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

Journal: The journals of gerontology. Series A, Biological sciences and medical sciences
Published Date: Nov 13, 2019

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.

Authors

  • Meredith L Wallace
    Department of Psychiatry, University of Pittsburgh, Pennsylvania.
  • Daniel J Buysse
    Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
  • Susan Redline
    Department of Medicine, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Harvard University Boston, MA.
  • Katie L Stone
    California Pacific Medical Center, Research Institute, San Francisco.
  • Kristine Ensrud
    Department of Medicine and Division of Epidemiology and Community Health, University of Minnesota, Minneapolis.
  • Yue Leng
    Department of Psychiatry, University of California, San Francisco.
  • Sonia Ancoli-Israel
    Department of Psychiatry, University of California, San Diego.
  • Martica H Hall
    Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. hallmh@upmc.edu.

Keywords

External Resources

Popular Topics
Recent Journals