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Updated: Dec 6, 2025

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running
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Human running performance from real-world big data.

Thorsten Emig1, Jussi Peltonen2

  • 1Université Paris-Saclay, CNRS, Laboratoire de Physique Théorique et Modèles Statistiques, 91405, Orsay, France. thorsten.emig@u-psud.fr.

Nature Communications
|October 7, 2020
PubMed
Summary
This summary is machine-generated.

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A minimal power model for human running performance.

PloS one·2018
See all related articles

Wearable trackers analyze running data using a validated model to predict athletic performance. This approach quantifies training impact and identifies key physiological markers for optimal performance.

Area of Science:

  • Exercise Physiology
  • Sports Science
  • Biomechanical Engineering

Background:

  • Wearable exercise trackers generate vast amounts of running data.
  • Understanding the relationship between training and performance is crucial for athletes.
  • Existing models may not fully capture the nuances of real-world running performance.

Purpose of the Study:

  • To apply a validated mathematical model to large-scale real-world running data.
  • To investigate the interplay between training load and running performance.
  • To quantify key physiological indices and predict athletic performance accurately.

Main Methods:

  • Utilized a previously validated mathematical model.
  • Analyzed data from approximately 14,000 individuals and 1.6 million exercise sessions.

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Last Updated: Dec 6, 2025

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  • Calculated aerobic power and endurance indices from running duration and distance data.

    • Main Results:

    • Demonstrated the feasibility of using wearable tracker data for performance analysis.
    • Accurately predicted race times.
    • Identified key performance parameters like lactate threshold.
    • Quantified correlations between training volume/intensity and performance indices.

    Conclusions:

    • Wearable tracker data, analyzed with appropriate models, can provide deep insights into athletic performance.
    • The model offers a novel way to quantify endurance and predict performance.
    • Findings suggest potential for optimizing training strategies based on individual data.