Up to 60% of individuals with binge-spectrum eating disorders (EDs; characterized by recurrent episodes of binge eating and/or compensatory behaviors) engage in maladaptive exercise which is designed to “compensate for” calories consumed and/or feels compulsive (e.g., continuing to exercise when injured). Engagement in maladaptive exercise further reinforces disordered eating behaviors. At the same time, up to 40% of individuals with binge-spectrum EDs also engage in exercise that is likely adaptive (i.e., neither compensatory, nor compulsive) and recent research suggests that adaptive exercise has the potential to decrease ED symptoms. In order to improve treatment outcomes for binge-spectrum EDs, a more nuanced understanding of both adaptive and maladaptive exercise is imperative. However, extant literature has three main flaws: 1) failure to account for the presence of adaptive exercise in ED populations, 2) extant theories are based on models of either other ED behaviors or addiction (each of which may not capture the full spectrum of risk-factors on their own), and 3) relies on retrospective self-report of both exercise and associated drivers over long periods (e.g., past 3 months). To address these limitations, we tested a supervised machine learning model using 28 days of data from wrist-worn passive sensors and ecological momentary assessment (EMA; 6 prompts per day) to classify exercise episodes as adaptive or maladaptive based on 26 transtheoretical drivers in a sample of 30 individuals with binge-spectrum EDs who exercised at least once per week over the past three months. Findings will inform targeted momentary interventions which can prevent maladaptive and promote adaptive exercise among individuals with eating disorders.

Role: Multiple Principal Investigator
Funding: 
Graduate Student Research Grant, Psi Chi Honor Society in Psychology
Exceptional Student Research Grant, WELL Center, Drexel University