Autoregulation in resistance training : addressing the inconsistencies

Autoregulation is a process that is used to manipulate training based primarily on the measurement of an individual's performance or their perceived capability to perform. Despite being established as a training framework since the 1940s, there has been limited systematic research investigating its broad utility. Instead, researchers have focused on disparate practices that can be considered specific examples of the broader autoregulation training framework. A primary limitation of previous research includes inconsistent use of key terminology (e.g., adaptation, readiness, fatigue, and response) and associated ambiguity of how to implement different autoregulation strategies. Crucially, this ambiguity in terminology and failure to provide a holistic overview of autoregulation limits the synthesis of existing research findings and their dissemination to practitioners working in both performance and health contexts. Therefore, the purpose of the current review was threefold: first, we provide a broad overview of various autoregulation strategies and their development in both research and practice whilst highlighting the inconsistencies in definitions and terminology that currently exist. Second, we present an overarching conceptual framework that can be used to generate operational definitions and contextualise autoregulation within broader training theory. Finally, we show how previous definitions of autoregulation fit within the proposed framework and provide specific examples of how common practices may be viewed, highlighting their individual subtleties

On self-propagating methodological flaws in performance normalization for strength and power sports

Performance in strength and power sports is greatly affected by a variety of anthropometric factors. The goal of performance normalization is to factor out the effects of confounding factors and compute a canonical (normalized) performance measure from the observed absolute performance. Performance normalization is applied in the ranking of elite athletes, as well as in the early stages of youth talent selection. Consequently, it is crucial that the process is principled and fair. The corpus of previous work on this topic, which is significant, is uniform in the methodology adopted. Performance normalization is universally reduced to a regression task: the collected performance data are used to fit a regression function that is then used to scale future performances. The present article demonstrates that this approach is fundamentally flawed. It inherently creates a bias that unfairly penalizes athletes with certain allometric characteristics, and, by virtue of its adoption in the ranking and selection of elite athletes, propagates and strengthens this bias over time. The main flaws are shown to originate in the criteria for selecting the data used for regression, as well as in the manner in which the regression model is applied in normalization. This analysis brings into light the aforesaid methodological flaws and motivates further work on the development of principled methods, the foundations of which are also laid out in this work

Effects of Jumping Exercise on Muscular Power in Older Adults: A Meta-Analysis.

Background Jump training (JT) can be used to enhance the ability of skeletal muscle to exert maximal force in as short a time as possible. Despite its usefulness as a method of performance enhancement in athletes, only a small number of studies have investigated its effects on muscle power in older adults. Objectives The aims of this meta-analysis were to measure the effect of JT on muscular power in older adults (≥ 50 years), and to establish appropriate programming guidelines for this population. Data sources The data sources utilised were Google Scholar, PubMed, and Microsoft Academic. Study eligibility criteria Studies were eligible for inclusion if they comprised JT interventions in healthy adults (≥ 50 years) who were free of any medical condition that could impair movement. Study appraisal and synthesis methods The inverse variance random-effects model for meta-analyses was used because it allocates a proportionate weight to trials based on the size of their individual standard errors and facilitates analysis while accounting for heterogeneity across studies. Effect sizes (ESs), calculated from a measure of muscular power, were represented by the standardised mean difference and were presented alongside 95% confidence intervals (CIs). Results Thirteen training groups across nine studies were included in this meta-analysis. The magnitude of the main effect was 'moderate' (0.66, 95% CI 0.33, 0.98). ESs were larger in non-obese participants (body mass index [BMI] < 30 vs. ≥ 30 kg/m²; 1.03 [95% CI 0.34, 1.73] vs. 0.53 [95% CI - 0.03, 1.09]). Among the studies included in this review, just one reported an acute injury, which did not result in the participant ceasing their involvement. JT was more effective in programmes with more than one exercise (range 1-4 exercises; ES = 0.74 [95% CI - 0.49, 1.96] vs. 0.53 [95% CI 0.29, 0.78]), more than two sets per exercise (range 1-4 sets; ES = 0.91 [95% CI 0.04, 1.77] vs. 0.68 [95% CI 0.15, 1.21]), more than three jumps per set (range 1-14 jumps; ES = 1.02 [95% CI 0.16, 1.87] vs. 0.53 [95% CI - 0.03, 1.09]) and more than 25 jumps per session (range 6-200 jumps; ES = 0.88 [95% CI 0.05, 1.70] vs. 0.49 [95% CI 0.14, 0.83]). Conclusions JT is safe and effective in older adults. Practitioners should construct varied JT programmes that include more than one exercise and comprise more than two sets per exercise, more than three jumps per set, and 60 s of recovery between sets. An upper limit of three sets per exercise and ten jumps per set is recommended. Up to three training sessions per week can be performed