Development, validation, and transportability of several machine-learned, non-exercise-based VO prediction models for older adults.

Journal: Journal of sport and health science
PMID: 38428731

Abstract

BACKGROUND: There exist few maximal oxygen uptake (VO) non-exercise-based prediction equations, fewer using machine learning (ML), and none specifically for older adults. Since direct measurement of VO is infeasible in large epidemiologic cohort studies, we sought to develop, validate, compare, and assess the transportability of several ML VO prediction algorithms.

Authors

  • Benjamin T Schumacher
    Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, USA. Electronic address: BTS70@pitt.edu.
  • Michael J LaMonte
    Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo-State University of New York, Buffalo, NY 14214, USA.
  • Andrea Z LaCroix
    Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, USA.
  • Eleanor M Simonsick
    Translational Gerontology Branch, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21225, USA.
  • Steven P Hooker
    College of Health and Human Services, San Diego State University, San Diego, CA 92182, USA.
  • Humberto Parada
    Division of Epidemiology and Biostatistics, School of Public Health, San Diego State University, San Diego, CA 92182, USA; University of California San Diego Moores Cancer Center, La Jolla, CA 92093, USA.
  • John Bellettiere
    Department of Family Medicine and Public Health, UCSD, La Jolla, CA.
  • Arun Kumar
    Department of Reproductive Medicine, Gunasheela Surgical and Maternity Hospital, Bengaluru, Karnataka, India.

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