Usefulness of Surface Electromyography Complexity Analyses to Assess the Effects of Warm-Up and Stretching during Maximal and Sub-Maximal Hamstring Contractions: A Cross-Over, Randomized, Single-Blind Trial

Abstract

This study aimed to apply different complexity-based methods to surface electromyography (EMG) in order to detect neuromuscular changes after realistic warm-up procedures that included stretching exercises. Sixteen volunteers conducted two experimental sessions. They were tested before, after a standardized warm-up, and after a stretching exercise (static or neuromuscular nerve gliding technique). Tests included measurements of the knee flexion torque and EMG of biceps femoris (BF) and semitendinosus (ST) muscles. EMG was analyzed using the root mean square (RMS), sample entropy (SampEn), percentage of recurrence and determinism following a recurrence quantification analysis (%Rec and %Det) and a scaling parameter from a detrended fluctuation analysis. Torque was significantly greater after warm-up as compared to baseline and after stretching. RMS was not affected by the experimental procedure. In contrast, SampEn was significantly greater after warm-up and stretching as compared to baseline values. %Rec was not modified but %Det for BF muscle was significantly greater after stretching as compared to baseline. The a scaling parameter was significantly lower after warm-up as compared to baseline for ST muscle. From the present results, complexity-based methods applied to the EMG give additional information than linear-based methods. They appeared sensitive to detect EMG complexity increases following warm-up.

Exploring Acute Changes in Hamstring EMG after Warm-up and Stretching Using a Multifractal Analysis

“…This result witnessed an increase in complexity after warm-up. Moreover, this parameter obviously confirmed our previous findings obtained with the a exponent extracted from the monofractal detrended fluctuation analysis (19). Indeed, one should remember that H 2 is a particular case that corresponds to a monofractal time series exponent (27).…”

“…MFDFA could therefore be considered another complexity-based tool that can be used to discriminate subtle neural adaptations. In contrast to sample entropy or the monofractal detrended fluctuation analysis (19), MFDFA computes different parameters favoring deepened findings.…”

“…Warm-up, the primary multicomponent of most physical activities, has multiple beneficial effects, such as muscle performance improvements. Results generally revealed a maximal voluntary torque increase after warm-up and a decrease immediately after stretching (19,41,42). All stretching modalities do not have the same consequences on the neuromuscular system.…”

Correction: Babault et al. Usefulness of Surface Electromyography Complexity Analyses to Assess the Effects of Warm-Up and Stretching during Maximal and Sub-Maximal Hamstring Contractions: A Cross-Over, Randomized, Single-Blind Trial. Biology 2022, 11, 1337

“…There was an error in the original publication [ 1 ]. The description of the neurodynamic nerve gliding technique was incorrect.…”

Efeitos agudos dos alongamentos estático e dinâmico sobre a preparação e ativação neuromuscular para a prática de exercicio resistido: uma revisão integrativa

“…Com base nessas respostas fisiológicas, alguns treinadores usam extensas rotinas de aquecimento e alongamento como parte de sua preparação para treinamento e competição (Türkmen et al, 2022). No entanto, há muitas evidências experimentais que não suportam essas crenças, fornecendo evidências de que o alongamento muscular agudo (nesse caso o estático) pode ser prejudicial para desempenhos cujo sucesso está relacionado à força máxima ou à produção de torque (Kokkonen et al, 1998;Fowles et al 2000Nelson et al, 2001Young & Behm, 2003, outros não mostraram efeitos (Kokkonen et al, 1998;Fowles et al, 2000Nelson et al, 2001Young & Behm, 2003) enquanto alguns sugerem AD como a para melhorias agudas do sistema neuromuscular (Moreira et al, 2022;Babault et al, 2022).…”