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Machine learning modeling for predicting adherence to physical activity guideline.

Ju-Pil Choe1, Seungbak Lee1, Minsoo Kang2

  • 1Health and Sport Analytics Laboratory, Department of Health, Exercise Science, and Recreation Management, The University of Mississippi, University, 38677, USA.

Scientific Reports
|February 15, 2025
PubMed
Summary

Machine learning models predict physical activity (PA) guideline adherence. Sedentary behavior, age, gender, and education are key factors influencing PA guideline compliance.

Keywords:
Artificial intelligenceMPAMeasurementPrediction modelSubjectively measuredVPA

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Area of Science:

  • Public Health
  • Health Informatics
  • Biostatistics

Background:

  • Physical activity (PA) guidelines are crucial for public health.
  • Understanding factors influencing adherence to PA guidelines is essential for effective health interventions.
  • Predictive modeling can offer novel insights into PA behavior.

Purpose of the Study:

  • To develop machine learning (ML) predictive models for adherence to physical activity (PA) guidelines.
  • To identify critical determinants influencing adherence to PA guidelines.
  • To explore the utility of data-driven methods in PA research.

Main Methods:

  • Analysis of 11,638 participants from the National Health and Nutrition Examination Survey (NHANES).
  • Categorization of variables into demographic, anthropometric, and lifestyle factors.
  • Development and evaluation of 18 ML models using algorithms like decision trees, assessed by accuracy, F1 score, and AUC.
  • Application of permutation feature importance (PFI) to determine variable significance.

Main Results:

  • A decision tree model demonstrated the highest predictive performance (accuracy=0.705, F1 score=0.819, AUC=0.542).
  • Permutation feature importance identified sedentary behavior, age, gender, and educational status as the most significant predictors of PA guideline adherence.
  • The study highlights the potential of ML in analyzing large health datasets for PA research.

Conclusions:

  • Machine learning models can effectively predict adherence to physical activity guidelines.
  • Sedentary behavior, age, gender, and education are critical determinants influencing PA guideline adherence.
  • These findings provide valuable insights for developing targeted interventions to improve PA levels.