Effects of endurance training on neuromuscular fatigue in healthy active men. Part I: Strength loss and muscle fatigue

Mira, José A.; Aboodarda, Saied Jalal; Floreani, Mirco; Jaswal, R; Moon, S J; Amery, K; Rupp, Thomas; Millet, Guillaume Y.

doi:10.1007/s00421-018-3950-8

Cited by 12 publications

(8 citation statements)

References 37 publications

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“…Sredhar et al [10] proofed the association between the sEMG and force of muscle contraction is accepted. Mira et al [11], conducted a study of the effects of a combined endurance training on fatigue kinetics during and immediately after a fatigue test. Recommending further study and methods, Robin et al [12] studied twelve young men performing three exercises with full measurements for the voluntary concentric or eccentric contractions until a reduction in maximal voluntary isometric contraction (MVC) torque of at least 40% MVC was achieved immediately post-exercise.…”

Section: Related Workmentioning

confidence: 99%

“…In previous research [11][12][13], the specialists, physiology authors conducted an interesting studies in the effects of cycling training on neuromuscular fatigue and its peripheral, there work come up with outcomes which can be used in future extended work to clarify the neuromuscular function using enhanced neural network infrastructure which is used in this article, the input and the output of their work can be used as a training data for specific ANN training for further cases included in their work. Different applications could benefit from the results of this research by applying artificial neural network as in [14,15].…”

Section: Related Workmentioning

confidence: 99%

“…The torque for the elbow joint is determined by using the standard torque Equation (11) [2]. τ = (r load F load ) + (r arm F arm ) cos θ (11) where r load is the distance from the elbow joint to the load, F load is known as in Equation 10, F arm is the force due to the mass of the lower arm and r arm is the distance from the elbow joint to the center of mass of the lower arm which is half of r load . The segment mass can calculate by Equation (12) [18].…”

Section: Elbow Joint Torque Modelmentioning

confidence: 99%

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Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

et al. 2019

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Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Elbow Joint Torque Modelmentioning

confidence: 99%

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Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

et al. 2019

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“…To our knowledge, there is only one study that has examined twitch contractility adaptation following short-term simultaneous strength and endurance training in elite swimmers at terrestrial altitude. Specifically, Tomazin and colleagues [ 24 ] showed that strength and endurance training at altitude leads to an increase in twitch contractile amplitude, likely due to the upregulation of physiological processes beyond sarcolemma, i.e., mechanisms of excitation-contraction coupling, which in turn would increase Ca 2+ available for cross-bridge formation, allowing for stronger contractions [ 29 , 30 ]. Currently, there is no information on how short-term power-oriented strength training at moderate altitude affects the contractile properties of twitch in elite athletes.…”

Section: Introductionmentioning

confidence: 99%

Neuromuscular Adaptations after an Altitude Training Camp in Elite Judo Athletes

Tomažin

Almeida

Štirn

et al. 2021

IJERPH

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The aim of this study was to investigate neuromuscular adaptations in elite judo athletes after three weeks of power-oriented strength training at terrestrial altitude (2320 m). Nineteen men were assigned to altitude training (AL) (22.1 ± 2.3 years) and sea level training (SL) (22.6 ± 4.1 years). Neuromuscular assessment consisted of: (1) maximal isometric knee extensor (KE) torque, (2) KE rate of torque development (RTD), (3) quadriceps activity and voluntary activation, (4) soleus H-reflex, (5) quadriceps single (TTW) and double twitch torque (TDB100) and contraction time (CTTW). There were no significant differences between groups at baseline for any of the observed parameters. Significant differences were found between groups in terms of change in RTD (p = 0.04). Cohen’s d showed a positive significant effect (0.43) in the SL group and a negative significant effect (−0.58) in the AL group. The difference between groups in changes in CTTW as a function of altitude was on the edge of significance (p = 0.077). CTTW increased by 8.1 ± 9.0% in the AL group (p = 0.036) and remained statistically unchanged in the SL group. Only the AL group showed a relationship between changes in TTW and TDB100 and changes in RTD at posttest (p = 0.022 and p = 0.016, respectively). Altitude induced differences in muscular adaptations likely due to greater peripheral fatigue.

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“…As the neuromuscular system is highly adaptable (Enoka and Stuart 1992), neuromuscular fatigue varies in response to different conditions. For instance, numerous studies examined the differential effect of age (Streckis et al 2007), ageing (Kent-Braun et al 2002), sex (Hunter et al 2004), training status (Mira et al 2018) and the mode of contraction (Souron et al 2018) on neuromuscular fatigue. However, less attention has been paid to the impact of maturation on the development and etiology of neuromuscular fatigue.…”

Section: Introductionmentioning

confidence: 99%

Maturation-related changes in the development and etiology of neuromuscular fatigue

et al. 2019

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The aim of the present study was to investigate the role of maturation on the etiology of neuromuscular fatigue induced by repeated maximal voluntary isometric contractions (MVIC). Methods: Nine prepubertal boys (9.9  1.3 yr.), eight male adolescents (13.6  1.3 yr.) and eleven men (23.4  3.0 yr.) performed a series of repeated isometric MVICs of the knee extensors until the MVIC torque reached 60% of its initial value. Magnetic stimulations were delivered to the femoral nerve every five MVICs to follow the course of voluntary activation level (VA) and the potentiated twitch torque (Qtwpot). Results: Task failure was reached after 52.9  12.7, 42.6  12.5 and 26.6  6.3 repetitions in boys, adolescents and men, respectively. VA remained unchanged in men whereas it decreased significantly and similarly in boys and adolescents (p<0.001). In contrast, Qtwpot remained unchanged in boys and decreased significantly less in adolescents than adults (p<0.05). Conclusions: Children and adolescents experience less peripheral and more central fatigue than adults. However, adolescents experience more peripheral fatigue than children for a comparable amount of central fatigue. This finding supports the idea that the tolerance of the central nervous system to peripheral fatigue could increase during maturation.

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Effects of endurance training on neuromuscular fatigue in healthy active men. Part I: Strength loss and muscle fatigue

Abstract: This study confirms fatigue attenuation at isotime after training. Yet lower or similar fatigue at EXH indicates that, unlike previously suggested, fatigue tolerance may not be upregulated after 9 weeks of cycling training.

Cited by 12 publications

References 37 publications

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Neuromuscular Adaptations after an Altitude Training Camp in Elite Judo Athletes

Maturation-related changes in the development and etiology of neuromuscular fatigue

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