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This page is an AI-assisted Turkish summary of the study presented at the 26th World Congress of Psychiatry. The study belongs to the presenting authors; please refer to the original source when citing it.

EV0442

Digital phenotyping in psychiatry: clinical applications and translational challenges

DIGITAL PHENOTYPING IN PSYCHIATRY: CLINICAL APPLICATIONS AND TRANSLATIONAL CHALLENGES

Digital phenotyping holds considerable clinical promise, but it cannot enter clinical practice without prospective validation, standardization, and ethical governance.

This review aimed to summarize the clinical relevance and translational barriers of digital phenotyping, which derives real-time behavioral information from smartphones and wearable devices, across major psychiatric disorders. A targeted narrative review of reviews indexed in PubMed and selected primary studies was conducted, focusing on depressive, anxiety, and schizophrenia spectrum disorders and emphasizing passive sensing, ecological validity, longitudinal monitoring, and relapse prediction. Relevant digital markers included sleep, mobility, sociability, physical activity, speech and voice, and phone use; the table in the poster linked sleep duration and circadian rhythm to mood changes and relapse risk, GPS variability and time spent at home to psychomotor changes and withdrawal, call and message frequency to social isolation, and speech pitch and prosody to affect and negative symptoms. Multimodal data were described as potentially useful for personalized monitoring, early warning signs, measurement-based care, and timely intervention. The main limitations were methodological heterogeneity, missing data, variable adherence, limited external validation and generalizability, privacy concerns, algorithmic bias, and difficulties with workflow integration. The poster proposed a six-stage clinical implementation pathway consisting of patient enrollment, data collection, secure data processing, risk stratification, clinical assessment and intervention, and follow-up, alongside safeguards for privacy, transparency, human oversight, equity, and interoperability. In conclusion, the review emphasized that clinical implementation requires prospective validation, standardized methods, transparent reporting, equitable algorithms, robust privacy safeguards, and ongoing human clinical oversight.

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