Fatigue reduces the complexity of knee extensor torque during fatiguing sustained isometric contractions

Sleep deprivation increases the regularity of isometric torque fluctuations

The regularity of the fluctuations present in torque signals represent the adaptability of the motor control. While previous research showed how it is affected by neuromuscular fatigue and ageing, the underlying mechanisms remain unclear. It is currently under debate whether these changes are explained by central or peripheral neuromuscular mechanisms. Here, we experimentally manipulated the sleep of thirteen young adults through a supervised 24 h-sleep deprivation protocol. This study aimed to investigate the effect of sleep deprivation on the regularity of torque fluctuations, and other standard torque-related outcomes (Peak Torque - PT - and Rate of Torque Development - RTD). The participants were asked to perform knee extension maximal voluntary contractions (MVC) and submaximal knee extensions at 40% of MVC for 30 s. PT and RTD were calculated from the MVC and the regularity of the torque fluctuations was determined on the submaximal task through Sample Entropy (SampEn). In addition, rate of perceived effort (RPE) was collected. We found no significant changes in PT and RTD. The regularity of torque fluctuations significantly increased (i.e., a decrease in SampEn) after 24 h-sleep deprivation (PRE = 1.76 ± 0.268, POS24 = 1.71 ± 0.306; p = 0.044). Importantly, we found a negative correlation between RPE and SampEn relative changes after sleep deprivation. This study brings new insights towards the understanding of the underlying mechanisms that explain changes in torque fluctuations, demonstrating that these changes are not limited to neuromuscular processes but are also likely to be affected by other domains, such as psychological profile, which can indirectly affect the neural drive to the muscles.

Network of muscle fibers activation facilitates inter-muscular coordination, adapts to fatigue and reflects muscle function

Fundamental movement patterns require continuous skeletal muscle coordination, where muscle fibers with different timing of activation synchronize their dynamics across muscles with distinct functions. It is unknown how muscle fibers integrate as a network to generate and fine tune movements. We investigate how distinct muscle fiber types synchronize across arm and chest muscles, and respond to fatigue during maximal push-up exercise. We uncover that a complex inter-muscular network of muscle fiber cross-frequency interactions underlies push-up movements. The network exhibits hierarchical organization (sub-networks/modules) with specific links strength stratification profile, reflecting distinct functions of muscles involved in push-up movements. We find network reorganization with fatigue where network modules follow distinct phase-space trajectories reflecting their functional role and adaptation to fatigue. Consistent with earlier observations for squat movements under same protocol, our findings point to general principles of inter-muscular coordination for fundamental movements, and open a new area of research, Network Physiology of Exercise.

Torque complexity of maximal knee extensor isometric contraction in individuals following anterior cruciate ligament reconstruction

BACKGROUND

Current rehabilitation goals following anterior cruciate ligament reconstruction are structured around the maximal force generating capabilities of the muscle. Force fluctuations, an index of force control, have been observed to alter post- anterior cruciate ligament reconstruction. The temporal structure, or "complexity" of force fluctuations may provide important insight into the post-operative muscular recovery. The aims of this study were 1) to compare quadriceps torque complexity in anterior cruciate ligament reconstructed patients to the contralateral limb and to healthy, controls and 2) to assess the relationships between torque complexity to patient outcomes.

METHODS

Data from 120 anterior cruciate ligament reconstructed participants (65 Females, 21.0 ± 8.3 years, 5.96 ± 0.48-months post-surgery) and 95 healthy controls (50 Females, 21.5 ± 2.9 years) were collected. A 30-s knee extensor maximal isometric contraction was completed to calculate approximate entropy, a measure of torque complexity.

FINDINGS

Approximate entropy was found to decrease throughout the 30-s trial (P < .001, Cohen's d = 1.87 [1.64,2.10]). The anterior cruciate ligament reconstructed limb demonstrated greater approximate entropy compared to the contralateral limb or to healthy controls (P < .001, Cohen's d = 0.64 [0.38,0.90]). approximate entropy at the end of the trial demonstrated weak, negatively relationships with peak torque, patient reported outcome measures, and knee extensor fatigue (r = -0.21 to -0.32, P < .05).

INTERPRETATION

A greater torque complexity in individuals following anterior cruciate ligament reconstruction was weakly related to lower quadriceps strength, lower subjective function, and quadriceps fatigue resistance. The complexity of force fluctuations during a sustained maximal task may draw clinical insight into the recovery of motor function following anterior cruciate ligament reconstruction.

Human Motor Noise Assessed by Electromagnetic Sensors and Its Relationship with the Degrees of Freedom Involved in Movement Control

Motor variability is a prominent feature of the human movement that, nowadays, can be easily measured through different sensors and analyzed using different types of variables, and it seems to be related to functional and adaptative motor behavior. It has been stated that motor variability is related to the system's flexibility needed to choose the right degrees of freedom (DoFs) to adapt to constant environmental changes. However, the potential relationship between motor variability and DoFs is unknown. The aim of this study was to analyze how motor variability, both the amount and structure, changes depending on the mechanical DoFs involved in the movement control. For this purpose, movement variability was assessed by a tracking sensor in five tasks with different DoFs, and the amount, using standard deviation, and the structure of variability, through fuzzy entropy and detrended fluctuation analysis, were also assessed. The results showed a higher amount of variability and a less predictable and more auto-correlated variability structure in the long-term when more mechanical DoFs are implied. The studies that analyze motor variability should consider the type of movement and the DoFs involved in the analyzed task since, as the findings have shown, both factors have a noticeable influence on the amount and the structure of motor variability.

The use of non-linear tools to analyze the variability of force production as an index of fatigue: A systematic review

Background: Fatigue is a process that results in a decreased ability to produce force, and which could eventually affect performance and increase the risk of injury. Force variability analysis has been proposed to describe the level of fatigue with the purpose of detecting the development of fatigue. Variability is credited to play a functional and adaptive role through which the components of a system self-organize to solve a motor problem. Non-linear tools have been applied to analyze the variability of physiological signals, revealing that the structure of motor fluctuations provides relevant information about the functional role of variability. It has been suggested that the presence of lower complexity in the variability structure could reveal a less functional and adaptative state (e.g., ageing or illness). In the last years, an increased number of studies have applied these techniques to force variability analysis in relation to fatigue. Objective: To provide an overview of the current knowledge on the use of non-linear tools on force variability as a fatigue index. Methods: Following PRISMA guidelines, a systematic search of SPORTDiscus, Scopus, Web of Science and PubMed was carried out. Studies included were: a) original studies that analyzed the effect of fatigue on humans during an action focused on force production; b) published studies with their title and abstract in English; c) studies that applied non-linear tools on a signal directly related to force production. Results: Twenty-five studies were included in this review. The relationship between fatigue and the complexity of force variability, the type of action and relative intensity, the nature of the signal and the non-linear tools used, and the methods of data acquisition and processing were identified. Conclusion: The articles reviewed suggest that fatigue leads to a decrease in complexity mostly in isometric contractions, but this is not as clear in dynamic contractions. This fatigue-induced loss of complexity seems to be a result of changes in the nervous system at the central level, albeit triggered by peripheral mechanisms. It should be noted that non-linear tools are affected by the relative intensity of contraction, non-stationarity, and the acquisition and treatment of the signal.

The Neuromuscular Fatigue-Induced Loss of Muscle Force Control

Neuromuscular fatigue is characterised not only by a reduction in the capacity to generate maximal muscle force, but also in the ability to control submaximal muscle forces, i.e., to generate task-relevant and precise levels of force. This decreased ability to control force is quantified according to a greater magnitude and lower complexity (temporal structure) of force fluctuations, which are indicative of decreased force steadiness and adaptability, respectively. The “loss of force control” is affected by the type of muscle contraction used in the fatiguing exercise, potentially differing between typical laboratory tests of fatigue (e.g., isometric contractions) and the contractions typical of everyday and sporting movements (e.g., dynamic concentric and eccentric contractions), and can be attenuated through the use of ergogenic aids. The loss of force control appears to relate to a fatigue-induced increase in common synaptic input to muscle, though the extent to which various mechanisms (afferent feedback, neuromodulatory pathways, cortical/reticulospinal pathways) contribute to this remains to be determined. Importantly, this fatigue-induced loss of force control could have important implications for task performance, as force control is correlated with performance in a range of tasks that are associated with activities of daily living, occupational duties, and sporting performance.

Variability and Complexity of Knee Neuromuscular Control during an Isometric Task in Uninjured Physically Active Adults: A Secondary Analysis Exploring Right/Left and Dominant/Nondominant Asymmetry

Work is needed to better understand the control of knee movement and knee health. Specifically, work is needed to further understand knee muscle force control variability and complexity and how it is organized on both sides of the body. The purpose of this study was to explore side-to-side comparisons of magnitude- and complexity-based measures of knee muscle force control to support future interpretations of complexity-based analyses and clinical reasoning in knee injury control. Participants (male/female n = 11/5) performed constant-force isometric efforts at 50% maximal effort. Force variability was quantified during the constant-force efforts using a coefficient of variation (CV%) and force complexity using approximate entropy (ApEn) and detrended fluctuation analysis (DFA) α. Outcomes were right/left and dominant/nondominant group-level and individual-level comparisons. A limb-symmetry index was calculated for each variable and clinically significant absolute asymmetry was defined (>15%). The only significant side-to-side difference was for right/left DFA α (p = 0.00; d = 1.12). Maximum absolute asymmetries were (right/left, dominant/nondominant): CV 18.2%, 18.0%; ApEn 34.5%, 32.3%; DFA α 4.9%, 5.0%. Different side-to-side comparisons yield different findings. Consideration for how side-to-side comparisons are performed (right/left, dominant/nondominant) is required. Because a significant difference existed for complexity but not variability, this indicates that both complexity-based and magnitude-based measures should be used when studying knee muscle force control.

Changes in torque complexity and maximal torque after a fatiguing exercise protocol

Torque outputs exhibit non-random fluctuations in their temporal structure, i.e., complexity. Fatigue has been shown to alter this structure. The torque outputs typically become more regular, resulting in decreased adaptability. Importantly, torque complexity was shown a different recovery pattern after fatigue compared to maximal torque. However, it remains to be understood if these uncoupled patterns of recovery are muscle dependent. In addition, it also remains to be investigated if changes in maximal torque and complexity are correlated. This study investigated (i) the effects of a fatiguing protocol on the complexity and maximal torque from plantar flexors and (ii) the relationship between changes in these two outputs. Ten participants visited the laboratory, and measures were taken at baseline, immediately after, 1 h after and 24 h after the fatiguing protocol. Maximum voluntary contraction, isometric contractions at 30% of maximum and pain pressure threshold were collected. Both legs were assessed, but only one was given the fatiguing protocol. Two-way ANOVAs and correlations were conducted. The fatiguing protocol decreased torque complexity (~35%) and maximal torque (~20%), and they exhibited uncoupled patterns of recovery. Moreover, the correlation analysis showed no correlation between changes in these parameters. These findings support that these parameters are independent of each other.

Influence of central and peripheral motor unit properties on isometric muscle force entropy: A computer simulation study

Approximate entropy of isometric force is a popular measure to characterize behavioral changes across muscle contraction conditions. The degree to which force entropy characterizes the randomness of the motor control strategy, however, is not known. In this study, we used a computational model to investigate the correlation between approximate entropy of the synaptic input to a motor neuron pool, the neural drive to muscle (cumulative spike train; CST), and the force. This comparison was made across several simulation conditions, that included different synaptic command signal bandwidths, motor neuron pool sizes, and muscle contractile properties. The results indicated that although force entropy to some degree reflects the entropy of the synaptic command to motor neurons, it is biased by changes in motor unit properties. As a consequence, there was a low correlation between approximate entropy of force and the motor neuron input signal across all simulation conditions (r² = 0.13). Therefore, force entropy should only be used to compare motor control strategies across conditions where motor neuron properties can be assumed to be maintained. Instead, we recommend that the entropy of the descending motor commands should be estimated from CSTs comprising spike trains of multiple motor units.

Physiological complexity: influence of ageing, disease and neuromuscular fatigue on muscle force and torque fluctuations

NEW FINDINGS

What is the topic of this review? Physiological complexity in muscle force and torque fluctuations, specifically the quantification of complexity, how neuromuscular complexityis altered by perturbations and the potential mechanism underlying changes in neuromuscular complexity. What advances does it highlight? The necessity to calculate both magnitude- and complexity-based measures for the thorough evaluation of force/torque fluctuations. Also the need for further research on neuromuscular complexity, particularly how it relates to the performance of functional activities (e.g. manual dexterity, balance, locomotion).

ABSTRACT

Physiological time series produce inherently complex fluctuations. In the last 30 years, methods have been developed to characterise these fluctuations, and have revealed that they contain information about the function of the system producing them. Two broad classes of metrics are used: (1) those which quantify the regularity of the signal (e.g. entropy metrics); and (2) those which quantify the fractal properties of the signal (e.g. detrended fluctuation analysis). Using these techniques, it has been demonstrated that ageing results in a loss of complexity in the time series of a multitude of signals, including heart rate, respiration, gait and, crucially, muscle force or torque output. This suggests that as the body ages, physiological systems become less adaptable (i.e. the systems' ability to respond rapidly to a changing external environment is diminished). More recently, it has been shown that neuromuscular fatigue causes a substantial loss of muscle torque complexity, a process that can be observed in a few minutes, rather than the decades it requires for the same system to degrade with ageing. The loss of torque complexity with neuromuscular fatigue appears to occur exclusively above the critical torque (at least for tasks lasting up to 30 min). The loss of torque complexity can be exacerbated with previous exercise of the same limb, and reduced by the administration of caffeine, suggesting both peripheral and central mechanisms contribute to this loss. The mechanisms underpinning the loss of complexity are not known but may be related to altered motor unit behaviour as the muscle fatigues.

Revealing Sex Differences During Upper and Lower Extremity Neuromuscular Fatigue in Older Adults Through a Neuroergonomics Approach

Background: Sex differences in neuromuscular fatigue is well-documented, however the underlying mechanisms remain understudied, particularly for the aging population. Objective: This study investigated sex differences in fatigability of the upper and lower extremity of older adults using a neuroergonomics approach. Methods: Thirty community-dwelling older adults (65 years or older; 15 M, 15 F) performed intermittent submaximal fatiguing handgrip and knee extension exercises until voluntary exhaustion on separate days. Muscle activity from prime muscles of the hand/arm and knee extensors were monitored using electromyography, neural activity from the frontal, motor, and sensory areas were monitored using functional near infrared spectroscopy, and force output were obtained. Results: While older males were stronger than females across both muscle groups, they exhibited longer endurance times and greater strength loss during knee extension exercises. These lower extremity findings were associated with greater force complexity over time and concomitant increase in left motor and right sensory motor regions. While fatigability during handgrip exercises was comparable across sexes, older females exhibited concurrent increases in the activation of the ipsilateral motor regions over time. Discussion: We identified differences in the underlying central neural strategies adopted by males and females in maintaining downstream motor outputs during handgrip fatigue that were not evident with traditional ergonomics measures. Additionally, enhanced neural activation in males during knee exercises that accompanied longer time to exhaustion point to potential rehabilitation/exercise strategies to improve neuromotor outcomes in more fatigable older adults.

Effects of Muscle Fatigue and Recovery on Complexity of Surface Electromyography of Biceps Brachii

This study aimed to investigate the degree of regularity of surface electromyography (sEMG) signals during muscle fatigue during dynamic contractions and muscle recovery after cupping therapy. To the best of our knowledge, this is the first study assessing both muscle fatigue and muscle recovery using a nonlinear method. Twelve healthy participants were recruited to perform biceps curls at 75% of the 10 repetitions maximum under four conditions: immediately and 24 h after cupping therapy (-300 mmHg pressure), as well as after sham control (no negative pressure). Cupping therapy or sham control was assigned to each participant according to a pre-determined counter-balanced order and applied to the participant's biceps brachii for 5 min. The degree of regularity of the sEMG signal during the first, second, and last 10 repetitions (Reps) of biceps curls was quantified using a modified sample entropy (E_ms) algorithm. When exercise was performed immediately or 24 h after sham control, E_ms of the sEMG signal showed a significant decrease from the first to second 10 Reps; when exercise was performed immediately after cupping therapy, E_ms also showed a significant decrease from the first to second 10 Reps but its relative change was significantly smaller compared to the condition of exercise immediately after sham control. When exercise was performed 24 h after cupping therapy, E_ms did not show a significant decrease, while its relative change was significantly smaller compared to the condition of exercise 24 h after sham control. These results indicated that the degree of regularity of sEMG signals quantified by E_ms is capable of assessing muscle fatigue and the effect of cupping therapy. Moreover, this measure seems to be more sensitive to muscle fatigue and could yield more consistent results compared to the traditional linear measures.

Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue.

Muscle pain from an intramuscular injection of hypertonic saline increases variability in knee extensor torque reproduction

The intensity of exercise-induced pain (EIP) reflects the metabolic environment in the exercising muscle, so during endurance exercise, this may inform the intelligent regulation of work rate. Conversely, the acute debilitating effects of EIP on motor unit recruitment could impair the estimation of force produced by the muscle and impair judgement of current exercise intensity. This study investigated whether muscle pain that feels like EIP, administered via intramuscular injection of hypertonic saline, interferes with the ability to accurately reproduce torque in a muscle group relevant to locomotive exercise. On separate days, 14 participants completed an isometric torque reproduction task of the knee extensors. Participants were required to produce torque at 15% and 20% maximal voluntary isometric torque (MVIT), without visual feedback before (baseline), during (pain/no pain), and after (recovery) an injection of 0.9% isotonic saline (Control) or 5.8% hypertonic saline (Experimental) into the vastus lateralis of the right leg. An elevated reported intensity of pain, and a significantly increased variance in mean contraction torque at both 15% (P = 0.049) and 20% (P = 0.002) MVIT was observed in the Experimental compared to the Control condition. Both 15 and 20% target torques were performed at a similar pain intensity in the Experimental condition (15% MVIT: 4.2 ± 1.9; 20% MVIT: 4.5 ± 2.2; P > 0.05). These findings demonstrate that the increased muscle pain from the injection of hypertonic saline impeded accurate reproduction of knee extensor torque. These findings have implications for the detrimental impact of EIP on exercise regulation and endurance performance.NEW & NOTEWORTHY We provide novel data demonstrating that the presence of muscle pain interferes with estimations of torque produced by the knee extensors, which could impair judgment of work rate during endurance exercise. The novelty of our study is in the application of the hypertonic saline experimental model into a quadriceps muscle during short, submaximal isometric contractions at an intensity that provides a more translatable assessment of the impact of exercise-induced pain on work-rate regulation during whole body exercise.

Complexity Analysis of Surface Electromyography for Assessing the Myoelectric Manifestation of Muscle Fatigue: A Review

The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles.

Effects of dual-task demands on the complexity and task performance of submaximal isometric handgrip force control

PURPOSE

To determine the effect of cognitive-motor dual-task load on temporal structure irregularity (complexity) of motor output and task performance of submaximal isometric contractions.

METHODS

Twelve young, sedentary subjects performed handgrip isometric contractions until failure at 50% of maximal voluntary contraction under mathematical self-regulated dual-task (own pace; SDT), regulated dual-task (imposed pace; RDT), and control. Force signal complexity was calculated by sample entropy at the initial, middle, and final thirds. Task performance was assessed by muscle fatigue (time to failure and rate of median frequency of the radial flexor of the carpus), force and math task error, and self-perceived difficulty.

RESULTS

Only RDT decreased complexity with respect to control (17.4% ± 12.6%, p = 0.005), all conditions decreased complexity by the final third (Control: 52.8% ± 18.7%, p < 0.001; SDT: 41.1% ± 32.1%, p = 0.003; RDT: 19.1% ± 21.9%, p = 0.035). Conditions did not affect time to failure, and only RDT decreased the rate of median frequency (0.1%/s ± 0.1%/s, p = 0.020). Inferior force error rate was increased by conditions (SDT: 1.5% ± 0.8%, p < 0.001; RDT: 2% ± 1.5%, p = 0.002). Math error was only augmented by RDT (from 9.9 ± 6.7 to 51.7 ± 18.8, p < 0.001), categorized as "very hard" in 85.7% of participants (p = 0.015).

CONCLUSION

Only the RDT condition reduced complexity and neuromuscular fatigue while increasing force error rate of the handgrip's motor output, without affecting time to failure. A highly demanding dual-task may become a strategy to modify the organization of the hand force motor output, which may contribute to its motor adaptations.

Effects of Nonstationarity on Muscle Force Signals Regularity During a Fatiguing Motor Task

Physiological signals present fluctuations that can be assessed from their temporal structure, also termed complexity. The complexity of a physiological signal is usually quantified using entropy estimators, such as Sample Entropy. Recent studies have shown a loss of force signal complexity with the development of neuromuscular fatigue. However, these studies did not consider the stationarity of the force signals which is an important prerequisite of Sample Entropy measurements. Here, we investigated the effect of the potential nonstationarity of force signals on the kinetics of neuromuscular fatigue-induced change in force signal's complexity. Eleven men performed submaximal intermittent isometric contractions of knee extensors until exhaustion. Neuromuscular fatigue was assessed from changes in voluntary and electrically evoked contractions. Sample Entropy values were computed from submaximal force signals throughout the fatiguing task. The Dickey-Fuller test was used to statistically investigate the stationarity of force signals and the Empirical Mode Decomposition was applied to detrend these signals. Maximal voluntary force, central voluntary activation and muscle twitch decreased throughout the task (all ), indicating the development of global, central and peripheral fatigue, respectively. We found an increase in Sample Entropy with fatigue ( p = 0.024 ) when not considering the nonstationarity of force signals (i.e., 43% of nonstationary signals). After applying the Empirical Mode Decomposition, we found a decrease in Sample Entropy with fatigue ( p = 0.002 ). These findings confirm the presence of nonstationarity in force signals during submaximal isometric contractions which influences the kinetics of Sample Entropy with neuromuscular fatigue.