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

Meredith L. Wallace, Daniel J. Buysse, Susan Redline, Katie L. Stone, Kristine Ensrud, Yue Leng, Sonia Ancoli-Israel, Martica H. Hall

Research output: Contribution to journalArticle

3 Scopus citations

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 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% confidence interval {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: Multidimensional 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.

Original languageEnglish (US)
Pages (from-to)1903-1909
Number of pages7
JournalJournals of Gerontology - Series A Biological Sciences and Medical Sciences
Volume74
Issue number12
DOIs
StatePublished - Nov 13 2019

Keywords

  • Elderly
  • Machine learning
  • Mortality
  • Random forest
  • Sleep health

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

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