Medicina (Sep 2020)

Allometric Modeling of Wingate Test among Adult Male Athletes from Combat Sports

  • Manuel J. Coelho-e-Silva,
  • Paulo Sousa-e-Silva,
  • Vinícius S. Morato,
  • Daniela C. Costa,
  • Diogo V. Martinho,
  • Luís M. Rama,
  • João Valente-dos-Santos,
  • André O. Werneck,
  • Óscar M. Tavares,
  • Jorge Conde,
  • Joaquim M. Castanheira,
  • Rui Soles-Gonçalves,
  • João P. Duarte

DOI
https://doi.org/10.3390/medicina56090480
Journal volume & issue
Vol. 56, no. 480
p. 480

Abstract

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Background and objectives: Athletes from combat sports are grouped into a series of weight categories that are intended to promote fair competition. Differences in performance are partly attributable to differences in body size. Consequently, ratio standards in which a performance variable is simply divided by an anthropometric characteristic such as body mass are often used, although this application is not recommended. This study aimed to obtain allometric models to interpret Wingate Anaerobic Test (WAnT) outputs among male adult athletes from combat sports. Materials and Methods: The sample was composed of 64 participants aged 18–39 years (24.2 ± 4.6 years). Stature and body mass (BM) were measured and air displacement plethysmography used to estimate fat mass and fat-free mass (FFM). Lower-limb lean soft tissue (LL-LST) was derived from dual energy X-ray absorptiometry. WAnT outputs were peak power (WAnT-PP) and mean power (WAnT-MP). Allometric models were obtained from simple and multiple linear regressions using log-transformed variables. Results: Models derived from a single three-dimension descriptor explained a large portion of variance: WAnT-PP (BM: 31.1%; FFM: 54%; LL-LST: 47.2%) and WAnT-MP (BM: 50.1%; FFM: 57.4%; LL-LST: 62.7%). Finally, the best proportional allometric models emerged from the combination of LL-LST and FFM (WAnT-PP: 55%; WAnT-MP: 65%). Conclusions: The relationship between weight categories and performance did not seem to be explained by the basic principles of geometric similarity.

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