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Statistical methods to model and evaluate physical activity programs, using step counts: A systematic review.

S S M Silva1, Madawa W Jayawardana1, Denny Meyer1

  • 1Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology, Hawthorn, Victoria, Australia.

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Physical activity programs use step counts to track progress. This review analyzes statistical models for step count data, finding general linear models most common and suggesting personalized programs for better health outcomes.

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Area of Science:

  • Public Health
  • Biostatistics
  • Exercise Science

Background:

  • Physical activity is crucial for preventing noncommunicable diseases and promoting health.
  • Global physical activity levels, especially among adolescents, remain suboptimal.
  • Step counts are widely used in physical activity programs to monitor performance.

Purpose of the Study:

  • To systematically review statistical methods used for analyzing step count data in physical activity programs.
  • To identify the most effective statistical models for evaluating individual and program performance.
  • To explore strategies for personalizing physical activity interventions based on data analysis.

Main Methods:

  • Systematic literature search adhering to PRISMA guidelines (January 2000 - August 2017).
  • Inclusion of studies using statistical models to analyze step counts from physical activity programs with healthy participants.
  • Focus on naturalistic physical activities integrated into daily life.

Main Results:

  • Identified 78 articles on statistical models for step count analysis.
  • General linear models and generalized linear models were the most frequently used statistical approaches.
  • Multilevel models were also employed; structural equation modeling focused on psychosocial factors; time series analysis was notably absent.

Conclusions:

  • Individual physical activity levels are influenced by psychosocial, demographic, and environmental factors.
  • Statistical modeling enhances understanding of these influences, enabling personalized physical activity programs.
  • Personalized programs are expected to yield improved short-term and long-term health benefits for participants.