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Systematic review of smartphone-based passive sensing for health and wellbeing
Open accessAbstract
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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“Multimodal and intensive sampling data streams (for example, experience sampling and passive measures gathered through smartphones and other electronic devices) used in some MBI studies133 and mental health research broadly134,135 might be particularly promising inputs for machine learning models, given that these models can often handle many correlated predictors136.”
From A common factors perspective on mindfulness-based interventions (2022) · cites this paper as [134]
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Metadata
- Authors
- Cornet, V. P., & Holden, R. J.
- Affiliations
- Indiana University Indianapolis (US)
- Regenstrief Institute (US)
- Corresponding author
- Holden, Richard J.
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 77
- Pages
- 120-132
- ISSN
- 1532-0464 (ISSN-L)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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