Can a mobile app improve prognostic accuracy in first episode psychosis? Evidence from ThinkApp using a machine learning approach

Abstract

Mobile app–based interventions for first-episode psychosis (FEP) can extend care, yet heterogenous engagement obscures which components drive benefit. Machine learning models integrating clinical variables with app-usage data may clarify trajectories and guide design. The aims of the study were to identify user profiles and usage patterns in a mobile intervention for youth with FEP (ThinkApp) and test whether adding usage features improves 6-month outcome prediction.