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Machine learning-based personalized training models for optimizing marathon performance through pyramidal and

Gang Qin1, Seongno Lee1, Sungmin Kim2,3,4

  • 1Major in Sport Science, College of Performing Arts and Sport, Hanyang University, 04763, Seoul, Republic of Korea.

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Summary
This summary is machine-generated.

Personalized marathon training intensity distribution is key. Machine learning identified that experienced runners benefit more from polarized training, while novices excel with pyramidal methods, improving performance significantly.

Keywords:
Endurance trainingIntensity distributionMachine learningMarathon performancePersonalized trainingTraining optimization

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Area of Science:

  • Exercise Physiology
  • Sports Science
  • Machine Learning in Sports

Background:

  • Individual responses to training intensity distribution vary significantly in marathon runners.
  • Understanding these individual differences is crucial for optimizing performance.
  • Current training prescriptions often lack personalization.

Purpose of the Study:

  • To compare pyramidal and polarized training methodologies for marathon runners.
  • To utilize machine learning for identifying personalized training intensity distribution strategies.
  • To predict optimal training approaches based on athlete characteristics.

Main Methods:

  • 120 recreational marathon runners were randomized into 16-week pyramidal or polarized training groups.
  • Machine learning models analyzed individual training responses using consumer-grade monitoring data.
  • Athlete characteristics were used to predict the most effective training methodology.

Main Results:

  • Polarized training yielded superior marathon performance improvements (11.3 min) compared to pyramidal training (8.7 min), a 30% greater enhancement.
  • Four distinct response clusters were identified: polarized responders (31.5%), pyramidal responders (31.9%), dual responders (18.7%), and non-responders (17.9%).
  • Training experience was the strongest predictor of effectiveness (r=0.72), with novices favoring pyramidal and experienced runners favoring polarized training.

Conclusions:

  • Significant inter-individual variability necessitates personalized training intensity distribution over universal approaches.
  • Machine learning successfully predicted optimal training methodologies using accessible athlete data.
  • This provides a practical framework for evidence-based, individualized marathon preparation.