A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults
Open accessAbstract
The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura Ring Generation 2, WHOOP 3.0 and Somfit, for assessing sleep. The secondary aim was to examine the validity of the six devices for assessing heart rate and heart rate variability during, or just prior to, night-time sleep. Fifty-three adults (26 F, 27 M, aged 25.4 ± 5.9 years) spent a single night in a sleep laboratory with 9 h in bed (23:00-08:00 h). Participants were fitted with all six wearable devices-and with polysomnography and electrocardiography for gold-standard assessment of sleep and heart rate, respectively. Compared with polysomnography, agreement (and Cohen's kappa) for two-state categorisation of sleep periods (as sleep or wake) was 88% (κ = 0.30) for Apple Watch; 89% (κ = 0.35) for Garmin; 87% (κ = 0.44) for Polar; 89% (κ = 0.51) for Oura; 86% (κ = 0.44) for WHOOP and 87% (κ = 0.48) for Somfit. Compared with polysomnography, agreement (and Cohen's kappa) for multi-state categorisation of sleep periods (as a specific sleep stage or wake) was 53% (κ = 0.20) for Apple Watch; 50% (κ = 0.25) for Garmin; 51% (κ = 0.28) for Polar; 61% (κ = 0.43) for Oura; 60% (κ = 0.44) for WHOOP and 65% (κ = 0.52) for Somfit. Analyses regarding the two-state categorisation of sleep indicate that all six devices are valid for the field-based assessment of the timing and duration of sleep. However, analyses regarding the multi-state categorisation of sleep indicate that all six devices require improvement for the assessment of specific sleep stages. As the use of wearable devices that are valid for the assessment of sleep increases in the general community, so too does the potential to answer research questions that were previously impractical or impossible to address-in some way, we could consider that the whole world is becoming a sleep laboratory.
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The 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 [46]
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Viswanath, V. K., Hartogenesis, W., Dilchert, S., Pandya, L., Hecht, F. M., Mason, A. E., Wang, E. J., & Smarr, B. L. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01125-5 · Cited by 20 · see citing works
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“Still, based on the post hoc G*Power analysis, the sample size is sufficient to allow a solid (Power [1-β err prob] 42–100%) evaluation; additionally, comparable studies have used a similar number of subjects (n = 26–56) [22,47,50,56,90].”
Deriving Accurate Nocturnal Heart Rate, rMSSD and Frequency HRV from the Oura Ring
Liang, T., Yılmaz, G., & Soon, C. S. (2024) · Sensors · doi:10.3390/s24237475 · Cited by 19 · see citing works
Bressler, S., Neely, R., Yost, R. M., & Wang, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-63385-1 · Cited by 19 · see citing works
Jasinski, S. R., Presby, D. M., Grosicki, G. J., Capodilupo, E. R., & Lee, V. H. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01394-0 · Cited by 18 · see citing works
“TheWHOOP cardiovascular and sleep measures have been validated in a general population against gold-standard electrocardiogram and polysomnography measures witha low degree of bias and low precisionerrors 30,31 .”
Consumer Wearable Sleep Trackers
Chiang, A. A.-P., & Khosla, S. (2023) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2023.05.005 · Cited by 19 · see citing works
Herberger, S., Aurnhammer, C., Bauerfeind, S., Bothe, T. L., Penzel, T., & Fietze, I. (2025) · Scientific Reports · doi:10.1038/s41598-025-93774-z · Cited by 17 · see citing works
“Both studies found good performance in sleep/wake discrimination but, in line with our results, found that sleep staging classification requires improvement (see also convergent results for the 2nd Generation Oura ring 17,18 ).”
Evaluating sleep in covert encephalopathy with wearable technology: results from the WATCHES study
Buckholz, A. P., Clarke, L., Paik, P. K., Jesudian, A. B., Schwartz, R. E., Krieger, A. C., Rosenblatt, R. E., & Brown, R. S. (2023) · Hepatology Communications · doi:10.1097/hc9.0000000000000028 · Cited by 17 · see citing works
“Despite multiple validation studies and meta-analyses suggesting that wearables have good performance for sleep stage detection against PSG, this is the first study evaluating this technology in cirrhosis.[22,33] Future research should confirm that wearables, and the WHOOP in particular, maintain similar fidelity agai…”
BackgroundSubclinical epileptiform activity and sleep disturbances in Alzheimer's disease
Devulder, A., Macea, J., Kalkanis, A., Winter, F. D., Vandenbulcke, M., Vandenberghe, R. R., Testelmans, D., Bossche, M. J. A. V. D., & Paesschen, W. V. (2023) · Brain and Behavior · doi:10.1002/brb3.3306 · Cited by 16 · see citing works
“The reliability of specific sleep staging with wearable devices is still quite low (Miller et al., 2022) and requires improvements before sleep parameters, like decreased REM%, could become a noninvasive, easy-to-measure biomarker to identify those patients at risk for/with SEA.”
BackgroundSchork, I. G., Manzo, I. A., de Oliveira, M. R. B., da Costa, F. V., Young, R. J., & de Azevedo, C. S. (2023) · Animals · doi:10.3390/ani13091467 · Cited by 16 · see citing works
Using New Technologies and Wearables for Characterizing Sleep in Population-based Studies
Jaiswal, S. J., Pawelek, J., Warshawsky, S., Quer, G., Trieu, M., Pandit, J. A., & Owens, R. L. (2024) · Current Sleep Medicine Reports · doi:10.1007/s40675-023-00272-7 · Cited by 16 · see citing works
“Most sleep architecture information from wearables is gained from accelerometry but also heart rate variability, which changes when transitioning from one sleep stage to the next [27].”
Gabb, V. G., Blackman, J., Morrison, H. D., Li, H., Kendrick, A. H., Turner, N. L., Greenwood, R., Biswas, B., Heslegrave, A., & Coulthard, E. J. (2025) · JMIR Aging · doi:10.2196/72824 · Cited by 14 · see citing works
Wearable Technology in Cardiology: Advancements, Applications, and Future Prospects
Jena, N. K., Singh, P. K., Chandramohan, D., Garapati, H. N., Gummadi, J., Mylavarapu, M., Shaik, B. F., Nanjundappa, A., Apala, D. R., Gahona, C. C. T., et al. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm39025 · Cited by 19 · see citing works
“Some common wearable sleep devices include Fitbit, Ocura ring, Whoop strap, Apple watch, and Garmin, which offer a wide range of functional capabilities of sleep analytics [100,101].”
The accuracy of Apple Watch measurements: a living systematic review and meta-analysis
Lambe, R., Baldwin, M., O’Grady, B., Schumann, M., Caulfield, B., & Doherty, C. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02238-1 · Cited by 17 · see citing works
“For sleep, however, agreement with polysomnography was lower for Apple Watch than for Whoop, Fitbit, and Garmin 88,100 .”
Joshi, J., Wang, K., & Cho, Y. (2023) · Sensors · doi:10.3390/s23198244 · Cited by 20 · see citing works
“With consumer wearables, data is first acquired and processed using the manufacturer’s proprietary algorithms and then exported directly from the manufacturer’s applications and often including remote servers [59].”
Methodvan Putten, L. D., Bamford, K. E., Veleslavov, I., & Wegerif, S. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-05848-8 · Cited by 15 · see citing works
Alzueta, E., Gombert, M., Javitz, H. S., Yüksel, D., Perez-Amparan, E., Camacho, L., Kiss, O., de Zambotti, M., Sattari, N. S., Pena, A., et al. (2024) · Journal of Biological Rhythms · doi:10.1177/07487304241265018 · Cited by 13 · see citing works
“The performance of agreement between night-time HR and RMSSD measures from Oura ring gen 2 consisted in Intraclass correlations of 0.85 and 0.63 respectively, compared to gold standard electrocardiography (Miller et al., 2022).”
InfluentialAn IoT-Based Noncontact ECG System: Sole of the Feet/Hands Palm
Irfan, M., Shun, P., Felix, B. B., Mustafa, N., Abbasi, S. F., Nahli, A., Subaşı, A., Westerlund, T., & Chen, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3283037 · Cited by 15 · see citing works
“This guarantees consistent and reliable data across a multitude of studies [27], [37], [38].”
BackgroundKhushhal, A. A., Abdelaal, A. A. M., & Elsayed, M. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s449071 · Cited by 14 · see citing works
“52 Also, excellent agreement (r = 0.98) was observed between the Apple Watch-HR and standard monitoring system-HR (lower limit: −5.84 and upper limit: 5.63), with the difference between the two mean readings being clinically non-significant (p = 0.93), and subgroup comparisons were also…”
MethodResultInfluentialPerformance Investigation of Somfit Sleep Staging Algorithm
McMahon, M., Goldin, J., Kealy, E. S., Wicks, D. J., Zilberg, E., Freeman, W., & Aliahmad, B. (2024) · Nature and Science of Sleep · doi:10.2147/nss.s463026 · Cited by 14 · see citing works
Insights about travel-related sleep disruption from 1.5 million nights of data
Willoughby, A. R., Vallat, R., Ong, J. L., & Chee, M. W. L. (2025) · SLEEP · doi:10.1093/sleep/zsaf077 · Cited by 14 · see citing works
Grosicki, G. J., Fielding, F., Kim, J., Chapman, C., Olaru, M., von Hippel, W., & Holmes, K. E. (2025) · Sensors · doi:10.3390/s25082437 · Cited by 16 · see citing works
“…and sleep measures were validated against gold-standard electrocardiogram and polysomnography, demonstrating a low degree of bias and high accuracy (e.g., <20 min bias and precision errors for sleep duration; 0.7 beats per minute for heart rate; 4.7 ms bias for heart rate variability) [9,18,19].”
InfluentialNemane, S. G., Sarad, V. M., Tulaskar, D. P., Marode, T. P., Bhangdiya, V. K., Agrawal, L., Avthankar, A., Kondylakis, H., Kulkarni, M. B., Bhaiyya, M. L., et al. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10629-0 · Cited by 13 · see citing works
“The fact that they collect data 24/7 and are embedded in our daily life makes them generate enormous time-series data offering real-time snapshots of the individual’s health state [121–123].”
Insomnia in primary care: Considerations for screening, assessment, and management
Yamamoto, M., Lim, C. T., Huang, H., Spottswood, M., & Huang, H. Y. (2023) · The Journal of Medicine Access · doi:10.1177/27550834231156727 · Cited by 15 · see citing works
Pearson, M. A., Weakley, J. J. S., McKay, A. K. A., Russell, S., Leota, J., Johnston, R. D., MINAHAN, C. L., Harris, R., Burke, L. M., & Halson, S. L. (2025) · European Journal of Sport Science · doi:10.1002/ejsc.70038 · Cited by 12 · see citing works
“The Oura ring is a validated (Miller et al. 2022) finger‐worn device containing a triaxial accelerometer and a photo-plethysmograph.”
InfluentialWei, J. C. J., van den Broek, T. J., van Baardewijk, J. U., van Stokkum, R., Kamstra, R. J. M., Rikken, L. F. A., Gijsbertse, K., Uzunbajakava, N. E., & van den Brink, W. J. (2024) · Scientific Reports · doi:10.1038/s41598-024-73557-8 · Cited by 12 · see citing works
“Other wearable designs have also been explored, including a watch or ring format 13,14 and integration into textiles 15 .”
BackgroundMethodGrosicki, G. J., Kim, J., Fielding, F., Jasinski, S. R., Chapman, C., von Hippel, W., & Holmes, K. E. (2024) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00809.2024 · Cited by 12 · see citing works
Reithe, H., Marty, B., Torrado, J. C., Førsund, E., Husebo, B. S., Erdal, A., Kverneng, S. U., Sheard, E. V., Tzoulis, C., & Pătraşcu, M. (2025) · BioMedical Engineering OnLine · doi:10.1186/s12938-025-01353-0 · Cited by 11 · see citing works
“The ring has shown promising results for measuring MET [71], HR during sleep [72], and mixed results for measuring sleep stages when compared to polysomnography [73, 74].”
Qin, S., Ng, K. K., Soon, C. S., Chua, X. Y., Zhou, J. H., Koh, W., & Chee, M. W. L. (2025) · Sleep Medicine · doi:10.1016/j.sleep.2025.106569 · Cited by 12 · see citing works
“The accuracy of Oura ring in detecting sleep and wake activities has been validated against the gold standard Poly-somnography by many independent research groups [21 – 26].”
Roberts, S., Owen, P. J., Warmington, S. A., Trevenen, J., Caeyenberghs, K., McDonald, S. J., Facer‐Childs, E. R., McKay, A., Bradshaw, E., Kara, S., et al. (2025) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2025.102072 · Cited by 11 · see citing works
de Jager, E., Caulfield, B., Angelidi, E., Namee, B. M., & Holden, S. (2026) · Sports Medicine · doi:10.1007/s40279-025-02388-y · Cited by 20 · see citing works
“This similarity supports the ecological validity of wearable devices for detecting autonomic variation observed across the menstrual cycle in real-world settings [39].”
Grasser, L. R., Erjo, T., Goodwin, M. S., Naim, R., German, R. E., White, J., Cullins, L. M., Tseng, W., Stoddard, J., & Brotman, M. A. (2023) · BMC Psychiatry · doi:10.1186/s12888-023-05421-4 · Cited by 11 · see citing works
“More broadly, previous studies have provided support for the use of wearable biosensors, like the Empatica devices as well as the Apple Watch and Fitbit, to provide valid and reliable indices of HR and HRV [39, 56, 66, 102].”
BackgroundThe Interplay of Sports and Nutrition in Neurological Health and Recovery
Clemente‐Suárez, V. J., Redondo-Flórez, L., Beltrán-Velasco, A. I., Belinchón-deMiguel, P., Ramos‐Campo, D. J., Curiel-Regueros, A., Martín-Rodríguez, A., & Tornero-Aguilera, J. F. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13072065 · Cited by 11 · see citing works
“Additionally, they aimed to assess the accuracy of these same devices in monitoring heart rate and heart rate variability either during nighttime sleep or in the period just before it [144].”
BackgroundAdaimi, R., Thigpen, N., Clausel, A., Gotlieb, N., Patel, K., & de Zambotti, M. (2025) · JMIR mhealth and uhealth · doi:10.2196/80213 · Cited by 10 · see citing works
Polysomnography in Transition: Reassessing Its Role in the Future of Sleep Medicine
Léger, D., Mutti, C., Rouen, A., & Parrino, L. (2025) · Journal of Sleep Research · doi:10.1111/jsr.70217 · Cited by 10 · see citing works
Leota, J., Messman, B., Le, F., Jasinski, S. R., Capodilupo, E. R., Facer‐Childs, E. R., & Wiley, J. F. (2026) · SLEEP · doi:10.1093/sleep/zsag040 · Cited by 10 · see citing works
“%) with polysomnography for two-stage categorization of sleep and wake [ 15, 16].”
InfluentialLee, M. P., Kim, D. W., Mayer, C., Walch, O., & Forger, D. B. (2024) · Journal of Biological Rhythms · doi:10.1177/07487304241288607 · Cited by 10 · see citing works
“…recent comprehensive review of sleep stage prediction algorithms using wearable data (Birrer et al., 2024), we found 4 algorithms (Lee et al., 2019; Walch et al., 2019; Chee et al., 2021; Miller et al., 2022) that analyzed wearable data collected like our Apple Watch data (Supplementary Table S7).”
Pang, Z., Su, L., & Zhang, Y. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1557533 · Cited by 10 · see citing works
Apple watch accuracy in monitoring health metrics: a systematic review and meta-analysis
Choe, J.-P., & Kang, M. (2025) · Physiological Measurement · doi:10.1088/1361-6579/adca82 · Cited by 10 · see citing works
Leota, J., Czeisler, M. É., Le, F., Presby, D. M., Capodilupo, E. R., Scott, H., Wiley, J. F., Drummond, S. P. A., Rajaratnam, S. M. W., & Facer‐Childs, E. R. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2420846122 · Cited by 10 · see citing works
Non-esterified erythrocyte linoleic acid, arachidonic acid, and subjective sleep outcomes
Sanders, A. E., Wallace, E. D., Ehrmann, B. M., Soma, P. S., Shaikh, S. R., Preisser, J. S., Ohrbach, R. K., Fillingim, R. B., & Slade, G. D. (2023) · Prostaglandins Leukotrienes and Essential Fatty Acids · doi:10.1016/j.plefa.2023.102580 · Cited by 10 · see citing works
Artese, A., Rawat, R., & Sung, A. D. (2023) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/mco.0000000000000970 · Cited by 10 · see citing works
“Devices made by Apple, Garmin, Polar, Samsung, andWhoop (WHOOP, Boston, Massachusetts, USA) show moderate-to-high accuracy for assessing HRV at rest and during sleep for parameters that include the average of normal-normal (N-N) intervals (AVNN), standard deviation of N-N intervals (SDNN), root mean square of successi…”
Wearable derived cardiovascular responses to stressors in free-living conditions
Presby, D. M., Jasinski, S. R., & Capodilupo, E. R. (2023) · PLoS ONE · doi:10.1371/journal.pone.0285332 · Cited by 9 · see citing works
“HRmotionless and HRV derived from the WHOOP device have been validated against gold-standard measures from electrocardiograms [11].”
MethodVanhanen, J., Kujala, J., Liljeström, M., Kalso, E. A., Virkkala, J., & Harno, H. (2023) · Brain and Behavior · doi:10.1002/brb3.3252 · Cited by 9 · see citing works
“No Zambotti et al., 2019; Kinnunen, 2016; Miller et al., 2022; Roberts etal.,2020),andOuraappearstodetectsleepandwakeaccurately.”
BackgroundBaseline Sleep Characteristics in NCAA Division I Collegiate Athletes
Goldman, J., Donohoe, B., Hatamiya, N. S., Boland, N. F., Vail, J., Holmes, K. E., Presby, D. M., Kim, J., & Duffaut, C. J. (2024) · Clinical Journal of Sport Medicine · doi:10.1097/jsm.0000000000001205 · Cited by 9 · see citing works
“19 Objective overnight sleep variables included time spent in light sleep, rapid eye movement (REM) sleep, and slow-wave sleep (SWS) cycles, time spent awake in bed after falling asleep (wake time), and TST defined as the sum of light sleep, REM, and SWS durations.”
Continuous Glycemic Monitoring Enabled by A Wi‐Fi Energy‐Harvesting Wearable Sweat‐Sensing Patch
Mirlou, F., Abbasiasl, T., Mirzajani, H., Istif, E., Akhtar, M. J., Cakır, C., & Beker, L. (2024) · Advanced Materials Technologies · doi:10.1002/admt.202301583 · Cited by 9 · see citing works
A digital, real-time, history-based sleep-management tool to enhance alertness
Song, Y. M., Choi, S. J., Lim, D., Wijaya, R. H., Jang, H. J., Park, H. R., Joo, E. Y., & Kim, J. K. (2025) · SLEEP · doi:10.1093/sleep/zsaf160 · Cited by 9 · see citing works
Sleep Health in the Student-Athlete: A Narrative Review of Current Research and Future Directions
Wilson, S. M. B., Gooderick, J., Driller, M. W., Jones, M. I., Draper, S. B., & Parker, J. K. (2025) · Current Sleep Medicine Reports · doi:10.1007/s40675-025-00341-z · Cited by 9 · see citing works
“Comparing results across studies is challenging due to differing definitions of variables and the reduced accuracy of actigraphy in detecting wakefulness versus sleep [48].”
A Longitudinal Multimodal Dataset of Type 1 Diabetes
Alsuhaymi, A., Bilal, A., García, D. G., Kongdee, R., Lubasinski, N., Thabit, H., Nutter, P. W., & Harper, S. (2025) · Scientific Data · doi:10.1038/s41597-025-05695-1 · Cited by 8 · see citing works
“Comparative studies show that under optimal conditions, Garmin’s sleep tracking is consistent with more specialized devices 32,33 .”
Loro, F. L., Martins, R., Ferreira, J. B., de Araújo, C. L. P., Prade, L. R., Both, C. B., Nobre, J. C., Monteiro, M. B., & Lago, P. D. (2024) · JMIR Biomedical Engineering · doi:10.2196/57373 · Cited by 10 · see citing works
Christensen, J. C., Blackburn, B. E., Kapron, C. R., Pelt, C. E., Peters, C. L., Archibeck, M. J., Anderson, L. A., & Gililland, J. M. (2025) · The Journal of Arthroplasty · doi:10.1016/j.arth.2025.01.042 · Cited by 8 · see citing works
Validation and Application of Wrist-Worn Wearable Physiological Measures in Psychotherapy
Mink, F., Lutz, W., Rafaeli, E., Hehlmann, M. I., Schwartz, B., & Uhl, J. (2025) · Cognitive Therapy and Research · doi:10.1007/s10608-025-10601-5 · Cited by 7 · see citing works
“For instance, Miller et al. (2022) compared the HR and HRV data of several wearables to gold-standard ECG measurements during night-time sleep.”
Biorhythms derived from consumer wearables predict postoperative complications in children
Hua, R., Carter, M. M., O’Brien, M. K., Pitt, J. B., Kwon, S., Manworren, R. C. B., Oscherwitz, G. L., Edobor, A., Chen, A., Ghomrawi, H., et al. (2025) · Science Advances · doi:10.1126/sciadv.adv2643 · Cited by 9 · see citing works
Zhang, H., Xu, J., Lu, X., & Xu, M. (2025) · Journal of Nursing Management · doi:10.1155/jonm/1278057 · Cited by 10 · see citing works
“Wearable devices, capable of continuously monitoring physiological parameters such as heart rate, blood pressure, and sleep patterns, provide real-time data that enable healthcare providers to remotely track the health status of elderly patients [8, 9].”
Cutsem, J. V., & Pattyn, N. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1028294 · Cited by 8 · see citing works
Effectiveness of the Apple Watch as a mental health tracker
Adil, M., Atiq, I., & Younus, S. (2024) · Journal of Global Health · doi:10.7189/jogh.14.03010 · Cited by 8 · see citing works
“The Apple Watch thus provides an easily accessible and widely acceptable method for people to monitor mental health and maintain mental well-being [19].”
MethodLiu, J., Ito, S., Ngo, T. M., Lawate, A., Ong, Q. C., Fox, T. E., Chang, S. Y., Phung, D., Nair, E., Palaiyan, M., et al. (2024) · Digital Health · doi:10.1177/20552076241241244 · Cited by 8 · see citing works
“77 Nonetheless, in the case of the Fitbit Inspire 2, the model used in this study, recent comparisons with polysomnography revealed no signi fi cant differences in SE, TIB and WASO, although it signi fi cantly overestimated TST. 74”
MethodOvercoming boundaries: Interdisciplinary challenges and opportunities in cognitive neuroscience
Brignol, A., Paas, A., Castro, L. C. S., St-Onge, D., Beltrame, G., & Coffey, E. B. J. (2024) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2024.108903 · Cited by 9 · see citing works
- Authors
- Miller, D. J., Sargent, C., & Roach, G. D.
- Affiliations
- Central Queensland University (AU)
- Corresponding author
- Roach, Gregory D.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-08-22
- Volume
- 22
- Issue
- 16
- Pages
- 6317
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9412437
- OpenAlex
- W4292962286
- Cited by
- 328
- References
- 21
- Citations by year
- 2026: 93 · 2025: 106 · 2024: 83 · 2023: 43 · 2022: 3
- FWCI
- 17.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Australian Institute of Sport
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- wearable devices, sleep monitoring, heart rate, heart rate variability, device validation, sleep stages, polysomnography, Apple Watch, Oura Ring, WHOOP, nocturnal sleep, healthy adults, electrocardiogram
- MeSH terms
- Wearable Electronic Devices, Adult, Heart Rate, Humans, Sleep, Sleep Stages, Polysomnography
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