How well does a commercially available wearable device measure sleep in young athletes?

Sargent, Charli; Lastella, Michele; Romyn, Georgia; Versey, Nathan G.; Miller, Dean J.; Roach, Gregory D.

doi:10.1080/07420528.2018.1466800

Cited by 41 publications

(25 citation statements)

References 9 publications

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“…Only 5 investigations out of 22 (23%) attempted to blind the sleep laboratory technicians from outcome measures [22,25,26,28,40]. A total of 13 of the qualifying 22 studies (59%) relied on PSG as reference to evaluate Fitbit performance [22,25-28,30,31,37-42]; the other 9 (41%) relied on a sleep log, actigraphy, or home EEG as reference. Since PSG is the gold standard for measurement of sleep stages and parameters, use of other methods of reference constitutes an additional potential source of bias.…”

Section: Resultsmentioning

confidence: 99%

“…Out of the 22 studies, 10 (45%) assessed early-generation nonsleep-staging Fitbit models in comparison with PSG in estimating sleep parameters [25,27,30,31,37-42]; 1 of these studies involved performance of Fitbit models when applied to individuals of two different cohorts (ie, good sleepers and insomniacs), thereby increasing the number of possible comparisons to 11. Eight (N=203) of the 10 potential comparisons reported significant overestimation of TST by Fitbit versus PSG of between 6.5 and 88.1 minutes, while the two others (N=52) found nonsignificant overestimation.…”

Section: Resultsmentioning

confidence: 99%

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Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis

et al. 2019

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BackgroundWearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way.ObjectiveWe conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages.MethodsIn adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria.ResultsThe search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P<.001; heterogenicity: I2=8.8%, P=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, P<.001; heterogenicity: I2=24.0%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I2=0%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0%, P=.92), TST (P=.29; heterogenicity: I2=0%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy.ConclusionsSleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis

et al. 2019

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“…The participants used to go to bed between 22:00 h and 23:00 h. They were instructed to sleep for 7 h at home, and this was recorded with a fitness tracker worn on the non-dominant hand (Xiaomi mi band 2, China), with the epoch length set to 1 min. With consideration of the limitations of wearable devices [24], participants were asked to record time to go to bed, time to sleep and time to wake up in a sleeping diary to confirm the sleep time data obtained with the wearable device. After explanation of the testing procedures, and benefits and possible risks of the study, informed consent forms were signed.…”

Section: Participantsmentioning

confidence: 99%

Effects of a 20-min Nap after Sleep Deprivation on Brain Activity and Soccer Performance

2020

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AbstractWe examined effects of a 20-min nap following 3 h of sleep deprivation on brain wave activity, auditory reaction time, the running-based anaerobic sprint test, leg muscle strength and the rating of perceived exertion in male college soccer players. Eleven players underwent three sleep conditions; normal sleep, sleep deprivation and 20-min nap after sleep deprivation. The sleep deprivation demonstrated an increase in the mean power of delta waves over the frontal area and a decrease in the mean power of alpha waves over the parietal area compared to the normal sleep. The nap and the sleep deprivation showed an increase in auditory reaction time compared with those in the normal sleep. The sleep deprivation demonstrated a decrease in the running-based anaerobic sprint test compared to the normal sleep, whereas the nap has partially reversed only minimal power and average power of the running-based anaerobic sprint test. The nap showed a recovery effect on leg muscle strength, but not on the rating of perceived exertion compared with the sleep deprivation. Thus, a 20-min nap after sleep deprivation did not completely return brain activity back to active state and did not entirely reverse the negative impact of sleep deprivation on soccer performance in soccer players.

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“…In case it is unavoidable to phone during a commute (e.g., an emergency call), it is advisable to bring the car to a stop. Sargent et al (2018) examined the validity of a commercially available wearable sleep detection device (FitBit) using polysomnography for comparison. Such a comparison is pertinent, given that several devices, i.e., phones, smart watches, activity trackers, already include functions to assess sleep parameters derived from quantifications of physical activity, heart rate, electrodermal activity, etc.…”

Section: Effects Of Total Sleep Deprivation and Partial Sleep Restricmentioning

confidence: 99%

23rd International Symposium on Shiftwork and Working Time: Towards a Global Consensus

Roach

Kantermann

Dawson

et al. 2018

Chronobiology International

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The Working Time Society (WTS), and the International Commission on Occupational Health (ICOH) Scientific Committee on Shiftwork and Working Time, are twin organisations focused on conducting research, and informing practice, regarding the impact of work hours in general, and shiftwork in particular, on the efficiency, productivity, safety, well-being, health, and biological rhythms, of employees. Every 2-3 years since 1969, the WTS and ICOH have conducted a series of international symposia in Europe, Asia, Australia, North America, and South America. The purpose of these symposia is to provide a forum for the exchange of knowledge, and the discussion of contested issues, with researchers, employee representatives, regulators, and employers. The most recent symposium in this series - the 23rd International Symposium on Shiftwork and Working Time, entitled "Toward a Global Consensus" - was held on 19-23 June 2017, at Yulara, Australia, near Uluru. Since 2004, Chronobiology International has released a special issue after each symposium, and that tradition continues with a special issue that includes 17 contributions based on a selection of the 128 papers that were presented at the most recent symposium. Here, we provide an overview of the papers that comprise the special issue, and we briefly comment on the implications of the findings for shiftworkers and their employers.

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How well does a commercially available wearable device measure sleep in young athletes?

Cited by 41 publications

References 9 publications

Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis

Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis

Effects of a 20-min Nap after Sleep Deprivation on Brain Activity and Soccer Performance

23rd International Symposium on Shiftwork and Working Time: Towards a Global Consensus

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