Literature DB >> 33661083

The influence of anthropometric variables, body composition, propulsive force and maturation on 50m freestyle swimming performance in junior swimmers: An allometric approach.

Marcos A M Dos Santos1,2, Rafael S Henrique1,3,4, Marlene Salvina4, Artur Henrique Oliveira Silva1, Marco Aurélio de V C Junior1, Daniel R Queiroz1, Michael J Duncan5, José A R Maia3, Alan M Nevill6.   

Abstract

The purpose of the current article was to use allometric models to identify the best body size descriptors and other anthropometric variables, body composition, and offset maturity that might be associated with the youngsters' 50m personal-best (PB) swim speeds (m·s-1). Eighty-five competitive swimmers (male, n=50; 13.5±1.8 y; female, n=35; 12.6±1.8 y) participated in this study. Height, body mass, sitting height, arm span, skinfolds, arm muscle area (AMA), and maturity offset were assessed. Swimming performance was taken as the PB time recorded in competition, and the propulsive force of their arm (PFA) was assessed by the tied swimming test. The multiplicative allometric model relating 50m PB swim speeds (m·s-1) to all the predictor variables found percentage body fat as a negative [(BF%) β= -.121±.036; P=0.001], and PFA (PFA β=.108±.033; P=0.001) and the girl's arm span (β=.850±.301; P=0.006), all log-transformed, as positive significant predictors of log-transformed swim speed. The adjusted coefficient of determination, Radj2 was 54.8% with the log-transformed error ratio being 0.094 or 9.8%, having taken antilogs. The study revealed, using an allometric approach, that body fatness and PFA were significant contributors to 50m freestyle swim performance in young swimmers.

Entities:  

Keywords:  Swim speed; allometric models; personal-best swim speeds; propulsive force

Mesh:

Year:  2021        PMID: 33661083     DOI: 10.1080/02640414.2021.1891685

Source DB:  PubMed          Journal:  J Sports Sci        ISSN: 0264-0414            Impact factor:   3.337


  1 in total

1.  Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model.

Authors:  Wei Wen; Tingyu Yang; Yanhao Fu; Siwen Liu
Journal:  Comput Intell Neurosci       Date:  2022-04-11
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.