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
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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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