Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data
This study demonstrates the feasibility and utility of smartphone-based digital phenotyping for predicting mental health risks in nonclinical, school-going adolescents. By integrating active and passive data with advanced machine modeling techniques, this approach shows promise for early detection and scalable intervention strategies in community settings.
Authors
- Teresa Bellido Bel (ORCID: https://orcid.org/0000-0003-3590-6390)
- Dasha Nicholls (ORCID: https://orcid.org/0000-0001-7257-6605)
- Martina Di Simplicio (ORCID: https://orcid.org/0000-0001-5701-9659)
- A. Aldo Faisal (ORCID: https://orcid.org/0000-0003-0813-7207)
- Balasundaram Kadirvelu (ORCID: https://orcid.org/0000-0001-9791-3006)
- Aglaia Freccero (ORCID: https://orcid.org/0009-0002-2072-9838)
Institutions
- Imperial College London (GB)
- University of Bayreuth (DE)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-12-05
- DOI
- https://doi.org/10.2196/72501
- Citations
- 2
- Primary Topic
- Digital Mental Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 3.09