Multidimensional Sleep and Mortality in Older Adults: A Machine-Learning Comparison with Other Risk Factors.

BACKGROUND: Sleep characteristics related to duration, timing, continuity, and sleepiness are associated with mortality in older adults, but rarely considered in health recommendations. We applied machine learning to: (1) establish the predictive ability of a multidimensional self-reported sleep domain for all-cause and cardiovascular mortality in older adults relative to other established risk factors; and (2) identify which sleep characteristics are most predictive.

METHODS: The analytic sample includes N=8,668 older adults (54% female) aged 65-99 with self-reported sleep characterization and longitudinal follow-up (≤15.5 years), aggregated from three epidemiological cohorts. We used variable Importance (VIMP) metrics from a random survival forest to rank the predictive abilities of 47 measures and domains to which they belong. VIMPs > 0 indicate predictive variables/domains.

RESULTS: Multidimensional sleep was a significant predictor of all-cause [VIMP (99.9% CI) = 0.94 (0.60, 1.29)] and cardiovascular [1.98 (1.31, 2.64)] mortality. For all-cause mortality, it ranked below that of the sociodemographic [3.94 (3.02, 4.87)], physical health [3.79 (3.01, 4.57)], and medication [1.33 (0.94, 1.73)] domains but above that of the health behaviors domain [0.22 (0.06, 0.38)]. The domains were ranked similarly for cardiovascular mortality. The most predictive individual sleep characteristics across outcomes were time in bed, hours spent napping, and wake-up time.

CONCLUSION: ultidimensional sleep is an important predictor of mortality that should be considered among other more routinely used predictors. Future research should develop tools for measuring multidimensional sleep - especially those incorporating time in bed, napping, and timing-and test mechanistic pathways through which these characteristics relate to mortality.