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Personalized Physical Activity Coaching: A Machine Learning Approach.

Talko B Dijkhuis1,2, Frank J Blaauw3,4, Miriam W van Ittersum5

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Machine learning algorithms can predict physical activity, enabling automated personalized coaching. This approach helps individuals achieve daily step goals and promotes healthier lifestyles by providing timely feedback.

Keywords:
coachingmachine learningphysical activitysedentary lifestyle

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Area of Science:

  • Health Promotion
  • Machine Learning
  • Wearable Technology

Background:

  • Sedentary lifestyles contribute to significant health issues.
  • Workplace health promotion programs aim to encourage physical activity.
  • Activity trackers and coaching sessions are used to monitor and improve step counts.

Purpose of the Study:

  • To automate personalized physical activity coaching.
  • To provide real-time feedback on progress towards daily step goals.
  • To investigate the efficacy of machine learning in predicting physical activity.

Main Methods:

  • Utilized daily step count data from activity trackers.
  • Trained eight machine learning algorithms for hourly step goal achievement probability estimation.
  • Developed a proof-of-concept web application for personalized feedback.

Main Results:

  • The Random Forest algorithm demonstrated superior performance in 80% of cases.
  • Achieved a mean accuracy of 0.93 and a mean F1-score of 0.90.
  • The web application successfully provided personalized, predictive feedback.

Conclusions:

  • Machine learning offers a valuable tool for automated, personalized health coaching.
  • Predictive algorithms can facilitate timely interventions to increase physical activity.
  • This technology can support individuals in achieving their physical activity targets.