Morales Pillado, CarlaGutiérrez, EnriqueNavas Loro, MaríaMayoral, MaríaLeón Quismondo, LeticiaOchoa, SusanaArango, CelsoRubio Abadal, ElenaIbáñez, ÁngelaCalvo, Ana2026-10-292026-10-292027Morales-Pillado, C., Gutiérrez, E., Navas-Loro, M., Mayoral, M., León-Quismondo, L., Ochoa, S., Arango, C., Rubio-Abadal, E., Ibáñez, Á., & Calvo, A. (2027). Can a mobile app improve prognostic accuracy in first episode psychosis? Evidence from ThinkApp using a machine learning approach. Journal of Psychiatric Research, 204, 249-257. https://doi.org/10.1016/j.jpsychires.2026.10.0210022-39561879-1379https://hdl.handle.net/11268/17518Mobile 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.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Psicologíajournal article10.1016/j.jpsychires.2026.10.021open accessMáquina de aprendizajePsicosisTecnología médicaGoal 3: Ensure healthy lives and promote well-being for all at all agesGoal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation