ArticleJMIR research protocols2026
Using Smartphone-Based Digital Phenotyping to Predict Relapse in Serious Mental Disorders Among Slum Residents in Dhaka, Bangladesh: Protocol for a Machine Learning Study.
Article in JMIR research protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Serious mental illnesses (SMIs) are associated with high relapse rates and limited access to continuous care, particularly in low-resource settings such as urban slums. Traditional clinical monitoring is constrained by accessibility and scalability challenges. Digital phenotyping, through passive smartphone data, offers a novel approach to predict relapse by capturing real-world behavioral changes. Objective: This study aims to evaluate the feasibility and predictive value of smartphone-based digital phenotyping for detecting relapse in individuals with SMIs living in the Korail slum of Dhaka, Bangladesh. Methods: This prospective 6-month cohort study will recruit 430 participants diagnosed with SMIs who own Android (Google LLC) smartphones. Passive data (eg, screen time, mobility, and call or text frequency) will be continuously collected using a custom-built app (DataDoc). Monthly active data, including symptom and functioning assessments, will be collected via self-report and clinical engagement. Machine learning models will integrate these data to detect early warning signs and predict relapse trajectories. Results: This study was funded by the NIHR (National Institute for Health and Care Research; award number NIHR200846) in October 2022. Data collection commenced in August 2025 and is ongoing. A total of 14 participants have been recruited, as of January 2026. Preliminary data analysis is ongoing, with expected results to be published in fall 2026. Conclusions: This study is one of the first to apply smartphone-based digital phenotyping and machine learning for relapse prediction in low- and middle-income countries' slum settings. The findings will inform scalable, low-cost digital interventions to address the mental health treatment gap in underresourced communities.
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