Russell A P, Le Rossignol P F, Sparrow W A
School of Human Movement, Deakin University, Burwood, Victoria, Australia.
J Sports Sci. 1998 Nov;16(8):749-54. doi: 10.1080/026404198366380.
In 19 elite schoolboy rowers, the relationships between anthropometric characteristics, metabolic parameters, strength variables and 2000-m rowing ergometer performance time were analysed to test the hypothesis that a combination of these variables would predict performance better than either individual variables or one category of variables. Anthropometric characteristics, maximal oxygen uptake (VO2max), accumulated oxygen deficit, net efficiency, leg strength and 2000-m rowing ergometer time were measured. Body mass, VO2max and knee extension correlated with 2000-m performance time (r= -0.41, -0.43 and -0.40, respectively; P< 0.05), while net efficiency and accumulated oxygen deficit did not. Multiple-regression analyses indicated that the prediction model using anthropometric variables alone best predicts performance (R = 0.82), followed by the equation comprising body mass, VO2max and skinfolds (R = 0.80). Although the regression equations increased the predictive power from that obtained using single variables, the hypothesis that a prediction model consisting of variables from different physiological categories would predict performance better than variables from one physiological category was not supported.
对19名精英男校划船运动员的人体测量特征、代谢参数、力量变量与2000米划船测力计表现时间之间的关系进行了分析，以检验这样一个假设：这些变量的组合比单个变量或某一类变量能更好地预测表现。测量了人体测量特征、最大摄氧量（VO2max）、累积氧亏、净效率、腿部力量和2000米划船测力计时间。体重、VO2max和膝关节伸展与2000米表现时间相关（r分别为-0.41、-0.43和-0.40；P<0.05），而净效率和累积氧亏则不相关。多元回归分析表明，仅使用人体测量变量的预测模型对表现的预测效果最佳（R = 0.82），其次是包含体重、VO2max和皮褶厚度的方程（R = 0.80）。尽管回归方程比使用单个变量时的预测能力有所提高，但由来自不同生理类别的变量组成的预测模型比来自一个生理类别的变量能更好地预测表现这一假设未得到支持。
