A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep
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 p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/11/6/185 |
| _version_ | 1797531006759075840 |
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| author | Dean J. Miller Gregory D. Roach Michele Lastella Aaron T. Scanlan Clint R. Bellenger Shona L. Halson Charli Sargent |
| author_facet | Dean J. Miller Gregory D. Roach Michele Lastella Aaron T. Scanlan Clint R. Bellenger Shona L. Halson Charli Sargent |
| author_sort | Dean J. Miller |
| collection | DOAJ |
| description | 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: <i>n</i> = 3; female: <i>n</i> = 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. |
| first_indexed | 2024-03-10T10:37:04Z |
| format | Article |
| id | doaj.art-55f72883d1734718b989697095a0fb7d |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-10T10:37:04Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-55f72883d1734718b989697095a0fb7d2023-11-21T23:11:37ZengMDPI AGBiosensors2079-63742021-06-0111618510.3390/bios11060185A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate SleepDean J. Miller0Gregory D. Roach1Michele Lastella2Aaron T. Scanlan3Clint R. Bellenger4Shona L. Halson5Charli Sargent6The Appleton Institute for Behavioural Science, CQUniversity, Adelaide, SA 5043, AustraliaThe Appleton Institute for Behavioural Science, CQUniversity, Adelaide, SA 5043, AustraliaThe Appleton Institute for Behavioural Science, CQUniversity, Adelaide, SA 5043, AustraliaHuman Exercise and Training Laboratory, CQUniversity, Rockhampton, QLD 4700, AustraliaAlliance for Research in Exercise, Nutrition and Activity (ARENA), Allied Health and Human Performance, University of South Australia, Adelaide, SA 5001, AustraliaSchool of Behavioural and Health Sciences, Australian Catholic University, Brisbane, QLD 4014, AustraliaThe Appleton Institute for Behavioural Science, CQUniversity, Adelaide, SA 5043, AustraliaThe 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: <i>n</i> = 3; female: <i>n</i> = 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.https://www.mdpi.com/2079-6374/11/6/185consumer sleep technologywearablesPSGsleep stagingsleep monitoringsleep quality |
| spellingShingle | Dean J. Miller Gregory D. Roach Michele Lastella Aaron T. Scanlan Clint R. Bellenger Shona L. Halson Charli Sargent A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep Biosensors consumer sleep technology wearables PSG sleep staging sleep monitoring sleep quality |
| title | A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep |
| title_full | A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep |
| title_fullStr | A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep |
| title_full_unstemmed | A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep |
| title_short | A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep |
| title_sort | validation study of a commercial wearable device to automatically detect and estimate sleep |
| topic | consumer sleep technology wearables PSG sleep staging sleep monitoring sleep quality |
| url | https://www.mdpi.com/2079-6374/11/6/185 |
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