Association Between Sleep Efficiency Variability and Cognition Among Older Adults: Cross-Sectional Accelerometer Study

Background: Sleep efficiency is often used as a measure of sleep quality. Getting sufficiently high -quality sleep has been associated with better cognitive function among older adults; however, the relationship between day-to-day sleep quality variability and cognition has not been well -established. Objective: We aimed to determine the relationship between day-to-day sleep efficiency variability and cognitive function among older adults, using accelerometer data and 3 cognitive tests. Methods: We included older adults aged >65 years with at least 5 days of accelerometer wear time from the National Health and Nutrition Examination Survey (NHANES) who completed the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer's Disease Word -Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Sleep efficiency was derived using a data -driven machine learning algorithm. We examined associations between sleep efficiency variability and scores on each cognitive test adjusted for age, sex, education, household income, marital status, depressive symptoms, diabetes, smoking habits, alcohol consumption, arthritis, heart disease, prior heart attack, prior stroke, activities of daily living, and instrumental activities of daily living. Associations between average sleep efficiency and each cognitive test score were further examined for comparison purposes. Results: A total of 1074 older adults from the NHANES were included in this study. Older adults with low average sleep efficiency exhibited higher levels of sleep efficiency variability (Pearson r =-0.63). After adjusting for confounding factors, greater average sleep efficiency was associated with higher scores on the DSST (per 10% increase, beta=2.25, 95% CI 0.61 to 3.90) and AFT (per 10% increase, beta=.91, 95% CI 0.27 to 1.56). Greater sleep efficiency variability was univariably associated with worse cognitive function based on the DSST (per 10% increase, beta=-3.34, 95% CI -5.33 to -1.34), CERAD-WL (per 10% increase, beta=-1.00, 95% CI -1.79 to -0.21), and AFT (per 10% increase, beta=-1.02, 95% CI -1.68 to -0.36). In fully adjusted models, greater sleep efficiency variability remained associated with lower DSST (per 10% increase, beta=-2.01, 95% CI -3.62 to -0.40) and AFT (per 10% increase, beta=-.84, 95% CI -1.47 to -0.21) scores but not CERAD-WL (per 10% increase, beta=-.65, 95% CI -1.39 to 0.08) scores. Conclusions: Targeting consistency in sleep quality may be useful for interventions seeking to preserve cognitive function among older adults.