Unveiling the acute neurophysiological responses to strength training: an exploratory study on novices performing weightlifting bouts with different motor learning models

Abstract

Currently, there is limited evidence regarding various neurophysiological responses to strength exercise and the influence of the adopted practice schedule. This study aimed to assess the acute systemic effects of snatch training bouts, employing different motor learning models, on skill efficiency, electric brain activity (EEG), heart rate variability (HRV), and perceived exertion as well as mental demand in novices. In a within-subject design, sixteen highly active males (mean age: 23.13 ± 2.09 years) randomly performed snatch learning bouts consisting of 36 trials using repetitive learning (RL), contextual interference (blocked, CIb; and serial, CIs), and differential learning (DL) models. Spontaneous resting EEG and HRV activities were recorded at PRE and POST training bouts while measuring heart rate. Perceived exertion and mental demand were assessed immediately after, and barbell kinematics were recorded during three power snatch trials performed following the POST measurement. The results showed increases in alpha, beta, and gamma frequencies from pre- to post-training bouts in the majority of the tested brain regions (p values ranging from < 0.0001 to 0.02). The CIb model exhibited increased frequencies in more regions. Resting time domain HRV parameters were altered following the snatch bouts, with increased HR (p < 0.001) and decreased RR interval (p < 0.001), SDNN, and RMSSD (p values ranging from < 0.0001 to 0.02). DL showed more pronounced pulse-related changes (p = 0.01). Significant changes in HRV frequency domain parameters were observed, with a significant increase in LFn (p = 0.03) and a decrease in HFn (p = 0.001) registered only in the DL model. Elevated HR zones (> HR zone 3) were more dominant in the DL model during the snatch bouts (effect size = 0.5). Similarly, the DL model tended to exhibit higher perceived physical (effect size = 0.5) and mental exertions (effect size = 0.6). Despite the highest psycho-physiological response, the DL group showed one of the fewest significant EEG changes. There was no significant advantage of one learning model over the other in terms of technical efficiency. These findings offer preliminary support for the acute neurophysiological benefits of coordination-strength-based exercise in novices, particularly when employing a DL model. The advantages of combining EEG and HRV measurements for comprehensive monitoring and understanding of potential adaptations are also highlighted. However, further studies encompassing a broader range of coordination-strength-based exercises are warranted to corroborate these observations.

Resonance Effects in Variable Practice for Handball, Basketball, and Volleyball Skills: A Study on Contextual Interference and Differential Learning

“…Hence, available literature proposes a framework that not only unifies previous approaches as different forms of noise [ 26 , 54 ] but in parallel suggests a quantitative methodology to address the unresolved issue in other learning models using a holistic ML approach [ 34 , 43 , 54 , 55 , 56 ]. In the context of learning a single movement, several studies have provided evidence supporting the superiority of stochastic DL training over repetitions-based training in the context of skill-related learning [ 34 , 56 , 57 , 58 , 59 , 60 , 61 ] as well as equality in strength training [ 62 , 63 , 64 ]. Meanwhile, successful applications of DL have been broadened with the learning of multiple skills simultaneously, hence reinforcing the superiority of DL over repetitive and CI learning in football [ 60 ] and volleyball [ 34 , 61 , 65 ].…”

Resonance Effects in Athletic Training: Unveiling the Impact of Exercise Similarity in Variable Practice for Handball, Basketball, and Volleyball Skills with CI and DL

“…Hence, available literature proposes a framework that not only unifies previous approaches as different forms of noise [26,54] but in parallel sug-gests a quantitative methodology to address the unresolved issue in other learning models using a holistic ML approach [34,43,[54][55][56]. In the context of learning a single movement, several studies have provided evidence supporting the superiority of stochastic DL training over repetitions-based training in the context of skill-related learning [34,[56][57][58][59][60][61] as well as equality in strength training [62][63][64]. Meanwhile, successful applications of DL have been broadened with the learning of multiple skills simultaneously, hence reinforcing the superiority of DL over repetitive and CI learning in football [60] and volleyball [34,61,65].…”