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A Pilot Study Using Entropy for Optimizing Self-Pacing during a Marathon
Florent Palacin1,2, Luc Poinsard1,2, Jean Renaud Pycke3
1Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles Neuroscience Institut, 1070 Bruxelles, Belgium.
Entropy (Basel, Switzerland)
|August 26, 2023
Summary
Marathon runners can improve performance by monitoring stride length entropy. A decrease in stride length information signals fatigue, helping runners self-pace effectively to avoid "hitting the wall".
Area of Science:
- Sports Science
- Human Physiology
- Biomechanics
Background:
- Marathon running involves self-pacing based on perceived exertion (RPE) rather than constant speed or heart rate.
- Understanding how runners use physiological and mechanical signals for self-pacing remains unclear.
Purpose of the Study:
- Investigate the relationship between information conveyed by running signals (Shannon Entropy), RPE, and marathon performance.
- Hypothesize that reduced physiological or mechanical information impacts performance.
Main Methods:
- Calculated Shannon Entropy for heart rate, speed, and stride length per kilometer.
- Analyzed the association between stride length entropy, RPE, and performance outcomes.
Main Results:
- Stride length exhibited the highest entropy among measured variables.
- Reduced stride length entropy (below 50% of max, H=3.3) correlated significantly with "hard exertion" (RPE 15) between kilometers 22-40 and overall performance (p < 0.001).
Conclusions:
- Stride length entropy is a key indicator of fatigue and performance in marathon runners.
- Integrating stride length entropy feedback into devices like cardioGPS watches could enhance marathon performance.
