Patoz Aurélien, Lussiana Thibault, Gindre Cyrille, Mourot Laurent
Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland.
Research and Development Department, Volodalen Swiss Sport Lab, Aigle, Switzerland.
Front Physiol. 2021 Jan 8;11:625557. doi: 10.3389/fphys.2020.625557. eCollection 2020.
Equations predicting stride frequency (SF) and duty factor (DF) solely based on running speed have been proposed. However, for a given speed, kinematics vary depending on the global running pattern (GRP), i.e., the overall individual movement while running, which depends on the vertical oscillation of the head, antero-posterior motion of the elbows, vertical pelvis position at ground contact, antero-posterior foot position at ground contact, and strike pattern. Hence, we first verified the validity of the aforementioned equations while accounting for GRP. Kinematics during three 50-m runs on a track ( = 20) were used with curve fitting and linear mixed effects models. The percentage of explained variance was increased by ≥133% for DF when taking into account GRP. GRP was negatively related to DF ( = 0.004) but not to SF ( = 0.08), invalidating DF equation. Second, we assessed which parameters among anthropometric characteristics, sex, training volume, and GRP could relate to SF and DF in addition to speed, using kinematic data during five 30-s runs on a treadmill ( = 54). SF and DF linearly increased and quadratically decreased with speed ( < 0.001), respectively. However, on an individual level, SF was best described using a second-order polynomial equation. SF and DF showed a non-negligible percentage of variance explained by random effects (≥28%). Age and height were positively and negatively related to SF ( ≤ 0.05), respectively, while GRP was negatively related to DF ( < 0.001), making them key parameters to estimate SF and DF, respectively, in addition to speed.
已经有人提出了仅基于跑步速度来预测步频(SF)和负荷因子(DF)的方程。然而,对于给定的速度,运动学因整体跑步模式(GRP)而异,即跑步时的整体个人运动,这取决于头部的垂直振荡、肘部的前后运动、地面接触时骨盆的垂直位置、地面接触时足部的前后位置以及着地模式。因此,我们首先在考虑GRP的情况下验证了上述方程的有效性。在跑道上进行的三次50米跑步(n = 20)过程中的运动学数据,使用曲线拟合和线性混合效应模型进行分析。考虑GRP时,DF的解释方差百分比增加了≥133%。GRP与DF呈负相关(p = 0.004),但与SF无关(p = 0.08),这使得DF方程无效。其次,我们使用跑步机上五次30秒跑步(n = 54)过程中的运动学数据,评估了除速度外,人体测量特征、性别、训练量和GRP中的哪些参数可能与SF和DF相关。SF和DF分别随速度呈线性增加和二次方下降(p < 0.001)。然而,在个体水平上,SF最好用二阶多项式方程来描述。SF和DF显示出由随机效应解释的不可忽略的方差百分比(≥28%)。年龄和身高分别与SF呈正相关和负相关(p ≤ 0.05),而GRP与DF呈负相关(p < 0.001),这使得它们分别成为除速度外估计SF和DF的关键参数。