Evidence map›Paper›PMID 33827552›Full record

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.

Sujen Man Maharjan, Anubhuti Poudyal, Alastair van Heerden, Prabin Byanjankar, Ada Thapa, Celia Islam, Brandon A Kohrt, Ashley Hagaman

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
4.7field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. The utility of bluetooth and smartphone technology to detect peer contact.Experimental and clinical psychopharmacology · 2026
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  12. 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 · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 3 countries.

Sujen Man Maharjan *Transcultural Psychosocial Organization (TPO) Nepal, Kathmandu, 44600, Nepal.
Anubhuti Poudyal *Division of Global Mental Health, Department of Psychiatry and Behavioral Sciences, George Washington School of Medicine and Health Sciences, 2120 L St NW Suite 600, Washington, DC, 20037, USA.
Alastair van HeerdenCenter for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa.
Prabin ByanjankarTranscultural Psychosocial Organization (TPO) Nepal, Kathmandu, 44600, Nepal.
Ada ThapaDivision of Global Mental Health, Department of Psychiatry and Behavioral Sciences, George Washington School of Medicine and Health Sciences, 2120 L St NW Suite 600, Washington, DC, 20037, USA.
Celia IslamGeorge Washington School of Medicine and Health Sciences, Washington, DC, 20037, USA.
Brandon A Kohrt *Division of Global Mental Health, Department of Psychiatry and Behavioral Sciences, George Washington School of Medicine and Health Sciences, 2120 L St NW Suite 600, Washington, DC, 20037, USA. bkohrt@gwu.edu.ORCID 0000-0002-3829-4820
Ashley Hagaman *Department of Social and Behavioral Sciences, Yale School of Public Health, Yale University, New Haven, CT, 06510, USA.
George Washington University · USNLR Nepal · NPHuman Sciences Research Council · ZAYale University · US

Funding

Opportunities for suicide prevention integration into task-shifted mental health interventions in low-resourced contextsK01MH125142 · NIMH · YALE UNIVERSITY · PI HAGAMAN, ASHLEY K · 2021 to 2025
$964k
NIMH NIH HHS K01 MH125142
6 · The paper itself

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.

Indexed as

Cell PhoneMental Health ServicesAdolescentAdultChildComputers, HandheldFeasibility StudiesFemaleHumansInfantMothersYoung AdultAdolescentChild healthDepressionDeveloping countriesDigital healthDigital phenotypeMental healthNepalPostpartum depressionPsychotherapy

Identifiers

PMID33827552
PMCPMC8025381
OpenAlexW3099867181

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.