Comparing cadence-based and machine learning based estimates for physical activity intensity classification: The UK Biobank.

Journal: Journal of science and medicine in sport
PMID: 38852004

Abstract

OBJECTIVES: Cadence thresholds have been widely used to categorize physical activity intensity in health-related research. We examined the convergent validity of two cadence-based intensity classification approaches against a machine-learning-based intensity schema in 84,315 participants (≥40 years) with wrist-worn accelerometers.

Authors

  • Le Wei
    Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, United States.
  • Matthew N Ahmadi
    Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
  • Mark Hamer
    School Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom.
  • Joanna M Blodgett
    Division of Surgery and Interventional Sciences, Institute of Sport Exercise and Health, Faculty of Medical Sciences, University College London, United Kingdom.
  • Scott Small
    Nuffield Department of Population Health, University of Oxford, United Kingdom.
  • Stewart Trost
    Institute of Health and Biomedical Innovation at QLD Centre for Children's Health Research, School of Exercise and Nutrition Sciences, Queensland University of Technology, 62 Graham St, South Brisbane, QLD, 4101, Australia. s.trost@qut.edu.au.
  • Emmanuel Stamatakis
    Mackenzie Wearables Research Hub, Charles Perkins Centre, The University of Sydney, Australia; School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia. Electronic address: emmanuel.stamatakis@sydney.edu.au.

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