Measures of sleep and cardiac functioning during sleep using a multi-sensory commercially-available wristband in adolescents
Open accessAbstract
To validate measures of sleep and heart rate (HR) during sleep generated by a commercially-available activity tracker against those derived from polysomnography (PSG) in healthy adolescents. Sleep data were concurrently recorded using FitbitChargeHR™ and PSG, including electrocardiography (ECG), during an overnight laboratory sleep recording in 32 healthy adolescents (15 females; age, mean±SD: 17.3±2.5years). Sleep and HR measures were compared between FitbitChargeHR™ and PSG using paired t-tests and Bland-Altman plots. Epoch-by-epoch analysis showed that FitbitChargeHR™ had high overall accuracy (91%), high sensitivity (97%) in detecting sleep, and poor specificity (42%) in detecting wake on a min-to-min basis. On average, FitbitChargeHR™ significantly but negligibly overestimated total sleep time by 8min and sleep efficiency by 1.8%, and underestimated wake after sleep onset by 5.6min (p<0.05). Within FitbitChargeHR™ epochs of sleep, the average HR was 59.3±7.5bpm, which was significantly but negligibly lower than that calculated from ECG (60.2±7.6bpm, p<0.001), with no change in mean discrepancies throughout the night. FitbitChargeHR™ showed good agreement with PSG and ECG in measuring sleep and HR during sleep, supporting its use in assessing sleep and cardiac function in healthy adolescents. Further validation is needed to assess its reliability over prolonged periods of time in ecological settings and in clinical populations.
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
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?
Some studies have used the gold-standard reference method of electrocardiogram, which directly measures heart rate via electrical impulses from the heart and is used for clinical accuracy.
“Although some studies have attempted to address wearable HR accuracy using the gold-standard reference method of ECG, which directly measures HR via electrical impulses from the heart and is used for clinical accuracy38–44”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [40]
On average, wearables slightly and negligibly underestimate absolute heart rate (−0.9 to −7.2 bpm) compared with gold-standard electrocardiogram.
“Current research verifying wearables to the gold-standard ECG, which uses electrodes to directly measure cardiac muscular contractions from electrical activity of the heart, have shown that on average wearables slightly and negligibly underestimate (−0.9 to −7.2 bpm) absolute HR40,41,44,52”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [40]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965 · see citing works
Beckett, A. H., Rowland, M., & TURNER, P. (1965) · The Lancet · doi:10.1016/s0140-6736(65)91037-8 · Cited by 16,917 · see citing works
(2007) · Cited by 6,530 · see citing works
The Role of Actigraphy in the Study of Sleep and Circadian Rhythms
Ancoli-Israel, S., Cole, R., Alessi, C., Chambers, M., Moorcroft, W., & Pollak, C. P. (2003) · Sleep · doi:10.1093/sleep/26.3.342 · Cited by 2,751 · cited 3× · see citing works
“We also computed two additional indices of performance – the predicted value for sleep (PVS, proportion of FitbitChargeHRTM epochs identified correctly as “Sleep” by PSG) and the predicted value for wakefulness (PVW, proportion of FitbitChargeHRTM epochs identified correctly as “Wake” by PSG) [7].”
BackgroundMethodBucholz, K. K., Cadoret, R. J., Cloninger, C. R., Dinwiddie, S. H., HESSELBROCK, V. M., Nürnberger, J. I., Reich, T., Schmidt, I., & Schuckit, M. A. (1994) · Journal of Studies on Alcohol · doi:10.15288/jsa.1994.55.149 · Cited by 1,758 · see citing works
Activity-Based Sleep-Wake Identification: An Empirical Test of Methodological Issues
Sadeh, A., Sharkey, M., & Carskadon, M. A. (1994) · Sleep · doi:10.1093/sleep/17.3.201 · Cited by 1,487 · see citing works
The role and validity of actigraphy in sleep medicine: an update
Sadeh, A. (2011) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2010.10.001 · Cited by 1,415 · 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 Technology of Accelerometry-Based Activity Monitors: Current and Future
CHEN, K. Y., & BASSETT, D. R. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185571.49104.82 · Cited by 996 · see citing works
Martin, J. L., & Hakim, A. D. (2011) · CHEST Journal · doi:10.1378/chest.10-1872 · Cited by 612 · see citing works
Further Validation of Actigraphy for Sleep Studies
de Souza, L., Benedito‐Silva, A. A., Pires, M. L. N., Poyares, D. R., Tufik, S., & Calil, H. M. (2003) · SLEEP · doi:10.1093/sleep/26.1.81 · Cited by 587 · see citing works
Autonomic activity during human sleep as a function of time and sleep stage
Trinder, J., Kleiman, J., Carrington, M., Smith, S., Breen, S., Tan, N., & Kim, Y. (2001) · Journal of Sleep Research · doi:10.1046/j.1365-2869.2001.00263.x · Cited by 507 · 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
Changes in Sleep as a Function of Adolescent Development
Colrain, I. M., & Baker, F. C. (2011) · Neuropsychology Review · doi:10.1007/s11065-010-9155-5 · Cited by 452 · see citing works
Self-citationRelationships between hours of sleep and health-risk behaviors in US adolescent students
McKnight-Eily, L. R., Eaton, D. K., Lowry, R., Croft, J. B., Presley-Cantrell, L., & Perry, G. S. (2011) · Preventive Medicine · doi:10.1016/j.ypmed.2011.06.020 · Cited by 397 · see citing works
Agreement Rates Between Actigraphy, Diary, and Questionnaire for Children's Sleep Patterns
Werner, H., Molinari, L., Guyer, C., & Jenni, O. G. (2008) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/archpedi.162.4.350 · Cited by 384 · see citing works
Wake Detection Capacity of Actigraphy During Sleep
Paquet, J., Kawinska, A., & Carrier, J. (2007) · SLEEP · doi:10.1093/sleep/30.10.1362 · Cited by 345 · see citing works
Direct Comparison of Two New Actigraphs and Polysomnography in Children and Adolescents
Meltzer, L. J., Walsh, C. M., Traylor, J. T., & Westin, A. M. L. (2011) · SLEEP · doi:10.5665/sleep.1608 · Cited by 339 · see citing works
Sleep Quality and Elevated Blood Pressure in Adolescents
Javaheri, S., Storfer‐Isser, A., ROSEN, C. L., & Redline, S. S. (2008) · Circulation · doi:10.1161/circulationaha.108.766410 · Cited by 310 · see citing works
Issues of Validity in Actigraphic Sleep Assessment
Tryon, W. W. (2004) · SLEEP · doi:10.1093/sleep/27.1.158 · Cited by 308 · 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
Brown, S. A., Brumback, T., Tomlinson, K., Cummins, K., Thompson, W. K., Nagel, B. J., De Bellis, M. D., Hooper, S. R., Clark, D. B., Chung, T., et al. (2015) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.2015.76.895 · Cited by 255 · see citing works
Self-citationHeart rate variability in healthy children and adolescents is partially related to age and gender
Silvetti, M. S., Drago, F., & Ragonese, P. (2001) · International Journal of Cardiology · doi:10.1016/s0167-5273(01)00537-x · Cited by 246 · see citing works
Meltzer, L. J., Hiruma, L., Avis, K. T., Montgomery‐Downs, H. E., & Valentin, J. (2015) · SLEEP · doi:10.5665/sleep.4918 · Cited by 228 · 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
Self-citationSleep disturbance and cardiovascular risk in adolescents
Narang, I., Manlhiot, C., Davies-Shaw, J., Gibson, D., Chahal, N., Stearne, K., Fisher, A., Dobbin, S. W., & McCrindle, B. W. (2012) · Canadian Medical Association Journal · doi:10.1503/cmaj.111589 · Cited by 146 · see citing works
Circadian variation and influence of risk factors on heart rate variability in healthy subjects
Mølgaard, H., Sørensen, K. E., & Bjerregaard, P. (1991) · The American Journal of Cardiology · doi:10.1016/0002-9149(91)90653-3 · Cited by 145 · see citing works
Wearable motion sensors to continuously measure real-world physical activities
Dobkin, B. H. (2013) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000026 · Cited by 142 · see citing works
Monitoring human health behaviour in one's living environment: A technological review
Lowe, S., & OLaighin, G. M. (2013) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2013.11.010 · Cited by 139 · see citing works
Evaluation of a consumer fitness-tracking device to assess sleep in adults
de Zambotti, M., Claudatos, S., Inkelis, S., Colrain, I. M., & Baker, F. C. (2015) · Chronobiology International · doi:10.3109/07420528.2015.1054395 · Cited by 127 · see citing works
Self-citationChakko, S. C., Mulingtapang, R., Huikuri, H. V., Kessler, K. M., Materson, B. J., & Myerburg, R. J. (1993) · American Heart Journal · doi:10.1016/0002-8703(93)90535-h · Cited by 121 · see citing works
Sleep and cardiovascular regulation
Trinder, J. C., Waloszek, J. M., Woods, M. J., & Jordan, A. S. (2011) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-011-1041-3 · Cited by 104 · see citing works
Lillehei, A. S., Halcón, L. L., Savik, K., & Reis, R. (2015) · The Journal of Alternative and Complementary Medicine · doi:10.1089/acm.2014.0327 · Cited by 102 · see citing works
An evaluation of wearable activity monitoring devices
Guo, F., Li, Y., Kankanhalli, M. S., & Brown, M. S. (2013) · Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia · doi:10.1145/2509352.2512882 · Cited by 80 · see citing works
Steins, D., Dawes, H., Esser, P., & Collett, J. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-36 · Cited by 71 · see citing works
Consumer Sleep Tracking Devices: A Critical Review
Jeon, L., & Finkelstein, J. (2015) · Studies in health technology and informatics · doi:10.3233/978-1-61499-512-8-458 · Cited by 66 · see citing works
Fitbit Flex: an unreliable device for longitudinal sleep measures in a non-clinical population
Baroni, A., Bruzzese, J., Bartolo, C. A. D., & Shatkin, J. P. (2015) · Sleep And Breathing · doi:10.1007/s11325-015-1271-2 · Cited by 37 · see citing works
Grifantini, K. (2014) · IEEE Pulse · doi:10.1109/mpul.2014.2339252 · Cited by 19 · see citing works
Fitness Tracker to Assess Sleep: Beyond the Market
Poyares, D. R., Hirotsu, C., & Tufik, S. B. (2015) · SLEEP · doi:10.5665/sleep.4966 · Cited by 10 · see citing works
Fischer, S. (2014) · IEEE Pulse · doi:10.1109/mpul.2014.2339251 · Cited by 4 · see citing works
Flagged first, then by citations
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 · see citing works
Self-citationAccuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis
Haghayegh, S., Khoshnevis, S., Smolensky, M. H., Diller, K. R., & Castriotta, R. J. (2019) · Journal of Medical Internet Research · doi:10.2196/16273 · Cited by 446 · see citing works
A validation study of Fitbit Charge 2™ compared with polysomnography in adults
de Zambotti, M., Goldstone, A., Claudatos, S. A., Colrain, I. M., & Baker, F. C. (2017) · Chronobiology International · doi:10.1080/07420528.2017.1413578 · Cited by 420 · see citing works
Self-citationRadin, J. M., Wineinger, N. E., Topol, E. J., & Steinhubl, S. R. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30222-5 · Cited by 300 · see citing works
Nelson, B. W., & Allen, N. B. (2019) · JMIR mHealth and uHealth · doi:10.2196/10828 · Cited by 310 · see citing works
The Sleep of the Ring: Comparison of the ŌURA Sleep Tracker Against Polysomnography
de Zambotti, M., Rosas, L., Colrain, I. M., & Baker, F. C. (2019) · Behavioral Sleep Medicine · doi:10.1080/15402002.2017.1300587 · Cited by 287 · see citing works
Self-citationde Zambotti, M., Goldstein, C. A., Cook, J. D., Menghini, L., Altini, M., Cheng, P., & Robillard, R. (2023) · SLEEP · doi:10.1093/sleep/zsad325 · Cited by 262 · 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
Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research
Nelson, B. W., Low, C. A., Jacobson, N., Areán, P., Torous, J., & Allen, N. B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0297-4 · Cited by 241 · see citing works
Walch, O., Huang, Y., Forger, D., & Goldstein, C. (2019) · Sleep · doi:10.1093/sleep/zsz180 · Cited by 240 · see citing works
How consumer physical activity monitors could transform human physiology research
Wright, S. P., Hall Brown, T. S., Collier, S. R., & Sandberg, K. (2017) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00349.2016 · Cited by 234 · see citing works
Assessment of physiological signs associated with COVID-19 measured using wearable devices
Natarajan, A., Su, H.-W., & Heneghan, C. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00363-7 · Cited by 213 · see citing works
Schuurmans, A. A. T., de Looff, P., Nijhof, K. S., Rosada, C., Scholte, R. H. J., Popma, A., & Otten, R. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01648-w · Cited by 210 · see citing works
Quer, G., Gouda, P., Galarnyk, M., Topol, E. J., & Steinhubl, S. R. (2020) · PLOS ONE · doi:10.1371/journal.pone.0227709 · Cited by 185 · see citing works
A Systematic Review of Sensing Technologies for Wearable Sleep Staging
Imtiaz, S. A. (2021) · Sensors · doi:10.3390/s21051562 · Cited by 164 · see citing works
Mehrabadi, M. A., Azimi, I., Sarhaddi, F., Axelin, A., Niela‐Vilén, H., Myllyntausta, S., Stenholm, S., Dutt, N. D., Liljeberg, P., & Rahmani, A. M. (2020) · JMIR mhealth and uhealth · doi:10.2196/20465 · Cited by 156 · see citing works
Bagot, K. S., Matthews, S. A., Mason, M., Squeglia, L. M., Fowler, J., Gray, K., Herting, M., May, A., Colrain, I., Godino, J., et al. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.03.008 · Cited by 145 · see citing works
Self-citationAssessment of the Fitbit Charge 2 for monitoring heart rate
Benedetto, S., Caldato, C., Bazzan, E., Greenwood, D. C., Pensabene, V., & Actis, P. (2018) · PLOS ONE · doi:10.1371/journal.pone.0192691 · Cited by 149 · see citing works
Kang, S.-G., Kang, J. M., Ko, K.-P., Park, S.-C., Mariani, S., & Weng, J. (2017) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2017.03.009 · Cited by 146 · see citing works
Haghayegh, S., Kang, H.-A., Khoshnevis, S., Smolensky, M. H., & Diller, K. R. (2020) · Physiological Measurement · doi:10.1088/1361-6579/ab86d6 · Cited by 152 · see citing works
Burkart, S. A., Parker, H., Weaver, R. G., Beets, M. W., Jones, A., Adams, E. L., Chaput, J., & Armstrong, B. (2021) · Pediatric Obesity · doi:10.1111/ijpo.12846 · Cited by 136 · see citing works
Soon, S., Svavarsdottir, H. S., Downey, C. L., & Jayne, D. G. (2020) · BMJ Innovations · doi:10.1136/bmjinnov-2019-000354 · Cited by 133 · see citing works
Comparison of Wearable Trackers’ Ability to Estimate Sleep
Lee, J. M., Byun, W., Keill, A. K., Dinkel, D. M., & Seo, Y. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15061265 · Cited by 121 · see citing works
A validation study of the WHOOP strap against polysomnography to assess sleep
Miller, D. J., Lastella, M., Scanlan, A. T., Bellenger, C. R., Halson, S. L., Roach, G. D., & Sargent, C. (2020) · Journal of Sports Sciences · doi:10.1080/02640414.2020.1797448 · Cited by 121 · see citing works
Lee, S. Y., & Kang, J. (2020) · Intensive and Critical Care Nursing · doi:10.1016/j.iccn.2020.102849 · Cited by 121 · see citing works
Rezaei, N., & Grandner, M. A. (2021) · Sleep Health · doi:10.1016/j.sleh.2021.02.008 · Cited by 118 · see citing works
Past, Present, and Future of Multisensory Wearable Technology to Monitor Sleep and Circadian Rhythms
Lujan, M. R., Perez-Pozuelo, I., & Grandner, M. A. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.721919 · Cited by 115 · see citing works
Moreau, D., Kirk, I. J., & Waldie, K. E. (2017) · eLife · doi:10.7554/elife.25062 · Cited by 114 · see citing works
Evaluating reliability in wearable devices for sleep staging
Birrer, V., Elgendi, M., Lambercy, O., & Menon, C. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01016-9 · Cited by 108 · see citing works
Godino, J. G., Wing, D., de Zambotti, M., Baker, F. C., Bagot, K., Inkelis, S., Pautz, C., Higgins, M., Nichols, J., Brumback, T., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237719 · Cited by 107 · see citing works
Self-citationLiang, Z., & Martell, M. A. C. (2018) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-018-0013-1 · Cited by 105 · see citing works
The Meaning of Sleep Quality: A Survey of Available Technologies
Crivello, A., Barsocchi, P., Girolami, M., & Palumbo, F. (2019) · IEEE Access · doi:10.1109/access.2019.2953835 · Cited by 112 · see citing works
Measurement of respiratory rate using wearable devices and applications to COVID-19 detection
Natarajan, A., Su, H.-W., Heneghan, C., Blunt, L., O’Connor, C., & Niehaus, L. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00493-6 · Cited by 99 · see citing works
Sleep in Neurodevelopmental Disorders
Esbensen, A. J., & Schwichtenberg, A. J. (2016) · International review of research in developmental disabilities · doi:10.1016/bs.irrdd.2016.07.005 · Cited by 97 · see citing works
Zhang, Y., Weaver, R. G., Armstrong, B., Burkart, S., Zhang, S., & Beets, M. W. (2020) · Journal of Sports Sciences · doi:10.1080/02640414.2020.1767348 · Cited by 88 · see citing works
Promises and Challenges in the Use of Consumer-Grade Devices for Sleep Monitoring
Roomkham, S., Lovell, D. R., Cheung, J., & Perrin, D. (2018) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2018.2811735 · Cited by 84 · see citing works
Sensors Capabilities, Performance, and Use of Consumer Sleep Technology
de Zambotti, M., Cellini, N., Menghini, L., Sarlo, M., & Baker, F. C. (2020) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2019.11.003 · Cited by 88 · see citing works
Self-citationQin, S., Leong, R. L. F., Ong, J. L., & Chee, M. W. L. (2022) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2022.101734 · Cited by 88 · see citing works
Mizuno, A., Changolkar, S., & Patel, M. S. (2020) · Annual Review of Medicine · doi:10.1146/annurev-med-050919-031534 · Cited by 80 · see citing works
Liang, Z., & Chapa-Martell, M. A. (2019) · JMIR mHealth and uHealth · doi:10.2196/13384 · Cited by 76 · see citing works
Katori, M., Shi, S., Ode, K. L., Tomita, Y., & Ueda, H. R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2116729119 · Cited by 75 · see citing works
Ong, J. L., Tandi, J., Patanaik, A., Lo, J. C.-Y., & Chee, M. W. L. (2019) · Scientific Reports · doi:10.1038/s41598-019-40156-x · Cited by 76 · see citing works
Pulmonary exposure to metal fume particulate matter cause sleep disturbances in shipyard welders
Chuang, H., Su, T.-Y., Chuang, K.-J., Hsiao, T.-C., Lin, H.-L., Hsu, Y.-T., Pan, C., Lee, K., Ho, S., & Lai, C. (2017) · Environmental Pollution · doi:10.1016/j.envpol.2017.09.082 · Cited by 74 · see citing works
Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages
Haghayegh, S., Khoshnevis, S., Smolensky, M. H., Diller, K. R., & Castriotta, R. J. (2019) · Chronobiology International · doi:10.1080/07420528.2019.1682006 · Cited by 73 · see citing works
Use of wearable devices for post-discharge monitoring of ICU patients: a feasibility study
for the WEARable Information Technology for hospital INpatients (WEARIT-IN) study group, Kroll, R. R., McKenzie, E. D., Boyd, J. G., Sheth, P., Howes, D., Wood, M., & Maslove, D. M. (2017) · Journal of Intensive Care · doi:10.1186/s40560-017-0261-9 · Cited by 69 · see citing works
A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep
Miller, D. J., Roach, G. D., Lastella, M., Scanlan, A. T., Bellenger, C. R., Halson, S. L., & Sargent, C. (2021) · Biosensors · doi:10.3390/bios11060185 · Cited by 65 · see citing works
Haghayegh, S., Khoshnevis, S., Smolensky, M. H., & Diller, K. R. (2019) · Chronobiology International · doi:10.1080/07420528.2019.1596947 · Cited by 62 · see citing works
Weaver, R. G., Armstrong, B., Hunt, E. T., Beets, M. W., Brazendale, K., Dugger, R., Turner‐McGrievy, G. M., Pate, R. R., Maydeu‐Olivares, A., Saelens, B. E., et al. (2020) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-020-01052-0 · Cited by 64 · see citing works
A novel machine learning unsupervised algorithm for sleep/wake identification using actigraphy
Li, X., Zhang, Y., Jiang, F., & Zhao, H. (2020) · Chronobiology International · doi:10.1080/07420528.2020.1754848 · Cited by 63 · see citing works
Degroote, L., Hamerlinck, G., Poels, K., Maher, C., Crombez, G., De Bourdeaudhuij, I., Vandendriessche, A., Curtis, R. G., & DeSmet, A. (2020) · JMIR mhealth and uhealth · doi:10.2196/16674 · Cited by 60 · see citing works
Stucky, B., Clark, I., Azza, Y., Karlen, W., Achermann, P., Kleim, B., & Landolt, H. (2021) · Journal of Medical Internet Research · doi:10.2196/26476 · Cited by 58 · see citing works
Meyer, N., Kerz, M., Folarin, A., Joyce, D. W., Jackson, R., Karr, C., Dobson, R., & MacCabe, J. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.8292 · Cited by 58 · see citing works
Teo, J. X., Dávila, S., Yang, C., Hii, A. A., Pua, C. J., Yap, J. J. L., Tan, S. Y., Sahlén, A., Chin, C. W.-L., Teh, B. T., et al. (2019) · Communications Biology · doi:10.1038/s42003-019-0605-1 · Cited by 57 · see citing works
Bustamante, C. M. V., Rodman, A. M., Dennison, M., Flournoy, J. C., Mair, P., & McLaughlin, K. A. (2020) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13234 · Cited by 53 · see citing works
Giddens, N. T., Juneau, P. L., Manza, P., Wiers, C. E., & Volkow, N. D. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2120009119 · Cited by 52 · see citing works
Application Experiences Using IoT Devices in Education
Francisti, J., Balogh, Z., Reichel, J., Magdin, M., Koprda, Š., & Molnár, G. (2020) · Applied Sciences · doi:10.3390/app10207286 · Cited by 53 · see citing works
Pyjamas, Polysomnography and Professional Athletes: The Role of Sleep Tracking Technology in Sport
Driller, M. W., Dunican, I. C., Omond, S. E. T., Boukhris, O. F., Stevenson, S., Lambing, K. A., & Bender, A. M. (2023) · Sports · doi:10.3390/sports11010014 · Cited by 51 · see citing works
Validation of Fitbit Inspire 2TM Against Polysomnography in Adults Considering Adaptation for Use
Lim, S. E., Kim, H. S., Lee, S. W., Bae, K.-H., & Baek, Y. (2023) · Nature and Science of Sleep · doi:10.2147/nss.s391802 · Cited by 50 · see citing works
The Boom in Wearable Technology: Cause for Alarm or Just What is Needed to Better Understand Sleep?
de Zambotti, M., Godino, J., Baker, F. C., Cheung, J., Patrick, K., & Colrain, I. M. (2016) · SLEEP · doi:10.5665/sleep.6108 · Cited by 50 · see citing works
Self-citationDong, X., Yang, S., Guo, Y., Lv, P., Wang, M., & Li, Y. (2022) · PLoS ONE · doi:10.1371/journal.pone.0275287 · Cited by 48 · see citing works
Cook, J. D., Prairie, M. L., & Plante, D. T. (2018) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.7120 · Cited by 47 · see citing works
Menghini, L., Yuksel, D., Goldstone, A., Baker, F. C., & de Zambotti, M. (2021) · Chronobiology International · doi:10.1080/07420528.2021.1903481 · Cited by 45 · see citing works
Self-citationSvensson, T., Chung, U., Tokuno, S., Nakamura, M., & Svensson, A. K. (2019) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2019.109822 · Cited by 47 · see citing works
Ear-EEG for sleep assessment: a comparison with actigraphy and PSG
Tabar, Y. R., Mikkelsen, K. B., Rank, M. L., Hemmsen, M. C., Otto, M., & Kidmose, P. (2020) · Sleep And Breathing · doi:10.1007/s11325-020-02248-1 · Cited by 46 · see citing works
Sleep behavior assessment via smartwatch and stigmergic receptive fields
Alfeo, A. L., Barsocchi, P., Cimino, M. G. C. A., Rosa, D. L., Palumbo, F., & Vaglini, G. (2017) · Personal and Ubiquitous Computing · doi:10.1007/s00779-017-1038-9 · Cited by 44 · see citing works
Burkart, S. A., Beets, M. W., Armstrong, B., Hunt, E. T., Dugger, R., von Klinggraeff, L., Jones, A., Brown, D. E., & Weaver, R. G. (2020) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.8980 · Cited by 43 · see citing works
Heart rate detection by Fitbit ChargeHR™: A validation study versus portable polysomnography
Benedetti, D., Olcese, U., Frumento, P., Bazzani, A., Bruno, S., D’Ascanio, P., Maestri, M., Bonanni, E., & Faraguna, U. (2021) · Journal of Sleep Research · doi:10.1111/jsr.13346 · Cited by 35 · see citing works
Grandner, M. A., & Rosenberger, M. E. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815373-4.00012-5 · Cited by 39 · see citing works
Liang, Z., & Martell, M. A. C. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.665946 · Cited by 40 · see citing works
Azza, Y., Grueschow, M., Karlen, W., Seifritz, E., & Kleim, B. (2019) · SLEEP · doi:10.1093/sleep/zsz310 · Cited by 39 · see citing works
Deviations from normal bedtimes are associated with short-term increases in resting heart rate
Faust, L. J., Feldman, K., Mattingly, S. M., Hachen, D. S., & Chawla, N. V. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0250-6 · Cited by 37 · see citing works
Trivedi, M. H., Fatt, C. C., Jha, M. K., Cooper, C. M., Trombello, J. M., Mason, B. L., Hughes, J. L., Gadad, B. S., Czysz, A. H., Toll, R. T., et al. (2019) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2019.12.004 · Cited by 37 · see citing works
Performance of a Convolutional Neural Network Derived From PPG Signal in Classifying Sleep Stages
Habib, A., Motin, M. A., Penzel, T., Palaniswami, M. S., Yearwood, J. L., & Karmakar, C. K. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3219863 · Cited by 36 · see citing works
Yang, F. N., Picchioni, D., & Duyn, J. H. (2023) · SLEEP · doi:10.1093/sleep/zsad259 · Cited by 42 · see citing works
How Does Fitbit Measure Brainwaves
Liang, Z., & Ploderer, B. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3380994 · Cited by 36 · see citing works
A Novel Wearable Real-Time Sleep Apnea Detection System Based on the Acceleration Sensor
Yüzer, A. H., Sümbül, H., & Polat, K. (2019) · IRBM · doi:10.1016/j.irbm.2019.10.007 · Cited by 35 · see citing works
Dugger, R., Brazendale, K., Hunt, E. T., Moore, J. B., Turner‐McGrievy, G. M., Vogler, K. E., Beets, M. W., Armstrong, B., & Weaver, R. G. (2020) · Pilot and Feasibility Studies · doi:10.1186/s40814-020-00617-x · Cited by 35 · see citing works
Xu, X., Conomos, M. P., Manor, O., Rohwer, J. E., Magis, A. T., & Lovejoy, J. C. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.223 · Cited by 33 · see citing works
Weaver, R. G., Beets, M. W., Perry, M., Hunt, E. T., Brazendale, K., Decker, L., Turner‐McGrievy, G. M., Pate, R. R., Youngstedt, S. D., Saelens, B. E., et al. (2018) · SLEEP · doi:10.1093/sleep/zsy205 · Cited by 33 · see citing works
Deep Sleep and Beeps: Sleep Quality Improvement Project in General Surgery Patients
Allen, R. W., Burney, C. P., Davis, A., Henkin, J. R., Kelly, J. L., Judd, B. G., & Ivatury, S. J. (2021) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2021.02.010 · Cited by 33 · see citing works
Sun, Y., Zhang, H., Yan, Y., Lan, L., Cao, T., Lian, Z., Fan, X., Wargocki, P. L., Zhu, J., & Xu, X. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.111894 · Cited by 33 · see citing works
Current and Future Roles of Consumer Sleep Technologies in Sleep Medicine
Goldstein, C. A. (2020) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2020.05.001 · Cited by 34 · see citing works
Flournoy, J. C., Bryce, N. V., Dennison, M., Rodman, A. M., McNeilly, E. A., Lurie, L. A., Bitrán, D., Reid-Russell, A., Bustamante, C. M. V., Madhyastha, T. M., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120503 · Cited by 30 · see citing works
Lister, N. B., Jebeile, H., Truby, H., Garnett, S. P., Varady, K. A., Cowell, C. T. C. T., Collins, C. E., Paxton, S. J., Gow, M. L., Brown, J. J., et al. (2019) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2019.11.005 · Cited by 32 · see citing works
Application of deep learning to improve sleep scoring of wrist actigraphy
Haghayegh, S., Khoshnevis, S., Smolensky, M. H., & Diller, K. R. (2020) · Sleep Medicine · doi:10.1016/j.sleep.2020.05.008 · Cited by 30 · see citing works
Quality of Sleep Data Validation From the Xiaomi Mi Band 5 Against Polysomnography: Comparison Study
Concheiro-Moscoso, P., Groba, B., Álvarez-Estévez, D., Miranda-Duro, M. D. C., Pousada, T., Nieto-Riveiro, L., Mejuto-Muiño, F. J., & Pereira, J. (2023) · Journal of Medical Internet Research · doi:10.2196/42073 · Cited by 29 · see citing works
Weatherall, J., Paprocki, Y., Meyer, T. M., Kudel, I., & Witt, E. A. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.8122 · Cited by 29 · see citing works
The state of wearable health technologies: a transdisciplinary literature review
Li, C., Lin, S., & Chib, A. I. (2020) · Mobile Media & Communication · doi:10.1177/2050157920966023 · Cited by 29 · see citing works
Mesfin, G., Saleme, E. B., Ademoye, O. A., Kani‐Zabihi, E., Santos, C. A. S., & Ghinea, G. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2992948 · Cited by 28 · see citing works
Chue, A. E., Gunthert, K. C., Kim, R. W., Alfano, C. A., & Ruggiero, A. R. (2018) · Journal of Adolescence · doi:10.1016/j.adolescence.2018.05.006 · Cited by 28 · see citing works
Ode, K. L., Shi, S., Katori, M., Mitsui, K., Takanashi, S., Oguchi, R., Aoki, D., & Ueda, H. R. (2022) · iScience · doi:10.1016/j.isci.2021.103727 · Cited by 27 · see citing works
Armstrong, B., Beets, M. W., Starrett, A., Brazendale, K., Turner‐McGrievy, G. M., Saelens, B. E., Pate, R. R., Youngstedt, S. D., Maydeu‐Olivares, A., & Weaver, R. G. (2020) · SLEEP · doi:10.1093/sleep/zsaa174 · Cited by 26 · see citing works
Benedetti, D., Olcese, U., Bruno, S., Barsotti, M., Maestri, M., Bonanni, E., Siciliano, G., & Faraguna, U. (2022) · Nature and Science of Sleep · doi:10.2147/nss.s352335 · Cited by 23 · see citing works
George, M. J., Rivenbark, J. G., Russell, M. A., Ng'eno, L., Hoyle, R. H., & Odgers, C. L. (2019) · Journal of Research on Adolescence · doi:10.1111/jora.12467 · Cited by 25 · see citing works
Luik, A. I., Machado, P. F., & Espie, C. A. (2018) · npj Digital Medicine · doi:10.1038/s41746-017-0010-4 · Cited by 24 · see citing works
Hermans, F., Arents, E., Blondeel, A., Janssens, W., Cardinaels, N., Calders, P., Troosters, T., Derom, E., & Demeyer, H. (2024) · JMIR mhealth and uhealth · doi:10.2196/56027 · Cited by 23 · see citing works
Li, X., Shen, L., & Califano, R. (2020) · Science and Technology for the Built Environment · doi:10.1080/23744731.2020.1720445 · Cited by 23 · see citing works
Vesterinen, V., Rinkinen, N., & Nummela, A. T. (2020) · JMIR Biomedical Engineering · doi:10.2196/16620 · Cited by 23 · see citing works
Nelson, B. W., Flannery, J., Flournoy, J. C., Duell, N., Prinstein, M. J., & Telzer, E. H. (2021) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13471 · Cited by 19 · see citing works
A validation study of a ballistocardiograph sleep tracker against polysomnography
Kholghi, M., Szollosi, I., Hollamby, M., Bradford, D., & Zhang, Q. (2021) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.9754 · Cited by 23 · see citing works
- Authors
- de Zambotti, M., Baker, F. C., Willoughby, A. R., Godino, J. G., Wing, D., Patrick, K., & Colrain, I. M.
- Affiliations
- SRI International (US)
- University of California San Diego (US)
- Corresponding author
- Colrain, Ian M.
- ORCID
- Venue
- Physiology & Behavior
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 158
- Pages
- 143-149
- ISSN
- 0031-9384 (ISSN-L)
- PMC
- PMC5498752
- OpenAlex
- W2295749803
- Cited by
- 222
- References
- 40
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 28 · 2023: 20 · 2022: 17 · 2021: 32 · 2020: 43 · 2019: 31 · 2018: 14 · 2017: 13 · 2016: 4
- FWCI
- 29.34
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Physiology & Behavior (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Alcohol Abuse and Alcoholism (U01AA021696, U24AA021695)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Circadian rhythm and melatonin
- Keywords
- wearable activity trackers, wristband, sleep monitoring, heart rate during sleep, adolescents, polysomnography validation, electrocardiogram, total sleep time, sleep efficiency, wake after sleep onset
- MeSH terms
- Adolescent, Electrocardiography, Female, Heart Rate, Humans, Male, Sleep, Time Factors, Wakefulness, Polysomnography, Young Adult, Actigraphy, Wireless Technology
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
