Applying machine learning to predict future adherence to physical activity programs.

Journal: BMC medical informatics and decision making
Published Date: Aug 22, 2019

Abstract

BACKGROUND: Identifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.

Authors

  • Mo Zhou
    Department of Industrial Engineering and Operations Research, University of California at Berkeley, 4141 Etcheverry Hall, Berkeley, CA, 94720, USA. mzhou@berkeley.edu.
  • Yoshimi Fukuoka
    Department of Physiological Nursing/Institute for Health and Aging, School of Nursing, University of fornia, San Francisco, CA 94143.
  • Ken Goldberg
    Department of Industrial Engineering and Operations Research & Electrical Engineering and Computer Sciences, University of California at Berkeley, 425 Sutardja Dai Hall, Berkeley, CA, 94720-1777, USA.
  • Eric Vittinghoff
    Department of Epidemiology & Biostatistics, School of Medicine, University of California at San Francisco, 550 16th. Street, San Francisco, CA, 94158, USA.
  • Anil Aswani
    Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720.

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