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Physiological, Anthropometric, Strength, and Muscle Power Characteristics Correlates With Running Performance in Young Runners

    1. [1] Universidade Federal do Paraná

      Universidade Federal do Paraná

      Brasil

    2. [2] Federal University of Santa Catarina
  • Localización: Journal of strength and conditioning research: the research journal of the NSCA, ISSN 1064-8011, Vol. 29, Nº. 6, 2015, págs. 1584-1591
  • Idioma: inglés
  • Texto completo no disponible (Saber más ...)
  • Resumen
    • The purpose of this study was to investigate the relationship between physiological, anthropometric, strength, and muscle power variables and a 5-km time trial (5kmT) in young runners. Twenty-three runners volunteered to participate in this study. Height, body mass, body fat, and fat-free mass (FFM) were measured. The subjects underwent laboratory testing to determine maximal oxygen uptake (V[Combining Dot Above]O2max), velocity at ventilatory threshold (VVT), running economy (RE), velocity associated with maximal oxygen uptake (vV[Combining Dot Above]O2max), and peak velocity (PV). Peak torque, total work, and power were measured by an isokinetic dynamometer at 60°·s-1 and 240°·s-1 angular velocities. Right and left knee flexor and extensor torques were evaluated. Finally, the participants performed a 5kmT. Multiple regression and correlation analysis were used to determine the variables that significantly related to 5kmT. Strength and muscle power variables did not correlate with 5kmT. However, most physiological variables were associated with 5kmT. Velocity at ventilatory threshold alone explains 40% of the variance in 5kmT. The addition of the RE at speed 11.2 km·h-1 (RE11.2) and FFM to the prediction equation allowed for 71% of the adjusted variance in 5kmT to be predicted. These results show that strength and muscle power variables are not good predictors of 5kmT; however, the physiological variables presented high prediction capacity in the 5kmT. Moreover, the anthropometric measures showed significant influence in performance prediction.


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