Evaluation of a consumer fitness-tracking device to assess sleep in adults
Open accessAbstract
Wearable fitness-tracker devices are becoming increasingly available. We evaluated the agreement between Jawbone UP and polysomnography (PSG) in assessing sleep in a sample of 28 midlife women. As shown previously, for standard actigraphy, Jawbone UP had high sensitivity in detecting sleep (0.97) and low specificity in detecting wake (0.37). However, it showed good overall agreement with PSG with a maximum of two women falling outside Bland-Altman plot agreement limits. Jawbone UP overestimated PSG total sleep time (26.6 ± 35.3 min) and sleep onset latency (5.2 ± 9.6 min), and underestimated wake after sleep onset (31.2 ± 32.3 min) (p's < 0.05), with greater discrepancies in nights with more disrupted sleep. The low-cost and wide-availability of these fitness-tracker devices may make them an attractive alternative to standard actigraphy in monitoring daily sleep-wake rhythms over several days.
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?
Compared with polysomnography, the UP device overestimates sleep onset latency (5.2 ± 9.6 min).
“One study reported that compared with polysomnography, the UP device has high sensitivity for detecting sleep (0.97), and low specificity for detecting wake (0.37), whereas it overestimates total sleep time (26.6 ± 35.3 min) and sleep onset latency (5.2 ± 9.6 min), and underestimates wake after sleep onset (31.2 ± 32.3 min) (de Zambotti et al., 2015).”
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 de Zambotti et al., 2015
Compared with polysomnography, the UP device underestimates wake after sleep onset (31.2 ± 32.3 min).
“One study reported that compared with polysomnography, the UP device has high sensitivity for detecting sleep (0.97), and low specificity for detecting wake (0.37), whereas it overestimates total sleep time (26.6 ± 35.3 min) and sleep onset latency (5.2 ± 9.6 min), and underestimates wake after sleep onset (31.2 ± 32.3 min) (de Zambotti et al., 2015).”
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 de Zambotti et al., 2015
Compared with polysomnography, the UP device has high sensitivity for detecting sleep (0.97).
“One study reported that compared with polysomnography, the UP device has high sensitivity for detecting sleep (0.97), and low specificity for detecting wake (0.37), whereas it overestimates total sleep time (26.6 ± 35.3 min) and sleep onset latency (5.2 ± 9.6 min), and underestimates wake after sleep onset (31.2 ± 32.3 min) (de Zambotti et al., 2015).”
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 de Zambotti et al., 2015
Compared with polysomnography, the UP device has low specificity for detecting wake (0.37).
“One study reported that compared with polysomnography, the UP device has high sensitivity for detecting sleep (0.97), and low specificity for detecting wake (0.37), whereas it overestimates total sleep time (26.6 ± 35.3 min) and sleep onset latency (5.2 ± 9.6 min), and underestimates wake after sleep onset (31.2 ± 32.3 min) (de Zambotti et al., 2015).”
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 de Zambotti et al., 2015
Compared with polysomnography, the UP device overestimates total sleep time (26.6 ± 35.3 min).
“One study reported that compared with polysomnography, the UP device has high sensitivity for detecting sleep (0.97), and low specificity for detecting wake (0.37), whereas it overestimates total sleep time (26.6 ± 35.3 min) and sleep onset latency (5.2 ± 9.6 min), and underestimates wake after sleep onset (31.2 ± 32.3 min) (de Zambotti et al., 2015).”
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 de Zambotti et al., 2015
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
(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 4× · see citing works
“Next, sensitivity (proportion of PSG sleep epochs scored as ‘‘sleep’’ by Jawbone UP) and specificity (proportion of PSG wake epochs scored as ‘‘wake’’ by Jawbone UP) were calculated (Ancoli-Israel et al., 2003).”
MethodResultInfluentialActivity-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
Marino, M., Li, Y., Rueschman, M. N., Winkelman, J. W., Ellenbogen, J. M., Solet, J. M., Dulin, H., Berkman, L. F., & Buxton, O. M. (2013) · Sleep · doi:10.5665/sleep.3142 · Cited by 1,056 · 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
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
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
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
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-citationMonitoring 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
Sassoon, S. A., de Zambotti, M., Colrain, I. M., & Baker, F. C. (2014) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0000000000000192 · Cited by 38 · see citing works
Self-citation
Flagged first, then by citations
1 of the 15 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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
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-citationPeake, J. M., Kerr, G., & Sullivan, J. P. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00743 · Cited by 523 · see citing works
Performance of seven consumer sleep-tracking devices compared with polysomnography
Chinoy, E. D., Cuellar, J. A., Huwa, K. E., Jameson, J., Watson, C. H., Bessman, S. C., Hirsch, D. A., Cooper, A. D., Drummond, S. P. A., & Markwald, R. R. (2020) · SLEEP · doi:10.1093/sleep/zsaa291 · Cited by 451 · see citing works
A survey on sleep assessment methods
Valle, V. I.-D., Silva, J., & Cauli, O. (2018) · PeerJ · doi:10.7717/peerj.4849 · Cited by 318 · 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-citationJohnson, D. A., Billings, M. E., & Hale, L. E. (2018) · Current Epidemiology Reports · doi:10.1007/s40471-018-0139-y · Cited by 287 · see citing works
de 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
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
de Zambotti, M., Baker, F. C., Willoughby, A. R., Godino, J. G., Wing, D., Patrick, K., & Colrain, I. M. (2016) · Physiology & Behavior · doi:10.1016/j.physbeh.2016.03.006 · Cited by 222 · see citing works
Self-citationBaron, 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
Clinical Applications of Mobile Health Wearable–Based Sleep Monitoring: Systematic Review
Guillodo, E., Lemey, C., Simonnet, M., Walter, M., Baca-García, E., Masetti, V., Moga, S., Larsen, M., HUGOPSY Network, Ropars, J., et al. (2020) · JMIR mHealth and uHealth · doi:10.2196/10733 · Cited by 167 · 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
Consumer sleep tracking devices: a review of mechanisms, validity and utility
Kolla, B. P., Mansukhani, S., & Mansukhani, M. P. (2016) · Expert Review of Medical Devices · doi:10.1586/17434440.2016.1171708 · Cited by 158 · see citing works
Wearable Medical Sensor-Based System Design: A Survey
Mosenia, A., Sur‐Kolay, S., Raghunathan, A., & Jha, N. K. (2017) · IEEE Transactions on Multi-Scale Computing Systems · doi:10.1109/tmscs.2017.2675888 · Cited by 143 · 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
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
Wen, D., Zhang, X., Liu, X., & Lei, J. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6874 · Cited by 111 · 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
Liang, Z., & Martell, M. A. C. (2018) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-018-0013-1 · Cited by 105 · see citing works
A Review of Approaches for Sleep Quality Analysis
Mendonça, F., Mostafa, S. S., Morgado‐Dias, F., Ravelo-García, A. G., & Penzel, T. (2019) · IEEE Access · doi:10.1109/access.2019.2900345 · Cited by 111 · see citing works
Svensson, T., Madhawa, K., Nt, H., Chung, U., & Svensson, A. K. (2024) · Sleep Medicine · doi:10.1016/j.sleep.2024.01.020 · Cited by 115 · see citing works
Cook, J. D., Prairie, M. L., & Plante, D. T. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.04.030 · Cited by 105 · see citing works
Lee, H. A., Lee, H., Moon, J.-H., Lee, T., Kim, M.-G., In, H., Cho, C., & Kim, L. (2017) · Psychiatry Investigation · doi:10.4306/pi.2017.14.2.179 · Cited by 102 · see citing works
Seshadri, D. R., Thom, M. L., Harlow, E. R., Gabbett, T. J., Geletka, B. J., Hsu, J. J., Drummond, C. K., Phelan, D. M. J., & Voos, J. E. (2021) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2020.630576 · Cited by 117 · 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
Contribution of sleep deprivation to suicidal behaviour: A systematic review
Porras‐Segovia, A., Pérez-Rodríguez, M. M., López-Esteban, P., Courtet, P., Barrigón, M. L., López‐Castromán, J., Cervilla, J. A., & Baca‐García, E. (2018) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2018.12.005 · Cited by 102 · 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-citationLi, J., Liu, X., Ma, L., & Zhang, W. (2018) · Informatics for Health and Social Care · doi:10.1080/17538157.2018.1434179 · Cited by 83 · 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
Schyvens, A.-M., Peters, B. T., Oost, N. C. V., Aerts, J., Masci, F., Neven, A., Dirix, H., Wets, G., Ross, V., & Verbraecken, J. A. (2025) · SLEEP Advances · doi:10.1093/sleepadvances/zpaf021 · Cited by 72 · 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
Personal Activity Trackers and the Quantified Self
Hoy, M. B. (2016) · Medical Reference Services Quarterly · doi:10.1080/02763869.2016.1117300 · Cited by 61 · see citing works
Smart, Secure, Yet Energy-Efficient, Internet-of-Things Sensors
Akmandor, A. O., Yin, H., & Jha, N. K. (2018) · IEEE Transactions on Multi-Scale Computing Systems · doi:10.1109/tmscs.2018.2864297 · Cited by 59 · see citing works
About the Accuracy and Problems of Consumer Devices in the Assessment of Sleep
Ameen, M. S., Cheung, L. M., Hauser, T., Hahn, M. A., & Schabus, M. (2019) · Sensors · doi:10.3390/s19194160 · Cited by 58 · see citing works
Sleep assessment devices: types, market analysis, and a critical view on accuracy and validation
Valle, V. I.-D., Silva, J., Navarro-Illana, E., & Cauli, O. (2019) · Expert Review of Medical Devices · doi:10.1080/17434440.2019.1693890 · Cited by 56 · see citing works
Dietch, J. R., Sethi, K., Slavish, D. C., & Taylor, D. J. (2019) · Sleep Medicine · doi:10.1016/j.sleep.2019.05.015 · Cited by 57 · 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
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-citationSleep Validity of a Non-Contact Bedside Movement and Respiration-Sensing Device
Schade, M. M., Bauer, C. E., Murray, B. R., Gahan, L., Doheny, E. P., Kilroy, H., Zaffaroni, A., & Montgomery‐Downs, H. E. (2019) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.7892 · Cited by 49 · 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
Sleep Tracking: a Systematic Review of the Research Using Commercially Available Technology
Robbins, R., Seixas, A. A., Masters, L. W., Chanko, N., Diaby, F., Vieira, D. L., & Jean‐Louis, G. (2019) · Current Sleep Medicine Reports · doi:10.1007/s40675-019-00150-1 · Cited by 45 · see citing works
Svensson, 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
Counting sleep: Ambiguity, aspirational control and the politics of digital self-tracking at work
Elmholdt, K. T., Elmholdt, C. W., & Haahr, L. (2020) · Organization · doi:10.1177/1350508420970475 · Cited by 47 · see citing works
Robbins, R., Affouf, M., Seixas, A. A., Beaugris, L., Avirappattu, G., & Jean‐Louis, G. (2019) · Journal of Medical Internet Research · doi:10.2196/14735 · 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
Forensic analysis for IoT fitness trackers and its application
Kang, S., Kim, S., & Kim, J. (2018) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-018-0708-3 · Cited by 44 · see citing works
Associations Between Adolescents’ Daily Digital Technology Use and Sleep
Burnell, K., George, M. J., Jensen, M. R., Hoyle, R. H., & Odgers, C. L. (2021) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2021.09.033 · Cited by 41 · 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
Integrating sleep, neuroimaging, and computational approaches for precision psychiatry
Goldstein‐Piekarski, A. N., Holt-Gosselin, B., O’Hora, K., & Williams, L. M. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0483-8 · Cited by 38 · 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
Lee, Y. J., Lee, J. Y., Cho, J. H., Kang, Y. J., & Choi, J. H. (2024) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.11460 · Cited by 35 · see citing works
Kubala, A. G., Gibbs, B. B., Buysse, D. J., Patel, S. R., Hall, M. H., & Kline, C. E. (2019) · Behavioral Sleep Medicine · doi:10.1080/15402002.2019.1651316 · Cited by 33 · see citing works
Ravindran, K. K. G., della Monica, C., Atzori, G., Lambert, D., Hassanin, H., Revell, V. L., & Dijk, D. (2023) · JMIR mhealth and uhealth · doi:10.2196/46338 · Cited by 35 · see citing works
Assessing 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
A practical validation study of a commercial accelerometer using good and poor sleepers
Dickinson, D. L., Cazier, J. A., & Cech, T. G. (2016) · Health Psychology Open · doi:10.1177/2055102916679012 · Cited by 32 · see citing works
Topalidis, P. I., Florea, C., Eigl, E., Kurapov, A., Leon, C. A. B., & Schabus, M. (2021) · Sensors · doi:10.3390/s21113774 · Cited by 30 · see citing works
Sleep Staging Monitoring Based on Sonar Smartphone Technology
Zaffaroni, A., Coffey, S., Dodd, S., Kilroy, H., Lyon, G., O’Rourke, D., Lederer, K., Fietze, I., & Penzel, T. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857033 · Cited by 27 · see citing works
Lorenz, C., & Williams, A. J. (2017) · Current Opinion in Pulmonary Medicine · doi:10.1097/mcp.0000000000000425 · Cited by 24 · see citing works
An International Study on the Determinants of Poor Sleep Amongst 15,000 Users of Connected Devices
Fagherazzi, G., Fatouhi, D. E., Bellicha, A., Gareh, A. E., Affret, A., Dow, C., Delrieu, L., Vegreville, M., Normand, A., Oppert, J., et al. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7930 · Cited by 24 · see citing works
Sleep in the Natural Environment: A Pilot Study
Chaudhry, F. F., Danieletto, M., Golden, E., Scelza, J., Botwin, G., Shervey, M. M., Freitas, J. K. D., Paranjpe, I., Nadkarni, G. N., Miotto, R., et al. (2020) · Sensors · doi:10.3390/s20051378 · Cited by 23 · see citing works
Maskevich, S., Jumabhoy, R., Dao, P., Stout, J. C., & Drummond, S. P. A. (2017) · Journal of Huntington s Disease · doi:10.3233/jhd-170251 · Cited by 23 · see citing works
Budig, M., Stoohs, R. A., & Keiner, M. (2022) · Sensors · doi:10.3390/s22239540 · Cited by 20 · see citing works
Kanady, J. C., Yack, L. M., Straus, L. D., Varbel, J., Metzler, T. J., Richards, A. L., Inslicht, S. S., O’Donovan, A., Hlavin, J. A., & Neylan, T. C. (2020) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.8362 · Cited by 23 · see citing works
Apps and fitness trackers that measure sleep: Are they useful?
Mansukhani, M. P., & Kolla, B. P. (2017) · Cleveland Clinic Journal of Medicine · doi:10.3949/ccjm.84a.15173 · Cited by 20 · see citing works
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
Green, S. F., Frame, T. V. H., Banerjee, L. V., Gimson, A., Blackman, J., Morrison, H. D., Lloyd, K., Rudd, S., Fotherby, W. F., Bartsch, U., et al. (2022) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2022.101665 · Cited by 19 · see citing works
Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Arsintescu, L., Kato, K. H., Hilditch, C. J., Gregory, K. B., & Flynn‐Evans, E. E. (2019) · Journal of Visualized Experiments · doi:10.3791/59851 · Cited by 19 · see citing works
Anxiolytic Effect and Improved Sleep Quality in Individuals Taking Lippia citriodora Extract
Martínez‐Rodríguez, A., Martínez-Olcina, M., Mora, J., Navarro, P., Caturla, N., & Jones, J. (2022) · Nutrients · doi:10.3390/nu14010218 · Cited by 16 · see citing works
Faizah, A., Hardian, A. F. A., Nandini, R. D., Handayani, P. W., & Harahap, N. C. (2024) · JMIR Human Factors · doi:10.2196/50957 · Cited by 16 · see citing works
Eating versus sleeping: Lunchtime meals and naps relation to afternoon creativity at work
Yun, M., & Beehr, T. A. (2023) · Applied Psychology Health and Well-Being · doi:10.1111/aphw.12438 · Cited by 15 · see citing works
Liu, J., Wong, W. T., Zwetsloot, I. M., Hsu, Y., & Tsui, K. (2019) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2018.0202 · Cited by 15 · see citing works
Darvariu, V.-A., Convertino, L., Mehrotra, A., & Musolesi, M. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3380997 · Cited by 14 · see citing works
Self-reported current sleep behaviors of adult athletes from different competitive levels and sports
Randell, R. K., Anderson, R., Carter, J. M., & Rollo, I. (2021) · Sleep Science · doi:10.5935/1984-0063.20200044 · Cited by 13 · see citing works
mHealth for Mental Health in the COVID-19 Era
Lee, S., Shin, H., & Hur, J. (2020) · The Korean Journal of Clinical Psychology · doi:10.15842/kjcp.2020.39.4.007 · Cited by 12 · 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
Müller, R., Kuhn, E. M., Ranisch, R., Hunger, J., & Primc, N. (2023) · Ethics and Information Technology · doi:10.1007/s10676-023-09677-y · Cited by 11 · see citing works
Gooderick, J., Wood, T., Abbott, W., Hayes, M., & Maxwell, N. (2024) · Science and Medicine in Football · doi:10.1080/24733938.2023.2297903 · Cited by 10 · 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
Liang, Z., & Martell, M. A. C. (2019) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2019.8730805 · Cited by 10 · see citing works
Pauley, A. M., Hohman, E. E., Leonard, K. S., Guo, P., McNitt, K. M., Rivera, D. E., Savage, J. S., & Downs, D. S. (2020) · Clocks & Sleep · doi:10.3390/clockssleep2040036 · Cited by 9 · see citing works
When experiencing nice interactions at work: Good sleep quality via well‐being
Yun, M., & Beehr, T. A. (2024) · Stress and Health · doi:10.1002/smi.3390 · Cited by 9 · see citing works
Zhang, S., Schaub, F., Feng, Y., & Sadeh, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-15742-5_71 · Cited by 9 · see citing works
Ellender, C. M., Zahir, S. F., Meaklim, H., Joyce, R., Cunnington, D., & Swieca, J. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-044015 · Cited by 8 · see citing works
Durrani, S., Cao, S., Bo, N., Pai, J. K., Baker, J., Rawlings, L., Qureshi, Z. P., Sigua, N. L., Manchanda, S., & Khan, B. A. (2022) · Advances in Therapy · doi:10.1007/s12325-021-02013-0 · Cited by 7 · see citing works
Addressing Security and Privacy Challenges in Internet of ThingsPreprint
Mosenia, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.06724 · Cited by 9 · see citing works
A Compact Deep Learning Network for Temporal Sleep Stage Classification
Vetek, A., Müller, K., & Lindholm, H. J. (2018) · IEEE Life Sciences Conference (LSC) · doi:10.1109/lsc.2018.8572286 · Cited by 6 · see citing works
Canazei, M., Turiaux, J., Huber, S. E., Marksteiner, J., Papousek, I., & Weiss, E. M. (2019) · Current Alzheimer Research · doi:10.2174/1567205016666191010124011 · Cited by 6 · see citing works
Consumer Technology for Sleep-Disordered Breathing: a Review of the Landscape
Rosa, T. D., Zitser, J., & Capasso, R. (2019) · Current Otorhinolaryngology Reports · doi:10.1007/s40136-019-00222-4 · Cited by 5 · see citing works
Motor Control and Dyscontrol in Sleep
Chokroverty, S., Bhat, S., & Allen, R. P. (2017) · Springer eBooks · doi:10.1007/978-1-4939-6578-6_39 · Cited by 5 · see citing works
How reliable are commercially available trackers in detecting daytime sleep
Lambrechtse, P., Ziesenitz, V. C., Cohen, A. F., van den Anker, J. N., & Bos, E. J. (2018) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.13475 · Cited by 4 · see citing works
Jumabhoy, R., Maskevich, S., Dap, P., Anderson, C., Stout, J. C., & Drummond, S. P. A. (2019) · doi:10.31234/osf.io/mx2ae · Cited by 4 · see citing works
Measuring sleep in the bedroom environment
Cook, J. D., Castelan, A. C., & Cheng, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822963-7.00365-0 · Cited by 3 · see citing works
Agreement between perceived and objective sleep duration in highly trained young male soccer players
Barreira, J., Brito, J., Nakamura, F. Y., Pimenta, R., & Figueiredo, P. (2025) · Science and Medicine in Football · doi:10.1080/24733938.2025.2593866 · Cited by 2 · see citing works
Utility of Activity Monitors and Thermometry in Assessing Sleep Stages and Sleep Quality
Swalve, N. L., Harfmann, B. D., Mitrzyk, J., & Montoye, A. H. K. (2018) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0012 · Cited by 2 · see citing works
Validity of Fitbit Flex® consumer device on the sleep evaluation
López-Flores, M., Fernandez, A. R., Villa-Vicente, J., Iglesias, D. S., & Marroyo, J. A. R. (2020) · Cultura Ciencia y Deporte · doi:10.12800/ccd.v15i43.1397 · Cited by 2 · see citing works
- Authors
- de Zambotti, M., Claudatos, S., Inkelis, S., Colrain, I. M., & Baker, F. C.
- Affiliations
- SRI International (US)
- Corresponding author
- Baker, Fiona C.
- Venue
- Chronobiology International
- Publisher
- Informa UK Limited
- Published
- 2015-07-09
- Volume
- 32
- Issue
- 7
- Pages
- 1024-1028
- ISSN
- 0742-0528 (ISSN-L)
- 1525-6073
- PMC
- PMC4780439
- OpenAlex
- W1606858143
- Cited by
- 127
- References
- 15
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 7 · 2023: 8 · 2022: 6 · 2021: 7 · 2020: 18 · 2019: 28 · 2018: 21 · 2017: 15 · 2016: 5 · 2015: 2
- FWCI
- 21.66
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Chronobiology International (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (HL 103688, R01HL103688)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Circadian rhythm and melatonin
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
- Keywords
- wearable activity trackers, sleep assessment, actigraphy, sleep-wake cycle, total sleep time, sleep latency, wake after sleep onset, middle-aged women, sleep disruption
- MeSH terms
- Activity Cycles, Equipment Design, Female, Humans, Middle Aged, Predictive Value of Tests, Sleep, Time Factors, Reproducibility of Results, Polysomnography, 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 ›
Are you an author, or do you see a problem with this page? Request a review
