Using Continuous Glucose Monitoring to Passively Classify Naturalistic Binge Eating and Vomiting Among Adults With Binge-Spectrum Eating Disorders: A Preliminary Investigation.

OBJECTIVE: Binge eating and self-induced vomiting are common, transdiagnostic eating disorder (ED) symptoms. Efforts to understand these behaviors in research and clinical settings have historically relied on self-report measures, which may be biased and have limited ecological validity. It may be possible to passively detect binge eating and vomiting using data collected by continuous glucose monitors (CGMs; minimally invasive sensors that measure blood glucose levels), as these behaviors yield characteristic glucose responses. METHOD: This study developed machine learning classification algorithms to classify binge eating and vomiting among 22 adults with binge-spectrum EDs using CGM data. Participants wore Dexcom G6 CGMs and reported eating episodes and disordered eating symptoms using ecological momentary assessment for 2 weeks. Group-level random forest models were generated to distinguish binge eating from typical eating episodes and to classify instances of vomiting. RESULTS: The binge eating model had accuracy of 0.88 (95% CI: 0.83, 0.92), sensitivity of 0.56, and specificity of 0.90. The vomiting model demonstrated accuracy of 0.79 (95% CI: 0.62, 0.91), sensitivity of 0.88, and specificity of 0.71. DISCUSSION: Results suggest that CGM may be a promising avenue for passively classifying binge eating and vomiting, with implications for innovative research and clinical applications.

A preliminary study of latent trajectories of change in dietary restraint during CBT-E for bulimia-spectrum eating disorders and their associations with treatment response.

OBJECTIVE: Dietary restraint is a primary target of CBT-E. However, little research has examined how specific types of dietary restraint change during CBT-E for bulimia-spectrum eating disorders (BN-EDs) or the association between changes in dietary restraint and treatment response. This study examined latent trajectories of change in eating enough, eating a range of macronutrients, and following dietary rules during CBT-E for BN-EDs and the relationships between these trajectories and pre- to post-treatment change in BN symptoms and remission. METHOD: Participants were 56 adults with BN-EDs who received 16 sessions of CBT-E and completed the Eating Disorder Examination and ecological momentary assessments (EMA) of eating behaviors and BN symptoms. Latent growth mixture modeling identified trajectories of change in dietary restraint, which were compared on pre- to post-treatment BN symptom change and remission. RESULTS: Three trajectories of change were identified for eating enough, eating a range of macronutrients, and food rules. Trajectories of change in eating enough were differentially associated with pre- to post-treatment change in BN symptoms, and trajectories of change in eating a range of macronutrients and food rules were differentially associated with remission. CONCLUSIONS: CBT-E yields heterogeneous trajectories of change in dietary restraint, which are associated with treatment response.

Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge-spectrum eating disorders: A proof-of-concept study.

OBJECTIVE: Going extended periods of time without eating increases risk for binge eating and is a primary target of leading interventions for binge-spectrum eating disorders (B-EDs). However, existing treatments for B-EDs yield insufficient improvements in regular eating and subsequently, binge eating. These unsatisfactory clinical outcomes may result from limitations in assessment and promotion of regular eating in therapy. Detecting the absence of eating using passive sensing may improve clinical outcomes by facilitating more accurate monitoring of eating behaviours and powering just-in-time adaptive interventions. We developed an algorithm for detecting meal consumption (and extended periods without eating) using continuous glucose monitor (CGM) data and machine learning. METHOD: Adults with B-EDs (N = 22) wore CGMs and reported eating episodes on self-monitoring surveys for 2 weeks. Random forest models were run on CGM data to distinguish between eating and non-eating episodes. RESULTS: The optimal model distinguished eating and non-eating episodes with high accuracy (0.82), sensitivity (0.71), and specificity (0.94). CONCLUSIONS: These findings suggest that meal consumption and extended periods without eating can be detected from CGM data with high accuracy among individuals with B-EDs, which may improve clinical efforts to target dietary restriction and improve the field's understanding of its antecedents and consequences.

Cannabis use and binge eating: Examining the relationship between cannabis use and clinical severity among adults with binge eating.

Cannabis use is prevalent among individuals with binge eating (BE; i.e., the inability to control eating behavior). Yet, only two studies to date (both over 20 years old) have tested if cannabis use relates to clinical severity among BE samples. Characterizing the relationship between cannabis use, eating disorder (ED) severity, and other psychiatric symptoms in BE samples is necessary for informing screening and clinical recommendations. The present study characterized cannabis use among adults with BE and tested between-group and within-group relationships between cannabis use and eating disorder symptoms, alcohol consumption and symptoms, and depression symptoms. Participants (N = 165) were treatment-seeking adults with at least once weekly BE in the past 3 months who completed clinical interviews and self-report measures before treatment. Over 23% of participants reported cannabis use in the past 3 months, with most persons using cannabis reported using "once or twice" or "monthly." Most persons using cannabis reported cannabis-related symptoms. Persons using cannabis reported significantly greater alcohol consumption and were more likely to report alcohol-related symptoms compared to persons not using cannabis. No associations were observed between cannabis use, eating disorder symptoms, and depressions symptoms. These findings indicate that a notable subset of patients with BE use cannabis and experience cannabis-related problems, and that cannabis and alcohol use may be related for these individuals. Considering legal and sociocultural shifts in cannabis availability and prevalence, results from the present study support screening for cannabis and alcohol use patterns in patients with BE. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Affective profiles of exercise episodes are associated with maladaptive and adaptive motivations for exercise.

OBJECTIVE: Maladaptive exercise (i.e., driven and/or compensatory exercise) is common in binge-spectrum eating disorders (EDs; e.g., bulimia nervosa, binge ED) and associated with adverse treatment outcomes. Alternatively, individuals with EDs are often also engaging in adaptive exercise (e.g., for enjoyment or health improvement), and increasing adaptive exercise may decrease ED symptoms. The current study aimed to understand which exercise episodes are likely to be maladaptive/adaptive so that interventions can appropriately decrease/increase maladaptive and adaptive exercise. METHOD: We used latent profile analysis (LPA) to identify pre-exercise affective profiles of 661 exercise episodes among 84 individuals with binge-spectrum EDs and examined associations between LPA-identified profiles and subsequent exercise motivations using ecological momentary assessment. RESULTS: A two-profile solution best fit our data: Profile 1 (n = 174), 'positive affectivity,' and Profile 2 (n = 487), 'negative affectivity.' Episodes in the 'negative affectivity' profile were more likely to be endorsed as both driven and intended to influence body shape/weight. Episodes in the 'positive affectivity' profile were more likely to be endorsed as exercising for enjoyment. CONCLUSIONS: Results support two phenotypes of exercise episodes, and differential associations of these phenotypes with adaptive and maladaptive motivations for exercise.

Using Wearable Passive Sensing to Predict Binge Eating in Response to Negative Affect Among Individuals With Transdiagnostic Binge Eating: Protocol for an Observational Study.

BACKGROUND: Binge eating (BE), characterized by eating a large amount of food accompanied by a sense of loss of control over eating, is a public health crisis. Negative affect is a well-established antecedent for BE. The affect regulation model of BE posits that elevated negative affect increases momentary risk for BE, as engaging in BE alleviates negative affect and reinforces the behavior. The eating disorder field's capacity to identify moments of elevated negative affect, and thus BE risk, has exclusively relied on ecological momentary assessment (EMA). EMA involves the completion of surveys in real time on one's smartphone to report behavioral, cognitive, and emotional symptoms throughout the day. Although EMA provides ecologically valid information, EMA surveys are often delivered only 5-6 times per day, involve self-report of affect intensity only, and are unable to assess affect-related physiological arousal. Wearable, psychophysiological sensors that measure markers of affect arousal including heart rate, heart rate variability, and electrodermal activity may augment EMA surveys to improve accurate real-time prediction of BE. These sensors can objectively and continuously measure biomarkers of nervous system arousal that coincide with affect, thus allowing them to measure affective trajectories on a continuous timescale, detect changes in negative affect before the individual is consciously aware of them, and reduce user burden to improve data completeness. However, it is unknown whether sensor features can distinguish between positive and negative affect states, given that physiological arousal may occur during both negative and positive affect states. OBJECTIVE: The aims of this study are (1) to test the hypothesis that sensor features will distinguish positive and negative affect states in individuals with BE with >60% accuracy and (2) test the hypothesis that a machine learning algorithm using sensor data and EMA-reported negative affect to predict the occurrence of BE will predict BE with greater accuracy than an algorithm using EMA-reported negative affect alone. METHODS: This study will recruit 30 individuals with BE who will wear Fitbit Sense 2 wristbands to passively measure heart rate and electrodermal activity and report affect and BE on EMA surveys for 4 weeks. Machine learning algorithms will be developed using sensor data to distinguish instances of high positive and high negative affect (aim 1) and to predict engagement in BE (aim 2). RESULTS: This project will be funded from November 2022 to October 2024. Recruitment efforts will be conducted from January 2023 through March 2024. Data collection is anticipated to be completed in May 2024. CONCLUSIONS: This study is anticipated to provide new insight into the relationship between negative affect and BE by integrating wearable sensor data to measure affective arousal. The findings from this study may set the stage for future development of more effective digital ecological momentary interventions for BE. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47098.

Latent profiles of emotion dysregulation among individuals with binge-spectrum eating disorders: Associations with eating disorder pathology.

OBJECTIVE: Emotion dysregulation (i.e., a multi-component term comprising nonacceptance of emotional responses, difficulty engaging in goal-directed behaviour, impulse control difficulties, lack of emotional awareness, limited access to emotion regulation strategies, and lack of emotional clarity) is a well-established transdiagnostic risk and maintenance factor for eating disorders. To date, there is limited information on how varying scores on subdomains of emotion dysregulation may yield distinct profiles in individuals with binge-spectrum eating disorders (B-EDs), and how these emotion dysregulation profiles may inform resultant symptomatology. METHOD: In the current study, treatment-seeking individuals with B-EDs (n = 315) completed the Difficulties in Emotion Regulation Scale (DERS) and Eating Disorder Examination. Latent profile analysis was conducted on the six subscales of the DERS. Identified latent profiles were examined as predictors of eating disorder pathology using linear regression, and a two-class model of emotion dysregulation fit the data. RESULTS: Class 1 (n = 113) was low in all of the DERS subscales, while Class 2 (n = 202) was high in all of the DERS subscales. Individuals in Class 2 had a significantly higher frequency of compensatory behaviours in the past month (F(1,313) = 12.97, p < 0.001), and significantly higher restraint scores (F(1,313) = 17.86, p < 0.001). The classes also significantly differed in terms of eating concern (F(1,313) = 20.89, p < 0.001) and shape concern (F(1,313) = 4.59, p = 0.03), with both being higher for Class 2. DISCUSSION: We found only two distinct classes of emotion dysregulation in B-ED's such that individuals were simply high or low in emotion dysregulation. These results suggest that it may be more valuable for future research to evaluate emotion dysregulation as a cohesive whole rather than conceptualising the construct as having truly distinct subdomains.

Unequivocally large, but not enormous: An examination of the nutritional content of objective and subjective binge-eating episodes using ecological momentary assessment data.

OBJECTIVE: This study compared the macronutrient profiles of subjective binge-eating episodes (SBEs), objective binge-eating episodes (OBEs), and typical eating episodes. METHOD: Twenty-one adults with binge eating completed ecological momentary assessment of all eating episodes for 2 weeks, including detailed monitoring of food types and portions. Binge-eating episodes (N = 237) were coded as OBEs (n = 76) or SBEs (n = 161). Calories and macronutrients were computed using manufacturer information and USDA Food and Nutrient Database for Dietary Studies. Multilevel regression models compared the eating episode types on caloric and macronutrient content. RESULTS: OBEs contained an average of 121.5 (95.1) g fat, 363.7 (289.1) g carbohydrates, 65.2 (38.2) g protein, 20.9 (16.4) g fiber, and 2856.2 (1869.2) calories. SBEs contained 31.6 (30.5) g fat, 76.5 (54.0) g carbohydrates, 20.5 (21.6) g protein, 5.3 (5.9) g fiber, and 695.1 (505.9) calories. Although OBEs contained significantly more calories and grams of all macronutrients than SBEs (p < .001), the macronutrient proportions of OBEs and SBEs did not differ. The proportions of carbohydrates (p = .005) and protein (p < .001) in SBEs significantly differed from typical eating episodes. DISCUSSION: Our findings offer preliminary evidence that OBEs and SBEs are more comparable in macronutrient profile than typical eating episodes. PUBLIC SIGNIFICANCE STATEMENT: The present study compared the calories and grams of macronutrients in objectively large binge-eating episodes, subjectively large binge-eating episodes, and typical meals and snacks. Results suggest that objectively and subjectively large binge-eating episodes demonstrate similar profiles of macronutrients, which are different from the macronutrient profile of meals and snacks. These results may help the eating disorder field better study the impact of subjectively large binge-eating episodes.

A closer look at homework compliance in behavior therapy for bulimia nervosa: does homework compliance in between-session period prospectively predict session-by-session change in bulimia symptoms?

INTRODUCTION: Homework assignments are considered key components of behavioral treatments for bulimia nervosa (BN), but little is known about whether homework compliance predicts BN symptom improvement. The present study is the first to examine whether session-by-session change in homework compliance predicts session-by-session changes in BN symptoms during behavioral treatment. METHOD: Patients with BN-spectrum eating disorders (n = 42) received 20 sessions of behavioral treatment. Each session, their clinicians completed surveys assessing compliance with self-monitoring, behavioral, and written homework assignments and BN symptom frequency during the previous week. RESULTS: Significant between-persons effects of self-monitoring and behavioral homework compliance were identified, such that patients with greater compliance in the past week experienced greater reductions in binge eating and purging the following week. There were significant within-persons effects of self-monitoring compliance on binge eating and behavioral homework compliance on restrictive eating, binge eating, and purging, such that greater than one's usual compliance predicted greater improvements in BN symptoms the following week. No significant effects of written homework compliance were identified. CONCLUSION: Compliance with self-monitoring and behavioral homework predict improvements in BN symptoms during behavioral treatment. These findings reinforce the importance of self-monitoring and behavioral homework compliance as drivers of change during treatment for BN.

A Randomized Controlled Trial of CBT+: A Clinician-Controlled, Just-In-Time, Adjunctive Intervention for Bulimia-Spectrum Disorders.

Cognitive Behavior Therapy (CBT) for bulimia nervosa (BN) requires patient skill utilization (use of treatment skills) and skill acquisition (successful skill use) for symptom improvement. Treatment outcomes are unsatisfactory, possibly due to poor skill acquisition and utilization by post-treatment. Just-in-time adaptive interventions (JITAIs), momentary interventions delivered at opportunities for skill practice, may improve skill acquisition and utilization. Participants (N = 56 individuals with bulimia-spectrum eating disorders) completed electronic self-monitoring in CBT+ and received JITAIs or no JITAIs alongside 16 sessions of CBT. Feasibility, acceptability, target engagement, and treatment outcomes were evaluated. JITAIs demonstrated feasibility and acceptability. Treatment outcomes and target engagement did not differ between conditions. The lack of group differences in target engagement and treatment outcomes may be explained by skill use self-monitoring promoting skill utilization and acquisition or low statistical power. Our findings suggest that JITAIs are feasible and acceptable during CBT for BN and warrant additional study.

Optimizing digital health technologies to improve therapeutic skill use and acquisition alongside enhanced cognitive-behavior therapy for binge-spectrum eating disorders: Protocol for a randomized controlled trial.

OBJECTIVE: Adjunctive mobile health (mHealth) technologies offer promise for improving treatment response to enhanced cognitive-behavior therapy (CBT-E) among individuals with binge-spectrum eating disorders, but research on the key "active" components of these technologies has been very limited. The present study will use a full factorial design to (1) evaluate the optimal combination of complexity of two commonly used mHealth components (i.e., self-monitoring and microinterventions) alongside CBT-E and (2) test whether the optimal complexity level of these interventions is moderated by baseline self-regulation. Secondary aims of the present study include evaluating target engagement associated with each level of these intervention components and quantifying the component interaction effects (i.e., partially additive, fully additive, or synergistic effects). METHOD: Two hundred and sixty-four participants with binge-spectrum eating disorders will be randomized to six treatment conditions determined by the combination of self-monitoring condition (i.e., standard self-monitoring or skills monitoring) and microinterventions condition (i.e., no microinterventions, automated microinterventions, or just-in-time adaptive interventions) as an augmentation to 16 sessions of CBT-E. Treatment outcomes will be measured using the Eating Disorder Examination and compared by treatment condition using multilevel models. RESULTS: Results will clarify the "active" components in mHealth interventions for binge eating. DISCUSSION: The present study will provide critical insight into the efficacy of commonly used digital intervention components (i.e., skills monitoring and microinterventions) alongside CBT-E. Furthermore, results of this study may inform personalization of digital intervention intensity based on patient profiles of self-regulation. PUBLIC SIGNIFICANCE: This study will examine the relative effectiveness of commonly used components of application-based interventions as an augmentation to cognitive-behavioral therapy for binge eating. Findings from this study will inform the development of an optimized digital intervention for individuals with binge eating.

Weight status is associated with clinical characteristics among individuals with bulimia nervosa.

Recent studies have found increasing rates of overweight and obesity in bulimia nervosa (BN). However, the relationships between body mass index (BMI) and BN symptoms and other clinically relevant constructs are unknown. Participants (N = 152 adults with BN) were assigned to three groups by BMI: group with no overweight or obesity (NOW-BN; BMI <25; N = 32), group with overweight (OW-BN; BMI ≥25 and <30; N = 66), and group with obesity (O-BN; BMI ≥30; N = 54). We compared the groups on demographics, diet and weight histories, body esteem, BN symptoms, and depression using chi square, analysis of variance, analysis of covariance, and Poisson regression models. The O-BN group was older (d = 0.57) and OW-BN and O-BN groups had greater proportions of race/ethnic minorities than NOW-BN group. The O-BN group was significantly younger at first diet (d = 0.41) and demonstrated significantly higher cognitive dietary restraint (d = 0.31). Compared to NOW-BN, O-BN participants had lower incidence of objective binge eating (incidence rate ratio [IRR] = 4.86) and driven exercise (IRR = 7.13), and greater incidence of vomiting (IRR = 9.30), laxative misuse (IRR = 4.01), and diuretic misuse (d = 2.08). O-BN participants also experienced higher shape (d = 0.41) and weight (d = 0.42) concerns than NOW-BN and OW-BN, although NOW-BN experienced higher shape (d = 0.44) and weight (d = 0.39) concerns than OW-BN. Groups did not differ on depression scores. These results were replicated when examining BMI as a continuous predictor across the full sample, with the exception of objective binge eating and driven exercise, which were not significantly associated with BMI. Individuals with BN and comorbid obesity have distinct clinical characteristics. Existing interventions may need to be adapted to meet clinical needs of these individuals.

Using Continuous Glucose Monitoring to Detect and Intervene on Dietary Restriction in Individuals With Binge Eating: The SenseSupport Withdrawal Design Study.

BACKGROUND: Dietary restraint is a key factor for maintaining engagement in binge eating among individuals with binge eating disorder (BED) and bulimia nervosa (BN). Reducing dietary restraint is a mechanism of change in cognitive behavioral therapy (CBT) for individuals with BN and BED. However, many individuals who undergo CBT fail to adequately reduce dietary restraint during treatment, perhaps owing to difficulty in using treatment skills (eg, regular eating) to reduce dietary restraint during their daily lives. The SenseSupport system, a novel just-in-time, adaptive intervention (JITAI) system that uses continuous glucose monitoring to detect periods of dietary restraint, may improve CBT to reduce dietary restraint during treatment by providing real-time interventions. OBJECTIVE: This study aimed to describe the feasibility, acceptability, and initial evaluation of SenseSupport. We presented feasibility, acceptability, target engagement, and initial treatment outcome data from a small trial using an ABAB (A=continuous glucose monitoring data sharing and JITAIs-Off, B=continuous glucose monitoring data sharing and JITAIs-On) design (in which JITAIs were turned on for 2 weeks and then turned off for 2 weeks throughout the treatment). METHODS: Participants (N=30) were individuals with BED or BN engaging in ≥3 episodes of ≥5 hours without eating per week at baseline. Participants received 12 sessions of CBT and wore continuous glucose monitors to detect eating behaviors and inform the delivery of JITAIs. Participants completed 4 assessments and reported eating disorder behaviors, dietary restraint, and barriers to app use weekly throughout treatment. RESULTS: Retention was high (25/30, 83% after treatment). However, the rates of continuous glucose monitoring data collection were low (67.4% of expected glucose data were collected), and therapists and participants reported frequent app-related issues. Participants reported that the SenseSupport system was comfortable, minimally disruptive, and easy to use. The only form of dietary restraint that decreased significantly more rapidly during JITAIs-On periods relative to JITAIs-Off periods was the desire for an empty stomach (t43=1.69; P=.049; Cohen d=0.25). There was also a trend toward greater decrease in overall restraint during JITAs-On periods compared with JITAIs-Off periods, but these results were not statistically significant (t43=1.60; P=.06; Cohen d=0.24). There was no significant difference in change in the frequency of binge eating during JITAIs-On periods compared with JITAIs-Off periods (P=.23). Participants demonstrated clinically significant, large decreases in binge eating (t24=10.36; P<.001; Cohen d=2.07), compensatory behaviors (t24=3.40; P=.001; Cohen d=0.68), and global eating pathology (t24=6.25; P<.001; Cohen d=1.25) from pre- to posttreatment. CONCLUSIONS: This study describes the successful development and implementation of the first intervention system combining passive continuous glucose monitors and JITAIs to augment CBT for binge-spectrum eating disorders. Despite the lower-than-anticipated collection of glucose data, the high acceptability and promising treatment outcomes suggest that the SenseSupport system warrants additional investigation via future, fully powered clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT04126694; https://clinicaltrials.gov/ct2/show/NCT04126694.

Development and Validation of an Eating-Related Eco-Concern Questionnaire.

Eco-concern, the distress experienced relating to climate change, is associated with mental health, yet no study has examined disordered eating related to eco-concern. This study developed and validated a 10-item scale assessing Eating-Related Eco-Concern (EREC). Participants (n = 224) completed the EREC, Climate Change Worry Scale (CCWS), and Eating Disorder Examination-Questionnaire (EDE-Q). Construct validity, convergent validity, and internal consistency were evaluated. Sex differences in EREC were evaluated using t-tests. Associations among the EREC, CCWS, and EDE-Q were evaluated using linear regression models. Sensitivity analyses were conducted in individuals below EDE-Q global score clinical cut-offs. Factor analysis suggested that all items loaded adequately onto one factor. Pearson's correlation and Bland-Altman analyses suggested strong correlation and acceptable agreement between the EREC and CCWS (r = 0.57), but weak correlation and low agreement with the EDE-Q global score (r = 0.14). The EREC had acceptable internal consistency (α = 0.88). No sex difference was observed in the EREC in the full sample; females had a significantly higher mean score than males in sensitivity analysis. The EREC was significantly positively associated with the CCWS and EDE-Q global and shape concern scores, but not in sensitivity analysis. The EREC is a brief, validated scale that can be useful to screen for eating-related eco-concern.

Glucose variability: A physiological correlate of eating disorder behaviors among individuals with binge-spectrum eating disorders.

OBJECTIVES: Elevated glucose variability may be one mechanism that increases risk for significant psychological and physiological health conditions among individuals with binge-spectrum eating disorders (B-EDs), given the impact of eating disorder (ED) behaviors on blood glucose levels. This study aimed to characterize glucose variability among individuals with B-EDs compared with age-matched, sex-matched, and body mass index-matched controls, and investigate the association between frequency of ED behaviors and glucose variability. METHODS: Participants were 52 individuals with B-EDs and 22 controls who wore continuous glucose monitors to measure blood glucose levels and completed ecological momentary assessment surveys to measure ED behaviors for 1 week. Independent samples t-tests compared individuals with B-EDs and controls and multiple linear regression models examined the association between ED behaviors and glucose variability. RESULTS: Individuals with B-EDs demonstrated numerically higher glucose variability than controls (t = 1.42, p = .08, d = 0.43), although this difference was not statistically significant. When controlling for covariates, frequency of ED behaviors was significantly, positively associated with glucose variability (t = 3.17, p = .003) with medium effect size (f2  = 0.25). Post hoc analyses indicated that binge eating frequency was significantly associated with glucose variability, while episodes of 5+ hours without eating were not. DISCUSSION: Glucose variability among individuals with B-EDs appears to be positively associated with engagement in ED behaviors, particularly binge eating. Glucose variability may be an important mechanism by which adverse health outcomes occur at elevated rates in B-EDs and warrants future study. PUBLIC SIGNIFICANCE: This study suggests that some individuals with binge ED and bulimia nervosa may experience elevated glucose variability, a physiological symptom that is linked to a number of adverse health consequences. The degree of elevation in glucose variability is positive associated with frequency of eating disorder behaviors, especially binge eating.

Latent profiles of dietary restraint among individuals with binge-spectrum eating disorders: Associations with eating disorder symptom severity.

OBJECTIVE: The relationship of dietary restraint in increasing risk for binge eating among individuals with binge-spectrum eating disorders (B-EDs) is well established. However, previous research has not yet identified whether these individuals exhibit heterogeneous profiles of dietary restraint and whether these profiles are associated with differences in eating pathology. METHODS: Individuals with B-EDs (N = 290) completed the Eating Disorder Examination. Latent profile analysis was conducted on dietary restraint frequency data, including restriction of overall amount of food consumed, avoidance of eating, desire for an empty stomach, food avoidance, and dietary rules. Identified latent profiles were compared on binge eating frequency, compensatory behaviors frequency, and ED pathology using the three-step procedure. RESULTS: A four-class model of dietary restraint best fit the data. Classes significantly differed in frequency of compensatory behaviors (F[3, 286] = 31.01, p < .001), EDE Eating Concern (F[3, 286] = 14.36, p < .001), EDE Shape Concern (F[3, 286] = 7.06, p < .001), EDE Weight Concern (F[3, 286] = 6.83, p < .001), and ED Pathology (F[3, 286] = 12.86, p < .001), but did not differ in frequency of objective (F[3, 286] = 2.45, p = .06) or subjective binge episodes (F[3, 286] = 1.87, p = .14). DISCUSSION: Individuals with B-EDs exhibit distinct profiles of dietary restraint, which are associated with frequency of compensatory behaviors and severity of ED pathology. PUBLIC SIGNIFICANCE STATEMENT: Individuals with binge-spectrum eating disorders have different patterns of restrictive eating symptoms. These profiles of restrictive eating behaviors are associated with differences in severity of compensatory behaviors and cognitive eating disorder symptoms, like shape and weight dissatisfaction. Understanding the relationships between profiles of restrictive eating behaviors and other eating disorder symptoms may allow for personalization of treatment and improvements in treatment efficacy.

Correcting the reward imbalance in binge eating: A pilot randomized trial of reward re-training treatment.

Behavioral treatments for psychological disorders characterized by reward-driven maladaptive behaviors (e.g., substance use disorder, eating disorders, behavioral addictions) primarily seek to reduce hyper-reward response to disorder-specific stimuli. Suboptimal outcomes for these treatments highlight the need to also target hypo-reward response to day-to-day life activities. The present study sought to conduct an initial test of a novel behavioral treatment, Reward Re-Training (RRT) to target hyper- and hypo-reward response in individuals with binge eating. Individuals with binge eating (N = 23) were randomly assigned to either 10 weeks of outpatient, group-based RRT treatment or a waitlist control. RRT was found to be feasible and acceptable, demonstrated large impacts on both hypo- and hyper-reward response (measured by self-report (pre-to post-treatment ηp2 range 0.38-0.58) and neural activation via fMRI), and was efficacious in reducing eating disorder pathology (ηp2 range 0.40-0.64, including binge eating, ηp2 = 0.64) compared to waitlist control (ηp2 range 0.00-0.04). This pilot data provides preliminary support for the feasibility, acceptability, and effectiveness of a novel treatment targeting reward imbalance for individuals with binge eating. Future evaluations of RRT may benefit from an active treatment comparison condition and a follow-up assessment to examine persistence of positive outcomes.

Sensor technology in eating disorders research: A systematic review.

OBJECTIVE: Sensor technologies offer exciting potential to objectively measure psychopathological correlates of eating pathology and eating disorder (ED) research utilizing sensors has rapidly proliferated in the past several years. The aims of the present review are: (1) characterize the types of sensors that have been utilized in ED research, (2) identify the psychopathological factors relevant to EDs that have been assessed using sensors, (3) describe the data supporting the validity and reliability of these sensors, (4) discuss limitations associated with these sensors, and (5) identify gaps that persist within the ED literature with regard to use of sensor technologies. METHOD: A systematic search was conducted of PubMed, PsycINFO, Web of Science, ProQuest, and "gray" literature sources. Eligible publications were empirical studies that utilized sensors to measure at least one psychological variable among clinical ED populations. RESULTS: Sensors have been utilized with ED samples to measure eating behaviors, physical activity, sleep, autonomic nervous system activity, eyeblink startle response, visual attention, and visual-haptic object integration. The reliability and validity of these sensors varies widely and there are a number of significant gaps that remain in the literature with regard to the types of sensors utilized, context in which sensors have been used, and populations studied. DISCUSSION: The existing literature utilizing sensors within ED research largely support the feasibility and acceptability of these tools. Sensors should continue to be utilized within the field, with a specific focus on examining the reliability and validity of these tools within ED samples and increasing the diversity of samples studied. PUBLIC SIGNIFICANCE STATEMENT: Sensor technologies, such as those included in modern smartwatches, offer new opportunities to measure factors that may maintain or contribute to symptoms of eating disorders. This article describes the types of sensors that have been used in eating disorders research, challenges that may arise in using these sensors, and discusses new applications of these sensors that may be pursued in future research.

Associations between emotion regulation and remission following cognitive behavioural therapy for adults with bulimia nervosa.

OBJECTIVE: Emotion regulation (ER) deficits are associated with illness severity in individuals with bulimia nervosa. We examined whether baseline ER abilities are associated with remission following enhanced cognitive behavioural therapy for eating disorders (CBT-E). METHOD: Participants (N = 50, 85.0% female) receiving CBT-E completed a measure (yielding a global score and six subscale scores) of ER pre-treatment. Remission was assessed by the Eating Disorder Examination at post-treatment and follow-up. Analyses tested associations between baseline ER and behavioural, cognitive, or full remission at post-treatment and three-month follow-up. RESULTS: Lower global ER abilities, measured by the Difficulties in Emotion Regulation Scale, were associated with lower likelihood of behavioural and full, but not cognitive, remission at post-treatment. Specifically, individuals low in emotional clarity and impulse control were less likely to be behaviourally remitted. Those low in emotional acceptance, awareness, clarity, or strategies to manage emotion were less likely to be fully remitted. Global ER scores were not associated with any remission type at follow-up. DISCUSSION: Baseline ER deficits were associated with lower likelihood of behavioural or full remission at post-treatment. However, ER was less associated with remission at follow-up, indicating that ER is most important during treatment. Findings highlight a need for targeted treatments aimed at improving ER.