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从库珀测试估算半程马拉松比赛时间的简单方程式。

A Simple Equation to Estimate Half-Marathon Race Time From the Cooper Test.

出版信息

Int J Sports Physiol Perform. 2020 May 1;15(5):690-695. doi: 10.1123/ijspp.2019-0518.

Abstract

BACKGROUND

Half-marathon races have become increasingly more popular with many recreational athletes all around the world. New and recreational runners are likely to have the greatest need for training advice to set running paces during long-distance races.

PURPOSE

To develop a simple equation to estimate half-marathon time from the Cooper test and verify its validity.

METHODS

One hundred ninety-eight recreational runners (177 men and 21 women, 40 [6.8] years and 33.7 [8] years, respectively) participated in this study. All runners completed the Cooper test 7 to 10 days prior to races. A stepwise multiple regression analysis was performed to select the main predictors of half-marathon time.

RESULTS

Simple correlation analysis showed that Cooper test performance (distance) was a good construct to estimate half-marathon time (r = -.906; 95% confidence interval, -0.927 to -0.877; P < .0001). The authors also derived an equation with a high predictive validity (R2 = .82; standard error of estimation = 5.19 min) and low systematic bias (mean differences between the predicted value and the criterion of 0.48 [5.2] min). Finally, the concordance coefficient of correlation (.9038) and proportional bias analysis (Kendall τ = -.0799; 95% confidence interval, -0.184 to 0.00453; P = .09) confirmed a good concurrent validity.

CONCLUSION

In this study, the authors derived an equation from the Cooper test data with a high predictive and concurrent validity and low bias.

摘要

背景

半程马拉松比赛在全世界范围内越来越受到许多休闲运动员的欢迎。新的和休闲的跑步者可能最需要训练建议,以在长跑比赛中设定跑步速度。

目的

从库珀测试中开发一个简单的方程来估计半程马拉松时间,并验证其有效性。

方法

198 名休闲跑步者(177 名男性和 21 名女性,年龄分别为 40 [6.8]岁和 33.7 [8]岁)参加了这项研究。所有跑步者在比赛前 7 至 10 天完成库珀测试。进行逐步多元回归分析,以选择半程马拉松时间的主要预测因素。

结果

简单相关分析表明,库珀测试表现(距离)是估计半程马拉松时间的良好结构(r = -.906;95%置信区间,-0.927 至 -0.877;P <.0001)。作者还推导出一个具有高预测有效性的方程(R2 =.82;估计标准误差= 5.19 分钟)和低系统偏差(预测值与标准值之间的平均差异为 0.48 [5.2] 分钟)。最后,相关性的一致性系数(.9038)和比例偏差分析(肯德尔 τ = -.0799;95%置信区间,-0.184 至 0.00453;P =.09)证实了良好的同时有效性。

结论

在这项研究中,作者从库珀测试数据中推导出一个具有高预测和同时有效性且偏差低的方程。

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