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Validated Predictions of Metabolic Energy Consumption for Submaximal Effort Movement

机译:次最大努力运动的代谢能消耗量的经过验证的预测

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摘要

Physical performance emerges from complex interactions among many physiological systems that are largely driven by the metabolic energy demanded. Quantifying metabolic demand is an essential step for revealing the many mechanisms of physical performance decrement, but accurate predictive models do not exist. The goal of this study was to investigate if a recently developed model of muscle energetics and force could be extended to reproduce the kinematics, kinetics, and metabolic demand of submaximal effort movement. Upright dynamic knee extension against various levels of ergometer load was simulated. Task energetics were estimated by combining the model of muscle contraction with validated models of lower limb musculotendon paths and segment dynamics. A genetic algorithm was used to compute the muscle excitations that reproduced the movement with the lowest energetic cost, which was determined to be an appropriate criterion for this task. Model predictions of oxygen uptake rate (VO2) were well within experimental variability for the range over which the model parameters were confidently known. The model's accurate estimates of metabolic demand make it useful for assessing the likelihood and severity of physical performance decrement for a given task as well as investigating underlying physiologic mechanisms.
机译:物理性能来自许多生理系统之间的复杂相互作用,而这些相互作用主要是由所需的代谢能驱动的。量化代谢需求是揭示身体机能下降的许多机制的重要步骤,但是不存在准确的预测模型。这项研究的目的是研究是否可以扩展最近开发的肌肉能量和力量模型来重现次最大努力运动的运动学,动力学和代谢需求。模拟了在各种水平的测力计负荷下的直立动态膝盖伸展。通过将肌肉收缩模型与下肢肌肉腱路径和节段动力学的经过验证的模型相结合来估计任务能量。遗传算法被用来计算以最低的能量消耗来再现运动的肌肉刺激,这被确定为完成该任务的合适标准。氧气吸收率(VO2)的模型预测值完全在实验可变性范围内,在该范围内,模型参数的确定范围是已知的。该模型对新陈代谢需求的准确估计,对于评估给定任务身体机能下降的可能性和严重性以及调查潜在的生理机制非常有用。

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