JMIR Publications

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.