The Sleep of the Ring: Comparison of the ŌURA Sleep Tracker Against Polysomnography
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Objective/Background: To evaluate the performance of a multisensor sleep-tracker (ŌURA ring) against polysomnography (PSG) in measuring sleep and sleep stages. Participants: Forty-one healthy adolescents and young adults (13 females; Age: 17.2 ± 2.4 years). Methods: Sleep data were recorded using the ŌURA ring and standard PSG on a single laboratory overnight. Metrics were compared using Bland-Altman plots and epoch-by-epoch (EBE) analysis. Results: Summary variables for sleep onset latency (SOL), total sleep time (TST), and wake after sleep onset (WASO) were not different between ŌURA ring and PSG. PSG-ŌURA discrepancies for WASO were greater in participants with more PSG-defined WASO (p < .001). Compared with PSG, ŌURA ring underestimated PSG N3 (~20 min) and overestimated PSG REM (~17 min; p < .05). PSG-ŌURA differences for TST and WASO lay within the ≤ 30 min a-priori-set clinically satisfactory ranges for 87.8% and 85.4% of the sample, respectively. From EBE analysis, ŌURA ring had a 96% sensitivity to detect sleep, and agreement of 65%, 51%, and 61%, in detecting “light sleep” (N1), “deep sleep” (N2 + N3), and REM sleep, respectively. Specificity in detecting wake was 48%. Similarly to PSG-N3 (p < .001), “deep sleep” detected with the ŌURA ring was negatively correlated with advancing age (p = .001). ŌURA ring correctly categorized 90.9%, 81.3%, and 92.9% into PSG-defined TST ranges of < 6 hr, 6–7 hr, > 7 hr, respectively. Conclusions: Multisensor sleep trackers, such as the ŌURA ring have the potential for detecting outcomes beyond binary sleep–wake using sources of information in addition to motion. While these first results could be viewed as promising, future development and validation are needed.
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Commonly used features are supported by prior work to be relevant to the effects of DM.
“commonly used features supported by prior work to be relevant to the effects of DM31–33,35–38,45,49”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [45]
The Discussion (page not provided) describes sleep parameters and heart-rate variability as useful features for distinguishing sleep stages. It does not link those features to the effects of DM. The Methods > Participants section (page not provided) says participants with diabetes were excluded. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe Oura Ring sleep stage algorithm has been validated against polysomnography and electroencephalogram.
“The Oura Ring predicts sleep stages with a proprietary algorithm that has been validated against Polysomnography (PSG) and Electro-encephalogram (EEG) by Miller et al., Ghorbani et al., and de Zambotti et al. 45–47.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [45]
Specificity of the Oura ring in detecting wake was 48%.
“Specificity in detecting wake was 48%.”
From Wearables and the Medical Revolution (2018) · cites this paper as [35]
Similar to PSG, deep sleep detected with the Oura ring was negatively correlated with advancing age.
“Similar to PSG, ‘deep sleep’ detected with the Oura ring was negatively correlated with advancing age [35].”
From Wearables and the Medical Revolution (2018) · cites this paper as [35]
The Oura Ring has been shown to have high agreement with polysomnography (PSG) in the whole night estimation of total sleep time (TST), sleep onset latency (SOL) and wake after sleep onset (WASO).
“The Oura Ring has been shown to have high agreement with polysomnography (PSG; the gold-standard for measuring sleep) in the whole night estimation of total sleep time (TST), sleep onset latency (SOL) and wake after sleep onset (WASO) (47).”
From Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data (2021) · cites this paper as [47]
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References 35 · Cited by 287
- Authors
- de Zambotti, M., Rosas, L., Colrain, I. M., & Baker, F. C.
- Affiliations
- Center for Health Sciences, SRI International, Menlo Park, California
- SRI International (US)
- ORCID
- Baker, Fiona C.
- Venue
- Behavioral Sleep Medicine
- Publisher
- Informa UK Limited
- Published
- 2017-03-21
- Volume
- 17
- Issue
- 2
- Pages
- 124-136
- ISSN
- 1540-2002 (ISSN-L)
- 1540-2010
- PMC
- PMC6095823
- OpenAlex
- W2602011993
- Cited by
- 287
- References
- 35
- Citations by year
- 2026: 42 · 2025: 48 · 2024: 40 · 2023: 39 · 2022: 33 · 2021: 32 · 2020: 30 · 2019: 15 · 2018: 6 · 2017: 2
- FWCI
- 18.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Behavioral Sleep Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- the National Consortium on Alcohol and NeuroDevelopment in Adolescence (AA021696)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Physiology › Obstructive Sleep Apnea Research
- Keywords
- polysomnography, sleep stage classification, REM sleep detection, wake after sleep onset, total sleep time, sleep latency, sleep-wake detection, adolescents and young adults, wearable sleep monitoring
- MeSH terms
- Adolescent, Adult, Female, Humans, Male, Sleep, Reproducibility of Results, Polysomnography, Young Adult, Actigraphy
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