INTRODUCTION Formulating individualized optimized jump training prescriptions based on the force-velocity profile has become popular, but its effectiveness has been contested. Such training programs have opposite effects on 'maximal average force' and 'maximal average shortening velocity', and we set out to investigate which training-induced changes in the neuromuscular system could cause such effects. METHODS We used a musculoskeletal simulation model with four body segments and six muscle-tendon actuators to simulate vertical squat jumps with different loads. Independent input was muscle stimulation over time, which was optimized for maximal jump height. We determined the force-velocity profile for a reference model and investigated how it changed when we modified muscle properties and initial postures. RESULTS We could not reproduce the reported training effects by realistically improving muscle properties (maximal force, shortening velocity and rate of force development) or modifying initial postures of the model. However, the profile was very sensitive to gains in jump height at low-loads but not high loads, or vice versa. Reaching maximal height in force-velocity profile jumps requires skill. We argued that submaximal performance in low-load or high-load jumps caused by lack of skill could be responsible for large imbalances in profiles before training. Differential skill training promoted by the individualized optimized approach could explain quick changes toward a balanced profile. CONCLUSIONS If the success of individualized optimized training studies is explained by selective skill improvements, training effects are unlikely to transfer to other tasks, and individualized optimized training will not be superior to other types of training.

中文翻译：

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自由跳跃的力-速度曲线是个性化跳跃训练处方的可靠基础吗？


引言 根据力-速度曲线制定个性化的优化跳跃训练处方已经变得流行，但其有效性一直存在争议。此类训练计划对“最大平均力量”和“最大平均缩短速度”具有相反的效果，我们着手研究哪些训练引起的神经肌肉系统变化会导致此类影响。方法 我们使用具有四个身体节段和 6 个肌肉肌腱致动器的肌肉骨骼模拟模型来模拟不同负载的垂直深蹲跳跃。独立输入是随着时间的推移对肌肉的刺激，它针对最大跳跃高度进行了优化。我们确定了参考模型的力-速度曲线，并研究了当我们修改肌肉特性和初始姿势时它如何变化。结果我们无法通过现实地改善肌肉特性 （最大力量、缩短速度和力量发展速度） 或修改模型的初始姿势来重现所报道的训练效果。然而，该曲线对低负载时跳跃高度的增加非常敏感，但对高负载下不敏感，反之亦然。在力-速度剖面跳跃中达到最大高度需要技巧。我们认为，由于缺乏技能而导致的低负载或高负载跳跃中的次最大性能可能是导致训练前配置文件出现巨大不平衡的原因。由个性化优化方法促进的差异化技能训练可以解释向平衡配置文件的快速变化。结论 如果个体化优化训练研究的成功可以用选择性技能提高来解释，那么训练效果不太可能转移到其他任务上，个体化优化训练不会优于其他类型的训练。