Impulsivity and affect reactivity prospectively predict disordered eating attitudes in adolescents: a 6-year longitudinal study.

Eating disorders (EDs) are associated with significant psychological and physical comorbidities, and adolescence is a particularly high-risk time for the development of EDs. Impulsivity (i.e., acting with little conscious judgment or forethought) and affect reactivity (i.e., changes in negative affect in response to a stressor) are hypothesized to contribute to the development of binge/purge ED pathology. The current study is the first to examine the prospective relationships between impulsivity and affect reactivity as predictors of the development of ED attitudes in adolescents over time. Two hundred six adolescents participated in a longitudinal study examining the development of psychopathology. ED attitudes were assessed via the College Eating Disorders Screen annually for 6 years. Baseline impulsivity and affect reactivity were also assessed. Affect reactivity, impulsivity, and their interaction were examined as baseline predictors of changes in ED attitudes over time using latent growth modeling. Results of latent growth modeling indicated that ED attitudes increased over time. The interaction between impulsivity and affect reactivity significantly predicted the slope of ED attitudes, such that the relationship between impulsivity and ED attitudes was strongest for those with elevated levels of affect reactivity. Findings suggest that greater levels of affect reactivity and impulsivity are key risk factors for the development of ED attitudes in adolescents. Subsequent research should examine the relation between affect reactivity and impulsivity in predicting objectively measured ED behaviors, in addition to ED attitudes. Further investigation may implicate affect reactivity and impulsivity as important targets for early intervention to prevent onset of ED symptoms in adolescents.

Just-in-time adaptive interventions: A novel approach for enhancing skill utilization and acquisition in cognitive behavioral therapy for eating disorders.

OBJECTIVE: The most widely researched treatment for bulimia nervosa (BN) and binge-eating disorder (BED) is cognitive behavioral therapy (CBT), a present-focused, active, skill-oriented treatment. However, despite the success of CBT, many patients fail to achieve sufficient rates of skill utilization (i.e., the frequency with which a patient practices or uses therapeutic skills) or adequate skill acquisition (i.e., the ability to successfully perform a skill learned in treatment) by the end of treatment and outcomes suffer as a result. One method for improving skill acquisition and utilization in patients with BN or BED could be the augmentation of in-person treatment with just-in-time adaptive interventions (JITAIs), which use smartphone technology to deliver real-time interventions during app-identified moments of need. The current article discusses how novel JITAI systems that utilize machine learning or other predictive algorithms could be used to detect momentary risk for eating disordered behavior and provide tailored interventions to enhance outcomes. We will consider technologies that may help reduce patient burden and suggest avenues for future research on developing acceptable and effective JITAIs that can be used as an adjunct to CBT protocols.