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Physical Activity Measurement in Children Accepting Table Tennis Training
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Published on: July 27, 2022

Modeling physical activity outcomes: a two-part generalized-estimating-equations approach.

Andy H Lee1, Liming Xiang, Fumi Hirayama

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Curtin University of Technology, Perth, Western Australia. Andy.Lee@curtin.edu.au

Epidemiology (Cambridge, Mass.)
|July 1, 2010
PubMed
Summary

Analyzing physical activity data presents challenges like non-normality and excess zeros. A novel 2-part generalized estimating equations (GEE) approach offers a robust method for analyzing complex physical activity patterns.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Physical activity is crucial for health, but its measurement and analysis in studies face methodological hurdles.
  • Common issues include non-normal distributions, a high frequency of zero values, and violated independence assumptions in observational data.
  • Standard regression models applied to physical activity data can yield inaccurate results.

Purpose of the Study:

  • To address the methodological challenges in analyzing physical activity data.
  • To propose and illustrate an alternative statistical approach for heterogeneous and correlated physical activity data.
  • To provide a more reliable method for understanding factors influencing physical activity.

Main Methods:

  • Developed a 2-part generalized estimating equations (GEE) approach.
  • Part 1: Logistic GEE model to analyze physical activity prevalence and influencing factors.
  • Part 2: Gamma GEE model to assess predictor effects among active individuals.

Main Results:

  • The proposed 2-part GEE method effectively handles the complexities of physical activity data.
  • Demonstrated application in an epidemiologic study of older adults.
  • Identified factors associated with physical activity participation and intensity.

Conclusions:

  • The 2-part GEE approach provides a superior alternative to standard regression for analyzing physical activity.
  • This methodology enhances the accuracy of findings in physical activity research.
  • Offers improved insights into physical activity patterns and determinants in populations.