Department of Physical Therapy, University of Minnesota, Minneapolis, MN 55455, USA; Department of Physical Therapy and Rehabilitation Science, University of Iowa, Iowa City, IA 52242, USA.
Department of Physical Therapy and Rehabilitation Science, University of Iowa, Iowa City, IA 52242, USA.
J Biomech. 2018 Aug 22;77:16-25. doi: 10.1016/j.jbiomech.2018.06.005. Epub 2018 Jun 18.
This study aimed to test whether adding a rest recovery parameter, r, to the analytical three-compartment controller (3CC) fatigue model (Xia and Frey Law, 2008) will improve fatigue estimates during intermittent contractions. The 3CC muscle fatigue model uses differential equations to predict the flow of muscle between three muscle states: Resting (M), Active (M), and Fatigued (M). This model uses a feedback controller to match the active state to target loads and two joint-specific parameters: F, fatigue rate controlling flow from active to fatigued compartments) and R, the recovery rate controlling flow from the fatigued to the resting compartments. This model does well to predict intensity-endurance time curves for sustained isometric tasks. However, previous studies find when rest intervals are present that the model over predicts fatigue. Intermittent rest periods would allow for the occurrence of subsequent reactive vasodilation and post-contraction hyperemia. We hypothesize a modified 3CC-r fatigue model will improve predictions of force decay during intermittent contractions with the addition of a rest recovery parameter, r, to augment recovery during rest intervals, representing muscle re-perfusion. A meta-analysis compiling intermittent fatigue data from 63 publications reporting decline in peak torque (% torque decline) were used for comparison. The original model over-predicted fatigue development from 19 to 29% torque decline; the addition of a rest multiplier significantly improved fatigue estimates to 6-10% torque decline. We conclude the addition of a rest multiplier to the three-compartment controller fatigue model provides a physiologically consistent modification for tasks involving rest intervals, resulting in improved estimates of muscle fatigue.
本研究旨在测试在分析性三房室控制器(3CC)疲劳模型(Xia 和 Frey Law,2008)中添加休息恢复参数 r 是否会提高间歇性收缩期间的疲劳估计值。3CC 肌肉疲劳模型使用微分方程来预测肌肉在三种肌肉状态之间的流动:休息(M)、活跃(M)和疲劳(M)。该模型使用反馈控制器将活跃状态与目标负荷以及两个关节特定参数匹配:F,疲劳速率控制从活跃到疲劳腔室的流动)和 R,恢复速率控制从疲劳到休息腔室的流动。该模型很好地预测了持续等长任务的强度-耐力时间曲线。然而,之前的研究发现,当存在休息间隔时,该模型会过度预测疲劳。间歇性休息期会导致随后的反应性血管扩张和收缩后充血。我们假设,通过添加休息恢复参数 r 来增强休息期间的恢复,模拟肌肉再灌注,修改后的 3CC-r 疲劳模型将改善间歇性收缩期间的力衰减预测。一项荟萃分析汇总了 63 项报告峰值扭矩下降(%扭矩下降)的间歇性疲劳数据,用于比较。原始模型高估了从 19%到 29%扭矩下降的疲劳发展;添加休息倍增器可显著提高疲劳估计值,达到 6-10%扭矩下降。我们得出结论,在三房室控制器疲劳模型中添加休息倍增器为涉及休息间隔的任务提供了生理上一致的修改,从而提高了肌肉疲劳的估计值。