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Related Concept Videos

Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

158
Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression...
158

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Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
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Multidimensional Analysis of Physiological Entropy during Self-Paced Marathon Running.

Florent Palacin1, Luc Poinsard1, Véronique Billat1,2

  • 1EA 4445-Movement, Balance, Performance, and Health Laboratory, Université de Pau et des Pays de l'Adour, 65000 Tarbes, France.

Sports (Basel, Switzerland)
|September 27, 2024
PubMed
Summary

Marathon pacing is optimized by analyzing physiological data variability using Shannon entropy and PCA. Real-time monitoring of these metrics can help runners avoid "hitting the wall" and improve performance.

Keywords:
entropyfatiguehitting the wallmarathon runningpacingperformancephysiological responses

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Area of Science:

  • Exercise Physiology
  • Sports Science
  • Data Analysis in Athletics

Background:

  • Marathon running performance is significantly impacted by pacing strategies.
  • Runners often experience a performance decline, known as
  • hitting the wall
  • around the 30 km mark.
  • Optimizing pacing requires understanding complex physiological responses during endurance events.

Purpose of the Study:

  • To investigate physiological responses and pacing strategies during marathon running.
  • To apply Shannon entropy and Principal Component Analysis (PCA) for quantifying cardiorespiratory measure variability.
  • To explore the potential for optimizing self-paced marathon performance through physiological data analysis.

Main Methods:

  • Continuous monitoring of oxygen uptake (V˙O2), carbon dioxide output (V˙CO2), tidal volume (Vt), heart rate, respiratory frequency (Rf), and running speed in nine recreational marathon runners.
  • Application of PCA to analyze entropy variance of cardiorespiratory measures and running parameters.
  • Utilizing Agglomerative Hierarchical Clustering to categorize runners' physiological responses and identify distinct entropy profiles.

Main Results:

  • PCA revealed distinct axes for metabolic (V˙O2, V˙CO2, Vt) and other (heart rate, cadence) physiological responses.
  • A shift in physiological state was observed post-26 km, indicated by changes in metabolic responses relative to distance.
  • Heart rate and cadence entropy variances appeared independent of distance, unlike typical linear changes.
  • Clustering identified 2-4 distinct physiological response profiles, often correlating with race phases (beginning, middle, end).

Conclusions:

  • Physiological responses and pacing strategies during marathons are highly individualized.
  • Real-time entropy monitoring offers potential for enhanced marathon performance insights.
  • Understanding physiological variability can help runners prevent performance degradation and
  • hit the wall.
  • Individualized pacing strategies based on physiological feedback are crucial for marathon success.