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实验室性能测试对山地自行车越野奥林匹克运动员的预测能力。

Predictive Ability of a Laboratory Performance Test in Mountain Bike Cross-country Olympic Athletes.

机构信息

Department of Sports Medicine, Medical Clinic, University of Tuebingen, Tuebingen.

Institute for Clinical Epidemiology and Applied Biometry, University of Tuebingen, Tuebingen.

出版信息

Int J Sports Med. 2019 May;40(6):397-403. doi: 10.1055/a-0858-9900. Epub 2019 Apr 1.

DOI:10.1055/a-0858-9900
PMID:30934091
Abstract

Mountain bike Cross-Country Olympic (XCO) has an intermittent performance profile, underlining the importance of anaerobic metabolism. Traditional performance tests in cycling primarily quantify aerobic metabolism and inadequately meet the demands in XCO. The aim was therefore to validate a specific test that quantifies these requirements by means of an XCO race.Twenty-three competitive XCO athletes (17.9±3.6 years) performed a previously developed performance test and an XCO race within one week. Correlations between individual anaerobic threshold (IAT), 4 mmol lactate threshold (LT), maximal aerobic power (MAP), maximal effort time trials (TT) for 10-300 s and mean power output of the race (POR) were calculated. In addition, a multiple regression model of the predictive value of the test was calculated.Variables correlated significantly (p<.01) with POR: IAT (r=.81), LT (r=0.79), MAP (r=0.91), TT (r=0.75), TT (r=0.85), TT (r=0.84) and TT (r=0.86). In the regression model, sex and body mass were set influencing variables (R²=0.70), whereby MAP had the highest correlation with POR and significantly improved the predictive value of the model (R²=0.86).The high correlation of collected performance variables with POR indicated the MTB-PT's additional benefit for performance testing in XCO because it is specific but very feasible.

摘要

山地自行车越野奥林匹克项目(XCO)具有间歇性的表现特征,强调了无氧代谢的重要性。传统的自行车运动性能测试主要量化有氧代谢,而不能充分满足 XCO 的需求。因此,本研究旨在验证一种特定的测试,通过 XCO 比赛来量化这些需求。23 名有竞争力的 XCO 运动员(17.9±3.6 岁)在一周内进行了先前开发的性能测试和 XCO 比赛。计算了个体无氧阈(IAT)、4mmol 乳酸阈(LT)、最大有氧功率(MAP)、10-300s 的最大努力计时赛(TT)和比赛平均功率输出(POR)之间的个体相关性。此外,还计算了测试预测价值的多元回归模型。与 POR 显著相关的变量(p<.01):IAT(r=.81)、LT(r=0.79)、MAP(r=0.91)、TT(r=0.75)、TT(r=0.85)、TT(r=0.84)和 TT(r=0.86)。在回归模型中,性别和体重被设定为影响变量(R²=0.70),其中 MAP 与 POR 的相关性最高,并显著提高了模型的预测价值(R²=0.86)。收集的性能变量与 POR 的高度相关性表明,MTB-PT 对 XCO 性能测试具有额外的益处,因为它具有特异性但非常可行。

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