Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
Strength and Conditioning, Australian Institute of Sport, Bruce, A.C.T., Australia.
PLoS One. 2019 Jul 10;14(7):e0219295. doi: 10.1371/journal.pone.0219295. eCollection 2019.
This study investigated the relationship between the ground reaction force-time profile of a countermovement jump (CMJ) and fatigue, specifically focusing on predicting the onset of neuromuscular versus metabolic fatigue using the CMJ.
Ten recreational athletes performed 5 CMJs at time points prior to, immediately following, and at 0.5, 1, 3, 6, 24 and 48 h after training, which comprised repeated sprint sessions of low, moderate, or high workloads. Features of the concentric portion of the CMJ force-time signature at the measurement time points were analysed using Principal Components Analysis (PCA) and functional PCA (fPCA) to better understand fatigue onset given training workload. In addition, Linear Mixed Effects (LME) models were developed to predict the onset of fatigue.
The first two Principal Components (PCs) using PCA explained 68% of the variation in CMJ features, capturing variation between athletes through weighted combinations of force, concentric time and power. The next two PCs explained 9.9% of the variation and revealed fatigue effects between 6 to 48 h after training for PC3, and contrasting neuromuscular and metabolic fatigue effects in PC4. fPCA supported these findings and further revealed contrasts between metabolic and neuromuscular fatigue effects in the first and second half of the force-time curve in PC3, and a double peak effect in PC4. Subsequently, CMJ measurements up to 0.5 h after training were used to predict relative peak CMJ force, with mean squared errors of 0.013 and 0.015 at 6 and 48 h corresponding to metabolic and neuromuscular fatigue.
The CMJ was found to provide a strong predictor of neuromuscular and metabolic fatigue, after accounting for force, concentric time and power. This method can be used to assist coaches to individualise future training based on CMJ response to the immediate session.
本研究探讨了反跳（CMJ）的地面反作用力-时间曲线与疲劳之间的关系，特别是使用 CMJ 预测神经肌肉与代谢性疲劳的发生。
10 名业余运动员在训练前、训练后即刻以及 0.5、1、3、6、24 和 48 小时后进行了 5 次 CMJ，这些运动员完成了低、中、高负荷的重复冲刺。使用主成分分析（PCA）和功能主成分分析（fPCA）对 CMJ 力-时间特征的同心部分在测量时间点的特征进行分析，以更好地了解给定训练负荷下疲劳的发生。此外，还开发了线性混合效应（LME）模型来预测疲劳的发生。
使用 PCA 的前两个主成分（PC）解释了 CMJ 特征变化的 68%，通过力、同心时间和功率的加权组合来捕捉运动员之间的变化。接下来的两个 PC 解释了 9.9%的变化，并揭示了训练后 6 至 48 小时 PC3 的疲劳效应，以及 PC4 中神经肌肉和代谢性疲劳效应的对比。fPCA 支持了这些发现，并进一步揭示了 PC3 中力-时间曲线前半部分和后半部分代谢性和神经肌肉疲劳效应之间的对比，以及 PC4 中的双峰效应。随后，使用训练后 0.5 小时内的 CMJ 测量值来预测相对峰值 CMJ 力，在 6 和 48 小时时的均方误差分别为 0.013 和 0.015，对应于代谢性和神经肌肉性疲劳。
在考虑力、同心时间和功率后，CMJ 被发现是神经肌肉和代谢性疲劳的有力预测指标。这种方法可以用于帮助教练根据 CMJ 对当前训练的反应来个性化未来的训练。
