# Predicting psychotic relapse through smartphone-based digital phenotyping

- URL: https://alisanburak.com/en/wcp-2026-posterler/so138
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- Page title: Predicting psychotic relapse through smartphone-based digital phenotyping | WCP 2026 Summary

> Digital phenotyping based on smartphone data may detect early warning signs days or weeks before psychotic relapse; however, its integration into routine clini…

This page was translated automatically from Turkish. [Read the original](https://alisanburak.com/wcp-2026-posterler/so138)

Bu sayfa, 26. Dünya Psikiyatri Kongresi'nde sunulan çalışmanın yapay zekâ desteğiyle hazırlanmış Türkçe özetidir. Çalışmanın sahibi sunan yazarlardır; atıf için orijinal kaynağa başvurun.

SO138

Kısa Sözlü Poster SunumuDijital Psikiyatri ve Yapay ZekâDerleme

# Predicting psychotic relapse through smartphone-based digital phenotyping

PREDICTING PSYCHOTIC RELAPSE USING SMARTPHONE DIGITAL PHENOTYPING

Sunan: Iolanda M. Marques

Ülke: Portugal

Oturum: 2830 - PARALLEL SHORT ORAL POSTER SESSIONS: DIGITAL & AI PSYCHIATRY - Part II

Tarih: Thu, 24.09.2026

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.

[Orijinal postere git](https://cslide.ctimeetingtech.com/wcp26/attendee/eposter/file/577)

Bu çalışmaya nasıl atıf yapılır?

APA 7: Marques, I. M., et al. (2026, September 24). Predicting psychotic relapse using smartphone digital phenotyping [Poster presentation]. 26th WPA World Congress of Psychiatry (WCP 2026), Stockholm, Sweden. https://cslide.ctimeetingtech.com/wcp26/attendee/eposter/file/577

Vancouver: Marques IM, et al. Predicting psychotic relapse using smartphone digital phenotyping [poster]. 26th WPA World Congress of Psychiatry (WCP 2026); 2026 Sep 23-26; Stockholm, Sweden. Available from: https://cslide.ctimeetingtech.com/wcp26/attendee/eposter/file/577

Yazar adları ve başlık biçimi kongre sisteminden otomatik oluşturulmuştur; kullanmadan önce orijinal posterle karşılaştırın.

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