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Intermuscular Coordination in the Power Clean Exercise: Comparison Between Olympic Weightlifters and Untrained Individuals-A Preliminary Study

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2021 Apr 3
PMID 33803182
Citations 2
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Abstract

Muscle coordination in human movement has been assessed through muscle synergy analysis. In sports science, this procedure has been mainly applied to the comparison between highly trained and unexperienced participants. However, the lack of knowledge regarding strength training exercises led us to study the differences in neural strategies to perform the power clean between weightlifters and untrained individuals. Synergies were extracted from electromyograms of 16 muscles of ten unexperienced participants and seven weightlifters. To evaluate differences, we determined the pairwise correlations for the synergy components and electromyographic profiles. While the shape of activation patterns presented strong correlations across participants of each group, the weightings of each muscle were more variable. The three extracted synergies were shifted in time with the unexperienced group anticipating synergy #1 (-2.46 ± 18.7%; < 0.001) and #2 (-4.60 ± 5.71%; < 0.001) and delaying synergy #3 (1.86 ± 17.39%; = 0.01). Moreover, muscle vectors presented more inter-group variability, changing the composition of synergy #1 and #3. These results may indicate an adaptation in intermuscular coordination with training, and athletes in an initial phase of training should attempt to delay the hip extension (synergy #1), as well as the upper-limb flexion (synergy #2).

Citing Articles

Effects of Expertise on Muscle Activity during the Hang Power Clean and Hang Power Snatch Compared to Snatch and Clean Pulls - An Explorative Analysis.

Geisler S, Havers T, Isenmann E, Schulze J, Lourens L, Nowak J J Sports Sci Med. 2023; 22(4):778-789.

PMID: 38045750 PMC: 10690512. DOI: 10.52082/jssm.2023.778.


An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking.

Ballarini R, Ghislieri M, Knaflitz M, Agostini V Sensors (Basel). 2021; 21(10).

PMID: 34064615 PMC: 8151057. DOI: 10.3390/s21103311.

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