Predicting responsiveness to a dialectical behaviour therapy skills training app for recurrent binge eating: A machine learning approach.

Objective: Smartphone applications (apps) show promise as an effective and scalable intervention modality for disordered eating, yet responsiveness varies considerably. The ability to predict user responses to app-based interventions is currently limited. Machine learning (ML) techniques have shown potential to improve prediction of complex clinical outcomes. We applied ML techniques to predict responsiveness to a dialectical behaviour therapy-based smartphone app for recurrent binge eating.

Method: Data were collected as part of a randomised controlled trial (RCT). The present sample was based on data from 576 participants with recurrent binge eating. 10 common classification and regression approaches were used to predict outcomes that represent key stages of the user experience, including initial intervention uptake, app adherence, study drop-out, and symptom change. Models were developed using 69 self-reported baseline variables (i.e., demographic, clinical, psychological) and several app usage variables (i.e., number of modules completed) as predictors.

Results: All models, using only baseline predictors, performed sub-optimally at predicting engagement (AUCs = 0.48-0.61; R = 0.00-0.04) and symptom level change (R = 0.00-0.07). Incorporating usage data improved prediction of study dropout (AUC = 0.69-0.76).

Conclusion: ML models were unable to accurately predict responsiveness using self-reported baseline predictors alone. Predicting outcomes with greater precision may require consideration of how predictors change over time and interact with a user's context. Modelling usage pattern data appears to improve prediction of dropout, highlighting the potential value of tracking intervention usage to identify individuals at risk of disengagement.