Literature DB >> 34201016

A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep.

Dean J Miller1, Gregory D Roach1, Michele Lastella1, Aaron T Scanlan2, Clint R Bellenger3,4, Shona L Halson5, Charli Sargent1.   

Abstract

The aims of this study were to: (1) compare actigraphy (ACTICAL) and a commercially available sleep wearable (i.e., WHOOP) under two functionalities (i.e., sleep auto-detection (WHOOP-AUTO) and manual adjustment of sleep (WHOOP-MANUAL)) for two-stage categorisation of sleep (sleep or wake) against polysomnography, and; (2) compare WHOOP-AUTO and WHOOP-MANUAL for four-stage categorisation of sleep (wake, light sleep, slow wave sleep (SWS), or rapid eye movement sleep (REM)) against polysomnography. Six healthy adults (male: n = 3; female: n = 3; age: 23.0 ± 2.2 yr) participated in the nine-night protocol. Fifty-four sleeps assessed by ACTICAL, WHOOP-AUTO and WHOOP-MANUAL were compared to polysomnography using difference testing, Bland-Altman comparisons, and 30-s epoch-by-epoch comparisons. Compared to polysomnography, ACTICAL overestimated total sleep time (37.6 min) and underestimated wake (-37.6 min); WHOOP-AUTO underestimated SWS (-15.5 min); and WHOOP-MANUAL underestimated wake (-16.7 min). For ACTICAL, sensitivity for sleep, specificity for wake and overall agreement were 98%, 60% and 89%, respectively. For WHOOP-AUTO, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 90%, 60%, 86% and 63%, respectively. For WHOOP-MANUAL, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 97%, 45%, 90% and 62%, respectively. WHOOP-AUTO and WHOOP-MANUAL have a similar sensitivity and specificity to actigraphy for two-stage categorisation of sleep and can be used as a practical alternative to polysomnography for two-stage categorisation of sleep and four-stage categorisation of sleep.

Entities:  

Keywords:  PSG; consumer sleep technology; sleep monitoring; sleep quality; sleep staging; wearables

Year:  2021        PMID: 34201016     DOI: 10.3390/bios11060185

Source DB:  PubMed          Journal:  Biosensors (Basel)        ISSN: 2079-6374


  6 in total

1.  Prior sleep-wake behaviors are associated with mental health outcomes during the COVID-19 pandemic among adult users of a wearable device in the United States.

Authors:  Mark É Czeisler; Emily R Capodilupo; Matthew D Weaver; Charles A Czeisler; Mark E Howard; Shantha M W Rajaratnam
Journal:  Sleep Health       Date:  2022-04-20

2.  Treatment Considerations for Compulsive Exercise in High-Performance Athletes with an Eating Disorder.

Authors:  Jordan A Martenstyn; Nikki A Jeacocke; Jana Pittman; Stephen Touyz; Sarah Maguire
Journal:  Sports Med Open       Date:  2022-03-03

3.  Performance of Four Commercial Wearable Sleep-Tracking Devices Tested Under Unrestricted Conditions at Home in Healthy Young Adults.

Authors:  Evan D Chinoy; Joseph A Cuellar; Jason T Jameson; Rachel R Markwald
Journal:  Nat Sci Sleep       Date:  2022-03-22

4.  Sleep and Alcohol Use Patterns During Federal Holidays and Daylight Saving Time Transitions in the United States.

Authors:  Rachel M Heacock; Emily R Capodilupo; Mark É Czeisler; Matthew D Weaver; Charles A Czeisler; Mark E Howard; Shantha M W Rajaratnam
Journal:  Front Physiol       Date:  2022-07-11       Impact factor: 4.755

5.  A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults.

Authors:  Dean J Miller; Charli Sargent; Gregory D Roach
Journal:  Sensors (Basel)       Date:  2022-08-22       Impact factor: 3.847

6.  Athlete experiences of communication strategies in applied sports nutrition and future considerations for mobile app supportive solutions.

Authors:  David Mark Dunne; Carmen Lefevre-Lewis; Brian Cunniffe; Samuel George Impey; David Tod; Graeme Leonard Close; James P Morton; Rebecca Murphy
Journal:  Front Sports Act Living       Date:  2022-09-12
  6 in total

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