Conceptualizing a load and volume autoregulation integrated velocity model to minimize neuromuscular fatigue and maximize neuromuscular adaptations in resistance training.

IF 2.8 3区 医学 Q2 PHYSIOLOGY
European Journal of Applied Physiology Pub Date : 2025-05-01 Epub Date: 2025-01-26 DOI:10.1007/s00421-025-05709-1
Landyn M Hickmott, Scotty J Butcher, Philip D Chilibeck
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引用次数: 0

Abstract

Resistance training (RT) load and volume are considered crucial variables to appropriately prescribe and manage for eliciting the targeted acute responses (i.e., minimizing neuromuscular fatigue) and chronic adaptations (i.e., maximizing neuromuscular adaptations). In traditional RT contexts, load and volume are generally pre-prescribed; thereby, potentially yielding sub-optimal outcomes. A RT concept that individualizes programming is autoregulation: a systematic two-step feedback process involving, (1) monitoring performance and its constituents (fitness, fatigue, and readiness) across multiple time frames (short-, moderate-, and long-term); and (2) adjusting programming (i.e., load and volume) to elicit the targeted goals (i.e., responses and adaptations). A growing body of load and volume autoregulation research has accelerated recently, with several meta-analyses suggesting that autoregulation may provide a small advantage over traditional RT. Nonetheless, the existing literature has typically conceptualized these current autoregulation methods as standalone practices, which has limited their extensive utility in research and applied settings. The primary purpose of this review was three-fold. Initially, we synthesized the current methods of load and volume autoregulation, while disseminating each method's main advantages and limitations. Second, we conceptualized a theoretical Integrated Velocity Model (IVM) that integrates the current methods for a more holistic perspective of autoregulation that may potentially augment its benefits. Lastly, we illustrated how the IVM may be compared to the current methods for future directions and how it may be implemented for practical applications. We hope that this review assists to contextualize a novel autoregulation framework to help inform future investigations for researchers and practices for RT professionals.

概念的负荷和体积自动调节集成速度模型,以减少神经肌肉疲劳和最大限度地提高神经肌肉适应阻力训练。
阻力训练(RT)负荷和量被认为是适当规定和管理的关键变量,以引起有针对性的急性反应(即,最小化神经肌肉疲劳)和慢性适应(即,最大化神经肌肉适应)。在传统的RT环境中,负载和体积通常是预先规定的;因此,可能产生次优结果。个性化编程的一个RT概念是自动调节:一个系统的两步反馈过程,包括:(1)跨多个时间框架(短期、中期和长期)监测性能及其组成部分(健康、疲劳和准备);(2)调整程序(即负荷和体积)以引出目标目标(即响应和适应)。最近,越来越多的负荷和体积自动调节研究加速发展,一些荟萃分析表明,自动调节可能比传统的自动调节提供一个小的优势。尽管如此,现有的文献通常将这些当前的自动调节方法概念化为独立的实践,这限制了它们在研究和应用环境中的广泛应用。本综述的主要目的有三个方面。首先,我们综合了当前的负载和体积自动调节方法,同时传播了每种方法的主要优点和局限性。其次,我们概念化了一个理论上的集成速度模型(IVM),该模型集成了当前的方法,可以从更全面的角度来看待自动调节,这可能会增加其好处。最后,我们说明了如何将IVM与未来方向的当前方法进行比较,以及如何在实际应用中实现它。我们希望这篇综述有助于建立一个新的自动调节框架,以帮助为研究人员和RT专业人员的实践提供未来的调查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.00
自引率
6.70%
发文量
227
审稿时长
3 months
期刊介绍: The European Journal of Applied Physiology (EJAP) aims to promote mechanistic advances in human integrative and translational physiology. Physiology is viewed broadly, having overlapping context with related disciplines such as biomechanics, biochemistry, endocrinology, ergonomics, immunology, motor control, and nutrition. EJAP welcomes studies dealing with physical exercise, training and performance. Studies addressing physiological mechanisms are preferred over descriptive studies. Papers dealing with animal models or pathophysiological conditions are not excluded from consideration, but must be clearly relevant to human physiology.
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