University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange
Doctoral Dissertations Graduate School
8-2011
Distress Tolerance, Experiential Avoidance, and Negative Affect: Implications for Understanding Eating Behavior and BMI
Christen Nicole Mullane University of Tennessee - Knoxville, [email protected]
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Recommended Citation Mullane, Christen Nicole, "Distress Tolerance, Experiential Avoidance, and Negative Affect: Implications for Understanding Eating Behavior and BMI. " PhD diss., University of Tennessee, 2011. https://trace.tennessee.edu/utk_graddiss/1103
This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council:
I am submitting herewith a dissertation written by Christen Nicole Mullane entitled "Distress Tolerance, Experiential Avoidance, and Negative Affect: Implications for Understanding Eating Behavior and BMI." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Psychology.
Derek R. Hopko, Major Professor
We have read this dissertation and recommend its acceptance:
Derek Hopko, Hollie Raynor, Greg Stuart, Betsy Haughton
Accepted for the Council:
Carolyn R. Hodges
Vice Provost and Dean of the Graduate School
(Original signatures are on file with official studentecor r ds.)
To the Graduate Council:
I am submitting herewith a dissertation written by Christen Mullane entitled “Distress Tolerance, Experiential Avoidance, and Negative Affect: Implications for Understanding Eating Behavior and BMI.” I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Psychology.
Derek Hopko Major Professor
We have read this dissertation and recommend its acceptance:
Hollie Raynor
Greg Stuart
Betsy Haughton
Accepted for the Council:
Carolyn R. Hodges, Ph.D. Vice Provost and Dean of the Graduate School
(Original signatures are on file with official student records.)
Distress Tolerance, Experiential Avoidance, and Negative Affect:
Implications for Understanding Eating Behavior and BMI
A Dissertation Presented for
the Doctor of Philosophy
Degree
The University of Tennessee, Knoxville
Christen Nicole Mullane
August 2011
ii
Copyright © 2010 by Christen N. Mullane All rights reserved. iii
Acknowledgements:
Thank you to the University of Tennessee Clinical Psychology department for funding the current study. Thank you to Drs. Gregory Stuart and Betsy Haughton, whom I am honored to have on my committee. Thanks to my family and friends, who have been so supportive of my efforts, and who have given me much needed guidance along the way. Finally, special thanks to Drs. Hollie Raynor and Derek Hopko. Through your efforts, I have learned about the kind of professional and mentor that I will strive to be. iv
ABSTRACT
Distress tolerance and experiential avoidance are important aspects of the coping process.
In the current study, both were examined in relation to Body Mass Index and self- reported disturbances in mood and eating behavior. Distress tolerance was measured behaviorally and via self-report to elucidate the manner in which a) the ability to tolerate emotional distress, and b) the ability to persist behaviorally in the presence of stress- inducing stimuli were related to self-reported levels of depression, anxiety, maladaptive eating habits, and bodily concerns. A sample of 73 undergraduate students participated, and height, weight, and waist circumference were measured. Increased experiential avoidance was associated with increased weight status; however, this was true only for the morbidly obese group (n = 1). Increased experiential avoidance and decreased self- efficacy were significantly associated with less rewarding eating experiences. Individuals with lower distress tolerance reported increased depression, anxiety, and experiential avoidance, and were more likely to indicate eating disturbances and concerns on self- report measures, although distress tolerance generally was unrelated to eating behaviors as indexed on food diaries. These results were not replicated utilizing a behavioral measure of distress tolerance. Future directions for research designed to examine these variables in overweight and obese populations are discussed. iii
TABLE OF CONTENTS
Introduction………………………………………………………………………… 1 Methods……………………………………………………………………………. 20 Results……………………………………………………………………………… 30 Discussion………………………………………………………………………….. 37 References………………………………………………………………………….. 46 Tables………………………………………………………………………………. 69 Appendices…………………………………………………………………………. 94 Vita…………………………………………………………………………………. 105 iv
LIST OF TABLES
Table 1. Descriptive Statistics for all Measures…………………………………………… 68 Table 2. Correlations: Anthropomorphic Measures, Distress Tolerance, & Experiential Avoidance ……………………………………………………………………………………………... 69 Table 3. Correlations Between Anthropomorphic Measures and Mood Measures……….. 70 Table 4. Correlations Between Distress Tolerance, Experiential Avoidance, and TFEQ Subscales …………………………………………………………………………………………….. 71 Table 5. Correlations Between Distress Tolerance, Experiential Avoidance, and EDE-Q Scales ……………………………………………………………………………………………... 72 Table 6. Correlations Between Anthropomorphic Measures & Food Record Variables….. 73 Table 7. BMI and WC Group Status Predicting Average Eating Episode Duration………. 74 Table 8. BMI and WC Group Status Predicting Total Eating Episodes…………………… 75 Table 9. BMI and WC Group Status Predicting Total Three-Day Caloric Intake………… 76 Table 10. BMI and WC Group Status Predicting Average Mood Change………………… 77 Table 11. BMI and WC Group Status Predicting Average Reward Ratings per Eating Episode ………………………………………………………………………………………..……. 78 Table 12. BMI and WC Group Status Predicting Average Perceived Control…………….. 79 Table 13. Distress Tolerance (DTS) as a Function of BMI and WC Group Status……….. 80 Table 14. Distress Tolerance (PASAT-C) as a Function of BMI and WC Group Status….. 81 Table 15. Experiential Avoidance as a Function of BMI and WC Group Status …………. 82 Table 16. BMI as a Function of Distress Tolerance and Experiential Avoidance…………. 83 Table 17. WC as a Function of Distress Tolerance and Experiential Avoidance………….. 84 Table 18. Total Caloric Intake & Average Reward per Eating Episode as a Function of Average Mood Change……………….……………………………………………………... 85 Table 19. Total Caloric Intake & Average Reward per Eating Episode as a Function of Depression and Anxiety…………………………………………………………... 86 Table 20. Mediation of the Effect of Total Caloric Intake over the course of Three Days on Body Mass Index Through Behavioral Distress Tolerance and Experiential Avoidance ……………………………………………………………………………………. 87 Table 21. Mediation of the Effect of Total Caloric Intake over the course of Three Days on Body Mass Index Through Self-Reported Distress Tolerance and Experiential Avoidance ……………………………………………………………………………….…… 88 Table 22. Mediation of the Effect of Total Caloric Intake over the course of Three Days on Waist Circumference Through Behavioral Distress Tolerance and Experiential Avoidance …………………………………………………………………………………….. 89 Table 23. Mediation of the Effect of Total Caloric Intake over the course of Three Days on Waist Circumference Through Self-Reported Distress Tolerance and Experiential Avoidance …………………………………………………………………………………… 90 Table 24. Summary of Hierarchical Regression Analysis for Variables Predicting Average Reward Ratings per Eating Episode……………………………………………… 91
1
Distress Tolerance, Experiential Avoidance, and Negative Affect:
Implications for Understanding Eating Behavior and BMI
The incidence of obesity, defined as a BMI [Body Mass Index] of over 30, has increased by 50% in the past 20 years (Carlson, 2004). According to the National Health and Nutrition Examination Survey (NHANES; Hedley et al., 2004), among American adults aged 20 years and over, 65% are overweight or obese. Of these individuals, approximately 30% are considered obese and 5% are considered extremely obese
(overweight BMI: 25.0-29.9 kg/m², class I obesity BMI: 30.0 - 34.9 kg/m², class II obesity BMI: 35.0 - 39.9 kg/m², and class III or extreme obesity BMI: ≥ 40.0 kg/m²).
Research consistently has shown that overweight and obese persons are vulnerable to increased physical and mental health problems. For example, obesity is significantly associated with cardiovascular disease, diabetes, high blood pressure, hypercholesterolemia, asthma, arthritis, and poor general health (Mokdad et al., 2003).
Obese individuals also are more likely to suffer from gout, gallbladder disease, certain cancers, and post-surgical complications (Straub, 2002; U.S. Department of Health and
Human Services: Obesity Action Coalition, 2010). Indeed, a BMI greater than 40 has been associated with a two-fold increase in mortality rates (Straub, 2002). In addition to affecting physical health, weight plays a substantial role in psychological functioning.
For example, overweight women are at increased risk for depression and suicidal ideation
(Straub, 2002), and both males and females with binge eating episodes or disorders are more likely to be diagnosed with depression, anxiety, and body image dissatisfaction 2
(Matos et al., 2002). Thus, variations in weight and eating behavior impact physical health as well as emotional states.
Given the prevalence of obesity and its impact on physical and mental health functioning, concerted efforts are needed to explore demographic and clinical correlates of obesity. Focus in this area is critical toward understanding the etiology of obesity and will be useful toward informing assessment and intervention strategies for problematic eating behavior and associated obesity. In line with these objectives, using a multi- method assessment methodology that included self-report measures, self-observation
(i.e., daily diaries), and a direct behavioral task, the current study was designed to explore how distress tolerance and experiential avoidance might be associated with obesity and problematic eating behavior.
Empirically-based Correlates of Obesity
Demographic Correlates of Obesity: Adults. Adults are classified as obese or overweight using data relating morbidity and mortality to weight status, in addition to reference population criteria (Pi-Sunyer, 1999). These data were used to create BMI categories: “underweight,” “normal,” “overweight,” and “obese.” According to
NHANES III survey data (1988-1994), prevalence rates for being overweight (BMI ≥ 25) are highest between the ages of 50 and 59 years, regardless of sex or ethnicity. When sex and ethnicity are taken into account, research shows that Mexican American men are at highest risk for being overweight. NHANES III data also show that obesity is most common among Mexican American men between the ages of 40 and 59, although obesity is more prevalent among non-Hispanic black men between the ages of 20 and 29 than 3 their Mexican-American and non-Hispanic white counterparts. Among men in general, the prevalence of obesity (BMI ≥ 30) is lowest at age 80 or higher, perhaps due to the survivor effect (individuals with lower weight are more likely to live past age 60).
Mexican American men over age 80 were observed to have the lowest prevalence of obesity.
Minority women are also more likely to be overweight than non-Hispanic white women (Crespo & Smit, 2003). In fact, NHANES III data suggest that obesity is more prevalent amongst non-Hispanic black and Mexican American women than amongst their non-Hispanic white counterparts in every age group. Most recent NHANES data (2003-
2004) indicate that the prevalence of extreme obesity (BMI ≥ 40) is greater among blacks than non-Hispanic whites (10.5% vs. 4.3%), greater among women than men (6.9% vs.
2.8%), and highest among black women (14.7%; Hensrud & Klein, 2006).
Demographic Correlates of Obesity: Children & Adolescents. Among children, the concepts of “overweight” and “obesity” are defined using a statistical approach due to the absence of outcome-based criteria (Bellizi & Dietz, 1999; Cole et al., 2000;
Guillaume, 1999; Malina & Katzmarzyk, 1999). Overweight and obesity are defined according to selected sex- and age-specific percentile rankings using a reference population. Utilizing these criteria, overweight is often defined as falling between the 85th and 95th percentile (85th ≤ x < 95th), while obesity is categorized as falling in the 95th percentile or higher (Crespo & Smit, 2003). Childhood obesity rates have tripled over the past 30 years (O’Donnell, Hoerr, Mendoza, & Goh, 2008). Currently, an estimated one in four children in the United States is overweight and roughly 11% are obese. Children 4 who are overweight tend to remain overweight as adults; in general, the risk for adult overweight is 1.5 – 2 times higher for individuals who were overweight as children.
The prevalence of overweight children and young adults increased dramatically between 1974 and 1994, with the largest increases observed among 19- to 24-year-olds
(Nicklas, Baranowski, Cullen, & Berenson, 2001). Children between the ages of 8 and 16 also were at high risk, with approximately 25% of children in this age group being overweight and 12% obese. Indeed, data from NHANES III (1988-1994) showed that
Mexican American boys and girls and non-Hispanic black girls aged 8-16 have some of the highest prevalence rates for obesity in the U.S. Among preschool children aged 4 and
5, notable increases in obesity have been observed between NHANES II and NHANES
III (Ogden et al., 1997). Among children aged 2 and younger, minority children are particularly at risk for being overweight and obesity. For example, the prevalence of obesity among Mexican American children (aged 1-2 years) was twice as high as that of non-Hispanic white children. Non-Hispanic black girls have also been observed to have higher rates of obesity than non-Hispanic white girls younger than age 2 (Dennison, Erb,
& Jenkins, 2002; Ogden et al., 1997). Given the prevalence of obesity among minority children, it is unsurprising that prevalence rates of obesity among minority adults are higher compared to their non-Hispanic white counterparts.
Demographic Correlates of Obesity: Socioeconomic Status. Research generally demonstrates a link between obesity and social class (Cole et al., 2000; Gortmaker et al.,
1993; Messina & Barnes, 1991; Stern et al., 1995). Education, income, and poverty were three social class indicators used by the NHANES III to predict weight classification 5
(Crespo & Smit, 2003). Prevalence of being overweight and obese was highest among the less educated and in households with income lower than $10,000 a year. In men, obesity was most prevalent among those with less than a high school education living in a household with incomes of less than $35,000 per year. Among women, obesity prevalence rates were highest amongst individuals with less than a high school education in every income category. Poverty and lower levels of education have been associated with obesity independent of ethnicity; however, minorities may be at higher risk for obesity due to the fact that these individuals are at increased risk for lower educational attainment and lessened income (Kumanyika & Golden, 1991).
Weight, Psychopathology, and Personality Traits.
Qualitative reviews of population-based sampling studies show that, on average, obese and non-obese individuals are psychologically comparable (Faith & Allison, 1996;
O’Neil & Jarrell, 1992; Striegel-Moore & Rodin, 1986; Wadden & Stunkard, 1985).
Obesity itself is not considered a mental disorder, though it is premature to conclude that obesity has no psychological correlates (Faith, Matz, & Allison, 2003). For example, an analysis of NHANES I data (Istvan, Zavela, & Weidner, 1992) demonstrated a positive correlation between BMI and depression among women, but not men. A recent study produced similar conclusions (Carpenter et al., 2000), demonstrating that Caucasian and
African American women who were obese were at increased risk for Major Depression over the course of the past year. Interestingly, the opposite was observed among men; males who were obese were at decreased risk for depression. These findings indicate that obesity could have more psychological implications for women than men. 6
Higher rates of body dissatisfaction are often found in individuals with increased
BMI. Such body image disparagement could be particularly problematic for obese individuals who also engage in binge eating behavior (Faith, Matz, & Allison, 2003).
Research on disordered eating behavior among overweight and obese individuals is in its nascent stages; however, it is currently recognized that a subgroup of obese patients also engage in binge eating and suffer from marked psychological impairment (Marcus,
1993). Among obese individuals who self-identified as binge-eaters, researchers found increased negative evaluations of specific body parts and overall appearance, greater “fat- anxiety,” and increased emphasis on physical appearance (Cash, 1991). Indeed, obese binge-eaters consistently demonstrate increased psychopathology compared to their non- binge eating obese or normal weight counterparts (Black, Goldstein, & Mason, 1992;
Marcus, Wing, & Hopkins, 1988; Marcus et al., 1990). For example, these individuals are more likely to experience anxiety, depression, obsessive-compulsive disorder, paranoid ideation, psychoticism, and borderline personality disorder (Faith, Matz, & Allison,
2003). Individuals diagnosed with Binge Eating Disorder (BED) have been described as showing greater somatization, hostility, and interpersonal sensitivity (Marcus, Wing, &
Hopkins, 1988). The difficulties faced by obese individuals who binge are further apparent in that these individuals report a continual struggle to avoid binge episodes, perfectionistic standards for dieting, less perceived control over eating, increased fear of weight gain, and greater preoccupation with food and weight (Marcus, 1993).
Psychopathology is not uncommon among individuals with “classic” eating disorders (Anorexia & Bulimia Nervosa). Eating disorders are often associated with 7 comorbid Axis I and II diagnoses, which negatively impacts treatment outcome (Coker,
Vize, Wade, & Cooper, 1993; Schork, Eckert, & Halmi, 1994). For example, individuals with Bulimia Nervosa (BN) often are diagnosed with depression, anxiety, personality, or substance abuse disorders, and sometimes report a history of sexual abuse (Coker, Vize,
Wade, & Cooper, 1993; Davis & Kaptein, 2006; Rossotto, 1998; Serpell et al., 2006;
Strober, Freeman, Lampert, & Diamond, 2007; Wilfley et al., 2000). One personality disorder most commonly associated with bulimia nervosa is Borderline Personality
Disorder (BPD; Masjuan, Aranda, & Raich, 2003). Common factors underlying both
Bulimia Nervosa and Borderline Personality Disorder are mood lability and difficulty with emotion regulation (Perugi, Toni, Travierso, & Akiskal, 2003). Both bulimic women and those with BPD have been described as displaying a need to quickly discharge affective experiences, which often takes the form of engaging in impulsive behaviors. In addition, this individuals also demonstrate difficulties regulating tension, a fragile sense of self, and a preference for immediate gratification (McDougall, 1989).
Studies that relate eating behavior to personality traits (Costa & McCrae, 1992) show mixed results. Some studies show that emotional eating (eating in response to negative affective states; Bruch, 1973) is positively associated with neuroticism, especially in the form of depression and low impulse control. Past studies exploring the relation between dietary restraint and depression demonstrated that, when depressed, individuals categorized as restrained eaters gained weight while unrestrained eaters lost weight (Polivy & Hermann, 1976; Zielinski, 1978). Later studies using the Three Factor
Eating Questionnaire clarified this relationship and showed that disinhibition rather than 8 restraint explained shifts in weight during a depressive episode, with higher disinhibition predicting greater weight gain (Weissenburg, Rush, Giles, & Stunkard, 1985).
Lower conscientiousness is fairly consistently observed in obese patients, and higher conscientiousness scores have been associated with increased eating restraint
(Claes et al., 2006; Elfag & Morey, 2008). In one study using cluster analysis (Claes et al., 2006), three distinct personality profiles were identified among individuals diagnosed with eating disorders: (1) a resilient group with no clinical elevations on personality scales; (2) an emotionally “dysregulated” or undercontrolled group, with elevated scores on neuroticism and lower conscientiousness scores; and (3) a “constricted” or overcontrolled group, showing high scores on conscientiousness. Individuals in the resilient group had significantly fewer Axis 1 and II disorders than both undercontrollers and overcontrollers. Compared to the overcontrollers, undercontrollers demonstrated more impulsive behaviors.
In a study by Van Strien et al. (1985), in addition to feeling anxious, worried, or emotionally unstable, women reporting increased emotional eating also reported lower self-esteem, less patience, less self-sufficiency, and decreased social desirability. By contrast, restrained eaters indicated higher self-esteem, a tendency to be authoritarian, and a need for social ascendance. Interestingly, women who had been overweight or obese at one point in their lives, but were not obese at the time of the study (i.e., BMI <
30), reported feeling more socially adequate, had less social anxiety, and were more outgoing than obese subjects. Similar to more restrained eaters, these women reported being more dominant and having a greater need for social ascendance. They showed a 9 higher preference for male occupations, were less sentimental, had higher levels of self- esteem than obese women, and reported more internal control.
In summary, it is evident that eating behaviors (and obesity) may be related to increased affective experiences and corresponding difficulties controlling and regulating negative emotions. The development and utilization of adaptive emotion regulation strategies would therefore seem important toward instituting healthy eating behaviors and preventing the onset and maintenance of binge and overeating behaviors. As a move in this direction, the current study involved exploring how distress tolerance and experiential avoidance, factors associated with decreased emotion regulation and mental health problems, were related to BMI and self-monitored eating behavior.
Distress Tolerance and Experiential Avoidance
Distress Tolerance. Distress tolerance refers to the ability to experience and cope with emotional discomfort and negative affective states (Simons & Gaher, 2005).
Individuals with low distress tolerance tend to: (a) describe the experience of emotional distress as unbearable, (b) appraise emotional distress as unacceptable or shameful, and appraise their own abilities to cope with stress as inferior to others, (c) avoid negative emotional states, or engage in efforts to alleviate negative emotional states rapidly when they occur; and (d) become absorbed in the negative emotional experience and unable to focus attention away from their feelings of distress, which impairs and disrupts daily functioning and quality of life.
Psychologists have long been interested in the ability to tolerate frustration and conflict, as it is clear that increasing the ability to cope with emotional distress and 10 frustration is vital to positive therapeutic outcome (Hybl & Stagner, 1952). Indeed, as early as the 1930s, theoretical models of frustration and its negative ramifications for logical reasoning and efficient problem-solving began to develop (Rosenweig, 1938).
These ideas have continued to evolve, with contemporary therapies such as Dialectical
Behavior Therapy (DBT; Linehan, 1993) and Acceptance and Commitment Therapy
(ACT; Hayes, Strosahl, & Wilson, 1999) emphasizing the importance of tolerating difficult emotions as a core element in effectively treating patients with a variety of mental health problems. Central to these therapies is the premise that developing acceptance of transient psychological and emotional discomfort, as well as exposure to and experiencing of difficult and often aversive emotions, results in positive mental health outcomes (Hayes, Wilson, Gifford, Follette, & Strosahl, 1996). In contrast, difficulties experiencing emotional distress and the perception that aversive emotions are to be avoided whenever possible are hypothesized to worsen psychological functioning, and to potentially intensify an individual’s suffering. Linehan’s DBT model, for example, operates partially on the principle that individuals with BPD have lower levels of distress tolerance. According to Linehan, patient perceptions of distress as unbearable may increase the use of impulsive and maladaptive behaviors to ease this distress (Linehan,
1993). Consistent with these ideas, a large body of research on ACT investigates the negative effects of avoiding or suppressing distress and negative emotions, and demonstrates the benefits of experiencing and accepting negative affect (Blackledge &
Hayes, 2001; Twohig & Hayes, 2008). 11
Experiential Avoidance. Strongly conceptually related to the concept of distress tolerance is the notion of experiential avoidance. Experiential avoidance occurs when a person is unwilling to remain in contact with particular private experiences (e.g. bodily sensations, emotions, thoughts, memories, images, behavioral predispositions), and therefore takes steps to alter the form or frequency of these experiences or the contexts that occasion them, even when these forms of avoidance cause behavioral harm (Hayes et al., 2004). Research demonstrates that through avoidance of unwanted experiences, the relationship between the stimulus and response paradoxically becomes strengthened rather than weakened (Hayes, Strosahl, & Wilson, 1999). For example, the thought suppression literature demonstrates that efforts to avoid thinking a certain thought may actually increase the frequency of that thought (Koster, Rassin, Crombez, & Naring,
2003; Purdon, 1999). Similarly, when individuals are encouraged to control symptoms of anxiety rather than mindfully observe (and accept) them, they demonstrate increased fear and catastrophic thoughts (Eifert & Heffner, 2003). Further, it has become quite clear that avoiding exposure to phobic stimuli inhibits the extinction process and increases rather than decreases fear of such stimuli (Barlow, 2002; Hayes et al., 2002). Despite the ineffectiveness of this strategy, individuals often continue to engage in avoidant behaviors, primarily due to short-term gains associated with avoiding aversive stimuli
(i.e., alleviation of discomfort).
To summarize, data generally suggest the process of experiential avoidance is largely counterproductive, and show that behavioral avoidance is strongly related to the etiology and persistence of anxiety and depressive disorders (Hayes et al., 1999; Orsillo, 12
Roemer, Block, LeJeune, & Herbert, 2005; Tull, Gratz, Salters, & Roemer, 2004). More specifically, experiential avoidance is a highly ineffective coping method that is associated with various forms of pathology, including increased severity of trichotillomania (Begotka, Woods, & Wetterneck, 2004), self-harm in borderline personality disorder (Chapman, Specht, & Cellucci, 2005), dissociation in trauma victims
(Marx & Sloan, 2005), depression in a substance dependent sample (Forsyth, Parker, &
Finlay, 2003), and anxiety and panic symptoms (Eifert & Heffner, 2003; Tull, Gratz,
Salters, & Roemer, 2004).
Experiential avoidance is likely related to decreased levels of distress tolerance in the sense that when individuals are unable to tolerate distress, they are presumably more inclined to engage in experiential avoidance. Along with cognitive reappraisal (Barlow,
Allen, & Choate, 2004) and radical acceptance strategies (Hayes et al., 1999), methods of increasing distress tolerance may be productive emotion regulation strategies that can assist individuals toward confronting rather than avoiding difficult and aversive experiences.
Distress Tolerance, Experiential Avoidance, and Eating Behavior
Researchers have recently begun to explore the relationship of distress tolerance and experiential avoidance to maladaptive eating behaviors such as binge eating or emotional eating, and to weight. Emotional eating, which shows a high degree of overlap with binge eating, refers to eating in response to negative emotional states (Bruch, 1973).
Emotional eating often is viewed as a coping behavior that enables individuals to manage depression, anxiety, or other negative emotions by serving as a distraction from negative 13 affect (Elmore & De Castro, 1990). This form of eating has been conceptualized as a form of avoidance coping, specifically through negative reinforcement processes that allow for successful alleviation of negative emotional states and related negative cognitions (Bekker & Spoor, 2008; Neckowitz & Morrison, 1991; Soukup, Beiler, &
Terrell, 1990; Troop, Holbrey, Trowler, & Treasure, 1994; Wardle, Steptoe, Olliver, &
Lipsey, 1999). In further support of the impact of negative emotions on eating behavior, higher levels of perceived distress combined with lower levels of distress tolerance have been demonstrated in obese binge eating populations (Kenardy, Arnow, & Agras, 1996).
Additionally, bulimia nervosa patients report that at least half of their binges are driven by emotions rather than hunger (Waller, 2002). Studies show that individuals with emotional and binge eating behaviors report higher levels of emotional avoidance, increased fear of emotions, and more frequent bouts of emotional eating, than do their non-binge eating counterparts (Pells, 2006). In a recent study, relative to a control group, individuals with BED reported greater negative emotions in response to emotional images and vignettes and less willingness to experience these stimuli (Pells, 2006).
In response to these findings, researchers hypothesize that individuals who have difficulty tolerating negative affect and emotional distress and who tend to react to such affect impulsively may be more prone to dysregulated eating behaviors, such as bingeing and purging (Anestis, Selby, Fink, & Joiner, 2007; Nasser, Gluck, & Geliebter, 2004).
Although binge eating episodes typically conclude with feelings of relief and the attenuation of uncomfortable or negative thoughts and feelings, this reprieve often is only temporary in that binge episodes regularly are followed by negative feelings such as guilt, 14 disgust, and misery (Weiner, 1998). Thus, not only is eating an attempt to cope with or modulate intense emotional states, it also serves to perpetuate or exacerbate such states.
It is important to note that these findings have not yet been consistently utilized in order to improve weight control results, and that even when coping skills are directly targeted in weight control treatment, better outcomes do not always occur (Bennett, 1986;
Glenny et al., 1997). However, results of recent studies are promising. For example, one study by Lillis & Bunting (2009) exposed 43 randomly assigned individuals that had completed a weight loss program within the past 2 years to a one day, 6 hour ACT workshop. An ACT workbook was also distributed to these individuals. The principal target of the workshop was “weight-related stigmatizing thoughts;” neither the workshop nor the workbook contained strategies for losing weight. At 3 month follow-up, however, individuals in the ACT condition were significantly more likely to have lost 5 pounds than those in the control group. This suggests that further exploration regarding the relationship between eating behavior, weight outcomes, and mindfulness- and acceptance-based skills (e.g. those designed to increase distress tolerance and decrease experiential avoidance) is needed.
Emotional States and the Reinforcing Value of Food
It is not surprising that eating food is a mechanism to regulate and cope with negative emotional states. Food is a primary reinforcer, with no direct learning required for food to motivate behavior. Different foods possess different reinforcement values for individuals, however, with reinforcement value typically measured by the frequency of responses an individual emits according to varying reinforcement schedules in order to 15 obtain that food (Epstein & Saelens, 2000). The reinforcement value of food has been measured within choice paradigms, where research participants are given the choice between eating two different foods, or between eating food and engaging in an alternative activity (Epstein & Leddy, 2006). Within choice paradigms, the decision to engage in eating behavior depends on the reinforcement associated with alternative activities, as well as the constraints or behavioral cost associated with obtaining food (Epstein &
Leddy, 2006). When given the choice between eating and engaging in another activity, studies show that participants will usually choose to eat, unless the behavioral cost associated with gaining access to a particular food becomes too high (Goldfield &
Epstein, 2002). Studies have found that the reinforcing value of eating food relative to other activities also varies as a function of BMI, with obese individuals willing to work harder to obtain food than non-obese individuals (Saelens & Epstein, 1996).
Furthermore, studies show that increased hunger elevates the reward value of food, and caloric intake increases as the reinforcing value of food increases (Epstein et al., 2007;
Epstein & Leddy, 2006).
When we consider the inherent motivating and rewarding aspects of food, the ease with which food is obtained, the distress or emotional cost of negative affect states, the resulting desire to avoid negative emotions in favor of experiencing positive emotional states, and the fact that food encourages positive emotional states, it is easy to see why individuals would utilize eating as an emotional coping strategy. Interestingly, research shows that mood affects not only motivation to eat, but the type of food eaten.
Basic research in both animals and humans has shown that when subjects are exposed to 16 a depressive mood induction, the reinforcing value of food shifts from a preference for healthier foods to those that are sweeter and less nutritious. In other words, increased emotional eating has been associated with a preference for sweet, non-nutritive foods
(Van Strien et al., 1985; Willner et al., 1998), foods which also typically exhibit greater energy density.
Meal Patterns and Weight: Eating Frequency and Duration
Eating frequency refers to how often an individual eats in a given time period
(e.g. 3 meals per day vs. 5 meals per day). Increased eating frequency is inversely related to weight, body mass index, and body fatness (Bellisle, McDevitt, & Prentice, 1997;
Lioret et al., 2008; Ma et al., 2003; Masheb & Grilo, 2006; Toschke, et al., 2005).
Researchers hypothesize that increases in the frequency of meals and snacks may reduce overall daily caloric intake by preventing excessive hunger and subsequent over-eating
(Kirk, 2000). Indeed, past research has shown that elevated hunger increases the reinforcing value of food, which in turn boosts caloric intake (Epstein et al., 2007;
Epstein & Leddy, 2006). Individuals who eat more frequently may be controlling their hunger through increased ability to regulate daily energy intake (Kirk, 2000; Westerterp-
Plantenga, Wijckmans-Duysens, & ten Hoor, 1994), or through other biological mechanisms such as improved maintenance of insulin and glucose levels (Carlson et al.,
2007; Solomon et al., 2008; Wadhwa et al., 1973), slowed gastric emptying (Capasso &
Izzo, 2008; Speechly & Buffenstein, 1999), or changes in the release of satiety hormones in response to food intake (Higgins, Gueorguiev, & Korbonits, 2007; Jayasena & Bloom,
2008; Solomon et al., 2008). Recent studies show that individuals who have been 17 successful in maintaining their weight loss eat on average nearly five times daily; it is rare to find a successful weight loss maintainer who eats less than twice a day (Klem,
Wing, McGuire, Seagle, & Hill, 1997).
Though these findings are suggestive, not all studies report a significant inverse relationship between eating frequency, caloric intake, and weight status (Berteus
Forslund et al., 2002; Duval et al., 2008; Howarth et al., 2007). Studies that do demonstrate significant findings also suggest that our understanding of the relationship between these variables and other parameters requires further elaboration. For example, though many current cross-sectional studies imply an inverse relationship between meal frequency and measures of body fatness in adults and in children (Lioret et al., 2008; Ma et al., 2003; Masheb & Grilo, 2006; Toschke, et al., 2005), research also indicates that this relationship may only exist in certain sub-groups, for example in males but not females (Drummond et al., 1998), or in post-menopausal women but not pre-menopausal women (Yannakoulia et al., 2007).
Eating duration is defined as the length of an eating episode. In the current study, participants were asked to record the length of time they spent eating or drinking for each episode. We made no change to the participant’s listed time unless he/she described drinking a beverage or chewing gum over the course of several hours, in which case we calculated the episode as persisting for 5 minutes per each hour recorded. For example, if a participant recorded drinking tea over the course of 4 hours, this was recorded as a 20 minute bout.
Measuring Eating Behavior: Daily Diaries 18
The present study was designed to assess the relations of distress tolerance (self- reported and behavioral) and experiential avoidance (self-reported) with BMI and eating behaviors as measured through a three day food record, conceptually similar to daily diary self-monitoring. Daily diaries have often been used within the domains of clinical and health psychology as a means of assessment. For instance, daily diaries such as those used in the current study have been used to assess the relations among mood state, overt behavior, and reward value of activities (Hopko et al., 2003; Hopko & Mullane, 2008).
Increasing evidence suggests that the daily diary approach can be considered both reliable and valid. For example, in depression research, self-reported depressive symptoms (as measured by the Beck Depression Inventory; Beck & Steer, 1987) were highly convergent with aversive behavioral experiences reported in daily diaries (e.g. conflictual experiences, feeling trapped; Robbins & Tanck, 1984). Diary methods have also been shown to have strong psychometric properties in research on anxiety (Fydrich, Dowdall,
& Chambless, 1992; Nelson & Clum, 2002), pain (Feldman, Downey, & Schaffer-Neitz,
1999; Grant, Long, & Willms, 2002), alcohol abuse (Watson, 1999), sexual behaviors
(Okami, 2002), gambling (Atlas & Peterson, 1990), and insomnia (Haythornthwaite,
Hegel, & Kerns, 1991).
Summary and Current Study
Individuals with decreased ability to tolerate negative affect and increased tendencies to avoid confronting negative emotions also exhibit difficulties with weight control. As increased distress tolerance and decreased experiential avoidance may be fundamental toward coping with negative affect, it is probable that individuals with 19 greater distress tolerance and decreased experiential avoidance will be less likely to report engaging in maladaptive eating behaviors (e.g. emotional or binge eating).
Additionally, higher levels of distress tolerance and decreased experiential avoidance likely indicate increased ability to engage in healthy eating behavior. However, these hypotheses have not been adequately or systematically addressed in the literature to date, particularly in the context of other variables associated with being overweight or obese.
With the objective of delineating the relationships between distress tolerance, experiential avoidance, BMI, and eating behavior within the context of past research, self-reported mood (trait anxiety, state anxiety, and depression) and self-efficacy, in addition to self-reported assessments of eating behavior and concerns about shape and weight, were assessed. The following hypotheses were generated:
1. Individuals with higher Body Mass Index (BMI) and higher waist circumference (WC) will exhibit decreased distress tolerance as measured by a behavioral task (the PASAT-C) and participant self-report (DTS).
2. Individuals with higher BMI and WC will exhibit increased experiential avoidance as measured by the AAQ.
3. Individuals with higher BMI and WC will report increased depression and anxiety as measured by the BDI-II and STAI-Y.
4. Individuals with higher BMI and WC will score lower on self-reported general and social self-efficacy as measured by the SES.
5. Individuals with lower distress tolerance and higher experiential avoidance are more likely to report dysregulated or maladaptive eating behavior and attitudes. 20
6. Based on daily diaries, individuals with higher BMI and increased WC will eat for longer time durations, less frequently, and exhibit increased caloric intake. They will report more positive change in affect pre- and post-eating bout, greater reward associated with eating, and less perceived control.
7. After controlling for other study variables, decreased distress tolerance and increased experiential avoidance will account for unique variance in BMI and WC.
8. Individuals reporting higher levels of depression and anxiety, and who report greater mood changes pre- and post-meal in their three day food diaries will eat, on average, more calories, and exhibit increased average reward scores associated with eating bouts.
9. Total caloric intake over the course of three days will be associated with BMI.
This relationship is proposed to be mediated by distress tolerance and experiential avoidance.
10. Based on daily diaries and utilizing mood variables as covariates, decreased distress tolerance and increased experiential avoidance will account for unique variance in reward value of food.
METHODS
Participants
A sample of university students (n = 73) was recruited from the general population of students at the University of Tennessee, Knoxville. Participants were recruited from undergraduate Introduction to Psychology courses, via an online research participation system [Human Participation in Research (HPR)]. To be eligible to 21 participate, in addition to being enrolled in an introductory psychology course, participants had to be 18 years of age or older and be willing to meet with researchers on two separate occasions. Recruitment for the study occurred during the Fall and Spring semesters of the 2009-2010 academic year. The current study was initially described online as an effort to examine relationships between mood and eating behavior. For most individuals recruited (n = 48) no suggested BMI for participation was listed. However, as recruitment continued, it became apparent that there was not sufficient variation in the sample to detect differences between weight categories. Thus, the online description of the research study was changed in order to encourage individuals of higher weight status to apply. A link to the NIH Standard BMI calculator website was provided on the recruitment site, and individuals were asked to calculate their BMI before registering for the experiment (http://www.nhlbisupport.com/bmi/bmicalc.htm). It is important to note that participants were not screened or determined to be ineligible for participation based on their weight status, and that it was never stated on the recruitment website that individuals of normal or underweight status would not be included in the study. The remaining participants (n = 25) were recruited in this fashion. These measures were enacted in order to increase sample variability and were effective; in fact, 64.0% of those who participated following the change in recruitment strategy were overweight or obese
(n = 16) whereas only 18.8% of the previous sample had fallen within these categories (n
= 9).
Of the 73 participants, 56.2% were female, and most were college freshmen or sophomores (Mean Age = 19.3 years, SD = 2.3). When classified utilizing BMI, the 22 majority of respondents were of normal weight status (63.0%), followed by overweight
(21.9%), obese (11.0%), and underweight status (2.7%). When classified according to waist circumference criteria, most individuals fell within the “low risk” category (78.1%), whereas relatively few fell within the “slight risk” (9.6%) or “high risk” (12.3%) categories. These rates are substantially lower than the statewide percentages, which show that 36.1% of the population is overweight and 32.9% is obese (CDC BRFSS,
2009). Participants were predominantly Caucasian (79.5%), with other individuals self- identifying as Black/African American (13.7%), American Indian/Alaskan Native
(4.1%), Asian (1.4%), and Other (1.4%). U.S. Census data from the year 2000 suggest that this distribution generally is representative of the East Tennessee region where the study was conducted (U.S. Census Bureau, 2010). In terms of relationship status, 96% of the sample was single or dating, with the remaining participants either married (3%) or divorced (1%). The sample was economically diverse, with 47% reporting annual
(parental) income greater than $50,000; 15% between $40,000 and $49,999; 15% between $30,000 and $39,999; 7% between $20,000 and $29,999; 10% between $10,000 and $19,999; and 6% between $0 and $9,999. In terms of employment status, 2.7% of participants were employed full-time and 27.4% were employed part-time. The remainder of the sample was unemployed. Approximately two-thirds of the students (68.5%) reported being on a university-sponsored meal plan for meals and snacks. Additionally, most reported living on campus in a dormitory (67.1%), whereas 26.0% were off-campus commuters, 4.1% were living in University-sponsored apartment housing, 1.4% 23 described their housing arrangements as “on campus, not in a dormitory,” and 1.4% described living arrangements as “other.”
Measures
Demographic Information and Anthropometric Data.
Demographics. Demographic information was obtained via participant self-report, and included variables such as gender, age, ethnicity, marital/relationship status, and socioeconomic status (see Appendix A for a sample Demographics form).
BMI. Body mass index (BMI) was assessed by measuring height and weight in the laboratory. Weight measurements were taken by researchers using a standard bathroom scale. Height measurements were taken using a stadiometer. All participants were weighed and measured using the same equipment.
Waist Circumference. Waist circumference (WC) was measured using a standard measuring tape, placed around the abdomen about one inch above the navel. Waist circumference measurements have been demonstrated to be an accurate measurement of central adiposity, and related to health outcomes (Janssen, Katzmarzyk, & Ross, 2002). It has been argued to be a more accurate measure of body fat and subsequent health outcomes than body mass index measurements (Wang & Hoy, 2004). We included multiple anthropometric measurement strategies to provide the most accurate data possible regarding relationships between body fat, distress tolerance, experiential avoidance, and eating behavior.
Self-report Measures. 24
The Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996) assesses the severity of depressive symptoms and includes 21 items rated on a 4-point Likert scale
(Score Range = 0-63). Higher scores suggest increased depression severity. Sample items include degree of “sadness” and “loss of pleasure.” The instrument has excellent reliability and validity with depressed younger and older adults (Nezu, Ronan, Meadows,
& McClure, 2000). In the present study, internal consistency was strong ( = .88).
The Distress Tolerance Scale (DTS, Simons & Gaher, 2005) is a 14-item self- report measure of distress tolerance. The scale includes four factors: perceived ability to tolerate emotional distress (sample item: “I can’t handle feeling distressed or upset”); subjective appraisal of distress (sample item: “My feelings of distress or being upset are not acceptable”); whether an individual’s attention is absorbed by negative emotions
(sample item: “When I feel distressed or upset, I cannot help but concentrate on how bad the distress actually feels”); and regulation efforts or avoidance of affect that include efforts to alleviate distress (sample item: “When I feel distressed or upset I must do something about it immediately”). Items are rated on a 5-point scale: (5) Strongly disagree, (4) Mildly disagree (3) Agree and disagree equally, (2) Mildly agree, (1)
Strongly agree. High scores represent high distress tolerance. Higher distress tolerance correlates strongly and negatively with emotional distress/negative affect (r = -0.59) and emotional lability (r = - 0.51; Simons & Gaher, 2005). In the present study, internal consistency was strong for the total scale ( = .84), and adequate for the subscales ( range = .61 to .78). 25
The Acceptance and Action Questionnaire (AAQ; Hayes et al., 2004) is a 9-item scale that assesses levels of experiential avoidance. Sample items from the scale include
“Anxiety is bad”, “If I could magically remove all the painful experiences I’ve had in my life, I would”, and “I’m not afraid of my feelings.” Responses range from 1 (never true) to 7 (always true). Increasing scores indicate increased levels of experiential avoidance.
This measure has adequate reliability (Hayes et al., 2004). Moderate internal consistency
( = .70) has been demonstrated in clinical and non-clinical samples (Hayes et al., 2004).
This was replicated in the present study ( = .69).
The State-Trait Anxiety Inventory – Form Y (STAI-Y; Spielberger, 1983) is a 40- item scale used to measure both state and trait anxiety. The scales are at times used separately, but were used together for the present study to control for the effects of both types of anxiety on study variables. The state (S) scale consists of 20 items that evaluate how participants feel “right now, at this moment” (sample items: “I feel upset;” “I feel at ease.”). Responses are rated on a Likert scale ranging from one “not at all” to four “very much so.” The trait (T) scale assesses how individuals generally feel (sample items: “I am a steady person;” “I lack self-confidence.”). Responses are again rated on a Likert scale ranging from one to four, with one being “almost never” and four being “almost always.” The STAI has excellent internal consistency (average s > .89), and the STAI –
Trait has good test-retest reliability across multiple time intervals (average r - .88;
Barnes, Harp, & Jung, 2002; Grös, Antony, Simms, & McCabe, 2007). In the present study, internal consistency for the state and trait scales was very strong ( = .92 and .91, respectively). 26
The Self-Efficacy Scale (SES; Sherer et al., 1982; Sherer & Adams, 1983) is a 23- item instrument that assesses generalized self-efficacy. The scale contains seven additional filler items that are not scored (30 items total). Initial factor analysis supported the presence of two subscales for the measure: General Self-efficacy (17 items) and
Social Self-efficacy (6 items). The measure was validated in young adult samples and demonstrated adequate internal consistency (α = .86 and .71 for the general and social subscales, respectively) and construct validity as measured through correlations with other personality characteristics (Sherer et al., 1982). In the present study, internal consistency for the total scale was good ( = .83). Cronbach’s alpha for the general self- efficacy subscale was .82, and for the social self-efficacy subscale was .69.
The Eating Disorder Examination-Questionnaire (EDE-Q, Version 4; Fairburn &
Beglin, 1994) is a 33-item self-report version of the EDE interview (Fairburn & Cooper,
1993). The self-report items were taken from analogous EDE interview items, with small changes to wording of items as needed (Mond et al., 2004). Similar to the EDE interview, each questionnaire item is rated via a 7-point forced-choice scale (0-6). The measure focuses on the previous 28 days, and assesses objective bulimic episodes (OBEs), subjective bulimic episodes (SBEs), and objective overeating episodes (OOEs; Reas,
Grilo, & Masheb, 2006). The EDE-Q includes four subscales: Dietary Restraint, Weight
Concern, Shape Concern, and Eating Concern. EDE-Q items addressing attitudinal aspects related to eating disorder psychopathology show high temporal stability, with
Pearson correlations across two time points ranging from 0.57 for the Restraint subscale to 0.77 for the Eating Concern subscale, and 0.79 for the global scale (Mond et al., 27
2004b). The EDE-Q also demonstrates good reliability across subscales (range = 0.66 to
0.77). Test-retest reliability for OBEs is considerably better than for SBEs or OOEs, regardless of time between administrations (Mond et al., 2004b). Overall, both validity and reliability data support the use of this instrument, especially in assessing binge eating behavior (Mitchell & Peterson, 2005). In the present study, internal consistency was strong for the total scale ( = .86) as well as subscales ( range = .76 to .86).
The Three Factor Eating Questionnaire (Stunkard & Messick, 1985), also known as the Eating Inventory, is a 51-item measure designed to assess dietary restraint (Factor
I), disinhibition or lability in behavior and weight (Factor II), and hunger and its behavioral manifestations (Factor III). Though current studies report mixed results regarding this measure’s psychometric properties (see Mitchell & Peterson, 2005), earlier research showed good criterion validity, as reflected in a study by Marcus and Wing
(1983) which showed that the severity of binge eating behavior was positively correlated with impulsivity (Factor II; r = 0.61, p < 0.001) and with perceived hunger (Factor III; r
= 0.54, p < 0.001), but not with cognitive restraint (Factor I; r = -0.14, NS). When a subscale from this measure was tested with other eating measures, it was found to have good test-retest reliability (r = .91) and internal consistency (α = .90; Allison, Kalinsky,
& Gorman, 1992). In the present study, internal consistency for the total scale was good
( = .69), but ranged widely for the subscales (Cronbach’s alpha for the restraint, disinhibition, and hunger subscales was .38, .61, and .75, respectively).
Behavioral Assessment. 28
Daily Food Records were used to monitor participant snacks and meals (see
Appendix B). These diaries recorded amount of food eaten, the time of day at which meals began and ended, specific foods eaten, type of meal (e.g., dinner, snack), context where food was eaten (e.g., home, restaurant, school cafeteria), reward value of food
(measured via 9-point Likert Scale), emotional valence prior to eating (negative, neutral, positive; measured via 9-point Likert scale), emotional valence after eating (negative, neutral, positive; measured via 9-point Likert scale), and perceived control over eating behavior (measured via 9-point Likert scale). Three days of food intake data were used to test whether BMI was differentially associated with these variables. Traditional nutrition assessments vary in the number and type of days included; however, three-day records including two week days and one weekend day are a common and reliable method utilized for assessment of caloric consumption in order to examine potential variations in eating behavior due to scheduling and responsibilities (Basiotis et al., 1987; Prochaska &
Sallis, 2004; Therrien et al., 2008; Tremblay, Sevigny, LeBlanc, & Bouchard, 1983). As the focus of the current study was on eating behavior in a university sample, students in this study were required only to record all food and beverage items consumed on two class days and one non-class day. It was thought that this would adequately capture changes in caloric intake due to variability in daily behaviors.
Nutrient calculations for these three-day food records were performed using the
Nutrient Data System for Research (NDSR) software version 2008, developed by the
Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN, Food and
Nutrient Database (2008) [see Schakel, Sievert, & Buzzard (1988) for a detailed 29 description of the sources and procedures used by the Nutrition Coordinating Center at the University of Minnesota for the development and maintenance of its nutrient database].
The PASAT-C (Lejuez, 2003) is a modified version of the Paced Auditory Serial
Addition Task (PASAT), originally developed for the assessment of information processing and capacity in patients with head trauma. The PASAT-C is a computer version of the original test, and has been used to produce psychological stress in laboratory examinations of experimental psychopathology. This task allows for the comprehensive examination of behavioral/motor, cognitive/self-report, and psychological response modes without sacrificing experimental precision and control. The PASAT-C requires participants to add a series of numbers presented on the computer screen. There are three levels, and the numbers are presented more quickly with each level. In the first two levels, the participant is not given the option of quitting the task. The last task, when the numbers are presented most quickly, does provide a “quit task” option. If the participant does not choose this option, the task continues for ten minutes before it ceases automatically. The PASAT-C thus allows researchers to examine the length of time individuals behaviorally persist at a stressful task.
Procedure
Initial Lab Visit: During the initial laboratory meeting, the project was explained, participants were given the opportunity to ask questions, and informed consent was obtained from each participant. Participants then had their height, weight, and waist circumference measured. Following these measurements, participants completed self- 30 report questionnaires and were given the option of taking a break before being asked to complete the PASAT-C. When the PASAT-C was completed, participants were provided with daily food records and detailed instructions for their completion. Participants were given the opportunity to ask questions, after which the initial laboratory visit concluded.
Follow-up Lab Visit: During the second meeting, which occurred approximately 1 week later, participants returned their food records. The records were examined by the researchers in the presence of participants to ensure completion and accuracy. Following clarification of the records, participants were debriefed and given full credit for their participation in the study.
Follow-up Mailings: Final follow-up mailings were sent to participants after their lab visits were completed. These were provided at participant request only (n = 58).
Participant-requested dietary feedback was provided in the form of a report generated by
NDSR software (2008). This report included information regarding the nutrient content of the specific foods eaten by that participant, in addition to total caloric intake per meal and per day. A sample NDSR report is included in Appendix C.
RESULTS
Bivariate Analyses
Data were first examined to assess correlations among BMI, WC, and demographic variables. Gender was correlated with WC but not BMI, with males more likely to exhibit higher WC (0.46, p < .01). Income was negatively correlated with both
BMI (-0.31, p < .01) and WC (-0.24, p < .05). Age was at first uncorrelated with any weight outcomes. However, there was one 35 year old individual in our sample. When 31 this individual outlier was dropped from the analysis, age was significantly correlated with both BMI (0.26, p<.05) and WC (0.26, p<.05). No other demographic variables were related to weight outcomes in the current sample. Data were next examined in order to assess relationships between depression, anxiety, self-efficacy, distress tolerance, and experiential avoidance. We hypothesized positive correlations between levels of depression, anxiety, and weight indices (BMI and WC), in addition to an expected negative correlation between self-efficacy and weight indices. Findings suggested only a significant negative correlation between social self-efficacy and WC measurements, as shown in Table 3 (-0.24, p < 0.05). It was also anticipated that negative correlations would exist between self-reported distress tolerance (DTS) and behavioral distress tolerance (PASAT-C) and weight indices, whereas positive correlations would be observed between experiential avoidance and weight indices. As presented in Table 2, however, these relationships were not demonstrated.
Based on daily food diary data, positive correlations between BMI, WC, total caloric intake, average reward ratings per eating episode, and frequency and duration of eating episodes were anticipated. Negative correlations were expected between BMI,
WC, affect change pre- to post-eating episode, and perceived control ratings.
Interestingly, none of these potential associations were supported, as demonstrated in
Table 6. Only the relationships of both WC and BMI with average reward rating following eating were significant; however, this was not in the expected direction.
Finally, we hypothesized that individuals with lower distress tolerance and higher experiential avoidance would be more likely to report dysregulated eating behavior and 32 maladaptive eating attitudes, as measured by the TFEQ and EDE-Q. As indicated in bivariate analyses, individuals who self-reported higher levels of eating disturbance also reported lower distress tolerance and higher experiential avoidance, thus supporting this hypothesis. Results of these analyses are reported in Tables 4 (TFEQ) and 5 (EDE-Q).
Univariate Analyses
For univariate analyses, both BMI and WC initially were conceptualized as categorical variables. For analyses using BMI, participants were categorized into five groups using body mass index scores (weight in kilograms divided by height in meters squared), according to CDC guidelines [BMI < 18.5 = underweight; 18.5 ≤ BMI ≤ 24.9 = normal weight; 25.0 ≤ BMI ≤ 29.9 = overweight; 30.0 ≤ BMI ≤ 39.9 = obese; BMI ≥ 40 morbidly obese]. For WC analyses, participants were categorized according to gender- based health risk guidelines. The accepted guidelines according to the National Institute of Health suggest that males with WC > 40 inches (about 102 cm) and females with WC
> 35 inches (about 88 cm) should be classified as high risk (NIH, 1998). It has also been noted that, in the United States, non-Asian adults with marginally increased waist circumference (in men, WC = 37-39 inches; in women, WC = 31-34 inches) might be predisposed to health risks including insulin resistance and can benefit from changes in life habits (Grundy, 2006). In the current study, three groups were formed based on estimated health risk: a “low risk” group consisting of women with WC < 30.9 inches and men with WC < 36.9 inches; a “slight health risk” group consisting of women with a
WC of 31 - 34.9 inches and men with a WC of 37 – 39.9 inches, and a “high health risk” 33 group consisting of women with a WC > 35 inches and men with WC > 40 inches as high health risk.
We expected that higher BMI and WC scores would be associated with lower levels of distress tolerance and higher levels of experiential avoidance. For these analyses, distress tolerance was measured by the DTS and the PASAT-C computer task.
The length of time individuals persisted in the third level of the PASAT-C was used as an index of behavioral distress tolerance, with increased task persistence indicated by higher
“quit time” scores. To evaluate the relationship between weight indices and distress tolerance as measured by the DTS and the PASAT-C, data were examined using a series of one-way ANCOVA’s, with BDI-II, STAI-Y, and SES scores as covariates in analyses.
The same statistical method was used to examine whether experiential avoidance (AAQ) differed as a function of BMI/WC. BMI and WC categories were used as independent variables in these analyses in order to examine differences between groups. Results of these analyses are presented in Tables 13 - 17. Estimated eta-squared (2; Keppel, 1991) was used as a measure of effect size (2 = .01 = small; 2 = .06 = medium; 2 = .16 = large). As indicated in Table 15, ANCOVA results revealed that experiential avoidance significantly differed as a function of BMI group [F (4, 58) = 3.16, p = .02, 2 = 0.18].
Post-hoc Bonferroni comparisons indicated that this effect was due to the morbidly obese group reporting significantly higher experiential avoidance than the obese group. Neither
BMI nor WC was significantly associated with self-reported distress tolerance or performance on the PASAT-C. 34
We further hypothesized that, based on three day food records and measured via
ANCOVAs, individual BMI and WC group status would be associated with average eating duration (Table 7), average reward ratings for each eating experience (Table 11), total number of eating episodes over three days (Table 8), total caloric intake over three days (Table 9), change scores in affect ratings prior to and after eating experiences (Table
10), and the degree of perceived control over eating behavior (Table 12). We found that, while controlling for levels of depression, anxiety, and self-efficacy, BMI [F (4, 55) =
4.97, p < .01, 2 = 0.27] and WC group status [F (2, 55) = 6.85, p < .01, 2 = 0.20] was significantly associated with average reward ratings for eating episodes. Post-hoc
Bonferonni analyses based on estimated marginal means indicated that the morbidly obese group rated meals as less rewarding than both the normal weight and overweight groups. Further, participants with a larger WC rated meals as less rewarding than those with a smaller WC. There were no other significant main effects of BMI group or WC group on eating behaviors.
Multivariate Analyses
When controlling for other demographic variables in a multivariate analysis, income significantly predicted BMI [F(33, 5) = 5.41, p < .01] and WC [F(33, 5) = 7.96, p<.01]. Age also significantly predicted BMI [F(33, 3) = 5.56, p<.01] and WC [F(33, 3)
= 6.30, p<.01], as did gender [BMI: F(33, 1) = 17.13, p<.01; WC: F(33, 1) = 52.25, p <
.01]. There were significant interactions between gender and income [BMI: F(33,4) =
5.61, p<.01; WC: F(33, 4) = 5.52, p<.01], gender and age [BMI: F(33, 3) = 5.56, p<.01;
WC: F(33, 3) = 6.30, p<.01], and income and age [for WC only: F(33, 11) = 2.19, p<.05], 35 but not between all three variables taken together. In terms of other demographic variables, there was likely not enough variation in the sample to detect significant differences in weight indices.
To further assess the relations between distress tolerance and experiential avoidance (predictor variables) with weight indices, BMI and WC were conceptualized as continuous criterion variables, and hierarchical regression analyses were conducted. In these two analyses, BMI (Table 16) and WC (Table 17) were the criterion variables. In block 1 of each analysis, depression, anxiety, and self-efficacy were entered as predictor variables. In Block 2, distress tolerance (DTS and PASAT-C) and experiential avoidance
(AAQ) were entered. Results revealed that, after including distress tolerance and experiential avoidance in the model, general self-efficacy accounted for unique variance in BMI (β = .33, p < .05), while social self-efficacy accounted for unique variance in WC
(β = -.46, p < .05). Distress tolerance and experiential avoidance were not associated with differences in BMI or WC.
Finally, to examine the effects of mood lability surrounding eating episodes on other eating variables, we first examined the average change in mood pre- and post-meal over the course of three days as it related to total caloric intake and average reward scores associated with eating episodes. BDI-II and STAI-Y scores were included as covariates in the analysis to control for existing levels of mood disturbance. Average mood change did not account for a significant amount of the variance in total calories consumed, or in the three-day average reward rated per eating bout (Table 18). We next examined relationships between existing mood disturbance and eating behavior, using as predictor 36 variables BDI-II scores and STAI-Y Trait scores, with no covariates in the analysis.
These mood variables also did not account for a significant amount of the variance in total caloric intake or average reward as recorded in the food records (Table 19).
Mediational Analysis
We proposed that the relationship between total caloric intake and BMI would be mediated by distress tolerance and experiential avoidance. To examine this model, we used the bootstrapping method advocated by Preacher & Hayes (2008), with a 95% confidence interval and number of re-sampling attempts set at 5,000. Total caloric intake over the course of three days was entered as the independent variable, and BMI (treated continuously) was entered as the dependent variable. To reduce the likelihood of collinearity, two separate tests of mediation were conducted. The first included PASAT-
C quit time scores and total AAQ scores as mediators. The second included DTS scores and AAQ scores. Results of these analyses are summarized in tables 19 and 20. As zero was included in the confidence interval for both tests, no mediation was found. The indirect effects of self-reported distress tolerance and experiential avoidance were then tested for the relationship between total caloric intake and waist circumference, as were the indirect effects of PASAT-C quit time. Again, two separate tests were conducted.
These results are summarized in tables 21 and 22. No mediation was found.
Linear Regression: Distress Tolerance, Experiential Avoidance, & Food Reward Value
Based on daily diaries, we hypothesized that levels of distress tolerance and experiential avoidance would account for unique variance in average reward ratings associated with each eating episode over the course of the three day food record. 37
Accordingly, hierarchical regression analysis was conducted (Block 1: depression, anxiety, self-efficacy; Block 2: distress tolerance and experiential avoidance). Results of this analysis indicated that generalized self-efficacy and experiential avoidance were the only two variables that accounted for significant variance in average reward ratings
(Table 24).
DISCUSSION
The purpose of this study was to examine the manner in which distress tolerance
(DT) and experiential avoidance (EA) were related to weight status as measured by Body
Mass Index (BMI) and waist circumference (WC), in the context of other psychological variables. Additional relationships examined included those between distress tolerance, experiential avoidance, self-reported reward associated with eating episodes over the course of three days, and self-reported disturbances in mood and eating behavior. This study was conducted in a predominantly high-functioning sample of young adults – individuals of fairly high SES that had graduated high school and proceeded to the university level. Although not atypical of the region in which the study was conducted, the current sample was not ethnically or racially diverse. Additionally, the sample consisted of individuals primarily of normal to overweight status and generally did not include significantly underweight or obese individuals. The findings of the current study suggest that within this relatively homogenous sample of undergraduates, differences in levels of experiential avoidance and perceived distress tolerance are associated with self- reported differences in eating disturbance, mood, and self-efficacy. The data further 38 demonstrate the importance of self-efficacy in accounting for variance in weight status in this sample.
In terms of eating behavior, bivariate data analyses demonstrated that individuals perceiving themselves as possessing lower distress tolerance and higher experiential avoidance also report more concerns regarding their shape, weight, and eating habits. For example, individuals who reported lower distress tolerance, specifically on the absorption and tolerance subscales of the DTS measure, reported higher levels of hunger and disinhibition on the TFEQ. These individuals were also more likely to score higher on the
EDE-Q global scale. Lower scores on each of the DTS subscales (tolerance, absorption, appraisal, & regulation) were associated with increased disturbance on the eating, shape, weight, and emotion subscales of the EDE-Q. When taken together with mood outcomes, which show that individuals reporting higher mood disturbance and lower self-efficacy also reported increased experiential avoidance and decreased distress tolerance, the data suggest a consistent psychological profile within the self-report measures. These findings support existing literature that demonstrates links between avoidance of internal emotional states and levels of disturbance in psychological functioning. They also provide further evidence for a link between DT, EA, and disturbances in eating attitudes and behaviors, associations demonstrated among eating disordered samples (Anestis,
Selby, Fink, & Joiner, 2007; Baer, Fischer, & Huss, 2005; Constorphine et al., 2005).
When anthropometric and behavioral measures of weight, distress tolerance, and eating behavior were included in analyses, however, the results were less clear. Results revealed non-significant correlations between DT, EA, and objectively-measured weight 39 indices. Furthermore, self-reported distress tolerance was negatively correlated with a behavioral measure of distress tolerance (-0.24, p < .05). Speculating on this finding, in previous studies participants have been rewarded for increased persistence on the
PASAT-C (Daughters, Richards, Gorka, & Sinha, 2009; McHugh, 2010). In the current study, however, this methodology was not utilized. It was thought that this would provide a measure of distress tolerance without complications such as a reward’s varying incentive value for participants; however, it may have impacted the utility of the PASAT-
C as a measure of distress tolerance in some way. A recent study by McHugh et al.
(2010) assessed relations between outcomes on self-report and behavioral measures of distress tolerance, and this study also showed no correlation between the DTS and the
PASAT-C. Indeed, it was found that, in general, there is little relation between scores on self-report and behavioral measures of distress tolerance. Thus, it is conceivable that in the current study the DTS and the PASAT-C might assess different aspects of the distress tolerance construct or that PASAT-C performance was influenced by factors other than distress tolerance (e.g. motivation to complete the task). The absence of expected correlations among DT, EA, and anthropometric data suggests that an additional explanation is that participants responded to self-report measures in a manner that was psychologically consistent, whereas their behavioral patterns and physical indicators of weight were more variable.
Continued examination of the data using ANCOVA demonstrated that higher weight status was associated with increased self-reported experiential avoidance and decreased reward associated with eating episodes, suggesting that individuals who 40 struggle to confront and cope with negative mood states are at risk for negative weight outcomes, and that these individuals do not, in fact, derive pleasure or reward from eating. Because power was limited in the current study due to limited sample size at extreme weight categories, and post-hoc analyses for these results indicated that they applied only to individuals in the morbidly obese group (n = 1), results should be interpreted with extreme caution.
Self-efficacy and weight status have been highly associated in the literature
(Clark, Abrams, Niaura, Eaton, & Rossi, 1991; O’Leary, 1985; Shannon, Bagby, Wang,
& Trenkner, 1990), and current findings are consistent with these data. Enhancing self- efficacy has been utilized as an additional target of dietary interventions in the past
(Ebbeling et al., 2003), and has been shown to mediate the effects of physical activity interventions on actual physical activity outcomes (Dishman et al., 2004). Increased self- efficacy also has been demonstrated to predict success, defined as maintenance of a weight loss of 10% or more of initial fat mass, at 16 months following a weight loss intervention (Teixeira et al., 2004). Given these findings in combination with present results, increasing levels of self-efficacy is an effective strategy for aiding individuals in reaching and maintaining healthy lifestyle goals, thereby positively impacting BMI and
WC.
Limitations and Future Directions
The current study was designed to augment the existing literature on relations between distress tolerance, experiential avoidance, and eating behavior by examining these constructs across BMI categories. Strengths of this study include its use of a multi- 41 method assessment strategy. For example, both waist circumference and body mass index were used to categorize participants, and distress tolerance was assessed via both self- report and a behavioral task. All data were double-entered before statistical analyses were conducted to maintain data integrity. Additionally, attrition was minimal in that, of a sample of 73 undergraduate students who were asked to complete food records as part of this study, only 3 failed to return their completed record to researchers.
This study also has some noteworthy limitations, including the small sample size discussed above. Second, the current study included no assessment of physical activity
(PA) levels for participants, which did not allow examination of energy expenditure; nor was there a measure of current/past dieting behavior, which could have impacted results regarding caloric intake. Assessing for physical activity in addition to total caloric intake could have provided a more balanced interpretation of results. Third, given the common critique that BMI does not consistently differentiate individuals of higher muscle density from individuals who are overweight, an assessment of muscle vs. fat mass using a technique such as bioelectrical impedance analysis would have improved the current study. Fourth, in terms of the sample utilized in the current study, it would have been beneficial to include a greater number of participants who were underweight, overweight, and obese. This would have improved the ability to detect significant effects.
Alternatively, recruiting a well-defined and focused sample of individuals who were overweight and who engaged in eating behavior as an emotions coping strategy could have improved results. Understanding relations among distress tolerance, experiential avoidance, and eating behavior in clinical samples is an important area not assessed by 42 the current study. Sixth, although the current study included multiple methods for measuring variables of interest, the study relied on self-report for assessment of caloric intake without validation using biomarkers. The food record method is often used; however, underreporting nutrient intake is a commonly reported problem in studies utilizing dietary assessment (Carpenter, 2006). More accurate measures of caloric intake include the doubly labeled water (DLW; Trabulsi & Schoeller, 2001) or urinary nitrogen analysis (Kroke et al., 1999) techniques. Utilizing food records in combination with biomarker data could improve the accuracy of results. Finally, given current statistics regarding the prevalence of overweight and obesity in low-income and minority households, targeting a more diverse sample would have improved the current study.
These findings, though they should be taken with caution, do demonstrate that the inclusion of an examination of distress tolerance and experiential avoidance in assessments of eating behavior could prove an interesting area for further exploration and clinical research. Both self-report and behavioral assessments of distress tolerance have proven clinically useful in the past, particularly in the treatment of individuals engaging in substance abuse (McHugh, 2010). Future investigations assessing applications to the treatment of individuals attempting to lose weight could prove fruitful.
Given their lack of overlap, however, any future research including both behavioral and self-report measures of distress tolerance should include some attempt to clarify whether they assess the same construct. In the current study, a positive correlation was expected between PASAT-C quit time scores and levels of distress tolerance reported on the DTS. On the contrary, a negative correlation was found. In addition to the 43 possibility that these instruments assess different constructs, it may be that there is an interaction between individual performances on these two measures, which could be assessed in future research. For example, individuals who perceive themselves as having high distress tolerance (as measured via self-report), but who actually exhibit low levels of distress tolerance (as measured via the PASAT-C), could prove at increased risk for certain forms of psychopathology or demonstrate certain personality characteristics.
Conversely, individuals who behaviorally exhibit high distress tolerance (as measured by the PASAT-C) but who do not perceive themselves as possessing the ability to withstand negative emotional states might be at increased risk for other clinical outcomes.
Assessing differences in eating behavior in these groups is an area for future research. A final area for future research regarding the PASAT-C would be to reward participants for their continued perseverance on the third level of the task, in a manner more consistent with past research. This would potentially allow researchers to examine differences in the reinforcement value of a stimulus across weight categories. Participant responses to variations in the type of reward offered would also be interesting to assess in future studies.
In terms of the dietary data collected during this study, there are several possible avenues for future research. For example, in the literature to date, it is common for eating bout data to be separated into meal data vs. snack data. Adult individuals who engage in increased snacking behavior have been shown to be at increased risk for higher BMI status (e.g. Forslund, Torgerson, Sjöström, & Lindroos, 2005); however, some studies demonstrate that snack food intake is not an important independent contributor to weight 44 gain among children and adolescents (Field et al., 2004). Conducting these analyses could prove fruitful in further explicating the relationships between BMI, WC, and food record variables. Additional analysis of the nutrient components of various meal items
(e.g. proteins, carbohydrates, and fats) as these variables relate to distress tolerance, experiential avoidance, and other study variables is also necessary. Finally, in the three- day food record, individuals were required to record the location in which they ate for each bout; however, they were not required to record whether others were present for these bouts or to keep track of interpersonal variables. Given findings regarding the lack of social self-efficacy demonstrated by individuals of increased weight status, it would be interesting to examine the interpersonal meal environment, including the various locations in which individuals ate (while controlling for whether or not they were on a meal plan), to see if participants were more likely to eat at home, in their dorm room, or elsewhere. As this was not a primary outcome of interest in the current study, new research would be necessary to determine the level of social avoidance in which individuals of higher weight status and lower social self-efficacy typically engage.
Summary and Conclusions
The current study demonstrates the need for further research on the relations between distress tolerance, experiential avoidance, and weight status of individuals.
Assessment of these variables as they relate to weight loss intervention outcomes, particularly in individuals with class III (morbid) obesity, is an area for future research.
This study supports new research regarding potential differences in behavioral and self- report measures of distress tolerance. It further supports evidence demonstrating the link 45 between self-efficacy and anthropometric weight measurements. It will be beneficial to take these variables into account as interventions for overweight and obesity continue to be designed and improved. 46
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TABLES 70
Table 1: Descriptive Statistics for all Measures
n Mean Std. Deviation Skewness Kurtosis Statistic Std. Error Statistic Std. Error BMI 73 24.5996 5.14205 1.385 .281 1.648 .555 WC 73 80.2023 12.44951 1.215 .281 1.649 .555 BDI 73 10.0959 7.30328 1.281 .281 1.898 .555 AAQ 73 31.5205 7.31268 -.129 .281 .560 .555 STAI: state subscale 73 32.6438 10.06558 1.333 .281 2.463 .555 STAI: trait subscale 73 36.1644 10.13796 .755 .281 .640 .555 DTS 73 3.6330 .63365 -.610 .281 .257 .555 TFEQ: restraint 73 4.4521 2.04152 .861 .281 .406 .555 subscale TFEQ: hunger 73 5.5753 2.94353 .007 .281 -1.077 .555 subscale TFEQ: disinhibition 73 4.8630 2.69421 .268 .281 -.624 .555 subscale EDE: global score 73 1.4345 1.01867 1.077 .281 2.120 .555 SES: general 73 66.8904 8.52050 -1.043 .281 1.993 .555 SES: social 73 21.5616 4.02073 -.957 .281 .975 .555 PASAT-C Quit time 73 273.7260 242.70428 .368 .281 -1.622 .555 (seconds) Mood Change 70 .9077 .76964 .960 .287 1.144 .566 Average Reward 70 6.4915 1.00227 .405 .287 .224 .566 Total Calories 70 5806.1571 2827.04383 2.222 .287 9.110 .566 Total Eating Episodes 70 11.3143 3.96559 .819 .287 .681 .566
71
Table 2
Correlations: Anthropomorphic Measures, Distress Tolerance, & Experiential Avoidance
Measure DTS total PASAT-C quit time AAQ
BMI -0.04 ns -0.10 ns -0.04 ns
WC -0.03ns -0.09 ns -0.07 ns
AAQ -0.49** 0.21 ns
PASAT-C -0.02*
Note. ns = non-significant. *p < .05, **p < .01
72
Table 3
Correlations Between Anthropomorphic Measures and Mood Measures
Measure BDI STAI-S STAI-Y SES (general) SES (social)
BMI .04 .00 -.01 .19 -.20
WC -.02 .01 -.02 .14 -.24*
Note. *p < .05, **p < .01 73
Table 4
Correlations Between Distress Tolerance, Experiential Avoidance, and TFEQ Subscales
Measure Restraint Hunger Disinhibition
DTS total -0.19 -0.30** -0.36**
DTS tolerance -0.17 -0.29* -0.33**
DTS absorption -0.16 -0.33** -0.38**
DTS appraisal -0.00 -0.05 -0.22
DTS regulation -0.21 -0.20 -0.14
AAQ -0.04 -0.14 0.20
Note. *p < .05, **p < .01
74
Table 5
Correlations Between Distress Tolerance, Experiential Avoidance, and EDE-Q Scales
Measure Global Restraint Eating Shape Weight Emotion
DTS total -0.46** -0.22 -0.52** -0.40** -0.44** -0.55**
DTS tolerance -0.31** -0.16 -0.37** -0.25* -0.30** -0.36**
DTS absorption -0.45** -0.26* -0.54** -0.38** -0.41** -0.53**
DTS appraisal -0.32** -0.04 -0.42** -0.31** -0.34** -0.33**
DTS regulation -0.27** -0.16 -0.21 -0.26* -0.27* -0.39**
AAQ 0.36** -0.12 0.43** 0.33** 0.36** 0.26*
Note. *p < .05, **p < .01
75
Table 6
Correlations Between Anthropomorphic Measures & Food Record Variables
Measure BMI WC
Total Caloric Intake -0.11 -0.03
Total Eating Episodes -.17 -0.12
Avg. Eating Episode Duration -0.24 -0.18
Affect Change -0.04 -0.02
Avg. Reward -0.34** -0.30**
Avg. Perceived Control 0.03 -0.07
Note. *p < .05, **p < .01 76
Table 7
BMI and WC Group Status Predicting Average Eating Episode Duration
ANCOVA: Between Subjects Effects
Source df F p 2
Corrected Model 14 2.05 0.03* 0.37
BDI 1 0.54 0.47 0.01
STAI-S 1 2.28 0.14 0.04
STAI-T 1 0.00 0.97 0.00
SES-G 1 0.02 0.88 0.00
SES-S 1 0.82 0.37 0.02
WC 2 2.85 0.07 0.10
BMI 4 1.52 0.21 0.11
BMI x WC 3 1.21 0.39 0.06
Error 58 (119.42)
2 2 Note. Values enclosed in parentheses represent mean square errors. R = 0.365 (Adjusted R = 0.187).
*p < .05, **p < .01
77
Table 8
BMI and WC Group Status Predicting Total Eating Episodes
ANCOVA: Between Subjects Effects
Source df F p 2
Corrected Model 14 1.32 0.23 0.25
BDI 1 0.00 0.95 0.00
STAI-S 1 0.04 0.85 0.00
STAI-T 1 0.12 0.73 0.00
SES-G 1 0.14 0.71 0.00
SES-S 1 2.08 0.16 0.04
WC 4 0.67 0.51 0.02
BMI 2 1.01 0.41 0.07
BMI x WC 3 1.45 0.24 0.07
Error 55 (14.77)
2 2 Note. Values enclosed in parentheses represent mean square errors. R = 0.251 (Adjusted R = 0.061).
*p < .05, **p < .01 78
Table 9
BMI and WC Group Status Predicting Total Three-Day Caloric Intake
ANCOVA: Between Subjects Effects
Source df F p 2
Corrected Model 14 0.49 0.93 0.11
BDI 1 0.50 0.48 0.01
STAI-S 1 0.06 0.81 0.00
STAI-T 1 0.03 0.87 0.00
SES-G 1 0.67 0.42 0.01
SES-S 1 0.16 0.69 0.00
WC 4 0.68 0.51 0.02
BMI 2 0.44 0.78 0.03
BMI x WC 3 0.11 0.96 0.01
Error 55 (19.92)
2 2 Note. Values enclosed in parentheses represent mean square errors. R = 0.110 (Adjusted R = -0.116).
*p < .05, **p < .01
79
Table 10
BMI and WC Group Status Predicting Average Mood Change
ANCOVA: Between Subjects Effects
Source df F p 2
Corrected Model 14 0.72 0.75 0.15
BDI-II 1 2.39 0.13 0.04
STAI-S 1 2.07 0.16 0.04
STAI-T 1 0.21 0.65 0.00
SES-G 1 0.05 0.82 0.00
SES-S 1 1.22 0.27 0.02
WC 4 0.38 0.69 0.01
BMI 2 0.30 0.88 0.02
BMI x WC 3 0.22 0.88 0.01
Error 55 (0.63)
2 2 Note. Values enclosed in parentheses represent mean square errors. R = 0.154 (Adjusted R = -0.061).
*p < .05, **p < .01 80
Table 11
BMI and WC Group Status Predicting Average Reward Ratings per Eating Episode
ANCOVA: Between Subjects Effects
Source df F p 2
Corrected Model 14 3.60 0.00** 0.48
BDI 1 0.15 0.70 0.00
STAI-S 1 1.28 0.26 0.02
STAI-T 1 2.93 0.09 0.05
SES-G 1 8.06 0.01** 0.13
SES-S 1 0.99 0.32 0.02
WC 2 6.85 0.00** 0.20
BMI 4 4.97 0.00** 0.27
BMI x WC 3 2.15 0.10 0.11
Error 55 (0.66)
2 2 Note. Values enclosed in parentheses represent mean square errors. R = 0.478 (Adjusted R = 0.346).
*p < .05, **p < .01
81
Table 12
BMI and WC Group Status Predicting Average Perceived Control
ANCOVA: Between Subjects Effects
Source df F p 2
Corrected Model 14 1.05 0.42 0.21
BDI 1 1.87 0.18 0.03
STAI-S 1 0.29 0.59 0.01
STAI-T 1 0.02 0.88 0.00
SES-G 1 0.79 0.38 0.01
SES-S 1 0.38 0.54 0.01
WC 4 1.91 0.16 0.07
BMI 2 0.82 0.52 0.06
BMI x WC 3 0.41 0.75 0.02
Error 54 (2.20)
2 2 Note. Values enclosed in parentheses represent mean square errors. R = 0.214 (Adjusted R = 0.010).
*p < .05, **p < .01
82
Table 13 Distress Tolerance (DTS) as a Function of BMI and WC Group Status
ANCOVA: Between Subjects Effects
Source df F p 2
Corrected Model 14 4.98 0.00** 0.55
BDI 1 11.44 0.00** 0.17
STAI-S 1 0.39 0.54 0.01
STAI-T 1 2.96 0.09 0.05
SES-G 1 6.55 0.01* 0.10
SES-S 1 4.47 0.04* 0.07
BMI 4 1.20 0.32 0.08
WC 2 0.22 0.81 0.01
BMI x WC 3 1.60 0.20 0.08
Error 58 (0.23)
2 2 Note. Values enclosed in parentheses represent mean square errors. R = 0.546 (Adjusted R = 0.436).
*p < .05, **p < .01
83
Table 14
Distress Tolerance (PASAT-C) as a Function of BMI and WC Group Status
ANCOVA: Between Subjects Effects
Source df F p 2
Corrected Model 14 1.21 0.30 0.23
BDI 1 0.03 0.88 0.00
STAI-S 1 3.59 0.06 0.06
STAI-T 1 0.01 0.93 0.00
SES-G 1 0.59 0.45 0.01
SES-S 1 3.21 0.08 0.05
BMI 4 1.34 0.27 0.08
WC 2 0.43 0.65 0.02
BMI x WC 3 0.29 0.83 0.02
Error 58 (0.29)
2 2 Note. Values enclosed in parentheses represent mean square errors. R = 0.225 (Adjusted R = 0.038).
*p < .05, **p < .01
84
Table 15
Experiential Avoidance as a Function of BMI and WC Group Status
ANCOVA: Between Subjects Effects
Source df F p 2
Corrected Model 14 5.41 0.00** 0.57
BDI 1 2.92 0.09 0.05
STAI-S 1 0.05 0.83 0.00
STAI-T 1 4.47 0.04* 0.07
SES-G 1 2.79 0.10 0.05
SES-S 1 0.26 0.61 0.00
BMI 4 3.16 0.02* 0.18
WC 2 1.50 0.23 0.05
BMI x WC 3 0.78 0.51 0.04
Error 58 (28.79)
2 2 Note. Values enclosed in parentheses represent mean square errors. R = 0.566 (Adjusted R = 0.462).
*p < .05, **p < .01 85
Table 16
BMI as a Function of Distress Tolerance and Experiential Avoidance
Independent Variable Standardized Coefficient (β) SE Partial r t p
Step 1
Depression -.00 .12 -.00 -.02 .99
Anxiety (State) .05 .09 -.03 -.28 .78
Anxiety (Trait) -.01 .12 -.01 -.05 .96
Self-efficacy (General) .29 .09 .24 2.05 .04*
Self-efficacy (Social) -.29 .17 -.26 -2.20 .03*
R2 = .11
Step 2
Depression .06 .13 -.04 -.29 .77
Anxiety (State) .06 .09 -.04 .33 .74
Anxiety (Trait) .04 .13 -.02 .15 .89
Self-efficacy (General) .33 .10 .25 2.07 .04*
Self-efficacy (Social) -.25 .19 -.20 -1.66 .10
Distress Tolerance (DTS) -.12 1.38 -.09 -.68 .66
Distress Tolerance (PASAT-C) -.06 .00 -.06 -.44 .50
Experiential Avoidance -.03 .12 -.03 -.20 .84
R2 = .12
∆R2 = .01
Note: Depression = BDI-II; Anxiety (Trait) = STAI-Y; Anxiety (State) = STAI-Y; General Self-Efficacy = SES; Social Self-Efficacy = SES; Experiential Avoidance = AAQ. 86
Table 17
WC as a Function of Distress Tolerance and Experiential Avoidance
Independent Variable Standardized Coefficient (β) SE Partial r t p
Step 1
Depression -.08 .28 -.06 -.48 .64
Anxiety (State) .07 .21 .05 .40 .69
Anxiety (Trait) -.06 .29 -.03 -.24 .81
Self-efficacy (General) .23 .21 .19 1.58 .12
Self-efficacy (Social) -.34 .41 -.30 -2.59 .01*
R2 = .12
Step 2
Depression -.12 .32 -.08 -.62 .54
Anxiety (State) .08 .22 .05 .44 .67
Anxiety (Trait) .01 .32 .00 .03 .98
Self-efficacy (General) .25 .23 .19 1.56 .12
Self-efficacy (Social) -.46 .46 -.25 -2.09 .04*
Distress Tolerance (DTS) -.10 3.34 -.07 -.58 .57
Distress Tolerance (PASAT-C) -.03 .01 -.03 -.24 .82
Experiential Avoidance -.08 .28 -.06 -.48 .64
R2 = .12
∆R2 = .01
Note: Depression = BDI-II; Anxiety (Trait) = STAI-Y; Anxiety (State) = STAI-Y; General Self-Efficacy = SES; Social Self-Efficacy = SES; Experiential Avoidance = AAQ.
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Table 18
Total Caloric Intake & Average Reward per Eating Episode as a Function of Average
Mood Change
Multivariate Analysis: Between Subjects Effects
Source DV df F p 2
Corrected Model Reward a 67 9.22 0.10 0.10
Kcal Total b 67 0.62 0.79 0.95
STAI-Y Trait Reward 1 11.75 0.08 0.86
Kcal Total 1 0.12 0.77 0.06
STAI-Y State Reward 1 23.73 0.04* 0.92
Kcal Total 39 0.20 0.70 0.09
BDI-II Reward 1 83.22 0.01* 0.98
Kcal Total 1 0.09 0.79 0.04
Mood Change Reward 64 8.45 0.11 0.98
Kcal Total 64 0.63 0.79 0.04
Error Reward 4 (0.112)
Kcal Total 4 (1.260E7)
Note. Values enclosed in parentheses represent mean square errors. 2 2 a. R = 0.997 (Adjusted R = 0.889) 2 2 b. R = 0.954 (Adjusted R = 0.576) *p < .05, **p < .01
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Table 19
Total Caloric Intake & Average Reward per Eating Episode as a Function of
Depression and Anxiety
Multivariate Analysis: Between Subjects Effects
Source DV df F p 2
Corrected Model Reward a 65 0.68 0.78 0.92
Kcal Total b 65 0.90 0.64 0.94
STAI-Y Trait Reward 27 0.49 0.89 0.77
Kcal Total 27 0.69 0.76 0.82
BDI-II Reward 18 0.87 0.64 0.80
Kcal Total 18 0.90 0.62 0.80
STAI-Y-T x BDI Reward 16 0.83 0.65 0.77
Kcal Total 16 1.55 0.36 0.86
Error Reward 4 (1.436)
Kcal Total 4 (8813461.417)
Note. Values enclosed in parentheses represent mean square errors. 2 2 a. R = 0.917 (Adjusted R = -0.429) 2 2 b. R = 0.936 (Adjusted R = -0.103) *p < .05, **p < .01
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Table 20 Mediation of the Effect of Total Caloric Intake over the course of Three Days on Body Mass Index Through Behavioral Distress Tolerance and Experiential Avoidance
Bootstrapping Percentile 95% CI BC 95% CI BCa 95% CI
Lower Upper Lower Upper Lower Upper
Indirect Effects -.0001 .0001 -.0001 .0001 -.0001 .0001 PASAT-C AAQ -.0001 .0001 -.0001 .0001 -.0001 .0000 -.0001 .0001 -.0001 .0001 -.0002 .0001 TOTAL Contrasts
PASAT-C vs. AAQ -.0001 .0001 -.0001 .0001 -.0002 .0001
Note: PASAT-C = Paced Auditory Serial Addition Task, Computer Version; AAQ = Acceptance & Action Questionnaire; BC = bias corrected; BCa = bias corrected and accelerated; 5,000 bootstrap samples. 90
Table 21 Mediation of the Effect of Total Caloric Intake over the course of Three Days on Body Mass Index Through Self-Reported Distress Tolerance and Experiential Avoidance
Bootstrapping Percentile 95% CI BC 95% CI BCa 95% CI
Lower Upper Lower Upper Lower Upper
Indirect Effects -.0001 .0001 -.0001 .0001 -.0001 .0001 DTS AAQ -.0001 .0001 -.0001 .0001 -.0001 .0000 -.0001 .0001 -.0001 .0001 -.0002 .0001 TOTAL Contrasts
DTS vs. AAQ -.0001 .0001 -.0001 .0001 -.0002 .0001
Note: DTS = Distress Tolerance Scale; AAQ = Acceptance & Action Questionnaire; BC = bias corrected; BCa = bias corrected and accelerated; 5,000 bootstrap samples. 91
Table 22 Mediation of the Effect of Total Caloric Intake over the course of Three Days on Waist Circumference Through Behavioral Distress Tolerance and Experiential Avoidance
Bootstrapping Percentile 95% CI BC 95% CI BCa 95% CI
Lower Upper Lower Upper Lower Upper
Indirect Effects -.0002 .0002 -.0003 .0001 -.0003 .0001 PASAT-C AAQ -.0002 .0002 -.0001 .0002 -.0001 .0002 -.0003 .0003 -.0003 .0003 -.0003 .0002 TOTAL Contrasts
PASAT-C vs. AAQ -.0002 .0003 -.0003 .0002 -.0003 .0002
Note: PASAT-C = Paced Auditory Serial Addition Task, Computer Version; AAQ = Acceptance & Action Questionnaire; BC = bias corrected; BCa = bias corrected and accelerated; 5,000 bootstrap samples. 92
Table 23 Mediation of the Effect of Total Caloric Intake over the course of Three Days on Waist Circumference Through Self-Reported Distress Tolerance and Experiential Avoidance
Bootstrapping Percentile 95% CI BC 95% CI BCa 95% CI
Lower Upper Lower Upper Lower Upper
Indirect Effects -.0003 .0002 -.0004 .0001 -.0004 .0001 DTS AAQ -.0002 .0002 -.0002 .0003 -.0002 .0003 -.0003 .0002 -.0003 .0002 -.0003 .0002 TOTAL Contrasts
DTS vs. AAQ -.0004 .0003 -.0006 .0002 -.0006 .0002
Note: DTS = Distress Tolerance Scale; AAQ = Acceptance & Action Questionnaire; BC = bias corrected; BCa = bias corrected and accelerated; 5,000 bootstrap samples. 93
Table 24
Summary of Hierarchical Regression Analysis for Variables Predicting Average
Reward Ratings per Eating Episode
Variable B SE B Beta
Step 1
Depression 0.02 0.02 0.15
Anxiety (State) -0.03 0.02 -0.25
Anxiety (Trait) -0.04 0.02 -0.39
Self-Efficacy (General) -0.05 0.02 -0.38**
Self-Efficacy (Social) 0.03 0.03 0.11
Step 2
Depression 0.01 0.02 0.10
Anxiety (State) -0.03 0.02 -0.25
Anxiety (Trait) -0.01 0.02 -0.09
Self-Efficacy (General) -0.05 0.02 0.40**
Self-Efficacy (Social) 0.05 0.03 0.21
Distress Tolerance -0.44 0.24 -0.26
Experiential Avoidance -0.07 0.02 -0.49**
*p < .05, **p < .01
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APPENDICES 95
APPENDIX A: DEMOGRAPHIC INFORMATION
Please provide the following information by circling or writing in your answer:
1. Gender a) Male b) Female
2. Age: ______
3. Race: Select 1 or more a) White b) Black or African American c) Asian d) American Indian or Alaskan Native e) Native Hawaiian or Other Pacific Islander f) Other: (please specify) ______
4. Ethnicity a) Hispanic b) Non-Hispanic
5. Your Marital Status a) Single b) Married c) Separated d) Divorced
6. Estimated Family Income (per year; if a dependent use parents’ income) a) $0-$9,999 b) $10,000-$19,999 c) $20,000-$29,999 d) $30,000-$39,999 e) $40,000-$49,999 f) Greater than $50,000
7. Occupational Status a) Employed full-time b) Employed part-time c) Unemployed
8. Educational Status: a) Part-time student b) Full-time student 96
c) Other: (please specify) ______
9. Education (years): _____ (example: Grade 12 + 1 year in college = 13 years; Grade 12 + 2 years in college = 14 years, etc.)
10. Meal Plan a) None b) Vol Block ($500 DD) c) Unlimited Access ($100 DD) d) Unlimited Access Plus ($300 DD) e) Any 10 ($300 DD) f) Any 8 ($450 DD) g) Apartment Any 8 ($200 DD) h) Apartment Any 5 ($500 DD) i) Apartment Dining Dollars Only ($989 DD) j) Commuter Dining Dollar Plan ($626 DD) k) Commuter 75 ($100 DD) l) Commuter 50 ($200 DD) m) Varsity Inn 15 (no DD) n) Other: (please specify) ______
11. Living Arrangements a) On campus, in a dormitory b) On campus, not in a dormitory: (please describe) ______c) Off campus, University Apartments: (please describe) ______d) Off-campus, Commuter: (please describe) ______e) Other: (please describe) ______
Following your participation in this study, would you like to receive a feedback report detailing your daily energy intake? □ yes □ no
If yes, please provide your address. A copy of your report will be mailed to you as soon as possible, following your participation in this research.
Street Address City State Zip Code
97
APPENDIX B: DAILY FOOD DIARY
Instructions: This Daily Food Dairy will be used to record your snacks and meals over the course of three days: 2 class days, and one non-class day. Please record as accurately as possible; DO NOT LEAVE ANYTHING OUT. Remember, this information will be kept private and protected. In your Daily Food Diary, please record the following information:
1. Meal Label: Breakfast (B), Lunch (L), Dinner (D), or Snack (S) 2. Time at which you began eating the meal 3. Location where meal occurred (e.g. “home,” or “restaurant”) 4. Your mood prior to eating or drinking, according to the 1 – 9 scale below:
1: Most 3: 5: 7: 9: Most Negative Negative Neutral Positive Positive Mood Mood Mood Mood Mood
5. A detailed description of the food(s) eaten (for example, give brand names if possible, the type of bread eaten, whether you used salt or added anything to the food, etc.) Please include a detailed description of the types of beverages consumed throughout the day, as well, including alcohol. 6. A detailed description of the amount of food eaten (for example, the number of cups of fruit, the number of tablespoons of butter or sugar, etc.) or of beverage consumed (for example, ½ liter, 8 oz). 7. The amount of pleasure or reward you experienced while eating or drinking, according to the 1 – 9 scale below:
1: Very 3: 5: 7: 9: Most Unpleasant Unpleasant Neutral Pleasant Pleasant
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8. Your mood following eating or drinking, according to the 1 – 9 scale below:
1: Most 3: 5: 7: 9: Most Negative Negative Neutral Positive Positive Mood Mood Mood Mood Mood
9. The amount of control you feel you had over your eating/drinking behavior, according to the 1 – 9 scale below:
1: 5: 9: Absolutely Neutral Absolute No Control Control
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Example: At lunch (12:00 pm), Tom ate a turkey sandwich, chips, a soda, and cookies.
Meal Time Time Location Mood Description of Food(s) Amount Reward Mood Perceived Label Eating Eating Prior Eaten Consumed Value After Control over Began Ended to of Food Eating Eating Eating Behavior L 12 p 1p Home 7 Turkey sandwich 9 2 White bread (Nature’s 2 slices 2 Own) Turkey luncheon meat 2 oz (2 5 (Oscar Meyer) slices) American cheese (Kraft) 1 slice 7 Mayonnaise – regular 2 Tbsp 2 (Hellman’s) Lettuce – iceberg 1 leaf 2 Lay’s regular potato chips 1 oz 7 Diet Coke 16 oz 6 Oreo cookies 3 8
100
Office Use Only DATE // M M D D Y Y Reference #:
______CIRCLE ONE: class day non-class day Day of the Week
Meal Time Time Location Mood Description of Food(s) Eaten Amount Reward Mood Perceived Label Eating Eating Prior to Consumed Value After Control Began Ended Eating of Food Eating over Eating Behavior
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APPENDIX C: SAMPLE NDSR REPORT
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VITA
Christen Mullane earned her B.A. in Psychology and Anthropology from Vanderbilt University in Nashville, Tennessee, in 2005. She completed her M.A. thesis in Clinical Psychology, entitled
“Body Mass Index: Effects on Overt Behaviors and Perceived Reward,” in 2008 under the mentorship of Dr. Derek Hopko at the University of Tennessee in Knoxville. Partial results from that study were published in volume 46 of the journal Behaviour Research and Therapy under the title “Exploring the Relation of Depression and Overt Behavior using Daily Diaries” in 2008.
Christen continued her graduate education at the University of Tennessee with Dr. Hopko and defended her dissertation in 2010. Partial results from that study were presented at the annual
Association for Behavioral and Cognitive Therapies convention in 2010. She will complete her internship in Clinical Psychology at the Temple University Health Sciences Center in the summer of 2011.