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SO138

Predicting psychotic relapse through smartphone-based digital phenotyping

PREDICTING PSYCHOTIC RELAPSE USING SMARTPHONE DIGITAL PHENOTYPING

Digital phenotyping based on smartphone data may detect early warning signs days or weeks before psychotic relapse; however, its integration into routine clinical practice remains an unresolved challenge.

This review aims to examine the role of smartphone-based digital phenotyping in predicting psychotic relapse, a common challenge in clinical practice. The method is a nonsystematic literature review based on a search of the PubMed database; the poster reports neither a sample nor a quantitative synthesis. The poster states that up to 40% of individuals diagnosed with schizophrenia experience a relapse within one year of discharge despite appropriate treatment, adversely affecting the course of illness. Digital phenotyping is defined as the continuous, moment-by-moment assessment of an individual's behavior and physiology using data obtained from smartphones and connected digital devices. A distinction is made between active data collection, in which data are voluntarily provided through specific tasks (ecological momentary assessment, EMA), and passive data collection, such as location and communication patterns, which requires no user input. Changes in sleep patterns, social withdrawal, anxiety, dysphoria, and attenuated psychotic symptoms are listed as potential early warning signs; ITAREPS, CrossCheck, EMPOWER, and mindLAMP are cited as major projects investigating early indicators of relapse in psychosis. The conference abstract also states that studies detected relapse 3 to 14 days in advance, while some studies focused on the 30-day period preceding relapse. In conclusion, these systems are highlighted as enabling personalized intervention, but their integration into routine clinical workflows is emphasized as essential for their effectiveness.

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