Cited by 406Mobile Health and mHealth ApplicationsDigital Mental Health InterventionsContext-Aware Activity Recognition Systems

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Systematic review of smartphone-based passive sensing for health and wellbeing

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Cornet, Victor P., Holden, Richard J.·2018·Journal of Biomedical Informatics

DOI 10.1016/j.jbi.2017.12.008PMC PMC5793918

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Abstract

Objective To review published empirical literature on the use of smartphone-based passive sensing for health and wellbeing.Material and methods A systematic review of the English language literature was performed following PRISMA guidelines. Papers indexed in computing, technology, and medical databases were included if they were empirical, focused on health and/or wellbeing, involved the collection of data via smartphones, and described the utilized technology as passive or requiring minimal user interaction.Results Thirty-five papers were included in the review. Studies were performed around the world, with samples of up to 171 (median n = 15) representing individuals with bipolar disorder, schizophrenia, depression, older adults, and the general population. The majority of studies used the Android operating system and an array of smartphone sensors, most frequently capturing accelerometry, location, audio, and usage data. Captured data were usually sent to a remote server for processing but were shared with participants in only 40% of studies. Reported benefits of passive sensing included accurately detecting changes in status, behavior change through feedback, and increased accountability in participants. Studies reported facing technical, methodological, and privacy challenges.Discussion Studies in the nascent area of smartphone-based passive sensing for health and wellbeing demonstrate promise and invite continued research and investment. Existing studies suffer from weaknesses in research design, lack of feedback and clinical integration, and inadequate attention to privacy issues. Key recommendations relate to developing passive sensing strategies matching the problem at hand, using personalized interventions, and addressing methodological and privacy challenges.Conclusion As evolving passive sensing technology presents new possibilities for health and wellbeing, additional research must address methodological, clinical integration, and privacy issues. Doing so depends on interdisciplinary collaboration between informatics and clinical experts.

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Background
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Authors: Cornet, V. P., & Holden, R. J.
Affiliations: Indiana University Indianapolis (US)
Regenstrief Institute (US)
Corresponding author: Holden, Richard J.
ORCID: Cornet, Victor P.
Holden, Richard J.
Venue: Journal of Biomedical Informatics
Publisher: Elsevier BV
Published: 2018-01
Volume: 77
Pages: 120-132
ISSN: 1532-0464 (ISSN-L)
DOI: 10.1016/j.jbi.2017.12.008
PMC: PMC5793918
OpenAlex: W2775819279
Cited by: 406
References: 90
Citations by year: 2026: 50 · 2025: 52 · 2024: 51 · 2023: 49 · 2022: 55 · 2021: 52 · 2020: 48 · 2019: 29 · 2018: 20
FWCI: 4.72
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
Locations: Journal of Biomedical Informatics (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Purdue University Indianapolis (Indiana University) (submittedVersion)
Funders: National Institute on Aging
National Institutes of Health (K01AG044439)
Topics: Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords: health monitoring, well-being, smartphone sensors, accelerometry, location data, audio data, usage data, bipolar disorder, schizophrenia, depression, older adults, privacy, clinical integration, personalized interventions
MeSH terms: Behavior Observation Techniques, Data Accuracy, Smartphone, Confidentiality, Health Status, Humans, Mental Health, Health Behavior, Monitoring, Ambulatory, Mobile Applications

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Systematic review of smartphone-based passive sensing for health and wellbeing

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