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Updated: Sep 19, 2025

Determining the Contribution of the Energy Systems During Exercise
Published on: March 20, 2012
Quantifying metabolic energy contributions in sprint running: a novel bioenergetic model
Jérémy Briand1,2, Pietro Enrico di Prampero3,4, Cristian Osgnach3
1École de kinésiologie et des sciences de l'activité physique (EKSAP), Faculté de médecine, Centre d'éducation physique et des sports (CEPSUM), Université de Montréal, 2100, Boul. Édouard-Montpetit, Montreal, QC, H3T 1J4, Canada. jeremy.briand@umontreal.ca.
Purpose:
To develop a bioenergetic model representing the dynamics of metabolic power-including aerobic, anaerobic lactic, and anaerobic alactic contributions-during 100-400 m sprints. This study calculates maximum anaerobic capacities using sprint data and assesses the model's ability to predict performance across various sprint distances.
Methods:
Sprint energetics were estimated applying di Prampero et al. (J Exp Biol 208:2809-2816, 2005) method using velocity and time-split data from the 2009 World Athletics Championships to model metabolic power over the men's and women's 100-200-400 m events. Aerobic power was modeled with an exponential function, anaerobic lactic power with a bi-exponential function, and anaerobic alactic power with a log-normal function. Maximal anaerobic lactic and alactic capacities were estimated from available performances. Simulations were made to predict the distance traveled by hypothetical male and female athletes achieving World Championship performances on the 100-200-400 m.
Results:
The model closely fit metabolic power trajectories (R2 = 0.94-0.98). Maximal anaerobic alactic capacities were 376 J kg-1 (male) and 259 J kg-1 (female), and maximal anaerobic lactic capacities were 1314 J kg-1 (male) and 1194 J kg-1 (female). Simulations of distance traveled revealed mean absolute errors of 0.31% and 1.63% for male and female, respectively. Higher female errors likely stem from underestimating anaerobic lactic contribution due to male-derived parameters and limited available data.
Conclusion:
This model aligns closely with theoretical bioenergetic principles and experimental findings, providing valuable insights that improve our understanding of sprint running energetics and performance. Further refinements, incorporating female-specific parameters and collecting data from various distances, could broaden the model's applicability.
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