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Optimal mechanical force-velocity profile for sprint acceleration performance.

Pierre SamozinoNicolas PeyrotPascal EdouardRyu NagaharaPedro Jimenez-ReyesBenedicte VanwanseeleJean-Benoît Morin
Published in: Scandinavian journal of medicine & science in sports (2021)
The aim was to determine the respective influences of sprinting maximal power output ( P H max ) and mechanical Force-velocity (F-v) profile (ie, ratio between horizontal force production capacities at low and high velocities) on sprint acceleration performance. A macroscopic biomechanical model using an inverse dynamics approach applied to the athlete's center of mass during running acceleration was developed to express the time to cover a given distance as a mathematical function of P H max and F-v profile. Simulations showed that sprint acceleration performance depends mainly on P H max , but also on the F-v profile, with the existence of an individual optimal F-v profile corresponding, for a given P H max , to the best balance between force production capacities at low and high velocities. This individual optimal profile depends on P H max and sprint distance: the lower the sprint distance, the more the optimal F-v profile is oriented to force capabilities and vice versa. When applying this model to the data of 231 athletes from very different sports, differences between optimal and actual F-v profile were observed and depend more on the variability in the optimal F-v profile between sprint distances than on the interindividual variability in F-v profiles. For a given sprint distance, acceleration performance (<30 m) mainly depends on P H max and slightly on the difference between optimal and actual F-v profile, the weight of each variable changing with sprint distance. Sprint acceleration performance is determined by both maximization of the horizontal power output capabilities and the optimization of the mechanical F-v profile of sprint propulsion.
Keyphrases
  • high intensity
  • resistance training
  • single molecule
  • body mass index
  • body composition
  • machine learning
  • weight loss
  • heart rate
  • finite element analysis