The Role of Diet Type, Dieting Strategies, Bmi, And

LOSING WEIGHT AND EATING DISORDER RISK: THE ROLE OF DIET TYPE, DIETING

STRATEGIES, BMI, AND PSYCHOLOGICAL FACTORS IN ACHIEVING

WEIGHT LOSS AND INCREASING EATING DISORDER RISK

OLESYA VLADIMIROVNA MIKHEEVA

A dissertation submitted in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

WASHINGTON STATE UNIVERSITY Department of Psychology

JULY 2019

To the Faculty of Washington State University:

The members of the Committee appointed to examine the dissertation of OLESYA

VLADIMIROVNA MIKHEEVA find it satisfactory and recommend that it be accepted.

Sarah Tragesser, Ph.D., Chair

Paul Kwon, Ph.D.

Paul Strand, Ph.D.

ACKNOWLEDGMENT

I would like to thank Sarah Tragesser for her support and education throughout my graduate training and for her guidance in creating this project. I also extend my thanks to Eric Desmarais for his assistance as a statistics consultant and his support throughout this project. Finally, I thank Jacob Mussman for his constant support throughout this process.

iii

LOSING WEIGHT AND EATING DISORDER RISK: THE ROLE OF DIET TYPE, DIETING

STRATEGIES, BMI, AND PSYCHOLOGICAL FACTORS IN ACHIEVING

WEIGHT LOSS AND INCREASING EATING DISORDER RISK

Abstract

by Olesya Vladimirovna Mikheeva, Ph.D. Washington State University July 2019

Chair: Sarah Tragesser

Although research has investigated various weight loss strategies among those with obesity, and the link between a desire to lose weight and eating disorders is well known, research focused on understanding how dieting attempts increase eating disorder risk is almost completely lacking.

The present study was the first to examine these constructs simultaneously to determine how psychological factors, dieting strategies, type of diet, and BMI contribute to weight loss and/or eating disorder risk. College students (n=918) answered a series of online questionnaires assessing dieting habits, psychological traits, and disordered eating. Results indicated that type of diet and dieting strategy was only a risk factor for some individuals, depending on other psychological and physical risk factors. Results point to the importance of dieting motives, dieting strategies, and psychological factors that combine with type of diet and BMI to put individuals at eating disorder risk. Findings also indicate which type of diet or dieting strategies can help individuals reach their weight loss goals without creating risk for eating disorders.

Furthermore, results demonstrate which factors only contribute to eating disorder risk, only promote or hinder weight loss, or impact both eating disorder risk and weight loss. The present study highlights psychological factors that can be used in interventions when attempting to treat or prevent eating disorders, according to one’s current weight category.

TABLE OF CONTENTS Page

ACKNOWLEDGMENT ...... iii

ABSTRACT ...... iv

LIST OF TABLES ...... ix

CHAPTERS

CHAPTER ONE: INTRODUCTION ...... 1

Obesity and Eating Disorders ...... 1

Diets ...... 3

Diet Plans ...... 4

Pre-packaged meal diets ...... 4

Balanced restriction diets ...... 5

Self-monitoring diets ...... 6

Vegetarian diets ...... 7

Low carbohydrate and low fat diets ...... 8

Meal replacement diets ...... 9

Very-low-calorie diets ...... 11

Cycling diets ...... 13

Comparing the effectiveness of diet programs ...... 13

Diets and Risk for Eating Disorders ...... 14

Low Risk ...... 15

Moderate Risk ...... 16

High Risk ...... 18

Summary of Diets and Eating Disorder Risk ...... 20

Dieting Strategies ...... 20

Dieting Strategies and Weight Loss ...... 20

Dieting Strategies and Eating Disorder Risk ...... 23

Psychological Factors ...... 24

Psychological Factors and Weight Loss ...... 24

Psychological Factors and Eating Disorder Risk...... 27

The Present Study ...... 29

CHAPTER TWO: METHODOLOGY ...... 32

Participants ...... 32

Measures ...... 32

Demographics Information ...... 32

Weight Loss ...... 33

Diets ...... 33

Dieting Strategies ...... 33

Eating Behavior ...... 34

Eating Disorder Risk ...... 34

Motivation for Dieting ...... 34

Dichotomous Thinking...... 35

Negative Urgency ...... 35

Coping ...... 35

CHAPTER THREE: RESULTS ...... 37

Analyses ...... 37

Preliminary Analyses ...... 37

Eating Disorder Risk ...... 38

Weight Loss ...... 39

CHAPTER FOUR: DISCUSSION...... 41

Eating Disorder Risk ...... 41

Diet Type ...... 41

BMI ...... 43

Psychological Risk Factors ...... 44

Weight Loss ...... 45

Diet Type ...... 45

BMI ...... 46

Dieting Strategies and Psychological Factors ...... 46

Eating Disorder Risk and Weight Loss ...... 48

Implications ...... 49

Implications for Eating Disorder Risk and Obesity ...... 49

Implications for Eating Disorder Risk for Those in the Normal BMI Range ...... 53

Implications for Weight Loss in General ...... 54

Limitations and Future Directions ...... 55

Classification of Diets ...... 55

Lack of Psychological Factors Predicting Weight Loss ...... 56

Need to Identify Other Factors Associated with Both Eating Disorder Risk and

Weight Loss……………………………………………….……………………....58

Methodological Considerations ...... 59

Use of College Student Sample ...... 60

vii

Gender Differences ...... 61

Directionality of Effects ...... 63

Conclusions ...... 68

REFERENCES ...... 70

viii

LIST OF TABLES

Page

Table 1: List of diets classified by level of risk ...... 86

Table 2: Descriptive statistics for measured variables ...... 88

Table 3: Frequencies of diets tried ...... 89

Table 4: Pearson correlations between predictor and outcome variables ...... 91

Table 5: Linear Regression Analyses Depicting Significant Relationships between Predictor and Outcome Variables, Controlling for Age and Gender…………………………………………...92

Table 6: One-way ANOVA Results for Between Group Differences on Eating Disorder Risk...92

Table 7: One-way ANOVA Results for Between Group Differences on Weight Loss………….92

Table 8: Mean Eating Disorder Risk and Weight Loss across Diet Groups……………………..93

Table 9: Mean Eating Disorder Risk and Weight Loss across BMI Range Groups……………..93

Table 10: ANCOVA Results Including Significant Predictors and Interaction Terms Predicting Eating Disorder Risk……………………………………………………………….…………….93

Table 11: ANCOVA Results Including Significant Predictors and Interaction Terms Predicting Weight Loss……….……………………………………………………………….…………….93

Table 12: Linear Regression Analyses Demonstrating Significant Interaction Effects between Dieting Strategies and Psychological Variables…..……………………………….…………….94

Table 13: List of Significant Predictors for Outcome Variables…………………..…………….94

CHAPTER ONE: INTRODUCTION

Over the past several decades, obesity has become an increasingly researched area of interest in both medical and psychological literature. Due to the continuously growing rate of obesity in both children and adults, combined with the multitude of associated illnesses and diseases, obesity research is at the forefront of the medical field. It is estimated that approximately 1 in 3 Americans are obese (Nicklas, Huskey, Davis, & Wee, 2012) and 1 in 3

American youth are overweight or at risk for overweight (Eddy, Tanofsky-Kraff, Thompson-

Brenner, Herzog, Brown, & Ludwig, 2007). This poses a significant health problem for the

American population. The National Institute of Health (2012) has identified an abundance of medical problems that are associated with obesity, including coronary heart disease, high blood pressure, stroke, type 2 diabetes, cancer, reproductive problems, and many others. As a result, researchers have aimed to identify interventions for obesity to inform medical practice and treatment recommendations. However, these interventions traditionally consist of weight loss or weight management strategies and do not consider how these dieting efforts might increase risk for eating disorders.

Obesity and Eating Disorders

Although obesity and eating disorders traditionally reflect two separate fields of research, these two problems are strongly related. In fact, Day, Ternouth, and Collier (2009) proposed that eating disorders and obesity share specific susceptibility factors and argued for an integration of research and treatment for these two concerns. For example, college students with overweight or obesity are significantly more likely to report disordered eating symptoms, such as binge eating and fasting (Kass et al., 2016). Similarly, prevalence of disordered eating in college students is highest among those who are overweight or obese (Kass et al., 2016). This suggests an important

link between overweight/obesity and eating disorders. Additionally, most eating disorder patients report a history of being overweight or obese (Sim, Lebow, & Billings, 2013), and dieting has been implicated as a risk factor that leads to eating disorders (Howard & Porzelius, 1999; Stice,

Marti, & Durant, 2011). It may be that overweight or obese individuals first initiate dieting behaviors with the goal of losing weight, which escalate into eating disorder symptoms and may eventually meet clinical criteria for an eating disorder. Therefore, although there is a known association between overweight/obesity and an increased risk for eating disorders, the underlying mechanisms for this association are unclear (Eddy et al., 2007). This is particularly problematic because among individuals with overweight or obesity, identifying eating disorder symptoms likely takes longer and is more challenging. Friends and family are likely to congratulate these individuals for achieving weight loss and ignore the particular means used to achieve weight loss. This leads to a longer duration of eating disorder symptoms and thus, a poorer prognosis

(Lebow, Sim, & Kransdorf, 2015). In fact, Eddy and colleagues (2007) reported that 1 in 10 youth seeking treatment for being overweight meet diagnostic criteria for an eating disorder and

1 in 3 of these youth report binge eating symptoms. Therefore, the link between obesity and eating disorders is evident, but the nature of this relationship is unclear.

Despite the clear association between obesity and eating disorders, these two topics are researched separately. This is concerning for a number of reasons. First, this does not address the factors underlying the relationship between overweight/obesity and eating disorders. Second, the directionality of the effect remains unknown. Third, from an applied perspective, separating the two fields of research may create even more risk. This may even contribute to a continued increase of eating disorder pathology in the population. For example, medical professionals might suggest a certain diet without accounting for eating disorder risk. Although the patient

might experience weight loss, they might also increase their risk for eating disorders or develop symptoms of an eating disorder. Therefore, it is important to not only further investigate the relationship between obesity and eating disorder risk, but it is necessary to identify and evaluate the particular dieting strategies and how they may contribute to disordered eating.

Furthermore, there appears to be an overall lack of focus on individuals within the obese

BMI range, which is particularly concerning, given their significant level of risk. While it is clear that those in the underweight range with symptoms of anorexia are at risk for eating disorders, it is less clear and seemingly overlooked that individuals in the obese BMI range may be equally at risk. In fact, others might completely disregard their level of risk because they want individuals with obesity to lose weight for medical reasons. However, their dieting efforts may lead to eating disorder symptoms that may be ignored, may worsen over time, and may even be reinforced by others. In the first part of this paper I will first focus on discussing specific diet programs and categorize them based on theoretical understanding into higher or lower risk diet types for eating disorders. Secondly, I will review the dieting strategies individuals may utilize when attempting weight loss. Third, I will discuss how these diets and dieting strategies may be relevant to eating disorder risk. Finally, I will present a study to investigate the relationship between dieting and eating disorder risk, with a focus on how diet types, dieting strategies, and psychological risk factors may impact weight loss and eating disorder risk.

Diets

When individuals pursue weight loss, they might try one of the hundreds of available diet programs. The popularity of the weight loss market is evidenced by the multibillion dollar dieting industry, with estimates approximating a 64 billion dollar revenue in 2014 (Kell, 2015).

Walk through any bookstore or checkout aisle at a grocery store and you are guaranteed to see

numerous books or magazines detailing the newest popular diet plans. Slim Fast, for example, has been around for over 4 decades and its net worth is still estimated at 2.3 billion dollars

(Forbes, 2018). Although some individuals might try using certain dieting strategies (e.g., calorie counting) without a specific plan, it is likely that many prefer a structured program. Furthermore, it may be easier for doctors to recommend a specific program (e.g., Weight Watchers, SlimFast) rather than suggesting a multitude of strategies.

Many diet programs have been proposed as solutions for weight loss. However, diets vary in their effectiveness for weight loss as well as the amount of supportive empirical evidence. More importantly, I suggest that these programs differ in how they might contribute to eating disorder risk. Table 1 includes a list of various diets, which are organized by categories that reflect shared features of the diets as well as their conceptualized level of risk for eating disorders. Of note, although this list includes many diets, it is not a comprehensive list of all available diet programs. Rather, these programs have been chosen as exemplars to represent the types of diets, and their characteristics, frequently used in each proposed risk category. This paper will first describe these different diet programs and review any existing evidence that supports the effectiveness of these programs. Then I will group these diets based on my theoretical assertion of their level of risk for eating disorders and discuss why different diets pose a greater or lower risk for eating disorders.

Diet Plans

Pre-packaged meal diets. A range of diets come in the form of prepackaged meals, where individuals do not need to make choices or prepare food, but rather choose from a variety of provided choices. Jenny Craig and Nutrisystem are two popular diet programs that provide reduced calorie pre-packaged meals to achieve weight loss. Similar to other diet programs, the

evidence is limited, but a few studies have been able to demonstrate their effectiveness for weight loss (e.g., Finley, Barlow, Greenway, Rock, Rolls, & Blair, 2007; Foster et al., 2009). For example, Foster and colleagues (2009) evaluated the effectiveness of Nutrisystem for patients with type 2 diabetes and found that Nutrisystem was superior to a support and education control condition in producing weight loss, improved glycemic control, and reduced cardiovascular risk.

Finley et al. (2007) found that individuals who adhered to Jenny Craig for 40-52 weeks lost an average 12% of their initial body weight. However, the retention rate for 1 year on the Jenny

Craig program was only 6.6% of participants. Although 73% of participants followed the program for 4 weeks, those individuals only lost 1.1% of their initial body weight. Similar to other programs, Finley and colleagues (2007) suggest that persistence of adhering to a diet program is essential to achieving weight loss. Thus, retention rates need to be addressed when evaluating the utility of a diet program. In other words, although participants on pre-packaged meal plans may have the potential to lose 12% of their initial body weight, this is unlikely for most, as most do not adhere to the program long enough to see these results. Therefore, although these outcomes are achieved in a study, they likely do not reflect reasonable expectations for the majority of individuals.

Balanced restriction diets. Balanced restriction diets can be characterized as programs that rely on various changes in one’s diet or dieting habits, without requiring extreme restriction.

There is an immense lack of research for most diets included under this category. However, one of these programs – the Mediterranean diet – was originally developed for cardiovascular health and has gained recent popularity for weight loss. Thus, researchers have investigated its effectiveness. For example, Paoli, Bianoc, Grimaldi, Lodi, and Bosco (2013) found that a

Mediterranean-based diet resulted in successful long-term weight loss, as well as overall

improved physical health for the majority of participants. They also emphasized that adherence to the Mediterranean diet was high. Furthermore, Mancini, Filion, Atallah, and Eisenberg (2016) conducted a systematic review of the Mediterranean diet and concluded that overall, individuals on this diet would achieve similar weight loss to other popular diet programs and would also reduce their cardiovascular risk after 12 months. These findings suggest that the Mediterranean diet is an equally effective diet program compared to other programs. However, the importance of adherence cannot be understated. If the Mediterranean diet produces a higher compliance rate, then it would be a better recommendation compared to other diet programs (e.g., Jenny

Craig, very-low-calorie diets) that have high dropout rates.

Self-monitoring diets. Diets such as Weight Watchers and the Zone diet are primarily grounded in self-monitoring strategies. On these diets, individuals can consume a large variety of foods and do not need to restrict specific foods or food groups. Instead, they rely on tracking food intake (e.g., calories/points, macronutrients) to produce weight loss. The majority of research on self-monitoring diets has been conducted with Weight Watchers, and overall, suggests that participants who adhere to the Weight Watchers program will experience significant weight loss (e.g., Heshika et al., 2000; Heshika et al., 2003; Johnston, Rost, Miller-

Kovach, Moreno, & Foreyt, 2013). Specifically, Heshika and colleagues (2000) demonstrated that Weight Watchers participants achieved a significant decrease in weight compared to those in a self-help program after 6 months (Heshika et al., 2000; Johnston et al., 2013) and after 2 years

(Heshika et al., 2003). Additionally, Mitchell, Ellison, Hill, and Tsai (2013) investigated the effects of implementing a Weight Watchers program for recipients of Medicaid in Tennessee.

They found that approximately 20% of participants experienced a 5% weight loss or greater of their initial body weight, which reflects a significant change in body weight and is considered

successful weight loss. Mitchell et al. (2013) also emphasized the importance of adherence, explaining that those who attended more program meetings experienced more weight loss. They hypothesized that perhaps those who attended more meetings benefited from added social support or increased motivation. They also suggested that perhaps attendance at meetings reflected the participant’s commitment to the program (Mitchell et al., 2013). Overall, the evidence suggests that self-monitoring diets may produce successful weight loss, but adherence and engagement in the program are equally important.

Vegetarian diets. Vegetarian diets are characterized by the exclusion of meat and food containing animal by-products. These programs have been widely implemented for various reasons (e.g., health, beliefs). More recently, they have also been proposed as helpful for weight loss. Vegetarian-based diets can be further broken down into subcategories (see Table 1). For example, vegan diets restrict any food produced by animals (e.g., dairy, honey), whereas pescetarian diets allow fish. A recent review by Huang, Huang, Hu, and Chavarro (2015) evaluated the effectiveness of vegetarian diets for weight loss and concluded that those who adhered to a vegetarian diet achieved a significantly greater weight loss than those assigned to non-vegetarian diets. They also identified that adhering to a vegan diet resulted in greater weight loss than a lacto-ovo-vegetarian diet (i.e. one that includes eggs and dairy). Moreover, Huang et al. (2015) found that those who restricted calories while on a vegetarian diet achieved greater weight loss compared to those who did not restrict calories on a vegetarian diet. In addition to weight loss, vegan diets have also been demonstrated to be effective for sustaining weight loss at

1 and 2-year follow-up (Turner, McGrievy, Barnard, & Scialli, 2007). Thus, the evidence suggests that adhering to a vegetarian diet can be effective for not only weight loss, but also weight loss maintenance, , particularly when combined with caloric restriction.

Low carbohydrate and low fat diets. Low carbohydrate diets, such as Atkins and South

Beach, are based on the assumption that limiting one’s intake of carbohydrates will lead to weight loss. Astrup, Meinert Larsen, and Harper (2004) conducted a systematic review of low- carbohydrate diet programs and reported that low carbohydrate diets do produce significant weight loss, but they questioned the mechanisms that lead to weight loss. Specifically, they found that, it is not the limitation of carbohydrates in one’s diet that leads to weight loss, but rather the length of time adhering to the diet as well as caloric restriction. The authors’ conclusion reflects an important discussion among diet researchers. That is, although diet programs may produce significant weight loss, perhaps it is more important to identify the underlying mechanisms contributing to weight loss (e.g., self-monitoring strategies such as calorie counting, adherence). Focusing on the latter would allow researchers to then investigate how to increase use of or adherence to these strategies. However, it is also necessary to continue gathering evidence regarding the effectiveness of specific diet programs because it may be more practical for health practitioners to recommend specific diet programs as opposed to general strategies without a structured plan. Therefore, it is both necessary for researchers to investigate the mechanisms that contribute to successful weight loss, as well as the effectiveness of specific programs.

Some studies have compared the effectiveness of low carbohydrates diets and low fat diets and found that participants in both programs are able to achieve significant reductions in body weight and BMI (Krebs, Gao, Gralla, Collins, & Johnson, 2010; Meckling, O’Sullivan, &

Saari, 2004). However, they noted some differences in outcome. For instance, Krebs et al. (2010) reported that those in the low carbohydrate group achieved greater weight loss compared to those in the low fat diet group. However, Meckling et al. (2004) argued that both programs lead to

equal reduction of body weight and fat. Meckling and colleagues (2004) noted differences, such that those in the low fat diet program preserved more lean body mass, whereas those in the low carbohydrate group significantly reduced their insulin levels. Therefore, the evidence suggests that both programs are helpful in achieving significant weight loss, but that the health benefits may vary and should be tailored to the needs of individuals. Furthermore, there is a dearth of research on how low carbohydrate diets may create higher cravings, and thus in some cases increase risk for binge eating. Therefore, it is equally essential to not only continue to examine the effectiveness of these programs, but investigate how they may confer risk for eating disorders.

Meal replacement diets. Meal replacement diets are those where products are provided to consume in place of meals. Overall, the research indicates that liquid meal replacement programs are safe and effective at producing substantial weight loss, specifically among those with obesity, and that these may particularly benefit those who are unable or unwilling to pursue surgical options (e.g., Hemmingson, Johansson, Eriksson, Sundstrom, Neovius, & Marcus, 2012;

Saris, 2001; Tsai & Wadden, 2006). Coleman, Kiel, Mitola, Langford, Davis, and Arterburn

(2015) investigated the effectiveness of Medifast – a meal replacement product – and concluded that those who used Medifast experienced significant weight loss, while preserving lean mass and decreasing cardiometabolic risk.

However, weight loss maintenance following these programs appears to be challenging

(Ames et al., 2014, Berkowitz et al., 2011). For example, long-term investigations concluded that individuals typically maintain a weight loss of approximately 5-6% at 4-5 year follow-up

(Anderson, Konz, Frederich, & Wood, 2001; Tsai & Wadden, 2006). In addition, Berkowitz et al. (2011) compared the effects of a meal replacement program to a more conventional diet

program among teens with obesity and found that teens in the meal replacement program initially achieved significantly more weight loss. However, continuing to use these products did not help with maintaining weight loss. Ames and colleagues (2014) examined methods of improving weight loss maintenance following liquid meal replacement programs and found that using strategies such as self-weighing, step counting, and continuing to use meal replacement products improved weight loss maintenance. Brindal, Hendrie, Taylor, Freyne, and Noakes (2016) created and assessed the efficacy of a 24-week partial meal replacement program with app support and found that adherence to this program yielded significant weight loss and overall improvements in health. However, despite the achieved results, the retention rate was only 57% at 24 weeks. This reiterates the idea that many individuals have difficulty adhering to diet programs for sustained periods of time, which decreases the effectiveness of these programs for most.

It is also important to note that meal replacement diet programs are often considered very-low-calorie diets – diets that are limited to 800kcal/day (Tsai & Wadden, 2006). Therefore, it is difficult to differentiate between the effects of the products versus the effect of severe caloric restriction. For example, it would be interesting to investigate whether meal replacement programs that do not include severe caloric restriction result in comparable weight loss. This would address the question of whether it is the products themselves contributing to weight loss or perhaps simply a decrease in calories. Furthermore, while there is considerable research on meal replacement diets and weight loss, there is a complete dearth of evidence on how this may create risk for eating disorders. This is an important question to investigate given the restrictive nature of meal replacement diets. These diets require not only significant caloric restriction, but also restriction of typical foods the individual would consume. Additionally, these diets seem

difficult to sustain long-term, which means more problems with maintaining weight loss. This could create a cycle of trying this diet plan, achieving weight loss, regaining lost weight, and trying it again without ever achieving long-term results. The individual may then pursue even more restrictive means when pursuing weight loss, giving up on weight loss, or conversely, improving their adherence and following a meal replacement diet long-term. All of these could result in disordered eating attitudes and behaviors with or without achieving weight loss.

Very-low-calorie diets. As mentioned previously, very-low-calorie diets are typically defined as those that restrict caloric intake to 800kcal/day (Tsai & Wadden, 2006). Overall, the evidence suggests that adhering to a very-low-calorie diet for a course of 3-4 months results in an average weight loss of 15-25% of one’s initial body weight (Anderson et al., 2001; Astrup &

Rossner, 2000; Saris, 2001). However, the long term effects of very-low-calorie diets are equal to that of low-calorie diets (which restrict calories to 1200-1500kcal/day for women and 1500-

1800kcal/day for men) because very-low-calorie diets result in greater weight regain (National

Heart Lung and Blood Institute, 1998). On the other hand, some suggest that very-low-calorie diets increase the likelihood of greater weight loss maintenance due to the higher initial weight loss (e.g., Anderson et al., 2001, Astrup & Rossner, 2000). Thus, Tsai and Wadden (2006) conducted a meta-analysis and concluded that overall, patients who adhere to very-low-calorie diets are unlikely to be successful at maintaining weight losses of 15-25% of initial body weight.

They suggest multiple factors that may contribute to difficulties with maintaining significant weight loss, including fatigue or monotony associated with adhering to strict dietary regimens and potential physiological variables (e.g., hormonal changes related to appetite regulation). In addition to difficulties with weight loss maintenance, those who adhere to very-low-calorie diets might increase their risk for eating disorders. Individual factors may differentiate those at

greater risk, and these factors might be physiological, as mentioned by Tsai and Wadden (2006), or psychological For example, hormonal imbalances could result in a combination of greater difficulty regulating mood and difficulty with appetite suppression, which would increase risk for binge eating. Researchers have found in animal models that hormonal responses to stress increase the likelihood of binge eating in rats (Calvez & Timofeeva, 2016). Furthermore, Ahn &

Phillips (2012) found that a restrict-binge cycle lead to decreased satiety in rats and continuous weight gain. Thus, binge eating may also exacerbate hormonal imbalances, leading to continued dysregulation of one’s ability to appropriately identify and respond to hunger cues, perpetuating a cycle of lack of satiety, binge eating, and weight gain. Finally, psychological factors, which will be discussed later in this paper, might differentiate those who pursue very-low-calorie diets and are at higher risk for eating disorders.

Questions remain regarding the effectiveness of long-term successful weight loss with very-low-calorie diets. Although very-low-calorie diets may result in successful weight loss for some, Maggard and colleagues (2005) suggest that, bariatric surgery may be the only effective method for, not only successful and substantial weight loss, but also weight loss maintenance of

20% or more of one’s initial body weight. However, this poses a significant barrier to obesity treatment for those who cannot afford, or do not desire, surgery. Additionally, this may be problematic for those who do not qualify for bariatric surgery for various reasons, including those who are not approved due to psychological reasons (e.g., disordered eating). For example, patients with obesity, who struggle with binge eating, are unlikely to be identified as good candidates for bariatric surgery due to the serious risks and complications that result if one were to engage in binge eating following surgery. This highlights a significant problem in regards to available and effective treatment for individuals with obesity. Furthermore, with the lack of

knowledge regarding the association between eating disorder risk and very-low-calorie diets, those with a desire and need to lose weight desperately lack productive options that would lead to successful weight loss without increasing eating disorder risk.

Cycling diets. These diets are characterized by a form of severe restriction for varying time intervals. However, the evidence is lacking to support their effectiveness for weight loss.

Intermittent fasting is classified under this diet category and involves manipulating when one consumes food and incorporates periods of fasting (i.e. periods of time when food or calorie consumption is prohibited; Tinsley & La Bounty, 2015). The evidence suggests that intermittent fasting can be successful in producing weight loss (Tinsley & La Bounty, 2015). Similarly,

Klempel, Kroeger, Bhutani, Trepanowski, and Varady (2012) found that severe caloric restriction 1day/week resulted in significant weight loss among obese women. More specifically,

Klempel and colleagues (2012) investigated a 10-week program and examined the effects of intermittent fasting with a food-based diet compared to intermittent fasting with a liquid diet.

Findings showed that, although both groups achieved significant weight loss, those on the liquid diet plan achieved more weight loss. Home, Muhlestein, and Anderson (2015) systematically reviewed the literature on intermittent fasting and only identified 3 randomized controlled trials to evaluate, indicating the lack of evidence for cycling diets. Home et al (2015) concluded that, although these studies demonstrated the effectiveness of intermittent fasting for weight loss and improving health, more research is needed prior to concluding both the overall effectiveness as well as the safety of these diets.

Comparing the effectiveness of diet programs. Researchers have tried to compare whether some diet programs are superior to others in producing weight loss. For example,

Gudzune and colleagues (2015) systematically reviewed 45 studies and concluded that after 12

months, the Jenny Craig diet produced the greatest weight loss (4.9%) compared to Weight

Watchers, which averaged a 2.6% of initial body weight loss. In addition, Nutrisystem resulted in a weight loss of 3.8% after 3 months, but information about longer term weight loss maintenance

(e.g., after 12 months) was not available. Gudzune et al. (2015) also evaluated Atkins and

SlimFast and found a wide range of results for Atkins, with weight loss varying from 0.1-2.9% after 12 months, whereas results for SlimFast were mixed. Truby and colleagues (2006) also compared Atkins, SlimFast, and Weight Watchers and did not find any significant differences in weight loss between these programs. In fact, they concluded that all three diet programs resulted in significant weight loss and body fat reduction after 6 months and were equally effective in producing these results (Truby et al., 2006). Overall, the evidence is mixed in terms of identifying a superior diet program, and perhaps this indicates the importance of previously discussed factors that may be more essential to achieving weight loss, such as adherence.

Diets and Risk for Eating Disorders

Different diet programs may pose varying levels of risk for eating disorders However, studies evaluating the effectiveness of diet programs rarely do not take into account psychological consequences of adherence to diet programs and rather rely on achieved weight loss, change in BMI, or improvement in health outcomes (e.g., blood pressure, insulin levels) as outcome measures. Other unintended consequences are not accounted for, such as development of disordered eating attitudes or behaviors, although it is widely known that dieting is a risk factor for eating disorders (e.g., Stice, Gau, & Rohde, 2016). The following section details how different diet categories might contribute to eating disorder risk and groups them according to the level of risk – low, moderate, or high.

Low risk. Balanced energy restriction diets. These diets, as indicated in the name of the category, tend to take a balanced approach to weight loss and avoid severe limitations and rigid rules, therefore posing a lower risk for eating disorders. These diets focus on strategies such as timeliness of eating (e.g., the 3-hour diet emphasizing small balanced meals every 3 hours), avoiding processed foods (Macrobiotic Diet), or cutting down portion sizes (Step Diet).

Additionally, some of these diets shift the focus to increasing consumption of healthy foods, such as fruit, vegetables, and whole grains (e.g., Spectrum Diet, Mediterranean Diet), as opposed to focusing on limitations and restrictions. Therefore, these diets seem to promote balanced and healthy lifestyle changes as opposed to imposing strict limitations and restrictions on one’s diet, making it unlikely that individuals on these diets will develop disordered eating concerns.

Pre-packaged meals. Diet programs that provide pre-packaged meals offer the convenience of ready-to-eat healthy meals and remove the stress related to cooking and grocery shopping. Anxiety and stress related to choosing, shopping for, and cooking meals may play a significant role, especially in the mental fatigue that is associated with adhering to a diet program long-term. Reducing this stress may increase the likelihood of adherence to these programs, particularly if they include a large variety of meals to choose from. However, these plans may lack variety and sacrifice taste for nutritional value, which would make long-term adherence more difficult due to increased fatigue and boredom. Furthermore, individuals may still experience feelings of deprivation because they are not able to consume foods that are familiar to them. Feelings of deprivation may lead to binge eating, which would increase eating disorder risk. However, these potential drawbacks are balanced with prior mentioned benefits of reduced stress. Therefore, although some aspects of these diet programs may pose a small risk for disordered eating, it is proposed that pre-packaged meal plans are low risk diets.

Moderate risk. Vegetarian diets. Although vegetarian diet programs typically include a restriction component (e.g., eliminating specific foods or food groups from one’s diet), it is unclear whether this type of restriction confers risk for eating disorders. For example, Timko,

Hormes, and Chubski (2012) found that those who strictly adhered to a vegetarian or vegan diet actually had lower levels of eating pathology, whereas those who only somewhat adhered to a vegetarian diet had increased risk for eating disorders. Thus, due to the variation in different vegetarian diet programs, the level of risk may differ depending on the severity of restriction in one’s diet. For example, individuals who adhere to a strictly vegan diet may be more likely to develop disordered eating due to the severe limitations placed on their lifestyle. However, this is contradictory to Timko and colleague’s findings (2012). Perhaps those who do not strictly adhere to a vegan or vegetarian diet are at greater risk due to their dieting motives. That is, those who pursue vegetarian diets for weight loss reasons rather than values are likely to adhere less strictly, which may increase their eating disorder risk. This highlights the importance of considering not just restrictiveness, but also one’s motivation for weight loss when evaluating risk for eating disorders. In comparison, a vegetarian diet that allows occasional meat (e.g., flexitarian) or fish consumption (e.g., pescetarian) may be a lower risk diet due to its flexible nature. Overall, most vegetarian diets are flexible in that, despite the limitations, they still allow a large variety of other foods and therefore do not impose severe restrictions that may lead to disordered eating problems. Furthermore, there are many carbohydrate-rich and sugary foods that are allowed, even on vegan diets, such as Oreos. Therefore, it may be that vegetarian diets are not strict enough for producing weight loss. For example, Huang and colleagues (2015) emphasized that, although vegetarian diets may promote weight loss, adding caloric restriction may be important

in achieving significant weight loss. Therefore, it is proposed that vegetarian diets do not pose a significant risk for eating disorders and are classified as moderate risk diets.

Self-monitoring diets. Self-monitoring programs, such as Weight Watchers, focus on self-monitoring strategies based on energy restriction. However, they also avoid strict limitations on specific foods or food groups and even encourage incorporation of sweets, but in limited quantities. Due to the potential lack of deprivation in these programs, it is unlikely that self- monitoring programs pose a high risk for eating disorders. However, excessive self-monitoring may pose some risk for eating disorders, as will be discussed later in this paper. Those who are unable to approach this program in a balanced or flexible manner may be more prone to adhering too rigidly. For example, they might choose not to incorporate any pleasurable foods into their diets, which would increase feelings of deprivation and perhaps hunger, thereby increasing risk for binge eating and eating disorders. This emphasizes the influence of psychological risk factors when assessing risk for eating disorders. For example, individuals with high levels of impulsivity may have significant difficulty with consuming pleasurable foods in limited quantities. To avoid overeating, they might completely eliminate these foods from their diet, which may actually increase their risk for binge eating. Similarly, individuals with dichotomous or rigid thinking may have difficulty approaching a diet program in a flexible manner because they tend to view things categorically (e.g., good foods versus bad foods), which would increase their risk for eating disorders. Overall, I propose that self-monitoring diets are classified as moderate risk and suggest that psychological factors should also be taken into account when evaluating one’s level of risk on these diets.

Low fat and low carbohydrate diets. It is proposed that diets that limit the amount of carbohydrate or fat intake pose a moderate risk for eating disorders. This is not only due to the

restriction component of these diets, but also due to the nature of the foods that are restricted.

When a diet program imposes strict limits on one’s consumption of carbohydrates or fats, it is also restricting particular foods (e.g., desserts, fast food) due to their carbohydrate or fat content.

This becomes problematic when individuals experience feelings of boredom, fatigue, and deprivation, which can lead to strong cravings, overeating, or binge eating. Furthermore, it is well known that when individuals with eating disorders engage in binge eating behavior, they typically consume foods that are high in carbohydrates and/or high in fat, and this could be due to the consistent deprivation of these foods in their diets. Therefore, adhering to a low carbohydrate or low fat diet may result in similar problems – feelings of deprivation, which lead to strong cravings, and impulsive binge eating. Binge eating may even lead to increased restriction to compensate, and all of this would increase eating disorder risk.

High risk. Very-low-calorie and liquid meal replacement diets. Very-low-calorie diets pose a particularly high risk for eating disorders due to the extreme nature of severe caloric restriction. Liquid meal replacement diets are classified under this category, as they typically conform to the standards of very-low-calorie diets. Although these diets may yield significant short-term weight loss (e.g., after 1 month), they are also notorious for poor long-term adherence and long-term weight loss maintenance. Furthermore, the severe deprivation factor of these diets significantly increases risk for eating disorders. In fact, severe caloric restriction, as imposed by very-low-calorie diets (i.e. 800kcal/day or less), leads to rapid weight loss at an unhealthy rate and actually qualifies as a symptom of eating disorders. Furthermore, individuals who are able to adhere to this diet may become underweight over time, particularly if they are in the overweight

BMI range when they start. I propose that such a severe restriction regimen places these diets in the high risk category. Participants would likely experience boredom, deprivation, or fatigue on

this type of diet or perhaps would simply be unable to control the natural hunger that would result from this level of caloric restriction. They might try to continue with this level of unhealthy restriction, or start a pattern of overeating or binge eating, all of which would greatly increase their risk for eating disorders.

Cycling diets. Home et al. (2015) warned that further research is needed prior to assuming the efficacy and, more importantly, the safety of cycling diets. Diets such as intermittent fasting can resemble or lead to disordered eating patterns. Engaging in periods of fasting might teach individuals to ignore internal physiological cues (e.g., hunger), which would lead to a dangerous pattern of ignoring other internal physiological cues (e.g., feeling full). This problematic way of adapting to an intermittent fasting program could lead some to engage in disordered eating behaviors (e.g., binge eating), especially if they are engaging in moderate to severe caloric restriction. Additionally, fasting itself is considered a symptom of eating disorders, and some participants may begin fasting and restricting calories excessively. They would rapidly lose weight and potentially also experience muscle mass or bone density loss, which could result in health complications. It remains unclear how intermittent fasting can contribute to an overall disruption of one’s internal system, including hormonal balances, and whether engaging in such a program can in fact increase one’s risk for weight gain after stopping the program. For example, if one must learn to ignore internal physiological cues of hunger and fullness, then it may be challenging to later establish a more normative diet plan because they will have learned to ignore cues of hunger and fullness. Individuals might struggle significantly with consuming appropriate portion sizes and most importantly, not know how to maintain weight loss without fasting. They might resort to excessive restriction or fasting to try to maintain weight loss, which could lead to overeating or binge eating, reflecting a pattern of bulimic symptoms. Overall,

cycling diets seem to not only lack evidence for effective weight loss, but also likely greatly increase eating disorder risk.

Summary of diets and eating disorder risk. Given the variety of diets available to those pursuing weight loss and the different elements included as part of these programs, it is suggested that some diets pose a greater risk for eating disorders than others. Those with overweight or obesity, who pursue weight loss, might attempt different programs with varying levels of success in regards to weight loss, but these attempts might also come with varying levels of risk for eating disorders. Based on my theoretical understanding of these diets and their shared elements, diets were categorized into low, moderate, and high risk diet types. Therefore, the first purpose of the present study is to investigate whether type of diet (i.e. level of risk of diet) is associated with an increased risk for eating disorders and/or weight loss.

Dieting Strategies

In addition to, or in combination with specific, diet programs, individuals also typically utilize dieting strategies when pursuing weight loss. It is important to assess these strategies in terms of not only their effectiveness in contributing to weight loss, but their potential contribution to increased eating disorder risk.

Dieting Strategies and Weight Loss

A number of helpful methods and techniques have been identified for successful and sustained weight loss. For example, self-monitoring strategies, such as keeping a food diary and regular weighing, have long been established as key behavioral factors in the treatment of obesity (e.g., Baker & Kirschenbaum, 1993; Boutelle & Kirschenbaum, 1998, Kolodziejcyk et al., 2014; Kruger, Blanck, & Gillepsie, 2006). Other strategies helpful for weight loss include meal planning (e.g., Fugelstad, Jeffrey, & Sherwood, 2012; Kruger et al., 2006), consuming less

fat (Kolodziejcyk et al., 2014), calorie and fat tracking (Kruger et al., 2006), and measuring food

(Kruger et al., 2006). Furthermore, in the largest study of individuals in the National Weight

Control Registry, Wing and Phelan (2005) assessed 4000 adults who lost and maintained a weight loss of a minimum of 30lbs over 1 year and found that the most commonly used and effective were restricting particular foods, limiting quantities, counting calories, and engaging in physical activity. Exercise has also been indicated as helpful for weight loss in other studies

(e.g., Kruger et al., 2006). However, this paper will focus on dieting strategies in particular (i.e. changes to food consumption) rather than a comprehensive look at all factors that contribute to weight loss, such as exercise. One of the main aims of this study is to investigate how dieting strategies relate to both weight loss and eating disorder risk. As food consumption patterns are most salient to eating disorder risk, this paper will solely focus on dieting strategies related to changes in food consumption. Nonetheless, exercise may be an important factor to take into account in future studies.

VanWormer, French, Pereira, and Welsh (2008) conducted a systematic literature review and concluded that frequent self-weighing predicted moderate weight loss and lower weight regain, further supporting the importance of self-monitoring strategies for weight loss. Fugelstad and colleagues (2012) identified important lifestyle factors that influence weight loss, including meal regularity, not eating while watching TV, reducing eating away from home, and using weight control strategies, such as self-weighing, food and exercise tracking, and meal and exercise planning. Specifically, they found that more frequent meal regularity predicted greater weight loss, whereas more frequent eating while watching TV predicted a higher BMI and increased fat and sugar intake. Additionally, Fugelstad et al. (2012) found that eating away from home was associated with greater fat and sugar intake as well as decreased physical activity,

whereas using weight control strategies was overall predictive of better weight, healthier diet, and greater physical activity. These findings suggest that certain lifestyle factors may be more conducive to weight loss and make it easier for individuals to engage in or sustain the use of helpful weight loss strategies. They also support the prior mentioned findings that self- monitoring is strongly predictive of weight loss.

To assess the extent to which people use various dieting strategies, and to develop a largely comprehensive measure of diet modification strategies, Keller and Siegrist (2015) developed the Weight Management Strategies Inventory (WMSI). They found that most of these strategies were significantly and positively correlated with successful dieting. For example, tracking caloric intake and weight, focusing on eating, sleeping and relaxing, distracting oneself from food temptations, implementing a low-carbohydrate or low-fat diet, and eating slowly were all behaviors that were significantly associated with dieting success. Their findings showed that dieting success was most strongly associated with the following strategies – substituting low for high caloric foods, monitoring portions, inhibiting oneself from eating, and implementing regular meal times. Some of these strategies were inversely associated with BMI, including inhibiting oneself from eating, and implementing regular mealtimes. Additionally, implementing a low-fat diet and planning food purchases were two strategies that were also negatively correlated with

BMI. It is unclear why some strategies are negatively associated with BMI, but it may be that some strategies are more helpful or necessary for those of higher weight. Overall, their results support the existing evidence regarding helpful behavioral weight loss strategies and generally indicate a positive association between using weight management strategies and successful weight loss. However, these strategies have not been evaluated in terms of how they contribute to eating disorder risk.

Dieting strategies and eating disorder risk. While some dieting strategies may contribute to successful weight loss and maintenance, they may also pose risk for eating disorders. For example, calorie counting may promote rigidity or excessiveness through a focus on strict limits for caloric intake, which may be at odds with the natural pattern for how the body typically consumes or craves calories. Low-carb or low-fat strategies may similarly foster rigidity through the elimination of specific foods or food groups. The unnatural rigidity of these strategies may result in greater feelings of deprivation and hunger, which would increase risk for binge eating. Similarly, although self-weighing can be helpful for weight loss, this strategy may also be problematic for some. For instance, individuals may put too much focus on their weight, or weigh themselves too frequently and become discouraged by mild fluctuations in weight. This might further contribute to an unrelenting focus on one’s body weight and lead to psychological or behavioral changes characteristic of disordered eating attitudes and behaviors. Other mentioned strategies, such as measuring food and planning meals, have similar implications in that some may implement these strategies in a rigid manner and begin to exhibit disordered eating. Therefore, the second purpose of this study was to investigate how these dieting strategies might contribute to eating disorder risk and/or weight loss. On the other hand, it is also necessary to consider the individual differences that differentiate those who become obsessive and rigid when utilizing certain strategies for weight loss to evaluate risk for eating disorders. It may be that individual differences in psychological factors, rather than the nature of the diet itself, play a more important role in determining eating disorder risk.

Psychological Factors

Psychological Factors and Weight Loss

In addition to dieting strategies, psychological factors have also been identified as contributors to successful weight loss. For example, Ohsiek and Williams (2011) identified several factors that negatively affect one’s ability to maintain weight loss, including an “all-or- nothing” thinking style (i.e. dichotomous thinking), eating to regulate mood (i.e. using food as a coping strategy for negative emotions), and impulsive eating. Dichotomous thinking can be defined as a rigid cognitive style with a tendency to categorize experiences in opposite categories

(Byrne, Cooper, & Fairburn, 2004). Byrne and colleagues (2004) found that a dichotomous thinking style predicted weight regain over the course of 1 year among individuals who were previously obese. Additionally, the role of coping in weight loss has been investigated. Kayman,

Bruvold, and Stern (1990) found that individuals who utilized avoidant coping strategies, which can be defined as cognitions and behaviors aimed at escaping the problem (Ghaderi & Scott,

2000), were more likely to regain weight they had initially lost. Similarly, Elfhag and Rossner

(2005) concluded that avoidant coping is an important risk factor for weight regain. These results indicate that certain psychological tendencies, such as dichotomous thinking and avoidant coping, interfere with people’s ability to achieve successful weight loss and maintenance. In particular, the evidence indicates that a rigid cognitive style, eating to cope with negative mood states, and a general tendency to use avoidant coping strategies negatively contribute to one’s ability to lose weight and maintain weight loss.

To assess how psychological factors are related to specific eating behaviors, Stunkard and Messick (1984) developed the Three Factor Eating Questionnaire, which is comprised of three scales – ability to restrain one’s food intake, disinhibition(i.e. disinhibited eating), and

susceptibility to hunger (i.e. frequency and intensity of hunger experienced throughout the day).

Marcus and Wing (1983) implemented this questionnaire in a study of women with obesity and identified that almost half of these women reported significant difficulties with binge eating.

Furthermore, they found that the disinhibition and hunger scales were associated with binge severity. These findings indicate that individuals with obesity are at a high risk for binge eating and this is associated with high levels of impulsivity and high susceptibility to feelings of hunger. Individuals with obesity may have difficulty with implementing weight loss strategies or a diet program successfully due to psychological factors, such as impulsivity and perceived hunger, perhaps because higher levels of these traits predict more problems with binge eating, which then interfere with one’s ability to successfully lose weight.

Another essential psychological factor to account for is motivation for pursuing weight loss. Putterman and Linden (2004) investigated the relationship between appearance versus health-based motivation for pursuing weight loss and found that those motivated to lose weight to improve their appearance were more likely to endorse unhealthy dieting practices (e.g., fasting, excluding entire food groups) and have difficulties with restraint. Interestingly, although these individuals tended to implement extreme restrictive dieting practices, they also reported more difficulties with restraint (Putterman & Linden, 2004). This may be due to higher levels of impulsivity or perhaps because they are engaging in more restrictive diets, which makes adherence difficult. Furthermore, those who reported being motivated to lose weight for others were also more likely to report difficulties with disinhibited eating and use unhealthy dieting strategies.

In contrast, Putterman and Linden (2004) found that participants who pursued weight loss for health-based reasons were less likely to endorse extreme dieting practices and less likely to

report problems with impulsive eating. These findings suggest that those primarily motivated to lose weight to improve their appearance, whether for themselves or for others, are more likely to struggle with impulsive eating and utilize unhealthy dieting strategies. However, this study did not assess how motivation was related to achieved weight loss. Results indicated that health- based motivation was associated with a higher BMI (Putterman & Linden, 2004), but achieved weight loss was not assessed. It may be that individuals of lower weight are less likely endorse health-based reasons for weight loss, as their weight may be within the normal BMI range.

However, perhaps body dissatisfaction drives these individuals to lose weight to improve their appearance reasons, compared to those of higher weight, who have medical (i.e. health-based) reasons for wanting to lose weight. Therefore, the evidence suggests that appearance-based motives for weight loss are associated with a higher risk for unhealthy dieting practices and disordered eating.

In regards to whether motivations for weight loss relate to dieting success, the research remains inconclusive. Brink and Ferguson (1998) found that health-related reasons were the most common for weight loss, followed by appearance-based reasons, but it is unclear how motivation actually predicts change in weight. For example, Gillett (1998) found that overweight women motivated by appearance were more likely to adhere to an exercise regimen. In contrast,

Ryan, Frederick, Lepes, Rubio, and Sheldon (1997) found that appearance motivation was not significantly correlated with adherence to an exercise program. It may be that the reason for weight loss does not specifically impact weight change or adherence. Perhaps other factors play a more important role in predicting weight loss success and the motivation for weight loss is more specifically related to eating disorder risk.

Psychological Factors and Eating Disorder Risk

It is interesting that a dichotomous thinking style and eating in response to negative mood or impulsive urges have been identified as unhelpful psychological factors for weight loss because these have also been identified eating disorder risk factors (e.g., Leehr, Krohmer, Schag,

Dresler, Zipfel, & Giel, 2015). For example, Lethbridge, Watson, Egan, Street, and Nathan

(2011) identified dichotomous thinking as a specific cognitive process that differentiates individuals with eating disorders from the general population. In addition, Leehr and colleagues

(2015) found that negative emotions serve as a trigger for binge eating for obese individuals with binge eating disorder, compared to those without binge eating disorder. This indicates that patients with obesity, who use food to help regulate negative emotions, are at a higher risk for eating disorders. Additionally, those with obesity who report high levels of disinhibition (i.e. impulsivity) also report more problems with binge eating (Marcus & Wing, 1983). More specifically, those who are more likely to act impulsive when in a negative mood state have been identified are especially at risk for eating disorders (e.g., Fischer, Peterson, & McCarthy, 2013).

This is consistent with the idea that individuals who tend to be impulsive when experiencing negative emotions may impulsively use food to cope in difficult situations. Thus, impulsivity, and perhaps more specifically being impulsive when experiencing negative mood states (i.e. negative urgency), serves as a barrier to achieving successful weight loss among obese individuals.

Finally, the relationship between maladaptive coping and eating disorders has been widely established. The evidence shows that avoidant coping is associated with disordered eating attitudes and behaviors in the college population (e.g., Freeman & Gil, 2004; Sulkowski,

Dempsey, & Dempsey, 2011) as well as in those with diagnosed eating disorders (e.g., Ghaderi

& Scott, 2000). In combination, these psychological traits – dichotomous thinking, negative urgency, and avoidant coping – present a particularly high risk for eating disorders. When these individuals experience negative emotions, they likely act impulsively, and, because they also exhibit a rigid thinking style, they are less likely to be able to reinterpret their current situation and alleviate their negative emotions, which corresponds with using food to cope with difficult emotions.

These psychological factors not only increase risk for eating disorders, but by doing so, may also result in difficulties with weight loss. For example, individuals high on negative urgency may be more likely to engage in binge eating when in a negative mood, which would make it difficult to consistently adhere to a diet program and may create a pattern of yo-yo dieting without achieving long-term weight loss. Those who tend to use avoidant coping strategies may use food to cope and therefore be less successful with adhering to a diet and unable to achieve weight loss. Individuals with a dichotomous thinking style may have problems with adapting to daily challenges while adhering to a diet program and therefore give up quickly, unable to achieve weight loss. The third purpose of this study is to investigate the role of these psychological factors (i.e. dichotomous thinking, coping, motivation, and negative urgency) in eating disorder risk, but particularly how these psychological factors might impact the relationship between dieting and weight loss or eating disorder risk.

Examining how these psychological factors are implicated in both difficulties with weight loss and increasing eating disorder risk would provide us with information that can be used to inform intervention and treatment for obesity. For example, many individuals who are overweight or obese struggle with body image concerns. They might also struggle with using food to cope and/or binge eating, which would make it more difficult to lose weight.

Furthermore, based on prior research that has identified the role of certain psychological factors

(e.g., impulsivity, rigid thinking, avoidance coping) in hindering weight loss (Byrne et al., 2004;

Elfhag & Rossner, 2005; Ohsiek & Williams, 2000) presence of these traits would lead to even greater difficulty with achieving weight loss. More importantly, dieting efforts are associated with increased eating disorder risk (Stice et al., 2011). It is hypothesized that those in the obese

BMI range, who are also high on these psychological risk factors, might be identified as a specific group with increased risk for eating disorders and greater difficulty with weight loss. For obese individuals with high levels of psychological risk, traditional diet programs may be ineffective. Instead, they may benefit from addressing psychological risk factors in order to achieve successful lifestyle changes that promote weight loss and decrease eating disorder risk.

Thus, it is hypothesized that individuals with high levels of psychological risk factors would be less likely to achieve weight loss. Furthermore, I hypothesize that individuals who report a history of high risk dieting or report using diet modification strategies (e.g., calorie counting, weight monitoring), would be particularly at risk for eating disorders if they are also high on psychological risk factors.

The Present Study

It is clear that overweight and obesity is an immensely concerning public health problem, and treating this problem requires weight loss. However, the role of weight loss attempts in risk for eating disorders is unknown. Various methods for weight loss among individuals with overweight or obesity have been suggested or shown to be effective, both in the form of specific diet plans as well as specific strategies, but little is known about the relationship between these diet programs or strategies and eating disorder risk. This is problematic especially given the high risk for eating disorders among obese individuals. In addition, there is a lack of knowledge about

the role of psychological factors, such as dichotomous thinking, avoidant coping, motivation for weight loss, and negative urgency, in achieving weight loss, and how these factors contribute to eating disorder risk.

The present study has multiple aims. The first aim is to identify whether type of diet (i.e. low, moderate, or high risk) is associated with eating disorder risk or weight loss. The second aim is to determine the relationships between dieting strategies and eating disorder risk or weight loss. The third goal is to determine how type of diet and dieting strategies might interact with psychological factors to predict eating disorder risk, as well as the role of these psychological factors in predicting (or preventing) weight loss. Finally, by examining weight loss in conjunction with eating disorder risk, the findings from this study will identify which factors relate only to eating disorder risk, only to weight loss, or to both weight loss and eating disorder risk. This would lead to potentially identifying certain factors, including diet type, dieting strategies, or psychological factors, that might promote weight loss without contributing to, or perhaps even protecting from, eating disorder risk. This would help inform how to address the obesity epidemic in a way that does not put individuals at risk for eating disorders. The present study examined the following hypotheses:

1) Eating disorder risk will be predicted by the following:

a. Consistent with prior research, obesity will be associated with eating disorder

risk, such that a higher BMI when initiating weight loss will be associated with

higher eating disorder risk.

b. A history of engaging in high-risk diets will predict higher eating disorder risk.

c. Psychological factors, including negative urgency, avoidant coping, and

dichotomous thinking, will moderate the relationship between dieting behaviors

and risk for eating disorders.

i. Specifically, individuals who report a history of high risk diets or using

self-monitoring strategies (e.g., calorie counting, weight monitoring), will

be at higher risk for eating disorders, if they report higher levels of

negative urgency, avoidant coping, appearance motivation, and

dichotomous thinking.

2) Weight loss success will be predicted by the following:

a. Psychological factors, including higher levels of negative urgency, avoidant

coping, disinhibition, susceptibility to hunger, and dichotomous thinking, will

inversely predict weight loss success.

b. Higher levels of self-reported boredom, feelings of deprivation, and overall stress

level while on a diet will predict less weight loss success.

c. Finally, consistent with prior research, calorie counting and weight monitoring

will predict greater weight loss success.

CHAPTER TWO: METHODOLOGY

Participants

Participants were recruited from the Washington State University Psychology

Department subject pool and received course credit for participation. The total sample was comprised of college students, who were currently enrolled in a Psychology course at

Washington State University. The total sample consisted of 76.7% female (n=1107) and 23.2% male (n=335) participants. The mean age of the sample was 20.88 (SD=6.295). The majority of participants identified as White (non-Hispanics) (n=914), followed by Hispanic-American

(n=179), Asian-American (n=134), African-American (n=74), Native American (n=15), and

Other (n=77). In regards to religious identity, 34.2% of the sample reported that they do not identify with any religion (n=493), while the rest of the sample identified as Protestant or

Christian (n=455), Catholic (n=278), Jewish (n=13), Buddhist (n=10), Hindu (n=3), and Other

(n=83). It should be noted that only those participants, who endorsed a history of dieting (n =

918) were included in the analyses below. The utilized sample consisted of 80.7% female

(n=741) and 18.5% male (n=170) and had a mean age of 20.94 (SD=4.325). The utilized sample did not show statistically significant differences compared to the total sample on other demographics (e.g., religion, ethnicity). Participants were asked to complete an online survey, which included measures of dieting habits, weight loss strategies, psychological factors, and disordered eating. The online survey included the following measures.

Measures

Demographic information. Demographics information was collected, including age, gender, race/ethnicity, and religious affiliation. In addition, information about height and weight was gathered in order to determine participants’ BMI pre and post weight loss attempt.

Weight Loss. Participants were asked to report their height and weight before and after

“[their] most recent attempt at weight loss.” They were also asked to report how much weight they lost during their “most recent attempt at weight loss.” This information was used to calculate their BMI (kg/m2) before (pre-BMI) and after their most recent weight loss attempt.

Participants were categorized into normal, overweight, and obese BMI range based on calculated

BMI. Total weight loss was operationalized as the percent of initial body weight lost during their most recent weight loss attempt.

Diets. A history of using each type of diet was obtained for each participant by asking participants to select from a list of diet programs to indicate those they had tried. Participants were then grouped into low, moderate, and high risk diet groups based on diets endorsed.

Specifically, those who endorsed at least one low risk diet, but no moderate or high risk diets, were grouped into the low risk group. Those who endorsed at least one moderate risk diet, but no high risk diets, were grouped into the moderate risk group. Those who endorsed at least one high risk diet were grouped into the high risk diet group. It was conceptualized that due to the extreme nature of high risk diets, trying at least one high risk diet would increase eating disorder risk, as it would require individuals to either excessively restrict their calories to the level that would be consistent with symptoms of anorexia or engage in a dieting pattern that mimics bulimic symptoms (i.e. intermittent fasting). Therefore, endorsing at least one high risk diet resulted in the participant being categorized into the high risk diet group.

Dieting strategies. The Weight Management Strategies Inventory (WMSI; Keller &

Siegrist, 2015) was included to gather information regarding participants’ utilization of various diet modification strategies. This questionnaire assesses five broad categories of weight management aimed at changing one’s diet or food intake – goal setting and monitoring (α=.82),

prospection of temptation and planning of goal-directed and alternative behaviors (α=.82), automating behavior and routines (α=.86), construal reinterpretation (α=.91), and effortful inhibition (α=.89). These broad scales are made up of 19 different weight management strategies/subscales. All of the broad scales were included in preliminary analyses. The following subscales were also included in preliminary analyses – calorie counting (α=.92), weight monitoring (α=.80), low carb/calorie/fat dieting (α=.83), and irregular meals (α=.81).

Eating behavior. The Three Factor Eating Questionnaire (TFEQ; Stunkard & Messick,

1984) measures a variety of eating behaviors with a total of 51 items. The items comprise three scales, including restraint or cognitive control of one’s behavior (α=.82), disinhibition (α=.88), and susceptibility to hunger (α=.85). Items are rated as true or false, reverse coded appropriately, and summed to provide a total score for each scale.

Eating disorder risk. Eating disorder attitudes and behaviors were measured using the

Eating Disorder Inventory – 3 (EDI-3; Garner, 2004). The eating disorder risk composite scale

(α=.92) is comprised of three subscales – drive for thinness (α=.87)), bulimia (α=.87), and body dissatisfaction (α=.84) – and was used to assess eating disorder symptoms. Answers are marked on a 0 to 4 point scale and reverse coded appropriately.

Motivation for dieting. The Weight Loss Motives Questionnaire (WLM-Q; Meyer,

Weissen-Schelling, Munsch, & Margraf, 2010) was used to assess participants’ reasons and motives for dieting and weight loss. This instrument is composed of 24 items, which comprise three scales that reflect three broad reasons for weight loss. These include health reasons (α=.91), appearance in relation to others (i.e. improved relationships with others due to weight loss)

(α=.93), and appearance in relation to oneself (i.e. improve one’s satisfaction with one’s

appearance) (α=.92). Items are answered on a 4-point Likert scale and summed to provide total scores for each scale.

Dichotomous thinking. Cognitive thinking style was measured using the Dichotomous

Thinking in Eating Disorders Scale-11 (DTEDS; Byrne, Allen, Dove, Watt, & Nathan, 2008).

This self-report questionnaire is comprised of two subscales, which assess dichotomous or rigid thinking related specifically to eating, dieting, or weight (α=.82), as well as more general dichotomous thinking (α=.86).

Negative urgency. Negative urgency was measured using the UPPS-P Impulsive

Behavior Scale (UPPS; Cyders et al., 2007; Whiteside & Lynam, 2001). The negative urgency

(α=.86) subscale refers to acting impulsively when in a negative mood state, including items such as “When I feel bad, I will often do things I later regret in order to make myself feel better now”. This 59-item questionnaire includes items rated on a scale of 1 to 4, with 1=”agree strongly” and 4=”disagree strongly”. Items were reverse-coded appropriately.

Coping. The Coping Styles Questionnaire (CSQ; Roger, Jarvis, & Najarian, 1993) was administered to gain information about participants’ general coping tendencies. This questionnaire is comprised of 60 items, which make up four coping scales – rational coping

(α=.91), detachment coping (α=.86), emotional coping (α=.89), and avoidant coping (α=.85).

Participants answered items on a 4-point Likert scale, and items for each scale were summed to provide a total score for each scale.

Other information regarding relevant factors contributing to dieting was gathered by asking participants to rate (on a scale of 0-4) to what extent they experience various issues while dieting, including feeling bored while on a diet, feeling deprived on the diet, and their overall

stress level while dieting. Participants were also asked to rate how strongly they typically adhere to a diet on a scale of 0-4.

CHAPTER THREE: RESULTS

Analyses

The first purpose of the present study was to investigate the relationship between type of diet and eating disorder risk or weight loss. The second goal of this study was to identify relationships between dieting strategies and eating disorder risk/weight loss. The third goal was to specifically identify the role of psychological risk factors in those relationships, including coping, motivation, dichotomous thinking, negative urgency, disinhibition, and hunger. These findings would then identify predictors specific to just eating disorder risk or weight loss, as well as identify factors that relate to both weight loss and eating disorders.

Preliminary analyses. Descriptive statistics for measured variables can be found in

Table 2. Frequencies for specific diets tried can be found in Table 3. Pearson correlations between predictor (e.g., dieting strategies, psychological risk factors,) and outcome variables

(e.g., weight loss, eating disorder risk) can be found in Table 4. The following preliminary analyses were conducted to identify significant relationships between predictor and outcome variables. Linear regression analyses were used to test relationships between continuous variables, while controlling for age, gender, and other scales/subscales from the same measure

(see Table 5). One-way analysis of variance (ANOVA) was then used to test for group difference between diet groups and BMI groups on eating disorder risk and weight loss. Results indicated a significant difference between diet groups and between BMI groups on both eating disorder risk and weight loss, such that a higher risk diet group or higher BMI range predicted a higher mean risk for eating disorders and higher mean weight loss (see Tables 6-9). Due to the high number of significant associations, and in order to test for the unique variance associated with eating disorder risk and weight loss, omnibus ANCOVAs and linear regression analyses were

conducted with all significant variables entered simultaneously. Nonsignificant predictors were left out of these subsequent analyses. For example, although it was hypothesized that avoidant coping would predict risk for eating disorders, instead, the emotion coping subscale significantly predicted eating disorder risk. Therefore, the emotion coping subscale was used in subsequent analyses.

Eating disorder risk. An ANCOVA was used to test for main effects for significant predictors as well as potential interactions between these predictors and diet group (e.g., low risk, moderate risk, high risk) or BMI group (normal range, overweight range, or obese range) in predicting eating disorder risk (see Table 10). Total number of diets tried was included as a covariate to control for the effect of number of diets tried on eating disorder risk in order to isolate the effect of level of risk of diet group. Results indicated main effects for negative urgency [F(1, 365)=5.681, p<.05], rigid thinking in relation to food or eating [F(1, 365)=9.069, p<.005], and feeling stressed while on a diet [F(1, 365)=14.014, p≤.000). No other main effects were identified, but results indicated a significant appearance-for-self motivation by BMI group interaction [F(2, 365)=4.945, p<.05] and health-based motivation by BMI group interaction [F(2,

365)=5.029, p<.05]. Follow-up tests showed that appearance-for-self motivation predicted eating disorder risk among those in the normal [F(1, 189) =37.757, p≤.000)] and obese [F(1, 77)=8.854, p≤.005)] BMI range, but not in the overweight BMI range, and that health-based motivation was inversely associated with eating disorder risk among those in the normal BMI range [F(1,

189)=13.754, p≤.000)]. Results also indicated a significant emotion coping by diet group interaction [F(2, 365)=5.335, p≤.005], such that emotion coping only predicted risk for eating disorders in the moderate risk diet group [F(1, 68)=27.464, p≤.000). Regression analyses indicated positive slopes, demonstrating that higher levels of negative urgency, rigid thinking in

relation to food or eating, and feeling stressed while dieting predicts increased risk for eating disorders.

In order to identify potential interactions between continuous variables, such as dieting strategies (i.e. calorie counting and weight monitoring) and psychological factors (i.e. coping, negative urgency, rigid thinking, and motivation), in predicting risk for eating disorders, linear regressions were conducted. These included the interaction term being tested, while controlling for the effect of other significant predictors (see Table 12). One significant interaction was identified for calorie counting by appearance-for-self motivation (β=.384, p<.05), such that appearance-for-self motivation strengthened the relationship between calorie counting and risk for eating disorders. That is, for those who reported a tendency to count calories as a weight management strategy, being motivated by appearance reasons increased their level of risk for eating disorders.

Weight loss. To test hypotheses for weight loss, a second ANCOVA was performed to test main effects for significant predictors as well as potential interactions between these predictors and diet group (e.g., low risk, moderate risk, high risk) or BMI group (normal range, overweight range, or obese range) on weight loss (see Table 11). Main effects for adherence to diet [F(1, 535)=6.672, p<.05] and weight monitoring [F(1, 535=4.914, p<.05)] were identified, indicating that consistently adhering to a diet program and weighing oneself regularly were associated with greater weight loss. Although there was also a main effect for diet type [F(2,

535)=3.671, p<.05), a significant interaction for health-based motivation by diet group was identified [F(2, 535)=3.143, p<.05)], such that health-based motivation only showed an association with weight loss among those in the low risk diet group [F(1, 89)=7.572, p<.05]. In addition, a significant interaction for appearance-for-others motivation by diet group was

identified [F(2, 535)=3.213, p<.05], such that appearance-for-others motivation was only associated with weight loss among those in the normal BMI range [F(1, 271)=4.895, p<.05], but not among those in the overweight and obese BMI range. Finally, in order to identify potential interactions between continuous variables, such as dieting strategies, specifically calorie counting and weight monitoring, and psychological factors (e.g., coping, negative urgency, rigid thinking, and motivation), in predicting weight loss, linear regressions were conducted. No significant interactions were identified to predict weight loss. A summary of significant predictors for eating disorder risk and weight loss can be found in Table 13.

CHAPTER FOUR: DISCUSSION

Obesity is a major public health concern in our society. The typical solution or treatment for obesity is dieting or diet-related strategies, but there is a lack of research addressing how dieting efforts might contribute to increased eating disorder risk. This is problematic given the association that exists between obesity and eating disorder risk (e.g., Day et al., 2009). While there are a variety of existing diets and weight loss strategies for people to choose from, it is unknown how these diet plans might contribute to eating disorders. Therefore, the goals of this study were to 1) identify how specific diets contribute to eating disorder risk and/or weight loss,

2) examine how dieting strategies are associated with eating disorder risk and weight loss, and 3) explore how certain psychological risk factors impact the relationship between these diets or dieting strategies and eating disorder risk or weight loss. Answering these questions would help illuminate the question of how individuals, when pursuing weight loss, can achieve weight loss

(if needed) and not increase, or perhaps even decrease, their risk for eating disorders.

Eating Disorder Risk

Diet type. It was hypothesized that a history of high risk dieting would be associated with an increased risk for eating disorders. Preliminary analyses found support for this hypothesis, indicating that a more risky type of diet was associated with a higher risk for eating disorders.

However, type of diet alone did not prove to be an important factor that was associated with eating disorder risk. Instead, it was a particular interaction between the type of diet and the way someone copes that was associated with eating disorder risk. Specifically, results indicated that a history of moderate risk dieting (i.e. self-monitoring diets, low carb/fat diets, vegetarian diets) was related to eating disorder risk among those who reported a tendency to use emotion-based coping strategies. That is, individuals who had tried diets like Weight Watchers were particularly

at risk for eating disorders if they reported using emotion-based coping. Although it was hypothesized that avoidant coping would be associated with eating disorder risk based on previous research (e.g., Freeman & Gil, 2004), it was emotion-based coping that was related to eating disorders in this study. This could be explained by the content of the items that comprise the emotion coping scale in the measure used in the present study.

The Coping Style Questionnaire (Roger et al., 1993) includes an emotion coping subscale, which consists of items depicting feelings of sadness, loneliness, helplessness, and other negative emotions as a reaction to stressful events. Thus, the emotion coping subscale may be identifying those with more difficulty regulating difficult emotions, or those with a higher propensity toward experiencing negative emotions more frequently in response to stressful events. This is consistent with prior research, which has demonstrated that individuals with disordered eating report higher levels of negative emotionality (e.g., Cassin & Von Ranson,

2005; Cervera et al., 2003). Furthermore, Goldschmidt, Wall, Loth, Le Grange, and Neumark-

Sztainer (2012) found that depression levels predicted binge eating among dieters. Thus, a measure of negative emotion, depression, and/or emotion dysregulation should be included in future studies to determine whether it is the negative emotionality trait, depression, or the form of coping with negative emotions that contributes to higher eating disorder risk. Nonetheless, the present findings identified that emotion-based coping is associated with increased eating disorder risk, among those with a history of moderate risk dieting. Regardless of whether these results point to the effect of overall negative emotionality or coping style, these findings suggest that increasing healthy coping strategies to be able to regulate and cope with difficult emotions in an adaptive manner would be an important intervention and prevention target to decrease eating disorder risk among college students.

BMI. Based on previous research (Day et al., 2009; Kass et al., 2016), it was hypothesized that obesity (i.e. a higher BMI range) would be associated with higher eating disorder risk. Preliminary analyses found support for this hypothesis, indicating that those in the obese BMI range were at higher risk for eating disorders, followed by those in the overweight

BMI range and those in the normal BMI range. Furthermore, one of the goals of this study was to explore the underlying mechanisms of the relationship between obesity and eating disorder risk.

More specifically, I hoped to identify how weight loss efforts among those in the obese BMI range might contribute to eating disorder risk. These findings demonstrated an important role for motives in increasing eating disorder risk, and this was dependent on participants’ BMI range.

That is, one’s primary motive for pursuing weight loss was differentially associated with eating disorder risk, depending on one’s weight.

Specifically, those in the obese BMI range, who reported being motivated to lose weight to improve their appearance, were more likely to be at risk for eating disorders. This indicates that pursuing weight loss to improve appearance may be a risk factor for eating disorders among those in the obese BMI range. Similarly, for those in the normal BMI range, dieting to improve appearance was also associated with eating disorder risk. These results are consistent with previous research, which identified an association between appearance motives and unhealthy dieting practices (Putterman & Linden, 2004). However, the present findings also identified that health-based motivation was associated with lower eating disorder risk among those in the normal BMI range. This suggests that for those who are not overweight, one’s motive for pursuing weight loss may distinguish those at risk for eating disorders. However, as this was only found for those in the normal BMI range, these results suggest that focusing on one’s health may not be helpful among those in the overweight or obese BMI range. Nonetheless, given that

dieting to improve appearance was associated with eating disorder risk for obese individuals, it is important to take into account dieting motives for those in the obese BMI range or the normal

BMI range when assessing eating disorder risk.

Psychological risk factors. Negative urgency. Consistent with prior research and what was hypothesized, higher levels of negative urgency were associated with higher eating disorder risk (e.g., Fischer, Smith, & Cyders, 2008; Fischer, Peterson, & McCarthy, 2013). It was hypothesized that among those who reported calorie counting, weight monitoring or a history of high risk diets, negative urgency would distinguish those that were at particular risk for eating disorders. However, the results of this study indicated that negative urgency was strongly associated with eating disorder risk, irrespective of type of diet or dieting strategies used. Thus, these findings indicate that negative urgency is a strong and consistent predictor of eating disorder risk among individuals in all BMI ranges, regardless of their history of dieting.

Dichotomous thinking. Similarly, it was hypothesized that dichotomous thinking would put individuals at risk for eating disorders when high risk diets, calorie counting, or weight monitoring were used. However, results showed that general dichotomous thinking was not associated with eating disorder risk. Although prior research has identified general dichotomous thinking as a specific risk factor for eating disorders in the general population (Lethbridge et al.,

2011), the present findings indicated that dichotomous thinking specifically related to food or eating was associated with eating disorder risk. Interestingly, dichotomous thinking about food or eating was related to eating disorder risk regardless of BMI range or history of dieting, suggesting that this is also a strong and consistent risk factor for eating disorders, at least in the college student population. These findings indicate that general cognitive rigidity may not be a

specific risk factor for eating disorder risk among college students, but exhibiting cognitive rigidity about food and eating may present particular risk for eating disorders.

Stress while dieting. Finally, higher levels of self-reported stress while dieting were associated with higher eating disorder risk. These findings are consistent with previous research.

For example, King, Vidourek, and Schwiebert (2009) found that higher levels of perceived job stress were associated with increased eating disorder risk. Beukes, Walker, and Esterhuyse

(2010) also found a significant correlation between perceived stress and disordered eating among college students. It is notable that the present results indicate overall stress level as an important predictor of eating disorder risk, above and beyond other psychological risk factors (e.g., coping) and dieting strategies included in the model. These results suggests that overall perceived stress is an essential factor that contributes to eating disorder risk and indicates a further importance for increasing healthy and adaptive coping strategies among college students.

Weight Loss

Diet type. The present results indicated that individuals motivated to lose weight for health reasons were more likely to achieve weight loss, particularly if they reported a history of low risk diets. This is especially interesting, given how the diets were categorized, as the low risk diet group including those who only endorsed a history of low risk diets. Those in the low risk diet group did not endorse ever trying any moderate or high risk diets. Prior research had remained inconclusive in regards to the impact of motivation on achieved weight loss, and these results demonstrated that pursuing weight loss for health reasons was associated with greater weight loss, particularly when using low risk diets (e.g., Jenny Craig). These findings suggest that for all individuals, focusing on health when pursuing weight loss and using a low risk diet might be the best approach to promote weight loss success.

BMI. This study also explored how one’s BMI range when initiating a weight loss attempt might influence weight loss success. Although it might seem obvious that a higher initial

BMI would result in higher amount of weight lost, weight loss was operationalized as the total percentage of body weight lost. BMI was also included in the model, therefore controlling for initial starting weight. It was examined whether individuals in the normal, overweight, or obese

BMI range reported differences in their weight loss. These results showed that those who were not overweight and were motivated to lose weight to improve their appearance-for-others were more likely to achieve weight loss. This means that individuals who want to lose weight to improve attractiveness or relationships with others are more likely to lose weight than those motivated due to dissatisfaction with their body image or for health reasons.

Dieting strategies and psychological factors. Consistent with prior research and hypotheses, weight monitoring was an important factor associated with a higher likelihood of weight loss success (e.g., Kruger et al., 2006). Thus, regular weighing appears to be an important weight loss strategy. In addition, those who reported a higher typical level of adherence to their diet program were more likely to achieve weight loss. This finding is also consistent with prior research, which has identified the importance of adherence in achieving weight loss. For example, Alhassan, Kim, Bersamin, King, and Gardner (2008) found that, regardless of diet plan, those who achieved the most weight loss exhibited higher levels of adherence to the diet plan. In other words, for achieving weight loss, it matters less which diet one chooses, and more whether one can stick to the diet long enough to lose weight. However, even having this knowledge is not sufficient for helping individuals actually achieve their goals. Many individuals will report that they have difficulty adhering to their diets. Therefore, future research is needed on what factors contribute to the ability to adhere to a diet plan, including both those related to the diet plan

itself, as well as psychological factors that correspond to behavior maintenance and change. It may be that conscientiousness is an important personality trait that is associated with higher adherence, or perhaps certain types of diets (e.g., low risk diets) are associated with higher adherence because they are easier to adhere to, or a combination of these factors.

In regards to psychological factors, the present results did not identify any specific psychological factors that were associated with weight loss besides motivation. Neither dichotomous thinking, negative urgency, or coping were significant. Similarly, self-reported levels of boredom, stress, or feelings of deprivation were not associated with weight loss success.

Although it was hypothesized that these psychological factors would be associated with a lower likelihood of weight loss success, this was not the case. In fact, psychological factors neither promoted nor hindered weight loss success, suggesting that they were largely unimportant to achieving weight loss. This is contrary to prior research, which has identified factors like dichotomous thinking as negatively predicting weight loss success (e.g., Byrne et al., 2004). This may be because Byrne and colleagues (2004) were specifically addressing weight loss maintenance rather than weight loss itself and perhaps dichotomous thinking prevents individuals from maintaining weight loss, but not achieving it. However, it is equally likely that other factors included in the model, such as weight monitoring and adherence, were simply more important than psychological factors in predicting weight loss. Furthermore, although it was hypothesized that calorie counting would predict weight loss based on prior research (e.g.,

Kruger et al., 2006), the results of this study did not support this hypothesis. It is possible that these factors become less important when other variables, such as weight monitoring and motivation are in the model, and previous research had not yet simultaneously included all factors in one model. This may help explain why calorie counting, as well as the included

psychological factors, did not prove to be as important in predicting weight loss. However, it is notable that weight monitoring showed an effect over calorie counting. Perhaps counting calories is not necessary or essential to achieving weight loss. In fact, given that calorie counting is associated with eating disorder risk among those who are motivated to lose weight for appearance, perhaps those who are at higher risk for eating disorders based on other psychological factors (e.g., appearance-based motivation, negative urgency) would benefit from staying away from counting calories when pursuing weight loss. An important intervention strategy could be to help individuals understand that not only is calorie counting less important

(potentially unimportant) to reaching their goal, but also puts them at risk for eating disorders.

Eating Disorder Risk and Weight Loss

One of the final aims of this study was to identify factors that influence both weight loss and eating disorder risk. Although preliminary analyses indicated some overlapping factors that were associated with both eating disorder risk and weight loss, when all factors were entered into one model, several differences emerged. For eating disorder risk, psychological factors played a more important role. For weight loss, adherence to a diet plan and weight monitoring were important. However, motivation (appearance-based versus health-based) proved to be influential in both. Among those who were not overweight, dieting to improve appearance was associated with eating disorder risk, but this also promoted weight loss. Therefore, while these individuals might be more successful in achieving weight loss when focusing on their appearance, it might also increase their risk for eating disorders. Conversely, these results show that focusing on health when attempting weight loss may decrease eating disorder risk for those who are not overweight. This means that when pursuing weight loss, those in the normal BMI range may

benefit from focusing on body acceptance and emphasizing that their BMI is in the healthy range to avoid eating disorder risk.

Implications

Implications for eating disorder risk and obesity. The present study had several aims, but most importantly, it was driven by the demanding question of how to reduce obesity without conferring risk for eating disorders. As previously mentioned, approximately 1 in 3 Americans are obese (Nicklas et al., 2012) and 1 in 3 American youth are overweight or at risk for being overweight (Eddy et al., 2007). Furthermore, the association between obesity and disordered eating is well known (Kass et al., 2016), but highly overlooked in clinical settings when assessing eating disorder risk. Moreover, it was not only the epidemic of obesity and the associated medical problems that inspired this study, but specifically the lack of knowledge about how weight loss can be achieved among those who medically need to pursue weight loss, without increasing risk for eating disorders. As mentioned previously, it has been suggested that many individuals with eating disorders initiate a diet with the goal of losing weight, and then develop disordered eating symptoms (Sim, Lebow, & Billings, 2013). The results of this study help shed light on the potential mechanisms for this relationship. Findings indicate that losing weight to improve one’s appearance is associated with an increased risk for eating disorders among individuals who are obese. Perhaps obese individuals initiate weight loss because they are dissatisfied with their body image. However, they might also exhibit high levels of psychological risk factors, which would further increase their risk for eating disorders. The results of this study therefore suggest that motivation is an important point of intervention for individuals who are obese and desire to lose weight. In particular, helping them shift their focus to improving health would decrease their eating disorder risk. Furthermore, once their focus has shifted to health,

encouraging a low risk diet like Nutrisystem would increase their likelihood of achieving weight loss.

It is important to note that approximately 2/3 of this study’s total sample reported a recent weight loss attempt, indicating that weight loss attempts are common among college students. In addition, among those who reported a recent weight loss attempt, although 1/2 fell in the normal

BMI range, about 30% fell in the overweight BMI range and 20% fell in the obese BMI range.

This indicates that not only is overweight and obesity a prevalent concern among college students, but that these students might be at particularly high risk for eating disorders.

Specifically, college students in the obese BMI range might feel self-conscious about their weight due to the surrounding social pressures to fit in with their peers. This might motivate them to pursue weight loss using various strategies or diet programs. Furthermore, these weight loss attempts would likely be reinforced by their peers, regardless of the strategies used (i.e. whether these strategies are drastic or whether they are even developing an eating disorder). This suggests that college students in the obese BMI range may be of particular focus in terms of intervention and prevention for eating disorder risk, and the findings from this study can inform medical professionals and others on how we can best help these students.

The present results inform our understanding of how medical professionals can facilitate weight loss among obese individuals in a way that minimizes risk for eating disorders. Medical professionals should focus on health indicators when patients report a desire to lose weight and screen for body dissatisfaction and appearance-based motivation. This would create an opportunity for a psychological referral, if individuals report a strong desire to change their appearance as their primary motivator. Referring to behavioral health could facilitate change in their motivation for weight loss, thereby decreasing their eating disorder risk. Furthermore,

medical professionals can encourage a low risk diet, which, combined with a focus on health, would increase the likelihood of weight loss and decrease the likelihood of eating disorder risk.

Medical professionals should also screen for psychological risk factors (i.e. negative urgency, dichotomous thinking in relation to food/eating, overall stress) and make a behavioral health referral for those who endorse higher levels of these factors.

For example, a primary care provider might see an obese patient with a recent diagnosis of Type II diabetes. The patient might report concerns about how their weight is impacting their health. The present results suggest that the provider should first administer brief instruments to screen for psychological risk factors. He/she should then have a conversation with the patient to provide psychoeducation about their risk factors. The provider should also encourage and reinforce the patient’s desire to manage their Type II diabetes and discuss other health benefits of weight loss, such as lowering blood pressure, reducing cardiometabolic risk, and improved quality of life.). The provider should then suggest a low-risk diet, such as Nutrisystem or the

Step Diet. Finally, the provider should emphasize the importance of regular weight monitoring and adherence to the low risk diet in order to promote the highest likelihood of successful weight loss, and perhaps make a psychological referral, if the results of the psychological screeners indicated higher levels of psychological risk factors.

From a clinical perspective, a clinician might see a student in a counseling center in the obese BMI range, who reports problems with binge eating and a desire to lose weight due to social pressures and dissatisfaction with her appearance. The clinician should also administer brief psychological screeners to assess for other psychological risk factors (i.e. negative urgency, dichotomous thinking about food/eating, and overall stress). Then, the clinician could provide psychoeducation about the student’s identified risk factors and have a conversation about how

these might be contributing to her symptoms and how they might impact weight loss success.

Furthermore, the clinician should help the student shift her focus to her health. For example, in collaboration with the student’s primary care provider, they could discuss how her weight is related to her physical health and identify physical health risks that may be an important area of focus (e.g., blood pressure, risk for diabetes). By shifting her focus away from her appearance, the clinician would be emphasizing ways to improve her physical health and quality of life versus body dissatisfaction. The clinician would also address any other identified psychological risk factors. For example, if the student is reporting high levels of stress, the clinician would introduce helpful stress management strategies and coping techniques. If the student reports high levels of dichotomous thinking about food or eating, the clinician would focus on increasing cognitive flexibility about food and eating. Finally, given this study’s findings, the clinician would work with the student’s primary care provider and perhaps a nutritionist, to encourage a low risk diet, such as Nutrisystem or the Step Diet, and adherence to the diet plan. Given the student’s initial reason for weight loss being appearance-based, the clinician would discourage calorie counting as a weight loss strategy for this student. The present findings indicated that pursuing weight loss to improve appearance increases eating disorder risk among those who count calories. Furthermore, focusing on appearance increased eating disorder risk among obese individuals in this study. Therefore, calorie counting would likely not be helpful for weight loss for this student and increase eating disorder risk. This type of approach, including both weight loss strategies and psychological factors, might help address the problem of obesity and disordered eating from a multidimensional approach that could help target both eating disorder symptoms (e.g., binge eating) as well as obesity.

Implications for eating disorder risk for those in the normal BMI range. Given the results of this study, students in the normal BMI range appear to be at risk for eating disorders, especially when motivated to lose weight to improve their appearance. However, these students are also more likely to achieve weight loss if focused on their appearance, as the results of this study indicated that focusing on appearance was associated with weight loss among those in the normal BMI range. This suggests that, although these individuals may be successful in achieving weight loss, they might also develop disordered eating attitudes or behaviors in the process. Most importantly, based off these students’ BMI range, weight loss is not medically beneficial or necessary and would likely increase their risk for eating disorders. The results of this study identify this group of students (i.e. those in the normal BMI range with a desire to lose weight to improve their appearance) as an important group to target for prevention and intervention.

It is more likely that individuals in the normal BMI range with a desire to lose weight or dissatisfaction with their body weight or shape would present in a clinical setting. The clinician should then be able to assess for psychological risk factors for eating disorders and provide psychoeducation about both eating disorder risk and weight loss. For example, the clinician could have a conversation, in combination with the individual’s primary care provider, about the lack of medical necessity for weight loss. This would provide an opportunity to discuss the individual’s motivation for weight loss, which would likely be focused on appearance, and allow for the clinician to focus on shifting the conversation to body dissatisfaction. The clinician and client could collaboratively create goals to work toward body acceptance and brainstorm other ways to improve body satisfaction that don’t involve weight loss (e.g., exercise, practicing gratitude). The clinician could then use the information from the screeners for psychological risk factors to address ones that may be concerning and particularly susceptible to treatment, such as

dichotomous thinking about food or eating and overall stress. With this approach, the clinician would not only work with the student on their current concerns (e.g., body dissatisfaction), but also target risk factors that might contribute to further development of eating disorder symptoms.

By addressing these risk factors, the clinician would be taking a preventative approach and decreasing the likelihood of an eating disorder in the future. If individuals who are not overweight present to primary care providers with concerns about weight loss, medical providers should provide psychoeducation about their level of risk for eating disorders and even screen for psychological risk factors. They should also make a psychological referral, as these individuals might benefit from interventions aimed at body acceptance and prevention from eating disorder risk.

Implications for weight loss in general. In terms of weight loss, higher levels of adherence and weight monitoring were associated with greater weight loss success, regardless of diet group or BMI range. These results suggest that weighing oneself regularly and consistently adhering to a diet program are most important in successfully losing weight, which is consistent with previous research (e.g., Kruger et al., 2006, Webber, Tate, Ward, & Bowling, 2010, respectively). Furthermore, the results of this study indicated that other strategies, such as pursuing a low carbohydrate/fat/calorie diet and calorie counting, were not important in achieving weight loss. These findings suggest that perhaps the type of diet or strategy may not be important when pursuing weight loss, but rather being consistent with the diet program chosen.

In terms of which type of diet individuals choose, initial analyses indicated that a history of trying higher risk diets was associated with a greater weight loss, which suggests that higher risk diets (e.g., very-low-calorie diet) might promote more weight loss. However, type of diet did

not emerge as an important factor in predicting weight loss when controlling for other factors.

Furthermore, it might be more difficult to adhere to a higher risk diet, which would decrease the likelihood of weight loss success. Moreover, initial analyses identified an association between history of high risk dieting and increased risk for eating disorders. Thus, it is possible that using high risk diets would not only make it more difficult to adhere to the diet and lose weight, but they may also increase risk for eating disorders. Therefore, it is important to encourage a low risk diet among those trying to lose weight, particularly in combination with focusing on improvements in health as a result of weight loss, and emphasize the importance of adherence.

However, further research is needed to investigate more specifically the relationship between type of diet and adherence.

Limitations and Future Directions

Classification of diets. One of the novel aspects of this study was the categorization of specific diet plans into low, moderate, and high risk diet groups and identifying how type of diet is associated with eating disorder risk and/or weight loss. In this study, diet group was operationalized by categorizing students into one group based on their self-reported history of diets tried, such that those who reported trying at least one high risk diet were categorized into the high risk diet group. This created a significantly higher number of students in the high risk diet group compared to the moderate or low risk diet group. Although this study controlled for total number of diets tried as a way of isolating the effect of the level of risk of diet on eating disorder risk, rather than the overall dieting attempts, future studies should statistically refine a classification system for specific diets (e.g., using latent class analysis).

This study classified specific named diets into low risk, moderate risk, and high risk groups based on theoretical understanding of shared elements of the diet and how these elements

might contribute to increased eating disorder risk. However, this system was not tested empirically, with the exception of demonstrating a progressive increase in mean level of risk for eating disorders from the low risk diet group to the high risk diet group. Furthermore, after examining the reported frequencies of diets tried, it is notable that a large portion of diets tried reported in the moderate risk diet group constitute plant-based or vegetarian-based diets. These diets may differ in nature from other diets in this group, such as Weight Watchers, particularly when accounting for the reasons that people pursue vegetarian or plant-based diets. Especially given that motivation was identified as an important factor that was associated with both eating disorder risk and weight loss. With respect to vegetarian diets, reasons might include weight loss, health, or values, which might be differentially associated with eating disorder risk or weight loss. Therefore, an empirical test of this classification system, which takes into account the potential differences in vegan and vegetarian diets, is needed to more clearly determine the best way to categorize diets.

Lack of psychological factors predicting weight loss. Another novel and unique aspect of this study is simultaneously investigating the constructs of weight loss and eating disorder risk and identifying contributing factors to both, including psychological factors. However, the results of this study did not identify any specific psychological factors contributing to weight loss, with the exception of an interaction with health-based motivation. Instead, adherence and weight monitoring were identified as most important in achieving weight loss. This is surprising, as it seems that psychological factors would play an important role in achieving weight loss, such that higher levels of certain traits would predict a higher or lower likelihood of achieving weight loss. In fact, prior research has identified dichotomous thinking, using food to cope, and eating impulsively as factors that hinder weight loss success and weight loss maintenance (Byrne et al.

2004; Ohsiek & Williams, 2011). However, these studies only examined the role of psychological factors in weight loss and did not include specific diets and dieting strategies as predictors.

Thus, further research is needed to identify important psychological factors or personality traits that distinguish those who are more likely to achieve weight loss when accounting for factors like weight monitoring and adherence. For instance, Aguera and colleagues (2015) identified that higher symptoms of anxiety and lower levels of depression were associated with successful weight loss among patients who completed bariatric surgery. Perhaps neuroticism or anxiety increases the likelihood of adherence, which predicts weight loss. In addition to anxiety, conscientiousness might be an important predictor of weight loss, as higher levels of conscientiousness might also be associated with better adherence and regular weight monitoring, which predict weight loss. In contrast, depression symptoms could negatively impact adherence due to symptoms of low energy, low mood, and lack of motivation. Emotion dysregulation might also inversely predict weight loss. Individuals with greater difficulties regulating day-to-day emotions may be more likely to use food as a coping strategy. All of these factors, including anxiety, depression, conscientiousness, emotion regulation, and using food to cope, would be important to take into account in future studies to identify how they relate to weight loss.

Furthermore, all of these factors might not only predict weight loss, but also contribute to eating disorder risk. For example, eating disorders are highly comorbid with both anxiety and depression (Blinder, Cumella, & Sanathara, 2006; Jordan et al., 2008). Furthermore, neuroticism and conscientiousness have been associated with increased eating disorder risk (Podar, Hannus,

& Alik, 1999; Ghaderi & Scott, 2000). Therefore, these personality traits may differentially predict weight loss and eating disorder risk. Perhaps moderate levels of conscientiousness are

helpful for weight loss, but higher levels of conscientiousness increase risk for eating disorders.

It may also be that there are important interactions with BMI or diet group and these factors. For example, it may be that conscientiousness predicts weight loss across all BMI ranges, but only predicts eating disorder risk among those in the normal BMI range. These psychological factors could be included in future studies in a latent profile analysis to distinguish between profiles of students at varying levels of weight loss and varying levels of eating disorder risk.

Need to identify other factors associated with both eating disorders and weight loss.

One of the questions I investigated was how individuals can pursue weight loss without creating risk for eating disorders, and the results of this study began to answer this question, particularly when taking into account BMI range. This study identified one factor – health-based motivation

– as potentially protective from eating disorder risk (i.e. inversely predicting eating disorder risk), and health-based motivation was also associated with greater weight loss. Future studies could further investigate whether there are certain dieting strategies or psychological factors that both promote weight loss and serve as protective factors for eating disorder risk.

While the present study was focused specifically on diet modification strategies, other relevant behaviors, such as exercise and alcohol intake, should be included in future research.

These may also indirectly contribute to both weight and eating disorder risk. For example, exercise interventions have been successful in achieving weight loss (e.g., McTiernan et al.,

2007). In fact, Cudjoe, Moss, and Nguyen (2007) suggested that exercise interventions might be equally effective to diet interventions for weight loss. Furthermore, exercise could be a protective factor from eating disorder risk. However, exercise could also increase eating disorder risk, particularly in combination with certain psychological traits. Additionally, alcohol use is associated with both eating disorder risk (e.g., Krug et al., 2009) and weight problems (Lloyd-

Richardson, Lucero, DiBello, Jacobson, & Wing, 2008; French, Norton, Fang, & Maclean,

2010). Thus, future studies could include alcohol use as an important factor that may differentiate those at risk for eating disorders and/or those who struggle with weight loss.

Perhaps a combination of wanting to lose or maintain weight, while continuing to drink alcohol, leads to unhealthy dieting practices and increased eating disorder risk. In fact, recent research investigating a new concept of “drunkorexia” has shown that college students who use alcohol report both exercising and restricting their diet either after or before using alcohol as a compensatory strategy (Bryant, Darkes, & Rahal, 2012). Although this study identified several factors associated with eating disorder risk and weight loss, future studies should examine how other factors (e.g., exercise, alcohol use) contribute to both. Future studies might also utilize these identified relationships and conduct a latent profile analysis to identify profiles of individuals at varying levels of eating disorder risk and/or weight loss based on BMI, psychological factors, and dieting strategies.

Methodological considerations. This was an initial and exploratory study on the relationship between dieting strategies and eating disorder risk and how psychological factors may impact this relationship. The results of this study indicated several important relationships; however, there are certain limitations to take into account. First, weight loss was operationalized as the total percent of body weight lost during participants’ most recent weight loss attempt.

However, the recency or length of this attempt was not taken into account. Participants were asked to report on weight lost during their most recent attempt at weight loss, and, while some denied any recent weight loss attempt and were excluded from analyses, the recency of weight loss attempts that were reported was unknown. In addition, diet group was operationalized based on history of diets tried, and this did not necessarily coincide with the most recent weight loss

attempt. Therefore, participants may have endorsed a history of certain diets, but may not have implemented these diets during their most recent weight loss attempt. However, the purpose of the present study was to get a sense for the overall dieting strategies used and history of dieting, and whether this set individuals up for weight loss success or failure, as well as culminated risk for eating disorders. Nonetheless, future research interested in examining the specific diet type and its relation to weight loss during a particular attempt could address this question directly.

Use of college student sample. Additionally, this study utilized a college student sample; thus, the generalizability of these findings is limited to similar populations. Future research should investigate these questions in other samples and settings (e.g., community, medical setting) to identify any similarities or differences. For example, obesity might be a more prevalent concern in a community sample or an older age sample (Edson, Sierra-Johnson, &

Curtis, 2009), which would increase their risk for eating disorders. Research has shown that impairment due to eating disorder symptoms is associated with age, such that older individuals experienced more impairment (Ro, Bang, Reas, & Rosenvinge, 2012). Dingemans and van Furth

(2012) identified that obese individuals with binge eating problems were significantly more likely to be older than non-obese individuals with binge eating. However, eating disorder symptoms are more common among younger age groups (Ng, Cheung, & Chou, 2013). Perhaps other factors aside from age (e.g., social pressures) increase eating disorder risk among younger populations. Thus, prior research appears mixed in regards to the association between age and eating disorder risk. Although this study included age as a control variable and results did not indicate age as a significant predictor, the distribution of age in this sample was mostly college- aged students (i.e. 18-22 years old). Therefore, results might vary based on the age of the sample.

For example, Svetkey and colleagues (2014) found that older adults experienced more weight

loss and better maintained weight loss over 3 years than younger adults. Perhaps this is due to differences in motivation, such that older adults are more focused on health and thus, achieve greater weight loss. In contrast, Alfonsson, Sundbom, and Ghaderi (2014) found that older women were less likely to experience weight loss success, if they reported a loss of control over eating, but these findings did not apply to younger women or men. It would be important to investigate this study’s questions in a community sample and/or an older age population to explore potential differences in weight problems, dieting efforts, and eating disorder risk.

The use of a college student sample in the present study also limits generalizability of findings to a university setting. Other settings (e.g., medical/in-patient) may yield different findings. For example, individuals in a medical setting (e.g., hospital patients) may endorse higher levels of health-based motivation due to higher levels of health concerns. These individuals might also be more likely to adhere to a diet plan. Due to the structured environment of a hospital, combined with the close supervision of several medical professionals, hospital patients may experience greater weight loss success. However, it is less clear how this would impact their risk for eating disorders. It would be informative to investigate the present study’s questions in a hospital setting to determine how one’s environment impacts weight loss as well as eating disorder risk. It may even be that some individuals with obesity require a structured, inpatient type of setting to achieve and maintain successful weight loss, not unlike some individuals with eating disorders.

Gender differences. While this study included gender as a control variable in all analyses, and it is notable that gender was consistently associated with eating disorder risk. It is also well-known that the prevalence of eating disorder symptoms is significantly different between men and women (e.g., Striegel-Moore et al., 2009). This study’s sample did not include

enough participants to be able to separate analyses by gender to determine gender differences in findings. Future research should examine gender differences in both weight loss and eating disorder risk, while including the role of dieting strategies and psychological risk factors. Prior findings indicate that this would be important. Notably for the present study, women are more likely to engage in an organized program to pursue weight loss, whereas men report that their weight loss is self-directed (Crane, Jeffery, & Sherwood, 2017; Tsai, Lv, Xiao, & Ma, 2016).

Therefore, perhaps women are more likely to try specific diet plans and thereby, more likely to pursue high risk diets, which increases their risk for eating disorders. In addition, Tsai and colleagues (2016) found gender differences in achieving weight loss. For example, among overweight and obese adults, men were less likely to report a history of weight loss attempts, but they were more likely to successfully lose and maintain weight loss than women (Tsai et al.,

2016). Perhaps men are overall less concerned with their weight, particularly if they are in the overweight versus the obese BMI range, which could explain their lower frequency of weight loss attempts. Interestingly, previous research has identified different motives for men and women for engaging in healthy versus unhealthy dieting practices. Markey and Markey (2005) found that men and women both reported using healthy dieting practices because they perceived themselves as heavier than others. However, they found that use of unhealthy dieting practices differed between men and women. Women reported using unhealthy dieting strategies because they wanted to be thinner, regardless of their BMI range. In comparison, men only reported using unhealthy dieting when they were overweight (Markey & Markey, 2005). Thus, future research should investigate gender differences in the present findings to examine how body dissatisfaction relates to eating disorder risk in different BMI ranges between men and women.

Related to the present study’s findings, it is interesting that women reported an improvement in self-esteem as motivation for weight loss (Crane et al., 2017). This might reflect the present study’s findings on the association between appearance-based motivation and eating disorder risk. Perhaps women experience their appearance as part of their sense of self-worth, and this increases their risk for eating disorders. For example, it may be that both men and women being motivated to lose weight to improve their appearance increases risk for eating disorders, but maybe the risk is stronger for those who closely connect their appearance to their sense of self (i.e. women). This is consistent with prior research which has found that women with binge eating problems reported higher levels of body dissatisfaction and drive for thinness than men (Barry, Grilo, & Masheb, 2002; Lewinsohn, Seeley, Moerk, & Striegel-Moore, 2002).

Perhaps the societal expectations and pressures for women to be thin create a stronger drive for thinness and body dissatisfaction among women. This may be associated with lower self-esteem in women, which could further drive continuous dieting efforts and increased eating disorder risk. Furthermore, given that women are more likely to experience body dissatisfaction, perhaps women experience body dissatisfaction at lower BMI levels than men, which leads to weight loss efforts when they are not necessary. This connects with the present study’s findings, as 50% of students in this sample who reported a recent weight loss attempt were in the normal BMI range, indicating that half of the students with a recent weight loss attempt did not actually need to lose weight. Overall, gender differences appear evident in terms of both dieting strategies, weight loss outcomes, and eating disorder risk. Thus, future research should investigate these differences.

Findings would help inform intervention approaches specific to men and women for both weight loss and eating disorder risk.

Directionality of effects. Finally, this study was cross-sectional in nature and the findings are only interpretable to the extent of identifying associations between variables.

Therefore, the directionality of effects is assumed and other potential interpretations are be discussed.

Eating disorder risk. Beginning with type of diet, it was assumed that the type of diet would predict one’s eating disorder risk. Conversely, it could be that someone’s level of eating disorder symptoms predicts the type of diet chosen. For example, an individual with higher levels of body dissatisfaction and drive for thinness (i.e. higher eating disorder risk) may be more likely to try a more risky diet. Particularly if someone who is not overweight desires to lose weight quickly, they could try a high risk diet (e.g., fasting) in order to achieve faster results.

This could create a dangerous feedback loop. Someone who is dissatisfied with their body weight or shape and has a strong drive for thinness is already at increased risk for eating disorders. This individual might not be overweight, but still want to lose weight. They would try a high-risk diet to achieve weight loss results quickly, which could further lead to increased eating disorder risk whether they achieve weight loss or not. In addition to type of diet, it was also assumed that dieting strategies predict eating disorder risk. Instead, it could be that eating disorder symptoms predicts the type of dieting strategies one utilizes when pursuing weight loss.

For example, an individual with higher levels of body dissatisfaction and drive for thinness might be more likely to monitor their weight regularly and count calories either to prevent weight gain or because they are continuously trying to lose weight.

In regards to BMI, it was assumed that obesity is a risk factor for eating disorders.

However, it is very likely that higher levels of eating disorder symptoms are a risk factor for obesity. For example, eating disorder symptoms, such as frequent binge eating, could contribute

to weight gain and a higher BMI, particularly if there is no use of compensatory behaviors. In fact, studies have identified that some individuals with binge eating problems report binge eating prior their first diet attempt, whereas others report a dieting attempt prior to their first binge eating episode (Spurrell, Wilfley, Tanofsky, & Brownell, 1997; Spitzer et al., 1993). Spurrell and colleagues (1997) also found that these groups are differentiated by the age of onset of their first binge, such that those who reported binge eating first reported a mean age of onset 12-years-old versus 26-years-old for those who reported trying dieting first. This may point to varying trajectories or etiology for different individuals with binge eating problems, indicating the importance of examining the impact of disordered eating on weight as well as how weight/BMI might predict eating disorder risk.

The present findings assumed that focusing on appearance when pursuing weight loss increases eating disorder risk among those in the normal or obese BMI range. However, perhaps it is that higher levels of eating disorder symptoms predict either a normal or obese BMI range, among those who are focused on appearance. There may be a third variable that differentially predicts obesity among some, but not others. For example, perhaps among people who are focused on appearance, using effective weight management strategies (e.g., weight monitoring, adherence to diet) predicts a normal BMI range, whereas using ineffective strategies leads to obesity. Future studies could investigate which factors differentiate those with high levels of eating disorder symptoms and a focus on appearance who are in the normal BMI range versus in the obese BMI range. This would help identify those with high eating disorder symptoms and a focus on appearance, who are also at risk for obesity. Furthermore, it was assumed that those in the normal weight range, who were motivated by health reasons, were at lower risk for eating disorders. However, it may be that those with less eating disorder symptoms are more likely to

be in the normal weight range if they are motivated by health reasons. That would mean that among those at low levels of eating disorder symptoms, being motivated by health predicts a normal weight. This would demonstrate that focusing on health is protective from being overweight or obese. It could be equally helpful to examine these effects in order to determine what differentiates individuals with equally high or equally low levels of eating disorder risk, who are in different weight ranges.

Finally, it was assumed that type of diet, dieting strategies, and psychological factors all predicted eating disorder risk. However, it could be that certain psychological factors predict dieting strategies or type of diet. For example, individuals with higher levels of dichotomous thinking might be more likely to utilize high risk diets because they prefer an “all-or-nothing” type of diet with rigid limitations as opposed to a more flexible diet (i.e. low risk diet). Thus, it is possible that type of diet could mediate the relationship between dichotomous thinking and eating disorder risk. Similarly, motivation might predict type of diet as well. For example, those motivated by appearance might be more likely to try high risk diets because they would like to see results quickly due to their focus on appearance. In comparison, those motivated by health might be more likely to try low risk diets because they are more focused on gradual changes to improve long-term health outcomes.

Weight loss. In regards to weight loss, it was also assumed that the type of diet would predict achieved weight loss. Specific to this study’s findings, it was assumed that a history of low risk dieting would increase the likelihood of weight loss success among those motivated by health reasons. Conversely, it could be that those who achieved weight loss are more likely to try a low risk diet. Particularly if taking into account adherence as an important factor, maybe those with more weight loss success are more focused on adherence and therefore more likely to

choose a lower risk diet (e.g., Jenny Craig). The same could be hypothesized for dieting strategies. For example, rather than certain dieting strategies leading to weight loss, it could be that achieving weight loss leads individuals to begin to use certain dieting strategies. For example, perhaps achieving weight loss leads individuals to monitor their weight to prevent weight regain.

These remaining questions provide an excellent platform for future research. One of the meaningful aspects of these results, particularly the interactions identified with BMI range and type of diet, is that they allow for identification of groups of students, who may be at varying levels of risk for eating disorders. Furthermore, assessing BMI range is informative in terms of determining whether individuals might medically benefit from pursuing weight loss as well as whether they are at risk for eating disorders. Future research could utilize these findings and conduct a longitudinal study, using a quasi-experimental design. Researchers could group students by type of diet and/or BMI range and examine changes in eating disorder risk and weight loss across time, while measuring psychological risk factors and dieting strategies as predictor variables. They could also be asked to focus on health reasons or appearance reasons using prompts given each day or each week. Cross-lagged panel design could also be used to simultaneously examine the effect of baseline eating disorder symptoms on future diet attempts and strategies as well as how those diet attempts and strategies then predict future eating disorder risk. This would further improve our understanding of how psychological factors, in combination with type of diet or dieting strategies and BMI, might contribute to eating disorder risk and/or weight loss over time.

Conclusions

This was the first study to simultaneously investigate the constructs of weight loss and eating disorder risk to determine how type of diet, BMI, dieting strategies, and psychological factors might contribute to risk for eating disorders and/or weight loss. This was also the first study to categorize specific diets into low, moderate, and high risk categories to assess their risk for eating disorders. This produced multiple important findings.

First, this study identified a role for type of diet in both weight loss and eating disorder risk. Specifically, those with a history of moderate risk dieting were at increased risk for eating disorders, if they tended to react to stress with experiences of negative emotions, such as sadness, loneliness, and helplessness. In addition, those with a history of low risk dieting were more likely to lose weight if they reported being motivated by health reasons. Second, this study identified the importance of negative urgency, dichotomous thinking in relation to food or eating, and overall stress while dieting as important factors associated with eating disorder risk generally. In contrast, these psychological factors did not seem to play an important role in contributing to weight loss. Finally, this study identified an essential role for motivation as well as BMI in both eating disorder risk and weight loss. Specifically, health-based motivation seemed to be a protective factor from eating disorder risk, particularly among those who are not overweight, whereas appearance-based motivation was associated with eating disorder risk among those in either the normal or obese BMI range. Additionally, among those who counted calories, appearance-based motivation particularly served as a risk factor for eating disorders.

Overall, this study has important theoretical and practical implications. Theoretically, it demonstrates roles for diet type, BMI, and dieting strategies in both weight loss and eating disorder risk and confirms the importance of psychological factors in eating disorder risk.

Furthermore, these findings indicate that one’s motivation for dieting can differentiate those at risk for eating disorders and highlights the importance of focusing on health to achieve weight loss. Practically, these results are helpful to individuals as well as clinicians/providers.

Individuals may benefit from being informed about which strategies are helpful when pursuing weight loss, given their weight range, as well as knowing which psychological factors put them at risk for eating disorders. Providers can use this information to tailor their approaches to patients interested in weight loss, while taking into account their weight range. They can also utilize these findings to screen patients for eating disorder risk and identify those who might benefit from psychoeducation and other psychological prevention efforts. Furthermore, this study provides insight into how we can identify those who may need to lose weight (e.g., for medical reasons) versus those who might desire to lose weight for appearance-based reasons, and allows us the opportunity to screen these individuals for psychological factors that would increase risk for eating disorders. This could help create appropriate referrals for intervention, which might include behavioral, psychological, and/or medical intervention to target indicated factors with the ultimate goal of promoting weight loss among those who might medically benefit from achieving weight loss, as well as decreasing risk for eating disorders among those who are at risk.

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Table 1 List of diets classified by level of risk. Category/Diet Description Low Risk Balanced Restriction Diets Restrict caloric/food in balanced way. Body for Life Whole grain carbohydrates and lean protein, includes one free day Eat-clean diet Exclude foods with preservatives, include lean protein and complex carbohydrates Macrobiotic diet Avoid processed foods Mediterranean diet Emphasizes fruit, vegetables, legumes, grains, and lean protein Best Life Diet Includes phase to address psychological part of eating, followed by weight-loss phase that emphasizes three meals and one snack daily Volumetrics diet Focus on eating for fullness; includes whole grains, fruit, vegetables, lean protein 3-Hour Diet Focus on eating small meals every 3 hours – balance of protein, carbohydrates, fruit, and vegetables Thin for Life diet Making small adjustments to diet, focusing on lowering fat and calories without deprivation Step diet Cut down portions by 25%, emphasizes walking Spectrum diet Increase consumption of fruit, vegetables, whole grains, legumes, decrease consumption of unhealthy fat and high fat and animal products Pre-packaged meals Nutrisystem Delivers low-calorie meals with balanced ratios of fat/protein/carbs Jenny Craig Pre-packaged meals, includes weight counseling

Moderate Risk Vegetarian Excludes meat and food containing animal by-products Frutarian Diet consisting mostly of raw fruit Lacto vegetarian Includes some dairy, excludes eggs and other animal by-products Lacto-ovo vegetarian Includes eggs and dairy Vegan Excludes food produced by animals (e.g., dairy, eggs, honey) Flexitarian Meat is occasionally consumed Pescetarian Includes fish Plant-based Includes limited animal products The Graham Diet Emphasizes whole grains, discourages stimulants Self-monitoring diets Emphasize tracking food intake The Hacker’s Diet Use self-monitoring to track calorie expenditure and consumption Weight Watchers Foods are assigned point values, eat any food within point limit Zone diet Caloric intake split into fat/carbs/protein ratio of 40:30:30 Low carbohydrate Limits consumption of carbohydrates Atkins Low carbohydrate diet Dukan Includes steps for weight loss and maintenance ITG diet Includes steps for long term maintenance South Beach Categorizes carbohydrates into “good” and “bad” Stillman diet Emphasizes 6 small meals per day

Low fat McDougall’s starch diet High calorie, high fiber, low fat diet, excludes animal foods Pritkin Principle Limits fat consumption to 10% of total calories

High Risk Meal Replacement Cookie diet Eat low-calorie cookies to replace breakfast, lunch, and snacks Shake diets Drink shakes in place of meals (e.g., Shakeology, Herbalife) Slim Fast Consume low-calorie shakes, bars, etc. except for dinner Baby Food Diet Consume baby food for meals Very low-calorie/Fasting Bretharian diet Excludes all food, belief that food is not necessary for survival KE diet Consume food through feeding tube Beverly Hills diet Fruit during first few days, gradually increase calories for 6 weeks Cabbage soup diet Heavy consumption of cabbage soup Grapefruit diet Heavy consumption of grapefruit at meal times Juice fasting Includes only fruit and vegetable juices Master Cleanse Specific form of juice fasting Liquid diet Consuming only liquids after gastric bypass surgery Cycling Diets Intermittent fasting Cycle between fasting and non-fasting Cheater’s Diet Exclude sugar, bread, alcohol, and saturated food Mon-Fri, Sat and Sun are cheat days 3-Day Diet Severe caloric restriction for 3 consecutive days out of the week

Table 2 Descriptive statistics for measured variables. Measure N Mean SD WMSI Goal 853 .0586 .05567 WMSI ProsPlan 851 82.64 17.17 WMSI Auto 846 100.64 22.05 WMSI Construal 894 16.00 6.61 WMSI Effort Inhib 902 11.39 4.54 WMSI Irregular Meals 897 11.02 4.57 WMSI Calorie Counting 903 9.40 5.22 WMSI Weight Monitoring 899 11.08 4.92 WMSI Mindful/Relax 881 36.90 7.88 WMSI Low cal/carb/fat 880 45.96 12.82 WMSI Health Eat/Portion 871 40.39 9.80 CSQ Rational 847 44.15 8.46 CSQ Detach 852 36.18 7.18 CSQ Emotion 874 38.67 8.77 CSQ Avoid 855 32.25 6.89 WLM-Q Health 720 21.50 5.76 WLM-Q App Others 789 21.55 8.80 WLM-Q App Self 730 20.79 6.24 DTEDS Eating 841 10.71 3.33 DTEDS General 822 17.20 5.31 UPPS Negative Urgency 798 25.34 6.74 TFEQ Disinhibition 893 8.87 2.48 TFEQ Hunger 885 7.34 2.47 Bored 899 1.72 1.38 Deprived 896 1.98 1.29 Stress 897 2.08 1.38 BMI 841 26.74 6.48 Total # of diets 917 2.50 2.14 Adherence 909 2.53 .78 Eating Disorder Risk 779 35.65 19.53 Weight Loss 853 .0586 .0557 Note: WMSI=Weight Management Strategies Inventory; WMSI Goal=Goal setting and monitoring; WMSI ProsPlan=Prospection (of temptation) and planning (of goal-directed and alternative behaviors); WMSI Auto=Automating behavior and routines; WMSI Construal=Reinterpreting food temptations; WMSI Effort Inhib=Inhibiting food temptations; CSQ=Coping Style Questionnaire; WLM-Q=Weight Loss Motivation Questionnaire; DTEDS=Dichotomous Thinking in Eating Disorders Scale; UPPS=UPPS-P Impulsive Behavior Scale; TFEQ=Three Factor Eating Questionnaire

Table 3 Frequencies of diets tried Diet N % of sample Low Risk Volumetrics diet 6 .7 Spectrum diet 7 .8 Step diet 12 1.3 Thin for Life diet 13 1.4 3-Hour Diet 17 1.9 Best Life Diet 26 2.8 Jenny Craig 31 3.4 Macrobiotic diet 32 3.5 Body for Life 43 4.7 Mediterranean diet 44 4.8 Nutrisystem 50 5.4 Eat-clean diet 363 39.5 Moderate Risk Stillman diet 1 .1 Pritkin principle 2 .2 The Graham Diet 2 .2 The Hacker’s Diet 2 .2 Dukan 3 .3 ITG diet 3 .3 MCDougall’s starch diet 4 .4 Zone diet 8 .9 Frutarian diet 14 1.5 Flexitarian diet 21 2.3 Lacto-ovo vegetarian diet 34 3.7 Lacto vegetarian diet 35 3.8 South Beach 38 4.1 Atkins 76 8.3 Weight Watchers 104 11.3 Plant-based diet 115 12.5 Pescetarian diet 116 12.6 Vegan diet 166 18.1 High Risk Bretharian diet 2 .2 Beverly Hills diet 4 .4 Cookie diet 7 .8 Baby food diet 8 .9 KE diet 9 1.0 Grapefruit diet 33 3.6 Cabbage soup diet 34 3.77 Master Cleanse 36 3.9 Cheater’s diet 49 5.3 Slim Fast 92 10.0

Liquid diet 99 10.8 3-Day diet 103 11.2 Shake diets 122 13.3 Juice fasting 151 16.4 Intermittent fasting 197 21.5

Table 4 Pearson correlations between predictor and outcome variables. Measure Eating Disorder Risk Weight Loss Eating Disorder Risk 1 .001 Weight Loss .001 1 Age -.001 .076** Gender .243** -.007 BMI .218** .034 WMSI Irregular Meals .348** .018 WMSI Calorie Counting .236** .148** WMSI Weight Monitoring .224** .167** WLM-Q Health .409** .160** WLM-Q Appearance-for-others .563** .091** WLM-Q Appearance-for-Self .654** .096** DTEDS Eating .540** .009 DTEDS General .379** -.022 UPPS Neg Urgency .381** .004 TFEQ Disinhibition .429** .052 TFEQ Hunger .347** -.076** CSQ Rational -.060* .042 CSQ Detach .028 .025 CSQ Avoid .317** .024 CSQ Emotion .436** -.009 Bored on diet .154** -.097** Deprived on diet .261** -.048 Stressed on diet .364** -.029 Total # of diets .235** .087* Adherence .038 .236** * <.05, ** <.005, *** ≤.000 Note: WMSI=Weight Management Strategies Inventory; CSQ=Coping Style Questionnaire; WLM-Q=Weight Loss Motivation Questionnaire; DTEDS=Dichotomous Thinking in Eating Disorders Scale; UPPS=UPPS-P Impulsive Behavior Scale; TFEQ=Three Factor Eating Questionnaire

Table 5 Linear regression analyses depicting significant relationships between predictor and outcome variables, controlling for age and gender. Measure Eating Disorder Risk (β) Weight Loss (β) BMI .229** * .022 WMSI Calorie Counting .139*** .066 WMSI Weight Monitoring .091** .109** WMSI Irregular Meals .260*** -.044 WLM-Q Health -.160*** .157** WLM-Q App Others .293*** .105* WLM-Q App Self .547*** -.085 DTEDS Eating .511*** .037 UPPS Neg Urgency .386*** .077 TFEQ Disinhibition .328*** .102** TFEQ Hunger .166*** -.114** CSQ Rational -.108** .007 CSQ Emotion .394*** -.091* Bored on diet -.004 -.087* Deprived on diet .111** .010 Stressed on diet .285*** -.016 Total # of diets .231*** .107** Adherence .070 .225*** * <.05, ** <.005, *** ≤.000 Note: WMSI=Weight Management Strategies Inventory; CSQ=Coping Style Questionnaire; WLM-Q=Weight Loss Motivation Questionnaire; DTEDS=Dichotomous Thinking in Eating Disorders Scale; UPPS=UPPS-P Impulsive Behavior Scale; TFEQ=Three Factor Eating Questionnaire

Table 6 One-way ANOVA results for between group differences on eating disorder risk

Group df(between, within) MSE F Sig. Diet type 2, 776 7231.53 19.878 .000 BMI group 2, 706 8932.57 25.800 .000 * <.05, ** ≤.005, *** ≤.000

Table 7 One-way ANOVA results for between group differences on weight loss

Group df(between, within) MSE F Sig. Diet type 2, 850 .015 4.725 .009 BMI group 2, 828 .072 24.553 .000 * <.05, ** ≤.005, *** ≤.000

Table 8 Mean eating disorder risk and weight loss across diet groups. Group N Eating Disorder Risk Weight Loss Mean (Std. Dev.) Mean (Std. Dev.) Low risk 170 28.34 (16.62) .0468 (.0472) Moderate risk 176 32.24 (19.68) .0573 (.0551) High risk 572 38.88 (19.55) .0623 (.0576)

Table 9 Mean eating disorder risk and weight loss across BMI range groups. Group N Eating Disorder Risk Weight Loss Mean (Std. Dev.) Mean (Std. Dev.) Normal range 399 31.61 (17.64) .0459 (.0421) Overweight range 246 37.95 (19.93) .0675 (.0583) Obese range 186 44.38 (18.68) .0767 (.0696)

Table 10 ANCOVA results including significant predictors and interaction terms predicting eating disorder risk.

Measure df(between) MSE F Sig. Negative urgency 1 821.27 5.681 * .018 DTEDS Eating 1 1310.93 9.069** .003 Stressed on diet 1 2025.86 14.014*** .000 BMI x WLM-Q Appearance-for-self 2 714.84 4.945** .008 BMI x WLM-Q Health 2 727.00 5.029* .007 Diet type x CSQ Emotion 2 771.83 5.335** .005 df within 365 * <.05, ** ≤.005, *** ≤.000 Note: DTEDS=Dichotomous Thinking in Eating Disorders Scale; CSQ=Coping Style Questionnaire; WLM-Q=Weight Loss Motivation Questionnaire

Table 11 ANCOVA results including significant predictors and interaction terms predicting weight loss.

Measure df(between) MSE (F) Sig. Adherence 1 .018 6.672* .010 WMSI Weight Monitoring 1 .013 4.914 .027 Diet type 1 .010 3.671* .026 BMI x WLM-Q App Others 2 .009 3.213* .041 Diet type x WLM-Q Health 2 .009 3.143* .044 Df within 535 * <.05, ** ≤.005, *** ≤.000 Note: WMSI=Weight Management Strategies Inventory; WLM-Q=Weight Loss Motivation Questionnaire

Table 12 Linear regression analyses demonstrating significant interaction effects between dieting strategies and psychological variables. Measure Eating Disorder Risk Unstandardized Beta (Std. Error) β Calorie counting x WLM-Q Appearance-for-self .042 (.020) .291* * <.05, ** ≤.005, *** ≤.000 Note: WLM-Q=Weight Loss Motives Questionnaire

Table 13 List of significant predictors for outcome variables. Factors that predict ED Risk Factors that predict weight loss Negative urgency Adherence Rigid thinking about food/eating Weight monitoring Feeling stressed on a diet BMI x Appearance-for-others motivation BMI x Appearance-for-self motivation Diet type x Health-based motivation BMI x Health-based motivation Diet type x Emotion coping Calorie counting x Appearance-for-self motivation