

12-week open-label trial

ADM with reference to the Texas Medication Algorithm

Self-report questionnaire

Psychological and sexual abuse predicted a lower probability of remission.

Johnstone et al. (2009)

195 adults

6-month RCT

fluoxetine, nortriptyline

Questionnaire of parenting style

Parental overprotection and neglect were significantly associated with poor outcome across conditions.

Interview of psychological, physical, or sexual abuse

Result

Abuse was not significantly associated with outcome in either condition.

Miniati et al. (2010)

312 adult outpatients with MDD

20-week RCT

ADM (citalopram or escitalopram), IPT

Clinical interview

Childhood abuse was significantly associated with longer time to remission across treatments.

Harkness et al. (2012)

203 adult outpatients with MDD

16-week RCT

CBT, IPT, ADM at psychiatrist discretion according to CANMAT guidelines

CECA contextual interview and rating system

Patients with a history of maltreatment were significantly less likely to respond to IPT than CBT or ADM, with no significant difference between CBT and ADM. (continues)

Childhood Adversity, Stressful Life Events, and Trauma 113

Sample

Authors

Sample

Design

Tx modalities

CM measure

Result

Nemeroff et al. (2003)

681 outpatients with chronic depression

12-week RCT

CBASP, nefazodone, CBASP + nefazodone

Self-report questionnaire

Patients with a history of maltreatment who were randomized to CBASP or CBASP + nefazodone were significantly more likely to respond than those randomized to nefazodone alone.

Bausch et al. (2017)

60 outpatients with chronic depression

28-week RCT

CBASP, escitalopram

Clinician-rated interview

Patients with a history of maltreatment randomized to escitalopram had significantly lower Week 8 depression severity than patients without maltreatment and all patients randomized to CBASP. No treatment differences at Week 28.

Asarnow et al. (2009)

287 adolescents

RCT

ADM (SSRI or venlafaxine), ADM + CBT

Self-report questionnaire

Childhood abuse associated with lack of response to CBT but not to pharmacotherapy or combination.

Lewis et al. (2010)

427 adolescents

RCT

Fluoxetine, CBT, combination, placebo

PTSD Criterion A

Childhood trauma associated with poor response to CBT but not with response to fluoxetine or combination treatment.

Barbe et al. (2004)

107 adolescents

16-week RCT

CBT, SBFT, NST

One interview question querying sexual abuse

Among adolescents with no history of sexual abuse, CBT predicted better response than NST. Among adolescents with a history of sexual abuse, response rates did not differ by treatment.

Note: ADM = antidepressant medication; CANMAT = Canadian mood and anxiety treatment; CBASP = cognitive behavioral analysis system of psychotherapy; CBT = cognitive behavioral therapy; CECA = childhood experience of care and abuse; CM = childhood maltreatment; CTQ = childhood trauma questionnaire; IPT = interpersonal psychotherapy; MDD = major depressive disorder; NST = nondirective supportive therapy; PTSD = posttraumatic stress disorder; RCT = randomized controlled trial; SBFT = systemic behavioral family therapy; Tx = treatment.

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TABLE 5.2.  Characteristics of Studies Examining Relation of Childhood Maltreatment to Treatment Response (Continued)

Childhood Adversity, Stressful Life Events, and Trauma 115

Proximal Life Events The prognostic role of proximal stressful life events was examined first in the context of antidepressant medication (ADM) trials. The three earliest of these found that exposure to proximal life events predicted poorer response to tricyclic ADMs (Lloyd et  al., 1981; Monroe et  al., 1992; Tomaszewska et  al., 1996). The only of these trials to use a rigorous contextual interview to define life events (i.e., the LEDS), found that both severe and nonsevere life events experienced during the first 6 weeks of treatment, as well as severe events experienced in the 3 months prior to the start of treatment, significantly predicted poor response (Monroe et al., 1992). This trial was also notable because all patients were receiving interpersonal psychotherapy (IPT) in addition to the tricyclic ADM imipramine. This addition of IPT did not protect patients, however, from the negative prognostic effect of life events. However, a subsequent large, naturalistic ADM trial failed to replicate these effects (Kim et al., 2011). In other words, higher levels of life events reported in the year prior to treatment did not significantly predict response in this trial. It is possible that these null effects are a result of the methodologically weak checklist life event measure, and the focus on such a broad period of exposure. As noted previously, the prognostic significance of life events declines the further back in time one goes; thus, it is unlikely that life events experienced a year prior to treatment entry would be powerful predictors of response. In addition, the ADMs in this trial were chosen at the discretion of the clinician. Approximately half of patients (48.1%) received selective serotonin reuptake inhibitors (SSRIs), and the other half (46.4%) received dual-action (serotonin and norepinephrine) ADMs. While analyses were not conducted stratified by ADM type, it is possible that life events may predict response differentially across these mechanisms of action. Tentative support for this latter hypothesis comes from the results of two studies including subsets of patients from the same large pharmacogenetics trial (genome-based therapeutic drugs for depression project [GENDEP]; Keers et al., 2010, 2011). In this trial, patients were randomly assigned to escitalopram (an SSRI) or nortriptyline (a tricyclic ADM). The presence of life events in the 6 months prior to treatment was not significantly associated with response to nortriptyline, but was significantly associated with better response in the escitalopram condition, particularly in terms of the cognitive symptoms of depression. This means that patients who reported higher levels of life events prior to treatment were significantly more likely to respond to escitalopram than those who reported low levels of proximal stress. Therefore, it is possible that ADMs with high serotonin affinity may be more effective than loweraffinity ADMs for patients with high levels of stress prior to and during treatment. This is a very intriguing result that, if replicated, could suggest that an assessment of life events at start of treatment would provide a basis upon which physicians could choose ADMs with the highest likelihood of being effective. Only two trials in adults have examined the role of life events in differentially predicting response to ADM versus psychotherapy. The results of these

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trials further complicate this picture. First, in direct contrast to above, in a trial of 180 patients randomized to cognitive therapy (CT) versus the SSRI paroxetine, Fournier et al. (2009) found that a greater number of stressful life events at start of treatment predicted poorer response (higher Week 16 Hamilton rating scale for depression [HRSD] score) in patients randomized to paroxetine, and significantly better response (lower Week 16 HRSD score) in patients randomized to CT. Similarly, in a sample of 113 patients with major depressive disorder (MDD) randomized to 16  weeks of personalized ADM, CT, or IPT, Bulmash et al. (2009) reported that the presence of a “severe” life event prior to or during treatment, contextually defined with the LEDS, predicted a poorer likelihood of response, but only in patients randomized to the ADM condition. Therefore, the results of these two studies suggest that the presence of life events around the time of treatment predicts better response to psychological interventions than ADM, even if the ADM is an SSRI. It should be noted, however, that only about half of the ADM group in the Bulmash et al. (2009) trial received an SSRI; the other half received a dual-action or atypical ADM, but cell sizes were too low to examine differences between medication types. Therefore, an important future direction is to clarify the relation of life events to response across the spectrum of ADM mechanisms of action. Further evidence for the important role of the serotonin transporter comes from the pharmacogenetics trial of Keers et al. (2011). Among patients receiving escitalopram who had a life event, those with the risk (short) allele of the serotonin transporter gene had significantly lower rates of response than patients heterozygous for the nonrisk (long) allele. In contrast, short and long allele carriers did not differ significantly in response among those with no life event exposure (see Mandelli et al., 2009). Further, much more research is needed on the relation between life event exposure and response to psychotherapy, in general, and differential response to psychotherapy versus ADMs. In contrast to the Fournier et al. (2009) trial, Bulmash et al. (2009) did not find a significant relation between life events and response to psychotherapy. However, in these analyses, the IPT and CT conditions were combined. Therefore, it is possible that the positive effects of stress on response are specific to cognitive interventions. Evidence for this possibility comes from a recent study of 276 patients with MDD receiving CT (Vittengl et  al., 2020). In this study, higher scores on an index of lifetime exposure to stressful life events predicted a lower likelihood of response, and a greater likelihood of relapse and recurrence, but only among patients with weaker cognitive therapy skills. Stated another way, the authors argued that patients who reported more stressful life events during treatment derived greater benefit from the skills learned in CT, and these skills served to “neutralize” risks for poor outcome that is generally predicted by stress. Childhood Maltreatment A meta-analysis of 10 trials reported an overall effect size of OR = 1.43, indicating poorer response to treatment in patients with versus without a

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maltreatment history (Nanni et  al., 2012). This difference was statistically significant for trials of psychotherapy (OR = 1.12), ADM (OR = 1.26), and psychotherapy + ADM (OR = 1.90). While, descriptively, patients with a history of maltreatment appear to fare the worst in combined treatment, the statistically significant differences among these effect sizes were not reported. Examining the individual studies included in the Nanni et  al. (2012) meta-analysis, as well as those published subsequently, reveals some interesting details. Five trials to date have focused on ADM and have generally found evidence for a negative prognostic effect (Enns & Cox, 2005; Johnstone et al., 2009; Klein et al., 2009; Quilty et al., 2016; L. M. Williams et al., 2016). For example, Enns and Cox (2005) found that patients with a history of sexual abuse were less likely to respond across a variety of naturalistic pharmacological interventions. This result is important, as it indicates that the negative prognostic role of maltreatment is not limited to strictly controlled clinical trials administered in academic settings. More recently, in the large International Study to Predict Optimized Treatment for Depression (iSPOT-D), childhood maltreatment was associated with poor response to ADM treatment (L. M. Williams et al., 2016). Relatedly, the two ADM trials that examined physical, sexual, and emotional abuse or neglect separately found that all types of maltreatment were significantly associated with lower likelihood of remission (Klein et al., 2009; L. M. Williams et al., 2016). These results highlight childhood maltreatment as a potent prognostic marker across heterogenous exposures. The trial by L. M. Williams et al. (2016) also found that maltreatment more strongly predicted poor response for patients randomized to sertraline than for those randomized to escitalopram or venlafaxine. Sertraline, in addition to blocking the re-uptake of serotonin, has dopamine-inhibiting effects. Given the strong association between stress and blunted dopamine-mediated reward function (Craske et  al., 2016), the investigators speculated that dopamine inhibition may be particularly harmful in patients with trauma histories. As noted earlier, dopamine agonists (e.g., aripiprazole) are promising candidates for this subgroup of patients, although no trials have yet been conducted. A smaller open-label trial also found evidence for a differential relation of childhood maltreatment to response based on the mechanism of ADM action (Quilty et al., 2016). Specifically, higher severity of childhood maltreatment was significantly associated with lower likelihood of response, but only in patients receiving antidepressant medications with a weak affinity for the serotonin transporter (e.g., tricyclics, serotonin and norepinephrine reuptake inhibitors [SNRIs]). This latter result is intriguing, as it is consistent with the proximal stress trials reviewed above (Keers et al., 2010, 2011). Several trials have compared ADM versus psychotherapy conditions. In a trial of 312 adults randomized to IPT or SSRI (citalopram or escitalopram), Miniati et al. (2010) found that a history of emotional and/or physical abuse predicted a longer time to remission, and greater likelihood of requiring combined treatment at Week 8, in the full sample. However, the investigators did not compare across IPT and SSRI conditions. Harkness et al. (2012) did make

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this comparison in their trial of 203 adults with major depression randomized to IPT, CT, or ADM, finding that patients with a history of childhood maltreatment were significantly less likely to respond to IPT than to either cognitive behavior therapy (CBT) or ADM, with no significant difference between the latter two conditions. In contrast, in their large trial of 681 outpatients with persistent depression, Nemeroff et al. (2003) found that patients with a history of maltreatment who were randomized to cognitive-behavioral analysis system of psychotherapy (CBASP), or CBASP + nefazodone, were significantly more likely to respond than those randomized to nefazodone alone. The investigators suggested that patients with a history of maltreatment may particularly benefit from working through their history of maltreatment in the context of psychotherapy. However, an alternative interpretation, based on these findings, is that the significantly poorer outcomes with nefazodone are because this ADM has a relatively weak serotonin affinity. In a small trial of 60 adults with persistent depression, Bausch et al. (2017) reported that patients with a history of maltreatment who were randomized to the higher serotonin affinity SSRI escitalopram had a significantly better response at Week 8 than maltreated patients randomized to CBASP and all patients without a history of maltreatment. All patients caught up eventually, though, and there were no differences in response across groups by Week 28. However, the exact opposite pattern of response emerged in two large treatment trials of depression in adolescents. In the Treatment of SSRI-Resistant Depression in Adolescents (TORDIA) trial (N = 287), Asarnow et al. (2009) reported that childhood maltreatment was significantly associated with poorer response among adolescents randomized to CBT; however, maltreatment did not predict response among those randomized to medication or the combination of CBT + medication. Similarly, in the treatment of adolescent depression study (TADS; N = 427), Lewis et al. (2010) reported that adolescents with a history of childhood trauma (not specific to maltreatment) responded better to fluoxetine, or to the combination of fluoxetine and CBT, than to CBT alone. Indeed, in this latter trial, adolescents with a history of maltreatment in the CBT arm fared no better than those in the placebo condition. Finally, Barbe et al. (2004) reported results from a smaller trial of 107 adolescents randomized to CBT, systemic behavioral family therapy (SBFT), or nondirective supportive therapy (NST). While CBT was associated with a significantly higher rate of response than NST in adolescents without a history of sexual abuse, this difference was not significant among those with a sexual abuse history. Therefore, results of the three most comprehensive RCTs to date of adolescent depression suggest that adolescents with a history of maltreatment and/or trauma may fare worse in CBT than in ADM, and, indeed, may not achieve a better response to CBT than to placebo. Reasons for the discrepancy between the findings of these adolescent trials versus those reported above in adults are likely complex. Childhood maltreatment is more proximal in time for adolescents than for adults. Perhaps some time and emotional distance are required before attempting to work through

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the thoughts and feelings associated with maltreatment. Relatedly, perhaps adolescents do not yet possess the requisite emotional and cognitive skill to work through their trauma in CBT. Finally, the CBT included in these adolescent trials was not specifically geared to addressing maltreatment or other traumas, and focused heavily on psychoeducation about the disorder. Therefore, more recently developed “trauma-focused CBT” may be associated with stronger effects in this young group (reviewed in a later section). In summary, while the role of childhood maltreatment as a negative prognostic marker in depression is fairly clear, evidence regarding differential prediction of response in particular treatment modalities over others is sparse, and results are inconsistent. Large differences across studies in the operationalization and assessment of maltreatment are likely at least partly to blame for these inconsistencies. Further, and more substantively, the literature on the effect of maltreatment on treatment response has to date lacked a clear theoretical framework and has generally ignored the vast amount of research on the mechanisms that translate childhood maltreatment to depression. This basic research could be drawn upon to develop clear hypotheses regarding the treatment modalities and/or techniques that would be expected to be most beneficial in the treatment of individuals with maltreatment histories, and could be used to refine existing treatments and/or develop new treatments to improve outcomes in depressed patients with a history of maltreatment. Some preliminary work addressing this issue is reviewed next.

TREATMENTS DEVELOPED FOR DEPRESSION IN THE CONTEXT OF STRESS EXPOSURE Treatments specifically addressing the effects of stressful and traumatic life events have a long history in the field of post-traumatic stress disorder (PTSD). A review of PTSD treatments goes beyond the current scope, and the reader is directed to numerous reviews and meta-analyses of this literature (e.g., Lewis et al., 2020). The current focus will be on the much smaller literature examining interventions specifically for the treatment of depression in individuals with a history of stress. This literature has focused almost exclusively on the treatment of depression in individuals with a history of traumatic stress, most often childhood maltreatment. Cognitive and Behavioral Interventions Most cognitive and behavioral interventions addressing trauma in adults were developed to treat PTSD, with prolonged exposure (PE; Foa et al., 2007) and cognitive processing therapy (CPT; Resick & Schnicke, 1992) having the strongest empirical bases. As noted, review of their evidence base is beyond the scope of this chapter. Specifically focusing on depression, a meta-analysis of PE trials for PTSD showed large effects on depression symptoms relative to inactive control conditions in acute treatment, Hedges’s g = 0.77, p < .001, and

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upon follow-up, Hedges’s g = 0.41, p = .03 (Powers et al., 2010). Similarly, a meta-analysis of CPT showed large effects on depression symptoms relative to control conditions in acute treatment, Hedges’s g = 1.01, p < .001, and upon follow-up, Hedges’s g = 0.82, p < .001 (Asmundson et al., 2019). Interestingly, in the latter meta-analysis, these effects were significantly moderated by sex, such that studies with a larger percentage of women reported larger effect sizes. In sum, empirically supported treatments for PTSD appear to also be very effective in treating co-occurring depression symptoms. Descriptively, comparing across these two meta-analyses, CPT was associated with stronger effect sizes for depression than PE. This may be because CPT includes a strong and explicit focus on restructuring maladaptive appraisals of the trauma and restructuring negative cognitions that drive negative mood. In contrast, PE focuses almost exclusively on repeated imaginal exposure of the trauma and in vivo exposure to trauma cues. However, in the absence of specific statistical comparison across treatments for depression outcomes, this remains a tentative hypothesis that should be tested in future research. CBT has been specifically adapted for the treatment of maltreatment in children and adolescents with depression. This advance is needed given findings from the large TADS and TORDIA trials showing that youth with a history of childhood maltreatment had significantly worse outcomes in the CBT arm relative to the medication arm (in contrast to trials in adults). Traumafocused CBT (TF-CBT; Deblinger et al., 2011) is a 16-week, manualized intervention that includes three modules delivered to children and their parents: (1) skills-building: addresses children’s affective, behavioral, biological, and cognitive self-regulation through graduated exposure exercises; addresses parents’ behavior management skills and coping; (2) trauma narrative: children describe and cognitively process the trauma memories through exposure; and (3) treatment closure: safety planning and relapse prevention. The efficacy of TF-CBT was tested in a sample of 179 children (ages 4–11 years) with a history of sexual abuse randomized to 8 or 16  weeks of TF-CBT that either included the trauma narrative component or did not (Deblinger et al., 2011). Treatment was associated with significant improvement in parent and child depression symptoms, with no significant differences across conditions. Mean posttreatment depression scores were mild, with a large mean effect size for pre–post differences across all outcomes (d = .96), and remission was maintained at 6- and 12-month follow-ups (Mannarino et al., 2012). Meta-analysis of the efficacy of TF-CBT on depression symptoms, specifically, revealed a significant effect size, Hedges’s g = −0.78, p < .01, favoring TF-CBT over no treatment (seven studies), and Hedges’s g = −0.25, p < .01, favoring TF-CBT over alternative treatments (10 studies; Lenz & Hollenbaugh, 2015). Effect sizes were larger for PTSD outcomes (Hedges’s g = −1.48 for no treatment comparisons; Hedges’s g = −0.28 for alternative treatment comparisons); however, PTSD was the primary diagnosis in most of the studies included in the review, and, thus, larger treatment effects on that outcome would be expected. The meta-analysis indicated no significant moderation of depression

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outcomes by participant age, ethnicity, or type of trauma. This is notable because the studies were very diverse, with several including large proportions of ethnic minority participants in traditionally underserved areas of North America and the world. Interpersonal Interventions Childhood maltreatment is associated with significant disruptions in interpersonal functioning (McLaughlin, 2020), and, thus, interpersonal treatments adapted for individuals with childhood trauma histories may be particularly effective. Talbot and Gamble (2008) adapted IPT for the treatment of women with sexual abuse histories (interpersonal psychotherapy–trauma [IPT-T]) by developing a fourth interpersonal problem area—“interpersonal patterns.” Work within this problem area involves helping women to understand how the interpersonal behavioral patterns that they developed as a result of their trauma in childhood negatively affect their relationships with others in adulthood. In an RCT of 70 women in a community mental health setting, IPT-T was associated with significantly greater decreases in depression symptom severity over 36 weeks than usual care (Talbot et al., 2011). However, Week 36 depression scores were still in the moderate range even in the IPT-T group (Beck Depression Inventory [BDI-II; Beck et  al., 1996] = 22.87; Hamilton Rating Scale for Depression [HRSD; Hamilton, 1960] = 16.10). IPT was also adapted to treat depression in women exposed to intimate partner violence (IPT for IPV; Cort et al., 2014). The main adaptations included administering IPT in a brief eight-session group format (to reduce social isolation and increase accessibility) and focusing exclusively on role disputes and interpersonal patterns. In an uncontrolled trial of 21 depressed women with IPV histories, depression and trauma symptoms improved significantly from pre- to posttreatment. However, the change in HRSD scores failed to exceed 50%, which is the standard psychiatric definition of “response,” and endpoint HRSD scores remained above 10 at posttreatment; thus, most women in this trial also failed to attain remission. Therefore, the results of both of these trials raise the question of how these modified IPT interventions would perform in comparison with other active treatments, such as modified CBT or CPT. Mindfulness Interventions One of the primary mechanisms mediating the relation of childhood maltreatment to depression is dysregulation of the neurobiological and psychological stress response. As such, treatments incorporating mindfulness training may be particularly effective for this group of patients. Mindfulness-based stress reduction (MBSR) is typically delivered in weekly 120-minute sessions over the course of 8 weeks in a group setting (Khoury et al., 2015). Patients are led through four mindfulness practices: (a) sitting meditation, (b) walking meditation, (c) mindful awareness of movement by holding yoga poses, and (d) body scan (i.e., focusing attention on different regions of the body). Patients

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are encouraged to notice thoughts, feelings, and sensations that arise and then to calmly refocus their attention on their breath and meditation. The overarching purpose of MBSR is to help patients develop a nonjudgmental awareness and acceptance of their internal physiological and mental states. In a meta-analysis, MBSR was associated with large effects on perceived stress (Hedges’s g = .74, p < .0001) and depression symptoms (Hedges’s g = .80, p < .0001; Khoury et al., 2015). In a small, uncontrolled trial of 27 adults with a history of sexual abuse, 8  weeks of MBSR was associated with significant decreases in depression symptoms (Kimbrough et  al., 2010), which were maintained at a 2.5-year follow-up (Earley et  al., 2014). Specifically, in the acute trial, BDI-II scores decreased 65% from moderate (BDI-II = 22.10) to mild (BDI-II = 7.80) levels of symptoms. These promising preliminary results suggest that further examination of MBSR in randomized controlled trials for depressed patients with maltreatment histories is warranted. Further, there is preliminary evidence for stress mechanisms mediating treatment effects. For example, in a trial of 42 young adults with histories of childhood maltreatment, Joss et al. (2020) reported that those randomized to MBSR showed a significant increase in hippocampal volume from pre- to posttreatment that was significantly correlated with reductions in depression and perceived stress. In contrast, those randomized to a wait-list condition showed a decrease in hippocampal volume. Mindfulness-based cognitive therapy (MBCT) was developed as an intervention to prevent depression relapse and recurrence by targeting the ruminative thought patterns that trigger dysphoric mood (Segal et al., 2018). Therefore, MBCT teaches patients in remission from depression to develop metacognitive awareness so that the sorts of automatic, ruminative thoughts that trigger relapse may be more accessible to effortful reflection. MBCT is a manualized group skills training program delivered in eight weekly 2-hour classes (Segal et al., 2018). Three themes are stressed during the classes: (a) developing moment-bymoment nonjudgmental awareness of bodily sensations, thoughts, and feelings through guided meditation exercises; (b) developing self-compassion and acceptance in the face of life difficulties; and (c) developing an “action plan” of strategies for responding to signs of relapse or recurrence with self-compassion. An individual patient-level meta-analysis of four MBCT trials showed that risk of depression relapse over a 60-week follow-up was significantly lower for patients randomized to MBCT compared with no treatment (hazard ratio = 0.69) and active treatment (hazard ratio = 0.79; Kuyken et al., 2016). Further, MBCT shows stronger effects in preventing relapse and recurrence among more severely depressed patients with greater lifetime number of episodes (Piet & Hougaard, 2011). In an RCT comparing CBASP, MBCT, and treatment-as-usual (TAU) in the treatment of 106 patients with persistent depression, Michalak et  al. (2016) reported that higher levels of childhood maltreatment were associated with significantly greater likelihood of remission over a 6-month follow-up period in both CBASP and MBCT relative to TAU. While level of childhood maltreatment did not significantly moderate the difference between CBASP

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and MBCT, the size of the effect comparing MBCT with TAU (coefficient = −7.59) was descriptively larger than that comparing CBASP with TAU (coefficient = −6.93). Second, in an RCT comparing MBCT, cognitive psychoeducation (CPE), and TAU in 274 adults in remission following at least three previous episodes, J. M. Williams et al. (2014) reported that, among patients with high severity of childhood maltreatment, MBCT was significantly better at preventing relapse over a 12-month follow-up period than CPE or TAU. Specifically, 41% of patients relapsed following MBCT compared with 54% and 65% following CPE and TAU, respectively. In contrast, among those with low severity of maltreatment, there were no significant differences in relapse rates across groups (51%, 45%, and 43% for MBCT, CPE, and TAU, respectively). These results suggest, then, that patients with the highest risk of relapse may benefit most from MBCT, which, indeed, was developed specifically to prevent relapse. In summary, traditional treatments for PTSD show positive effects on secondary depression outcomes in youth and adults with maltreatment histories. Further, tentative evidence suggests that interventions that include cognitive restructuring show stronger effects on depression outcomes than exposure only or than amendments of IPT. However, more research is needed in examining the efficacy of interventions specifically adapted to address trauma in patients with a primary diagnosis of MDD. Interventions such as TF-CBT may be a particularly effective option for youth with MDD given the poor outcomes associated with non–trauma-focused CBT in this age group. Finally, childhood maltreatment is associated with a high risk of relapse and recurrence regardless of the modality of acute treatment. The preliminary findings reviewed above suggest that mindfulness training is significantly more effective than other types of relapse prevention specifically in patients with maltreatment histories.

CASE EXAMPLE Nia is a 33-year-old cisgender married woman referred by her family physician to a university psychology clinic.1 A structured diagnostic assessment revealed that Nia is currently in an episode of MDD that started 1 month ago. Her history revealed three previous depressive episodes but no other comorbid mental disorders. Two months ago, Nia was fired from her job in technical support at a human resources software company following two negative performance evaluations. She has been looking for work, but it has been difficult due to job scarcity during the COVID-19 pandemic. Nia’s wife, Harper (a cisgender woman), has picked up more hours at her job as an auto parts manager at a garage to help alleviate the loss of income. This has created additional conflict in an already strained marriage. Nia reported that she has frequent fears of

The specifics of this case were disguised to protect the identity of the patient.

1

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rejection in her marriage that have increased recently. Nia expressed guilt about being a financial burden as well as anxiety that Harper now risks additional exposure to COVID-19 because of her increased work hours. Nia is sure that Harper will soon leave her (“she deserves someone better than me”). When questioned whether Harper has told Nia this, she responded, “She says she loves me, but how could she? She just says that to get me to shut up.” An assessment of family of origin revealed a chronic history of emotional maltreatment from her mother. Nia reported that her mother (a cisgender woman) was an “angry woman” who expressed a high degree of hostility toward Nia. She was frequently critical and could be verbally abusive, calling Nia names (e.g., “fat cow,” “little bitch”). This antipathy was directed preferentially toward Nia and was not expressed toward either of her two brothers. Nia reported that her father (a cisgender man) left the family when she was a young child, and Nia has few memories of him. Nia has not interacted with her mother in several years. The clinic takes a personalized approach based on the empirical evidence for prescriptive predictors of treatment response. Given Nia’s history of emotional abuse and recurrent depression, she is at risk for a poor response. Further, given her severe proximal stress (job loss + relationship conflict) and absence of personality disorder comorbidity, the empirical evidence suggests that she has a better chance of responding to CBT than to ADM. The clinic follows a standard 16-week manualized CBT protocol (Greenberger & Padesky, 2016). The first phase of treatment focuses on restructuring negative automatic thoughts that drive negative mood, particularly in Nia’s interactions with Harper. Working through exercises, such as thought records and in-session and in-home practice, Nia starts to recognize themes that relate to core beliefs of abandonment, emotional deprivation, and dependency. Through Socratic dialogue with her clinician, Nia realizes that the ways in which she was treated by her mother led to the development of patterns of thinking that now influence her behavior in her marriage. The appraisals about herself, others, and the world that Nia made about her abuse, though perhaps adaptive in her childhood abusive context, do not accurately reflect the reality of her current context. She begins to recognize how these core beliefs are driving maladaptive behaviors, such as excessively seeking reassurance (see Chapter 6, this volume) of Harper’s feelings for her, while, at the same time, challenging such reassurance when it is provided. The second phase of treatment focuses on behavioral application of the insights learned through the cognitive work. Nia is encouraged to test out some of the underlying assumptions that she has about Harper and then to reflect on the implications of the results of these behavioral experiments for the veracity of her core beliefs. At the same time, she is encouraged to schedule positive and rewarding activities, both with Harper and alone, to further challenge these beliefs. By Week 16 of treatment, Nia no longer meets criteria for MDD and reports that her communication with Harper is more open and positive. Nevertheless, Nia is at high risk for relapse based on her history of childhood maltreatment

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and three previous depressive episodes. Therefore, Nia’s clinician refers her to an MBCT group. In addition to learning mindfulness techniques and developing a stance of acceptance and self-compassion, Nia identifies a list of relapse warning signs and develops strategies for addressing them. Specifically, Nia realizes that she knows that her mood is slipping when she starts having trouble falling asleep due to ruminative, spiraling thoughts related to rejection (e.g., “Harper doesn’t love me,” “What am I going to do if she leaves me?”) that are accompanied by images and memories of specific abusive incidents from her childhood. Through problem solving and brainstorming discussion with her MBCT group, Nia’s list of strategies include engaging in mindfulness practice, replacing ruminative thoughts with more balanced thoughts, and reminding herself of her accepting and compassionate stance (e.g., “thoughts are just thoughts,” “my past doesn’t get to decide my present”), as well as making sure she is engaging in activities that provide pleasure and mastery.

SUMMARY AND FUTURE DIRECTIONS Proximal stressful life events and childhood maltreatment are significant predictors of a poor response to depression treatment. More interesting, however, is their role as prescriptive predictors. The evidence consistently suggests that high levels of proximal stressful life events are associated with poor response in patients treated with tricyclic ADMs. However, results of trials with ADMs with serotonergic mechanism of action are unclear, with some studies showing that proximal stress predicts better response (Keers et al., 2010, 2011), and others showing either no relation or poorer response in patients with high exposure to proximal life events (Bulmash et al., 2009; Fournier et al., 2009; Kim et al., 2011). Results of the two trials that have examined the relation of proximal life events to response to psychotherapy are also inconsistent, with one showing that proximal life event exposure predicts better response to CT (Fournier et al., 2009) and the other showing no evidence of a significant relation of life events to response in a combined CT–IPT group (Bulmash et al., 2009). The meta-analytic evidence suggests that childhood maltreatment is a significant predictor of poor response across treatment modalities. And integrating across studies suggests some preliminary conclusions regarding differential effects. ADMs with high serotonin affinity appear to be more effective in patients with maltreatment histories than low serotonin-affinity ADMs (e.g., tricyclics, SNRIs, dopamine antagonists). Further, psychological interventions that include cognitive restructuring, and that have been specifically adapted for use in patients with a history of trauma, appear to be associated with better response than purely exposure-based behavioral interventions, IPT, or psychoeducation heavy CBT. In addition, mindfulness-based approaches are more effective than strategies that do not include mindfulness in targeting the persistence and recurrence of depression in patients with maltreatment histories.

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This is an area of tremendous opportunity for investigation, with potentially huge benefits for patients with depression. A brief list of questions to emerge from this review is presented next. First, are there particular types of proximal life events that are especially predictive of bad or good outcomes (e.g., interpersonal vs. noninterpersonal, dependent vs. independent)? Similarly, to date the majority of studies investigating the relation of childhood maltreatment to treatment outcome have focused on a composite maltreatment variable; however, emotional maltreatment is more strongly associated with the onset and clinical characteristics of depression than sexual and physical maltreatment (Vallati et al., 2020). Does this distinction extend to predictions of treatment outcome? Second, proximal stressful life events and childhood maltreatment often present with additional risk factors, which themselves are prognostic and prescriptive indicators. Therefore, research is needed that integrates across these risk indicators. For example, childhood maltreatment is associated with the generation of proximal stressful life events. Given that exposure to proximal life events may predict better response to CBT than ADM (Bulmash et  al., 2009; Fournier et al., 2009), a reasonable hypothesis is that when a history of childhood maltreatment presents in the context of high levels of proximal life events, CBT may be particularly indicated over ADMs. However, childhood maltreatment is also associated with higher rates of comorbid personality disorder and higher levels of maladaptive personality traits such as neuroticism. These personality markers have been associated with better response to ADM over CBT (Bagby et al., 2008; Fournier et al., 2008). Therefore, it is possible that when a history of childhood maltreatment presents in the context of personality dysfunction, ADM may be particularly indicated over CBT. Results bearing on these questions would help in the development of empirically informed strategies for treatment assignment based on a careful assessment of patients’ current context of stress exposure and personality functioning. Third, and relatedly, almost no studies have examined whether the prognostic or prescriptive relation of stress exposure to treatment outcome is moderated by gender, sexual orientation, ethnicity, or their intersection. Research bearing on this important question could be informed by the rich research base examining adaptations of depression treatments and service delivery for individuals with diverse identities (Legha & Miranda, 2020; Sugarman et al., 2021). Finally, are there specific cognitive behavioral strategies that predict positive outcomes in patients with stress exposures? For example, are cognitive skills, such as restructuring negative attributions about one’s life circumstances, more beneficial than behavioral strategies, such as exposure? Relatedly, what are the neurobiological and psychological mechanisms that mediate response to ADMs (and differential response to particular ADMs) in patients with stress exposure? In all of this research, outcomes should not simply focus on remission of depression symptoms but should also include stress-related outcome measures. In particular, longitudinal follow-up studies should examine the differential effects across

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6 Dependency and Excessive Reassurance Seeking Lisa R. Starr, Angela C. Santee, and Meghan Huang

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nterpersonal dependency is the tendency to rely upon others for support, guidance, nurture, or protection even when functioning autonomously is possible (Bornstein, 2011). Synthesizing decades of research on the dependency construct, Bornstein (2011) proposed a cognitive/interactionist model of interpersonal dependency that includes four core components: (a) cognitive (e.g., helpless self-schema, core belief of oneself as powerless), (b) affective (e.g., fear of negative evaluation, fear of abandonment, pronounced anxiety when required to act autonomously), (c) motivational (e.g., a strong need to obtain support, guidance, nurturance, and protection from others), and (d) behavioral (e.g., use of a broad range of strategies to strengthen relationships, elicit support, and preclude abandonment from others). The cognitive, affective, and motivational components of dependency are theorized to remain relatively consistent across time and situations, whereas the behavioral manifestations of dependency vary considerably across contexts and range widely from more adaptive to maladaptive self-presentation strategies and inter­ personal behaviors. Underlying these behavioral manifestations of dependency is the helpless self-schema. Researchers have long recognized that dependency is associated with a variety of self-presentation and behavioral strategies that range from adaptive to maladaptive. On the one hand, individuals with higher levels of dependency show increased sensitivity to verbal and nonverbal social cues (Masling et al., 1980), increased willingness to seek support when needed (Bornstein & Kennedy, 1994), and a decreased delay in seeking treatment following

https://doi.org/10.1037/0000332-007 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  133

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illness onset (Greenberg & Fisher, 1977). The self-presentation strategies of individuals high on dependency can contribute to others’ perception of them as especially warm, kind, and agreeable (Lowe et al., 2009). On the other hand, individuals high in dependency are particularly sensitive to interpersonal rejection (Allen et al., 1996) and relationship conflict (Mongrain et al., 1998), especially when it involves authority figures or potential caregivers. People high in dependency are commonly thought of as passive, meek, or overly compliant, but evidence suggests that they can also be overly assertive or aggressive. More active, aggressive expressions of dependency may be employed after other strategies have failed to preclude abandonment when an important relationship appears to be threatened (Bornstein, 1995a; Pincus & Wilson, 2001). Dependency has long been considered a risk factor for depression, with some of the earliest research on this construct conceptualizing dependency as one of two vulnerability subtypes for depression (e.g., Blatt & Zuroff, 1992). There is also a high degree of conceptual overlap between interpersonal dependency and personality dimensions that have been proposed to underlie risk for depression (in particular following interpersonally relevant stressors), including sociotropy (Beck et al., 1983) and anaclitic personality style (Blatt & Zuroff, 1992; Lowe et al., 2009). In analogous models, Blatt and Zuroff (1992) contrasted dependency (e.g., concerns about helplessness and abandonment) with self-criticism (e.g., failing to meet personal standards), whereas Beck et al. (1983) contrasted sociotropy (e.g., investment in preserving relationships with others) with autonomy (e.g., investment in independent goals; see Zuroff et al., 2004, for a review and further nuance). Numerous measures have been developed to assess interpersonal dependency (Bornstein & Hopwood, 2017; Disney, 2013). Some of the most commonly used self-report measures include the following: the Depressive Experiences Questionnaire (DEQ; Blatt et  al., 1976, 1992), which includes items that assess anaclitic tendencies/dependency and can be further divided into subscales for maladaptive dependency and relatedness or “mature” dependency; the Interpersonal Dependency Inventory (IDI; Hirschfeld et al., 1977), which comprises three subscales theorized to tap subcomponents of dependency, including emotional reliance on others, lack of social self-confidence, and assertion of autonomy; the Personal Style Inventory (PSI; Robins & Luten, 1991), a scale designed to assess sociotropy or social dependency, with three subscales that assess concerns about what others think, dependency, and pleasing others; the Relationship Profile Test (RPT; Bornstein et al., 2003), which consists of three subscales for destructive overdependence, dysfunctional detachment, and healthy dependency; and the sociotropy subscale from the Sociotropy-Autonomy Scale (SAS; Bieling et al., 2000). Although most of these measure interpersonal dependency using self-report questionnaires, researchers in this area commonly recommend combining these tools with other forms of assessment, including informant reports (Disney, 2013). These alternate approaches may help in situations where individuals have limited insight into their own dependency-related drives and behaviors.

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Excessive Reassurance Seeking Coyne’s (1976) seminal interactional theory of depression proposes a cyclical process in which initially mild symptoms are recursively amplified via dysfunctional interpersonal processes (see Figure 6.1). Specifically, people with mild depression attempt to regulate their mood by requesting assurance from close others (e.g., about their own self-worth). This reassurance seeking is initially provided, but—being inconsistent with the negative self-schema of the person with depression—its veracity is ultimately questioned, leading them to return for more. Over time, this grows tiresome for the provider of the reassurance, so with self-prophetic irony, the support provider engages in rejecting behaviors that serve to confirm the reassurance seeker’s negative beliefs and propagate their negative mood. The rejected person then attempts to assuage their mood via further reassurance seeking, and the cycle continues. Joiner et al. (1992, 1999) identified excessive reassurance seeking (ERS) as the active ingredient in this process. ERS has been defined as the statelike tendency to repeatedly elicit requests for assurance, despite prior attempts and often to the point of negative interpersonal consequences. Across the broad

FIGURE 6.1.  A Conceptual Diagram of the Interactional Theory of Depression

Depressive Symptoms

Interpersonal Rejection by Others

Doubts About Self-Worth

Excessive Reassurance Seeking Reassurance Sought Again

Reassurance Initially Sought

Reassurance Given, but Veracity Doubted Note. Data from Coyne (1976).

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literature on ERS, the construct has almost exclusively been operationalized using a four-item measure, the Reassurance Seeking subscale of the Depressive Interpersonal Relationships Inventory (DIRI-RS; see Joiner et al., 1992). The DIRI-RS contains two items assessing whether respondents engage in reassurance seeking and two items assessing whether people they feel close to act “irritated” or “fed up” following these behaviors. Other Dependency-Relevant Interpersonal Behaviors In addition to excessive reassurance seeking, dependency is associated with other interpersonal behaviors that are corrosive to relationships and mental health. We briefly discuss two here: negative feedback seeking and corumination. Negative Feedback Seeking According to self-verification theory, individuals with depression seek to confirm their negative self-perceptions by selecting into rejecting contexts. Specifically, they may engage in negative feedback seeking (NFS), or the tendency to elicit negative evaluations from others. Although people who engage in NFS experience negative affective responses when they hear negative feedback, they nonetheless find it reinforcing because it aligns with their negative schemas and provides a sense of predictability (Hames et al., 2013; Swann et al., 1992). NFS is typically assessed using the Feedback Seeking Questionnaire (FSQ; Swann et al., 1992), which asks participants to select among a series of questions (positive and negative; e.g., what are their least attractive features) that they would like for a close other (e.g., roommate) to answer about them. Individuals who request that their peers rate them on more negative traits are described as high on NFS. Corumination Corumination may represent another important behavioral manifestation of interpersonal dependency. Corumination is defined as excessive discussion of problems in a dyadic context, in which the causes, consequences, and negative affect associated with problems are repetitively and excessively discussed (Rose, 2002). It is often conceptualized as a social support-seeking behavior, as people may attempt to regulate their distress resulting from problems in their lives by seeking another person with whom to rehash their problems. Given that corumination involves dwelling on negative content and cognitions surrounding the problem, this maladaptive social support-seeking behavior is associated with increased emotional distress (Spendelow et al., 2017). Notably, corumination is also linked with relationship benefits, including increased emotional intimacy and connectedness within the relationship, which may stem from the self-disclosure and support-seeking and provision involved during problem talk (Rose, 2002). At present, corumination is mainly measured using the Co-rumination Questionnaire (Rose, 2002), a 27-item self-report measure that assesses nine domains relevant to corumination, including frequency and repetitiveness of problem

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talk, focus on negative emotions, and discussions about the causes and consequences of the problem within a specific relationship (traditionally a samegender friendship).

RISK LITERATURE Dependency Higher levels of dependency are associated with risk for a range of negative outcomes, including depression and anxiety (Mazure et  al., 2000; Overholser, 1997), increased functional impairment (Bornstein, 2012b), somatic symptoms (Bornstein & Gold, 2008), physical illness (Bornstein, 1998, 2012b), self-harm (Klonsky et al., 2003), and suicidal thoughts and behaviors (Bornstein & O’Neill, 2000; Loas et al., 2005; Loas & Defélice, 2012). These individuals appear to be at increased risk for physical illness particularly when they experience heightened interpersonal stress (Bornstein, 1995b). Individuals high on dependency are more likely to perpetrate intimate partner violence, and they are more likely to remain in dysfunctional or abusive relationships longer (Bornstein, 2006; Watson et al., 1997). Some research shows that individuals high on dependency are also at greater risk of perpetrating child abuse (Bornstein, 2005b). As research on dependency has evolved over time, findings suggest that individuals high in dependency may be at greater risk for depression follow­ing the occurrence of major interpersonal stressors (Hammen, 2005; Zuroff et al., 2004). Additionally, some research suggests that dependency remains high among individuals whose depression has remitted compared to those with no history of depression (e.g., Bagby et al., 1994). Together, these results suggest that dependency may be a relatively stable underlying vulnerability factor that contributes to the onset and recurrence of depressive episodes, particularly following major interpersonal life stress. Development of Dependency Researchers have identified a handful of factors that appear to contribute to the development of maladaptive interpersonal dependency. First, authoritarian and overprotective parenting behaviors contribute to the development of dependency by facilitating the development of children’s helpless self-schema (Baker et al., 1996; Bornstein, 2012b). Overprotective parents signal to children that the world is dangerous and that they are unable to survive without the support or protection of a powerful caregiver. Authoritarian parents may signal the same while also emphasizing the need to conform to the expectations or demands of authority figures. Some research suggests that serious illness early in childhood is associated with the development of dependency, perhaps because it may elicit overprotective parenting from caregivers and contribute to the child’s sense of helplessness or frailty (Hoare, 1984; Parker & Lipscombe, 1980). Further, researchers speculate that infant temperament plays

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a role in the development of dependency, as certain temperamental factors (e.g., shyness, timidness) may elicit more parental overprotectiveness (Bornstein, 2016; Bornstein et al., 1993). Beyond parenting factors, ample research suggests that social and cultural factors (e.g., gender role socialization, the degree to which interconnectedness versus independence is valued) impact the degree to which dependency is expressed, accepted, and reinforced by others. Women consistently score higher than men on measures of interpersonal dependency (Bornstein et al., 1993). Individuals from sociocentric rather than individualistic cultures generally score higher on measures of dependency, and these cultures are generally more tolerant of dependency-related behaviors (Bornstein, 2016). Excessive Reassurance Seeking Relationship to Dependency The link between ERS and interpersonal dependency makes intuitive sense. Within Coyne’s (1976) model, ERS can fundamentally be construed as an overreliance on close others for emotion regulation, which is conceptually similar to dependency. Further, both ERS and dependency share cores of fear of negative evaluation (Bornstein, 2005b; McClintock et al., 2014). Fitting with this conceptual alignment, several studies have shown significant associations between ERS and dependency/sociotropy (Beck et al., 2001; Birgenheir et al., 2010; Joiner & Metalsky, 2001; McClintock et al., 2014; Shahar et al., 2004), even beyond associations with depression (McClintock et al., 2014), with bivariate correlations ranging from r = .25 to r = .47. In contrast, ERS is unrelated to autonomy after accounting for shared variance (Beck et al., 2001; Birgenheir et al., 2010; Davila, 2001). ERS may act as a behavioral mechanism linking trait-like dependency/sociotropy and negative outcomes, such as negative life events (Birgenheir et al., 2010). Relationship to Depression ERS shows a robust cross-sectional association with depressive symptoms across a large number of studies. Starr and Davila (2008) conducted a metaanalysis of 38 studies and generated an aggregate effect size (r) of .32. A more recent expanded and updated meta-analysis of 91 studies (Wakeling et al., 2020) generated very comparable results (meta r = .33). However, the cross-sectional correlation does little to clarify whether ERS serves as a risk factor for the onset of depression, especially as Coyne’s (1976) model predicts that depressive symptoms motivate people to engage in ERS. Prospective studies have been much more limited in number and somewhat more mixed in findings, with some studies suggesting that ERS predicts escalating depressive symptoms over time, especially under stressful environmental conditions, but a few studies suggest that correlated variables (e.g., dependency, poor social support) may better account for ERS’s prospective effects on depression (for a review, see Starr & Davila, 2008).

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Relationship to Other Diagnostic Outcomes Many ERS researchers have argued that the construct is uniquely depressogenic, based on evidence that ERS correlates with and predicts depression but not other anxiety and other diagnostic outcomes when controlling for shared variance (e.g., Burns et al., 2006; Joiner & Schmidt, 1998). However, patterns of reassurance seeking have long been observed in obsessive compulsive disorder (Salkovskis, 1985). Rector et  al. (2011) suggested that the apparent specificity of ERS to depression may be a measurement relic, as the DIRI-RS items (e.g., “Do you frequently seek reassurance from people you feel close to as to whether they really care about you?” [emphasis ours]) capture reassurance about issues most relevant to depression (i.e., low self-worth). Their research team developed an alternative measure, the Reassurance Seeking Scale, with subscales assessing ERS in different content areas and found that while depression was generally associated with all forms of ERS, anxiety was uniquely linked to ERS about potential threats (Rector et al., 2011). In a subsequent study, Rector et al. (2019) found that ERS was elevated in a variety of diagnostic groups among anxiety disorders and obsessive–compulsive disorder (OCD) in a treatment-seeking population, but that the domain of reassurance being sought differed by disorder. Taken together, these results may suggest that ERS may be more transdiagnostic than was once believed by many depression researchers, and that overreliance on a psychometrically limited measure may have obscured important findings. Relationship to Interpersonal Rejection In the Starr and Davila (2008) meta-analysis, the weighted mean effect size for the association between ERS and interpersonal rejection was significant but weak (r = .14), and prospective studies were minimal and mixed. Further, effect sizes were significantly stronger when perceptions of rejection were assessed (r = .30) rather than informant-reported rejection (r = .09) although both effect sizes were significant. Further work is needed to determine the extent to which the ERS process is truly interpersonal (i.e., evoking actual responses from others), rather than intrapersonal. Development of ERS If ERS is linked to critical outcomes, it is important to next address how individual differences in ERS emerge, using a developmentally informed framework. Research in this area has been comparatively limited. Several researchers have framed ERS in the context of attachment theory. Bowlby (1980) posited that the development of secure base functioning encompassed the ability to self-soothe and develop positive working models of the self; individuals with inconsistent attachment figures may fail to develop these features of security and learn to (excessively) rely on others to validate their self-worth. Aligning with this theory, numerous studies have linked ERS to anxious attachment and characteristics of insecure attachment style (for a review, see Evraire & Dozois, 2011). Bridging attachment and cognitive behavioral models, Evraire and Dozois (2011, 2014) have argued that early maladaptive core schemas

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about rejection and abandonment fuel reassurance seeking efforts. In a new direction, an intriguing recent study points to the role of social cognitive impairments, showing that among individuals with depression, theory of mind deficits were associated with higher ERS (Hudson et al., 2018). However, as with much of the ERS literature, this research was conducted in adults and is fully cross-sectional. Far more longitudinal work is needed to better understand how and when ERS develops and consolidates into a stable interpersonal pattern. Other Relevant Interpersonal Behaviors Negative Feedback Seeking In a recent meta-analysis of 31 effect sizes, Wakeling et al. (2020) found an aggregate r of .26 for the association between NFS and depression. The apparent contradiction that depression would be associated with the solicitation of both positive reassurance (ERS) and negative verification (NFS) has generated substantial discussion. Researchers have argued that NFS is cognitively satisfying but affectively troubling, whereas ERS has the opposite effects, leading the individual to oscillate between the behaviors, potentially snowballing their negative interpersonal effects (Joiner et  al., 1993). Researchers have also argued that the selection of NSF versus reassurance seeking may depend on factors such as cognitive load (Swann et al., 1990), the specificity versus globality of the domain (Neff & Karney, 2002), and other factors (for more detailed reviews and integrated models, see Evraire & Dozois, 2011; Hames et al., 2013). A few studies have supported the notion that, like ERS, NFS acts as a self-fulfilling prophecy, leading to eventual rejecting behaviors by others (itself a potent form of negative feedback) although limited longitudinal studies have examined this question in comparison to the larger literature on ERS (for a review, see Evraire & Dozois, 2011). Corumination Like excessive reassurance seeking, corumination is an interpersonal behavior that individuals may engage in for its potential benefits (e.g., self-disclosure with a close other, emotional support relating to problems) that may also increase psychological distress (Rose, 2002). These two interpersonal constructs are positively correlated and may reflect a general propensity for certain individuals to engage in maladaptive social support patterns, to the detriment of their mental health (Starr, 2015). Of note, these constructs are related yet distinct; for example, research suggests that corumination and excessive reassurance seeking both interact with daily hassles to uniquely predict depressed mood (Starr, 2015). Although many studies on corumination largely focus on its link with depression, the broader literature suggests that corumination may not be uniquely depressogenic. For instance, a metaanalytic review by Spendelow et al. (2017) found small-to-moderate effect sizes when examining the relationship between corumination and several internalizing outcomes, including depression and anxiety (rs ranging from

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.14 to .26). Further, corumination has been linked to externalizing symptoms (Tompkins et al., 2011) and other negative outcomes, including interpersonal stress generation (Hankin et  al., 2010). Recent research has also examined interpersonal contexts that may enhance the deleterious effects of corumination. For example, in a sample of 240 adolescent friend dyads, Schwartz-Mette and Smith (2018) found that corumination facilitated depression contagion particularly when the following characteristics were present: a high-quality friendship, high excessive reassurance seeking on the friend’s part, and the individual’s propensity to experience high personal distress in the face of others’ distress, highlighting how these maladaptive interpersonal behaviors may flourish and confer risk within close relationships (Schwartz-Mette & Smith, 2018).

INTERVENTIONS AND EVIDENCE BASE Dependency Despite the clear need for interventions that target excessive interpersonal dependency, research evaluating treatment approaches that specifically target dependency is sparse (Bornstein, 2004; Disney, 2013; Dixon-Gordon et al., 2011; Perry, 2007; Versaevel, 2012). Nevertheless, numerous theoretical models have been applied to conceptualize dependency and articulate targets for treatment (e.g., Bornstein, 2005a, 2016). A few common goals emerge across theoretical approaches to treatment of dependent individuals, including (a) helping clients develop insight into the nature and origins of their dependency, (b) bringing awareness to dependency-related thoughts, feelings, and behaviors as they arise, and (c) reducing the use of maladaptive dependency-related behaviors and shifting toward more adaptive expressions of dependency needs. Overall, the goal is not to extinguish dependency in its entirety but rather to help clients achieve a flexible, healthy balance of autonomous functioning and interpersonal connectedness and support seeking. Dependency is associated with several strengths in the context of psychotherapy. For example, individuals high in dependency exhibit shorter delays in seeking treatment, miss fewer psychotherapy sessions, and show higher levels of treatment adherence and compliance (Bornstein, 1992, 1993, 2005a). At the same time, these individuals use health services at higher levels than their peers with comparable demographics and levels of functioning, make more contact with providers between sessions, and incur higher health care costs than others even after accounting for severity of illness and impairment (Bornstein, 2012a; Bornstein & Hopwood, 2017; O’Neill & Bornstein, 2001; Porcerelli et al., 2009). In treating individuals with high interpersonal dependency, clinicians should take care to choose a treatment approach and build a therapeutic relationship that does not inadvertently reinforce maladaptive aspects of dependency (e.g., clients’ perception of themselves as passive or helpless). For

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example, failure to recognize and explicitly address dependency in the context of the therapist–client relationship may hamper treatment progress; when a client with high dependency develops a strong attachment to the therapist in their supportive role, they may prolong treatment to avoid ending the relationship. Given certain cognitive (e.g., helpless self-schema) and affective (e.g., pronounced distress when required to behave autonomously) features of dependency, clinicians may find it more challenging to enhance clients’ sense of autonomy. Clinicians may also find that clients high in dependency withhold important information or avoid certain topics in therapy with the goal of preserving the therapist’s positive view of them. This phenomenon may be particularly present with clients for whom expression of dependency-related thoughts or feelings are less socially acceptable (e.g., men). Cognitive Behavioral Approaches Treatment approaches in this area will target cognitive, affective, and behavioral components of dependency for change. From the cognitive behavioral perspective, the maladaptive self-schemas (i.e., perceiving the self as helpless or powerless) of individuals with dependency contributes to negative automatic thoughts and pronounced distress when they perceive or anticipate failure, which, in turn, shapes the behavioral strategies they use to avoid the possibility of failure and reduce distress. Overholser and Fine (1994) presented one model for cognitive behavioral treatment of dependency that includes four stages: (a) active guidance from the therapist to facilitate early behavioral change, (b) self-esteem enhancement, (c) autonomy promotion through problem-solving training and self-control strategy use, and (d) relapse prevention. Following the publication of this model, Overholser (1997) outlined five areas that effective treatment for dependency must address: (a) stabilizing emotional reactions to dependency-related situations, (b) enhancing clients’ self-efficacy and selfesteem, (c) improving social functioning, (d) reducing the use of maladaptive dependency behaviors, and (e) developing insight into developmental origins of dependency. Mindfulness-Based Interventions Two studies have evaluated mindfulness interventions for interpersonal dependency (McClintock & Anderson, 2015; McClintock et al., 2015). Dependency is characterized by the habitual devaluing of one’s own thoughts, feelings, and ability to function autonomously while highly valuing the capabilities and guidance of others. For individuals with high dependency, mindfulnessbased interventions were developed to promote attention to their moment-tomoment experiences and foster a sense of curiosity, openness, and acceptance of their own experiences, thereby reducing the perceived need to rely on other people in situations where autonomous functioning is possible. In a study by McClintock and Anderson (2015), 70 undergraduate students with high levels of dependency were prompted to complete a mood induction to activate core cognitive (i.e., perceived helplessness) and affective (i.e., fear of abandonment) features of dependency, then randomly assigned to complete

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either a brief (20-minute) mindfulness or a control (distraction) intervention. Participants in the mindfulness condition reported greater reductions in anxiety and negative affect following the intervention, suggesting that a mindfulness-based intervention may help to alleviate distress associated with cognitive and affective features of interpersonal dependency. In a second study (McClintock et al., 2015), 48 undergraduate students high in dependency were randomized to either five sessions of mindfulness therapy for maladaptive interpersonal dependency or a minimal contact control condition over a 4-week period. Participants were evaluated at posttreatment and 4-week follow-up with measures of mindfulness, interpersonal dependency, and related symptoms (including perceived helplessness, fear of negative evaluation, and excessive reassurance seeking). Those who completed the mindfulness intervention exhibited greater improvements on all measures at posttreatment and follow-up. Moreover, increases in mindfulness appeared to mediate symptom improvements. Dialectical Behavioral Therapy Treatment of dependency can also be considered within the dialectical behavior therapy (DBT) framework. The biopsychosocial model posits that emotion dysregulation and associated behaviors in response to these emotions may result from sensitivity to emotions, combined with an invalidating environment that leaves individuals ill-equipped to regulate their emotions, tolerate distress, and effectively manage and communicate within their relationships (Koerner, 2012; Linehan, 1993). Thus, DBT strives to promote mindful awareness and to develop skills relating to emotion regulation, distress tolerance, and interpersonal effectiveness. Within the context of interpersonal dependency, individuals may feel helpless, experience distress when acting autonomously, and engage in ineffective behaviors to garner support and communicate distress to others. Through DBT, they may gain greater awareness of their emotional and behavioral patterns relating to dependency, develop skills to reduce vulnerability to and manage distressing emotions (e.g., fear of abandonment), learn to sit with and accept uncomfortable emotions and thoughts, and improve their abilities to communicate their personal needs within relationships. The application of DBT to dependency traits merits empirical investigation. Couple Therapy Couple interventions may be key in addressing dependency-related concerns in the context of intimate relationships. In the face of relationship difficulties and the potential loss of a critical relationship in their lives, individuals with dependent characteristics may be more willing to seek couple therapy to address these concerns (Links & Stockwell, 2004). Couples in which relationship difficulties arise from low self-esteem and submissiveness (i.e., low assertiveness) stemming from dependency may particularly benefit from couple therapy focused on identifying relational patterns and improving communication skills and problem-solving skills as a couple (Links & Stockwell, 2004).

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In treatment, a couple may also examine their own emotional responses and patterns of emotional engagement and responsiveness as a couple, working to foster secure and healthy patterns of emotional attachment (Johnson, 2007). In contrast, for couples whose patterns of dependency manifest in an enmeshed relationship or those who may be unaware of interpersonally dependent patterns, individual therapy may be beneficial first before engaging in couple therapy in order to build awareness and motivation (Kupers, 1997; Links & Stockwell, 2004). Psychodynamic Approaches Bornstein (2005a) identified three central elements across psychodynamic approaches to treating dependency, including analysis of core relational themes, corrective object relations, and transference or countertransference. Through identifying and exploring core relational themes—interpersonal dynamics that repeatedly recur across time and contexts—therapists may help clients with dependency issues to gain insight into the origins and maintenance of maladaptive dependency-related relationship patterns. From a psychodynamic perspective, the therapist–client relationship can serve as an important context for providing a corrective object relations experience to clients high in dependency. That is, by maintaining a positive, autonomysupportive relationship with clear and consistent boundaries, the therapist enables their client to experience new ways of relating to others in supportive roles. Finally, discussions of transference in psychodynamic therapy lend themselves to further development of clients’ insight into their dependency by, for example, explicitly addressing the client’s perceptions of the therapist as a supportive, nurturing, or powerful caregiver in their life. More detailed descriptions of psychodynamic conceptualizations of dependency and treatment strategies may be found elsewhere (e.g., Coen, 1992; van Sweden, 1995). Excessive Reassurance Seeking in a Treatment Context The depression treatment literature has given surprisingly little attention to ERS. However, for many years, researchers of anxiety and OCD have observed patterns of reassurance seeking in anxious individuals, often functioning as a safety behavior or a compulsive ritual (Gillihan et al., 2012; Parrish & Radomsky, 2010; Rector et al., 2019). Notably, the ERS literature on depression and anxiety has until recently been fairly siloed (for exceptions, see Katz et al., 2020; Parrish & Radomsky, 2010; Rector et  al., 2011), which may inadvertently send the message that ERS in depression and ERS in anxiety are inherently different constructs. Although reassurance seeking within anxiety and OCD differs somewhat in nature from that in depressive disorders (e.g., often characterized by seeking safety-related information or reducing threat concerns rather than assuaging negative self-concepts; Parrish & Radomsky, 2010; Rector et al., 2011), they share largely common behavioral elements. Consequently, anxiety research treatment that has explored ERS may have analogous implications for depression treatment. As such, here we draw mostly

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from research on treatment of ERS in the context of anxiety but encourage future research to explore the development of treatment modules (or transdiagnostic models) that more directly target depression-based ERS. Repeated engagement in reassurance seeking behavior may predict worse therapy outcomes. Within the anxiety literature, ERS has been often conceptualized as a safety behavior (Rector et al., 2019) or a coping signal employed in the presence of threat to reduce anxiety. As with other safety behaviors, ERS is believed to reduce distress in the short term but contribute to the maintenance of anxiety and repeated reassurance seeking behaviors over time. This may be because the reassurance seeking interferes with inhibitory learning or habituation during exposure to feared stimuli and prevents the person from learning to cope independently (Craske et al., 2014; Rector et al., 2019). That is, people engage in ERS because it provides some level of affective relief; however, once the reassurance is no longer provided, the source of the distress may remain, and the person feels ill-equipped to independently approach the source of distress without additional reassurance. Within the context of CBT, safety behaviors are generally viewed as detrimental to the exposure process, and, although some research suggests that engagement in safety behaviors may not be as problematic as once believed (and may actually facilitate initial engagement in exposures; e.g., Deacon et al., 2010), best practice guidelines advocate for gradual elimination of safety behaviors for optimal treatment outcomes, with elimination early in treatment ideal when feasible (Craske et al., 2014). Consistent with this conceptualization, Rector et al. (2019) found that reductions in ERS over the course of group CBT treatment for anxiety disorders predicted greater symptom improvement posttreatment; a second study replicated this finding in patients with OCD and depression (Katz et al., 2020). These findings suggest that patients who can reduce their reliance on ERS as a safety behavior may benefit more from treatment. This raises another question: How can therapists help patients diminish their engagement in reassurance seeking behaviors? Given (a) the amount of literature linking ERS to depression and its putative role in onset and maintenance of symptoms and (b) evidence that the reduction of ERS may improve treatment outcomes, it is surprising that (to our knowledge) no evidence-based treatments for depression explicitly address this maladaptive interpersonal behavior. However, several treatment protocols for nondepressive disorders (e.g., OCD, health anxiety) directly target reassurance seeking as a safety behavior (e.g., Abramowitz, 2018; Salkovskis et al., 2003), and these may be adapted to fit with depression treatment manuals. Some early, indirect evidence suggests that treatment may be helpful for ERS, especially when it is addressed directly. Rector et al. (2019) examined ERS levels over the course of treatment in patients enrolled in CBT for depression and CBT for OCD. The authors found that ERS diminished over the course of treatment in both treatment groups (perhaps as a simple function of symptom reduction), but more sharply for those receiving OCD treatment,

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which the authors speculated may be because the OCD treatment directly targeted reassurance seeking reduction as a safety behavior whereas the depression treatment did not. Although treatment targeting ERS can take a variety of forms, it may include directly working with the patient to reduce their reliance on reassurance seeking as a form of coping. Treatment may incorporate monitoring reassurance seeking behaviors, identifying triggers, tracking both short- and long-term consequences, and learning to differentiate problematic reassurance seeking from adaptive support seeking. Moreover, as the ERS cycle is an inherently interpersonal process, many treatments recruit the relationship partner (or whoever is the recipient of the reassurance seeking efforts) to dampen the need for further ERS. For example, reducing accommodation (Gillihan et al., 2012; Thompson-Hollands et al., 2015) involves stripping the reassurance of its reinforcing elements by instructing partners to disengage. Reducing accommodation can take a number of forms but, as one illustrative example, let’s say Fred (a cisgender man), who is in CBT for depression, asks his spouse, Wilma (a cisgender woman), for reassurance that other people like him. Wilma normally would provide this reassurance, which gives Fred an affective boost in the short run (reinforcing the reassurance seeking) but wears off when Wilma is no longer present to provide reassurance, causing him to return for more. In reducing accommodation, Wilma may simply say, “I cannot answer that” or “Fred, you are seeking reassurance. What can you do instead?” If Fred persists, Wilma may choose to discontinue the conversation by leaving the room or provide a statement that engages with Fred’s core fear or negative belief (“Nobody can ever really know, with 100% certainty, what other people think of them”). Fred receives no reinforcement, is directed toward treatment efforts, and ideally, the habit is broken. Research in OCD has suggested that incorporating accommodation reduction in family members, including avoiding provision of reassurance, accelerates treatment response (Thompson-Hollands et al., 2015). Neal and Radomsky (2019) suggested that while providing reassurance may fuel toxic patterns, providing support to seekers of reassurance may help facilitate adaptive coping (possibly relieving some of the negative affect that drives ERS) without inviting negative interpersonal consequences. Whereas providing reassurance directly “answers” the person’s inquiries about their concerns (“No, you are not fat,” “Yes, I love you,” “No, that is not cancer”), providing support aims to alleviate the person’s distress without directly addressing their question (“I can see that this is hard for you, but I know that you can handle this”). In a proof-of-concept study in which unselected undergraduates were trained to provide either supportive feedback or accommodation reduction in a cooperative task, Neal and Radomsky (2019) found that participants in the supportive feedback condition reported that the feedback was more helpful and reported no higher levels of reassurance seeking, suggesting that allowing for a “softer” (and potentially more engaging) supportbased approach may refrain from feeding the ERS cycle.

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CASE EXAMPLE “Laura”1 is a 19-year-old, White, cisgender woman. She is currently enrolled in college, is nonreligious, and identifies as heterosexual. She was born and raised in the United States and was raised in an upper middle-class family. She has no developmental disabilities identified although she reported a history of chronic health problems since early childhood requiring chronic pain management and a strict dietary plan. She also reported having seen a psychiatrist for anxiety in middle school. Relevant History, Presenting Concerns, and Presentation Laura’s parents have been divorced since her early childhood, and she has lived primarily with her mother since the divorce. She is the youngest of five siblings in her family. Laura describes herself as being extremely close to her mother, voicing that her mother is her best friend, closest confidante, and the person she seeks whenever she needs help or is feeling distressed. She also reported that since her childhood, her mother has played a key role in managing her physical health (e.g., appointments, medications, diet). She was also homeschooled by her mother up until her junior year of high school when her physical health became more stable and manageable. She described her relationship with her mother as her greatest protective factor in helping her navigate the stressors she experienced over the course of her childhood, noting that she did not know what she would do without her mother in her life. Laura endorsed recent depressed mood and increased anxiety and heightened stress since starting college. A college freshman several states away from her hometown, she has felt homesick and had been missing her mother in particular; she reported that, although they talk often, it has been difficult being several hours away from her mother and establishing her own schedule and routine as a student. She also reported feeling out of place at times when interacting with her peers and having anxiety about developing and maintaining new friendships in college. She has been feeling stressed about all the options and decisions to be made since starting college and described feeling “lost,” “anxious,” and “unsure” in these moments. Laura has also been struggling with managing her physical health since beginning college, noting that living away from home has meant taking a bigger role in managing her physical health needs. Given that this is something her mother typically handled prior to college, Laura has often felt unsure and helpless in the face of this greater degree of autonomy. She endorsed generally getting more quickly and frequently overwhelmed by these stressors and her emotions, such that even a small stressor would set her off. She has been contemplating a medical leave of absence and returning home or instead potentially transferring to a local university so that she can be closer to her mother.

Names and identifying information have been changed to protect client confidentiality.

1

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During the intake, Laura presented as eager and motivated for treatment, expressing interest in individual therapy to address her symptoms and guidance in navigating college life. Throughout the intake process, as she discussed ongoing stressors and worries in her life, she would solicit advice and validation from the therapist about her decisions and dilemmas (e.g., “Did I make the right decision choosing to take a linguistics class?” “What do you think about that?”). Additionally, despite the therapist’s attempts to make treatment planning and goal-setting a collaborative process, Laura repeatedly expressed a desire to hear her therapist’s opinions rather than providing her own thoughts and reactions. Case Conceptualization and Considerations for Treatment Upon assessment during the intake, Laura was diagnosed with adjustment disorder with mixed anxiety and depressed mood. It became clear from the intake that these symptoms were precipitated by changes brought by the transition to college and a new social environment, the stress of which was amplified by Laura’s interpersonally dependent tendencies. In the past, her mother often stepped in to guide her and to take care of things for her, which helped manage Laura’s stress and regulate her emotions. However, starting college has presented new challenges and related negative emotions, which Laura has felt ill-equipped to deal with on her own. Important considerations and potential targets for treatment include the following: • Supporting the development of effective coping strategies that she could implement independently to regulate her emotions and tolerate distress rather than seeking others (namely her mother) for support. She may also benefit from engaging in potential exposures targeting intolerance of uncertainty. • Fostering greater self-esteem and self-competence. Laura would likely benefit from learning to identify and address thoughts arising from maladaptive self-schemas. Teaching Laura the tools to set attainable goals for herself also may help in this regard, particularly in her approach to her physical health. On an interpersonal level, she may also benefit from assertiveness training. • Exploring emotions and beliefs that feed dependent tendencies and developing awareness of urges to engage in these behaviors. Laura’s upbringing has likely shaped her dependent tendencies, her beliefs about herself, and her emotions. Further insight and awareness (such as through behavior chains and mindfulness) on Laura’s part may be crucial in helping her change how she responds to problems or distress in her life. • Considering incorporating her mother into treatment, to bring awareness to their interpersonal dynamics and discuss strategies to reduce accommodation. For example, if Laura seeks reassurance about a decision she has made or help making a future decision, rather than engaging in patterns where she provides reassurance or makes decisions for Laura, her mother can provide her with emotional support (“I know that this uncertainty is

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difficult for you, and I also know that you are strong enough to tolerate those feelings, and that you have the information that you need to come to a decision on your own”).

SUMMARY AND FUTURE DIRECTIONS The construct of dependency has been researched by generations of psychologists, and yet we have much to learn about the causes, mechanisms, and treatment of this personality trait. Here, we note some limitations in the literature, with an eye toward future directions. First, the treatment literature on dependency is relatively thin. Although numerous researchers have developed treatment models and piloted techniques, there is little rigorous work that meets the highest standards of evaluating empirical support (e.g., Chambless & Hollon, 1998). This may be related to a divestment in interpersonal targets of treatment by the National Institute of Mental Health and other public entities in favor of neurobiological mechanisms. Second, more research is needed on the development of dependency, ERS, and other related behaviors, and particularly more longitudinal research is needed that tracks the emergence of these constructs in children. Third, aspects of the dependency literature may be improved with greater attention to measurement. ERS research, in particular, has largely relied on a four-item measure that features items that are double-barreled and rooted in depression pathology. Moreover, results from studies utilizing more novel methods to assess ERS (e.g., behavioral observation coding) have not always aligned with self-report methods or with predictions from Coyne’s (1976) model (Lord et al., 2020; Stewart & Harkness, 2017). Nonetheless, the dependency literature is empirically robust and conceptually rich, offering useful clinical guidance in the treatment of depression and related problems.

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Linehan, M. M. (1993). Cognitive-behavioral treatment of borderline personality disorder. Guilford Press. Links, P. S., & Stockwell, M. (2004). Is couple therapy indicated for patients with dependent personality disorder? Journal of Family Psychotherapy, 15(3), 63–79. https:// doi.org/10.1300/J085v15n03_05 Loas, G., & Defélice, E. (2012). Absolute and relative short-term stability of interpersonal dependency in suicide attempters. Journal of Nervous and Mental Disease, 200(10), 904–907. https://doi.org/10.1097/NMD.0b013e31826ba141 Loas, G., Guilbaud, O., Perez-Diaz, F., Verrier, A., Stephan, P., Lang, F., Bizouard, P., Venisse, J. L., Corcos, M., Flament, M. F., & Jeammet, P. (2005). Dependency and suicidality in addictive disorders. Psychiatry Research, 137(1–2), 103–111. https://doi.org/ 10.1016/j.psychres.2004.06.022 Lord, K. A., Harkness, K. L., Suvak, M. K., & Stewart, J. G. (2020). The affective dynamics of reassurance-seeking in real-time interactions. Journal of Social and Personal Relationships, 37(2), 676–694. https://doi.org/10.1177/0265407519876991 Lowe, J. R., Edmundson, M., & Widiger, T. A. (2009). Assessment of dependency, agreeableness, and their relationship. Psychological Assessment, 21(4), 543–553. https:// doi.org/10.1037/a0016899 Masling, J., Shiffner, J., & Shenfeld, M. (1980). Client perception of the counselor and orality. Journal of Counseling Psychology, 27(3), 294–298. https://doi.org/10.1037/ 0022-0167.27.3.294 Mazure, C. M., Bruce, M. L., Maciejewski, P. K., & Jacobs, S. C. (2000). Adverse life events and cognitive-personality characteristics in the prediction of major depression and antidepressant response. The American Journal of Psychiatry, 157(6), 896–903. https://doi.org/10.1176/appi.ajp.157.6.896 McClintock, A. S., & Anderson, T. (2015). The application of mindfulness for inter­ personal dependency: Effects of a brief intervention. Mindfulness, 6(2), 243–252. https://doi.org/10.1007/s12671-013-0253-3 McClintock, A. S., Anderson, T., & Cranston, S. (2015). Mindfulness therapy for maladaptive interpersonal dependency: A preliminary randomized controlled trial. Behavior Therapy, 46(6), 856–868. https://doi.org/10.1016/j.beth.2015.08.002 McClintock, A. S., McCarrick, S. M., & Anderson, T. (2014). Excessive reassurance-seeking and interpersonal dependency: Assessing incremental associations. Personality and Individual Differences, 64, 94–97. https://doi.org/10.1016/j.paid.2014.02.023 Mongrain, M., Vettese, L. C., Shuster, B., & Kendal, N. (1998). Perceptual biases, affect, and behavior in the relationships of dependents and self-critics. Journal of Personality and Social Psychology, 75(1), 230–241. https://doi.org/10.1037/0022-3514.75.1.230 Neal, R. L., & Radomsky, A. S. (2019). How do I say this? An experimental comparison of the effects of partner feedback styles on reassurance seeking behaviour. Cognitive Therapy and Research, 43(4), 748–758. https://doi.org/10.1007/s10608-019-10007-0 Neff, L. A., & Karney, B. R. (2002). Self-evaluation motives in close relationships: A model of global enhancement and specific verification. In P. Noller & J. A. Feeney (Eds.), Understanding marriage: Developments in the study of couple interaction (pp. 32–58). Cambridge University Press. https://doi.org/10.1017/CBO9780511500077.004 O’Neill, R. M., & Bornstein, R. F. (2001). The dependent patient in a psychiatric inpatient setting: Relationship of interpersonal dependency to consultation and medication frequencies. Journal of Clinical Psychology, 57(3), 289–298. https://doi.org/10.1002/ jclp.1012 Overholser, J. C. (1997). Treatment of excessive interpersonal dependency: A cognitivebehavioral model. Journal of Contemporary Psychotherapy, 27(4), 283–301. https://doi. org/10.1023/A:1025614524578 Overholser, J. C., & Fine, M. A. (1994). Cognitive-behavioral treatment of excessive interpersonal dependency: A four-stage psychotherapy model. Journal of Cognitive Psychotherapy, 8(1), 55–70. https://doi.org/10.1891/0889-8391.8.1.55

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Parker, G., & Lipscombe, P. (1980). The relevance of early parental experiences to adult dependency, hypochondriasis and utilization of primary physicians. The British Journal of Medical Psychology, 53(4), 355–363. https://doi.org/10.1111/j.2044-8341.1980. tb02563.x Parrish, C. L., & Radomsky, A. S. (2010). Why do people seek reassurance and check repeatedly? An investigation of factors involved in compulsive behavior in OCD and depression. Journal of Anxiety Disorders, 24(2), 211–222. https://doi.org/10.1016/ j.janxdis.2009.10.010 Perry, J. C. (2007). Dependent personality disorder. In G. O. Gabbard, J. S. Beck, & J. Holmes (Eds.), Oxford textbook of psychotherapy (pp. 321–328). Oxford University Press. Pincus, A. L., & Wilson, K. R. (2001). Interpersonal variability in dependent personality. Journal of Personality, 69(2), 223–251. https://doi.org/10.1111/1467-6494.00143 Porcerelli, J. H., Bornstein, R. F., Markova, T., & Huprich, S. K. (2009). Physical health correlates of pathological and healthy dependency in urban women. Journal of Nervous and Mental Disease, 197(10), 761–765. https://doi.org/10.1097/NMD. 0b013e3181b97bbe Rector, N. A., Kamkar, K., Cassin, S. E., Ayearst, L. E., & Laposa, J. M. (2011). Assessing excessive reassurance seeking in the anxiety disorders. Journal of Anxiety Disorders, 25(7), 911–917. https://doi.org/10.1016/j.janxdis.2011.05.003 Rector, N. A., Katz, D. E., Quilty, L. C., Laposa, J. M., Collimore, K., & Kay, T. (2019). Reassurance seeking in the anxiety disorders and OCD: Construct validation, clinical correlates and CBT treatment response. Journal of Anxiety Disorders, 67, Article 102109. https://doi.org/10.1016/j.janxdis.2019.102109 Robins, C. J., & Luten, A. G. (1991). Sociotropy and autonomy: Differential patterns of clinical presentation in unipolar depression. Journal of Abnormal Psychology, 100(1), 74–77. https://doi.org/10.1037/0021-843X.100.1.74 Rose, A. J. (2002). Co-rumination in the friendships of girls and boys. Child Development, 73(6), 1830–1843. https://doi.org/10.1111/1467-8624.00509 Salkovskis, P. M. (1985). Obsessional-compulsive problems: A cognitive-behavioural analysis. Behaviour Research and Therapy, 23(5), 571–583. https://doi.org/10.1016/ 0005-7967(85)90105-6 Salkovskis, P. M., Warwick, H. M., & Deale, A. C. (2003). Cognitive-behavioral treatment for severe and persistent health anxiety (hypochondriasis). Brief Treatment and Crisis Intervention, 3(3), 353–367. https://doi.org/10.1093/brief-treatment/mhg026 Schwartz-Mette, R. A., & Smith, R. L. (2018). When does co-rumination facilitate depression contagion in adolescent friendships? Investigating intrapersonal and interpersonal factors. Journal of Clinical Child and Adolescent Psychology, 47(6), 912–924. https:// doi.org/10.1080/15374416.2016.1197837 Shahar, G., Joiner, T. E., Jr., Zuroff, D. C., & Blatt, S. J. (2004). Personality, interpersonal behavior, and depression: Co-existence of stress-specific moderating and medi­ a­ting effects. Personality and Individual Differences, 36(7), 1583–1596. https://doi.org/ 10.1016/j.paid.2003.06.006 Spendelow, J. S., Simonds, L. M., & Avery, R. E. (2017). The relationship between co-rumination and internalizing problems: A systematic review and meta-analysis. Clinical Psychology & Psychotherapy, 24(2), 512–527. https://doi.org/10.1002/cpp.2023 Starr, L. R. (2015). When support seeking backfires: Co-rumination, excessive reassurance seeking, and depressed mood in the daily lives of young adults. Journal of Social and Clinical Psychology, 34(5), 436–457. https://doi.org/10.1521/jscp.2015.34.5.436 Starr, L. R., & Davila, J. (2008). Excessive reassurance seeking, depression, and interpersonal rejection: A meta-analytic review. Journal of Abnormal Psychology, 117(4), 762–775. https://doi.org/10.1037/a0013866 Stewart, J. G., & Harkness, K. L. (2017). Testing a revised interpersonal theory of depression using a laboratory measure of excessive reassurance seeking. Journal of Clinical Psychology, 73(3), 331–348. https://doi.org/10.1002/jclp.22338

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Swann, W. B., Hixon, J. G., Stein-Seroussi, A., & Gilbert, D. T. (1990). The fleeting gleam of praise: Cognitive processes underlying behavioral reactions to self-relevant feedback. Journal of Personality and Social Psychology, 59(1), 17–26. https://doi.org/10.1037/ 0022-3514.59.1.17 Swann, W. B., Wenzlaff, R. M., Krull, D. S., & Pelham, B. W. (1992). Allure of negative feedback: Self-verification strivings among depressed persons. Journal of Abnormal Psychology, 101(2), 293–306. https://doi.org/10.1037/0021-843X.101.2.293 Thompson-Hollands, J., Abramovitch, A., Tompson, M. C., & Barlow, D. H. (2015). A randomized clinical trial of a brief family intervention to reduce accommodation in obsessive-compulsive disorder: A preliminary study. Behavior Therapy, 46(2), 218–229. https://doi.org/10.1016/j.beth.2014.11.001 Tompkins, T. L., Hockett, A. R., Abraibesh, N., & Witt, J. L. (2011). A closer look at co-rumination: Gender, coping, peer functioning and internalizing/externalizing problems. Journal of Adolescence, 34(5), 801–811. https://doi.org/10.1016/j.adolescence. 2011.02.005 van Sweden, R. C. (1995). Regression to dependence. Aronson. Versaevel, C. (2012). Personnalité dépendante et dépendance affective: Stratégies psychothérapeutiques [Dependent patient and interpersonal dependency: Psychotherapeutic strategies]. L’Encéphale, 38(2), 170–178. https://doi.org/10.1016/j.encep.2011.08.006 Wakeling, S., Stukas, A. A., Wright, B. J., & Evans, L. (2020). Negative feedback seeking and excessive reassurance seeking behavior and depression: A meta-analytic review. Journal of Social and Clinical Psychology, 39(9), 788–823. https://doi.org/10.1521/jscp. 2020.39.9.788 Watson, C. G., Barnett, M., Nikunen, L., Schultz, C., Randolph-Elgin, T., & Mendez, C. (1997). Lifetime prevalences of nine common psychiatric/personality disorders in female domestic abuse survivors. The Journal of Nervous and Mental Disease, 185(10), 645–647. https://doi.org/10.1097/00005053-199710000-00011 Zuroff, D. C., Mongrain, M., & Santor, D. A. (2004). Conceptualizing and measuring personality vulnerability to depression: Comment on Coyne and Whiffen (1995). Psychological Bulletin, 130(3), 489–511. https://doi.org/10.1037/0033-2909.130.3.489

7 Marriage and Relationship Issues Mark A. Whisman and Anna L. Gilmour

T

he global point prevalence of major depressive disorder (MDD; i.e., the proportion of people with MDD at a particular point in time) was 4.4% in 2015, which is equivalent to approximately 322 million people living with depression (World Health Organization, 2017). Additionally, many more people live with subthreshold but clinically significant levels of depressive symptoms (Yu et al., 2020). Globally, depressive disorders are the single largest contributor to nonfatal health loss (World Health Organization, 2017). Given the widespread prevalence and impact of depression, developing treatments that target modifiable causal risk factors for depression is a major priority for researchers. One such widely studied risk factor is the quality of intimate relationships, such as marriage, given that most adults form committed relationships in adulthood. For example, the worldwide percentage of women aged 45 to 49 who have ever married is estimated to be 95.7% (UN Women, 2020). This chapter focuses on intimate relationship quality as a risk factor for depression and discusses the impact of interventions that improve relationship quality as a treatment for depression.

DEFINITIONAL ISSUES Beach et al. (1990) developed a marital discord model of depression in which they hypothesized that poor relationship quality may increase risk for depression by increasing a partner’s experience of social stress or decreasing their experience of social support, or both. Specifically, they proposed that marital https://doi.org/10.1037/0000332-008 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  157

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discord may increase the level of major stressors in the domains of verbal and physical aggression; threats of separation or divorce; spousal denigration, criticism, and blame; disruption of scripted routines; and other idiosyncratic marital stressors. They also proposed that marital discord may decrease available support from the partner through decreasing couple cohesion, acceptance and encouragement of emotional expression, actual and perceived coping assistance, self-esteem support, perceptions of spousal dependability and commitment to the relationship, and intimacy. Beach et al. proposed that these 11 facets of a couple’s relationship could be considered “points of intervention” (p. 53) to guide clinicians in working with couples in which one or both partners are depressed. The marital discord model of depression has evolved since it was introduced in 1990. For example, the model has been expanded to make it more relevant not only to married couples but also to a diverse range of families and circumstances (Beach, 2014). The model has further been expanded to include the stress generation framework of depression (Hammen, 1991). As described elsewhere (Beach et al., 2014), this expanded model proposes that individuals who are depressed or depression-prone may generate stress in their social relationships, including their relationship with their romantic partner, and that this interpersonal stress may serve to maintain or exacerbate the person’s depression. This perspective is consistent with studies demonstrating that individuals with depression tend to seek negative feedback, engage in excessive reassurance seeking, avoid conflict and withdraw, and elicit changes in their partners’ view of them (Joiner, 2000). Consistent with what has been observed in longitudinal studies (Davila et al., 1997), relationship distress and depression may best be viewed as components of a larger vicious cycle that creates a self-sustaining loop. Much of the research on intimate relationship functioning as a risk factor for depression has focused on relationship quality. Relationship quality has been operationalized using various measures, including measures of satisfaction, which assess intrapersonal aspects of relationship functioning (e.g., subjective evaluations), and measures of adjustment, which assess interpersonal aspects of relationship functioning (e.g., communication) and typically also include intrapersonal, subjective assessment of the quality of the relationship (Fincham & Rogge, 2010). Because the focus of this chapter is on poor relationship functioning as a risk factor for depression and the impact of interventions on poor relationship functioning, we use the term relationship distress to refer to low levels of relationship satisfaction or adjustment, reframing the results of reviewed studies as necessary so that higher scores on the original measures indicate greater relationship distress. Although researchers typically measure relationship distress as a dimensional construct (i.e., a varying level of severity), cutoff scores have also been used to identify people who may be at greatest risk for depression and other adverse outcomes; such cutoff scores have been used to operationalize couples (or individuals) as discordant. For example, Jacobson et al. (1984) proposed a cutoff of 97 on the Dyadic Adjustment Scale (DAS; Spanier, 1976) to define

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relationship discord; couples who fall below a score of 97 are considered discordant. The DAS is the most widely cited measure of relationship adjustment, and the cutoff of 97 was identified as the “point at which a couple was equally likely to fall into either the functional or dysfunctional population” (Jacobson et al., 1984, p. 500). Researchers have also identified cutoff scores on other measures of relationship distress that correspond to the cutoff of 97 on the DAS. For example, Funk and Rogge (2007) provided cutoff scores to define relationship discord for a variety of self-report measures. This study was based on data from a sample of over 5,300 individuals who responded to an online survey and used response-operating curves to optimize sensitivity and specificity to classify individuals below a cutoff of < 97.5 on the DAS. Determining whether a construct such as relationship distress should be conceptualized as a continuum or a category can be evaluated using taxometric analyses. Taxometric analyses refer to a set of procedures developed by Paul Meehl and colleagues (Waller & Meehl, 1998) that search for discrete changes in the structure of data (e.g., covariances, slopes) that may suggest the presence of a latent subgroup or taxon. The presence of a discrete latent class suggests that members of the taxon (i.e., discordant couples) differ qualitatively from those who are not members, referred to as the complement (i.e., nondiscordant couples). Beach et al. (2005) conducted a taxometric analysis in a sample of 447 couples who had been married for approximately 2 years and found evidence for a latent category of marital discord, with a base rate of approximately 20%. Whisman et al. (2008) conducted a taxometric analysis in a sample of 1,020 couples who approximated the United States’ population with respect to region, education, race, and ethnicity. Their results suggested the presence of a latent category of marital discord, with a base rate of approximately 31% and a more specific base rate of 26% among couples who had been married for 1 to 3 years, a period of time comparable to the couples in the Beach et al. (2005) study. Additional analyses with several samples of community and clinic couples indicated a much higher base rate of the taxon in clinical samples of couples relative to community samples of couples and that the taxon classification was associated with clinician-rated indices of relationship functioning, thereby providing evidence for the validity of the taxon classification. Taken together, these studies suggest that discordant couples differ qualitatively and quantitatively from nondiscordant couples. The conceptualization of relationship distress as a category rather than as a dimension may have important implications for its assessment and etiology as well as its association with other outcomes, such as depression. In support of this perspective, one study found that taxon status was associated with depressive symptoms in a community sample of middle-aged and older couples (Whisman et al., 2015). Additionally, taxon status was uniquely associated with level of depressive symptoms over and above the association between depressive symptoms and the mean of continuously distributed indices of relationship quality used to create the taxon. The presence of a nonarbitrary category of relationship discord may have important treatment implications. Specifically, it may be that members of the

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taxon are those for whom couple therapy may be most appropriate. Whisman et al. (2009) developed a 10-item screening measure for detecting the relationship discord taxon. They developed an interview version of the screen and a self-report version (called the MSI-B, available from Western Psychological Services). Administration and scoring of the screen are brief (< 5 minutes) and straightforward, making it easy to determine whether someone is a member of the relationship discord taxon. The screen may be useful in decision making regarding couple-based treatment for depression: If someone scores above the cutoff on the screen, they could be referred for a more detailed assessment of their relationship and possibly for couple-based intervention.

RISK LITERATURE In studying intimate relationship functioning as a risk factor for depression, researchers have focused primarily on relationship distress but have also examined a variety of relationship domains. The following sections provide a selective review of the risk literature on relationship functioning (for a more thorough review and discussion of this literature, see Whisman, Sbarra, et al., 2021). Relationship Distress A large body of literature has established a cross-sectional and longitudinal association between relationship distress and depressive symptoms (for a review, see Whisman, Sbarra, et al., 2021). Several meta-analyses have been conducted that examine the cross-sectional association between these constructs. The first meta-analysis yielded a weighted effect size (r) between relationship distress and depressive symptoms of .42 for women and .37 for men (Whisman, 2001a), and a second meta-analysis examining the association between relationship distress and well-being (e.g., depressive symptoms, self-esteem, life satisfaction, global happiness, and physical health) similarly yielded a weighted effect size of .37 (Proulx et al., 2007). Two additional meta-analyses found that relationship distress is significantly associated with postpartum depression, with weighted effect sizes ranging from .29 to .39 (Beck, 1996, 2001), suggesting that relationship distress and depression are associated with one another in more specific samples of individuals as well. Population-based probability samples are considered the most effective samples to elicit generalizable findings, and they have been used to establish a positive cross-sectional association between relationship distress and depressive symptoms and disorders. As reviewed by Whisman, Sbarra, et al. (2021), relationship distress covaries with depressive symptoms in probability samples conducted in many parts of the world. For example, one study found that relationship distress was significantly associated with depressive symptoms in a probability sample of over 4,700 couples from 11 European countries (Salinger et al., 2021). Studies based on probability samples from the United

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States and Canada have similarly found significant associations between relationship distress and depressive disorders, including major and minor depressive disorder and dysthymia (Goldfarb et al., 2019; Whisman, 2007). Other studies based on probability samples have indicated that the associations between relationship distress and depressive disorders in the United States do not vary by race or ethnicity (McShall & Johnson, 2015; Uebelacker & Whisman, 2006). The cross-sectional association between relationship distress and MDD has also been found in probability samples of specific populations, including active-duty soldiers in the United States Army (Whisman et al., 2020). Longitudinal associations between relationship distress and depressive symptoms have been found in community samples from several countries, including the United States (e.g., Beach et al., 2003) and Brazil (Hollist et al., 2007). Replicating this association, research involving probability samples indicated that relationship distress is associated with increases in depressive symptoms in longitudinal studies from the United States (P. A. Thomas, 2016) and several countries in Europe (Whisman et al., 2018; Whisman & Uebelacker, 2009). Further, research based on community samples of understudied groups of individuals, such as racial and ethnic minorities (e.g., Thomas et al., 2019) and sexual minorities (e.g., Gilmour et al., 2019), has also found that relationship distress at baseline predicts changes in depressive symptoms from baseline to follow-up. A meta-analysis of the longitudinal association between relationship distress and well-being yielded a weighted effect size of .25 (Proulx et al., 2007). Researchers have also examined the association between relationship distress at baseline and incidence of depressive disorders at follow-up. For instance, baseline relationship distress was associated with incidence of major depressive episode (MDE) at a 1-year follow-up in a U.S. probability sample, and this association remained significant when statistically adjusting for participants’ history of MDE (Whisman & Bruce, 1999). Similarly, a significant association between baseline relationship distress and major or minor depression at 12-month follow-up was observed in a probability sample in Canada (Goldfarb et al., 2019). Additionally, in a probability sample from the Netherlands, relationship distress at baseline was associated with 2-year total incidence of MDD (i.e., all onsets of MDD) as well as new case incidence of MDD (i.e., first onsets of MDD) assessed at 1-year and 3-year follow-up (Overbeek et al., 2006). The association between relationship distress and incidence of major depression has also been examined over longer follow-up periods. For instance, an association between relationship distress and depressive disorders was found in a community sample of men in Finland, such that relationship distress at baseline was associated with incidence of depressive disorder 5 years later (Kivelä et al., 1996); relationship distress at baseline also predicted incident MDE 5 years later in a probability sample of active-duty soldiers from the U.S. Army (Whisman, Salinger, et al., 2021). An even longer term association was observed in a U.S. probability sample study that found that baseline relationship distress predicted incidence of MDE 10  years later (Teo et al., 2013).

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Research has also examined within-subject longitudinal associations between relationship distress and depressive symptoms, which allows for an examination of how changes in relationship distress are associated with changes in depressive symptoms in the same individual over time, controlling for each variable’s trajectory over time. Results from studies using within-subjects analyses suggest that increases in relationship distress are associated with increases in depressive symptoms over a variety of time intervals (Davila et  al., 2003; Kouros et  al., 2008; Whitton et  al., 2008). These findings indicate that people’s depressive symptoms tend to be higher than usual during time points when their relationship distress is higher than usual. Additional research has found that within-person fluctuations in relationship quality over time predict depressive symptoms beyond their average level of relationship quality. One study found that women whose relationship quality fluctuated more widely from week to week tended to have higher depressive symptoms (Whitton & Whisman, 2010), and another study showed that greater variation in relationship quality measured every 4 months was associated with lower well-being (i.e., psychological distress and life satisfaction) beyond initial levels of and linear changes in relationship quality (Whitton et al., 2014). These findings suggest that changes in relationship quality over time may be particularly associated with depressive symptoms, possibly because individuals with higher fluctuations in the quality of their relationships are less confident about the stability of their relationship. Some research has also examined the degree to which separate dimensions of positive relationship quality and negative relationship quality are associated with depression. Although researchers commonly conceptualize relationship quality along a single dimension, some theorists have proposed that relationship satisfaction and adjustment are better conceptualized as consisting of independent positive and negative valence dimensions (Fincham & Rogge, 2010). One study conducted in a U.S. probability sample found that people with a current MDD reported both fewer positive interactions and more negative interactions with their partner relative to people with other current psychiatric disorders and people with no current psychiatric diagnosis (Zlotnick et al., 2000). Poor Communication Specific aspects of relationship functioning have also been found to be associated with depressive symptoms. One aspect of relationship functioning that has been particularly well-studied in relation to depression is communication between partners. Communication is often studied using observational data. Couples are observed interacting in a laboratory setting, often as they discuss an ongoing problem or difficulty in their relationship, and researchers code their interactions for particular behaviors. A review of relationship interactions among individuals with depression found that these interactions include a greater frequency of negative communication behaviors and fewer positive communication behaviors relative to individuals without depression (Rehman et al., 2008).

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Relationship Stressors Although the majority of research examining associations between relationship functioning and depression has been conducted in samples of couples who were not recruited for particular relationship stressors or behaviors, some studies have examined specific, severe relationship stressors and their association with depression. For instance, learning that one’s partner is engaging in a relationship affair or having a partner threaten to end the relationship have both been associated with depression (Cano & O’Leary, 2000). Additionally, one study involving a U.S. probability sample found that discovery of a partner affair was associated with 12-month prevalence of MDD, over and above their shared association with relationship distress (Whisman, 2016). Intimate partner violence (IPV) victimization and perpetration have also been associated with depression. Across genders, a meta-analysis of this research found that the mean effect size (r) was .21 for the association between IPV perpetration and depressive symptoms whereas the mean effect size was .25 for the association between IPV victimization and depressive symptoms (Spencer et  al., 2019). Further, the correlation between IPV victimization and depressive symptoms was significantly stronger for women than for men. High Expressed Emotion and Perceived Criticism An additional interpersonal process that has been studied among depressed individuals and their partners and family members is expressed emotion. Expressed emotion refers to the degree to which family members express criticism, hostility, and emotional overinvolvement toward an individual with a psychiatric disorder and is normally measured during a private interview with a researcher. A meta-analysis of the longitudinal association between expressed emotion and relapse in mood disorders found medium to large mean effect sizes (r), ranging from .39 to .45 (Butzlaff & Hooley, 1998). Researchers have also examined the degree to which the perception of others’ criticism (i.e., perceived criticism; Hooley & Teasdale, 1989) among individuals with depression is associated with symptoms. A literature review found that higher perceived criticism is associated with later worsening of depressive symptoms and depressive relapse (Renshaw, 2008). Partner Effects for Relationship Distress on Depression Most research involving the marital discord model of depression has focused on the nature of the association between relationship distress or aspects of relationship functioning (e.g., poor communication) and depression in one or both partners. However, there may be other ways in which relationship partners influence the mental health and well-being of the other member of the dyad in addition to through the pathways of specific aspects of relationship functioning. For example, it has been proposed that depression in one person induces negative affect in others (Coyne, 1976), a process that has been labeled depression contagion (Joiner & Katz, 1999). A meta-analysis of cross-sectional

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studies found evidence for depression contagion in dating and married couples (Joiner & Katz, 1999). Furthermore, two-panel longitudinal studies examining the cross-partner effects of depression have found that husbands’ depressive symptoms at baseline predicted wives’ depressive symptoms at follow-up in probability samples (Whisman & Uebelacker, 2009). Similarly, multiwave within-person analyses have found that higher levels of husbands’ depressive symptoms predicted higher levels of wives’ depressive symptoms over time (Kouros & Cummings, 2010); this association was stronger for discordant couples relative to nondiscordant couples. Thus, the results from studies conducted to date support the contagion hypothesis, particularly for discordant couples, and underscore the need for continued research on partners’ depression to fully map out the range and extent of dyadic influences on depression.

INTERVENTION In response to the risk literature finding that relationship distress is a predictor of depression, couple-based treatments for depression have been developed and tested through clinical trials since the early 1990s. In particular, cognitive behavioral couple therapy (CBCT) has been recognized as an effective treatment for depression by several national agencies, including the U.S. Department of Veteran Affairs and the National Institute for Health and Care Excellence (NICE). For example, NICE guidelines recommend CBCT be considered as a treatment for depression for “a person with less or more severe depression who has problems in the relationship with their partner if the relationship problem(s) could be contributing to their depression or involving their partner may help in the treatment of their depression” (NICE Clinical Guidelines, 2018, p. 341). CBCT for depression is a variant of CBCT for relationship distress (Epstein & Baucom, 2002), designed to treat depression. It is a time-limited approach to couple therapy (typically 20 or fewer sessions) with sessions generally scheduled once a week. Based on the premise that improving relationship distress should improve depression, the treatment may be most appropriate for couples who experience both relationship distress and depression. Consistent with most cognitive behavioral treatments, sessions typically involve (a) working with the couple to collaboratively set a session agenda; (b) reviewing homework completed during the week (and troubleshooting anything that was not completed); (c) addressing a problem, learning a new skill, or practicing new ways of interacting with one another; (d) assigning a task or homework assignment for the week; and (e) summarizing the content of the session and eliciting feedback. As discussed in greater detail elsewhere (Whisman & Beach, 2015), CBCT for depression can be divided into three stages. The first stage is focused primarily on increasing positive behavior. Each partner is encouraged to increase the frequency of “caring gestures,” which are small behaviors that they can do to increase the satisfaction of the other person. Ideally, caring gestures should

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be done frequently (e.g., daily), and each person is encouraged to increase the frequencies of caring behavior independent of the other person’s engagement in their behaviors. Each person is encouraged to acknowledge and express appreciation for partner caring behaviors. Couples can be encouraged to engage in “companionship activities,” such as date nights, recreational activities, and activities with other couples. Whereas these exercises are focused on increasing couple cohesion, partners are also encouraged to engage in independent pleasant activities, as undue pressure can be placed on the relationship if partners expect to derive all their satisfaction or enjoyment from each other or the relationship. Increasing the frequency of activities each person enjoys may help not only the individual with depression but also their partner, as partners often experience significant levels of depressed mood and report feeling overwhelmed or burdened with caring for the individual with depression (Benazon & Coyne, 2000). Furthermore, partners can be instructed in ways to encourage and support each other in the completion of these positive activities. Finally, the first stage of treatment focuses on increasing the frequency of self-esteem support, including commenting on positive qualities of or feelings toward the partner and verbalizing thanks for things their partner does for them. Couples may have difficulty focusing on increasing positive behavior when severe negative stressors are present. Relationship-threatening behaviors may be eliminated for some couples by discussing the dangers and consequences of such behaviors and encouraging partners to agree to refrain from such behaviors. For example, therapists can discuss the harmful impact that denigrating, criticizing, and blaming comments can have on the partner. If one partner is making threats to leave the relationship, even if these threats reflect temporary feelings, it is possible to discuss how these threats can undermine the confidence the partner has about the stability of the relationship. If one or both partners are unable to refrain from such behaviors after a few sessions, then therapists may want to consider pausing couple therapy and providing or referring the person or couple for individual treatments to increase their self-control of disruptive behavior prior to conducting CBCT. The second stage of CBCT for depression focuses on communication and problem-solving training. Therapists typically begin with discussing receptive, “listener” skills (e.g., making eye contact, nodding, reflecting key points) and expressive, “speaker” skills (e.g., sharing thoughts and feelings, using “I” statements). Depending on the skill level of the couple, these discussions may involve teaching new skills for some couples or encouraging the use of existing skills for other couples. The goals of these couple conversations are to build intimacy and closeness. Couples practice these conversations in session, with the therapist providing coaching and feedback, as well as at home for homework. Therapists then move to discussing steps involved in effective problem solving (i.e., problem definition, generating solutions, evaluating and selecting solutions, implementing solutions). These conversations can be useful not only for solving current and future problems, but also for providing a framework for discussing and making decisions that do not necessarily involve solving

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problems. Couples practice these types of conversation in session and between sessions as part of homework. The final phase of CBCT for depression focuses on maintenance and generalization. Couples are encouraged to write out what they learned in therapy for homework during the final phase of treatment and to keep these written summaries in a secure place to review as necessary in the future. They are also encouraged to adopt a mindset of anticipating upcoming challenges or difficult times or situations and problem solving how to deal with such situations in advance of their occurrence. The frequency of sessions is typically tapered during the final stages of treatment, with sessions occurring every other week and then monthly, which allows the couple more time to practice the skills they learned in therapy and get help and support from their therapist if they encounter difficulties with implementing these skills in their daily lives.

OUTCOME RESEARCH In this section, we review the literature on CBCT as a treatment for depression. The first trial of CBCT for depression was conducted among 45 couples in which the wife met DSM-III criteria for MDD or dysthymia and both partners reported relationship distress (Beach & O’Leary, 1992; O’Leary & Beach, 1990). Couples were assigned to CBCT, individual cognitive behavior therapy (CBT), or a wait-list control group for 15 to 20 treatment sessions. It was hypothesized that the improvement in depressive symptoms as a result of CBCT would be mediated by improvement in relationship adjustment. Further, it was hypothesized that individuals with greater levels of relationship distress would exhibit poorer outcomes in individual CBT than in CBCT whereas individuals with greater cognitive dysfunction would exhibit poorer outcomes in CBCT than in individual CBT. Results indicated that both CBCT and individual CBT led to clinically significant reductions in depression, with both treatments performing equally well and significantly better than the control group. Wives in the CBCT condition exhibited increases in relationship adjustment after therapy, whereas wives in the CBT and wait-list conditions did not exhibit such increases and did not significantly differ over time in relationship adjustment. Further, the pattern of results suggested that change in relationship adjustment partially mediated reductions in depressive symptoms among the CBCT group. Additionally, some evidence suggested that individuals with greater relationship distress and fewer cognitive distortions at baseline were particularly well treated by CBCT, whereas individuals with more cognitive distortions and lower relationship distress at baseline were best treated by individual CBT. Finally, improvements in depressive symptoms at 12-month follow-up did not significantly differ between the CBCT and CBT groups; however, the CBCT group reported significantly higher relationship adjustment at 12-month follow-up than the CBT group. A second trial of CBCT for depression compared the relative efficacy of CBCT, CBT, and a treatment combining CBT and CBCT in treating depression

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and improving relationship adjustment (Jacobson et al., 1991). The sample consisted of 60 couples in which the wife met DSM-III criteria for MDD. Further, the sample included couples who exhibited relationship discord, as defined by scoring ≤ 97 on the DAS, along with couples who were classified as nondiscordant, as defined by scoring > 97 on the DAS. Twenty sessions of CBCT, CBT, or combined treatment were administered. It was hypothesized that CBCT and CBT would be equally effective in treating depression and that CBCT would be more effective than CBT in improving relationship adjustment among couples with significant relationship distress. Additionally, it was hypothesized that the combination of CBCT and CBT would be more effective than CBCT and CBT in treating depression and more effective than CBCT alone in improving relationship adjustment. The results suggested that all three treatments were effective in reducing depressive symptoms among both discordant and nondiscordant couples, with efficacy of the treatments moderated by whether the couple was discordant. For couples who were nondiscordant, individual CBT was more effective than CBCT in reducing depression, and the combined condition performed equivalently to CBT. Among discordant couples, CBCT and CBT were equally effective, and the combined condition did not outperform either CBCT or CBT. Therefore, the authors did not find evidence that combining CBCT and CBT was a more effective form of treatment for depression than either of the components on their own, possibly because the couples in the combined treatment received fewer sessions of both CBCT and CBT than in either of the component treatment conditions. However, when examining outcomes for relationship adjustment, combined treatment was the only treatment to result in significant improvement in relationship adjustment for nondiscordant couples whereas discordant couples in both the CBCT and combined treatment conditions exhibited significant improvement in relationship adjustment. Rates of depressive relapse at 6-month and 12-month follow-up were equal across treatment conditions (Jacobson et al., 1993). Teichman et al. (1995) examined the efficacy of cognitive marital therapy (CMT) relative to individual CBT and a wait-list control condition in a sample of couples living in Israel. The sample included 45 couples in which at least one member of the couple was depressed. CMT was developed based on a theory that individuals’ depression and interpersonal relationships mutually affect one another, such that one’s relationship is negatively influenced by their depression and maladaptive relationship patterns reinforce and maintain depression. These relationship patterns influence individuals’ thoughts, feelings, and behaviors related to their partner and their relationship, which are targeted through CMT. Specifically, CMT aims to help each partner understand how they maintain depression and to then help build more adaptive relationship patterns. The results indicated that depressed individuals in all three groups had significant reductions in depression after treatment and that the level of depression in the CMT group was significantly lower than in the CBT and wait-list control group. Further, spouses of depressed individuals in the CMT group had significantly lower levels of depression posttherapy than those in the CBT and wait-list control groups. Depressive symptoms continued to decrease at the 6-month

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follow-up for individuals in the CMT and CBT groups, and levels of depression did not significantly differ between the two treatment groups at that time. Additionally, satisfaction with treatment was significantly higher among depressed individuals and their spouses in the CMT group than in the CBT group. Overall, the results suggested that CMT and CBT are equally effective in treating depression long-term, with some advantages for CMT short-term. Emanuels-Zuurveen and Emmelkamp (1996) compared a modified version of CBCT with individual CBT in a study of 27 individuals with depression and their partners. The CBCT treatment condition focused first on treating problems associated with depression that could impede relationship therapy, and then the focus of therapy shifted to improving couples’ communication skills, conflict resolution, and problem solving. Both treatment conditions resulted in reduced depressive symptoms, with no significant differences between the CBCT and CBT groups. Individuals in the CBCT condition exhibited greater improvements in relationship adjustment and communication and greater reductions in expressed emotion than those in the CBT condition. However, the CBCT condition had more dropouts, including several couples who were disappointed that the focus of the treatment was on relationship problems rather than an explicit, specific focus on depressive symptoms. The studies described so far were all efficacy studies, which were conducted within university settings and involved highly trained therapists who received frequent supervision and had high adherence to a detailed treatment protocol. In contrast, Baucom et  al. (2018) conducted an effectiveness study of CBCT provided in London services that were part of the Improving Access to Psychological Therapies (IAPT) program in England. As such, the study provides an assessment of the outcome of the treatment as provided in a routine clinical care setting. Twenty-three therapists working in community clinics were trained to administer CBCT for depression during a 5-day workshop followed by monthly group supervision for 1 year. Baucom et al. analyzed data from 63 couples in London who chose to receive CBCT for depression and for whom at least one partner had a likely diagnosis of depression. Results indicated that 57% of individuals treated with CBCT recovered (i.e., scored in the nonclinical range on self-report measures for both depressive and anxiety symptoms) compared to a recovery rate of 41% for IAPT services in London during the same time period for all treatments of depression (i.e., antidepressants, CBT, interpersonal psychotherapy [IPT], behavioral activation, and CBCT). Additionally, individuals’ relationship adjustment significantly improved at posttreatment. These results suggest that CBCT for depression is effective not only in highly controlled clinical trials but also in real-world clinical settings with therapists who have received a short amount of specialized training. In addition to the trials that have focused on treating couples with co-occurring relationship distress and depression, a few studies have examined couple-based treatments for depression in couples who are not necessarily experiencing relationship distress. Bodenmann et  al. (2008) compared the efficacy of CBT, IPT, and coping-oriented couple therapy (COCT) in treating depression in 60 outpatients with depression. COCT includes elements of CBCT

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(e.g., communication and problem-solving training) as well as additional interventions based on the stress and coping literature. These additional interventions are designed to help partners effectively communicate with each other about their personal stress and mutually provide emotion- and problem-focused support in dealing with stress experiences, including reducing criticism and emotional overinvolvement. Results suggested that all three treatment groups evidenced decreases in depressive symptoms, with no significant differences among treatment groups in posttest depressive symptoms. Additionally, a nonsignificant trend in the data suggested that depressive relapse rates 18 months after ending treatment were lower in the COCT condition than the CBT or IPT conditions. There were no significant differences in relationship adjustment posttreatment among the three treatment conditions although expressed emotion was significantly lower in the COCT condition than the other two conditions. Overall, these results suggest that COCT is equally effective in treating short-term depressive symptoms and in preventing depressive relapse up to 18 months posttreatment as two other evidence-based individual therapy treatments. Cohen et al. (2010) examined the efficacy of a brief five-session problemfocused couple intervention (brief couples therapy) compared with a wait-list control group. Thirty-five couples who were experiencing mild to moderate levels of relationship distress were recruited, in each couple the wife was experiencing major depression or dysthymia and the husband was not clinically depressed. The treatment included five 2-hour sessions targeting depression and psychological distress through psychoeducation and cognitive-behavioral skills building. Fully 67% of the women in the intervention group exhibited reductions in depressive symptoms posttreatment, which was significantly greater than improvements in the wait-list control group (17%). Additionally, 40% to 47% of women in the intervention group exhibited recovery at 3-month follow-up, compared with 8% among the wait-list control group. The results also showed that the treatment significantly improved relationship adjustment and reduced husbands’ distress with having a partner with depression. Overall, the results suggested that this brief couple-based intervention for depression may be an effective and efficient way to treat depression in couples in which one partner is depressed and in which both partners experience mild to moderate levels of relationship distress. In addition to the above intervention trials, several other couple-based approaches to treat depression that do not come from a cognitive behavioral framework have been developed and evaluated. Leff et al. (2000) examined the efficacy of systemic couple therapy for depression relative to treatment with antidepressants. The therapy focused on helping both partners to gain new perspectives on their problems, to practice new forms of interaction, and to change their perspective on depressive behaviors. Dessaulles et al. (2003) conducted a pilot study comparing the efficacy of pharmacotherapy with emotionally focused couple therapy for individuals with depression. Emotionally focused couple therapy is grounded in attachment theory, family systems, and experiential approaches and is focused on elucidating the conflict pattern

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between the partners, examining partners’ feelings, promoting acceptance, and building more effective interaction patterns. Additionally, other modifications of individual treatments for depression that have included one’s partner have been evaluated (for a review, see Barbato & D’Avanzo, 2020). Barbato and D’Avanzo (2020) conducted a meta-analysis of 14 clinical trials that compared couple therapy for depression with individual psychotherapy (most often CBT), drug therapy, or no/minimal intervention. Results indicated that couple therapy improved depressive symptoms at the end of treatment and at 6-month follow-up, with no significant differences between couple therapy or individual therapy with respect to remission rates or level of depressive symptoms (Barbato & D’Avanzo, 2020). Couple therapy was more effective than individual therapy in reducing relationship distress, with a standardized mean difference of .50 between groups. The authors concluded that “couple therapy may be a viable option for the treatment of a depressed partner, especially in discordant couples” (Barbato & D’Avanzo, 2020, p. 369). In addition to research finding that couple interventions improve depression among couples recruited for the presence of depressive symptoms or disorders, research has also found that couple therapy has led to reductions in depressive symptoms among couples who were not recruited for the presence of depression (Doss et  al., 2019). For example, an effectiveness study conducted in Germany and Austria provided by therapists in the community who were trained in a variety of approaches found a within-group effect size (d) of .72 for a self-report measure of depressive symptoms following couple therapy (Klann et al., 2011).

CASE EXAMPLE Jada1 was a 28-year-old cisgender Black woman who presented to the clinic for treatment of depression. She reported a history of two prior episodes of major depression, with the first episode precipitated by an abusive relationship she was in during college and the second episode following the ending of a close friendship she had maintained since childhood. Her current episode of depression was characterized by depressed mood, loss of appetite, insomnia, fatigue, and difficulty concentrating. During the intake session, she focused on the troubles she was having with her husband of 3 years. She reported that he criticized her frequently and did not seem interested in spending time with her. She expressed concerns that he no longer loved her and was going to leave her. The therapist (a White Latino cisgender man) asked her if she would like to include him in the next session, to which she agreed. Consequently, Tyrell, a 30-year-old cisgender Black man, attended the following session with Jada. He agreed with Jada that they were arguing much more frequently and having a much harder time getting along. Furthermore, he reported feeling guilty about the problems that the couple was experiencing. Upon further questioning, Tyrell reported guilt not only about the Names and identifying information have been changed to protect client confidentiality.

1

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couples’ ongoing relationship problems but also about several other areas in his life. He also described how he was experiencing little enjoyment in his life and felt restless and irritable all the time. Even though Tyrell did not meet criteria for major depression, the therapist described how these symptoms were common of depression. Jada was surprised to hear her husband talk about the problems he was experiencing and was even more surprised to hear that some of the problems he was experiencing were symptoms of depression, given that his symptoms differed from hers. Both partners said they felt trapped and unsure of how to break out of the patterns they had fallen into. The therapist asked them if they would like to work on their relationship in couple therapy, and they both agreed that they would like to do so. The next session involved an assessment of the relationship, including each partner’s perceptions of the ongoing problems in the relationship; the frequency, intensity, and sources of conflicts in their relationship; and the ways each of them dealt with conflict. The therapist also discussed circumstances in which they experienced positive feelings about each other and the past and present strengths of the relationship. The therapist ended the session with more psychoeducation about the differing manifestations of depression and concluded the session with a brief summary of the efficacy of CBCT for treating depression. Jada appeared visibly relieved that Tyrell’s frequent irritability and frustration was partly due to his depression and did not necessarily reflect that he was upset with her. Tyrell said that hearing that Jada’s poor concentration was in part due to her depression was helpful; he had previously been frustrated with her apparent forgetfulness. The next several sessions focused on increasing the frequency of positive interactions in the relationship through the use of caring behaviors. Jada and Tyrell were each asked to privately write a list of things they could do to increase the other person’s satisfaction and then select items from their list to do during the coming week for homework. In the following session, they each described what they had noticed their partner doing during the week. Jada reported that she had observed Tyrell unloading the dishwasher once during the week, a task that he normally left for Jada. Tyrell reported that Jada appeared more understanding of his irritability than normal, and that on two occasions she had brought him a cup of tea in the evening when he was in a bad mood. He noted that he appreciated the gesture of Jada bringing him tea but stated that it seemed a little bit like she was trying to “fix” his irritability, which made him feel more irritable. Jada was surprised by this admission, and Jada and Tyrell had a conversation about what Tyrell found helpful when he felt irritable. He described that physical touch—holding hands or receiving a backrub—was most helpful for him in coping with irritability. Jada also noted that although she appreciated Tyrell unloading the dishwasher, she would also like increased verbal gratitude of the tasks that she did around the house rather than only physical support of sharing these tasks. She also asked whether Tyrell would be willing to give her a massage at night, which might help her improve her sleep. Tyrell and Jada agreed that having a conversation about what each of them found most helpful or caring from their spouse was beneficial. Both partners

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were encouraged to continue to do caring behaviors for one another and to modify the types of behaviors based on their partner’s feedback. The therapist also introduced the concept of self-esteem support, which was presented to the couple in terms of verbally acknowledging appreciation for characteristics about each other and the things that they do that the person normally takes for granted. Subsequent sessions were spent reviewing the couple’s caring behaviors and self-esteem support as well as encouraging them to increase the frequency of going out together. Jada said the idea of a date night did not sound appealing as she could not think of anything she would enjoy doing. The therapist encouraged her to predict how much enjoyment she anticipated and then compare that with the amount of enjoyment she experienced. In the following session, she reported that they had gone dancing and that she had enjoyed it more than she anticipated. Tyrell had also enjoyed their date, noting that they had felt more connected than in a long time. The therapist shifted the focus of treatment to communication training. The therapist reviewed expressive and receptive communication skills with the couple, and they practiced the skills with each other in the session with the therapist providing feedback. Jada and Tyrell agreed to try this approach when they walked their dog each evening, during which they talked about the events of their day. In the subsequent session, they both reported how much they appreciated having the other person’s attention, as they both had busy schedules and often felt the other person was not really listening and did not understand how much stress they were under. This discussion resulted in the couple deciding to make sure to include one positive thing that had happened to them in their day as part of their walk. The therapy proceeded to problem-solving and decision-making training. The couple initially experienced difficulty with this type of communication, as they both acknowledged that their general tendency was to avoid acknowledging or talking about problems in their relationship. The therapist discussed the pros and cons of avoidance with them, noting that avoidance can feel helpful in the short term but can also limit partners’ ability to resolve difficulties and feel emotionally close in the long term. The couple agreed that it was important to discuss issues so that they could get out of the hole they felt they were in, but that they would also help to remind one another of the fact that just because they were experiencing some problems did not mean they could not work these problems out or that they did not care for one another. They also thought it was best to start small and work up to bigger issues. Over the next several sessions, they addressed several issues, including making decisions about some home renovations that were needed. They also resolved an ongoing source of conflict about the amount of time Tyrell spent playing in a band that performed in local clubs. Although the couple reported that they had other problems to work out, they felt confident they could work these out on their own. The final sessions were devoted to generalization and relapse prevention. Jada and Tyrell each wrote out what they had learned in therapy and took turns reading their summaries in session. The therapist was impressed by how

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they had decided on their own to not only summarize the skills and content that they learned in therapy but also what they learned about each other and their relationship. They put their summaries in a notebook along with several handouts the therapist had given them and agreed they would review these materials if needed in the future. As part of a problem-solving session, the couple agreed to sit down at the beginning of each month to pay their bills and go through their finances together, and they decided to expand this session by also discussing any upcoming issues or potential sources of conflict and ways of handling such issues. In the final session, the self-report measure of depressive symptoms they had both been completing weekly indicated that both Jada and Tyrell were no longer depressed, and a post-treatment assessment indicated they were no longer in the discordant range on a measure of relationship distress, which was consistent with the feedback they provided to the therapist in their final session.

SUMMARY AND FUTURE DIRECTIONS In a recent review of the correlational (i.e., observational), genetically informed, and intervention (i.e., experimental) research, Whisman, Sbarra, et al. (2021) concluded that relationship distress is a causal risk factor for depression. In future research, it may be fruitful to consider how relationship distress may best be integrated with other risk factors for depression. For example, other risk factors for depression may serve to increase the strength of the association between relationship distress and depression. Consistent with this perspective, one study found that the association between relationship distress and MDD in married individuals was moderated by neuroticism, such that the association was stronger at higher levels of neuroticism (Uebelacker & Whisman, 2006), and another study found that within-subject associations between relationship distress and depressive symptoms were stronger for people with higher levels of neuroticism (Davila et al., 2003). These findings suggest that researchers studying relationship influences on depression may want to consider individual and relationship characteristics that may moderate this association. Furthermore, researchers studying diathesis stress models of depression may want to consider relationship distress as a specific form of interpersonal stress that may increase risk for depression. In addition, research is needed to identify individual and relationship resiliency factors that serve to protect against or buffer the adverse impact of relationship distress on depression. It is important to consider the demographic characteristics of couples who have been included in clinical trials conducted to date. Although basic research on relationship distress and depression has found no evidence of differences in the strength of the association between relationship distress and MDD across race or ethnicity (McShall & Johnson, 2015; Uebelacker & Whisman, 2006), and although some research exists on the association between relationship distress and depression in sexual minority couples (Gilmour et al., 2019), participants in clinical trials of couple therapy for depression have generally been

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White, middle- or upper-class different-gender couples. There is an urgent need for research on the use of CBCT and other approaches to couple therapy in treating depression among underserved groups of people, including race and ethnic minority couples, sexual and gender minority couples, and lower income couples (see also Barbato & D’Avanzo, 2020). Whereas much of the research on couple therapy for depression has focused on the impact of couple therapy on depression as a stand-alone treatment, combining couple-based interventions with other treatments for depression could yield improved outcomes. This perspective is based on findings that relationship distress is associated with poorer outcome for individuals with depression who receive other treatments for depression. For example, in the multisite Treatment of Depression Collaborative Research Program—which examined outcomes for individuals with depression who received treatment with imipramine, IPT, or CBT—greater relationship distress (i.e., lower marital adjustment) after treatment predicted higher levels of depressive symptoms at 6, 12, and 18 months posttreatment (Whisman, 2001b). Therefore, improving relationship functioning may enhance outcomes for individuals receiving medication or evidence-based psychosocial treatments for depression. One study evaluated the combination of emotionally focused therapy for couples with antidepressant medication in the treatment of women with MDD and comorbid relationship distress (Denton et al., 2012). Women who received the combined treatment reported more improvements in their relationship quality relative to women who received medication management alone. These results suggest that combining couple-based interventions with other treatments may improve outcomes for partnered individuals with depression. It may be helpful to view couple-based interventions as adjunct treatments for depression from a stepped care approach. In stepped care, the least resource intensive treatment is delivered first, and more intensive treatment is offered only if there is a higher level of distress or need. As applied to couple-based interventions as an adjunct treatment for depression, it may be helpful to develop stepped approaches for work with couples. For example, a single session (or a few sessions) focusing on psychoeducation regarding depression and relationship strategies for dealing with the challenges and problems that accompany depression may be useful for couples who report mild levels of relationship distress. This minimal intervention could be stepped up with additional efforts for couples who report moderate levels of relationship distress, such as Cohen et al.’s (2010) brief couples therapy approach, culminating in CBCT or other evidence-based couple approaches that have been shown to effectively treat depression for couples who report more severe levels of relationship distress that they view as contributing to one or both partner’s depression. An important question for researchers to consider is the degree to which preventing relationship distress is associated with the prevention of depression. That is, can improving relationship functioning reduce the probability of developing depressive symptoms and lower the incidence of depressive dis­ orders? Research has shown that marriage and relationship education programs are effective in reducing relationship distress. A meta-analysis of the

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efficacy of marriage and relationship education programs on relationship quality for experimental studies yielded effect sizes (i.e., Cohen’s d) ranging from .30 to .36 (Hawkins et al., 2008). However, little is known regarding the impact of such interventions on individual functioning, including their impact on the prevention of depression. One study found evidence for declines in depressive symptoms following participation in a couple and relationship education program (Lucier-Greer et al., 2012); additional research that examines long-term outcomes for depressive symptoms and incidence of MDD would strengthen the evidence base for such programs for the prevention of depression. In conclusion, relationship distress and other indicators of poor relationship functioning are well-established risk factors for the onset, severity, and course of depression. In addition, couple therapy has been shown to be an effective treatment for depression, particularly among couples who are in discordant relationships. REFERENCES Barbato, A., & D’Avanzo, B. (2020). The findings of a Cochrane meta-analysis of couple therapy in adult depression: Implications for research and clinical practice. Family Process, 59(2), 361–375. https://doi.org/10.1111/famp.12540 Baucom, D. H., Fischer, M. S., Worrell, M., Corrie, S., Belus, J. M., Molyva, E., & Boeding, S. E. (2018). Couple-based intervention for depression: An effectiveness study in the National Health Service in England. Family Process, 57(2), 275–292. https://doi.org/ 10.1111/famp.12332 Beach, S. R. H. (2014). The couple and family discord model of depression: Updates and future directions. In C. R. Agnew & S. C. South (Eds.), Interpersonal Relationships and Health: Social and Clinical Psychological Mechanisms (pp. 133–155). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199936632.003.0007 Beach, S. R. H., Fincham, F. D., Amir, N., & Leonard, K. E. (2005). The taxometrics of marriage: Is marital discord categorical? Journal of Family Psychology, 19(2), 276–285. https://doi.org/10.1037/0893-3200.19.2.276 Beach, S. R. H., Katz, J., Kim, S., & Brody, G. H. (2003). Prospective effects of marital satisfaction on depressive symptoms in established marriages: A dyadic model. Journal of Social and Personal Relationships, 20(3), 355–371. https://doi.org/10.1177/ 0265407503020003005 Beach, S. R. H., & O’Leary, K. D. (1992). Treating depression in the context of marital discord: Outcome and predictors of response of marital therapy versus cognitive therapy. Behavior Therapy, 23(4), 507–528. https://doi.org/10.1016/S0005-7894(05)80219-9 Beach, S. R. H., Sandeen, E. E., & O’Leary, K. D. (1990). Depression in marriage: A model for etiology and treatment. Guilford Press. Beach, S. R. H., Whisman, M. A., & Bodenmann, G. (2014). Couple, parenting, and interpersonal therapies for depression in adults: Toward common clinical guidelines within a stress-generation framework. In I. H. Gotlib & C. L. Hammen (Eds.), Handbook of depression (3rd ed., pp. 552–570). Guilford Press. Beck, C. T. (1996). A meta-analysis of predictors of postpartum depression. Nursing Research, 45(5), 297–303. https://doi.org/10.1097/00006199-199609000-00008 Beck, C. T. (2001). Predictors of postpartum depression: An update. Nursing Research, 50(5), 275–285. https://doi.org/10.1097/00006199-200109000-00004 Benazon, N. R., & Coyne, J. C. (2000). Living with a depressed spouse. Journal of Family Psychology, 14(1), 71–79. https://doi.org/10.1037/0893-3200.14.1.71

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Bodenmann, G., Plancherel, B., Beach, S. R. H., Widmer, K., Gabriel, B., Meuwly, N., Charvoz, L., Hautzinger, M., & Schramm, E. (2008). Effects of coping-oriented couples therapy on depression: A randomized clinical trial. Journal of Consulting and Clinical Psychology, 76(6), 944–954. https://doi.org/10.1037/a0013467 Butzlaff, R. L., & Hooley, J. M. (1998). Expressed emotion and psychiatric relapse: A meta-analysis. Archives of General Psychiatry, 55(6), 547–552. https://doi.org/ 10.1001/archpsyc.55.6.547 Cano, A., & O’Leary, K. D. (2000). Infidelity and separations precipitate major depressive episodes and symptoms of nonspecific depression and anxiety. Journal of Consulting and Clinical Psychology, 68(5), 774–781. https://doi.org/10.1037/0022-006X.68.5.774 Cohen, S., O’Leary, K. D., & Foran, H. (2010). A randomized clinical trial of a brief, problem-focused couple therapy for depression. Behavior Therapy, 41(4), 433–446. https://doi.org/10.1016/j.beth.2009.11.004 Coyne, J. C. (1976). Toward an interactional description of depression. Psychiatry, 39(1), 28–40. https://doi.org/10.1080/00332747.1976.11023874 Davila, J., Bradbury, T. N., Cohan, C. L., & Tochluk, S. (1997). Marital functioning and depressive symptoms: Evidence for a stress generation model. Journal of Personality and Social Psychology, 73(4), 849–861. https://doi.org/10.1037/0022-3514.73.4.849 Davila, J., Karney, B. R., Hall, T. W., & Bradbury, T. N. (2003). Depressive symptoms and marital satisfaction: Within-subject associations and the moderating effects of gender and neuroticism. Journal of Family Psychology, 17(4), 557–570. https://doi.org/ 10.1037/0893-3200.17.4.557 Denton, W. H., Wittenborn, A. K., & Golden, R. N. (2012). Augmenting antidepressant medication treatment of depressed women with emotionally focused therapy for couples: A randomized pilot study. Journal of Marital and Family Therapy, 38(Suppl. 1), 23–38. https://doi.org/10.1111/j.1752-0606.2012.00291.x Dessaulles, A., Johnson, S. M., & Denton, W. H. (2003). Emotion-focused therapy for couples in the treatment of depression: A pilot study. The American Journal of Family Therapy, 31(5), 345–353. https://doi.org/10.1080/01926180390232266 Doss, B. D., Roddy, M. K., Nowlan, K. M., Rothman, K., & Christensen, A. (2019). Maintenance of gains in relationship and individual functioning following the online OurRelationship program. Behavior Therapy, 50(1), 73–86. https://doi.org/ 10.1016/j.beth.2018.03.011 Emanuels-Zuurveen, L., & Emmelkamp, P. M. G. (1996). Individual behaviouralcognitive therapy v. marital therapy for depression in maritally distressed couples. The British Journal of Psychiatry, 169(2), 181–188. https://doi.org/10.1192/bjp.169.2.181 Epstein, N. B., & Baucom, D. H. (2002). Enhanced cognitive-behavioral therapy for couples. American Psychological Association. Fincham, F. D., & Rogge, R. (2010). Understanding relationship quality: Theoretical challenges and new tools for assessment. Journal of Family Theory & Review, 2(4), 227–242. https://doi.org/10.1111/j.1756-2589.2010.00059.x Funk, J. L., & Rogge, R. D. (2007). Testing the ruler with item response theory: Increasing precision of measurement for relationship satisfaction with the Couples Satisfaction Index. Journal of Family Psychology, 21(4), 572–583. https://doi.org/10.1037/ 0893-3200.21.4.572 Gilmour, A. L., Whisman, M. A., & Randall, A. K. (2019). Relationship discord and depressive symptoms in sexual minority individuals over time. Journal of Family Psychology, 33(8), 994–999. https://doi.org/10.1037/fam0000554 Goldfarb, M. R., Trudel, G., Boyer, R., & Preville, M. (2019). Till depression do us part? Examining the prospective associations between depression and marital adjustment in older adults. European Review of Applied Psychology, 69(2), 41–49. https://doi.org/ 10.1016/j.erap.2019.02.001 Hammen, C. (1991). Generation of stress in the course of unipolar depression. Journal of Abnormal Psychology, 100(4), 555–561. https://doi.org/10.1037/0021-843X.100.4.555

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Hawkins, A. J., Blanchard, V. L., Baldwin, S. A., & Fawcett, E. B. (2008). Does marriage and relationship education work? A meta-analytic study. Journal of Consulting and Clinical Psychology, 76(5), 723–734. https://doi.org/10.1037/a0012584 Hollist, C. S., Miller, R. B., Falceto, O. G., & Fernandes, C. L. (2007). Marital satisfaction and depression: A replication of the marital discord model in a Latino sample. Family Process, 46(4), 485–498. https://doi.org/10.1111/j.1545-5300.2007.00227.x Hooley, J. M., & Teasdale, J. D. (1989). Predictors of relapse in unipolar depressives: Expressed emotion, marital distress, and perceived criticism. Journal of Abnormal Psychology, 98(3), 229–235. https://doi.org/10.1037/0021-843X.98.3.229 Jacobson, N. S., Dobson, K., Fruzzetti, A. E., Schmaling, K. B., & Salusky, S. (1991). Marital therapy as a treatment for depression. Journal of Consulting and Clinical Psychology, 59(4), 547–557. https://doi.org/10.1037/0022-006X.59.4.547 Jacobson, N. S., Follette, W. C., Revenstorf, D., Hahlweg, K., Baucom, D. H., & Margolin, G. (1984). Variability in outcome and clinical significance of behavioral marital therapy: A reanalysis of outcome data. Journal of Consulting and Clinical Psychology, 52(4), 497–504. https://doi.org/10.1037/0022-006X.52.4.497 Jacobson, N. S., Fruzzetti, A. E., Dobson, K., Whisman, M., & Hops, H. (1993). Couple therapy as a treatment for depression: II. The effects of relationship quality and therapy on depressive relapse. Journal of Consulting and Clinical Psychology, 61(3), 516–519. https://doi.org/10.1037/0022-006X.61.3.516 Joiner, T. E., Jr. (2000). Depression’s vicious scree: Self-propagating and erosive processes in depression chronicity. Clinical Psychology: Science and Practice, 7(2), 203–218. https://doi.org/10.1093/clipsy.7.2.203 Joiner, T. E., Jr., & Katz, J. (1999). Contagion of depressive symptoms and mood: Meta-analytic review and explanations from cognitive, behavioral, and interpersonal viewpoints. Clinical Psychology: Science and Practice, 6(2), 149–164. https://doi.org/ 10.1093/clipsy.6.2.149 Kivelä, S. L., Köngäs-Saviaro, P., Laippala, P., Pahkala, K., & Kesti, E. (1996). Social and psychosocial factors predicting depression in old age: A longitudinal study. Inter­ national Psychogeriatrics, 8(4), 635–644. https://doi.org/10.1017/S1041610296002943 Klann, N., Hahlweg, K., Baucom, D. H., & Kroeger, C. (2011). The effectiveness of couple therapy in Germany: A replication study. Journal of Marital and Family Therapy, 37(2), 200–208. https://doi.org/10.1111/j.1752-0606.2009.00164.x Kouros, C. D., & Cummings, E. M. (2010). Longitudinal associations between husbands’ and wives’ depressive symptoms. Journal of Marriage and the Family, 72(1), 135–147. https://doi.org/10.1111/j.1741-3737.2009.00688.x Kouros, C. D., Papp, L. M., & Cummings, E. M. (2008). Interrelations and moderators of longitudinal links between marital satisfaction and depressive symptoms among couples in established relationships. Journal of Family Psychology, 22(5), 667–677. https://doi.org/10.1037/0893-3200.22.5.667 Leff, J., Vearnals, S., Brewin, C. R., Wolff, G., Alexander, B., Asen, E., Dayson, D., Jones, E., Chisholm, D., & Everitt, B. (2000). The London Depression Intervention Trial. Randomised controlled trial of antidepressants v. couple therapy in the treatment and maintenance of people with depression living with a partner: Clinical outcome and costs. The British Journal of Psychiatry, 177(2), 95–100. https://doi.org/10.1192/ bjp.177.2.95 Lucier-Greer, M., Adler-Baeder, F., Ketring, S. A., Harcourt, K. T., & Smith, T. (2012). Comparing the experiences of couples in first marriages and remarriages in couple and relationship education. Journal of Divorce & Remarriage, 53(1), 55–75. https:// doi.org/10.1080/10502556.2012.635970 McShall, J. R., & Johnson, M. D. (2015). The association between relationship distress and psychopathology is consistent across racial and ethnic groups. Journal of Abnormal Psychology, 124(1), 226–231. https://doi.org/10.1037/a0038267

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National Institute for Health and Care Excellence. (2018). Depression in adults: Treatment and management. https://www.nice.org.uk/guidance/gid-cgwave0725/documents/ full-guideline-updated O’Leary, K. D., & Beach, S. R. (1990). Marital therapy: A viable treatment for depression and marital discord. The American Journal of Psychiatry, 147(2), 183–186. https:// doi.org/10.1176/ajp.147.2.183 Overbeek, G., Vollebergh, W., de Graaf, R., Scholte, R., de Kemp, R., & Engels, R. (2006). Longitudinal associations of marital quality and marital dissolution with the incidence of DSM-III-R disorders. Journal of Family Psychology, 20(2), 284–291. https:// doi.org/10.1037/0893-3200.20.2.284 Proulx, C. M., Helms, H. M., & Buehler, C. (2007). Marital quality and personal well-being: A meta-analysis. Journal of Marriage and the Family, 69(3), 576–593. https://doi.org/10.1111/j.1741-3737.2007.00393.x Rehman, U. S., Gollan, J., & Mortimer, A. R. (2008). The marital context of depression: Research, limitations, and new directions. Clinical Psychology Review, 28(2), 179–198. https://doi.org/10.1016/j.cpr.2007.04.007 Renshaw, K. D. (2008). The predictive, convergent, and discriminant validity of perceived criticism: A review. Clinical Psychology Review, 28(3), 521–534. https://doi.org/ 10.1016/j.cpr.2007.09.002 Salinger, J. M., Whisman, M. A., Randall, A. K., & Hilpert, P. (2021). Associations between marital discord and depressive symptoms: A cross-cultural analysis. Family Process, 60(2), 493–506. https://doi.org/10.1111/famp.12563 Spanier, G. B. (1976). Measuring dyadic adjustment: New scales for assessing the quality of marriage and similar dyads. Journal of Marriage and the Family, 38(1), 15–28. https:// doi.org/10.2307/350547 Spencer, C., Mallory, A. B., Cafferky, B. M., Kimmes, J. G., Beck, A. R., & Stith, S. M. (2019). Mental health factors and intimate partner violence perpetration and victimization: A meta-analysis. Psychology of Violence, 9(1), 1–17. https://doi.org/10.1037/ vio0000156 Teichman, Y., Bar-el, Z., Shor, H., Sirota, P., & Elizur, A. (1995). A comparison of two modalities of cognitive therapy (individual and marital) in treating depression. Psychiatry, 58(2), 136–148. https://doi.org/10.1080/00332747.1995.11024721 Teo, A. R., Choi, H., & Valenstein, M. (2013). Social relationships and depression: Ten-year follow-up from a nationally representative study. PLOS ONE, 8(4), Article e62396. https://doi.org/10.1371/journal.pone.0062396 Thomas, A. L., Caughy, M. O., Anderson, L. A., & Owen, M. T. (2019). Longitudinal associations between relationship quality and maternal depression among low-income African American and Hispanic mothers. Journal of Family Psychology, 33(6), 722–729. https://doi.org/10.1037/fam0000548 Thomas, P. A. (2016). The impact of relationship-specific support and strain on depressive symptoms across the life course. Journal of Aging and Health, 28(2), 363–382. https://doi.org/10.1177/0898264315591004 Uebelacker, L. A., & Whisman, M. A. (2006). Moderators of the association between relationship discord and major depression in a national population-based sample. Journal of Family Psychology, 20(1), 40–46. https://doi.org/10.1037/0893-3200.20.1.40 U. N. Women. (2020). Progress of the world’s women 2019–2020: Families in a changing world. https://www.unwomen.org/en/digital-library/progress-of-the-worlds-women?y= 2019&y=2019 Waller, N. G., & Meehl, P. E. (1998). Multivariate taxometric procedures: Distinguishing types from continua. Sage. Whisman, M. A. (2001a). The association between depression and marital dissatisfaction. In S. R. H. Beach (Ed.), Marital and family processes in depression: A scientific foundation for clinical practice (pp. 3–24). American Psychological Association. https://doi.org/ 10.1037/10350-001

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Whisman, M. A. (2001b). Marital adjustment and outcome following treatments for depression. Journal of Consulting and Clinical Psychology, 69(1), 125–129. https://doi.org/ 10.1037/0022-006X.69.1.125 Whisman, M. A. (2007). Marital distress and DSM-IV psychiatric disorders in a population-based national survey. Journal of Abnormal Psychology, 116(3), 638–643. https:// doi.org/10.1037/0021-843X.116.3.638 Whisman, M. A. (2016). Discovery of a partner affair and major depressive episode in a probability sample of married or cohabiting adults. Family Process, 55(4), 713–723. https://doi.org/10.1111/famp.12185 Whisman, M. A., & Beach, S. R. H. (2015). Couple therapy and depression. In A. S. Gurman, J. L. Lebow, & D. K. Snyder (Eds.), Clinical handbook of couple therapy (5th ed., pp. 585–605). Guilford Press. Whisman, M. A., Beach, S. R. H., & Snyder, D. K. (2008). Is marital discord taxonic and can taxonic status be assessed reliably? Results from a national, representative sample of married couples. Journal of Consulting and Clinical Psychology, 76(5), 745–755. https://doi.org/10.1037/0022-006X.76.5.745 Whisman, M. A., & Bruce, M. L. (1999). Marital dissatisfaction and incidence of major depressive episode in a community sample. Journal of Abnormal Psychology, 108(4), 674–678. https://doi.org/10.1037/0021-843X.108.4.674 Whisman, M. A., Robustelli, B. L., Beach, S. R., Snyder, D. K., & Harper, J. M. (2015). Marital discord and depression in middle-aged and older couples: Is taxon status associated with depression? Journal of Social and Personal Relationships, 32(7), 967–973. https://doi.org/10.1177/0265407514554519 Whisman, M. A., Robustelli, B. L., & Labrecque, L. T. (2018). Specificity of the association between marital discord and longitudinal changes in symptoms of depression and generalized anxiety disorder in the Irish Longitudinal Study on Ageing. Family Process, 57(3), 649–661. https://doi.org/10.1111/famp.12351 Whisman, M. A., Salinger, J. M., Gilmour, A. L., Steele, B. A., & Snyder, D. K. (2021). Love and war: Prospective associations between relationship distress and incidence of psychiatric disorders in active-duty Army personnel. Journal of Abnormal Psychology, 130(1), 3–8. https://doi.org/10.1037/abn0000642 Whisman, M. A., Salinger, J. M., Labrecque, L. T., Gilmour, A. L., & Snyder, D. K. (2020). Couples in arms: Marital distress, psychopathology, and suicidal ideation in active-duty Army personnel. Journal of Abnormal Psychology, 129(3), 248–255. https:// doi.org/10.1037/abn0000492 Whisman, M. A., Sbarra, D. A., & Beach, S. R. H. (2021). Intimate relationships and depression: Searching for causation in the sea of association. Annual Review of Clinical Psychology, 17(1), 233–258. https://doi.org/10.1146/annurev-clinpsy-081219-103323 Whisman, M. A., Snyder, D. K., & Beach, S. R. H. (2009). Screening for marital and relationship discord. Journal of Family Psychology, 23(2), 247–254. https://doi.org/ 10.1037/a0014476 Whisman, M. A., & Uebelacker, L. A. (2009). Prospective associations between marital discord and depressive symptoms in middle-aged and older adults. Psychology and Aging, 24(1), 184–189. https://doi.org/10.1037/a0014759 Whitton, S. W., Rhoades, G. K., & Whisman, M. A. (2014). Fluctuation in relationship quality over time and individual well-being: Main, mediated, and moderated effects. Personality and Social Psychology Bulletin, 40(7), 858–871. https://doi.org/10.1177/ 0146167214528988 Whitton, S. W., Stanley, S. M., Markman, H. J., & Baucom, B. R. (2008). Women’s weekly relationship functioning and depressive symptoms. Personal Relationships, 15(4), 533–550. https://doi.org/10.1111/j.1475-6811.2008.00214.x Whitton, S. W., & Whisman, M. A. (2010). Relationship satisfaction instability and depression. Journal of Family Psychology, 24(6), 791–794. https://doi.org/10.1037/ a0021734

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8 Emotion Dysregulation Natasha H. Bailen and Renee J. Thompson

D

epressive disorders are characterized by extensive emotion dysregulation, defined as a pattern of emotional experience or expression that interferes with goal-directed activity (R. A. Thompson, 2019). The two cardinal symptoms of major depressive disorder (MDD) involve persistent negative emotion and blunted positive emotion (American Psychiatric Association, 2013). Consistent with this model, research using a variety of methodologies has found that adults with MDD experience elevated negative affect and diminished positive affect compared with healthy controls (e.g., Nelson et al., 2020). These symptoms are often associated with severe functional, social, and occupational impairment (American Psychiatric Association, 2013). The full picture of emotion dysregulation in MDD is complex. Dysregulated emotion is theorized not only to be a symptom of depression (i.e., a feature that indicates the presence of the disorder) but also a risk factor (i.e., a feature that precedes the development of the disorder) and a “scar” (i.e., a feature that remains after remission). Various forms of dysregulated emotion precede the development and predict the maintenance of depressive episodes and symptoms. For instance, elevated emotional instability has been shown to precede the onset of depressive episodes (Eldesouky et al., 2018). In addition, individuals in full remission from depressive episodes show diminished positive emotion and elevated negative emotion as compared with healthy controls (BargeSchaapveld & Nicolson, 2002). Positive bidirectional associations have been established between depressive symptoms and rumination (e.g., Whisman et al., 2020).

https://doi.org/10.1037/0000332-009 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  181

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In this chapter, we examine the role of emotion dysregulation as a risk factor for depression. Because the focus of the book is on risk factors, other than briefly noting established cross-sectional findings, we focus on longitudinal studies to evaluate which aspects of emotion dysregulation precede changes in depressive symptoms, MDD onset, and MDD recurrence. We then point to evidence that emotion dysregulation can be successfully targeted in treatment and discuss the extent to which different aspects of emotion dysregulation change over the course of leading treatments for depression, focusing on emotional awareness, emotional experience, and emotion regulation strategies. As an exhaustive literature review on these topics is beyond the scope of the chapter, we synthesize the longitudinal research and highlight notable patterns. We present a case example of the role of emotion dysregulation in treatment for depression. Finally, we summarize the evidence for emotion dysregulation as a risk factor for depression that changes over the course of treatment, review limitations of the existing literature, and provide directions for future research.

DEFINITIONAL AND CONCEPTUAL ISSUES Emotion Dysregulation Emotion dysregulation has been defined in different ways throughout the literature, and the specific emotional phenomena encompassed by the term vary widely. For instance, Cole et al. (2017) identified four types of dysregulated emotion, including (a) ineffective regulation attempts that result in persistent emotional states, (b) interference of emotions with appropriate behavioral responses, (c) context-inappropriate emotional experience or expression, and (d) emotional changes that are unusually fast or slow. Gratz and Roemer (2004) identified facets of emotion dysregulation that touch on (a) poor awareness of and attention to emotions, (b) poor emotional clarity, (c) rejection of one’s own emotional states, (d) poor impulse control in response to emotions, (e) poor concentration and goal pursuit in the face of emotional distress, and (f) poor use of emotion regulation strategies. A taxonomy of emotional disturbances (Berenbaum et  al., 2003) includes (a) deficits or excesses in pleasant or unpleasant emotion, (b) emotional reactivity, and (c) emotional disconnections (e.g., disconnect between subjective experience and expressions, low emotional clarity). Finally, the framework by Gross and Jazaieri (2014) includes disturbances in (a) emotional intensity and reactivity, (b) emotional duration, (c) emotion frequency, and (d) emotion type. Several similarities can be seen across models of emotion dysregulation. For instance, most models describe emotional experiences that are experienced in atypical or maladaptive quantities and contexts as forms of dysregulation. Multiple models also consider specific strategies used to modify emotional experience as well as the awareness one has of their own emotional experience. In efforts to circumscribe the boundaries of emotion dysregulation for

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our purposes, we took commonalities into consideration and arrived at the following three parsimonious categories of emotion dysregulation: (a) deficiencies in the awareness of emotion (e.g., emotional clarity and attention to emotions), (b) disturbance of the experience of emotion (i.e., emotional intensity, reactivity, instability, variability, and inertia), and (c) unhelpful patterns in the use of emotion regulation strategies. Relation to Psychopathology In this chapter, we examine emotion dysregulation as it relates to the changes in depressive symptom scores (e.g., as assessed using self-report measures) and the onset and recurrence of clinical depressive disorders (i.e., MDD, persistent depressive disorder, or dysthymia, as self-reported or assessed via clinical interview). We do not include findings related to bipolar depression or samples in which depression is not differentiated from other diagnoses such as anxiety disorders. We indicate when cited studies make use of clinical samples; in all other instances, the samples were recruited without regard to depression (e.g., community sample). It is worth noting that emotion dysregulation is not a phenomenon that is specific to depressive pathology. Indeed, it is difficult to find a psychological disorder that is not characterized by dysregulation of emotional experience, awareness, or strategy use. Broadly defined, dysregulated emotion features in bipolar disorder, borderline personality disorder, anxiety disorders, traumarelated disorders, and even many disorders that are widely considered externalizing, such as substance use disorders. Therefore, although this chapter limits its review to emotion dysregulation as a risk factor for depression, it is important to keep in mind that some aspects of emotion dysregulation are trans­ diagnostic risk factors. A Note on the Use of Emotion Regulation Strategies The emotion regulation strategies that are more avoidance based (e.g., expressive suppression, rumination) have traditionally been considered maladaptive, and the strategies that are more approach oriented (e.g., problem solving, acceptance) have traditionally been considered adaptive due to their associations with negative and positive socioemotional outcomes, respectively (e.g., Goldin et al., 2008). However, as many have argued, it is difficult to label any one strategy as categorically maladaptive, or representative of emotion dysregulation, as some putatively maladaptive strategies can be adaptive in certain situations (e.g., suppressing amusement during a funeral). Further, the outcomes of a given strategy can vary by sociodemographic characteristics. For instance, race has been found to moderate the positive association between depressive symptoms and emotional suppression, such that the association was stronger in European American than in Asian American college students (Cheung & Park, 2010). Research indicates that it is not the use of any one strategy that

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most reflects emotion dysregulation; it is rather the inflexible, context-insensitive use of emotion regulation strategies (e.g., Westphal et al., 2010). Although in this chapter we review associations between specific strategies and depression, in alignment with more current theorizing, we also incorporate more recent research on emotion regulation flexibility. Assessment of Emotion Dysregulation When possible, we refer to specific forms of emotion dysregulation, not to composite emotion dysregulation scores. We also distinguish when experience sampling methodology (ESM) and physiological markers (e.g., cardiac reactivity) are used to assess emotional experience as opposed to trait self-report measures. The use of ESM to collect momentary reports of emotional experiences helps to minimize the retrospective recall bias associated with trait selfreports and with depressive symptomatology (LeMoult & Gotlib, 2019).

EMOTION DYSREGULATION AS A RISK FACTOR FOR DEPRESSION Emotional Awareness as a Risk Factor Cross-sectional studies show a link between depression and emotional awareness, which itself is a multifaceted construct that involves the ability to notice and identify one’s emotional experience. Low emotional awareness overlaps to some degree with alexithymia, which involves difficulty identifying, understanding, and communicating emotional experiences and is also cross-sectionally associated with depression (Visted et al., 2018). The longitudinal research examining these factors predicting depression has mixed findings. Two studies have found that deficits in general emotional awareness at baseline predicted increases in depressive symptoms over time in adolescents (Kranzler et al., 2016) and adults (Berking et al., 2014). However, baseline alexithymia did not predict the onset of MDD in adults (e.g., Honkalampi et al., 2010). It may be that these results are equivocal because they have examined broad constructs. Emotional awareness encompasses both emotional clarity and attention to emotion, two related but distinct dimensions that have unique predictive validity (Boden & Thompson, 2017). Emotional clarity is the degree to which one can identify, distinguish, and describe one’s emotions, whereas attention to emotion is the degree to which one notices and thinks about one’s emotions (Gohm & Clore, 2000). Research examining the associations between these individual dimensions of emotional awareness and depression seem to provide a more consistent picture than examining emotional awareness more broadly. Deficits in trait emotional clarity prospectively predicted increases in depressive symptoms in adolescents (e.g., Stange et al., 2013) and adults (Berking et al., 2014). Although these findings are promising, more research is needed to examine emotional clarity in clinical samples with respect to the onset and course of MDD.

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Preliminary evidence supports the role of attention to emotion in depression. Increased trait attention to emotion was associated with increased depressive symptoms over time in a sample of adolescents (Salguero et al., 2012). Research has also examined people’s momentary attention to emotion—an intra-individual variable reflecting the extent to which someone is attending to their emotions at a particular point in time. Average levels of attention to emotion assessed while participants were in a depressive episode predicted their depressive status one year later: those whose MDD was in remission had reported lower levels of attention to emotion than did those who still met criteria for MDD. Importantly, attention to emotion was assessed when participants had current MDD, not one year later when some people’s MDD was in remission. The study also included a healthy control group, and their levels of attention to emotion did not differ from those whose MDD was in remission a year later. That is, paying some but not too much attention to emotion was associated with MDD remission. Increasing one’s attention to and clarity about one’s emotions is consistent with many depression treatments. Therefore, it might seem counterintuitive that higher attention to emotion is associated with poorer depression outcomes. However, it is important to note that such treatments include psychoeducation about the adaptive nature of emotions and emphasize nonjudgmental attention to emotion. For individuals with depression, negative biases might interact with high attention and clarity to produce negative reactions to one’s emotional experience (e.g., “I can’t tolerate this sadness,” “It’s stupid that I feel lonely”), which is quite different from the nonjudgmental clarity and attention encouraged in treatment. Further, as described above, R. J. Thompson et  al. (2013) found that it is moderate, but not high, levels of attention to emotion that seem most adaptive for those with MDD at least with regard to the course of their illness. Emotional Experience as a Risk Factor Emotion intensity, or the subjective strength of one’s emotional experience, has mixed support as a risk factor for depression. Both high negative and low positive emotional intensity assessed via ESM were associated with increases in depressive symptoms in both adults with depression and healthy controls over 6 months (Panaite et al., 2020) and in adolescents at elevated risk of developing externalizing disorders over 12 months (Neumann et al., 2011). However, neither mean negative emotion nor positive emotion assessed via ESM predicted whether people’s MDD was in remission 12 months later (R. J. Thompson et al., 2013). Trait emotional intensity did not predict changes in depressive symptoms over 2 months in an unselected adult sample (R. J. Thompson et al., 2011). The predictive value of emotional intensity may be most clearly observed when intensity is assessed using ESM (as opposed to using trait measures) with regard to depressive symptoms (not MDD status). However, additional research is needed to test this assertion.

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Emotion reactivity is the strength of one’s emotional response to emotionprovoking events or stimuli. Cross-sectional findings in relation to depression tend to differ as a function of study methodology. ESM research shows that emotional reactivity to daily stressors is generally higher in individuals with MDD as compared to controls. In contrast, laboratory research has found that emotional reactivity to positive and negative material is lower in individuals with MDD as compared with controls (van der Stouwe et al., 2019). Findings from longitudinal studies follow the same pattern. ESM studies generally suggest that negative emotion reactivity in response to interpersonal daily stressors is associated with subsequent increases in depressive symptoms (e.g., O’Neill et al., 2004) while laboratory studies have found diminished emotion reactivity is associated with depression risk. For example, cardiac underreactivity to a sadness induction predicted increases in depressive symptoms over 2  years in children (Somers et al., 2015), and low self-reported positive reactivity to a sadness induction predicted recurrence 12 months later in adults with remitted MDD (Lethbridge & Allen, 2008). In another study, low cardiac and behaviorally assessed emotion reactivity to an amusing film was associated with higher likelihood of MDD recurrence (Rottenberg et al., 2002). A related construct to emotional reactivity is emotional instability, or the degree and rate at which emotions fluctuate over time (Houben et al., 2015). Importantly, research has found that emotional reactivity accounts for some, but not all, of the variance of elevated emotional instability that characterizes those with MDD (R. J. Thompson et al., 2012). Researchers assess emotional instability using a variety of methods, such as self-report trait measures and variation of mean levels of momentary emotion assessed via ESM. For the latter, instability is most often computed as the sum of the squared differences between consecutive observations (mean of squared successive differences; MSSD) of momentary emotion. Across these methods, cross-sectional research shows that both depressive symptoms and depressive disorders are associated with heightened emotional instability (for a review, see Houben et al., 2015). The results from research examining instability predicting depression are relatively inconsistent. Neither positive nor negative emotion instability (Sperry et al., 2020) nor general emotion instability (Eldesouky et al., 2018) prospectively predict increases in depressive symptoms. In contrast, general emotion instability has predicted increased depressive symptoms (R. J. Thompson et al., 2011), and negative emotion instability has predicted increased depressive symptoms (Panaite et al., 2020). The discrepancy between studies could lie in the difference in lengths of measurement periods: Sperry et al. (2020) and Eldesouky et al. (2018) took place over multiple years while R. J. Thompson et al. (2011) and Panaite et al. (2020) took place between 2 and 6 months, suggesting that instability might only be predictive of depressive symptoms within a relatively short time frame. Although trait emotional instability has been found to predict the onset of major depressive episodes in adults (e.g., Eldesouky et al., 2018), instability as assessed by MSSD via ESM did not (Sperry et al., 2020).

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Of note, MSSD is a function of both variance and autocorrelation (Jahng et al., 2008), which map onto variability and inertia, respectively. Emotion variability is the variance or the average deviation of one’s momentary emotions from their average levels across time (Kuppens et al., 2012), whereas emotional inertia is the degree to which levels of emotion remain consistent from one moment to the next (Koval & Kuppens, 2012). Consequently, instability as measured by MSSD does not differentiate between these two constructs even though they represent different aspects of emotional experience, and both are cross-sectionally associated with depressive symptoms and diagnosis (see Houben et al., 2015, for a review). Cross-sectional research finds that those with MDD experience greater emotional variability and inertia, especially regarding negative emotions (see Houben et al., 2015, for a meta-analysis). Although little research has examined emotional variability predicting depression, evidence suggests it predicts increases in depressive symptoms over time in adolescents (Neumann et  al., 2011) and community adults (e.g., Panaite et al., 2020) but not in young adults (Sperry et al., 2020). Similarly, increased inertia of negative emotion has been shown to precede increases in depressive symptoms in community adults over periods of 2–6 months (e.g., Panaite et al., 2020). Interestingly, when inertia of negative emotion was examined over a period of 2 to 3 years, it did not significantly predict changes in depressive symptoms (e.g., Sperry et al., 2020). The only study to examine inertia predicting onset of first depressive episodes in adolescents found that inertia of both negative and positive emotion were predictors of depression (Kuppens et  al., 2012). This pattern could suggest that inertia of positive emotion is a risk factor specific to adolescents, or specific to the onset of depressive episodes as opposed to symptom increases. Alternatively, results may vary because different methods were used to assess momentary emotion. Kuppens et al. (2012) assessed emotion by independent raters who coded participants’ behaviors during an interaction task whereas the other studies assessed participants’ self-reports of momentary emotion. Emotion Regulation Strategy Use as a Risk Factor In this section, we review research that examines trait or habitual use of emotion regulation strategies using self-report measures, as this methodology characterizes the majority of research on this topic. We focus on the strategies of rumination, cognitive reappraisal, experiential avoidance and acceptance, and expressive suppression because they are most examined with regard to depression. Finally, we review literature that has examined emotion regulation inflexibility as a risk factor for depression. Rumination Rumination is defined as repetitive negative thinking about symptoms of distress (Nolen-Hoeksema, 2000), often in an attempt to alleviate that distress. Most of the research linking depression and rumination has been limited to

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the brooding, not the reflective pondering, component of rumination, including the studies described below. Cross-sectional research has shown that individuals with current depression report higher rumination than healthy controls (for reviews, see Joormann & Stanton, 2016; Liu & Thompson, 2017; Visted et al., 2018). Longitudinally, rumination predicts increases in depressive symptoms in youth (for a review, see Rood et al., 2009) and adults (e.g., Everaert & Joormann, 2020). Similarly, rumination predicts the onset of MDD in youth (e.g., Abela & Hankin, 2011) and adults (e.g., Nolen-Hoeksema, 2000). For a more comprehensive analysis of rumination as a risk factor for depression, refer to Chapter 13, this volume. Cognitive Reappraisal Cognitive reappraisal involves the reinterpretation of a situation or event emotion-eliciting stimulus to change its emotional impact. Cross-sectional research generally shows that individuals with MDD use cognitive reappraisal to a lesser extent than healthy controls (Joormann & Stanton, 2016; Liu & Thompson, 2017; Visted et  al., 2018). However, considering the central role of cognitive restructuring in treatments for depression, a surprising dearth of research has examined the longitudinal association between this emotion regulation strategy and depression. In fact, we found only one study showing that greater use of positive cognitive reappraisal was associated with decreases in depressive symptoms in women with breast cancer over 1 month (Wang et al., 2014). The other two studies showed no association between positive cognitive reappraisal and changes in depressive symptoms in an adult sample over 5 months (Everaert & Joormann, 2020) or recovery status in adults with MDD over 6 months (Arditte & Joormann, 2011). Interestingly, the Wang et al. (2014) study was conducted over a briefer period and with participants who had lower levels of psycho­pathology than the two studies with null findings, so significant findings may be limited to relatively short periods of time among relatively healthy samples. Experiential Avoidance and Acceptance Experiential avoidance involves efforts to avoid contact with unwanted internal experiences. Its antithesis, acceptance, is somewhat lower in those with MDD than healthy controls (Joormann & Stanton, 2016; Liu & Thompson, 2017; Visted et al., 2018). Although little research examines experiential avoidance and acceptance predicting changes in depression, existing evidence supports its role as a risk factor of depression. For instance, higher experiential avoidance was associated with higher depressive symptoms 4, 8, and 12 months later in women with borderline personality disorder (Berking et  al., 2009). Similarly, higher acceptance prospectively predicted lower depressive symptoms 1 month later in women with breast cancer (Wang et al., 2014). Interestingly, in a sample of community women at elevated risk of developing depression, higher experiential avoidance was associated with higher depressive symptoms among participants who experienced higher

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levels of life stress (Shallcross et  al., 2010), suggesting that life stress could play a moderating role in this association. Expressive Suppression Expressive suppression is when one inhibits the outward expression of emotions. It has traditionally been associated with negative psychological outcomes, including higher depressive symptoms (Aldao et al., 2010) and MDD (Joormann & Stanton, 2016; Liu & Thompson, 2017; Visted et al., 2018). Perhaps surprisingly, there is little evidence that expressive suppression predicts depression (Liu & Thompson, 2017). For example, expressive suppression did not significantly predict depressive symptoms among adolescents over a year (Larsen et al., 2013). In adults with MDD, expressive suppression did not predict MDD recovery status over 6 months (Arditte & Joormann, 2011). The predictive effects may vary by the specific emotion that is suppressed. For example, only suppression of anger, not suppression of anxiety or depressed mood, was associated with depressive symptoms across 3-month chemotherapy treatment among patients with breast cancer (Schlatter & Cameron, 2010). Emotional specificity could explain the null effects in other studies that examine suppression of negative emotion. Emotion Regulation Flexibility In addition to trait reports of habitual emotion regulation strategy use, the flexibility or rigidity with which people use different emotion regulation strategies has been the subject of depression theory and research. There is evidence that the effectiveness of specific emotion regulation strategies is not static but instead varies across contexts (e.g., English & Eldesouky, 2020). For instance, cognitive reappraisal is more effective in uncontrollable, as opposed to controllable, situations (e.g., Haines et al., 2016). In an ESM study in which emotion regulation flexibility was operationalized as within-strategy variability (i.e., the variation of use of a single strategy across time) and between-strategy variability (i.e., varied endorsement of different strategies on single occasions), higher levels of both were significantly associated with lower depressive symptoms (Wang et al., 2021). The within-strategy form likely reflects participants choosing the same strategy in different contexts whereas the latter may reflect that people utilize different strategies within a given context. Since this area of research is relatively new, the field is still trying to discern how best to assess emotion regulation flexibility. Little longitudinal work has explored emotion regulation flexibility predicting changes in depression. However, awareness of the success or failure of a chosen emotion regulation strategy has been associated with decreased depressive symptoms over time, even above and beyond the habitual use of any emotion regulation strategy (e.g., Kato, 2012). Further, the ability to abandon an ineffective coping strategy and devise and implement an alternative strategy were both predictive of decreases in depressive symptoms above and beyond the habitual use of any emotion regulation strategy (Kato, 2020).

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INTERVENTIONS Many leading psychotherapeutic and biological treatments for depressive disorders are theorized to function mechanistically by acting, either directly or indirectly, on the awareness, experience, and regulation of emotion. Therefore, it is vital to have an empirical understanding of whether, and which aspects of, emotion dysregulation are impacted in the manners theorized. In this section, we examine how emotion dysregulation changes over the course of leading treatments for MDD. First, we examine cognitive and behavioral interventions with a focus on cognitive behavior therapy (CBT), which has perhaps the strongest evidence base for treating active MDD and is identified as a frontline treatment in national and international guidelines (Gelenberg et al., 2010). We also examine mindfulness-based therapies with a focus on mindfulness-based cognitive therapy (MBCT), which is an empirically supported treatment to prevent MDD recurrence among those with recurrent MDD (Sipe & Eisendrath, 2012). Then, we review antidepressant medications and their relationship with emotion dysregulation. Within each of these sections, we examine how each intervention is theorized to alleviate depression by way of reducing emotion dysregulation and evidence that either emotion dysregulation is reduced over the course of each intervention or depression is reduced via the reduction of emotion dysregulation, or both. Finally, we discuss recent evidence that the incorporation of more explicit emotion regulation training can improve existing treatments. Cognitive and Behavioral Interventions Theorized Role of Emotion Dysregulation Cognitive and behavioral interventions—including CBT, exposure therapy, behavioral activation, and problem-solving therapy—use cognitive and behavioral strategies to combat low mood. CBT posits that cognition, behavior, and emotion are inextricably linked and mutually reinforcing (e.g., Beck & Haigh, 2014). In nonclinical samples, this association is adaptive. For example, if the end of a friendship is interpreted as a significant loss, that cognitive appraisal might trigger an emotional response of sadness, which might facilitate adaptive behavior to prevent further losses (e.g., attending more closely to other friendships). However, individuals with MDD have negative schematic representations of themselves and the world that are activated by congruent life experiences and lead to biases in information processing. In turn, faulty or unreliable information processing can lead to maladaptive emotional and behavioral responses. Therefore, emotion dysregulation in depression is attributable to faulty information processing in which dysfunctional cognitive schemas, negative biases, and thinking errors lead to emotional and behavioral responses that are not situationally appropriate or helpful (see Chapter 9, this volume). CBT provides skills to challenge inaccurate or maladaptive beliefs and promote adaptive behaviors. Flexible skill use is emphasized, for not every tool is appropriate for every situation. These changes in beliefs and behaviors are

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posited to then lead to more adaptive emotional responses. There are a multi­ tude of reasons that emotional awareness, experience, and strategy use could be expected to change over the course of CBT. For one, CBT for depression often includes explicit psychoeducation about the purpose and adaptive nature of emotions (e.g., Gilson et al., 2009). Along with CBT’s focus on logging thoughts and corresponding emotions, psychoeducation is intended in part to build awareness of emotional responses. Behavioral strategies such as activity scheduling (i.e., scheduling and participating in positive activities) and problem solving (i.e., generating possible solutions for a particular issue) are intended to decrease experiential avoidance, an emotion regulation strategy that is regularly associated with increased negative emotion (e.g., Wenze et al., 2018) and is a risk factor for depression (Berking et al., 2009). Activity scheduling also aims to build more frequent and intense positive emotion. Finally, the core CBT skill of cognitive restructuring is very similar to the emotion regulation strategy of cognitive reappraisal, which is associated with decreased negative emotions, increased positive emotions, and lower depressive symptoms (e.g., Aldao et al., 2010). Emotion Dysregulation Outcomes Despite the clear theoretical implications for the impact of cognitive and behavioral treatments on emotion dysregulation, relatively few studies have examined emotion outcomes over the course of CBT. There is limited evidence that emotional awareness and emotional experience change throughout the course of CBT treatment. For instance, community adults with a range of depressive symptoms have shown improved ability to identify and describe feelings (i.e., higher levels of emotional clarity), higher levels of positive emotion, and lower levels of negative emotion after CBT treatment (Baker et al., 2012). Further, over the course of treatment, among patient samples in major depressive episodes, negative emotion intensity and emotional variability have been shown to decrease and positive emotion intensity, emotional awareness, and emotional clarity to increase (e.g., Berking et al., 2013). A handful of studies have examined the change in emotion regulation strategies over the course of CBT, with some strategies showing significant change. Expressive suppression does not change significantly over the course of CBT in hospital inpatients with MDD (e.g., Forkmann et al., 2014). Similarly, experiential avoidance shows no change with CBT in outpatients with MDD (A-Tjak et al., 2021) or community adults (Baker et al., 2012). In contrast, acceptance of negative emotions has been shown to increase over the course of CBT in hospital inpatients with MDD (e.g., Berking et al., 2013), and preliminary evidence shows that cognitive reappraisal increases as well (Forkmann et al., 2014). There is also some evidence that rumination decreases over the course of both cognitive therapy (Jones et al., 2008) and rumination-focused cognitive therapy (Watkins et al., 2011). Overall, CBT seems to lead to increases in strategies that involve adaptive engagement with one’s emotional experience (e.g., acceptance, appraisal) and decreases in strategies that involve maladaptive (i.e., perseverative) engagement (e.g., rumination). However, it does

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not necessarily lead to changes in strategies that are used to disengage from one’s emotional experience (e.g., avoidance, suppression). Mindfulness-Based Interventions Theorized Role of Emotion Dysregulation Mindfulness-based interventions help patients build mindful awareness of, and attention to, internal experiences, including thoughts, emotions, and physiological sensations (Sipe & Eisendrath, 2012). One such intervention is MBCT, an offshoot of CBT that incorporates elements of mindfulness-based stress reduction and was originally developed to prevent future recurrence in individuals with a history of multiple depressive episodes (Teasdale et al., 2000). Another common mindfulness-based treatment for depression is acceptance and commitment therapy (Hayes et al., 2004). In both interventions, rather than attempting to reappraise negative cognitions to decrease negative emotions as in CBT, patients are encouraged to practice acceptance of negative thoughts and emotions. They are taught that acknowledged and accepted emotions will pass in their own time (Sipe & Eisendrath, 2012). Mindfulness-based interventions are theorized to target emotion dysregulation in several ways. First, they encourage mindful attention to emotional states and emotional responses, leading to reduced reactivity to internal and external experiences (e.g., Grabovac et al., 2011). Further, by encouraging a present awareness, these interventions minimize experiential avoidance and rumination—emotion regulation strategies whose habitual and inflexible use is associated with depressive symptoms (e.g., Hayes et  al., 2004)—and instead build acceptance as a more theoretically adaptive emotion regulation strategy. Mindfulness theory posits that when someone ceases to “feed” a negative emotional response with negative judgment and denial and instead accepts the emotional response, the emotion will eventually pass, theoretically leading to lower emotional intensity and variability over time (Teasdale et al., 2000). Emotion Dysregulation Outcomes Limited research examines emotional awareness over the course of mindfulnessbased therapies. However, research suggests that MBCT targets several dimensions of emotional experience. Trait and momentary positive emotion intensity has been shown to increase, and trait and momentary negative emotion to decrease, over the course of MBCT for individuals with remitted MDD (e.g., Bakker et al., 2014). Evidence from a randomized controlled trial (RCT) also showed that decreases in emotion reactivity partially mediated decreases in depressive symptoms over the course of MBCT (Britton et al., 2012). Considerably more research has examined changes in the use of various emotion regulation strategies over the course of mindfulness-based interventions, with a focus on mindfulness (i.e., nonjudgmental awareness and attention to internal experiences; Sipe & Eisendrath, 2012). Indeed, a strong body of research has shown that mindfulness increases among individuals with remitted MDD over the course of MBCT (e.g., van Aalderen et al., 2012). Of note, mindfulness is itself associated with other positive emotion regulation effects,

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including decreases in emotion instability, lower reactivity, and increased positive emotion intensity (e.g., Brown & Ryan, 2003). Evidence also supports that acceptance—a component of mindfulness—increases while experiential avoidance—its antithesis—decreases over the course of MBCT (e.g., Hamidian et al., 2016). Rumination is another strategy that has been shown to decrease over the course of MBCT in individuals with current (e.g., van Aalderen et al., 2012) and remitted (e.g., Jones et al., 2008) depressive disorders. Biological Interventions Theorized Role of Emotion Dysregulation Pharmacotherapeutic and neurobiological interventions are often used either in addition or as an alternative to psychotherapy in the treatment of depression. It is theorized that cognitive interventions such as CBT and MBCT target emotion dysregulation in MDD via alterations in “top-down” processing, which is effortful, rational information processing that occurs chiefly via the prefrontal and anterior cingulate cortices (e.g., Bruijniks et al., 2019). Anti­ depressant medications target emotion regulation by alterations in “bottom-up” processing, which is automatic or implicit information processing via serotonergic pathways in the hippocampus, amygdala, and basal ganglia (e.g., Godlewska & Harmer, 2021). In other words, psychotherapy is theorized to facilitate the effortful, intentional use of adaptive emotion regulation strategies while antidepressant medication decreases automatic emotional reactivity. Both treatments can work extremely well in combination, as CBT increases effortful cognitive control while antidepressants take effect at the level of neurotransmission and neuroplasticity (Godlewska & Harmer, 2021). In support of this theory, increased activation has been observed primarily in the prefrontal cortex and anterior cingulate cortex after CBT for MDD (Goldapple et  al., 2004) versus in the limbic system after treatment with a medication (Kennedy et al., 2001). Emotion Dysregulation Outcomes Imaging research has explored neuropsychological correlates of emotion regulation before and after antidepressant use (e.g., Stoy et al., 2012) and provided rich insights into how activation in these areas changes with treatment. However, the clinical applicability of these data are limited, in that imaging data cannot convey, for example, which emotions are being felt, how strongly, how well they are understood, or the ways in which they are regulated. However, hardly any studies have examined self-reported emotional awareness, experience, and strategy use before and after treatment. In one rare exception, a large study of adults with MDD showed decreased use of suppression and increased use of reappraisal after 8 weeks of antidepressant treatment (McRae et al., 2014). Incorporating Emotion Regulation Skills Training Into Psychotherapy Some research suggests that while effective psychotherapies target emotion dysregulation on their own, they can be enhanced with specialized emotion

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regulation training. Greater depressive symptom reductions in CBT enhanced with emotion regulation skills training, in comparison with standard CBT, has been observed in several outpatient samples (e.g., Gratz & Gunderson, 2006). In a controlled trial, inpatients with heterogeneous psychopathology received CBT, and a random subset also received additional emotion regulation skills training (e.g., emotional acceptance, distress tolerance; Berking et al., 2008). Both groups showed improved scores on a general emotion regulation measure (assessing a composite of awareness, experience, and strategies) over the course of treatment, and both reported increased positive emotion and decreased negative emotion. However, participants in the emotion regulation-enhanced group experienced significantly greater changes than patients in the CBT-only group, including their depressive symptoms decreasing to a greater degree. These findings were replicated in an RCT of inpatients with MDD (Berking et al., 2013). Several offshoots of CBT that target depressive disorders have incorporated a greater emphasis on psychoeducation about emotion and emotion regulation skills training. For instance, affect regulation training (Berking & Lukas, 2015) is a transdiagnostic intervention that teaches seven skills to help at-risk and clinical populations better regulate their emotions: muscle relaxation, breathing relaxation, nonjudgmental awareness of emotions, acceptance and tolerance of emotions, compassionate self-support, analysis of the antecedents and consequences of one’s emotional reactions, and active modification of emotions. Similarly, emotion regulation therapy (Mennin & Fresco, 2014) helps patients cultivate mindful attention to and awareness of their emotional experience and fosters the development of new emotion regulation skills. Patients are taught to respond skillfully and flexibly to regulate intense negative emotional experience. Finally, the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (Allen et al., 2008) was designed to treat a variety of emotional disorders. It focuses on reactions to emotional experience (e.g., emotional non-acceptance; avoidance of emotions) and teaches approach-oriented ways to manage emotional experiences.

CASE EXAMPLE The following is a description of a case seen by the second author of the chapter. It provides an example of how emotion dysregulation can be targeted in multiple ways to alleviate mood and anxiety symptoms. All potentially identifying information has been altered, and the description of the therapy is limited to what is central to the issue of emotion dysregulation. The patient was a cisgender White woman in her mid-30s who was referred to the clinic for worsening symptoms of depression in the context of increasing demands and responsibilities at work. During the intake session, the patient reported symptoms that included depressed mood, lack of interest, decreased appetite, insomnia, psychomotor retardation, loss of energy, feelings of worthlessness and excessive and inappropriate guilt, and diminished ability to

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concentrate. In particular, she reported that she had lost interest in most hobbies she used to enjoy, including photography and listening to music. Instead, she reported that she spent most of her free time watching YouTube videos and sleeping. She also described mild nonsuicidal self-injury and passive suicidal ideation. The intake evaluation revealed that this was the second such mood episode that the patient had experienced in her lifetime. Of note, she also reported anxiety symptoms, particularly in the context of social situations, such as meeting someone new, having conversations, and speaking with members of the opposite sex. Because of this clinical presentation, DSM-5 diagnoses of MDD (Recurrent, Moderate) and social anxiety disorder were assigned. During the first phase of treatment, behavioral activation was used to address the patient’s depressive symptoms by incorporating pleasant activities into her schedule to increase frequency and intensity of positive emotion. We—the patient and the therapist—also worked on scheduling regular sleep and mealtimes to decrease biological vulnerability to negative emotions. We discussed distress tolerance strategies to use instead of nonsuicidal self-injury when very intense feelings of sadness, loneliness, and anxiety arose. Once the patient was regularly meeting her sleeping and eating goals and was refraining from non-suicidal self-injury, we began the second phase of treatment, in which we targeted lingering anxiety and mood symptoms with CBT. The therapist provided the patient with psychoeducation about the adaptive nature and functional utility of emotions, which the patient had never considered, and their associations with thoughts and behaviors. The patient learned to challenge distorted thoughts to help regulate negative and unhelpful emotions. We further focused on the removal of behaviors intended to numb or distract from negative emotions—for example, watching YouTube, avoiding eye contact, scrolling through her phone, and listening to music on headphones. Once the patient had decreased her use of these behaviors, we began to engage in exposures to deliberately evoke and practice tolerating difficult negative emotions. During these exposures, the patient would recall memories that evoked loneliness or sadness and would practice sitting with (i.e., accepting, instead of rejecting) those emotions without the use of safety behaviors. After several weeks, the patient reported significant gains in social and emotional engagement. Our third and final phase of treatment targeted automatic thoughts and cognitive distortions that left the patient with lingering feelings of “loneliness and inadequacy.” During this phase, we also used acceptance and commitment therapy techniques to clarify her values and use them to guide her behaviors when faced with difficult emotions. At the end of treatment, the patient no longer met full DSM-5 criteria for any initial diagnosis. Her mood, anxiety symptoms, and overall life satisfaction had significantly improved. Her scores on the Beck Depression Inventory (BDI-II; Beck et al., 1996) had decreased from 42 (severe) to 6 (minimal). Further, by the end of treatment, she had started eating and sleeping more regularly, showed increased work productivity and social engagement, and successfully replaced self-injurious behaviors with more adaptive coping strategies.

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Emotion dysregulation was clearly central to the patient’s pathology in the above case example. The patient had poor awareness of her emotions, experienced low positive emotion and highly intense negative emotion, and persistently used strategies such as experiential avoidance and suppression that were intended to avoid her emotions. The combined treatment approach served to help the patient to more skillfully (a) understand what emotions were and why she had them (i.e., become more aware, attentive, and clear about them); (b) use strategies to increase the intensity of positive emotion and decrease the intensity of negative emotion during daily life, leading to a longer term pattern of decreased emotion variability, instability, and reactivity; and (c) respond flexibly and adaptively during moments of especially high emotion intensity using skills such as cognitive reappraisal and emotional acceptance.

SUMMARY AND FUTURE DIRECTIONS Status of Emotion Dysregulation as a Risk Factor for Depression The evidence indicates that many key aspects of emotion dysregulation are risk factors for depression, including changes in depressive symptoms, MDD onset, and recurrence. Emotional intensity, reactivity, instability, variability, and inertia (particularly of negative affect) have promising evidence for predicting changes in depressive symptomatology. There is also support for the emotion regulation strategies of rumination and experiential avoidance acting as risk factors for depression. Further, recent research suggests that emotion regulation inflexibility use could serve as a warning sign for increases in depressive symptoms. Methodologies to Study Emotion Dysregulation as a Risk Factor for Depression Research is needed to disentangle whether any emotion dysregulation variables are specific predictors of depressive disorders versus transdiagnostic predictors of internalizing disorders, or even psychopathology in general. For instance, there is some evidence that attention to emotion predicts both depressive and anxiety symptoms one year later (Salguero et al., 2012). However, no studies to date have identified emotion dysregulation variables that are unique risk factors for depressive disorders. This research will be especially important when comparing risk factors for depressive disorders to those for disorders that show high comorbidity or similar etiology, course, and presentation, such as anxiety and bipolar disorders. Many existing studies have either focused on negative emotion dysregulation or failed to examine emotion dysregulation separately by valence. Because MDD is defined in part by lack of enjoyment in everyday activities (American Psychiatric Association, 2013), it is a mistake to disregard the role

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of positive emotion dysregulation as a potential risk factor for later depression. Indeed, preliminary evidence suggests that positive instability, variability, and inertia could be risk factors for depression. Further research of both positive and negative emotion in relation to later depression will allow us to evaluate differential associations reliably. We have focused on longitudinal research because the temporal nature of findings (i.e., emotion dysregulation followed by changes in depression) addresses the possibility of risk. However, these findings do not prove causality; it is always possible that an unassessed variable explains or is driving the longitudinal association. We wanted to note that the large existing body of (nonlongitudinal) research using a variety of study designs can provide important insights into potential risk factors. Cross-sectional studies of emotion dysregulation in youth with and without biological and environmental predispositions to depression (e.g., the children of parents with depression) can provide clues as to what emotion dysregulation factors may constitute risk. In addition to these studies, research focusing on individuals with remitted depression (i.e., those at risk for future depression) can also highlight potential risk factors. For instance, those with remitted MDD had higher intensity and variability of negative and positive emotion than did a healthy control group (R. J. Thompson et al., 2021). Future research could examine whether elevated emotion intensity or variability could be a risk factor for MDD recurrence. Trait and State Assessment of Emotion Dysregulation Most of the longitudinal research examined in this chapter used trait selfreport measures of emotion dysregulation. This method is consistent with how emotion regulation is typically assessed in the broader cross-sectional literature. For instance, research on trait emotion regulation strategy use has typically found that those with MDD more strongly endorse putatively maladaptive emotion regulation strategies and less strongly endorse putatively adaptive strategies than those without MDD (Joormann & Stanton, 2016; Liu & Thompson, 2017; Visted et al., 2018). However, a growing body of research has examined different stages of the emotion regulation process in real time. This research has yielded exciting preliminary findings that are not always consistent with trait emotion regulation research. Although it is beyond the scope of this chapter to cover all such research, we highlight select research that we think has promise for future research on depression risk. To provide a framework for these ideas, we refer to the extended process model. This model identifies three stages of emotion regulation: identification (i.e., deciding whether to regulate), selection (i.e., choosing a strategy), and implementation (i.e., putting a strategy into effect; Gross, 2015). In laboratory studies assessing the strategy selection stage, participants are often instructed to freely choose any emotion regulation strategy after a negative mood induction. Those with current MDD showed similar levels of selecting cognitive reappraisal (a putatively adaptive strategy) and distraction (a putatively

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maladaptive strategy) as did those who were never depressed (e.g., Smoski et al., 2014). These findings contrast with people with MDD, who report lower trait cognitive reappraisal and similar or higher trait distraction than do healthy controls (Joormann & Stanton, 2016; Liu & Thompson, 2017). It is important to see if future research continues to support this discrepant pattern of findings and whether it extends to other emotion regulation strategies assessed in controlled settings like the lab as well as in more naturalistic settings. If this pattern holds, it suggests that individuals with MDD do not have issues with selecting emotion regulation strategies. Trait measures may be capturing another stage of the emotion regulation process, or they may not be accurate reflections of people’s emotion regulation. In laboratory research that assesses the implementation stage, participants are often instructed to use a particular emotion regulation strategy after the negative mood induction. Regardless of depression status, participants’ moods either similarly improved when instructed to cognitively reappraise or similarly worsened when instructed to ruminate (Joormann & Stanton, 2016; Liu & Thompson, 2017). Although this work has only focused on a handful of emotion regulation strategies, it points to people with MDD being able to successfully implement strategies in a controlled setting for some strategies. Research is still needed to examine whether people with depression can also successfully implement other emotion regulation strategies in controlled settings as well as how successful they are at implementing these in their everyday lives. We expect that people with depression will struggle to successfully select and implement these strategies in their own lives; therefore, identifying the factors that impact their success may help hone treatment targets for depression. Further, longitudinal research is needed to tell whether success in implementing instructed emotion regulation strategies predicts later depression. Another important avenue for future research is to expand the understanding of negative beliefs about emotion; that is, stable beliefs about the meaning, value, or consequences of one’s emotions (e.g., that one’s emotions are useless, uncontrollable, or dangerous). Developmental psychologists conceptualize beliefs about emotion as being present from childhood and arising from early experiences, parental modeling, and cultural milieu (e.g., Parker et  al., 2012). Clinical theorists suggest that the internalization of negative beliefs about emotion in childhood can lead to emotion dysregulation and later psychopathology, including depression (e.g., Greenberg, 2006). Empirically, the belief that emotions are uncontrollable has been associated with depressive symptoms in community samples longitudinally (e.g., Romero et  al., 2014), suggesting that this belief could be a potential risk factor for depression. More longitudinal research, developmental research, and research in samples with remitted MDD are needed on other types of beliefs and in clinical samples to further the understanding of which beliefs might serve as risk factors for the onset, recurrence, and course of depression. Moreover, negative beliefs about emotion may interact with other forms of emotion dysregulation. For example, if one is highly emotionally reactive and holds a

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belief that their emotions are dangerous, then one’s own emotional reactions might theoretically generate further distress due to their perceived danger. These questions are yet to be explored empirically. Effect of Treatment Interventions on Emotion Dysregulation Despite the theoretical and empirical evidence for emotion dysregulation as a risk factor for depression, few treatment studies have examined emotion dysregulation variables as treatment outcomes or as mediators of change in depression across treatment. In RCTs and other treatment studies, change scores on psychopathology and general quality of life measures have often featured as primary outcomes. It is also important to assess change over time of emotion dysregulation variables—including emotional awareness, experience, and strategy use—as outcomes of treatment. This research will allow a clearer understanding of which variables are most successfully targeted in treatment, so that existing treatments can be modified and new treatments developed that emphasize these variables. There is preliminary evidence that emotion dysregulation is modifiable and successfully targeted by leading treatments. As described previously, it appears that emotional awareness and experience change over the course of CBT, such that emotional awareness and positive emotion intensity increase and negative emotion intensity and variability decrease (e.g., Baker et  al., 2012). Further, over the course of CBT, patients’ use of emotion regulation strategies involving adaptive engagement with one’s emotional experience (e.g., acceptance, appraisal) increases and use of strategies involving maladaptive (i.e., perseverative) engagement (e.g., rumination) decrease (e.g., Berking et al., 2013; Jones et al., 2008). Evidence also demonstrates that emotion dysregulation changes over the course of MBCT, that negative emotion intensity and emotion reactivity decrease while positive emotion intensity increases, and that patients more often use approach-oriented strategies such as mindfulness and acceptance rather than rumination (e.g., Bakker et al., 2014; Britton et al., 2012; van Aalderen et al., 2012). Finally, there is promising evidence that adaptations of CBT that incorporate explicit training in emotion regulation provide incremental value in decreasing emotion dysregulation and alleviating depression (e.g., Berking et al., 2008). However, theory tells us that other factors should change over the course of each treatment, but for which we do not yet have an empirical grasp. For instance, despite the central role of cognitive restructuring in CBT, and despite strong cross-sectional evidence that MDD involves an underutilization of cognitive reappraisal (see Dryman & Heimberg, 2018, for a review), only one existing study has examined changes in cognitive reappraisal over the course of CBT (Forkmann et al., 2014). Without studies such as these, we cannot know whether treatments shown to be effective in treating depression are successful for the suggested reason. Targeted longitudinal research is needed to test theorized treatment mechanisms, including emotion dysregulation as a longitudinal mediator of depression outcomes.

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Significant evidence from imaging studies shows that pharmacotherapeutic treatment facilitates functional changes in the brain related to emotion processing. However, little research examines self-reported emotional awareness, experience, and strategy use before and after a course of antidepressants. To our knowledge, the exception to this rule showed an increase in cognitive reappraisal and a decrease in suppression over the course of an antidepressant regimen (McRae et al., 2014). More research, and particularly regarding emotional awareness and experience, is needed to flesh out the clinical picture regarding the effects of biological interventions on emotion dysregulation. Ultimately, more longitudinal research must be conducted to disclose which aspects of emotion dysregulation are unique risk factors for depression and to inform which aspects are best targeted by different interventions. As unique and modifiable risk factors are identified, treatment protocols can be finetuned to ensure that they emphasize the identified risk factors. On the other hand, if key modifiable risk factors appear to be largely transdiagnostic, treatments such as the Unified Protocol for Emotional Disorders could be employed for more efficient and comprehensive treatment. Given the picture that is slowly but clearly emerging—that emotion dysregulation is an important risk factor for depression—it will be essential for future research to continue to probe and refine our understanding of these issues. REFERENCES Abela, J. R. Z., & Hankin, B. L. (2011). Rumination as a vulnerability factor to depression during the transition from early to middle adolescence: A multiwave longitudinal study. Journal of Abnormal Psychology, 120(2), 259–271. https://doi.org/10.1037/ a0022796 Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2010). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clinical Psychology Review, 30(2), 217–237. https://doi.org/10.1016/j.cpr.2009.11.004 Allen, L. B., McHugh, R. K., & Barlow, D. H. (2008). Emotional disorders: A unified protocol. In D. H. Barlow (Ed.), Clinical handbook of psychological disorders: A step-bystep treatment manual (pp. 216–249). Guilford Press. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596 Arditte, K. A., & Joormann, J. (2011). Emotion regulation in depression: Reflection predicts recovery from a major depressive episode. Cognitive Therapy and Research, 35(6), 536–543. https://doi.org/10.1007/s10608-011-9389-4 A-Tjak, J. G. L., Morina, N., Topper, M., & Emmelkamp, P. M. G. (2021). One year follow-up and mediation in cognitive behavioral therapy and acceptance and commitment therapy for adult depression. BMC Psychiatry, 21(1), Article 41. https://doi.org/ 10.1186/s12888-020-03020-1 Baker, R., Owens, M., Thomas, S., Whittlesea, A., Abbey, G., Gower, P., Tosunlar, L., Corrigan, E., & Thomas, P. W. (2012). Does CBT facilitate emotional processing? Behavioural and Cognitive Psychotherapy, 40(1), 19–37. https://doi.org/10.1017/ S1352465810000895 Bakker, J. M., Lieverse, R., Menne-Lothmann, C., Viechtbauer, W., Pishva, E., Kenis, G., Geschwind, N., Peeters, F., van Os, J., & Wichers, M. (2014). Therapygenetics in mindfulness-based cognitive therapy: Do genes have an impact on therapy-induced

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cancer treatment. Annals of Behavioral Medicine, 40(1), 15–29. https://doi.org/10.1007/ s12160-010-9204-6 Shallcross, A. J., Troy, A. S., Boland, M., & Mauss, I. B. (2010). Let it be: Accepting negative emotional experiences predicts decreased negative affect and depressive symptoms. Behaviour Research and Therapy, 48(9), 921–929. https://doi.org/10.1016/ j.brat.2010.05.025 Sipe, W. E., & Eisendrath, S. J. (2012). Mindfulness-based cognitive therapy: Theory and practice. Canadian Journal of Psychiatry, 57(2), 63–69. https://doi.org/10.1177/ 070674371205700202 Smoski, M. J., LaBar, K. S., & Steffens, D. C. (2014). Relative effectiveness of reappraisal and distraction in regulating emotion in late-life depression. The American Journal of Geriatric Psychiatry, 22(9), 898–907. https://doi.org/10.1016/j.jagp.2013.01.070 Somers, J. A., Borelli, J. L., Smiley, P. A., West, J. L., & Hilt, L. M. (2015). Concurrent and prospective associations between emotion reactivity and depressive symptoms in middle childhood. Journal of Psychopathology and Behavioral Assessment, 37(4), 692–704. https://doi.org/10.1007/s10862-015-9491-0 Sperry, S. H., Walsh, M. A., & Kwapil, T. R. (2020). Emotion dynamics concurrently and prospectively predict mood psychopathology. Journal of Affective Disorders, 261, 67–75. https://doi.org/10.1016/j.jad.2019.09.076 Stange, J. P., Alloy, L. B., Flynn, M., & Abramson, L. Y. (2013). Negative inferential style, emotional clarity, and life stress: Integrating vulnerabilities to depression in adolescence. Journal of Clinical Child and Adolescent Psychology, 42(4), 508–518. https:// doi.org/10.1080/15374416.2012.743104 Stoy, M., Schlagenhauf, F., Sterzer, P., Bermpohl, F., Hägele, C., Suchotzki, K., Schmack, K., Wrase, J., Ricken, R., Knutson, B., Adli, M., Bauer, M., Heinz, A., & Ströhle, A. (2012). Hyporeactivity of ventral striatum towards incentive stimuli in unmedicated depressed patients normalizes after treatment with escitalopram. Journal of Psychopharmacology, 26(5), 677–688. https://doi.org/10.1177/0269881111416686 Teasdale, J. D., Segal, Z. V., Williams, J. M. G., Ridgeway, V. A., Soulsby, J. M., & Lau, M. A. (2000). Prevention of relapse/recurrence in major depression by mindfulnessbased cognitive therapy. Journal of Consulting and Clinical Psychology, 68(4), 615–623. https://doi.org/10.1037/0022-006X.68.4.615 Thompson, R. A. (2019). Emotion dysregulation: A theme in search of definition. Development and Psychopathology, 31(3), 805–815. https://doi.org/10.1017/S0954579419000282 Thompson, R. J., Bailen, N. H., & English, T. (2021). Everyday emotional experiences in current and remitted major depressive disorder: An experience-sampling study. Clinical Psychological Science, 9(5), 866–878. https://doi.org/10.1177/2167702621992082 Thompson, R. J., Berenbaum, H., & Bredemeier, K. (2011). Cross-sectional and longitudinal relations between affective instability and depression. Journal of Affective Disorders, 130(1–2), 53–59. https://doi.org/10.1016/j.jad.2010.09.021 Thompson, R. J., Mata, J., Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Gotlib, I. H. (2012). The everyday emotional experience of adults with major depressive disorder: Examining emotional instability, inertia, and reactivity. Journal of Abnormal Psychology, 121(4), 819–829. https://doi.org/10.1037/a0027978 Thompson, R. J., Mata, J., Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Gotlib, I. H. (2013). The role of attention to emotion in recovery from major depressive disorder. Depression Research and Treatment, 2013, Article 540726. https://doi.org/10.1155/2013/ 540726 van Aalderen, J. R., Donders, A. R. T., Giommi, F., Spinhoven, P., Barendregt, H. P., & Speckens, A. E. M. (2012). The efficacy of mindfulness-based cognitive therapy in recurrent depressed patients with and without a current depressive episode: A randomized controlled trial. Psychological Medicine, 42(5), 989–1001. https://doi.org/10.1017/ S0033291711002054

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9 Negative Thinking Cognitive Products and Schema Structures David J. A. Dozois and Aaron T. Beck

F

or nearly 60  years, A. T. Beck (1963, 1964) has developed and tested a comprehensive and evolving theory of psychopathology that corresponds to specific treatment approaches for various cognitive risk factors (A. T. Beck & Dozois, 2011, 2014; A. T. Beck & Haigh, 2014; A. T. Beck et al., 1979).1 The cognitive model of depression contends that maladaptive thinking and negative appraisals of life circumstances serve as important risk factors and that shifting cognition to be more evidence-based effectively disrupts the depressive process (Dozois et al., 2019). Most contemporary cognitive vulnerability models of depression have involved refinements and expansions of this basic conceptual framework (e.g., Abramson et al., 2002; A. T. Beck et al., 2021; A. T. Beck & Haigh, 2014; Hankin & Abramson, 2001; Ingram, 1984; Ingram et al., 1998, 2006; Teasdale, 1997; Teasdale & Barnard, 1993). This chapter examines the nature of risk in terms of various levels of the cognitive taxonomy in depression (Ingram & Kendall, 1986; Ingram et al., 1998). By taxonomy, we mean that the cognitive system related to vulnerability to depression is comprised of interrelated components ranging from deeper

Aaron T. Beck (1921–2021), affectionately known as “Tim” to his friends and colleagues, passed away on November 1, 2021, at the age of 100. Considered the founding father of cognitive therapy, Dr. Beck was arguably the most important contributor to the development of cognitive behavior therapy (CBT) and worked tirelessly, right up until the end of his remarkable life, to advance the theory, research, and treatment of CBT and improve the lives of millions of individuals worldwide (see Dozois, 2008; Thase, 2022). A week before his death, Dr. Beck provided feedback and approved this chapter. He will be sorely missed.

1

https://doi.org/10.1037/0000332-010 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  207

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cognitive structures to more surface-level thoughts (see Figure  9.1). We describe the components of the cognitive taxonomy and highlight the evidence for, and some important conceptual issues related to, negative thoughts and core beliefs/schemas as modifiable risk factors for depression. Various cognitive restructuring strategies, aimed at modifying negative cognition and, in turn, ameliorating depressive symptomatology, are reviewed and the empirical support for cognitive behavioral interventions is highlighted. A case example is used to illustrate the application of cognitive restructuring techniques. The chapter highlights several important research directions to enhance our understanding of cognitive vulnerability and the treatment of depression.

FIGURE 9.1.  The Cognitive Taxonomy Cognitive Structure/Organization Annoying

Funny

Inconsiderate Boring

Rejected Kind

Unworthy Unloved Lazy

Cold

Caring

Note: Thicker lines represent stronger associations among core beliefs.

Cognitive Processes/Operations (e.g., memory biases for times when one was rejected; attentional biases for cues that one is boring; interpretive biases that one is unlovable and unworthy)

Cognitive Products (e.g., negative automatic thoughts: “I was so annoying in that situation”; “They don’t like me”) Note. From “Exceptional Canadian Contributions to Research on Cognitive Vulnerability to Depression,” by D. J. A. Dozois and E. P. Hayden, 2022, Canadian Journal of Behavioural Science, 54(2), p. 97 (https://doi.org/10.1037/cbs0000301). Copyright 2022 by the American Psychological Association.

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DEFINITIONAL ISSUES Cognitive vulnerabilities are believed to be organized hierarchically in three main levels (see Figure 9.1), with deeper schemas affecting more superficial cognitive processes, beliefs, and assumptions (Dozois, 2021; Dozois & Beck, 2008; Dozois & Hayden, 2022). Ingram and Kendall (1986) first distinguished cognitive structure from cognitive operations/processes and cognitive products. Although there is no universal definition of schemas (Clark & Guyitt, 2016), most clinical researchers conceptualize them as well-organized, interconnected negative internal structures of stored information that contain perceptions of self and others, and guide an individual’s attention, encoding, memory, and interpretation of incoming stimuli and experiences (A. T. Beck & Dozois, 2011, 2014; Clark et al., 1999; Dozois & Beck, 2008; Marchetti et al., 2016; Scher et al., 2005). Schemas are comprised of both content (e.g., core beliefs about self) and organization or structure (Ingram & Kendall, 1986; Ingram et al., 1998). When negative schemas are highly interconnected, negative content becomes more accessible and available, facilitating cognitive biases and efficient processing of negative information (Ingram et al., 1998; Segal, 1988). Depressogenic schemas are believed to develop during early childhood, often because of insecure attachment experiences, childhood maltreatment, or other adverse events (e.g., Harkness, 2008; Lumley & Harkness, 2009; Lumley et al., 2012; see also Chapter 5, this volume). However, they remain quiescent until triggered by potent negative events later in life (see Dozois & Beck, 2008). Someone who is vulnerable to depression, for example, may have core beliefs that they are incompetent or unlovable. This individual may not experience depression, however, until this schema is triggered by corresponding life stress (e.g., failure or rejection). Once depressed, the individual is more likely to engage in information processing biases (Ingram et al., 2008; see also Chapter 10, this volume) and to experience an increase in cognitive products—negative automatic thoughts about self, others, and the future, and dysfunctional attitudes focused on themes of loss, failure, worthlessness, defectiveness, incompetence, and inadequacy (e.g., “I am a loser,” “I am a failure,” “Nobody likes me,” “I will never succeed”; see J. S. Beck, 2021; Dozois & Beck, 2008; Hofmann et al., 2018). A. T. Beck’s theory has evolved over time. A. T. Beck (1983) highlighted two specific personality dimensions. Sociotropy involves defining self-worth based on interpersonal approval and acceptance, whereas autonomy is concerned with achievement, independence, mobility, and control. Beck argued that depression is more likely to occur when negative life events match one’s personality dimension (e.g., rejection in the case of sociotropy; failure in the case of autonomy). Although empirical support for the “congruency hypothesis” has been equivocal (e.g., Coyne & Whiffen, 1995), negative thinking related to interpersonal themes appears to confer a particular risk factor for depression (e.g., Dozois, 2021). More recently, the model has been expanded to include the notion of modes, comprised of cognitive, affective, motivational, and

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behavioral elements. The relative (mal)adaptiveness of these modes depends on the “goodness of fit” between an individual’s internal goals, impulses, and yearnings and external situational factors (see A. T. Beck, 1996; A. T. Beck & Haigh, 2014; A. T. Beck et al., 2021; Clark et al., 1999). Given that information processing biases are covered extensively elsewhere in this volume (see Chapters 10 and 13, this volume), we focus on the scientific evidence pertaining to cognitive products and schema structures as risk factors for depression and describe strategies for modifying them.

EMPIRICAL FINDINGS ON COGNITIVE PRODUCTS AND STRUCTURES In this section, we distinguish cognitive products from cognitive structures and examine the empirical evidence that supports each of these variables as risk factors in depression. Both constructs are important conceptually and clinically, and each can be targeted effectively using cognitive behavioral interventions. Cognitive Products Cognitive products are the ensuing thoughts that derive from the activation of negative schema structures and may manifest as negative automatic thoughts, cognitive distortions, dysfunctional attitudes, inferential styles, or biased assumptions. Cross-sectional studies have demonstrated that depression is associated with increased negative thinking in the form of cognitive products (see Alloy et al., 2018, for a review). Cognitive products expressed as self-reported early maladaptive schemas (see Dozois & Beck, 2008)—in particular, disconnection/ rejection, impaired autonomy/performance, and other-directedness—may also be vulnerability factors for depression (Dozois, Martin, et al., 2009; Tariq et al., 2021). Some studies indicate that individuals who remit from an episode of depression continue to demonstrate elevations in cognitive products (see Alloy et al., 2017, 2018). For example, Romens et al. (2009) examined the stability of cognitive risk in adolescents, operationalized as extreme scores on a composite measure of dysfunctional attitudes and inferential styles. This composite risk factor was quite stable over a 7-year period. Most research, however, has demonstrated that the negative thinking seen during a depressive episode improves once depression subsides. In other words, cognitive products may reflect episode rather than vulnerability markers for depression (see Dozois & Dobson, 2001a; Dozois & Hayden, 2022). In contrast, research based on the mood-congruency hypothesis has demonstrated that, when priming methodologies are used to activate and assess them, cognitive products may operate as stable markers for depression (see Ingram et al., 1998; Segal, 1988). Following a negative mood manipulation intended to “prime” latent cognitive structures, for instance, individuals with a history of

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depression show increases in dysfunctional attitudes that are not present in those who have never been depressed (see Ingram et al., 1998; Scher et al., 2005, for reviews). Such cognitive reactivity has also been shown to predict first onset depression (Struijs et al., 2021) and subsequent relapse (Segal et al., 2006). It is possible that priming through mood manipulations may be more important for some cognitive products (e.g., dysfunctional attitudes) than for others (e.g., inferential styles) because the latter is measured in such a way that they may already have “built-in hypothetical life event primes” (Alloy et al., 2018, p. 109). The observation that some cognitive products are present in individuals who have remitted from an episode of depression does not rule out the possibility that they represent “scars” of the disorder (e.g., Oei et al., 2006), rather than causes. Therefore, showing that cognitive products predict future depression is important. Several longitudinal studies have supported the idea that dysfunctional attitudes and negative inferential styles, independently or in combination with negative life events, predict depressive symptoms and even the initial onset of depression (e.g., Abela & D’Alessandro, 2002; Alloy et al., 2006; Faissner et al., 2018; Iacoviello et al., 2006; Joiner et al., 1999; Kwon & Oei, 1992; Perez & Rohan, 2021; Zuroff et al., 1990). In perhaps the most compelling, prospective test of cognitive products, Alloy et al. (2006) found that cognitively high-risk participants (i.e., individuals who scored in the upper quartile on a composite index of the Dysfunctional Attitudes Scale [DAS] and the Cognitive Style Questionnaire) were four to seven times more likely to experience depression over a two-and-a-half-year time frame than were lowrisk participants. Although the empirical evidence remains inconclusive regarding when or if priming procedures are necessary to activate the cognitive system when studying cognitive products in depression, the preponderance of evidence indicates that negative thinking is a risk factor for the onset of depression and its recurrence. Cognitive Structures Schemas are defined by both their content and their structure. Despite the importance of schema structures to cognitive vulnerability models of depression (Dozois & Beck, 2008), a dearth of research has investigated the organizational coherence of schema content or how various elements of the cognitive taxonomy are organized and hierarchically structured (see Segal et al., 1988; Segal & Gemar, 1997; Segal & Vella, 1990; Segal et al., 1995, for exceptions). One strategy for measuring the cognitive structure of schematic content is the Psychological Distance Scaling Task (PDST; Dozois & Dobson, 2001a, 2001b). Using a computer screen or digital device, respondents place adjectives on a two-dimensional space based on their self-descriptiveness and valence. The distance among self-referential adjectives is then computed for positive and negative content, with the assumption that smaller distances among adjectives reflect greater interconnectedness or consolidation of self-referent content

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and larger distances among adjectives indicate less interconnectedness or consolidation (see Dozois, 2021). The psychometric properties of the PDST have been supported in several studies (e.g., Diehl et al., 2017; Dozois & Dobson, 2001a, 2001b). Highly interconnected negative content and loosely clustered positive content have been consistently demonstrated in adults (e.g., Diehl et al., 2017; Dozois & Dobson, 2001a; Dozois & Frewen, 2006) and youth (Dozois et al., 2012; Lumley et al., 2012) with depression, and in individuals with remitted depression (e.g., Dozois & Dobson, 2003). Interconnected negative cognitive structure also interacts with life stress to prospectively predict future depressive symptoms after accounting for baseline scores (Seeds & Dozois, 2010). Cognitive structure, assessed by the PDST, also appears to predict depressive symptoms beyond negative schema content (Lumley et al., 2012) and persists despite symptom amelioration (Dozois & Dobson, 2001b; Dozois, 2007). To illustrate, Dozois and Dobson (2001b) administered the PDST and various information processing tasks to a sample of females with depression. At the 6-month follow-up, when participants were retested, half continued to experience depression and half no longer met diagnostic criteria for major depressive disorder. In contrast to information processing, which improved once depression lifted, negative cognitive structures remained well interconnected in the sample of people who had experienced clinical remission. This finding was replicated in a subsequent study, which also found that the stability of negative cognitive structure was specific to interpersonal self-referent content (Dozois, 2007). These results suggest that negative interpersonal self-structures may be important vulnerability factors for depression and its recurrence (see Dozois, 2021, for a review).

CONCEPTUAL AND METHODOLOGICAL ISSUES RELATED TO THE STUDY OF COGNITIVE VULNERABILITY Several conceptual issues are important to our understanding of cognitive products and structures as risk factors for depression. First, it is important to point out that schemas are really a heuristic. There are many other ways to describe how the human brain represents and stores information (Clark & Guyitt, 2016; Disner et al., 2011). The processes involved in the cognitive taxonomy are also undoubtedly much more complicated than what has been described here, and involve many other systems, including neurological processes (e.g., prefrontal control) and dysregulated biological stress responses (e.g., hypothalamic–pituitary–adrenal axis, or HPA axis) that interact and affect attentional biases, rumination, and other cognitive processes in depression (e.g., De Raedt & Koster, 2010; Disner et al., 2011; Dobson & Dozois, 2008; Kertz et al., 2019). In addition, these processes likely operate in a multi­ directional fashion. Increasingly, the literature is shifting from investigating putative cognitive vulnerability factors in isolation (e.g., dysfunctional attitudes,

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cognitive structure), assessed from only one vantage point (e.g., self-report), to testing the integration and reciprocal influences of biological, cognitive, and environmental/contextual indices of risk over time. Another important issue in the literature has to do with the measurement of cognitive vulnerability itself (see Evraire et  al., 2015). Aside from issues surrounding the replication crisis in science (Open Science Collaboration, 2015), the ability to conduct appropriate tests of cognitive vulnerability models relies on reliable and valid measurement instruments and techniques (NaragonGainey & Brown, 2015). Numerous measurement approaches and instruments are used in research. However, even those that are well established by virtue of their longevity are suboptimal psychometrically or require research about their psychometric properties with larger, more diverse samples, and using the latest data analytic strategies (Dozois & Hayden, 2022). Moreover, some of the methodological approaches and measurement instruments that scientists have used to test models of cognitive vulnerability may be confounded by other variables (e.g., issues of residual confounding, omitted-variables bias; see Dozois & Hayden, 2022; Lorenzo-Luaces et al., 2015). Gillies and Dozois (2021) examined the persistence of sad moods following various mood induction procedures (MIPs). As described earlier, MIPs are used widely in research on cognitive vulnerability to depression to test the idea that individuals vulnerable to depression have latent schemas that become activated via naturalistic- or experimentally induced primes. Numerous studies have compared individuals with remitted depression to never-depressed controls prior to and following a mood manipulation. The general finding, supportive of the idea that the schemas of individuals with previous depression are available/accessible but dormant until activated, is that these individuals show an increase in negative thinking (e.g., dysfunctional attitudes) following the MIP that is not exhibited by control groups who have never experienced depression. However, if the induced mood dissipates prior to the completion of the full battery of outcome measures (self-report questionnaires or information processing tasks), the validity of this paradigm is dubious. Gillies and Dozois (2021) found that MIP-induced sad moods do not persist for a sufficient period to allow most participants to complete the tasks or measures that are frequently used in research to assess the outcome variable of interest. Related to the issue of measurement is the importance of developing and validating culturally sensitive indices of cognitive vulnerability to depression. With some exceptions, most of the instruments that assess cognitive risk have been validated primarily in samples of White participants. Research is urgently needed to create and test measures and models that incorporate diversity in the context of cognitive vulnerability to depression. This research is essential to advance the study of psychopathology and enhance clinical assessment and intervention. For example, the field has often either ignored or treated ethnicity as a covariate, rather than explore important questions about how culture, ethnicity, oppression, and other related issues contribute to the onset and maintenance of cognitive vulnerability (Dozois & Hayden, 2022). More fully

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integrating diversity issues in the study of cognitive vulnerability to depression has the potential to enhance the efficacy and effectiveness of cognitive approaches to treating depression in marginalized and racialized communities (Metzger et al., 2021; Muñoz & Mendelson, 2005). Other important conceptual issues that have not been resolved in the research literature include whether cognitive content or schema structures are more important aspects of cognitive vulnerability to depression (Clark & Guyitt, 2016; Dozois & Beck, 2008) and how different levels of the cognitive taxonomy affect one another. More longitudinal research is necessary to test the causal status of cognitive products and schema structures in depression. As Clark and Guyitt (2016) pointed out, researchers also need to ascertain whether self-report measures are the optimal ways to assess schema content or whether responses to information processing tasks and other indices that involve less potential demand characteristics may be more sensitive predictors.

INTERVENTIONS AND EVIDENCE BASE Although Beck’s (A. T. Beck, 1967; A. T. Beck & Haigh, 2014; A. T. Beck et al., 2021; Dozois & Beck, 2008) cognitive theory is top down (i.e., activated schemas affect negative information processing, which, in turn, triggers cognitive products), cognitive therapy works primarily from the bottom up, as treatment begins by examining, testing, and modifying more proximal cognitive products and then proceeds to focus on more distal, deeper cognitive structures. Cognitive therapy aims to help individuals shift their cognitive appraisals and beliefs from unhealthy and maladaptive to evidence-based and adaptive. Three underlying principles characterize this work: (a) cognition affects behavior and affect; (b) cognitive activity may be monitored and modified; and (c)  by changing one’s beliefs, one can exert desired changes in behavior and experience more balanced emotional reactions (Dozois et al., 2019). Although cognitive therapy uses various behavioral and acceptance-based strategies (see Dozois & Beck, 2012; Leahy et al., in press), the focus is ultimately on altering beliefs. Patients learn how to treat thoughts as hypotheses rather than facts, to provide clients the opportunity to test their validity, consider alternative explanations, and gain distance from a thought to allow for more objective scrutiny (see Beck et al., 2021; DeRubeis et al., 2019). The following section outlines some of the therapeutic strategies involved in restructuring cognitive products and cognitive schemas (content/structure). After briefly highlighting behavioral activation, we focus on strategies for modifying negative automatic thoughts and dysfunctional assumptions. We then concentrate on some techniques for helping patients shift deeper core beliefs and structures. Following this exposition, we briefly review the evidence that supports cognitive therapy for depression and discuss some of the mechanisms of change.

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Behavioral Activation Behavioral activation (BA) is based on the idea that lack of positive reinforcement contributes to negative mood (Forbes, 2020; Manos et al., 2010), and has been an integral component of cognitive behavior therapy (CBT) for depression since its inception (Beck et al., 1979). In a groundbreaking study intended to test the active components of Beck’s cognitive therapy, Jacobson et al. (1996) randomly assigned outpatients with major depression to one of three conditions (BA, BA and interventions aimed at modifying negative automatic thoughts, or the full treatment package that included BA, automatic thoughts, and techniques to modify core schemas). The results indicated that the BA alone was as efficacious as the full CBT condition (Jacobson et al., 1996). Follow-up analyses showed that these treatment gains were maintained over time (Gortner et al., 1998). These findings called into question the necessity of cognitive change interventions (or at least their efficiency) and led to the development of BA as a stand-alone treatment for depression (see Leahy et al., in press, for a review). Both randomized controlled trials and meta-analyses support BA as an evidencebased treatment for depression (see Dimidjian et al., 2011; Martell et al., 2010). Although the conceptual, empirical, and practical aspects of BA are described in detail in Chapter 15 of this volume, we highlight BA briefly here as an important strategy for changing cognition. BA strategies in CBT are predicated on the assumption that changing behavior both alters reinforcement contingencies and affords clients an opportunity to identify, test, and modify negative cognitions (DeRubeis et al., 2019). Individuals with depression often describe their lives as devoid of pleasure, gratification, a sense of accomplishment, and rewarding social interactions (see Carvalho & Hopko, 2011, for a review). Given the symptoms of fatigue and low motivation, when coupled with loss of reinforcement, it is understandable that individuals with depression withdraw from many day-to-day activities. However, the less one does, the less one feels like doing, and continued avoidance eventually creates a downward spiral toward more negative affect and cognition. As J. S. Beck (2021) pointed out, inactivity contributes to low mood as clients “have a paucity of opportunities to gain a sense of mastery, pleasure, or connection, which leads to more negative thinking, which leads to increased dysphoria and inactivity, in a vicious cycle” (p. 119). BA helps individuals with depression reengage and improve their mood by modifying their avoidance, withdrawal, and inactivity (Dobson & Dobson, 2017; Martell et al., 2010; see also Chapter 15, this volume). Specific strategies used to help individuals with depression include gradually engaging in behaviors designed to increase positive reinforcement (e.g., pleasure- and mastery-oriented experiences), reducing activities that maintain or exacerbate depression (e.g., avoidance, rumination), and learning how to effectively problem solve. BA typically starts by having patients record their daily activities and rate their mood throughout the day. This activity log provides an inventory of the person’s daily activity and enhances understanding that variations in mood

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might be related to the patient’s routines. A number of beliefs can also be tested using self-monitoring (e.g., “I always feel miserable,” “I never have energy,” “I am too busy to accomplish what I need to do,” “It is useless for me to get out of bed”), which can then be evaluated with behavioral experiments and by scheduling activities (see J. S. Beck, 2021; DeRubeis et al., 2019; Dobson & Dobson, 2017). Scheduling activities (also known as graded task assignments) has two main benefits. First, becoming more active can reduce depressive symptoms (e.g., low mood, fatigue, sleep dysregulation). Second, there is often a shift in patients’ cognitions. For example, patients may start to experience a sense of accomplishment and pleasure and begin to see themselves as capable and competent. Thus, such assignments are likely to produce both functional and cognitive benefits (see Chapter 15, this volume). Techniques to Work With Automatic Thoughts After patients start to improve using behavioral strategies, therapy often quickly transitions to more cognitive work, beginning with identifying, testing, and restructuring negative automatic thoughts. Using Socratic questioning and guided discovery, therapists help patients monitor their thoughts and learn to recognize the relations between thoughts and mood. A common tool to help patients identify and work with their automatic thoughts involves some variant of a thought record. Several different types of thought records exist, each with slightly different formats. One particularly useful thought record is the sevencolumn tool by Greenberger and Padesky (2016). On this record, patients describe the situation that occurred, rate their mood, note their automatic thoughts, detail evidence that supports a particular belief and evidence that is inconsistent with the belief, and generate alternative/balanced thoughts. After going through these steps, patients also rerate their mood to determine whether modifying the thought helped to elevate their mood. If affect did not shift significantly, it is possible that the patient was not capturing a “hot thought” (defined as the thought that carried the greatest “emotional charge”), that the evidence was not reviewed exhaustively enough, or that the alternative thought was not balanced or believable to them. What is particularly useful about Greenberger and Padesky’s (2016) thought record is that patients are encouraged to identify the hot thought and list the evidence that not only refutes it but also supports the hot thought. Focusing on the hot thought is an efficient way to examine automatic thoughts as doing so bypasses the tendency to get sidetracked by thoughts that are not as strongly associated with the main emotion and minimizes the probability that there will be a chain of rumination. Further, by examining the evidence both in support and against a particular hot thought, the patient is able to develop more believable and balanced alternative thoughts. Once patients learn to reliably identify their hot thoughts, the process of testing them with evidence (or putting them on trial) begins. Socratic questioning is a defining characteristic of cognitive therapy (J. S. Beck, 2021; DeRubeis

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et al., 2019) and is particularly critical at this stage of therapy (see Vittorio et al., 2022). Socratic questioning involves a series of related questions designed to help patients reach a more logical, objective conclusion about their inner experiences. Four steps in this questioning process involve (1) characterizing the problem specifically and accurately; (2) identifying the associated thoughts, beliefs, and interpretations; (3) understanding the meanings of the thoughts; and (4) assessing the consequences of thoughts and their basis in evidence (Beck & Weishaar, 2019). In general, questions that gather evidence to test an automatic thought usually involve ascertaining the situational parameters related to a negative thought, helping the patient shift their appraisal of the situation by considering alternative perspectives, weighing the veracity of different conclusions, and formulating a more balanced and helpful thought that takes all the evidence into account (see J. S. Beck, 2021; Greenberger & Padesky, 2016). At times, there is insufficient evidence or information with which to draw meaningful conclusions about a belief or situation. In these instances, patients are often encouraged to conduct an experiment to gather the information they need to reach a conclusion about the accuracy of a negative thought. The experiment in cognitive therapy embodies collaborative empiricism and asking questions in an open-minded manner. Many experiments involve some form of returning to the situation and gathering more information, but the essence of any experiment is to form a hypothesis and determine an operationally defined way to test it. Techniques for Modifying Core Beliefs and Schemas The cognitive model of depression asserts that the activation of deeply held core beliefs and schema structures incites other levels of the cognitive taxonomy (see Dozois & Beck, 2008). Psychoeducation is typically used to explain the interactions among observable thoughts, early life events, and deeper levels of cognition. Repetitive problematic situations and thoughts, and certain cognitive themes (e.g., beliefs about the self, others, the world; absolutist statements such as “I’m incompetent,” “I am unworthy,” or “I am unlovable”), can be identified by working through different thought records. Thought records are also often used to identify, test, and change core beliefs. The process of understanding how early learning affects patients’ beliefs and current problems, however, typically involves a more fluid process. Helping patients understand their underlying beliefs assists them with changing the factors that give rise to many of their troubling automatic thoughts and provides alternatives to self-defeating coping strategies. One common strategy to identify core beliefs and schema content is the “downward arrow” technique (J. S. Beck, 2021; DeRubeis et al., 2019). This approach begins with an automatic thought—ideally, one that reflects a recurrent theme. The therapist asks the patient to assume the thought is true and, rather than testing it with evidence, promotes a deeper level of affect and

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exploration of the meaning of the thought (“What would it mean if this thought was true?”). When the patient reveals their response, they typically express another, deeper thought. The therapist continues to probe, “If that was true, what would that mean about you?” “What would be so bad about that?” This process often leads to the emergence of an underlying conditional assumption, a cognition that typically takes the form of “if–then” statements. These “conditional rules” or intermediate beliefs frequently specify a circumstance and a maladaptive or unhelpful consequence (e.g., “If I am not approved of by everyone, then I am not worthwhile” or “If I am not completely competent, then I am a failure”). Patients are often unaware of their internalized rules until the therapist helps bring them to their awareness. Continuing with the downward arrow is one way to reveal the underlying core belief and schema. The content of core beliefs and schemas varies for everyone, although there are common themes across patients (e.g., unlovable, incompetent, defective, unworthy; see J. S. Beck, 2021). Consistent with Beck’s recent expansion of cognitive therapy (e.g., the goodness of fit between modes and external factors; A. T. Beck & Haigh, 2014; A. T. Beck et al., 2021), it can be beneficial for patients to understand that their negative core beliefs did not develop randomly or by accident but, rather, are understandable given their previous experiences. Indeed, thoughts that were functional early in life may no longer serve the same purpose or be grounded in evidence given different circumstances. Patients usually experience increased negative emotions when their core beliefs or schemas are exposed. The strategies used to change automatic thoughts are also applied to working with core beliefs and schemas, although changing beliefs often takes longer and requires more effort than altering cognitive products. Working with patients to generate a narrative concerning the development of their beliefs can be helpful. In this way, patients can begin to view themselves and their experiences more objectively and compassionately, acknowledging that it is not their fault that they learned something that was negative and potentially damaging. Finally, it is important to engender hope that these beliefs can be modified. Core beliefs and schemas can be modified in a number of different ways. These techniques include strengthening adaptive beliefs that have become deactivated, testing the validity of schemas using Socratic questioning, behavioral experiments, discussing the advantages and disadvantages of adopting a new schema versus the old schema, evaluating the costs and benefits of alternative coping styles, recognizing continua versus categorical beliefs, examining the evidence for and against old and new schemas, role plays, imagery work, reframing the evidence that supports the schema (e.g., data from early childhood and from the patient’s life since childhood), acting “as if,” confronting and rescripting the past, developing nurturing self-statements, and more. Many of these strategies are discussed extensively in other volumes (e.g., J. S. Beck, 2021; Dobson & Dobson, 2017; Leahy, 2017; Young et al., 2003).

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Behavioral experiments are a powerful technique (J. S. Beck, 2021; BennettLevy et al., 2004) to modify core beliefs and schemas. According to A. T. Beck et al. (1979), behavioral experiments begin by clarifying the assumption or belief to be tested. The therapist and patient then collaboratively identify an assignment that will permit an experimental test of the belief. To do this, it is important that the patient makes clear predictions about the outcome of the experiment (see Bennett-Levy et al., 2004). After working out the logistics, the experiment is then carried out either with the therapist (in session) or as a homework assignment. The results of the experiment are then reviewed in detail relative to the original predictions. Behavioral experiments can be as simple as testing a belief with the activity log that “I am not doing anything with my day.” They can also involve basic surveys. For example, a client might think they need to have a lot of interesting things to say to be accepted by others and engage in conversation. Patients could be encouraged to go to a food court or coffee shop and listen in on conversations; that may allow them to recognize that most conversations are mundane. For others, therapists can help their patients test the validity of specific thoughts (e.g., “If I criticize myself, I will be motivated to work harder” vs. “If I am kind to myself, I will be motivated to work harder”). For example, a graduate student was quite anxious about their performance and clearly experienced the “impostor syndrome” as they thought that they needed to work harder than everyone else to do a reasonable job. The therapist and patient decided to test this belief. During graduate work, the student worked approximately 18 hours a day and predicted that their academic productivity would plummet if they lived a more balanced life. Although it was highly anxiety provoking to do so, the patient decided to treat graduate school like a job, by going into the office from 8:00 to 6:00 each day, and bringing no work home with them in the evening or on weekends. Much to the patient’s surprise, their productivity increased. Instead of spinning their wheels working around the clock, the patient was able to experience pleasure and relaxation, which provided the necessary energy, focus, and motivation to also be productive. Exposure tasks designed for the treatment of anxiety are also a form of behavioral experiments intended to test beliefs. In fact, in addition to habituation, some of the fundamental mechanisms of exposure involve inhibitory learning and expectancy violation (Craske et al., 2014; Sewart & Craske, 2020). It is important to design behavioral experiments collaboratively with patients and to develop experiments that are testable and will provide objective data to test their core beliefs and schemas. Sometimes behavioral experiments can be used to test one component of a patient’s deeper beliefs, which can then lead to changes in other schema structures. For instance, if a therapist can help a patient recognize that part of their belief system does not hold up to the evidence, other aspects of their beliefs may become more open to question and modification (see the Case Example later).

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EVIDENCE FOR COGNITIVE RESTRUCTURING CBT is the most extensively studied psychological intervention to date (A. T. Beck et  al., 2021; David et  al., 2018; Hollon & DeRubeis, 2018). Numerous randomized controlled trials and meta-analyses support the efficacy and effectiveness of CBT for treating unipolar depression (see DeRubeis et al., 2019; Hofmann et al., 2012; Hofmann et al., 2013; López-López et al., 2019), although it has been argued that estimates of effect sizes may be overestimated due to publication bias (see Cuijpers et al., 2010). CBT is comparable to behavior therapy (Mazzucchelli et al., 2009), other bona fide psychological treatments (Wampold et al., 2002), and antidepressant medication for an acute episode of depression, with these treatments each producing superior results than placebo control conditions (see DeRubeis et al., 2019). In addition to its efficacy for the acute phase of major depressive disorder, CBT is also more effective than pharmacotherapy for the prevention of relapse and at least as effective as continuance mediation (Cuijpers et al., 2013). Numerous studies have demonstrated that CBT is associated with changes in cognitive products (see DeRubeis et al., 2019, for a review) and cognitive structures (Dozois, Bieling, et al., 2009; Dozois et al., 2014; Quilty et al., 2014). Segal et al. (1999), for example, compared patients who were treated with either cognitive therapy or pharmacotherapy. After successful treatment, participants were administered a measure of dysfunctional attitudes. Participants were then given a mood induction and subsequently administered an alternate index of dysfunctional attitudes. Individuals who were treated with antidepressant medication showed elevated scores following the mood manipulation, but this increase in negative thinking was not seen in patients who had received cognitive therapy. Dozois, Bieling, et al. (2009) evaluated whether CBT can modify negative interpersonal schema structures that previous research had shown are stable into remission (Dozois, 2007; Dozois & Dobson, 2001b). Participants with depression were randomly assigned to receive either CBT together with pharmacotherapy or antidepressant medication alone. Those individuals treated with both interventions showed a significant shift in their cognitive organization for negative interpersonal content and greater cognitive organization for positive interpersonal content following treatment than did those treated with medications alone. An important limitation of this study, however, was that it was not possible to ascertain whether the effects were due to the impact of CBT or the combination of treatments. Subsequent research has yielded discrepant findings (e.g., Dozois et al., 2014; Quigley et al., 2019; Quilty et al., 2014). Quilty et al. (2014), for example, compared CBT alone with pharmacotherapy alone in patients with depression. Positive content became more interconnected and negative content less consolidated over treatment in both conditions, with no significant between-group differences. These results suggest that enduring cognitive risk factors can be modified with multiple treatment modalities.

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An abundance of research studies supports the idea that CBT is associated with a reduction in cognitive products and an improvement in schema structures, but there is a lack of empirical data that directly tests cognitive mediation (DeRubeis et al., 2019; Hofmann et al., 2018). Although some studies have supported changes in cognition as a mediator of treatment change, a direct causal relation is currently inconclusive (see Crits-Christoph et  al., 2017; DeRubeis et  al., 2019; Hofmann et  al., 2018; Lorenzo-Luaces et  al., 2015). For instance, Crits-Christoph et  al. (2017) conducted a randomized controlled trial that compared cognitive therapy and manualized psycho­ dynamic therapy. These researchers found that dysfunctional attitudes changed concurrently with symptoms of depression. In contrast, no evidence was obtained that shifts in cognitive products or schema structures predicted subsequent change in depression. These findings, coupled with the fact that cognitive change may be equivalent in other evidence-based psychological treatments and in pharmacotherapy (e.g., Quilty et al., 2014), suggest that specificity of cognitive change in CBT for depression is equivocal, and raise questions as to whether cognitive change is the primary mechanism involved in treatment change in CBT.

CASE EXAMPLE Susan,2 a 29-year-old cisgender woman, was referred for CBT. Assessment, using the Structured Clinical Interview for DSM-5, indicated that Susan met the diagnostic criteria for major depressive disorder. Key features included sadness, anhedonia, loss of appetite, insomnia, loss of energy, difficulty concentrating, and feelings of worthlessness. There was no comorbid diagnosis. Susan noted that she had experienced symptoms of depression off and on since high school. She estimated that she had experienced three separate episodes of depression. During the initial assessment, Susan was administered several self-report questionnaires on depressive symptomatology (Beck Depression Inventory-II, Beck et al., 1996; Depression Anxiety Stress Scale-21, Lovibond & Lovibond, 1995) and negative thinking (Automatic Thoughts Questionnaire, Hollon & Kendall, 1980; Cognitive Distortions Scale, Covin et al., 2011; DAS, Weissman & Beck, 1978). She was also administered the PDST (Dozois & Dobson, 2001a). Susan’s scores on the cognitive product measures indicated that her automatic thoughts and dysfunctional attitudes were significantly elevated and outside of the range of the general population (Dozois et al., 2003). Scores on the Cognitive Distortions Scale also revealed maladaptive thoughts and assumptions related to mind reading, all-or-nothing thinking, emotional reasoning, labeling, and personalizing. Cognitive organization, assessed via the PDST, indicated that Susan had a highly interconnected negative interpersonal schema structure and a less well-consolidated positive structure. Case specifics have been changed to protect the identity of the patient.

2

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The initial course of psychological treatment focused on behavioral activation to help Susan engage in antidepressant behavior. This was an important component of treatment to help increase her energy and interest, begin to shift maladaptive thoughts about herself (e.g., “I can’t do this,” “I am incompetent”), and prepare her for subsequent cognitive work. Cognitive restructuring then became a central focus in therapy, beginning with automatic thoughts in early sessions and identifying and modifying negative core beliefs later in therapy. Through Socratic questioning and the use of thought records, Susan learned how to monitor, test, and alter negative thoughts. Susan was very compliant with treatment and found that BA and cognitive restructuring helped to alleviate her depressed mood. Her scores on the Beck Depression Inventory-II dropped from severe (33) to minimal (10), and the scores on other symptom-based and cognitive product measures also showed marked improvement. After working for several sessions on identifying the evidence about various negative thoughts and predictions, and generating more helpful and adaptive automatic thoughts, the therapist used a downward arrow to help Susan identify some of her deeper core beliefs. This work was supplemented with a review of past thought records. Susan noticed themes in her automatic thoughts (i.e., “I am no good,” “I’m not wanted,” “I am unlovable”). The therapist then used several strategies to work collaboratively with Susan to engage in deeper schema change intended to maintain the gains made in therapy and prevent relapse. Initially, the therapist and patient began with some behavioral experiments that tackled related aspects of Susan’s core belief but that were somewhat less threatening to her. One belief that was associated with her deeper belief that she was “no good” was that she thought she was completely unattractive. Through Socratic questioning, she was able to see that this may not be entirely true. However, she was easily able to disqualify the evidence. For example, although other important people in her life told her that she was beautiful or “pretty,” she negated this evidence by saying they were compelled to say so. The therapist worked collaboratively with Susan to set up an experiment to test the belief that she was unattractive. They decided to test this belief with people who did not know her personally and who had no stake in the decision. They agreed that the therapist would run the actual experiment to minimize demand characteristics and worked through the logistics of testing this belief. The therapist and patient agreed that a survey of 20 people would provide a reliable experiment. They also set up the parameters for this experiment. To maintain confidentiality, they agreed to include her picture along with eight other photos. The therapist encouraged Susan to only use a forcedchoice response regarding the attractiveness of each photo, but Susan was adamant that she wanted to also include a rating from 1 to 10. She had predicted that she would be viewed as unattractive by most raters and that her “score” would be no more than 3 out of 10. The therapist downloaded a variety of pictures from the internet and printed them off on a sheet of paper

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along with the patient’s picture and surveyed 20 different people of different ages and backgrounds, asking them to (a) indicate whether the person in each picture was attractive or not and (b) provide a rating from 1 to 10, with 10 being extremely attractive. The following session began with a review of Susan’s predictions. The thera­ pist and patient also discussed the idea that beauty is in the eye of the beholder. For example, some people might see a given image as attractive whereas others did not. The therapist then started with the categorical response and told her that all 20 people perceived her as attractive. In addition, although virtually every picture had a range of scores, Susan’s scores exhibited a very small range, as she was consistently “rated” an 8 out of 10. When the therapist revealed this information to Susan, she burst into tears, as she was previously convinced she was unattractive. This behavioral experiment helped to change this belief. More important, it opened the door to experimentation as a method and to changing other beliefs. Susan was soon able to discuss what her childhood adoption might have meant to her and realized that it had little or nothing to do with her not being good enough, and much more about the fact that her mother was too young to adequately care for her. A two-chair technique was used so that Susan could consider her own cognitive and emotional responses and those of her biological mother. Eventually, Susan decided to meet her biological mother and assess her new beliefs regarding the adoption. Through a series of behavioral experiments, Susan was able to develop a new belief about herself that fundamentally changed how she thought, acted, and felt. Her core beliefs of being “unlovable,” “unwanted,” and “no good” shifted, and she was able to develop healthier alternative beliefs. This, in turn, helped to modify related coping/compensatory behaviors. For instance, one of Susan’s conditional beliefs was “If people got to know the real me, then they would reject me.” Her compensatory strategies involved building a protective wall around herself so that others would not gain access to her true self. In subsequent behavioral experiments, Susan was able to let her guard down with others slowly and reveal parts of her true self to others. This process was met with an increase in intimacy and genuineness in her platonic and romantic relationships, which further strengthened her new beliefs about herself. Susan’s scores on measures of cognitive products shifted and were within the distribution of individuals in the general population (cf. Dozois et  al., 2003). Her scores also further decreased on the Beck Depression Inventory-II and Depression Anxiety Stress Scale-21 and were within the minimal range. Susan’s scores on the PDST also showed more dispersed organization for negative interpersonal content and more interconnected positive organization, a dramatic turnaround from the initial assessment. The last couple of sessions of therapy focused on relapse prevention strategies (see J. S. Beck, 2021; Dobson & Dobson, 2017). Notwithstanding some of the methodological issues raised earlier regarding mood induction techniques (e.g., Gillies & Dozois, 2021), Susan was also administered the DAS

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and participated in a mood manipulation paradigm in which she was asked to think about a negative event in her life while listening to sad music. She was readministered an alternate form of the DAS (see Segal et al., 1999). She showed no notable cognitive reactivity, which increased confidence that treatment could be terminated safely and with limited risk of relapse (see Segal et al., 2006).

SUMMARY AND FUTURE DIRECTIONS This chapter described cognitive products and core beliefs/schema structures as risk factors for depression. Although earlier research suggested that cognitive products may operate merely as concomitants of depression, subsequent empirical work has demonstrated that this form of negative thinking represents an important risk factor for depression, particularly if primed before assessment. Moreover, longitudinal research has supported cognitive products as risk factors for depression (see Scher et al., 2005). There is also empirical evidence that negative schema structures, particularly for interpersonal content (see Dozois, 2021), may be important vulnerability factors for depression. Several conceptual issues relevant to the assessment and investigation of cognitive products and structures were also discussed, and important clinical tools to modify them were highlighted. Cognitive vulnerability research has progressed significantly since the original formulation of the cognitive model of depression (A. T. Beck, 1967; A. T. Beck et al., 1979). Notwithstanding cogent evidence that cognitive products and the content/structure of schemas in depression are important modifiable risk factors, there remain numerous unanswered questions. Given space constraints, we highlight just a few directions for future research. Additional research is necessary to further understand how different vulnerability factors affect one another within the cognitive taxonomy (e.g., schema structures, rumination, attentional processes, dysfunctional attitudes) and interact with other biological and behavioral systems and modes (A. T. Beck & Haigh, 2014). Fortunately, cognitive vulnerability research is becoming less siloed and more multidisciplinary, which will afford more complex tests of some of the more recent integrative models being developed (e.g., De Raedt & Koster, 2010; Disner et  al., 2011). Research is also necessary to ascertain whether cognitive content or schema structures are more important predictors of cognitive vulnerability to depression (Clark & Guyitt, 2016; Dozois & Beck, 2008). Multiwave longitudinal research is also needed to test the causal status of various cognitive risk factors in depression and how cognitive vulnerability develops and changes over time. Another important direction for future research pertains to the measurement of cognitive vulnerability itself. The field has relied primarily on selfreport questionnaires, methodologies, and measurement approaches that have not been subjected to rigorous psychometric evaluation or that require additional research using more advanced data analytic techniques to assess their

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reliability and validity adequately. Moreover, the research literature needs to extend beyond samples of convenience to ensure greater representation of underserved and underrepresented groups. To better test the applications and limits of various cognitive models, for example, we need to do a better job of incorporating diversity into cognitive vulnerability research. Finally, additional research is required to determine whether cognitive change is a necessary ingredient responsible for treatment outcomes in depression. Although CBT is highly efficacious for the treatment of depression, the extent to which cognitive change is a primary mechanism is equivocal. Some researchers (e.g., Kazdin, 2007) contend that the evidence does not support cognitive change as the basis for symptom change in CBT. Others (e.g., LorenzoLuaces et al., 2015) argue that definitional and methodological issues on the investigation of mechanisms make it difficult to provide a definitive answer: The evidence for bivariate associations between cognitive procedures and symptom change, cognitive procedures and cognitive change, and cognitive change and symptom change is clear. . . . By contrast, in the literature on psychotherapeutic change, the direction of causality between cognitive change and symptom change has not been well established. (Lorenzo-Luaces et al., 2015, p. 13)

It is important to point out that the absence of evidence is not the same as the “evidence of absence” and that the handful of studies that control for temporal confounds have provided supportive evidence that cognition is a mediator of change (Lorenzo-Luaces et al., 2015, p. 12). Research is also needed to determine which levels of the cognitive taxonomy may account for changes in sympto­ matology (Hofmann et al., 2018). Such investigations are critical to further refine models of cognitive vulnerability and to improve treatment outcomes. REFERENCES Abela, J. R., & D’Alessandro, D. U. (2002). Beck’s cognitive theory of depression: A test of the diathesis-stress and causal mediation components. British Journal of Clinical Psychology, 41(2), 111–128. https://doi.org/10.1348/014466502163912 Abramson, L. Y., Alloy, L. B., Hankin, B. L., Haeffel, G. J., MacCoon, D. G., & Gibb, B. E. (2002). Cognitive vulnerability-stress models of depression in a self-regulatory and psychobiological context. In I. H. Gotlib & C. L. Hammen (Eds.), Handbook of depression (3rd ed., pp. 268–294). Guilford Press. Alloy, L. B., Abramson, L. Y., Whitehouse, W. G., Hogan, M. E., Panzarella, C., & Rose, D. T. (2006). Prospective incidence of first onsets and recurrences of depression in individuals at high and low cognitive risk for depression. Journal of Abnormal Psychology, 115(1), 145–156. https://doi.org/10.1037/0021-843X.115.1.145 Alloy, L. B., Burke, T., O’Garro-Moore, J., & Abramson, L. Y. (2018). Cognitive vulnerability to depression and bipolar disorder. In R. L. Leahy (Ed.), Science and practice in cognitive therapy: Foundations, mechanisms, and applications (pp. 105–123). Guilford Press. Alloy, L. B., Salk, R. H., Stange, J. P., & Abramson, L. Y. (2017). Cognitive vulnerability and unipolar depression. In R. J. DeRubeis & D. R. Strunk (Eds.), The Oxford handbook of mood disorders (pp. 142–153). Oxford University Press. https://doi.org/10.1093/ oxfordhb/9780199973965.013.13 Beck, A. T. (1963). Thinking and depression: 1. Idiosyncratic content and cognitive distortions. Archives of General Psychiatry, 9(4), 324–333. https://doi.org/10.1001/archpsyc. 1963.01720160014002

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Quigley, L., Dozois, D. J. A., Bagby, R. M., Lobo, D. S. S., Ravindran, L., & Quilty, L. C. (2019). Cognitive change in cognitive behavioural therapy versus pharmaco­therapy for adult depression: A longitudinal mediation analysis. Psychological Medicine, 49, 2626–2634. https://doi.org/10.1017/s0033291718003653 Quilty, L. C., Dozois, D. J. A., Lobo, D. S. S., Ravindran, L. N., & Bagby, R. M. (2014). Cognitive structure and processing over pharmacotherapy vs. cognitive behavioural therapy for depression. International Journal of Cognitive Therapy, 7, 235–250. https:// doi.org/10.1521/ijct.2014.7.3.235 Romens, S. E., Abramson, L. Y., & Alloy, L. B. (2009). High and low cognitive risk for depression: Stability from late adolescence to early adulthood. Cognitive Therapy and Research, 33(5), 480–498. https://doi.org/10.1007/s10608-008-9219-5 Scher, C. D., Ingram, R. E., & Segal, Z. V. (2005). Cognitive reactivity and vulnerability: Empirical evaluation of construct activation and cognitive diatheses in unipolar depression. Clinical Psychology Review, 25(4), 487–510. https://doi.org/10.1016/j.cpr. 2005.01.005 Seeds, P. M., & Dozois, D. J. A. (2010). Prospective evaluation of a cognitive vulnerabilitystress model for depression: The interaction of schema self-structures and negative life events. Journal of Clinical Psychology, 66(12), 1307–1323. https://doi.org/10.1002/ jclp.20723 Segal, Z. V. (1988). Appraisal of the self-schema construct in cognitive models of depression. Psychological Bulletin, 103(2), 147–162. https://doi.org/10.1037/0033-2909. 103.2.147 Segal, Z. V., & Gemar, M. (1997). Changes in cognitive organization for negative self-referent material following cognitive behavior therapy for depression: A primed Stroop study. Cognition and Emotion, 11(5–6), 501–516. https://doi.org/10.1080/ 026999397379836a Segal, Z. V., Gemar, M., Truchon, C., Guirguis, M., & Horowitz, L. M. (1995). A priming methodology for studying self-representation in major depressive disorder. Journal of Abnormal Psychology, 104(1), 205–213. https://doi.org/10.1037/0021-843X.104.1.205 Segal, Z. V., Gemar, M., & Williams, S. (1999). Differential cognitive response to a mood challenge following successful cognitive therapy or pharmacotherapy for unipolar depression. Journal of Abnormal Psychology, 108(1), 3–10. https://doi.org/10.1037/ 0021-843X.108.1.3 Segal, Z. V., Hood, J. E., Shaw, B. F., & Higgins, E. T. (1988). A structural analysis of the self-schema construct in major depression. Cognitive Therapy and Research, 12(5), 471–485. https://doi.org/10.1007/BF01173414 Segal, Z. V., Kennedy, S., Gemar, M., Hood, K., Pedersen, R., & Buis, T. (2006). Cognitive reactivity to sad mood provocation and the prediction of depressive relapse. Archives of General Psychiatry, 63(7), 749–755. https://doi.org/10.1001/archpsyc. 63.7.749 Segal, Z. V., & Vella, D. D. (1990). Self-schema in major depression: Replication and extension of a priming methodology. Cognitive Therapy and Research, 14(2), 161–176. https://doi.org/10.1007/BF01176207 Sewart, A. R., & Craske, M. G. (2020). Inhibitory learning. In J. S. Abramowitz & S. M. Blakey (Eds.), Clinical handbook of fear and anxiety: Maintenance processes and treatment mechanisms (pp. 265–285). American Psychological Association. https://doi.org/ 10.1037/0000150-015 Struijs, S. Y., de Jong, P. J., Jeronimus, B. F., van der Does, W., Riese, H., & Spinhoven, P. (2021). Psychological risk factors and the course of depression and anxiety disorders: A review of 15 years NESDA research. Journal of Affective Disorders, 295, 1347–1359. https://doi.org/10.1016/j.jad.2021.08.086 Tariq, A., Reid, C., & Chan, S. W. Y. (2021). A meta-analysis of the relationship between early maladaptive schemas and depression in adolescence and young adulthood. Psychological Medicine, 51(8), 1233–1248. https://doi.org/10.1017/S0033291721001458

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10 Negative Information Processing Wisteria Deng and Jutta Joormann

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epression is defined by a state of sustained negative affect and difficulties experiencing positive affect, but research on depression has also identified cognitive deficits as well as biases in cognition as important features of this disorder (Everaert et al., 2012). Importantly, many studies have examined whether these alterations in cognition are linked to the changes in negative and positive affect that characterize the disorder. There is a long tradition of examining interactions between cognition and emotion in research on emotion (Blair et al., 2007) and on clinical disorders (Power & Dalgleish, 2015), and the most frequently used intervention for clinical depression, cognitive behavior therapy (CBT), explicitly targets cognitive processes and cognitive biases to alleviate negative affect and repair the experience of positive affect. This close link between cognitive processing and affect has resulted in increased research efforts to more closely characterize the link between cognition and emotion in depression and to develop more targeted interventions that can supplement existing treatment protocols such as CBT. This chapter provides an overview of this area of research. Recent models of the interaction of cognition and emotion in depression have paid particular attention to emotion regulation and its link to cognitive biases. For example, Joormann and Stanton (2016) argued that cognitive aspects of depression interfere with emotion regulation, thereby resulting in sustained negative affect and difficulties experiencing positive affect. Cognitive biases in depression change the way people process information, that is, how they attend to their environment, interpret ambiguous information, and

https://doi.org/10.1037/0000332-011 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  233

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remember past events, all of which may affect emotion and emotion regulation in profound ways. In addition to biases in cognition, deficits in cognitive processes such as executive control may contribute to regulation deficits by, for example, interfering with updating of working memory content that may contribute to depression-related processes such as rumination (Everaert et al., 2012; Gotlib & Joormann, 2010). There is evidence that these deficits appear both in the acute phase of illness and long term (Everaert et al., 2012). Biased information processing and cognitive deficits also serve as important risk factors, which are frequently seen in those with an elevated familial risk for depression who have not yet developed the disorder (Gotlib et al., 2014) and predict first onsets, remission, and recurrence of depressive episodes (Everaert et al., 2012). Given this central role of cognition for emotional experience, emotion regulation, and affective symptoms of depression, the following sections provide an overview of our current knowledge on cognitive biases and deficits in depression and in those at high risk for depression. We then present work on the development of interventions that directly target these processes.

COGNITIVE BIASES AS RISK AND MAINTENANCE FACTORS OF DEPRESSION Cognitive biases underlying the emergence and development of depression exist at various stages of information processing: attention to new stimuli, subsequent interpretations, memory, and overall executive control. Attention Biases in attention, specifically negative attentional biases as related to depression, refer to the tendency to preferentially focus on negative stimuli (Mennen et al., 2019). Such a tendency is manifested through the initial stage of orienting to new information and the subsequently maintained focus on, and the difficulty disengaging from, the negative stimuli. In many studies, individuals diagnosed with depression or with elevated depression scores have demonstrated negative biases during the early attentional allocation stage, showing preferred engagement with the mood-congruent (negative) emotional cues and diminished engagement with the positive stimuli (Armstrong & Olatunji, 2012; Stange et al., 2017). Most of the work conducted in this area has used the dot probe task or a similar reaction-time-based assessment, such as the Posner task or modified versions of the Stroop task (Amir et al., 2010; Wingenfeld et al., 2006). In the dot probe task, participants are presented with pairs of stimuli (usually one neutral and one emotional). The primary task is to detect a dot probe that replaces one of these stimuli as fast as possible. With the assumption that participants may respond faster to probes presented at the spatial location that was previously attended, researchers have shown negative attentional biases with shorter reaction times measured in detecting dot probes following negative

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stimuli, along with longer reaction times following positive or neutral stimuli (MacLeod et  al., 2002). Other review papers reveal that, while there may seem to be attentional biases toward negative information, this could be characterized more by an inability to disengage one’s attention from the negative information, rather than an enhanced engagement with the negative information (De Raedt & Koster, 2010; Everaert et al., 2012). Further, while earlier assessments of attentional biases, such as the dot probe task, have been criticized for low reliability (Chapman et al., 2019), converging evidence from a wide array of newer methodologies supports the claim that attentional biases in depression are mostly manifested as issues with disengagement from negative stimuli (Sanchez et al., 2013). Eye-tracking, for example, allows for assessments of participants’ fixation time, fixation frequency, and glance duration for negative stimuli as compared with neutral ones. Using these indices, individuals with depression demonstrated a longer fixation time and longer glance duration for dysphoric pictures than did controls (De Raedt & Koster, 2010), adding evidence that depression is associated with impaired disengagement from negative stimuli. In addition to variation in tasks and components of attention that may be differentially affected by depression, studies have also examined the role of contextual variation in stimuli presented. Ji and colleagues (2017), for example, examined the role of self-referential processing in attentional biases toward emotional information. Using the dot probe task, these researchers compared attentional responses to negative (vs. positive) information after the information was processed in either a self-referential (e.g., imagine the words to be judgments about you) or an other-referential (e.g., evaluate if the words describe a television news anchor) manner. Individuals with high levels of depression exhibited reduced attention toward only self-referential information that was positive (Ji et al., 2017). These findings provide evidence that the link between depression and attentional selectivity may be sensitive to contextual factors and the type of stimuli. It is worth noting that not all studies have found a significant relationship between attentional biases and depression. Research has rendered null findings using the exogenous cueing task to measure attentional biases in depression (Krings et al., 2020). The exogenous cueing task is similar to the dot probe task in its reaction-time-based design. Participants were asked to respond to letter cues on either the left or the right side of the screen, following the exposure to a negative stimulus. The null findings could be due to such factors as the lack of reliability in these earlier measurements (Chapman et al., 2019), disparities in stimuli exposure times, and other inconsistencies in aims and methodologies (Ao et al., 2020; Ji et al., 2017). Overall, while there is abundant evidence on attentional biases in depression, it is important to consider the task characteristics and contextual factors in interpreting findings (Mathews & MacLeod, 2005). Focusing on attention biases in interventions seems especially promising as researchers have proposed that attentional control is a crucial link between

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biological and cognitive vulnerability to depression. For example, De Raedt and Koster (2010) proposed the following framework: during depressive episodes, the hypothalamic–pituitary–adrenal axis grows continuously more impaired due to the high levels of cortisol, which creates a higher reactivity to stressors. This can lead to decreased activity in the dorsolateral prefrontal cortex (Beevers et al., 2015), causing impairments to attention and emotional stress responses, and sustaining overall negative affect (De Raedt & Koster, 2010). Maintained attention toward negative stimuli and decreased inhibitory control may lead to an impaired ability to stop processes such as rumination, which contribute heavily to sustained negative affect (De Raedt & Koster, 2010) by affecting emotion regulation. Attention biases may play a crucial role in the maintenance of depressive symptoms (Mathews & MacLeod, 2005; Platt et al., 2017). Researchers have theorized that individuals with depression struggle with negative schemas on themes of failure, worthlessness, separation, loss, and rejection (see Chapter 9, this volume). Related to these schemas, individuals with depression tend to have selective attention toward information that is consistent with their schemas. The negative schemas in individuals with depression further accentuate their biases and exacerbate the symptoms of depression. Especially when presented with stressors, the schemas give rise to negative automatic thoughts that center on oneself, the world, and the future (Dozois & Beck, 2008; Gotlib & Joormann, 2010). Recent work has built upon these theories, citing attentional biases and self-referential schemas as major contributing factors to maintaining low mood and other symptoms of depression. Further, there is empirical evidence from longitudinal research that connects negative self-referential schemas with higher levels of depressive symptoms, and negative attentional biases with a greater worsening of symptoms over time, which shows that these biases can not only maintain depression but also increase depression severity (Disner et al., 2017). In sum, given the evidence in support of attention biases elevating risk for and maintaining symptoms of depression, as well as the role of attention in emotion regulation and stress responding, interventions that directly target attention processes in depression are particularly promising. Interpretation Following attentional biases involved in the initial stage of information gather­ ing, interpretation biases may relate to depression as individuals interpret ambiguous contexts in ways that cohere with their preexisting beliefs. Interpretation biases, specifically ones that favor negative interpretations of social stimuli, are implicated in both clinical (Everaert et al., 2012; Gotlib & Joormann, 2010; Krahé et al., 2019; Mathews & MacLeod, 2005) and subclinical depressive symptoms (Krahé et al., 2019). Most studies that used self-report measures have found that participants diagnosed with depression interpret ambiguous information in negative ways (e.g., Mathews & MacLeod, 2005). For example,

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the Ambiguous Scenario Test–Depression prompts participants to form interpretations about open-ended scenarios, such as “It is New Year’s Eve. You think about the year ahead of you” (Berna et al., 2011). People with depressive symptoms tend to report less pleasantness associated with this kind of mental imagery, underlining the negative biases in interpreting ambiguous situations (Berna et al., 2011). Another widely used measurement for interpretation biases involves similar open-ended scenarios. Participants are asked to fill in words that complete the scenarios in either a negative or a neutral way. For example, “The doctor examined little Emily’s growth” is paired with a negative interpretation (target word: tumor) and a neutral interpretation (target word: growth). Interpretation biases are then captured by measuring response latency—participants exhibit a negative interpretation bias when they are faster at endorsing negative target words as compared with neutral ones (Lawson & MacLeod, 1999). Other studies, however, that use measurements of response latency have failed to find negative interpretation biases, but this may be due to the latency indices being limited and insensitive in participants with depression, especially given the increased response variability and psychomotor slowing often seen in this disorder (Gotlib & Joormann, 2010; Mathews & MacLeod, 2005). Interpretation biases may play an important role in the emergence and maintenance of depression. The cognitive theory of depression postulates that biased interpretation of emotional information is a risk factor for depression (Dozois & Beck, 2008; Gotlib & Joormann, 2010; Platt et al., 2017). Cognitive vulnerability theories of depression argue that those most at risk can be characterized by how they explain the cause and consequence of negative events, which reflect their vulnerability to depression (Bernstein et al., 2019). In particular, a negative cognitive style may involve a pattern of making attributions that are internal, stable, and global (as opposed to external, temporary, or specific), assuming negative events will have further negative consequences, and making negative inferences about oneself, such as assuming the events that have occurred are a reflection of the self (Bernstein et al., 2019). Distorted cognitive processes like these have been found not only in individuals with active depression but also in individuals at risk for the disorder and with individuals in remission (Everaert et al., 2012). There is also evidence of negative inter­ pretation biases specifically appearing in never-depressed daughters of mothers with depression, which provides further evidence that these biases may be a risk factor for the onset of this disorder (Gotlib & Joormann, 2010). More longitudinal studies are needed, however, before making causal inferences about the relationship between a negative cognitive style and interpretation biases in the prediction of depression. Interpretation biases have also been associated with higher levels of rumination, worry, and negative intrusive thoughts (Krahé et al., 2019). Rumination and worry are forms of repetitive negative thinking common to depression, and interpreting ambiguous information in a negative manner may maintain these repetitive negative thinking patterns (Krahé et al., 2019). Such negative beliefs then manifest as attentional biases to negative stimuli, as they are congruent to

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one’s existing beliefs. Together with impaired executive control that prevents individuals with depression from disengaging and updating their negative perceptions, interpretation and attentional biases feed into and sustain each other. This further supports the claim that cognitive biases may play a role in the vulnerability to and onset, severity, and maintenance of depression (Everaert et al., 2012; Gotlib & Joormann, 2010). In addition to examining interpretation biases, more recent research has started to focus on individual differences in the ability to update these biased interpretations as more (disconfirming) information becomes available. Indeed, recent research has developed novel behavioral tasks that capture both interpretation biases and flexibility in revising existing interpretations. For example, the emotional bias against disconfirmatory evidence (BADE) task provides comprehensive indices that evaluate participants’ interpretation biases of a social scenario, as well as the dynamic revision of such interpretation biases as more information about the scenario is revealed (Everaert et al., 2018). The emotional BADE task focuses on the process of interpretation in a social context charged with emotions, resembling interactions one may engage with in real life. The significant relationship between negative interpretation bias and depression suggests that depression-related cognitive biases are manifested not only in the initial stage of attention allocation but also in the following interpretation phase, persisting as individuals integrate subsequent information from the environment. Findings from such tasks highlight the value of examining interpretation—both the biases involved and the inflexibility in revision— as an integral cognitive process that is affected in depression. While modifying interpretation biases has been a focus in depression treatment research, the specific process of interpretation revision and flexibility may be important to address in future intervention designs. Memory Memory biases have been found to be reliably linked with depression (Gotlib & Joormann, 2010; Ji et al., 2017; Mathews & MacLeod, 2005). Memory biases are especially consistent in relation to explicit memory, as individuals with depression often remember more negative material than positive material in recall tasks (Everaert et al., 2012; Gotlib & Joormann, 2010). In recalling specific daily emotions, people with past or current depression tend to over­ estimate experiences of negative emotions (such as sadness, anxiety, and anger; Urban et al., 2018). In an integrative model, low verbal fluency and brooding/rumination interact to reduce memory specificity in those with a history of depression (Sumner et al., 2014). Similarly, early life adversity and chronic interpersonal stress, in interaction with overgeneralized memory, predict the course of depression among adolescents (Sumner et al., 2011). Memory biases in depression also go beyond the emotional valence of memory content, affecting individuals’ ability to form specific memory, especially for positive events. Review papers have shown that people with depressive symptoms tend to overgeneralize past experiences, a finding consistent

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with different methods of testing memory recall (King et al., 2010; Liu et al., 2013). One of the most commonly used tasks in assessing memory specificity is the Autobiographical Memory Test (Williams & Broadbent, 1986). In this test, participants are presented with cue words (e.g., safe, clumsy) and are asked to retrieve memories about a specific period in time and place (30 to 60 seconds), and then describe the specific memory. Participants’ descriptions are scored as categoric (i.e., a repeated event, such as “every time when I am in bed”), extended (i.e., memories that extend over a long period of time, such as “the first year after I leave home”), or specific (i.e., localized in time and space and reflect a unique occurrence lasting no longer than 24 hours, such as “the day I learned how to swim, my grandfather held my arms” (King et al., 2010). Memory deficits in depression are manifested as people have enhanced recall of categoric/extended memories and impoverished recollection of specific events. Such reduced autobiographical memory specificity has also been associated with poor clinical outcomes in those with major depressive disorder (King et al., 2010). Overall, the existing literature reveals that people with depression may experience difficulties recalling positive information and forming specific memories (Everaert et al., 2012; Gotlib & Joormann, 2010; Ji et al., 2017; Mathews & MacLeod, 2005). Memory trainings, especially those that encourage the recollection of positive, specific past events, may be a crucial aspect of depression treatment design. Executive Control Cognitive control deficits have been proposed to underlie the relation between depression and difficulties inhibiting negative information, along with processes involved in shifting/updating representations (both emotional and nonemotional) in working memory (Everaert et al., 2012). Indeed, research has uncovered several executive processes affected in depression, such as perseverative errors in task switching and inhibition deficits (Harvey et al., 2004). Tasks such as the Wisconsin Card Sorting Task (WCST) allow researchers to examine cognitive rigidity in depression (Gormezano & Grant, 1958). In the WCST, participants are expected to learn from negative feedback that their previous sorting pattern is no longer correct and that they need to form a new rule (Gormezano & Grant, 1958). People with depression are found to perseverate or take a longer time to switch away from the previous pattern (Whitmer & Gotlib, 2012). It is worth noting that the WCST, while being widely used in testing cognitive flexibility, may not be the best paradigm for assessing taskswitching ability. The WCST involves more cognitive processes beyond task switching—the ability to integrate reward information and learn from negative feedback, for example, is essential to facilitate the switching. To better isolate the cognitive process that is affected in depression, other researchers have developed a task that explicitly instructs participants to either switch task set or repeat the same task, and then measures the switch cost (i.e., additional time spent on a switching trial; Lo & Allen, 2011). With this newer paradigm,

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researchers found that people with depressive symptoms have an impaired switching ability, especially when switching between affective stimuli (Lo & Allen, 2011). As another subcomponent of the executive control, behavioral inhibition deficit is also found to characterize depression. The go/no-go paradigm (including “go” stimuli that prompt participants to respond and “no-go” stimuli that require participants to withhold responses after viewing) evaluates participants’ ability to inhibit responses specifically in the “no-go” portion—as participants are prompted to withhold responses when a rare tone is played (Kaiser et al., 2003). While people with depression exhibit no difficulty in reacting to the “go” task (by responding to the rare tone), they experience difficulties inhibiting such response (Kaiser et al., 2003). Subsequent review papers have further supported the role of cognitive inhibition impairments in depression (Joormann & Gotlib, 2010). In particular, people with elevated depressive symptoms, clinical depression, or a history of depression tend to experience greater inhibition difficulties both in general and in responses to negative stimuli (Joormann & Gotlib, 2010). Impaired cognitive control is also observed in people at risk for depression. Different from the broad range of cognitive impairments in people with clinical depression, cognitive control deficits in the at-risk population mainly exist during the processing of emotionally negative information (e.g., negative selfreferring words or angry facial expressions; Snyder, 2013). Research suggests that cognitive impairments (e.g., difficulty in task switching and inhibition control) are likely to co-occur with rumination (Whitmer & Gotlib, 2012). Taken together, these findings indicate that cognitive control deficits not only co-occur with clinical depression but also predict rumination and the development of depressive symptoms in healthy (Zetsche & Joormann, 2011) and at-risk groups (Demeyer et al., 2012). In summary, people with depression exhibit broad impairments in cognitive control processes, highlighting the need for targeted interventions to better assess and treat each affected aspect of the executive functions (Snyder, 2013).

COGNITIVE BIASES INTERVENTIONS Interventions for depression can target cognitive biases at different stages of information processing, including biases in attention, interpretation, memory, and executive control. Attention Research on cognitive biases in depression has provided the foundation for the development of many novel and targeted interventions for depression. The first group of tasks used to modify cognitive biases can be summarized under attention bias modification (ABM). This group of interventions aims to modify attentional biases in depression. An early version of the ABM uses the

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dot probe task. The dot probe version of ABM involves a brief, simultaneous presentation of two stimuli: one congruent with depression-related, negative attentional biases, the other neutral. After the stimuli offset, a probe appears at the neutral (or non-depression-related) side consistently and, in so doing, increases people’s tendency to orient toward the neutral stimuli and introduces a contingency to the negative attentional biases (MacLeod et al., 2002). Other similar studies have modified the emotional–spatial cueing task for attention training (Bar-Haim, 2010). The emotional–spatial cueing task pre­sents a neutral and an emotional cue at two sides of the screen, followed by a target appearing at either the neutral or the emotional cue position (Posner, 2016). By having the target never appear at the location previously occupied by the negative cue, such a training paradigm is effective in reducing negative attention biases (Bar-Haim, 2010). Findings from randomized controlled trials indicate that ABM is effective in reducing negative attentional bias, increasing resting-state connectivity in neural circuits that support control over emotional information (e.g., middle frontal gyrus and dorsal anterior cingulate cortex; Beevers et al., 2015), and significantly improving depressive symptoms (Hilland et al., 2018). Given that the dot probe task has been criticized for its reliability (Chapman et al., 2019), recent work has explored other versions of ABM (Everaert et al., 2015). A novel design of the ABM procedure involves reward-based eyetracking, obscuring the negative stimuli while enhancing image quality for the positive stimuli (Woolridge et al., 2021). Such a free-viewing procedure significantly reduced negative attentional biases by decreasing the time spent looking at negative stimuli during the free-viewing task, as well as prompting disengagement from negative information. This novel training paradigm also had lasting effects beyond the modification of biases in attention. Participants had an improved ability to recall happy words, highlighting that the modified-ABM treatment effects may be generalized to reduce memory biases as well (Woolridge et al., 2021). Multiple review studies have shown that ABM has potential efficacy in treating residual symptoms of depression, with evidence of it having a direct, positive impact on depressive symptoms such as amotivation, which, in turn, alleviated other symptoms of depression (Bar-Haim, 2010; Grist et al., 2019). Additionally, there is evidence that ABM significantly modifies negative attentional biases in adolescents at risk for depression (LeMoult et al., 2016). However, it is impor­ tant to note that, while ABM has become a widely accepted treatment for depression, some studies showed mixed results about its treatment efficacy (Rengasamy et al., 2021). These findings highlight that many design aspects may affect treatment outcomes, including the type of stimuli shown in treatment, stimuli exposure times, and the number of training sessions. For example, the length and frequency of ABM training sessions range from four to twelve, over four weeks, with no clear comparisons on the outcome (Lowther & Newman, 2014). Further, the setting in which the ABM is delivered is not standardized: participants may engage with the material at home, in a laboratory, or at school. More research is

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needed to confirm the optimal design and most efficacious environment for the intervention (Woolridge et al., 2021). ABM training may influence both observable depression symptoms and neural correlates. ABM has been linked to decreased neurophysiological threat avoidance, reduced activation in the amygdala, and changes in the prefrontal and occipital cortices (Rengasamy et al., 2021). A recent review also showed consistent evidence that ABM training toward positive information enhanced the electrocortical measures involved in reward processing (yet this was found only in females; Carlson, 2021). This finding shows that ABM could reduce biases toward negative information by altering electrocortical activity, with sex serving as a potential moderator for this effect (Sylvain et al., 2020). Overall, treatments modifying negative attentional biases in depression have shown promise in encouraging positive information processing and reducing depressive symptoms. Future work can continue to improve these treatment designs by optimizing the presentation of stimuli and the length of training programs. Interpretation Another intervention with cognitive biases as a treatment target is cognitive bias modification for interpretation (CBM-I). A typical CBM-I procedure involves displaying an ambiguous picture that depicts everyday situations, with captions clarifying positive outcomes about the scenario (Blackwell et al., 2015). Other similar treatment designs include narrating stories that start off ambiguous but always resolve to a positive outcome (Blackwell et al., 2015). Thus far, CBM-I has been used mostly for anxiety disorders, but these interventions are valuable as low-intensity options for symptom relief, and may work best as an addition to treatment, rather than a stand-alone one (Blackwell, 2020). Different versions of the CBM-I, such as CBM-IS (cognitive bias modification of interpretations of self), seek to modify the negative biases of individuals in regard to themselves and/or events by targeting factors such as repetitive negative thinking in depression (Hirsch et al., 2020). Reviews show that CBM-I has yielded promising results in increasing positive interpretation biases, serving as a buffer to stressor vulnerability, and reducing symptoms of anxiety and depression (Jones & Sharpe, 2017). Other newer treatment designs also aim to modify interpretation biases to alleviate depressive symptoms. The Ambiguous Hallmark Program is a training program similar to the CBM-I. By presenting participants with ambiguous visual stimuli and engaging them in word completion and emotional recognition tasks that resolve in positive outcomes, the Ambiguous Hallmark Program seeks to modify attention and interpretation biases in depression. This treatment has shown promising preliminary evidence for its efficacy (Nejati et al., 2019). As with the dot probe task, other tasks that were initially developed to measure cognitive biases in depression may also have the potential to be adjusted into bias modification interventions. For example, the Scrambled Sentences Task involves 20 sentences with their words scrambled out of order,

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and each sentence can be unscrambled to create a positive or negative sentence (Cowden Hindash & Amir, 2012). For example, “born winner am loser a I” can be unscrambled to form two sentences, one positive and the other negative: “I am born a winner” or “a loser.” The more a participant unscrambles the sentences with negative words, the more a negative interpretation bias is demonstrated (Cowden Hindash & Amir, 2012). Overall, the task has dem­ onstrated the ability to reveal negative interpretation patterns and predict depressive symptoms (Cowden Hindash & Amir, 2012). In a similar priming task, the Word Sentence Association Paradigm, participants are shown either a positive (e.g., “funny”) or a negative word (e.g., “embarrassing”), followed by a scenario (e.g., “people laugh after something you said”). Participants are then asked to respond if the scenario is associated with the previously shown word. Cowden Hindash and Amir (2012) found that individuals with depression endorsed an association between ambiguous sentences and negative words significantly faster, which suggests that negative interpretations may be primed in depression (Cowden Hindash & Amir, 2012). These measurements of biases may be revised to reward positive biases and/or reduce negative biases in depression. To conclude, numerous treatments have been developed to modify interpretation biases in depression, targeting cognitive styles such as repetitive negative thinking. These treatments show promise in controlling depressive symptoms and reducing stress vulnerability. Memory Apart from attention and interpretation biases modifications, treatments targeting memory also show promise in reducing depressive symptoms. Memory Specificity Training (MeST), for example, often begins with psychoeducation that differentiates specific memories from the generic ones (Barry et al., 2019). Subsequent training sessions then encourage participants to recall past events with as many spatiotemporal and contextual details as possible. Participants are often prompted to recall unique positive and neutral events, before being asked to recall negative ones. A meta-analysis has shown that MeST significantly improved the ability of patients with depression to form specific autobiographical memories, alleviating depressive symptoms as a result (Barry et al., 2019). In addition to MeST, positive memory enhancement training (PMET) specifically targets the repair of negative moods by eliciting positive emotions. PMET exposes participants in a negative mood state by recounting sad memories and then prompts the participants to recall a specific, positive memory (Arditte Hall et al., 2018). PMET is found to improve participants’ ability to recall vivid, positive memories, encourage memory specificity, and aid mood repair (Arditte Hall et al., 2018). Recent research has incorporated novel technology, such as virtual reality, in MeST. After viewing a 12- to 15-minute-long virtual reality scene, participants are prompted to recount the scene, noting the most positive part of the scene and positive emotions experienced (Chen et al., 2021). Over the 7-week

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training period, participants experience increased reward sensitivity and a significant drop in anhedonia (Chen et al., 2021). In sum, memory training targeting memory specificity, especially that of positive memories, has shown promise in reducing anhedonia and negative moods. Similar to the ABM and the CBM-I, memory training programs also require more work to optimize the design. Specifically, additional research is needed to investigate the optimal number of sessions, as well as how different treatment delivery platforms (e.g., online vs. in person) may alter treatment effectiveness. Executive Control Cognitive control training (CCT) is another potential intervention for depressionrelated biases, with evidence suggesting that the training may help reduce both depressive symptoms and underlying physiological mechanisms of the disorder (Siegle et al., 2007). CCT targets cognitive processes affected by depression, including mental set shifting (i.e., reducing perseverative errors), updating working memory representations (i.e., reducing mood-congruent memory biases), and inhibition control (e.g., disengaging from negative content). Among the various CCT designs, the adaptive Paced Auditory Serial Addition Test (PASAT) is widely used (Tombaugh, 2006). PASAT presents a number every 3 seconds as an auditory cue and prompts individuals to add to the number they heard previously, with the goal to improve individuals’ working memory, attention, and concentration (Tombaugh, 2006). Similar to PASAT in its aim to increase working memory capacity and improve the filtering of irrelevant information, the dual n-back task is also often used as a CCT module in those diagnosed with or at risk for depression (Owens et al., 2013). The dual n-back task pre­ sents a sequence of paired audio and visual stimuli, prompting participants to determine whether one or both of the current pair(s) matched those previously presented in a number (n) of trails back in the sequence (Owens et al., 2013). Newer CCT designs have targeted inhibition. For example, participants are first prompted to ignore incongruent, distractor stimuli (e.g., side arrows pointing to the left) and capture the relevant information (e.g., clicking the right button in response to the central arrow pointing to the right; Cohen et al., 2015). After viewing a negative picture (e.g., someone crying), participants are then asked to engage in a cognitive task (e.g., selecting a green block on the screen). Such training paradigms show that recruiting inhibition control before processing emotional stimuli can help reduce emotional interference and rumination in the subsequent cognitive tasks, alleviating associated depressed mood (Cohen et al., 2015). Several studies have reported beneficial effects of administering CCT as an early intervention or preventive method for healthy populations or those at risk for depression (Koster et al., 2017). Promising findings support the use of CCT in reducing cognitive vulnerability for depression, improving information processing ability, and facilitating adaptive emotion regulation (Kennedy et al., 2006). When given to the clinical population, CCT is found to alleviate

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symptom severity and improve working memory and daily functioning in depression (Motter et al., 2016). Overall, it is worth noting that CCT targets specific cognitive processes that are linked to depression risk and severity. This line of treatments provides unique value to cognitive control remediation, a factor not covered by traditional interventions such as antidepressant medication (Shilyansky et al., 2016).

CASE EXAMPLE “Evelyn” is a 25-year-old cisgender woman living alone. She self-referred to the clinic for anxious distress and low moods.1 Upon diagnostic assessment, Evelyn is diagnosed with major depressive disorder with comorbid social anxiety disorder. Her history revealed multiple previous depressive episodes. Evelyn reported experiencing “cycles of depression” since 2016, when her parents started having conflicts. She described her sadness as a “suspended” state of being that usually lasts a couple of days a week for 2 weeks, every 3 to 4 months, with the worst episodes taking place in 2016. During these periods of depression, Evelyn stated that she experienced hypersomnia and had feelings of fatigue, worthlessness, and guilt. During the same 2-week period, she also reported having difficulty concentrating. In addition, Evelyn expressed feelings of hopelessness and a lack of motivation in seeking pleasurable experiences. Such amotivation is worsened by her comorbid social anxiety, as she often draws from social interactions negative interpretations, such as evidence that “people are bored by [her].” While Evelyn denied any impairment in academic performance or job functioning, she mentioned that these episodes had caused significant distress and insecurity. The clinic takes a personalized approach in recommending the appropriate treatment for Evelyn. Considering her symptoms of amotivation and negative beliefs about the world, the clinic follows a standard 16-week manualized CBT protocol (Greenberger & Padesky, 2016) to restructure negative automatic thoughts and address negative interpretations top down. As a homework assignment that adds on to the CBT, the clinic has integrated CBM-I into her treatment plan for two rounds, in Weeks 8 and 16 during her 16-week treatment module. The CBM-I approach is deemed effective in modifying negative interpretations from the bottom up, which may strengthen the CBT treatment outcomes. Specifically, the CBM-I procedure involves displaying an ambiguous picture that depicts everyday situations, with captions clarifying positive outcomes of the scenario (Blackwell et al., 2015). The procedure can be completed at home on a computer between sessions. In particular, Evelyn participates in passive viewing of ambiguous situations and is then prompted to read sentences that clarify the scenario with a positive outcome. For each round of the CBM-I, Evelyn is asked to go through the 15-minute

Specific details of the case have been changed to protect the identity of the patient.

1

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task at any time during the day, for 7 consecutively days. The second round of the CBM-I is carried out as a “booster” session to support the maintenance of effects from the first round. Such procedure is in line with the CBT treatment goals and aims to challenge Evelyn’s existing negative interpretation bias, especially in everyday situations and social interactions. Through these at-home CBM-I exercises, Evelyn practices the cognitive restructuring skills learned during the CBT sessions and starts to endorse positive resolutions to ambiguous situations. Evelyn also realizes that everyday interactions with others may help accumulate positive experiences. By experiencing previously ambiguous everyday situations as potentially rewarding and pleasurable, Evelyn develops motivation to further seek positive experiences (e.g., by engaging in conversations with friends) as an effective means to repair negative moods.

SUMMARY AND FUTURE DIRECTIONS Cognitive deficits and biases at various stages of gathering, processing, and retaining information have been shown to interact with emotional experiences, emotion regulation, and stress reactivity (Everaert et al., 2012). Together, biases in cognition are associated with depression risk, maintenance, and symptom severity. As an increasing number of depression interventions have focused on cognitive bias modification, it is crucial to recognize the variability in treatment outcomes and explore specific determinants of treatment efficacy. Given that many training paradigms are modified from existing tasks that focus on the assessment of psychopathology (to assess cognitive functioning and measure cognitive biases), the studies on the reliability of such tasks are needed to provide a strong foundation for subsequent treatment conception. Low reliability of older tasks, such as the dot probe task, may translate into limited treatment applicability (Chapman et al., 2019). In addition, novel task development may help capture additional aspects of cognitive biases and advance treatment designs. For example, while there is abundant literature on negative interpretation biases in depression, prompting treatments that target interpretation biases modifications, sparse attention has been paid to interpretation inflexibility and belief revision difficulties in treating depression. Designing novel tasks that capture both interpretation biases and inflexibility may be a crucial first step to creating interventions that address this aspect of cognition deficits that has yet to be adequately explored (Everaert et al., 2020). Research is needed to address two long-standing concerns regarding the plethora of cognitive training and biased modification paradigms. First, more work is needed to understand optimal treatment designs, especially the specific details, such as the number of treatment sessions needed, the frequency of administering these sessions, and the duration of the entire treatment program. Given the increasing popularity of online administration and the need for treatment scalability, it is crucial to investigate if and how different delivery

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platforms (e.g., internet, smartphone app, in person) affect treatment outcomes. Second, future research needs to continue exploring whether treatments have lasting and generalizable effects beyond the content and duration of training. Longitudinal studies are needed to evaluate whether treatment efficacy in reducing symptoms or alleviating depression risk persists over time. Research designs can also be improved by including a more thorough evaluation of outcomes beyond the specific cognitive processes targeted by the treatment. For example, measuring changes in pivotal mechanisms underlying depression, such as emotion regulation and stress reactivity, may provide important information about the transfer of positive treatment outcomes and the generalizability of skills learned across contexts. Other research methodologies, such as ecological momentary assessment, can be promising in measuring real-world effectiveness of existing treatments. Overall, interventions targeting cognitive biases in depression have shown great promise. With a flexible delivery medium (e.g., allowing for online administrations outside a treatment setting), these interventions are scalable and can be used for remote treatments, which can be especially crucial under public health restrictions (such as social distancing during the COVID-19 pandemic). Such treatments also directly link to basic science research on the role of cognitive biases in depression, providing a strong empirical foundation for treatment development and for capturing treatment efficacy. Given the challenges of understanding the details of the treatment design and the generalizability of training effects across real-life situations, future work may continue to improve these treatment designs with research on the optimal design and most efficacious environment for treatment delivery. REFERENCES Amir, N., Bomyea, J., & Beard, C. (2010). The effect of single-session interpretation modification on attention bias in socially anxious individuals. Journal of Anxiety Disorders, 24(2), 178–182. https://doi.org/10.1016/j.janxdis.2009.10.005 Ao, X., Mo, L., Wei, Z., Yu, W., Zhou, F., & Zhang, D. (2020). Negative bias during early attentional engagement in major depressive disorder as examined using a two-stage model: High sensitivity to sad but bluntness to happy cues. Frontiers in Human Neuro­ science, 14, 593010. Advance online publication. https://doi.org/10.3389/fnhum. 2020.593010 Arditte Hall, K. A., De Raedt, R., Timpano, K. R., & Joormann, J. (2018). Positive memory enhancement training for individuals with major depressive disorder. Cognitive Behaviour Therapy, 47(2), 155–168. https://doi.org/10.1080/16506073.2017. 1364291 Armstrong, T., & Olatunji, B. O. (2012). Eye tracking of attention in the affective disorders: A meta-analytic review and synthesis. Clinical Psychology Review, 32(8), 704–723. https://doi.org/10.1016/j.cpr.2012.09.004 Bar-Haim, Y. (2010). Research review: Attention bias modification (ABM); A novel treatment for anxiety disorders. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 51(8), 859–870. https://doi.org/10.1111/j.1469-7610.2010.02251.x Barry, T. J., Sze, W. Y., & Raes, F. (2019). A meta-analysis and systematic review of Memory Specificity Training (MeST) in the treatment of emotional disorders. Behaviour Research and Therapy, 116, 36–51. https://doi.org/10.1016/j.brat.2019.02.001

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Nejati, V., Fathi, E., Shahidi, S., & Salehinejad, M. A. (2019). Cognitive training for modifying interpretation and attention bias in depression: Relevance to mood improvement and implications for cognitive intervention in depression. Asian Journal of Psychiatry, 39, 23–28. https://doi.org/10.1016/j.ajp.2018.11.012 Owens, M., Koster, E. H., & Derakshan, N. (2013). Improving attention control in dysphoria through cognitive training: Transfer effects on working memory capacity and filtering efficiency. Psychophysiology, 50(3), 297–307. https://doi.org/10.1111/ psyp.12010 Platt, B., Waters, A. M., Schulte-Koerne, G., Engelmann, L., & Salemink, E. (2017). A review of cognitive biases in youth depression: Attention, interpretation and memory. Cognition and Emotion, 31(3), 462–483. https://doi.org/10.1080/02699931. 2015.1127215 Posner, M. I. (2016). Orienting of attention: Then and now. Quarterly Journal of Experimental Psychology, 69(10), 1864–1875. https://doi.org/10.1080/17470218.2014.937446 Power, M., & Dalgleish, T. (2015). Cognition and emotion: From order to disorder. Psychology Press. https://doi.org/10.4324/9781315708744 Rengasamy, M., Woody, M., Kovats, T., Siegle, G., & Price, R. B. (2021). What’s in a face? Amygdalar sensitivity to an emotional threatening faces task and transdiagnostic internalizing disorder symptoms in participants receiving attention bias modification training. Cognitive Therapy and Research, 45(4), 795–804. https://doi.org/10.1007/ s10608-021-10205-9 Sanchez, A., Vazquez, C., Marker, C., LeMoult, J., & Joormann, J. (2013). Attentional disengagement predicts stress recovery in depression: An eye-tracking study. Journal of Abnormal Psychology, 122(2), 303–313. https://doi.org/10.1037/a0031529 Shilyansky, C., Williams, L. M., Gyurak, A., Harris, A., Usherwood, T., & Etkin, A. (2016). Effect of antidepressant treatment on cognitive impairments associated with depression: A randomised longitudinal study. The Lancet Psychiatry, 3(5), 425–435. https://doi.org/10.1016/S2215-0366(16)00012-2 Siegle, G. J., Ghinassi, F., & Thase, M. E. (2007). Neurobehavioral therapies in the 21st century: Summary of an emerging field and an extended example of cognitive control training for depression. Cognitive Therapy and Research, 31(2), 235–262. https:// doi.org/10.1007/s10608-006-9118-6 Snyder, H. R. (2013). Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: A meta-analysis and review. Psychological Bulletin, 139(1), 81–132. https://doi.org/10.1037/a0028727 Stange, J. P., MacNamara, A., Barnas, O., Kennedy, A. E., Hajcak, G., Phan, K. L., & Klumpp, H. (2017). Neural markers of attention to aversive pictures predict response to cognitive behavioral therapy in anxiety and depression. Biological Psychology, 123, 269–277. https://doi.org/10.1016/j.biopsycho.2016.10.009 Sumner, J. A., Griffith, J. W., Mineka, S., Rekart, K. N., Zinbarg, R. E., & Craske, M. G. (2011). Overgeneral autobiographical memory and chronic interpersonal stress as predictors of the course of depression in adolescents. Cognition and Emotion, 25(1), 183–192. https://doi.org/10.1080/02699931003741566 Sumner, J. A., Mineka, S., Adam, E. K., Craske, M. G., Vrshek-Schallhorn, S., WolitzkyTaylor, K., & Zinbarg, R. E. (2014). Testing the CaR-FA-X model: Investigating the mechanisms underlying reduced autobiographical memory specificity in individuals with and without a history of depression. Journal of Abnormal Psychology, 123(3), 471–486. https://doi.org/10.1037/a0037271 Sylvain, R., Gilbertson, H., & Carlson, J. M. (2020). Single session positive attention bias modification training enhances reward-related electrocortical responses in females. International Journal of Psychophysiology, 156, 10–17. https://doi.org/10.1016/ j.ijpsycho.2020.07.002 Tombaugh, T. N. (2006). A comprehensive review of the Paced Auditory Serial Addition Test (PASAT). Archives of Clinical Neuropsychology, 21(1), 53–76. https://doi.org/ 10.1016/j.acn.2005.07.006

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11 Optimism and Pessimism Max Genecov and Martin E. P. Seligman

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his chapter discusses the relationship between pessimism and depression, exploring the importance of optimistic beliefs about positive events to mental health. Pessimism has an important place as a predisposing factor to depression, and relieving pessimism is crucial in the course of cognitive therapy. In contrast, optimism faces an uphill battle for recognition as an independent construct. First, optimism is often seen as a Panglossian perspective that deludes a person into vulnerability to disappointment (Kahneman & Lovallo, 1993). It has even been argued that dysphoric pessimists have more realistic assessments of probabilities and events than optimists (Moore & Fresco, 2012). This antioptimistic perspective falters in the face of abundant evidence about the benefits of optimism, much of which is covered in this chapter. Nevertheless, many believe optimism should be discouraged rather than encouraged. A second challenge for the construct of optimism is that it has been sometimes positioned as merely the lack of pessimism and that the best we could ever do in life was to minimize our suffering (Freud, 2015, p. 15; Schopenhauer, 2012, p. 372). In this tradition, prominent depression inventories like the Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977) and the Beck Depression Inventory-II (BDI-II; Beck et al., 1996) stop at zero depressive symptoms, thus making the implicit assumption that the absence of depression is the peak of functioning. For instance, only four of the 20 statements on the CES-D are positive: “I felt I was just as good as other people,” “I felt hopeful about my future,” “I was happy,” and “I enjoyed life.” In everyday

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life, being very hopeful about the future has strong protection against future depression (Valle et al., 2006), but the intensity of that hopefulness does not register on the CES-D. Focusing on symptoms of distress and omitting the states of positive well-being is a mistake that positive psychology attempts to correct (Seligman & Csikszentmihalyi, 2000). The third challenge for the construct of optimism is that it is often seen as only the polar opposite of pessimism. This is a subtle problem, but one that has been present in much of the optimism research over the past 40  years. For example, the Revised Life Orientation Test (LOT-R), the most common optimism– pessimism survey instrument, was proposed as a unidimensional measure of optimism, even though a two-factor model with optimism and pessimism as separate factors is a better fit (Scheier et al., 1994). When Seligman (1991) first worked on explanatory style, the concept of “optimism” or optimistic style combined both magnifying good events and minimizing bad events. An optimistic style for bad events was unstable (temporary), specific (local), and external, whereas an optimistic style for good events was stable, global, and internal. The combination of these two indices into a composite measure leads to muddled, low-resolution data (Peterson, 2000). In this chapter, we define optimism and pessimism and differentiate them from similar constructs. We separate optimism from pessimism and from pessimism’s behavioral correlates. Next, we connect both to depressive symptoms and evaluate treatments that focus on optimism, contrasting them with the usual methods of reducing pessimism. We conclude with some unsettled questions about optimism and its enhancement.

CASE EXAMPLE Ben (cisgender man) is a 28-year-old clerical worker living in the Philadelphia area seeking first-time treatment for recurrent major depressive disorder.1 His mother, the only emotional support in his young life, died when he was a teenager. He attributes his first depressive episode to this event. After Ben’s mother’s death, his father remained emotionally distant, and moved and remarried while Ben was in college. Ben’s older sister (a cisgender woman) has schizoaffective disorder and substance use disorder. She often calls upon Ben to get her out of difficult situations, which Ben does sporadically and reluctantly, causing him significant distress. Ben graduated college with a focus on art preservation and received a certificate for it after college but found that jobs in the field, on top of being badly paid, were not available without a master’s degree, which he could not afford. Ben’s most recent depressive episode was precipitated by a cross-country move during the COVID-19 pandemic for a job that was mischaracterized: manual labor that Ben was not trained to do with dilapidated sponsored housing.

Case specifics have been changed to protect the identity of the patient.

1

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The job reinforced Ben’s belief that optimism and working toward something better were foolish. He lost the ability to motivate himself for potential opportunities. After returning home, he found a “dead-end” data-entry position and has been working there throughout treatment. Throughout the pandemic, he has had a consistent romantic partner, but he lives with his grandma as a helper as she goes through treatment for cancer. Ben sought treatment roughly 6 weeks after the onset of his depressive episode. Ben’s intake revealed moderate depression with indecision among his most prominent symptoms on the BDI-II (total score of 19). He also responded with moderate hopelessness on the Beck Hopelessness Scale (total score of 10), endorsing mildly positive items, mildly negative items, and items about the future being uncertain. Ben showed himself to be capable in his job, caretaking, and various activities, though he was quick to discount his accomplishments and to criticize himself. Ben’s attributional style appeared to be generalized about negative events and self-effacing about positive events. Ben’s optimism was targeted in cognitive behavior therapy, to help him commit to concrete goals connected to his values and increase his motivation to find practical solutions to his problems. Ben also exhibited black-and-white thinking, as events were perceived as either perfect and acceptable or irrevocably flawed. Bringing attention to this style of thinking and the development of more balanced thoughts helped to increase his ability to handle ambivalence and pursue his goals. Ben’s treatment also consisted of identifying positive thoughts and enhancing self-attributions to positive events in the manner of Riskind et al. (1996). Ben’s treatment consisted mostly of cognitive reframing that led to identification of a core belief of being a failure, which Ben found increasingly easy to dispute. Ben’s main hobby—birding—serves as a prime example of the optimism component of treatment. At first, he would bemoan the fact that he loved birding but could never enjoy it fully, leading him to avoid going out on trips. Ben was encouraged to visualize and narrate positive, but realistic, scenarios for how a full morning of birding might reinvigorate him after a week of caretaking and mindless work. Ben had not gone on one of these trips for months, until he was motivated by gains in treatment to actually act upon his desire. His first birding trip was compromised by circumstances but was moderately enjoyable. Two weeks later, he went out again of his own volition and had a moving experience that taught him a lesson about patience and resilience. Ben’s depression decreased slowly during the first several weeks of treatment, but by about 2 months, his symptoms dropped suddenly and stayed in the mid-single digits on the BDI-II for about a month. This corresponded to Ben’s increasing skill at locating and disputing the core belief that he’s a failure. During this euthymic period, Ben nevertheless found it hard to believe in his competence and act on it. He believed that he needed more confidence first, though he did not immediately recognize that competence needs to be tried and developed before turning into confidence. He also continued to discount positive outcomes.

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Soon, a conglomerate of negative life events and disappointments caused his maladaptive generalizations about the future to reemerge, and his scores on the BDI-II rebounded to the high teens and low 20s. After reflexively discounting them, Ben couldn’t use positive traits and experiences to keep his goals and values clear amid the negativity; his pessimism took hold, demotivating him and drawing his attention toward negative outcomes of goal pursuit. Pessimism was addressed directly through cognitive reframing and approach-oriented problem solving, and Ben’s optimism was fostered by promoting appreciation of his positive traits and activities. Though Ben was initially skeptical about these appreciation exercises, he found a lot in himself to be proud of while navigating difficult interpersonal situations and enjoying a long birding trip. He enjoyed this exercise and continued it of his own volition. Simultaneously, Ben began to see many positive steps he could make in his life. Even when some fell through, Ben knew that something he was passionate about—a volunteer opportunity, a new job, or a new class—would work out for him. In all, these interventions helped Ben’s symptoms decrease in the short term as well as reduce the effects of negative events and enhance the effects of positive events in the long term.

CONCEPTUAL AND DEFINITIONAL ISSUES Here we explicate different definitions of optimism used in the scientific literature. We use the term optimism with respect to good events. Increased optimism reflects the belief that good events are more likely. In contrast, decreases in pessimism soften catastrophizing beliefs about negative events. In principle, optimism and pessimism are independent, as one can believe both that good events are very likely and that bad events are very likely. An intervention can, in principle, alleviate pessimism and increase the belief that bad events are less likely without changing the belief about how likely good events are. Defined in this manner, optimism and pessimism align respectively with the approach/ reward and the avoidance/punishment neural systems, which are similarly related but separable (McNaughton et al., 2016). Two major types of optimism appear in the literature. Dispositional optimism reflects optimism about future events, whereas explanatory optimism (also called explanatory style) is about the causes of past events. As discussed further below, each has a strong predictive value for depression and related outcomes (Peterson, 2000). Dispositional Optimism Dispositional optimism is close to the colloquial definition of optimism: the general expectation that the future will be good. The glass is half full. Dispositional optimism dominates much of the literature, perhaps because it is much simpler to measure than an explanatory style about the past. The Life

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Orientation Test (LOT; Scheier & Carver, 1985) is the prominent measure of dispositional optimism. The LOT consists of eight statements rated on a 5-point Likert scale. The optimism items are about good events: for instance, “In uncertain times, I usually expect the best.” The pessimism items are mostly about bad events: for instance, “If something can go wrong for me, it will.” Some authors (e.g., Smith et al., 1989) have critiqued the scale as being overly correlated with neuroticism, leading to a shorter, six-item scale with little loss of reliability or validity (LOT-R; Scheier et al., 1994). The original LOT contained two negatively correlated factors related to optimism and pessimism. The correlation between these scales (r = −0.64) led the authors to treat the LOT as a unidimensional measure. In other samples, the correlation between LOT optimism and pessimism scales is weaker or even nonsignificant (e.g., Mroczek et al., 1993; Plomin et al., 1992). The LOT-R also contains two separate optimism and pessimism factors (Herzberg et al., 2006). In a meta-analysis, the correlation between the LOT-R factors appears to weaken with age, from r = −0.5 for people under 39 and r = 0.05 for people over 70 (Hinz et al., 2017). A greater acceptability in older adults for mixed emotions and tolerance of ambiguity may be related to this pattern (Williams & Aaker, 2002). The optimism and pessimism factors also appear to be more correlated at high levels of education (Glaesmer et al., 2012). The LOT-R’s factor structure has been controversial. The authors advanced a unidimensional model with correlated positive items, which had similar model fit statistics to a two-factor structure, but work on the instrument in multiple contexts has consistently shown a two-factor structure (Glaesmer et al., 2012; Herzberg et al., 2006; Robinson-Whelen et al., 1997). The optimism factor is often more predictive of satisfaction with life and even depression inventory scores (Glaesmer et al., 2012; Zenger et al., 2013). Nevertheless, this statistical work is neglected, leading to papers that refer to only an overall LOT-R score as optimism. Total LOT-R scores predict protection from depression in numerous contexts (Scheier & Carver, 2018). Different behavioral correlates emerge when optimism and pessimism are considered unique dimensions (Whitfield et al., 2020). Both predict risk for mortality separately in five studies that measured both (Craig et al., 2021). Pessimism appears to have a greater effect on physiological outcomes related to illness, including inflammation (Roy et al., 2010), blood pressure variation (Felt et al., 2020), and abstinence from smoking (Serlachius et al., 2015). The optimism factor predicts recovery from stressful tasks (Stephenson, 2018) and psychological and financial recovery from negative life events, such as tornadoes (Carbone & Echols, 2017). Dispositional optimism buffers against depression and other psychopathology when controlling for pessimism (Hirsch et al., 2014; Treharne et al., 2007). One study (Kleiman et al., 2012) separated the components of optimism measures and found that positive expectations (high positive event subjective probabilities) protected against depression, whereas a sense of invulnerability (low negative event subjective probabilities) protected against anxiety.

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Distinguishing Dispositional Optimism From Related Constructs Dispositional optimism is not self-efficacy. Whereas self-efficacy is the belief in one’s ability to perform an action to accomplish a goal (Bandura, 1977), dis­ positional optimism is about a good outcome expectancy, regardless of whether one’s own action achieves the goal. Self-efficacy is causally related to, rather than contained within, dispositional optimism. Thus, self-efficacy cognitions lead to optimistic ones. Optimism appears to mediate the relationship between self-efficacy and well-being in longitudinal studies (Karademas, 2006). Hope is also different from dispositional optimism. Hope is “the perceived capability to derive pathways to desired goals and motivate oneself via agency thinking to use those pathways” (Snyder, 2002, p. 249). This definition implies a personal cause and goal-directed behavior that are absent in the definition of dispositional optimism. While hope resembles self-efficacy, it also includes a skill called “pathways thinking,” which is the ability to imagine the ways one could realistically achieve life goals. Neither dispositional optimism nor selfefficacy includes this imaginative component in their definitions. The constructs of hope, self-efficacy, and dispositional optimism correlate at about 0.50 but maintain independent relationships with both well-being (Magaletta & Oliver, 1999) and depression. Both high hope and optimism weaken the relationship between rumination and suicidal ideation (Tucker et al., 2013). Low hope predicts future frequency and severity of physical illness, whereas low optimism does not predict illness (Scioli et al., 1997). Hope predicts job performance (Youssef & Luthans, 2007) and satisfaction with life (Bailey et al., 2007) better than does optimism. The personal agency factor in hope was especially predictive of life satisfaction. In more specific domains of quality of life, however, hope and optimism had equivalent predictive value. Dispositional optimism is best used to measure a generalized expectancy about the future, rather than personal agency about the future. Explanatory Optimism Explanatory optimism is unique among constructs related to positive outlook in that it focuses explicitly on the past and the present. Explanatory optimism is the tendency to view the causes of negative events as temporary and situational. In contrast, explanatory pessimism is the tendency to view the causes of past negative events as permanent and pervasive. Though attributions explain past events in questionnaires, the classic version of the explanatory style suggests that optimists are energized by their successes and unfazed by their failures, whereas pessimists are enervated by their defeats and dismiss their successes. Explanatory optimism arose as an explanation of resilience in the learned helplessness experiments. Studies converged on the fact that long-lived and global learned helplessness arose only in conditions where the negative circumstances themselves seemed long lived and global (e.g., Roth & Kubal, 1975). These moderators became the key components of the reformulated

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helplessness theory of depression (Abramson et  al., 1978). Depression was hypothesized to arise when one attributed internal, stable, and global causes to negative events (Abramson et al., 1989). A further refinement of explanatory style, hopelessness theory, added a dimension of perceived meaning (importance) of the past events (Alloy et al., 1988). If a negative event was important and had stable and global causes, then a specific “hopelessness” depression was expected, with unique cognitive biases. Negative self-concept symptoms and low self-esteem might also occur, but they are not necessary for hopelessness depression. Explanatory optimism is often measured by the Attributional Style Questionnaire (ASQ; Peterson et al., 1982). The original ASQ included six positive and six negative hypothetical events that the respondent was asked to imagine happening to themself. The respondent is asked to write the most likely cause of this event and then evaluate it on the internal–external, global–specific, and stable–unstable axes. These scores are tallied for good and bad events, creating two separate scores. The scores for positive and negative ASQ events are not strongly correlated (Peterson, 1991; Xenikou et al., 1997). ASQ scores are also not consistent across domains (Cutrona et al., 1984), although the explanatory style is moderately consistent over time (Burns & Seligman, 1989; Tiggemann et al., 1991). Importantly, explanatory style research has focused on negative events. Attributions for negative events tend to be more predictive of depression than those for positive events (Abramson et al., 1989; Sweeney et al., 1986). Optimistic styles (about good events) have been understudied (Peterson & Steen, 2002). Even so, attributions about positive events predict recovery from depression (Johnson et al., 1998; Needles & Abramson, 1990). An interaction between positive events and an enhancing explanatory style about positive events also buffers against depression, even in those with pessimistic explanatory styles about negative events (Haeffel & Vargas, 2011). Other research has found that positive life events in depression are related to more gratitude and more meaning, leading toward remission (Disabato et al., 2017). A wider attributional theory based on well-being might generate more specific hypotheses related to explanatory optimism. However, positive event attributions appear to have predictive power, as the belief in stable, global causes for positive events helps people recover from the psychological impact of negative events. The Content Analysis of Verbatim Explanations (CAVE; Peterson et al., 1992) was developed to measure explanatory dimensions for either verbatim speech or writing. All events in a given corpus are identified and blindly scored on the ASQ dimensions, resulting in an explanatory style for positive and negative events. Quickly trained raters achieve respectable interrater reliability (Peterson et al., 1985). The technique allows for retrospective analysis, assuming sufficient verbatim data. Explanatory style as tabulated by the ASQ and by CAVE demonstrate modest correlations (r ∼0.3; Peterson, 1991). The Cognitive Style Questionnaire (CSQ; Alloy et al., 2000) is an elaboration on the ASQ with twice as many events. Participants also describe the meaning of the event and whether it implies that positive/negative events

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will follow, whether the participant is special or flawed, and whether it matters to them. These additions allow a more comprehensive evaluation of the reformulated helplessness theory. The CSQ predicts changes in depressive symptoms in periods as short as 1  day (Hankin et  al., 2005) to as long as 2  years (Hankin et al., 2004) and can predict depressive disorder (Alloy et al., 2006). A short form of the CSQ removes the positive scenarios and reduces the number of negative scenarios. It has good reliability and good correlations (r ∼0.3) with depression measures (Meins et al., 2012). A contribution to the theory of explanatory style is the importance of explanatory flexibility. Ideally, accurate recognition of the cause of an event would lead to the selection of the proper coping mechanism. For example, correctly assessing an intractable romantic relationship as doomed would lead to a healthy end of the relationship, whereas holding out hope for it to change might just yield more misery. Flexibility and nonpessimistic explanatory style have a significant but small positive correlation (Fresco et al., 2007; Moore & Fresco, 2007), indicating that some pessimists are relatively flexible in their explanatory style across life domains. Pessimistic style with low flexibility predicts depression (Fresco et al., 2007). Further, when people with a history of depression are put in negative moods, their explanatory style becomes less flexible (Fresco et al., 2006). The Relationship Between Dispositional and Explanatory Optimism There are theoretical reasons to expect that the two types of optimism are related, as both use mental simulation of possible events (Klein & Zajac, 2009). People envision possible scenarios in the past, the present, and the future. It is likely that similar biases and habits are deployed when thinking about both past and future events. However, these statements are not necessarily transitive. Views about the past are not always views about the future: a trivial past cause might set off an extremely negative consequence. Correlations between dispositional and explanatory optimism are also inconsistent among samples (Hirsch & Conner, 2006). That implies that the two types of optimism often have different correlates, causes, and consequences. For instance, dispositional optimism correlates with nurturing paternal and maternal behaviors, while explanatory style is associated only with paternal behaviors (Hjelle et al., 1996). Too few studies examine both types of optimism, let alone for both positive and negative events. Unrealistic Optimism The perception of optimism as being blissfully blind to the real world has led many researchers to examine “unrealistic optimism.” There is, indeed, some evidence for negative effects of optimism. In one study, HIV-positive men were more likely to decline in their immune functioning if they started with a more optimistic explanatory style (Tomakowsky et al., 2001). Tomakowsky and colleagues suggested that HIV being a persistent, uncontrollable stressor—

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one that disrupts intimate relationships as well as one’s life span—is incompatible with optimism. They point to research showing that the immune system of optimists is dysregulated by uncontrollable stressors (Sieber et al., 1992). People being treated for attention-deficit/hyperactivity disorder (ADHD) often express overly optimistic self-evaluations and goals to relieve unpleasant emotions and sustain the behavioral avoidance that maintains those deficits (Knouse & Mitchell, 2015). Kahneman and Lovallo (1993) argued that there is a fundamental, deleterious optimistic bias in normal mental health. People often focus on the specific, unique, and personal aspects of a problem rather than how the problem is similar to those faced by other people. When they do attend to those similarities, they make a statistically more dismal forecast. The “inside view” bias is found in both individuals and organizations and can lead to disastrous results. Landau and Chisholm (1995) blamed many industrial disasters in part on ever-increasing optimism. However, the authors also noted that this type of optimism appears itself to be a product of management, which views analysis of possible problems as expensive and unnecessary. This is organizational optimism under economic pressure rather than individual optimism. On the individual level, even unrealistic optimism might be helpful. Illusions of positivity and unrealistic optimism also lead to more goal pursuit, caring behaviors, and general happiness (Taylor & Brown, 1988). Unrealistic optimism may not be a completely self-fulfilling prophecy, but it may lead to behaviors that increase the likelihood of success—at least in a benign world and with adequate personal resources. There is also the issue of calibrating one’s dispositional optimism: Who can say what is unrealistic except in hindsight? Accurate assessments of whether someone is being overly optimistic are also hard to come by, especially for rare events (Harris & Hahn, 2011). Therefore, people might assume that a person is being unrealistically optimistic even when objective data on base rates are not available, diluting the utility of “unrealistic.” It is unclear how unrealistically optimistic assessments relate to depression. Undue optimism may lead to more distress after a failure, and increase the chances of a depressive episode, although research about dispositional optimism indicates a linear relationship with well-being measures (Scheier & Carver, 1988). Though very high dispositional positive affect is negatively correlated with business success (Baron et al., 2011), no such studies have measured the relationship between optimism and clinically relevant consequences. Unrealistic optimism may be a result of risky or protective behavior, rather than a cause (Shepperd et al., 2017). Defensive Pessimism A defensive pessimist is a person who sets overly low expectations in spite of past performance in order to anticipate failure scenarios, avoid disappointment, and motivate preparation. The strategy of defensive pessimism may not be conscious, but it can be effective in the short term for those who are able

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to overcome the initial wave of anxiety and use it as motivation. Defensive pessimism is a tactic, not a global or stable style or a disposition (Norem & Chang, 2001). Defensive pessimists do spend time reflecting on good or ideal outcomes (Spencer & Norem, 1996), and they may also use strategic optimism at other times (Suárez Riveiro, 2014). Defensive pessimism is an effective practice for those who use it regularly. Within the domains defensive pessimism is regularly used, it is not associated with the avoidant coping strategies that are indicative of attributional or dispositional pessimism (Showers & Ruben, 1990). Distracting a defensive pessimist from their anxious preparation or trying to relax them hampers their performance (Norem & Illingworth, 1993; Spencer & Norem, 1996). In contrast, normal pessimism can be self-­ fulfilling, by promoting avoidance of preparation and worse performance (Showers & Ruben, 1990). In line with its strategic deployment of anxiety, defensive pessimism is more associated with anxiety and worry than with depression and hopelessness (Norem & Chang, 2001). The correlation with depression is not null: Academic, physical, and psychological outcomes are worse for defensive pessimists than for strategic optimists in the long run, even if they have similar performance in the short term (Cantor & Norem, 1989; Norem & Illingworth, 1993). Defensive pessimists do not update their beliefs about themselves after success, and that may lead to low self-esteem, high negative affect, and generally higher neuroticism.

CONNECTING OPTIMISM AND PESSIMISM TO DEPRESSIVE COGNITION AND BEHAVIOR Having defined optimism and pessimism, we now discuss their association with depressive symptoms. Pessimism and Depressive Symptoms Social and cultural forces influence the development of pessimism. Parental pessimism and authoritarianism correlate with pessimism in children (Hasan & Power, 2002), implicating social modeling and self-efficacy pathways to developing pessimism. Twin studies and adoption research finds significant genetic associations for both optimism and pessimism but an environmental association only with optimism (Plomin et al., 1992). Neither helplessness theory nor Scheier and Carver’s life orientation perspective explicitly details prototypical pessimistic thinking. Neural imaging finds that pessimists’ brains imagining ambiguous future events resemble the nonpessimists’ brains imagining events with certainly negative valence (Herwig et al., 2010). Pessimists also exert less proactive control on the level of neuro­ circuits in anticipation of stressful events (De Raedt & Hooley, 2016). Low expectations lead to less preparation for stress and difficulty. Negative expectancies

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also intensify physiological responses to stress, leading to a greater perception of threat (Pulopulos et al., 2020). Repetitive negative thinking causes depressive symptom severity (Spinhoven et al., 2019), and pessimistic thinking is also associated with rumination (Miranda & Mennin, 2007). Depressive rumination can be elicited by inducing pessimistic expectancies along with self-consciousness (Strack et al., 1985). This relationship is bidirectional: Rumination induces pessimistic thinking and ineffective problem-solving (Nolen-Hoeksema, 1998). Pessimism also leads to depressive symptoms through beliefs about one’s lack of control over one’s environment (Sherman & Cotter, 2013). Many individuals with pessimism are not depressed (Luten et al., 1997), but pessimism promotes rumination and avoidance, which may then lead to depression. A pessimistic explanatory style is associated with anhedonia in hospital settings (Gutkovich et al., 2011). Nonetheless, pessimists are sensitive to negative stimuli and demonstrate sensitivity to rejection (Downey et al., 1998). Rejection sensitivity may lead to social avoidance and, in turn, other social skill deficits of depression (Cole & Milstead, 1989). There is a bidirectional relationship between pessimism and sleep: Pessimism can affect sleep quality via anxiety symptoms, leading to worse sleep quality and subsequent reductions in optimism (Lau et al., 2017). Optimism and Goal-Direction Protect From Depression Optimism leads to approach rather than avoidance, and it capitalizes on psychological and material gains to buffer against depression. Optimism is associated with increased goal-directed behavior, which leads to feelings of efficacy and mastery (Gaudreau & Blondin, 2004). Optimism also leads to perceptions of progress toward goals independent of goal commitment or values (Monzani et al., 2015). These effects protect against negative events and attributions that lead to depression. Whereas high pessimism may lead a person with depression to think that there is no effective treatment at all, low optimism may only lead a person with depression to minimize self-efficacy and seek treatment for their depression (Karlsson et al., 2011). One well-studied aspect of optimism as protection against depression is in the selection of coping strategies with pain (Basten-Günther et  al., 2019). Laboratory-induced optimism via the “Best Possible Self” task erases the effect of pain on executive function tasks (Boselie et al., 2014). Inducing a low pain expectation for a cold-pressor task also increases optimism in relation to the task without decreasing pessimism (Hanssen et al., 2014). Optimism promotes longer goal-directed and approach-oriented coping strategies in painful situations and can reduce pain-induced cognitive impairments. When problemfocused coping strategies are unavailable or not useful, optimists tend toward emotion-focused coping strategies (Nes & Segerstrom, 2006). Dispositional optimism further leads to psychological adaptation via approach-oriented coping for other stressors, such as in first-year students adjusting to college

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(Perera & McIlveen, 2014). Together, the evidence indicates that optimists can see the bright future beyond current difficulty and they work to either change the present or, if necessary, endure it.

OPTIMISM INTERVENTIONS If optimism can be taught, then such teaching can complement other approaches that reduce pessimism to prevent or reduce depression. Given its unique behavioral correlates, optimism should improve coping strategies and goal direction in ways that alleviating pessimism alone would not. Here we evaluate relevant interventions. Traditional Therapies and Optimism Cognitive behavior therapy (CBT) addresses pessimism in alleviating depression, as pessimism is embedded in the negative self, the world, and future concepts that describe depression (Beck & Alford, 2009). CBT identifies sources of pessimism and corrects pessimistic automatic thoughts via psychoeducation and cognitive restructuring. For instance, the attribution of depression to unchangeable biochemical mechanisms is a pessimistic belief. Education on the biopsychosocial reality of depression alleviates overall pessimism (Lebowitz & Ahn, 2015). CBT also addresses pessimism by targeting the behavioral avoidance related to pessimism (Ottenbreit & Dobson, 2004), in that behavioral experiments are used to disconfirm pessimistic beliefs. Explanatory-style pessimism typically improves after CBT (Seligman et al., 1988). Moore and colleagues (2017) found that cognitive therapy improved explanatory flexibility and explanatory style about negative events, which lowered the likelihood of depression relapse. There are limits to how much CBT can increase optimism or decrease pessimism (Pretzer & Walsh, 2001). Various authors have investigated including specific optimism training in CBT. Rather than just dispute negative thoughts and their attributions and find the most “realistic” cause, participants in optimism training list the best and worst events of a day, as well as their explanations of the events. Then participants brainstorm other possibilities for both events, find acceptable replacement causes, and rate those replacement causes on the explanatory style axes. Participants are not guided toward better explanations of positive or negative events, as the practice of effortful, concrete processing, working against automatic thoughts, is theoretically the active ingredient in optimism training. After 4 weeks of optimism training, participants are less pessimistic about negative events (Fresco et al., 2009). It should be noted that Fresco and colleagues did not examine changes in positive event attributions, so it is unclear whether this training also increased optimism. They did not discount that the training might work because of its focus on positive events but argued that the measurement of positive event attributions was less reliable and less related to the alleviation of depression. Riskind and

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colleagues (1996) focused on positive cognitions and enhancing attributions to positive events, and they found that 1 week of optimism training increased positive cognitions beyond psychoeducation and homework. Those in the optimism condition also performed better on a creative word problem task. Optimism is not a component of psychological flexibility, which is the core focus of acceptance and commitment therapy (ACT), but some studies of ACT examine optimism. A basic tenet of ACT is that every person will encounter stress and pain in their lives, and what determines outcomes is the ability to detach from inflexible patterns related to stress and pain (Ciarrochi et  al., 2010). ACT also makes committed, values-based action a key component in reaction to difficulty. It is clear that optimistic goal direction in regard to positive events shares aspects of these tenets, as optimism implies that past and current states of affairs may not be the same as future states. Optimists do not delude themselves into thinking the present or past world was always positive but look to the future as a place for hope. In terms of the components of psychological flexibility, an increase in dispositional and explanatory optimism indicates a loosening grip of negative views of the self as a failure in the past and in the future. Some researchers assert that optimism is incompatible with ACT because ACT considers negative thoughts as natural and that they only become problematic when the person having them holds too inflexibly to them (Burckhardt et al., 2016). However, total LOT-R score and psychological flexibility have moderate correlations (r = ∼0.5; Arslan et al., 2021; Woldgabreal et al., 2016). More research should examine optimism as positive event attributions and expectancies in the context of ACT. How Treatments Targeting Optimism Differ From Traditional Therapy Evidence suggests that the most successful path to encouraging optimism is through affect and experience rather than cognitive style. The lack of positive mental imagery in depression is as pervasive as the presence of negative mental imagery (Holmes et al., 2016); the latter leads to a negative availability bias and an attentional bias based on habitual negative mental imagery (Holmes et al., 2009). Overgeneralized memory interacts with negative attentional biases to inhibit the retrieval of vivid positive memories and inhibit visual imagery generation (Hach et al., 2014). In treatment, the vividness of positive imagery predicts future optimism in participants who are euthymic and depressed (Blackwell et al., 2013; Ji et al., 2017). A 4-week treatment to increase positive imagery can decrease anhedonia and decrease depressive symptoms (Blackwell et al., 2015). An emotion regulation perspective makes sense of the disparate findings in positive intervention research. Positive emotions broaden the scope of attention and cognition (Fredrickson, 2001; Isen, 1999). Optimism, but not low pessimism, is associated with rapid emotion regulation (Larcom & Isaacowitz, 2009). Quoidbach and colleagues (2015) argued that increasing positive

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emotions requires modifying attention, appraisal, and responses to positive situations. Especially highlighted in this frame was the anticipation of the experience of the positive event, becoming immersed in the experience, and reminiscing vividly about the event. Repeatedly simulating, experiencing, and reexperiencing positive emotion in the past, the present, and the future leads to increases in positive emotion. These perspectives reflect the growing body of optimism interventions in both clinical and self-help contexts. The interventions focus on eliciting, remembering, and noticing positive experiences and images. Through this practice, optimistic goals become more vivid and motivating, and positive affect is approached rather than avoided or ignored. Clients may also learn to disengage from impossible or unconstructive tasks (Aspinwall & Richter, 1999). Optimism interventions are not merely positive affect inductions, however. In general, positive affect inductions also tend to decrease negative affect and vice versa (Joseph et al., 2020), and it is therefore difficult to isolate the effects of positive affect just on optimism. One study (Lewis et  al., 1995) found that, in women but not men, a negative affect induction led to higher pessimism and lower optimism scores relative to a positive affect induction. The lack of a neutral induction meant that no inferences could be made as to whether the affect inductions influence both optimism and pessimism or whether they only affected one. Optimism and positive affect appear linked through attention to positive thoughts. Experimentally induced attentional biases to positive and negative information appear to increase positive and negative affect, respectively (Grafton et  al., 2012). While positive and negative words yield differences in attentional bias (and skin conductance) between pessimists and mildly optimistic people, differences in the same variables between mild optimists and high optimists occur only with positive words (Segerstrom, 2001). The Best Possible Self intervention, which robustly increases optimism, also increases positive mood and affect, yet it does not change the negative thoughts caused by a sad mood induction that preceded it (Renner et al., 2014). This result implies that the increases in positive affect were related to increases in optimism rather than decreases in negative thoughts. Positive Activity Interventions Positive activity interventions (PAIs) increase optimism. Visualizing one’s Best Possible Self (BPS; Sheldon & Lyubomirsky, 2006) may increase optimism more than any other PAI (Peters et al., 2013). It is also one of the most studied PAIs (Malouff & Schutte, 2017). The BPS intervention is based on King’s (2001) research related to writing about life goals, which is itself based on Pennebaker’s expressive writing paradigm (Pennebaker & Chung, 2007). Sheldon and Lyubomirsky (2006) hypothesized that the BPS task improves self-regulation by structuring one’s self-concept and its relationship to goals. The experiential, affective component of simulating what it feels like to have achieved one’s goals may also play a part. The BPS task is short, however, and optimism induced

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by the BPS task decays within hours, probably because optimism requires maintenance via repeated thought and exercise (Lyubomirsky et al., 2005). Nevertheless, people rate positive events as more probable and negative events as less probable after a single BPS session (MacLeod & Byrne, 1996; Peters et al., 2010). A recent meta-analysis of the BPS task (Carrillo et al., 2019) found significant positive changes in well-being, positive affect, and optimism. Online and face-to-face delivery methods were effective, though individual administration was more effective than group administration. Curiously, in some cases, higher intensity improves the effects of the intervention but in others reduces the effects (Lyubomirsky et al., 2005). Individuals with depression do benefit more than people without depression from a weeklong, daily version of the BPS task (Shapira & Mongrain, 2010). Reductions in depressive symptoms were maintained for over a month, and increases in happiness were maintained for 6 months. Other PAIs include gratitude letters, counting one’s blessings, using one’s strengths in a new way, affirming one’s values, performing acts of kindness, and meditating on positive feelings. PAIs have been found to be efficacious in increasing well-being in several meta-analyses, though their effects are small overall (Bolier et al., 2013). People with depression may benefit from PAI as well as people without depression (Sin & Lyubomirsky, 2009), but few studies use participants with a clinical depression diagnosis. Positive Psychotherapy Positive psychotherapy (PPT; Rashid & Seligman, 2018) seeks to bypass the individual ambiguities of self-administered PAIs and integrate several of them in a cognitive behavioral setting. PPT consists of 15 clinical sessions with homework. Some sessions focus on pessimism, such as understanding how negative interpretations of events rather than the events themselves lead to psychological symptoms. Other sessions build optimism; one session focuses on hope and optimism, and several others touch on optimism through exercises and psychoeducation. For instance, a session about satisficing may enhance the number of positive futures above the single, ideal, all-or-nothing success. Another session about goal-setting and positive emotions increases optimism similarly to the BPS task. PPT focuses on the future-oriented aspects of optimism. Prior events are examined for accurate and inaccurate attributions and appraisals. Realistic appraisals then allow the patient to respond with the proper tools in their expanding repertoire of coping skills. Hope and optimism are also harnessed as character strengths that lead one toward meaningful goals. The therapy also ameliorates the self-sabotaging attributions that come with a pessimistic explanatory style. Several small studies indicate that PPT is a feasible, promising treatment for mild to moderate depression (see Rashid, 2015). In a comparison with group dialectical behavior therapy for patients with “severe emotion dysregulation,”

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PPT had similar but less dramatic outcomes, including relief of depression (Uliaszek et  al., 2016). The significantly worse attrition rates and worse working alliance in PPT exacerbated the differential effects of the therapies in this study. A recent meta-analysis concerning multi-component positive psychotherapies like PPT (Hendriks et al., 2020) found small but significant effect sizes in depression treatment. Hendriks and colleagues did not analyze PPT specifically, and also did not examine optimism as an outcome. No meta-analysis has yet examined positive psychotherapy because of the heterogeneity of the diagnoses treated by it in studies (Walsh et al., 2017). Though promising, PPT requires more outcome research with larger samples, more specific optimism measures, and longer follow-up. Well-Being Therapy Well-being therapy was developed to alleviate residual affective symptoms after remission (Fava, 1999). The therapy begins with two sessions of listing and examining recent episodes of well-being and good feeling, continues with three sessions that resemble cognitive therapy, and ends with three sessions that prepare the patient for positive experience and increases in well-being beyond remission, using Ryff’s (1989) well-being dimensions (environmental mastery, personal growth, purpose in life, autonomy, self-acceptance, and positive relations) as the model. Optimism is related to the “purpose in life,” “personal growth,” and “self-acceptance” dimensions (Ruini & Fava, 2012). The rationale for well-being therapy is that patients need to improve beyond the absence of symptoms, as residual negative thoughts blunt their experiences of well-being and increase the chance for depressive relapse. Adding well-being therapy at the end of CBT treatment led to more reduction in depression compared to continued CBT for residual symptoms, and it protected better against relapse (Fava et al., 1998, 2005). Dismantling studies further suggest that well-being therapy has clinical utility over and above CBT (Fava et al., 2005; Fava et al., 2011; Ryff, 2014). Well-being therapy is well integrated into the CBT framework and can be added to a treatment plan relatively easily. Well-being therapy has not been evaluated as a stand-alone treatment, but it has been found to prevent depression relapse better than clinical management alone for people who are tapering off antidepressants (Belaise et al., 2014). Penn Resilience Program The Penn Resilience Program (PRP) is a program focused on research into depression and predictors of depression in children, including optimism and pessimism (Gillham & Reivich, 2004). PRP promotes cognitive skills and problem-solving training through lectures and hands-on activities for middle school students. Much of the focus on pessimism includes restructuring negative self-talk. The optimism component is contained in the “putting it in perspective” skill: Children are trained to imagine better outcomes apart from

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the one they most fear (Reivich et al., 2013). The better outcomes might be improbable or even silly, but this practice trains children to generate positive possibilities and then select more realistic ones. The problem-solving component teaches children to develop short-term goals that scaffold toward larger goals. Together, these skills help children become optimistic by training them to imagine positive futures that motivate the pursuit of goals that would bring about those futures. Though PRP improves explanatory style for positive events and prevents depression (Gillham et al., 2006), results are modest (Brunwasser et al., 2009) and vary significantly by site (Gillham et al., 2007). In a meta-analysis of 22 studies, PRP modestly reduced depressive symptoms for at least 6 months compared to controls (Hedges’ g = ∼0.22; Ma et al., 2020). The major adaptation of PRP is Master Resilience Training (MRT), created for trainers in the U.S. Army (Reivich et al., 2011). MRT incorporates many aspects of PRP, self-enhancement strategies from sports psychology, and PAIs that strengthen positive self-conceptions. The program’s definition of optimism focuses primarily on relieving the negative: The program describes optimism as a strategy that can be used to challenge “counterproductive beliefs” about explanatory style with negative events. However, the program also incorporates a gratitude exercise to cultivate the identification and experience of positive events. Soldiers are taught “active constructive responding” for positive events so that they investigate the events for further vivid, positive details. The course consists of a 10-day face-to-face or teleconferenced program for trainers. Once they complete the program, trainers teach core MRT material to soldiers in 12 regularly scheduled sessions. In this way, the training is disseminated across the entire army. Large studies of MRT suggest the program is successful. Griffith and West (2013) found that the vast majority of National Guard soldiers in a study of MRT trainers (92%) indicated that the program was helpful, and the extent of this helpfulness was correlated with self-reported improvement in resilience, which in turn was negatively correlated with stress and worry. Survey participants also indicated that they used MRT skills across domains of life, including military work, psychological counseling, and civilian life. One other study over a 5-year period (Harms et al., 2013) of over 7,000 soldiers found being trained in MRT by MRT trainers was associated with a lower incidence of diagnosed mental disorders via increasing optimism (as measured by total LOT-R score). Remarkably, the brigades with MRT trainers in them were deployed more often and were exposed to more potentially stressful military events but still maintained better mental health than controls.

SUMMARY AND FUTURE DIRECTIONS Successful therapy has a long history of relieving depression by correcting distorted beliefs about bad events and incidentally relieving pessimism. We suggest that building optimism about good events is a useful complement

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to past efforts. In this regard, optimism should be thought of as pertaining to positive thoughts and positive events, rather than reductions in negative ones. This perspective goes against traditional theories of depression that emphasize the importance of negative events but is supported by the data that emphasizes the LOT-R optimism factor and by the predictive power of enhancing explanatory styles about positive events with respect to depression recovery and prevention. A person may become optimistic because of wealth and privilege and their associated opportunities. A person without privilege may need to work harder to develop or maintain optimism. The role of genetics also needs consideration. There is evidence that explanatory style is heritable (Schulman et al., 1993), and the heritable personality trait of positive affectivity surely predisposes one to optimism and amplifies the ability to capitalize on positive events (Boardman et al., 2008). Research about heritable individual differences and the relationship between life events and these factors is needed. We evaluated treatments that target optimism and pessimism with special attention to the components that make optimism treatment different from pessimism treatment. Effective optimism treatments focus on imagining and cherishing positive events, not just on disputation of negative thoughts. The evidence shows that high optimists recover from negative events (e.g., bereavement) faster than low optimists, as measured by the number of sick days taken, regardless of their pessimism scores (Kivimäki et al., 2005). Optimism produces resilience from negative events. Optimism in older adults is related only to the presence of major good events (Schwaba et al., 2019), and so an emphasis on good events and optimism are robust complements to other treatment methods. Most important, research should explore integrating optimism interventions into traditional therapies, to see if building optimism improves the effectiveness of therapy. REFERENCES Abramson, L.  Y., Metalsky, G.  I., & Alloy, L.  B. (1989). Hopelessness depression: A theorybased subtype of depression. Psychological Review, 96(2), 358–372. https://doi.org/ 10.1037/0033-295X.96.2.358 Abramson, L. Y., Seligman, M. E., & Teasdale, J. D. (1978). Learned helplessness in humans: Critique and reformulation. Journal of Abnormal Psychology, 87(1), 49–74. https://doi.org/10.1037/0021-843X.87.1.49 Alloy, L. B., Abramson, L. Y., Hogan, M. E., Whitehouse, W. G., Rose, D. T., Robinson, M. S., Kim, R. S., & Lapkin, J. B. (2000). The Temple-Wisconsin Cognitive Vulnerability to Depression Project: Lifetime history of Axis I psychopathology in individuals at high and low cognitive risk for depression. Journal of Abnormal Psychology, 109(3), 403–418. https://doi.org/10.1037/0021-843X.109.3.403 Alloy, L. B., Abramson, L. Y., Metalsky, G. I., & Hartlage, S. (1988). The hopelessness theory of depression: Attributional aspects. British Journal of Clinical Psychology, 27(1), 5–21. https://doi.org/10.1111/j.2044-8260.1988.tb00749.x Alloy, L. B., Abramson, L. Y., Whitehouse, W. G., Hogan, M. E., Panzarella, C., & Rose, D. T. (2006). Prospective incidence of first onsets and recurrences of depression in individuals at high and low cognitive risk for depression. Journal of Abnormal Psychology, 115(1), 145–156. https://doi.org/10.1037/0021-843X.115.1.145

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12 Perfectionism Paul L. Hewitt, Martin M. Smith, Sabrina Ge, Marcia Mössler, Gordon L. Flett, and Samuel F. Mikail

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epressive disorders and related dysfunctions are common, recurrent, widespread, and debilitating mental health problems. In addition to the significant suffering depressive disorders cause to patients and relatives, they place a considerable burden on the economy (Findlay, 2017; Mathers & Loncar, 2006; Vieta et al., 2021). Indeed, in 2020, the economic burden of major depressive disorder (MDD) was roughly $326 billion (U.S. dollars). Although numerous treatments exist for depression, its recurrence among those treated (Vittengl et al., 2010) alludes to pathogenic mechanisms that may influence the cause and maintenance of depression, even after the defining symptoms abate. As epidemiological research has demonstrated, following a single episode of MDD, there is a 50% chance of developing a subsequent episode and the likelihood increases to between 70% and 90% following two or more episodes (Bulloch et al., 2014; Kessler et al., 1993; Wang et al., 2014), suggesting that addressing symptoms either through medication or behavioural strategies is insufficient. Given the increasing prevalence of depression (World Health Organization, 2017), it is paramount that researchers continue to identify contributing and predisposing factors for the disorder, and both establish and refine appropriate treatments that not only relieve symptoms but also reduce the probability of future depressive episodes.

This work was supported by a grant from the Social Sciences and Humanities Research Council of Canada (SSHRC; 435-2015-0412) awarded to Paul L. Hewitt. https://doi.org/10.1037/0000332-013 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  281

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TREATMENT OF A RISK FACTOR IN DEPRESSION: PERFECTIONISM Treatment goals beyond symptom or depressive episode reduction are fundamental when treating disorders such as depression. Sydney Blatt framed this as addressing disruptions in the deeply ingrained relational and self-definitional personality organization of patients with depression (see Blatt, 2004). Additionally, we have encouraged researchers and clinicians to focus on “patient characteristics and personality vulnerabilities that bear directly and indirectly on the psychopathology the patient exhibits rather than on the symptoms of the clinical syndrome per se” (Hewitt et al., 2008, p. 116). Although several vulnerability factors have been proposed and researched, one set of such factors that are particularly appropriate targets involves personality factors that confer stable and consistent predispositions. We suggest that although reducing depression symptoms is an important goal, reducing demonstrated vulnerability factors for depression that are amenable to change (Hewitt et al., 1998, 2008, 2015; Hewitt, Qiu, et al., 2020) is equally important. Hence, in the present chapter, we discuss the treatment of one such vulnerability factor, namely, perfectionism. In doing so, we discuss the role of perfectionism in producing and maintaining depression from a dynamic–relational perspective and present the extant research on the effectiveness and efficacy of this treatment for perfectionism. Definitional Issues in Perfectionism Several definitions of perfectionism exist, and one overarching conceptualization and operationalization derives from a psychodynamic–interpersonal perspective (Hewitt et al., 2017). This perspective views perfectionism as a broad and multifarious personality style that reflects a deeply ingrained “way of being in the world” (see Cheek et al., 2018; Hewitt et al., 2017; Horney, 1950). Researchers and clinicians who adopt this perspective tend to focus on the interplay of internal motivational, affective, cognitive, and defensive/coping processing elements that involve the expression of perfectionism (see Hewitt et al., 2017, 2018). Psychodynamic Relational Perspective In our work over the past 30 years, we have conceptualized and demonstrated that perfectionism, rather than a set of self-related attitudes, is a broad, ingrained, and multidimensional personality style that leaves people vulnerable to numerous forms of dysfunction, including depression. In conceptualizing perfectionism, we presented the comprehensive model of perfectionistic behavior (CMPB; Hewitt et al., 2017). According to the CMPB, perfectionism is composed of stable and enduring trait dimensions that drive and energize perfectionistic behavior. Hewitt and Flett (1991a, 1991b) identified three trait perfectionism dimensions: self-oriented perfectionism (i.e., the requirement of perfection for

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oneself), other-oriented perfectionism (i.e., the requirement of perfection for others), and socially prescribed perfectionism (i.e., the perception that others require perfection of oneself). In addition to trait perfectionism dimensions, Hewitt et  al. (2003) proposed perfectionistic self-presentational styles that capture the interpersonal expression and communication of one’s purported perfection to others. These include perfectionistic self-promotion (i.e., promoting and proclaiming oneself as perfect), the nondisplay of imperfection (i.e., concealing overt displays of any imperfect behavior), and the nondisclosure of imperfection (i.e., not disclosing or verbally revealing any imperfection). Finally, we have also introduced an intrapersonal or self-relational component of perfectionism that is reflected, in part, by an individual’s internal dialogue with the self. We suggested that this involves not only automatic perfectionistic self-statements and thoughts (Flett et al., 1998) but also automatic critical self-recriminations (Hewitt, Kealy, et  al., 2022) and concerns over shortfalls and perceived errors (see Frost et al., 1990). Moreover, we have indicated that this component reflects a behavioral element involving neglect of the self in terms of limited self-care and self-denial. Finally, we and others have demonstrated the independence of these perfectionism components and their pernicious nature for adults, adolescents, and children (for reviews, see Flett & Hewitt, 2002; Hewitt et al., 2017; Shafran & Mansell, 2001; Sirois & Molnar, 2016).

IMPORTANCE OF PERFECTIONISM AS A FOCUS We have argued that perfectionistic behavior should be an important focus of treatment in depression for several reasons (Hewitt, Smith, et al., 2020). First, perfectionistic behavior can act as a core vulnerability factor (Hewitt & Flett, 2002), influencing the cause and maintenance of depressive disorders. For example, there is compelling clinical evidence that the trait components of perfectionism act as vulnerability factors in unipolar depression (Enns & Cox, 1999; Hewitt & Flett, 1993; Hewitt et al., 1996). Similarly, these trait components are associated differentially with state, chronic unipolar, and chronic bipolar symptoms of depression (see Hewitt et al., 1996; Huggins et al., 2008) and certain elements of perfectionism are associated with dysthymia (Huprich, 1998) and depressive personality disorder (Huprich et  al., 2008). Likewise, certain trait components of perfectionism are also associated with related dysfunctions (e.g., suicide ideation, risk, attempts) in both adolescents and adults (see Hewitt et al., 2006, 2017; for a review, see Smith et al., 2018). Second, perfectionism interferes with the therapeutic process and outcomes (e.g., Blatt et al., 1995; Hewitt et al., 2008; Hewitt, Smith, et al., 2020), is associated with negative help-seeking attitudes and fears of psychotherapy (Dang et al., 2020; Martin & Anderson, 2020; Zeifman et al., 2015), negatively influences the therapeutic alliance (Hewitt et al., 2008, 2021; Shahar et al., 2004) and, ultimately, limits the success of psychotherapy for depression (Hewitt, Smith,

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et al., 2020). Third, although treatments focusing on symptoms or disorders associated with perfectionism have been found to reduce symptoms, such treatments typically yield limited to no clinically significant reductions of core perfectionistic features (e.g., Ashbaugh et al., 2007; Bastiani et al., 1995; Blatt et al., 2010; Radu, 2012; Riley et al., 2007). Despite these limited gains, there remains substantial agreement among clinical writers that continued development of perfectionism-specific treatments is required (Fredtoft et al., 1996; Greenspon, 2008; Hewitt et al., 2008, 2015; Salzman, 1980; Shafran & Mansell, 2001). Several specific treatments have been discussed, and the process of evaluating the effectiveness and efficacy of these treatments has begun in the literature.

MODELS OF PERFECTIONISM AND DEPRESSION Over the years, we have developed, refined, and supported several different stress mechanism models to explain why perfectionism confers vulnerability to depression and related forms of psychopathology. These models posit that people with perfectionism experience more intensely and elicit more frequently distressing events and failures due to the generation, enhancement, and perpetuation of stressful experiences. Likewise, these models maintain that chronic or prolonged experiences of stress contribute to depressive symptoms and related dysfunctions. We merged each of these models in a single dynamic-relational model that we term the perfectionism social disconnection model (Hewitt et al., 2006, 2017). Stress Generation Individuals with perfectionism think, feel, behave, and relate in ways that often generate stressful experiences and stressful failures (Dunkley et al., 2003; Ellis, 2002; Hewitt & Flett, 2002; Smith et al., 2020). Furthermore, some people with perfectionism are highly cognizant of this tendency as part of their pattern of self-recriminations (see Flett et  al., 2020). To illustrate, self-oriented perfectionism involves the requirement of perfection from the self at all times. Satisfaction with performance is not in their repertoire, and never is there a performance that is good enough (see the case report of “Robert” in Hewitt et al., 2017). Hence, those with excessive self-oriented perfectionism encounter a higher frequency of stressful failures and a lower frequency of perceived successes (Besser et al., 2004; Hewitt & Flett, 2002). Those with excessive other-oriented perfectionism experience perpetual disappointment in and hostility toward others and, as a result, push people away (Sherry et al., 2016; Smith et al., 2018) thereby limiting their connection with others. And the tendency for individuals with socially prescribed perfectionism to see the world as a malevolent place in which others are overly judgmental and continually disappointed with them can foster a profound

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sense that nothing they do will ever be good enough (Smith et al., 2018). This, in turn, can lead individuals with socially prescribed perfectionism to perceive criticism and censure that may or may not truly exist. Stress Enhancement Besides stress generation, people higher in perfectionism tend to experience stressors and failures more adversely (Hewitt & Genest, 1990). In part, this reflects the tendency for individuals with perfectionism to view their selfworth as contingent on the attainment of perfection (Hewitt et al., 2017; Sturman et al., 2009). Hence, any evidence that they, or another person, performed less than perfectly can result in a wounding blow to their fragile self-esteem. Additionally, individuals with perfectionism tend to use splitting as they struggle to bring together a dichotomy of both the positive and negative aspects of the self and others into a cohesive whole. That is, for individuals with perfectionism, things are either perfect (i.e., all good) or imperfect (i.e., all bad) with nothing in between. One consequence of this splitting is that people with perfectionism frequently interpret relatively minor setbacks as monumental catastrophes. Furthermore, in our specific vulnerability model (Hewitt & Flett, 1993), we theorized that failures that occur in domains that are more relevant to the self-concept of individuals with perfectionism are experienced more adversely, resulting in depressive experiences. For instance, as mentioned, individuals with self-oriented perfectionism can prize perfection in the achievement domain above others, such as the relational domain. Consequently, individuals with self-oriented perfectionism often experience achievement-related failures (e.g., job loss) more painfully than interpersonal-related failures (e.g., romantic break-up). Conversely, the specific vulnerability hypothesis asserts that, because individuals with socially prescribed perfectionism overtly strive to please others, often at the expense of their own desires and preferences, they experience interpersonal stressors as particularly distressing (Hewitt et al., 1996). Although initial tests of our specific vulnerability model with depression were supportive (e.g., Hewitt & Flett, 1993; Hewitt et al., 1996; Joiner & Schmidt, 1995), recent findings are more mixed. Hewitt & Flett (2002), for example, found that children’s self-oriented perfectionism, but not children’s socially prescribed perfectionism, interacted with achievement-related stress and interpersonal stress in predicting depression. Likewise, Enns and Cox (2005) studied a large outpatient sample and found that, consistent with the specific vulnerability hypothesis, self-oriented perfectionism interacted with achievement-related stress, but not interpersonal stress, in predicting longitudinal increases in depressive symptoms one year later. Yet, as with Hewitt and Flett (2002), although socially prescribed perfectionism was a robust predictor of depression, it interacted with neither achievement-related nor interpersonal stress in predicting depression. Finally, Curran and Hill (2018) tested the

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extent to which college athletes’ perfectionism predicts changes in shame and guilt following competitive failure. Their results suggest that athletes who were higher in self-oriented perfectionism or socially prescribed perfectionism experienced greater guilt following competitive failures. These findings, considered together, suggest that whereas there is ample evidence that perfectionism is a vulnerability factor for depression in the presence of stress, the boundary between self-relational stressors and interpersonal stressors may be less distinct than initially perceived. Stress Perpetuation Individuals who are perfectionistic also perpetuate the negative experience and effects of stress through various means. First, individuals with perfectionism rarely simply accept their failures and move on. Instead, many patients with perfectionism perseverate on failures, sometimes for years, through a negative and punitive internal dialogue involving self-related criticisms and admonishments regarding the need to have been or to have appeared perfect and harshly critical statements about the self (Hewitt et al., 2017). As noted by Hewitt and Genest (1990), individuals high on perfectionism are continuously appraising themselves through an idealized selfconcept lens. This can, in turn, manifest in a seemingly unending and painful internal dialogue concerning the perceived discrepancy between the actual self (i.e., disgraceful failure) and the idealized self-concept (i.e., unassailable, respected, and loved person). Their consistent comparison with the ideal self subsequently perpetuates this internal dialogue, thereby sustaining stressful feelings of failure. In support, Flett et al. (2002) found that perfectionistic cognitions were associated with a ruminative response orientation characterized by intrusive thoughts and images following the experience of a stressful event. Likewise, Nepon et al. (2011) reported that, following an interpersonal stressor, both socially prescribed perfectionism and perfectionistic self-presentation were associated with interpersonal rumination. Recently, Xie et al. (2019) presented meta-analytic evidence that self-oriented and socially prescribed perfectionism indirectly predicted depressive symptoms through rumination. An important caveat is that individuals with perfectionism seem not to make judgments of their performance per se but rather vituperative judgments of the self in relation to that performance. Performances, in general, are either perfect or somehow wanting; however, the self, irrespective of performance, is blameworthy and never “good enough.” Last, similar to how individuals with perfectionism lack in self-soothing and social forms of defenses and coping (see Cheli et al., 2020; Matos & Steindl, 2020), they usually avoid seeking help managing their stress from others, including mental health professionals (Dang et al., 2020). This occurs for several reasons. First, perfectionistic self-presentation can cause people to avoid seeking help because they view it as tantamount to a public admission of weakness. Second, other persons with perfectionism may refuse to admit

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to themselves that they need help as a means, albeit an ineffective one, of protecting a sense of self as vulnerable and inadequate. Third, underscoring the relational importance of perfectionism, individuals with perfectionism have a deeply entrenched fear that abandonment and rejection will occur if their friends, family, therapist, or others ever discover how flawed and defective they truly are. Indeed, Ey et al. (2000) found that socially prescribed perfectionism tended to be higher among dental students who reported not seeking treatment despite also reporting clinically significant levels of distress. Dunkley and colleagues (2006) studied patients recruited from various clinical sites. They found that perfectionistic attitudes were associated positively with maladaptive relational elements involving negative perceptions of social support and avoidant coping two years later, which, in turn, predicted increased depressive symptoms one year later. Shannon et al. (2018) found positive associations between perfectionistic self-promotion components and negative attitudes toward help-seeking behaviors. Finally, Dang et al. (2020) demonstrated that all perfectionistic self-presentation facets display moderate negative relationships with stigma tolerance and interpersonal closeness and moderate positive relationships with concern over therapist responsiveness and therapist coercion.

THE PERFECTIONISM SOCIAL DISCONNECTION MODEL The self-relational and interpersonal stress mechanisms described previously are among many components subsumed by the perfectionism social disconnection model (PSDM; Hewitt et al., 2017). In a broad sense, the PSDM views perfectionism as a defensive position that emerges over development as a means of securing caring and acceptance, as well as avoiding rejection and not mattering to others. However, the behaviors that perfectionistic persons use to secure approval and avoid rejection often have the opposite effect. According to the PSDM, perfectionism generates, enhances, and perpetuates various forms of subjective and objective social disconnection that increase vulnerability for depression and related dysfunctions. Subjective social disconnection is a “between the ears” phenomenon that involves the tendency for perfectionistic individuals to view other people as more nonaccepting and critical than they may be. On the other hand, objective social disconnection reflects the veridical reality that other people often find perfectionistic behavior socially repellant and actively move away from individuals with perfectionism. To illustrate, individuals with excessive self-oriented perfectionism exhibit a rigid focus on agentic achievement at the expense of communal goals, which can cause them to miss or ignore chances for forging meaningful connections (Sherry et al., 2016). As well, hypercompetitiveness often makes them see others more as potential competitors than as collaborators (Sherry et al., 2016). Likewise, the tireless need to please others exhibited by individuals with socially prescribed perfectionism can be experienced

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by others as needy and exhausting. Additionally, the hostile, critical, and controlling behaviors associated with other-oriented perfectionism directed toward others is experienced by many as socially repellant. Lastly, the tendency for people higher in perfectionistic self-presentation to conceal flaws and mistakes or to proclaim their perfection is often perceived as inauthentic, cold, guarded, distant, and unavailable. Evidence supporting the various forms of subjective and objective social disconnection involved in the perfectionism-depression link is accumulating (see Hewitt, Smith, et al., 2022). For example, using a longitudinal mixedmethod design, Mackinnon, Sherry, et al. (2014) found that the relationship between socially prescribed perfectionism and change in depressive symptoms was strongest when participants also had low autobiographical friendship intimacy. Likewise, another methodologically rigorous study by Mackinnon, Battista, and colleagues (2014) demonstrated through a daily diary design that both perfectionistic cognitions and perfectionistic self-presentation add incrementally to the prediction of social anxiety and depressive symptoms by trait perfectionism dimensions. In line with these findings, Goya Arce and Polo (2017) found that social anxiety mediated the relationship between perfectionistic self-presentation and depressive symptoms in low-income Latino and African American youth. Magson et  al. (2019) presented correlational and experimental evidence that, in adolescent girls, the socially prescribed perfectionism–depression link was accounted for by rejection sensitivity and associated feelings of social disconnection. Finally, using a diverse sample of community adults, Rnic et al. (2021) demonstrated that all trait perfectionism dimensions and perfectionistic self-presentation facets predicted increased depression symptoms through one or more forms of social disconnection, with social hopelessness and loneliness showing the most consistent mediating effect. Overall, congruent with the PSDM, research suggests subjective social disconnection is vital for understanding how and why perfectionism increases vulnerability for depression. Although studied to a lesser extent, evidence also suggests that perfectionistic behavior leads to objective social disconnection. In a longitudinal, dailydiary study, Smith et al. (2017) found that mothers with higher other-oriented perfectionism tended to have daughters with lower social self-esteem (i.e., feeling liked and accepted by others), and that those daughters tended to experience increased depressive symptoms (Smith et  al., 2018). Similarly, Smith et al. (2019) reported that mothers’ (and siblings’) higher other-oriented perfectionism was predictive of daughters’ higher socially prescribed perfectionism (Smith et al., 2019). Although more research is needed, these findings are in accord with the PSDM, as they suggest that other-oriented perfectionism can contribute to other people’s social disconnection and depression, particularly during critical periods of development when self-concept may still be forming (Cheek et al., 2018; Sherry et al., 2016). Besides research on other-oriented perfectionism, Mandel and colleagues (2015) reported that a factor composed of socially prescribed perfectionism

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and related attitudes predicted interpersonal sensitivity, which, in turn, predicted greater clinician-rated social disconnection four years later. Likewise, Hoffmann et al. (2015) asked participants to rate vignettes describing individuals high and low in perfectionism in terms of their desire to go on a date with them. Findings revealed that most participants rated the hypothetical individuals with self-oriented, other-oriented, and socially prescribed perfectionism as significantly less desirable than individuals without perfectionism. Similarly, Kleszewski and Otto (2020) conducted a conceptual replication of this finding by asking participants to rate vignettes in terms of how much they would like to have the person described as a coworker. In line with Hoffmann et al. (2015), the majority of participants rated individuals with self-oriented, other-oriented, and socially prescribed perfectionism as significantly less desirable coworkers relative to those who were not perfectionistic. Considering these findings together, it appears that individuals with perfectionism exist in a bleak psychosocial milieu, in which they are both more likely to perceive and elicit the very abandonment and rejection they so desperately wished to avoid by being or appearing perfect.

DYNAMIC-RELATIONAL TREATMENT OF PERFECTIONISM As our conceptualization of and research on perfectionism is driven by psychodynamic and interpersonal perspectives, our treatment model for perfectionism integrates psychodynamic (e.g., Bowlby, 1988; Horney, 1939; Kohut, 1971; Strupp & Binder, 1984) and interpersonal (Benjamin, 1996; Kiesler & Anchin, 1982; Sullivan, 1953) principles (see also Hewitt et al., 2017). The approach emphasizes the relational basis of human behavior—particularly the thwarted needs for belonging and self-esteem, focusing on how concerns with attempting to be or appear perfect offer a false promise of securing these unfulfilled needs. These thwarted needs are proposed to ensue often early in life (childhood and adolescence) as the person learns to navigate their world in an as safe and secure manner as possible. Moreover, perfectionism is thought to develop as a way of attempting to meet these unmet relational needs and as a means of keeping aversive affective states arising from experiences of needs not being met. The early developmental origins of perfectionism are consistent with research suggesting that depression can also begin early in life and set a course for continued depression throughout life. For example, It is commonly supposed that major depressive disorder (MDD) largely begins during adolescence, but epidemiological evidence suggests that its onset may be as early as the preschool years. In any case, recent studies suggest that the prevalence of MDD markedly increases during adolescence and young adulthood. (Lingiardi & McWilliams, 2017, p. 155)

These are the phases of development most critical to the emergence of selfconcept, identity, and relational patterns, and there exists a vast body of

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attachment research that links them to early experiences with significant others. In other words, depression and perfectionism share the same developmental foundation; neither is merely a function of a particular way of thinking or unrealistic standards but are instead aspects of experience and development that are deep and pervasive. We use the developmental relational elements as treatment aims first to develop awareness regarding the relational dynamics and unique inter­ personal patterns underlying their need for perfection and then work with the patient to move toward more adaptive, flexible, and healthy ways of securing these needs of belonging and self-esteem. Treatment begins with an extensive psychodiagnostic assessment to develop an initial clinical formulation describing the individual’s unique and idiosyncratic model of how their perfectionism manifests and how and why perfectionism evolved as a strategy to meet their needs for belonging and self-esteem (see Hewitt et al., 2018). The formulation also identifies the effects of perfectionism, including disconnection and distress, and the disorders and dysfunctions, in the present case, depression, that arise from the patient’s perfectionistic behavior (see Hewitt et al., 2017, 2018). Several heuristic devices help define and refine the formulation throughout treatment, including the Triangle of Adaptation, including relational needs, affects, and defenses, and the Triangle of Object Relations, including relational patterns in past, current, and therapeutic relationships. These heuristics aid the clinician in understanding the specific nature of the internal relational needs, affective reactions, and defenses and behaviors to deal with the affect and meet the relational needs as well as the relational behaviors and patterns used to meet the relational needs (for details, see Cheek et  al., 2018; Hewitt et  al., 2017). In addition to the triangles, the formulation includes the cyclical relational pattern outlining the patient’s specific behaviors, expectations of others’ behavior in response to the patient’s behaviors, the actual behaviors of others, and how they relate and incorporate these behaviors to their self-concept. The clinician initially shares the formulation with the patient, encouraging collaboration to ensure accuracy and engagement and establishing a safe and secure therapeutic alliance and connection with the patient. In exploring the formulation over the course of treatment, especially in the here and now of the therapeutic relationship, the patient builds a deeper experiential connection to the formulation. This allows for the consideration and acceptance of more flexible and adaptive patterns of relating to self and others. Importantly, treatment focuses not only on relational patterns in the past, present, and therapeutic context but also on the patient’s relationship with the self—the self-directed esteem, self-acceptance, trust of the self, and self-caring that are so antithetical for perfectionistic individuals. As treatment progresses, the patient works towards engaging in behaviors more aligned with their desires and needs while learning to tolerate accompanying anxiety. Through this work, the patient internalizes new ways of relating with the therapist and others and develops healthier models of self and others, negating the need for maladaptive patterns of defending and

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relating. Parenthetically, individuals with perfectionism often prefer information over emotional experiences. Hence, within this process-oriented treatment, it is imperative that the clinician ensures learning that occurs during therapy does not remain exclusively at the didactic or intellectual level but also occurs at the experiential level. Indeed, learning through the therapeutic relationship promotes real and sustained growth through reduction of limit­ing and punishing perfectionism. In sum, our dynamic-relational treatment (DRT) is formulation-driven and attempts to make changes in the relational elements that underlie perfectionism and the subsequent experience of depression. That is, rather than focusing on depression symptoms per se or even specific perfectionism behaviors directly, we effect change by focusing on the relational underpinnings that produce perfectionistic behavior. A reduction in perfectionism will, in turn, reduce presenting symptoms (see Hewitt et al., 2015) and, importantly, the recurrence of depression (see Blatt & Zuroff, 2002; Blatt et al., 1995; Cheek et al., 2018; Hewitt et al., 2008, 2015, 2017).

TREATMENT EVIDENCE In this section, we provide a brief review of the extant research conducted to assess the effectiveness and efficacy of the DRTs designed specifically for perfectionism and associated symptoms, such as depression. Dynamic-Relational Group Psychotherapy Outcome research for our DRT is in the initial stages and focused, at this point, on group psychotherapy. We have demonstrated the effectiveness of our DRT of perfectionism by assessing the changes in CMPB components (i.e., traits, self-presentational facets, self-relational components) and psychological symptoms, including depression, in a group psychotherapy format in the University of British Columbia Treatment of Perfectionism Study (UBC TPS). The first study reported from this project (Hewitt et al., 2015) was based on 60 patients who were initially screened for extreme scores on our extensively validated measures of trait perfectionism, self-presentation, and automatic thoughts, completed a clinical interview, extensive psychometric testing, and met specific inclusion and exclusion criteria for acceptance into the treatment study. The treatment, discussed above, was provided by senior clinical psychology students under the direct supervision of Paul L. Hewitt (PLH) and Samuel F. Mikail (SFM) to ensure fidelity. The first report from this study involved patients’ self-reports and shows that, following 10 sessions of DRT for perfectionism (Cheek et al., 2018; see also Hewitt et al., 2017, for a detailed description of the treatment), all components of trait, self-presentation, and self-relational cognitive elements of perfectionism significantly improved posttreatment (with 92% showing clinically significant improvements, based on the Reliable Change Index [Jacobson & Truax, 1991] on at least one perfectionism measure

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and 82% reporting clinically significant improvements on two or more perfectionism measures). Clinically significant improvements were also seen in depression, anxiety, social anxiety, and interpersonal problems (Hewitt et al., 2015). Moreover, at the 4-month follow-up, perfectionism and associated symptoms, including depression, continued to improve, a result often found with psychodynamic treatments (Shedler, 2010). In the study, we also assessed whether changes in particular perfectionism components were associated with changes in depression symptoms. Indeed, reductions in levels of self-oriented perfectionism and nondisplay of imperfections were associated with subsequent and significant changes in depression severity. In addition to self-report measures of perfectionism, we also included measures of our CMPB components using close informant reports. Significant or close others provided ratings of the three perfectionism traits and three perfectionistic self-presentational styles at pre- and post-treatment as well as at the 4-month follow-up (self-relational cognitions are not as evident to others and thus were not measured). Close other measures of patients’ self-oriented and otheroriented perfectionism, and all three facets of perfectionistic self-presentation, were significantly reduced at posttreatment and follow-up (Hewitt, Qiu, et al., 2020). Close other measures of patients’ socially prescribed perfectionism did not show change over the course of treatment and follow-up, likely due to socially prescribed perfectionism being more internal than other measures and not directly observable. In this study, we also calculated reliable change indices (RCIs) and found that 67% of participants showed clinically significant improvement on at least one perfectionism subscale measure. Overall, the findings of close others support the effectiveness of the DRT and corroborate earlier results using self-reports of patients (Hewitt et al., 2015). The UBC TPS demonstrated significant changes in perfectionism and depression as well as other symptom outcomes. Importantly, as some have suggested that the trait features of perfectionism are immutable and not amenable to change (Shafran et al., 2002) and numerous studies using cognitive behavior therapy (CBT) approaches show that these treatments do not change trait elements of perfectionism in comparison to wait list controls (e.g., Riley et al., 2007; Radu, 2012), the DRTs showed that large and clinically significant changes occurred not only in cognitive elements of perfectionism, but also in the deeply ingrained trait and interpersonal style variables and these were also evident in the informant data. Overall, the treatment had a significant effect on all elements of perfectionism and was associated with subsequent changes in symptoms of depression. The findings of these two studies suggest that not only does a treatment that focuses on the underlying putative cause of depression reduce the vulnerability factor (i.e., perfectionism), it also reduces the depressive symptoms themselves. Extending this work, we recently completed a randomized controlled trial comparing DRT with a supportive psychotherapy (PST) control (the UBC TPS II; Hewitt, Kealy, et al., 2022). Again, we used well-trained senior clinical psychology graduate students under the direct supervision of PLH and SFM for the DRT (Hewitt et al., 2017) or Dr. David Kealy, an expert

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in psychodynamic supportive treatment (e.g., Kealy et al., 2019; Rasmussen & Kealy, 2020) for the PST, to ensure fidelity and adherence in both treatment approaches. Thirty-seven patients in the DRT group and 33 patients in the PST groups completed 12 sessions of group psychotherapy and follow-up. In this study, we had a sample of patients with extreme levels of perfectionism randomly assigned to 12 group therapy sessions of either the DRT (initially n = 41, with 37 completers) or the PST (initially n = 39, with 33 completers). Multilevel modelling analyses revealed significant changes in all elements of perfectionism and depression for patients who received DRT and PST. Moreover, of the 37 individuals who completed the DRT, 36 (97%) showed clinically significant improvement (i.e., RCI > 1.96) on at least one perfectionism measure. Of the 33 individuals who completed the PST, 28 (90%) individuals showed clinically significant improvement on at least one perfectionism measure. Moreover, the average reduction in depressive symptoms, and the percentage of patients who showed clinically significant improvements in depressive symptoms, was significantly larger for patients who received DRT than patients who received PST. Analyses showed significant changes in all perfectionism components and depression from pre-treatment to post treatment and follow up (Hewitt, Kealy, et al., 2022) in both groups. A second set of analyses from this RCT measured various attitudinal elements of perfectionism used in CBT research. Concern over mistakes (as measured by the Frost Multidimensional Perfectionism Scale [MPS]) and perfectionistic dysfunctional attitudes (as assessed by the Dysfunctional Attitude Scale; Weissman & Beck, 1978) were reduced significantly in the treatments. In addition, greater changes in both concern over mistakes and dysfunctional attitudes were found in DRT than in the PST. In terms of clinically significant change in concern over mistakes, in the DRT, 22 of 37 (60%) patients showed clinically significant change; in the PST, 16 of 33 (49%) showed clinically significant change. Similarly, using the DAS perfectionism measure, in the DRT, 26 of the 37 (71%) participants showed clinically significant change compared with 18 of 33 (55%) in the PST. Finally, the American Psychological Association (APA) Presidential Task Force (APA, 2005) indicated that case reports can be considered in the evidence base for psychotherapy. In that respect, Hewitt, Mikail, et al. (2020) published a case study on the DRT of perfectionism that detailed not only the model but also specifics as to the assessment, formulation, and treatment of a patient, Azure.1 In addition, we provided data as to the outcome of the treatment. In this case study, the patient completed an initial assessment and a clinical formulation according to our model was developed (see Hewitt et al., 2017). Azure completed 12 group therapy sessions of DRT. Perfectionism measures as well as symptom measures were administered pre- mid- and self-relational elements of perfectionism as well as depression, anxiety, and overall psychiatric symptoms as measured by the Brief Symptom Inventory

Azure is a pseudonym to protect the identity of an actual patient in this case study.

1

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(Derogatis, 1982). The case study adds to the evidence base of the effectiveness and efficacy of DRT in the treatment of perfectionism. Evidence appears to be accumulating for the effectiveness and efficacy of the DRT. Moreover, psychodynamic treatments, including both the DRT and PST groups, that focus on the relational underpinnings and use relational interventions (Hewitt et al., 2015; Hewitt, Kealy, et al., 2022; Hewitt, Qiu, et al., 2020) appear to reduce not only depression symptoms but, importantly, the putative vulnerability that contributes to the experience of depression. The DRT, in particular, appears to offer significant benefit to individuals with perfectionism by effecting changes not only in perfectionism traits, self-presentation, self-relational, and attitudinal elements but also in symptoms of depression.

CASE EXAMPLE The case of Anita2 is presented in detail in Hewitt et al. (2017, pp. 156–160) as an illustration of the dynamic-relational approach we use in conducting a psychodiagnostic assessment. A summary of Anita is presented next, along with more specific information regarding her treatment. Anita is a happily married 44-year-old cisgender woman. She has a daughter with whom she is close and an extensive social network that seems warm and supportive. Anita was formerly employed as a biologist, but her career ended abruptly when she sustained a physical injury. Following this injury, she developed symptoms of severe depression and marked suicidal thoughts. Before seeing PLH, Anita sought various treatment options, including medication and both behavioral therapy and CBT. Although these were somewhat helpful, none were seen as effective for her. Upon hearing an interview with Dr. Hewitt on the role of perfectionism in suicide and depression, she identified with his description of perfectionism and subsequently arranged for an initial clinical evaluation. Anita’s scores on the MPS and Perfectionistic Self-Presentation Scale ranged between the 87th and 99th percentiles, with self-oriented perfectionism and perfectionistic self-promotion the highest subscales. She met diagnostic criteria for major depressive disorder, severe and chronic, and her profile from the Minnesota Multiphasic Personality Inventory-2 (MMPI-2; Butcher et al., 2001), as well as her score from the Beck Depression Inventory (BDI; BDI = 40; Beck et al., 1996), indicated she was experiencing significant levels of depression that included elevated somatic, affective, and cognitive symptoms. She endorsed significant suicidal ideation but denied a plan or intention, and she indicated that her children and family were her reasons for living. The findings also indicated that she had significant symptoms of anxiety, pessimism, guilt, and social isolation. Additionally, she had difficulty trusting others and a

Anita is a pseudonym to protect the identity of an actual patient in this case study.

2

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tendency toward passive dependency and meticulousness. Finally, although the MMPI-2 showed that she was experiencing an extremely high level of distress and that her functioning was markedly compromised (e.g., Global Assessment of Functioning = 55), she noted that she kept her pain and distress to herself, and that very few people knew the actual depth of her pain. Anita described her childhood as idyllic and her family as open and supportive. She reported having a close relationship with her family, especially with her mother, whom she described as her absolute best friend and confidante. Her mother died about ten years prior, and Anita described it as a massive loss. She reported experiencing initial anger and rage followed by a sadness that never seemed to abate. Following the death of her mother, Anita began to swim to cope with the loss. Over time, she went from swimming just a few lengths at a time to swimming competitively and for extremely long distances. She described a feeling of relief whenever she pushed herself to swim longer distances and added that sometimes during long-distance swims, she would experience a “runner’s high for swimmers.” When in this state, she fantasized that her mother was still alive. Unfortunately, an injury abruptly deprived Anita of her primary means of coping. Relevant History and Dynamic Formulation In one of her initial sessions, Anita recalled being separated from her parents at around age 5 years. She and her sister were sent to live with relatives for a few months, while her parents dealt with a family crisis. It was clear that Anita had found the separation extremely distressing. She reported that, at the time, she felt abandoned and was unable to comprehend her parents’ reasons for sending her and her sister away. She recalled that when her mother finally returned to bring Anita and her sister home, she was struck by how beautiful her mother looked when she first saw her approaching. Over the course of treatment, it became apparent that the separation from her mother was a formative experience for Anita that marked the beginning of her extremely perfectionistic approach to dealing with the world. Specifically, Anita personalized her parents’ decision to “leave” her and determined to never behave in a manner that could provoke another separation from her mother or, indeed, anyone else in her life with whom she was emotionally connected. Thus, Anita arranged her life in a way that ensured the closest possible proximity to her mother, including choosing to work in the same field, facility, and even the same unit and shift time as her mother as well as first renting and then purchasing a home just two doors down from her parents’ home. As Anita and PLH continued to discuss the death of her mother, it became apparent that she had not yet fully accepted that her mother was gone and that Anita had somehow failed. Once this element of the formulation was uncovered, treatment focused on helping Anita process her grief. Anita

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expressed feeling powerless in preventing the loss of her mother. This came to be understood in the therapy that, despite Anita’s efforts to remain tightly connected with and in close proximity to her mother throughout her life, her mother’s death reawakened the pain and distress of Anita’s childhood experience. Thus, she was left with undifferentiated and overwhelming powerlessness in response to perceived abandonment while being deprived of her primary means of coping. Due to her inability to contain her emotional pain by redoubling efforts at work and distance swimming, her work injury took away these vital coping mechanisms and left her to confront feelings she had worked hard to avoid her entire life. As treatment continued to focus on the death of her mother, Anita experienced heightened suicidality, depression, and rage. The clinician’s response was one of encouraging the expression of contained affect while assuming a stance of active empathy, compassion, and an intentional therapeutic presence intended to counter Anita’s feeling that she is alone in her grief and powerlessness. Concerning the Triangle of Adaptation, Anita’s attachment style was characterized by a powerful need to avoid experiences of abandonment and felt rejection, which she encountered early on in her life. This need was manifested in several ways, including maintaining close proximity with her mother by working and living near her. When her mother died and Anita was no longer able to avoid experiences of abandonment, she responded with intense feelings of despair, hopelessness, abandonment anxiety, loneliness, and failure. As her defense, she made efforts to focus on being perfect in other areas of her life and even entertained the fantasy that if she perfected her athletic achievements, her mother might return. Anita’s interpersonal pattern can be understood using the Triangle of Object Relations. Her relationship with her mother revolved around a need to ensure proximity to in an attempt to ward off feelings of rejection and abandonment. Anita assumed a stance of perfectionism as a means of guarding against ever causing difficulties that would be distressing for her mother. This pattern extended to her relationships with her family and friends. In order to avoid causing difficulties to those around her, Anita made efforts to conceal any signs that she was hurting, even when she was extremely distressed. Not surprisingly, Anita’s interpersonal stance extended to the therapeutic relationship, making it especially critical for the clinician to target the deeper layers of contained affect. Treatment Anita’s treatment was guided by this initial formulation. Early sessions focused on encouraging Anita to “tell her story” with the therapist communicating understanding, empathy, and uncovering unprocessed pain stemming from memories that had been relegated to factual accounts devoid of affect. This served as a means of elaborating the unique elements making up Anita’s clinical formulation while creating an environment that allowed Anita to feel

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sufficiently safe to take risks necessary for therapeutic growth. Treatment progressed to a focus on helping Anita recognize that perfectionism became a self-limiting means of shielding her from the possibility of rejection or losing people in her life. The middle phase of treatment emphasized gaining a deeper understanding of her attempts to meet relational needs of mattering to others, of not being disposable, and of being “good enough” to be cared for and remain connected with others. Moreover, treatment addressed Anita’s harsh view of and relationship with the self. The latter part of the treatment involved relinquishing both the fantasy that attaining perfection was the solution to a lack of connection and esteem, and the views of herself as needing to “earn” others’ caring and guarantee of never leaving. This is often a painful process for the patient whereby individuals come to realize that the approval they long for to make everything ok or “cure” them of the sense of being flawed, alone, or not good enough may never come from the source they have focused on (significant figures who may be dead, alive, or estranged). Moreover, this work emphasized reconsidering her sense of vulnerability to wounding rejection experiences and her sense of being flawed, unlovable, and never quite good enough. Overall, Anita worked very hard throughout treatment, often expressing how much she valued the therapist and the therapy. At the conclusion of treatment, she added that she never revealed how truly suicidal she had felt at times (she knew how to assess suicide risk and the process that would be engaged should she reveal her true level of intent). Her hard work included never missing a session, never being late, and wanting to be a referral source for the therapist’s practice—a residue of the original dynamic that remained. Even after treatment ended, Anita continues to let the therapist know, once a year, that she is doing well.

SUMMARY AND FUTURE DIRECTIONS The link between perfectionism and depression is a long-standing one with early researchers and clinicians describing the link (e.g., Arieti & Bemporad, 1980; Bibring, 1953; Horney, 1950). We presented our model of perfectionism and depression in the context of this personality style reflecting a core vulnerability factor for many disorders and dysfunctions and as a risk factor for depression that has been demonstrated empirically. Additionally, we argued that targeting foundational elements of functioning that contribute to the increased vulnerability for depression, such as perfectionism, is crucial to effect long standing change. We further argued that perfectionism is a viable target of intervention to mitigate both current depressive symptoms but also future episodes of depression, and we described how we believe perfectionism functions in resultant depression and outlined our model of treatment that has evolved over 30  years of treating individuals with perfectionistic behavior. Moreover, we showed that there is significant promise for a psychodynamic-interpersonal

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13 Rumination Ed Watkins

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his chapter focuses on rumination as a psychosocial risk factor robustly implicated in the onset and maintenance of depression (Nolen-Hoeksema, 2000; Nolen-Hoeksema et al., 2008) and as a transdiagnostic pathological process cutting across multiple disorders (Ehring & Watkins, 2008; Nolen-Hoeksema & Watkins, 2011; Watkins, 2008). The chapter summarizes evidence-based interventions for rumination and provides detail on effective elements of cognitive behavior therapy (CBT) for rumination. In this chapter, rumination is defined as recurrent and repetitive thinking on symptoms, feelings, problems, upsetting events, and negative aspects of the self, typically with a focus on their causes, circumstances, meanings, and implications (see Nolen-Hoeksema, 1991).

DEFINITIONAL ISSUES Rumination is one example of repetitive thinking about negative content (repetitive negative thought [RNT]), along with worry, perseverative cognition, and obsessions (Ehring & Watkins, 2008; Watkins, 2008). Worry has been defined as “a chain of thoughts and images, negatively affect-laden and relatively uncontrollable,” and as “an attempt to engage in mental problem-solving on an issue whose outcome is uncertain but contains the possibility of one or more negative outcomes” (Borkovec et al., 1983, p. 9). There is debate as to whether worry and rumination reflect distinct but related processes versus the same underlying process applied to different disorder-specific contents (Segerstrom et al., 2000; Watkins, 2008). https://doi.org/10.1037/0000332-014 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  305

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Worry and rumination are highly related, with a high correlation (0.6–0.7) between the standardized questionnaire measures of worry and rumination (Penn State Worry Questionnaire, Meyer et al., 1990; Response Styles Questionnaire, Nolen-Hoeksema & Morrow, 1991). Structural equation modeling finds that these measures load on a common factor, and that both forms of RNT are similarly related to symptoms of anxiety and depression (Fresco et al., 2002; Segerstrom et al., 2000). Moreover, few differences are found in ratings of cognitive dimensions for worry and rumination, except that worry is more future-focused and rumination is more past-focused (Papageorgiou & Wells, 1999; Watkins, 2008). Experimental studies find that inducing worry and rumination each has similar effects on increasing anxiety and depression relative to control conditions (e.g., Blagden & Craske, 1996). Convergent evidence thus suggests considerable similarities between the processes and consequences of worry and rumination, leading to the hypothesis that they share are common underlying process but differ in specific content and goals. Nonetheless, in clinical practice, it is hard to distinguish worry and rumination. Patients often use the terms interchangeably and may use “worry” as a label for rumination. Moreover, people tend to dwell both on past events and future uncertainties, with one triggering the other. Thinking about a past event that went badly is likely to lead into thoughts about what could go wrong in the future and vice versa. Thus, in practice, worry and rumination tend to merge and flow together dynamically in the moment-to-moment thoughts of patients. For these reasons, targeting RNT in general may be more helpful (Ehring & Watkins, 2008; Watkins, 2008); interventions that are effective at tackling rumination typically also reduce worry (Watkins et al., 2011).

RUMINATION AS A PSYCHOSOCIAL RISK FACTOR There is extensive prospective and experimental evidence that rumination (or RNT) is a major psychosocial risk factor for depression and anxiety (NolenHoeksema et al., 2008; Watkins, 2008). Multiple longitudinal prospective studies find that rumination prospectively predicts the onset of subsequent major depressive episodes and depressive symptoms in individuals with and without depression, including in twin studies (Johnson et al., 2016), as well as substance abuse, eating disorders (Nolen-Hoeksema et al., 2007), alcohol abuse (Caselli et al., 2010), and posttraumatic stress disorder symptoms following trauma (Ehring et al., 2008) even after controlling for initial symptoms (see reviews by Nolen-Hoeksema et al., 2008; Nolen-Hoeksema & Watkins, 2011; Watkins, 2008; Watkins & Roberts, 2020). A meta-analysis revealed that rumination was significantly related to four distinct symptom types (i.e., depression, anxiety, eating, alcohol abuse; Aldao et al., 2010). Further, rumination explained the concurrent and prospective associations between symptoms of anxiety and depression (McLaughlin & Nolen-Hoeksema, 2011) and between anxiety disorders and depressive disorders 5 years later, with these effects mediated by repetitive negative thought at 2 years follow-up (Spinhoven et al.,

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2019). There is also emerging evidence implicating rumination in impulsive and dysregulated behaviors, insomnia, and psychosis (for a review, see Watkins & Roberts, 2020). All these findings support the argument that rumination is a transdiagnostic process, that is, a process present across multiple psychiatric disorders and that causally contributes to the onset and maintenance of these disorders (Ehring & Watkins, 2008; Nolen-Hoeksema & Watkins, 2011). Thus, interventions that target rumination may also have the benefit of better addressing comorbid presentations, as well as depression. Prospective studies find that rumination mediates the association between stressful life events and later anxiety and depression (McLaughlin & Hatzenbuehler, 2009; Michl et al., 2013). Moreover, rumination is elevated in those with family histories of mental health difficulties and those experiencing interpersonal stress, socioeconomic disadvantage, stressful life transitions, and bullying or abuse, and it acts as a common mediator between these risk factors and later psychopathology (Kinderman et al., 2013; Michl et al., 2013; Nolen-Hoeksema, 2000; Spasojevic´ & Alloy, 2001). Rumination is also a common residual symptom, remaining elevated after both partial and full remission from depression and in those who have recovered from depression (Riso et al., 2003; Roberts et al., 1998). Studies utilizing ecological momentary assessment (EMA) designs, in which occurrences of negative affect, rumination, and stress are measured multiple times per day, find that rumination predicts, mediates, and moderates sub­ sequent distress over days and weeks. Momentary rumination predicts sub­ sequent negative affect (and vice versa; Moberly & Watkins, 2008a) and mediates the role of life stress and negative events in prospectively predicting negative affect and depressive symptoms over several weeks in both nonclinical (Genet & Siemer, 2012; Moberly & Watkins, 2008b) and clinical samples (Ruscio et al., 2015) and moderates the role of life stress and current depressive symptoms in predicting future depressive symptoms in undergraduates (Connolly & Alloy, 2017). Rumination, therefore, appears to be an important mechanism that acts as a common pathway from both distal and recent stressful life events to psychopathology. Targeting rumination thus provides a means to tackle multiple risk factors for psychopathology that cannot themselves be directly changed (e.g., family and personal history). This stress-exacerbation mechanism is consistent with the evidence from experimental studies in which rumination is manipulated in the laboratory, which suggests that rumination plays a causal role in exacerbating negative mood, negative cognition, and unhelpful behavior. The typical study compares a rumination induction, which prompts participants to repetitively focus on their feelings, symptoms, and their causes and consequences, versus distraction, which instructs participants to imagine visual images unrelated to their self or feelings (Lyubomirsky & Nolen-Hoeksema, 1995). These studies find that rumination acts to magnify, exacerbate, and prolong existing negative emotions (e.g., sadness, anger, anxiety, depression) and to exacerbate negative thoughts about the past, present, and future in participants already in a

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depressed or dysphoric mood, but it has limited effects in participants with euthymic mood (e.g., Lyubomirsky & Nolen-Hoeksema, 1995; Lyubomirsky et al., 1999; for a review, see Watkins & Roberts, 2020). Induced rumination slows emotional recovery to a prior failure event (Watkins, 2004) and increases negative emotional reactivity to a subsequent stressful event (Watkins et al., 2008). Second, rumination impairs problem solving by making individuals more pessimistic and by making thinking more abstract and less able to access specific details of how to resolve a difficulty (Lyubomirsky & Nolen-Hoeksema, 1995; Lyubomirsky et al., 1999; Watkins & Baracaia, 2002; Watkins & Moulds, 2005). Third, experimental studies have found that rumination impairs concentration; occupies working memory (Lyubomirsky et al., 2003; Watkins & Brown, 2002); and reduces sensitivity to context, such as reduced responsiveness by mothers to infants (e.g., Tester-Jones et al., 2017).

CONCEPTUAL ISSUES FOR RUMINATION There are several theoretical models of rumination, including response styles theory (Nolen-Hoeksema, 1991), the control theory account (Martin & Tesser, 1996), the processing mode account (Watkins, 2008), the attentional disengagement model (Koster et al., 2011), and the habit-goal model (Watkins & Nolen-Hoeksema, 2014). More recently, a new model (H-EX-A-GO-N; see Figure 13.1) attempted to integrate and synthesize existing models and evidence to create an overarching model, proposing that pathological rumination emerges from the interaction of five mechanisms: habit development, executive control, abstract processing, goal discrepancies, and negative-information biases (for further detail, see Watkins & Roberts, 2020). Across these models, several key concepts have important clinical implications. A first key concept is that dwelling on problems and upsets is a normal and universal process, which is not necessarily dysfunctional. This was first made explicit in the control theory account (Martin & Tesser, 1996), which conceptualizes rumination as recurrent instrumental thinking about unsatisfactory goal progress, wherein rumination is triggered by the perception of slower than anticipated progress in pursuing a goal. In this account, rumination continues until either satisfactory progress is made in reducing the goal discrepancy or the individual disengages from or abandons the goal. Rumination is not necessarily pathological: this recurrent thinking could help to highlight and address unresolved difficulties and act as a self-focused attempt at problem solving. Alternatively, it could make the discrepancy more salient and elaborate further negative thoughts about the unrealized goal, further worsening mood. Consistent with this theory, naturalistic diary and EMA studies find that unsatisfactory progress on personally important goals is associated with increased rumination on a momentary basis (Moberly & Watkins, 2010; Verkuil et al., 2015). Further, experimentally inducing participants to focus on an unresolved goal generated more frequent and prolonged rumination

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FIGURE 13.1.  The H-EX-A-GO-N Model of Rumination DISTAL RISK FACTORS

ENVIRONMENTAL

BIOLOGICAL

Early Adversity

Elevated DMN activity and connectivity

Interpersonal Stress

Specific genotypes may be linked to

Parenting style

increased rumination

Socio-cultural expectancies

R

1 H

Habit 13 Executive functioning

EX

5

11

N

7

Depressive 2 Rumination 10 4 3 8

12

A

Negative biases 9

GO

6

Abstract processing style

Goal discrepancies Arrows indicate direction of causal relationship between variables: Established causal relationship Hypothesized causal relationship

1Habit development leads to trait rumination through repeated rehearsal of repetitive thinking with low mood. Repeated rumination further elaborates the habit. 2The perception of goal discrepancies causes state rumination. 3Repeated/prolonged goal discrepancies provide the setting conditions for rumination to become habitual. 4Abstract thinking prolongs and intensifies state rumination. Reducing abstract processing causally reduces trait rumination. 5Rumination exacerbates negative biases, negative biases increase susceptibility to rumination. 6Abstract processing impairs problem-solving, perpetuating goal discrepancies. 7Executive functioning deficits causally contribute to depressive rumination, state rumination impairs concurrent executive functioning. 8Executive functioning deficits impair flexible problem-solving and are hypothesized to perpetuate goal discrepancies. 9Negative biases are hypothesized to increased the perception of unsatisfactory goal progress. 10Abstract processing is hypothesized to become habitual in some environmental/learning contexts 11Negative information processing is hypothesized to become habitual in some environmental/learning contexts. 12Executive functioning deficits are hypothesized to impair the ability to shift out of an abstract processing mode. 13Executive functioning deficits impair the ability to inhibit habitual responses

Note. From “Reflecting on Rumination: Consequences, Causes, Mechanisms and Treatment of Rumination,” by E. R. Watkins and H. Roberts, 2020, Behaviour Research and Therapy, 127, 103573 (https://doi.org/10.1016/j.brat.2020.103573). Copyright 2020 by Elsevier. Reprinted with permission.

as indexed by thought probes than did focusing on a resolved goal (Roberts et al., 2013). Within this model, people are more likely to get stuck in rumination if they set goals that are difficult to attain and hard to abandon, which is a greater risk for individuals with extremely high standards or perfectionism (see Chapter 12, this volume), or if they do not know how best to achieve their goals (e.g., poor problem solving, patterns of unhelpful thinking; Watkins, 2008). The key implication of this research is that rumination should not be treated as always pathological, because it can be a normal and helpful response to difficulties (Hollon, 2020). It may be helpful to normalize the experience of rumination by emphasizing that it is something that everyone does and not something weird or that reflects weakness. It also implies that instructions to

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stop ruminating or to only distract from concerns are unlikely to be effective— if someone is faced with an unresolved personally important goal, it will be hard to put it out of mind for long. Rather, it would be more helpful to both patients and therapists to discriminate between when focusing on a concern is helpful versus unhelpful. A second key concept is the hypothesis that pathological rumination is an automatic response conditioned to triggering stimuli such as low mood, that is, a mental habit (Watkins & Nolen-Hoeksema, 2014). This conceptualization is central to response styles theory (Nolen-Hoeksema, 1991), which hypothesizes that depressive rumination is a stable, enduring, and habitual traitlike tendency to engage in repetitive self-focus in response to depressed mood. It is also consistent with conceptualizations of rumination as a habit of thought that often starts automatically and involuntarily (Hertel, 2004). Rumination occurs frequently, unintentionally, and repetitively in the same emotional context of low or depressed mood, and thus it fulfills the usual definitions of a habit (Verplanken et al., 2007; Wood & Neal, 2007). Consistent with the habit account, individuals who engage in depressive rumination report that rumination occurs without conscious intent and that they are unable to control it (Watkins & Baracaia, 2001), and a self-reported index of habitual negative thinking, assessing dimensions of habits (e.g., lack of conscious awareness, lack of conscious intent, mental efficiency, hard to control) is positively correlated with both trait and state rumination (Verplanken et al., 2007), including in patients with past depression (Ólafsson et al., 2020). Moreover, an EMA study revealed that the extent to which affect prospectively predicted increased rumination at the next sampling point was moderated by habitual characteristics of negative thinking, such that a stronger habitual quality was associated with a stronger relationship between mood and subsequent rumination (Hjartarson et al., 2021). Depressive rumination is hypothesized to be learned in childhood, either because it was modeled by parents who themselves had a passive coping style (Nolen-Hoeksema, 1991; Nolen-Hoeksema et al., 2008) or because the child failed to learn more active coping strategies for negative affect as a consequence of overcritical, intrusive, and overcontrolling parents or early physical/sexual abuse. Consistent with this hypothesis, rumination is associated with selfreported overcontrolling parents (Spasojevic´ & Alloy, 2002) and with physical, emotional, and sexual abuse (Conway et al., 2004; Shaw et al., 2019). Wood and Neal (2007) proposed that habits develop through a process of automatic association between a behavioral response (e.g., rumination) and any context that occurs repeatedly with performance of the behavior (e.g., sad mood). Such contextual cues become automatic triggers for the response, such that the behavior is controlled by the presence or absence of the cue, rather than via the mediation of an implicit or explicit goal. Thus, any response repeated frequently (e.g., dwelling on a problem) that is contingent on a particular context (e.g., sad mood) could develop a habitual response to that context, consistent with classic stimulus–response theories of learning. The initial ruminative response could start as one driven by difficult events and

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voluntary goals (e.g., dwelling on an unresolved goal) but with repeated and prolonged association with the accompanying low mood (such as when people experience chronic difficulties) could become a habit automatically triggered by low mood alone (Watkins & Nolen-Hoeksema, 2014). Wood and Neal (2007) further argued that “habits arise from context-response learning that is acquired slowly with experience. As a result, habit dispositions do not alter in response to people’s current goals or occasional counter-habitual responses” (p. 844). Habits are thus resistant to changes in goals, outcomes, intentions, and difficult to restrain (Hertel, 2004), consistent with the phenomenology of pathological rumination. The habit model suggests that once rumination becomes a habit, it will be hard to stop even if new behavioral goals are adopted, even if it has negative consequences or is at odds with an individual’s attitudes and intentions. This habit analysis suggests lessons for the treatment of rumination. Interventions focused on changing individual’s beliefs, attitudes, and intentions and providing new information are not effective at changing habitual behaviors (Verplanken & Wood, 2006), because they do not directly address the patterns of context-response learning. Focus on changing thought content alone (e.g., thought challenging) is insufficient to stop rumination as a habit. Rather, successful habit change involves either (a) disrupting the environmental factors (time, place, mood, prior behavior) that automatically cue the habit or (b)  “counterconditioning or training to associate the triggering cue with a response that is incompatible and thereby conflicts with the unwanted habit” (Wood & Neal, 2007, p. 859), in effect, learning a new helpful habit to replace the original dysfunctional habit. Where the cueing context for rumination involves a particular location (e.g., bedroom), person, routine (e.g., sitting down for a coffee after work), or environmental feature (e.g., sad music), environmental modification to remove or avoid the triggering context ought to interrupt depressive rumination. Alternatively, repeated practice at utilizing an alternative incompatible coping strategy (e.g., relaxation) in response to the triggering cue (e.g., tension) can develop a new context–response association to replace rumination. Such direct targeting of the automatic context-response association is hypothesized to improve the efficacy and durability of interventions for rumination and depression. A third key concept is that there are different styles of information processing when thinking about negative content, which can determine whether such thinking becomes dysfunctional rumination or adaptive problem solving (Watkins, 2008). The processing style characteristic of unhelpful RNT is abstract and analytical, and involves general, superordinate, and decontextualized mental representations that convey the essential meaning, causes, and implications of goals and events (the “why” aspects of an action). In contrast, the adaptive processing style is more concrete and focuses on the direct, specific, detailed, and contextualized experience of an event and represents details of goals, events, and actions that denote the feasibility, mechanics, and means of “how” to do the action (Watkins, 2008).

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Experimental manipulations have found that inducing abstract rumination (with questions like “Why did this problem happen?”) impaired social problem solving in individuals who had recovered from depression, who performed as well as participants who had never been depressed in a no-prompt control condition. In contrast, prompting concrete thinking (“How are you deciding what to do next?”) ameliorated problem-solving deficits found in patients with current depression (Watkins & Baracaia, 2002). In patients with depression and in participants with dysphoria, compared with abstract rumination, concrete rumination improved social problem solving (Watkins & Moulds, 2005), increased specificity of autobiographical memory recall (Watkins & Teasdale, 2001) and reduced negative generalizations (Van Lier et al., 2015). Repeated training to think in a concrete mode reduced subsequent emotional reactivity to analogue loss events, relative to training in an abstract mode (Watkins et al., 2008). This research suggests that targeting processing style may help individuals to shift from maladaptive repetitive thought to adaptive repetitive thought, from rumination to problem solving. More specifically, it suggests that being more concrete (asking, “How?”) is more adaptive when responding to negative situations than being abstract (asking, “Why?”).

INTERVENTIONS FOR RUMINATION Several CBT-based therapies are intended to target rumination, most notably rumination-focused cognitive behavioral therapy (RFCBT; Watkins, 2016; Watkins et al., 2011). RFCBT directly builds on the research reviewed above to incorporate novel elements to tackle rumination-as-a-habit and to shift thinking style, in addition to standard CBT elements, organization, principles, and techniques including a structured format, here-and-now focus, collaborative empiricism, agenda setting, feedback, summaries, homework, guided discovery, and behavioral experiments. To target rumination-as-a-habit, RFCBT incorporates the functional–analytic and contextual approach developed in behavioral activation (BA; Jacobson et al., 2001; see also Chapter 15, this volume). Functional analysis is used to examine how, when, and where rumination does and does not occur, and its antecedents and consequences; to formulate its possible functions; and to make plans that systematically reduce or replace it. In addition, RFCBT uses functional analysis, imagery, behavioral experiments, and experiential approaches to shift a patient from the unconstructive abstract processing style to the more constructive concrete style. Within RFCBT, general CBT assessment procedures are followed with respect to understanding the symptoms, difficulties, concerns, goals, and circumstances of the individual patient. The specific assessment of rumination focuses on understanding the nature, scope, and consequences of rumination for the patient and then moving to a more detailed functional analysis of specific

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TABLE 13.1.  Key Principles and Interventions of RFCBT Engage, empower, and motivate

Shift from unhelpful to helpful thinking style

Target rumination-as-ahabit

Principle 1: Normalize patient’s experience of rumination

Principle 3: Encourage active, concrete, experiential, specific behavior

Principle 4: Take a functionalanalytic approach

Principle 2: Make rumination an explicit target of therapy

Principle 7: Shift to an adaptive style of thinking

Principle 5: Link behaviours to triggers and warning signs

Principle 8: Focus on warmth, empathy, optimism, validation, persistence

Principle 6: Emphasize the importance of repetition and practice

Note. RFCBT = rumination-focused cognitive behavioral therapy.

occurrences of rumination. The therapy is principle based, with eight key principles underpinning the delivery of the intervention (see Table 13.1). Therapy typically starts by establishing if rumination is a major problem for the patient (e.g., “How often do you find yourself dwelling on your problems and difficulties?”; “How long does this rumination tend to last?”; “What are the consequences?”). Examples of worry and rumination are discussed and their role in the patient’s difficulties explored so that collaborative agreement is reached that rumination is a central process in maintaining their symptoms and difficulties and to make rumination a key focus of therapy (Principle 2: Make rumination an explicit target of therapy).1 The therapist is explicit that rumination is likely to occur before, during, and after treatment sessions, and suggests that both therapist and patient will point out when it might be happening so it can be tackled directly in-session. The Importance of Functional Analysis of Rumination The patient’s experience is validated by emphasizing how rumination can be a normal and helpful response and that it is natural and understandable given their history (e.g., “It is not surprising that you spend a lot of time thinking about . . . everyone else dwells on what is important to them too”; Principle 1: Normalize the patient’s experience of rumination). The patient’s developmental history is tied into the rationale and to the shared understanding of their rumination. It is explained that such thinking becomes problematic when it is used excessively or inappropriately and that the therapy is about determining when thinking is helpful versus unhelpful to increase more helpful thinking. To do this, the therapy builds from the patient’s experience with

Note that the eight principles are not presented in numerical order but as related to therapy progress and main goals of treatment for brevity.

1

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the therapist coaching the patient to increase use of their own strategies that work. This rationale includes a review of the idea that rumination is a learnt habit, which can be replaced by learning new more helpful habits. This model of rumination-as-a-habit is helpful because it reduces stigma: Most people understand the idea of habits and what it might take to change them (e.g., spotting the triggers to the habit; repeated practice at doing something different to the same triggers), and they find it empowering and hopeful because it moves away from ideas of rumination being unchangeable. Once a focus on rumination is established, the therapist assesses what influences the rumination and gets an overview on its content and contexts (e.g., “When and where do you tend to ruminate?” “When is it better?” “When is it worse?” “What do you think about?”). The therapist then moves onto a detailed functional analysis (FA) to delineate the sequence and contents of specific episodes of rumination. FA is used to determine the functions and contexts under which desired and undesired behaviors occur and, thereby, find ways to systematically increase or reduce the target behavior (Principle 4: Take a functional-analytic approach). It examines the variability and context of behavior within an individual’s personal experience and uses this to guide interventions. Within RFCBT, FA is focused on identifying (a) the antecedents or triggers to rumination and how the sequence of rumination unfolds (Principle 5: Link behaviors to triggers and warning signs); (b) the consequences of rumination; and (c) the factors that influence whether rumination is helpful versus unhelpful, short versus long-lived (Principle 7: Shift to an adaptive style of thinking). ABC (Antecedent–Behavior–Consequence) and CUDOS (Context, Usefulness, Development, OptionS) are mnemonic guides for conducting FA. Identifying the antecedents to the rumination provides clues as to what purpose it may serve and helps to plan where and when to make changes to reduce the habit. Knowing its consequences provides clues to its use and functions and helps to consider what alternative actions might usefully replace rumination. Exploring CUDOS is used to compare between and across episodes of rumination to identify patterns and contingencies underpinning the rumination and to look for variability in rumination and its effects. The therapist conducts FA across multiple specific episodes of rumination, starting with recent episodes of rumination to identify common and repeating patterns of triggers, warning signs, thoughts, and behaviors during rumination. FA is then used to identify moderators of rumination by seeking out recent examples that have matching stressful contexts, triggers, and warning signs to episodes of rumination but where the outcome was more positive such as no or short-lived rumination or constructive problem solving. For example, if a recent episode of rumination was triggered by an argument with a friend, the therapist would ask the patient to think of another situation where they had an argument with someone close to them but where they didn’t then get caught up in dwelling about it. Comparing similar emotive events where there is variability in the duration of rumination or the usefulness

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of patient’s response can identify changes in the environment or the patient’s behavior (e.g., different focus of attention; different self-talk) that influence rumination. To explore context, the therapist asks about conditions under which rumination does and does not occur (what, when, where, how, with whom it happens or does not happen). Context includes external factors such as location, other people, environment, time of day, and internal factors, such as the individual’s mood, mental state, physical state, thoughts, focus of attention, and behavior. To explore usefulness, the therapist asks about the effects and consequences of rumination and when it is helpful versus unhelpful. A fundamental goal is to help patients to identify when thinking on a difficulty is helpful or not, discriminate under what circumstances such thinking is helpful and recognize how helpful thinking differs from unhelpful thinking to systematically increase helpful thinking. Thus, rather than stopping thinking about important personal concerns, RFCBT aims is to make such thinking more functional and adaptive. Useful rules of thumb to spot when thinking is unhelpful include asking, “Is it an answerable question?” and “Is this leading to a useful decision or plan?”; negative answers suggest the thinking is unhelpful. To explore development, the therapist will ask when rumination started, whether it was learnt from significant others such as parents, or whether it is associated with any memories. For example, childhood bullying is a common memory tied into onset of rumination, with patients reporting rumination about understanding and preventing the bullying (e.g., “Why are they picking on me?”). To explore options, the therapist looks for examples of when the patient escaped from rumination or focused on a problem in an adaptive way. We want these alternatives to be drawn from the patient’s own experience because this validates and empowers the patient and makes it more likely that associated plans will be enacted. For example, FA can usefully explore the end of bouts of rumination (e.g., “What happened just before you stopped ruminating?” “What did you do when the rumination stopped?”), as this can reveal external events (e.g., distraction from a friend calling) or changes in behavior (e.g., deciding what to do; changes in thinking) that bring rumination to an end. Spotting these junctures provides possible levers to systematically reduce the rumination. Similarly, spotting the loops of thinking during an episode of rumination can be helpful. An episode of rumination can start with a genuine concern (e.g., difficult meeting with boss), leading into attempts to problem solve (e.g., “How can I plan for this?”), which are hijacked by more abstract rumination (e.g., “Why do I find this so hard?”), before eventually returning to problem solving. Using a detailed FA to identify what happened as the patient went off-track and then what happened when they got back on-track can lead to plans to cut out the maladaptive rumination in the middle of the sequence. Therapy with patients who ruminate, and particularly FA, needs to be detailed, specific, and concrete because people who ruminate typically think and talk about the meaning and gist of events rather than the detail of what

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happened. The therapist, therefore, works with clients to facilitate rich reexperiencing of ruminative episodes and alternative states of mind incorporating vivid sensory perceptual descriptions of the context and what happened. This level of detail is essential to identify triggers and cues for the rumination habit and to spot the specific elements that influence the duration or helplessness of dwelling on a problem (Principle 3: Encourage active, concrete, experiential, and specific [ACES] behavior). The therapist models more concrete behavior by making plans and asking questions that are action focused, concrete, specific, and that pertain to direct here-and-now experience rather than meaning. Exact phrasing of questions and summaries is important. Vague and general questions are unhelpful (“What kind of thoughts do you have?”), as are questions about meanings and implications (asking, “Why?”; “What do you think that means?”). In contrast, questions focusing on exact behaviors and the sequence of events (“How did it happen?”) and that link a response to a particular context at a narrowly defined time (e.g., “What did he say then?” “How did he say it?” “What did you notice immediately after you said to yourself, ‘Why can’t I do this?’”) are more useful. When exploring recent events, the therapist will seek more detail until there is a precise description of the context and behavior sufficient to generate a vivid mental picture—exactly what happened, when it happened, where it happened, how it happened, and with whom it happened. The ACES principle guides therapist questions and strategies: When unsure as to the next step or when encountering a hurdle, the emphasis is on doing something and engaging directly with experience rather than talking about it. Assessment continues throughout therapy, with detailed explorations and FA of recent episodes of rumination used in each session to assess progress and fine-tune the formulation and potential interventions. Early in therapy, it is also often useful to encourage participants to self-monitor, by completing recording forms rating the frequency, duration, and controllability of rumination or writing down recent examples of rumination, with columns for what comes before, during, and after each bout of rumination. Such self-monitoring increases awareness of the ruminative habit, which is the first step to changing the habit, and helps to get detailed descriptions of specific episodes for FA. Formulation in RFCBT involves hypothesizing the underlying process and functions of the rumination. Rumination is conceptualized as escape and avoidance behavior that has been negatively reinforced in the past by the removal of short-term aversive experience or because it has perceived or actual functions. Rumination often involves attempts to solve problems or to secondguess how other people will respond, all to avoid bad things happening. It can avoid facing unpleasant situations by putting them off and by dealing with problems “in the head” rather than “in the real world.” A common function is an attempt to understand and make sense of events and losses, which can reflect trying to increase control and reduce uncertainty and leads to the abstract “Why?” processing style. This abstract processing itself can be unintentionally avoidant: such “thinking about” the meaning of upsetting events can remove an individual from the contextualized details of upsetting events and

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memories, providing some emotional distance, which in the short-term can be negatively reinforced but which in the longer term can slow exposure and habituation to these events. Rumination can also function as an internalization of external criticism by anticipating other’s negative responses, as an attempt to minimize the emotional impact of possible future criticism or rejection. A further function is rumination acting as a form of self-motivation, for example, by pointing out one’s deficits and faults to “spur oneself on” and to avoid becoming lazy, arrogant, or complacent. Key Interventions for Rumination Once the idiosyncratic functions and contexts for rumination for each patient are formulated via iterative FAs, these guide the selection of treatment interventions. The selection of intervention builds directly from the patient’s experience across several steps, sometimes occurring within a single session. First, FA exploring the experience of the patient highlights a potential change in environment or behavior that could reduce unhelpful rumination and that generates a hypothesis of the possible function of the rumination. Second, this suggests in-session or between-session experiments to manipulate this identified variable to see if this helps the patient better handle situations. Third, if these experiments are successful, this information guides the introduction of specific treatment strategies that can be routinely practiced in homework and in daily life. This stepwise process increases the likelihood of success because it builds from the patient’s own repertoire, checks if any strategy is effective in a behavioral experiment, and gives the patient a direct positive experience of the approach working to increase motivation and positive expectations. The mnemonic for this process is E6, which stands for Explore Experience, Experiment with Experience, Exercise and Engage. Because RFCBT is principle based and process focused, there is not a standard set or order of interventions; instead, the interventions result from the specific functional-analytic formulation for each patient. However, interventions typically fall into two broad classes: (a) disrupting and/or removing the triggers to the ruminative habit and (b) repeated practice of alternative adaptive strategies to these triggers so the patient learns a new more helpful habit (i.e., the counterconditioning of alternative responses to rumination-triggering cues). FA and self-monitoring are used to recognize warning signs and environmental or behavioral contingencies maintaining rumination. Behavioral plans are then made to change these contingencies. For example, if rumination is triggered first thing in the morning just after waking up, activity scheduling is used to change the early morning routine, such as getting up and being active rather than lying in bed brooding. Similarly, stimulus control approaches would be used if rumination occurs while lying in bed not falling asleep, with patients encouraged to leave the bedroom if not falling asleep within 15 to 20 minutes and to do something relaxing, to break the association between lying in bed and ruminating. Experiments test the impact of changing environments and behavior. For example, if rumination is triggered when listening to sad music

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in the car, the effects of listening to other music can be tested to see if this impacts on rumination. Common patterns amongst individuals who ruminate include a focus on chores and obligations at the cost of self-fulfilling and absorbing activities, less structured days with irregular mealtimes and sleep patterns, taking on too many things at once and rushing around. Building in more daily routines, increasing absorbing positive activities, slowing things down, building in relaxing activities, and only focusing on one task at a time can all lower stress and feelings of being under pressure, and thus potentially reduce the triggering of rumination. Alternative behaviors are chosen to address or replace the same function(s) as that served by rumination (i.e., functionally equivalent), so that they are more likely to be reinforcing and become routine habits for the patient. This includes the full repertoire of CBT actions (i.e., activity scheduling, mastery-pleasure activities, graded task assignments, verbal rehearsal, managing situational contingencies, role-play, environmental control, Socratic dialogue, problem solving, relaxation, assertiveness training, exposure, behavioral experiments) but also more specific experiential approaches and imagery exercises developed within RFCBT to specifically shift individuals from maladaptive to adaptive thinking styles (Principle 7: Shift to an adaptive style of thinking). In these interventions, patients use directed imagery to vividly recreate previous states when a more helpful thinking style was active, to generate mental states at odds with rumination that can be used as a functional alternative strategy in response to warning signs for rumination. The effective generation of these alternative styles of processing involves vividly imagining all the elements contributing to the original experience: thoughts, feelings, posture, sensory experience, bodily sensations, attitudes, motivations, facial expressions, and urges. The patient recalls a vivid memory that recreates the desired experience and is then guided into a deeper, more elaborated experience via therapist questions focusing the patient’s attention and imagination on each detail of the experience. The patient is encouraged to imagine the event in the present tense and from a field perspective, as if they were looking into the scene right now. Concreteness training involves the patient’s shifting away from an unhelpful abstract thinking style to a more helpful concrete thinking style. It involves repeatedly focusing on the sensory details of a negative event, its specific context and circumstances and concentrating on the sequence of how the event unfolded and how active steps can be taken. Concreteness training can help patients to keep emotional events in perspective, reduce negative emotional responses, and improve problem solving (Watkins, 2008; Watkins et al., 2012). Behavioral experiments test out alternative strategies and illustrate the potential value of changing thinking style. For example, in the Why–How experiment, patients compare thinking about an upsetting situation in an abstract way (e.g., asking, “Why did it happen?” using abstract questions characteristic of the patient and identified in earlier FAs) versus thinking about it in a concrete way (e.g., asking, “How did it happen?”) to learn that thinking styles influence the effects of rumination.

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Absorption training involves recapturing the experience of being immersed and absorbed in activities by focusing on the memory of being completely absorbed in an activity (e.g., “flow” or “peak” experiences of being creative, immersed in sensory experience, or involved in highly focused physical activities like rock climbing or skiing) to provide a counterexperience to rumination. This helps patients to get in the right frame of mind to make the most of engaging in positive activities by preventing them from slipping into a negative ruminative “running commentary.” It can also enhance motivation. These imagery exercises can feed into FAs to identify how individuals can plan and prepare for activities to increase the likelihood that they will be positively absorbed in them, and lead to activity scheduling to increase the frequency of activities that the patient finds absorbing and immersive. Compassion training involves the use of imagery and visualization exercises to focus on past experiences of being compassionate and supportive or of receiving compassion and kindness to develop feelings of compassion toward the self and others. This provides a counterexperience to rumination and a functional alternative to motivate and encourage oneself. Compassion work also includes identifying activities that need to be increased and activities that need to be decreased to take care of oneself. All new alternative behaviors are repeatedly practiced in the context of the triggers and warning signs that set off rumination (e.g., feeling low, recalling an upsetting memory) with the intent of building a new more adaptive habit to replace rumination (Principles 4, 5, and 6). If becoming tense in the shoulders and noticing attention narrowing and turning inward are warning signs for rumination, exercises within the session would induce these experiences (e.g., by recalling a recent stressful experience) and then practice shifting to a new strategy, such as shifting attention outward and using helpful concrete questions. These exercises would then be audio-recorded and set as homework for further practice. For example, the trigger for one client’s rumination was becoming angry and irritated. Their rumination was about why they were overreacting and being oversensitive. The consequence of their rumination was to reduce anger but to also then result in increased depressed mood. It was hypothesized that a possible function of their rumination was to control anger. When this formulation was collaboratively reviewed with the patient, they noted that they were particularly afraid of losing control of their anger and becoming like their father, who was violent and aggressive. In this case, we looked at functionally equivalent but constructive alternatives to practice to control the patient’s anger when they noticed they were becoming irritated such as relaxation exercises and appropriate assertiveness. To support the repeated practice of these helpful alternative behaviors and the development of new habits, self-coping homework plans of the form “If I notice a warning sign for rumination, then I will do my alternative action” are made (e.g., “If I notice I am getting tense, then I will focus my attention outwards and ask, ‘How can I do something about this?’”). These plans build on

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the research showing that such implementation intention plans linking an action to a context make it more likely that the action will be enacted.

EVIDENCE BASE FOR RUMINATION INTERVENTIONS A recent systematic review and meta-analysis of 36 randomized controlled trials (RCTs) involving 3,307 patients found that treatments intended to target rumination had medium-sized effects on reducing rumination relative to control arms (RFCBT g = 0.76, p < .01; mindfulness-based cognitive therapy [MBCT], g = 0.44, p < .01; CBT g = 0.57, p < .01) and had significantly larger effect sizes than treatments that did not specifically target rumination, including antidepressant medication (Spinhoven et al., 2018). The effects on rumination post-treatment were only significantly associated with reductions in depression severity in RFCBT: “in particular RNT-focused CBT may have a more pronounced effect on RNT than other types of interventions” (Spinhoven et al., 2018, p. 71). In parallel, higher levels of rumination pretreatment are associated with less of a treatment response to standard CBT (and antidepressant medication; Ciesla & Roberts, 2002; Jones et al., 2008; Schmaling et al., 2002; Teismann et al., 2008). The first RCT of RFCBT (Watkins et al., 2011) found that the addition of 12 sessions of individual RFCBT to ongoing antidepressant medication and outpatient clinical management (treatment-as-usual [TAU]) significantly reduced rumination and depression relative to TAU alone (remission rates: TAU 21%; TAU+RFCBT 62%) in adult patients with treatment-refractory residual depression. An independent trial confirmed that group-delivered RFCBT improved depressed mood and reduced rumination relative to a waiting list condition in patients with residual depression, with treatment gains maintained over 1-year follow-up (Teismann et al., 2014). A third RCT in adults with depression who had not responded to primary care treatment found that a groupdelivered RFCBT reduced depression significantly more than group-delivered standard CBT (Hvenegaard et al., 2019). A fourth RCT in adolescents with a history of major depressive disorder found that RFCBT significantly reduced rumination and depressive symptoms relative to an assessment only control condition. fMRI revealed that patients treated with RFCBT had significant decreases in connectivity between the default mode network and cognitive control network, with this change in connectivity correlated with changes in depression and rumination (Jacobs et al., 2016). Two RCTs tested whether a key component of RFCBT—–encouraging a more concrete adaptive processing style—was effective as a stand-alone treatment. This cognitive bias modification approach involved repeated practice at focusing on the specific details, context, and sequence (e.g., asking, “How?”) of difficult events using audio-recorded mental exercises in response to warning signs for rumination. An RCT in individuals with dysphoria found that training to be more concrete for one-week reduced depression, anxiety, and rumination relative to a no-treatment control and a credible attention placebo

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control (Watkins et  al., 2009). An RCT of adults with major depression recruited in primary care found that guided self-help concreteness training and guided self-help relaxation training were superior to TAU provided by general practitioners in reducing depression. Both training conditions were matched for rationale, therapist contact, identification of warning signs and daily practice via audio-recording over 6 weeks and delivered via one faceto-face session and three 30-min telephone sessions to encourage treatment completion. Concreteness training was superior to relaxation training in reducing rumination and outperformed relaxation training at reducing depression when the training was self-reported to be habitual (Watkins et al., 2012). Because rumination has been implicated as a risk factor for depression onset, RFCBT was tested as a preventative intervention for depression and anxiety. In 251 Dutch adolescents and young adults with elevated rumination but without current major depression or anxiety disorder in a high-risk prevention design, relative to wait list control, both group RFCBT and therapist-supported internet-delivered RFCBT significantly reduced worry, rumination, anxiety, and depression at post-intervention and 1-year follow-up and halved one-year incidence rates of major depression and generalized anxiety disorder, as indexed by standard cutoffs on self-report measures (Topper et al., 2017). A follow-on trial compared supported internet-delivered CBT versus usual practice for UK undergraduates with elevated rumination and worry and found that relative to usual practice, guided online RFCBT significantly reduced the subsequent onset of major depression, as assessed by structured diagnostic interview, for young people reporting higher levels of stress at baseline (Cook et al., 2019). This evidence confirms that rumination is a major psychosocial risk for the onset of major depression and generalized anxiety disorder, and that targeting rumination has transdiagnostic preventive benefits. Other psychological treatments explicitly focusing on rumination are meta­ cognitive therapy and MBCT. Based on the theory that rumination is initiated by positive metacognitive beliefs about the usefulness of rumination and exacerbated by negative metacognitive beliefs about the negative consequences of rumination (Wells, 2009), metacognitive therapy involves challenging these beliefs and training patients to disengage their attention from self-focus to external stimuli (see Chapter 16, this volume). Metacognitive therapy has been examined in one small RCT against waiting list control, with significant benefits over 1 year (Hjemdal et al., 2019), but has not yet been compared with an active control or active treatment. MBCT is a psychosocial group-based relapse prevention program that incorporates meditational practice within the framework of CBT principles to increase resilience against depression (Segal et al., 2013). The meditational practice typically involves participants maintaining their attention to their breath, body thoughts, and feelings, and to hold such experiences in awareness, in a non-judgmental, detached, compassionate and accepting way. Mindfulness is hypothesized to enable individuals to develop alternative responses to negative thoughts, and, thereby, step out of habitual patterns of rumination. Although found to be an effective relapse prevention treatment for individuals with three or more episodes of depression,

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the effects of MBCT on rumination are mixed, with rumination reduced in experimental analogue studies (Feldman et al., 2010) and in RCTs for patients with a history of recurrent major depression relative to waiting list control (Geschwind et al., 2011) but not to continuation antidepressants (Kuyken et al., 2015).

CASE EXAMPLE The following fictional case example amalgamating observations across multiple real patients illustrates RFCBT in more detail. Charlotte is a cisgender woman with a long-standing history of major depression, as well as comorbid GAD, PTSD, and bulimia. Her rumination is often very self-critical and focused on judging and evaluating herself as lazy, arrogant, and selfish. She has a difficult relationship with her mother, whom she experiences as insensitive, domineering, and critical. The initial sessions established rumination as an unhelpful habit and as a target for therapy. The initial assessment and first FAs of specific episodes of rumination indicated that her common warning signs for rumination were tiredness, inactivity, irritability, and getting hot and tense. Charlotte’s ruminative content was often about why she wasn’t trying hard enough or being thoughtful enough, and the consequences were to maintain her depression, reduce her motivation, and erode her self-confidence. Exploring recent examples of when she had ruminated versus times when she hadn’t ruminated highlighted variability in recent interactions with her ex-partner. On one recent occasion, she went to her ex-partner’s apartment to sort through their possessions and work out what was hers, but it ended up in an argument and she got caught up in rumination. Later in the week, she went back to finish the sorting through possessions and coped much better. Looking closely at the detail of the two situations revealed key differences: the first time, she was already in a rush to get there and a bit stressed before she went but got stuck in a traffic jam, whereas the second time, she was more prepared and had made a list of what she wanted to collect before going and had spent some time with a friend before going so she was feeling calmer. This difference illustrates how changes in environment and behavior influence the likelihood of triggering rumination. This led to Charlotte’s making plans to slow things down, to schedule difficult things when she wasn’t in a rush and alongside more positive activities and to actively plan before events. The FAs also indicated that when ruminating Charlotte often asked lots of “Why?” questions, for example, “Why does he always do this?” “Why does he always put me down?” “Why am I so irritable?” “Why am I so selfish?” and “Why do I find this so difficult?” These questions further exacerbated negative emotions and the ruminative loop, leading to several hours of rumination, withdrawal, and tearfulness. When looking for variability, the therapist sought a recent example where Charlotte had the same triggers and warning signs for rumination, such as feeling irritable, hot, and tense but didn’t get trapped in “Why” questions and rumination. Charlotte identified an example

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where she had started to dwell on why she was a failure but had then shifted across to more helpful active problem solving by asking questions, including “What would someone else do to cope?” “What is the best way to get a positive result?” “What can I do differently?” and “How can I handle this?” In this situation, her thinking only persisted 25  minutes, and she rapidly calmed down and moved on to get on with her day. This example illustrates spontaneously occurring adaptive concrete thought. To check whether this thinking style was genuinely useful and to reinforce it as a helpful strategy, the therapist moved into an experiential experiment (the “Why–How” experiment). The therapist asked Charlotte to imagine herself back in a recent upsetting situation that triggered rumination and then first prompted her to focus on this situation while asking her typical “Why” questions and then to reimagine this situation while prompting her with her own more concrete “What” and “How” questions. Mood, thinking, concentration, physical sensations, and motivation were rated after each imaginal induction. This experiment revealed that there was a strong difference in how Charlotte felt between the two manipulations, with her feeling more active, calm and empowered when she used the “What” and “How” questions. Because these questions proved to be useful in this in-session experiment, the therapist and Charlotte agreed they would be a useful strategy to try and use in daily life. To help this strategy to become a habit, the therapist worked with Charlotte to develop a collaborative “If–Then” plan (i.e., If I notice I am getting irritable, hot, tense or asking “Why” questions, then I will ask, “What is the best way to get a positive result?” and “How can I handle this?”), using the questions which resonated most with Charlotte. The implementation of this If–Then plan was then reviewed over subsequent weeks, and Charlotte observed that it helped to get more control over her rumination. However, there were still episodes of rumination, particularly when Charlotte had thoughts about being lazy or selfish. Examples of these situations were explored in further FAs. The possible effects of not ruminating were investigated (e.g., “What would happen if you didn’t dwell on thoughts about not trying hard enough?”). Charlotte reported a concern that if she didn’t ruminate, then she might become complacent and arrogant. It was hypothesized that one function of her rumination may be to avoid becoming lazy and arrogant. On collaboratively reviewing this with her, she reflected on how much she feared becoming this kind of person and that she saw becoming an arrogant and lazy person as very aversive. This formulation suggested that further tackling her rumination required the development of another alternative response that more constructively served this function. For this reason, compassion work was chosen because this is a constructive way to promote motivation and to approach becoming a better person (rather than avoiding being an unwanted person) that provides an effective, positive, calming, and beneficial alternative to rumination. A hierarchy of what would be easier or harder to do about feeling compassion was drawn up with Charlotte: this indicated that it was easy to feel compassion to others and harder to feel compassion to herself. Imaginal and memory

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exercises were used to generate the experience of compassion starting with easier examples (e.g., compassion to loved ones), and gradually moving up to more difficult situations (e.g., compassion to an annoying colleague), in small steps over several sessions until Charlotte was able to begin to direct some self-compassion to herself. In later examples, the feeling of irritation as a warning sign and trigger to rumination was induced first by recalling upsetting events, before practicing compassion to others and compassion to self, to associate the new response with this cue to rumination. Over several weeks, this compassionate approach became easier for Charlotte and her residual rumination declined.

SUMMARY AND FUTURE DIRECTIONS One key future direction for improving the treatment of rumination is a better understanding of and focus on its habitual nature. The habit account proposes that any interventions that improve mood state may temporarily reduce depressive rumination by removing the context (low mood) that triggers rumination (i.e., limiting its expression but not necessarily modifying the underlying habit; Watkins & Nolen-Hoeksema, 2014). If the underlying habit is not changed, once the triggering context returns during another period of depressed mood or stress, the tendency to ruminate would be reactivated, increasing vulnerability to another episode of depression. It remains an open question as to how well interventions change the underlying habit to produce long-lasting reductions in psychosocial risk, versus producing only short-lived reductions in the expression of rumination. Interventions that do not directly target rumination but that improve mood such as supportive counseling, antidepressants, and attention placebo may only temporarily prevent the expression of rumination. Future studies therefore need to evaluate the longer-term impact of treatment on rumination and relapse/recurrence of depression and anxiety and include measures that capture all dimensions of the habitual quality of pathological rumination (e.g., Hjartarson et  al., 2021). Recent research has indicated that change in habitual behaviors can mediate the effect of psychological interventions on depression (Owens et al., 2021). The habit account also suggests that the use of EMA and ecological momentary interventions (EMIs), such as delivered by smartphone apps, could enhance the reduction of rumination. Random prompts to assess mood, rumination, and context throughout the day provide a more effective and real-time form of self-monitoring for users to spot their patterns of rumination and to identify the triggers and warning signs for their rumination. With sufficient collection of such EMA data, EMI apps could then automatically prompt patients to use appropriate strategies based on self-reported data or sensor data when thresholds for triggers or warning signs are reported or detected. These thresholds could be derived from common preestablished patterns (e.g., increased anxiety, low mood) or, ideally, use machine learning to develop individualized thresholds of

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when rumination is likely for each user. Such EMIs would support the realworld implementation of If–Then plans. EMA and EMIs could be a useful adjunct to existing evidence-based treatments such as RFCBT or potentially even a standalone intervention. The conceptualization of rumination-as-a-mental habit suggests that repeated cognitive bias modification (CBM) training may be a useful intervention because it involves the same associative and instrumental learning processes as habit formation and has been shown to change automatic habitual processes (Hertel et al., 2014; Hertel & Mathews, 2011). Along with concreteness training’s being effective at reducing rumination and depression (Watkins et al., 2012), CBM focused on repeated training of positive interpretations of ambiguous situations via internet-delivery has been found to reduce worry, rumination and depressive symptoms relative to a control condition in patients with major depression or generalized anxiety disorder (Hirsch et al., 2018). A second key area for development is to increase the scalability of interventions for rumination. To date, the best evidence-based interventions for rumination involve therapist-supported face-to-face or online therapy or group interventions and therefore have a limited capacity and scalability. For such a common and highly impactful psychosocial risk, scalable and ideally nonconsumable interventions that multiple users can access at the same time without limit are required, such as massive open online interventions or effective self-help apps. Developments in online CBM and EMA/EMI apps may be part of the solution to scalable interventions. Preliminary data from Cook et al. (2019) suggested that an unguided internet RFCBT package targeting worry and rumination may act as an effective prevention for depression in a high-risk group. A large-scale European study is currently testing an unguided personalized self-help app that incorporates EMA and various RFCBT strategies for rumination where indicated (Newbold et al., 2020). In summary, there is extensive evidence that rumination is a major psycho­ social risk for depression as well as anxiety and other mental health conditions. Pathological rumination has negative effects by exacerbating and prolonging negative affect and cognition, reducing active problem solving, and impairing sensitivity to context and reward. Moreover, rumination is itself a consequence of numerous important risk factors for depression, including past history of depression, family history of poor mental health, stressful life events, history of abuse or neglect, bullying, and neuroticism, such that it provides a common route to vulnerability, albeit one that is modifiable. Pathological rumination is distinguished from normal and sometimes adaptive repetitive thinking about losses, unexpected events, and important unresolved goals in terms of having become an automatic habit triggered by mood states and daily hassles and in involving a more abstract, generalized, and decontextualized thinking style. There is emerging evidence for effective psychological interventions to ameliorate rumination, particularly for those interventions that explicitly target rumination and that tackle these proximal underlying mechanisms.

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Spinhoven, P., van Hemert, A. M., & Penninx, B. W. (2019). Repetitive negative thinking as a mediator in prospective cross-disorder associations between anxiety and depression disorders and their symptoms. Journal of Behavior Therapy and Experimental Psychiatry, 63, 6–11. https://doi.org/10.1016/j.jbtep.2018.11.007 Teismann, T., von Brachel, R., Hanning, S., Grillenberger, M., Hebermehl, L., Hornstein, I., & Willutzki, U. (2014). A randomized controlled trial on the effectiveness of a rumination-focused group treatment for residual depression. Psychotherapy Research, 24(1), 80–90. https://doi.org/10.1080/10503307.2013.821636 Teismann, T., Willutzki, U., Michalak, J., & Schulte, D. (2008). Bedeutung von rumination und ablenkung für den therapieerfolg depressiver patienten [Relevance of rumination and distraction to therapy outcome in depressed patients]. Verhaltenstherapie, 18(4), 215–222. https://doi.org/10.1159/000165687 Tester-Jones, M., Karl, A., Watkins, E., & O’Mahen, H. (2017). Rumination in dysphoric mothers negatively affects mother-infant interactions. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 58(1), 38–45. https://doi.org/10.1111/jcpp.12633 Topper, M., Emmelkamp, P. M. G., Watkins, E., & Ehring, T. (2017, March). Prevention of anxiety disorders and depression by targeting excessive worry and rumination in adolescents and young adults: A randomized controlled trial. Behaviour Research and Therapy, 90, 123–136. https://doi.org/10.1016/j.brat.2016.12.015 Van Lier, J., Vervliet, B., Boddez, Y., & Raes, F. (2015, June). “Why is everyone always angry with me?!”: When thinking ‘why’ leads to generalization. Journal of Behavior Therapy and Experimental Psychiatry, 47, 34–41. https://doi.org/10.1016/j.jbtep.2014. 11.008 Verkuil, B., Brosschot, J. F., Gebhardt, W. A., & Korrelboom, K. (2015). Goal linking and everyday worries in clinical work stress: A daily diary study. British Journal of Clinical Psychology, 54(4), 378–390. https://doi.org/10.1111/bjc.12083 Verplanken, B., Friborg, O., Wang, C. E., Trafimow, D., & Woolf, K. (2007). Mental habits: Metacognitive reflection on negative self-thinking. Journal of Personality and Social Psychology, 92(3), 526–541. https://doi.org/10.1037/0022-3514.92.3.526 Verplanken, B., & Wood, W. (2006). Interventions to break and create consumer habits. Journal of Public Policy & Marketing, 25(1), 90–103. https://doi.org/10.1509/jppm. 25.1.90 Watkins, E. (2004). Adaptive and maladaptive ruminative self-focus during emotional processing. Behaviour Research and Therapy, 42(9), 1037–1052. https://doi.org/10.1016/ j.brat.2004.01.009 Watkins, E. (2008). Constructive and unconstructive repetitive thought. Psychological Bulletin, 134(2), 163–206. https://doi.org/10.1037/0033-2909.134.2.163 Watkins, E. (2016). Rumination-focused cognitive-behavioral therapy for depression. Guilford Press. Watkins, E., Baeyens, C. B., & Read, R. (2009). Concreteness training reduces dysphoria: Proof-of-principle for repeated cognitive bias modification in depression. Journal of Abnormal Psychology, 118(1), 55–64. https://doi.org/10.1037/a0013642 Watkins, E., & Baracaia, S. (2001). Why do people ruminate in dysphoric moods? Personality and Individual Differences, 30(5), 723–734. https://doi.org/10.1016/S01918869(00)00053-2 Watkins, E., & Baracaia, S. (2002). Rumination and social problem-solving in depression. Behaviour Research and Therapy, 40(10), 1179–1189. https://doi.org/10.1016/S00057967(01)00098-5 Watkins, E., & Brown, R. G. (2002). Rumination and executive function in depression: An experimental study. Journal of Neurology, Neurosurgery, and Psychiatry, 72(3), 400–402. https://doi.org/10.1136/jnnp.72.3.400 Watkins, E., Moberly, N. J., & Moulds, M. L. (2008). Processing mode causally influences emotional reactivity: Distinct effects of abstract versus concrete construal on emotional response. Emotion, 8(3), 364–378. https://doi.org/10.1037/1528-3542.8.3.364

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Watkins, E., & Moulds, M. (2005). Distinct modes of ruminative self-focus: Impact of abstract versus concrete rumination on problem solving in depression. Emotion, 5(3), 319–328. https://doi.org/10.1037/1528-3542.5.3.319 Watkins, E., Mullan, E., Wingrove, J., Rimes, K., Steiner, H., Bathurst, N., Eastman, R., & Scott, J. (2011). Rumination-focused cognitive-behavioural therapy for residual depression: Phase II randomised controlled trial. The British Journal of Psychiatry, 199(4), 317–322. https://doi.org/10.1192/bjp.bp.110.090282 Watkins, E., & Nolen-Hoeksema, S. (2014). A habit-goal framework of depressive rumination. Journal of Abnormal Psychology, 123(1), 24–34. https://doi.org/10.1037/ a0035540 Watkins, E., Taylor, R. S., Byng, R., Baeyens, C., Read, R., Pearson, K., & Watson, L. (2012). Guided self-help concreteness training as an intervention for major depression in primary care: A Phase II randomized controlled trial. Psychological Medicine, 42(7), 1359–1371. https://doi.org/10.1017/S0033291711002480 Watkins, E., & Teasdale, J. D. (2001). Rumination and overgeneral memory in depression: Effects of self-focus and analytic thinking. Journal of Abnormal Psychology, 110(2), 353–357. https://doi.org/10.1037/0021-843X.110.2.333 Watkins, E. R., & Roberts, H. (2020). Reflecting on rumination: Consequences, causes, mechanisms and treatment of rumination. Behaviour Research and Therapy, 127, 103573. https://doi.org/10.1016/j.brat.2020.103573 Wells, A. (2009). Metacognitive therapy for anxiety and depression. Guilford Press. Wood, W., & Neal, D. T. (2007). A new look at habits and the habit-goal interface. Psycho­ logical Review, 114(4), 843–863. https://doi.org/10.1037/0033-295X.114.4.843

14 Ineffective Social Problem Solving Arthur M. Nezu, Christine Maguth Nezu, Jenna L. Damico, and Holly R. Gerber

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onventional wisdom suggests that the experience of stress is ubiquitous and omnipresent. Conventional wisdom also posits that a modicum of stress is important for our daily lives—if we did not have it, we would not get up in the morning and go to school or work. A further truism (which research findings confirm; e.g., Vrshek-Schallhorn et al., 2020) indicates that too much stress can lead to negative consequences, such as depression. However, we opine that it is not the presence of stress per se that engenders psychopathology but how we handle or manage such stress that best predicts the likelihood of experiencing depressive reactions. As such, this chapter focuses on ineffective social problem solving (SPS) as one major risk or vulnerability factor for depression, as well as on how psychosocial interventions aimed at improving SPS can attenuate such distress.

DEFINITION OF CONSTRUCTS We begin by differentiating between the constructs of problem solving and social problem solving. The topic of problem solving has been the focus of many decades of research within the fields of cognitive and experimental psychology. As such, human problem solving has been conceptualized as a higher order set of cognitive processes and one major aspect of executive functioning. Such research generally addresses how individuals attempt to solve cognitive, mathematical, or intellectual problems, as compared with the types of problems that people experience in living (e.g., dealing with financial stressors, https://doi.org/10.1037/0000332-015 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  333

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interpersonal difficulties, uncontrollable environmental disasters). Whether individuals’ emotional well-being is associated with their competency to effectively manage or cope with such everyday problems was not a special focus of scientific scrutiny until the 1960s (D’Zurilla & Nezu, 2007). Whereas certain processes or activities may be involved in addressing both types of problems (e.g., brainstorming different possible solutions), it should be noted that they differ in many significant ways (Nezu & Nezu, 2021). For example, problems-in-living, such as interpersonal difficulties or financial dilemmas, are more likely to engender stress and negative emotional reactions as compared with math problems, such as solving an algebraic equation. Moreover, they usually involve other individuals (e.g., difficulty with coworkers), have real-world consequences if not solved (e.g., inability to pay rent will likely lead to eviction), and generally have multiple possible solutions to reach a goal (i.e., different means of transportation to reach a travel destination). Moreover, differing goals, values, and circumstances usually greatly impact whether a given solution is effective, even for individuals experiencing a similar problem (e.g., how can I meet new people, having recently moved to a new neighborhood?). Problems-in-Living Problems-in-living are those real-life situations that (a) entail the presence of obstacles preventing people from reaching a goal; (b) if not handled effectively, can engender negative consequences; and (c) lack an immediately recognizable solution. A problem can be pictorially represented by the proverbial stick figure located at Point A having a question mark in a “thought bubble,” with that figure wondering how to get to Point B, given that a brick wall or other barrier lies between the two points. Description Problems can occur within one’s social (e.g., ending of a romantic relationship) or physical (e.g., car accident) milieu. They can be internal to the individual (e.g., a wish to obtain a promotion at work) or involve others (e.g., differences in parenting techniques within a couple). Why a given set of circumstances represents a problem to a particular individual depends on the presence of various individually unique obstacles to goal attainment. Such barriers are varied, such as novelty (e.g., going to college for the first time), uncertainty (e.g., uncertainty regarding how one is performing on a new job), difficulty predicting the future (e.g., lack of control over the economy), conflicts between internal (e.g., uncertainty between career aspirations) and interpersonal (e.g., family disputes regarding where to live) goals, behavioral skill deficits (e.g., difficulty communicating), and limited resources (e.g., financial problems). Problems can be a singular occurrence (e.g., forgetting one’s car keys at work), a group of related events (e.g., difficulties with coworkers), or a situation that is chronic in nature (e.g., medical illness). Further, problems-in-living are

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generally characterized as the interaction between people and their environment. Further, problems can be described by differences between the demands of a situation and people’s ability to adequately meet such demands. Solutions A response in reaction to the emergence of a problem can be thought of as a solution. However, this definition would be contrasted with what would be considered an effective solution. Effective solutions are ones that (a) attain people’s stated goals via overcoming relevant barriers, (b) maximize positive consequences, and (c) attenuate related negative outcomes. Such consequences involve various personal and interpersonal/social effects as well as short-term and long-range consequences. Note that the overall quality of a given solution varies as a function of individuals’ unique values and goals as well as the specific circumstances involved. SPS: A Multidimensional Model We define SPS as the process by which individuals attempt to identify, discover, or create adaptive means of coping with a wide variety of stressful problems, both acute and chronic, encountered during the course of living. . . . [It] reflects the process whereby people direct their coping efforts at altering the problematic nature of a given situation, their reactions to such problems, or both. Rather than representing a singular type of coping behavior or activity, SPS represents the multidimensional meta-process of ideographically identifying and selecting various coping responses to implement in order to adequately address the unique features of a given problematic situation at a given time. (Nezu & Nezu, 2019, p. 14)

Based on research, two major components of SPS have been identified: problem orientation and problem-solving style (D’Zurilla et al., 2002, 2004). Problem Orientation Problem orientation (originally termed general orientation; see D’Zurilla & Goldfried, 1971) involves various cognitive–affective factors, including beliefs, attitudes, and emotional reactions regarding problems-in-living. This also includes individuals’ estimations regarding their ability to effectively handle such stressors. Problem orientation was initially thought to represent a continuum with anchors positioned at the two ends, one being a “positive problem orientation” and the second being a “negative problem orientation.” However, based on subsequent research, it was later concluded that these two orientation factors can function independently within the same person depending on the type of problem being addressed (Nezu, 2004). In other words, a person can be characterized as having a positive orientation when addressing one type of problem (e.g., achievement-oriented difficulties, such as attempting to obtain a promotion), while simultaneously displaying a negative orientation regarding a different type of stressful event (e.g., affiliation problems, such as maintaining a healthy relationship with a partner).

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A positive problem orientation (PPO) is characterized by the propensity to perceive problems as challenges rather than catastrophes, accept that problems are a normal part of life, regard most problems-in-living as generally solvable, think of oneself as being able to successfully cope with most problems, presume that the act of problem solving usually entails time and effort, and view negative emotions as signals that a problem exists. In contrast, a negative problem orientation (NPO) is represented by tendencies to view problems as significant threats to one’s well-being, regard problems as being very difficult to resolve or manage, doubt one’s ability to solve problems successfully, and feel generally upset when problems occur. Problem-Solving Styles Problem-solving styles are the second major SPS dimension. Styles involve cognitive and behavioral activities that people engage in while attempting to cope with problems-in-living. Three SPS styles exist: planful problem solving (previously termed “rational” problem solving), avoidant problem solving, and impulsive–careless problem solving (D’Zurilla et al., 2002, 2004). Planful (rationale) problem solving (RPS) is the adaptive and effective method of addressing problems and involves the application of a set of problem-solving skills. These include (a) defining a problem (i.e., accurately describing a problem, articulating a set of realistic goals, and identifying specific barriers that prevent the protagonist from attaining such goals); (b) generating solution ideas (i.e., articulating a group of alternative solutions aimed at reaching the previously stated goals); (c) deciding which alternatives are likely to solve the problem (i.e., predicting the outcomes of the alternatives, comparing positive with negative consequences of the various solution ideas, and developing an “action plan” based on this cost–benefit analysis); and (d) enacting the solution (i.e., carrying out the plan, observing the consequences, and based on the outcome, determining how one needs to continue addressing the problem if not solved). The remaining two problem-solving styles are generally considered to be ineffective and maladaptive methods of addressing stressful problems in living. The first is referred to as an impulsive/careless style (ICS), and it is characterized by the propensity to be impulsive, rushed, and unplanned when attempting to solve a problem. The avoidant problem-solving style (AS) entails the tendency to procrastinate and delay one’s attempts to cope with an emerging problem. Often this style is also characterized by the propensity to depend heavily on others to solve one’s problems. Both styles not only can result in unsuccessful problem resolution, but they likely increase the possibility of exacerbating existing difficulties, as well as create new problems. Note that problem-solving styles should not be viewed as inflexible “traits.” Rather, the problem-solving styles employed are influenced heavily by various contextual factors, especially the type of problem being addressed. Each of the two orientations and the three problem-solving styles represents a propensity to view and behave toward life’s difficulties from a particular perspective. The emergence of these factors is likely to be based on people’s unique learning experiences. As such, whereas there are some people who can be

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characterized overall by a given orientation or style, the existence of individual difference factors would suggest that a person-by-situation perspective is probably more valid to describe most people.

SPS AS A RISK FACTOR FOR DEPRESSION Research linking SPS with depression has a history going back several decades. Early studies, for example, compared college students with and without dysphoria and found significant differences in their SPS (Dobson & Dobson, 1981; Gotlib & Asarnow, 1979; Nezu, 1986). In addition, SPS has been found to be significantly correlated with depression among adults (Marx et al., 1992) and children (Goodman et al., 1995; Sacco & Graves, 1984). The research strategy that evaluated differences between effective versus ineffective problem solvers also identified significant differences regarding depression symptom severity (e.g., Nezu, 1985). To describe the overall empirical literature documenting the strong relationship between SPS and depression since that time would require much additional chapter space. As such, the reader is directed to those sources that provide listings of this research that cuts across different age groups, behavioral health clients, medical patient samples, and ethnically diverse populations (D’Zurilla & Nezu, 2007; Nezu, 1987; Nezu & Nezu, 2019; Nezu et al., 2013). However, we wish to provide a sample of more recent research focused on diverse age and ethnic samples. SPS and Depression Among Adolescents Research focusing on adolescents has examined the association between SPS and depression, evaluated sex differences, assessed these and other variables among older youth, and explored how depression and SPS relate to suicidality. For example, Siu and Shek (2010) identified mild-to-moderate positive correlations between the negative components of SPS (i.e., NPO, ICS, and AS) and depression, as well as a negative association between PPO and depression among 235 Chinese adolescents. Similarly, in a sample of 800 East Indian adolescents, ineffective problem-solving (i.e., NPO, AS, and ICS) strategies were positively correlated with depression, whereas effective problem-solving (i.e., PPO and RPS) dimensions were negatively associated with depression (Singhal et al., 2016). In an investigation focused on Turkish youth, Özdemir et al. (2013) found SPS types to be significantly associated with both aggressive and depressive symptoms, with depression partially mediating the relationship between SPS and aggressive behavior. Vatanasin et  al. (2012) included 800 high school students from Thailand and found that ineffective SPS mediated the effects of rumination and negative life events. Additional research has identified significant differences in SPS as a function of gender among adolescents. For example, Crockett et al. (2020) recently reported a study that included 2,022 adolescents from high schools in Chile.

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Using the Patient Health Questionnaire-9 (Johnson et al., 2002), the adolescents were divided into three groups based on specific criteria regarding their symptoms during the previous two weeks: no depressive episode, subthreshold depressive episode (SDE), and major depressive episode (MDE). Results indicated that SPS was negatively associated with SDE among female students, but not male high schoolers, whereas SPS was negatively associated with MDE among both sexes. In studies that have included aspects of suicidality, NPO, PPO, and AS were significant predictors of depression among 439 adolescents who were receiving treatment, where NPO and PPO served as moderators of treatment outcome regarding suicide ideation (Becker-Weidman et al., 2010). In addition, when assessing for AS, depression, and suicide ideation at 3-month intervals in a sample of 110 adolescents, López. and colleagues (2020) found that depression severity at 3 months mediated the association between AS at baseline and suicide ideation at 6 months, accounting for half of the variance in suicide ideation severity at 6 months. SPS and Depression Among Diverse College Students Research investigating the relationship between SPS and depression, as well as other relevant variables (e.g., emotional intelligence, suicide, nonsuicidal self-injury), has also focused on college students of diverse backgrounds. For instance, Hasegawa and colleagues (Hasegawa, Hattori, et al., 2015; Hasegawa, Yoshida, et al., 2015; Hasegawa et al., 2016, 2018) conducted several studies examining SPS and ruminative depression among Japanese university students. Continuing along the lines of prior significant associations that have been found between depression and SPS (Hasegawa, Yoshida, et al., 2015), Hasegawa, Hattori, et al. (2015) found longitudinal relationships between ICS and depression among nonclinical students. Furthermore, Hasegawa and colleagues (2018) found that, after controlling for initial depressive symptoms in their sample, ICS and rumination was longitudinally predictive of subsequent depression. Among Iranian college students, Ranjbar et al. (2013) identified mild-tomoderate correlations between components of SPS and depression. Research on depression and SPS has been conducted at Chinese universities as well, with Chow et al. (2011) finding that SPS moderated the association between emotional intelligence and depression. Among Hispanic college students, Chesin and Jeglic (2012) found that both depression and PPO were significant predictors of suicide in their sample of Latina undergraduates. Further, among students in Northern Spain, low PPO and high NPO predicted depressive symptoms, whereby NPO significantly interacted with stress in predicting depression (de la Fuente et al., 2019). Additionally, in a racially and ethnically inclusive sample of Hispanic, Black, White, Indigenous, and “other” racial/ethnic college students, a correlation between SPS and depression was found. Further, when controlling for age, sex, and ethnicity, SPS moderated the relationship between nonsuicidal self-injury (NSSI) and suicidal behaviors with SPS, depression, and NSSI being independent contributors to the model (Walker et al., 2017).

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SPS and Depression Among Older Adults Among older individuals, research exploring the relationship between SPS and depression has included other psychological constructs, expanding prior research that demonstrated the association between SPS and depression (e.g., Kant et al., 1997). In a randomized trial assessing two interventions aimed at adults who met criteria for subsyndromal depression, higher SPS and late life functioning/disability were predictive of mental health-related quality of life (MHRQOL) postintervention, with no significant differences found between treatments. More specifically, effective problem-solving styles (i.e., PPO and RPS) were associated with increased MHRQOL, whereas ineffective styles (i.e., NPO, ICS, and AS) were associated with decreased MHRQOL among participants (Jimenez et al., 2015). In addition, Paterson and colleagues (2016) found that higher depression scores predicted lower everyday problem-solving abilities among older adults living in the community. Akin to other age groups, the association between SPS and depression among older adults has also been studied within the context of suicidality. For example, when examining differences among older adults with depression who had attempted suicide, adults with depression who had no suicide history, and controls with no psychiatric history, those who had attempted suicide reported lower SPS abilities compared with the other groups (Szanto et al., 2012). These results are congruous with a study by Clark et al. (2011) in which older adults presenting with depression and a history of suicide attempts showed significantly lower overall SPS ability and reported higher NPO, AS, and ICS scores than participants with depression and no suicidality. SPS, Depression, and Autism Spectrum Disorder Characteristics In addition to examining SPS and depression throughout various developmental stages, research has also explored these relationships aimed at individuals with neurodevelopmental disorders, such as autism spectrum disorder (ASD). When investigating autism phenotype severity, Jackson and Dritschel (2016) found that SPS partially mediated the association between phenotype severity and depression in their sample. Focused on the assessment of autistic traits, SPS has emerged as both a partial (Rosbrook & Whittingham, 2010) and full (Liew et al., 2015) mediator of the relationship to depression. Moreover, using structural equation modeling, Ebrahimi et al. (2017) provided further support for these existing relationships in their findings that SPS plays an intermediary role in the association between autistic trait severity and depression.

CONCEPTUAL ISSUES In addition to research demonstrating a link between SPS and depression among a wide variety of populations, additional variables have been identified that play a contextual role in this association. These include stress, personality traits, and rumination.

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The Role of Stress At the beginning of this chapter, we noted that a plethora of research has dem­ onstrated a strong link between stress and depression. However, we further posited that it is not the experience per se of stressful events that can engender depression. Rather, it is how one handles or copes with such stress that is the more valid predictor. In essence, then, the relationship between SPS and depression can be characterized as a diathesis–stress association—ineffective SPS is considered to be a vulnerability factor that, when triggered by a stressful event, increases the probability of depression occurring. Several studies have directly addressed this concept and found that SPS is a significant moderator of the association between stress and depression, suggesting that effective SPS serves to mitigate against the negative impact of stress (Bell & D’Zurilla, 2009a; Brack et al., 1992; Cheng, 2001; de la Fuente et al., 2019; Frye & Goodman, 2000; Goodman et al., 1995; Nezu et al., 1986; Nezu & Ronan, 1985, 1988; Priester & Clum, 1993; Spence et al., 2003; Yang & Clum, 1994). Stress can also impact SPS such that it leads to emotional distress (Nezu et al., 2013). For example, early life stress can negatively affect the ability of children to learn effective coping and SPS skills. Moreover, early-life stress is predictive of increased reactivity to stress later in life as well as cognitive deficits in adulthood (Lupien et al., 2009). Therefore, such individuals might be less resilient to stressful events that occur in adolescence and adulthood. Acute stress can also impact negatively divergent (i.e., less flexibility) and convergent (i.e., poorer accuracy) problem solving (Duan et al., 2020). Heightened stress can also negatively affect cognition in general, leading to biased decisionmaking that is hurried, unsystematic, and lacking in full consideration of alternative options (Galván & Rahdar, 2013). The Role of Personality Investigators have also attempted to assess the relationship between SPS and personality traits or characteristics. For example, D’Zurilla et al. (2011) found that neuroticism was highly correlated with a negative problem orientation and lowered SPS ability, whereas conscientiousness, openness, and positive affectivity predicted more effective SPS. Focusing on a sample of criminal offenders with mental disorders, McMurran et al. (2001) showed that neuro­ ticism was the personality factor most strongly associated with SPS. In fact, neuroticism was positively related to all three maladaptive SPS factors (i.e., NPO, ICS, and AS) and negatively correlated with the two constructive SPS dimensions (i.e., PPO and RPS). Relevant to the present issue, neuroticism itself has been found to be significantly related to depression (Chioqueta & Stiles, 2005; Enns & Cox, 1997; Hakulinen et al., 2015). Whether SPS and personality traits function independently, additively, or are overlapping constructs remains to be determined via future research. However, it may be important to bridge these two lines of research to obtain a clearer picture of how they relate to depression.

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The Role of Rumination Rumination, within the context of depression, is defined as a response style involving a focus on the causes, consequences, and meanings of depressed mood (Nolen-Hoeksema, 1991; see Chapter 13, this volume). Depressive severity has been found to be associated with higher levels of rumination (Hasegawa et al., 2018). Research also indicates that a ruminative response style negatively impacts SPS. For example, Lyubomirsky and Nolen-Hoeksema (1995) found that university students with dysphoria who engaged in depressive rumination generated less effective solutions to a series of interpersonal problems, in addition to offering very pessimistic explanations for the existence of these problems. Further, Lyubomirsky et al. (1999) found that individuals with dysphoria who engaged in self-focused rumination had a more negative problem orientation, and that they rated their own problems as having a reduced probability of being solved. More recently, Hasegawa et al. (2018) found that a nega­ tive problem orientation and ruminative style mutually enhanced each other. Moreover, these authors demonstrated that an ICS of problem-solving and rumination independently act to intensify depression. Given these relationships, it may be important for future research to determine if targeting both deficit SPS and rumination can enhance overall treatment efficacy. A particularly interesting area of research and theory regarding the interplay among rumination, SPS, and depression can be found in the field of evolutionary psychology. Specifically, the analytic rumination hypothesis (ARH) posits that rumination is actually an adaptive process whereby depression initially engenders rumination such that individuals engage in attempts to address a triggering problem in living (Andrews & Durisko, 2017; Hollon, 2020). Depressive symptoms, then, initially promote “analytical rumination,” or “causal analysis,” which involves thinking about why a problem has occurred and, secondly, how it can be solved, which is referred to as “problem-solving analysis” (Bartoskova et al., 2018). This description sounds very similar to a combination of PPO (i.e., acceptance that life is filled with problems; belief that if one spends time and energy addressing the problem, it can be managed) and a RPS style (i.e., attempts to accurately define the problem [causal analysis], generating alternative solutions, deciding which solutions to implement, and carrying out and monitoring a solution plan [problem-solving analysis]). What is not discussed in this theory is the issue of the quality of this process as it relates to the existence and/or persistence of the depressive symptoms. In other words, individuals who tend to be characterized by PPO, along with a RPS style, are likely to engage in both effective causal and problem-solving analyses, thus mitigating against the possibility that stress automatically leads to significant depressive symptoms. However, individuals who are characterized by more maladaptive SPS dimensions (i.e., NPO, ICS, and AS) are likely to engage in ineffective causal and problemsolving analyses thus engendering and/or prolonging depressive episodes. Whether future research determines if depression is an evolutionary adaptive response in reaction to life’s problems or not, this conceptualization posited by

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evolutionary psychologists represents an exciting future area of research. In pursuing this avenue, however, it may be extremely important to differentiate attempts to understand why a problem occurred (causal analysis) from why an emotional reaction occurred, as well as assessing the role of one’s attri­ butions regarding where to place the “blame” for the initial occurrence of the problem (i.e., internal versus external causation) and how such attributions are handled.

CLINICAL GUIDELINES If ineffective SPS has been consistently found to be linked to psychological distress and maladaptive behavior, it would appear logical that helping people to become better problem solvers in reaction to life stress could lead to a decrease in pathology. D’Zurilla and Goldfried (1971) developed a model based on this supposition aimed at training individuals in adaptive problem-solving attitudes and skills. Nezu (1986) was the first to tailor and evaluate this approach as a treatment for adults with major depressive disorder (MDD; note that this treatment was originally termed “social problem-solving therapy”). Over the past several decades, the theory and practice of “problem-solving therapy” (PST) emanating from the D’Zurilla, Goldfried, and Nezu guidelines has under­gone several refinements and revisions. In particular, as a function of wanting to underscore the importance of the interplay between emotions and SPS (see Nezu et al., 2019, for an overview of the history and evolution of PST), it has more recently evolved into emotion-centered problem-solving therapy (EC-PST). In this section, we provide a brief description of the clinical components of EC-PST (see Nezu & Nezu, 2019, for a comprehensive treatment manual). The specific treatment modules (referred to as “toolkits”) that comprise EC-PST are predicated on the hypothesis that four ubiquitous barriers to successful problem solving exist when individuals endeavor to manage problems in living. These include the presence of: (a) ineffective SPS, (b) stimulus overload, (c) feelings of hopelessness, and (d) emotion dysregulation. Conceptually, the latter three barriers represent factors that can potentially negatively impact one’s ability to solve, manage, or cope with stressful problems in living. For each of these obstacles, EC-PST provides for a set of tools aimed at helping individuals to overcome them. The four toolkits include (using the labels provided to patients): (a) Planful Problem Solving; (b) Problem-Solving “Multitasking”; (c) Motivation for Action; and (d) “Stop & Slow Down.” Planful Problem-Solving Toolkit: Fostering Effective Problem Solving This first toolkit entails teaching individuals to apply a systematic approach to solving problems in living. It includes the four major planful problem-solving tasks previously described. The first task is accurately defining the problem. The major goal of this activity is to describe a problem accurately and validly such that it is easier to identify effective means of solving it. An adage we

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promote is the following quotation: “A problem well defined is half solved.” Here, patients are taught to differentiate between “facts” and “assumptions,” delineate a set of realistic goals, and identify those obstacles that exist that make it difficult to reach such goals. The second planful problem-solving task involves generating a set of possible solutions that can reach the stated goal by overcoming the barriers to that goal. Patients are taught to use various brainstorming principles to think of a set of possible solutions. These include “quantity breeds quality” (i.e., the more ideas produced, the higher the probability of generating effective ones); “defer judgment” (i.e., postponing an evaluation of any idea can foster creativity); and “think of strategies and tactics” (i.e., brainstorming general categories of ideas, as well as specific ways to carry out these strategies, enhances the likelihood of generating a wide range of ideas in concert with the notion that there are multiple ways to reach one’s goals). The decision-making task involves having individuals predict the likely positive and negative consequences of each of the generated alternatives, if they are carried out. Consequences would include both personal and interpersonal outcomes, as well as short-term and long-term effects. Based on these predictions, people are directed to conduct a cost–benefit analysis to identify those ideas that are highly likely to be effective in reaching the problem-solving goals. An action or solution plan is then developed. The final planful problem-solving task involves implementing the action plan, monitoring the outcome, and determining whether one’s goals were reached satisfactorily. If not, then the problem solver is directed to “troubleshoot,” to identify what parts of the original plan need to be revised (e.g., additional obstacles to goal attainment may need to be identified; more ideas may need to be generated). Problem-Solving “Multitasking” Toolkit: Overcoming Brain Overload This toolkit is provided to patients to foster their ability to effectively handle the omnipresent human limitation of “brain or stimulus overload,” especially when attempting to cope with a stressful circumstance. More specifically, as a function of the restricted ability of people to effectively manipulate sizable amounts of information within their working memory, it becomes especially difficult to attend to a problem under stress. As such, patients are taught to apply three “multi-tasking” tools: externalization, visualization, and simplification. These tools reduce the burden on individuals’ working memory and can help them concentrate on other more focused problem-solving activities (e.g., generating solution ideas). Externalization involves placing information in a forum that is more easily retrievable rather than relying on one’s working memory. This includes lists, diagrams, tape recordings, or journal writings. The visualization tool involves having people engage in visual imagery to finetune a problem description, creatively think of solution ideas, rehearse a solution plan, and attenuate negative arousal (i.e., guided imagery). Simplification entails “breaking down” complex problems into smaller ones to make them

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more manageable. In addition, people are encouraged to use more concrete language when describing a problem and developing an action plan. Enhancing Motivation for Action Toolkit: Overcoming Reduced Motivation and Feelings of Hopelessness The third toolkit helps patients to overcome aspects of a negative problem orientation regarding reduced motivation and feelings of hopelessness. One tool requests the individuals to develop a list of possible consequences, both positive and negative, that could occur if they are hesitant to continue to actively work on solving a problem. The goal would be to allow a cost–benefit analysis comparison of these two lists, which can lead to enhanced motivation to implement one’s solution plan. Further, it can engender the potential identification of a deficient or limited action plan that could signal to both the therapist and patient that they need to potentially revise the plan. A second tool entails the use of visualization as a means of attenuating feelings of hopelessness. The use of visualization here involves having patients “use their mind’s eye” to conjure an image where the “problem is already solved.” Specifically, patients are directed to experience what it “feels like to be on the other side of the barrier.” They are encouraged to experience “the light at the end of the tunnel” and to vicariously experience any positive affect associated with such an outcome. In other words, the major thrust of this tool is to have individuals create their own positive consequences (in the form of affect, thoughts, physical sensations, and behavior) associated with solving a difficult problem as a major motivational step toward overcoming low motivation and feelings of hopelessness, as well as minimizing the tendency to engage in avoidant problem solving. (Nezu et al., 2019, pp. 367–368)

“Stop & Slow Down”: Overcoming Emotional Dysregulation This fourth toolkit teaches patients to apply a set of skills to foster their ability to decrease significant emotional distress (e.g., depression, suicidal ideation). It is also aimed at preventing initial negative emotional reactions from becoming problematic (i.e., intense and long lasting). Specifically, patients are taught to: (a) become more aware, mindful, and attentive to their emotional reactions when they occur; (b) “STOP” their activities and focus on such emotional reactions, including emergence of any negative arousal; and then (c) “Slow Down” to decrease the accelerated pace of the negative arousal. Individuals are provided various “slowing down” tools, such as counting down from 10 to 1, deep breathing, guided imagery, “fake” yawning, meditation, exercise, talking to others, and prayer. They are also asked to brainstorm additional strategies. The overarching theme of this toolkit is to help individuals to become more mindful of their emotional reactions to stressful circumstances, as well as to improve their ability to manage such arousal by engaging in activities that help delay urges to be impulsive or avoidant, and then to be able to engage in planful problem solving. The acronym, S.S.T.A., is used to label this process, where the

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“S. S.” represents “Stop and Slow Down” and the “T. A.” letters represent “Think” and “Act” (i.e., applying the planful problem solving toolkit). In essence, “it is only once individuals are ‘slowed down’ are they able to ‘think and act’ in a rational manner as a means of attempting to cope with the stressful problem situation that ‘fuels’ the negative emotional reactions” (Nezu et al., 2019, p. 368). Guided Practice In addition to learning the skills comprising the four major EC-PST toolkits, a substantial component of this intervention is to provide opportunities to apply the tools to various problems that patients are currently experiencing. Last, as a means of preventing potential emotional distress from occurring, individuals are encouraged to adopt a “future forecasting” perspective to better identify potential difficulties that may occur in the future.

EVIDENCE BASE FOR PST FOR DEPRESSION Because multiple studies evaluating the efficacy of PST in general have been undertaken, it has been possible for researchers to conduct various metaanalyses to determine its efficacy. The first such investigation focused specifically on PST interventions for depression; it included 13 studies of 1,133 adults with a diagnosed depressive disorder (Cuijpers et al., 2007). Results showed moderate and large effects of PST on depressive symptoms regarding fixed and random effects models, respectively.1 In a subsequent meta-analysis, Malouff et al. (2007) examined potential moderators of PST effectiveness addressing various mental and physical health problems, including depression. Initial findings were consistent with Cuijpers et al. (2007) in that, PST was more effective than control conditions in reducing depressive and other psychological symptoms and equally as effective as other bona fide psychological treatments. Further analyses found that the following factors were significant moderators of treatment outcome: whether training in the problem-orientation component was provided, whether the treatment protocol required homework assignments, and whether the study included a developer of the treatment. Bell and D’Zurilla (2009b) sought to take a more nuanced view of factors that could explain the efficacy of PST in reducing depressive symptoms. They included an additional seven studies beyond those examined by Cuijpers et al. (2007) and attempted to replicate and extend findings by Malouff et al. (2007) regarding the inclusion of specific treatment components. Overall, their findings indicated that participants who received PST reported a significant reduction in symptoms relative to participants who did not receive PST (d = 0.40). Results

Because EC-PST is a relatively recent revision of PST, the outcome literature referred to in this chapter is generally based on all or parts of the model described in Nezu (1987).

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were further broken down by specific types of comparison groups. For example, PST was equally as effective as other types of psychosocial therapies and medication, and it was significantly more effective than supportive therapies and attention-control groups. Moreover, studies that included training in problemorientation reported significantly larger effect sizes than those that did not. Similarly, including the complete PST package according to the Nezu (1987) model (i.e., training in problem-orientation and rationale problem solving) was associated with significantly larger treatment effects. Cuijpers et al. (2018) provided an updated meta-analysis of their 2007 study that focused on PST for adult depression and sought to examine its comparative efficacy in relation to other treatments. They also sought to identify sources of heterogeneity as described in prior meta-analyses. Thirty randomized controlled trials (RCTs) of PST for depression were included. Effect sizes varied greatly among the studies, and the authors noted a high risk of bias. A large effect favoring PST to control conditions was found (g = 0.78) in reducing depression. Among a subset of studies with lower risk of bias, the effect size was smaller (g = 0.28), although the results of those studies were consistent with the effect sizes of other high-quality treatment studies of evidence-based psychotherapies. In subgroup analyses, the authors identified that community samples, group treatment, use of waitlist controls, and higher risk of bias predicted larger effect sizes. When PST was compared with other psychotherapies, however, there was a small but significant effect in favor of PST and no significant difference between PST and medication. Nieuwsma and colleagues (2012) were interested in the comparative efficacy of various forms of brief (i.e., defined as a protocol involving eight or fewer sessions) psychotherapy interventions for depression. They examined 15 RCTs, including cognitive behavioral therapy (CBT), PST, and mindfulness-based cognitive therapy. Following their systematic review and meta-analysis, these authors concluded that depression can be efficaciously treated with brief psycho­ therapy, especially CBT and PST. Additional meta-analyses have addressed specific patient populations that provide for a further understanding of the overall efficacy of PST in treating depression. These include the evaluation of PST in primary care settings, as well as PST for older adult patients with depression. PST in Primary Care Cape et al. (2010) examined the effectiveness of several brief psychotherapies administered in primary care settings for common mental health conditions, including PST for depression. Among the 34 investigations included in the analysis, 12 examined PST in the treatment of patients with depression or comorbid depression and anxiety. Results showed that PST was significantly more effective than care as usual. Although the mean effect size was rather small (d = −0.21), it was consistent with the effect sizes of the other brief treatments examined in the analysis, including CBT and counseling.

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Linde et al. (2015) examined whether various psychological treatments (including PST) are effective in reducing depression symptoms among primary care patients. Thirty studies representing 5,159 patients met their inclusion criteria, of which four involved face-to-face PST studies and two were studies in which PST was delivered remotely. Treatment effects for PST were examined separately in each of these modalities and the results were not significant. These null findings could be explained by the problem of high hetero­ geneity among studies, small sample sizes, or the fact that most PST studies in this paper treated less severe depressive symptomatology. However, when the treatment effect was examined among the various psychotherapies (e.g., CBT, interpersonal psychotherapy), no significant differences between PST and these other treatments were found. Zhang et al. (2018) published the first meta-analysis that specifically examined PST separately for primary care patients with depression and/or anxiety. Eleven RCTs representing 2,072 patients were included, wherein PST was delivered in a primary care setting or tele-PST was prescribed by a physician. Results showed an overall significant effect of PST for depression and/or anxiety (d = 0.67) compared with participants in control groups. When differences in effect between depression and anxiety were examined, no significant difference was identified. Subgroup analyses indicated the overall treatment effect of anxiety was also nonsignificant, suggesting a greater treatment effect for primary care depression in this sample of studies. Among subgroup analyses examining demographic differences, age emerged as a significant moderator of treatment effect, such that older age was associated with improved treatment outcomes. Other demographics, such as racial identity and marital status, were not significant moderators. Zhang and colleagues additionally examined differences by treatment characteristics and provider background. The overall treatment effect was not moderated by treatment characteristics, such as modality (individual vs. group), delivery method (face-to-face vs. telehealth), number of sessions, or length of sessions. Analyses examining provider background did yield significant moderators. Specifically, and unexpectedly, master’s-level providers reported a significantly greater effect than doctoral-level providers. In addition, PST without physician involvement showed greater treatment effects than did PST with physician involvement, although both resulted in significant reductions in symptoms. Contrary to the Linde et al. (2015) metaanalysis, the authors concluded that PST is an effective treatment for depression in primary care settings. PST for Depression Among Older Adults Kirkham et al. (2016) published the first meta-analysis examining PST for MDD among adults 60 years of age and older. Nine publications, representing eight unique studies and 569 participants, were included in this analysis. The authors reported results of the efficacy of PST by various outcome measures used. PST was effective at reducing depression compared with control conditions regardless of the outcome measure employed, and the effect sizes were

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large (d > 1.00 for all measures). As with other meta-analyses, the heterogeneity of effect sizes across studies was large. When two studies with the largest effect sizes were removed, heterogeneity decreased, and the effect of PST on depression continued to be significant. Subgroup analyses also showed the effect of PST on depression was significant regardless of the presence of executive dysfunction. Compared with meta-analyses with mixed-age samples (e.g., Bell & D’Zurilla, 2009b; Cuijpers et al., 2007), PST for MDD was shown to be as or more effective for older adults and equally effective to other evidence-based therapies (e.g., CBT). In a recent meta-analysis, Shang and colleagues (2021) examined PST for older adults and included articles written in both English and Chinese. Their review yielded three additional studies not included by Kirkham et al. (2016) and removed others that did not meet their inclusion criteria. Shang et al.’s sample resulted in 10 studies, including 892 participants. The authors applied a random effects model and standard mean difference analyses to account for heterogeneity and the use of several outcome measures. Depression scores in the intervention groups were significantly lower than those in the control groups (SMD = 1.06). Due to the high heterogeneity, differences in PST effectiveness were calculated between several subgroups. Results indicated hetero­ geneity may be due to differences in measurements of depression, whether participants were recruited from clinical or community settings, duration of treatment, or whether waitlist or active controls were used. However, the authors stressed the need for more research to resolve the heterogeneity.

CASE EXAMPLE In addition to serving as a system of psychotherapy in its own right, the following brief clinical case description provides an example of how EC-PST principles can be part of ongoing integrated care for individuals with severe and chronic disorders. Brief Description of the Patient Tim was a 41-year-old Black cisgender male military veteran who requested treatment at a specialty clinic that offered time-limited (six sessions) EC-PST for veterans at risk for suicide.2 He reported a diagnostic history of MDD comorbid with generalized anxiety disorder for which he was receiving medication. Experiencing meager resources, Tim was living in a group residence for homeless veterans. He reported that he wanted to understand his psychiatric symptoms better and develop more effective coping strategies. Tim described himself as Although this clinic was developed to provide a six-session protocol, the number of sessions clinically prescribed is greatly dependent on the individual circumstances of a patient. Additionally, certain demographic characteristics of “Tim” have been altered to protect his confidentiality.

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“very emotional” and noted that he has difficulty communicating with other people and frequently engages in interpersonal arguments at his residence. Relevant History In addition to his diagnostic history, Tim reported a personal history of neglect, group residence placement during adolescence, and a previous suicide attempt. He exhibited a desire to appear knowledgeable and in control of interpersonal situations. However, he believed that others would reject and abandon him and generally felt socially isolated and lonely. Tim described past incidents of self-injury, aggression, and self-harm triggered by feelings of rejection and isolation. He noted that his suicide attempt during late adolescence was related to a desire to get attention from others. His responses to several intake questionnaires indicated that his SPS ability had a particularly NPO. His problem-solving style demonstrated both impulsivity and avoidance. His service dog appeared to be his only current social support. Explanation of Factors Instrumental to Tim’s Current Symptoms Tim’s neglect and abandonment history likely contributed to a hopeless pessimism and exaggerated anticipation of rejection. Moreover, these expectations triggered strong emotions that were overwhelming and difficult to manage. Further, he learned that he was more likely to be noticed by others when he lashed out or threatened his own life. He appeared to have a strong desire to be accepted by others and, therefore, attached quickly, but he pushed others away when he perceived impending abandonment. A tendency to present himself as superior to others appeared to help Tim maintain control over relationships and situations. These characteristics appeared to be longheld, ingrained interpersonal patterns, and they occurred rapidly and automatically, making them challenging to interrupt. Thus, motivating Tim to practice applying the various EC-PST toolkits experientially was viewed as critical. Description of EC-PST Toolkits Deployed Toward Instrumental Subgoals Relevant to Patient’s Ultimate Goal of Improved Self-Management and Social Connection Developing a working alliance with Tim required reducing his expectation of rejection, as he tended to argue intellectually with his therapist when such fears were triggered. Thus, his therapist focused on empathizing with the desire be “heard and belong.” Orienting him to a problem-solving perspective was framed as “skills training” rather than a criticism or mental disorder diagnosis. He related well to metaphors of learning skills in the military that increased his competency and resilience.

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Tim often experienced racing thoughts and rapid speech that compromised his executive functioning. As such, the “Multitasking toolkit” was taught specifically to foster his recognition that, when racing thoughts, grandiosity, and the cognitive overload associated with facing a complex problem occurred, he could focus and organize all the “moving parts” in his brain. The “Stop & Slow Down” toolkit helped Tim recognize the physical and experiential reactions associated with his emotional arousal, which often triggered erratic and ambivalent behavior toward others. This toolkit also provided user-friendly psychoeducation regarding how important it is to recognize and “listen to” emotions. This message was particularly salient for Tim. He learned that his early emotional learning and conditioning were part of a normal learning process in reaction to early life stress, and this was “how he learned to survive.” This toolkit also helped to mitigate those extant barriers in other counseling or medical situations. For example, he previously expended energy trying to “prove” to his health care providers that he was not “crazy” and continually tested them. The planful problem-solving toolkit was especially helpful for Tim because of his inflexibility, difficulty with perspective taking, and distortions concerning interpersonal relationships. One beneficial aspect of the problem-solving toolkit was to help him separate “facts versus assumptions” when defining a problem. This approach met with much less resistance compared to labeling his thinking as “errors.” Instead, he learned to understand that assumptions were causally and predictably linked to his negative learning experiences as a child. Finally, the enhancing motivation toolkit was very effective in moving his goals forward and reducing feelings of hopelessness. The use of the toolkit’s imagery exercises to visualize a future moment in time when he had successfully reached his goals helped him to see a possible “light at the end of the tunnel.” Summary of Tim’s Final Session The ultimate desired outcome for Tim during this brief six-session inter­vention focused on providing him with EC-PST skills that augmented his psychiatric medical care, reducing problems at his residence, increasing social connections, fostering self-awareness, improving communication with others, and more effectively managing his negative emotional arousal. In his final session, Tim reported that he had been working to manage his life and symptoms more effectively. He used the EC-PST worksheets to develop positive goals of forming new friendships and better managing his emotion reactivity. Tim reported that he was learning to accept his experience of emotional triggers as a combination of his temperament and early learning and to be taking a more objective (“facts vs. assumptions”) perspective. His most recent attempts at planful problem solving initially identified a person at the veteran center he recently met with the articulated goal of building a friendship. He was able to identify

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critical barriers to building friendships, including his tendency to overcommunicate or overwhelm others with unrealistic expectations. Tim also reported that his emotional reactivity was a barrier to his relationship goals; the ideas he generated to move this goal forward considered these barriers. Tim looked forward to implementing a realistic action plan. He reported that EC-PST training created a safe space and found the materials and exercises helpful.

FUTURE DIRECTIONS Based on a review of the literature pertaining to SPS as a risk factor for depression, the following are possible areas for future research and program development. SPS as a Vulnerability Factor for Depression Although the preponderance of research clearly has identified a strong association between SPS and depression, important additional issues remain unclear: • What are the SPS dimensions (e.g., PPO, NPO, RPS, ICS, AS) that are most associated with depression? Does this vary as a function of person-related variables (e.g., sex, gender orientation, ethnicity, age, cultural background)? • What are the types of stressors in combination with ineffective SPS that best predict depression? Does this vary by person variables? Does it vary by stressor × SPS factor interaction? • What specific role does personality play within the relationship between SPS and depression? Is personality a moderator or mediator of this association? Are these relationships universal or culture-dependent? • Is the evolutionary psychology theory that depression is a trigger for the causal analysis and problem-solving analysis valid? Does the quality of these two processes make a huge difference? • Do SPS deficits and rumination act synergistically and independently to engender depression? PST and EC-PST as a Treatment for Depression There are also multiple questions in need of empirically sound answers regarding PST and/or EC-PST as interventions to reduce depression. These include: • Is the recent revision of PST (i.e., EC-PST) more effective than the “traditional” version? Are there differences as a function of person-related variables? • Is EC-PST plus antidepressant medication more effective than either one alone? Are there differences in the short-term and/or long-term outcome?

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• Are there important person-related variables that serve as significant moderators of this type of treatment? • What accounts for the heterogeneity among RCTs that have been identified? • What is the best means to disseminate this treatment approach? • Can EC-PST be an important prevention approach for individuals considered at high risk for depression? For depression-associated suicide? • What is the most valid mechanism of action for why PST has been found to be efficacious?

SUMMARY We began this chapter by differentiating between the types of problems that are the targets of (social) problem-solving therapy (now referred to as EC-PST) and the problems usually addressed in cognitive and experimental psychology research. Specifically, problems-in-living (e.g., relationship difficulties, health problems) are characterized by involving others, having more than one solution, often engender negative emotional arousal, and generally exist as a function of the unique characteristics of the protagonist involved. Definitions of certain major constructs (e.g., problems, effective solutions) were also provided, including the definition of SPS. A multidimensional model of SPS comprising two major components: problem orientation (i.e., various cognitive–affective factors concerning how people perceive problems in living) and problem-solving styles (i.e., adaptive and maladaptive cognitive–behavioral activities engaged in when confronted with an emerging problem situation) was then described. The next section provided a brief overview of the research linking SPS and depression across various clinical and nonclinical populations. Recent research was highlighted that focused on this association among multiple diverse populations, particularly those studies conducted internationally, as well as among older adults. Collectively, a large set of studies document the positive correlation between ineffective or impaired SPS and depressive symptomatology. The role of stress, personality, and rumination was then addressed. This section attempted to highlight various contextual factors that are potentially involved in how SPS and depression are related on a more complex level. Stress is conceived of as the trigger or precipitating factor that requires individuals to react to such events in an effective manner, otherwise emotional distress may occur. SPS was characterized as an important moderating variable such that, more effective SPS serves to mitigate against the negative impact of stressful events. Research has also linked various personality traits or characteristics (especially neuroticism) with various SPS dimensions. Similarly, research has identified a strong association between ineffective SPS and rumination, both of which are related to depression. An interesting theory emanating from evolutionary psychology suggests that depression is an adaptive reaction

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to problems-in-living that triggers a sequence of activities beginning with “causal analysis” followed by “problem-solving analysis.” The strong similarities between a SPS model of depression (e.g., Nezu, 1987) and this “analytic rumination hypothesis” was noted. A brief overview of EC-PST was next described as a psychosocial intervention—a skills-oriented approach that teaches and trains patients in a group of four major “toolkits.” These tools were described as addressing the four major barriers to successful problem solving: ineffective SPS; stimulus overload; hopelessness; and emotional dysregulation. The four toolkits include planful problem solving, problem-solving multitasking, enhancing motivation for action, and “Stop & Slow Down.” The objectives and specific activities of these toolkits were briefly described. An overview of the empirical research literature supportive of the efficacy of this approach was also presented. Because many RCTs have been conducted evaluating PST for depression, it is possible to rely on the results of several meta-analyses rather than on individual investigations. PST conducted with primary care patients, as well as with older adults with depression, was highlighted. Overall, reviews of this literature provide for substantial evidence supportive of its efficacy. However, for those meta-analyses that directly looked at PST versus control conditions, significant heterogeneity was identified among studies. Reviews of RCTs involving comparisons with other interventions, including medication and CBT, basically found PST to be equivalent in effectiveness. A case example involving “Tim” was presented to illustrate how EC-PST can be conducted as an intervention to reduce depression, suicidal risk, and inter­ personal difficulties. Finally, we outlined a number of questions that researchers may wish to address to provide more clarity about the relationship between SPS and depression, as well as the relative efficacy of PST/EC-PST approaches for depression.

REFERENCES Andrews, P. W., & Durisko, Z. (2017). The evolution of depressive phenotypes. In R. J. DeRubeis & D. R. Strunk (Eds.), The Oxford handbook of mood disorders (pp. 24–36). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199973965.013.3 Bartoskova, M., Sevcikova, M., Durisko, Z., Maslej, M. M., Barbic, S. P., Preiss, M., & Andrews, P. W. (2018). The form and function of depressive rumination. Evolution and Human Behavior, 39(3), 277–289. https://doi.org/10.1016/j.evolhumbehav.2018. 01.005 Becker-Weidman, E. G., Jacobs, R. H., Reinecke, M. A., Silva, S. G., & March, J. S. (2010). Social problem-solving among adolescents treated for depression. Behaviour Research and Therapy, 48(1), 11–18. https://doi.org/10.1016/j.brat.2009.08.006 Bell, A. C., & D’Zurilla, T. J. (2009a). The influence of social problem-solving ability on the relationship between daily stress and adjustment. Cognitive Therapy and Research, 33, 439–448. https://doi.org/10.1007/s10608-009-9256-8 Bell, A. C., & D’Zurilla, T. J. (2009b). Problem-solving therapy for depression: A metaanalysis. Clinical Psychology Review, 29(4), 348–353. https://doi.org/10.1016/j.cpr. 2009.02.003

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Brack, G., LaClave, L., & Wyatt, A. S. (1992). The relationship of problem solving and reframing to stress and depression in female college students. Journal of College Student Development, 33(2), 124–131. Cape, J., Whittington, C., Buszewicz, M., Wallace, P., & Underwood, L. (2010). Brief psychological therapies for anxiety and depression in primary care: Meta-analysis and meta-regression. BMC Medicine, 8, 38. https://doi.org/10.1186/1741-7015-8-38 Cheng, S. K. (2001). Life stress, problem solving, perfectionism, and depressive symptoms in Chinese. Cognitive Therapy and Research, 25(3), 303–310. https://doi.org/10.1023/ A:1010788513083 Chesin, M. S., & Jeglic, E. L. (2012). Suicidal behavior among Latina college students. Hispanic Journal of Behavioral Sciences, 34(3), 421–436. https://doi.org/10.1177/ 0739986312445271 Chioqueta, A. P., & Stiles, T. C. (2005). Personality traits and the development of depression, hopelessness, and suicide ideation. Personality and Individual Differences, 38(6), 1283–1291. https://doi.org/10.1016/j.paid.2004.08.010 Chow, B. W.-Y., Chiu, M. M., & Wai-Lap Wong, S. (2011). Emotional intelligence, social problem-solving skills, and psychological distress: A study of Chinese undergraduate students. Journal of Applied Social Psychology, 41(8), 1958–1980. https://doi.org/ 10.1111/j.1559-1816.2011.00787.x Clark, L., Dombrovski, A. Y., Siegle, G. J., Butters, M. A., Shollenberger, C. L., Sahakian, B. J., & Szanto, K. (2011). Impairment in risk-sensitive decision-making in older suicide attempters with depression. Psychology and Aging, 26(2), 321–330. https:// doi.org/10.1037/a0021646 Crockett, M. A., Martínez, V., & Jiménez-Molina, Á. (2020, July). Subthreshold depression in adolescence: Gender differences in prevalence, clinical features, and asso­ ciated factors. Journal of Affective Disorders, 272, 269–276. https://doi.org/10.1016/ j.jad.2020.03.111 Cuijpers, P., de Wit, L., Kleiboer, A., Karyotaki, E., & Ebert, D. D. (2018). Problem-solving therapy for adult depression: An updated meta-analysis. European Psychiatry, 48(1), 27–37. https://doi.org/10.1016/j.eurpsy.2017.11.006 Cuijpers, P., van Straten, A., & Warmerdam, L. (2007). Problem solving therapies for depression: A meta-analysis. European Psychiatry, 22(1), 9–15. https://doi.org/10.1016/ j.eurpsy.2006.11.001 de la Fuente, A., Chang, E. C., Cardeñoso, O., & Chang, O. D. (2019). The psychological impact of social problem solving under stress in adults: Debased life satisfaction, heightened depressed mood, or both? Personality and Individual Differences, 146, 46–52. https://doi.org/10.1016/j.paid.2019.03.033 Dobson, D. J. G., & Dobson, K. S. (1981). Problem-solving strategies in depressed and nondepressed college students. Cognitive Therapy and Research, 5(3), 237–249. https:// doi.org/10.1007/BF01193408 Duan, H., Wang, X., Hu, W., & Kounios, J. (2020). Effects of acute stress on divergent and convergent problem-solving. Thinking & Reasoning, 26(1), 68–86. https://doi.org/ 10.1080/13546783.2019.1572539 D’Zurilla, T. J., & Goldfried, M. R. (1971). Problem solving and behavior modification. Journal of Abnormal Psychology, 78(1), 107–126. https://doi.org/10.1037/h0031360 D’Zurilla, T. J., Maydeu-Olivares, A., & Gallardo-Pujol, D. (2011). Predicting social problem solving using personality traits. Personality and Individual Differences, 50(2), 142–147. https://doi.org/10.1016/j.paid.2010.09.015 D’Zurilla, T. J., & Nezu, A. M. (2007). Problem-solving therapy: A positive approach to clinical intervention (3rd ed.). Springer Publishing. D’Zurilla, T. J., Nezu, A. M., & Maydeu-Olivares, A. (2002). Social problem-solving inventory-Revised (SPSI-R): Technical manual. Multi-Health Systems. D’Zurilla, T. J., Nezu, A. M., & Maydeu-Olivares, A. (2004). Social problem solving: Theory and assessment. In E. C. Chang, T. J. D’Zurilla, & L. J. Sanna (Eds.), Social

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Nezu, A. M., & Nezu, C. M. (2019). Emotion-centered problem-solving therapy: Treatment guidelines. Springer Publishing. https://doi.org/10.1891/9780826143167 Nezu, A. M., & Nezu, C. M. (2021). Emotion-centered problem-solving therapy. In A. Wenzel (Ed.), Handbook of cognitive behavioral therapy, Vol. 1. Overview and approaches (pp. 463–489). American Psychological Association. https://doi.org/10.1037/ 0000218-016 Nezu, A. M., Nezu, C. M., & D’Zurilla, T. J. (2013). Problem-solving therapy: A treatment manual. Springer Publishing. https://doi.org/10.1891/9780826109415 Nezu, A. M., Nezu, C. M., & Gerber, H. R. (2019). (Emotion-centered) problem-solving therapy: An update. Australian Psychologist, 54(5), 361–371. https://doi.org/10.1111/ ap.12418 Nezu, A. M., Nezu, C. M., Saraydarian, L., Kalmar, K., & Ronan, G. F. (1986). Social problem solving as a moderating variable between negative life stress and depressive symptoms. Cognitive Therapy and Research, 10(5), 489–498. https://doi.org/10.1007/ BF01177813 Nezu, A. M., & Ronan, G. F. (1985). Life stress, current problems, problem solving, and depressive symptoms: An integrative model. Journal of Consulting and Clinical Psychology, 53(5), 693–697. https://doi.org/10.1037/0022-006X.53.5.693 Nezu, A. M., & Ronan, G. F. (1988). Social problem solving as a moderator of stressrelated depressive symptoms: A prospective analysis Journal of Counseling Psychology, 35(2), 134–138. https://doi.org/10.1037/0022-0167.35.2.134 Nieuwsma, J. A., Trivedi, R. B., McDuffie, J., Kronish, I., Benjamin, D., & Williams, J. W., Jr. (2012). Brief psychotherapy for depression: A systematic review and metaanalysis. International Journal of Psychiatry in Medicine, 43(2), 129–151. https://doi.org/ 10.2190/PM.43.2.c Nolen-Hoeksema, S. (1991). Responses to depression and their effects on the duration of depressive episodes. Journal of Abnormal Psychology, 100(4), 569–582. https://doi.org/ 10.1037/0021-843X.100.4.569 Özdemir, Y., Kuzucu, Y., & Koruklu, N. (2013). Social problem solving and aggression: The role of depression. Australian Journal of Guidance & Counselling, 23(1), 72–81. https://doi.org/10.1017/jgc.2013.1 Paterson, T. S. E., Yeung, S. E., & Thornton, W. L. (2016). Positive affect predicts everyday problem-solving ability in older adults. Aging & Mental Health, 20(8), 871–879. https://doi.org/10.1080/13607863.2015.1043619 Priester, M. J., & Clum, G. A. (1993). Perceived problem-solving ability as a predictor of depression, hopelessness, and suicide ideation in a college population. Journal of Counseling Psychology, 40(1), 79–85. https://doi.org/10.1037/0022-0167.40.1.79 Ranjbar, M., Bayani, A. A., & Bayani, A. (2013). Social problem solving ability predicts mental health among undergraduate students. International Journal of Preventive Medicine, 4(11), 1337–1341. Rosbrook, A., & Whittingham, K. (2010). Autistic traits in the general population: What mediates the link with depressive and anxious symptomatology? Research in Autism Spectrum Disorders, 4(3), 415–424. https://doi.org/10.1016/j.rasd.2009.10.012 Sacco, W. P., & Graves, D. J. (1984). Childhood depression, interpersonal problem-solving, and self-ratings of performance. Journal of Clinical Child Psychology, 13(1), 10–15. https://doi.org/10.1080/15374418409533163 Shang, P., Cao, X., You, S., Feng, X., Li, N., & Jia, Y. (2021). Problem-solving therapy for major depressive disorders in older adults: An updated systematic review and metaanalysis of randomized controlled trials. Aging Clinical and Experimental Research, 33(6), 1465–1475. https://doi.org/10.1007/s40520-020-01672-3 Singhal, M., Manjula, M., & Vijay Sagar, K. J. (2016). Subclinical depression in Urban Indian adolescents: Prevalence, felt needs, and correlates. Indian Journal of Psychiatry, 58(4), 394–402. https://doi.org/10.4103/0019-5545.196727

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15 Cognitive and Behavioral Avoidance Christopher R. Martell and Ajeng J. Puspitasari

I

n this chapter, we discuss avoidance as a factor in the development and maintenance of depression. We provide some common definitions related to the concept of avoidance from previous research and literature. Next, we discuss the development of research examining the role of avoidance as a risk factor for depression and summarize previous models and theories that conceptualize avoidance as a precipitant or maintaining factor of depression. We also review several evidence-based psychotherapeutic interventions (EBPIs) that both implicitly and explicitly target avoidance as a strategy to manage depression toward the end of the chapter. We end with a case example and propose future research directions for this topic.

DEFINITIONAL ISSUES Current and emerging psychological research is increasingly examining the concept of avoidance as a multidimensional construct. When viewed only from a unidimensional approach, avoidance is defined as an attempt to escape from an activity, social engagement or people, or from a general stimulus (Ottenbreit & Dobson, 2004). Some people avoid overt external stimuli, such as certain places, situations, activities, or people. However, avoidance could also be an attempt to escape from internal stimuli, such as avoiding feelings of shame or fear, memories or thoughts, or physiological reactions.

https://doi.org/10.1037/0000332-016 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  359

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The multidimensional research and literature proposed several avoidance categories. Avoidance is often divided into behavioral and cognitive avoidance. Behavioral avoidance is defined as overt behavior to escape a problem or avoid doing activities that are productive, meaningful, and value guided. In the problemsolving-style literature, behavioral avoidance is divided into passive and active behavioral avoidance (Nezu & Perri, 1989; see also Chapter 14, this volume). Active behavioral avoidance is defined as a person’s way of running away from a problem and/or choosing to continue doing activities in which the only purpose is to distract or avoid solving the problem directly. A person who is depressed may choose to skip school for days to avoid failing an exam and choose to spend time playing video games to try to forget about school responsibilities. A similar concept is the distraction response style, in which a person prefers to draw their attention away from a problem or negative feeling. In several studies, behavioral avoidance is also divided into social and nonsocial behavioral avoidance (Arean et al., 1993; D’Zurilla et al., 2004; Quigley et al., 2017). Social behavioral avoidance arises when a person actively avoids social interaction or dealing with certain people. Nonsocial behavioral avoidance occurs when a person avoids an activity related to accomplishment or nonsocial interaction. Another concept related to behavioral avoidance is harm avoidance, which comes from the personality dimension research literature (Cloninger et al., 1994), where a person avoids doing to avoid punishment, events that are uncertain or uncontrollable, or are less likely to get rewarded. One of the other broad categories for avoidance is cognitive avoidance, which is defined as the use or activity of thinking to avoid a problem or to avoid seeking solutions by means of active problem solving. Several constructs related to cognitive avoidance have been studied in the depression literature. Rumination is a repetitive way of thinking in which a person only focuses on problems, past events, and the impact of these events on their depressed mood (NolenHoeksema, 2000). Rumination differs from active cognitive problem solving in that one focuses on organized, step-by-step thinking to generate concrete solutions. Worry is another form of repetitive thinking process that is similar to rumination and involves abstract thinking, does not involve active problem solving, and is often future oriented. Some emphasize that rumination focuses on the past, while worrying often focuses on the future (K. S. Dickson et al., 2012; see also Chapter 13, this volume). Thought suppression involves consciously trying to avoid certain forms of thinking. For example, someone with depression may try not to think about an upcoming exam or a recent loss of family member. Schema avoidance is another form of cognitive avoidance wherein someone may intend to not activate their early and maladaptive schemas (Young, 1999). Cognitive avoidance can also appear in the form of denial or minimization. Similar to behavioral avoidance, some researchers divide cognitive avoidance into social and nonsocial cognitive avoidance (Ottenbreit & Dobson, 2004). It includes avoidance of thoughts about things related to self-achievement or situations (e.g., trying not to think about how to solve a problem). In contrast, social cognitive avoidance includes avoiding thinking about situations related to

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social relationships and other people (e.g., trying not to think about how to improve relationships with partners). Another construct that has been studied in the literature is experiential avoidance, which includes efforts to escape from difficult unwanted thoughts, feelings, and physiological reactions (Hayes et al., 1996). Experiential avoidance as a construct focuses less on the strict division of behavioral and cognitive forms of avoidance, and stresses the process of unwillingness to experience unpleasant internal experiences resulting in engagement in nondirected and nonvalue guided avoidance behavior. Last, it is important to note that not all avoidance is maladaptive. Adaptive avoidance is a functional and strategic coping style when it can realistically prevent loss, harm, and exposure to a dangerous situation (Leventhal, 2008).

RISK FACTOR LITERATURE Considerations of avoidance as a risk factor and mechanism in depression have increasingly been reviewed in the literature. Behavioral, cognitive, and experiential avoidance have clearly been associated with anxiety disorders and viewed as a common mechanism in anxiety. However, more recently, research has shown an association between avoidance and depression. Aldao et al. (2010) found that the relationship between avoidance and depression was stronger in a clinical sample than in a nonclinical group. To some extent, this result may account for the substantial co-occurrence of depression and anxiety, with avoidance being a common mechanism. While the presence of anxiety symptoms may predict future depressive episodes, Jacobson and Newman (2014) found in a longitudinal study that the relationship between the two was partially mediated by avoidance. Thus, as the authors suggested, the avoidance behaviors of anxious clients may contribute to future depression. Both cognitive processes (e.g., thought suppression) and behavioral processes (e.g., disengaging from activities) have been linked to depression processes (Moulds et al., 2007). This finding held true after controlling for anxiety and rumination, suggesting that the process of avoidance is associated with the link between anxiety and depression. In a study examining the relationship among depression, avoidance, and rumination in an undergraduate nonclinical sample, Cribb et  al. (2006) controlled for anxiety in a correlation analysis. Their results indicated that participants who reported more depressed mood also indicated a ruminative thinking style and a tendency to engage in cognitive, behavioral, and experiential avoidance. Ottenbriet et al. (2014) compared levels of avoidance across two groups of women with clinical depression. One group of women were depressed with co-occurring social anxiety disorder (SAD), and the other group did not have it. These two groups were also compared with a nonclinical women sample. The authors hypothesized that nonsocial forms of avoidance would be more closely related to depression and that social avoidance would be related to social anxiety disorder. Ottenbreit and colleagues also hypothesized that avoidance would be

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associated with rumination and that rumination would be positively correlated with behavioral and cognitive avoidance. Avoidance was found to be significantly higher in all clinical groups relative to the nonclinical. Avoidance was highest in the comorbid major depressive disorder (MDD) with SAD group, providing further evidence that it may provide a psychological link between depression and anxiety disorders. Avoidance has been recognized as a potential factor in both episodic and persistent depression. Patients with chronic depression reported more avoidance than did healthy controls. (Brockmeyer et al., 2015). This study also suggested that there might be a differential quality of avoidance between patients with chronic versus episodic depression. These authors found that individuals with chronic depression reported more cognitive and behavioral social and nonsocial avoidance and emotional avoidance (more restricted affect) than individuals with episodic depression. Depression has also been linked with avoidance coping (Kuyken & Brewen, 1994) and with avoidance goals and plans. Avoidance goals are intentional plans on the part of an individual to move away from some undesired state (J. M. Dickson & MacLeod, 2004). They have been linked to higher levels of depression and anxiety (Chambers, 2007; Sideridis, 2005). The research suggests that individuals with depression have more avoidance goals and plans than individuals without depression. In a study of adolescents, those who experienced dysphoric mood reported more avoidance goals and fewer approach goals than those who did not report dysphoric mood (J. M. Dickson & MacLeod, 2006). Those dysphoric adolescents in the sample who did not report avoidance goals still reported more avoidance plans (i.e., immediate strategies to move away from undesired states), suggesting that avoidance may manifest at the more immediate behavioral level, if not generally (J. M. Dickson & MacLeod, 2004). At the more general level, avoidance goals may contribute to poor personal adjustment and well-being, poorer performance and satisfaction with progress, and may ultimately contribute to fewer positive and more negative experiences (Gable et al., 2000; Elliot & Sheldon, 1997; Lau & Nie, 2008). Low rates of response contingent positive reinforcement, which may be translated into fewer positive experiences, higher rates of punishment, or more negative experiences are consistent with Lewinsohn’s (1974) model of depression, which has informed behavioral treatments for depression. As Lewinsohn elucidated, there may be various reasons for decreases in positive reinforcement. There may be environmental changes, such as the loss of a loved one, a relocation to a new living situation, and other such events that remove formerly reinforcing peoples, places, or things from the individual, or the individual may not experience reward as they become more depressed. Individuals with depression may avoid difficult or aversive situations by engaging in other activities that, ultimately, are less rewarding (Hopko, Armento, et al., 2003). It has been suggested that engaging in avoidance may redirect attention from activities that would be positively reinforced (Eifert et al., 1998), which is consistent with Ferster’s (1973) analysis that individuals with depression often behave in a manner that is reinforced negatively—in other words, through avoidance of

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aversive rather than approach of positive experiences. Leventhal (2008) suggested that access to sources of positive reinforcement may be reduced when individuals avoid anticipated disappointments. Many theorists and researchers have noted that depression is both an individual and interpersonal experience. Interpersonal factors, including social avoidance and avoidance of interpersonal conflict, have been associated with depression (Joiner, 2000; Joiner et al., 1999). Social withdrawal and avoidance of conflict is consistent with avoidance goals and plans. These strategies to decrease negative interpersonal experiences may also limit access to social support (Ottenbreit & Dobson, 2004). These interpersonal avoidance processes can have a cascading affect, in that the individual anticipates negative outcomes, and avoids potential interpersonal conflict. However, as a result, they also experience less potential for rewarding opportunities. Consistent with the interpersonal model of depression, rejection or negativity from others may not only be a perception of individuals with depression; in fact, avoidance may elicit negative responses from others (Segrin & Abramson, 1994). Thus, the cascading effect is that depression may be associated with avoidance behavior, which is then a maintenance factor in depression. This results in less potential for experiences that would be antidepressant and positively reinforced, and can also remove some sources of social support, both in actuality and as augmented by the individual’s perceptions. Gray (1982) proposed a neurobiological model of motivation, in which he described the behavioral activation system (BAS), which regulates reward and appetitive experiences, and the behavioral inhibition system (BIS), which regulates aversive experiences. The relationship between BIS sensitivity, which increases an individual’s sensitivity to punishment or nonreward, and depression has been supported in several studies (Campbell-Sills et al., 2004; Johnson et al., 2003; Jones & Day, 2008; Kasch et al., 2002). Finally, according to Jacobson and colleagues (2001), behavioral avoidance reduces the likelihood that individuals will contact potential positive reinforcers in their environment. Carvalho and Hopko (2011) found that self-reported environmental reward, as determined through analysis of daily diaries, significantly mediated the relationship between avoidance and depression among men and women. These findings lend further support to the importance of reinforcement contingencies in depression and to the place of avoidance behaviors in access to potential sources of positive reinforcement. According to the authors, this investigation provided initial support for reinforcement as a mediator between avoidance and depression (Carvalho & Hopko, 2011), consistent with the behavioral conceptualization of depression and activation. Depression has also been linked to rumination (Nolen-Hoeksema, 2000). Rumination is a passive, private, cognitive process of repetitive thinking that does not lead to finding solutions to problems. Rumination has been linked to behavioral and experiential avoidance (Giorgio et al., 2010; Moulds et al., 2007). Cribb and colleagues (2006) noted that highly dysphoric undergraduate participants reported more ruminative thinking, as well as more of the three types of avoidance. Cross-sectional research has demonstrated a positive relationship

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between rumination and behavioral and cognitive avoidance. Some researchers have concluded that rumination mediates the effect of cognitive avoidance on sadness and anxiety (K. S. Dickson et al., 2012), which also provides even more evidence of connections among avoidance, rumination, and depression or anxiety. Experiential avoidance, or the effort to escape from unwanted thoughts, feelings, or physiological sensations has also been associated with depression. Experiential avoidance was found to predict onset, relapse, and maintenance of depressive disorders during a 4-year period, although it did not moderate the effect of rumination (Spinhoven et al., 2016). Biglan et al. (2015) found that experiential avoidance was associated with depression among teens, and more so for teens from families with high conflict. Gender differences were also found to contribute to experiential avoidance, with females having higher levels of experiential avoidance. Although the associations among various forms of avoidance, depression, rumination, lack of reward, and increased activity of the BIS are generally supported by research, not all studies have found these links. Self-report studies (e.g., Jones & Day, 2008) have failed to find such an association. Avoidance processes also may promote negative information processing biases, thus contributing to the maintenance of unhelpful cognitions targeted in cognitive behavioral therapy (CBT), although there is limited research in this area (Trew, 2011).

CONCEPTUAL ISSUES The role of avoidance in the development and maintenance of depression has been mainly integrated within a behavioral conceptualization of depression. In the early writings of behavioral theory of depression, it was postulated that depression results from the removal of positive reinforcement in one’s behaviors, that results in the reduction of healthy and desirable behaviors (Ferster, 1973; Skinner, 1965). This model led to the development of behavioral treatment for depression, which had the focus to support persons with depression to engage in pleasurable activities. Treatment interventions included the scheduling of pleasant events to increase contact with response-contingent positive reinforcement (Lewinsohn, 1974; Lewinsohn & Libet, 1972). A more contemporary theory of depression expanded the view of the earlier empirical work by explicitly noting the role of negative reinforcement in the development and maintenance of depressive symptoms (Jacobson et al., 2001; Martell et al., 2001). While this model continued to emphasize the loss or decrease of response-contingent positive reinforcement, it posited that one’s environment may also be filled with significant aversive stimuli that increased the likelihood for escape and avoidance behaviors. It highlighted the cyclical nature of depression in which the reduction of positive reinforcement paired with excessive increase in avoidance behaviors due to the function of negative reinforcement and/or punishment generated further removal of positive reinforcement in one’s life.

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An illustration of this contemporary psychopathological model of depression is someone who unexpectedly lost a job that used to bring significant rewards and benefits in life (i.e., reduced positive reinforcement) and consistently received rejection after job interviews (i.e., increased punishment and aversive stimuli from job searching). As time passed, the activities of revising one’s résumé, searching for positions, and completing job interviews became more aversive and triggered the urge to avoid and to instead spending time playing video games or sleeping. These avoidance behaviors generated immediate relief from not having to actively search for a new job or avoiding the aversive emotions (i.e., hopelessness) and thoughts (i.e., “I will never find a job”) of job searching activities and, as such, are negatively reinforced and naturally increased in frequency. In other words, avoidance behaviors were maintained from immediate short-term relief and avoidance of aversive stimuli, even though they brought long-term negative consequences (e.g., more financial problems and difficulties to secure a new job). As a result, avoidance often either exacerbates the existing problems or generates new secondary problems in one’s life. While the behavioral conceptualization of depression emphasizes the role of contingencies of reinforcement, avoidance is clearly a response that maintains disengagement from activities that could be positively reinforced in a person’s life. Individuals with depression often experience anhedonia, pointing to difficulties with reward system function. Research using functional magnetic resonance imaging and event-related potentials suggest that individuals with MDD have a general reduction in neural resources allocated to categorize or evaluate stimuli (White et al., 2021). White et al. (2021) compared event related potential data on individuals meeting criteria for MDD (with or without co-occurring anxiety disorders) with a control group of healthy individuals. The results of the study indicated that individuals with depression displayed less attention and engagement to tasks as indicated by lower P300 amplitude responses to cue stimuli, compared with the control group. Lower P300 amplitude responses were also predictive of higher treatment dropout. In this study treatment consisted of behavior therapy, either behavioral activation (BA) or exposure therapy. These findings have implications for informing treatment planning, and they provide physiological data to support Lewinsohn’s (1974) model of the importance of experience of reward in depression.

MEASURES OF AVOIDANCE Avoidance measures are mostly self-report inventories. A self-report measure that conceptualized avoidance multidimensionally and associated the construct with depression was the Cognitive and Behavioral Avoidance Scale (CBAS; Ottenbreit & Dobson, 2004). This measure categorizes avoidance into behavioral avoidance and cognitive avoidance. Within each type of avoidance, the construct is further divided into nonsocial and social behavioral avoidance, as well as nonsocial and social cognitive avoidance. The psychometric properties of the CBAS were examined in several studies indicating

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the positive correlation between avoidance and depression among populations with clinical depression (Ottenbreit et al., 2014; Quigley et al., 2017). Moos (1988) developed the Coping Responses Inventory (CRI) as a broad inventory of coping strategies for adolescents aged 12 to 18 years and adults. The CRI assesses four approach types and four avoidance types of coping. Several measures were created as measures associated with treatments that address avoidance behavior. The Behavioral Activation for Depression Scale (BADS; Kanter et al., 2007) was originally a 25-item self-report measure of activation, avoidance, and social functioning. A nine-item short form of the BADS was developed (BADS-SF; Manos et al., 2011) and demonstrated good internal consistency, and the construct validity of the BADS-SF was as good as the original BADS. The Acceptance and Action Questionnaire (AAQ; Hayes et  al., 2004) is a nine-item measure that contains items related to the avoidance of thoughts and feelings, negative evaluations of feelings, and behavioral adjustment to difficult thoughts or feelings. Hence, the AAQ has been used as a measure of experiential avoidance. Gámez et al. (2011) noted a need for a broad measure that did not focus on specific aspects of experiential avoidance and developed the Multidimensional Experiential Avoidance Questionnaire (MEAQ; Gámez et al., 2011). While the MEAQ contains substantial overlap with the AAQ, regression analyses demonstrated that its subscales contained unique content and, therefore, had additional use as a measure of experiential avoidance (Gámez et  al., 2011). Further, the authors found that there was good discriminant validity. The MEAQ has six subscales: distress aversion, behavioral avoidance, distraction/ suppression, repression/denial, procrastination, and distress endurance, making it a good overall measure of various types of client avoidance. One disadvantage of the MEAQ is its length. The authors suggested that a shorter form (10–20 items) would make a more clinically useful tool. Also, it is not clear whether the MEAQ is a good measure for change over time.

INTERVENTIONS AND EVIDENCE BASE The Society of Clinical Psychology (Division 12 of the American Psychological Association) established criteria for a treatment to be considered to have minimal, moderate, or strong empirical support (Chambless & Hollon, 1998). They list a number of empirically supported treatments by diagnosis according to the amount of empirical support for the efficacy of the treatment. According to the 1998 criteria, BA, cognitive therapy, problem-solving therapy, and mindfulness-based cognitive therapy (MBCT) all have strong empirical support as treatments for depression, while acceptance and commitment therapy (ACT) has moderate empirical support (see https://div12.org/diagnosis/depression/). While outcomes of randomized controlled trials demonstrate treatment efficacy, understanding mechanisms of change has only recently been a focus of research. In this section, we review the treatments for which the literature

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suggests that changes in avoidance may be a mechanism of action associated with treatment outcomes. Treatments for anxiety (Barlow & Craske, 2000), posttraumatic stress disorder (Foa & Kozak,1986), and obsessive–compulsive disorder (Foa et  al., 1980) have either predominantly utilized exposure or incorporated exposure as part of a treatment package. In contrast, explicitly targeting exposure has not been the predominant model of treatment for depression until recently. Nevertheless, treatments for depression that target avoidance developed alongside the theoretical associations of depression and avoidance in the 1960s and 1970s. While Ferster (1973) noted that many behaviors of individuals with depression function as avoidance, which had obvious treatment implications, it was Lewinsohn and Graf (1973) whose behavioral conceptualization of depression led to strategies to reverse depressed mood. Their model identified low rates of response-contingent positive reinforcement, and, instead, encouraged pleasant activities that a client no longer experiences and worked with the client to schedule activities to increase reward. Pleasant events scheduling mostly targets overt avoidance, helps clients to identify activities that they no longer participate in, and then to make plans to increase the number of these activities in their daily lives. BA utilizes the activity scheduling strategies developed by Lewinsohn and Graf (1973), and also used by Beck and colleagues (Beck et al., 1979), with a strong emphasis on the identification of barriers to activation, particularly avoidance. Jacobson et al. (2001) described an overarching goal of BA to bring clients in touch with possible reinforcers in their environment. Following Ferster’s (1973) functional analysis of depression, and, more recently, the descriptions of avoidance goals and plans (J. M. Dickson & MacLeod, 2004), BA identifies when depressive behavior results in patterns of avoidance that are maintained via negative reinforcement (i.e., acts to remove or avoid undesirable stimuli) rather than antidepressant behaviors (Martell et al., 2022) that are oriented toward goals that may result in reward, and are more likely maintained via positive reinforcement. Activity scheduling and mastery and pleasure ratings in Beck’s treatment for depression (Beck et al., 1979) may also target avoidance, but the model considers cognitive reappraisal as more central to change than the behavioral avoidance targets that have been emphasized in BA. Activity scheduling is a mainstay of BA, to increase positive reward and decrease avoidance, and it is incorporated into every form of behavioral activation treatment (Dimidjian et al., 2011). There have been several strategies employed in BA that help clients increase activities and attempt to increase contact reinforcers in the environment. Hopko and colleagues have developed a behavioral activation treatment for depression (BATD) in which clients identify valued activities in various areas of life such as interpersonal relationships, leisure, work, and so forth. Clients set weekly goals and identify and schedule stepped activities to help them arrive at these goals. BATD has efficacy in the treatment of depression and anxiety (Hopko et al., 2004), and with patients with cancer who have depression

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(Hopko & Lejuez, 2007), and it is considered one of the major contemporary behavioral treatments for depression (Hopko, Lejuez, Ruggiero, et  al., 2003) sharing much in common with the approach that has been utilized by Jacobson and colleagues (2001). The main treatment targets of BATD and BA are similar, as both increase engagement with activities that have an antidepressant function. Both look at client values as a means to identify activities that will have meaning and be potentially more reinforcing. Values are emphasized in BATD, and, more recently, have been incorporated in BA as researchers and clinicians have encouraged clients to act in line with value-driven behavior rather than mood-driven behavior (Richards et al., 2017). BATD has begun to target avoidance as a barrier to activation (Hopko, Armento, et al., 2003). Although there is some variation as to the materials used in BA and BATD (e.g., activity charts, daily diaries), the ultimate goal of behavioral strategies is to increase activity in order to lift mood, help clients function better in daily life, and cope with co-occurring problems. Avoidance is emphasized in BA because it is a primary barrier to activation, and because the early formulations of Ferster (1973). The fact that other behaviorists have identified avoidance as common in “neuroses” has strongly influenced the contemporary conceptualizations of BA. Clinical trials of BA, BATD, and pleasant events scheduling have been considered in meta-analyses of treatment outcome literature. Activation has been shown to improve depression symptoms whether practiced in a therapist’s office (Dimidjian et al., 2006), on an inpatient hospital ward (Hopko, Lejuez, LePage, et al., 2003), or through an internet application (Nyström et al., 2017). Several meta-analyses attest to the efficacy of BA in the treatment of depression (Cuijpers et al., 2013; Ekers et al., 2014; Mazzucchelli et al., 2009). An innovative computerized treatment of BA was recently used to target explicit avoidance behavior and to target implicit approach-avoidance behavior. This model yielded decreased depressive symptoms, anxiety scores, and anhedonia and increased positive affect and social relationship functioning (Sweet et al., 2021). Although the sample was small (N = 25) the results were promising in that a brief computerized version of BA, along with approach-avoidance training, could have a positive impact on these several areas. Acierno and colleagues (2012) combined BA with therapeutic exposure for bereaved older adults and found statistically significant improvement on measures of complicated grief and depression. It is interesting to observe that in both of these studies the researchers added additional treatment strategies to BA in order to target avoidance. As such, it is difficult to parse which aspects of the intervention were most effective and whether BA alone would have been sufficient. Nevertheless, Acierno and colleagues demonstrated that a brief, multimedia, paraprofessionalled intervention was efficacious and holds promise for wider dissemination. Other researchers have conducted studies to directly test whether BA has an effect on avoidance tendencies in adults with depression. Nasrin et al. (2017) used one session of BATD in a randomized controlled trial to assess mechanisms of change in BA. Their results indicated that BA increased approach tendencies and there was a small effect size in improvement in depressive symptoms for the BATD group compared with the control group. While not

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addressing avoidance per se, Santos et al. (2017) found that activation was a mechanism of change in their analysis from a randomized-controlled trail of BA for Latinos, compared with treatment as usual. As activation requires approach behaviors, this analysis may provide some evidence for activation and avoidance-modification as important treatment factors. Inasmuch as anhedonia is associated with avoidance, recent studies of inflammatory processes in depression have provided initial evidence that BA may positively impact these processes. For example, Savitz and colleagues (2020) looked at the inflammatory-induced imbalance of the kynurenine pathway (KP) and the ratio of tryptophan metabolites kynurenic acid (KYNA) with the neurotoxin quinolinic acid (QA) and the levels of their metabolites following treatment. Citing literature that suggests that there is a decrease in KYNA and or an increase in QA in individuals with depression compared with nonclinical populations, the authors looked at the relative effects of prolonged exposure and BA on modulating the KP pathway. The authors found that a significant decrease in QA following treatment with BA. The study had limitations and it was not clear that the reduction in QA was due to BA treatment or to the general improvement from depressive symptoms or other factors. However, the study suggests the possibility that BA affects biological processes related to the reward system. The cognitive-behavioral analysis system of psychotherapy (C-BASP; McCullough, 2000) is an empirically supported treatment for chronic depression. C-BASP combines cognitive, behavioral, and interpersonal problem-solving using situational analyses (SAs) to identify clients’ cognitions and actions that interfere with goals in interpersonal situations. During an SA, therapists ask clients to identify and describe a problematical interpersonal situation. Then, they inquire as to the client’s interpretations of the situation, the client’s behavior in the situation, their expected outcome and the actual outcome. Problematic interpretations and maladaptive behaviors are identified, and clients discuss with their therapists how else they may have interpreted the situation or behaved to obtain a different outcome. A large clinical trial compared C-BASP and nefazadone to each of these interventions alone (Keller et al., 2000). The combination treatment outperformed either treatment alone. Further analysis of these data indicated that C-BASP, nefazadone, and the combined treatment reduced avoidant coping and that this was one mechanism of change, along with maladaptive cognitive coping, that accounted for positive treatment outcomes (Blalock et al., 2008). While C-BASP offers a very specific approach to treat maladaptive cognitive coping through the process of SAs, the intriguing idea that avoidance may mediate information processing bias, as mentioned earlier (Trew, 2011), suggests that this effect may also be true in the most widely studied and robustly supported treatment for depression, that of Beck and colleagues (1979). Cognitive therapy has always treated inactivity as part of the protocol and has included activity scheduling and mastery or pleasure ratings. As such, it may also influence cognitive avoidance through the process of cognitive restructuring and hypothesis testing. While this is speculative, some researchers have noted that depression may have an evolutionarily adaptive function and that rumination may be an

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adaptation to attempt problem resolution (Hollon et al., 2021). These authors suggested that cognitive therapy promotes productive problem solving, and we suggest that it may also interrupt cognitive avoidance. A thorough examination of this idea remains to be studied and is outside the scope of this chapter. Nevertheless, we believe that we should consider whether cognitive therapy has similar positive effects on avoidance modification as seen in BA. Similar effects could be examined in adaptations of cognitive therapy such as C-BASP, rumination-focused cognitive behavioral therapy (RFCBT), or MBCT; or the “third wave” treatments such as ACT. As Hollon et al. (2021) noted regarding cognitive therapy, “it is easier to detect an effect than it is to explain it” (p. 404). We suggest, however, that the evidence is mounting that decreasing avoidance and increasing approach behaviors may be one key active mechanism in several of these treatments. A mediation analysis at follow-up of a randomized clinical trial comparing CBT and ACT in the treatment of depression, in which the authors hypothesized that CBT would outperform ACT, did not demonstrate significant differences in the treatments. Both treatments resulted in significant improvements in depressive symptoms and reported quality of life (A-Tjak et al., 2015). There were no significant differences in the rate of change for participants from either condition at 6-month and again at 12-month follow-up. However, the authors found that while both treatments appeared to work through changes in dysfunctional thinking and decentering, changes in experiential avoidance were a specific mechanism within ACT. BA and several treatments for depression target maladaptive rumination that can serve the function of cognitive avoidance. In BA, ruminative thinking is considered to be a “private behavior” (Skinner, 1957) that pulls one away from full engagement in their activities. The two ways that BA attempts to help clients stop ruminating is to work with them to practice attending to their experiences or to do active problem solving (Addis & Martell, 2004; Martell et al., 2022). Changes in avoidance may also be an effective ingredient of change in MBCT (Segal et al., 2013). In a controlled study of female adolescents with social anxiety and depression, Ghadampour et  al. (2017) found that MBCT reduced cognitive-behavioral avoidance, as well as maladaptive mental rumination. In a study of narrative–emotional disclosure, Moore et al. (2009) found that increasing mindfulness while writing narratives, and decreasing experiential avoidance while writing, was associated with improved mental health, regardless of the content of the writing. Jacobson et al. (2001) conceptualized rumination as a possible avoidance behavior, and there is some support for this concept in the literature (Cribb et al., 2006). Watkins et al. (2007) followed the functional analytic model of rumination from BA and developed a treatment that directly targets unconstructive rumination and, like BA, addresses the process of thinking rather than the content as in traditional cognitive therapy. As discussed in greater detail in Chapter 13 of this volume, RFCBT has been compared with treatment as usual (TAU) in a randomized controlled trial in the United Kingdom (Watkins et al., 2011). Results indicated that the RFCBT group reported fewer residual

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symptoms of depression than did the TAU group. Mindfulness and rumination focused treatments may decrease cognitive avoidance and help clients utilize more constructive, problem-oriented rumination rather than brooding. Experiential avoidance, or attempts to avoid aversive emotions, thoughts or physiological sensations, has also been associated with depression. ACT (Hayes et al., 1999) is designed as a transdiagnostic model to help clients move from avoidance to its counterpart, acceptance. Clients are encouraged in ACT to make contact with their psychological experiences without attempting to escape from those experiences they judge to be aversive (i.e., acceptance), as well as to act in an efficacious manner consistent with their values (i.e., commitment). Both ACT and BA invite clients to act constructively despite negative affect, although ACT is based on a model of psychological flexibility as a means of decreasing suffering, whereas BA is based on a reinforcement model. As a treatment that targets experiential avoidance, we turn now to reviewing ACT in the treatment of depression and consider some of the data supporting this intervention. A recent meta-analysis of ACT, MBCT, and positive psychotherapy for major depression (Seshadri et al., 2021) concluded that ACT was an efficacious treatment for depression and that it was superior to inactive or TAU controls. In a study comparing ACT with BA in the treatment of anxiety and depression in cancer survivors, Fernández-Rodríguez et al. (2021) showed that both treatments were better than a waitlist control, although clients in the BA condition showed greater improvement in activation. The authors noted that treating avoidance (as in ACT) without increasing activation (as in BA) may have less benefit than doing both in the treatment of depression and anxiety. This in some ways is consistent with the need to not only decrease negative affect but to also increase positive affect as part of a treatment plan (Persons, 2008). Thus, this study suggests that engaging clients with difficult thoughts, feelings, and behaviors may be necessary but not sufficient for successful treatment. In summary, behavioral and cognitive behavioral treatments have targeted avoidance in the treatment of depression. While the strategies differ somewhat from the exposure treatments seen as a dominant approach within the anxiety disorders, they are not completely unique. While we have discussed several treatments, we now present a case example of how BA can be successfully employed as an evidence-based treatment for depression with the overarching goal of countering behavioral, cognitive, and emotional avoidance through improving client engagement in the events of their lives.

CASE EXAMPLE Karen was a 30-year-old, cisgender, heterosexual, Black woman who recently went through a divorce and received full custody of her two children under the age of 5.1 She had a history of depression that started when she was a The specifics of this case example have been altered to protect the identities of all individuals.

1

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teen, and the episodes often typically reoccurred when she went through major life changes. This current episode started about 8 months prior to beginning treatment when her divorce was finalized. Due to the divorce, Karen decided to leave her past job as an accounting manager, moved to a different state with her two children so she could be closer to her mother and siblings, who offered to provide support raising her children, and took a new position as an accounting manager at a new company. Several life changes and losses were the core contributors to the elevation of Karen’s depressive symptoms. She lost a marriage and a romantic partner of 13  years. She gave up a career that she enjoyed and was professionally meaningful to her to be closer to her family. Socially, most of her friends and social network lived in the previous state, so she suddenly had very limited opportunity to engage with a group of women who used to provide social support both emotionally and logistically (e.g., picking her kids up from day care when needed). Additionally, Karen found her new company and colleagues to be rather difficult to adjust to. The company culture and climate were different than her past company, focusing more on goals and outcomes than on relationships and teamwork. She found it challenging to feel like she could belong and be accepted for who she was (i.e., an experienced leader) at work. She often experienced microaggressions, as colleagues or her supervisor made comments about her hair and her manner of speech, and asked her to complete tasks that were not within her role description (e.g., taking notes during a meeting or other tasks that the other accounting managers in sister sites did not have to complete). Karen struggled severely with the feeling of depression, sadness, guilt, and shame. She also had a difficult time enjoying most of daily activities, even routines that she used to enjoy in the past such as going for a run, volunteering in the community, and playing with her children. She struggled with insomnia, poor appetite, and difficulties concentrating since her mind kept rehearsing her past relationship, causes for her divorce, and how she was treated at work. At least once a week, especially when she woke up in the middle of the night, she struggled with suicidal ideation, thinking that it would be better if she was dead and her mother raised her children. Despite passive and fleeting suicidal ideation, she never developed a suicidal intent or plan. Karen sought professional support and started outpatient psychotherapy a month after she moved to the new city. The primary treatment modality that she received was BA. Early in treatment, Karen worked with her therapist to understand the cycle of her depression and the connection between the major life changes and losses that she experienced with her depressive symptoms and her emotional, cognitive, and behavioral responses. She learned the role of avoidance and how this pattern of behavior maintains and, at times, perpetuates her depression and even led to more problems in her life. Karen became more aware that she would often feel unwanted sadness while doing or thinking about doing desirable activities. She would remember how in the past, despite having engaged in those types of activities, she still had to go

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through significant loss. These emotional and cognitive reactions then led to the urge to avoid and isolate and got into a rumination cycle. Karen and her therapist identified common behavioral avoidance, which included staying in her room and isolating in the evening and over the weekend. She also noticed that her pattern of endlessly scrolling social media content was another behavior that functioned to avoid other healthy, productive, and socially meaningful activities. By staying at home and in her room, Karen actively avoided social interaction as well as pleasurable and healthy activities. Karen was asked to monitor her rumination patterns and frequency to assess the function as possible cognitive avoidance. Karen noticed that throughout the day, she often got stuck in a ruminative thinking pattern where she rehearsed her past marital problems, current job stress, and unpleasant interactions with work colleagues. This rumination occurred while she engaged in many activities such as driving, eating, working, and even while spending time with her children and family. She also often avoided thinking about social activities that she could start planning to join such as volunteering or a book club. As part of BA, Karen started to set a goal to resist and/or limit avoidance behaviors such as playing on her phone and staying and isolating in her room. Instead, she created a behavioral hierarchy where she listed a number of activities that fall within the categories of healthy routine, productive and value-guided, pleasurable, and socially connecting activities. Every week, Karen scheduled those activities that she would like to engage in and would often break these goals into smaller steps to avoid feeling overwhelmed, which can increase the risk of future avoidance. When she started to notice an emerging avoidance pattern, Karen committed to replace the avoidance behaviors with behavioral activation that she had scheduled in advance with her therapist. In the process of resisting avoidance behaviors, Karen made herself aware that while avoiding by isolating or playing on her phone often brought immediate relief and momentarily helped her to escape, in the long run, engaging in her behavioral activation would bring long-term positive changes in her life or effectively solve the problem in front of her. Since rumination was a significant cognitive avoidance for Karen, her therapist shared some strategies to manage rumination effectively. First, Karen was asked to practice more awareness and noticed the context when rumination typically occurred more often. When Karen caught herself ruminating, instead of getting further hooked by the thoughts, she practiced returning her attention intentionally and mindfully to her current activity. For example, if she caught herself ruminating while playing with her children, she practiced turning her attention to the children, the activities that they were doing, the conversation that they were having, and the environmental context she was in (e.g., paying attention to nature or the walking path). While one of the main goals of therapy was to reduce avoidance and increase activation, Karen and her therapist also discussed the potential benefit of engaging in adaptive avoidance regarding her current work environment. The microaggressions that Karen experienced and the organizational climate

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and culture had brought significant stress and anxiety for her. Despite practicing interpersonal effectiveness, direct and assertive communication, Karen noticed that a systemic change might not happen in a short period of time. Thus, she and her therapist considered the potential benefit of looking for a different job, which could be conceptualized as an adaptive avoidance at this point of her depression recovery.

SUMMARY AND FUTURE DIRECTIONS Understanding avoidance as a feature of depression is not new (Ferster, 1973), and, at this point, the inclusion of avoidance in treatment conceptualizations of depression is no longer new (Jacobson et al., 2001). In this chapter, we have reviewed the emerging literature on avoidance as a risk factor in depression. This literature supports the rationales used in a number of the treatments for depression reviewed in this chapter. Inasmuch as avoidance behavior is one mechanism in the development and maintenance of depression, treatments that focus on avoidance are welcomed. As other chapters in this volume demonstrate, there is support for other risk factors and mechanisms in depression and there is room in the field for variety in entry points into treatment. In our opinion, this gives individuals coping with depressive disorders cause for hope. As BA experts, we continue to be encouraged by the evidence that supports its efficacy. We are particularly encouraged by neurophysiological data suggesting how BA may affect neuropsychological processes in depression (White et al., 2021). While the current data cannot specifically support a contention that BA per se has psychophysiological effects, but insofar as BA has demonstrated efficacy in treating depression, and reductions in depression are associated with biological change, further research looking at mediators is optimistically anticipated. One conclusion that we can draw from research on avoidance in depression is that it is important to decrease behavioral, cognitive, and experiential avoidance, but it is also, perhaps, equally important to increase exposure to potentially rewarding (reinforced) activities. According to the BA model of depression, and Lewinsohn and colleagues (1985) integrated model, contextual events or antecedents result in low rates of response contingent positive reinforcement or high rates of punishment, creating a need for the client to manage the associated emotional responses (affective, behavioral, and cognitive). It is understandable that there is a tendency to focus more on the self, attend to negatively valenced stimuli, and to engage in behaviors that function to escape or avoid aversive experiences when coping with negative life events and challenges. The research on avoidance, rumination, reinforcement, and depression generally lends credibility to these behavioral models. Studies show that behavioral activation whether branded as BA or BATD with the slight difference in emphasis is efficacious in the treatment of depression. Where do we go from here? BA (Martell et al., 2001) offers a treatment package that has several components apart from activation such as teaching

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clients to assess the function of their behavior and directly address avoidance patterns. However, the focus is ultimately on activation and engagement, with avoidance behaviors and rumination being conceptualized as barriers to activation. Thus, within the treatment, several possible mechanisms are addressed. BATD (Hopko, Lejuez, Ruggiero, et al., 2003) provides an approach emphasizing activity scheduling, yet the mechanism of change may be more than just increasing activity and improving access to response contingent positive reinforcement. The exact change processes are still unclear. Contemporary cognitive behavioral treatments, explicitly or implicitly, target avoidance directly or cognitive and behavioral processes that function as avoidance. We have reviewed a sampling of the literature on evidence-based, empirically supported therapies. The various therapies, while presenting differing protocols or principles, do not contradict one another and, as we stated earlier, provide choice and hope for clients with depression. Furthermore, in much of the literature that was reviewed, repeated associations between depression and avoidance have been revealed. CBT and research supported by government granting agencies has needed to follow the medical model and evaluate treatments proposed for specific “mental disorders.” While this strategy has certainly led to the development of innovative treatments, it has also led to a potentially overwhelming proliferation of treatments. No clinician can learn all of the protocols for all of the disorders, and it is even difficult to faithfully practice treatments for a specific disorder like depression. The emphasis on disorder specific treatments is beginning to give way to process-based treatments (Hayes & Hofmann, 2018), which may be a welcomed advancement in treatment for human suffering. The various forms of avoidance, as well as processes such as inactivity and cognitive bias, warrant continued study and intervention efforts, both within the area of depression and related disorders. REFERENCES Acierno, R., Rheingold, A., Amstadter, A., Kurent, J., Amella, E., Resnick, H., Muzzy, W., & Lejuez, C. (2012). Behavioral activation and therapeutic exposure for bereavement in older adults. The American Journal of Hospice & Palliative Care, 29(1), 13–25. https://doi.org/10.1177/1049909111411471 Addis, M. E., & Martell, C. R. (2004). Overcoming depression one step at a time: The new behavioral activation approach to getting your life back. New Harbinger. Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2010). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clinical Psychology Review, 30(2), 217–237. https://doi.org/10.1016/j.cpr.2009.11.004 Arean, P. A., Perri, M. G., Nezu, A. M., Schein, R. L., Christopher, F., & Joseph, T. X. (1993). Comparative effectiveness of social problem-solving therapy and reminiscence therapy as treatments for depression in older adults. Journal of Consulting and Clinical Psychology, 61(6), 1003–1010. https://doi.org/10.1037/0022-006X.61.6.1003 A-Tjak, J.G.L., Davis, M. L., Morina, N., Powers, M. B., Smits, J.A.J., & Emmelkamp, P. M. G. (2015). A meta-analysis of the efficacy of acceptance and commitment therapy for clinically relevant mental and physical health problems. Psychotherapy and Psychosomatics, 84, 30–36. https://doi.org/10.1159/000365764

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Nolen-Hoeksema, S. (2000). The role of rumination in depressive disorders and mixed anxiety/depressive symptoms. Journal of Abnormal Psychology, 109(3), 504–511. https:// doi.org/10.1037/0021-843X.109.3.504 Nyström, M. B. T., Stenling, A., Sjöström, E., Neely, G., Lindner, P., Hassmén, P., Andersson, G., Martell, C., & Carlbring, P. (2017). Behavioral activation versus physical activity via the internet: A randomized controlled trial. Journal of Affective Disorders, 215, 85–93. https://doi.org/10.1016/j.jad.2017.03.018 Ottenbreit, N. D., & Dobson, K. S. (2004). Avoidance and depression: The construction of the cognitive-behavioral avoidance scale. Behaviour Research and Therapy, 42(3), 293–313. https://doi.org/10.1016/S0005-7967(03)00140-2 Ottenbreit, N. D., Dobson, K. S., & Quigley, L. (2014). An examination of avoidance in major depression in comparison to social anxiety disorder. Behaviour Research and Therapy, 56, 82–90. https://doi.org/10.1016/j.brat.2014.03.005 Persons, J. B. (2008). The case formulation approach to cognitive-behavior therapy. Guilford Press. Quigley, L., Wen, A., & Dobson, K. S. (2017). Avoidance and depression vulnerability: An examination of avoidance in remitted and currently depressed individuals. Behaviour Research and Therapy, 97, 183–188. https://doi.org/10.1016/j.brat.2017.07.015 Richards, D. A., Rhodes, S., Ekers, D., McMillan, D., Taylor, R. S., Byford, S., Barrett, B., Finning, K., Ganguli, P., Warren, F., Farrand, P., Gilbody, S., Kuyken, W., O’Mahen, H., Watkins, E., Wright, K., Reed, N., Fletcher, E., Hollon, S. D., . . . Woodhouse, R. (2017). Cost and Outcome of BehaviouRal Activation (COBRA): A randomised controlled trial of behavioural activation versus cognitive-behavioural therapy for depression. Health Technology Assessment, 21(46), 1–366. https://doi.org/10.3310/hta21460 Santos, M. M., Rae, J. R., Nagy, G. A., Manbeck, K. E., Hurtado, G. D., West, P., SantiagoRivera, A., & Kanter, J. W. (2017). A client-level session-by-session evaluation of behavioral activation’s mechanism of action. Journal of Behavior Therapy and Experimental Psychiatry, 54, 93–100. https://doi.org/10.1016/j.jbtep.2016.07.003 Savitz, J., Ford, B. N., Yeh, H-W., Akeman, E., Cosgrove, K., Clausen, A. N., Martell, C. Kirlic, N., Santiago, J., Teague, T. K., Irwin, M. R., Paulus, M. P., & Aupperle, R. L. (2020). Behavioral activation therapy for depression is associated with reduction in the concentration of circulating quinolinic acid. Psychological Medicine, 1–10. https:// doi.org/10.1017/s0033291720004389 Segal, Z. V., Williams, J. M. G., & Teasdale, J. D. (2013). Mindfulness-based cognitive therapy for depression (2nd ed.). Guilford Press. Segrin, C., & Abramson, L. Y. (1994). Negative reactions to depressive behaviors: A communication theories analysis. Journal of Abnormal Psychology, 103(4), 655–668. https://doi.org/10.1037/0021-843X.103.4.655 Seshadri, A., Orth, S. S., Adaji, A., Singh, B., Clark, M. M., Frye, M. A., McGillivray, J., & Fuller-Tyszkiewicz, M. (2021). Mindfulness-based cognitive therapy, acceptance and commitment therapy, and positive psychotherapy for major depression. American Journal of Psychotherapy, 74(1), 4–12. https://doi.org/10.1176/appi.psychotherapy. 20200006 Sideridis, G. D. (2005). Goal orientation, academic achievement, and depression: Evidence in favor of a revised goal theory framework. Journal of Educational Psychology, 97(3), 366–375. https://doi.org/10.1037/0022-0663.97.3.366 Skinner, B. F. (1957). Verbal behavior. Appleton-Century-Crofts. https://doi.org/10.1037/ 11256-000 Skinner, B. F. (1965). Science and human behavior. Simon and Schuster. Spinhoven, P., Drost, J., de Rooij, M., van Hemert, A. M., & Penninx, B. W. (2016). Is experiential avoidance a mediating, moderating, independent, overlapping, or proxy risk factor in the onset, relapse and maintenance of depressive disorders? Cognitive Therapy and Research, 40(2), 150–163. https://doi.org/10.1007/s10608015-9747-8

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Sweet, A. M., Pearlstein, S. L., Paulus, M. P., Stein, M. B., & Taylor, C. T. (2021). Computerdelivered behavioural activation and approach-avoidance training in major depression: Proof of concept and initial outcomes. British Journal of Clinical Psychology, 60(3), 357–374. https://doi.org/10.1111/bjc.12287 Trew, J. L. (2011). Exploring the roles of approach and avoidance in depression: An integrative model. Clinical Psychology Review, 31(7), 1156–1168. https://doi.org/10.1016/ j.cpr.2011.07.007 Watkins, E., Scott, J., Wingrove, J., Rimes, K., Bathurst, N., Steiner, H., Kennell-Webb, S., Moulds, M., & Malliaris, Y. (2007). Rumination-focused cognitive behaviour therapy for residual depression: A case series. Behaviour Research and Therapy, 45(9), 2144–2154. https://doi.org/10.1016/j.brat.2006.09.018 Watkins, E. R., Mullan, E., Wingrove, J., Rimes, K., Steiner, H., Bathurst, N., Eastman, R., & Scott, J. (2011). Rumination-focused cognitive-behavioural therapy for residual depression: Phase II randomised controlled trial. The British Journal of Psychiatry, 199(4), 317–322. https://doi.org/10.1192/bjp.bp.110.090282 White, E. J., Nacke, M., Akeman, E., Cannon, M. J., Mayeli, A., Touthang, J., Al Zoubi, O., McDermott, T., Kirlic, N., Santiago, J., Kuplicki, R., Bodurka, J., Paulus, M. P., Craske, M. G., Wolitzky-Taylor, K., Abelson, J., Martell, C., Clausen, A., Stewart, J. L., & Aupperle, R. L. (2021). P300 amplitude during a monetary incentive delay task predicts future therapy competition in individuals with major depressive disorder. Journal of Affective Disorders, 295, 873–882. https://doi.org/10.1016/j.jad.2021.08.106 Young, J. E. (1999). Cognitive therapy for personality disorders: A schema-focused approach (3rd ed.). Professional Resource Exchange.

16 Metacognition and Mental Regulation Adrian Wells and Henrik Nordahl

M

etacognitive therapy (MCT) is gaining support as an effective form of psychotherapy for depression and anxiety within mental and physical health, with indications that it can be more effective and/or efficient than cognitive and behavioral treatments for generalized anxiety, trauma, and depression. In this chapter, we present the metacognitive model of depression on which MCT is based and consider the evidence supporting the role of metacognition in depression symptoms, rumination, and depressive disorder. Metacognition is an important aspect of information processing; it is that part of cognition involved in regulating thinking and includes the executive system’s information about the current status of processing and stored knowledge of strategies and repertoires (commands) for cognitive regulation. The concept of metacognition is central in MCT, the theoretical grounding of which can be traced to the self-regulatory executive function (S-REF) model (Wells & Matthews, 1994, 1996). This model is based on the principle that psychological disorder is caused by a common set of processes that constitute a syndrome of extended difficult to control negative thinking and misplaced coping called the cognitive attentional syndrome (CAS). The CAS comprises worry, rumination, threat monitoring, and self-regulation strategies that inadvertently maintain repetitive negative thinking patterns. The CAS configuration is, in turn, hypothesized to result from biases in underlying metacognitions, including, but not limited to, beliefs about thinking.

https://doi.org/10.1037/0000332-017 Treatment of Psychosocial Risk Factors in Depression, D. J. A. Dozois and K. S. Dobson (Editors) Copyright © 2023 by the American Psychological Association. All rights reserved.  383

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THE METACOGNITIVE MODEL OF DEPRESSION The metacognitive model of depression (Wells, 2009) is grounded in the common principles of the S-REF theory. The patient with depression is not able to exert appropriate control over processing in response to negative thoughts and feelings. As a result, negative repetitive mental processes persist and are extended, leading to a maintenance and worsening of low mood. Instead of reducing negative emotion-focused processing, the person with depression suffers extended negative processing. Such processing is dominated by the CAS, a prolonged and biased negative thinking style expressed in the form of rumination and worry, monitoring for signs and symptoms of threat (e.g., mood fluctuation) and other unhelpful coping behaviors that have unintended and counterproductive effects. The CAS is the result of underlying biases in the metacognitive control system, including metacognitive beliefs that act against motivated efforts to reduce rumination. Compounding effects on control, the patient with depression often has diminished awareness of the extent of their rumination and unhelpful coping strategies. This can stem from the type of relationship the individual has with their thoughts. In many cases, CAS processes such as rumination are seen as the solution to depression rather than as a cause; in effect, the person remains immersed in negative thought content and is said to be in object mode. In addition, depressed mood can have effects on processing by, for example, reducing “mental energy” and increasing the subjective effort required to engage in control, which mitigates against more active self-regulation. In most cases, patients with depression believe erroneously that their thinking is out of control. In some cases, especially involving repeated depressive episodes or treatment resistance, there is a belief that the mind or brain is weak, defective, chemically compromised, or damaged. These are powerful negative metacognitions that have significant effects on motivation and choice of coping behaviors. For example, believing that one has no control leads to refraining from directly using the mind and strengthens the selection of indirect methods of control through distraction or self-harm or external control through alcohol or drugs. Such CAS coping behaviors are maladaptive because they have counter­ productive effects (e.g., alcohol may reduce meta-awareness and control) and, when they are helpful, prevent the individual from reconnecting with the direct metacognitive control they actually have. The metacognitive model presents a different approach to depressive cognition compared with schema theory (Beck, 1976; see also Chapter 9, this volume), where the assumption is that schemas (e.g., “I’m a failure”) lead to negative biases and distortions in interpreting events that maintain depression. Metacognitive theory, in contrast, proposes that the control of processing is independent of schemas, since an individual retains flexibility and choice over their cognitive appraisals. In particular, a person retains choice over whether or not to continue thinking about “failure.” In the metacognitive model, a person who is not prone to depression responds to negative beliefs by maintaining task-focus and external goal-directed behavior, whereas the person who is prone to depression shifts to self-focus and engages in rumination, worry, and self-

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analysis. According to the metacognitive model, this difference in response styles is a consequence of the way the metacognitive control system is configured, with biased metacognitive knowledge (e.g., “I must process all my failures in order to recover” and/or “I cannot control my thinking”) in that system being a major cause of the CAS in clinical patients. The metacognitive model assumes that symptoms of psychological disorder tend to naturally subside, through a process of reflexive self-regulation, but that the individual with depression fails to exit modes of sustained negative thinking. The two important subsets of metacognitions that are involved in this process are negative beliefs about thinking (e.g., “I have lost control of my mind”) and positive beliefs about rumination and worry (e.g., “I must analyze my feelings to find an answer to my depression”). In some cases, negative metacognitive beliefs include the idea that cognition is damaged or “broken.” Positive metacognitive beliefs lead the individual to engage sustained negative thinking to deal with moods, thoughts, and threats, whereas negative metacognitive beliefs concerning uncontrollability and mental brokenness reduce motivated efforts to exercise direct mental control over repetitive thinking. The patient with depression is more likely to rely on misdirected control through strategies such as thought suppression, substance use, self-harm, or excessive sleep rather than by directly reducing thinking. Awareness of repetitive thinking such as rumination may be distorted because the patient with depression exhibits biased meta-awareness (i.e., is not fully aware of the extent of rumination) or views the process as a solution to depression rather than a factor maintaining low mood. A diagrammatic depiction of the metacognitive model of depression, with the central factors described above is presented in Figure 16.1. There are five major components: (a) positive metacognitive beliefs (e.g., “I must analyze my feelings of emptiness to know who I am”); (b) negative metacognitive beliefs (e.g., “I have no control over my depressive thinking”); (c) rumination (a repetitive negative thinking pattern that also includes worry); (d) low meta-awareness (incomplete awareness or knowledge that overthinking is a problem for selfregulation); and (e) misdirected coping behaviors such as mood monitoring, inactivity, substance use, and self-harm. The CAS components are represented in two parts of the model, in the central cycle under “rumination” and under depression where “behaviors” are listed. Initially, negative self-relevant thoughts and feelings are detected as a deviation from the “normative” or desired state by the metacognitive control system. For most individuals, the deviation is transitory, as the control system maintains flexibility over processing. However, individuals with depression experience reduced dynamic control over processing because of biases in metacognition. The patient with depression engages in extended negative processing of the self, dominated by rumination and worry, driven by metacognitive beliefs about the usefulness of rumination and beliefs about the uncontrollability of the process, low levels of awareness of the activity, and other coping strategies. Such responses do not provide evidence of direct internal control, and, so, biased meta-awareness and dysfunctional metacognitive beliefs persist. As depicted in Figure 16.1, negative metacognitive beliefs about the uncontrollability of

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FIGURE 16.1.  The Metacognitive Model of Depression

Trigger

Positive Metabeliefs (Strategy selection)

Rumination

Negative Metabeliefs (Low meta-awareness)

Depression behavior

affect

cognition

Note. From Metacognitive Therapy for Anxiety and Depression (p. 34), by A. Wells, 2009, Guilford Press. Copyright 2009 by Guilford Press. Reprinted with permission.

thinking are of special importance in linking rumination to clinical depression, not only because they lead to disturbances in effective use of mental control but because they also lead to an increasing sense of being powerless to effect change. Negative metacognitions can be reinforced, depending on the sense that the individual makes of their depression and the information they receive from others (e.g., health care providers). For example, an individual may develop the belief that their depression is caused by a neurochemical disturbance. Metacognitions of this kind increase the likelihood that they will make unhelpful attributions about the source of their problem and will continue to engage in ineffective coping strategies. In depression, through various pathways the internal capacity and flexibility to use effective forms of metacognitive control and to consciously disengage extended negative processing is gradually diminished.

METACOGNITION AND RISK OF VULNERABILITY TO DEPRESSION In the following sections, we review the empirical evidence supporting the proposed link between dysfunctional metacognitions, rumination, depression symptoms, and vulnerability. After that, we present a case example to illustrate how the model is used in the practice of metacognitive therapy.

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Metacognition and Depressive Thinking (Rumination) In their systematic review and meta-analysis, Cano-Lòpez et al. (2022) summarized the empirical evidence of the link between metacognitive beliefs and rumination and metacognitive beliefs and depression symptoms, in clinical and nonclinical samples. The authors identified 41 eligible studies (N = 10,607), of which 16 used the Positive Beliefs About Rumination Scale (PBRS; Papageorgiou & Wells, 2001b) and/or the Negative Beliefs About Rumination Scale (NBRS; Papageorgiou & Wells, 2001a), and the remaining 25 used the 30-Item Metacognitions Questionnaire (MCQ-30; Wells & Cartwright-Hatton, 2004). A synthesis of the correlations indicated that positive beliefs and negative beliefs showed moderate positive associations with rumination with correlations of r = 0.50 and r = 0.46, respectively. These relationships were consistent across different metacognitive belief measures and across clinical and nonclinical samples. Metacognitions positively correlated both with rumination and depression symptoms, with positive metacognitive beliefs (assessed with PBRS) correlating more strongly with rumination and negative metacognitive beliefs (assessed with NBRS) correlating more strongly with depression symptoms. This pattern of data is consistent with the metacognitive model (see Figure 16.1). A smaller number of longitudinal studies have evaluated the relationships between metacognitions and rumination, with both positive (Kubiak et  al., 2014; Weber & Exner, 2013) and negative (Matsumoto & Mochizuki, 2018; Papageorgiou & Wells, 2009) beliefs about rumination being significant predictors. In sum, there is clear evidence of reliable positive associations between metacognitive beliefs and rumination that are consistent with the metacognitive model. However, caution is required in interpreting the data, since they are correlational in nature with few prospective studies to support causal relationships and directionality. Metacognition and Depressive Symptoms Sun et al. (2017) conducted a meta-analytic review of dysfunctional metacognitive beliefs as assessed with the MCQ and found that patients with major depressive disorder scored significantly higher on all metacognitive belief domains than did healthy controls. In their review, Cano-López et al. (2022) reported correlations of r = 0.27 between positive metacognitions and depressive symptoms and higher correlations (r = 0.49) between negative metacognitive beliefs and symptoms. This difference in the strength of associations, depending on type of belief measured, is consistent with the metacognitive model which places greater emphasis on negative metacognitions having a direct link with depression (see Figure 16.1). The direct and indirect relationships between different domains of metacognitive beliefs and rumination on the one hand and depressive symptoms on the other have been the subject of investigation. Papageorgiou and Wells (2003) tested the statistical fit of the metacognitive model of depression in which positive beliefs link directly to rumination which, in turn, links to depressive

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symptoms through paths mediated by negative metacognitions. The model was tested in a group of individuals with depression and a group of nondepressed controls. The model appeared to be a good fit to the data. Cano-Lòpez and colleagues (2022) also tested the fit of the metacognitive model of rumination and depression using a two-stage structural equation modeling approach in which they included a subset of the reviewed studies that used the PBRS or the NBRS. Sixteen studies (N = 4,477) were included in the model, which was specified in line with the metacognitive model of depression (Wells, 2009; Figure  16.1). The model provided a good fit to the data and indicated that positive metacognitive beliefs were moderately associated with rumination, whereas rumination was both directly and indirectly associated with depression via negative metacognitive beliefs. In longitudinal studies, both positive (Webner & Exner, 2013) and negative metacognitive beliefs (Matsumoto & Mochizuki, 2018; McEvoy et al., 2013; Papageorgiou & Wells, 2009; Yilmaz et al., 2011) prospectively predicted depression symptoms in nonclinical samples. Moreover, negative metacognitive beliefs prospectively predicted depression levels in clinical samples (Faissner et al., 2018; Kraft et al., 2019). In addition to metacognitive beliefs, the use of particular thought control strategies has been linked to suicidal ideation. Using experience sampling methodology, Hallard et  al. (2021) asked individuals with depression who had suicidal ideation to monitor their thought control strategies, rumination, and suicidal ideation over a 6-day period. The study showed that thought control strategies of reappraisal, distraction and social support, were negatively predictive of suicidal thinking. The use of worry, rumination, and self-punishment, on the other hand, independently predicted greater suicidal ideation, with the use of self-punishment to regulate thoughts emerging as the strongest individual predictor. The relationship between dysfunctional metacognitions and depression symptoms is evident in a wide range of populations. For example, negative metacognitive beliefs are linked to depression symptoms in physical illnesses and in chronic medical conditions (Capobianco et al., 2020; Lenzo et al., 2020). In patients with primary social anxiety disorder, metacognitive beliefs are positively correlated with depression symptoms, even when controlling for levels of social anxiety and social phobic cognitions (Nordahl, Nordahl, et al., 2018). Metacognitions also show predicted correlations with depression symptoms in children and adolescents (Myers et al., 2019). Metacognition as a Transdiagnostic Risk Factor The self-regulation model presented by Wells and Matthews (1994) proposes that the cognitive attentional syndrome is a common mechanism cutting across psychological disorders. Thus, biased metacognitions should be a source of psychological vulnerability and thereby contribute a general risk of mental ill-health. A growing number of studies have tested for relationships between

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metacognitions and psychological vulnerability constructs or risk factors such as trait-anxiety (which is multifactorial containing trait-depression) and neuroticism. Significant positive correlations have been reported between trait-anxiety and metacognitive beliefs assessed with the MCQ-30 at the cross-sectional level (Wells & Cartwright-Hatton, 2004), even when controlling for the presence of a diagnosed mental disorder (Nordahl & Wells, 2017). In addition, metacognitions prospectively predict trait-anxiety (and a trait-depression factor) in longitudinal data (Nordahl et al., 2019). Other risk factors also share variance with metacognitive beliefs. Positive correlations have been found consistently between negative metacognitive beliefs about cognitive control and neuroticism (Bailey & Wells, 2013; Carciofo, 2020; Marino et al., 2016, 2018; Nordahl et al., 2021; Spada et al., 2016). Treatment studies have demonstrated that reductions in dysfunctional metacognitive beliefs are associated with improvements in risk factors, such as trait-anxiety (Nordahl, Berkovec, et al., 2018; van der Heiden et al., 2012, 2013; Wells et al., 2010) and neuroticism (Kennair et al., 2021), in patients with generalized anxiety disorder. These findings suggest that treatments that target metacognition may impact a wide range of risk markers. In the area of depression, one study compared individuals currently, previously, and never depressed. On measures of metacognitive beliefs and strategies, individuals with current depression scored significantly higher than individuals with previous depression who, in turn, scored significantly higher than those never depressed. These results are consistent with the idea that metacognitions and strategies are not simply symptoms of current depression but remain elevated and represent potential risk markers (Halvorsen et al., 2015). However, caution should be exercised because one study using latent growth curve modeling to test metacognitions as a prospective predictor for depression recurrence did not find support for an association in a sample of individuals with previous depression (Kraft et al., 2019).

METACOGNITIVE THERAPY IN DEPRESSION MCT (Wells, 2000, 2009) is a protocol-based treatment for psychological disorders that is designed to reduce the CAS and modify dysfunctional metacognitions. It has demonstrated efficacy in the treatment of anxiety and depression (see Normann & Morina, 2018). The Nature of MCT MCT for depression is typically implemented in eight to 12 sessions and begins with a case-conceptualization based on the metacognitive model. The therapist uses the formulation to help the patient identify the CAS and understand the role of rumination and unhelpful coping responses in symptom maintenance (see Figures 16.1 and 16.2). Treatment follows a sequence in which strategies

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FIGURE 16.2.  Metacognitive Therapy Case Formulation “Why can’t I be happy?” Analyzing my feelings will help me recover

Rumination (most days)

I’ve lost control of my thinking (80%)

Depression avoid, rest

sad, empty

hopeless, trapped

such as the attention training technique (Wells, 1990) and detached mindfulness (Wells, 2009; Wells & Matthews, 1994) are used to increase meta-awareness, enhance knowledge of choice in response to negative thoughts, and develop an alternative stance in relation to those thoughts that would normally trigger rumination and other unhelpful CAS responses. An illustration of using these techniques is given later in the case example. An important feature of treatment is the dialogue the therapist maintains with the patient throughout. This is a metalevel discourse that keeps discussion of the content of cognitions to a minimum. Instead, discussion focuses on regulating and reducing reactions to thoughts and altering patient beliefs about thoughts rather than beliefs about other things. As an illustration, a contrast with the type of dialogue used in cognitive behavior therapy (CBT) can be made; here, the therapist might deal with a negative cognition such as “I’ll never feel better” by asking “Is that an example of fortune telling? What are the steps you could take to find out? Let’s make a list of some pleasurable activities we might focus on to improve your mood?” In contrast, the MCT therapist does not deal with content (i.e., never feeling better) but focuses on mental regulation: “How much time do you analyze your feelings? What’s the point of that? Can you reduce the activity?” Thus, as this example illustrates, in MCT a solution is sought by reducing processing whereas in CBT a solution is sought by increasing processing. MCT involves the use of behavioral experiments that are metacognitively focused, since they are explicitly designed to modify erroneous metacognitive beliefs. An important experiment is one in which patients are asked to postpone

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rumination (in session and for homework) in response to usual “trigger thoughts.” This is followed by further experiments in which the individual is asked to deliberately start and stop rumination. Throughout this process, the therapist monitors metacognitive belief levels to ensure they are changing and refines the experiments as needed. For example, because of restrictions in meta-awareness, refinements involve widening the identification of “trigger thoughts” and the application of rumination postponement to a wider array of cognitions. Patients are also helped to relinquish other unhelpful coping behaviors, such as excessive symptom/mood checking, inactivity, self-punishment, and withdrawal of effort. The therapist explores and modifies beliefs about the nature of depression and faulty mental models about how the mind works. Once such metacognitions are effectively challenged, treatment focuses on weakening positive beliefs about the need to analyze thoughts and feelings and the need to continue use of rumination or extended thinking as a coping strategy. Toward the end of therapy, the patient is asked to synthesize all that they have learned and to develop a replacement and more adaptive “theory of depressive cognition” with an accompanying set of strategies that can be used to maintain more effective self-regulation strategies in the future. To obtain a more comprehensive sense of this treatment, the reader is referred to the case example presented later in this chapter. The Evidence for MCT The effects of MCT for depression have been evaluated across studies using different methodologies, including single case-replication series, open trials, and randomized controlled trials (RCTs). Here, we briefly summarize these studies and report the effects of MCT for depression delivered in individual and group formats. These studies also cover the impact on depression symptoms in patients with physical illnesses. We also briefly report on the effects of MCT on depression symptoms when these are secondary outcomes. The approach used in the evaluation of MCT for depression demonstrates a stepwise progression. It began with testing the attention training technique (ATT; Wells, 1990) as a standalone treatment before the effects of full MCT were evaluated. Papageorgiou and Wells (2000) examined the effects of ATT in a single-case replication of four patients with recurrent major depression. Following ATT, all patients showed clinically significant reductions in depression symptoms and fell within the normative range. On secondary outcomes, the patients also showed substantial reductions on measures of anxiety symptoms, negative automatic thoughts, rumination, and metacognitive beliefs, with gains associated with treatment maintained at 12  months. Subsequent research has provided further support for the positive effects of ATT in depression and across other symptom types (Fergus & Bardeen, 2016). Case Series Studies The full MCT treatment package for depression was first evaluated in a case series (Wells et  al., 2009) consisting of four patients with recurrent and/or chronic major depressive disorder who received six to eight sessions of MCT

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following a baseline period. Treatment was associated with large and clinically significant improvements in depressive symptoms, rumination and metacognitive beliefs and gains were maintained at 6-months follow-up. Three of the patients were classified as recovered at 6-months follow-up as defined using Frank et  al.’s (1991) criteria, consisting of no longer having a diagnosis of depression and exhibiting a Beck Depression Inventory (BDI) score of 8 or less. MCT for depression has also been evaluated using an A–B design in a Danish study. In five to 11 sessions of up to 1 hour, three out of four of the patients fully recovered using Frank et al.’s criteria, with the outcomes maintained at 3- and 6-months follow up (Callesen et al., 2014). Bevan et  al. (2013) conducted a case series of the effects associated with MCT for postpartum depression. Six women were assigned to a baseline period followed by eight to 12 sessions of MCT. At posttreatment, 83% of the participants were classified as recovered using Jacobson’s reliable change index criteria (Jacobson et al., 1984). Five of the six participants experienced clinically significant reductions in the Beck Depression Inventory-II (BDI-II; Beck et al., 1996), with a large pre–post effect size (Cohen’s d = 3.12). In addition, participants reported improvements in their relationship and bond with their infants, and improvements on measures of metacognition. Treatment associated gains were largely maintained at 3- and 6-months follow-up, indicating stability of improvements. MCT has also been piloted in three cases diagnosed with bipolar II disorder, using the BDI-II as the primary outcome measure (Callesen, Pederson et al., 2020). In this study, positive effects associated with MCT for depression symptoms were reported for all three patients and were maintained at 6- and 12-months follow-up. Uncontrolled Trials Wells and colleagues (2012) delivered eight sessions (mean 6.5) to individuals with treatment-resistant depression following a baseline period. Ten out of 12 patients completed the treatment and large statistically significant improvements were observed in all symptom measures at posttreatment, which were maintained at 6- and 12-months follow-up. From the end of baseline to posttreatment, the treatment effect size (Cohen’s d) was 1.65 on the BDI and 2.72 on the Hamilton Rating Scale for Depression (HRSD; Hamilton, 1960). Using Jacobson criteria, 80% of the completers were classified as recovered at posttreatment, whereas 20% did not change. One year after treatment, 70% were classified as recovered, 10% as improved, and 20% unchanged. In a subsequent open trial, Hjemdal et al. (2017) treated 10 patients with severe major depression and comorbid disorders in 10 sessions. In this study, none of the patients fulfilled the criteria for major depressive disorder following treatment and, at post treatment, only three out of 21 pretreatment total diagnoses remained. The effect sizes (Hedges’s g) were large for symptoms of depression (2.89 from pre- to posttreatment, and 2.40 from pretreatment to 6-months follow-up). Using Jacobson’s criteria, 90% of patients were classified as recovered, and 10% were classified as improved using the BDI. At 6-months

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follow-up, 70% were classified as recovered, 20% as improved, and 10% as unchanged. In addition to demonstrating the effects of MCT for depression, this study indicated that MCT is associated with transdiagnostic improvement at the level of clinician-assessed diagnostic status. Winter et  al. (2020) evaluated the feasibility and outcome of MCT for depression in a German sample of 20 outpatients. All patients completed the treatment and the 6-months follow-up phase and were satisfied with MCT. MCT was associated with large effect sizes on depression symptoms and metacognitions. Winter et al. (2019) evaluated the effects of MCT for major depressive disorder (MDD) in comparison with persistent depressive disorder (PDD), as some have argued that more chronic depressions require a somewhat different approach to treatment than nonchronic depression. Those with PDD had at least 2 years duration of depressive symptoms, had not responded to antidepressant medication, and most participants had at least one trial of CBT without response. MCT was associated with large effect sizes (Cohen’s d) from pre- to posttreatment in those with MDD (d = 2.7) and in those with PDD (d = 3.1), and the effects were maintained from pretreatment to 6-months follow-up (d = 2.9 for MDD and 3.4 for PDD). Large effect sizes were also reported on secondary outcome measures. MCT was associated with substantial and comparable effects in both groups, indicating that persistent depression was not associated with a reduced outcome following MCT. Randomized Controlled Trials MCT for depression has been evaluated in several RCTs. In an early study, Jordan and colleagues (2014) conducted a randomized controlled pilot study of MCT versus CBT. Although the therapists in this study had no formal training in MCT and there was greater comorbidity in the MCT arm, MCT produced equal effects to CBT with a pre- to posttreatment effect size (Cohens d) of 1.03 on the clinician rating version of the Quick Inventory of Depressive Symptomatology (QIDS-16-C; Rush et al., 2003)—intention to treat—which was the primary outcome measure. Interestingly, MCT produced superior effects on improved executive control (Groves et  al., 2015). In a follow-up study, the treatment effects of MCT were maintained 2 years after treatment and MCT was associated with substantial positive change in anxiety symptoms and social functioning (Carter et al., 2022). Hagen et al. (2017) conducted an RCT of MCT for depression with fully trained therapists. MCT was compared with a waitlist control condition and delivered over 10 weekly sessions. On the BDI, the within group effect size (Cohens d) from pre to post MCT was 3.50 compared with 0.49 for the waitlist, with a controlled effect size of 2.51. Using Jacobson’s criteria, 80% of those treated with MCT could be classified as recovered, 15% as improved, and 5% as unchanged at posttreatment. At 6-months follow-up, the effect size for MCT on the BDI compared with pretreatment was 2.11 (total sample), and 75% were classified as recovered, 15% as improved, and 10% as unchanged. MCT was associated with large effects on secondary outcome measures such

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as anxiety and metacognitions from pre- to posttreatment and at 6-months follow-up. MCT was associated with return to work or studying for a substantial number of the participants who had been out of work or studies before treatment, and unemployment or disability benefits were reduced by 22%. At 1-year follow-up (Hjemdal et al., 2019), 73% of the completers were classified as recovered, 12% as improved, and 15% unchanged. Only five of the patients (13%) who were in remission at posttreatment experienced relapse at this point. Within-group effect sizes were large for reductions in symptoms of depression (d = 2.09) and anxiety (d = 1.16). At 3-year follow-up (Solem et al., 2019), 34 (87%) of the original sample of 39 participated in further assessments. Sixty percent had not experienced any new depressive episodes in the 3-year follow-up period, and recovery rates ranged from 69% to 97%, depending upon the criteria used. On average there were large reductions in depression symptoms (e.g., Cohen’s d from pretreatment to 3 years follow-up was 3.22 on the BDI). Eight out of 13 who were initially out of work and on disability benefits were no longer receiving benefits. More recently, MCT has been evaluated against gold-standard CBT in a large-scale single-blind RCT (Callesen, Reeves et  al., 2020). Eighty-five patients with MDD were randomized to MCT, and 89 to CBT. Each treatment arm consisted of up to 24 sessions of up to 60  minutes each (time under therapy varied to maximize individual patient benefits), delivered by clinical psychologists trained and supervised in MCT and CBT. Both MCT and CBT were associated with similar large gains in depression assessed with the Hamilton Depression Rating Scale at post treatment and at 6-months follow up. However, a significant difference favoring MCT was found on the coprimary outcome (i.e., the BDI-II) at posttreatment that was maintained at 6-months follow-up. Most secondary measures also significantly favored the MCT arm. Following MCT, 74% of the patients were classified as recovered at posttreatment and at follow-up, in comparison 52% were classified as recovered at post treatment and 56% at follow-up, after receiving CBT. In this study, the mean number of sessions was significantly higher for CBT than MCT, with MCT associated with more rapid treatment effects. Group MCT for Depression MCT for depression can also be delivered in a group format and has been evaluated in two studies with trained MCT therapists. Dammen et al. (2015) treated 11 patients diagnosed with MDD in an uncontrolled trial and evaluated feasibility and preliminary effects. The treatment was associated with significant and large improvements across measures of depression, anxiety, rumination, and metacognitive beliefs, and these gains were maintained at 6-months follow-up. The intervention was also associated with significant reductions in comorbid diagnoses. The authors reported that, on average, it required 2.5 to 3 hours to treat each of the patients. At 1-year follow-up patients appeared to maintain their gains (Dammen et al., 2016). Papageorgiou and Wells (2015) evaluated the effects of group MCT for treatment-resistant depression defined as a lack of or limited response to

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current recommended treatments. Ten patients received 12 weekly group sessions of 2 hours, and two booster sessions of 1 hour each. The treatment was associated with significant improvements and large effect sizes. The authors report that on average it required 2 hours and 36 minutes to treat each of the patients in the group MCT. Effectiveness of MCT on Secondary Depression Symptoms The metacognitive approach is based on the principle that there are core similarities in underlying mechanisms across symptoms and disorders, especially negative metacognitive beliefs about the control of cognitions (Wells, 2009). We would, therefore, expect that metacognitive change would have a broad impact on symptoms. In line with this notion, a meta-analysis of the efficacy of MCT identified 12 studies that reported depression as a secondary outcome (anxiety was typically the primary outcome in these studies). The pooled effect size (Hedges’s g) for depression from pre- to posttreatment was 1.12, and from pretreatment to follow-up, it was 0.97, indicating a large effect for secondary depression symptom change in anxious patients (Normann & Morina, 2018). Effectiveness of MCT on Depression Symptoms in Physical Illnesses Depression and anxiety symptoms are common in individuals with physical illnesses and are linked to poorer outcomes, increased mortality, and poorer quality of life. Effective management of anxiety and depression is, therefore, important, and MCT may be particularly suited to addressing the psychological needs of medical patients. This is because the treatment focuses on beliefs about thinking (i.e., metacognition) and regulation of cognition rather than negative beliefs about the self and the future (which are often realistic in these patients). Among individuals with cardiovascular disease who reported elevated anxiety and depression symptoms, Wells and colleagues (2021) conducted a large (N = 332) single-blind multicenter RCT comparing cardiac rehabilitation with cardiac rehabilitation plus group MCT (six sessions of 60–90  minutes delivered by therapists with limited training). Group MCT plus treatment as usual was associated with significantly greater improvements than treatment as usual, with a standardized mean difference on the total Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983) at 4-months follow-up of 0.52 and also a difference favoring MCT of 0.33 at 12 months. Fisher and colleagues (2019) conducted an uncontrolled trial of MCT for emotional distress in adult cancer survivors. Twenty-seven patients were included, and each participant received a maximum of six 1-hour sessions of MCT. MCT was associated with significant and large improvements in symptoms of anxiety, depression, fear of cancer, and trauma symptoms, as well as metacognitive beliefs and strategies. The effect size (Cohen’s d) for depression symptoms was 1.37 from pre- to posttreatment, and 1.23 from pretreatment to 6-months follow-up.

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Metacognitive Change in Other Depression Therapies As suggested by the metacognitive model (Wells & Matthews, 1994), metacognitive change may account for outcome in other depression therapies as well. In line with this suggestion, metacognitions in the form of beliefs about the need to control thoughts significantly predicted improvements in patients with depression who were treated with “metacognitive training” (Moritz et  al., 2014), while dysfunctional attitudes (negative cognitions) did not (Jelinek et al., 2017). In patients with social anxiety disorder, treated with group CBT, change in metacognitions positively correlated with change in depression symptoms following treatment (McEvoy et al., 2009).

CASE EXAMPLE OF MCT FOR DEPRESSION In this section, we present an illustrative case example of an individual treated with MCT. We will refer to the client as Shona, who was 46 years of age at the time of treatment.1 Shona (a cisgender woman) had suffered from recurrent depressive episodes since her teenage years, with the present depressive episode lasting 14 months. She had declined antidepressant treatment, as she had found this of little benefit in the past, but she had received some benefit from counseling and CBT in previous depressive episodes. Treatment was conducted over nine treatment sessions, with eight to 12 sessions being typical of MCT. In the first treatment session (which occurred 2  weeks after the initial intake assessment), the therapist focused on constructing Shona’s case formulation based on the metacognitive model (Figure  16.2). The therapist identified a recent time in the last week when Shona had found herself dwelling on her feelings and thoughts and feeling more distressed. This event was used as a reference point for the depression case formulation interview (Wells, 2009, p. 280). This enabled the therapist to identify a trigger thought, the nature and extent of a rumination response, positive and negative meta­ cognitive beliefs, the nature of depressive symptoms, and CAS coping strategies. Shona described how she had felt “dreadful” in the last week when she was meeting with some of her friends. She described having the thought “why can’t I be happy?” and then ruminating about how she had nothing to be sad about and trying to figure out “what is wrong with me?” She described leaving the gathering early and going home to be alone. She spent all evening thinking about why she could not be as happy as others seemed to be and how she would never have a “normal life” that others seemed to have. The therapist questioned Shona about the advantages of thinking this way, and she disclosed that analyzing herself and comparing herself with others was a means of understanding her feelings and what was “normal.” The therapist asked her to estimate how much time each week she engaged in thinking this

Features of the case have been changed to protect confidentiality.

1

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way and labeled this process “rumination.” Shona described how she experienced rumination “every day” and “most of the time.” This was an opportunity to question the emotional consequences of engaging in the activity. The therapist asked, “How does spending so much time on this thinking project make you feel?” Shona could easily recognize that it was not helpful and that it did not get her any closer to finding an answer to her depression. When she was asked if the process made her feel better or worse, Shona was able to recognize that it had negative effects on her mood, but this was not something she had been conscious of before. The therapist then went on to explore if she could stop ruminating, but Shona was uncertain, saying, “I think it’s out of my control, I’m not sure I can stop it?” She was asked how much she believed it was uncontrollable (0–100%), to which she replied, “80%, when I feel so bad; it’s like my mind is taken over and I don’t know what to do anymore.” The therapist explored symptoms and other behaviors with Shona, and these were added to the case formulation (see Figure  16.2). The next step involved sharing the formulation through detailed discussion of each of the elements and how they link with depressive symptoms. Part of this socialization process involved asking further questions about the consequences of rumination and helping Shona to see how, if she wanted to reduce negative thinking, this could not be achieved by engaging in prolonged negative analysis of herself. The therapist asked hypothetical questions (e.g., “If you discovered that you could reduce rumination time what would happen to your feelings of depression?”) and suggested therapy could be viewed as an experiment to find out. Shona agreed that this would be useful but doubted her ability to influence her thinking, believing that her moods controlled her and admitting that she did not know how to stop “being so negative all the time.” Next, the therapist introduced the idea that it might be useful to experiment and discover how much control Shona had over her mind by using a technique of attention training. Shona was guided through a series of auditory attention exercises in which the therapist explored her awareness and reactions to spontaneously occurring internal (thoughts) and external (sounds) events. For example, she was asked to focus on sounds that might occur on her right-hand side and then move attention and focus on her left and back and forth. The therapist helped Shona identify thoughts that occurred during the exercise and used this to examine if there were any reactions to the thoughts. Shona described that while focusing she had heard a church bell and how this reminded her of her wedding. This had led her to think how her depression had “stolen” the happiness from her marriage. Through discussion, the therapist helped Shona to see how she had entered into a process of negative thinking in response to this memory. The memory or initial negative thought about her wedding was labelled as a “trigger thought,” and the subsequent thinking process was labeled as an example of rumination. In continuing the therapy dialogue, the therapist guided Shona to discover that she might have decided to leave the memory or trigger thought alone and not react with extended thinking, but, instead, continue by prioritizing

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the attention task. As it often does, this suggestion led into deeper discussion concerning her ability to leave thoughts alone and the important distinction between dealing with thoughts (e.g., through distraction or analyzing or ruminating) and choosing to do nothing with them. It became apparent that Shona confused distraction and pushing thoughts away, with the strategy of doing nothing (i.e., leaving mental events alone), and this was an important distinction to learn in this session. For homework, Shona was asked to practice with the attention task once a day and notice how she could be flexible in the control of her attention, irrespective of the occurrence of internal (trigger thoughts) and external events. In the sessions that followed, the exercise of detached mindfulness (Wells & Matthews, 1994) was introduced as a means of improving awareness of control and developing a new stance in relation to negative thoughts and memories. Here the therapist used the free association task (Wells, 2009), in which Shona was asked to sit back and passively observe what happened in her mind without controlling thoughts or analyzing or dealing with inner events in any way. Shona was asked to close her eyes while the therapist slowly said a series of neutral words: “apple, bicycle, tree, friends, chocolate, holiday . . .” for a period of 2  minutes. A discussion followed, focusing on the mental events that occurred during the exercise, and exploring how Shona had reacted or not reacted to her inner cognitive experiences. Through this process the therapist enabled Shona to strengthen conscious distinctions between action and inaction in response to spontaneous mental events. She was also able to sharpen her discrimination between trigger thoughts and subsequent and more purposeful mental reactions such as rumination and analyzing. The therapist suggested that Shona might try to step back and leave the thoughts alone that normally trigger rumination. However, Shona doubted this was possible, as she described such thoughts as “more important” than other thoughts. The therapist used this opportunity to repeat the free association task but explained that among the neutral words the therapist would use a trigger word related to one of her important negative thoughts. After a brief discussion, the word that Shona thought would be appropriate and important was “trapped,” since she felt trapped by depression with little hope of a future or a return to a happy marriage. She described that the word made her feel a “weight in my chest.” The therapist then repeated the free association task and used the trigger word. An exploration of Shona’s experiences followed and during this discourse it was discovered that Shona had felt a “gripping” sensation in her stomach on hearing the trigger word, and this was associated with an image of being alone and separated from her partner. The image had acted as a trigger for analyzing her reaction and worrying about the future. It was useful for Shona that the therapist identified this process and contrasted it with the idea of “leaving the image alone.” The therapist asked her if she could leave such a thought alone and not question her reasons for thinking it or entering into a process of worry. For homework, Shona was asked to step back from trigger thoughts and postpone rumination or worry or any attempt to deal with them.

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In the next sessions, the focus shifted on explicitly challenging beliefs about the uncontrollability of depressive rumination and worry. The therapist asked Shona to reflect on her experiences so far in treatment (e.g., “Have you had some success in leaving thoughts alone? How often have you stepped back and postponed your rumination?”). A review of the experiences of attention training, detached mindfulness, and rumination postponement were used to create new explicit knowledge of control and challenge metacognitive beliefs concerning uncontrollability. To prove that control was present and could not be readily lost, the therapist asked Shona to conduct an experiment by actively ruminating in the session and then to try losing control of the activity. Although Shona found it easy to ruminate on command, she found it impossible to lose control of her mind. However, this activated an important question for Shona: “Okay, maybe I do have control, but why do I ruminate so much in the first place? Isn’t that evidence I’m mentally weak?” The therapist asked Shona to consider the evidence for two possibilities, the first being that she was “mentally weak” and the second being that she had simply depended on unhelpful mental regulation strategies in the past that were ineffective. Shona discovered that she had not been aware of her options for control and that her strategies were biased toward those that she now realized did not work (e.g., more negative thinking and analysis of feelings as a fix for negative ideas and sadness). By Session 5, Shona had noticed an improvement in her mood. The therapist asked her to quantify how often she had been successful in not to reacting to trigger thoughts. Her success rate was about 70%, which is not far from the 75% rule of thumb that the MCT therapist aims for. Her belief that rumination was uncontrollable had decreased from 80% to 30%. This presented an opportunity to explore what was maintaining her 30% belief. The therapist discovered that Shona was interpreting her repeated past episodes of depression as an indication that depression might control her mind rather than her being in control. This dysfunctional metacognition was tackled by asking her to consider the evidence: “Had she used the strategies that she had discovered in this treatment in previous episodes? If she had, what might have happened? What strategies had she used instead and what had she learned about the effects of those strategies such as rumination?” By the end of the session, Shona’s belief in uncontrollability and in having a mental weakness was 0% and 10%, respectively. In the remaining sessions, the focus shifted to positive beliefs about rumination and modifying residual unhelpful CAS behaviors. The therapist questioned the advantages of engaging in rumination and analysis of moods. Shona answered that she had believed that ruminating was a way of “making sense of my depression,” but she now realized that it did not offer any understanding or solution. However, she disclosed that “it seems like I don’t want to be happy. I have to level out my feelings by thinking that the positives won’t last.” The therapist asked her, “What’s the worst that can happen if you don’t level it out?” Shona indicated that she believed that by keeping a pessimistic mindset, she could avoid feelings of disappointment and that such feelings were

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dangerous because they would trigger a depressive episode. A positive metacognitive belief was formulated, “being pessimistic avoids depression,” and was added to the case formulation and the therapist worked on challenging it. There was evidence to counter this belief; because Shona had already reduced the amount of time being pessimistic (i.e., ruminating) earlier in treatment, it was a simple task to question whether she had become more or less disappointed and depressed during this time. This was followed by an experiment for homework in which Shona was asked if she could practice increasing pessimism as a means of avoiding depression more tomorrow and then on the following day to abandon pessimism completely to see what happened to her mood. Shona discovered that disappointment was not a constant threat for depression, but that pessimism had been a process that weighed her down and affected her ability to fully appreciate events. In the final two sessions, the therapist explored residual unhelpful coping strategies and worked on relapse prevention. These issues were tackled by writing out a “Plan A” that consisted of Shona’s old CAS strategies (including rumination) and the metacognitions behind them. In contrast, the therapist worked collaboratively with Shona to develop a “Plan B,” which was a replacement set of behaviors and more adaptive metacognitions. Plan B was a summary of what had been learned in therapy and a blueprint for dealing adaptively with disappointments, mood fluctuations, and negative thoughts in the future. By the end of therapy, Shona was no longer depressed, and she felt more confident that she had a plan that could make a difference in the future. She described how she felt “quite excited about ending therapy” and how she was much less worried about whether she might become depressed in the future. She described how MCT had given her a perspective that was going to make a difference in the way she approached problems.

SUMMARY AND FUTURE DIRECTIONS The metacognitive model explains depression and other psychological disorders as resulting from the influence of metacognition on thinking styles. It identifies the CAS—a style of extended negative thinking comprised of rumination and worry, attention to threat, and unhelpful coping behaviors as a central mechanism of disorder. Specifically, metacognitive beliefs concerning the uncontrollability of thinking and the value of CAS processes such as rumination have a detrimental effect on the choice and execution of self-regulation strategies. As such, the individual with depression uses extended negative thinking (e.g., rumination) in an attempt to understand and deal with negative thoughts and feelings. Metacognitions can compromise direct mental efforts to stop ruminating and they bias the choice of strategies, leading to responses that provide limited experiences of direct internal control and that contribute to additional self-regulation and social difficulties. MCT (Wells, 2009) was developed to reduce the CAS and modify specific unhelpful features of metacognition. A growing number of studies demonstrate

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the effectiveness of MCT for depression and anxiety. In major depression and generalized anxiety disorders, the results of comparative trials suggest that MCT can be more effective or efficient than cognitive behavioral interventions. MCT consists of methods that increase awareness of thinking styles, bring repetitive thinking under direct control, challenge negative metacognitive beliefs about uncontrollability and mental brokenness, and modify positive beliefs concerning the value of extended negative processing. In doing so, the goal is to revise the content and procedures of the metacognitive control system. In MCT, the therapist does not work on the content of cognition such as the negative automatic thoughts or schemas of CBT, as interrogating these mental products is not considered to be a direct route to modification of dysfunctional metacognition and mental regulation. MCT is a recent development in understanding and treating depression and further theory testing and evaluation of its clinical effectiveness is needed. Theoretical developments since the original S-REF model have focused on clearer separation of the metacognitive and cognitive systems and mapping in more detail the structures and information content of the metacognitive control system (Wells, 2019). Such work will lead to further refinements in MCT and the development of additional metacognitive change techniques. One of the issues identified, which has important implications for the development of psychotherapy, is to understand the mechanisms by which the metacognitive control system represents the current status of cognition and how neural networks may be recruited (and modified in therapy) in the execution of this process. Further research is required to investigate the causal status of metacognition in the development of stages of depression. MCT gives particular importance to the negative metacognitions concerning control in MDD, with positive beliefs about rumination playing a more minor role. It is feasible that positive and negative metacognitions have different temporal relationships with depression. In particular, positive beliefs may contribute to an overuse of rumination and a general pessimistic outlook, presenting an earlier risk for psychological maladjustment but the development of negative metacognitions presents a later mechanism for actual disorder. The respective importance of positive and negative metacognitions in the subsequent maintenance and relapse of depression, especially in recurrent depression, remains to be investigated. Future trials might aim to test the clinical effects of MCT against a wider range of evidence-based therapies, such as problem-solving therapy, behavioral activation, and interpersonal therapy. It will be useful for trials of MCT to be conducted by independent research groups that do not have an allegiance to the method. One of the advantages is that MCT has a well-defined set of methods that can be distinguished from these other approaches. However, MCT is a complex therapy and effective practice requires adequate training. There are further cautions to be considered, namely, that the techniques and goals of MCT are not entirely compatible with other treatment approaches. This means that combining MCT with techniques form other therapies may interfere with the effectiveness of individual treatment components.

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In conclusion, separating the metacognitive and cognitive systems and treating depression as predominantly a disturbance of the former may present a significant forward step. Metacognitive theory and therapy are supported by empirical data, but a cautious approach is recommended, since evidence of the causal significance of metacognitions in depression is limited by the existence of a small number of studies. However, there is growing evidence, including data from large randomized trials, that MCT offers an effective and brief treatment for depressive symptoms and for MDD. REFERENCES Bailey, R., & Wells, A. (2013). Does metacognition make a unique contribution to health anxiety when controlling for neuroticism, illness cognition, and somatosensory amplification? Journal of Cognitive Psychotherapy, 27(4), 327–337. https://doi.org/10.1891/ 0889-8391.27.4.327 Beck, A. T. (1976). Cognitive therapy and the emotional disorders. International Universities Press. Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Beck Depression Inventory manual (2nd ed.). Psychological Corporation. Bevan, D., Wittkowski, A., & Wells, A. (2013). A multiple-baseline study of the effects associated with metacognitive therapy in postpartum depression. Journal of Midwifery & Women’s Health, 58(1), 69–75. https://doi.org/10.1111/j.1542-2011.2012.00255.x Callesen, P., Jensen, A. B., & Wells, A. (2014). Metacognitive therapy in recurrent depression: A case replication series in Denmark. Scandinavian Journal of Psychology, 55(1), 60–64. https://doi.org/10.1111/sjop.12089 Callesen, P., Pedersen, M. L., Andersen, C. K., & Wells, A. (2020). Metacognitive therapy for bipolar II disorder: A single case series study. Neurology, Psychiatry & Brain Research, 38(1), 107–113. https://doi.org/10.1016/j.npbr.2020.08.004 Callesen, P., Reeves, D., Heal, C., & Wells, A. (2020). Metacognitive therapy versus cognitive behaviour therapy in adults with major depression: A parallel single-blind randomised trial. Scientific Reports, 10(1), 7878. https://doi.org/10.1038/s41598-02064577-1 Cano-López, J. B., García-Sancho, E., Fernández-Castilla, B., & Salguero, J. M. (2022). Empirical evidence of the metacognitive model of rumination and depression in clinical and nonclinical samples: A systematic review and meta-analysis. Cognitive Therapy and Research, 46, 367–392. https://doi.org/10.1007/s10608-021-10260-2 Capobianco, L., Faija, C., Husain, Z., & Wells, A. (2020). Metacognitive beliefs and their relationship with anxiety and depression in physical illnesses: A systematic review. PLOS ONE, 15(9), e0238457. https://doi.org/10.1371/journal.pone.0238457 Carciofo, R. (2020). Morning affect, eveningness, and amplitude distinctness: Associations with negative emotionality, including the mediating roles of sleep quality, personality, and metacognitive beliefs. Chronobiology International, 37(11), 1565–1579. https://doi.org/10.1080/07420528.2020.1798978 Carter, J. D., Jordan, J., McIntosh, V. V., Frampton, C. M., Lacey, C., Porter, R. J., & Mulder, R. T. (2022). Long-term efficacy of metacognitive therapy and cognitive behaviour therapy for depression. The Australian and New Zealand Journal of Psychiatry, 56(2), 137–143. https://doi.org/10.1177/00048674211025686 Dammen, T., Papageorgiou, C., & Wells, A. (2015). An open trial of group metacognitive therapy for depression in Norway. Nordic Journal of Psychiatry, 69(2), 126–131. https://doi.org/10.3109/08039488.2014.936502 Dammen, T., Papageorgiou, C., & Wells, A. (2016). A two year follow up study of group metacognitive therapy for depression in Norway. Journal of Depression & Anxiety, 5(2), 1000227. https://doi.org/10.4172/2167-1044.1000227

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Faissner, M., Kriston, L., Moritz, S., & Jelinek, L. (2018). Course and stability of cognitive and metacognitive beliefs in depression. Depression and Anxiety, 35(12), 1239–1246. https://doi.org/10.1002/da.22834 Fergus, T. A., & Bardeen, J. R. (2016). The attention training technique: A review of a neurobehavioral therapy for emotional disorders. Cognitive and Behavioral Practice, 23(4), 502–516. https://doi.org/10.1016/j.cbpra.2015.11.001 Fisher, P. L., Byrne, A., Fairburn, L., Ullmer, H., Abbey, G., & Salmon, P. (2019). Brief metacognitive therapy for emotional distress in adult cancer survivors. Frontiers in Psychology, 10, 162. https://doi.org/10.3389/fpsyg.2019.00162 Frank, E., Prien, R. F., Jarrett, R. B., Keller, M. B., Kupfer, D. J., Lavori, P. W., Rush, A. J., & Weissman, M. M. (1991). Conceptualization and rationale for consensus definitions of terms in major depressive disorder. Remission, recovery, relapse, and recurrence. Archives of General Psychiatry, 48(9), 851–855. https://doi.org/10.1001/ archpsyc.1991.01810330075011 Groves, S. J., Porter, R. J., Jordan, J., Knight, R., Carter, J. D., McIntosh, V. V. W., Fernando, K., Frampton, C. M., Mulder, R. T., Lacey, C., & Joyce, P. R. (2015). Changes in neuropsychological function after treatment with metacognitive therapy or cognitive behavior therapy for depression. Depression and Anxiety, 32(6), 437–444. https://doi.org/10.1002/da.22341 Hagen, R., Hjemdal, O., Solem, S., Kennair, L. E. O., Nordahl, H. M., Fisher, P., & Wells, A. (2017). Metacognitive therapy for depression in adults: A waiting list randomized controlled trial with six months follow-up. Frontiers in Psychology, 8, 31. https:// doi.org/10.3389/fpsyg.2017.00031 Hallard, R. I., Wells, A., Aadahl, V., Emsley, R., & Pratt, D. (2021). Metacognition, rumination and suicidal ideation: An experience sampling test of the self-regulatory executive function model. Psychiatry Research, 303, 114083. https://doi.org/10.1016/ j.psychres.2021.114083 Halvorsen, M., Hagen, R., Hjemdal, O., Eriksen, M. S., Sørli, Å. J., Eisemann, M., & Wang, C. E. (2015). Metacognitions and thought control strategies in unipolar major depression: A comparison of currently depressed, previously depressed, and never-depressed individuals. Cognitive Therapy and Research, 39(1), 31–40. https:// doi.org/10.1007/s10608-014-9638-4 Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23(1), 56–62. https://doi.org/10.1136/jnnp.23.1.56 Hjemdal, O., Hagen, R., Solem, S., Nordahl, H., Kennair, L. E. O., Ryum, T., Nordahl, H. M., & Wells, A. (2017). Metacognitive therapy in major depression: An open trial of comorbid cases. Cognitive and Behavioral Practice, 24(3), 312–318. https://doi.org/ 10.1016/j.cbpra.2016.06.006 Hjemdal, O., Solem, S., Hagen, R., Kennair, L. E. O., Nordahl, H. M., & Wells, A. (2019). A randomized controlled trial of metacognitive therapy for depression: Analysis of 1-year follow-up. Frontiers in Psychology, 10, 1842. https://doi.org/10.3389/fpsyg. 2019.01842 Jacobson, N. S., Follette, W. C., & Revenstorf, D. (1984). Psychotherapy outcome research: Methods for reporting variability and evaluating clinical significance. Behavior Therapy, 15(4), 336–352. https://doi.org/10.1016/S0005-7894(84)80002-7 Jelinek, L., Van Quaquebeke, N., & Moritz, S. (2017). Cognitive and metacognitive mechanisms of change in metacognitive training for depression. Scientific Reports, 7(1), 3449. https://doi.org/10.1038/s41598-017-03626-8 Jordan, J., Carter, J. D., McIntosh, V. V., Fernando, K., Frampton, C. M., Porter, R. J., Mulder, R. T., Lacey, C., & Joyce, P. R. (2014). Metacognitive therapy versus cognitive behavioural therapy for depression: A randomized pilot study. The Australian and New Zealand Journal of Psychiatry, 48(10), 932–943. https://doi.org/10.1177/ 0004867414533015

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Kennair, L. E. O., Solem, S., Hagen, R., Havnen, A., Nysæter, T. E., & Hjemdal, O. (2021). Change in personality traits and facets (Revised NEO Personality Inventory) following metacognitive therapy or cognitive behaviour therapy for generalized anxiety disorder: Results from a randomized controlled trial. Clinical Psychology & Psychotherapy, 28(4), 872–881. https://doi.org/10.1002/cpp.2541 Kraft, B., Jonassen, R., Ulset, V., Stiles, T., & Landrø, N. I. (2019). A prospective test of the metacognitive model of depression in previously depressed individuals. Cognitive Therapy and Research, 43(3), 603–610. https://doi.org/10.1007/s10608-018-9972-z Kubiak, T., Zahn, D., Siewert, K., Jonas, C., & Weber, H. (2014). Positive beliefs about rumination are associated with ruminative thinking and affect in daily life: Evidence for a metacognitive view on depression. Behavioural and Cognitive Psychotherapy, 42(5), 568–576. https://doi.org/10.1017/S1352465813000325 Lenzo, V., Sardella, A., Martino, G., & Quattropani, M. C. (2020). A systematic review of metacognitive beliefs in chronic medical conditions. Frontiers in Psychology, 10, 2875. https://doi.org/10.3389/fpsyg.2019.02875 Marino, C., Vieno, A., Lenzi, M., Fernie, B. A., Nikcˇevic´, A. V., & Spada, M. M. (2018). Personality traits and metacognitions as predictors of positive mental health in college students. Journal of Happiness Studies, 19(2), 365–379. 10.1007/s10902-016-9825-y Marino, C., Vieno, A., Moss, A. C., Caselli, G., Nikcˇevic´, A. V., & Spada, M. M. (2016). Personality, motives and metacognitions as predictors of problematic Facebook use in university students. Personality and Individual Differences, 101, 70–77. https://doi.org/ 10.1016/j.paid.2016.05.053 Matsumoto, N., & Mochizuki, S. (2018). Why do people overthink? A longitudinal investigation of a meta-cognitive model and uncontrollability of rumination. Behavioural and Cognitive Psychotherapy, 46(4), 504–509. https://doi.org/10.1017/S1352465818000103 McEvoy, P. M., Mahoney, A., Perini, S. J., & Kingsep, P. (2009). Changes in post-event processing and metacognitions during cognitive behavioral group therapy for social phobia. Journal of Anxiety Disorders, 23(5), 617–623. https://doi.org/10.1016/j.janxdis. 2009.01.011 McEvoy, P. M., Moulds, M. L., & Mahoney, A. E. (2013). Mechanisms driving pre- and post-stressor repetitive negative thinking: Metacognitions, cognitive avoidance, and thought control. Journal of Behavior Therapy and Experimental Psychiatry, 44(1), 84–93. https://doi.org/10.1016/j.jbtep.2012.07.011 Moritz, S., Andreou, C., Schneider, B. C., Wittekind, C. E., Menon, M., Balzan, R. P., & Woodward, T. S. (2014). Sowing the seeds of doubt: A narrative review on metacognitive training in schizophrenia. Clinical Psychology Review, 34(4), 358–366. https:// doi.org/10.1016/j.cpr.2014.04.004 Myers, S. G., Solem, S., & Wells, A. (2019). The Metacognitions Questionnaire and its derivatives in children and adolescents: A systematic review of psychometric properties. Frontiers in Psychology, 10, 1871. https://doi.org/10.3389/fpsyg.2019.01871 Nordahl, H., Hjemdal, O., Hagen, R., Nordahl, H. M., & Wells, A. (2019). What lies beneath trait-anxiety? Testing the self-regulatory executive function model of vulnerability. Frontiers in Psychology, 10, 122. https://doi.org/10.3389/fpsyg.2019.00122 Nordahl, H., Hjemdal, O., & Wells, A. (2021). Metacognitive beliefs uniquely contribute to interpersonal problems: A test controlling for adult attachment, big-5 personality traits, anxiety, and depression. Frontiers in Psychology, 12, 694565. https://doi.org/ 10.3389/fpsyg.2021.694565 Nordahl, H., Nordahl, H. M., Vogel, P. A., & Wells, A. (2018). Explaining depression symptoms in patients with social anxiety disorder: Do maladaptive metacognitive beliefs play a role? Clinical Psychology & Psychotherapy, 25(3), 457–464. https://doi.org/ 10.1002/cpp.2181 Nordahl, H., & Wells, A. (2017). Individual differences in metacognitive knowledge contribute to psychological vulnerability more than the presence of a mental disorder does. Mental Health & Prevention, 7, 17–20. https://doi.org/10.1016/j.mhp.2017.07.003

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Nordahl, H. M., Borkovec, T. D., Hagen, R., Kennair, L. E. O., Hjemdal, O., Solem, S., Hansen, B., Haseth, S., & Wells, A. (2018). Metacognitive therapy versus cognitivebehavioural therapy in adults with generalised anxiety disorder. BJPsych Open, 4(5), 393–400. https://doi.org/10.1192/bjo.2018.54 Normann, N., & Morina, N. (2018). The efficacy of metacognitive therapy: A systematic review and meta-analysis. Frontiers in Psychology, 9, 2211. https://doi.org/10.3389/ fpsyg.2018.02211 Papageorgiou, C., & Wells, A. (2000). Treatment of recurrent major depression with attention training. Cognitive and Behavioral Practice, 7(4), 407–413. https://doi.org/ 10.1016/S1077-7229(00)80051-6 Papageorgiou, C., & Wells, A. (2001a). Metacognitive beliefs about rumination in recurrent major depression. Cognitive and Behavioral Practice, 8, 160–164. https://doi.org/ 10.1016/S1077-7229(01) 80021-3. Papageorgiou, C., & Wells, A. (2001b). Positive beliefs about depressive rumination: Development and preliminary validation of a self-report scale. Behavior Therapy, 32(1), 13–26. https://doi.org/10.1016/S0005-7894(01)80041-1 Papageorgiou, C., & Wells, A. (2003). An empirical test of a clinical metacognitive model of rumination and depression. Cognitive Therapy and Research, 27(3), 261–273. https://doi.org/10.1023/A:1023962332399 Papageorgiou, C., & Wells, A. (2009). A prospective test of the clinical metacognitive model of rumination and depression. International Journal of Cognitive Therapy, 2(2), 123–131. https://doi.org/10.1521/ijct.2009.2.2.123 Papageorgiou, C., & Wells, A. (2015). Group metacognitive therapy for severe anti­ depressant and CBT resistant depression: A baseline-controlled trial. Cognitive Therapy and Research, 39(1), 14–22. https://doi.org/10.1007/s10608-014-9632-x Rush, A. J., Trivedi, M. H., Ibrahim, H. M., Carmody, T. J., Arnow, B., Klein, D. N., Markowitz, J. C., Ninan, P. T., Kornstein, S., Manber, R., Thase, M. E., Kocsis, J. H., & Keller, M. B. (2003). The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): A psychometric evaluation in patients with chronic major depression. Biological Psychiatry, 54(5), 573–583. https://doi.org/10.1016/S0006-3223(02)01866-8 Solem, S., Kennair, L. E. O., Hagen, R., Havnen, A., Nordahl, H. M., Wells, A., & Hjemdal, O. (2019). Metacognitive therapy for depression: A 3-year follow-up study assessing recovery, relapse, work force participation, and quality of Life. Frontiers in Psychology, 10, 2908. https://doi.org/10.3389/fpsyg.2019.02908 Spada, M. M., Gay, H., Nikcˇevic´, A. V., Fernie, B. A., & Caselli, G. (2016). Meta-cognitive beliefs about worry and pain catastrophising as mediators between neuroticism and pain behaviour. Clinical Psychologist, 20(3), 138–146. https://doi.org/10.1111/cp.12081 Sun, X., Zhu, C., & So, S. H. W. (2017). Dysfunctional metacognition across psycho­ pathologies: A meta-analytic review. European Psychiatry, 45, 139–153. https://doi.org/ 10.1016/j.eurpsy.2017.05.029 van der Heiden, C., Melchior, K., & de Stigter, E. (2013). The effectiveness of group metacognitive therapy for generalized anxiety disorder: A pilot study. Journal of Contemporary Psychotherapy, 43(3), 151–157. https://doi.org/10.1007/s10879-013-9235-y van der Heiden, C., Muris, P., & van der Molen, H. T. (2012). Randomized controlled trial on the effectiveness of metacognitive therapy and intolerance-of-uncertainty therapy for generalized anxiety disorder. Behaviour Research and Therapy, 50(2), 100–109. https://doi.org/10.1016/j.brat.2011.12.005 Weber, F., & Exner, C. (2013). Metacognitive beliefs and rumination: A longitudinal study. Cognitive Therapy and Research, 37(6), 1257–1261. https://doi.org/10.1007/ s10608-013-9555-y Wells, A. (1990). Panic disorder in association with relaxation induced anxiety: An attentional training approach to treatment. Behavior Therapy, 21(3), 273–280. https://doi.org/ 10.1016/S0005-7894(05)80330-2

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17 Investigating and Treating Psychosocial Risk Factors in Depression An Integrative Summary David J. A. Dozois and Keith S. Dobson

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linical depression is a highly prevalent disorder associated with significant cognitive, behavioral, emotional, interpersonal, and somatic impairments. According to the World Health Organization (WHO; 2017), more than 322 million people live with depression, and it is reported that this condition is the largest single contributor to disability worldwide. A systematic review of the prevalence of depression, combining data from 1 million community adults from 30 countries (1994–2014), found a 1-year prevalence rate of 7.2% and a lifetime prevalence rate of 10.8% (Lim et al., 2018), with higher rates in more recent years (Hasin et al., 2018; Wang et al., 2021; but see Kessler et al., 2015). Among continents, the highest aggregate prevalence rate was 20.6% in South America, 16.7% in Asia, 13.4% in North America, 11.9% in Europe, 11.5% in Africa, and 7.3% in Australia (Lim et al., 2018). Using a structured diagnostic interview to measure symptomatology according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013) criteria, Hasin et al. (2018) found that the 12-month and lifetime prevalence rates of major depressive disorder (MDD) in the United States were 10.4% and 20.6%, respectively. Depression is characterized by both high prevalence and recurrence (Buckman et al., 2018; Pettit et al., 2013; Verduijn et al., 2017). Between 50% and 90% of individuals with depression experience multiple subsequent episodes (Kessler et al., 2015; Klein & Allmann, 2014), and the risk of recurrence increases with each episode (an effect known as sensitization and kindling; see Post, 2021). In addition, depression is highly comorbid with other mental

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and physical health conditions (Dozois et al., 2020; Kessler et al., 2015). For example, more than 50% of individuals with depression also have an anxiety disorder, which raises the question of whether there might be common risk factors between these disorders that could have important implications for transdiagnostic interventions (Dozois et al., 2009; Kessler et al., 2015). Together, the prevalence, recurrence, and comorbidity of depression contribute to enormous complexity in understanding this disorder, studying risk and vulnerability factors, and effectively preventing and treating this condition. The objective of the current volume was to examine key evidence-based psycho­ social risk factors for clinical depression and consider each from the perspective of intervention. In this chapter, we provide a rationale for the overall approach we have taken in this volume to describe the evidence and interventions for different individual risk factors. We then highlight some of the salient risk factors and interventions covered in this volume and outline some future directions to study risk and apply this knowledge to intervention science. We highlight the fact that although highly efficacious interventions for the treatment of depression exist, there is also considerable room for improvement.

EFFICACY (AND GAPS) OF PSYCHOLOGICAL INTERVENTIONS FOR DEPRESSION There are several highly efficacious and effective psychosocial interventions for MDD (Cuijpers et al., 2011; Cuijpers, Quero, et al., 2019; Driessen et al., 2015; Hofmann et al., 2012; Hollon & Ponniah, 2010; Munder et al., 2019). For instance, Hollon and Ponniah (2010) found that cognitive behavior therapy (CBT), behavioral activation (BA), and interpersonal psychotherapy (IPT) each demonstrated efficacy for the treatment of depression and that brief psycho­ dynamic therapy and emotion-focused therapy are possibly efficacious. That said, evidence-based treatments for depression do not benefit all patients, and outcomes are not always optimal. For example, although CBT is equivalent to pharmacotherapy for the treatment of an acute and even severe episode of depression, and substantially reduces the risk of relapse (Cuijpers et al., 2013; DeRubeis et al., 2019; Strunk et al., 2017), many patients drop out of therapy, do not achieve remission, or relapse after treatment has ended (Cohen & DeRubeis, 2018; Dozois et al., 2019; Vittorio et al., 2021). The literature also lacks clarity regarding which treatments are most appropriate for a given client or the key ingredients and mechanisms of change (Cuijpers et al., 2017; Cuijpers, Reijnders, et al., 2019; Dozois et al., 2019). As Truijens et al. (2021) argued, understanding the complex mechanisms of change is not only interesting scientifically but also a clinical and ethical imperative. Increased empirical and clinical focus on treating specific mechanisms and risk factors related to depression represent important steps toward potentially personalizing and optimizing treatment. Intervention science has typically focused on treatment packages rather than on treatment components or interventions that target putative vulnerability

Investigating and Treating Psychosocial Risk Factors in Depression 409

mechanisms. As Hewitt et al. (Chapter 12, this volume) pointed out, “although reducing depression symptoms is an important goal, reducing demonstrated vulnerability factors for depression that are amenable to change . . . is equally important” (p. 282).

A MYRIAD OF RISK FACTORS Risk factors in depression have been defined and operationalized in a variety of ways. As Hammen (2018) pointed out, the “term risk factor has been variously used to describe risk mechanisms as well as risk triggers, moderators as well as mediators, and causal risk factors as well as noncausal risk markers” (p. 5). Risk is conceptualized within this volume, as evidence-based variables that are associated with a greater probability that a person or group will experience depression relative to other individuals or groups who do not possess a given characteristic (see also Ingram & Price, 2001; Ingram et al., 2004). The literature on risk factors in depression is vast, and numerous biological (e.g., Kendler, 2019; Kennis et al., 2020), psychological (e.g., Dozois & Hayden, 2022; Goodman, 2020; Hammen, 2018; LeMoult & Gotlib, 2019; Platt et al., 2017; Struijs et al., 2021), and social (e.g., Goodman, 2020; Sanchez & Sanchez, 2021) variables have been identified (see also Dobson & Dozois, 2008). It is important to note, however, that the identification of a risk factor simply refers to an increased statistical likelihood of the onset or recurrence of depression and does not necessarily relay information about the causal mechanisms involved. For example, depression affects twice as many women as it does men, and this ratio is consistent across ages and nations (Hyde & Mezulis, 2020; Lim et al., 2018; Rnic & Dozois, 2020). This gender difference typically appears by the age of 12 years and peaks at ages 13 to 15 (Hankin & Abramson, 2001; Hyde & Mezulis, 2020). Knowing the odds ratio for sex differences in depression, however, does not necessarily inform us about vulnerability toward depression associated with gender or the mechanisms involved (Ingram et al., 2011). Across multiple levels of scientific inquiry (e.g., molecular genetics, systems neuroscience, cognition, individual and family environment, community, society, culture), Kendler (2019) identified 37 variables that could be considered causes of depression. These potential causes of depression included genome wide biological risks, functional magnetic resonance imaging resting state, reduced cortical brain volumes, genetic influences, abnormalities in the hypothalamic– pituitary–adrenal axis, immune activation, reduced serum brain-derived neuro­ trophic factor, poor diet, smoking, the postpartum period, affective processing biases, deficits in executive functioning, deficits in memory and attention, neuro­ ticism, other psychiatric disorders (borderline personality disorder, anxiety disorders, alcohol use disorder), negative cognitive styles, rumination, stressful life events, long-term contextual threat, loss, humiliation, childhood sexual abuse, lack of exercise, low socioeconomic status, unemployment, low social support,

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intergenerational transmission, low parental warmth, parental death in childhood, low levels of religiosity, and economic downturns. This review also noted that depression rates were demonstrably lower among people of East Asian heritage. Many, though not all, of the variables on this long list represent modifiable risk factors. For instance, genetic vulnerability and early life adversity are two risk factors for depression that have acquired substantial empirical support, but which are not modifiable in adults (Choi et al., 2020). Using an exposure-wide association study design, Choi et al. (2020) tested the relationship between 106 modifiable risk factors and subsequent depression 6 to 8 years later. These authors identified numerous factors across different domains that included social, media, sleep, dietary, and physical activity. The sample was large and consisted of individuals who were at risk for depression based on polygenic risk (n = 112,589) or previous self-reported trauma (n = 11,258). The association between each risk factor and depression onset was tested conservatively, controlling for multiple comparisons (using the Bonferroni correction of .05/106; p = 0.000157). To account for the possibility of reverse causation and residual confounding, Mendelian randomization analyses were used to investigate each association and identify modifiable risk factors. Among those with genetic risk, computer use time and salt intake were significant risk factors, whereas confiding in others and sleep duration were protective factors. After controlling for covariates, three protective factors (confiding in others, exercise, and sleep duration) and one risk factor (television watching time) were found in the sample identified as at risk due to previous trauma. The Choi et al. (2020) study represents an innovative approach to the study of risk in depression. However, their study did not include a wide range of other psychological and social variables known to increase risk for depression. To illustrate this, Hammen (2018) identified cognitive content and processes, parental depression, life stress, and other behavior patterns (e.g., stress generation) as important risk factors for depression. In a narrative review, Struijs et  al. (2021) examined the specificity and predictive utility of various risk factors associated with the onset, maintenance, comorbidity, and recurrence of affective disorders. High neuroticism, low self-esteem, and repetitive negative thinking emerged as transdiagnostic variables relevant to both anxiety and depression, whereas cognitive reactivity was the most specific to depression. It, thus, appears that different authors emphasize different variables, or, in some cases, may have access to only certain potential risk variables and so the emergent picture of risk factors varies among studies and authors.

TARGETING MODIFIABLE RISK FACTORS In this volume, we have attempted to distill some of the main evidence-based psychosocial risk factors for depression that have been identified in the literature. The risk factors selected for consideration in this volume include those

Investigating and Treating Psychosocial Risk Factors in Depression 411

psychological and social factors that have a substantive literature related to depression. The risk factors covered in this volume were selected because (a) they have the potential to contribute to our knowledge base regarding the onset, maintenance, relapse, or recurrence of depression; (b) they are modifiable; and (c) there are evidence-based psychological interventions available to target them. Table 17.1 lists the risk factors that were included in this volume. As it summarizes, many of these factors have a data base to substantiate them as modifiable risk factors, and, in some instances, there is evidence to support a broad-based intervention or psychotherapy to modify a particular risk factor. Examples include CBT to treat parental depression (see Pine & Garber, Chapter 2, this volume), CBT, mindfulness-based interventions, dialectical behavior therapy, couple therapy, and psychodynamic approaches to target behaviors and problems related to interpersonal dependency (see Starr et al., Chapter 6), and dynamic–relational psychotherapy for the treatment of depression related to perfectionism (see Hewitt et al., Chapter 12). In other instances, the interventions are comprised of specific techniques to alter given risk variables (e.g., attention bias modification; memory specificity training; Deng & Joorman, Chapter 10) or modifications of a broader approach to specifically target risk (e.g., rumination-focused cognitive behavioral therapy; Watkins, Chapter 13). Our approach to this book involved having authors define the risk factor, discuss pertinent conceptual issues, review the scientific evidence pertaining to a particular risk factor, and outline the therapeutic implications of this research. We further asked authors to describe evidence-based strategies that target these risk and vulnerability factors in depression, and to use a case example to illustrate the techniques. As readers will discern, the chapters in this volume provide strong evidence that it is feasible to translate risk research into effective, evidence-based interventions. Although the knowledge base related to psychosocial risk factors for depression is vast and complex (Dobson & Dozois, 2008; Hammen, 2018; Hankin, 2012; Rnic & Dozois, 2020; Robinaugh et al., 2020), many modifiable risk factors can be targeted effectively. There are modifiable risk factors that were not covered in this text. For example, we know that anxiety is often a precursor to depression and that effectively treating anxiety disorders can reduce the risk of developing depression (Kendler, 2019; Sutton, 2007). We also know that dysregulated sleep is a modifiable risk for depression (Choi et al., 2020; Kendler, 2019). We refer the reader to the many treatment manuals for these disorders and other risk factors not included in this volume. It is also important to point out that although the focus of this book was on different individual risk factors, this does not imply that these variables do not interact with other biological, psychological, and social risk factors in complex ways (Alba & Calvette, 2019; Gotlib et al., 2020; Hammen & Gotlib, 2014; Hankin, 2012) or that risk can only be treated with psychosocial interventions. Indeed, the field is increasingly moving toward more complex biopsychosocial models of risk of depression that integrate these variables (e.g., De Raedt & Koster, 2010; Disner et al., 2011; Vargas et al., 2020).

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TABLE 17.1.  Modifiable Risk Factors of Clinical Depression Chapter in this volume 2

Risk factor

Intervention

Family member with a history of depression

Interventions to treat parental depression (e.g., sequenced treatment alternatives to relieve depression [STAR*D]; cognitive behavioral therapy)

Intergenerational transmission (heritability, innate dysfunctional neuroregulation, high stress, exposure to negative parental cognitions, behaviors, and affect) Child characteristics (e.g., temperament, emotion regulation, intelligence)

Interventions that target parenting or the parent–child relationship (homevisiting interventions; learning through play plus program; toddler-parent psycho­therapy; Triple P-Positive Parenting Program; EFFEKT-E; Family Talk; Let’s Talk About the Children; family group cognitive behavioral preventive intervention; Keeping Families Strong; The Family Check-Up; The Family Bereavement Program; Program for Children of Divorce)

3

Low perceived social support

Mathematically defined relationship effects in psychotherapy

4

Role transitions

Interpersonal psychotherapy

Role disputes Interpersonal deficits Grief 5

Major proximal life events (e.g., divorce, job loss) Major distal events (e.g., history of trauma in childhood)

Cognitive and behavioral interventions (e.g., prolonged exposure; cognitive processing therapy) Interpersonal psychotherapy Mindfulness-based stress reduction Mindfulness-based cognitive therapy Biological interventions

6

7

Interpersonal dependency

Cognitive behavioral therapy

Excessive reassurance seeking

Mindfulness-based interventions

Negative feedback seeking

Dialectical behavioral therapy

Corumination

Couple therapy

Interpersonal stress generation

Psychodynamic approaches

Marriage and relationship issues

Cognitive behavioral couples therapy

Relationship distress Poor communication Relationship stressors (e.g., high expressed emotion and perceived criticism)

Investigating and Treating Psychosocial Risk Factors in Depression 413

TABLE 17.1.  Modifiable Risk Factors of Clinical Depression (Continued) Chapter in this volume 8

Risk factor

Intervention

Emotion dysregulation

Cognitive behavior therapy

Emotional awareness

Mindfulness-based cognitive therapy

Emotional experience/intensity

Biological interventions

Emotion regulation strategy use •  Rumination •  Cognitive reappraisal •  Experiential avoidance and acceptance •  Expressive suppression •  Emotion regulation flexibility 9

Negative thinking in depression

Behavioral activation

Cognitive products (e.g., negative automatic thoughts, cognitive distortions, dysfunctional attitudes, inferential styles, or biased assumptions)

Cognitive restructuring (e.g., working with automatic thoughts) Modifying core beliefs and schemas (e.g., behavioral experiments)

Schema structures 10

Attention biases

Attention bias modification

Interpretation biases Memory biases

Cognitive bias modification (interpretation)

Executive control

Memory specificity training Cognitive control training

11

Unrealistic optimism

Building optimism

Defensive pessimism

Cognitive behavior therapy Acceptance and commitment therapy Positive activity interventions Positive psychotherapy Well-being therapy Penn Resilience program

12

Perfectionism

Dynamic–relational psychotherapy

13

Rumination

Rumination-focused cognitive behavioral therapy (concreteness training, absorption training, compassion training) Cognitive bias modification

14

Social problem solving

Problem-solving therapy (continues)

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TABLE 17.1.  Modifiable Risk Factors of Clinical Depression (Continued) Chapter in this volume 15

16

Risk factor

Intervention

Avoidance (behavioral, cognitive, and experiential avoidance)

Behavioral activation

Cognitive attentional syndrome

Metacognitive therapy

Also: cognitive therapy, cognitive behavioral analysis system of psychotherapy, rumination-focused cognitive behavioral therapy, mindfulness-based cognitive therapy, other “third wave” treatments

Sustained negative thinking Negative beliefs about thinking Positive beliefs about rumination and worry Biased meta-awareness

We hope that this book provides readers with both the research evidence and set of techniques to intervene at the level of the underlying mechanism and obtain a better understanding of how a given treatment technique alters different risk factors for depression. With this knowledge in hand, it may become more possible to individualize models of care.

FUTURE DIRECTIONS TO STUDY RISK AND INTERVENTION SCIENCE As the authors of the chapters in this volume have so aptly pointed out, clinical scientists have made significant advances in the understanding of psychosocial risk factors in depression. Notwithstanding these important strides, much empirical and clinical work remains to be done. In this section, we highlight several broad domains we see as worthy priorities for future research on psychosocial risk factors in depression. Conceptualizing and Investigating Risk Major depression is a phenotypically heterogeneous construct, and there are different ways to define and study its vicissitudes, each of which impacts our ability to understand risk, prevent its onset, and intervene once symptomatology has developed to the point where day-to-day functioning has become impaired. The most used criteria to define clinical depression, or what researchers and clinicians refer to as MDD, is the categorical approach advanced by the DSM-5. According to the DSM-5 classification system, the criteria for MDD are met when an individual exhibits a minimum of five out of nine symptoms which are present and cause notable distress or impairment nearly every day for a

Investigating and Treating Psychosocial Risk Factors in Depression 415

period of at least 2  weeks. At least one of these symptoms must be either depressed mood or anhedonia. There are, of course, limitations with the traditional categorical approach to understanding depression. For example, instead of acknowledging that depression is heterogeneous and with multiple presentations, researchers often study the construct as though it were a single entity (the uniformity myth) or with the implicit assumption that all individuals with depression are equivalent on various psychosocial variables (Hammen & Gotlib, 2014; Ingram et al., 2014). These assumptions are incorrect. Given that only five out of nine DSM-5 criteria are necessary for a diagnosis of depression, for instance, it is possible for two individuals with depression to share only one symptom: “Indeed, except for a diagnosis of depression, these two individuals could have virtually nothing in common, including the variables that caused the disorder and that determine its course” (Ingram et al., 2014, p. 49). Further, even if two individuals experience the same symptoms, the severity, duration, and quality of these experiences may be quite distinct. In partial response to these vagaries of the diagnostic approach, other methods have been proposed to conceptualize the nature of depression. Alternatives include the National Institute of Mental Health’s Research Domain Criteria (RDoC; Cuthbert & Insel, 2013) and the Hierarchical Taxonomy of Psychopathology (HiTOP; Kotov et al., 2017), although these alternatives also have their inherent strengths and weaknesses (see Haeffel et al., 2022). Further empirical work will help to determine the optimal strategies to classify and investigate depression. Whether a diagnostic or alternative model is employed, the field needs to move beyond studying depression as a single phenomenon with relatively straightforward research designs, and to test more integrative and complex models, using more advanced and nuanced statistical and methodological designs. As pointed out in Chapter 1 of this volume, although it is easier to examine single risk factors, and unifactorial studies do continue to contribute important knowledge to the field, there is generally an inverse relation between the simplicity of the research design its informative value. In addition, the ability to identify stable and reliable risk factors does not necessarily mean that they can be easily modified. The chapters in this book indicate that many psychosocial risk factors for depression are malleable and can be successfully targeted with a variety of interventions. In addition to the continued research that further aims to define, operationalize, conceptualize, and investigate risk factors for depression, continued empirical investigation needs to be devoted to translating this risk research into effective interventions. Integrative Models and Studies on Risk The biological, psychological, and social risk factors that are associated with (and, in some cases, causally related to) depression are incredibly complex and interact in dynamic and reciprocal ways (Alba & Calvete, 2019; Fried & Robinaugh, 2020; Herrman et al., 2022; Kendler, 2019; Masten & Cicchetti,

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2010). Although isolated risk factors continue to be studied and add importantly to our understanding of the development and maintenance of depression, the field is moving toward more sophisticated research strategies to test more complex mechanisms and processes that trigger depression onset and relapse (e.g., Backs-Dermott et al., 2010; De Raedt & Koster, 2010; Disner et al., 2011; Hankin & Abramson, 2001; Ingram et al., 2011, 2014; Vargas et al., 2020). As one example, De Raedt and Koster (2010) developed an integrated model of cognitive vulnerability to depression that incorporates neurobiological processes. In this model, the hypothalamic–pituitary–adrenocortical (HPA) axis and negative cognitive schemas are activated by stressors. The activation of negative schemas and the HPA axis contributes to decreased activity in the prefrontal cortex, excessive activation of the amygdala, and impaired ability to inhibit attentional control over negative (e.g., ruminative) thinking, leading to a downward spiral toward increased negative affect and depression. Several chapters in this volume stress the importance of research that progresses beyond monocausal explanations, to integrate various indicators of risk, and embrace theoretical, methodological, and empirical complexity (Fried & Robinaugh, 2020; Kendler, 2019). Research does not support the idea that any one risk factor is necessary and sufficient to cause depression (Hankin, 2012; Herrman et al., 2022). Instead, the variables that cause depression are characterized by both equifinality (different risk factors can lead to the same disorder) and multifinality (the same risk factors can lead to different mental health problems; Cicchetti & Rogosch, 1996; Hankin, 2012). In addition, these risk factors interact in dynamic and complex ways (Fried & Robinaugh, 2020; Robinaugh et al., 2020). Some of this complexity is described in the chapters contained in this volume. Although the focus herein was on individual, modifiable psychosocial risk factors and their interventions, we recognize that risk factors for depression are multifactorial and “intertwined in a web of complex interactions” (Fried & Robinaugh, 2020, p. 1). We affirm the importance of investigating this developmental progression toward depression onset, maintenance, relapse, and recurrence. For example, it would now be possible to take the various risk factors that are named and validated in this volume and conduct a large-scale epidemiological study of the relative risk of psychosocial factors for depression, in an analogous study to that of Choi et al. (2020). Many of the strategies discussed in this book focus at the level of individual risk factors not only to treat depression but also potentially to prevent the disorder, by altering the trajectory that a particular risk factor takes. Such interventions are provided to also propel positive change on other interacting factors. As Ingram et al. (2011) stated, “vulnerability to unipolar depression is multidetermined and set in motion by variables operating across the continuum from neuron to environment” (p. 152). Additional research is needed to develop more comprehensive models and examine how the modification of one risk factor can influence other biological, psychological, and social systems. As just one example, it can be imagined that reducing rumination might modify other cognitive and information processing biases which, in turn, could lower

Investigating and Treating Psychosocial Risk Factors in Depression 417

amygdala activation and reduce cortisol responses, and, thus, contribute to less reassurance seeking, lower dependency, and reduced avoidance. Similarly, other feedback loops and reciprocal relationships can be imagined that involve a dynamic interplay among biological, psychological, and social constructs. Personalizing and Sequencing Interventions Another important direction discussed in many of the chapters in this volume pertains to the possibility that we might be able to personalize or match interventions based on an understanding of individual risk profiles or other individual difference variables either prior to treatment or as treatment progresses (DeRubeis et al., 2019; Hankin et al., 2018). As has been noted, major depression presents in a highly individualized manner across individuals who may meet the criteria for the diagnosis, and even within the same individual over time. There is every reason to believe that different interventions may be more or less applicable to different presentations of what is nominally the same condition. Consistent with the idea of a personalized approach to depression care, DeRubeis et al. (2014) developed multivariate statistical algorithms (called the Personalized Advantage Index [PAI]) based on machine learning to determine whether individual difference variables, assessed prior to treatment can be used to predict treatment outcome (see also Cohen & DeRubeis, 2018). Data from 154 patients with MDD treated with CBT or medication were used to predict posttreatment depression symptoms based on regression analyses from five variables (marital status, employment status, comorbid personality disorders, life events, and prior medication) that predicted differential treatment outcome. Sixty percent of the sample had a meaningful PAI score, suggesting that they would do better in one treatment than another (see also Harkness, Chapter 5, this volume). Patients who received what the PAI indicated was their optimal treatment demonstrated greater improvements than did patients who did not. Although the work of DeRubeis and colleagues (2014) presents an initial foray into the development of individualized treatment approaches, their use of a limited set of primarily sociodemographic variables necessarily limits the model that they could have developed. Additional research is necessary to make the possibility of individualized matching a reality and enhancing actuarial prediction. This work needs to consider and study the psychosocial factors included in this volume. Currently, there is a paucity of research related to individualized risk profiles that can reliably be used to ideographically match individuals to specific evidence-based strategies (Hankin et al., 2018). Research is also needed to better understand which risk factors demonstrate specificity to depression and which may represent more transdiagnostic predictors. The last decade has witnessed an increase in transdiagnostic protocols designed to treat a broad array of presenting problems and issues that share underlying mechanisms (e.g., risk factors that are shared between depression and anxiety). For example, Barlow et al. (2011) developed a unified protocol

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for emotional disorders, and Chorpita and Weisz (2009) advanced a modular approach to therapy for children with anxiety, depression, trauma, or conduct problems (MATCH-ADTC). Although such protocols may have the advantage of treating comorbid mental health problems (e.g., internalizing disorders) more efficiently, research on their efficacy and effectiveness is presently limited. As we gain a better understanding of which risk factors are unique to depression or represent transdiagnostic mechanisms, we may be able to adjust our interventions to optimally target various risk factors (either by treating a specific or transdiagnostic risk factor), reduce depressive symptomatology, and prevent relapse and recurrence. In addition to matching specific or transdiagnostic interventions is the issue of the timing and sequencing of various risk factor treatments. Pine and Garber (Chapter 2, this volume), for example, contend that future research is needed to ascertain whether treatments for parents with depression and their children should occur simultaneously or sequentially. Moreover, various risk factors for depression are complex and reciprocally intertwined. For instance, we know that interpersonal difficulties (e.g., interpersonal stress, dependency, excessive reassurance seeking) contribute to depression but also that depression can increase interpersonal problems (e.g., stress generation, rejection; see Hammen, 2018; see also Harkness, Chapter 5; Starr et al., Chapter 6; and Young et al., Chapter 4, this volume). Additional research is needed to determine the optimal treatments for psychosocial risk factors as well as to elucidate important logistics of intervention such as the dosing (e.g., how many sessions are required), timing (e.g., when is the best time to intervene with a particular risk factor), and sequencing (e.g., whether it is advantageous to treat some risk factors prior to other risk factors) of treatment strategies. Measurement of Risk Understanding psychosocial risk factors and translating this knowledge into evidence-based interventions requires that researchers pay close attention to the critical issue of measurement (Evraire et al., 2015; Hankin, 2012). Our ability to conduct valid tests of risk factors and models relies on rigorous statistical models and measures (Naragon-Gainey & Brown, 2015). Unfortunately, as pointed out by some of the authors of this volume (Starr et al., Chapter 6), tests of causal mechanisms are frequently limited by suboptimal measurement approaches. As Dozois and Hayden (2022) stated, even “measures in the field that are well established (and used by investigators for their very longevity) are oftentimes long overdue for a fuller investigation of their psychometric properties with larger samples, using contemporary methods of data analysis” (p. 102). In addition, some of the methodological approaches and measurement instruments that researchers use to test conceptual models of risk and vulnerability may be confounded by third party variables (e.g., issues of residual confounding, omitted-variables bias; see Dozois & Hayden, 2022). As pointed out in the opening chapter of this volume, tests of empirically established, stable, valid, and modifiable risk factors require methodologically

Investigating and Treating Psychosocial Risk Factors in Depression 419

sophisticated measures (using multiple approaches), advanced statistical procedures, and appropriate longitudinal and experimental designs. Scientists should also dedicate resources to study the initial development and trajectory of risk over time (e.g., using daily diaries, prospective multiwave studies, ecological momentary assessment). Methodologies such as ecological momentary assessment, for example, allow researchers to examine the associations among different risk factors and depression across various time points, ranging from minutes to months (Hankin, 2012; see also Watkins, Chapter 13, this volume). Equity, Diversity, and Inclusion The development of culturally sensitive models of risk and vulnerability to depression is another important direction for the field, and a direction that has been neglected for far too long (Dozois & Hayden, 2022; Herrman et al., 2022; Polo et al., 2019). Many measures of risk in depression have been validated largely in White samples. In addition, individuals from most studies of risk come from WEIRD (Western, educated, industrialized, rich, democratic) samples, which are not representative of the population at large. Many studies of depression also capitalize on the gender difference in the disorder and either solely or predominantly study female samples. These biases can be seen in studies that develop and evaluate interventions as well (see Polo et al., 2019; Whisman & Gilmore, Chapter 7, this volume). The field needs to advance beyond samples of convenience to ensure greater representation of underserved and underrepresented groups in tests of risk or vulnerability, intervention, and prevention for psychopathology in general, and depression in particular (Patalay & MacDonald, 2022; Sanchez & Sanchez, 2021). For example, in a systematic review Hall (2018) identified important and unique risk factors related to depression (e.g., LGBQ-related oppression, stress experienced from hiding and dealing with stigmatized identity, parental rejection, abuse and other traumatic events, bullying, being victims of violence) among lesbian, gay, bisexual, or queer (LGBQ) youth (see also Johnson et al., 2019). Another systematic review identified several psychosocial risk factors associated with societal discriminatory attitudes and behaviors among people who identify as transgender (McCann & Brown, 2018). The research literature needs to stop considering variables such as ethnicity, gender identity, and sexual orientation as covariates, and, instead, examine these variables as potentially important elements of models of risk to depression in their own right. Cultural sensitivity is fortunately gaining greater attention in the field (e.g., Polo et al., 2019; Wenzel, 2017). Muñoz and his colleagues, for example, have long provided culturally informed treatments to Latino communities by translating standard CBT approaches (e.g., Aguilera et al., 2016; Muñoz & Mendelson, 2005). However, the field has a long way to go to increase representation. Polo et al. (2019), for instance, examined diversity in psychotherapy trials across 36 years (1981–2016). A total of 342 randomized controlled trials for depression that included 61,283 participants were evaluated. Although diversity in psychotherapy outcome research had improved over this period of time

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(e.g., more ethnic minority and low-income groups were included in more recent trials), most studies continued to exclude linguistic minorities and most had what were considered insufficient numbers of Asian American, Native Hawaiian or Pacific Islander, Native American or Native Alaskan, and multiethnic participants. In addition, ethnicity was rarely examined as a moderator of treatment. Human diversity is, by definition, a broad construct. Studying the numerous potential ways in which humans vary, and how these aspects of diversity interact with the construct of depression is a vital need. No doubt, some of these aspects of diversity also interact with some of the psychosocial constructs that are the object of the current volume, so complex and multidimensional models will be required to fully describe these relations. Even further, it is broadly recognized that humans do not have or possess a single diversity element, but are, instead, characterized by intersectionality (Hancock, 2007; Holvino, 2010; Viruell-Fuentes et al., 2012). Although the concept of intersectionality was originally introduced related to feminism and gender issues, it is often now used as a broader way to consider the many combinations and permutations of human variability. Protective Factors For more than 7 decades, the WHO (1948) has defined health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity” (p. 100; see Larsen, 2022, for an historical review). Despite this dual emphasis on well-being, as well as the absence of illness, the predominant focus of research has been heavily oriented toward what was aberrant or deficient, and how to treat or prevent problems or dysfunction (i.e., a psychopathology orientation; see Dozois, 2018). In some respects, this emphasis is understandable, as it is, in some ways, easier to treat individuals once they struggle or become ill than to attempt to prevent dysfunction. The development of effective health promotion and prevention models, however, has the potential to obviate untold suffering, as well as save immense amounts of global health expenditures (Dozois & Dobson, 2004; Hoare et al., 2020). Over the past couple of decades, there has been an increasing focus on factors that are potentially protective from depression in the scientific literature. For example, numerous studies have identified critical hedonic (a person’s general happiness with their life) and eudaimonic (self-realization, personal growth, and meaning well-being) variables related to subjective well-being (Ryan & Deci, 2001; Ryff & Keyes, 1995; Vallerand, 2012). Moreover, Ruini and Cesetti (2019) found that eudaimonic well-being was strongly and inversely related to depression and that several interventions can effectively promote eudaimonia (e.g., CBT, positive psychotherapy, acceptance and commitment therapy, mindfulness). The focus on both pessimism as a risk factor for depression, but optimism as a potential protective factor (see Genecov & Seligman, Chapter 11, this volume) is an excellent example of this type of development. A number of protective factors for depression have been identified in the research literature (e.g., Choi et al., 2020). One of the most important of these

Investigating and Treating Psychosocial Risk Factors in Depression 421

is social connection. Choi et al. (2020), for example, found that confiding in others was the principal modifiable protective factor for depression. Indeed, considerable evidence points to social connection as important factor in living fully (Dozois, 2018; Diener et al., 2017). Thus, although the focus of this volume is the identification and treatment of psychosocial risk factors for depression, the field also needs to continue to examine protective factors not only to treat and prevent depression, but also to help people flourish (Jankowski et al., 2020; Seligman, 2011).

CONCLUSION This volume represents the most exciting of research in psychosocial risk for depression. The chapters herein allow readers to compare and contrast different models of risk and their empirical evidence, and to examine the predictive power of these models as they relate to depression onset, maintenance, and relapse or recurrence. It is hoped that these descriptions will stimulate the development of theory and research related to risk factors for depression, further integrative models of depression, and facilitate new and more sophisticated methodological and statistical approaches to the study of risk. This growing body of work can then be utilized to consider its therapeutic implications, and to develop and evaluate evidence-based strategies for various risk and vulnerability factors in depression. As discussed throughout this volume, there are many complexities to the study of psychosocial risk factors in depression, including its heterogeneous and polythetic nature, waxing and waning course, and issues of comorbidity. Considerable discussion remains about how to best conceptualize and classify depression, the nature of risk and vulnerability, the accurate measurement of risk, the identification of modifiable distal and proximal risk factors, and how to optimally convert risk models into evidence-based interventions. This chapter outlines several directions for future research. Research and treatment of psychosocial risk factors for depression will progress by testing more integrative and complex models; using more advanced and nuanced statistical and methodological designs; evaluating more personalized interventions; investigating the optimal sequencing of risk factor interventions; improving the measurement of risk; focusing on equity, diversity and inclusion; and studying protective factors. Numerous modifiable risk factors were discussed in the pages of this volume and evidence-based interventions to target these risk factors were reviewed. We hope that this volume will serve as a pivotal resource in the field for the conceptualization and treatment of risk factors for depression. REFERENCES Aguilera, A., Miranda, J., Aguilar-Gaxiola, S., Organista, K. C., González, G. M., McQuaid, J., Kohn-Wood, L. P., Le, H.-N., Ghosh-Ippen, C., Urizar, G. G., Soto, J., Mendelson, T., Barrera, A. Z., Torres, L. D., Leykin, Y., Schueller, S., Liu, N., & Muñoz, R. F. (2016). Depression prevention and treatment interventions: Evolution of the

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INDEX

A AAQ (Acceptance and Action Questionnaire), 366 Abandonment, fear of, 133, 134, 140, 287, 289 ABC (Antecedent–Behavior–Consequence) mnemonic, 314 ABM. See Attention bias modification Absolutist statements, 217 Absorption training, 319 Abstract rumination, 311–312, 316–317 Abusive relationships, 137. See also Intimate partner violence (IPV) Academic impairment, 27 Acceptance, 122, 188–189, 191–193, 413 Acceptance, Distraction, Activities, and Positive Thinking (ADAPT) skills, 44 Acceptance and Action Questionnaire (AAQ), 366 Acceptance and commitment therapy (ACT), 413 avoidance reduction with, 370, 371 emotion regulation and, 192, 195 empirical support for, 366 optimism and, 265 Accommodation reduction, 146 ACEs. See Adverse childhood experiences ACES (active, concrete, experiential, and specific) behavior, 316 Achievement-related stress, 285 Acierno, R., 368

ACT. See Acceptance and commitment therapy Action, distinguishing inaction from, 398 Action–control framework, 33 Action plans, 122, 343 Activation, as mechanism for change, 369 Active, concrete, experiential, and specific (ACES) behavior, 316 Active behavioral avoidance, 360 Active cognitive problem solving, 360 Active constructive responding, 269 Activity logs, 215–216, 219 Activity scheduling, 191, 216, 367, 373, 375 Actual self, idealized self-concept vs., 286 Acute “severe” life events, 109, 116 Adaptive avoidance, 361, 373–374 Adaptive beliefs, strengthening, 218 Adaptive emotion regulation strategies, 183 Adaptive processing style, 311, 318, 320–321 ADAPT (Acceptance, Distraction, Activities, and Positive Thinking) skills, 44 ADHD (attention-deficit/hyperactivity disorder), 261 Adjunct treatments, couple-based, 174 Adjustment of child, with depressed parent, 28, 29, 33 relationship, 158, 159, 166–167, 169 Adjustment disorder, 147–149

 429

430  Index

Adolescents, 81, 289. See also Interpersonal Psychotherapy–Adolescent Skills Training (IPT-AST); Interpersonal psychotherapy for depressed adolescents (IPT-A) attentional bias modification for, 241 avoidance for, 364, 366, 370 childhood maltreatment and treatment response for, 114, 118–119 cognitive risk of depression in, 210 corumination by, 141 emotional awareness for, 184, 185 emotional intensity for, 185 emotional variability for, 187 estimates of provider effects by, 62 interpersonal risk factors of depression for, 82–86 interventions in context of stress exposure for, 120 metacognitive beliefs for, 388 parental depression and treatment outcomes for, 35 parental support as for, 85 parenting behavior and depression in, 34 perfectionism for, 288 RFCBT with, 320, 321 role transition for, 82 social problem solving by, 337–338 Adverse childhood experiences (ACEs), 12–13, 31–32, 209 Adversity early life, 31–32, 238, 410 parental reactions to, 31 Affairs, engaging in, 163 Affect. See also Negative affect in cognitive therapy, 214 with major depressive disorder, 181 positive, 63, 233, 261, 266, 267, 340 relational regulation of, 63, 64 during remission from depressive episode, 268 Affective component of dependency, 133, 142 Affective processing biases, 409 Affect regulation training, 194 Africa, 407 Age, prevalence of clinical depression by, 9 Agency, 258 Age of exposure to parental depression, 29, 32 Age of parenthood, 31 Aggressive behavior, 38, 337 Alcohol use and abuse, 306, 384, 409 Aldao, A., 361 Alexithymia, 184 Allen, J. P., 84 Alloy, L. B., 211 Alternate behaviors, 318–319 Alternatives, predicting outcome of, 336

Ambiguous events, pessimism about, 262 Ambiguous Hallmark Program, 242 Ambiguous information, interpreting, 236–237, 242, 243 Ambiguous Scenario Test–Depression, 237 American Psychiatric Association, 8, 106, 407 American Psychological Association (APA), 4, 93, 293, 366 Amir, N., 243 Amitriptyline, 111 Ammerman, R. T., 36, 38 Amoxapine, 111 Amygdala, 193, 242, 416 Anaclitic personality style, 134 Analytical rumination, 341–342 Analytic rumination hypothesis (ARH), 341–342 Anderson, T., 142–143 Anger, suppression of, 189 Anhedonia antidepressants to reduce, 110 avoidance and, 365 behavioral activation to reduce, 369 as cardinal symptom, 8, 415 explanatory pessimism and, 263 MeST to reduce, 244 positive visual imagery and, 265 Antecedent–Behavior–Consequence (ABC) mnemonic, 314 Anterior cingulate cortex, 193 Antidepressants. See also Pharmacotherapy; specific drugs and bottom-up processing, 193 directions for future research on, 126, 200 with EFCT, 174 and emotion dysregulation, 190 history of childhood maltreatment and response to, 111–112, 116–118 mediation of response to, 126 proximal life events and response to, 111–112 with RFCBT, 320 systemic couple therapy for depression vs., 169 treating anhedonia with, 110 well-being therapy while tapering off, 268 Anxiety (generally) attention training technique and, 391 and defensive pessimism, 262 and dependency, 137 dispositional optimism and, 257 dynamic-relational group psychotherapy for treating, 292 exposure-based treatments for, 367 interventions for, 144–145, 389 MCT for treating, 394, 395 RFCBT for treating, 321



Index 431

as risk factor for depression, 409, 411 targeting emotion dysregulation to relieve, 194–196 Anxiety disorders, 29, 139, 183, 242 Anxiety tolerance, 290 Anxiety with depression, 9, 408 avoidance in, 361 BATD for treating, 367 economic toll associated with, 3 MCT for treating, 383 PST for treating, 347 risk factors in, 410 and rumination, 306–308 Anxious attachment, 139 APA. See American Psychological Association Appraisals, of life circumstances, 207, 214 Approach-avoidance training, 368 Approach-oriented coping, 199, 256, 263–264 ARH (analytic rumination hypothesis), 341–342 Aripiprazole, 117 AS (avoidant problem-solving style), 336–340 Asarnow, J. R., 114, 118 Asia, 407 Asian Americans, 183 Asmundson et al. G. J. G., 120 ASQ (Attributional Style Questionnaire), 259 Assumptions, facts vs., 343, 350 ATT. See Attention training technique Attachment, to therapist, 142 Attachment theory, 33, 39, 139 Attention deficits in, as risk factor, 409 to emotion, 184, 185 with major depressive disorder, 365 Attentional bias, 17, 234–236, 238, 240–242, 413 Attentional disengagement model, 308 Attention bias modification (ABM), 240–242, 411, 413 Attention-deficit/hyperactivity disorder (ADHD), 261 Attention training technique (ATT), 390, 391, 397–398 Attitudes dysfunctional, 64, 211, 293 help-seeking, 283 Attributional Style Questionnaire (ASQ), 259 Australia, 407 Authoritarianism, parental, 137, 262 Authoritative parenting, 34 Autism spectrum disorder, 339 Autobiographical friendship intimacy, 288 Autobiographical Memory Test, 239 Automatic response, pathological rumination as, 310–311 Automatic thoughts, 64, 215–218, 236

Autonomy, 82, 134, 138, 141, 142 Availability bias, 265 Avoidance, 359–375. See also Experiential avoidance adaptive, 361, 373–374 and attentional bias modification, 242 behavioral, 360–366, 368, 370 and behavioral activation, 215 case example, 371–374 cognitive, 360–362, 364–366, 369, 370 correlational studies of, 15 defensive pessimism and, 262 definitional issues with, 359–361 directions for future research on, 374–375 interactions between other psychosocial risk factors and, 5 interactions between other risk factors and, 417 of interpersonal conflict, 363 interventions targeting, 366–371 measures of, 365–366 multidimensional approach to, 359–360 and perfectionism, 287 as risk and maintenance factor in depression, 364–365, 414 risk literature on, 361–364 rumination as, 316–317 schema, 360 social, 360, 361, 363, 365 unidimensional approach to, 359 Avoidance-based emotion regulation strategies, 183 Avoidance goals, 362–363 Avoidance plans, 362, 363 Avoidant problem-solving style (AS), 336–340 Awareness of dependency-related thoughts, 141 emotional, 184–185, 190, 192, 199, 413 meta-, 122, 385, 390, 391, 414 nonjudgmental, 122 B BA. See Behavioral activation BADS (Behavioral Activation for Depression Scale), 366 BADS-SF (Behavioral Activation for Depression Scale–Short Form), 366 Barbato, A., 170 Barbe, R. P., 114 Barlow, D. H., 417–418 Barrera, M., Jr., 59 Barry, T. J., 243 BAS (behavioral activation system), 363 Basal ganglia, 193 Baseline relationship distress, 161 BATD (behavioral activation treatment for depression), 367–369, 375

432  Index

Battista, S. R., 288 Baucom, D. H., 168 Bausch, P., 114, 118 BDI. See Beck Depression Inventory BDI-II. See Beck Depression Inventory-II Beach, S. R. H., 157–160, 173 Beardslee, W. R., 37, 40 Beck, A. T., 14, 134, 207, 209, 214, 218, 367, 369 Beck, C. T., 160 Beck, J. S., 215 Beck Depression Inventory (BDI), 294, 392, 393 Beck Depression Inventory-II (BDI-II), 222, 253, 392 Becker-Wiedman, E. G., 338 Beck Hopelessness Scale, 255 Befriending interventions, 55, 57–60, 62 Behavioral activation (BA), 413, 414 case example of, 371–374 cognitive vulnerabilities as target of, 215–216, 222 directions for future research on, 374–375 for emotion regulation, 195 empirical support for, 366 functional analysis in, 312 to reduce avoidance, 367–369 rewarding activities in, 110 role of emotion dysregulation in, 190 rumination in, 370 as treatment for MDD, 408 Behavioral Activation for Depression Scale (BADS), 366 Behavioral Activation for Depression Scale–Short Form (BADS-SF), 366 Behavioral activation system (BAS), 363 Behavioral activation treatment for depression (BATD), 367–369, 375 Behavioral avoidance, 360–366, 368, 370 Behavioral component of dependency, 133, 142 Behavioral experiments, 217–219, 222, 390–391 Behavioral inhibition, 239, 240, 244 Behavioral inhibition system (BIS), 363 Behavioral interventions, 190–192, 412 Behavioral model of depression, 364, 365 Behavioral plans, 317 Behavioral problems, of child with depressed parent, 39 Behavioral skill deficits, 334 Behavioral treatment for depression, 364 Behavior therapy, 119, 220 Beliefs about emotion, 198–199 about rumination and worry, 385, 387–388, 391, 399, 401, 414 about thinking, 385–388, 395, 414

adaptive, 218 in cognitive therapy, 214 core, 217–219, 413 counterproductive, 269 interactions between other risk factors and, 5 narratives for developing, 218 testing, 219 Bell, A. C., 345 Belonging, need for, 289, 290 Berenbaum, H., 182 Bernal, G., 93 Best Possible Self (BPS) task and intervention, 263, 266–267 Between-strategy emotion regulation flexibility, 189 Bevan, D., 392 Biased meta-awareness, 385, 414 Biases. See also Cognitive biases; Information processing biases affective processing, 409 attentional, 17, 234–236, 238, 240–242, 413 availability, 265 interpretation, 236–238, 242–243, 413 memory, 238–239, 243–244, 413 response, 107, 108 Bieling, P. J., 220 Biglan, A., 364 Biological interventions, 193, 412, 413 Biological risk factors, 4, 13, 236, 409, 411 Biological sensitivity to context theory (BSCT), 32 Biological theories of depression, 3 Biopsychosocial framework of risk, 13, 143 Biopsychosocial model of depression, 19 Bipolar disorder, 183, 283, 392 BIS (behavioral inhibition system), 363 Black Americans, 64, 288, 338 Blatt, S. J., 134, 282 Block design, for estimating social influences, 60–61 Blueprints for Health Youth Development, 98 Bodenmann, G., 168–169 Body scan, 121 Borderline personality disorder, 183, 409 Bornstein, R. F., 133, 144 Bowlby, 1980, 139 BPS (Best Possible Self) task and intervention, 263, 266–267 Brain (stimulus) overload, 343–344 Brainstorming, in EC-PST, 343 Brazil, 161 Brief problem-focused couple intervention, 169 Brief psychodynamic therapy, 408 Brief psychotherapy interventions, PST vs. other, 346



Index 433

Brief Symptom Inventory, 293–294 Brockmeyer, T., 362 Brown, G. S., 65 BSCT (biological sensitivity to context theory), 32 Bühler, A., 37, 40 Bullying, 315 Bulmash, E., 112, 116 Butzlaff, R. L., 163 C California Evidence-Based Clearinghouse, 98 Callesen, P., 392 Canada, 161 Canadian mood and anxiety treatment (CANMAT), 113 Cancer survivors and patients emotion regulation strategies for, 188, 189 MCT with, 395 support groups for, 58–59 treatments for anxiety and depression for, 367–368, 371 CANMAT (Canadian mood and anxiety treatment), 113 Cano-López, J. B., 387, 388 Cape, J., 346 Cardiac underreactivity, 186 Cardinal symptoms, of clinical depression, 8, 415 Cardiovascular disease, 395 Caretaking, by children with depressed parents, 43–44 Caring gestures (behaviors), 164–165, 171–172, 261 Carrillo, A., 267 Carvalho, J. P., 363 Carver, C. S., 262 CAS. See Cognitive attentional syndrome Case-conceptualization, for metacognitive therapy, 389, 390 Case series study, of MCT, 391–392 Case studies, 14, 71–73 Categorical approach to depression, 414–415 Categoric memories, 239 Causal analysis, 341–342 Causality, 10–11, 16–18, 409, 418 CAVE (content analysis of verbatim explanations), 259 CBAS (Cognitive and Behavioral Avoidance Scale), 365–366 C-BASP. See Cognitive-behavioral analysis system of psychotherapy CBCT (cognitive behavior couple therapy), 164–173, 412 CBM. See Cognitive bias modification CBM-I (cognitive bias modification for interpretation), 242, 245–246

CBM-IS (cognitive bias modification of interpretations of self), 242 CBT. See Cognitive behavior therapy CCT (cognitive control training), 244–247, 413 CECA (Childhood Experience of Care and Abuse) Interview, 108 Center for Epidemiologic Studies Depression Scale (CES-D), 253–254 Cesetti, G., 420 Change, mechanisms of, 408 Chaudhry, I. B., 36 Chen, E. C., 66–67 Chen, K., 243–244 Chesin, M. S., 338 Child abuse, 137. See also Childhood maltreatment Child characteristics, 32, 46, 412 Childhood Experience of Care and Abuse (CECA) Interview, 108 Childhood maltreatment case example, 123–125 definitional issues with, 106 and depressive rumination, 310 depressogenic schemas and, 209 directions for future research on, 125–127 interactions of other risk factors with, 126 interventions for individuals with history of, 121–123 as risk factor for depression, 109–110 stress exposure during, 107–108 stress response associated with, 107 and treatment response, 113–114, 116–119 Childhood sexual abuse, 117, 118, 121, 122, 409 Childhood trauma, history of, 105 Children. See also Adolescents; Infants; Youth dependency development for, 137 with depressed parents. See Parental psychopathology emotion reactivity of, 186 estimates of provider effects by, 62 with internalizing symptoms, 30 interventions in context of stress exposure for, 120 learning of SPS skills by, 340 loss of parent for, 42, 85, 410 malnourished, 39 metacognitive beliefs of, 388 and parental perfectionism, 288 in Penn Resilience Program, 268–269 perfectionism for, 285 preventing intergenerational transmission of depression to, 34–42 rumination by, 310–311, 315 sequencing treatment for, 418 social problem solving for, 337

434  Index

Chile, 337–338 China, 337, 338 Chisholm, D., 261 Choi, K. W., 410, 416, 421 Chorpita, B. F., 418 Chow, B. W.-Y., 338 Chronic (persistent) depression, 3 avoidance in, 362 C-BASP for treating, 369 home-visiting interventions targeting, 38 for individuals with history childhood maltreatment, 113, 114, 118 interventions in context of stress exposure for, 122–123 MCT for treating, 391–393 parents with, 28–29 perfectionism and, 283 Chronic interpersonal stress, memory biases and, 238 Chronic medical conditions, 388 Chronic pain, 9 Chronic problems-in-living, 334 Cicero, 105 Citalopram, 113, 117 Clarity, emotional, 184, 185, 191 Clark, D. A., 214 Clark, L., 339 Clinical depression avoidance across women with, 361 challenges with studying, 8–10, 408 comorbid conditions with, 8–9, 407–408 criteria for inclusion in studies of, 9–10 demographics of subgroups with, 9 diagnostic criteria for, 8–10 intervention perspective on evidencebased risk factors in, 4–5 investigations of, 3–4 PAI studies of individuals with, 267 perceived social support as risk factor for, 56 prevalence of, 3, 407 severity and chronicity of, 8 Clinical settings CBCT for treating depression in, 168 relationship effects in, 74–75 Closeness, interpersonal, 287 Closeness circle, 87 CMPB. See Comprehensive model of perfectionistic behavior (CMPB) Cognitions emotions and, 233–234 impact of stress on, 340 Cognitive activity, in cognitive therapy, 214 Cognitive and Behavioral Avoidance Scale (CBAS), 365–366 Cognitive attentional syndrome (CAS), 383–385, 388–390, 399–400 Cognitive avoidance, 360–362, 364–366, 369, 370

Cognitive-behavioral analysis system of psychotherapy (C-BASP), 114, 118, 122–123, 369, 370, 414 Cognitive behavioral interventions avoidance as target of, 375 in context of stress exposure, 119–121 culturally informed, 419 dependency as target in, 142 rumination and, 312–313, 320 stressful life events and response to, 105 well-being therapy after, 268 Cognitive behavior couple therapy (CBCT), 164–173, 412 Cognitive behavior therapy (CBT), 412, 413 ACT as depression treatment vs., 370 avoidance and outcomes of, 364 behavioral activation in, 215 boosting perceived support with, 55 case example, 221–224 CBCT with, 166–168 childhood maltreatment and response to, 113, 118, 119 cognitive biases targeted by, 233 cognitive marital therapy vs., 167–168 in context of stress exposure, 120, 123–125 coping-oriented couple therapy vs., 168–169 directions for future research on, 126, 375 efficacy and effectiveness research on, 220–221 emotional regulation/outcomes over course of, 191, 199 excessive reassurance seeking as target of, 145 FGCB, 37, 41, 43–45, 412 focused model of, 18 group, 320, 396 improving emotion regulation with, 194, 195 improving relationship functioning with, 174 in-home, 35, 38 internet-delivered, 321 internet-delivered RFCBT vs., 321 in Learning Through Play Plus Program, 38–39 metacognitive change in, 396 nomothetic assumption in, 68 optimism/pessimism as target of, 255, 264 with pharmacotherapy, 193 problem solving therapy vs., 346 proximal life events and response to, 112 randomized controlled trial of MCT vs., 394 role of emotion dysregulation in, 190–191 rumination as focus of, 411 rumination-focused, 191, 312–316, 318, 320–325, 370–371, 411, 413, 414



Index 435

therapist–patient dialogue in, 390 and trait perfectionism, 292 trauma-focused, 119–121, 123 for treating postpartum depression, 35 as treatment for MDD, 190, 408 as treatment for parental depression, 411 Cognitive biases, 233–247 case example, 245–246 directions for future research on, 246–247 as feature of depression, 233 interactions between other risk factors and, 416–417 interventions targeting, 240–245 as risk and maintenance factors of depression, 234–240 Cognitive bias modification (CBM), 320–321, 325, 413 Cognitive bias modification for interpretation (CBM-I), 242, 245–246 Cognitive bias modification of interpretations of self (CBM-IS), 242 Cognitive change, 221, 225 Cognitive component of dependency, 133, 142 Cognitive content, 214, 224, 410 Cognitive control deficits, 240 Cognitive control network, 320 Cognitive control training (CCT), 244–247, 413 Cognitive coping, C-BASP to reduce maladaptive, 369 Cognitive deficits, 233, 234, 340 Cognitive distortions, 166 Cognitive Distortions Scales, 221 Cognitive flexibility, 238, 239, 249 Cognitive interventions, 190–193, 412 Cognitive marital therapy (CMT), 167–168 Cognitive model of depression, 207, 217, 224 Cognitive (interactionist) model of interpersonal dependency, 133 Cognitive problems, solving, 333–334 Cognitive processes (operations), 208, 209, 214, 224, 410 Cognitive processing therapy (CPT), 119–120, 412 Cognitive products, 413 CBT and changes in, 220, 221 in cognitive taxonomy, 208 in cognitive therapy, 214 empirical research on, 210–211 and schemas, 209 Cognitive psychoeducation (CPE), 123 Cognitive reactivity, 211, 410 Cognitive reappraisal and cognitive restructuring, 191 directions for future research on, 199 effectiveness of, 189 as emotion regulation strategy, 188

by individuals with MDD, 197 as risk factor, 413 as thought control strategy, 388 Cognitive reframing, 255, 256 Cognitive restructuring, 413 in case example, 222 and cognitive reappraisal, 188, 191 decreasing avoidance with, 369 for depression in context of stress exposure, 123, 125 evidence base for, 220–221 Cognitive structures, 209, 211–212, 214. See also Cognitive restructuring; Schema structures Cognitive style, 237, 243, 409 Cognitive Style Questionnaire (CSQ), 211, 259–260 Cognitive taxonomy, 207–208, 212, 214, 224, 225 Cognitive theory of depression, 110, 214, 237 Cognitive therapy, 414. See also Mindfulnessbased cognitive therapy (MBCT) avoidance as target of, 369–370 and behavioral activation, 215 bottom-up mechanism of, 214 in context of stress exposure, 119 empirical support for, 366 modifying core beliefs and schemas in, 218 pessimism as target of, 253 proximal life events and response to, 112, 116 Cognitive vulnerabilities, 207–225 case example, 221–224 cognitive control training to reduce, 244–245 conceptual/methodological issues in study of, 212–214 culturally sensitive indices of, 213–214 definitional issues with, 209–210 directions for future research on, 224–225 empirical findings on, 210–212 and evidence base for cognitive restructuring, 220–221 interventions targeting, 214–219 measurement of, 213, 224–225 Cognitive vulnerability models of depression, 207 Cohen, J. L., 70 Cohen, S., 169 Cole, P. M., 182 College students perfectionism for, 285–286 rumination by, 341 social problem solving by, 337, 338, 341 Communication after loss of parent, 85 poor, as risk factor, 162, 412 Communication analysis, 89–90, 95–96

436  Index

Communication strategies for adolescents, 83, 84 in IPT-A, 88–90, 92–93 in IPT-AST, 95–98 Communication training, in CBCT, 165–166, 172 Comorbid conditions. See also Anxiety with depression; specific conditions and avoidance, 361–362 with clinical depression, 8–9, 407–408 MCT for treating depression with, 392–394 studies of, 3 transdiagnostic interventions for treating, 417–418 Companionship activities, 165 Compas, B. E., 37 Compassion training, 319 Complicated grief, 86, 368 Comprehensive model of perfectionistic behavior (CMPB), 282–283, 291, 292 Computer use time, 410 Concentration, rumination and, 308 Concrete adaptive processing style, 320–321 Concreteness training, 318, 321, 325 Concrete rumination, 311, 312 Conditional assumptions (conditional rules), 218 Conduct problems, 42 Confiding in others, 410 Conflict. See Interpersonal conflict Congruency hypothesis, 209–210 Connection, social, 16, 287–289, 420–421 Conradt, E., 30 Conscientiousness, 340 Content analysis of verbatim explanations (CAVE), 259 Context, Usefulness, Development, OptionS (CUDOS) mnemonic, 314–315 Context of rumination, 308, 315 Context-response learning, 311 Contextual factors, in problem solving, 336, 339–342 Contextual life interview, 108 Control, in metacognitive model, 384–386 Control theory account for rumination, 308–310 Convenience samples, 419 Convergent problem solving, 340 Cook, L., 325 Coping behaviors and strategies approach-oriented, 199, 256, 263–264 for dependent individuals, 148 early life stress and, 340 excessive reassurance seeking as, 145, 146 in IPT-A, 89

in metacognitive model, 384–386 optimism and, 263 with perfectionism, 286, 287 relinquishing, in MCT, 391 rumination as, 310 self-coping plans, 319–320 Coping-oriented couple therapy (COPT), 168–169 Coping Responses Inventory (CRI), 366 Core assumptions, of interventions targeting social support, 59–65 Core beliefs, modifying, 217–219, 413 Core relational themes, 144 Core schemas, 139–140 Core vulnerability factor, perfectionism as, 283 Correlational studies, 14–16, 19 Cortical brain volume, 409 Cortisol, 236, 417 Corumination, 136–137, 140–141, 412 Co-rumination Questionnaire, 136–137 Cost–benefit analysis, 218, 344 Counterproductive beliefs, 269 Countertransference, 144 Couple therapy, 411 to address dependency, 143–144 as adjunct treatment, 174 brief problem-focused intervention, 169 cognitive behavior, 164–173, 412 coping-oriented, 168–169 dependency as target in, 143–144 emotionally focused, 169–170, 174 for relationship discord, 160 with sexual and gender minority couples, 173–174 systemic couple therapy for depression, 169 treating depression with individual psychotherapy vs., 170 Covariate adjustments, 15 Cox, B. J., 113, 117, 285 Coyne, J. C., 135, 138 CPE (cognitive psychoeducation), 123 CPT (cognitive processing therapy), 119–120, 412 CRI (Coping Responses Inventory), 366 Cribb, G., 361, 363 Criminal history, 31 Criticism, 34, 134, 163, 286, 288, 317 Crits-Christoph, P., 221 Crockett, M. A., 337–338 Cronin, A., 56, 57 Cross-sectional studies, 160–161, 197 CSQ (Cognitive Style Questionnaire), 211, 259–260 CUDOS (Context, Usefulness, Development, OptionS) mnemonic, 314–315 Cuijpers, P., 345, 346 Cultural sensitivity, 138, 213–214, 419–420



Index 437

Cultural theories of depression, 3 Cummings, E. M., 164 Curran, T., 285–286 Cutoff scores, relationship distress, 158 Cyclical relational pattern, 290 D Daily hassles, 13, 140, 325 Dammen, T., 394 Dang, S. S., 287 DAS (Dyadic Adjustment Scale), 158–159 DAS (Dysfunctional Attitudes Scale), 211, 293 Dating, 84, 289. See also Relationship functioning D’Avanzo, B., 170 Davila, J., 139 DBT. See Dialectical behavior therapy Death, traumatic, 86 Deblinger, E., 120 Decision analysis, 90, 98 Decision-making task, EC-PST, 343 Decision-making training, 172 Declaration of Helsinki, 20–21 Default mode network, 320 Defensive pessimism, 261–262, 413 DEI (equity, diversity, and inclusion), 419–420 Denton, W. H., 174 Dependency-relevant interpersonal behaviors, 133–149 case example, 147–149 corumination, 136–137 directions for future research on, 149 excessive reassurance seeking, 135–136 interventions targeting, 141–146 negative feedback seeking, 136 risk literature on, 137–141 Depressed mood, as diagnostic criteria, 415 Depression. See also specific types and disorders as cause of disability, 81 clinical characteristics of, 28–29 defined, 233 economic burden of, 281 economic toll associated with, 3, 281 emotion dysregulation in, 181 episodic nature of, 19 excessive reassurance seeking with, 138 intergenerational transmission of, 29–32, 45, 410, 412 interpersonal problems and, 81–82 Depression checklist, IPT-AST, 96 Depression contagion, 141, 163–164 Depressive cognition, theory of, 391 Depressive Experiences Questionnaire (DEQ), 134 Depressive Interpersonal Relationships Inventory (DIRI), 136

Depressive personality disorder, 283 Depressive symptoms. See also Severity of depressive symptoms assessment of, in IPT-A, 87 avoidance and, 368–369 and CBCT for depression, 166–168 and changes in relationship quality, 162 cognitive structure and, 212 and emotional suppression, 183 emotion regulation to relieve, 194–196 and excessive reassurance seeking, 138–139 global point prevalence of, 157 maintenance of, 236, 305, 307, 364–365 and metacognition, 387–388 and pessimism, 262–263 with physical illness, 395 and relationship distress, 160–162 residual, 241–242, 307 and rumination, 181, 306 secondary, 123, 395 social disconnection, perfectionism, and, 288 transmission of, in couples, 164 DEQ (Depressive Experiences Questionnaire), 134 De Raedt, R., 236, 416 DeRubeis, R. J., 74, 417 Descriptive studies of experience, 3 Desirability, perfectionism and, 289 Dessaulles, A., 169–170 Detached mindfulness, 390, 398 Developmental model for understanding mechanisms of transmission, 29–30 Diabetes support groups, 59 Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5), 8, 106, 407, 414–415 Diagnostic and Statistical Manual of Mental Disorders, third edition (DSM-III), 166, 167 Diagnostic and Statistical Manual of Mental Disorders, third edition (DSM-III-R), 57 Diagnostic models of depression, 3, 415 Dialectical behavior therapy (DBT), 143, 267–268, 411, 412 Diathesis–stress model, 340 Dickson, J. M., 362 Diet, 409 Differential susceptibility theory (DST), 32 Directed imagery, 318–319 Direct observation, 100 DIRI (Depressive Interpersonal Relationships Inventory), 136 Disconfirming information, 238 Discordant couples. See Relationship discord Discourse analysis, 14 Dispositional optimism, 256–258, 260, 263–264

438  Index

Distal risk factors, 12–13, 412 Distraction, 197–198, 360, 388 Divergent problem solving, 340 Diverse populations couple therapy for depression in, 173, 174 depression research with, 419–420 interventions in context of stress exposure for, 121, 126 measuring cognitive vulnerabilities in, 213–214 SPS and depression in, 338 stressful life events in, 109 unique risk factors for depression in, 419 Divorce, parental, 42–43, 83 Dobson, K. S., 4, 56, 212 Dopamine, 110, 117 Dorta, K. P., 94 Dosing, of intervention, 418 Dot probe task, 234–235, 241 Downward arrow technique, 217, 222 Dozois, D. J. A., 4, 56, 139–140, 212, 213, 220, 418 Drew, M., 14 Dritschel, B., 339 DRTs. See Dynamic-relational treatments DSM-5. See Diagnostic and Statistical Manual of Mental Disorders, fifth edition DSM-III (Diagnostic and Statistical Manual of Mental Disorders, third edition), 166, 167 DSM-III-R (Diagnostic and Statistical Manual of Mental Disorders, third edition–revised), 57 DST (differential susceptibility theory), 32 Dual-action antidepressant medications, 115, 116 Dual n-back task, 244 Dunkley, D. M., 287 Dyadic Adjustment Scale (DAS), 158–159 Dynamic-relational treatments (DRTs), 413 effectiveness and efficacy research on, 291–294 group psychotherapy, 291–294 perfectionism as target of, 289–291, 411 Dysfunctional attitudes, 64, 211, 293 Dysfunctional Attitudes Scale (DAS), 211, 293 Dysphoria, 320–321, 337, 341, 362–364 Dysthymia, 161, 166, 169, 283 D’Zurilla, T. J., 340, 342, 345 E E6 (Explore Experience, Experiment with Experience, Exercise and Engage) process, 317 Early Head Start, 38 Early life adversity, 31–32, 238, 410 Early life experience, interventions associated with, 4

Early life stress, 340 East Asians, 410 East Indian adolescents, 337 Eating disorders, 306 Ebrahimi, A., 339 Ecological momentary assessment (EMA), 100, 307, 308, 310, 324, 325, 419 Ecological momentary interventions (EMIs), 324–325 Economic downturn, 410 EC-PST (emotion-centered problem-solving therapy), 342–352 Educational attainment, maternal, 31 Educational interventions, 58–59 Educational transitions, 83 Effectiveness and efficacy research, 408–409 on avoidance-targeted interventions, 366–371 on cognitive behavior therapy, 220–221 on dynamic-relational treatments, 291–294 on interventions in context of stress exposure, 120–121 on IP-AST, 98–100 on IPT-A, 93–94, 99–100 on metacognitive therapy, 391–396 on perfectionism-targeted interventions, 291–294 on problem-solving therapy, 345–348 on relationship functioning-targeted interventions, 166–170 on rumination-targeted interventions, 320–322, 325 on social support-targeted interventions, 57–59 EFFEKT-E program, 37, 40, 412 Eldesouky, L., 186 Ellis, B. J., 32 EMA. See Ecological momentary assessment Emanuels-Zuurveen, L., 168 EMIs (ecological momentary interventions), 324–325 Emmelkamp, P. M. G., 168 Emotional abuse, 117, 126, 310 Emotional awareness, 184–185, 190, 192, 199, 413 Emotional BADE task, 238 Emotional clarity, 184, 185, 191 Emotional development, maternal depression and child’s, 39 Emotional disorders, unified protocol for, 417–418 Emotional distress, 143, 340, 395 Emotional disturbances, taxonomy of, 182 Emotional experience, 185–187, 190, 199, 413 Emotional inertia, 187, 196, 197 Emotional instability, 181, 186–187, 193, 196, 197



Index 439

Emotional intelligence, 338 Emotionally focused couple therapy, 169–170, 174 Emotional maltreatment, 17 Emotional reactions, in cognitive therapy, 214 Emotional reactivity, 186, 196 Emotional recovery, rumination and, 308 Emotional–spatial cueing task, 241 Emotional suppression, 183 Emotional variability, 187, 196, 197 Emotion-centered problem-solving therapy (EC-PST), 342–352 Emotion dysregulation, 181–200 assessment of, 184 case example, 194–196 categories of, 182–183 definitional issues with, 182–183 directions for future research on, 196–200 effect of depression interventions on, 199–200 and excessive reassurance seeking, 138 and interpersonal dependency, 143 and interventions for MDD, 190–193 interventions targeting, 190–194 and maladaptive regulation strategies, 183–184 methodologies to study, 196–197 overcoming, in EC-PST, 344–345 and psychopathology, 183 as risk factor for depression, 184–189, 196, 413 trait and state assessment of, 197–199 Emotion-focused therapy, 408 Emotion intensity, 185, 192, 193, 196, 413 Emotion regulation in cognitive training, 247 flexibility of, 184, 189, 413 and intergenerational transmission of depression, 32 in interventions for postpartum depression, 35 interventions targeting, 190 for parents with depression, 33 and positive intervention research, 265–266 Emotion regulation skills training, 193–194 Emotion regulation strategy use, 187–189, 192–193, 413 Emotion regulation therapy, 194 Emotions and cognitions, 233–234 cognitions and, 233–234 listening to, 343 rumination and negative, 307 Empirical research on cognitive vulnerabilities, 210–212 criteria for, 366 Encomium of Helen (Gorgias), 105

Endogeneity, 10 Enhanced group counseling, 99 Enhanced Triple-P, 39 Enhancing Motivation for Action Toolkit, 344, 350 Enns, M. W., 113, 117, 285 Environmental modification, 311, 317–318 Environmental risk factors, 109 Epidemiological studies, 3, 8, 9 Epigenetic pathway analysis, 30 Episodic depression, 362 Equifinality, 416 Equity, in depression research, 419–420 ERS. See Excessive reassurance seeking Escitalopram, 111–117 ESM (experience sampling methodology), 184, 185 Ethical framework for depression research, 20–21 Ethnic minority status, individuals with. See Racial or ethnic minority status, individuals with Eudaimonic variables, in well-being, 420 Europe, 160, 161, 407 European American college students, 183 Evidence-based interventions cognitive restructuring in, 220–221 evaluating MCT against other, 401 to improve parenting, 34 matching individuals to, 417 for maternal depression, 35–42 for MDD, 408 modifiable psychosocial risk factors as basis for, 21, 410–411 problem solving therapy vs. other, 346–348 relationship effects and impact of, 72–74 Evidence-based risk factors, 4–5, 408 Evolutionary psychology, 341–342 Evraire, L. E., 139–140 Excessive reassurance seeking (ERS), 412 with anxiety disorders and OCD, 139 case example, 148 with corumination, 140, 141 and dependency, 138 and depression, 138 development of, 139–140 in interactional theory of depression, 135–136 interactions between other risk factors and, 417 and interpersonal rejection, 139 risk literature on, 138–140 in treatment context, 144–146 Executive control, 239–240, 244–245, 393, 413 Executive functioning deficits, 348, 409 Exercise, 409, 410 Exogenous cueing task, 235

440  Index

Expectation clarification, in relationships, 89 Experience sampling methodology (ESM), 184, 185 Experiential avoidance ACT for reducing, 371 anxiety and, 361 CBT strategies to reduce, 191 defined, 361 and depression, 364 as emotion regulation strategy, 188 measures of, 366 and mindfulness-based interventions, 192, 193 as risk factor for depression, 196, 413 and rumination, 363–364 Experiential learning, 291 Experimental studies, 16–17 Explanatory optimism (explanatory style), 254, 256, 258–260, 270 Explicit avoidance behavior, 368 Explicit memory, 238 Explore Experience, Experiment with Experience, Exercise and Engage (E6) process, 317 Exposure-based treatments, 190, 367, 368 Expressed emotion, 163 Expressive suppression, 188–189, 191, 413 Extended memories, 239 Extended process model of emotion regulation, 197–198 External causation, 342 Externalization (tool), 343 Externalizing symptoms, 29, 30, 43, 141, 183 Ey, S., 287 Eye-tracking, 235 F Facts, assumptions vs., 343, 350 Failure, 142, 285–286 Family Bereavement Program, 42, 412 Family Check-Up, 42, 412 Family group cognitive behavioral preventive intervention (FGCB), 37, 41, 43–45, 412 Family members, with history of depression, 57, 412. See also Parental psychopathology Family Talk, 37, 40, 412 Fatigue, 215 Fear of negative evaluation, 138 of psychotherapy, 283 of rejection and abandonment, 133, 134, 140, 287, 289 Feedback Seeking Questionnaire (FSQ), 136 Fernández-Rodríguez, C., 371 Ferster, C. B., 362, 367, 368 Fetal exposure to maternal mood disorders, 30

FGCB. See Family group cognitive behavioral preventive intervention Fine, M. A., 142 Finland, 40, 161 Fisher, P. L., 395 Flett, G. L., 282–283, 285, 286 Flexibility cognitive, 238, 239, 249 emotion regulation, 184, 189, 413 psychological, 265 in revising existing interpretations, 238, 239, 246 Flow, 319 Fluoxetine, 113, 114, 118 Focused model of cognitive behavioral therapy for depression, 18 Fortalezas Familiares, 42 Fournier, J. C., 112, 116 Frank, E., 392 Free association task, 398 Free-viewing procedure, 241 Fresco, D. M., 264 Fried, E. I., 416 Friendships autobiographical friendship intimacy, 288 conflict in, 84, 90 during role transitions, 83 Frost Multidimensional Perfectionism Scale (MPS), 293, 294 FSQ (Feedback Seeking Questionnaire), 136 Functional analysis, 312–317, 367, 370 Functional impairment, dependency and, 137 Functional magnetic resonance resting state, 409 Funk, J. L., 159 “Future forecasting” perspective, 345 Future orientation, for worry, 306, 360 G Gamble, S. A., 121 Gámez, W., 366 Garber, J., 35 GENDEP. See Genome-based therapeutic drugs for depression project Gender differences in experiential avoidance, 364 in impact of parental depression on child, 32 in interpersonal dependency, 138 in negative affect and pessimism, 266 in prevalence of depression, 9, 27–28, 409 in sensitivity to stressful life events, 109 in social problem solving, 337–338 in stress exposure and treatment outcome, 126



Index 441

Gender identity, 419 Gender minority couples, couple therapy with, 173–174 Generalizable research, 160 Generalization stage of CBCT, 166, 172–173 Generalized anxiety disorder, 321, 348–351, 389 General orientation (problem orientation), 335–336 Genest, M., 286 Genome-based therapeutic drugs for depression project (GENDEP), 111, 112, 115 Ghadampour, F., 370 Gillies, J. C. P., 213 Girls. See Women and girls Global intelligence, 19–20 Goal-directed behavior, optimism and, 263–264 Goal pursuit, 261, 308–310 Goals, avoidance, 362–363 Goal setting, 95, 97, 267 Goldfried, M. R., 342 Go/no-go paradigm, 240 Gorgias, 105 Goya Arce, A. B., 288 Graded task assignments, 216. See also Activity scheduling Graf, M., 365 Gratitude, 259 Gratz, K. L., 182 Gray, J., 363 Greece, 41 Greenberger, D., 216 Grief, 85–89, 368, 412 Griffith, J., 269 Gross, J., 70, 182 Group therapy cognitive behavior, 320, 396 IPT-A delivered in, 93 metacognitive, 394–395 relationship effects in, 66–67 Guided discovery, 216 Guided practice, in EC-PST, 345 Guild, D. J., 36 Guyitt, B., 214 H Habit(s) changing, 311, 324 rumination as, 310–314 Habit-goal model, 308, 310–311 HADS (Hospital Anxiety and Depression Scale), 395 Hagen, R., 393 Hall, W. J., 419 Hallard, R. I., 388

Hamilton Rating Scale for Depression (HRSD), 116, 121, 392 Hammen, C., 409, 410 Happiness, 261 Harkness, K. L., 113, 117–118 Harm avoidance, 360 Harms, P. D., 269 Hasegawa, A., 338, 341 Hasin, D. S., 407 Hattori, Y., 338 Hawkins, A. J., 175 Hayden, E. P., 418 Head Start programs, 32 Health, defined, 420 Hedonic variables in well-being, 420 Hegelson, V. S., 58 Heller, K., 58 Helplessness theory, 258–259, 262 Helpless self-schema, 133, 137, 142 Help-seeking attitudes, 283 Hendriks, T., 268 Heritability of depression, 30, 409, 410 of explanatory style, 270 Hewitt, P. L., 282–283, 285, 286, 291–294 H-EX-A-GO-N model of rumination, 308, 309 Hierarchical Taxonomy of Psychopathology (HiTOP), 415 Hill, A. P., 285–286 Hindash, A. H. C., 243 Hippocampus, 122, 193 Hispanic college students, 338 HiTOP (Hierarchical Taxonomy of Psychopathology), 415 HIV, 261–262 Hjemdal, O., 392–393 Hoffmann, A., 289 Holahan, W., 66 Hollon, S. D., 370, 408 Home-visiting interventions, 31–32, 34–36, 38, 412 Hooley, J. M., 163 Hope, 258, 267 Hopefulness, 254 Hopelessness, 64, 288, 344, 350 Hopelessness theory, 259 Hopko, D. R., 363, 367–368 Hospital Anxiety and Depression Scale (HADS), 395 “Hot thoughts,” 216 Hoyt, W. T., 67 HPA axis. See Hypothalamic–pituitary– adrenal axis HRSD. See Hamilton Rating Scale for Depression Hudson, C. C., 140 Humiliation, 409 Husain, M. I., 36

442  Index

Hypercompetitiveness, 287 Hypothalamic–pituitary–adrenal (HPA) axis, 30–31, 212, 236, 409, 416 Hypothesis testing, 369 I IAPT (Improving Access to Psychological Therapies) program, 168 ICS. See Impulsive/careless problem-solving style Identification stage of extended process model, 197 IDI (Interpersonal Dependency Inventory), 134 If–Then plans, 323, 325 IH-CBT (in-home cognitive behavior therapy), 35, 38 Illusions of positivity, 261 Imipramine, 111, 115, 174 Immune function, 261, 409 Imperfection, nondisclosure/nondisplay of, 283 Implementation stage of extended process model, 197 Implicit approach-avoidance behavior, 368 Impostor syndrome, 219 Improving Access to Psychological Therapies (IAPT) program, 168 Impulse control problems, parental, 29 Impulsive behavior, 307 Impulsive/careless problem-solving style (ICS), 336–341 Inaction, distinguishing action from, 398 Inactivity, mood and, 215 Inclusiveness, of depression research, 419–420 Indigenous college students, 338 Individualistic cultures, 138 Individual psychotherapy couple therapy for depression vs., 170 relationship effects in, 67–68 Induced rumination, 307–308 Industrial disasters, 261 Infants, 35, 39, 137–138 Inferential styles, 211 Information processing biases, 233–234. See also Cognitive biases avoidance and, 364, 369 in hierarchy of vulnerabilities, 209, 210 interactions between other risk factors and, 416–417 during remission, 212 Informed consent, 21 Ingraham, L. J., 66 Ingram, R. E., 10, 209, 415, 416 Inhibition, behavioral, 239, 240, 244 In-home cognitive behavior therapy (IH-CBT), 35, 38

Insecure attachment, 209 Insight, into dependency, 141, 142 Insomnia, 307 Integrated interventions for parenting and depression, 35 Integrated model of cognitive vulnerability, 416 Integrative models of risk, 40, 238, 415–417 Intellectual problems, 333–334 Intelligence, 32 Interactional theory of depression, 135 Interactionist model of interpersonal dependency, 133 Intergenerational transmission of depression, 29–32, 45, 410, 412 Internal causation, 342 Internalizing problems, 28–32, 35, 41 Internal problems-in-living, 334 International Study to Predict Optimized Treatment for Depression (iSPOT-D), 117 Internet-delivered therapy, 321 Interpersonal closeness, 287 Interpersonal conflict avoidance of, 363 in friendships, 84, 90 IPT-AST to address, 99–100 parent–child, 34, 82–84, 88–94, 99 between parents, 32 sensitivity to, 134 between siblings, 84 Interpersonal deficits, 84–85, 87–90, 412 Interpersonal dependency defined, 133 development of, 137–138 and excessive reassurance seeking, 138 interactions between other risk factors and, 417 interventions targeting, 141–144 measures of, 134 and personality-based risk factors, 134 psychodynamic approaches targeting, 411 as risk factor for depression, 134, 412 risk literature on, 137–138 Interpersonal Dependency Inventory (IDI), 134 Interpersonal formulation, in IPT-A, 88 Interpersonal interventions, 100, 121 Interpersonal inventories, 87–88, 95 Interpersonal loss. See Loss Interpersonal model of depression, 363 Interpersonal patterns, 121, 289, 290 Interpersonal problems, 292, 334 Interpersonal problem-solving, in IPT-A, 88, 90 Interpersonal psychotherapy (IPT), 168–169, 412 childhood maltreatment and response to, 113, 118



Index 443

described, 81 identifying role transitions in, 83 IPT for IPV, 121 for MDD, 408 proximal life events treatment response to, 111, 112, 115, 116 relationship functioning of individuals receiving, 174 Interpersonal Psychotherapy–Adolescent Skills Training (IPT-AST), 94–99 case example, 97–98 described, 81 efficacy research on, 98–99 initial phase of, 95–96 interpersonal problem areas targeted by, 82, 84, 85 middle phase of, 96 pregroup sessions, 94–95 termination phase of, 96–97 Interpersonal psychotherapy for depressed adolescents (IPT-A), 84–94 case example, 91–93 described, 81 efficacy research on, 93–94 initial phase, 87–88 interpersonal problem areas targeted by, 82, 84–86 middle phase, 88–90 termination phase, 90–91 Interpersonal psychotherapy–trauma (IPT-T), 121 Interpersonal rejection, 139 Interpersonal risk factors, 81–100 directions for future research on, 99–100 IPT-AST targeting, 94–99 IPT-A targeting, 86–94 literature on, 82–86 Interpersonal role disputes, 83–84 Interpersonal sensitivity, 289 Interpersonal strategies, in IPT-AST, 94, 96 Interpersonal stressors, 137, 285, 286, 412 Interpersonal theories of depression, 81–82 Interpretation biases, 236–238, 242–243, 413 Intersectional identities, 420 Intervention science, 408–409 Interventions for depression. See also Effectiveness and efficacy research avoidance as target of, 366–371 cognitive biases as target of, 240–245 cognitive vulnerabilities as target of, 214–219 in context of stressful life events, 119–123 converting models of risk into, 21–22 dependency-relevant behaviors as targets of, 141–146

emotion dysregulation as target of, 190–194 evidence-based risk factors in, 4–5 habitual behaviors and effect of, 324 impact of, on emotion dysregulation, 199–200 and interactions between risk factors, 416–418 metacognition as target of, 389–396 parental psychopathology as target of, 34–43 perfectionism as target of, 283–284, 289–294 personalizing, 46, 105–106, 417–418 relationship functioning as target of, 164–166 rumination as target of, 312–322 sequencing, 418 and static variables, 12 utilization of optimism in, 264–269 Intimate partner violence (IPV), 121, 137, 163 Intimate relationships, 143–144, 157, 158, 160. See also Relationship functioning Invulnerability, sense of, 257 IPT. See Interpersonal psychotherapy IPT-A. See Interpersonal psychotherapy for depressed adolescents IPT-AST. See Interpersonal Psychotherapy– Adolescent Skills Training IPT for IPV program, 121 IPT-T (interpersonal psychotherapy– trauma), 121 IPV. See Intimate partner violence Iran, 338 iSPOT-D (International Study to Predict Optimized Treatment for Depression), 117 Italy, 64 J Jackson, S. L. J., 339 Jacobson, N. C., 361 Jacobson, N. S., 158, 167, 215, 363, 367, 368, 370, 392 Japan, 338 Jazaieri, H., 182 Jeglic, E. L., 338 Ji, J. L., 235 Jimenez, D. E., 339 Job performance, hope and, 258 Johnstone, J. M., 113 Joiner, T. E., 135 Jones, J. D., 99 Joormann, J., 233 Jordan, J., 393 Joss, D., 122 Judgment, perfectionism and, 285, 286

444  Index

K Kahneman, D., 261 Kant, G. L., 339 Kealy, David, 292–293 Keeping Families Strong, 37, 41–42, 412 Keers, R., 111, 112, 115–117 Kendall, P. C., 209 Kendler, K. S., 57, 409–410 Kim, J. M., 111, 115 Kimbrough, E., 122 King, L. A., 266 Kirkham, J. G., 347–348 Kleiman, E. M., 257 Klein, D. N., 113 Kleszewski, E., 289 Koster, E. H., 236, 416 Kouros, C. D., 164 Kuppens, P., 187 Kynurenic acid (KYNA), 369 L Lakey, B., 56, 57, 62, 67, 69–71 Landau, M., 261 Late life functioning, quality of life, SPS, and, 339 Latency, 10 Latinx individuals behavioral activation with, 369 culturally informed treatments for, 419 interventions for maternal depression with, 38, 42 social disconnection, social anxiety, and perfectionism for, 288 Learned behavior depressive rumination as, 310–311 in parenting, 33 Learned helplessness, 258–259 Learning Through Play Plus Program (LTP Plus), 36, 38–39, 412 LEDS. See Life Events and Difficulties Schedule Leff, J., 169 Lesbian, gay, bisexual, or queer (LGBTQ) youth, 419 Let’s Talk About the Children, 40–41, 412 Leventhal, A. M., 363 Lewinsohn, P. M., 365 Lewinsohn’s model of depression, 362, 365, 374 Lewis, C., 114, 118, 119 Lewis, L. M., 266 LGBTQ (lesbian, gay, bisexual, or queer) youth, 419 Life events, stressful. See Stressful life events Life Events and Difficulties Schedule (LEDS), 108, 111, 115, 116

Life orientation perspective, 262 Life Orientation Test (LOT), 257 Life satisfaction, hope and, 258 Lifespan, risk factor studies across, 9 Life stress, 188–189, 209, 410 Life Stress Interview (LSI), 108 Limited sick role, 87 Linde, K., 347 Linguistic minorities, 419–420 “Listener” skills, 165 Lloyd, C., 111, 115 Loma Prieta earthquake, 16 Loneliness, 58, 84–85, 288 Longitudinal studies, 15, 16 Long-term contextual threat, 409 López, R., Jr., 338 Lorenzo-Luaces, L., 225 Loss, 42, 85, 86, 109, 409, 410 LOT (Life Orientation Test), 257 Lovallo, D., 261 Lower income levels, individuals with, 27, 174 LSI (Life Stress Interview), 108 LTP Plus. See Learning Through Play Plus Program Lucier-Greer, M., 175 Lyubomirsky, S., 266, 341 M Ma, L., 269 Mackinnon, S. P., 288 MacLeod, A. K., 362 Magson, N. R., 288 Main effects, in studies of depression, 57 Maintenance of depressive symptoms, 236, 305, 307, 364–365 Maintenance stage of CBCT, 166 Major depressive disorder (MDD), 8. See also Clinical depression attention and engagement to tasks with, 365 avoidance for women with, 362 behavioral activation for treating, 215 brief problem-focused couple intervention for treating, 169 causal role of stress in, 108 CBCT for treating, 166, 167 CBT for treating, 220–224 cognitive reappraisal by individuals with, 188 concreteness training and relaxation training for treating, 321 diagnostic criteria for, 414–415 economic burden of, 281 emotion dysregulation with, 181, 184–187, 197–198 estimates of provider effects by individuals with, 62



Index 445

expressive suppression and, 189 global point prevalence of, 157 group MCT for treating, 394 heterogeneity in characteristics of, 28 interventions for parents with, 41 interventions targeting emotion dysregulation for treating, 190–194 low social support as risk factor for, 56–57 MCT for treating, 391–393 onset of, 289 PAI score to personalize treatment of, 417 perceived social support and, 55 problem solving therapy for older adults with, 347–348 psychosocial interventions for treating, 408 relationship distress and, 161, 173 relationship stressors and prevalence of, 163 RFCBT for adolescents with, 320 rumination and, 188 treatment response for individuals with history of childhood maltreatment and, 113, 118 treatment response for individuals with proximal stressful life events and, 111–112, 116 U.S. prevalence of, 407 Major depressive episode, 8, 85, 161 Major life events, daily hassles vs., 13 Maladaptive dependency-related behavior, 141, 142 Maladaptive emotion regulation strategies, 183–184 Maladaptive grief, 85 Maladaptive thinking, 207 Mallinckrodt, B., 66–67 Malnourished children, 39 Malouff, J. M., 345–346 Mandel, T., 288–289 Mannarino, A. P., 120 Manualized treatment, 68 Marcus, D. K., 66 Marginalized communities, 213–214. See also Diverse populations Marital discord model of depression, 157–158, 163 Marriage and relationship education programs, 174–175 Marriage and relationship issues, 412. See also Relationship functioning Master Resilience Training (MRT), 269 Mastery, 367 MATCH (modular approach to therapy for children with anxiety, depression, trauma, or conduct disorder), 418 Maternal depression age of child at exposure to, 29 case study of, 43–45

chronicity, severity, and impact on child of, 28 effects of exposure to, 27 epigenetic changes with exposure to, 30 evidence-based interventions for, 35–42 neurodevelopment and exposure to, 30–31 and parenting behaviors, 33–34 Maternal mood disorders, 30 Mathematically defined relationship effects in psychotherapy, 412 Mathematical problems, solving, 333–334 Matthews, G., 388 McClintock, A. S., 142–143 McMurran, M., 340 MCQ-30 (Metacognitions Questionnaire, 30-item version), 387 MCT. See Metacognitive therapy MDD. See Major depressive disorder Meaning making, 259 Mean of squared successive differences (MSSD), 186, 187 MEAQ (Multidimensional Experiential Avoidance Questionnaire), 366 Mediating variables, studies of, 3 Medical model, 375 Meditation, 321 Meehl, Paul, 159 Memories, 239, 244 Memory biases, 238–239, 243–244, 413 Memory deficits, 409 Memory Specificity Training (MeST), 243–244, 411, 413 Memory training, 239 Men. See also Gender differences disclosure of dependency-related behaviors by, 142 paternal depression, 28, 34, 39 Mental health child’s neurodevelopment and maternal, 31 impact of parenting on child’s, 33 Master Resilience Training and, 269 partner effects on, 163 unrealistic optimistic bias in, 261 Mental-health related quality of life (MHRQOL), 339 Mental set shifting, 244 MeST. See Memory Specificity Training Meta-awareness, 122, 385, 390, 391, 414 Metacognition(s) case example, 396–400 changes in, with non-MCT interventions, 396 directions for future research on, 400–402 in information processing, 383 interventions targeting, 389–396 MCT and changes in, 389, 394 as risk factor in depression, 386–389

446  Index

Metacognitions Questionnaire, 30-item version (MCQ-30), 387 Metacognitive control system, 401 Metacognitive model of depression, 384–388, 396 Metacognitive therapy (MCT), 389–396, 414 case example of, 396–400 case series studies of, 391–392 for depression symptoms in physical illness, 395 for depression with anxiety, 383 directions for future research on, 401 effectiveness and efficacy research on, 391–396 group, 394–395 nature of, 389–391 in randomized controlled trials, 393–394 rumination as target of, 321 and secondary depression symptoms, 395 in uncontrolled trials, 392–393 Metacognitive training, 396 Mezulis, A. H., 28 MHRQOL (mental-health related quality of life), 339 Michalak, J., 122–123 Mikail, S. F., 291–293 Mindfulness detached, 390, 398 in remission from depressive episode, 192–193 Mindfulness-based cognitive therapy (MBCT), 412–414 avoidance reduction with, 370, 371 in context of stress response, 122–123, 125 emotional dysregulation over course of, 192, 199 emotion outcomes of, 192–193 empirical support for, 366 problem solving therapy vs., 346 rumination as target of, 321–322 for treating recurrent MDD, 190 Mindfulness-based interventions, 411, 412 in context of stress exposure, 121–123 dependency as target in, 142–143 emotion outcomes of, 192–193 role of emotion dysregulation in, 192 Mindfulness-based stress reduction (MBSR), 121–122, 412 Miniati, M., 113, 117 Minnesota Multiphasic Personality Inventory-2 (MMPI-2), 294–295 Minor depressive disorder, relationship distress and, 161 MIPs. See Mood induction procedures Mistakes, concern over, 293 MMPI-2 (Minnesota Multiphasic Personality Inventory-2), 294–295

Models of risk biopsychosocial framework, 13 converting, into interventions, 21–22 multifactorial, 19–20 research methods to study, 13–18 Moderating variables, studies of, 3 Modes, in congruency hypothesis, 209–210 Modifiable risk factors, 10 evidence-based interventions incorporating, 21 investigations of, 409–410 measurement of, 418–419 perceived support as, 59 psychosocial, 4, 410–414 relative likelihood studies on, 15 static vs., 12 Modified CBCT, 168 Modular approach to therapy for children with anxiety, depression, trauma, or conduct disorder (MATCH), 418 Momentary attention to emotion, 185 Momentary rumination, 307 Monroe, S. M., 111, 115 Mood, 95, 215, 324 Mood-congruency hypothesis, 210 Mood disorders, 30, 40, 163 Mood induction procedures (MIPs), 17, 142–143, 210–211, 213, 224 Moore, M. T., 264 Moore, S. D., 370 Moos, R., 366 Morale, befriending interventions and, 58 Mortality risk, 57, 257 Motivation, 215, 344, 350 Motivational component of dependency, 133 Motivational interventions, patient– treatment matching in, 70–71 Moving, during adolescence, 83 MRT (Master Resilience Training), 269 MSI-B screen, 160 MSSD (mean of squared successive differences), 186, 187 Mufson, L., 94 Multidimensional approach to avoidance, 359–360 Multidimensional Experiential Avoidance Questionnaire (MEAQ), 366 Multidimensional model of social problem solving, 335–337 Multifactorial models of risk for depression, 19–20 Multifinality, 416 Muñoz, R. F., 419 Mutually supportive relationships, 64–65 N Nanni, V., 116–117 Narratives, for belief development, 218 National Guard of the United States, 269



Index 447

National Institute for Health and Care Excellence (NICE), 164 National Institute of Mental Health, 415 National Registry of Evidence-based Programs and Practices, 93 Native American mothers, 38 Naturalistic studies, 16, 308 NBRS (Negative Beliefs About Rumination Scale), 387, 388 Neal, D. T., 310 Neal, R. L., 146 Nefazadone, 114, 118, 369 Negative affect and attentional biases, 236 and defensive pessimism, 262 with depression contagion, 163–164 and negative feedback seeking, 136 and optimism interventions, 266 for parents with depression, 33 provider effects on, 62–63 and rumination, 307 as target of CBT, 233 Negative appraisals of life circumstances, 207, 214 Negative arousal, slowing pace of, 344 Negative attentional biases, 234, 240–242 Negative automatic thoughts, 64, 215–218, 236 Negative availability bias, 265 Negative beliefs about emotion, 198–199 about thinking, 385–388, 395, 414 interactions between other psychosocial risk factors and, 5 metacognitive, 385, 386 Negative Beliefs About Rumination Scale (NBRS), 387, 388 Negative cognitive structures, 212 Negative cognitive style, 237 Negative emotional instability, 186 Negative emotions, rumination and, 307 Negative evaluation, fear of, 138 Negative events, attributions for, 259 Negative feedback seeking (NFS), 136, 140, 412 Negative information processing. See Cognitive biases Negative intrusive thoughts, 237 Negative life events, SPS and, 337 Negative metacognitive beliefs, 385, 386 Negative parenting behaviors, 33, 34 Negative problem orientation (NPO), 335–338, 340, 341, 344 Negative reinforcement, 362–364, 367 Negative relationship quality, 162 Negative schemas, 110, 236 Negative stimuli, disengagement from, 235

Negative thinking. See also Cognitive vulnerabilities; Repetitive negative thinking (RNT) in CAS, 383 case studies on, 14 and depressive symptom severity, 263 in metacognitive model of depression, 384–385 as risk factor for depression, 413, 414 rumination and, 307–308 Neglect, 117 Nemeroff, C. B., 114, 118 Nepon, T., 286 Netherlands, 64, 161 Neurobiological interventions, 193 Neurobiological model of motivation, 363 Neurodevelopment of child, with depressed parent, 30–31 Neuroticism, 126, 173, 262, 340, 389, 409, 410 Newman, M. G., 361 Nezu, A. M., 342, 344, 346 NFS. See Negative feedback seeking NICE (National Institute for Health and Care Excellence), 164 Nieuwsma, J. A., 346 Nilsen, W., 85 Nolen-Hoeksema, S., 341 Nomothetic assumption of psychological therapy, 71 of psychological therapy for depression, 68 of stress and coping social support theory, 60, 62–63 Nomothetic social influences, estimating, 60–62 Nondirective supportive therapy (NST), 114, 118 Nondisclosure of imperfection, 283 Nondisplay of imperfection, 283 Nonjudgmental awareness, 122 Nonsocial avoidance, 360, 361, 365 Nonsuicidal self-injury (NSSI), 338 Nonverbal communication, 95 Normalization, of rumination, 308–310, 313 North America, 407 Nortriptyline, 111–113, 115 Novelty, 334 NSSI (nonsuicidal self-injury), 338 Nuclear families, 64 Nuisance variables, 15 Nurturing paternal behaviors, 260 O Objective assessment of stress exposure, 107 Objective social disconnection, 287, 288 Object mode, 384 Object relations, 144 Observational studies, 18, 19, 21

448  Index

Obsessive-compulsive disorder (OCD), 139, 144–146, 367 Occipital cortex, 242 Older adults, 339, 347–348 Ondersma, S., 70–71 One-with-many design, for estimating social influences, 61–62 Open-ended scenarios, 237 Openness, 340 Opiate dependence, 64 Oppression, 213 Optimal parenting, 32, 33, 45 Optimism building, 413 case example, 254–256 definitional issues with, 256–261 directions for future research on, 269–270 dispositional, 256–258, 260, 263–264 explanatory, 254, 256, 258–260, 270 as independent construct, 253–254 interventions utilizing, 264–269 as protective factor, 263–264, 420 relational regulation of, 64 strategic, 262 and traditional therapies, 264–265 unrealistic, 260–261, 413 Optimism interventions, 265–266, 270 Optimism training, 264–265 O’Shea, G., 93 Other-oriented perfectionism, 283, 284, 288, 289 Ottenbreit, N. D., 14, 361–362 Otto, K., 289 Outcome assessments, referring patients based on, 70 Outcome research. See also Effectiveness and efficacy research on autobiographical memory deficits, 239 on emotion dysregulation, 199–200 on excessive reassurance seeking, 145 on interventions targeting emotion dysregulation, 191–193 involving history of childhood maltreatment, 113–114, 116–119 involving stressful life events, 105–106, 110–112, 115–116 on relationship functioning, 166–170 with youth with depressed parents, 35 Overcontrolling parents, 310 Overgeneralization, 238–239, 265 Overholser, J. C., 142 Overprotective parents, 137–138 Özdemir, Y., 337 P Paced Auditory Serial Addition Test (PASAT), 244 Padesky, C. A., 216

PAI (Personalized Advantage Index), 417 Pain, 9, 263 PAIs (positive activity interventions), 266–267, 413 Panaite, V., 186 Papageorgiou, C., 387, 391, 394–395 Paradoxical symptoms of clinical depression, 8 Parent(s) involvement of, in IPT-A, 86, 88, 89, 92 involvement of, in IPT-AST, 94 loss of, 42, 85, 410 over-controlling, 310 overprotective, 137–138 social support from, 85 Parental authoritarianism, 137, 262 Parental depression, 45, 237, 410, 411, 418. See also Maternal depression; Paternal depression Parental divorce, 42–43, 83 Parental perfectionism, 288 Parental pessimism, 262 Parental psychopathology, 27–46 case example, 43–45 and clinical characteristics of depression, 28–29 directions for future research on, 45–46 interventions targeting, 34–43 and parenting as mechanism of transmission, 33–34 risk factors for intergenerational transmission of depression, 29–32 as static risk factor, 12 Parental warmth, 410 Parent–child conflict conflict with, 94, 99 and depressive episodes in youth, 34 role dispute as source of, 82–84, 88–93 Parent–child relationship, home-visiting interventions targeting, 38 Parent factors, in personalization of interventions, 46 Parenting authoritative, 34 interventions to improve efficacy of, 39 as mechanism of transmission, 33–34 optimal, 32, 33, 45 Parenting behaviors, interventions targeting, 39–41 Parenting competence, 33 Parenting satisfaction, 39 Parenting stress, 31 Park, M., 59 Paroxetine, 112, 116 Partner effects, in relationship distress, 163–164 PASAT (Paced Auditory Serial Addition Test), 244 Past orientation, for rumination, 306, 360



Index 449

Paternal depression, 28, 34, 39 Paterson, T. S. E., 339 Pathways thinking, 258 Patient Health Questionnaire-9, 338 Patient–therapist matching, 65, 68–72 Patient–treatment matching, 65, 70–73 PBRS (Positive Beliefs About Rumination Scale), 387, 388 PDD (persistent depressive disorder), 393 PDSM (perfectionism social disconnection) model, 284–289 PDST (Psychological Distance Scaling Task), 211, 221 PE. See Prolonged exposure Pedersen, M. L., 392 Peer, loss of, 86 Peer relationships, 83, 84 Peer support, 94 Pennebaker, J. W., 266 Penn Resilience Program (PRP), 268–269, 413 People pleasing, 287–288 Perceived criticism, 163 Perceived support, 56, 59–65 Perfectionism, 281–298 case example, 294–297 definitional issues with, 282 directions for future research on, 297–298 dynamic-relational treatment targeting, 289–291, 411 effectiveness/efficacy of treatments targeting, 291–294 as focus of treatment, 283–284 models of depression and, 284–287 other-oriented, 283, 284, 288, 289 and PDSM model, 284–289 relational regulation of, 64 as risk factor for depression, 413 as risk factor in depression, 282–283 rumination and, 309 self-oriented, 282–289 socially prescribed, 283–289, 292 Perfectionism social disconnection (PSDM) model, 287–289 Perfectionism-specific treatments, 284 Perfectionistic self-presentation, 283, 286–288 Perfectionistic self-promotion, 283 Perseveration, 286 Persistent depression. See Chronic depression Persistent depressive disorder (PDD), 393 Personal agency, 258 Personality, 60, 340 Personality disorders, 126 Personalized Advantage Index (PAI), 417 Personalized interventions, 46, 105–106, 417–418 Personal Style Inventory (PSI), 134

Pessimism association of, with depressive symptoms, 262–263 defensive, 261–262, 413 definitional issues with, 256, 261–262 directions for future research on, 269–270 interactions between other psychosocial risk factors and, 5 on Life Orientation Test, 257 in Penn Resilience Program, 268 positive psychotherapy targeting, 267 as risk factor, 420 social problem solving and, 341 as target of cognitive therapy, 253 view of optimism in opposition to, 254 Pharmacotherapy. See also Antidepressants; specific drugs and CBT, 193, 220 directions for future research on, 200 with emotionally focused couple therapy, 169–170 emotion outcomes of, 193 stressful life events and response to, 105 Physical abuse, 117, 310 Physical illness, 137, 257, 258, 388, 395 Physical problems-in-living, 334 Physiological markers of emotional experience, 184 Planful problem solving style. See Rational problem solving (RPS) style Planful Problem-Solving Toolkit, 342–343, 350 Pleasant events scheduling, 364, 367, 368 PMET (positive memory enhancement training), 243 Polo, A. J., 288, 419 Ponniah, K., 408 Population-based probability samples, 160–161 Positive activity interventions (PAIs), 266–267, 413 Positive affect, 63, 233, 261, 266, 267, 340 Positive behavior, CBCT to improve, 164–165, 171–172 Positive beliefs about rumination and worry, 385, 387–388, 391, 399, 401 metacognitive, 385 Positive Beliefs About Rumination Scale (PBRS), 387, 388 Positive emotional instability, 186 Positive emotion dysregulation, 196–197 Positive events, attributions for, 259 Positive expectations, 257 Positive interventions, 265–266 Positive memory enhancement training (PMET), 243 Positive parenting behaviors, 33–34

450  Index

Positive problem orientation (PPO), 335–339, 341 Positive psychotherapy, 413 Positive psychotherapy (PPT), 267–268, 371 Positive reinforcement, 215, 362–365, 367 Positive relationship quality, 162 Possible solutions, generating, in EC-PST, 343 Postpartum depression, 35, 160, 392, 409 Posttraumatic stress disorder (PTSD), 119–120, 123, 306, 367 Potential threats, 139 Powers, M. B., 119–120 PPO (positive problem orientation), 335–339, 341 Predicting the future, 334 Prefrontal cortex, 193, 212, 236, 242, 416 Pregnancy, 29–31, 35 Pregroup sessions, IPT-AST, 94–95 Prenatal exposure to maternal depression, 30–31 Preventative Intervention Project (Family Talk), 37, 40, 412 Preventative interventions to build social support, 57–59 cognitive control training, 244–245 to improve relationship functioning, 174–175 individualizing, 100 interventions treating maternal depression as, 34–42 IPT-AST, 94 relational regulation theory-based, 64 rumination-focused cognitive behavior therapy, 321 Price, J. M., 10 Primary-care settings, 94, 346–347 Privilege, optimism and, 270 Problem definition, 336, 342–343 Problem orientation, 335–336 Problem-orientation training, 346 Problems-in-living, 333–336, 342–343 Problem solving in CBT, 191 in Penn Resilience Program, 269 for problems-in-living, 333–334 rumination and, 308–312, 315, 370 social. See Social problem solving [SPS] Problem-solving analysis, 341 Problem-Solving “Multitasking” Toolkit, 343–344, 350 Problem-solving styles, 336–337 Problem-solving therapy (PST), 413. See also Emotion-centered problemsolving therapy (EC-PST) directions for future research on, 351–352 empirical support for, 366 evidence base for, 345–348 evolution of, 342

for older adults with depression, 347–348 in primary care, 346–347 role of emotion dysregulation in, 190 Problem-solving training, in CBCT, 165–166, 172 Processing mode account for rumination, 308, 311–312 Program for Children of Divorce, 42–43, 412 Prolonged exposure (PE), 119–120, 284, 412 Prospective studies, 56–57, 211 Protective factors in depression, 263–264, 410, 420–421 Proulx, C. M., 160 Provider effects, 60–63, 65–68 Proximal risk factors, 13, 105, 412 Proximal stressful life events assessing exposure to, 107–108 childhood maltreatment and sensitivity to, 110 defined, 106 interactions of other risk factors with, 126 as prescriptive predictors, 125 as risk factor for depression, 109 and treatment response in depression, 110–112, 115–116 PRP (Penn Resilience Program), 268–269, 413 PSI (Personal Style Inventory), 134 PST. See Problem-solving therapy PST (psychodynamic supportive psychotherapy), 292–294 Psychodynamic approaches, 144, 411, 412 Psychodynamic-interpersonal perspective on perfectionism, 282 Psychodynamic-relational perspective on perfectionism, 282–283 Psychodynamic supportive psychotherapy (PST), 292–294 Psychoeducation, 264 on emotion dysregulation, 185, 191, 194 in interventions for maternal depression, 40–41 in IPT-A, 87 in IPT-AST, 95 to modify core beliefs and schemas, 217 Psychological Distance Scaling Task (PDST), 211, 221 Psychological flexibility, 265 Psychological risk factors, 4, 13, 21, 409, 411 Psychological theories of depression, 3 Psychological therapy (psychotherapy) childhood maltreatment and response to, 117–118 with dependent individuals, 141 emotion regulation skills training in, 193–194 fear of, 283 life event exposure and response to, 115–116



Index 451

optimism in, 264–265 perceived support and, 55, 59 problem solving therapy vs. other, 346, 347 relational regulation in, 55, 65–68 relationship effects in, 68–71 Psychopathological model of depression, 364–365 Psychopathology, 183, 333, 409. See also Comorbid conditions; Parental psychopathology severe, befriending interventions for individuals with, 58 Psychosis, 9, 307 Psychosocial interventions, 346, 411 Psychosocial risk factors (generally), 407–421 assumption of equivalence on, 415 diversity of samples for studying, 419–420 and efficacy of psychological interventions, 408–409 and future investigations of risk, 414–415 in integrative models and studies, 415–417 interactions between, 5 investigating, 409–410 and measurement of risk, 418–420 modifiable, 4, 410–414 personalizing and sequencing interventions based on, 417–418 and protective factors, 420–421 PTSD. See Posttraumatic stress disorder Puerto Rico, 93 Punishment sensitivity, 363 “Putting it in perspective” skill, 268–269 Q QIDS-16-C (Quick Inventory of Depressive Symptomatology, clinician rating version), 393 Qualitative studies, 14 Quasi-experimental designs, 16–17 Questioning, in functional analysis, 316 Quick Inventory of Depressive Symptomatology, clinician rating version (QIDS-16-C), 393 Quilty, L. C., 113, 220 Quinolinic acid, 369 Quoidbach, J., 265–266 R Racial discrimination, 109 Racial or ethnic minority status, individuals with. See also specific groups couple therapy for depression with, 173, 174 depression research with, 419–420

emotional suppression and depressive symptoms for, 183 interventions in context of stress exposure with, 121, 126 measures of cognitive vulnerability for, 213 SPS and depression for, 338 stressful life events for, 109 Radomsky, A. S., 146 Ranjbar, M., 338 Rational problem solving (RPS) style, 336, 337, 339, 341 RDoC (Research Domain Criteria), 415 Reactivity cognitive, 211, 410 emotional, 186, 196 stress, 31–32, 247, 340 Reassurance seeking. see Excessive reassurance seeking (ERS) Reassurance Seeking subscale, DIRI, 136 Recipient effects, 60–63 Recovery from depression, 8, 189, 259 from stressful tasks, 257 Rector, N. A., 139, 145 Recurrent depression, 19, 407 adolescent-onset, 81 cognitive products and, 210–211 MBCT for treating, 322 MCT for treating, 391–392, 396–400 prevalence of, 281 proximal life events and treatment response for individuals with, 112 Reflexive self-regulation, 385 Reformulated helplessness theory of depression, 258–259 Regression techniques, 14, 15 Rehman, U. S., 162 Reinforcement negative, 362–364, 367 positive, 215, 362–365, 367 Rejection fear of, 133, 134, 140, 287, 289 targeted, 109 Rejection sensitivity, 134, 139, 263, 288 Relapse prevention, 122–123, 172–173, 223, 400 Relational forecasting, 68–70 Relational needs, and DRTs, 289, 290 Relational regulation, 55, 65–68 Relational regulation theory (RRT), 55, 63–65, 68–73 Relational support model for forecasting, 69 Relationship(s). See also specific types benefits of corumination for, 136–137 definition of, in RRT, 63 link between mood and, 95 threats to end, 163, 165

452  Index

Relationship adjustment, 158, 159, 166–167, 169 Relationship conflict, sensitivity to, 134 Relationship discord CBCT for depression with, 167 cutoff scores for, 158–159 depression contagion with, 164 interventions for couples experiencing, 159–160 Relationship distress brief couples therapy for depression and, 169 as category, 159–160 CBCT for depression in couples without, 168–169 CBCT for treating, 164 defined, 158 directions for future research on, 173 and effectiveness of CBCT for depression, 166 measures of, 158–159 partner effects for, 163–164 as risk factor for depression, 173, 412 risk literature on, 160–162 and severity of depressive symptoms, 174 Relationship effects directions for future research on, 74–75 and nomothetic assumptions in therapy, 68 and patient–therapist/patient–treatment matching, 68–71 in psychological therapy for depression, 68–71 in psychological therapy generally, 65–68 relative strength of provider, recipient, and, 62–63 in SRM designs, 60–62 Relationship functioning, 157–175 case example, 170–173 definitional issues with, 157–160 directions for future research on, 173–175 interventions targeting, 164–166 outcome research on, 166–170 risk literature on, 160–164 Relationship Profile Test (RPT), 134 Relationship quality, 158, 162 Relationship satisfaction, 158 Relationship stressors, 163, 412 Relationship-threatening behaviors, 165 Relative likelihood studies, 15 Relaxation training, 321 Reliability, of risk measures, 20 Reliable Change Index, 291–292, 392 Religiosity, 410 Remission from depressive episode attention to emotion in, 185 childhood maltreatment and likelihood of, 117 cognitive products in, 210–211

cognitive structures in, 212 dependency and likelihood of, 137 emotion intensity and likelihood of, 185 with evidence-based interventions, 74 with interventions in context of stress exposure, 120, 121 MBCT during, 122 mindfulness in, 192–193 for parents, 35, 45 positive emotion in, 181 well-being therapy during, 268 Renshaw, K. D., 163 Repetitive negative thinking (RNT) as cognitive avoidance, 360 interventions targeting, 243 processing style of, 311–312 as psychosocial risk factor for depression and anxiety, 306–307 as risk factor for depression, 385, 410, 414 rumination and worry as, 305–306 Research Domain Criteria (RDoC), 415 Research methods, for studying risk, 13–18 Residual symptoms of depression, 241–242, 307 Resilience, 20, 258–259, 270, 340 Response bias, in stress exposure assessments, 107, 108 Response latency, 237 Response styles, 384–385 Response styles theory, 308, 310 Revised Life Orientation Test (LOT-R), 254 Reward processing, 33, 34, 110, 244, 365, 374 Risk biopsychosocial framework of, 13 conceptualization of, 10–12, 409, 414–415 future investigations of, 414–415 integrative models and studies on, 415–417 measurement of, 20, 418–420 relationship of vulnerability and, 10–12 research methods for studying, 13–18 Risk factors (generally), 11–13 Risk factors for depression. See also specific entries evidence-based. see Evidence-based risk factors interactions between, 82, 126, 224, 416–418 intergenerational transmission, 29–32 issues in establishing, 18–21 operationalization of, 409 polythetic nature of, 18–19 transdiagnostic, 183, 196, 307, 388–389, 417–418 Risk Factors in Depression (Dobson & Dozois), 4 Riskind, J. H., 255, 264–265 Rnic, K., 288



Index 453

Robinaugh, D. J., 416 Roemer, L., 182 Rogge, R. D., 159 Role disputes, 83–84, 87–93, 412 Role playing, 90, 95, 96, 98 Role transition, 82–83, 87, 89, 90, 412 Romantic relationships, role disputes in, 84, 89, 91–93. See also Intimate relationships Romens, S. E., 210 Rosselló, J., 93 Round-robin design for estimating social influences, 61 Routine building, 318 RPS style. See Rational problem solving style RPT (Relationship Profile Test), 134 Rudolph, K. D., 84 Ruini, C., 420 Rumination, 305–325 abstract, 311–312, 316–317 avoidance, depression, and, 361–364, 373 as avoidance, 360, 370–371 case example, 322–324 with CBT, 191 and cognitive control deficits, 240 conceptual issues with, 308–312 concrete, 311, 312 corumination, 136–137, 140–141 defined, 341 definitional issues with, 305–306 depressive symptoms and, 181 directions for future research on, 324–325 as emotion regulation strategy, 187–188 evidence base for interventions targeting, 320–322 evolutionarily adaptive function of, 369–370 functional analysis of, 312–317 interactions between other risk factors and, 5, 416–417 and interpretation biases, 237–238 interventions targeting, 312–322 memory biases and, 238 and metacognition, 384, 385, 387 metacognitive therapy to reduce, 394 and mindfulness-based interventions, 192, 193 for parents with depression, 33 and perfectionism, 286 and pessimism, 263 positive beliefs about, 385, 387–388, 391, 399, 401, 414 postponing/starting and stopping, 390–391, 399 as risk factor for depression, 196 as risk factor for depression with anxiety, 306–308 as risk factor in depression, 409, 413

and SPS, 337, 341–342 suicidal ideation and, 258 and working memory, 234 Rumination-as-a habit model, 310–314, 324 Rumination-focused cognitive behavioral therapy (RFCBT), 411, 413, 414 avoidance reduction with, 370–371 case example of, 322–324 emotion dysregulation with, 191 evidence base for, 320–321 factor analysis and, 314–316, 318 as preventative intervention, 321 scalability of, 325 Ryff, C. D., 268 S Sadness, as cardinal symptom, 8 Safety behaviors, 144–146 Salt intake, 410 SAMHSA (Substance Abuse and Mental Health Services Administration), 93 Sanders, M. R., 37 Santos, M. M., 369 SAs (situational analyses), 369 SAS (Sociotropy-Autonomy Scale), 134 Satisficing, 267 Savitz, J., 369 Sbarra, D. A., 160, 173 SBFT (systemic behavioral family therapy), 114, 118 Scalability, of interventions for rumination, 325 Scheier, M. F., 262 Schema avoidance, 360 Schemas childhood maltreatment and, 110 cognitive products and, 210 cognitive structure of content of, 211–212 core, 139–140 as heuristic for information storage, 212 in hierarchy of cognitive vulnerabilities, 209 modifying, 217–219, 413 negative, 110, 236 Schema structures, 209. See also Cognitive restructuring CBT and changes in, 220, 221 childhood maltreatment and, 110 in cognitive model of depression, 217 cognitive products and activation of, 210 directions for future research on, 224 importance of, 224 relative importance of, 214 research on, 211 as risk factor in depression, 413 Schema theory, 384–385 School-based clinicians, 94, 98–99 Schwartz-Mette, R. A., 141

454  Index

Scrambled Sentences Task, 242–243 Secondary depressive symptoms, 123, 395 Secure attachment, 33, 39 Segal, Z. V., 220 Selection stage of extended process model, 197 Selective attention, 236 Selective serotonin reuptake inhibitors (SSRIs), 115–118 Self neglect of, 283 relationship with, 290 Self-compassion, 122 Self-competence, 148 Self-concept, 14, 286 Self-coping plans, 319–320 Self-criticism, dependency vs., 134 Self-discrepancies, relational regulation of, 64 Self-efficacy, 142, 258, 263 Self-esteem, 262 dependency and, 143, 148 in DRTs for perfectionism, 289, 290 in interventions targeting dependency, 142 parental perfectionism and, 288 relational regulation of, 64 as risk factor in depression, 410 support for, in CBCT, 165, 172 Self-harm, dependency and, 137 Self-monitoring, for rumination, 316, 317 Self-motivation, 317 Self-oriented perfectionism, 282–289 Self-presentation by individuals high on dependency, 133–134 perfectionistic, 283, 286–288 Self-promotion, perfectionistic, 283 Self-referential processing, 235, 236 Self-regulation, 383, 385 Self-regulation model, 388 Self-regulatory executive function (S-REF) model, 383, 384, 401 Self-report measures of avoidance, 364–366 of emotional experience, 184 of emotion regulation, 197 of interpersonal dependency, 134 of social support, 56 of stress response, 107 Self-schema, helpless, 133, 137, 142 Self-verification theory, 136 Self-worth, perfectionism and, 285 Seligman, M. E., 254 Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial, 35, 412 Sequencing of interventions for depression, 417–418 Serotonin, 110, 117, 125 Serotonin transporter gene, 116

Sertraline, 113, 117 Serum brain-derived neurotrophic factor, 409 Seven-column tool, 216 Severe life events, 109, 116 Severity index, sampling based on, 9–10 Severity of depressive symptoms attention training technique to reduce, 391 cognitive control training to reduce, 245 cognitive reappraisal and, 188 couple therapy to reduce, 170 and emotional awareness, 184, 185 emotional instability and, 186 emotional variability and, 187 and emotion intensity, 185 emotion reactivity and, 186 emotion regulation flexibility and, 189 experiential avoidance and, 188 expressive suppression and, 189 impact of CMT vs. CBT on, 167–168 and impact of parental depression on child, 28–29 maternal stress and, 31 and negative thinking, 263 and problem solving style, 338 problem solving therapy to reduce, 345, 347 and relationship distress, 174 RFCBT to reduce, 320 rumination and, 188, 341 social problem solving and, 337 and stress response, 107 Sexual abuse, 117, 118, 121, 122, 310, 409 Sexual minority couples, couple therapy with, 173–174 Sexual orientation, 126, 419 Shang, P., 348 Shannon, A., 287 Shek, D. T. L., 337 Sheldon, K. M., 266 Sherry, S. B., 288 Short-term mood induction studies, 17 Sibling, loss of, 86 Sibling conflict, 84 Siette, J., 58, 59 Simplification, in EC-PST, 343–344 Singhal, M., 337 Singular occurrence, problems-in-living with, 334 Situational analyses (SAs), 369 Siu, A. M. H., 337 Skills training, framing EC-PST as, 349–350 Sleep duration, 410 Sleep dysregulation, 8, 411 Sleep quality, 263 Smith, M. M., 288 Smith, R. L., 141 Smoking, 409



Index 455

Snyder, C. R., 258 Social anxiety, 288, 292, 361, 396 Social avoidance, 360, 361, 363, 365 Social-behavioral deficits, 84–85 Social cognitive impairments, 140 Social connection, 16, 287–289, 420–421 Social cues, sensitivity to, 133 Social development, 39 Social factors, in development of dependency, 138 Social functioning, improving, 99–100, 142 Social hopelessness, 288 Social impairment, for children with depressed parents, 27 Social influences, 60–62 Social isolation, 31, 84 Socially prescribed perfectionism, 283–289, 292 Social problems-in-living, 334 Social problem solving (SPS), 333–353 case example, 348–351 clinical guidelines of EC-PST, 342–345 contextual variables in association of depression and, 339–342 defined, 335 directions for future research on, 351–352 and evidence base for PST for depression, 345–348 multidimensional model of, 335–337 and problems-in-living, 334–335 problem solving vs., 333–334 as risk factor in depression, 337–339, 351, 413 Social problem-solving therapy, 342. See also problem solving therapy (PST) Social relations model (SRM), 60–63, 66 Social risk factors, 4, 13, 409, 411 Social stress, 157–158 Social support, 55–75 for adolescents, 85 case examples, 71–73 core assumptions of interventions targeting, 59–65 directions for future research on, 73–75 effectiveness of interventions targeting, 57–59 in marital discord model of depression, 157 and relational regulation in therapy, 65–68 and relationship effects in therapy, 68–71 as risk factor in depression, 56–57, 409, 412 during role transitions, 83 as thought control strategy, 388 Social support theory, 55 Social theories of depression, 3 Society of Clinical Psychology, 366 Sociocentric cultures, 138

Sociodemographic variables, emotion regulation strategies and, 183 Socioeconomic status, 409 Socioemotional functioning, improving, 38 Sociotropy, 134, 138, 209 Sociotropy-Autonomy Scale (SAS), 134 Socratic questioning, 216–217 Soltani, S., 17 Solutions comparing, 336 enacting, 336 generating ideas for, 336 to problems-in-living, 335 Somatic symptoms, dependency and, 137 South America, 407 Spain, 338 “Speaker” skills, 165 Specific memories, 239, 244 Specific vulnerability hypothesis, 285 Spencer, C., 163 Spendelow, J. S., 140 Sperry, S. H., 186 Spring, B., 11 S-REF model. See Self-regulatory executive function model SSRIs (selective serotonin reuptake inhibitors), 115–118 S.S.T.A. (Stop, Slow Down, Think, and Act) process, 344–345 Stable risk factors, 57 Stanton, C. H., 233 STAR*D trial, 35, 412 Starr, L. R., 139 State assessment of emotion dysregulation, 197–198 Static risk factors, 4, 12 Stein, A., 36 Stepped care approach, 94, 174 Stice, E., 85 Stigma tolerance, 287 Stimulus control approaches, 317 Stimulus overload, 343–344 Stimulus–response theories of learning, 310–311 Stop, Slow Down, Think, and Act (S.S.T.A.) process, 344–345 “Stop & Slow Down” Toolkit, 344–345, 350 Strategic optimism, 262 Stress achievement-related, 285 activation of risk factors by, 11–12 chronic interpersonal, 238 conceptualization of, 106–108 early life, 340 in etiology of depression, 108 exacerbation of, by rumination, 307–308 life, 188–189, 209, 410 parenting, 31 presence of, 333

456  Index

social, 157–158 social support as protection from effects of, 56, 57 SPS, depression, and, 340 as universal human experience, 105 Stress and coping social support theory (standard model), 56, 57, 59–65 Stress enhancement, 285–286 Stress exposure, 31–32, 106–108, 284 Stressful life events case example, 123–125 and conceptualization of stress, 106–108 definitional issues with, 106 directions for future research on, 125–127 perceived social support, depression, and, 56, 57 as risk factor for depression, 109, 409 rumination, anxiety/depression, and, 307 and treatment response in depression, 110–112, 115–116 treatments developed for depression in context of, 119–123 Stress generation, in PDSM, 284–285 Stress generation framework of depression, 158 Stress management, 31, 333 Stress mechanism models of perfectionism and depression, 284 Stressors emotion reactivity to, 186 interpersonal, 137, 285, 286, 412 marital, 158 relationship, 163, 412 Stress perpetuation, in PDSM model, 286–287 Stress reactivity, 31–32, 247, 340 Stress reduction, in interventions targeting rumination, 318 Stress response, 30, 107 Structural equation modeling, 15 Struijs, S. Y., 410 Subjective social disconnection, 287, 288 Submissiveness, 143 Substance Abuse and Mental Health Services Administration (SAMHSA), 93 Substance abuse and use, 29, 306 Subsyndromal depression, 85, 339 Suicidal ideation, 258, 388 Suicidality, 137, 337–339 Sun, X., 387 Support-based intervention for reassurance seeking, 146 Support groups, 55, 58–59, 62 Supportiveness, working alliance and, 67 Supportive therapies, 346 Support seeking, 133, 146

Symptom review, IPT-A, 86 Systemic behavioral family therapy (SBFT), 114, 118 Systemic couple therapy for depression, 169 T TADS (treatment of adolescent depression study), 118, 120, 174 Talbot, N. L., 121 Tanner, S. M., 66 Targeted rejection, 109 Task engagement, 365 Task switching errors, 239–240 Taxometric analyses, 159 Teichman, Y., 167–168 Television watching time, 410 Temperament, 30, 32 Temple-Wisconsin Cognitive Vulnerability to Depression Project, 16–17 Testing beliefs, 219 Texas Medication Algorithm, 113 Theory of depressive cognition, 391 Theory of explanatory style, 258–260 Theory of mind deficits, 140 Therapeutic alliance, 283 Therapeutic relationship, 141–142, 290 Therapeutic statements, effectiveness of, 66 Therapist–client relationship, in psycho­ dynamic approaches, 144 Therapist effects, 65–68 Therapist–patient dialogue, in MCT vs. CBT, 390 Thinking, negative beliefs about, 414 Third variables, 16, 18 “Third wave” treatments, 370, 414 Thompson, R. J., 185, 186 Thought control strategies, 388, 396 Thought records, 216, 217, 222 Thought suppression, 360 Threat monitoring, 383 Threats to end relationship, 163, 165 Timing, of intervention, 418 Toddler-parent psychotherapy (TPP), 36, 39, 412 Tomakowsky, J., 260–261 Tomaszewska, W., 111, 115 Topper, M., 321 TORDIA (Treatment of SSRI-Resistant Depression in Adolescents) study, 118, 120 TPP. See Toddler-parent psychotherapy Trait-anxiety, 389 Trait assessment of emotion dysregulation, 197–198 Trait attention to emotion, 185 Trait-depression, 389 Trait perfectionism, 288, 292



Index 457

Transdiagnostic risk factors, 183, 196, 307, 388–389, 417–418 Transference, 144 Transgender individuals, 419 Trauma, childhood, 105 Trauma-focused CBT, 119–121, 123 Trauma-related disorders, 183 Traumatic death, 86 Treatment approach, with dependent individuals, 141–142 Treatment design optimization, for cognitive training, 246–247 Treatment of adolescent depression study (TADS), 118, 120 Treatment of Depression Collaborative Research Program, 174 Treatment of SSRI-Resistant Depression in Adolescents (TORDIA) study, 118, 120 Treatment-refractory residual depression, 320 Treatment-resistant depression, 392, 394–395 Treatment seeking, 133–134, 263, 286–287 Treatment studies, 17–18, 21 Triangle of Adaptation, 290, 296 Triangle of Object Relations, 290, 296 Tricyclic antidepressants, 115, 125 Triggers, rumination, 319, 391 Triple P-Positive Parenting Program, 37, 39, 412 Truijens, F., 408 Turkey, 337

University of British Columbia Treatment of Perfectionism Study (UBC TPS), 291–292 University of British Columbia Treatment of Perfectionism Study II (UBC TPS II), 292–293 Unrealistic optimism, 260–261, 413 U.S. Army, 161, 262 U.S. Department of Veteran Affairs, 164 U.S. Marine Corps, 64 V Valdez, C. R., 37 Validity, of risk measures, 20 Validity testing, 219 Van Doesum, K. T. M., 36 Veenstra, A., 69–70 Venlafaxine, 113, 114, 117 Verbal communication, 95 Verbal fluency, memory bias and, 238 “Vicious spiral” of psychosocial risk factors, 5 Video, relational forecasting via, 70 Video feedback intervention for maternal depression, 38 Virtual reality, MeST with, 243–244 Visual imagery, positive, 265 Visualization, 319, 343, 344 Vittengl, J. R., 112 Vulnerability, risk and, 10–12 Vulnerability factors, reducing, 282 W

U UBC TPS (University of British Columbia Treatment of Perfectionism Study), 291–292 UBC TPS II (University of British Columbia Treatment of Perfectionism Study II), 292–293 Uebelacker, L. A., 164 Uncertainty, 334 Uncontrollability of emotions, 198 of thinking, 384–386 Unemployment, 409 Unidimensional approach to avoidance, 359 Unified Protocol for Transdiagnostic Treatment of Emotional Disorders, 194 Uniformity myth, 415 Unipolar depression CBT for treating, 220 perfectionism as vulnerability factor in, 283 proximal life events and treatment response for individuals with, 111 United States, 27, 160–163, 407 Universal prevention model, 98

Wade, T. D., 57 Wakeling, S., 140 Walker, K. L., 338 Wampold, B. E., 65 Wang, X., 188 Watkins, E., 18, 320–321, 370 WCST (Wisconsin Card Sorting Task), 239 WEIRD (Western, educated, industrialized, rich, democratic) samples, 419 Weisz, J. R., 418 Well-being Best Possible Self task and, 267 dimensions of, 268 dispositional optimism and, 261 hedonic and eudaimonic variables in, 420 hope, self-efficacy, dispositional optimism, and, 258 maternal, 30 parental depression and child’s, 45 partner effects on, 163 relationship distress and, 160 Well-being therapy, 268, 413 Wells, A., 387, 388, 391, 392, 394–395 West, C., 269 Western, educated, industrialized, rich, democratic (WEIRD) samples, 419

458  Index

Whisman, M. A., 159, 160, 162, 164, 173 White study participants, 213, 338 Whitton, S. W., 162 WHO (World Health Organization), 407, 420 Why–How experiment, 318 Wickramaratne, P., 94 Williams, J. M., 123 Williams, L. M., 113, 117 Winter, L., 393 Wisconsin Card Sorting Task (WCST), 239 Within-strategy emotion regulation flexibility, 189 Within-subject longitudinal studies, 162 Wolchik, S. A., 42–43 Women and girls. See also Gender differences; Maternal depression avoidance for, 361, 362 interventions in context of stress exposure with, 121 parental support for, 85 perfectionism and depression for, 288 social problem solving by, 338 Wood, W., 310 Word Sentence Association Paradigm, 243 Working alliance, 65–67 Working Alliance Inventory, 65 Working memory, 234, 239, 244, 308 World Health Organization (WHO), 407, 420 Worry in CAS, 383 as cognitive avoidance, 360

and interpretation biases, 237–238 positive beliefs about, 385, 387–388, 391, 399, 401, 414 RFCBT and, 321 and rumination, 305–306 Wright, T. L., 66 Written summaries of CBCT, 166 X Xie, Y., 286 Y Young, J. F., 98–99 Youth. See also Adolescents; Children directions for future research with, 197 Family Talk for, 40 parental depression and treatment outcomes for, 35 perfectionism and depression for, 288 rumination for, 188 Triple P Program with preadolescent, 39 Z Zhang, A., 347 Zimmerman, T., 58–59 Zlotnick, C., 162 Zubin, J., 11 Zuroff, D. C., 134

ABOUT THE EDITORS

David J. A. Dozois, PhD, is a professor of psychology and psychiatry, and director of the clinical psychology graduate program at the University of Western Ontario. Dr. Dozois is a fellow of the Association for Behavioral and Cognitive Therapies, the Canadian Association of Cognitive and Behavioural Therapies (CACBT), the International Association of Applied Psychology, the Academy of Cognitive Therapy, the Canadian Psycho­logical Association (CPA), and the CPA Section on Clinical Psychology. He is also a former Beck Institute Scholar at the Beck Institute for Cognitive Therapy and Research. Dr. Dozois’s research focuses on cognitive vulnerability to depression and cognitive behavioral theory or therapy. He has published 197 scientific articles, book chapters, and books, and 89 non–peer-reviewed papers, and has presented over 360 research presentations at national and international conferences. He is editor of Cognitive-Behavioral Therapy: General Strategies (2014) and Abnormal Psychology: Perspectives (7th ed., 2023), and the coeditor of the Handbook on the State of the Art in Applied Psychology (2021), Handbook of Cognitive-Behavioral Therapies (4th ed., 2019), Risk Factors in Depression (2008), and The Prevention of Anxiety and Depression: Theory Research and Practice (2004, American Psychological Association). Dr. Dozois received the Distinguished Contributions to Psychology as a Profession award from CPA in 2020. He was president of the CACBT (2020–2021) and twice president of the CPA (2011–2012, 2016–2017). He serves on the board of directors for Mental Health Research Canada and the International Association of Applied Psychology. In addition, he maintains a small private practice. Follow him on Twitter @Dozois_Mood_Lab Keith S. Dobson, PhD, is a professor of clinical psychology at the University of Calgary, where has served in roles such as the director of the clinical psychology  459

460  About the Editors

program, and head of the Department of Psychology. His research has focused on both models and the treatment of depression, particularly using cognitive behavioral therapies. In addition to his research in depression, Dr. Dobson has examined psychological approaches and the integration of evidence-based treatments in primary care. Further, he has written about developments in professional psychology and ethics, and he has been actively involved in organized psychology in Canada, including a term as president of the Canadian Psychological Association. Dr. Dobson is also a principal investigator for the Opening Minds program of the Mental Health Commission of Canada, with a focus on stigma reduction related to mental disorders in the workplace. Dr. Dobson’s research has resulted in over 320 published articles and 80 chapters, 17 books, and conference and workshop presentations in many countries. His recent books include the Handbook of Cognitive-Behavioral Therapies (4th ed., 2019), Law, Standards and Ethics in the Practice of Psychology (4th ed., 2021), and The Stigma of Mental Illness (2021). In recognition of his work, he has received numerous awards, including the Canadian Psychological Association Gold Medal for Lifetime Contributions to Psychology, and fellow status with several organizations, including the Canadian Academy of Health Sciences and the Royal Society of Canada.