Bioenergetic Modeling

Creating computational models to interpret physiological data and predict athletic performance.

Overview

This project involves creating computational models to interpret physiological data and predict athletic performance. By integrating metabolic and cardiorespiratory data, we develop mathematical frameworks that can forecast performance outcomes and guide training decisions.

The bioenergetic modeling work represents a core strength of the LPEBA laboratory, combining exercise physiology principles with data science approaches.

Objectives

  1. Develop predictive models for athletic performance
  2. Integrate metabolic and cardiorespiratory data streams
  3. Validate models with field data from various sports

Methodology

  • Mathematical modeling of energy systems during exercise
  • Machine learning approaches for performance prediction
  • Cross-validation with laboratory and field data
  • Integration of multiple physiological parameters

Team

  • Jérémy Briand (Lead Investigator)
  • Jonathan Tremblay (Principal Investigator)
  • Guy Thibault (Consultant)

Funding

Self-funded research

Key Publications

  • Briand, J. et al. (2025). Quantifying metabolic energy contributions in sprint running: a novel bioenergetic model. European Journal of Applied Physiology

  • Briand, J. et al. (2025). Bridging inductive and deductive reasoning: a proposal to enhance the evaluation and development of models in sports and exercise science. Sports Medicine

Timeline

Status: Active
Start Date: January 2022
End Date: Ongoing

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