Translational Psychiatry· 2026Q1
Predicting relapse risk in first episode psychosis using machine learning and sleep data
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- Q1SCImago
- 2026year
Short summary
A Random Forest model accurately predicted relapse in first episode psychosis (FEP) patients with 72% accuracy (AUC 0.75) using daily smartphone-reported sleep data, identifying insufficient and irregular sleep duration as key risk factors.
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Key points
- A Random Forest classifier predicted relapse in FEP patients with 72% accuracy (AUC 0.75 ± 0.13).
- Daily sleep data (difficulty falling asleep, wake-up frequency, insufficient sleep, duration) were collected via smartphone app over 30 days.
- Insufficient sleep and irregular sleep duration were the strongest predictors of relapse risk.
- An open-access web-based platform was developed to predict relapse risk based on sleep disturbance.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Sleep disturbance is common in First Episode Psychosis (FEP) and is associated with psychosis symptom exacerbation and functional decline, yet it remains unclear which sleep features predict relapse. In this longitudinal study, 269 FEP participants reported daily sleep during the first 30 days of the 12-month follow-up period via a smartphone app, including difficulty falling asleep, wake-up frequency, insufficient sleep, and sleep duration. These sleep characteristics were used to predict relapse and positive symptoms over the 12-month follow-up period, with outcomes assessed using structured interviews and clinical records. A Random Forest classifier was developed and validated using stratified nested cross-validation, achieving an AUC of 0.75 ± 0.13 and accuracy of 0.72 ± 0.16. Insufficient sleep and irregular sleep duration were identified as the strongest predictors of relapse risk. These findings were used to develop an open-access interactive web-based platform (a research prototype not intended for clinical use) to predict risk of relapse based on sleep disturbance, demonstrating the potential for real-time monitoring and personalised relapse prevention.
The authors' abstract, as published at the source. Translational Psychiatry, 2026 · DOI ↗
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Field: Experimental and Cognitive Psychology
Experimental and Cognitive PsychologyPsychology