Weight Change: Patterns, Risks and Psychosocial Effects : Patterns, Risks and Psychosocial Effects [1 ed.] 9781614709091, 9781614708865

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Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved. Weight Change: Patterns, Risks and Psychosocial Effects : Patterns, Risks and Psychosocial Effects, Nova Science Publishers,

Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved. Weight Change: Patterns, Risks and Psychosocial Effects : Patterns, Risks and Psychosocial Effects, Nova Science

PUBLIC HEALTH IN THE 21ST CENTURY

WEIGHT CHANGE

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PATTERNS, RISKS AND PSYCHOSOCIAL EFFECTS

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Weight Change: Patterns, Risks and Psychosocial Effects : Patterns, Risks and Psychosocial Effects, Nova Science

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Weight Change: Patterns, Risks and Psychosocial Effects : Patterns, Risks and Psychosocial Effects, Nova Science

PUBLIC HEALTH IN THE 21ST CENTURY

WEIGHT CHANGE

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PATTERNS, RISKS AND PSYCHOSOCIAL EFFECTS

CAMILO GOUVEIA and DIEGO MELO EDITORS

Nova Biomedical Books New York

Weight Change: Patterns, Risks and Psychosocial Effects : Patterns, Risks and Psychosocial Effects, Nova Science

Copyright © 2012 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com

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Library of Congress Cataloging-in-Publication Data Weight change : patterns, risks, and psychosocial effects / editors, Camilo Gouveia and Diego Melo. p. ; cm. Includes bibliographical references and index. ISBN  ((%RRN) 1. Body weight--Regulation. 2. Obesity. 3. Weight loss. I. Gouveia, Camilo. II. Melo, Diego. [DNLM: 1. Body Weight Changes. 2. Body Weight--physiology. 3. Obesity--prevention & control. QT 104] QP171.W452 2011 613.7'12--dc23 2011028031

Published by Nova Science Publishers, Inc. † New York

Weight Change: Patterns, Risks and Psychosocial Effects : Patterns, Risks and Psychosocial Effects, Nova Science

Contents

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Preface

vii

Chapter 1

Out-of-Home Eating and Weight Gain Cintia Curioni, Ilana Bezerra and Rosely Sichieri

Chapter 2

Effects of Structured Exercise on NonStructured Physical Activity and Food Intake: Can Compensation Limit Weight Loss? Marie-Ève Riou and Éric Doucet

Chapter 3

Chapter 4

Calories and their Role in Weight Gain/ Loss Renata A.M. Luvizotto, André F. Nascimento, Vania S. Nunes, and Célia R. Nogueira Weight Gain in Kidney Transplant Recipients: Risks, Cardiovascular Outcome and Management Alparslan Ersoy, M.D., Canan Ersoy, M.D. and Abdulmecit Yildiz, M.D.

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29

69

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vi Chapter 5

Chapter 6

Chapter 7

Contents Combined Effect of Exercise Training and Protein Diet on Beating Obesity Naziha El Elj, Gérard Lac, Monia Zaouali, Zouhair Tabka, Abdelaziz Kammoun, Najoua Gharbi, and Saloua El Fezaa Determinants of Successful Body Weight Reduction Makiko Nakade, Naomi Aiba, Akemi Morita, and Shaw Watanabe Obesity Management: Is Weight Loss Always Justified? AR Gallant, A Tremblay and V Drapeau

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Index

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137 173

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Preface

Obesity is a public health and policy problem because of its prevalence, costs, and health effects. In this book, the authors gather topical research on calories and their role in weight loss/gain and nutrition as well as the patterns, risks and psychosocial effects of weight change. Topics discussed include out-of-home eating and weight gain; effects of structured exercise and food take in weight loss and the combined effect of a protein diet and exercise training in beating obesity. Chapter 1- Obesity is a worldwide phenomenon that has reached epidemic proportions in many developed and developing countries. Dietary habits, influenced by modern lifestyles and time scarcity, are identifiable as one of the most important modifiable factors that contribute to weight gain. Among these habits, out-of-home eating has gained increasing attention because the consumption of food away from home had risen, this increasing trend is likely to continue around the world and many factors favors an unhealthy eating pattern out of home. Thus, food prepared away from home has larger portion sizes, significantly high amounts of energy, saturated fat content, sodium and sugar, and are poorer in fiber, vitamins and minerals than food prepared at home. It has been hypothesized that out-of-home eating may lead to a positive energy balance, influencing on weight gain and obesity. This chapter discusses the context associated with the habit of consuming food away from home, evaluating worldwide trends in the frequency of out-of-home eating, and the scientific rationale of the association between out-of-home eating and the risk of weight gain or becoming overweight/obese. Additionally, the authors discuss the main results of published studies examining the association between out-of-home eating and excess weight or weight gain, and also

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Camilo Gouveia and Diego Melo

alternatives within the context of out-of-home eating for preventing weight gain and overweight/obesity. Chapter 2- A dominant factor promoting obesity is the apparent difficulty of matching energy intake (EI) to the seemingly low level of daily energy expenditure (EE). Consequently, physical activity would appear to represent an interesting strategy to facilitate the matching of EI to EE and also possibly an efficient tool to prevent and possibly to treat obesity. However, the impact of structured exercise on energy balance (EB) and body weight and composition is often much less than anticipated. Truth be told, even though there is no doubt that physical activity has been associated with a favourable impact on physiological and psychological well being, it remains a fact that the weight loss following an structured exercise intervention is usually less than 2 to 3 kg of initial body weight. So far, even if more research is needed as far as acute and short term exercise interventions are concerned, longer term studies seem to support a compensation of the exercise EE equivalent to 44%. Furthermore, sex, adiposity and training duration have also been associated to have an important impact on compensation. In an attempt to better understand this compensation, a summary of evidence pertaining to both sides of the EB during an acute and long term structured physical activity will be presented and discussed in this chapter. For the most part, EI does not seem to increase over the next few meals following acute or short term structured physical activity but seems to increase following longer term exercise interventions. In addition, even if discrepancies persist, a reduction of EE from normal daily activities after exercise would also seem to occur. Finally, the contribution of the level of adiposity, the possible differences between women and men, the impact of the intensity of exercise, the initial fitness level, as well as the impact of certain cognitive factors will be described and discussed. Chapter 3- Trends on nutritional changes occurring in this century in different countries around the world is consequence of a high-fat diet, rich sugar diet and refined foods, and low in complex carbohydrates and fiber, also known as the Western Diet. In association with this nutritional change studies show a progressive decline in physical activity of individuals. Together, the increased availability and consumption of highly palatable and energy diets and decreased energy expenditure could explain the growing incidence of obesity worldwide. Chapter 4- Kidney transplantation (KT), which is by far the most frequently carried out transplantation globally, is generally accepted as the best treatment both for quality of life and cost effectiveness in end stage renal disease patients although other renal replacement therapies are present. In spite

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Preface

ix

of considerable progress in immunosuppressive and supportive treatments, a number of factors still interfere with the complete success of KT. Chapter 5- Obesity is emerging as a health epidemic around the world. It is responsible for various health disorders, which underscores the importance of developing effective strategies to counteract overweight. Dieting and exercise training have long been regarded as the conventional methods in the fight against overweight. The possible advantage for weight loss of diet and physical exercise association is not fully clear. The aim of this study was to examine the combined effect of exercise training and high-protein diet on reducing weight gain. Chapter 6- The subjects were 111 middle-aged men and women who participated in a one-year weight loss program by changing their lifestyle in 2006. The subjects were classified into two groups by the amount of body weight loss during the program: 1) moderate or no weight loss (MNWL) (loss of less than 5% body weight) group and 2) successful weight loss (SWL) (loss of more than 5% weight) group, and their eating behaviors, thoughts about losing weight, and the proportions of subjects who had obstacles to weight loss, stress and support at the start of the program were compared. Chapter 7- Obesity is a problem worldwide. It is associated with adverse physical and mental health consequences, including diabetes, cardiovascular disease, depression, low self-esteem, and body dissatisfaction. The current solution is to encourage weight loss in all individuals whose weight exceeds a specific cutoff limit, usually BMI >25 kg/m2 or a specific waist circumference. Overweight cutoffs are justified because health risks increase beyond these thresholds. However, these cutoffs also reinforce perceptions of being outside the norm or larger than one should be. The emotional consequences of this stigmatization are serious, and unfortunately, they motivate individuals to seek weight loss treatment more than a desire to improve their health. Traditional obesity treatments, which focus on weight loss through dietary restriction and exercise, are physically and mentally demanding. In addition, they usually require a behaviour change that is difficult to maintain in the long term. Consequently, most individuals who embark on a weight loss journey gain back most of the weight, if not more, within the next five years. For some individuals, failing to lose weight or regaining lost weight can have more negative health consequences than maintaining a stable, above ―standard‖ weight. Moreover, in some people, traditional treatments reinforce the very psychological anguish they aim to correct. The authors‘ clinical experience of testing the impact of weight loss over time suggests that psychosocial pressure to lose a substantial amount of weight can also result in biological

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Camilo Gouveia and Diego Melo

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vulnerability that hinders the regulation of macronutrient and energy balance. This chapter examines weight-loss barriers and explores the pros and cons of the new versus the traditional paradigm. They also discuss the need to develop individually tailored obesity-related interventions by taking into account individual clinical profiles.

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In: Weight Change: Patterns, Risks, Effects ISBN 978-1-61470-886-5 Editor: Camilo Gouveia, Diego Melo © 2012 Nova Science Publishers, Inc.

Chapter 1

Out-of-Home Eating and Weight Gain Cintia Curioni1, Ilana Bezerra2 and Rosely Sichieri3 Institute of Nutrition – State University of Rio de Janeiro, Brazil Institute of Social Medicine – State University of Rio de Janeiro, Brazil 3 Institute of Social Medicine – State University of Rio de Janeiro, Brazil 1

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Abstract Obesity is a worldwide phenomenon that has reached epidemic proportions in many developed and developing countries. Dietary habits, influenced by modern lifestyles and time scarcity, are identifiable as one of the most important modifiable factors that contribute to weight gain. Among these habits, out-of-home eating has gained increasing attention because the consumption of food away from home had risen, this increasing trend is likely to continue around the world and many factors favors an unhealthy eating pattern out of home. Thus, food prepared away from home has larger portion sizes, significantly high amounts of energy, saturated fat content, sodium and sugar, and are poorer in fiber, vitamins and minerals than food prepared at home. It has been hypothesized that out-of-home eating may lead to a positive energy balance, influencing on weight gain and obesity. This chapter discusses the context associated with the habit of consuming

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Cintia Curioni, Ilana Bezerra and Rosely Sichieri food away from home, evaluating worldwide trends in the frequency of out-of-home eating, and the scientific rationale of the association between out-of-home eating and the risk of weight gain or becoming overweight/obese. Additionally, we discuss the main results of published studies examining the association between out-of-home eating and excess weight or weight gain, and also alternatives within the context of out-of-home eating for preventing weight gain and overweight/obesity.

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Introduction Over the past decades, obesity and overweight have become a global public health crisis in both developed and developing countries. Data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative sample of the US population, shows that the prevalence of obesity in the United States is high, exceeding 30% in most age and sex groups. Considering overweight and obesity combined in adults (body mass index > 25 Kg/m²), the overall prevalence reaches 68% with a higher percentage among men 72.%, compared to women 64.% [1]. Around the world, prevalence of obesity is also increasing. In Europe, according to the World Health Organization, overweight affects between 30% and 80% of adults. The highest prevalence was found in Albania (in Tirana), Bosnia and Herzegovina and the United Kingdom (in Scotland); Turkmenistan and Uzbekistan had the lowest rates [2]. In addition, obesity has become commonplace in many developing countries, for example, in Brazil 50% of the population is overweight [3]. The excessive weight gain is a result of multiple factors, including genetic, metabolic, behavioral and environmental factors. Increasing energy intake and decreasing energy expenditure, leading to a positive energy balance explain the excessive weight gain [4]. However, an availability of food in the US twice the population requirement is the main explanation for the obesity epidemic according to Marion Nestle [5]. Due to changes in population eating habits it has been suggested an increasing trend in energy intake over the past several decades [4]. One common food habit, recently observed, that is shared by many countries is the increasing consumption of food away from home and one indicator of this increase is the percentage of expenses on away-from-home eating [6;7]. In Australia, the expenditures on food away from home raised 6.5% between the 70‘s and 80‘s [8]. In USA, the expenditures on eating out increased from

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Out-of-Home Eating and Weight Gain

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27% in the 60‘s to 40% in 1995, reaching around 46% in 2002 [6; 9]. It is estimated that China presents an annual increase of 8.5% with expenses on eating out [10]. In Brazil, the expenditures on food consumed away from home were 24% in 2002-2003 and 29% in 2008-2009 [11]. In United Kingdom, these expenditures doubled between 1992 and 2004 [12]. Other countries have also experienced high intakes of foods away from home, as Greek, Turkey, Spain, Korea, Malaysia and Egypt [13-18]. Many studies have hypothesized that the consumption of food away from home contributes to an excessive energy intake and thereby might be contributing to overweight and obesity [7; 8; 19]. Calories from food away from home have been replacing those from food prepared and consumed at home. In United States, the contribution of food away from home to energy intake increased from 18% to 32% between 1977/1978 and 1994/1996 [20]. In this chapter, we discuss the context associated with the habit of consuming food away from home. We present the scientific rationale of the association between out-of-home eating and the risk of weight gain, pointing the main results of published studies examining the association between outof-home eating and obesity. Additionally, we discuss some alternatives within the context of out-of-home eating for preventing weight gain and overweight/obesity. For the purpose of this chapter out-of-home (OH) eating, eating out (EO), food away from home (FAFH), and away-from-home eating are considered synonymous and refer to eating or drinking occasions at restaurants, cafeterias, bars, fast food outlets, etc. In literature, there is not a common definition of the out-of-home eating concept. Some studies define out-of-home eating as the consumption of food prepared or purchased away from home independently of the place of consumption, whereas others define out-of-home eating as the consumption of food away from home independently of the place of purchasing. In this chapter, we considered both definitions and EO included foods from fast food services, restaurants or from other premises.

Associated Factors with the Habit of Eating Out of Home Although eating out is not a new habit, it has increased in the last decades due to innumerous factors. It follows the factors that mostly have

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important implications on consumers‘food choices and eating behavior associated with EO. The Growing Number of Women in the Labor Force and Working Out of Home In many societies, female members of the family are responsible for food preparation [18]. Thus, the increasing number of women working outside home decreased the available time to prepare food at home, contributing to out-of-home eating. Households in which the woman is the head of the family have a high probability of expenditures on meals away from home [21]. Moreover, these households have a less acquisition of items that require more time to be prepared, as rice, beans and wheat flour [22]. Many families exchanged the traditional model in which parents had well defined roles according to gender and all the members sit together at the same time to have their meals to a family model where both father and mother work outside the home and the occurrence of family meals declined [23].

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Time Scarcity Time scarcity is considered an important factor that mostly influenced away-from-home food. The amount of time to elaborate a home-prepared meal, which includes shopping, cooking and cleaning up, decreased. In USA, 64% of men and 35% of women between 21 and 64 years old reported no time spend in daily food preparations; 1/3 of American parents reported eat out of home in a regular base, and 1/5 of the meals are eaten in the car [23]. These changes in family‘s behaviors contributed not only with increasing away-from-home food but also with unhealthy food habits among family members. Boutelle and colleagues (2007) studied 902 middle-school and high-school adolescents and their parents and found that purchasing fast food for family meals was associated with several potentially unhealthy habits within home environment, as higher consumption of salty snacks and decreased intake of vegetables serving among adults, and higher levels of salty snacks consumption, as well as energy from total fat and saturated fat among adolescents [24]. In contrast, family dinner frequency was associated with less soft drink consumption and healthier eating among Canadian children [25]. Adolescents also associate family meals with healthy foods and

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identify parents as important influences of their consumption patterns. The presence of parents at the evening meal was associated with lower risk of poor consumption of fruits, vegetables and dairy products [26]. Family income also has influence on the family eating pattern. In Brazil, comparing parents and children food habits, Veiga & Sichieri (2006) found that among higher-income families, the consumption of some foods by adolescents, such as sodas and sweets, was independent of their parents, whereas in the low-income families parents and adolescents eating habits were similar [27].

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Increases in Household Income and Dual-Income Households The increase in eating out is also driven by increases in household incomes and two-income households [18; 28]. The demand for food away from home is usually related to household income and, as a result, higher income is associated with a greater share of food purchased away from home. A 10% increase in income increased the share of food purchased away from home by 3% in Brazil [29]. In USA, a 10% increase in a household per capita income would increase the per capita weekly spending on full-service food by 6.4% and on fast food by 3.2% [28]. In China, 10% rise in income generates an increase in expenditure on FAFH of 17.4% [30].

Changes in Household Composition Household‘s composition has also an influence on eating out of home. Households with a single person or with few members may have higher time and monetary costs for eating at home comparing to larger households that tend to spend less money on food away from home [21; 28; 31]. Eating out represents a convenient meal option for such households. Away-from-home expenditures are typically higher for single-person households and childless households [28]. The presence of children in the households is inversely associated to away-from-home food expenditures [32; 33].

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Urbanization, Industrialization and Globalization

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Urbanization and industrialized processes have raised the offer of preprepared products at food establishments, as full-service restaurants, grocery stores, fast foods, food acquired from vending machines, street food and at establishments where food is not the main purpose service, as gas station, pharmacies, etc [33; 34]. Degrees of urbanization play a role in the likelihood of eating outside home. Rural households expend less money on food away from home than urban households [31; 35]. It should be highlighted the desire to imitate Western culture around the world stimulated by the globalization of the media and advertising. In most developing countries, there has also been a movement towards liberalization of trade and investment which has brought the global supermarket chains onto the scene [36]. Food industry contributed for these fast and growing changes on eating habits. Away-from-home food sector responded in a fast way to consumer‘s demand for convenience and for less time to cook and eat, offering an increasing number of food establishments with convenient products, fast and cheap meals at strategic places with easy accessibility.

Marketing Marketing is a powerful way of reaching consumers. The fast food market has grown faster than other segments of away-from-home foods in United States [37]. Fast food advertising is an important component of fast food marketing. Advertising creates overall awareness and establishes brand equity. Price promotions of specific menu items provide purchase incentives, or create repeat purchases among frequent customers [38]. Data from Rudd Center for Food Policy & Obesity at Yale shows the advertising spending by fast food restaurants in United States, with approximately 200 restaurants spent the amount of U$4.2 billion in advertising, including television, magazines, radio, newspapers, freestanding insert coupons, and outdoor advertising in 2009. Comparing with 2008, there was an increase of 2%. Only twelve restaurants contributed with 76% or U$3.2 billion [39].

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Food Costs Nowadays foods are cheaper than they were in the past. It is seemed that food consumption or expenditure is generally price-sensitive. According to Chou et al. (2004) the development of new technologies led to reductions in food prices, which may have contributed to increase the demand for food away from home. Some studies showed that adult weight is sensitive to restaurant prices and that fast food prices have a negative impact on adolescents‘ weight outcomes [40; 41].

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Consumers’ Characteristics Costumers‘ characteristics, as age, gender, race/ethnic, schooling and employment, may also have an influence on eating out. Age has a negative effect on eating out, mainly when it is related to fast foods restaurants [21]. Men eat out of home more frequently than women and black people tend to be less likely to eat out at table service facilities [21; 31]. Years of education has a positive effect on expenditures on eating out as well as employment status [31; 33]. Households where the head of the family has a high education level spend more money on eating out [28]. Consumer‘s choice of eating out is affected by different factors which can be related to individual or environmental variables. Eating out may occurs as a celebration or social occasion, thus it is usually considered a special moment with an opportunity for indulgence. Nowadays, however, eating out is also related to time scarcity and convenience. People do not want to spend time on cooking or commuting home during their work time is impractical and they have to eat out [35]. The fact is that people may still consider eating out as an opportunity for indulgence even if they eat out more often today than they used to do in the past. Some restaurants‘ attributes, such as quality of food, diversity, food costs, nutritional value of food, service and convenience, also contribute to consumers‘ overeating [42]. School meals and meals offered at workplaces also contribute to the proportion of foods eaten out of home. This is particular true in households where both spouses work full time away from home and where children attend to schools. However, the majority of school meals and meals offered at workplaces are provided free of charge by the public sector or offered under special government programs and should fellow nutrition guidelines.

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Characteristics of Food Consumed Away From Home and Mechanisms Associated with Excessive Weight Gain Many mechanisms have been suggested to explain the pathway between the consumption of food away from home and excessive weight gain. Some characteristics of these foods, as high energy content and low micronutrient content, high energy density, large portion size, high total and saturated fat content, high cholesterol content, excessive amounts of sugar and sodium, and sugar added beverages, have been proposed as casual links between outof-home eating and obesity [9; 20; 43-45]. Comparing to food prepared at home, away-from-home foods are poor in fiber, calcium, iron and vitamin content [20; 44]. It has been found that eating out often result in greater energy intake [4648]. Children and adolescents who ate fast food consumed more calories, sugar-sweetened beverages, less milk, and fewer fruits and vegetables than children who did not. They also consumed more total and saturated fat, more total carbohydrate and added sugars, less dietary fiber, and more energy per gram of solid food [49-51]. Thus, many studies have shown that out-of-home eating contribute to a lower diet quality [8; 52-54]. Among Irish children, the intake of calcium, iron, folate and vitamin C outside home was half of the intake at home [54]. These findings are particularly important since out-ofhome eating is getting more frequent and replacing foods eaten at home, indicating that other nutrition-related diseases can emerge or getting worse, as micronutrient deficiencies, osteoporosis, diabetes, cardiovascular diseases etc. Haines and colleagues (1992) have demonstrated a high risk of nutritional inadequacy in women with approximately 70% of food energy purchased from fast food or other restaurants [52]. Todd et al (2010) studied the impact of meals consumed away from home on energy intake and diet quality. The authors showed that each meal consumed away from home add extra calories to total daily energy and decrease diet quality [55]. In general, foods prepared out of home present higher energy density [56]. High-energy dense foods are rich in fat and have low contents of fiber, leading to an overconsumption of calories [57]. The fiber content induces satiation, consequently, reduce food consumption and the risk of obesity [58]. Portion sizes have been increasing both at home and out-of-home eating and have been pointed as one factor that contributes to increased energy

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intake [59]. Nielsen & Popkin (2003) determined the patterns and trends in food portion sizes according to type of food and place of consumption. The authors found that between 1977 and 1996 food portion sizes increased for salty snacks, desserts, soft drinks, fruit drinks, French fries, hamburgers, cheeseburgers and Mexican food in all locations, with the largest portions consumed at fast foods establishments [60]. Big portions stimulate overconsumption, without caloric compensation in subsequent meals, indicating that people are not aware of an excessive consumption and do not notice body satiation signals [61]. A worsening aspect of this situation is the high availability of high energy-dense foods offered in big portions with low prices [60]. Palatability and variability of food have also been pointed as linkers between out-of-home eating and obesity. Foods prepared out of home are rich in sugar and fat, being more attractive to consumer. Sorensen and colleagues (2003) reviewed the literature related to the palatability influence on appetite and energy intake and found that the highest the palatability of a food the highest is its consumption in a meal [62]. Palatability is also positive related to energy density [62; 63]. It is estimated that the consumption of food is increased when different food items is available [64]. Monotony diets lead to a small amount of food consumed [63; 65]. Moreover, individuals tend to eat higher energy intakes when a variety of highly palatable food is available [66]. Another hypothesis linking out-of-home eating and excess weight is the elevated consumption of soft drinks away from home. In many studies outof-home eating was associated to a higher consumption of soft drinks, identified as the most consumed away-from-home item [31; 67-69]. The mechanisms in which these beverages contribute to weight gain is not well understood; it is suggested that the intake of calories in liquid form confer less satiety, decreasing the mechanisms of caloric intake compensation in comparison to solid foods [70; 71]. Additionally, food environment has been associated to obesity. Some studies described a positive relationship between the density of fast food, non-fast food restaurants or other food facilities, and obesity [72; 73]. These associations have mostly been described among deprived areas where fewer healthy options are available in away-from-home outlets [74]. It has also been suggested that fast food outlets and more advertising over unhealthy alternatives are more prevalent in poorer areas [75]. It is important to note that OH eating may not be a major independent factor contributing to obesity. Behaviors that contribute to an

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overconsumption of energy, such as easy availability of inexpensive foods, high energy-density foods, higher food portion size, and higher consumption of sweetened beverages occur for both at-home and OH consumption [60; 76; 77]. Physical inactive, such increasing hours of television watching and videogame playing and decreasing level of activity required for work and daily living, leads to a decrease in energy expenditure and can also contribute to weight gain [4]. Orfanos et al. (2007) found an inverse association between eating at least 25% of daily energy intake through eating out and physical activity [19].

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Results From Studies: Food Away From Home and Weight Gain Foods and meals prepared out of home are an increasingly important part of the diet from around the world, a trend that has coincided with a dramatic rise in the prevalence of obesity. The question of whether food away from home contributes to overweight and obesity is an important issue to be considered. It has been hypothesized that eating away from home may lead to a positive energy balance and thereby might be contributing to the current obesity epidemic [78]. The following is a general overview of existing scientific literature on the subject. However, few of the existing studies are longitudinal, they have only been performed during the last few years and most of these studies have been conducted in the USA, mainly have focused solely on fast foods. In adults, a positive association between frequency of eating out and BMI or overweight/obesity was described by several authors [79-83]. However, some studies did not find any association between out-of-home eating and BMI or overweight/obesity [8; 46; 84; 85]. Orfanos et al (2007) compared the average out-of-home consumption of foods and beverages, as well as energy intake, among populations from 10 European countries. They did not find association between substantial OH eaters, those who consumed more than one-quarter of their respective total energy out of home, and BMI [19]. Similar results were also observed in a study carried out by Vandevijvere et al (2009) in Belgium [68]. Another study conducted by Naska et al (2011) among the same 10 European countries used a different definition of out-of-home eating, considering any eating or drinking occasion at restaurants, cafeterias, bars and fast food

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outlets. The authors found a positive association between eating at restaurants and BMI in cross-sectional analysis and no associations was observed among women [86]. The effect of food away from home may also vary across demographic groups. Several studies have found that men and women differ significantly in dietary habits [87-89]. Bezerra et al. found that eating out among Brazilian women was a protection for overweight/obesity, whereas among men it was associated with overweight and obesity [89]. Kant & Graubard (2004) studied the weekly frequency of consuming commercially prepared meals in three American national surveys (1987, 1992 and 1999-2000). They did not identify any association of eating commercially prepared meals with BMI, with exception for 1999-2000 survey, in which the frequency of consuming commercially prepared meals was a positive predictor for BMI among women [45]. Cornelisse-Vermaat et al (2007) described the associations among different ethnics groups in The Netherlands and found a negative association between times per month of eating out and BMI only among the Dutch group [90]. Ma et al (2003) investigated the proportion of main meals eaten away from home and found a negative association between breakfast and obesity and no association with lunch and dinner [80]. An ecological study reported by Maddock (2004), showed that states with higher levels of obesity had more quick-service restaurants per person [91]. Other types of studies support these findings. Using 1994 to 1996 data from the U.S. Department of Agriculture (USDA) Continuing Survey of Food Intakes by Individuals (CSFII), Binkley et al (2000) found that the source of food is a significant determinant of body mass index [87]. For females, the correlation was significant for fast food outlets only, but for males, the correlation was significant for restaurants generally as well as fast food outlets specifically. From the same data (CSFII - 1994 to 1996), Bowman and Vinyard (2004) showed that 25% of adults reported eating fast food. The study found that such fast food provided greater than 33% of total calorie intake, and it found a positive association between fast food consumption and overweight status [48]. Other study, with African-Americans, found that eating in fast food restaurants was associated with higher total fat intake, higher saturated fat intake, and lower vegetable intake. Frequent eaters in such establishments were more likely to be younger, never married, obese, and/or physically inactive [92].

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Boutelle et al (2007) evaluate family purchase of fast food for meals and its associations with home food availability, dietary intake, and BMI among adolescents and their parents. The study showed that more frequent fast food purchases was associated with overweight among female parents [24]. In a study that examined whether living or working near fast food restaurants is associated with body weight, Jeffery et al (2006) did not found relationships between proximity to fast food restaurants and obesity [93]. On the other hand, the availability of different types of food retailers around people's homes was associated with obesity among adults in Canada. This, lower the ratio of fast food restaurants and convenience stores to grocery stores and produce vendors near the home, the lower the odds of being obese [73]. Prospective cohort studies are more consistent and most of them found positive association between consumption of food away from home and BMI or weight gain. Duffey et al (2007) compared the associations of restaurant food and fast food consumption with current and 3-y changes in BMI. It was observed that increased consumption of fast food only and of both restaurant food and fast food was positively associated with BMI change [94]. Chung et al (2007) found that a reduction in spending on eating out was related to a reduction on BMI over 10 years [95]. In a prospective Spanish dynamic cohort of 9,182 university graduates (the SUN Study), followed up for an average of 4,4 years, it was showed that eating-out consumers (two times or more per week) had higher average weight gain (129 g/year) and higher risk of gaining 2 kg or more per year than non-eating-out consumers [96]. Ball et al (2002) investigated the prevalence and predictors of weight maintenance in a large sample of young Australian women during 4-y of follow-up. They observed that low consumption of takeaway food was one of the factors associated most clearly with weight maintenance [97]. French et al (2000) reported that fast food use (defined as the number of meals per week eaten from fast food restaurants), which was particularly high among young adult women, was associated with higher body weight and increased weight gain over a 3 y period [47]. Also, in the Coronary Artery Risk Development in Young Adults (CARDIA) study with 3,031 females and males from 18 to 30 years of age who were followed up during 15 years fast food frequency was positively associated with changes in body weight. Specifically, participants who ate fast food more than twice a week at baseline and at the 15-year follow-up gained an extra 4.5 kg of body weight

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compared to those who consumed fast food less than once a week at these time points [98]. Bowman, et al. (2004) using CSFII data from 1994 to 1996 and the Supplemental Children‘s Survey from 1998, found that, for 4- to 19-yearolds, 30% of the sample population consumed fast food on a typical day. Those who ate fast food consumed more calories per gram of food and had poorer diet quality [51]. Similarly, Schmidt et al (2005) observed that consuming fast food was positively associated with intakes of energy, sodium, total fat, and saturated fat among a sample of 2,379 adolescent girls, aged 9 to 19 years that participated in the National Heart, Lung, and Blood Growth and Health Study [99]. Many studies have analyzed eating out among children and adolescents. French et al (2001), in a study of more than 4,700 children between 11 and 18 years, found that boys who ate fast food regularly consumed 800 extra calories per week, and girls consumed an extra 660 calories per week. This could add a weight gain of 10 or more pounds per year [49]. Zoumas-Morse et al (2001) combined data from two populations: 376 children, 7 to 11 years old, and 435 adolescents, 12 to 17 years old. It was found, in both population, that the largest consumption of calories took place in restaurants. Of almost 2,500 calories consumed per day, restaurants contributed 31.3% of the total calories, followed by home at 17.3% of calories. It was also observed that children typically eat almost twice as many calories when they eat a meal at a restaurant (765 calories) compared to an average meal at home (425 calories) [100]. To determine if eating food away from home in childhood is associated with longitudinal change in relative overweight, Thompson et al (2006) investigated 101girls at two points in time: at baseline, between the ages of 8 and 12 y, and at follow-up, between the ages of 11 and 19 y. They found that those who ate quick service food twice a week or more at baseline experienced the highest increase in mean BMI z-score compared with those who ate quick service food once a week or not at all [101]. Although the large amount of data relating eating out and body weight this topic still needs clarification since few studies compared for the same individuals their pattern of eating at home and out of home. Also, definition of out-of-home eating includes many possibilities that should be disaggregated in further analysis.

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Recommendations to Promote Healthy Eating Out This section is based on two important initiatives dealing with out-ofhome eating context: The Keystone Forum on Away-From-Home Foods: Opportunities for Preventing Weight Gain and Obesity, carried out in 2006 in USA [102], and The Healthy Eating Out Project – HECTOR, conducted between 2006 and 2009 in several European countries [103]. The first was designed in the context of preventing overweight and obesity in North America, while the latter aimed at establish a platform for collaboration between scientific communities, consumer associations and catering sector to enhance knowledge on eating out in Europe and to develop and evaluate strategies and concrete measures for promoting healthy out-of-home dietary choices among European consumers. Although it is possible to eat healthy away-from-home [104], incorporating eating out into a healthy plan faces daunting barriers that challenge consumers to accomplish the adoption of a healthful dietary pattern. And while consumers have become increasingly conscientious about nutrition, the obesogenic environment promotes increasingly unhealthy food options, behaviors and lifestyle incompatible with a healthy eating. Also, people tend to prioritize more short-term benefits such as cost and all the work related to move away from ready-to-eat-foods, than long-term consequences. Thus, convenience, price, affordability and satisfaction when eating out affect more consumers` choice than health and nutrition. Moreover, it is difficult to change people‘s day-to-day habits and to maintain the changes in a long term [105]. It is recognized that to achieve and maintain a healthy diet requires more than individual efforts. To successfully observe sustainable healthful changes, all society sectors must be involved in diverse strategies that contribute to prevent undue weight gain. Individuals, families, educators, communities, consumer organizations, physicians, health professionals, policy makers at local, national and international levels, scientists, small and large businesses, including agricultural producers, food manufactures, food industry, food services and food retails, should be engaged in actions and efforts to address excessive weight gain and obesity-related diseases [105; 106]. Consumers should be concerned about foods that contribute to an excessive energy intake and few nutrients and be stimulated to require

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healthy practices at food establishments, schools, hospitals, public places. Also nutritional information of food, foods low in sodium and limited in added sugar, refined grains and fats, and smaller portion sizes should be available in all places where food is delivered including the small businesses, such as street food. Consumers should also be stimulated to increase the demand for fruits and vegetables, to prefer desserts with less sugar and fruitbase, and to reduce the consumption of sugar-sweetened beverages. Away-from-home food establishments should provide consumers with nutritional information of their menu items. Although some studies suggest that providing calorie information for away-from-home foods may have little or no effect on food choices [107-109], consumers may use the information to make more healthful dietary choices, and this may result in better health outcomes [110; 111]. Moreover, a labeling policy could encourage food establishments on reformulation their products to a healthier nutrient content [6]. Restaurants should also provide appropriate portion sizes. Young & Nestle (2007) examined response of fast food chains to health authorities claiming to decrease sizes of menu items and found that they have responded little or not at all to calls to reduce the portion sizes of soda, French fries, and hamburgers [112]. Many efforts need to be done to require fast food and other restaurants to decrease their food portion size in order to help consumers manage their energy intake [112]. Another alternative that can be implemented is to develop food options less energy dense in their menus. This can be implemented with the replacement of high-caloric ingredients for natural condiments with less caloric content and less amount of saturated fat and trans fat, as pepper, lemon, oregano, basil, olive oil, vinegar, mustard, salsa etc. Increased opportunity for customers to adapt their meals with less-calorie dense option is also a good alternative. Caloric density of many food items can be changed keeping palatability of the food [113], for example replacing fat meat with free-fat meat, increasing the consumption of fish, including vegetables in the recipes, and cooking using less caloric techniques. Diversified options of fruits and vegetables in salads and sandwiches should also be available in their menus. For these achievements, food services should provide their employees with education, resources and skill to produce healthier menu options. Another important issue that can be addressed by food industry is its power to influence and shape consumers` choice. Food sector should use all its powerful marketing, creativity and resources to promote healthy eating

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choices increasing marketing that highlights low-calorie foods and encourage small portion sizes with a reduction in marketing those promoting highcalorie foods. Public policies should be incorporated in order to regulate a more health food marketing including eating out-of-home. Health managers, policy makers and public health professionals involved in food and nutrition should emphasize health strategies that assist consumers in choosing healthier foods. Public policies should be developed to improve nutrition literacy and empower and motivate population to prepare and consume healthy foods at home [105]. Comprehensive programs with alternatives to reduce caloric intake, including the choice of low-energy density foods, such as fruit and vegetables, less caloric preparations, non sweetened beverages and decrease the portion size should also be done. Other actions could improve healthy habits likewise to encourage consumers to request nutritional information for menu products; implementation of guidelines in many countries related to healthy away-from-home foods, including the consumption of fruits, vegetables, dairy products, whole grains and foods low in saturated fat and trans fat. Another strategy being considered in literature to control unhealthy food and beverages intake is their price manipulation. Duffey et al (2010) showed that an increase in the price of sodas and pizza is associated to decrease energy intake [114]. Powell & Chaloupka (2009) reviewed nine papers and concluded that pricing interventions might have an impact on weight outcomes, mainly among children, adolescents and those at low socioeconomic levels [115]. Thus, food price polices may be a potential mechanism for improving diet and avoiding excessive weight gain [114-116].

Conclusion Although fast foods better represent the icon of modern lifestyle characterized by time scarcity, eating out of home includes street vendors, restaurants, canteens at school and other public places, establishments that had incorporate a quickly-food service to attend consumers` demand. Thus, it is easy to find prepared or pre-prepared food and equipments, as microwaves, to cook the product in minutes at gas stations, pharmacies, newsstands and others. While a causative link between eating out and obesity remains unclear, the rising in expenditures on food away from home in concomitance with the

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increasing obesity epidemic suggests a positive relationship between eating out and higher caloric intake, promoting a positive energy balance. Moreover, out-of-home eating has become part of modern life and the increasing trend continues. Therefore, strategies in preventing consumers to gain excessive weight as a consequence of food away from home are imperative. It is well recognized that accomplishing this goal requires the involvement of various stakeholders in promoting strategies for a healthier eating out. In addition, it is necessary to develop a task force on improving nutritional quality of food away from home in more countries around the world, including developing countries that are also experiencing a rising in obesity prevalence. Further longitudinal studies are necessary to clarify the relationship between the consumption of food away from home, according to different places and more research is necessary to understand consumer‘s behavior when eating out. We need to understand the reasons to eat out, perception of this habit, perceptions of healthy eating and choices.

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[67] Lachat C, Khanh le NB, Khan NC, Dung NQ, Nguyen do VA, Roberfroid D, et al. Eating out of home in Vietnamese adolescents: socioeconomic factors and dietary associations. Am J Clin Nutr 2009 Dec; 90(6): 1648-55. [68] Vandevijvere S, Lachat C, Kolsteren P, Van Oyen H. Eating out of home in Belgium: current situation and policy implications. Br J Nutr 2009 Sep; 102(6): 921-8. [69] Nago ES, Lachat CK, Huybregts L, Roberfroid D, Dossa RA, Kolsteren PW. Food, energy and macronutrient contribution of out-ofhome foods in school-going adolescents in Cotonou, Benin. Br J Nutr 2010 Jan; 103(2): 281-8. [70] Drewnowski A, Bellisle F. Liquid calories, sugar, and body weight. Am J Clin Nutr 2007 Mar; 85(3): 651-61. [71] Wolf A, Bray GA, Popkin BM. A short history of beverages and how our body treats them. Obes Rev 2008 Mar; 9(2): 151-64. [72] Mujahid MS, Diez Roux AV, Shen M, Gowda D, Sánchez B, Shea S, et al. Relation between neighborhood environments and obesity in the Multi-Ethnic Study of Atherosclerosis. Am J Epidemiol 2008 Jun; 167(11): 1349-57. [73] Spence JC, Cutumisu N, Edwards J, Raine KD, Smoyer-Tomic K. Relation between local food environments and obesity among adults. BMC Public Health 2009 Jun; 9: 192. [74] Lewis LB, Sloane DC, Nascimento LM, Diamant AL, Guinyard JJ, Yancey AK, et al. African Americans' Acess to health Food Options in South Los Angeles Restaurants. Am J Public Health 2005 Apr; 95(4): 668-73. [75] Cummins S, Macintyre S. Food environments and obesity-neighbourhood or nation? Int J Epidem 2006 Feb; 35(1): 100-4. [76] Nielsen SJ, Popkin BM. Changes in beverage intake between 1977 and 2001. Am J Prev Med 2004 Oct; 27(3): 205-10. [77] Monsivais P, Drewnowski A. The rising cost of low-energy-density foods. J Am Diet Assoc 2007 Dec; 107(12): 2071-2076. [78] Kral TVE, Roe LS, Rolls BJ. Combined effects of energy density and portion size on energy intake in women. Am J Clin Nutr 2004 Jun; 79(6): 962–968. [79] McCrory MA, Fuss PJ, Hays NP, Vinken AG, Greenberg AS, Roberts SB. Overeating in America: association between restaurant food consumption and body fatness in healthy adult men and women ages 19 to 80. Obes Res 1999 Nov; 7(6): 564-71.

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[80] Ma Y, Bertone ER, Stanek EJ, Reed GW, Hebert JR, Cohen NL, et al. Association between eating patterns and obesity in a free-living US adult population. Am J Epidemiol 2003 Jul; 158(1): 85-92. [81] Ko GT, Chan JC, Tong SD, Chan AW, Wong PT, Hui SS, et al. Associations between dietary habits and risk factors for cardiovascular diseases in a Hong Kong Chinese working population--the "Better Health for Better Hong Kong" (BHBHK) health promotion campaign. Asia Pac J Clin Nutr 2007; 16(4): 757-65. [82] Ayala GX, Rogers M, Arredondo EM, Campbell NR, Baquero B, Duerksen SC, et al. Away-from-home food intake and risk for obesity: examining the influence of context. Obesity 2008 May; 16(5): 1002-8. [83] Smith KJ, McNaughton SA, Gall SL, Blizzard L, Dwyer T, Venn AJ. Takeaway food consumption and its associations with diet quality and abdominal obesity: a cross-sectional study of young adults. Int J Behav Nutr Phys Act 2009 May; 6: 29. [84] Simmons D, McKenzie A, Eaton S, Cox N, Khan MA, Shaw J, et al. Choice and availability of takeaway and restaurant food is not related to the prevalence of adult obesity in rural communities in Australia. Int J Obes 2005 Jun; 29(6): 703-10. [85] Marin-Guerrero AC, Gutierrez-Fisac JL, Guallar-Castillon P, Banegas JR, Rodriguez-Artalejo F. Eating behaviours and obesity in the adult population of Spain. Br J Nutr 2008 Nov; 100(5): 1142-8. [86] Naska A, Orfanos P, Trichopoulou A, May AM, Overvad K, Jakobsen MU, et al. Eating out, weight and weight gain. A cross-sectional and prospective analysis in the context of the EPIC-PANACEA study. Int J Obes 2011 Mar; 35(3): 416-26. [87] Binkley JK, Eales J, Jekanowski M. The relation between dietary change and rising US obesity. Int J Obes Relat Metab Disord 2000 Aug; 24(8): 1032-9. [88] Kuchler F, Lin BH. The influence of individual choices and attitudes on adiposity. Int J Obes Relat Metab Disord 2002 Jul; 26(7): 1017-22. [89] Bezerra IN, Sichieri R. Eating out of home and obesity: a Brazilian nationwide survey. Public Health Nutr 2009 Nov; 12(11): 2037-43. [90] Cornelisse-Vermaat JR, van den Brink HM. Ethnic differences in lifestyle and overweight in the Netherlands. Obesity 2007 Feb; 15(2): 483-93. [91] Maddock J. The relationship between obesity and the prevalence of fast food restaurants: state-level analysis. Am J Health Promot 2004 NovDec; 19(2): 137-43.

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[92] Satia JA, Galanko JA, Siega-Riz AM. Eating at fast-food restaurants is associated with dietary intake, demographic, psychosocial and behavioural factors among African Americans in North Carolina. Public Health Nutr 2004 Dec; 7(8): 1089-96. [93] Jeffery RW, Baxter J, McGuire M, Linde J. Are fast food restaurants an environmental risk factor for obesity? Int J Behav Nutr Phys Act 2006 Jan; 3: 2. [94] Duffey KJ, Gordon-Larsen P, Jacobs DR, Jr., Williams OD, Popkin BM. Differential associations of fast food and restaurant food consumption with 3-y change in body mass index: the Coronary Artery Risk Development in Young Adults Study. Am J Clin Nutr 2007 Jan; 85(1): 201-8. [95] Chung S, Popkin BM, Domino ME, Stearns SC. Effect of retirement on eating out and weight change: an analysis of gender differences. Obesity 2007 Apr; 15(4): 1053-60. [96] Bes-Rastrollo M, Basterra-Gortari FJ, Sanchez-Villegas A, Marti A, Martinez JA, Martinez-Gonzalez MA. A prospective study of eating away-from-home meals and weight gain in a Mediterranean population: the SUN (Seguimiento Universidad de Navarra) cohort. Public Health Nutr 2010 Sep; 13(9): 1356-63. [97] Ball K, Brown W, Crawford D. Who does not gain weight? Prevalence and predictors of weight maintenance in young women. Int J Obes Relat Metab Disord. 2002 Dec; 26(12): 1570-8. [98] Pereira MA, Kartashov AI, Ebbeling CB, Van Horn L, Slattery ML, Jacobs DR, Jr., et al. Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet 2005 Jan; 365(9453): 36-42. [99] Schmidt M, Affenito SG, Striegel-Moore R, et al. Fast-food intake and diet quality in black and white girls: the National Heart, Lung, and Blood Institute Growth and Health Study. Arch Pediatr Adolesc Med 2005 Jul; 159(7): 626-31. [100] Zoumas-Morse C, Rock CL, Sobo EJ, Neuhouser ML. Children's patterns of macronutrient intake and associations with restaurant and home eating. J Am Diet Assoc 2001 Aug; 101(8): 923-5. [101] Thompson OM, Ballew C, Resnicow K, Must A, Bandini LG, Cyr H, et al. Food purchased away from home as a predictor of change in BMI z-score among girls. Int J Obes 2006; 28: 282-9.

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[102] Center, T. K. The Keystone Forum on Away-From-Home Foods: Opportunities for Preventing Weight Gain and Obesity. The Keystone Center. Washington, DC. 2006 [103] Hector. Healthy eating out. 2009 Available from: http://www.nut.uoa.gr/hector/. [104] You W, Zhang G, Davy BM, Carlson A, Lin BH. Food consumed away from home can be a part of a healthy and affordable diet. J Nutr 2009 Oct; 139(10): 1994-9. [105] USDA. Report of the Dietary Guidelines Advisory Committee on the Dietary Guidelines for Americans, 2010. Washington, DC: US Department of Agriculture: Available from URL: http://www.cnpp.usda.gov/dietaryguidelines.htm 2010. [106] Lachat C, Naska A, Trichopoulou A, Engeset D, Fairgrieve A, Marques HA, et al. Essential actions for caterers to promote healthy eating out among European consumers: results from a participatory stakeholder analysis in the HECTOR project. Public Health Nutr 2011 Feb; 14(2): 193-202. [107] Harnack LJ, French SA, Oakes JM, Story MT, Jeffery RW, Rydell SA. Effects of calorie labeling and value size pricing on fast food meal choices: Results from an experimental trial. Int J Behav Nutr Phys Act 2008 Dec 5; 5:63. [108] Yamamoto JA, Yamamoto JB, Yamamoto BE, Yamamoto LG. Adolescent fast food and restaurant ordering behavior with and without calorie and fat content menu information. J Adol Health 2005 Nov; 37(5): 397-402. [109] Krukowski R, Harvey-Berino J, Kolodinsky J, Narsana R, DeSisto TP. Consumers may not use or understand calorie labeling restaurants. J Am Diet Assoc 2006 Jun, 106(6): 917-20 [110] Burton S, Creyer E, Kees J, Huggins K. Attacking the obesity epidemic: The potential health benefits of providing nutrition information in restaurants. Am J Pub Health 2006 Sep; 96(9): 1669-75 [111] Roberto CA, Larsen PD, Agnew H, Baik J, Brownell KD. Evaluating the impact of menu labeling on food choices and intake. Am J Public Health 2010 Feb; 100(2): 312-8. [112] Young LR, Nestle M. Portion sizes and obesity: responses of fast-food companies. J Public Health Policy 2007 Jul; 28(2): 238-48. [113] Rolls BJ, Roe LS, Meengs JS. Reductions in portion size and energy density of foods are additive and lead to sustained decreases in energy intake over two days. Am J Clin Nutr 2006 Jan; 83(1): 11–7.

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[114] Duffey KJ, Gordon-Larsen P, Shikany JM, Guilkey D, Jacobs DR Jr, Popkin BM. Food price and diet and health outcomes: 20 years of the CARDIA Study. Arch Intern Med 2010 Mar; 170(5): 420-6. [115] Powell LM, Chaloupka FJ. Food prices and obesity: evidence and policy implications for taxes and subsidies. Milbank Q 2009 Mar; 87(1): 229-57. [116] French SA. Pricing effects on food choices. J Nutr 2003 Mar; 133(3): 841S-843S.

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In: Weight Change: Patterns, Risks, Effects ISBN 978-1-61470-886-5 Editor: Camilo Gouveia, Diego Melo © 2012 Nova Science Publishers, Inc.

Chapter 2

Effects of Structured Exercise on Non-Structured Physical Activity and Food Intake: Can Compensation Limit Weight Loss? Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved.

Marie-Ève Riou+ and Éric Doucet* School of Human Kinetics, University of Ottawa, Ontario, Canada +

Alternate Address for correspondence: Marie-Ève Riou Behavioral and Metabolic Research Unit School of Human Kinetics University of Ottawa Ottawa, Ontario Canada, K1N 6N5 Phone: (613) 562-5800 ext. 7361 Fax: (206) 260-6790 Email: [email protected] * Address for correspondence: Éric Doucet Behavioral and Metabolic Research Unit School of Human Kinetics University of Ottawa Ottawa, Ontario Canada, K1N 6N5 Phone: (613) 562-5800 ext. 4271 Fax: (613) 562-5149 Email: [email protected]

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Marie-Ève Riou and Éric Doucet

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Abstract A dominant factor promoting obesity is the apparent difficulty of matching energy intake (EI) to the seemingly low level of daily energy expenditure (EE). Consequently, physical activity would appear to represent an interesting strategy to facilitate the matching of EI to EE and also possibly an efficient tool to prevent and possibly to treat obesity. However, the impact of structured exercise on energy balance (EB) and body weight and composition is often much less than anticipated. Truth be told, even though there is no doubt that physical activity has been associated with a favourable impact on physiological and psychological well being, it remains a fact that the weight loss following an structured exercise intervention is usually less than 2 to 3 kg of initial body weight. So far, even if more research is needed as far as acute and short term exercise interventions are concerned, longer term studies seem to support a compensation of the exercise EE equivalent to 44%. Furthermore, sex, adiposity and training duration have also been associated to have an important impact on compensation. In an attempt to better understand this compensation, a summary of evidence pertaining to both sides of the EB during an acute and long term structured physical activity will be presented and discussed in this chapter. For the most part, EI does not seem to increase over the next few meals following acute or short term structured physical activity but seems to increase following longer term exercise interventions. In addition, even if discrepancies persist, a reduction of EE from normal daily activities after exercise would also seem to occur. Finally, the contribution of the level of adiposity, the possible differences between women and men, the impact of the intensity of exercise, the initial fitness level, as well as the impact of certain cognitive factors will be described and discussed.

Introduction Exercise has been widely investigated and recommended to prevent obesity or to promote weight loss because of its important contribution to energy balance (EB). However, even if exercise has been associated with a favourable impact on physiological and psychological well being [1], the fact remains that exercise induced-weight loss is usually less that 3% of initial body (Figure 1), which does not necessarily support the efficacy of exercise as a means of promoting weight reduction [2-4].

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Effects of Structured Exercise on Non-Structured Activity ...

31

Adapted from Miller et al. 1997) (Miller, Koceja et al. 1997.

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Figure 1. Weight loss during exercise, diet and diet and exercise.

In line with these results, we have recently reported with results from a systematic review (Riou et al., data unpublished), that the mean weight loss following a structured physical activity without planed dietary intervention was ~2.0 kg, when women and men are pooled together. More specifically, in a separate analysis, we reported that men lose more than twice the weight and fat mass with exercise when compared to women. This suggests that compensation from structured exercise may well be greater in women. To further understand the less than expected weight loss following structured physical activity, compensation was investigated. Results from our systematic review revealed an overall compensation of ~44% when including studies done with women, men and with studies that included women and men together (Riou et al., unpublished). More specifically, the compensation was ~57% and 52% when considering women and men, respectively (Riou et al., unpublished). Data also suggested that compensation was higher in exercise studies lasting less than three months and longer than six, but was lower between in studies lasting between 3-6 months in women (76% in studies 6 months) and in men (39% between 3 to 6 months and 54% in studies >6 months) (Riou et al., unpublished). Interestingly, results also revealed that, in women, compensation increased with the level of adiposity (Riou et al., unpublished). Conversely, when both men and women are pooled, compensation seems to decrease with the level of adiposity (Riou et al., unpublished), which seems

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Marie-Ève Riou and Éric Doucet

to suggest an interaction between sex and adiposity. These results tend to indicate that the impact of exercise on body weight loss should be further scrutinised, especially in obese women, due to an apparently larger degree of compensation. In this chapter, we will thus discuss the impact of an acute and long term effect of structured physical activity on energy intake (EI) and non-structured physical activity compensatory response. In general, it is suggested that when energy expenditure (EE) is increased due to an acute or short term physical activity, individuals do not necessarily compensate for the energy deficit induced by exercise over the next few meals. Nevertheless, since it could be hypothesized that this mismatch between food intake and the energy expended during the following day will not indefinitely persist [5], it has been proposed that subjects will gradually increase their food intake to match the energy deficit following a medium and/or long term exercise EE [6], or will slightly decrease their non-structured physical activity likely due to fatigue and other undetermined factors. It should be noted that most studies rarely take sex, adiposity, and training duration into account when it comes to compensation. In order to better understand the compensation that occurs following structured physical activity, this chapter will present a comprehensive overview of the effects of exercise on both sides of the EB. Factors that influence the degree of caloric compensation, such as sex, the intensity of training, fitness level, adiposity, as well as certain cognitive factors will also be reviewed.

1. Acute and Short Term EI Compensatory Response Following Structured Physical Activity Energy Expenditure It has been proposed that structured physical activity EE induces an automatic physiological impact on hunger and food intake [7]. This hypothesis was probably based on the fact that food deprivation creates an energy deficit and significantly increases hunger as well as EI at test meals and increases food cravings during the day [8-10]. However, this notion was subsequently contradicted when Hubert et al. [8] showed that a structured

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Effects of Structured Exercise on Non-Structured Activity ...

33

physical activity EE induced energy deficit, did not modify hunger and EI to the same extent when compared to food restriction [8]. Accordingly, it was proposed that both food deprivation and structured physical activity EE, with the same acute energy deficit, could differently affect hunger [11]. By extension, it is often assumed that exercise should lead to weight loss based on the observation that individuals with relatively large volumes of exercise are generally leaner. However, one aspect that is often overlooked is the fact that lean individuals who are regular exercisers typically maintain a stable body weight, which suggests a compensation of the exercise induced energy deficit of 100%. As such, evidence suggests that when EE is increased due to an acute bout of physical activity, individuals do not necessarily compensate for the energy deprivation over the next few meals. However, when carefully considering men and women as well as their level of fitness (e.g. sedentary vs. active, obese vs. lean and other), it remains that specific conclusions, when considering these confounds, are difficult to reach. An overview of the impact of acute and short term structured physical activity EE on EI and appetite is presented in Table 1. Evidence presented in this table will be further discussed in the following sections. For the purpose of this discussion, acute effects on EI will be considered as the meal that follows exercise, while short term effects on EI will be considered as the food ingested during the same day as the exercise session. In addition, the discussion will focus specifically on the compensation in response to aerobictype exercise since the energy expended following resistance training is typically lower [12].

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Table 1. Overview of the impact of acute and short-term effect of exercise on EI and hunger

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Study Participants Women [15] 9 lean (BMI: 22.14 ± 1.78 kg/m²) and 9 obese (27,72 ± 0.90 kg/m²)

[16]

12 lean (BMI: 22±1 kg/m²) and 12 overweight (BMI: 28±1 kg/m²)

Condition

Age

Intervention

Results

Sedentary

18-35

- Rest (40 min) - Moderate (30 W) (40 min) - Strenuous cycling (90 W) (40 min)

Lean: EI - ↓ after strenuous exercise vs. moderate exercise Hunger - NS

Sedentary

35±8

15 minutes after the condition, a strawberry yogurt test meal was served. - Exercise (walking on a treadmill at 60% VO2peak ) - No-exercise

Obese: EI - NS Hunger - ↑ after the moderate exercise vs. strenuous exercise and rest

EI - ↑ in overweight vs. lean in both exercise and. non-exercise

Ad libitum meal was taken at the cafeteria

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[23]

[26]

13 lean subjects (22.2 ± 2.4 kg/m²).

25 lean unrestrained (21.9 ± 1.6 kg/m²) and restrained (22.6 ± 1.9 kg/m²) subjects.

Moderately active

Regularly exercising

22.2±2,0

22.6 ± 2.3 and 21.7 ± 2.2, respectively

- Control (reading or writing) - Low intensity exercise (40% of the VO2peak and 350 kcal) - High intensity (70% of the VO2peak and 350 kcal) One hour after the exercise a buffet-type meal was served and snacks and diner were served during the afternoon and the evening - Rest and low fat (LF) - Rest and high fat (HF) - Exercise and LF (50 min of exercise at 70% of their VO2max ) - Exercise and HF (50 min of exercise at 70% of their VO2max )

EI (lunchtime) - ↑ after high intensity exercise Hunger - NS Relative EI (lunch time) - ↓ after high and low intensity exercise vs control session EI (day) - NS Relative EI (day) - NS

EI - ↑ with HF vs. LF ↑ in restrained subjects vs. unrestrained during rest NS during exercise between restrained and not restrained Relative EI - ↑ with HF vs. LF ↓ with exercise vs. rest ↓ in restrained subjects vs. non restrained subjects (exercise)

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Table 1. Continued

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[27]

[22]

Twelve lean (22.6 ± 1.9 kg/m²) restrained eaters subjects.

13 lean (BMI = 21.9 ± 1.6 kg/m²), unrestrained females

Regularly exercising

Regular exercisers

21.7 ± 2.2

22.6 ± 2.3

- Rest and low fat (LF) - Rest and high fat (HF) - Exercise and LF (50 min of exercise at 70% of their VO2max ) - Exercise and HF (50 min of exercise at 70% of their VO2max )

- Rest: high fat/low carbohydrate (HF) - Rest: low fat/high carbohydrate (LF) - Exercise (50 min at 70% of the VO2max in cycling or running) HF - Exercise (50 min at 70% of the VO2max max in cycling or running) LF

EI (lunch time) - NS after exercise (↓ 3%) ↑ by 67 % with HF vs. LF Relative EI (lunch time) - ↓ 43% after exercise ↑ by 99 % with HF vs. LF Hunger - NS EI (day) - NS after exercise (↓ 3%) ↑ by 17% with HF vs. LF Relative EI (day) - ↓ 19% after exercise ↑ by 19% with HF vs. LF EI - ↑ by 9% with exercise vs. rest condition (NS) ↑ by 64% with HF (exercise or rest condition) REI - ↓ with exercise vs. rest ↓ with LF vs. HF Hunger - ↓ during exercise ↑ immediately after exercise (NS)

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[8]

11 lean (21.5 ± 1.1 kg/m²) subjects

Regular exercisers

23.2 ± 2.7 y

- Exercise (40 min at 70% VO2max) + high energy breakfast (H) - Exercise (40 min at 70% VO2max) + low energy breakfast (L) - No-exercise + H (50 min) - No-exercise + L (50 min)

EI (lunch) - ↑ with L vs. H NS with exercise vs. no-exercise Hunger (morning + lunch) - ↑ with L vs. H NS with exercise vs. no-exercise Hunger (afternoon) - NS with L vs. H NS with exercise vs. no-exercise

An Ad libitum lunch test meal was served 4 hours after the condition Men [13]

[18]

10 lean (22.8 ± 1.6 kg/m²) and 10 obese (28.5 ± 1.6 kg/m²).

15

Sedentary

25 ± 7 and 25 ± 6 y, respectively

College age

8 weeks + 8 visits: - 4 visits on an ergometer bicycle at 60% W2max (2hrs) - 4 visits for reading and/or studying (2hrs)

EI - ↓ after exercise - NS between lean and obese subjects.

No-exercise control Cycle exercise (35% VO2max ) Cycle exercise (68% VO2max )

EI - NS

Hunger - ↓ after exercise - NS between lean and obese subjects.

Hunger - ↓ after high intensity vs. low intensity exercise and control

Foods were given after 1 h post-exercise

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Table 1. Continued

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[14]

24 lean subjects

Active (> 3 hrs per week)

24.4 ± 5.3 for cycle and 21.4 ± 3.4 for the runner

- Rest high fat/low carbohydrate (HF/LC) - Rest low fat/high carbohydrate (LF/HC) - Exercise (50 min at 70% of the VO2max in cycling or running) high fat/low carbohydrate - Exercise (50 min at 70% of the VO2max in cycling or running) low fat/high carbohydrate

EI (lunch time) - ↑ by 6% with exercise vs rest condition (NS) ↑ with HF/LC vs LF/HC NS between cycling and running REI (lunch time) - ↓ after LF vs HF EI (day) - NS (exercise vs rest) ↑ with HF vs LF Hunger - NS between cycling and running ↓ during and after exercise

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[30]

[19]

9 lean (24.0 kg/m²)

11 lean (23.2 ± 2.3 kg/m²)

Sedentar y to moderate ly active

Moderately active

28.3±6.1 y

24.4 ± 3.3 y

Four experimental conditions: - 60 min of treadmill exercise at 55-60% VO2max with either a low fat (L), high fat (H) or a mixed diet (L+H) - Control session with a mixed diet

- Control - Low-intensity (35% of the VO2max) - High-intensity exercise (75% of the VO2max) with the same energy cost during the exercise.

Energy balance - ↓ 6.4 MJ with exercise + L ↓ 4.5 MJ with exercise + L+H ↑ 0.9 MJ with exercise and H EI - ↑ with H vs L and H+L EE - ↓ in control session vs exercise + L EI - NS after exercise Hunger - NS after exercise Relative EI - ↓ after high intensity exercise vs low intensity exercise and control group

An ad libitum buffet meal was served after the exercise.

[11]

8 lean (22.4 ± 1.8 kg/m²) subjects

Moderat ely active

26 ± 5.2 y

- Exercise (2x50 min at 70% of the VO2max in the same day, 1000kcal) (E1) vs. rest condition (R) - No prescribed exercise for the following 3 days (E2, R1, R2)

EI –NS within 48 hrs Hunger – NS within 48 hrs

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.

Table 1. Continued [75]

16 lean (22.8 ± 3.0 kg/m²) and unrestrained subjects

Regular exerciser s

21.3 ± 12.4 y

After a period of exercising (70% VO2max for 50 min) 3 drink conditions were served to the subjects: - Plain water (W), - Low energy drink (sweeteners with aspartame and acesulfame-K) (L) - High energy drink (sucrose-sweetened drink) (H)

EI (test meal) - ↑ after L vs W and H Hunger - ↓ Total EI - ↓ with W vs L and H

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A test meal was given to the subjects BOTH – Women and Men [29] 13 lean subjects (Women: 19.5 ± 1.7 kg/m² and men: 23.4±1.5 kg/m²)

Athletic activity for 2 h per day, 2 days per week

- Exercise session (2 h, 500 kcal) + meal served at 0, 30, 60 , or 120 min after - Rest condition + meal served after 60 min

EI - ↑ with time vs. rest condition NS between male and female Hunger - ↑ with time vs. rest condition

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Effects of Structured Exercise on Non-Structured Activity ...

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1.1. Sedentary Women and Men There is limited data related to the short term effects of exercise on EI. Overall, Table 1 reveals that EI as well as hunger decrease or remain the same after exercise in sedentary lean men [13-14]. However, when considering sedentary women, some groups have found an increase and some a decrease in EI [15-16]. As such, an overall conclusion including men and women is not achievable with regards to the impact of an acute or short term exercise on EI. However, in men, acute exercise is generally recognised to have no impact on EI [14], while in women, more studies are needed to clarify the nature of their acute and short-term response to exercise.

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1.2. Lean and Obese Women and Men In terms of acute and short term EI following aerobic training only a few studies have compared obese with lean men and women. In fact, when nonobese were compared to obese individuals, it was demonstrated that obese women tended to not compensate following structured physical activity EE when compared than non-obese women [15]. However, it could also be hypothesized that obese subjects will reward themselves by increasing the amount of calorie intake after their workouts and will consequently overcome the exercise induced suppression of EI [16]. Nevertheless, after moderate exercise (2 hours of cycling at 60% of Wmax), it has been shown that obese and lean men reacted in the same way by decreasing their desire to eat and EI [13], which suggests sex differences in the regulation of EI and hunger after an acute or a short term exercise intervention.

1.3. Intensity of the Exercise The intensity of exercise has also been postulated to be an important mediator of the effects exercise on appetite and EI. Studies have indicated that exercise induced anorexia is characterized by a brief suppression of hunger, which is followed by a delay in the onset of eating [10, 15, 17-19]. In this regard, it has been proposed that the increased sympathetic nervous system activity during exercise may be enhanced to overcome a short term energy deficiency, while the reduction of hunger may be related to changes in blood glucose, free fatty acids, ghrelin and insulin [13, 20]. The redistribution

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of the blood flow to muscles and away from the splanchnic circulation may also explain this brief suppression of hunger [21]. However, in women, it has been shown that the feelings of hunger are not suppressed to the same extent as that seen in men [14, 22-23]. Because the duration of the exercise induced anorexia is very brief in men and does not impact food intake to a great extent, it may not in the end lead to sex differences in EI over longer periods. Along the same lines, Pomerleau et al. [23] have shown in young lean women that an acute bout of exercise at low and high intensity was , compensated at 25% and 41%, respectively. In fact, this translated into an increase in EI at both exercise intensities of a given caloric cost when compared to baseline, but also suggested that EI increased to a greater extent after an exercise at a higher intensity. After one day, the low intensity exercise bout was compensated at 41%, while the high intensity exercise bout was compensated at 91%. It is thus reasonable to postulate that high exercise intensity, in young lean women at least, is followed by a greater increase in EI when compared to a bout of lower intensity exercise. In contrast, work done by Tremblay et al. [24] showed that high-intensity intermittent-training in men when compared to endurance training was better at reducing subcutaneous adiposity. The outcome of increasing exercise intensity on EI and ultimately adiposity may potentially differ in men and women. The relative EI (REI) is a different way to express compensation and is calculated by subtracting the EE during the exercise from the EI during the test meal [10]. In the study done by Pomerleau et al. [23], the REI was decreased in low and high intensity groups when compared to the control group (Figure 2).

Pomerleau et al., 2004. Figure 2. Relative EI after the lunch time and either the control session as well as the low and high intensity EE session.

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Conversely, even if REI was decreased after the exercise at lunch time, no significant differences were observed for the daily REI between the three conditions (control, low, and high intensity) (Figure 3).

Pomerleau et al., 2004.

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Figure 3. Daily relative EI after the control session and the low and high intensity EE session.

Finally, this may suggest a different response in regard to high intensity training when compared to low intensity training. However, the fact remains that there simply is not enough data to come to a definitive conclusion on the matter, especially when considering the paucity of data on the influence of adiposity levels and sex differences.

1.4. Cognitive Factors We have recently demonstrated that physical activity may influence the relationship between cognitive dietary factors and BMI [25]. As such, cognitive dietary restraint, dietary disinhibition and susceptibility to hunger, measured with the Three-Factors Eating Questionnaire (TFEQ), should be taken into consideration when determining the impact of exercise on appetite and EI. In fact, it has been shown that unrestrained women ate less in the control condition than restrained individuals, but ate similarly after exercise [26]. Conversely, as described by Lluch (2000), while there was no significant correlation between dietary restraint and EI after a bout of exercise, there was a positive correlation following a control session [26-27].

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Interestingly, restrained eaters seemed to stop eating after a physical activity, not in response to satiety, but rather because they had reached a cognitive setlimit, which is determined by the amount of food eaten [27]. It is also possible that exercise acts as a controller of EI and that restrained individuals may relax their control of food intake without physical activity. As such, ambiguity remains in regards to cognitive factors and deserves further investigation. In addition, it is possible to hypothesize that compensation from increased EI following acute exercise may be explained by the pleasure associated to food (food reward) [28]. Food reward can be divided into the ―strength of motivational response to obtain available food (implicit wanting) and the subjective pleasure it induces (explicit liking and wanting)‖ [28]. In their study conducted on 24 non-dietary restrained females (18-40 years, BMI=22.3±2.9 kg/m²), Finlayson et al reported that when compared to resting for 50 min, exercise (50 min at 70% of their VO2max) induced a large inter-individual variability in regards to EI following the structured acute intervention. As such, the authors divided the participants into compensators and non-compensators. Non-compensators were part of a group of individuals in whom the predicted weight loss was similar or greater to the actual weight loss with an exercise intervention, while compensators were those in whom the actual weigh loss was lower than the predicted weight loss with an exercise intervention. Based on this dichotomisation, significant differences were seen between compensators and non-compensator and the authors suggested that implicit wanting for food after exercise is important to better understand the differences in EI compensation in those two groups. Finally, implicit wanting could thus explain why non-compensators may be less predisposed to overcompensate and lose more weight in response to exercise [28].

1.5. Limitation of Food Intake Measurement To explain the discrepancies between studies in regards to the impact of physical activity on appetite and EI, it is important to consider that EI is one of the most difficult components to measure in the field. In fact, currently available methods are accompanied by limitations than often hinder extrapolation of results to real-life settings. Another aspect which deserves consideration is the time interval between the exercise and the consumption of the foods as well as the macronutrient composition proposed of foods

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presented [23]. For example, some have measured the EI 15 minutes after the exercise [15, 19], while others as much 1 hour later [23]. Given the relatively short-lived anorectic effects of exercise, this factor should be taken into consideration and should also be standardized and interpreted accordingly [29]. The macronutrient composition of the test meal after an exercise session, even if it remains constant within studies, is also important to consider when comparing different studies, since it has been shown that the consumption of a high fat diet could completely overcomes the negative EB induced by exercise [14, 30]. Another important factor that can impact the level of compensation is a poor evaluation of the amount of energy expended [30]. An insufficient cost of exercise that would fail to exert a significant impact on EB could also further complicate the interpretation of the data. As such, studies must propose a sufficient amount of kcal per session and per week. In contrast, it has been demonstrated that in a larger EE study (4.5 MJ/day), when two periods of intense exercise were done on the same day, that there was no impact on EI and hunger on the day of, and the day immediately after exercise [11]. Prolonging the study period to days following exercise enables the investigation of a possible ―delayed compensatory response‖ to physical activity as proposed by Edholm in 1955. However, this finding has never been replicated [10, 31-33] and suggests that there is no subsequent response with a large energy deficits [11]. As such, factors such as timing, macronutrient, standardization of energy cost and intensity, should be considered when comparing studies.

1.6. Summary for the Short and Acute Effects of Exercise on EI Given that it has been shown that when subjects are obligated to become sedentary (i.e. calorimetric chamber), their EI is not systematically decreased [34], it would therefore be interesting to postulate that perturbations in EE do not always contribute to a commensurate modification of appetite and EI. It is however important to highlight that exceptions occur at extremely high rates of EE [35] when, for example, professional athletes know that a mismatch between EI and EE could impair their performance [11]. In this case, EI compensation must be obtain, suggesting that similar adaptations may also be involved in medium and long term EE. Hence, even if more data related to the sex, intensity, adiposity and cognitive behaviours is needed

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evidence suggests a lack of compensation in acute and short term interventions [5, 13, 15, 17-18, 36-38].

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2. Acute and Short Term Non-Structured Physical Activity Compensatory Response to Structured Physical Activity Energy Expenditure Non-structured physical activity is defined as all movement (transportation, work-related movement and/or activity of daily living) that is not related to training or to an exercise session [39-40]. To our knowledge, no studies have specifically investigated the impact of acute or short term physical activity on EE under free-living conditions. However, some have measured non-structured physical activity with radar in a whole-body indirect calorimeter. It has been demonstrated that non-structured physical activity could explain a variability of 100-800 kcal between subjects [41] and that non-structured physical activity was inversely related to body weight in a cross-sectional study [42]. However, it has also been demonstrated that trained and untrained men had the same level of non-structured physical activity following a bout of exercise [43]. Nevertheless, the difficulty to measure this component under free-living conditions could partially explain why this component has not been thoroughly investigated in the past. With newly available technologies, it is now possible to better document how nonstructured physical activity can be impacted by a structured physical activity EE aimed at decreasing body weight. Indeed, some studies have used the multiple sensor approach [44-47]. Accelerometers (e.g. biaxial and triaxial accelerometers) are currently widely investigated due to their small size, their low power consumption and high memory capability [48]. As such, in addition to the impact of an acute bout of exercise on EI, there is also a need to further investigate the effects of such a bout on EE, in order to have a more comprehensive picture of the effects structured physical activity on EB.

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3. Medium and Long Term EI Compensatory Responses to Structured Physical Activity Energy Expenditure In general, even if more research is needed in regard to lean and obese women, evidence suggests a lack of compensation in acute and short term interventions. Nevertheless, even if EI does not seem to acutely increase proportionally to EE, this uncoupling does not seem to continue indefinitely. In cross-sectional studies, when considering physically active individuals, a positive relationship between physical activity and food intake has been observed [49-52]. This may suggest that after a certain period of time, an increase in EI is made to match the high level of EE, and thus to maintain the EB, which in fact, suggests a compensation of 100%. However, when considering the impact of a physical activity intervention on EI the relation is uncertain; some authors have found no association [53-55], while others have found a positive relationship [56-57] or a negative association between the two [58-59]. Overall, the review done by King et al. [11] showed that 19% of the intervention studies reported an increase in EI, 65% showed no change, and 16% showed a decrease lasting no more than two to five days after the onset of exercise. For the purpose of this discussion, medium and long term effects on EI will be considered as a period longer than 2 days. As previously mentioned, the discussion will be more specifically related to aerobic training since the energy expended following resistance training is typically lower and recent results have shown no compensation of PAEE over 8 months [12, 60]. More consistent results are available when it comes to the effects of vigorous prolonged exercise induced EE. In this context, large energy deficits are tolerated for a considerable amount of time since the individuals engaged in these vigorous prolonged exercise are not capable to eat the same amount of calories expend by their EE (i.e. trans-Atlantic swimming [61], Greenland trekking [62-63], or high-altitude climbing [64]). However, even with the high energy deficits induced by exercise under such conditions, it should be noted that there generally is overcompensation, upon completion of these events. Indeed, studies by Tremblay et al., reported that these expeditions (North and South Poles) were associated with weight regain [62-63], which was explained by the fact that following the expedition total daily EE was decreased while ad libitum EI increased, which thus suggests that delayed

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compensation mechanisms are involved during these large exercise-induced energy deficits. As such, based on studies done in a ―controlled‖ environment there is a need to examine the impact of long term physical activity on ad libitum EI and appetite as far as the potential of exercise to induce weight loss is concerned. Table 2 shows an overview of compensation that occurs with medium and long term exercise intervention and the evidence is presented and discussed in the next sections.

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Table 2. Overview of the impact of medium and long term effect of exercise on EI and hunger Study Participants Women [68] 6 lean (21.4 ± 1.0 kg/m²) women

Condition

Age

Intervention

Results

Not specified

23.0 ± 0.6

EI - ↑ in Hex when compared to Nex (compensation of 25-30%) Hunger - NS Appetite - NS

[56]

Sedentary

Mean age of 37

3 conditions in 9 days protocol: - Control (Nex) (0 MJ/day) - Moderate exercise (Mex): 2 X 40 minute bouts of exercise/day (1.9 MJ/day) - High exercise (Hex): 3 X 40 minute bouts of exercise/day (3.4 MJ/day) 62 days with 3 phases (19 days): - Evaluation: 5 days, - No exercise - Mild exercise (110% of sedentary expenditure; 378 ± 63 kcal/day) - Moderate exercise (125% of sedentary exercise; 772 ± 40 kcal/day). 62 days with 3 phases (19 days): - Evaluation: 5 days, - No exercise - Mild exercise (110% of sedentary expenditure; 281 kcal/day) - Moderate exercise (125% of sedentary exercise; 694 kcal/day). 62 days with 3 phases (19 days): - Evaluation: 5 days, - Moderate exercise (125% of sedentary exercise).

[54]

[55]

5 lean women (21.3 kg/m²)

6 obese (mean 92 kg) women

3 obese (mean 100.4 kg) women

Sedentary

Sedentary

Mean age of 42

Mean age of 30

Body Weight - NS EI - ↑ significantly

Weight loss - NS EI - NS

Weight loss - ↓ EI - NS

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Table 2. Continued

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Men [67]

6 lean (23.3 ± 2.4 kg/m²) men not highly restrained

Active

31.0 ± 5.0

[34]

8 lean (23.9 ± 2.2 kg/m²) men

Sedentary to moderately active

29.5 ± 6.0

3 conditions in 9 days protocol: - Control (Nex) (0 MJ/day) - Moderate exercise (Mex) (2 X 40 minute bouts of exercise/day (1.9 MJ/day)) - High exercise (Hex) (3 X 40 minute bouts of exercise/day (3.4 MJ/day)) 4 conditions in 9 days protocol: 1 - high fat (50% of energy from fat) with exercise (3 X 40 minute bouts of exercise (42.8 kJ/kg/day) per day) 2 - high fat (50% of energy from fat) with no exercise 3 - low fat (25% of energy from fat) with exercise (3 X 40 minute bouts of exercise (42.8 kJ/kg/day) per day) 4 - low fat (25% of energy from fat) with no exercise

EI - NS Hunger - NS Appetite - NS

2-5 days: adjustment period to the diet 5 days: exercise or rest 5 days: exercise or rest

Men EI - ↑ (9/10 increased EI) Body Weight - NS

They were training 60 min/day at 68 % of their VO2max during 1 hour.

Women EI - NS (4/10 increased EI) Body Weight - NS

Weight loss - NS EI related to a high fat diet decreased over time. However, a low fat diet increases EI but does not really track EE during these seven days. Hunger - ↑ following low fat and exercise

BOTH – Women and Men [65]

10 lean men and 10 lean women

Active

Young people

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[52]

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[70]

[66]

6 lean men (24.2 ± 2.2 kg/m²) and 6 lean women (22.9 ± 1.6 kg/m²). Highly restrained eaters were excluded.

35 overweight and obese (31.8 ± 4.1 kg/m²) men and women

8 lean men (25.3±5.3) and women (21.9 ± 1.6)

Sedentary to moderately active

Sedentary

Moderately active

Aged 29.7 ± 5.9 (men) and 24.7 ± 5.9 (women)

3 conditions in 16 days protocol: - Control (Nex) (0 MJ/day) -Moderate exercise (Mex) (2 X 40 minute bouts of exercise/day (2.0 MJ/day)) - High exercise (Hex) (3 X 40 minute bouts of exercise/day (4.0 MJ/day))

39.6 ± 11.0 y

Training for 12 week X 5 days/week X 500 kcal/session at 70% of the maximal heart rate Exercise mode: ergometers, stepping machines, rowers, and treadmills

23 ± 1 (male) and 24 ± 3 (women)

16 days of testing: - Control session of 8 days - 4 days of testing (day 1, 3, 5 and 7) during a 8 days period The EE during the training on the ergometer was 2092 kJ + BMR at 90% of the lactate threshold.

Women: EI – NS between condition; no change over time Hunger - NS Body Weight - NS Men: EI - ↑ between condition; regressed over time Hunger - NS Body Weight - NS Compensators EI - ↑ Hunger - ↑ (NS) RMR - ↓ (NS) Non-compensators EI - ↓ Hunger - NS RMR - NS Women: EI - NS Body Weight - ↓ Men: EI - ↑ Body Weight - NS

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Table 2. Continued

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[71]

58 overweight and obese (31.8 ± 4.5 kg/m²) men (19) and women (39)

Not specified

39.6 ± 9.8

[72]

17 obese boys and 21 obese girls (mean BMI of 34.8±5.68 kg/m²)

Not specified

13.9±1.5 7

[57]

12 obese (BMI of 37.6±7.5 kg/m²) and 4 lean (BMI of 21.8±2.1 kg/m²) men and women

Not specified

27±10 (obese) and 22±3 (lean)

Training for 12 week X 5 days/week X 500 kcal/session at 70% of the maximal heart rate Exercise mode: Ergometers, stepping machines, rowers, and treadmills were used.

Training: 6 hours of activities per day for 6 weeks (1h of dance or aerobics, 1h of canoeing or swimming, 2h of student choice session and 2h of netball or rugby) Caloric restriction program (1300-3300 per/day) for of 6 weeks. Training (at least 1000 revolutions) during 3 days of a 6 days protocol. Mean EE in the lean group was 118±32 kcal/day and 100±21 kcal/day in obese individuals. EI measured with a food dispensing machine (6 days).

Responders: EI - NS AUC Hunger - NS Body weight - ↓ Non-responders: EI - ↑ AUC Hunger - ↑ Body Weight - ↓ Body weight - ↓ Hunger - ↑ Fullness - ↓

Obese: EI - ↓ Hunger - ↓ Appetite - ↓ Lean: EI - ↑ Hunger - ↑ Appetite- ↑

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3.1. Women and Men It has been demonstrated in sedentary lean men and women that EI, following five days of exercise training (60 min/day at 68-70% of the VO2max during 1 hour), was increased in men, while it was not the case for women [65]. In physically active subjects, it has also been shown that women, following 4 days of training done over a 2-week period, did not modify their EI, while men increased their EI [66]. As such, based on these studies, it was suggested that men increased their EI in response to repeated exercise bouts while women did not present any significant changes EI. The inspection of results from a series of studies revealed that after 7 days of moderate (2X40min/day; 21.4 kj/kg/day) or high (3X40min/day; 42.8 kj/kg/day) intensity structured exercise, the compensatory response was not noticeable in lean men, while in lean women a EI compensation of 25 to 30% did take place [67-68]. When increasing from seven to 14 days of training, the same compensatory response (30%) was seen in both men and women [52]. Specifically, at moderate intensity the compensation coming from EI was 18% for women and 43% for men while, at high-intensity the compensation was equivalent to 46% in women and to 32% in men. As such, it could be hypothesized that EI increases in response to longer term exercise and this is likely done in an attempt to re-establish EB [11]. Unfortunately, limitations such as the fact that women were not tested during their follicular phase [69], the use of skin fold thickness instead of an objective measure to measure body composition as well as the fact that both men and women were living at the research institute for the duration of the testing warrant that results be interpreted in light of these factors. A more precise body composition assessment should be taken since body composition, in long term study, is actually the best proxy of caloric compensation. In addition, instead of only focussing on the amount of energy expended during physical activity, the intensity of exercise should also be investigated. Nevertheless, following structured physical activity EE, these studies demonstrated that a period of 7 or 14 days were associated with an increased EI (compensation between 25-30%), and suggests that women compensate more than men after an exercise done at high intensity. By extension, it is also important to note that lean individuals who are regular exercisers typically maintain a stable body weight, which suggests a compensation of the exercise induced energy deficit of 100%.

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3.2. Lean and Obese There are results to suggest that differences in exercise compensation may be influenced by adiposity. Indeed, it has been demonstrated that when lean and obese women and men are compared, three days of exercise (1000 revolutions per day: mean EE in the lean group was 118 kcal/day and 100 kcal/day in obese individuals) increases EI in lean by 155 kcal per day but not in obese individuals [57]. In addition, a comparison between studies done in lean individuals and between obese/overweight women showed that lean subjects increased their EI (measured by a diary journal) by 125 % to match the EE (measured with an activities diary) while overweight individuals did not increase their EI [56]. One hypothesis suggests that fat mass is a possible predictor of an increase EI, while other hypotheses suggest that fat mass might serve as an energy buffer, so that the energy deficit induced by exercise might not increase EI as much [6, 21]. In fact, it was suggested that the compensatory mechanism in obese individuals will only occur when fat reserves will be totally depleted [54, 56]. In addition, by using more subjects and with objective methods, King et al. were able to show that there was no indication of compensation after 12 weeks of training designed to expend 25000 kcal (5 sessions per week and 500 kcal per session) based on the result obtained in 35 overweight and obese individuals. In their study, the mean group weight loss was 3.7 kg and suggested a large variability when each participant was further scrutinized (14.7 kg to + 1.7 kg). As previously described, the authors divided the participants into compensators and non-compensators. Non-compensators (37% body fat) were those in whom the predicted weight loss was similar or superior to the actual weight loss while compensators (33% body fat) were those in whom the actual weigh loss was lower than the predicted weight loss. Findings revealed that compensators presented an increase in EI, a greater hunger, and a small but non-significant decrease in resting EE when compared to non-compensators [70]. Even if this study was well designed to measure differences in compensation between men and women, this data was not provided. In addition, even if basal metabolic rate and non-structured physical activity were reported with indirect calorimetry and accelerometry, total daily EE was not measured and should thus be investigated to determine directly and accurately the impact of physical activity on both sides of the EB equation. King et al. also demonstrated an increase drive to eat as well as an increase in satiety following a standardized breakfast in 58 overweight and

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obese sedentary women following a 12-week exercise regimen [71]. As well, following a 6-week camp based intervention (mean weight loss of 8.4 kg) that included a caloric restriction and a structured physical activity component, an increase in hunger and a decrease in fullness were also reported [72]. In summary, the sum of these studies suggests that adiposity as well as sex may have an impact on EI in response to structured physical activity. In addition, it may also be important to closely inspect individual data to clearly delineate the effects of exercise on compensation. In fact, the effectiveness of exercise to induce weight loss is highly variable and suggests large interindividual variability with regards to the possible compensatory mechanisms.

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3.3. Cognitive Factors Behavioural factors, such as psychological and cognitive factors, are also important to consider when investigating the relationship between exercise, appetite, and EB. As such, following a training intervention, it is possible that people might relax their cognitive dietary restraint since they think that the energy cost of exercise allows them to indulge in unrestricted eating [8, 25, 73]. Nevertheless, it has been shown in lean women that exercise can exert a positive impact on women with high disinhibition scores, suggesting a decreased motivation to eat and an increased preference for a low fat diet [74]. In addition, physical activity may improve the satiety signalling systems suggesting that individuals would be able to better discriminate an energy rich and a non-energy rich beverage after an acute bout of physical activity [75]. It is also possible that exercise could modify macronutrient preferences, food choices, and the hedonic value of foods and could thus favour a better control of feeding during a long term physical activity [6]. For example, Lluch et al. demonstrated that exercise increased the palatability, pleasantness, and tastiness of food [26-27]. Finally, it has been suggested that emotions are largely involved in the regulation of EI [76], and it has been proposed by Lluch and colleagues that, if dietary compensation does occur, this may be due to cognitive factors rather than to a direct physiological linkage between EE and intake [27, 77].

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3.4. Summary for the Medium and Long Effects of Structured Physical Activity Energy Expenditure on Energy Intake

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There is some evidence showing that men and women do not compensate to the same extent as far as EI is concerned in response to structured physical activity [65, 67-68]. Obese and lean individuals also seem to respond differently [55-57]. Additionally, it remains that psychological factors could impact EI, more so than physiological determinants, which thus could make the interpretation of the data even more challenging. Nevertheless, following a medium and long structured physical activity, it is suggested that EI is increased to match the EE and thus suggest an important compensation. Indeed, even if results from our systematic review revealed a compensation of ~57% and 52% when considering women and men respectively (Riou et al., unpublished), it is also important to note that these results are higher than the EI compensation observed in medium and long term structured physical activity (~30%). This suggested that EE and more specifically non-structured physical activity could be decreased following a structured physical activity. As such, the fact remains that both side of the EB should be considered and this especially during a period long enough to appropriately capture the compensatory response.

4. Medium and Long Term NonStructured Physical Activity Compensatory Response to Structured Physical Activity Energy Expenditure As previously described, non-structured physical activity is defined as all movement (transportation, work-related movement and/or activity of daily living) that is not related to structured physical activity EE [39-40]. While little evidence is available following an acute and/or short term intervention, many have suggested that when structured physical activity fails to induce weight loss, it may be related to the fact that individuals adopt a sedentary lifestyle and thus reduce their total daily EE. However, others purport that structured physical activity, in an opposite way, increases non-structured physical activity due to an increased ability to perform daily activities [78].

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As such, the effect of exercise on non-structured physical activity deserves more attention.

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4.1. Structured Physical Activity Energy Expenditure and Decreased Non-Structured Physical Activity One of the first studies to address this issue was performed in elderly individuals. Results from this study revealed, with measures of doubly labelled water, that non-structured physical activity was decreased by 0.97 MJ/day (231 kcal/day) in response to exercise training, which thus presents a reduction of non-structured physical activity of 40% [79]. Similarly, a moderate intensity walking program designed to expend 1500 kcal/week during two months was shown to favour a significant decrease in nonstructured physical activity 22% (175 kcal/d) in a group of obese individuals [39]. This decrease was explained by a significant increase in sleeping and by a decrease in the time spent performing light physical activity without any change in time spent in sedentary and moderate activity. In addition to doubly labelled water, accelerometers provide the advantage of being able to capture information related to patterns of physical activity during the day. A study performed with tri-axial accelerometers showed that 12 weeks of training decreased non-training physical activity in elderly subjects [80]. The role of non-structured physical activity has also been investigated by Levine et al., in an attempt to determine differences in posture allocation between lean and obese individuals [81]. Results showed that obese individuals were seated 164 min/day more than were lean individuals and that lean individuals were upright for 152 min/day longer than obese individuals [81]. This method adds to the literature by objectively determining posture allocation time instead of only providing the energy expended. As such, this method could help to identify areas of intervention that should be targeted in order to increase the resolution of structured exercise in weight management.

4.2. Structured Physical Activity Energy Expenditure and Increased Non-Structured Physical Activity In contrast to results presented in the previous section, some have shown that non-structured physical activity was not reduced during the day [82-83].

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It should be noted that physical activity was measured with physical activity diary or questionnaire in these studies. Black et al., demonstrated that obese boys increased their non-structured physical activity by 1.3MJ/day measured with heart rate monitor following a cycling program of 4 weeks (5 sessions/weeks) at 50-60% of the VO2max [84]. Eight days of training in young lean women and men showed similar results when training at 48-59% of the VO2max [66]. These results are also in line with those of Stubbs et al., who reported that the energy expended by non-structured physical activity in subjects expending either 0 MJ/day, 2 MJ/day or 3.5 MJ/day from exercise over a 7 days of training was not significantly different [67-68] but decreased during the course of each treatment period (0.3 – 0.6 MJ/d) [67-68]. The method used to estimate non-structured physical activity, the FLEX HR (based on the mean higher heart rate obtained during rest and the lower HR during sitting and standing condition [85]), nevertheless was associated with considerable limitations and results should be interpreted accordingly. One of the limiting factors related to this methodology is that HR may vary substantially throughout the day and that these variations are not necessarily related to physical activity (e.g. stress). With measures obtained with triaxial accelerometry, Van Etten showed no significant difference in regards to non-structured physical activity in sedentary men (23-41 years old) following an 18-week weight training program [86]. Similarly, it has been shown that men and women (aged between 28-41 years old) who were preparing to run a half marathon increased their non-structured physical activity after 20 weeks of training [87] and also after 20 additional weeks of training [88]. Additionally, after 8 months of exercise designed to expend 59-96 kJ/kg, middle aged sedentary obese and overweight men and women, did not modify their non-structured physical activity measured with triaxial accelerometry [78]. Finally, 4 months of aerobic training in overweight individual resulted in a non-compensation of non-structured physical activity [60]. In summary, these studies suggest that non-structured physical activity was not affected by structured physical activity, when this component is measured with accelerometry. In order to explain the discrepancies with the previous section, it should be noted that the selection of the population is an important factor since healthy and obese young individuals have been shown to present no decrease or even an increase in their non-structured physical activity EE in response to structured physical activity. On the other hand, elderly subjects have been shown to present a decrease in their non-structured physical activity in response to structured physical activity.

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4.3. Summary for the Medium and Long Effects of Structured Physical Activity Energy Expenditure on EE As previously mentioned, an important compensation from EI follows medium and long structured physical activity (~30%). However, since these results are slightly different from our systematic review (~57% and 52% when considering women and men respectively) (Riou et al., unpublished), this suggests that EE and more specifically non-structured physical activity could also be decreased following structured physical activity. This highlights the need for studies that consider the inclusion of measures that could clearly delineate the extent of the contribution of both EI and EE to the compensation that occurs in response to structured exercise.

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Conclusion Based on the literature reviewed, it would seem that weight loss in response to structured physical activity may well be impeded by compensation. More specifically, following acute and short term interventions, a lack of EI compensation has been observed while non structured physical activity has not been yet thoroughly investigated in the free-living state. In contrast, following medium and long term structured physical activity interventions; it would appear that EI is increased to match the EE, which thus suggest a more important compensation. Indeed, even if results from our systematic review revealed a compensation of ~57% and 52% when considering women and men respectively (Riou et al., unpublished), it is also important to note that these results are higher than the EI compensation usually observed in medium and long term structured physical activity (~30%). This observation suggests that EE, and more specifically non-structured physical activity EE, could be decreased following a structured physical activity. The fact does remain that both sides of the EB should be considered during long enough periods in order to obtain a clearer portrait of the compensatory response to structured exercise. And finally, it should be pointed out that sex, age, adiposity, intensity of training and psychological factors should also be considered when interpreting data as they have been shown to affect the compensatory response to structured physical activity.

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In: Weight Change: Patterns, Risks, Effects ISBN 978-1-61470-886-5 Editor: Camilo Gouveia, Diego Melo © 2012 Nova Science Publishers, Inc.

Chapter 3

Calories and their Role in Weight Gain/Loss Renata A.M. Luvizotto, André F. Nascimento, Vania S. Nunes, and Célia R. Nogueira Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved.

Department of Clinical Medicine, Botucatu School of Medicine, University of São Paulo State, Brazil

Abstract Trends on nutritional changes occurring in this century in different countries around the world is consequence of a high-fat diet, rich sugar diet and refined foods, and low in complex carbohydrates and fiber, also known as the Western Diet. In association with this nutritional change studies show a progressive decline in physical activity of individuals. Together, the increased availability and consumption of highly palatable and energy diets and decreased energy expenditure could explain the growing incidence of obesity worldwide. Importantly, the increase in the number of obese people has been seen as a public health problem, since obesity is considered an important risk factor for the development of comorbidities such as type 2 diabetes mellitus, dyslipidemia, cardiovascular disease, among others, which are involved with the declining quality of life and increased human morbidity and mortality. For this reason, the development of

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R.A.M. Luvizotto, A.F. Nascimento, V.S. Nunes, et al. effective strategies that work in prevention and treatment of excess body weight has been an important challenge facing humanity. Low-calorie diets play a central role in reducing body fat in obese subjects. However, the adaptation to a calorie-restricted diet is characterized by metabolic, endocrine, and immunologic changes. In humans, the weight loss induced by lower food intake was associated with lower risk factors for cardiovascular disease, decreased incidence of type 2 diabetes mellitus and increased life quality. In addition, calorie restriction may be considered a safe method of weight loss, as it reduces fat mass without altering muscle mass. This review aims to discuss the influence of calories on the weight gain / loss, as well as what is currently known about diets composition on body weight.

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Introduction Both excess and restriction of calories have great impact upon adipose tissue. Adipose tissue is no longer considered just a passive depot of stored triglycerides. It is currently recognized as an endocrine organ with multiple functions (Rondinone, 2006; Kershaw & Flier, 2004). It actively participates in the body energy regulation, mainly through a network of endocrine (paracrine and autocrine) signals, which allows the adipocyte to play a metabolic role in other tissues. Fat cells produce various biologically active substances of different physiological functions. These substances, called adipocytokines or adipokines, can influence both the function and structural integrity of other tissues. Not only that, but such substances may control the intake, thermogenesis, immune system, reproductive system, thyroid hormones and neuroendocrine function (Tilg & Moschen, 2006; Ahima et al., 2006-a). Some examples of these adipokines are the tumor necrosis factor- (TNF-), acetylation stimulating protein (ASP), interleukin-6 (IL-6), leptin, resistin and adiponectin (Kershaw & Fier, 2004; Ahima et al., 2006-a; Ahima, 2006-b). Thus, the adipose tissue is a versatile organ, which changes according to the body needs, for example, it displays hypertrophy when there is an excessive calorie intake (Bell-Anderson & Bryson, 2004). The increase in adipose tissue, leading to obesity, is a major risk factor that leads to the development of both chronic and disabling diseases (WHO, 2011). Adults maintain a constant body weight owing to the complex neural mechanisms, hormones and chemicals that keep the balance between energy intake and energy loss within precisely regulated limits and genetic factors

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contribute to individual differences (Bouchard et al., 1993; Proserpi et al., 1997). Abnormalities in these mechanisms, many not fully understood, result in excessive weight fluctuations. The most common of them are overweighting and obesity (Ravussin & Swinburn, 1992, Larson et al., 1995).

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Excess of Calorie Intake: Obesity The increased availability of both high-energy and palatable diets in combination with the significant changes in the population eating habits, as well as the decrease in energy consumption are responsible for the worldwide obesity epidemic (Astrup et al., 1994). Obesity is defined as an excessive accumulation of body fat that can deteriorate the health and it is diagnosed through the body mass index (BMI), obtained by the ratio between weight and squared height. By definition, according to his/her BMI, one may be considered underweight (20 years, women 55%).7 In a population-based, national survey in Turkey consisting of over 10,000 participants, the prevalence of CKD in Turkey is 15.7%.8 CV risk factors were significantly more prevalent in CKD patients. The prevalence rates of dyslipidaemia (83.4% vs. 75.8%), hypertension (HT) (56.3% vs 31%), metabolic syndrome (46% vs. 29.8%), obesity (29.2% vs. 20%) and diabetes mellitus (DM) (26.6% vs. 10.1%) were significantly higher in subjects with CKD compared with subjects without CKD. Sedentary lifestyles, cultural and behavioral factors are the most commonly utilized explanations for the blooming epidemic of obesity. In one of our previous studies in which we compared obesity prevalances in three district municipalities of our city Bursa with different socioeconomic status. We found that the participants living in the district municipality with the highest socioeconomical status and level of education had the lowest BMI and body fat percentage [9]. In this study, sedentary life style and dyslipidemia in men, being unemployed, having lower level of education and HT in women and familial obesity in both gender were found to be related to increased obesity risk [9]. In another study, conducted by our group consisting of 1209 Turkish women, obesity and overweight has been found to be associated with sedentary lifestyles, cultural and behavioral factors. Lack of employment outside home and being traditionally unaccustomed to sporting activities were found to be additional factors for the development of obesity for Turkish women [10]. Obesity has major adverse effects on health. The Framingham study revealed that obesity is associated with an increased mortality, mostly due to CVD [11]. Obesity has a clear relationship with insulin resistance, HT, dyslipidemia and type 2 DM, and also an independent risk factor for CVD [12, 13]. The triad of atherogenic process atherosclerosis, inflammation, and thrombosis is modulated by insulin resistance and adipokines and increased exposure of hepatocytes to free fatty acids by inadequate adipocyte fatty acid oxidation that related with visceral obesity promoted dyslipidemia [14].

Weight Gain after Kidney Transplantation Malnutrition is common in patients on maintenance dialysis. Nutritional status of patients usually improves after KT. There is a tendency to an increase in recipient body weight and BMI after KT. Obesity is also

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Weight Gain in Kidney Transplant Recipients

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extremely common in the post-transplant period. The average weight gain after successful KT is about 10% in the first year, ranging from 0% to 20%.2 The prevalence of obesity increased from 19% at the time of transplantation to 36% 1 year after KT and mean body weight increased by 10.9% during the first year and by 15.3% over 5 years [15]. A few studies have examined the associations of post-transplant weight change with survival in this population. In Australian and New Zealand Dialysis and Transplant Registry data between 1991 and 2004 consisting of 3,899 RTRs, it has showed that significant post-transplant weight gain was associated with poorer transplant outcomes [16]. Majority of RTRs can be classified as obese [17]. Despite the fact that sedentary lifestyles, cultural and behavioral factors are major causes of obesity in the normal population, the reason for weight gain in RTRs are diverse and can be related with clearance of uremia, feeling of wellbeing, immunosuppressive agents, disabilities like avascular femur necrosis, unrestriction of dietary habits, ethnicity, age at the time of transplantation, sex, pre-existing obesity and dialysis modality. Increased transplantation among older type 2 diabetics, most of whom are obese, is another important factor in recent years. Similar to general population, overweight and obesity in RTRs can predispose to insulin resistance, DM and HT. Among RTRs, the presence of obesity appears to be associated with an increased number of adverse CV events. Increased body weight does not always mean obesity. Some lean but very muscular individuals may be overweight without having increased adiposity. For that reason parameters other than body weight are necessary to define obesity effectively and practically. There are numerous methods of assessing overweight and obesity other than BMI. Some techniques are applicable at primary care facilities, such as measurements of weight, abdominal sagittal diameter, waist (WaC) and hip circumferences (HC) and calculation of waist to hip ratio (WHR). Increasing central adiposity called abdominal obesity is defined as WaC ≥102 cm in men and ≥88 cm in women, WHR ≥0.9 in men and ≥0.85 in women. Central obesity is a strong predictor of mortality after adjustment for total-body fat, and WaC correlates highly with abdominal fat. Koster et al. [18] showed that persons with a normal BMI but a large WaC had a higher mortality risk in the general population. In a single center observational study consisting of over 1000 RTRs, WaC (>103 cm in males, >93 cm in females) but not elevated BMI was associated with higher mortality rate. The impact of WaC on mortality in RTRs was reported possibly due to visceral adiposity [19]. In dialysis population, it was reported

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Alparslan Ersoy, Canan Ersoy and Abdulmecit Yildiz

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that WaC is a direct, strong predictor of death and CVD after adjustment for BMI and other risk factors [20]. WaC and WHR appear to be better indicators of CVD than BMI. An international group of experts gathered by the World Health Organization formally recommends measurement of WaC and WHR as valid instruments to estimate the health burden posed by the obesity epidemic and these measurements are incorporated in the Adult Treatment Panel III guidelines. These considerations may extend to the CKD population because recent studies documented that WaC is strongly related to visceral fat in these patients [21-23]. Recently, Postorino et al. [24] have showed that dyslipidemia related CV complications in hemodialysis patients are strongly associated with WaC. To our knowledge, there is no follow-up study measuring WaC and WHR and relating these metrics to clinical outcomes in patients with KT. In our unpublished cohort study consisting of 30 RTRs, WHR, WaC, HC and percent body fat were evaluated after KT. Anthropometric measurements were performed before transplantation and at 1st, 3rd, 6th, 12th months after transplantation. All patients were treated with prednisolone, cyclosporine (n: 14) or tacrolimus (n: 16), and mycophenolic acid. The results are shown in the Table 1. Table 1. The follow-up of anthropometric measurements of recipients after kidney transplantation

Weight (kg) BMI (kg/m2) Body fat (%) WaC (cm) HC (cm) WHR

Pre-Tx

1.month

3.month

6.month

12.month

64.0 ± 12.2 22.9 ± 4.2 22.3 ± 9.4 83.7 ± 10.4 89.0 ± 10.3 0.94 ± 0.05

63.9 ± 10.8 22.7 ± 3.4 21.3 ± 10.3 85.7 ± 8.5 91.8 ± 10.1 0.93 ± 0.04

65.5 ± 11.1 23.4 ± 3.4 23.3 ± 8.3 87.3 ± 8.4 93.2 ± 10.1 0.93 ± 0.05

67.9 ±12.6 24.2 ± 3.7 23.8 ± 9.1 89.1 ± 9.7 95.3 ± 9.6 0.93 ± 0.05

68.2 ± 14.0 24.3 ± 4.6 23.7 ± 15.5 89.4 ± 10.6 97.0 ± 10.6 0.92 ± 0.04

P value