Digital Phenotyping for Relapse Prediction in Depression and Schizophrenia: A Narrative Review of Signals, Models, and Clinical Translation
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Gansu University of Traditional Chinese Medicine
James Cook University Singapore
Gansu University of Traditional Chinese Medicine
Abstract
Digital phenotyping brings measurement of daily behaviour and physiology into the study of psychiatric relapse. Smartphones capture mobility, communication-related activity, and device interactions, while wearable sensors trace sleep, rest–activity rhythms, and selected autonomic signals. This narrative review synthesises evidence on these approaches in depression and schizophrenia and examines their development into clinically useful warning systems. Smartphone studies demonstrate the value of longitudinal behavioural observation, pilot work in schizophrenia has directly examined relapse-related changes, and prospective actigraphy research in depression links sleep and activity patterns with subsequent relapse. Multimodal models provide a framework for integrating these complementary signals. The central challenge is to turn measurement into dependable decisions: a relapse-warning system needs a defined clinical outcome, a meaningful forecast horizon, reproducible sensor features, independent validation, and an agreed response to alerts. Patient-reported information strengthens the interpretation of passive data, while privacy, consent, equitable access, and usability shape implementation. We propose an evidence-to-care framework connecting digital signals, prospective risk estimation, and clinical action. This approach positions digital phenotyping as a practical component of future longitudinal mental-health care, with clinical effectiveness to be established through prospective implementation studies.
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No new participant-level data were collected or generated during this study. All materials examined in this review were obtained from previously published literature, and the relevant sources are cited in the reference list.
This work is licensed under a Creative Commons Attribution 4.0 International License.