由不同踩踏時間功率評估與預測高中自由車選手臨界功率

Assessment and prediction of critical power using power outputs at different cycling intervals for senior high school cyclists

10.6222/pej.202303_56(1).0005

呂松庭(Sung-Ting Lu)；葉哲偉(Che-Wei Yeh)；陳麒先(Chi-Hsien Chen)；黃智盈(Chih-Ying Huang)；陳韋翰(Wei-Han Chen)；劉強(Chiang Liu)

自行車 ； 踩踏功率 ； 能量系統 ； bike ； pedaling power ； energy system

體育學報

56卷1期（2023 / 03 / 01）

77 - 90

繁體中文;英文

緒論：臨界功率（critical power, CP）為評估自由車選手騎乘能力的重要指標，CP是由多次不同踩踏時間之平均功率輸出所建立，傳統CP檢測較為耗時且費力，容易造成選手產生疲勞及受傷的風險。本研究為分析高中自由車選手於不同踩踏時間之平均功率輸出，以及探討踩踏時間是否影響平均功率輸出的表現，並建立CP預測公式。方法：29名高中自由車選手，以最大努力進行5秒、15秒、30秒、60秒、240秒、600秒及1200秒等功率輸出。以重複量數單因子變異數分析比較不同踩踏時間的平均功率輸出，再以多元逐步迴歸建立簡化CP的測試方法。結果：5秒平均功率輸出（819.3 W ± 142.2 W）、15秒平均功率輸出（677.5 W ± 104.5 W）、30秒平均功率輸出（571.9 W ± 80.3 W）、60秒平均功率輸出（435.6 W ± 50.7 W）、240秒平均功率輸出（302.0 W ± 29.7 W）、600秒平均功率輸出（266.7 W ± 24.1 W）及1200秒平均功率輸出（244.3 W ± 23.1 W）；不同踩踏時間之平均功率輸出均達顯著差異（p < .001）；其中CP與1200秒相關性呈現高度相關（r= .997），且經由多元逐步迴歸分析後發現1200秒的平均功率輸出為CP的最佳預測指標（R^2 = .993）。結論：不同踩踏時間之平均功率輸出能夠分析選手短、中、長時間的騎乘能力；本研究發現經測量1200秒的平均功率輸出，透過預測公式計算後能精準地推估CP值，並提供新穎的CP檢測方式，除了能簡易得到CP的數值還能有效節省測試的時間。

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 (R^2 = .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.

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