Which Sleep Health Characteristics Predict All-Cause Mortality in Older Men? An Application of Flexible Multivariable Approaches. Issue 1 (20th November 2017)

• Record Type: Journal Article
• Authors: Wallace, Meredith L; Stone, Katie; Smagula, Stephen F; Hall, Martica H; Simsek, Burcin; Kado, Deborah M; Redline, Susan; Vo, Tien N; Buysse, Daniel J
• Is Part Of: Sleep. Volume 41:Issue 1(2018)
• Publication Date: 2017-11-20
• Subjects: sleep health -- circadian rhythm -- multivariable analyses -- mortality -- men -- late-life -- survival tree -- random survival forest; Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsx189
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Study Objectives: Sleep is multidimensional, with domains including duration, timing, continuity, regularity, rhythmicity, quality, and sleepiness/alertness. Individual sleep characteristics representing these domains are known to predict health outcomes. However, most studies consider sleep characteristics in isolation, resulting in an incomplete understanding of which sleep characteristics are the strongest predictors of health outcomes. We applied three multivariable approaches to robustly determine which sleep characteristics increase mortality risk in the osteoporotic fractures in men sleep study. Methods: In total, 2, 887 men (mean 76.3 years) completed relevant assessments and were followed for up to 11 years. One actigraphy or self-reported sleep characteristic was selected to represent each of seven sleep domains. Multivariable Cox models, survival trees, and random survival forests were applied to determine which sleep characteristics increase mortality risk. Results: Rhythmicity (actigraphy pseudo- F statistic) and continuity (actigraphy minutes awake after sleep onset) were the most robust sleep predictors across models. In a multivariable Cox model, lower rhythmicity (hazard ratio, HR [95%CI] =1.12 [1.04, 1.22]) and lower continuity (1.16 [1.08, 1.24]) were the strongest sleep predictors. In the random survival forest, rhythmicity and continuity were the most important individual sleep characteristics (ranked as the sixth and eighth most important among 43 possible sleep and non-sleep predictors); moreover, the predictive importance of all sleep information considered simultaneously followed only age, cognition, and cardiovascular disease. Conclusions: Research within a multidimensional sleep health framework can jumpstart future research on causal pathways linking sleep and health, new interventions that target specific sleep health profiles, and improved sleep screening for adverse health outcomes.