Upper and lower limb performance fatigability in people with multiple sclerosis investigated through surface electromyography: a pilot study

Abstract

Objective: Fatigue experienced by people with multiple sclerosis (pwMS) is multidimensional, consisting of different components, such as perceived, physical and cognitive fatigue and performance fatigability. At present, there is no gold standard to assess performance fatigability in pwMS; therefore, we aimed to determine whether, during a fatiguing task, average rectified value (ARV), mean frequency of the power spectrum (MNF), muscle fiber conduction velocity (CV) and fractal dimension (FD) of surface electromyography (sEMG) may be used as indirect indices of performance fatigability. Moreover, we analyzed whether a three-week rehabilitation program impacts on performance fatigability in pwMS, and whether a relationship between sEMG parameters and trait levels of perceived fatigability, before and after rehabilitation, does exist. Approach: Twenty-one pwMS performed a 20% maximal voluntary contraction (MVC) of 1 min, and afterwards a 60% MVC held until exhaustion. sEMG signals were detected from the biceps brachii, vastus medialis and vastus lateralis. Performance fatigability was determined at entry to (t 0) and discharge from (t 1) rehabilitation. Perceived fatigability was measured at t 0 and t 2, one month after rehabilitation. Main results: ARV, MNF, CV and FD rates of change showed significant changes at t 0 and t 1 (p < 0.05) during the high-level contraction in the BB, but rather limited in the vastii muscles. Moreover, rehabilitation did not induce any reductions in either perceived or performance fatigability. No significant correlations between ARV, MNF, CV and FD rates of change during the 60% MVC and perceived fatigability, at t 0 and t 2, were found. Significance: Our findings suggest that the sEMG parameters are useful for indirectly assessing performance fatigability in pwMS during sub-maximal fatiguing contractions, particularly in the biceps brachii.

Perceived fatigue, lower limb muscle force and performance fatigability after a rehabilitation program in Multiple Sclerosis

“… 14 Similarly to our results, Hameau et al (2018) 15 found that a short, intensive, combined 4-week rehabilitation programme, focused on gait, balance, endurance and resistance training, did not modify fatigability in moderate impaired MS subjects. Same findings was revealed by Beretta-Piccoli et al (2020) 16 while testing an exercise intervention based mainly on progressive resistance training for lower limb. Anyway, these studies used different protocols and fatigability was investigated with different tasks, making it difficult to draw unit conclusion.…”

Increased resistance towards fatigability in patients with facioscapulohumeral muscular dystrophy

“…Indeed, fatigability during isometric constant force contractions, can be observed by the decrease of CV mainly related to a decrease of intracellular pH (Komi and Tesch 1979 ). A decrease in the fractal dimension (FD) of the sEMG signal has been associated with fatigability, aging, and disease (Beretta-Piccoli et al 2020 ; Goldberger et al 2002 ; Arjunan and Kumar 2013 ). These findings suggest a potential utility of the fractal analysis of the sEMG signal as a complementary tool for assessing fatigability during a performance test.…”

Evaluation of performance fatigability through surface EMG in health and muscle disease: state of the art

“…Later, FD was proposed as an index to monitor changes in sEMG signal during a fatiguing task (Beretta-Piccoli et al, 2015;Boccia et al, 2016;Mesin et al, 2009a). Indeed, we clearly showed a significant negative normalized slope of FD, during fatiguing isometric contractions in different muscles (vastus lateralis and medialis and BB) and at different intensities (Beretta-Piccoli et al, 2020;Beretta-Piccoli et al, 2015;Beretta-Piccoli et al, 2017;Boccia et al, 2016;Meduri et al, 2016), suggesting a reduction in signal complexity (Figure 2).…”

“…FD values close to 1 reflect smoothed signals whereas values approaching 2 are typical of signals with high spacefilling propensity (Beretta-Piccoli et al, 2015). The box-counting algorithm has been used to evaluate sEMG signals during isometric contractions in healthy subjects (Beretta-Piccoli et al, 2017;Mesin et al, 2009a;Troiano et al, 2008), elderly (Boccia et al, 2016) and persons with multiple sclerosis (Beretta-Piccoli et al, 2020).…”

“…Figure2. Time course of fractal dimension (FD), muscle fiber conduction velocity (CV), mean frequency of the power spectrum (MNF) and average rectified value (ARV) for a representative person with multiple sclerosis (fromBeretta-Piccoli et al, 2020). BB, biceps brachii; VV, vastus lateralis and medialis.…”