Wearable Sleep Technology in Clinical and Research Settings
Open accessAbstract
: The accurate assessment of sleep is critical to better understand and evaluate its role in health and disease. The boom in wearable technology is part of the digital health revolution and is producing many novel, highly sophisticated and relatively inexpensive consumer devices collecting data from multiple sensors and claiming to extract information about users' behaviors, including sleep. These devices are now able to capture different biosignals for determining, for example, HR and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large data sets (big data), with the potential of offering an unprecedented window on users' health. Unfortunately, little guidance exists within and outside the scientific sleep community for their use, leading to confusion and controversy about their validity and application. The current state-of-the-art review aims to highlight use, validation and utility of consumer wearable sleep-trackers in clinical practice and research. Guidelines for a standardized assessment of device performance is deemed necessary, and several critical factors (proprietary algorithms, device malfunction, firmware updates) need to be considered before using these devices in clinical and sleep research protocols. Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health; however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine.
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Sleep monitoring wearables now incorporate multi-sensor setups using accelerometers, heart rate monitors, and peripheral oxygen saturation sensors to capture comprehensive sleep-related data.
“These devices now incorporate multi-sensor setups, utilizing accelerometers, heart rate monitors, and peripheral oxygen saturation sensors to capture comprehensive sleep-related data [239].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [239]
Sleep monitoring wearable devices have gained significant popularity in consumer health and sleep research in recent years as a non-invasive and convenient tool for tracking sleep patterns and assessing sleep quality.
“Sleep monitoring wearable devices have gained significant popularity in consumer health and sleep research in recent years as a non-invasive and convenient tool for tracking sleep patterns and assessing sleep quality [197,239] (Figure 7d).”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [239]
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“It seems appealing to end users to automatically track the steps they have taken during a day, count the repetitions of a free weights workout, analyze their sleep, or even estimate the calories they have burnt.”
- Authors
- DE ZAMBOTTI, M., CELLINI, N., GOLDSTONE, A., COLRAIN, I. M., & BAKER, F. C.
- Affiliations
- Center for Health Sciences, SRI International, Menlo Park, CA;
- University of Padua (IT)
- SRI International (US)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2019-02-19
- Volume
- 51
- Issue
- 7
- Pages
- 1538-1557
- ISSN
- 0195-9131 (ISSN-L)
- 1530-0315
- PMC
- PMC6579636
- OpenAlex
- W2918103210
- Cited by
- 564
- References
- 102
- Citations by year
- 2026: 125 · 2025: 106 · 2024: 88 · 2023: 89 · 2022: 58 · 2021: 54 · 2020: 29 · 2019: 15
- FWCI
- 5.82
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion, public-domain)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, public-domain)
- Funders
- Dipartimenti di Eccellenza
- Ministero dell’Istruzione, dell’Università e della Ricerca
- Università degli Studi di Padova
- National Institutes of Health
- Dipartimento di Psicologia Generale, Università degli Studi di Padova
- National Institute on Alcohol Abuse and Alcoholism (U01AA021696)
- National Heart, Lung, and Blood Institute (R01HL139652)
- National Institute of Biomedical Imaging and Bioengineering (U54EB020405)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- wearable sleep trackers, consumer wearable devices, sleep assessment, device validation, sleep research, heart rate variability, skin conductance, activity monitoring, proprietary algorithms, equipment failure, firmware updates, big data, digital health
- MeSH terms
- Sleep Latency, Wearable Electronic Devices, Age Factors, Equipment Failure, Humans, Monitoring, Physiologic, Sleep, Sleep Wake Disorders, Wakefulness, Reproducibility of Results, Polysomnography, Biomedical Research, Actigraphy
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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