کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
928423 | 1474231 | 2014 | 9 صفحه PDF | دانلود رایگان |
• Longitudinal leg dimensions are the most significantly related with PTS.
• Relative lean leg mass is the only body composition variable related with PTS.
• Body proportions are better PTS predictors than single anthropometric variables.
• The best allometric model explains 50.4% of PTS variance in normal weight males.
The purpose of this study was to explore the relationships between preferred transition speed (PTS) and anthropometric characteristics, body composition and different human body proportions in males. In a sample of 59 male students, we collected anthropometric and body composition data and determined individual PTS using increment protocol. The relationships between PTS and other variables were determined using Pearson correlation, stepwise linear and hierarchical regression. Body ratios were formed as quotient of two variables whereby at least one significantly correlated to PTS. Circular and transversal (except bitrochanteric diameter) body dimensions did not correlate with PTS. Moderate correlations were found between longitudinal leg dimensions (foot, leg and thigh length) and PTS, while the highest correlation was found for lower leg length (r = .488, p < .01). Two parameters related to body composition showed weak correlation with PTS: body fat mass (r = −.250, p < .05) and amount of lean leg mass scaled to body weight (r = .309, p < .05). Segmental body proportions correlated more significantly with PTS, where thigh/lower leg length ratio showed the highest correlation (r = .521, p < .01). Prediction model with individual variables (lower leg and foot length) have explained just 31% of PTS variability, while model with body proportions showed almost 20% better prediction (R2 = .504). These results suggests that longitudinal leg dimensions have moderate influence on PTS and that segmental body proportions significantly more explain PTS than single anthropometric variables.
Journal: Human Movement Science - Volume 34, April 2014, Pages 196–204