Accepted for/Published in: JMIR Research Protocols
Date Submitted: Jun 29, 2025
Date Accepted: Dec 12, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Digital Phenotyping and Serious Mental Disorders, Predicting Relapse among Slum Residents in Dhaka, Bangladesh (A Machine Learning Study): Rationale and Protocol
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 (DP), through passive smartphone data, offers a novel approach to predict relapse by capturing real-world behavioural 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 smartphones. Passive data (e.g., screen time, mobility, call/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. ML models will integrate these data to detect early warning signs and predict relapse trajectories.
Results:
The study is currently in the data collection phase. Outcomes will include validated relapse prediction models and evaluation of data completeness, participant engagement, and user-level barriers to implementation in slum settings.
Conclusions:
This study is one of the first to apply smartphone-based DP and ML for relapse prediction in LMIC slum settings. The findings will inform scalable, low-cost digital interventions to address the mental health treatment gap in under-resourced communities.
Citation
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Copyright
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