Associations between wearable device-measured sleep variability and cognition among older adults

Abstract

Importance: Healthy sleep habits are protective against adverse health outcomes, but it is unclear how strongly sleep intraindividual variability is associated with cognitive function among older adults. Objective: To examine how intraindividual variability in sleep duration, efficiency, onset timing, and offset timing is associated with cognition among older adults in the United States. Design: Cross-sectional Setting: 2011-2014 waves of the National Health and Nutrition Examination Survey (NHANES) Participants: Older adults aged 60+ with valid accelerometer and cognitive test data Exposures: Accelerometer-derived variability in sleep duration, efficiency, onset timing, and offset timing. Average metrics were also considered for comparison purposes. Main Outcome and Measures: A composite cognitive measure derived by summing z-scores from the Digit Symbol Substitution Test (DSST), Consortium to Establish a Registry for Alzheimer&rsquo;s Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Results: A final cohort of 2508 older adults aged 60+ with at least three days of accelerometer wear time who completed all three cognitive tests in the NHANES 2011-2014 waves were included in this study. After adjusting for demographic factors, the presence of diabetes, depressive symptoms, and measures of functional independence, we found that increased intraindividual variability in sleep onset timing was associated with worse cognition (&beta;, -0.12; 95% CI, -0.19 to -0.05), as was increased intraindividual variability in sleep efficiency (&beta;, -0.12; 95% CI, -0.20 to -0.05), and increased intraindividual variability in sleep duration (&beta;, -0.10; 95% CI, -0.17 to -0.03). Conclusion and Relevance: This study found that greater intraindividual variability in sleep duration, efficiency, and onset timing were significantly associated with worse cognition among older adults. Sleep variability metrics can be useful targets for interventions seeking to decrease the risk of cognitive impairments.</p

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