Gómez-Molina Josué, Ogueta-Alday Ana, Camara Jesus, Stickley Christoper, Rodríguez-Marroyo José A, García-López Juan
Faculty of Education and Sport, University of the Basque Country, UPV/EHU, Spain.
Department of Physical Education and Sports, Institute of Biomedicine (IBIOMED), Faculty of Physical Activity and Sports Sciences (FCAFD) University of León. Spain.
J Sports Sci Med. 2017 Jun 1;16(2):187-194. eCollection 2017 Jun.
The aims of this study were to establish and validate various predictive equations of half-marathon performance. Seventy-eight half-marathon male runners participated in two different phases. Phase 1 (n = 48) was used to establish the equations for estimating half-marathon performance, and Phase 2 (n = 30) to validate these equations. Apart from half-marathon performance, training-related and anthropometric variables were recorded, and an incremental test on a treadmill was performed, in which physiological (VO, speed at the anaerobic threshold, peak speed) and biomechanical variables (contact and flight times, step length and step rate) were registered. In Phase 1, half-marathon performance could be predicted to 90.3% by variables related to training and anthropometry (Equation 1), 94.9% by physiological variables (Equation 2), 93.7% by biomechanical parameters (Equation 3) and 96.2% by a general equation (Equation 4). Using these equations, in Phase 2 the predicted time was significantly correlated with performance (r = 0.78, 0.92, 0.90 and 0.95, respectively). The proposed equations and their validation showed a high prediction of half-marathon performance in long distance male runners, considered from different approaches. Furthermore, they improved the prediction performance of previous studies, which makes them a highly practical application in the field of training and performance.
本研究的目的是建立并验证半程马拉松成绩的各种预测方程。78名男性半程马拉松跑者参与了两个不同阶段。第一阶段(n = 48)用于建立估算半程马拉松成绩的方程,第二阶段(n = 30)用于验证这些方程。除了半程马拉松成绩外,还记录了与训练相关的变量和人体测量学变量,并在跑步机上进行了递增测试,记录了生理变量(摄氧量、无氧阈速度、峰值速度)和生物力学变量(接触时间和腾空时间、步长和步频)。在第一阶段,与训练和人体测量学相关的变量可将半程马拉松成绩预测到90.3%(方程1),生理变量可预测到94.9%(方程2),生物力学参数可预测到93.7%(方程3),通用方程可预测到96.2%(方程4)。使用这些方程,在第二阶段,预测时间与成绩显著相关(r分别为0.78、0.92、0.90和0.95)。从不同方法考虑,所提出的方程及其验证显示出对长距离男性跑者半程马拉松成绩的高度预测性。此外,它们提高了先前研究的预测性能,使其在训练和成绩领域具有高度的实际应用价值。