Matthew Fuller‐Tyszkiewicz

Objective Theoretical models highlight body image disturbances as central to the maintenance of eating disorders, with empirical evidence finding negative body image to be a robust predictor of treatment nonresponse, relapse and symptom persistence. Accessible, scalable, and inexpensive interventions that can effectively target negative body image across the eating disorders are needed. We developed an online single session intervention (SSI) for negative body image and evaluated its acceptability and efficacy in a randomized controlled trial in individuals with recurrent binge eating. Method Participants with recurrent binge eating were randomly assigned to the SSI ( n = 99) or a waitlist ( n = 101). Assessments were conducted at baseline, 4 week follow‐up, and 8 week follow‐up. Results 81% of participants accessed the SSI, demonstrating reasonable uptake. However, issues with attrition were apparent; a 32% study dropout rate was observed at posttest, while a 58% dropout rate was observed at follow‐up. 87% of participants who completed satisfaction measures would recommend the SSI. SSI participants experienced greater improvements in negative body image at both 4 ( d = −0.65) and 8 week ( d = −0.74) follow‐up. Significant between‐group effects were also observed on most other secondary symptom measures, though no differences were found for motivations and help‐seeking beliefs and intentions. Conclusion Body image‐focused self‐guided SSIs for recurrent binge are largely accepted by those who are retained in the trial, but are associated with significant attrition. Although SSIs are not intended to replace standard treatment, they may help with short‐term symptom management and could play an important role in eating disorder care. Public Significance Single session interventions (SSI) are a potentially accessible, scalable, and cost‐effective way to deliver evidence‐based treatment content to people with eating disorders. This study shows that an online (SSI) designed to target body image among people with recurrent binge eating is accepted and produced short‐term symptom relief.

Objective Our ability to predict responsiveness to digital interventions for eating disorders has thus far been poor, potentially for three reasons: (1) there has been a narrow set of predictors explored; (2) prediction has mostly focused on symptom change, ignoring other aspects of the user journey (uptake, early engagement); and (3) there is an excessive focus on the unique effects of predictors rather than the combined contributions of a predictor set. We evaluated the univariate and multivariate effects of outcome predictors in the context of a randomized trial ( n = 398) of digitally delivered interventions for recurrent binge eating. Method Thirty baseline variables were selected as predictors, ranging from specific symptoms, to key protective factors, to technological acceptance, and to online treatment attitudes. Outcomes included uptake, early engagement, and remission. Univariate ( d ) and multivariate ( D ) standardized mean differences were calculated to estimate the individual and combined effects of predictors, respectively. Results At the univariate level, few predictors produced an effect size larger than what is considered small ( d > .20) across outcomes. However, our multivariate approach enhanced prediction ( Ds = .65 to 1.12), producing accuracy rates greater than chance (63%–71% accuracy). Less than half of the chosen variables proved to be useful in contributing to predictions in multivariate models. Conclusion Findings suggest that accuracy in outcome prediction from digitally delivered interventions may be better driven by the aggregation of many small effects rather than one or several largely influential predictors. Replication with different data streams (sensor, neuroimaging) would be useful. Public Significance Our ability to predict who will and will not benefit from digital interventions for eating disorders has been poor. We highlight the viability of a multivariate approach to outcome prediction, whereby prediction may be better driven by the aggregation of many small effects rather than one or a few influential predictors.