英文摘要 |
Introduction: Critical Power (CP) is the most representative characteristic for evaluating a cyclist’s performance. Traditional CP is established by measuring the average power output of multiple different pedaling times. Measuring CP is time-consuming and arduous, which is likely to cause fatigue and risk of injury. This study aimed to understand power output at different time intervals and the effects of pedaling time on power output and to establish a predictive model for CP. Methods: Elite high school cyclists (n = 29) performed at their highest level to obtain their power output at intervals of 5 s, 15 s, 30 s, 60 s, 240 s, 600 s, and 1200 s. A repeated measures one-way ANOVA was used to compare the difference in power output between intervals. A stepwise multiple regression model was used to establish the CP prediction model. Results: The average power output at various intervals was 819.3 ± 142.2 W (5 s), 677.5 ± 104.5 W (15 s), 571.9 ± 80.3 W (30 s), 435.6 ± 50.7 W (60 s), 302.0 ± 29.7 W (240 s), 266.7 ± 24.1 W (600 s), and 244.3 ± 23.1 W (1200 s) and showed a significant difference (p < .001). CP at 1200 s showed a high correlation with power output (r = .997), and stepwise multiple regression analysis indicated that the power output at 1200 s was the best index to predict CP (R2 = .993). Conclusion: Our results indicate that the power output at 1200 s can be used to predict CP with the predictive model and to analysis the various distances achieved in cyclist group performance. This study provides an innovative model to precisely predict CP and reduce the time spent on unnecessary interval testing. |