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

ABSTRACT:

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: (i) 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 (ii) to identify which sleep characteristics are most predictive.

Methods

The analytic sample includes N = 8,668 older adults (54% female) aged 65-99 years 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 pred

SUBMITTER: Wallace ML 

PROVIDER: S-EPMC6853700 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature