ArticleBMC medical informatics and decision making2021
Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Leveraging artificial intelligence in the prediction, diagnosis and treatment of depression and anxiety among perinatal women in low- and middle-income countries: a systematic review.BMJ mental health · 2025Pooled it
- Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review.JMIR mHealth and uHealth · 2024Pooled it
- The utility of bluetooth and smartphone technology to detect peer contact.Experimental and clinical psychopharmacology · 2026Article
- Evaluation of Wellby, a Cocreated Mobile App and Wearable to Support Stress Management and Overall Well-Being: Mixed Methods Acceptability and Usability Study.JMIR human factors · 2026Article
- Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data.Journal of medical Internet research · 2026Article
- Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study.JMIR formative research · 2025Article
- Digital Health Interventions for Depression and Anxiety in Low- and Middle-Income Countries: Rapid Scoping Review.JMIR mental health · 2025Article
- Exploring the Requirements for an mHealth App to Prevent and Manage Perinatal Anxiety and Depression.Health science reports · 2025Article
- Multidisciplinary perspectives on personalised prevention in youth mental health.Frontiers in digital health · 2025Article
- Association between sleep and physical activity data, and depressive symptoms in Thai elderly.PloS one · 2025Article
- Engaging husbands in a digital mental health intervention to provide tailored counseling for women experiencing postpartum depression: A mixed methods study in Nepal.SSM. Mental health · 2024Article
- Healthcare Professionals' Views on the Use of Passive Sensing and Machine Learning Approaches in Secondary Mental Healthcare: A Qualitative Study.Health expectations : an international journal of public participation in health care and health policy · 2024Article
- Rapid and Online Microvolume Flow-Through Dialysis Probe for Sample Preparation in Veterinary Drug Residue Analysis.Sensors (Basel, Switzerland) · 2024Article
- Article
- Feasibility and utility of mobile health interventions for depression and anxiety in rural populations: A scoping review.Internet interventions · 2024Article
- A collaborative realist review of remote measurement technologies for depression in young people.Nature human behaviour · 2024Review
- Barriers and facilitators of using health information technologies by women: a scoping review.BMC medical informatics and decision making · 2023Article
- A framework for precision "dosing" of mental healthcare services: algorithm development and clinical pilot.International journal of mental health systems · 2023Article
- Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study.JMIR formative research · 2023Article
- Smartphone addiction and its impact on knowledge, cognitive and psychomotor skills among dental students in India: An observational study.Journal of education and health promotion · 2023Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundPassive sensor data from mobile devices can shed light on daily activities, social behavior, and maternal-child interactions to improve maternal and child health services including mental healthcare. We assessed feasibility and acceptability of the Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) platform. The StandStrong passive data collection platform was piloted with adolescent and young mothers, including mothers experiencing postpartum depression, in Nepal.
methodsMothers (15-25 years old) with infants (< 12 months old) were recruited in person from vaccination clinics in rural Nepal. They were provided with an Android smartphone and a Bluetooth beacon to collect data in four domains: the mother's location using the Global Positioning System (GPS), physical activity using the phone's accelerometer, auditory environment using episodic audio recording on the phone, and mother-infant proximity measured with the Bluetooth beacon attached to the infant's clothing. Feasibility and acceptability were evaluated based on the amount of passive sensing data collected compared to the total amount that could be collected in a 2-week period. Endline qualitative interviews were conducted to understand mothers' experiences and perceptions of passive data collection.
resultsOf the 782 women approached, 320 met eligibility criteria and 38 mothers (11 depressed, 27 non-depressed) were enrolled. 38 mothers (11 depressed, 27 non-depressed) were enrolled. Across all participants, 5,579 of the hour-long data collection windows had at least one audio recording [mean (M) = 57.4% of the total possible hour-long recording windows per participant; median (Mdn) = 62.6%], 5,001 activity readings (M = 50.6%; Mdn = 63.2%), 4,168 proximity readings (M = 41.1%; Mdn = 47.6%), and 3,482 GPS readings (M = 35.4%; Mdn = 39.2%). Feasibility challenges were phone battery charging, data usage exceeding prepaid limits, and burden of carrying mobile phones. Acceptability challenges were privacy concerns and lack of family involvement. Overall, families' understanding of passive sensing and families' awareness of potential benefits to mothers and infants were the major modifiable factors increasing acceptability and reducing gaps in data collection.
conclusionPer sensor type, approximately half of the hour-long collection windows had at least one reading. Feasibility challenges for passive sensing on mobile devices can be addressed by providing alternative phone charging options, reverse billing for the app, and replacing mobile phones with smartwatches. Enhancing acceptability will require greater family involvement and improved communication regarding benefits of passive sensing for psychological interventions and other health services. Registration International Registered Report Identifier (IRRID): DERR1-10.2196/14734.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.