Publicação
Changes in torque complexity with fatigue : unravelling the role of neuromuscular coordination mechanisms
| Resumo: | Physiological complexity is believed to reflect a system’s adaptability to environmental challenges. Torque complexity reflects the adaptability of motor control and has been proposed as an indirect indicator of the functional capacity of the neuromuscular system. While torque complexity has been shown to decrease with aging, disease and fatigue, its underlying mechanisms are not yet fully understood. Thus, the present study aimed to investigate the neurophysiological mechanisms underlying torque complexity. Twenty-one healthy and young adults (age: 24.62 ± 3.51yrs; height: 1.77 ± 0.07m; weight: 74.57 ± 13.34kg; BMI: 23.68 ± 3.30kg/m2) took part in the present study and visited the laboratory on one occasion. Participants performed three extension and flexion Maximal Voluntary Isometric Contractions (MVIC) proceeded by two repetitions of a thirty-second long submaximal isometric contraction at 30% MVIC. Participants then performed the fatiguing protocol, which consisted of a series of concentric and eccentric knee extensions until exhaustion at 90º/s. Immediately after, participants performed the same tests as prior to the fatiguing protocol. Peak Torque (PT) and Rate of Torque Development (RTD) were determined from the MVIC trials. Torque signals were sampled continuously, and the metrics of variability and complexity were calculated based on submaximal contractions trials. The coefficient of variation (CV) was used to quantify torque variability, while torque complexity was determined through Sample Entropy (SampEn). Electromyographic (EMG) signals, specifically, motor unit-related parameters, EMG amplitude, EMG CV and EMG co-contraction index (CCi) were also extracted from the submaximal trials. Paired sampled t-tests or Wilcoxon Signed-rank tests were used to test the effect of fatigue in all the dependent variables. Additionally, a stepwise multiple linear regression analysis was conducted to examine the contribution of other parameters to explain changes in torque complexity. Torque SampEn and CV were not altered with fatigue. PT and RTD significantly decreased whereas EMG amplitude, CCi, motor unit action potential amplitude (MUAPa) and average firing rate (FRa) significantly increased with fatigue. The multiple linear regression analysis revealed that FR/MUAPslope, FR/MUAPintercept and torque’s CV significantly explained changes in torque complexity accounting for 80.5% of its variance. Interestingly, changes in torque complexity were mainly attributed to intramuscular coordination processes which should be taken into consideration when planning training process and competition cycles. |
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| Autores principais: | Gomes, João Silveira de Sá |
| Assunto: | Sistemas Dinâmicos Não-lineares Complexidade do Torque Controlo Motor Coordenação Intramuscular Fadiga Neuromuscular Eletromiografia de Alta-Densidade Non-linear Dynamics Torque Complexity Motor Control Intramuscular Coordination Neuromuscular Fatigue High-Density Electromyography |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | Physiological complexity is believed to reflect a system’s adaptability to environmental challenges. Torque complexity reflects the adaptability of motor control and has been proposed as an indirect indicator of the functional capacity of the neuromuscular system. While torque complexity has been shown to decrease with aging, disease and fatigue, its underlying mechanisms are not yet fully understood. Thus, the present study aimed to investigate the neurophysiological mechanisms underlying torque complexity. Twenty-one healthy and young adults (age: 24.62 ± 3.51yrs; height: 1.77 ± 0.07m; weight: 74.57 ± 13.34kg; BMI: 23.68 ± 3.30kg/m2) took part in the present study and visited the laboratory on one occasion. Participants performed three extension and flexion Maximal Voluntary Isometric Contractions (MVIC) proceeded by two repetitions of a thirty-second long submaximal isometric contraction at 30% MVIC. Participants then performed the fatiguing protocol, which consisted of a series of concentric and eccentric knee extensions until exhaustion at 90º/s. Immediately after, participants performed the same tests as prior to the fatiguing protocol. Peak Torque (PT) and Rate of Torque Development (RTD) were determined from the MVIC trials. Torque signals were sampled continuously, and the metrics of variability and complexity were calculated based on submaximal contractions trials. The coefficient of variation (CV) was used to quantify torque variability, while torque complexity was determined through Sample Entropy (SampEn). Electromyographic (EMG) signals, specifically, motor unit-related parameters, EMG amplitude, EMG CV and EMG co-contraction index (CCi) were also extracted from the submaximal trials. Paired sampled t-tests or Wilcoxon Signed-rank tests were used to test the effect of fatigue in all the dependent variables. Additionally, a stepwise multiple linear regression analysis was conducted to examine the contribution of other parameters to explain changes in torque complexity. Torque SampEn and CV were not altered with fatigue. PT and RTD significantly decreased whereas EMG amplitude, CCi, motor unit action potential amplitude (MUAPa) and average firing rate (FRa) significantly increased with fatigue. The multiple linear regression analysis revealed that FR/MUAPslope, FR/MUAPintercept and torque’s CV significantly explained changes in torque complexity accounting for 80.5% of its variance. Interestingly, changes in torque complexity were mainly attributed to intramuscular coordination processes which should be taken into consideration when planning training process and competition cycles. |
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