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Loss of control eating is more likely to occur in the evening and is uniquely associated with distress. No studies have examined the effect of treatment on within-day timing of loss of control eating severity. We examined whether time of day differentially predicted loss of control eating severity at baseline (i.e. pretreatment), end-of-treatment, and 6-month follow-up for individuals with binge-eating disorder (BED), hypothesizing that loss of control eating severity would increase throughout the day pretreatment and that this pattern would be less pronounced following treatment. We explored differential treatment effects of cognitive-behavioral guided self-help (CBTgsh) and Integrative Cognitive-Affective Therapy (ICAT).
Individuals with BED (N = 112) were randomized to receive CBTgsh or ICAT and completed a 1-week ecological momentary assessment protocol at baseline, end-of-treatment, and 6-month follow-up to assess loss of control eating severity. We used multilevel models to assess within-day slope trajectories of loss of control eating severity across assessment periods and treatment type.
Within-day increases in loss of control eating severity were reduced at end-of-treatment and 6-month follow-up relative to baseline. Evening acceleration of loss of control eating severity was greater at 6-month follow-up relative to end-of-treatment. Within-day increases in loss of control severity did not differ between treatments at end-of-treatment; however, evening loss of control severity intensified for individuals who received CBTgsh relative to those who received ICAT at 6-month follow-up.
Findings suggest that treatment reduces evening-shifted loss of control eating severity, and that this effect may be more durable following ICAT relative to CBTgsh.
Family-based treatment (FBT) is an efficacious intervention for adolescents with an eating disorder. Evaluated to a lesser degree among adolescents, enhanced cognitive-behavior therapy (CBT-E) has shown promising results. This study compared the relative effectiveness of FBT and CBT-E, and as per manualized CBT-E, the sample was divided into a lower weight [<90% median body mass index (mBMI)], and higher weight cohort (⩾90%mBMI).
Participants (N = 97) aged 12–18 years, with a DSM-5 eating disorder diagnosis (largely restrictive, excluding Avoidant Restrictive Food Intake Disorder), and their parents, chose between FBT and CBT-E. Assessments were administered at baseline, end-of-treatment (EOT), and follow-up (6 and 12 months). Treatment comprised of 20 sessions over 6 months, except for the lower weight cohort where CBT-E comprised 40 sessions over 9–12 months. Primary outcomes were slope of weight gain and change in Eating Disorder Examination (EDE) Global Score at EOT.
Slope of weight gain at EOT was significantly higher for FBT than for CBT-E (lower weight, est. = 0.597, s.e. = 0.096, p < 0.001; higher weight, est. = 0.495, s.e. = 0.83, p < 0.001), but not at follow-up. There were no differences in the EDE Global Score or most secondary outcome measures at any time-point. Several baseline variables emerged as potential treatment effect moderators at EOT. Choosing between FBT and CBT-E resulted in older and less well participants opting for CBT-E.
Results underscore the efficiency of FBT to facilitate weight gain among underweight adolescents. FBT and CBT-E achieved similar outcomes in other domains assessed, making CBT-E a viable treatment for adolescents with an eating disorder.
While negative affect reliably predicts binge eating, it is unknown how this association may decrease or ‘de-couple’ during treatment for binge eating disorder (BED), whether such change is greater in treatments targeting emotion regulation, or how such change predicts outcome. This study utilized multi-wave ecological momentary assessment (EMA) to assess changes in the momentary association between negative affect and subsequent binge-eating symptoms during Integrative Cognitive Affective Therapy (ICAT-BED) and Cognitive Behavior Therapy Guided Self-Help (CBTgsh). It was predicted that there would be stronger de-coupling effects in ICAT-BED compared to CBTgsh given the focus on emotion regulation skills in ICAT-BED and that greater de-coupling would predict outcomes.
Adults with BED were randomized to ICAT-BED or CBTgsh and completed 1-week EMA protocols and the Eating Disorder Examination (EDE) at pre-treatment, end-of-treatment, and 6-month follow-up (final N = 78). De-coupling was operationalized as a change in momentary associations between negative affect and binge-eating symptoms from pre-treatment to end-of-treatment.
There was a significant de-coupling effect at follow-up but not end-of-treatment, and de-coupling did not differ between ICAT-BED and CBTgsh. Less de-coupling was associated with higher end-of-treatment EDE global scores at end-of-treatment and higher binge frequency at follow-up.
Both ICAT-BED and CBTgsh were associated with de-coupling of momentary negative affect and binge-eating symptoms, which in turn relate to cognitive and behavioral treatment outcomes. Future research is warranted to identify differential mechanisms of change across ICAT-BED and CBTgsh. Results also highlight the importance of developing momentary interventions to more effectively de-couple negative affect and binge eating.
Psychiatric disorders, including eating disorders (EDs), have clinical outcomes that range widely in severity and chronicity. The ability to predict such outcomes is extremely limited. Machine-learning (ML) approaches that model complexity may optimize the prediction of multifaceted psychiatric behaviors. However, the investigations of many psychiatric concerns have not capitalized on ML to improve prognosis. This study conducted the first comparison of an ML approach (elastic net regularized logistic regression) to traditional regression to longitudinally predict ED outcomes.
Females with heterogeneous ED diagnoses completed demographic and psychiatric assessments at baseline (n = 415) and Year 1 (n = 320) and 2 (n = 277) follow-ups. Elastic net and traditional logistic regression models comprising the same baseline variables were compared in ability to longitudinally predict ED diagnosis, binge eating, compensatory behavior, and underweight BMI at Years 1 and 2.
Elastic net models had higher accuracy for all outcomes at Years 1 and 2 [average Area Under the Receiving Operating Characteristics Curve (AUC) = 0.78] compared to logistic regression (average AUC = 0.67). Model performance did not deteriorate when the most important predictor was removed or an alternative ML algorithm (random forests) was applied. Baseline ED (e.g. diagnosis), psychiatric (e.g. hospitalization), and demographic (e.g. ethnicity) characteristics emerged as important predictors in exploratory predictor importance analyses.
ML algorithms can enhance the prediction of ED symptoms for 2 years and may identify important risk markers. The superior accuracy of ML for predicting complex outcomes suggests that these approaches may ultimately aid in advancing precision medicine for serious psychiatric disorders.
Habits are behavioral routines that are automatic and frequent, relatively independent of any desired outcome, and have potent antecedent cues. Among individuals with anorexia nervosa (AN), behaviors that promote the starved state appear habitual, and this is the foundation of a recent neurobiological model of AN. In this proof-of-concept study, we tested the habit model of AN by examining the impact of an intervention focused on antecedent cues for eating disorder routines.
The primary intervention target was habit strength; we also measured clinical impact via eating disorder psychopathology and actual eating. Twenty-two hospitalized patients with AN were randomly assigned to 12 sessions of either Supportive Psychotherapy or a behavioral intervention aimed at cues for maladaptive behavioral routines, Regulating Emotions and Changing Habits (REaCH).
Covarying for baseline, REaCH was associated with a significantly lower Self-Report Habit Index (SRHI) score and significantly lower Eating Disorder Examination-Questionnaire (EDE-Q) global score at the end-of-treatment. The end-of-treatment effect size for SRHI was d = 1.28, for EDE-Q was d = 0.81, and for caloric intake was d = 1.16.
REaCH changed habit strength of maladaptive routines more than an active control therapy, and targeting habit strength yielded improvement in clinically meaningful measures. These findings support a habit-based model of AN, and suggest habit strength as a mechanism-based target for intervention.
Background. There is empirical evidence suggesting that individuals with bulimia nervosa vary considerably in terms of psychiatric co-morbidity and personality functioning. In this study, latent profile analysis was used to attempt to identify clusters of bulimic subjects based on psychiatric co-morbidity and personality.
Method. A total of 178 women with bulimia nervosa or a subclinical variant of bulimia nervosa completed a series of self-report inventories of co-morbid psychopathology and personality, and also provided a buccal smear sample for genetic analyses.
Results. Three clusters of bulimic women were identified: an affective-perfectionistic cluster, an impulsive cluster, and a low co-morbid psychopathology cluster. The clusters showed expected differences on external validation tests with both personality and eating-disorder measures. The impulsive cluster showed the highest elevations on dissocial behavior and the lowest scores on compulsivity, while the affective-perfectionistic cluster showed the highest levels of eating-disorder symptoms. The clusters did not differ on genetic variations of the serotonin transporter gene.
Conclusions. This study corroborates previous findings suggesting that the bulimia nervosa diagnostic category is comprised of three classes of individuals based on co-morbid psychopathology and personality. These differences may have significant etiological and treatment implications.
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