ALTERED COGNITIVE AND PSYCHOPHYSIOLOGICAL COMPONENTS OF PSYCHOLOGICAL FLEXIBILITY IN INDIVIDUALS WITH OVERWEIGHT/OBESITY

Tanya S. Watford

A Dissertation

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

August 2020

Committee:

Abby Braden, Advisor

Amilcar Challu Graduate Faculty Representative

Dara Musher-Eizenman

William O'Brien

ii ABSTRACT

Abby Braden, Advisor

The prevalence of overweight and obesity has continued to rise among adults. While biological factors contribute to overweight (Frayling et al., 2007), body weight regulation is influenced by behavior (Fuglestad, Jeffery, & Sherwood, 2012). Behavioral weight loss interventions often demonstrate only modest reductions in weight, possibly because the psychological factors that modulate obesity-related behaviors are rarely targeted in treatment.

Thus, psychological flexibility may be of particular importance, given it describes the ability to perform goal-consistent behavior in the presence of contrary urges or environmental demands.

To characterize the dynamic factors involved in psychological flexibility (Kashdan &

Rottenberg, 2010), the present study examined between-group (overweight/obese: n=33, normal weight: n=47) differences in executive function, cognitive traits of awareness and acceptance, and heart rate variability (HRV). Individuals with overweight/obesity demonstrated poorer attention-shifting (Wisconsin Card Sorting Task, p = .009) and lower attention and awareness

(Mindful Attention Awareness Scale, p = .01 and Difficulties in Emotion Regulation subscale emotional clarity, p = .006). Participants with overweight/obesity also demonstrated greater vagally-mediated HRV compared to normal weight with marginal significance (p = .07) and a

2 medium effect size (ηp = .042) across all HRV conditions (rest, stress, and recovery). No other significant differences were found regarding executive function (i.e., inhibition and working memory), cognitive features of acceptance and awareness (i.e. subscales of the Difficulties in

Emotion Regulation scale), or HRV reactivity and recovery. Findings suggest some factors essential to psychological flexibility may be altered in individuals with overweight/obesity compared to normal-weight. iii

This work is dedicated to my husband, Lee. I could not imagine navigating the challenges of this

journey without his patience, faith, humor, and compassion. I am truly grateful to him for his

unconditional support and for showing me what it means to be an amazing human. iv ACKNOWLEDGEMENTS

This project would not have come into being without the influence and support of many

wonderful people in my life. First, I would like to thank my advisor, Dr. Abby Braden, for her

consistent feedback and compassionate guidance during the dissertation process. Your warmth

and sincerity in conjunction with your excellent skills as a scholar and researcher created an

environment that has fostered my growth and my well-being. I am thankful for the critical

thinking and expertise of my committee members, Drs. William O’Brien, Dara Musher-

Eizenman, and Amilcar Challu, whose feedback significantly strengthened this study. A special thanks to Dr. William O’Brien for offering me the opportunity to be a part of this BGSU family.

I have learned so much! I am grateful for the support and friendship of the clinical faculty, administrative staff, and fellow students in the Psychology Department at Bowling Green State

University. I must also thank Dr. Jane Stafford, whose ongoing support and mentorship have been essential elements throughout the successful pursuit of this doctoral degree.

I would like to thank the wonderful research assistants that provided their time and

energy to this project. A special thank you to Bret Williamson for his excellent work and

amazing contribution to analyzing a mountain of psychophysiological data. Thank you for your

commitment to completing this project.

As a scientist-practitioner, I have been significantly impacted by my clinical supervisors

during my graduate training. A special thanks to Drs. William O’Brien, Abby Braden, Joshua

Grubbs, Marlys Reetz, Jane Stafford, Maureen Carrigan, and Anne Ellison for your excellent training. You are exceptional role models.

Finally, I would like to thank my family and friends for their incredible support through this process. To my mother, your love, encouragement, and unconditional support have been invaluable. I am in awe when I reflect on the powerful impact of being part of my wonderful v clan, with special recognition of my brother and sister-in-law and my godmother. A special thanks to my husband. This project would not have been possible without your patience and support. Thank you for believing in me through every challenge. I am incredibly grateful to be surrounded by so many wonderful people. vi

TABLE OF CONTENTS

Page

INTRODUCTION ...... 1

Psychological Flexibility ...... 6

Executive Function (EF) ...... 7

Cognitive Traits of Acceptance and Awareness ...... 10

Heart Rate Variability (HRV) ...... 14

Present Study ...... 16

Hypotheses ...... 17

METHOD ...... 19

Participants ...... 19

Measures ...... 19

Demographics ...... 19

Anthropometry ...... 19

Heart Rate Variability (HRV) ...... 20

Attention-shifting ...... 21

Working Memory...... 21

Inhibition ...... 22

Psychological Health and Treatment History ...... 22

Health Behaviors ...... 22

Mindfulness...... 22

Mindful Emotion Regulation ...... 23

Positive and Negative Affect ...... 24 vii

Stressor ...... 24

Procedure ...... 25

Data Analytic Plan ...... 25

Preliminary Analyses ...... 26

Primary Analyses ...... 26

RESULTS ...... 29

DISCUSSION ...... 33

Executive Function ...... 33

Cognitive Traits of Acceptance and Awareness ...... 37

Heart Rate Variability ...... 40

Limitations ...... 43

Conclusions ...... 44

REFERENCES ...... 52

APPENDIX A. MINDFUL ATTENTION AWARENESS SCALE (MAAS) ...... 78

APPENDIX B. DIFFICULTIES IN EMOTION REGULATION, SHORT FORM

(DERS-SF) ...... 79

APPENDIX C. POSITIVE AND NEGATIVE AFFECT SCHEDULE (PANAS) ...... 80 viii

LIST OF FIGURES

Figure Page

1 Components of Psychological Flexibility ...... 48

2 Time Periods Analyzed for Heart Rate Variability (HRV) ...... 49

3 Procedure Flow Chart ...... 50

4 HRV Means by Group and Time ...... 51 ix

LIST OF TABLES

Table Page

1 Effect Sizes Between BMI and Study Variables in Previous Research ...... 45

2 Demographics and Health Behaviors...... 46

3 Differences Between Overweight/Obese and Normal-weight Participants on

Components of Psychological Flexibility ...... 47 1

INTRODUCTION

The prevalence of obesity has continued to rise among adults, growing from 33.7% in

2008 to 39.6% in 2015-16 (Hales, Fryar, Carroll, Freedman, & Ogden, 2018). Obesity and overweight are associated with significant health risks, including congestive heart failure, Type 2

diabetes, high blood pressure, stroke, osteoarthritis, and premature mortality (Burnette, 2017).

Accordingly, the burden of obesity-related health care costs reflects this (Biener, Cawley, &

Meyerhoefer, 2018). Overweight and obesity are associated with negative psychosocial

outcomes like low quality of life (Lillis, Hayes, Bunting, & Masuda, 2009), and increased risk of

disordered eating (Austin, 2011), self-esteem issues, weight discrimination and stigma, and body

dissatisfaction (Peckmezian & Hay, 2017).

Behavioral weight loss interventions commonly focus on a combination of

psychoeducation, diet, and physical activity, and, even when well-controlled and comprehensive,

often demonstrate only modest reductions in weight (Avenell et al., 2004). Individuals

participating in weight loss interventions often drop-out before completion (Steinhausen &

Weber, 2009) and have difficulties with weight loss maintenance (Lillis & Hayes, 2008). Part of

the problem may be that overweight partially results from obesity-related behaviors, and that the

psychological factors that contribute to obesity-related behaviors are rarely targeted in treatment

(Lillis, Hayes, Bunting, & Masuda, 2009). Psychological flexibility is a psychological factor that

may affect obesity-related behaviors (Lillis, Hayes, Bunting, & Masuda, 2009). Psychological

flexibility is considered the ability to persist in or change behavior, so it is values-consistent and

effective given the context (Hayes, Strosahl, & Wilson, 1999). Theory suggests that

psychological flexibility is essential to achieving sustained behavior change in the presence of

uncomfortable internal experiences (i.e., thoughts, emotions, physical sensations) and 2 complicated environmental demands of daily life. This is consistent with research showing that, compared to individuals lower in psychological flexibility, individuals higher in psychological flexibility respond more adaptively to emotional experience (Kashdan & Rottenberg, 2010).

Psychological flexibility could be a useful intervention target if individuals with overweight/obesity have lower levels of psychological flexibility compared to normal weight individuals.

Obesity is partially caused by obesity-related behaviors. While biological factors contribute to overweight and obesity (Frayling et al., 2007), body weight regulation likely depends on the interaction between genetic, environmental, and behavioral factors (Jequier,

2002). Body weight is influenced by behavior, in particular obesity-related behaviors like activity level (Michaud et al., 2001; Fuglestad, Jeffery, & Sherwood, 2012), eating habits like meal regularity and fruit and vegetable intake (Blokstra, Bums, & Seidell, 1999; Fuglestad,

Jeffery, & Sherwood, 2012), television-related viewing and eating (Robinson, 1998; Fuglestad,

Jeffery, & Sherwood, 2012), and greater use of weight control strategies (Fuglestad, Jeffery, &

Sherwood, 2012). Overweight individuals report that obesity arises from shortcomings in their personal behavior (i.e., motivational and physical; Greener, Douglas, and van Teijlingen, 2010).

Research has shown that behavior change results in weight change. Hankonen and colleagues

(2014; N = 239) examined whether behavior change techniques (e.g., goal setting) resulted in weight loss in individuals recently diagnosed with type 2 diabetes. Thirty-six percent of participants reported using the recommended techniques and greater use of techniques correlated with reductions in body mass index (BMI). Ross, Flynn, & Pate (2016) synthesized the data from published review articles (k = 65) and, while the literature revealed no consensus regarding the primary cause of obesity, the factor most frequently identified by adults as contributing to 3

changes in weight was behavioral: combined diet and physical activity. Therefore, while

overweight and obesity are physical conditions, individual behavior plays a crucial role in this

important health factor.

Obesity-related behaviors are associated with psychological factors. Individuals

performing successful weight loss behaviors demonstrate more approach-oriented and flexible

styles of responding to difficult emotions and other psychological experiences (Fergueson,

Brink, Wood, & Koop, 1992). Individuals who successfully maintain weight loss commonly

adopt flexible behavior and attitudes when responding to environmental demands (Westenhoefer,

2001). Difficulty maintaining weight loss after treatment was associated with limited access to

coping strategies (Drapkin, Wing, & Shiffman, 1995), and avoidant and impulsive coping styles

(Byrne, Cooper, & Fairburn, 2003; Fassino et al., 2002). Overweight individuals reported higher impulsivity, anxiety, angry hostility, and lower self-discipline compared to normal-weight

individuals, after controlling for a genetic contribution to BMI (i.e., FTO Single Nucleotide

Polymorphism: Frayling et al., 2007). Given that behaviors resulting in overweight are

associated with psychological factors, differences may exist between overweight and normal-

weight adults in various psychological domains.

Psychological flexibility may be a psychological factor that differs between overweight

and normal-weight adults. Psychological flexibility is essential to sustaining behavior that is

values-consistent when experiencing difficult or unwanted emotions (Hayes, Strosahl, & Wilson,

1999). Obesity-related behaviors may be considered values-inconsistent behavior, given they

persist despite being tied to poor psychological outcomes, weight gain, and a stated desire to lose

weight (Bourdier et al., 2018). Obesity-related behaviors have been shown to be greater in the

presence of unwanted emotions and stressful experiences (Klatzkin, Gaffbey, Cyrus, Bigus, & 4

Brownley, 2015). Considering this and given that obesity is partially caused by obesity-related behaviors, psychological flexibility may be lower in overweight/obese individuals compared to lean individuals.

The relationship between psychological flexibility and overweight/obesity has been examined in treatment-seeking samples. In a sample of overweight and obese persons enrolled in a psychological lifestyle intervention study (Sairanen et al., 2015), psychological flexibility

(Acceptance and Action Questionnaire: AAQ-II, Bond et al., 2011) accounted for unique variance in adaptive eating, including permission to eat when hungry, eating for physical rather than emotional reasons, and reliance on internal hunger cues. For overweight individuals participating in a weight-loss intervention, psychological flexibility (AAQ-II) has been inversely correlated with pre-treatment weight (Sairanen, Lappalainen, Lapvetelainen, Tolvanen, &

Karhunen, 2014) and has been predictive of weight loss (Juarascio, Forman, Timko, Butryn, &

Goodwin, 2011). For overweight individuals who previously attempted structured weight-loss programs, Lillis, Hayes, Bunting, and Masuda (2009) found that increased post-intervention self- reported psychological flexibility (AAQ for Weight; Lillis & Hayes, 2008) predicted reductions in weight at 3-month follow-up compared to wait-listed controls.

Given the effectiveness of psychological flexibility interventions for weight loss and management (Lillis, Hayes, Bunting & Masuda, 2009), one study (n= 7884) examined differences in psychological flexibility between people with overweight/obesity and normal weight. Ciarrochi, Sahdra, Marshall, Parker and Horwath (2014) conceptualized psychological flexibility within the model proposed by ACT (Acceptance and Commitment Therapy: Hayes,

Strosahl, & Wilson 1999) and assessed components of psychological flexibility - experiential acceptance, contact with the present moment, believing unhelpful thoughts, values, and 5

committed action. Results of this study describe the complicated nature of psychological

flexibility, in that all components were correlated with the most well-established self-report measure of psychological flexibility (AAQ-II). Yet, differences between weight classes were not parallel as theory would have predicted. BMI had a main effect on almost all flexibility components (i.e., not the male striving aspect of committed action), but effect sizes were small.

Overweight, obese, and normal weight groups did not differ significantly across flexibility

components. Severely obese participants reported significant inflexibility in believing unhelpful

thoughts and avoidance compared to all other weight classes (underweight, normal weight,

overweight). Other significant findings for severely obese varied by gender: deficits were found

in an aspect of committed action (striving) for females and emotional awareness for males,

compared to males and females of other weight classes. Authors asserted that small effect sizes

may reflect the inability of included measures to distinguish between normal, overweight, and

obese groups. Study findings support the conceptualization of psychological flexibility as having

multiple components rather than being a single factor.

The examination of cognitive and psychophysiological measures associated with

psychological flexibility may capture its dynamic nature (Kashdan & Rottenberg, 2010) and

reveal important differences between individuals with overweight and normal-weight. The

following section describes psychological flexibility and presents support for the utility of

examining psychophysiological and cognitive factors that may be essential to psychological

flexibility. These include executive function, the cognitive features of acceptance and awareness,

and HRV. Medium to large effect sizes have been found in each of these domains related to BMI

(e.g., Godfrey et al., 2019; Mantzios & Egan, 2018; Steenbergen & Colzato, 2017). The

neurovisceral integration model provides support for examining these factors in concert, given 6

the neural substrates of this model - pre-frontal cortex, amygdala, and brain regions of the central

autonomic network – likely support attention, executive function, and adaptive HRV (Thayer &

Lane, 2000). These work together to organize emotional, cognitive, physiological and behavioral

responses in service of goal-directed behavior (Wei, Chen, & Wu, 2018). Compared to relying

on participant self-report, examining psychophysiological and cognitive measures may capture a

more complete picture of psychological flexibility and may more accurately characterize

differences between overweight/obese and normal weight individuals in psychological flexibility

(Figure 1).

Psychological Flexibility

Psychological flexibility is considered the ability of an individual to be aware of experiences in the present moment and, depending upon what the context requires, to choose behaviors in the service of values (Hayes, Strosahl, & Wilson 1999). Psychological flexibility

may include the allocation of mental resources, adapting to contextual demands, shifting

perspective, and balancing short-term desires with long-term goals (Kashdan & Rottenberg,

2010). Flexible implementation of behaviors based on contextual demands has predicted lower

pathology (Aldao & Nolen-Hoeksema, 2012), suggesting it is the ability to flexibly implement

strategies appropriate to the context - psychological flexibility - that is associated with desired

outcomes, rather than just the strategy itself. As psychological flexibility increases, the ability to

commit energy to meaningful interests also increases, even in the context of emotional

experience (Hayes, Strosahl, & Wilson, 1999). Psychologically flexible individuals might bring

awareness to the experiences that increase eating and maintain commitment to healthy behaviors,

reducing or eliminating maladaptive eating behaviors (Ciarrochi et al., 2014). As such,

overweight individuals may have less psychological flexibility than normal weight individuals. 7

Research has demonstrated a significant relationship between psychological flexibility

and positive outcomes in a variety of domains. In a meta-analysis (k = 21), psychological

flexibility (Acceptance and Action Questionnaire: AAQ; Hayes et al., 2004) demonstrated a

large effect size (.30) with quality of life measures, including measures of stress, physical pain,

and negative affect. Additional research has found psychological flexibility to be positively

associated with reductions in distress (Dalrymple & Herbert, 2007), greater pain tolerance

(Feldner et al. 2006) and more effective reduction in depression over the course of treatment for

Borderline Personality Disorder (Berking, Neacsiu, Comtois, & Linehan, 2009). Given the

association between psychological flexibility and positive outcomes, like mental health, life

satisfaction (Hayes et al. 2006), and weight-loss (Juarascio et al., 2011), examining

psychological flexibility is of ever-greater importance.

Executive Function (EF)

EF may be essential to psychological flexibility given that it provides the neurological

support necessary to rapidly shift cognitive sets and attention which is critical to goal-directed

behavior (Goldberg, 2002). Based on an accepted model of EF (Miyake et al., 2000), EF is

comprised of three aspects: attention (i.e., the ability to shift attention when situationally

appropriate), working memory, and inhibition. This is not the only model that describes an

executive component to cognitive functioning. Baddeley (2001) proposed a multicomponent

model of working memory that describe the structures that temporarily maintain and manipulate

information, which includes a central executive component. The central executive manages attentional processes to facilitate the use of a phonological loop (short-term verbal memory) and

a visuospatial sketchpad (maintains and manipulates visuospatial information) for the

accumulation of knowledge. This model, because of its focus on structures rather than processes, 8 has been met with some criticism (Baddeley, 2001). The Miyake model was employed in the current study for a conceptualization of EF that is not bound by attention and memory and may better describe the processes necessary to accomplish executive tasks. The Miyake and colleagues (2000) model describes three often posited executive functions— attention-shifting, information updating and monitoring (also called working memory), and inhibition of automatic responses - that have been hypothesized to be generated and organized by the frontal lobe.

Research has supported these separable but correlated functions as essential for completing executive tasks using confirmatory factor analysis and structural equation modeling (Miyake et al., 2000). Attention is critical for appropriately determining contextual demands. Working memory allows a person to mentally represent multiple aspects of a complex situation. Inhibition of automatic responses allows the individual to choose behaviors in the service of values

(Goldberg, 2002). Adequate executive functioning as posited by the Miyake model (2000) may thus describe abilities necessary for an individual to choose responses that suit the context and achieve desired outcomes.

Research has shown each of these three aspects of EF to be associated with improved functioning. Attention, specifically shifting attention back and forth between relevant material predicted lower impact of pain on health (Boggero, Eisenlohr-Moul, & Segerstrom, 2016).

Sustained attention (scores on a continuous performance task) predicted increased high- frequency heart rate variability (HF-HRV), a psychophysiological correlate of psychological flexibility (Suess, Porges, & Plude, 1994). Attention may thus be important for psychological flexibility.

Working memory (WM) facilitates the ability to represent numerous aspects of a complex situation in the mind (Miyake et al., 2000). Individuals with higher WM scores perform 9 better on the Stroop color-word interference task (Kane & Engle, 2003), on tasks of visual attention (Kane, Bleckley, Conway, & Engle, 2001), and are better at ignoring unimportant cues

(Conway, Cowan, & Bunting, 2001). As such, sufficient WM is critical to goal-directed behavior in the presence of emotional experience. This assertion is consistent with research demonstrating that individuals with higher WM scores are more successful at emotion regulation (e.g.,

Schmeichel, Volokhov, & Demaree, 2008; Unsworth et al. 2005; Schmeichel & Demaree, 2010).

WM may thus be important for psychological flexibility.

Inhibition of automatic or habitual responses allows an individual to choose goal- consistent behaviors when experiencing goal-inconsistent urges and may be conceptualized as the overriding of an automatic response to a stimulus (Bari & Robbins, 2013). Successful inhibition is positively correlated with the use of adaptive emotion regulation strategies

(Joormann & Gotlib, 2010) and has been associated with higher resting HF-HRV, a psychophysiological correlate of psychological flexibility (Park, Van Bavel, Vasey, & Thayer,

2012). Particularly since individuals commonly experience action urges to perform behaviors that are values-inconsistent (e.g., feeling sadness may prompt the urge to eat despite a desire to restrict food intake), inhibition of values-inconsistent behavior is important to goal-directed behavior and to psychological flexibility (Appelhans, 2009).

Medium to large effect sizes have been found between lower executive function and both overweight (e.g., Steenbergen & Colzato, 2017; Coppin et al., 2014; Hendrick et al., 2012) and obesity (Yang, Shields, Guo, and Liu (2017). People who demonstrate lower levels of EF in these specific areas (i.e., attention, working memory, and inhibition) are at risk for engaging in obesity-related behaviors (e.g., increased sedentary behavior) and becoming overweight/obese

(Appelhans, 2009). Yang, Shields, Guo, and Liu (2017: k = 72) found impairment in obese 10

individuals on tasks of inhibition, attention shifting, working memory and impairment in overweight individuals on tasks of inhibition and working memory compared to normal-weight

individuals. Lavagnino, Arnone, Cao, Soares, and Selvaraj (2016; k = 15) found obese

participants demonstrated lower inhibitory control compared to normal-weight individuals.

Consistent with these findings, research has found that obese participants demonstrated poorer

EF compared to normal-weight participants on tasks of inhibition (Stroop test: Gameiro, Perea,

Ladera, Rosa, & García, 2017; Fagundo et al., 2012) and attention shifting (Color Trails test:

Gameiro et al., 2017; Wisconsin Card Sorting task: Fagundo et al., 2012). Evidence suggests that

EF deficits are associated with difficulties managing and maintaining healthy weight. While the

majority of EF findings have focused on deficits in more extreme weight difficulties (e.g.,

obesity), it would be valuable to develop a better understanding of EF deficits in participants

with overweight, given the important role of EF in goal-directed behavior and, thus, weight

management.

Cognitive Traits of Acceptance and Awareness

Acceptance and awareness processes are cognitive functions that facilitate an individual’s

ability to be psychologically flexible. The ability to cultivate a curious and open attitude toward

experiences is associated with the ability to remain in contact with distressing emotions, thoughts

and sensations (Arch et al., 2016; Masuda, Hill, & Tone, 2012). Avoidance and judgement of

unwanted thoughts and emotions, the opposite of acceptance and awareness, is associated with

poor outcomes and reduced well-being (Hayes et al., 1996; Delaney & O’Brien, 2012; Ferguson,

Taylor, & McMahon, 2017; Robinson, Vargas, Tamir & Solberg, 2004). Furthermore, attempts

to avoid unwanted internal experiences requires substantial resources, particularly when these

experiences cannot be avoided, and diminishes resources for attention and decision-making 11

(Wegner, 1994). Acceptance and awareness have been associated with neurological mechanisms

- greater prefrontal activity and inhibition of limbic activation - which facilitate the ability to label emotions and reduce negative affect (Creswell, Way, Eisenberger, & Lieberman, 2007).

The abilities of acceptance and awareness may thus represent cognitive aspects of individual functioning that are essential to psychological flexibility.

Assessing these cognitive features may be best facilitated by measuring trait mindfulness.

Acceptance and awareness are the cognitive hallmarks of mindfulness – the ability to tolerate distress and remain open to and accepting of emotions, thoughts, and sensations (Shapiro,

Carlson, Astin, & Freedman, 2006). Trait mindfulness represents the cross-situational and long- standing capacity to be nonjudgmentally aware and accepting of experience which may be an inherent quality or acquired via life experiences (Brown & Ryan, 2003). Trait mindfulness has been associated with improved well-being (for review see Keng, Smoski, & Robins, 2011), including self-reports of increased life satisfaction (Brown & Ryan, 2003), decreased psychological distress (Raes, Dewulf, Van Heeringen, & Williams, 2009), and decreased experiential avoidance (Baer, Smith, & Allen, 2004). Trait mindfulness has been shown to be related to self-report measures of psychological flexibility (Glick, Millstein, & Orsillo, 2014;

Levin, Hildebrandt, Lillis, & Hayes, 2012). Trait mindfulness may represent the cognitive features of acceptance and awareness that are components of psychological flexibility.

Overweight/obesity has shown an inverse association with mindfulness with a medium effect size (Mantzios & Egan, 2018). This is consistent with the inverse association found with mindfulness and both disordered eating behaviors and BMI in mixed samples of overweight and normal-weight individuals (Loucks et al., 2016; Masuda, Price, & Latzman, 2012; Masuda,

Price, Anderson, & Wendell, 2010). In a longitudinal study, individuals lower in trait 12

mindfulness demonstrated increased likelihood to be overweight adults even when not

overweight in childhood (Loucks, Britton, Howe, Gutman, Gilman, Brewer, & ... Buka, 2016).

Mindfulness has demonstrated an inverse association in overweight individuals with binge and

emotional eating (Levin, Dalrymple, Himes, & Zimmerman, 2014), emotional and external

eating (Ouwens, Schiffer, Visser, Raeijmaekers, & Nyklíček, 2015), and emotional eating

(Dalrymple, Clark, Chelminski, & Zimmerman, 2018). While research examining differences

between individuals with overweight/obesity and normal weight is limited, it appears possible that overweight/obese individuals may report lower levels of trait mindfulness compared to lean individuals.

The cognitive features of acceptance and awareness can also be assessed by examining

emotion regulation (ER) (Gratz & Roemer, 2004). Gross (1998) suggested that ER is “the

process by which individuals influence which emotions they have, when they have them, and

how they experience or express their emotions” (p. 275). An integrative review suggested that

ER, particularly when characterized by acceptance and awareness, may represent a separate yet

overlapping construct with mindfulness (Chambers, Gullone, & Allen, 2009), which further

supports its inclusion as an aspect of psychological flexibility. The model of ER developed by

Gratz and Roemer (2004) describes ER difficulties in terms consistent with the cognitive traits of

acceptance and awareness. This includes the lack of: a) awareness, clarity, and understanding of

emotions; b) acceptance of emotions; c) the ability to inhibit impulsive behavioral responses and

to perform action in service of desired goals in the presence of emotional experience; and d) the

ability to flexibly employ adaptive emotion regulation strategies. Higher ER difficulties are

associated with impaired mental health (Akbari & Hossaini, 2018; Saxena, Dubey, & Pandey,

2011), lower levels of life satisfaction (Akbari, & Hossaini, 2018; Saxena, Dubey, & Pandey, 13

2011; Phillips, Henry, Nouzova, Cooper, Radlak, & Summers, 2014), and poorer physical health

(Akbari, & Hossaini, 2018). Adaptive ER as described by the Gratz & Roemer (2004) model may index the cognitive features of acceptance and awareness that are important features of psychological flexibility.

Overweight/obese adults with deficits in attention and awareness may have greater ER difficulties compared to normal weight adults. Obesity-related behaviors, such as sedentary behavior and eating foods higher in sugar and fat, may be attempts to avoid uncomfortable self- awareness (Heatherton & Baumeister, 1991), to manage difficult emotional states

(Cooper, O’Shea, Atkinson, & Wade, 2014), or result from a lack of awareness of internal experiences (Moon & Berenbaum, 2009). These findings are consistent with the affect regulation model that conceptualizes obesity-related eating behaviors as poor coping strategies in the face of unwanted internal experiences (Heatherton & Baumeister, 1991; Polivy & Herman,

1993; Wiser & Telch, 1999).

Research on ER differences between overweight and normal-weight individuals is limited. However, BMI has shown an inverse correlation with access to adaptive strategies

(subscale of the Difficulties in Emotion Regulation scale (DERS): Gratz & Roemer, 2004) in undergraduate male students with a medium-large effect size (Lafrance Robinson, Kosmerly,

Mansfield-Green, & Lafrance, 2014). Also, obese female candidates for bariatric surgery reported greater difficulties on DERS subscales of goal-consistent behavior, impulsivity, and access to adaptive strategies compared to normal-weight controls (Fereidouni, Atef-Vahid,

Fathali Lavasani, Jamshidi Orak, Klonsky, & Pazooki, 2015). Differences between normal- weight and overweight individuals in ER may indicate differences in the cognitive features of awareness and acceptance. 14

Heart Rate Variability (HRV)

A growing body of literature suggests that HRV, the beat-to-beat variation in heart rate, may index a physiological ability that is important for psychological flexibility (e.g., Kemp &

Quintana, 2013; Rusciano, Corradini, & Stoianov, 2017). The heart is stimulated by both the sympathetic and parasympathetic branches of the autonomic nervous system (ANS; Malik et al.,

1996). Analyzing HRV can tease apart these influences on the heart. The high frequency HRV band (HF-HRV), which falls between 0.15 and 0.4 Hz, has been shown to be a reliable and valid indicator of parasympathetic activation (Berntson et al., 1997; Shaffer & Ginsberg, 2017). While the difference may be measured in milliseconds, sympathetic influence, primarily governed by the release of norepinephrine and catecholamines, is slower than the parasympathetic, which is primarily controlled by the vagus nerve (Berntson et al., 1997). Thus, it is parasympathetic

(vagal) influence that is reflected in HF-HRV, which is believed to reflect a flexible ANS

capable of generating behaviors required to navigate challenges in the environment (Appelhans

& Leuken, 2006; Friedman & Thayer, 1998).

Support for HF-HRV as an important aspect of psychological flexibility can be found in

theory and research. First, the polyvagal theory suggests the vagal nerve that influences HF-HRV

also innervates the physiology necessary for emotional communication and rapid mobilization of

the body to cope with emotional threat (Porges, 1995; Porges, 2007). Second, the Neurovisceral

Integration Model suggests that HF-HRV indexes the cognitive and emotional processes in

cortical and sub-cortical brain areas that facilitate appropriate responding and inhibition that is

contextually appropriate (Thayer & Lane, 2000; Thayer & Lane, 2009). Third, HF-HRV is

significantly related to other dimensions of psychological flexibility, such as self-regulation

(Fabes & Eisenberg, 1997; Segerstrom & Nes, 2007; Geisler, Kubiak, Siewert, & Weber, 2013), 15 executive functioning (Hansen, Johnsen, & Thayer, 2003; Suess, Porges, and Plude, 1994;

Laborde, Furley, & Schempp, 2015), and resilience (Souza et al., 2013; Souza et al., 2007).

Fourth, HF-HRV at rest is inversely associated with psychopathology, such as depression

(Rottenberg, 2007; Paniccia, Paniccia, Thomas, Taha, & Reed, 2017), anxiety (Friedman, 2007;

Paniccia et al., 2017), PTSD (Gillie & Thayer, 2014), and disordered eating (Green et al., 2009;

Hilbert, Vögele, Tuschen-Caffier, & Hartmann, 2011). Finally, HF-HRV is positively correlated with desired outcomes, such as social engagement (Geisler, Kubiak, Siewert, & Weber, 2013) and lower pain interference in functioning (Allen et al., 2018). Together, these factors support the assertions of Appelhans and Luecken (2006), who argue that HRV indexes the physiological resources necessary for an individual to generate behavioral responses to meet environmental concerns - the essence of psychological flexibility.

Assessing the link between HRV and psychological flexibility is often measured when the individual is at rest. However, flexible responses to difficult experiences may be better assessed by measuring the physiological response before and after a stressor (Walker, Pfingst,

Carnevali, Sgoifo, & Nalivaiko, 2017). In fact, Thayer and Lane (2009) reported that not only resting HRV but also phasic activation of HRV has been shown to be important for responding adaptively to emotional experience. This response may be measured as reactivity to or recovery from an event. For example, Stange, Hamilton, Fresco, and Alloy (2017) found that vagal withdrawal (reactivity) to a sad film and vagal increases during recovery were associated with the use of more adaptive emotion regulation strategies. Also, Souza and colleagues (2015, 2007) found that participants who reported higher resilience also demonstrated more efficient respiratory sinus arrhythmia (RSA: index of parasympathetic activation) recovery from stressors. 16

Research on both resting and phasic HF-HRV suggest it may be important for psychological flexibility.

Obese/overweight participants have demonstrated significant HRV reactivity to stressors with a large effect size (Godfrey et al., 2019). HRV has been shown to be lower at rest in obese compared to normal weight participants (Karason, Mølgaard, Wikstrand, & Sjöström, 1999).

BMI has shown an inverse relationship with resting HF-HRV (Koenig et al., 2015; Murakami,

Matsuda, & Koitabashi, 2012; Spitoni et al., 2017). Research between overweight/obese and normal weight individuals is limited. However, given that HRV during rest, reactivity, and recovery may index an essential component of psychological flexibility, it is possible that there are differences in HRV between adults with overweight/obesity and normal weight.

Present Study

Biological factors contribute to overweight and obesity (Frayling et al., 2007), yet there is a significant role for obesity-related behaviors in body weight regulation (Fuglestad, Jeffery, &

Sherwood, 2012). As such, the factors that influence behavior may be of particular importance when examining overweight and obesity. The construct of psychological flexibility describes an individual’s ability to perform goal-consistent behavior, even in the presence of contrary emotional urges or environmental demands (Kashdan & Rottenberg, 2010). Given the role of behavior in body weight regulation and the important implications of psychological flexibility for goal-consistent behavior, it is important to explore potential differences in psychological flexibility between adults with overweight/obesity and normal weight.

Ciarrochi et al. (2014) is the only study to examine differences in psychological flexibility between individuals with overweight/obesity and normal weight. This study examined components of psychological flexibility consistent with the model proposed by Acceptance and 17

Commitment Therapy (ACT: Hayes, Strosahl, & Wilson 1999). Findings indicated no significant

differences between overweight/obese compared to normal-weight participants. However,

significant deficits were revealed for severely obese participants in two components, believing

unhelpful thoughts and avoidance. Ciarrochi et al. (2014) demonstrated the value of

conceptualizing psychological flexibility as having multiple components rather than being a

single factor. A limitation of the Ciarrochi et al. (2014) study is the use of self-reported height

and weight to calculate BMI. Research on EF, cognitive features of attention and awareness, and

HRV suggests examining these factors may more accurately capture the dynamic abilities

involved in psychological flexibility than self-report alone (Kashdan & Rottenberg, 2010).

Therefore, in addition to measuring height and weight in the laboratory, the present study will

examine executive function, cognitive factors of awareness and acceptance, and HRV in

individuals with overweight/obesity and normal weight. Measuring the cognitive and

psychophysiological factors essential to psychological flexibility may reveal differences in

psychological flexibility between overweight/obese and normal-weight individuals.

Hypotheses

Research question 1: Executive function plays an important role in goal-directed and

eating behavior (Gameiro, Perea, Ladera, Rosa, & García, 2017). Given the contribution of

behavior to weight management, does executive function performance – attention shifting,

inhibition, and working memory- differ between overweight/obese and normal-weight

individuals?

Hypothesis 1: Overweight/obese individuals will demonstrate deficits on tests of attention-shifting (Wisconsin Card Sorting Task: WCST), inhibition (Stroop task), and working memory (Digit span task) compared to normal-weight individuals. 18

Research question 2: The cognitive traits of awareness and acceptance have been shown to facilitate goal-directed behavior and reduce disordered eating behaviors, particularly in the

presence of emotional experience (Lillis, Hayes, Bunting, & Masuda, 2009). Are acceptance and

awareness lower in overweight/obese compared to normal-weight individuals?

Hypothesis 2: Overweight/obese individuals will report lower trait mindfulness (Mindful

Attention Awareness Scale: MAAS) and greater difficulties in emotion regulation (Difficulties in

Emotion Regulation scale: DERS) compared to normal weight individuals.

Research question 3: As an index of parasympathetic activation, HRV may index an individual’s ability to flexibly generate goal-consistent behaviors despite environmental demands

(Appelhans & Luecken, 2006). Do overweight/obese individuals demonstrate less adaptive parasympathetic activation compared to normal-weight individuals?

Hypothesis 3: Overweight/obese individuals will demonstrate lower resting HRV, greater

HRV reactivity, and poorer HRV recovery in response to a stressor compared to normal-weight individuals. 19

METHOD

Participants

A sample size of sufficient power (0.80) was determined using g*Power software (Faul et al., 2007). Given a f2(V) = .099, the power analysis for a MANOVA revealed a total sample size

of 73 (α err prob = 0.05; Critical F = 2.17). See Table 1 for support of medium effect size in

previous research. A general sample (n = 84) of individuals were recruited from a large

midwestern university through advertisement in a campus online newsletter and through brief

presentations in undergraduate classes. Subjects were asked to enroll in a study examining the

effect of emotion on cardiovascular reactivity. Participants were compensated with their choice

of either class credit or a $15 gift card. Inclusion required that participants be at least 18 years of

age. Individuals were excluded if they had a history of cardiovascular disease or diabetes or are

currently taking medication for psychological or cardiovascular conditions. The data of 4

participants were excluded after participation for failing to meet study criteria (i.e., use of

medication for psychological disorder: n=2) and BMI not matching research hypotheses (i.e.,

BMI fell into the underweight category: n=2). Final sample size was 80 (normal weight n= 47,

overweight/obese n = 33).

Measures

Demographics

Participants were asked to report information on age, gender, and race.

Anthropometry

Participant height (cm) and weight (kg) were assessed in the laboratory. Body mass index

(BMI) was calculated using the calculator on the CDC website:

https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/metric_bmi_calculator/bmi_calcula 20

tor.html. Participants’ BMI was classified into categories based on the World Health

Organization classification system (WHO, 2004). Normal weight BMI falls within the range of

18.5 to < 24.9, overweight BMI falls within the range of 25 to < 29.9, obese individuals have

BMI greater than or equal to 30.0.

Heart Rate Variability (HRV)

Heart rate variability was collected using a Biopac Systems MP160 and Biopac analysis

software (Acqknowledge 5.0). Electrocardiograph (ECG) electrodes were attached to the

participant using a Lead II configuration with the negative electrode placed below the left collar

bone, the positive electrode placed on the left side of the thorax, and the ground electrode placed

below the right collar bone. The Biopac MP160 used a sampling rate of 1000Hz for the ECG

signal, which is higher than the suggested sampling rate of 500Hz required for accurate HRV

measurement (Allen, Chambers, & Towers, 2007). Using Acqknowledge 5.0 software, the ECG

signal was visually inspected and large artifacts were removed (less than .003% of recorded data) with the connect endpoints function (Rottenberg, Clift, Bolden, & Salomon, 2007).

For HRV analysis, ECG data was uploaded into Kubios software (Tarvainen, Niskanen,

Lipponen, Ranta-Aho, & Karjalainen, 2014). A band-pass filter was applied with values between

0.5 and 35Hz to correct for baseline drift and high-frequency noise artifacts. After band-pass filtering, template matching was used to identify individual QRS complexes. HF-HRV was calculated using spectral analysis with the autoregressive method, which has been shown to produce normalized spectral parameters and better spectrum resolution in short-term data compared to Fast Fourier Transformation (Dantas et al., 2012). The R-R intervals were partitioned into very low frequency (.0033 to 0.04 Hz), low frequency (0.04 to 0.15 Hz), and high frequency (0.15 to .4 Hz) bins. High-frequency HRV (HF-HRV) data were examined in 21 this study because it has been shown to be most reliably associated with parasympathetic activation (Berntson et al., 1997; Camm et al., 1996) and the associated factors of adaptive responding (Appelhans & Lueken, 2006). The HRV index used for the present analyses was high-frequency HRV in normalized units (HF-HRVnu). This HRV data was normally distributed and did not require transformation for use in analyses.

Attention-shifting

The Wisconsin Card Sorting task (WCST: Grant & Berg, 1948) is a well-established measure of attention-shifting ability (Puente, 1985). Participants completed this as a computer task adapted by Katja Borchert (2016) for use in Inquisit (Millisecond Software, 2015). In this task, participants are asked to match one card with one of four category cards according to several criteria: color, number, or shape. The rule for matching cards changes unpredictably.

After each attempt, the participant is given feedback indicating if they matched the card correctly. The task used a 64-card deck and was completed in an average of 2 minutes. Scores for total correct responses, perseverative errors, and error shifting to 2nd category were used in analyses.

Working Memory

The auditory digit span task is a test of working memory implemented in Inquisit

(Millisecond Software, 2015) as described in Woods et al. (2011). Digit span forward and digit span backward tasks are among the most widely implemented tests for assessing maintenance and manipulation of information in short-term memory (Nejati et al., 2018; Vallar & Papagno,

1995; Engle & Kane, 2003). The participant is auditorily presented a series of numbers. At the completion of each series, the participant must indicate by clicking with the mouse the numbers that were reported. The task consists of two parts: 1) recalling the numbers in forward order, and 22

2) recalling the numbers in backward order. The task was completed in 10-minutes on average.

Scores for number of trials completed in forward recall and backward recall were examined in analyses.

Inhibition

The Stroop task (Stroop, 1935) is considered a reliable measure of inhibition. Participants completed a computerized version in Inquisit (Millisecond Software, 2015). Participants were asked to name written color words despite being written in an incongruent color (e.g., the word

‘blue’ is written in green ink). The participant therefore must process the information and inhibit the irrelevant information (Gameiro, Perea, Ladera, Rosa, & García, 2017). This task took an average of 4 minutes to complete. Scores for proportion of correct trials and latency of correct responses were examined in analyses.

Psychological Health and Treatment History

Psychological well-being has been shown to impact psychological and physical health

(Keng, Smoski, & Robins, 2011), thus participants were asked to report previous psychological diagnoses and any history of psychological diagnosis and treatment for comprehensive assessment of the sample.

Health Behaviors

Participants were asked to rate their current use of caffeine and tobacco and the frequency of exercise behavior. Participants also indicated their history of psychological disorder and treatment.

Mindfulness

The Mindful Attention Awareness Scale (MAAS; Brown & Ryan, 2003) is a 15-item measure with a single-factor scale structure. Participants were asked to rate their ability to pay 23

attention to the present moment and to maintain nonjudgmental awareness of whatever is

experienced from 1(almost always) to 6 (almost never). Higher scores indicate higher dispositional mindfulness. Internal consistency has been found to range from .80 to .90 (Erisman

& Roemer, 2010) and have strong test–retest reliability, discriminant, and convergent validity

(Brown & Ryan, 2003). Internal consistency on the present study was high, α = 0.90. The

MAAS total score was used in analyses. Higher scores equal higher mindfulness. See Appendix I for measure.

Mindful Emotion Regulation

The Difficulties in Emotion Regulation scale, short form (DERS-SF: Kaufman, Xia,

Fosco, Yaptangco, Skidmore, & Crowell; Gratz & Roemer, 2004) is an 18-item measure that asks participants to rate their difficulty in regulating emotions. Item responses range from 1

(almost never) to 5 (almost always), and the scale includes several dimensions: nonacceptance of emotional responses, difficulties engaging in goal directed behavior, impulse control difficulties, lack of emotional awareness, limited access to emotion regulation strategies, and lack of emotional clarity. Example items include “I pay attention to how I feel” and “When I am upset, I lose control over my behaviors.” The DERS-SF has high internal reliability (nonacceptance α =

0.87, goal directed behavior α = 0.90, impulse control difficulties α = 0.88, lack of emotional awareness α = 0.79, limited access to strategies α = 0.96, and lack of emotional clarity α = 0.86)

and a high correlation with the full DERS (nonacceptance .97, goal directed behavior .96,

impulse control difficulties .91, lack of emotional awareness .94, limited access to strategies .96,

and lack of emotional clarity .93) which exhibits high internal consistency (.91) and good test-

retest reliability (.88) (Gratz and Roemer, 2004). Internal consistency in the present study was

good: nonacceptance α = 0.87, goal directed behavior α = 0.88, impulse control difficulties α = 24

0.91, lack of emotional awareness α = 0.77, limited access to strategies α = 0.81, lack of

emotional clarity α = 0.77, and DERS-SF total α = 0.89. Higher scores indicate higher difficulties with emotion regulation. The DERS-SF total and 6 subscales were used in analyses.

See Appendix II for measure.

Positive and Negative Affect

The Positive and Negative Affect Schedule (PANAS: Watson, Clark, & Tellegen, 1988)

is a 20-item self-report scale that includes two mood scales – positive affect and negative affect.

As a manipulation check, participants completed the negative and positive scales before and after

the stressor to assess effectiveness of the stressor. Participants will be asked to rate how much

they are experiencing an emotion (e.g., distressed, upset, happy, excited) “right now” from 1

(Slight or Not at All) to 5 (Extremely). Positive and negative affect will be scored by summing

the items in each scale. Internal consistency (positive: .89, negative: .85) is high and test-retest

reliability (positive: .54, negative: .45) is acceptable for PANAS ratings “in the moment”

(Watson, Clark, & Tellegen, 1988). Internal reliability in the present study was good pre-stressor

(positive α=0.88, negative α=0.79) and post-stressor (positive α=0.90, negative α=0.82). Higher

scores equal either higher positive or negative affect. See Appendix III for measure.

Stressor

The Trier Social Stress Test has been shown to instigate a significant stress response

(Het, Rohleder, Schoofs, Kirschbaum, & Wolf, 2009) and reliably reduce parasympathetic

activation (Woody et al., 2017; Petrowski et al., 2017). Participants were instructed to start at

1022 and subtract 13 concurrently from this number. If participants made any errors, they were

instructed to restart this task from the beginning. Participants were told that their performance

would be graded. The task duration was 3 minutes. 25

Procedure

See Figure 3 for study procedure. Participants scheduled their experimental session online. Participants were emailed the link to complete informed consent and self-report questionnaires online prior to attending the experimental session in the laboratory. Online questionnaires took approximately 25 minutes to complete. For the experimental session, participants came to the laboratory and were given the opportunity to review the informed consent and ask questions before signing a hard copy. Participants confirmed compliance with inclusion and exclusion criteria (age 18 or older, no current use of medication for psychological or cardiovascular conditions, no caffeine or tobacco use within 4 hours of session). Height and weight were measured. Electrodes for recording ECG were attached. Participants were instructed to hold as still as possible while seated with eyes open to complete a 10-minute baseline of physiological recording. They then completed executive function tasks. To ensure participants returned to baseline levels of activation before beginning the stressor, a 5-minute recovery period was completed to establish a new baseline prior to the stressor. Participants then completed the

(PANAS) and then the 3-minute stressor was administered. Participants then completed a 5- minute recovery period followed by the PANAS. Electrocardiogram (EEG) was recorded throughout the experimental procedure. HRV was examined during a resting baseline period as well as for reactivity to and recovery from a stressor. The experimental session took approximately 1 hour (Figure 3).

Data Analytic Plan

All statistical analyses were performed in SPSS. Data was checked for missing values.

Before running analyses, data were checked for outliers, which were addressed with 26

Windzorizing (Field, 2009). Significant deviations (e.g., skewness or kurtosis) were addressed

through logarithmic transformation (log 10) (Field, 2009).

Preliminary Analyses

BMI was calculated using the calculator on the CDC website. Weight class groups were calculated to create the normal-weight and overweight/obese groups. Manipulation check was

performed to examine the effectiveness of the stressor by performing paired t-tests using PANAS

positive and negative scores pre- and post-stressor. Descriptive statistics were calculated to

describe the characteristics of the study sample. Mean scores and standard deviations were

calculated for primary study variables.

Prior to HRV analyses, while 10-minutes of rest was recorded, baseline was analyzed as

the last 5 minutes of the rest period to increase consistency between participants in acclimating

to the setting. For HRV reactivity to the stressor, HRV was assessed for two minutes of rest prior

to the stressor and during the 3-minute stressor. Regarding HRV recovery from the stressor,

physiological recovery is considered the return to homeostasis demonstrated at rest (Baert,

Casier, & De Raedt, 2012). Recovery was assessed from two consecutive periods following the

stressor: immediate HRV recovery (first 2-minutes following the stressor) and delayed HRV

recovery (2-minutes analyzed at 3-minutes after stressor completion). See Figure 2 for HRV time

periods.

Primary Analyses

Analyses were performed to determine whether overweight/obese and normal-weight

participants differ on proposed components of psychological flexibility. MANOVA was used to

maximize power and reduce issues of multiple comparisons and type I error. Effect size was 27

2 2 calculated as partial eta squared (ηp ). Suggested norms for ηp are small = 0.0099, medium =

0.0588, and large = 0.1379 (Norouzian & Plonsky, 2017; Cohen, 1988).

The executive function component was examined with 3 separate MANOVAs, with

group (overweight/obese and normal weight) as the IV and attention-shifting, working memory,

and inhibition scores as the DVs (hypothesis 1). Attention-shifting was measured by the

Wisconsin Card Sorting Task (WCST). Scores for total correct responses, sum perseverative

errors, and percent error shifting to 2nd category were used. Working memory was measured by

the auditory digit span task (DS). Scores for forward digit span trials completed and backward

digit span trials completed were examined. Inhibition was measured by the Stroop task. Scores

for proportion of correct trials and latency of correct responses were examined. MANOVAS

were initially run without covariates, and then models were calculated a second time with the

addition of history of mental health treatment as a covariate.

For the acceptance and awareness component, one MANOVA was performed with group

as the IV and MAAS and DERS scores as the DVs (hypothesis 2). The MAAS total score, the

DERS total score, and DERS subscales (nonacceptance, goal directed behavior, impulse control difficulties, lack of emotional awareness, limited access to strategies, and lack of emotional clarity) were examined. MANOVAS were initially run without covariates, and then models were calculated a second time with the addition of history of mental health treatment as a covariate.

For the HRV component (hypothesis 3), a 2(overweight-obese/normal weight) by 5(time)

repeated-measures ANOVA was conducted with planned follow-up pairwise comparisons using

the Bonferroni correction (p <.0125). HRV baseline, rest before the stressor, stressor, immediate

recovery, and delayed recovery were the 5 timepoints. For baseline, while 10-minutes of rest was

recorded, baseline was analyzed as the last 5 minutes of the rest period to increase consistency 28

between participants in acclimating to the setting. Immediate HRV recovery is the first 2-minutes following the stressor and delayed HRV recovery is the 2-minutes analyzed at 3-minutes after stressor completion. See Figure 3 for HRV time points. 29

RESULTS

Preliminary analyses revealed there was no missing data in the data set. Outliers were addressed through Windzorizing for digit span forward and backward (n=2), Stroop correct trials

(n=5) and latency (n=2), HRV baseline (n=7), stressor baseline (n=6), stressor (n=5), immediate recovery (n=6), delayed recovery (n=8), and DERS subscales impulse control difficulties (n=5), limited access to strategies (n=4), and lack of clarity (n=1). Variables included in MANOVAs were analyzed using Box’s M (homogeneity of covariance matrices) and Levene’s Test of

Equality of Error Variances. The following variables were transformed (Log 10) to meet statistical assumptions before inclusion in analyses: error at 2nd category (WCST).

As a manipulation check, paired-sample t-tests of PANAS positive and negative scores before and after the stressor were calculated. The stressor was effective, given a significant increase in negative PANAS scores (t(75) = -3.99, p < .001) and a significant decrease in positive PANAS scores (t(76) = 5.82, p < .001) from pre- to post-stressor. To ensure participants returned to baseline levels of HRV activation before completing the stressor, pairwise comparisons were evaluated. Similar levels of HRV were observed between baseline activation

(M = 39.10, SE = 1.89) and rest before the stressor (M = 38.30, SE = 1.97), p > .05.

Table 2 describes the demographic and health characteristics of the study sample (n=80,

62.5% female, 68.4% Caucasian). Participant BMI of the present sample was 21.3 % obese (n =

17), 20% overweight (n = 16), and 58.7% normal weight (n = 47). Participants with overweight/obesity reported higher frequency of history of mental health treatment than those with normal weight. There were no other significant group differences on demographic or health characteristics. 30

Table 3 describes the test statistic, mean and standard deviation of primary study

variables. Group differences in executive function were examined with 3 MANOVAs. The first

MANOVA examined attention-shifting. Group (overweight-obese/normal weight) was the IV

and 3 scores from the Wisconsin Card Sorting Task (total correct, perseverative errors, error at

second category) were the DVs. Box’s test indicated the equality of covariance matrices was

acceptable, F (6, 14244.37) = 1.54, p = .16. Multivariate analyses indicated a significant difference with a large effect size in attention-shifting between overweight/obese and normal weight participants. Planned follow-up ANOVA indicated that normal weight participants demonstrated significantly greater scores for total correct and fewer errors shifting to the second category compared to overweight/obese participants. There were no significant differences in perseverative errors. Results did not change when conducting an ANCOVA with history of mental health treatment included as a covariate.

The second MANOVA examined working memory. Group (overweight-obese/normal weight) was the IV and 2 scores from the digit span (total forward span, total backward span) were the DVs. Box’s test indicated the equality of covariance matrices was acceptable, F(3,

335443.91) = 2.33, p = .07. Multivariate analyses indicated no significant differences between groups in working memory. Results did not change when conducting an ANCOVA with history of mental health treatment included as a covariate.

The third MANOVA examined inhibition. Group (overweight-obese/normal weight) was the IV and 2 scores from the Stroop (proportion or correct responses, latency of responses) were the DVs. Box’s test indicated the equality of covariance matrices was acceptable, F(3,

261964.53) = 2.40, p = .07. Multivariate analyses indicated no significant differences between 31 groups in inhibition. Results did not change when conducting an ANCOVA with history of mental health treatment included as a covariate.

One MANOVA was performed to examine group differences in the cognitive features of acceptance and awareness. Box’s test indicated the equality of covariance matrices was acceptable, F(36, 14988.82) = 1.36, p = .07. Multivariate analyses indicated a marginally significant difference with a large effect size in the cognitive features of awareness and acceptance between overweight/obese and normal weight participants. Planned follow-up

ANOVA indicated that normal weight participants demonstrated significantly lower scores for the DERS subscale emotional clarity and significantly higher scores for the MAAS compared to overweight/obese participants. There were no significant differences in DERS total or subscales of nonacceptance, goals, impulsivity, awareness, or access to strategies. Results did not change when conducting an ANCOVA with history of mental health treatment included as a covariate.

A 2(group) x 5(time) ANOVA was conducted to examine HRV (Figure 4). Box’s test was not significant, F(15, 19004.77) = 1.49, p = .10. Thus, the assumption of equality of covariance matrices had been met. Mauchley’s test indicated that the assumption of sphericity had been violated, x2(9) = 23.67, p = .005, therefore Greenhouse-Geisser corrected tests are reported (Ƹ = .86). The results show a significant and medium-large effect for time, indicating

HRV activation was significantly affected by stressor exposure, F(3.42, 263.57) = 6.24, p < .001,

2 ηp = .075. Follow-up pairwise comparisons indicated that HRV during the stressor was significantly lower than all other experiment conditions: baseline HRV, p = .005, resting HRV immediately before the stressor, p = .01, immediate HRV recovery, p = .04, and delayed HRV recovery, p = .004. This pattern of responding was consistent with the expectation that the stressor would evoke parasympathetic withdrawal. A marginally significant effect for group with 32 a medium effect size was observed. An examination of means indicated that the HRV for the overweight/obese group was significantly higher than the normal-weight group. The group by

2 condition interaction was not significant, F(3.42, 263.57) = .18, p = .93, ηp = .002. 33

DISCUSSION

The current investigation examined factors that have been shown to support psychological flexibility. Given that differences in body mass index result in part from behavior

(Fuglestad, Jeffery, & Sherwood, 2012), which is influenced by psychological flexibility (Lillis,

Hayes, Bunting & Masuda, 2009), present results clarify the contribution of cognitive and psychophysiological components of psychological flexibility to differences in body mass index

(BMI). Consistent with hypotheses, participants with overweight/obesity demonstrated deficits in the attention-shifting aspect of executive function and the cognitive features of mindful attention and clarity of emotional experience. Contrary to hypotheses, participants with overweight/obesity demonstrated greater HRV compared to normal weight participants across conditions. Also contrary to hypotheses, there were no significant differences between overweight/obese and normal weight participants on measures of inhibition and working memory or other aspects of acceptance and awareness (i.e., the total DERS score or DERS subscales nonacceptance of emotional responses, difficulties engaging in goal directed behavior, impulse control difficulties, lack of emotional awareness, or limited access to emotion regulation strategies). Present findings begin to describe the complex nature of psychological flexibility and the potential significance for healthy BMI.

Executive Function

Executive function is a neurocognitive process comprised of attention-shifting, inhibitory control, and working memory (Mayake et al., 2000) that is robustly associated with goal-directed behavior and dysregulated eating (Forman, Goldstein, Flack, Evans, Manasse, & Douchat, 2018).

Present findings indicated that participants with overweight and obesity performed significantly worse on the attention-shifting task and made significantly more errors than normal weight 34 participants in shifting from the first rule set to the second on the Wisconsin Card Sorting Task

(WCST). This suggests less abstraction ability and cognitive flexibility compared with normal weight individuals. However, there were no significant differences between groups in number of perseverative errors. The WCST is a complex test that draws on multiple cognitive functions

(Fagundo et al., 2012). While obese/overweight participants showed limitations in attention- shifting, particularly when initially presented with a new rule, other factors contributing to

WCST task performance may not be significantly different from normal weight individuals, such as memory, sustained attention, and response suppression to irrelevant material. This assertion is consistent with present findings of no significant differences between groups regarding performance on a tasks of inhibition (Stroop) and working memory (Digit Span forward and backward).

Differences in attention-shifting, as indicated by present findings, have implications for body mass index (BMI). There is a large body of research suggesting that attention bias towards palatable food cues increases the likelihood of consuming those foods (e.g., Castellanos et al.,

2009; Graham, Hoover, Ceballos, & Komogortsev, 2011; Nijs, Franken, & Muris, 2010; Nijs,

Muris, Euser, & Franken, 2010). Cognitive inflexibility could provide an explanation for the inability to regulate food intake, which is present in obesity. Obese participants (n=27) showed poor performance on tasks of attention-shifting (WCST) and decision-making (Iowa Gambling

Task) compared to healthy controls (n = 39)( Perpiña, Segura, & Sanchez-Reales, 2017).

Maladaptive behavioral choices (e.g., overeating) that contribute to overweight/obesity may thus be associated with deficits in cognitive flexibility and contribute to differences in weight class.

Current findings suggest that deficits in executive function are inconsistent across tasks in participants with overweight/obesity compared to normal weight. This is consistent with 35

systematic reviews that have confirmed variation in the association between aspects of executive

function and overweight/obesity (Fitzpatrick et al., 2013; Vainik et al., 2013; Emery & Levine,

2017; Gettens & Gorin, 2017; Gluck et al., 2017; Favieri. Forte, & Casagrande, 2019). A recent

meta-analysis by Yang, Shields, Guo, and Liu (2018) found that obese participants demonstrated

worse performance compared to normal weight participants in inhibition (k = 50), cognitive

flexibility (k = 33), and working memory (k = 35). While overweight participants demonstrated

worse performance on tasks of inhibition (k = 20) and working memory (k = 17) compared to normal weight individuals, meta-analytic results indicated non-significant differences in cognitive flexibility (k = 6). Future research would benefit from examining the variables that impact the relationship between executive function and body weight.

Characteristics of the present sample may explain limited differences in executive function in present findings. The present sample has no extreme obesity (overweight/obese BMI,

M = 31.37, SD = 5.99). This decreased the likelihood of encountering physiological factors more common in severe obesity that have been shown to impact EF (Yang et al., 2018; Favieri, Forte,

& Casagrande, 2019). As in the present study, studies often exclude participants with chronic medical conditions to avoid the confounding of results (Fagundo et al., 2012; Galioto et al.,

2013; Galioto Wiedemann et al., 2014). When physiological aspects are assessed (e.g., Maayan,

Hoogendoorn, Sweat, & Convil, 2011; Perpina et al., 2017; Heymsfield & Wadden, 2017), obese participants have shown poor values in blood pressure, cholesterol levels, insulin resistance, and levels of glycolic metabolism activation. These physiological factors associated with obesity may impact brain structural factors and contribute to decreased executive function through structural brain deficits (e.g., Gustafson, Lissner, Bengtsson, Björkelund, & Skoog, 2004; Convit, Wolf,

Tarshish, & de Leon, 2003; Maayan et al., 2011). The current sample is fairly homogenous and 36 free from health concerns, given the exclusion criteria to insure good psychophysiological data – no abnormal cardiovascular variables, no history of diabetes or cardiovascular disease. It is possible that the lack of between group differences in health factors, like diabetes, may account for limited differences in executive function.

We are not able to suggest a causal direction between the variables of executive function and overweight/obesity given the reciprocal nature of the relationship between executive function and eating behavior that contributes to poor weight regulation. Recent reviews (Dohle,

Diel, & Hofmann, 2018; Allan, McMinn, & Daly, 2016) supported the hypothesis that food behaviors affect executive functioning and that healthy eating behaviors are protective of executive function. Research has suggested a relationship between eating behaviors and brain circuits related to motivation, inhibition, and behavior involving reward (Volkow, Wang, Fowler,

Tomasi, & Baler, 2011). Consumption of elevated amounts of food can disrupt the balance of the circuits related to control and inhibition, which might result in compulsive food consumption

(Volkow, Wang, Fowler, Tomasi, & Baler, 2011; Volkow, Wang, Fowler, & Telang, 2008;

Fagundo et al., 2012). Other authors have asserted the opposite point of view, in which executive function predicts goal-directed behavior, and thus food behaviors, which ultimately impacts body weight changes (Dohl et al., 2018). This hypothesis is the basis of the present study. However, in the present cross-sectional data, we are unable to clarify whether deficits in cognitive flexibility are predictive or the result of eating behaviors associated with obesity. We acknowledge that this relationship may be bidirectional.

Some limitation of between group differences in executive function in the current study may be due to the lack of between group differences in psychopathology (i.e., eating disorders or other psychological diagnoses). Covariate analyses indicated no relationship between the report 37 of history of mental health treatment and results, despite greater endorsement by overweight/obese participants of mental health treatment history compared to normal weight.

Previous research has shown differences in executive function between overweight/obese and normal weight participants may be related to co-morbid psychopathology. A systematic review by Fitzpatrick, Gilbert, and Serpell (2013) only found a relationship between digit span (working memory) and BMI when combined with mood measures (e.g., Cserjesi, Luminet, Poncelet, &

Lenard, 2009). Obese patients seeking treatment for an eating disorder (n = 24) demonstrated the need for greater time to complete executive function tasks as well as a greater number of errors compared to healthy controls (n=49) (Spitoni et al., 2017). Perhaps most telling, obese participants without BED (n=38) performed better on Digit Span Backward and demonstrated fewer perseverative and set-shifting errors on the WCST than those with BED (n = 38)

(Duchesne, Mattos, Appolinario, Freitas, Coutinho, Santos, & Coutinho, 2010). The literature has not clarified the crucial factors nor the causality in the relationship between executive function and overweight/obesity (Favieri, Forte, & Casagrande, 2019). Non-significant differences on these factors between groups in the present study may partially explain the limited differences between groups in executive function.

Cognitive Traits of Acceptance and Awareness

Consistent with hypotheses, present findings suggest that participants with overweight/obesity demonstrate lower cognitive traits of acceptance and awareness than participants with normal weight. Specifically, overweight/obese participants reported significantly lower mindfulness (scores on the MAAS) and lower emotional clarity (subscale of the DERS) compared to normal weight participants. Research examining differences in acceptance and awareness between individuals with overweight/obesity and normal weight is 38 limited. However, present findings are consistent with research (Loucks et al., 2016) suggesting that participants (n=394) reporting lower mindfulness, even if not obese in childhood, became obese in adulthood compared to participants who never became obese. Also, an inverse correlation has been demonstrated between mindfulness and BMI in undergraduate students

(Mantzios & Egan, 2018).

We proposed that individuals lower in acceptance and awareness would have higher

BMI, due at least in part to maladaptive eating behavior, which could be considered values- inconsistent behavior. Present findings indicating deficits in the cognitive traits of acceptance and awareness in overweight/obese participants compared to normal weight participants are consistent with our premise and with previous research showing a relationship between the cognitive traits of awareness and acceptance, eating behavior, and BMI. Lower acceptance and awareness have been associated with greater maladaptive eating behavior in obese outpatients seeking bariatric surgery (Levin, Dalrymple, Himes, & Zimmerman, 2014; Ouwens et al., 2015) and overweight/obese adults reporting eating in response to negative emotions including boredom (Watford, Braden, & Emley, 2019). Cognitive traits of acceptance and awareness have been associated with more adaptive eating behavior in overweight and obese individuals

(Dalrymple, Clark, Chelminski, & Zimmerman, 2018; Moor, Scott, & McIntosh, 2013; Sairanen et al., 2015; Beshara, Hutchinson, & Wilson, 2013; Jordan, Wang, Donatoni, & Meier, 2014).

Increases in acceptance and awareness have resulted in larger reductions in body mass (Lillis,

Hayes, Bunting & Masuda, 2009) and lower food consumption after food cue exposure (Fischer,

Lattimore, & Malinowski, 2016) compared to a control group. While we do not measure eating behavior in the present study, we may hypothesize that greater acceptance and awareness facilitates adaptive eating behavior through strengthening sensitivity to internal signals of hunger 39

and satiety (Ouwens et al., 2015; Sairanen et al., 2015) and maintaining goal-consistent eating

behavior even when factors that normally increase eating are present (Fischer, Lattimore, &

Malinowski, 2016).

While brain activation was not measured in the current study, present findings are

consistent with research suggesting participants lower in acceptance and awareness have

difficulty with weight regulation due to changes in brain activation. Paolini and colleagues

(2012) examined moderation of the brain networks of older, obese adults (n=19) using fMRI.

Mindfulness (MAAS scores) appeared to be a strong moderator of brain connectivity following

exposure to palatable food cues. The brain networks of participants low in mindfulness continued

activity in the auditory, insular, sensorimotor, visual, and orbital prefrontal cortices, suggesting

continued preoccupation with the food cues. However, during the post-exposure rest period,

participants higher in mindfulness returned to their default network activation after food cues. It

appears that acceptance and awareness facilitate a return to adaptive brain activation after cues

that would otherwise trigger eating.

Contrary to hypotheses, no significant differences were found between weight classes on

the DERS total score or DERS subscales nonacceptance of emotional responses, difficulties

engaging in goal directed behavior, impulse control difficulties, lack of emotional awareness, or

limited access to emotion regulation strategies. We hypothesize that, while DERS subscales are shaped by the concepts of acceptance and awareness, they may also index other factors. For example, the impulse control difficulties subscale of the DERS has been employed as a measure of impulsivity (Coffino, Orloff, & Hormes, 2016). Further, previous research has shown that subscales of the DERS may vary between weight classes inconsistently. For example, obese women reported higher scores than normal weight women on difficulties engaging in goal 40 directed behavior, impulse control difficulties, and limited access to emotion regulation strategies but not on other subscales (Fereidouni et al., 2014). Present findings are a needed addition to the limited literature examining differences in acceptance and awareness between weight classes. Future research would benefit from the examination of potential mechanisms that contribute to differences in acceptance and awareness between weight classes.

Heart Rate Variability

Heart rate variability may index an individual’s ability to flexibly generate goal- consistent behaviors despite environmental demands (Appelhans & Luecken, 2006), which would be essential to choosing adaptive eating behaviors and regulating body weight. We hypothesized that participants with overweight/obesity would thus demonstrate less adaptive

HRV - lower resting, greater stressor reactivity, and poorer recovery -compared to normal- weight individuals. This was not supported. However, overweight/obese participants demonstrated altered HRV across conditions compared to normal weight participants.

Specifically, overweight/obese participants had higher HRV with marginal significance and a medium effect size. There were no interactions between groups in HRV activation.

The present HRV results for overweight/obese participants will be described as persistent because overweight/obese participants remained at a higher level of parasympathetic activation regardless of experience during the session. Consistent with expectations, all groups demonstrated reductions in HRV during the stressor and a return to baseline HRV during recovery, which is consistent with previous research examining the impact of a stressful math task on parasympathetic indices of HRV (e.g., Watford et al., 2020; Petrowski et al., 2017;

Woody et al., 2017; Het, Rohleder, Schoofs, Kirschbaum, & Wolf, 2009). This, in conjunction with self-report of decreased positive and increased negative emotion after the stressor, suggests 41 the stressor had the expected effect on all participants. However, there was no significant interaction between groups and time period (i.e., rest, reactivity, recovery). Thus, reactivity and recovery did not differ by group, which could suggest similar parasympathetic nervous system functioning. Yet, the HRV demonstrated by overweight/obese participants was higher throughout, regardless of experience encountered in the session, which has important implications.

Present findings of higher HRV for overweight/obese individuals contrast with research suggesting higher HRV compared to controls, particularly at rest, represents a flexible autonomic nervous system with the ability to respond adaptively to emotional and environmental demands

(Appelhans & Luecken, 2006). We hypothesized this pattern of HRV would facilitate goal- consistent behavior, and therefore be associated with normal BMI. Interestingly, some research has shown an association between persistent, increased vagal activation and factors that may contribute to weight dysregulation. Vagal hyperactivation is associated with more disordered eating behavior compared to individuals demonstrating little or no disordered eating behavior

(Watford, Braden, & O’Brien, in press). Maladaptive eating behaviors, such as overeating, may repeatedly exceed the normal threshold for vagus nerve activation thereby raising the threshold and resulting in vagal hyperactivation for adequate stimulation (Faris et al., 2008). Vagal hyperactivity also has implications for the regulation of ghrelin secretion which would disrupt hunger and satiety signals (Tanaka et al., 2006; Het et al., 2015). Vagal hyperactivity also has implications for emotion dysregulation through inflexibility in responding to and desensitization to experiences in the environment (Casu et al., 2002; Faris et al., 2008). The present study did not examine disordered eating behavior. However, present findings suggesting lower acceptance, awareness, and attention-shifting ability in overweight/obese compared to normal weight 42 participants are consistent with the possibility of poor emotion regulation in overweight/obese participants (Kashdan & Rottenberg, 2010). While present HRV findings are contrary to our original conceptualization, it is possible that vagal hyperactivation demonstrated by overweight/obese participants compared to normal weight participants may be a better index of psychological flexibility deficits, particularly given the implications of vagal hyperactivation for factors that are known to increase body mass.

Present findings showed significant reactivity and recovery to the stressor for participants with overweight/obesity. However, this pattern did not differ significantly from normal weight participants. Much of the research examining HRV reactivity and recovery in obese and overweight individuals did not compare it to a normal weight control group (e.g., Godfrey et al.,

2019), so interpretation in light of previous findings is limited. Some research has found between group differences (e.g., overweight/obese and normal weight) in HRV reactivity and recovery to food-related stimuli. For example, Spitoni and colleagues (2017) found that overweight/obese participants had greater HRV reduction and impaired HRV recovery in response to food cues compared to normal weight participants. However, the examination of a participant’s response to food-related stimuli, while relevant to eating behavior, would not necessarily facilitate the evaluation of a participant’s response to stress or difficult emotion. The present study sought to characterize psychological flexibility, rather than examine HRV in relation to solely obesogenic stimuli. Thus, while individuals with overweight/obesity may have a more significant parasympathetic response to food-specific stimuli (Spitoni et al., 2017), present findings suggest that individuals with overweight/obesity do not differ significantly from normal weight participants in parasympathetic responding to everyday stressors. 43

Present findings indicate that participants with overweight/obesity demonstrate significantly greater HRV across all conditions compared to normal weight participants. While this is contrary to hypotheses, it is consistent with research suggesting, at the very least, the possibility of a reciprocal relationship between vagal hyperactivation and maladaptive eating behaviors (Watford, Braden, & O’Brien, in press), which we hypothesize and research suggests contributes to higher BMI (Gibson, 2012; Kakoschke, Kemps & Tiggemann, 2017). Persistent, increased, vagal activation is also associated with poor emotion regulation (Casu et al., 2002;

Faris et al., 2008) and decreased sensitivity to internal cues of hunger and satiety (Tanaka et al.,

2006; Het et al., 2015) which may also contribute to poor weight regulation, and may explain the presentation of persistent vagal activation in overweight/obese participants.

Limitations

While the present findings provide insights into psychological flexibility and body weight, the limitations of this project should be considered. First, the present sample is healthy.

Future research would benefit from the examination of cognitive and psychophysiological components of psychological flexibility in treatment-seeking and clinical samples. This may be particularly important given that BMI and weight regulation are significantly impacted by psychopathology (e.g., Duchesne, Mattos, Appolinario, Freitas, Coutinho, Santos, & Coutinho,

2010) and physical health concerns (e.g., Yang et al., 2018). Second, this study was cross sectional making it impossible to determine causality. Third, we performed power analysis and exceeded the resulting recommended number of participants to discover significant differences if they are present. However, it is still possible that future research would detect more significant effects with a larger sample size. Finally, participants were young, college students enrolled in a 44

midwestern university. Future research should replicate these findings in a more diverse sample

to improve external validity.

Conclusions

The current investigation examined cognitive and psychophysiological variables that may

be essential to psychological flexibility in overweight/obese and normal weight participants.

Participants with overweight/obesity showed alterations compared to those with normal weight

in all three components examined: executive function, cognitive traits of acceptance and

awareness, and heart rate variability. These findings differ from Ciarrochi et al. (2014) who

found lower psychological flexibility only in extreme weight classes. While they also examined

psychological flexibility as a multi-component construct, their reliance on self-report measures interferes with the ability to compare our results with theirs. Present findings begin to describe the complex nature of psychological flexibility and its implications for BMI. Further, current results support the assertion that differences in body mass index result in part from behavior, which is influenced by psychological flexibility. 45

Table 1

Effect Sizes Between BMI and Study Variables in Previous Research

Variable Weight class Sample Effect Size Study Size 2 HRV reactivity to a math Obese n =28 Large (ηp = 0.71) Godfrey et al., 2019 stressor

Vagal withdrawal observed normal, k =76 Medium-Large (Hg = -0.582) Brindle, R. C., during psychological stress overweight, Ginty, A. T., obese Phillips, A. C., & Carroll, D. (2014). Awareness (Five Factor normal, n = 152 Medium (r = -.21) Mantzios, M., & Mindfulness Questionnaire) overweight, Egan, H. (2018) obese Executive function – normal, k =33 Medium (Hg = -0.37) Yang, Shields, Guo, Attention-shifting obese and Liu (2018)

Executive function – normal, k =6 Small (Hg = -0.10) Yang, Shields, Guo, Attention-shifting overweight and Liu (2018)

2 Executive function – normal, n = 52 Medium-Large (ηp = .08) Steenbergen & Attention-shifting overweight Colzato, 2017

Executive Function - normal, k =50 Medium (Hg = -.36) Yang, Shields, Guo, Inhibition obese and Liu (2018)

Executive Function - normal, k =20 Small (Hg = -0.23) Yang, Shields, Guo, Inhibition overweight and Liu (2018)

Executive Function - Normal, n = 43 Large (Hg = -.073) Hendrick et al., Inhibition overweight 2012

Executive function – normal, k =35 Medium (Hg = -0.33) Yang, Shields, Guo, Working memory obese and Liu (2018)

Executive function – normal, k =17 Small ( Hg = -0.13) Yang, Shields, Guo, Working memory overweight and Liu (2018)

Executive function – normal, n = 60 Large (Hg = -0.66) Coppin et al., 2014 Working memory overweight 46

Table 2

Demographics and Health Behaviors

Total Sample Normal Overweight/Obese Between groups Weight

Mean Age (SD) 20.49(2.81) 20.51(2.90) 20.47(2.72) t(77) = -.07, p = .95 Mean BMI (SD) 26.16(5.96) 22.5(1.69) 31.37(5.99) t(78) = 9.64, p < .001 Sex (%) X2(1) = 2.39, p = .12 Female 62.5 70.2 51.5 Male 36.3 29.8 45.5 Race(%) X2(4) = 4.39, p = .36 Caucasian 68.4 59.6 81.3 African American 15.2 19.1 9.4 Asian 7.6 10.6 3.1 Hispanic or Latino/a 5.1 6.4 3.1 Other 3.8 4.3 3.1 General caffeine use (%) X2(4) = 6.65, p = .16 Never 15.2 17.0 12.5 Once a week 20.3 25.5 12.5 A few times a week 34.2 27.7 43.8 Once a day 17.7 12.8 25.0 Multiple times a day 12.7 17.0 6.3 Using any tobacco products (SD) 1.94 (.25) 1.86 (.35) 1.98 (.02) X2(1) = 3.45, p = .06 Exercise Frequency (SD) Overall 2.78 (1.10) 2.55 (1.21) 2.89 (1.03) X2(4) = 5.15, p = .27 In last 2 weeks 2.48 (1.04) 2.24 (1.02) 2.57 (1.04) X2(3) = .99, p = .80 Psychological diagnosis (%) 23.2 19.1 27.3 X2(1) = .87, p = .35 Eating disorder diagnosis (%) 2.4 4.3 0 X2(1) = 1.40, p = .24 History of mental health treatment 22 12.8 33.3 X2(1) = 5.26, p = .02 (%)

Currently in counseling/therapy 6.1 4.3 9.1 X2(1) = .84, p = .35 (%) Note. n = 80, overweight/obese n = 33, normal weight n = 47. 47

Table 3

Differences Between Overweight/Obese and Normal-weight Participants on Components of Psychological Flexibility

Variable df F p ES OO M(SD) NW M(SD) Executive Function Attention Shifting* 3,46 4.30 .001 .18 Total correct responses 1,48 5.44 .02 .10 49.50 (3.52) 51.79 (3.38) Sum Perseverative Errors 1,48 1.91 .17 .04 5.82 (1.94) 6.57 (1.89) Error to 2nd category 1,48 12.75 .001 .21 1.31 (.29) 1.07 (.18) Working Memory* 2,77 .37 .69 .18 Forward recall 1,78 .35 .55 .01 6.53 (.79) 6.38 (1.21) Backward recall 1,78 .61 .42 .01 5.61 (.96) 5.44 (.89) Inhibition* 2,76 .38 .68 .01 Proportion of correct trials 1,77 .33 .57 .004 .97 (.03) .97 (.03) Latency of correct responses 1,77 .48 .49 .01 1078.90 1119.08 (183.12) (290.93)

Acceptance and Awareness* 8,70 1.98 .06 .18 Mindfulness 1,77 8.07 .006 .10 3.84 (.79) 4.41 (.93) Mindful Emotion Regulation Nonacceptance 1,77 .49 .49 .01 7.25 (3.62) 6.68 (3.51) Goal-directed behavior 1,77 .003 .95 <.001 8.53 (2.46) 8.49 (3.52) Impulse control difficulties 1,77 .01 .93 <.001 4.59 (1.79) 4.55(2.03) Lack emotional awareness 1,77 1.57 .21 .02 7.59 (3.13) 6.77 (2.70) Limited access to strategies 1,77 .48 .49 .01 6.31 (2.26) 5.91 (2.66) Lack of emotional clarity 1,77 6.50 .01 .08 6.44 (3.23) 5.00 (1.76) Total 1,77 1.28 .26 .02 41.63 (12.07) 38.45 (12.35)

Heart Rate Variability* 1,77 3.38 .07 .042 39.62 (2.26) 34.17 (1.91) Baseline 42.70 (14.95) 35.51 (17.62) Rest before stressor 40.99 (18.21) 35.61 (16.61) Stressor 33.68 (17.73) 29.07 (10.23) Recovery, Immediate 39.74 (17.21) 35.23 (17.26) Recovery, Delayed 40.97 (16.25) 35.44 (16.32) 2 Note. n = 80, overweight/obese (OO) n = 33, normal weight (NW) n = 47, ES = effect size (ηp ). Values marked with (*) are for overall component. 48

Figure 1

Components of Psychological Flexibility 49

Figure 2

Time Periods Analyzed for Heart Rate Variability (HRV) 50

Figure 3

Procedure Flow Chart 51

Figure 4

HRV Means by Group and Time

Note. HRV Time: 1=Baseline, 2=Rest before Stressor, 3=Stressor, 4=Immediate Recovery,

5=Delayed Recovery. 52

REFERENCES

Akbari, M., & Hossaini, S. M. (2018). The relationship of spiritual health with quality of life,

mental health, and burnout: The mediating role of emotional regulation. Iranian Journal of

Psychiatry, 13(1), 22-31. Retrieved

from http://ezproxy.bgsu.edu:8080/login?url=http://search.ebscohost.com/login.aspx?direct

=true&db=a9h&AN=128085101&site=ehost-live&scope=site

Aldao, A., & Nolen-Hoeksema, S. (2012). The influence of context on the implementation of

adaptive emotion regulation strategies. Behaviour Research and Therapy, 50(7), 493-501.

Allan, J. L., McMinn, D., & Daly, M. (2016). A bidirectional relationship between executive

function and health behavior: Evidence, implications, and future directions. Frontiers in

Neuroscience, 10, 386.

Allen, T. M., Struemph, K. L., Toledo‐Tamula, M. A., Wolters, P. L., Baldwin, A., Widemann,

B., & Martin, S. (2018). The relationship between heart rate variability, psychological

flexibility, and pain in neurofibromatosis type 1. Pain Practice.

Appelhans, B. M. (2009). Neurobehavioral inhibition of reward driven feeding: Implications for

dieting and obesity. Obesity, 17(4), 640-647.

Appelhans, B. M., & Luecken, L. J. (2006). Heart rate variability as an index of regulated

emotional responding. Review of General Psychology, 10(3), 229.

Arch, J. J., Brown, K. W., Goodman, R. J., Della Porta, M. D., Kiken, L. G., & Tillman, S.

(2016). Enjoying food without caloric cost: The impact of brief mindfulness on laboratory

eating outcomes. Behaviour Research and Therapy, 79, 23-34.

doi:10.1016/j.brat.2016.02.002 53

Arnow, B., Kenardy, J., & Agras, W. S. (1995). The emotional eating scale: The development of

a measure to assess coping with negative affect by eating. International Journal of Eating

Disorders, 18(1), 79-90.

Austin, S. B. (2011). The blind spot in the drive for childhood obesity prevention: Bringing

eating disorders prevention into focus as a public health priority. American Journal of

Public Health, 101(6), e1-e4. doi:10.2105/AJPH.2011.300182

Avenell, A., Brown, T. J., McGee, M. A., Campbell, M. K., Grant, A. M., Broom, J., . . . Smith,

W. (2004). What are the long‐term benefits of weight reducing diets in adults? A systematic

review of randomized controlled trials. Journal of Human Nutrition and Dietetics, 17(4),

317-335.

Baer, R. A., Smith, G. T., & Allen, K. B. (2004). Assessment of mindfulness by self-report: The

Kentucky inventory of mindfulness skills. Assessment, 11(3), 191-206.

doi:10.1177/1073191104268029.

Bari, A., & Robbins, T. W. (2013). Inhibition and impulsivity: Behavioral and neural basis of

response control. Progress in Neurobiology, 108, 44-79.

Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994). Losing control: How and why people

fail at self-regulation. Academic Press.

Berking, M., Neacsiu, A., Comtois, K. A., & Linehan, M. M. (2009). The impact of experiential

avoidance on the reduction of depression in treatment for borderline personality

disorder. Behaviour Research and Therapy, 47(8), 663-670.

Berntson, G. G., Bigger, J. T., Eckberg, D. L., Grossman, P., Kaufmann, P. G., Malik, M., . . .

Stone, P. H. (1997). Heart rate variability: Origins, methods, and interpretive

caveats. Psychophysiology, 34(6), 623-648. 54

Beshara, M., Hutchinson, A. D., & Wilson, C. (2013). Does mindfulness matter? Everyday

mindfulness, mindful eating and self-reported serving size of energy dense foods among a

sample of South Australian adults. Appetite, 67, 25-29.

Biener, A., Cawley, J., & Meyerhoefer, C. (2018). The impact of obesity on medical care costs

and labor market outcomes in the US. Clinical Chemistry, 64(1), 108-117.

Blaine, B. (2008). Does depression cause obesity?: A meta-analysis of longitudinal studies of

depression and weight control. Journal of Health Psychology, 13(8), 1190-1197.

doi:10.1177/1359105308095977

Blokstra, A., Burns, C. M., & Seidell, J. C. (1999). Perception of weight status and dieting

behaviour in Dutch men and women. International Journal of Obesity, 23(1), 7.

Boggero, I. A., Eisenlohrâ-Moul, T., & Segerstrom, S. C. (2016). Task switching ability protects

against the adverse effects of pain on health: A longitudinal study of older adults. British

Journal of Health Psychology, 21(2), 434-450.

Bonanno, G. A., Papa, A., Lalande, K., Westphal, M., & Coifman, K. (2004). The importance of

being flexible: The ability to both enhance and suppress emotional expression predicts long-

term adjustment. Psychological Science, 15(7), 482-487. doi:10.1111/j.0956-

7976.2004.00705.x

Bond, F. W., Hayes, S. C., Baer, R. A., Carpenter, K. M., Guenole, N., Orcutt, H. K., . . . Zettle,

R. D. (2011). Preliminary psychometric properties of the acceptance and action

Questionnaire–II: A revised measure of psychological inflexibility and experiential

avoidance. Behavior Therapy, 42(4), 676-688. 55

Borrell, L. N., & Samuel, L. (2014). Body mass index categories and mortality risk in US adults:

The effect of overweight and obesity on advancing death. American Journal of Public

Health, 104(3), 512-519. doi:10.2105/AJPH.2013.301597

Bourdier, L., Orri, M., Carre, A., Gearhardt, A. N., Romo, L., Dantzer, C., & Berthoz, S. (2018).

Are emotionally driven and addictive-like eating behaviors the missing links between

psychological distress and greater body weight? Appetite, 120, 536-546.

Brown, K. W., & Ryan, R. M. (2003). The benefits of being present: Mindfulness and its role in

psychological well-being. Journal of Personality and Social Psychology, 84(4), 822.

Brunette, G. W. (2017). CDC yellow book 2018: Health information for international

travel Oxford University Press.

Byrne, S., Cooper, Z., & Fairburn, C. (2003). Weight maintenance and relapse in obesity: A

qualitative study. International Journal of Obesity, 27(8), 955.

Campbell-Sills, L., & Barlow, D. H. (2007). Incorporating emotion regulation into

conceptualizations and treatments of anxiety and mood disorders. Handbook of emotion

regulation, 2.

Castellanos, E. H., Charboneau, E., Dietrich, M. S., Park, S., Bradley, B. P., Mogg, K., &

Cowan, R. L. (2009). Obese adults have visual attention bias for food cue images: Evidence

for altered reward system function Nature Publishing Group. doi:10.1038/ijo.2009.138

Casu, M., Patrone, V., Gianelli, M. V., Marchegiani, A., Ragni, G., Murialdo, G., & Polleri, A.

(2002). Spectral analysis of R-R interval variability by short-term recording in anorexia

nervosa. Eating and Weight Disorders, 7(3), 239-243. doi:10.1007/BF03327462

Chambers, R., Gullone, E., & Allen, N. B. (2009). Mindful emotion regulation: An integrative

review. Clinical Psychology Review, 29(6), 560-572. 56

Ciarrochi, J., Sahdra, B., Marshall, S., Parker, P., & Horwath, C. (2014). Psychological

flexibility is not a single dimension: The distinctive flexibility profiles of underweight,

overweight, and obese people. Journal of Contextual Behavioral Science, 3(4), 236-247.

Coffino, J. A., Orloff, N. C., & Hormes, J. M. (2016). Dietary restraint partially mediates the

relationship between impulsivity and binge eating only in lean individuals: The importance

of accounting for body mass in studies of restraint. Frontiers in Psychology, 7, 1499.

Convit, A., Wolf, O. T., Tarshish, C., & De Leon, M. J. (2003). Reduced glucose tolerance is

associated with poor memory performance and hippocampal atrophy among normal

elderly. Proceedings of the National Academy of Sciences, 100(4), 2019-2022.

Conway, A. R., Cowan, N., & Bunting, M. F. (2001). The cocktail party phenomenon revisited:

The importance of working memory capacity. Psychonomic Bulletin & Review, 8(2), 331-

335.

Cooper, J. L., O'Shea, A.,E., Atkinson, M. J., & Wade, T. D. (2014). Examination of the

difficulties in emotion regulation scale and its relation to disordered eating in a young

female sample. The International Journal of Eating Disorders, 47(6), 630-639.

doi:10.1002/eat.22278

Creswell, J. D., Way, B. M., Eisenberger, N. I., & Lieberman, M. D. (2007). Neural correlates of

dispositional mindfulness during affect labeling. Psychosomatic Medicine, 69(6), 560-565.

Cserjési, R., Luminet, O., Poncelet, A., & Lénárd, L. (2009). Altered executive function in

obesity. exploration of the role of affective states on cognitive abilities. Appetite, 52(2), 535-

539. 57

Dalrymple, K. L., Clark, H., Chelminski, I., & Zimmerman, M. (2018). The interaction between

mindfulness, emotion regulation, and social anxiety and its association with emotional

eating in bariatric surgery candidates. Mindfulness, doi:10.1007/s12671-018-0921-4

Dalrymple, K. L., & Herbert, J. D. (2007). Acceptance and commitment therapy for generalized

social anxiety disorder: A pilot study. Behavior Modification, 31(5), 543-568.

Delaney, E., & O'Brien, W. H. (2012). The association between acceptance and mental health

while living with HIV. Social Work in Mental Health, 10(3), 253-266.

doi:10.1080/15332985.2011.649107

Dohle, S., Diel, K., & Hofmann, W. (2018). Executive functions and the self-regulation of eating

behavior: A review. Appetite, 124, 4-9.

Drapkin, R. G., Wing, R. R., & Shiffman, S. (1995). Responses to hypothetical high risk

situations: Do they predict weight loss in a behavioral treatment program or the context of

dietary lapses? Health Psychology, 14(5), 427.

Duchesne, M., Mattos, P., Appolinário, J. C., Freitas, S. R. d., Coutinho, G., Santos, C., &

Coutinho, W. (2010). Assessment of executive functions in obese individuals with binge

eating disorder. Brazilian Journal of Psychiatry, 32(4), 381-388.

Emery, R. L., & Levine, M. D. (2017). Questionnaire and behavioral task measures of

impulsivity are differentially associated with body mass index: A comprehensive meta-

analysis. Psychological Bulletin, 143(8), 868.

Essayli, J. H., Murakami, J. M., Wilson, R. E., & Latner, J. D. (2017). The impact of weight

labels on body image, internalized weight stigma, affect, perceived health, and intended

weight loss behaviors in normal-weight and overweight college women. American Journal

of Health Promotion, 31(6), 484-490. doi:10.1177/0890117116661982 58

Estrada, C. A., Isen, A. M., & Young, M. J. (1997). Positive affect facilitates integration of

information and decreases anchoring in reasoning among physicians. Organizational

Behavior and Human Decision Processes, 72(1), 117-135.

Fabes, R. A., & Eisenberg, N. (1997). Regulatory control and adults' stress-related responses to

daily life events. Journal of Personality and Social Psychology, 73(5), 1107.

Fagundo, A. B., De la Torre, R., Jiménez-Murcia, S., Agüera, Z., Granero, R., Tárrega, S., . . .

Rodríguez, R. (2012). Executive functions profile in extreme eating/weight conditions:

From anorexia nervosa to obesity. PloS One, 7(8), e43382.

Fairburn, C. G., & Beglin, S. J. (1994). Assessment of eating disorders: Interview or self‐report

questionnaire? International Journal of Eating Disorders, 16(4), 363-370.

Faris, P. L., Hofbauer, R. D., Daughters, R., VandenLangenberg, E., Iversen, L., Goodale, R. L.,

. . . Hartman, B. K. (2008). De-stabilization of the positive vago-vagal reflex in bulimia

nervosa. Physiology & Behavior, 94(1), 136-153.

Fassino, S., Leombruni, P., Pier, A., Daga, G. A., Amianto, F., Rovera, G., & Rovera, G. G.

(2002). Temperament and character in obese women with and without binge eating

disorder. Comprehensive Psychiatry, 43(6), 431-437.

Faul, F., Erdfelder, E., Lang, A., & Buchner, A. (2007). G* power 3: A flexible statistical power

analysis program for the social, behavioral, and biomedical sciences. Behavior Research

Methods, 39(2), 175-191.

Favieri, F., & Casagrande, M. (2019). The executive functions in overweight and obesity: A

systematic review of neuropsychological cross-sectional and longitudinal studies. Frontiers

in Psychology, 10, 2126. 59

Favieri, F., Forte, G., & Casagrande, M. (2019). The executive functions in overweight and

obesity: A systematic review of neuropsychological cross-sectional and longitudinal

studies. Frontiers in Psychology, 10, 2126.

Feldner, M. T., Hekmat, H., Zvolensky, M. J., Vowles, K. E., Secrist, Z., & Leen-Feldner, E. W.

(2006). The role of experiential avoidance in acute pain tolerance: A laboratory test. Journal

of Behavior Therapy and Experimental Psychiatry, 37(2), 146-158.

Fereidouni, F., Atef-Vahid, M. K., Lavasani, F. F., Orak, R. J., Klonsky, E. D., & Pazooki, A.

(2015). Are iranian obese women candidate for bariatric surgery different cognitively,

emotionally and behaviorally from their normal weight counterparts? Eating and Weight

Disorders-Studies on Anorexia, Bulimia and Obesity, 20(3), 397-403.

Ferguson, K. J., Brink, P. J., Wood, M., & Koop, P. M. (1992a). Characteristics of successful

dieters as measured by guided interview responses and restraint scale scores. Journal of the

American Dietetic Association (USA).

Ferguson, S. J., Taylor, A. J., & McMahon, C. (2017). Hope for the future and avoidance of the

present: Associations with well-being in older adults. Journal of Happiness Studies, 18(5),

1485-1506. doi:10.1007/s10902-016-9787-0

Fisher, N., Lattimore, P., & Malinowski, P. (2016). Attention with a mindful attitude attenuates

subjective appetitive reactions and food intake following food-cue exposure. Appetite, 99,

10-16.

Fitzpatrick, S., Gilbert, S., & Serpell, L. (2013). Systematic review: Are overweight and obese

individuals impaired on behavioural tasks of executive functioning? Neuropsychology

Review, 23(2), 138-156. 60

Forman, E. M., Goldstein, S. P., Flack, D., Evans, B. C., Manasse, S. M., & Dochat, C.

(2018). Promising technological innovations in cognitive training to treat eating-related

behavior Elsevier Science. doi:10.1016/j.appet.2017.04.011

Franquesa, A., Cebolla, A., Garca-Campayo, J., Demarzo, M., Elices, M., Pascual, J. C., & Soler,

J. (2017). Meditation practice is associated with a values-oriented life: The mediating role of

decentering and mindfulness. Mindfulness, 8(5), 1259-1268. doi:10.1007/s12671-017-0702-

5

Frayling, T. M., Timpson, N. J., Weedon, M. N., Zeggini, E., Freathy, R. M., Lindgren, C. M., . .

. Rayner, N. W. (2007). A common variant in the FTO gene is associated with body mass

index and predisposes to childhood and adult obesity. Science, 316(5826), 889-894.

Friedman, B. H. (2007). An autonomic flexibility–neurovisceral integration model of anxiety and

cardiac vagal tone. Biological Psychology, 74(2), 185-199.

Friedman, B. H., & Thayer, J. F. (1998). Autonomic balance revisited: Panic anxiety and heart

rate variability. Journal of Psychosomatic Research, 44(1), 133-151.

Fuglestad, P. T., Jeffery, R. W., & Sherwood, N. E. (2012). Lifestyle patterns associated with

diet, physical activity, body mass index and amount of recent weight loss in a sample of

successful weight losers. International Journal of Behavioral Nutrition and Physical

Activity, 9(1), 79.

Galioto Wiedemann, R., Calvo, D., Meister, J., & Spitznagel, M. B. (2014). Self‐reported

physical activity is associated with cognitive function in lean, but not obese

individuals. Clinical Obesity, 4(6), 309-315. 61

Galioto, R., Gunstad, J., Heinberg, L. J., & Spitznagel, M. B. (2013). Adherence and weight loss

outcomes in bariatric surgery: Does cognitive function play a role? Obesity Surgery, 23(10),

1703-1710.

Gameiro, F., Perea, M. V., Ladera, V., Rosa, B., & García, R. (2017). Executive functioning in

obese individuals waiting for clinical treatment. Psicothema, 29(1)

Garland, E. L., Farb, N. A., Goldin, P. R., & Fredrickson, B. L. (2015). The mindfulness-to-

meaning theory: Extensions, applications, and challenges at the attention–appraisal–emotion

interface. Psychological Inquiry, 26(4), 377-387.

Geisler, F. C., Kubiak, T., Siewert, K., & Weber, H. (2013). Cardiac vagal tone is associated

with social engagement and self-regulation. Biological Psychology, 93(2), 279-286.

Gettens, K. M., & Gorin, A. A. (2017). Executive function in weight loss and weight loss

maintenance: A conceptual review and novel neuropsychological model of weight

control. Journal of Behavioral Medicine, 40(5), 687-701.

Gibson, E. L. (2012). The psychobiology of comfort eating: Implications for

neuropharmacological interventions. Behavioural Pharmacology, 23(5-6), 442-460.

doi:10.1097/FBP.0b013e328357bd4e [doi]

Gillie, B. L., & Thayer, J. F. (2014). Individual differences in resting heart rate variability and

cognitive control in posttraumatic stress disorder. Frontiers in Psychology, 5, 758.

Glick, D. M., Millstein, D. J., & Orsillo, S. M. (2014). A preliminary investigation of the role of

psychological inflexibility in academic procrastination. Journal of Contextual Behavioral

Science, 3(2), 81-88.

Gluck, M. E., Viswanath, P., & Stinson, E. J. (2017). Obesity, appetite, and the prefrontal

cortex. Current Obesity Reports, 6(4), 380-388. 62

Godfrey, K. M., Juarascio, A., Manasse, S., Minassian, A., Risbrough, V., & Afari, N. (2019).

Heart rate variability and emotion regulation among individuals with obesity and loss of

control eating. Physiology & Behavior, 199, 73-78.

Goldberg, E. (2002). The executive brain: Frontal lobes and the civilized mind Oxford

University Press, USA.

Graham, C. D., Gouick, J., Ferreira, N., & Gillanders, D. (2016). The influence of psychological

flexibility on life satisfaction and mood in muscle disorders. Rehabilitation

Psychology, 61(2), 210-217. doi:10.1037/rep0000092; 10.1037/rep0000092.supp

(Supplemental)

Graham, R., Hoover, A., Ceballos, N. A., & Komogortsev, O. (2011). Body mass index

moderates gaze orienting biases and pupil diameter to high and low calorie food

images. Appetite, 56(3), 577-586.

Gratz, K. L., & Roemer, L. (2004). Multidimensional assessment of emotion regulation and

dysregulation: Development, factor structure, and initial validation of the difficulties in

emotion regulation scale. Journal of Psychopathology and Behavioral Assessment, 26(1),

41-54.

Green, M. A., Hallengren, J. J., Davids, C. M., Riopel, C. M., & Skaggs, A. K. (2009). An

association between eating disorder behaviors and autonomic dysfunction in a nonclinical

population. A pilot study. Appetite, 53(1), 139-142.

Greener, J., Douglas, F., & van Teijlingen, E. (2010). More of the same? conflicting perspectives

of obesity causation and intervention amongst overweight people, health professionals and

policy makers. Social Science & Medicine, 70(7), 1042-1049.

doi:10.1016/j.socscimed.2009.11.017 63

Gross, J. J. (1998). Antecedent-and response-focused emotion regulation: Divergent

consequences for experience, expression, and physiology. Journal of Personality and Social

Psychology, 74(1), 224.

Gustafson, D., Lissner, L., Bengtsson, C., Björkelund, C., & Skoog, I. (2004). A 24-year follow-

up of body mass index and cerebral atrophy. Neurology, 63(10), 1876-1881.

Hales, C. M., Fryar, C. D., Carroll, M. D., Freedman, D. S., & Ogden, C. L. (2018). Trends in

obesity and severe obesity prevalence in US youth and adults by sex and age, 2007-2008 to

2015-2016. Jama,

Hankonen, N., Sutton, S., Prevost, A. T., Simmons, R. K., Griffin, S. J., Kinmonth, A. L., &

Hardeman, W. (2014a). Which behavior change techniques are associated with changes in

physical activity, diet and body mass index in people with recently diagnosed

diabetes? Annals of Behavioral Medicine, 49(1), 7-17.

Hankonen, N., Sutton, S., Prevost, A. T., Simmons, R. K., Griffin, S. J., Kinmonth, A. L., &

Hardeman, W. (2014b). Which behavior change techniques are associated with changes in

physical activity, diet and body mass index in people with recently diagnosed

diabetes? Annals of Behavioral Medicine, 49(1), 7-17.

Hansen, A. L., Johnsen, B. H., & Thayer, J. F. (2003). Vagal influence on working memory and

attention. International Journal of Psychophysiology, 48(3), 263-274.

Hayes, S. C., Strosahl, K. D., & Wilson, K. G. (1999). Acceptance and commitment therapy: An

experiential approach to behavior change. Guilford Press.

Hayes, S. C., Strosahl, K., Wilson, K. G., Bissett, R. T., Pistorello, J., Toarmino, D., . . . Bergan,

J. (2004). Measuring experiential avoidance: A preliminary test of a working model. The

Psychological Record, 54(4), 553-578. 64

Heatherton, T. F., & Baumeister, R. F. (1991). Binge eating as escape from self-

awareness. Psychological Bulletin, 110(1), 86-108. doi:10.1037/0033-2909.110.1.86

Hertel, P. T. (1998). Relation between rumination and impaired memory in dysphoric

moods. Journal of Abnormal Psychology, 107(1), 166.

Het, S., Rohleder, N., Schoofs, D., Kirschbaum, C., & Wolf, O. T. (2009). Neuroendocrine and

psychometric evaluation of a placebo version of the ‘Trier social stress

test’. Psychoneuroendocrinology, 34(7), 1075-1086.

Heymsfield, S. B., & Wadden, T. A. (2017). Mechanisms, pathophysiology, and management of

obesity. New England Journal of Medicine, 376(3), 254-266.

Hilbert, A., Vögele, C., Tuschen-Caffier, B., & Hartmann, A. S. (2011). Psychophysiological

responses to idiosyncratic stress in bulimia nervosa and binge eating disorder. Physiology &

Behavior, 104(5), 770-777. doi:10.1016/j.physbeh.2011.07.013

Horstmann, A., Dietrich, A., Mathar, D., Pssel, M., Villringer, A., & Neumann, J. (2015). Slave

to habit? obesity is associated with decreased behavioural sensitivity to reward

devaluation. Appetite, 87, 175-183. doi:10.1016/j.appet.2014.12.212

Hou, W. K., Ng, S. M., & Wan, J. H. Y. (2015). Changes in positive affect and mindfulness

predict changes in cortisol response and psychiatric symptoms: A latent change score

modelling approach. Psychology & Health, 30(5), 551-567.

Ingram, R. E. (1990). Self-focused attention in clinical disorders: Review and a conceptual

model. Psychological Bulletin, 107(2), 156.

Jaeger, J., Berns, S. M., & Czobor, P. (2003). The multidimensional scale of independent

functioning: A new instrument for measuring functional disability in psychiatric

populations. Schizophrenia Bulletin, 29(1), 153. 65

Jequier, E. (2002). Pathways to obesity. International Journal of Obesity, 26(S2), S12.

Joormann, J., & Gotlib, I. H. (2010). Emotion regulation in depression: Relation to cognitive

inhibition. Cognition & Emotion, 24(2), 281-298. doi:10.1080/02699930903407948

Jordan, C. H., Wang, W., Donatoni, L., & Meier, B. P. (2014). Mindful eating: Trait and state

mindfulness predict healthier eating behavior. Personality and Individual Differences, 68,

107-111.

Juarascio, A., Forman, E., Timko, C. A., Butryn, M., & Goodwin, C. (2011). The development

and validation of the food craving acceptance and action questionnaire (FAAQ). Eating

Behaviors, 12(3), 182-187. doi://dx.doi.org/10.1016/j.eatbeh.2011.04.008

Kakoschke, N., Kemps, E., & Tiggemann, M. (2017). Differential effects of approach bias and

eating style on unhealthy food consumption in overweight and normal weight

women. Psychology & Health, 32(11), 1371-1385.

Kane, M. J., Bleckley, M. K., Conway, A. R., & Engle, R. W. (2001). A controlled-attention

view of working-memory capacity. Journal of Experimental Psychology: General, 130(2),

169.

Kane, M. J., & Engle, R. W. (2003). Working-memory capacity and the control of attention: The

contributions of goal neglect, response competition, and task set to stroop

interference. Journal of Experimental Psychology: General, 132(1), 47.

Kashdan, T. B., & Rottenberg, J. (2010). Psychological flexibility as a fundamental aspect of

health. Clinical Psychology Review, 30(7), 865-878.

Kemp, A. H., & Quintana, D. S. (2013). The relationship between mental and physical health:

Insights from the study of heart rate variability. International Journal of

Psychophysiology, 89(3), 288. doi:10.1016/j.ijpsycho.2013.06.018 66

Keng, S., Smoski, M. J., & Robins, C. J. (2011). Effects of mindfulness on psychological health:

A review of empirical studies. Clinical Psychology Review, 31(6), 1041-1056.

Klatzkin, R. R., Gaffney, S., Cyrus, K., Bigus, E., & Brownley, K. A. (2015). Binge eating

disorder and obesity: Preliminary evidence for distinct cardiovascular and psychological

phenotypes. Physiology & Behavior, 142, 20-27.

Koball, A. M., Meers, M. R., Storfer-Isser, A., Domoff, S. E., & Musher-Eizenman, D. R.

(2012). Eating when bored: Revision of the emotional eating scale with a focus on

boredom. Health Psychology, 31(4), 521.

Koenig, J., Windham, B. G., Ferrucci, L., Sonntag, D., Fischer, J. E., Thayer, J. F., & Jarczok,

M. N. (2015). Association strength of three adiposity measures with autonomic nervous

system function in apparently healthy employees. The Journal of Nutrition, Health &

Aging, 19(9), 879-882.

Kozak, A. T., Nguyen, C., Yanos, B. R., & Fought, A. (2013). Persuading students to exercise:

What is the best way to frame messages for normal-weight versus overweight/obese

university students? Journal of American College Health, 61(5), 264-273.

doi:10.1080/07448481.2013.799477

Laborde, S., Furley, P., & Schempp, C. (2015). The relationship between working memory,

reinvestment, and heart rate variability. Physiology & Behavior, 139, 430-436.

doi:10.1016/j.physbeh.2014.11.036

Lafrance Robinson, A., Kosmerly, S., Mansfield-Green, S., & Lafrance, G. (2014). Disordered

eating behaviours in an undergraduate sample: Associations among gender, body mass

index, and difficulties in emotion regulation. Canadian Journal of Behavioural Science /

Revue Canadienne Des Sciences Du Comportement, 46(3), 320-326. doi:10.1037/a0031123 67

Lavagnino, L., Arnone, D., Cao, B., Soares, J. C., & Selvaraj, S. (2016). Inhibitory control in

obesity and binge eating disorder: A systematic review and meta-analysis of neurocognitive

and neuroimaging studies. Neuroscience & Biobehavioral Reviews, 68, 714-726.

Levin, M. E., Dalrymple, K., Himes, S., & Zimmerman, M. (2014). Which facets of mindfulness

are related to problematic eating among patients seeking bariatric surgery? Eating

Behaviors, 15(2), 298-305. doi:10.1016/j.eatbeh.2014.03.012

Levin, M. E., Hildebrandt, M. J., Lillis, J., & Hayes, S. C. (2012). The impact of treatment

components suggested by the psychological flexibility model: A meta-analysis of

laboratory-based component studies. Behavior Therapy, 43(4), 741-756.

doi:8080/10.1016/j.beth.2012.05.003

Lillis, J., & Hayes, S. C. (2008). Measuring avoidance and inflexibility in weight related

problems. International Journal of Behavioral Consultation and Therapy, 4(4), 348.

Lillis, J., Hayes, S. C., Bunting, K., & Masuda, A. (2009). Teaching acceptance and mindfulness

to improve the lives of the obese: A preliminary test of a theoretical model. Annals of

Behavioral Medicine, 37(1), 58-69.

Liu, Y., & Wang, Z. (2014). Positive affect and cognitive control: Approach-motivation intensity

influences the balance between cognitive flexibility and stability. Psychological

Science, 25(5), 1116-1123.

Loucks, E. B., Britton, W. B., Howe, C. J., Gutman, R., Gilman, S. E., Brewer, J., . . . Buka, S.

L. (2016). Associations of dispositional mindfulness with obesity and central adiposity: The

New England Family Study. International Journal of Behavioral Medicine, 23(2), 224-233.

doi:10.1007/s12529-015-9513-z 68

Maayan, L., Hoogendoorn, C., Sweat, V., & Convit, A. (2011). Disinhibited eating in obese

adolescents is associated with orbitofrontal volume reductions and executive

dysfunction. Obesity, 19(7), 1382-1387.

Malik, M. (1996). Task force of the European society of cardiology and the North American

society of pacing and electrophysiology. heart rate variability. standards of measurement,

physiological interpretation, and clinical use. European Heart Journal, 17, 354-381.

Mantzios, M., Egan, H., Hussain, M., Keyte, R., & Bahia, H. (2018). Mindfulness, self-

compassion, and mindful eating in relation to fat and sugar consumption: An exploratory

investigation. Eating and Weight Disorders-Studies on Anorexia, Bulimia and

Obesity, 23(6), 833-840.

Masuda, A., Hill, M. L., & Tone, E. B. (2012). The role of mindfulness and disordered eating

cognitions in psychological distress among college females with elevated disordered

eating. Mindfulness, 3(2), 132-138. doi:10.1007/s12671-012-0087-4

Masuda, A., Price, M., Anderson, P. L., & Wendell, J. W. (2010). Disordered eating-related

cognition and psychological flexibility as predictors of psychological health among college

students. Behavior Modification, 34(1), 3-15. doi:10.1177/0145445509351569

Masuda, A., Price, M., & Latzman, R. (2012). Mindfulness moderates the relationship between

disordered eating cognitions and disordered eating behaviors in a non-clinical college

sample. Journal of Psychopathology & Behavioral Assessment, 34(1), 107-115.

doi:10.1007/s10862-011-9252-7

Michaud, C. M., Murray, C. J., & Bloom, B. R. (2001). Burden of disease: Implications for

future research. Jama, 285(5), 535-539. 69

Mischel, W., Shoda, Y., & Peake, P. K. (1988). The nature of adolescent competencies predicted

by preschool delay of gratification. Journal of Personality and Social Psychology, 54(4),

687.

Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D.

(2000). The unity and diversity of executive functions and their contributions to complex

“Frontal lobe” tasks: A latent variable analysis. doi://doi.org/10.1006/cogp.1999.0734

Moon, A., & Berenbaum, H. (2009). Emotional awareness and emotional eating. Cognition and

Emotion, 23(3), 417-429. doi:10.1080/02699930801961798

Moor, K. R., Scott, A. J., & McIntosh, W. D. (2013). Mindful eating and its relationship to body

mass index and physical activity among university students. Mindfulness, 4(3), 269-274.

Murakami, R., Matsuda, T., & Koitabashi, K. (2012). Effect on cardiovascular system and

autonomic nervous system in healthy adults with different body types while performing

movements simulating washing of the lower limbs for cardiac rehabilitation. Japan Journal

of Nursing Science, 9(2), 149-159.

Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., . . . Oti, S. O. (2014).

Global, regional, and national prevalence of overweight and obesity in children and adults

during 1980-2013: A systematic analysis for the global burden of disease study 2013. The

Lancet, 384(9945), 766-781. doi:10.1016/S0140-6736(14)60460-8

Nijs, I. M., Franken, I. H., & Muris, P. (2010). Food-related Stroop interference in obese and

normal-weight individuals: Behavioral and electrophysiological indices. Eating

Behaviors, 11(4), 258-265. 70

Nijs, I. M., Muris, P., Euser, A. S., & Franken, I. H. (2010). Differences in attention to food and

food intake between overweight/obese and normal-weight females under conditions of

hunger and satiety. Appetite, 54(2), 243-254.

Ogden CL, Carroll MD, Kit BK, Flegal KM. (2014). Prevalence of childhood and adult obesity

in the united states, 2011-2012. Jama, 311(8), 806-814. doi:10.1001/jama.2014.732

Ouwens, M. A., Schiffer, A. A., Visser, L. I., Raeijmaekers, N. J. C., & Nyklícek, I. (2015).

Mindfulness and eating behaviour styles in morbidly obese males and females. Appetite, 87,

62-67. doi:10.1016/j.appet.2014.11.030

Paniccia, M., Paniccia, D., Thomas, S., Taha, T., & Reed, N. (2017). Clinical and non-clinical

depression and anxiety in young people: A scoping review on heart rate

variability. Autonomic Neuroscience,

Paolini, B., Burdette, J. H., Laurienti, P. J., Morgan, A. R., Williamson, D. A., & Rejeski, W. J.

(2012). Coping with brief periods of food restriction: Mindfulness matters. Frontiers in

Aging Neuroscience, 4, 13.

Park, G., Van Bavel, J. J., Vasey, M. W., & Thayer, J. F. (2012). Cardiac vagal tone predicts

inhibited attention to fearful faces. Emotion, 12(6), 1292-1302. doi:10.1037/a0028528

Paulus, D. J., Vanwoerden, S., Norton, P. J., & Sharp, C. (2016). From neuroticism to anxiety:

Examining unique contributions of three transdiagnostic vulnerability factors. Personality

and Individual Differences, 94, 38-43.

Peckmezian, T., & Hay, P. (2017). A systematic review and narrative synthesis of interventions

for uncomplicated obesity: Weight loss, well-being and impact on eating disorders. Journal

of Eating Disorders, 5 Retrieved 71

from http://ezproxy.bgsu.edu:8080/login?url=http://search.ebscohost.com/login.aspx?direct

=true&db=psyh&AN=2017-19755-001&site=ehost-live&scope=site

Perpina, C., Segura, M., & Sánchez-Reales, S. (2017). Cognitive flexibility and decision-making

in eating disorders and obesity. Eating and Weight Disorders-Studies on Anorexia, Bulimia

and Obesity, 22(3), 435-444.

Petrowski, K., Wichmann, S., Siepmann, T., Wintermann, G., Bornstein, S. R., & Siepmann, M.

(2017). Effects of mental stress induction on heart rate variability in patients with panic

disorder. Applied Psychophysiology and Biofeedback, 42(2), 85-94.

Phillips, L. H., Henry, J. D., Nouzova, E., Cooper, C., Radlak, B., & Summers, F. (2014).

Difficulties with emotion regulation in multiple sclerosis: Links to executive function,

mood, and quality of life. Journal of Clinical & Experimental Neuropsychology, 36(8), 831-

842. doi:10.1080/13803395.2014.946891

Polivy, J., & Herman, C. P. (1993). Etiology of binge eating: Psychological mechanisms.

Porges, S. W. (1995). Cardiac vagal tone: A physiological index of stress. Neuroscience &

Biobehavioral Reviews, 19(2), 225-233.

Porges, S. W. (2007). The polyvagal perspective. Biological Psychology, 74(2), 116-143.

Powell, L., & Amsbary, J. H. (2004). Self-reported obesity and obesity-related

behaviors. Individual Differences Research, 2(2), 118-124. Retrieved

from http://ezproxy.bgsu.edu:8080/login?url=http://search.ebscohost.com/login.aspx?direct

=true&db=psyh&AN=2004-19396-004&site=ehost-live&scope=site

Raes, F., Dewulf, D., Van Heeringen, C., & Williams, J. M. G. (2009). Mindfulness and reduced

cognitive reactivity to sad mood: Evidence from a correlational study and a non-randomized

waiting list controlled study. Behaviour Research and Therapy, 47(7), 623-627. 72

Robinson, B. E. (1998). The workaholic family: A clinical perspective. American Journal of

Family Therapy, 26(1), 65-75.

Robinson, M. D., Vargas, P. T., Tamir, M., & Solberg, E. C. (2004). Using and being used by

categories. the case of negative evaluations and daily well-being. Psychological

Science, 15(8), 521-526. doi:10.1111/j.0956-7976.2004.00713.x [doi]

Ross, S. E., Flynn, J. I., & Pate, R. R. (2016). What is really causing the obesity epidemic? A

review of reviews in children and adults. Journal of Sports Sciences, 34(12), 1148-1153.

Rottenberg, J. (2007). Cardiac vagal control in depression: A critical analysis. Biological

Psychology, 74(2), 200-211.

Rusciano, A., Corradini, G., & Stoianov, I. (2017). Neuroplus biofeedback improves attention,

resilience, and injury prevention in elite soccer players. Psychophysiology, 54(6), 916-926.

Sairanen, E., Lappalainen, R., Lapveteläinen, A., Tolvanen, A., & Karhunen, L. (2014).

Flexibility in weight management. Eating Behaviors, 15(2), 218-224.

doi:10.1016/j.eatbeh.2014.01.008

Sairanen, E., Tolvanen, A., Karhunen, L., Kolehmainen, M., Järvelä, E., Rantala, S., . . .

Lappalainen, R. (2015). Psychological flexibility and mindfulness explain intuitive eating in

overweight adults. Behavior Modification, 39(4), 557-579. doi:10.1177/0145445515576402

Saxena, P., Dubey, A., & Pandey, R. (2011). Role of emotion regulation difficulties in predicting

mental health and well-being. Journal of Projective Psychology & Mental Health, 18(2),

147-155. Retrieved

from http://ezproxy.bgsu.edu:8080/login?url=http://search.ebscohost.com/login.aspx?direct

=true&db=psyh&AN=2011-21803-003&site=ehost-live&scope=site 73

Schmeichel, B. J., & Demaree, H. A. (2010). Working memory capacity and spontaneous

emotion regulation: High capacity predicts self-enhancement in response to negative

feedback. Emotion, 10(5), 739.

Schmeichel, B. J., Volokhov, R. N., & Demaree, H. A. (2008). Working memory capacity and

the self-regulation of emotional expression and experience. Journal of Personality and

Social Psychology, 95(6), 1526.

Segerstrom, S. C., & Nes, L. S. (2007). Heart rate variability reflects self-regulatory strength,

effort, and fatigue. Psychological Science, 18(3), 275-281.

Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and

norms. Frontiers in Public Health, 5, 258.

Shapiro, S. L., Carlson, L. E., Astin, J. A., & Freedman, B. (2006). Mechanisms of

mindfulness. Journal of Clinical Psychology, 62(3), 373-386. doi:10.1002/jclp.20237

Souza, G. G. L., Magalhaes, L. N., Da Cruz, Thiago Amorim Ribeiro, Mendonça-De-Souza, A.

C. F., Duarte, A. F. A., Fischer, N. L., . . . Gleiser, S. (2013). Resting vagal control and

resilience as predictors of cardiovascular allostasis in peacekeepers. Stress, 16(4), 377-383.

Souza, G., Mendonça-de-Souza, A., Barros, E. M., Coutinho, E., Oliveira, L., Mendlowicz, M.

V., . . . Volchan, E. (2007). Resilience and vagal tone predict cardiac recovery from acute

social stress. Stress, 10(4), 368-374.

Spitoni, G. F., Ottaviani, C., Petta, A. M., Zingaretti, P., Aragona, M., Sarnicola, A., &

Antonucci, G. (2017). Obesity is associated with lack of inhibitory control and impaired

heart rate variability reactivity and recovery in response to food stimuli. International

Journal of Psychophysiology, 116, 77-84. 74

Stange, J. P., Hamilton, J. L., Fresco, D. M., & Alloy, L. B. (2017). Perseverate or decenter?

differential effects of metacognition on the relationship between parasympathetic

inflexibility and symptoms of depression in a multi-wave study. Behaviour Research and

Therapy, 97, 123-133.

Steinhausen, H., & Weber, S. (2009). The outcome of bulimia nervosa: Findings from one-

quarter century of research. American Journal of Psychiatry, 166(12), 1331-1341.

Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental

Psychology, 18(6), 643.

Suess, P. E., Porges, S. W., & Plude, D. J. (1994). Cardiac vagal tone and sustained attention in

school‐age children. Psychophysiology, 31(1), 17-22.

Tanaka, M., Nakahara, T., Muranaga, T., Kojima, S., Yasuhara, D., Ueno, H., . . . Inui, A.

(2006). Ghrelin concentrations and cardiac vagal tone are decreased after pharmacologic

and cognitive-behavioral treatment in patients with bulimia nervosa. Hormones and

Behavior, 50(2), 261-265. doi:10.1016/j.yhbeh.2006.03.009

Tarvainen, M. P., Niskanen, J., Lipponen, J. A., Ranta-aho, P. O., & Karjalainen, P. A. (2014).

Kubios HRV – heart rate variability analysis software. Computer Methods and Programs in

Biomedicine, 113(1), 210-220. doi://dx.doi.org/10.1016/j.cmpb.2013.07.024

Taube-Schiff, M., Van Exan, J., Tanaka, R., Wnuk, S., Hawa, R., & Sockalingam, S. (2015).

Attachment style and emotional eating in bariatric surgery candidates: The mediating role of

difficulties in emotion regulation. Eating Behaviors, 18, 36-40.

doi:10.1016/j.eatbeh.2015.03.011 75

Terracciano, A., Sutin, A. R., McCrae, R. R., Deiana, B., Ferrucct, L., Schlessinger, D., . . .

Costa, P. T. J. (2009). Facets of personality linked to underweight and

overweight. Psychosomatic Medicine, 71(6), 682-689. doi:10.1097/PSY.0b013e3181a2925b

Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation

and dysregulation. Journal of Affective Disorders, 61(3), 201-216.

Thayer, J. F., & Lane, R. D. (2009). Claude bernard and the heart–brain connection: Further

elaboration of a model of neurovisceral integration. Neuroscience & Biobehavioral

Reviews, 33(2), 81-88.

Unsworth, N., Heitz, R. P., Schrock, J. C., & Engle, R. W. (2005). An automated version of the

operation span task. Behavior Research Methods, 37(3), 498-505.

Urada, D. I., & Miller, N. (2000). The impact of positive mood and category importance on

crossed categorization effects. Journal of Personality and Social Psychology, 78(3), 417.

Vainik, U., Dagher, A., Dubé, L., & Fellows, L. K. (2013). Neurobehavioural correlates of body

mass index and eating behaviours in adults: A systematic review. Neuroscience &

Biobehavioral Reviews, 37(3), 279-299.

Volkow, N. D., Wang, G. J., Fowler, J. S., Tomasi, D., & Baler, R. (2011). Food and drug

reward: Overlapping circuits in human obesity and addiction. Brain imaging in behavioral

neuroscience (pp. 1-24) Springer.

Volkow, N. D., Wang, G., Fowler, J. S., & Telang, F. (2008). Overlapping neuronal circuits in

addiction and obesity: Evidence of systems pathology. Philosophical Transactions of the

Royal Society B: Biological Sciences, 363(1507), 3191-3200. 76

Walker, F. R., Pfingst, K., Carnevali, L., Sgoifo, A., & Nalivaiko, E. (2017). In the search for

integrative biomarker of resilience to psychological stress. Neuroscience & Biobehavioral

Reviews, 74, 310-320.

Watford, T. S., Braden, A. L., & Emley, E. A. (2019). Mediation of the association between

mindfulness and emotional eating among overweight individuals. Mindfulness, 10(6), 1153-

1162.

Watford, T. S., O’Brien, W. H., Koerten, H. R., Bogusch, L. M., Moeller, M. T., Sonia Singh, R.,

& Sims, T. E. (2020). The mindful attention and awareness scale is associated with lower

levels of high‐frequency heart rate variability in a laboratory

context. Psychophysiology, 57(3), e13506.

Watford, T. S., Braden, A., & O’Brien, W. H. (in press, 2020). Resting State Heart Rate

Variability in Clinical and Subthreshold Disordered Eating: A meta-analysis.

International Journal of Eating Disorders.

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures

of positive and negative affect: The PANAS scales. Journal of Personality and Social

Psychology, 54(6), 1063.

Wee, C. C., Davis, R. B., Chiodi, S., Huskey, K. W., & Hamel, M. B. (2015). Sex, race, and the

adverse effects of social stigma vs. other quality of life factors among primary care patients

with moderate to severe obesity. Journal of General Internal Medicine, 30(2), 229-235.

doi:10.1007/s11606-014-3041-4

Wegner, D. M. (1994). Ironic processes of mental control. Psychological Review, 101(1), 34-52.

doi:10.1037/0033-295X.101.1.34 77

Wei, L., Chen, H., & Wu, G. (2018). Heart rate variability associated with grey matter volumes

in striatal and limbic structures of the central autonomic network. Brain Research, 1681, 14-

20.

Westenhoefer, J. (2001). The therapeutic challenge: Behavioral changes for long-term weight

maintenance. International Journal of Obesity & Related Metabolic Disorders, 25, S85.

Retrieved

from http://ezproxy.bgsu.edu:8080/login?url=http://search.ebscohost.com/login.aspx?direct

=true&db=a9h&AN=9384158&site=ehost-live&scope=site

Wiser, S., & Telch, C. F. (1999). Dialectical behavior therapy for binge‐eating disorder. Journal

of Clinical Psychology, 55(6), 755-768.

Woody, A., Hamilton, K., Livitz, I. E., Figueroa, W. S., & Zoccola, P. M. (2017). Buccal

telomere length and its associations with cortisol, heart rate variability, heart rate, and blood

pressure responses to an acute social evaluative stressor in college students. Stress, 20(3),

249-257.

Wooley, S. C., & Garner, D. M. (1994). Dietary treatments for obesity are ineffective. BMJ:

British Medical Journal, 309(6955), 655.

Yang, Y., Shields, G. S., Guo, C., & Liu, Y. (2018). Executive function performance in obesity

and overweight individuals: A meta-analysis and review. Neuroscience & Biobehavioral

Reviews, 84, 225-244. 78

APPENDIX A. MINDFUL ATTENTION AWARENESS SCALE (MAAS)

Below is a collection of statements about your everyday experience. Using the 1-6 scale below, please indicate how frequently or infrequently you currently have each experience.

Please answer according to what really reflects your experience rather than what you think your experience should be.

1. I could be experiencing some emotion and not be conscious of it until some other time. 2. I break or spill things because of carelessness, not paying attention, or thinking of something else. 3. I find it difficult to stay focused on what's happening in the present. 4. I tend to walk quickly to get where I'm going without paying attention to what I experience along the way. 5. I tend NOT to notice feelings of physical tension or discomfort until they really grab my attention. 6. I forget a person's name almost as soon as I have been told it for the first time. 7. It seems I am "running on automatic", without much awareness of what I'm doing. 8. I rush through activities without being really attentive to them. 9. I get so focused on the goal I want to achieve that I lose touch with what I'm doing right now to get there. 10. I do jobs or tasks automatically, without being aware of what I'm doing. 11. I find myself listening to someone with one ear, doing something else at the same time. 12. I drive places on "automatic pilot" and then wonder why I went there. 13. I find myself preoccupied with the future or the past. 14. I find myself doing things without paying attention. 15. I snack without being aware that I'm eating. 79

APPENDIX B. DIFFICULTIES IN EMOTION REGULATION, SHORT FORM (DERS- SF)

Please indicate how the following statements apply to you IN GENERAL by choosing the appropriate number from the scale below.

Scale: Almost never (1 – 10%) to Almost Always (91 – 100%)

1. When I’m upset, it takes me a long time to feel better. 2. When I’m upset, I believe there is nothing I can do to make myself feel better. 3. When I’m upset, I believe that I will end up feeling very depressed. 4. When I’m upset, I become embarrassed for feeling that way. 5. When I’m upset, I feel guilty for feeling that way. 6. When I’m upset, I become irritated at myself for feeling that way. 7. When I’m upset, I become out of control. 8. When I’m upset, I lose control over my behavior. 9. When I’m upset, I have difficulty controlling my behavior. 10. When I’m upset, I have difficulty focusing on other things. 11. When I’m upset, I have difficulty concentrating. 12. When I’m upset, I have difficulty getting work done. 13. I care about what I am feelings. 14. When I’m upset, I acknowledge my emotions. 15. I pay attention to how I feel. 16. I am confused about how I feel. 17. I have difficulty making sense out of my feelings. 18. I have no idea how I am feeling. 80

APPENDIX C. POSITIVE AND NEGATIVE AFFECT SCHEDULE (PANAS)

This scale consists of a number of words that describe different feelings and emotions. Read each item and then indicate the extent to which you have felt this way in general.

1. Interested 2. Excited 3. Strong 4. Enthusiastic 5. Proud 6. Alert 7. Inspired 8. Determined 9. Attentive 10. Active 11. Distressed 12. Upset 13. Guilty 14. Scared 15. Hostile 16. Irritable 17. Ashamed 18. Nervous 19. Jittery 20. Afraid