• Using Smartphone-Based Digital...

*Result*: Using Smartphone-Based Digital Phenotyping to Predict Relapse in Serious Mental Disorders Among Slum Residents in Dhaka, Bangladesh: Protocol for a Machine Learning Study.

Title:
Using Smartphone-Based Digital Phenotyping to Predict Relapse in Serious Mental Disorders Among Slum Residents in Dhaka, Bangladesh: Protocol for a Machine Learning Study.
Authors:
Alam N; Warwick Medical School, University of Warwick, B 020, Gibbet Hill Road, Coventry, CV4 7AL, United Kingdom, 07300311294., Das CK; Department of Clinical Psychology, University of Dhaka, Dhaka, Bangladesh., Roy N; Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India., Giacco D; Department of Clinical Psychology, University of Dhaka, Dhaka, Bangladesh.; Coventry and Warwickshire Partnership NHS Trust, Coventry, United Kingdom., Singh SP; Warwick Medical School, University of Warwick, B 020, Gibbet Hill Road, Coventry, CV4 7AL, United Kingdom, 07300311294.; Coventry and Warwickshire Partnership NHS Trust, Coventry, United Kingdom., Jilka S; Warwick Medical School, University of Warwick, B 020, Gibbet Hill Road, Coventry, CV4 7AL, United Kingdom, 07300311294.
Source:
JMIR research protocols [JMIR Res Protoc] 2026 Feb 04; Vol. 15, pp. e79826. Date of Electronic Publication: 2026 Feb 04.
Publication Type:
Clinical Trial Protocol; Journal Article
Language:
English
Journal Info:
Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101599504 Publication Model: Electronic Cited Medium: Internet ISSN: 1929-0748 (Electronic) Linking ISSN: 19290748 NLM ISO Abbreviation: JMIR Res Protoc Subsets: MEDLINE
Imprint Name(s):
Original Publication: Toronto : JMIR Publications
MeSH Terms:
Mental Disorders*/diagnosis , Mental Disorders*/epidemiology , Poverty Areas* , Smartphone* , Mobile Applications* , Predictive Learning Models*, Bangladesh/epidemiology ; Prospective Studies ; Feasibility Studies ; Humans ; Male ; Female ; Recurrence ; Phenotype ; Observational Studies as Topic
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Contributed Indexing:
Keywords: digital phenotyping; low-income; mental health; mobile phone; passive sensing; urban settings
Entry Date(s):
Date Created: 20260204 Date Completed: 20260206 Latest Revision: 20260206
Update Code:
20260206
PubMed Central ID:
PMC12872212
DOI:
10.2196/79826
PMID:
41637756
Database:
MEDLINE

*Further Information*

*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.
(© Nadia Alam, Chayon Kumar Das, Neelabja Roy, Domenico Giacco, Swaran P Singh, Sagar Jilka. Originally published in JMIR Research Protocols (https://www.researchprotocols.org).)*