Predictors, mediators, and moderators of response to digital interventions for eating disorders: A systematic review

Digital interventions show promise as an effective prevention or self-management option for eating disorders (EDs). However, it remains unclear how, for whom, and through what mechanisms they work in this population, as a synthesis of outcome predictors, moderators, and mediators is lacking. This systematic review synthesized empirical research investigating predictors, mediators, and moderators of response to digital interventions for EDs.

Six databases were searched (PROSPERO CRD42022295565) for studies that assessed predictors, moderators, or mediators of response (i.e., uptake, drop-out, engagement, and symptom level change) to a digital prevention or treatment program for EDs. Variables were grouped into several overarching categories (demographic, symptom severity, psychological, etc.) and were synthesized qualitatively across samples without a formally diagnosed ED (typically prevention-focused) and samples with a formally diagnosed ED (typically treatment-focused).

Eighty-six studies were included. For studies recruiting samples without a formal diagnosis (n = 70 studies), most predictors explored were statistically unrelated to outcome, although participant age, baseline symptom severity, confidence to change, motivation, and program engagement showed preliminary evidence of prognostic potential. No robust moderators or mediators were identified. Few studies recruiting samples with a formal diagnosis emerged (n = 16), of which no reliable predictors, moderators, or mediators were identified.

It remains unclear how, for whom, and under what circumstances digital programs targeting EDs work. We offer several recommendations for future research with the aim of advancing understanding of client characteristics and intervention elements that signal success from this intervention modality.

Digital interventions have shown potential as an effective, scalable, and accessible intervention option for EDs. However, responsiveness varies, so advancing understanding of predictors, mediators, and moderators of outcome to digital interventions for EDs is needed. Such knowledge is important for enabling safe and efficient treatment matching, and for informing future development of effective digital interventions.