Can a mobile app improve prognostic accuracy in first episode psychosis? Evidence from ThinkApp using a machine learning approach
| dc.contributor.author | Morales Pillado, Carla | |
| dc.contributor.author | Gutiérrez, Enrique | |
| dc.contributor.author | Navas Loro, María | |
| dc.contributor.author | Mayoral, María | |
| dc.contributor.author | León Quismondo, Leticia | |
| dc.contributor.author | Ochoa, Susana | |
| dc.contributor.author | Arango, Celso | |
| dc.contributor.author | Rubio Abadal, Elena | |
| dc.contributor.author | Ibáñez, Ángela | |
| dc.contributor.author | Calvo, Ana | |
| dc.date.accessioned | 2026-10-29T09:26:01Z | |
| dc.date.available | 2026-10-29T09:26:01Z | |
| dc.date.issued | 2027 | |
| dc.description.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. | en |
| dc.description.filiation | UEM | spa |
| dc.description.impact | 3.7 Q2 JCR 2025 | |
| dc.description.impact | 1.327 Q1 JCR 2025 | |
| dc.description.impact | No data IDR 2025 | |
| dc.description.sponsorship | PSI2017-82542-R | |
| dc.description.sponsorship | Fundación Alicia Koplowitz, XVI 2020 | |
| dc.identifier.citation | Morales-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.2026.10.021 | |
| dc.identifier.doi | 10.1016/j.jpsychires.2026.10.021 | |
| dc.identifier.issn | 0022-3956 | |
| dc.identifier.issn | 1879-1379 | |
| dc.identifier.uri | https://hdl.handle.net/11268/17518 | |
| dc.language.iso | eng | |
| dc.peerreviewed | Si | |
| dc.relation.publisherversion | http://doi.org/10.1016/j.jpsychires.2026.10.021 | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | en |
| dc.rights.accessRights | open access | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject.other | Psicología | |
| dc.subject.sdg | Goal 3: Ensure healthy lives and promote well-being for all at all ages | |
| dc.subject.sdg | Goal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation | |
| dc.subject.unesco | Máquina de aprendizaje | |
| dc.subject.unesco | Psicosis | |
| dc.subject.unesco | Tecnología médica | |
| dc.title | Can a mobile app improve prognostic accuracy in first episode psychosis? Evidence from ThinkApp using a machine learning approach | en |
| dc.type | journal article | |
| dc.type.hasVersion | VoR | |
| dspace.entity.type | Publication |

