The DOI matches its Crossref record; 14 of the 15 references it cites were found in a registry or an index.
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.
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“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
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Citations
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
(2007) · Cited by 6,530
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×
“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
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
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
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
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
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
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
Research works printed but not linked: 1
- 10.1111/jsr.2011.20.issue-1pt2 · DOI
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
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
Self-citationPeake, J. M., Kerr, G., & Sullivan, J. P. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00743 · Cited by 523
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
A survey on sleep assessment methods
Valle, V. I.-D., Silva, J., & Cauli, O. (2018) · PeerJ · doi:10.7717/peerj.4849 · Cited by 352
Metadata
- 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
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