O058 Sleep estimates from an under-mattress sensor can predict vigilance, working memory, and mental arithmetic performance

Abstract Introduction Sleep is vitally important to maintain cognitive function. Sleep trackers can reliably estimate several sleep metrics to help predict cognitive performance, but associations between sleep-tracker estimated sleep and cognitive domains are unclear. This study examined associations between sleep tracker-estimated sleep and next-day cognitive performance across several domains, including vigilant attention, working memory, and risky decision-making. Methods Twenty four participants (mean[SD] age=28[9] years, 12/12 male/female) attended the sleep laboratory twice for 8-day simulated shift-work experimental protocols under two lighting conditions (dim, blue-depleted vs. blue-enriched circadian-informed lighting). Following a baseline sleep, participants remained awake for 27h and transitioned to sleeping between 10:00-19:00 and undertaking cognitive tasks from 00:00-08:00 for four days. Cognitive tasks included the Digit-Symbol Substitution task (DSST), Continuous Performance task, Iowa Gambling task, Operation Span task, Psychomotor Vigilance test (PVT), and Stroop task. Sleep was assessed using an under-mattress sensor (Withings Sleep Analyzer). Mixed effects and random forest models tested associations between estimated sleep and cognitive performance. Results Significant associations were found between sleep metrics and PVT reaction time (R²=.13, p=.008), Operation Span arithmetic errors (R²=.15, p=.023), proportion correct on DSST and Stroop tasks, (R²=.06, p=.045; R²=.09, p=.003), and Stroop reaction time (R²=.19, p=.004). Random forest models demonstrated strong associations between cognitive performance and estimates of sleep architecture, snoring, and cardiovascular estimates. Conclusion Sleep trackers can help predict next-day cognitive performance, particularly in vigilance, working memory, and mental arithmetic. This may be a practical strategy to help manage sleep-related cognitive impairments and enhance worker safety in shift work conditions.

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Publication Details

Journal
SLEEP Advances
Published
2024-10-01
DOI
https://doi.org/10.1093/sleepadvances/zpae070.058
Primary Topic
Sleep and Work-Related Fatigue
Type
article
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article

O058 Sleep estimates from an under-mattress sensor can predict vigilance, working memory, and mental arithmetic performance

A Guyett, Eva Kemps, Peter Catcheside, N Stuart et al.
1 links
SLEEP Advances
Sleep and Work-Related Fatigue
article

O058 Sleep estimates from an under-mattress sensor can predict vigilance, working memory, and mental arithmetic performance

A Guyett, Eva Kemps, Peter Catcheside, N Stuart, John M. Manners, Helen Scott
article en

Abstract

Abstract Introduction Sleep is vitally important to maintain cognitive function. Sleep trackers can reliably estimate several sleep metrics to help predict cognitive performance, but associations between sleep-tracker estimated sleep and cognitive domains are unclear. This study examined associations between sleep tracker-estimated sleep and next-day cognitive performance across several domains, including vigilant attention, working memory, and risky decision-making. Methods Twenty four participants (mean[SD] age=28[9] years, 12/12 male/female) attended the sleep laboratory twice for 8-day simulated shift-work experimental protocols under two lighting conditions (dim, blue-depleted vs. blue-enriched circadian-informed lighting). Following a baseline sleep, participants remained awake for 27h and transitioned to sleeping between 10:00-19:00 and undertaking cognitive tasks from 00:00-08:00 for four days. Cognitive tasks included the Digit-Symbol Substitution task (DSST), Continuous Performance task, Iowa Gambling task, Operation Span task, Psychomotor Vigilance test (PVT), and Stroop task. Sleep was assessed using an under-mattress sensor (Withings Sleep Analyzer). Mixed effects and random forest models tested associations between estimated sleep and cognitive performance. Results Significant associations were found between sleep metrics and PVT reaction time (R²=.13, p=.008), Operation Span arithmetic errors (R²=.15, p=.023), proportion correct on DSST and Stroop tasks, (R²=.06, p=.045; R²=.09, p=.003), and Stroop reaction time (R²=.19, p=.004). Random forest models demonstrated strong associations between cognitive performance and estimates of sleep architecture, snoring, and cardiovascular estimates. Conclusion Sleep trackers can help predict next-day cognitive performance, particularly in vigilance, working memory, and mental arithmetic. This may be a practical strategy to help manage sleep-related cognitive impairments and enhance worker safety in shift work conditions.

SLEEP AdvancesVol. 5(Supplement_1)
Flinders University (AU), Bury College (GB), Adelaide Institute for Sleep Health (AU)
Openalex Percentile: Top 72%
Sleep and Work-Related Fatigue
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