Reliability of commercially available sleep and activity trackers with manual switch-to-sleep mode activation in free-living healthy individuals
Abstract
Introduction Wearable health devices have become trendy among consumers, but it is not known whether they accurately measure sleep and physical activity parameters. To address this question, we have studied the measured data of two consumer-level activity monitors (Up Move Jawbone® (U) and Withings Pulse 02® (W)) and compared it with reference methods for sleep and activity recordings, namely the Bodymedia SenseWear Pro Armband® actigraph (SWA) and home-polysomnography (H-PSG).Methods Twenty healthy patients were assessed at home, during sleep, with the four devices. An additional 24-h period of recording was then planned during which they wore the 2 trackers and the SWA. Physical activity and sleep parameters obtained with the 4 devices were analyzed.Results Significant correlations with H-PSG were obtained for total sleep time (TST) for all the devices: r=0.48 for W (p=0.04), r=0.63 for U (p=0.002), r=0.7 for SWA (p=0.0003). The best coefficient was obtained with SWA. Significant correlations were also obtained for time in bed (TIB) for U and SWA vs PSG (r=0.79 and r=0.76, p<0.0001 for both) but not for W (r=0.45, p=0.07). No significant correlations were obtained for deep sleep, light sleep, and sleep efficiency (SE) measurements with W, U and SWA. Sleep latency (SL) correlated with H-PSG only when measured against SWA (r=0.5, p=0.02). Physical activity assessment revealed significant correlations for U and W with SWA for step count (both r=0.95 and p<0.0001) and active energy expenditure (EE) (r=0.65 and 0.54; p=0.0006 and p<0.0001). Total EE was also correctly estimated (r=0.75 and 0.52; p<0.0001 and p=0.001).Conclusion Sleep and activity monitors are only able to produce a limited set of reliable measurements, such as TST, step count, and active EE, with a preference for U which performs globally better. Despite the manual activation to sleep mode, U and W were not suitable for giving correct data such as sleep architecture, SE, and SL. In the future, to enhance accuracy of such monitors, researchers and providers have to collaborate to write algorithms based reliably on sleep physiology. It could avoid misleading the consumer.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Nine studies (22 comparisons) examined energy expenditure in free-living settings compared with doubly labeled water and accelerometry.
“Nine studies (22 comparisons) examined energy expenditure in free-living settings compared with the criterion measures of doubly labeled water [104] and accelerometry [29,49,79,101,131,172,174,175].”
From Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review (2020) · cites this paper as [175]
Measurements obtained using the UP device correlate with time in bed (r = 0.79).
“Another study reported that measurements obtained using the UP device correlated with total sleep time (r = 0.63) and time in bed (r = 0.79), but did not correlate with measurements of deep sleep, light sleep or sleep efficiency (Gruwez 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 Gruwez et al., 2017
Measurements obtained using the UP device do not correlate with measurements of deep sleep.
“Another study reported that measurements obtained using the UP device correlated with total sleep time (r = 0.63) and time in bed (r = 0.79), but did not correlate with measurements of deep sleep, light sleep or sleep efficiency (Gruwez 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 Gruwez et al., 2017
Measurements obtained using the UP device do not correlate with measurements of light sleep.
“Another study reported that measurements obtained using the UP device correlated with total sleep time (r = 0.63) and time in bed (r = 0.79), but did not correlate with measurements of deep sleep, light sleep or sleep efficiency (Gruwez 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 Gruwez et al., 2017
Measurements obtained using the UP device correlate with total sleep time (r = 0.63).
“Another study reported that measurements obtained using the UP device correlated with total sleep time (r = 0.63) and time in bed (r = 0.79), but did not correlate with measurements of deep sleep, light sleep or sleep efficiency (Gruwez 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 Gruwez et al., 2017
Measurements obtained using the UP device do not correlate with measurements of sleep efficiency.
“Another study reported that measurements obtained using the UP device correlated with total sleep time (r = 0.63) and time in bed (r = 0.79), but did not correlate with measurements of deep sleep, light sleep or sleep efficiency (Gruwez 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 Gruwez et al., 2017
Cites
Berry, R. B., Budhiraja, R., Gottlieb, D. J., Gozal, D., Iber, C., Kapur, V. K., Marcus, C. L., Mehra, R., Parthasarathy, S., Quan, S. F., et al. (2012) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.2172 · Cited by 5,302 · see citing works
Principles and Practice of Sleep Medicine
Kryger, M. H., Roth, T. G., & Dement, W. C. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-59875-3 · Cited by 5,118 · see citing works
Kushida, C. A., Littner, M. R., Morgenthaler, T., Alessi, C. A., Bailey, D., Coleman, J., Friedman, L., Hirshkowitz, M., Kapen, S., Kramer, M., et al. (2005) · Sleep · doi:10.1093/sleep/28.4.499 · Cited by 2,054 · see citing works
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 · see citing works
The SBSM Guide to Actigraphy Monitoring: Clinical and Research Applications
Ancoli‐Israel, S., Martin, J. L., BLACKWELL, T. L., Buenaver, L. F., Liu, L., Meltzer, L. J., Sadeh, A., Spira, A. P., & Taylor, D. J. (2015) · Behavioral Sleep Medicine · doi:10.1080/15402002.2015.1046356 · Cited by 561 · see citing works
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 · see citing works
van de Water, A. T. M., HOLMES, A., & Hurley, D. A. (2010) · Journal of Sleep Research · doi:10.1111/j.1365-2869.2009.00814.x · Cited by 454 · see citing works
Morgenthaler, T. I., Aurora, R. N., Brown, T., Zak, R., Alessi, C., Boehlecke, B. A., Chesson, A. L., Friedman, L., Kapur, V. K., Maganti, R. K., et al. (2008) · SLEEP · doi:10.1093/sleep/31.1.141 · Cited by 333 · see citing works
Physical Activity and Weight Gain Prevention
Lee, I.-M. (2010) · JAMA · doi:10.1001/jama.2010.312 · Cited by 331 · see citing works
Mantua, J., Gravel, N., & Spencer, R. (2016) · Sensors · doi:10.3390/s16050646 · Cited by 314 · see citing works
Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis
Mercer, K., Li, M., Giangregorio, L., Burns, C., & Grindrod, K. (2016) · JMIR mHealth and uHealth · doi:10.2196/mhealth.4461 · Cited by 304 · see citing works
Movement toward a novel activity monitoring device
Montgomery-Downs, H. E., Insana, S. P., & Bond, J. A. (2012) · Sleep and Breathing · doi:10.1007/s11325-011-0585-y · Cited by 301 · see citing works
Schimpl, M., Moore, C. S., Lederer, C., Neuhaus, A., Sambrook, J., Danesh, J., Ouwehand, W. H., & Däumer, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023299 · Cited by 202 · see citing works
Comparison of Four ActiGraph Accelerometers during Walking and Running
John, D., Tyo, B. M., & Bassett, D. R. (2010) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3181b3af49 · Cited by 183 · see citing works
Validation of Sleep-Tracking Technology Compared with Polysomnography in Adolescents
de Zambotti, M., Baker, F. C., & Colrain, I. M. (2015) · SLEEP · doi:10.5665/sleep.4990 · Cited by 160 · see citing works
Bhat, S., Ferraris, A., Gupta, D., Mozafarian, M., DeBari, V. A., Gushway-Henry, N., Gowda, S. P., Polos, P. G., Rubinstein, M., Seidu, H., et al. (2015) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.4840 · Cited by 130 · see citing works
O’Driscoll, D. M., Turton, A. R., Copland, J. M., Strauss, B. J., & Hamilton, G. S. (2012) · Sleep And Breathing · doi:10.1007/s11325-012-0662-x · Cited by 87 · see citing works
Shin, M., Swan, P., & Chow, C. M. (2015) · Sleep Science · doi:10.1016/j.slsci.2015.02.003 · Cited by 87 · see citing works
Validation and reliability of two activity monitors for energy expenditure assessment
Brazeau, A., Beaudoin, N., Bélisle, V., Messier, V., Karelis, A. D., & RABASA-LHORET, R. P. R. (2014) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2014.11.001 · Cited by 58 · see citing works
Sleep-Related Movement Disorders
Anees, S., Wu, W. P., & Thorpy, M. J. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-378610-4.00386-7 · Cited by 5 · see citing works
Woodman, J. A. (2015) · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Fuller, D., Colwell, E., Low, J., Orychock, K., Tobin, M. A., Simango, B., Buote, R., Van Heerden, D., Luan, H., Cullen, K., et al. (2020) · JMIR mHealth and uHealth · doi:10.2196/18694 · Cited by 715 · see citing works
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 · see citing works
Henriksen, A., Haugen Mikalsen, M., Woldaregay, A. Z., Muzny, M., Hartvigsen, G., Hopstock, L. A., & Grimsgaard, S. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9157 · Cited by 542 · see citing works
Peake, J. M., Kerr, G., & Sullivan, J. P. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00743 · Cited by 523 · see citing works
Shin, G., Jarrahi, M. H., Fei, Y., Karami, A., Gafinowitz, N., Byun, A., & Lu, X. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103153 · Cited by 326 · see citing works
The assessment and management of insomnia: an update
Krystal, A. D., Prather, A. A., & Ashbrook, L. H. (2019) · World Psychiatry · doi:10.1002/wps.20674 · Cited by 256 · see citing works
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 · see citing works
Baron, K. G., Duffecy, J., Berendsen, M. A., Cheung Mason, I., Lattie, E. G., & Manalo, N. C. (2018) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2017.12.002 · Cited by 186 · see citing works
Liang, J., Xian, D., Liu, X., Fu, J., Zhang, X., Tang, B., & Lei, J. (2018) · JMIR mhealth and uhealth · doi:10.2196/11066 · Cited by 145 · see citing works
A systematic review of the accuracy of sleep wearable devices for estimating sleep onset
Scott, H., Lack, L. C., & Lovato, N. (2019) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2019.101227 · Cited by 140 · see citing works
Physiological markers of sleep quality: A scoping review
McCarter, S. J., Hagen, P. T., Louis, E. K. S., Rieck, T. M., Haider, C. R., Holmes, D. R., & Morgenthaler, T. I. (2022) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2022.101657 · Cited by 122 · see citing works
Stress and Strain among Seafarers Related to the Occupational Groups
Oldenburg, M., & Jensen, H.-J. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16071153 · Cited by 106 · see citing works
Smart Environments and Social Robots for Age-Friendly Integrated Care Services
Anghel, I. M., Cioara, T., Moldovan, D., Antal, C., Pop, C. D., Salomie, I., Pop, C. B., & Chifu, V. R. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17113801 · Cited by 77 · see citing works
Sleep and Inflammatory Bowel Disease: An Important Bi-Directional Relationship
Qazi, T., & Farraye, F. A. (2018) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izy334 · Cited by 75 · see citing works
Gruwez, A., Bruyneel, A.-V., & Bruyneel, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210569 · Cited by 64 · see citing works
Self-citationGiurgiu, M., Timm, I., Becker, M. L., Schmidt, S., Wunsch, K., Nissen, R., Davidovski, D., Bussmann, J. B. J., Nigg, C. R., Reichert, M., et al. (2022) · JMIR mhealth and uhealth · doi:10.2196/36377 · Cited by 46 · see citing works
The Future of Sleep Measurements
Arnardóttir, E. S., Íslind, A. S., & Óskarsdóttir, M. (2021) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2021.05.004 · Cited by 45 · see citing works
Validity of Wrist-Worn Activity Trackers for Estimating VO2max and Energy Expenditure
Passler, S., Bohrer, J., Blöchinger, L., & Senner, V. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16173037 · Cited by 43 · see citing works
Oldenburg, M., & Jensen, H.-J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0217904 · Cited by 39 · see citing works
Kankanhalli, A., Saxena, M., & Wadhwa, B. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.12.005 · Cited by 35 · see citing works
Photoplethysmography in Normal and Pathological Sleep
Vulcan, R. S., André, S., & Bruyneel, M. (2021) · Sensors · doi:10.3390/s21092928 · Cited by 35 · see citing works
Self-citationAssessing the performance of a commercial multisensory sleep tracker
Mouritzen, N. J., Larsen, L. H., Lauritzen, M. H., & Kjær, T. W. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243214 · Cited by 33 · see citing works
Matzke, I., Huhn, S., Koch, M., Maggioni, M. A., Munga, S., Muma, J. O., Odhiambo, C. O., Kwaro, D., Obor, D., Bärnighausen, T., et al. (2024) · JMIR mhealth and uhealth · doi:10.2196/54669 · Cited by 21 · see citing works
Sleepiness of day workers and watchkeepers on board at high seas: a cross-sectional study
Oldenburg, M., & Jensen, H.-J. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-028449 · Cited by 20 · see citing works
Thapa-Chhetry, B., ARGUELLO, D. J., John, D., & Intille, S. S. (2022) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002973 · Cited by 17 · see citing works
Psychophysical stress and strain of maritime pilots in Germany. A cross-sectional study
Barbarewicz, F., Jensen, H.-J., Harth, V., & Oldenburg, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221269 · Cited by 14 · see citing works
Can we rely on wearable sleep-tracker devices for fatigue management?
Jaques Reifman, A., Priezjev, N. V., & Vital-Lopez, F. G. (2023) · SLEEP · doi:10.1093/sleep/zsad288 · Cited by 12 · see citing works
Collier, E., Varon, C., Huffel, S. V., & Bogaert, G. (2019) · Neurourology and Urodynamics · doi:10.1002/nau.24215 · Cited by 11 · see citing works
Quigg, M., Bazil, C. W., Boly, M., Louis, E. K. S., Liu, J. S.-H., Ptáček, L. J., Maganti, R. K., Kalume, F., Gluckman, B. J., Pathmanathan, J. S., et al. (2021) · Epiliepsy currents/Epilepsy currents · doi:10.1177/15357597211004566 · Cited by 11 · see citing works
Connectomic disturbances underlying insomnia disorder and predictors of treatment response
Lu, Q., Zhang, W., Yan, H., Mansouri, N., Tanglay, O., Osipowicz, K. Z., Joyce, A. W., Young, I. M., Zhang, X., Doyen, S., et al. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.960350 · Cited by 9 · see citing works
Cárthaigh, S. M., Perry, J. L., & Griffin, C. P. (2022) · Behaviour and Information Technology · doi:10.1080/0144929x.2022.2136533 · Cited by 6 · see citing works
Elements of Effective Population Surveillance Systems for Monitoring Obesity in School Aged Children
Hardy, L. L., & Mihrshahi, S. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17186812 · Cited by 6 · see citing works
Bliwise, D. L., Chapple, C. R., Maislisch, L., Roitmann, E., & Burtea, T. (2020) · SLEEP · doi:10.1093/sleep/zsaa141 · Cited by 6 · see citing works
Association of Behavioral Phenotypes With Changes in Sleep During a Workplace Wellness Program
Waddell, K. J., Changolkar, S., Szwartz, G., Godby, S., & Patel, M. S. (2021) · American Journal of Health Promotion · doi:10.1177/08901171211015089 · Cited by 3 · see citing works
Liaskopoulos, A., Kakouris, V., Liaskopoulos, N., Lappas, A. S., Christodoulou, G. N., & Samara, M. T. (2024) · Sleep Science · doi:10.1055/s-0044-1791238 · Cited by 3 · see citing works
Agostinho, M., Borges, M., Pereira, T. S., Borges, D. F., & Soares, J. I. (2026) · Behavioral Sleep Medicine · doi:10.1080/15402002.2026.2673893 · Cited by 2 · see citing works
Essais sur l'adoption des technologies de quantification de soi : une approche critique
Moya, J. D. (2019) · theses.fr (ABES) · doi:10.70675/71fa5e23zbc1bz4e98zb8baz67f821674d90 · Cited by 2 · see citing works
Real-World Use of Consumer Sleep Devices
Landvatter, J., Ryan, J. T., Depner, C. M., & Baron, K. G. (2026) · CHEST Journal · doi:10.1016/j.chest.2025.10.039 · Cited by 1 · see citing works
Insomnia: An Update on Epidemiology, Diagnosis and Non-pharmacological Management
Francis, A. (2024) · Indian Journal of Sleep Medicine · doi:10.5005/jp-journals-10069-0131 · Cited by 1 · see citing works
van Berkel, M., Beurskens, J., Buters-Ambrosius, N., van der Hagen, E. A. E., Waal, G. H., & Wanten, G. (2025) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2025.01.040 · Cited by 1 · see citing works
Associations Between Daily Step Counts and Sleep Parameters in Parkinson’s Disease: A Scoping Review
Milane, T., Bianchini, E., Chardon, M., Barbieri, F. A., Hansen, C., & Vuillerme, N. (2025) · Italian National Conference on Sensors · doi:10.3390/s25144447 · Cited by 1 · see citing works
McCall, C., & Watson, N. F. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815373-4.00010-1 · Cited by 1 · see citing works
Analysis of a Person’s Movement Activity during Sleep by Bioradiolocation
Davydova, I., Anishchenko, L. N., Коростовцева, Л. С., Викторович, Б. М., & Sviryaev, Y. V. (2020) · Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) · doi:10.1109/usbereit48449.2020.9117715
Karvinen, S. (2020) · Jyväskylä University Digital Archive (University of Jyväskylä)
Technologies to Assess Psycho-Behavioural Symptoms
Emmert, K., & Maetzler, W. (2023) · Practical issues in geriatrics · doi:10.1007/978-3-031-32246-4_8
- Authors
- Gruwez, A., Libert, W., Ameye, L., & Bruyneel, M.
- Affiliations
- Centre Hospitalier Universitaire de Saint-Pierre (BE)
- Institut Jules Bordet (BE)
- Corresponding author
- Bruyneel, Marie
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 102
- Pages
- 87-92
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W2597555856
- Cited by
- 45
- References
- 21
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 2 · 2022: 5 · 2021: 4 · 2020: 7 · 2019: 14 · 2018: 5 · 2017: 1
- FWCI
- 9.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- 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
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- wearable activity trackers, sleep trackers, device reliability, actigraphy, home polysomnography, total sleep time, time in bed, sleep latency, step count, energy expenditure, consumer health devices, home sleep monitoring
- MeSH terms
- Fitness Trackers, Adolescent, Adult, Algorithms, Energy Metabolism, Female, Humans, Male, Prospective Studies, Sleep, Reproducibility of Results, Polysomnography, Monitoring, Ambulatory, Young Adult
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
