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

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...

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Detalles Bibliográficos
Autores principales: Coelho-e-Silva, Manuel J., Sousa-e-Silva, Paulo, Morato, Vinícius S., Costa, Daniela C., Martinho, Diogo V., Rama, Luís M., Valente-dos-Santos, João, Werneck, André O., Tavares, Óscar M., Conde, Jorge, Castanheira, Joaquim M., Soles-Gonçalves, Rui, Duarte, João P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7558848/
https://www.ncbi.nlm.nih.gov/pubmed/32967169
http://dx.doi.org/10.3390/medicina56090480
Descripción
Sumario: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.