The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep
Open accessAbstract
The objectives of this review were to evaluate the use of consumer-targeted wearable and mobile sleep monitoring technology, identify gaps in the literature and determine the potential for use in behavioral interventions. We undertook a scoping review of studies conducted in adult populations using consumer-targeted wearable technology or mobile devices designed to measure and/or improve sleep. After screening for inclusion/exclusion criteria, data were extracted from the articles by two co-authors. Articles included in the search were using wearable or mobile technology to estimate or evaluate sleep, published in English and conducted in adult populations. Our search returned 3897 articles and 43 met our inclusion criteria. Results indicated that the majority of studies focused on validating technology to measure sleep (n = 23) or were observational studies (n = 10). Few studies were used to identify sleep disorders (n = 2), evaluate response to interventions (n = 3) or deliver interventions (n = 5). In conclusion, the use of consumer-targeted wearable and mobile sleep monitoring technology has largely focused on validation of devices and applications compared with polysomnography (PSG) but opportunities exist for observational research and for delivery of behavioral interventions. Multidisciplinary research is needed to determine the uses of these technologies in interventions as well as the use in more diverse populations including sleep disorders and other patient populations.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Proprietary algorithms used for data processing, the lack of access to data by independent scientists, and non-random assignment of device use are also factors that are restricting open engagement between the technology industry and the public at the present time (Baron et al., 2017).”
From A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations (2018) · cites this paper as Baron et al., 2017
Experimental — accuracy not yet measuredIn the citing paper
“Proprietary algorithms used for data processing, the lack of access to data by independent scientists, and non-random assignment of device use are also factors that are restricting open engagement between the technology industry and the public at the present time (Baron et al., 2017).”
From A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations (2018) · cites this paper as Baron et al., 2017
In the citing paper
“Scientific validation may be more achievable in healthy populations compared with populations who have certain health conditions (Baron et al., 2017).”
From A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations (2018) · cites this paper as Baron et al., 2017
In the citing paper
“Proprietary algorithms used for data processing, the lack of access to data by independent scientists, and non-random assignment of device use are also factors that are restricting open engagement between the technology industry and the public at the present time (Baron et al., 2017).”
From A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations (2018) · cites this paper as Baron et al., 2017
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Scoping studies: towards a methodological framework
Arksey, H., & O'Malley, L. (2005) · International Journal of Social Research Methodology · doi:10.1080/1364557032000119616 · Cited by 36,742
A typology of reviews: an analysis of 14 review types and associated methodologies
Grant, M. J., & Booth, A. (2009) · Health Information & Libraries Journal · doi:10.1111/j.1471-1842.2009.00848.x · Cited by 10,817
Guidance for conducting systematic scoping reviews
Peters, M. D. J., Godfrey, C. M., Khalil, H., McInerney, P., Parker, D., & Soares, C. B. (2015) · International Journal of Evidence-Based Healthcare · doi:10.1097/xeb.0000000000000050 · Cited by 7,472
Systematic review of the validity and reliability of consumer-wearable activity trackers
Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-015-0314-1 · Cited by 1,232
Swan, M. (2012) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan1030217 · Cited by 934 · cited 1×
“Movements such as the “quantified self” and the “in ter et of things (IOT)” demonstrate an increasing ability to collect granular data as well as connect behaviors to places, things and social networks [58].”
Jakicic, J. M., Davis, K. K., Rogers, R. J., King, W. C., Marcus, M. D., Helsel, D., Rickman, A. D., Wahed, A. S., & Belle, S. H. (2016) · JAMA · doi:10.1001/jama.2016.12858 · Cited by 621
Espie, C. A., Kyle, S. D., Williams, C., Ong, J. C., Douglas, N. J., Hames, P., & Brown, J. S. L. (2012) · Sleep · doi:10.5665/sleep.1872 · Cited by 550
Ferguson, T., Rowlands, A. V., Olds, T., & Maher, C. (2015) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-015-0201-9 · Cited by 468
Ben-Zeev, D., Scherer, E. A., Wang, R., Xie, H., & Campbell, A. T. (2015) · Psychiatric Rehabilitation Journal · doi:10.1037/prj0000130 · Cited by 429
Ritterband, L. M., Thorndike, F. P., Ingersoll, K. S., Lord, H. R., Gonder-Frederick, L., Frederick, C., Quigg, M. S., Cohn, W. F., & Morin, C. M. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2016.3249 · Cited by 325
Research works printed but not linked: 3
- Veritas Health Innovation. Covidence systematic review software. Melbourne, Australia: Veritas Health Innovation.
- Shambroom, 2010, Objective sleep differences among men and women measured in the home, J Sleep Res, 19, 106
- Shambroom, 2010, Differences in objective sleep quantity and quality in young, middle-aged and older subjects measured in the home, J Sleep Res, 19, 128
Cited by
Sorted by citations
Wearable Sleep Technology in Clinical and Research Settings
DE ZAMBOTTI, M., CELLINI, N., GOLDSTONE, A., COLRAIN, I. M., & BAKER, F. C. (2019) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001947 · Cited by 564
Peake, J. M., Kerr, G., & Sullivan, J. P. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00743 · Cited by 523
Performance of seven consumer sleep-tracking devices compared with polysomnography
Chinoy, E. D., Cuellar, J. A., Huwa, K. E., Jameson, J., Watson, C. H., Bessman, S. C., Hirsch, D. A., Cooper, A. D., Drummond, S. P. A., & Markwald, R. R. (2020) · SLEEP · doi:10.1093/sleep/zsaa291 · Cited by 451
Depner, C. M., Cheng, P., Devine, J. K., Khosla, S., de Zambotti, M., Robillard, R., Vakulin, A., & Drummond, S. P. A. (2019) · SLEEP · doi:10.1093/sleep/zsz254 · Cited by 245
Walch, O., Huang, Y., Forger, D., & Goldstein, C. (2019) · Sleep · doi:10.1093/sleep/zsz180 · Cited by 240
Metadata
- Authors
- Baron, K. G., Duffecy, J., Berendsen, M. A., Cheung Mason, I., Lattie, E. G., & Manalo, N. C.
- Affiliations
- Rush University Medical Center (US)
- University of Illinois Chicago (US)
- Northwestern University (US)
- Harvard University (US)
- Corresponding author
- Baron, Kelly Glazer
- Venue
- Sleep Medicine Reviews
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 40
- Pages
- 151-159
- ISSN
- 1087-0792 (ISSN-L)
- PMC
- PMC6008167
- OpenAlex
- W2778523487
- Cited by
- 186
- References
- 57
- Citations by year
- 2026: 13 · 2025: 22 · 2024: 30 · 2023: 15 · 2022: 23 · 2021: 34 · 2020: 29 · 2019: 17 · 2018: 3
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Sleep Medicine Reviews (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Foundation for the National Institutes of Health (K08 MH112878, 1K23 HL109110, 5T32 HL007909)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Physiology (medical) › Obstructive Sleep Apnea Research
- Keywords
- wearable devices, mobile health, sleep monitoring, consumer health technology, behavioral interventions, sleep disorders, polysomnography comparison
- MeSH terms
- Wearable Electronic Devices, Humans, Sleep Wake Disorders, Reproducibility of Results, 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 ›
Are you an author, or do you see a problem with this page? Request a review