Personalized Treatment Selection for Digital Eating Disorder Interventions: A Proof‐of‐Concept for the Personalized Advantage Index

OBJECTIVE: Although a range of evidence-based treatments for eating disorders exist, treatment response varies substantially. The ability to match individuals to a treatment which they are most likely to benefit from may help improve treatment efficiency and therapeutic outcomes. The present study applies a treatment selection approach called the personalized advantage index (PAI) and evaluates its utility for matching individuals to a broad versus focused digital program for eating disorder symptoms. METHOD: Data were used from a randomized non-inferiority trial comparing the two interventions (n = 214). Machine learning models (elastic net and random forest) were trained to predict post-intervention symptom severity for a broad and focused digital intervention using 40 self-reported baseline predictors. The PAI was calculated to identify the predicted optimal treatment for each participant. RESULTS: = 0.26). Independent samples t-tests indicated no significant differences in post-intervention outcomes between participants who received their PAI-indicated treatment and those who did not in both the full sample and a subsample with larger predicted differential responses. DISCUSSION: PAI-based treatment matching did not improve post-treatment outcomes in this context. Greater utility of the PAI approach may emerge when applied to different treatment orientations or delivery formats, enabling greater opportunity for differential effects to emerge.