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

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.