Hug F, Turpin NA, Couturier A, Dorel S. Consistency of muscle synergies during pedaling across different mechanical constraints. J Neurophysiol 106: 91-103, 2011. First published April 13, 2011 doi:10.1152/jn.01096.2010The purpose of the present study was to determine whether muscle synergies are constrained by changes in the mechanics of pedaling. The decomposition algorithm used to identify muscle synergies was based on two components: "muscle synergy vectors," which represent the relative weighting of each muscle within each synergy, and "synergy activation coefficients," which represent the relative contribution of muscle synergy to the overall muscle activity pattern. We hypothesized that muscle synergy vectors would remain fixed but that synergy activation coefficients could vary, resulting in observed variations in individual electromyographic (EMG) patterns. Eleven cyclists were tested during a submaximal pedaling exercise and five all-out sprints. The effects of torque, maximal torque-velocity combination, and posture were studied. First, muscle synergies were extracted from each pedaling exercise independently using non-negative matrix factorization. Then, to crossvalidate the results, muscle synergies were extracted from the entire data pooled across all conditions, and muscle synergy vectors extracted from the submaximal exercise were used to reconstruct EMG patterns of the five all-out sprints. Whatever the mechanical constraints, three muscle synergies accounted for the majority of variability [mean variance accounted for (VAF) ϭ 93.3 Ϯ 1.6%, VAF muscle Ͼ 82.5%] in the EMG signals of 11 lower limb muscles. In addition, there was a robust consistency in the muscle synergy vectors. This high similarity in the composition of the three extracted synergies was accompanied by slight adaptations in their activation coefficients in response to extreme changes in torque and posture. Thus, our results support the hypothesis that these muscle synergies reflect a neural control strategy, with only a few timing adjustments in their activation regarding the mechanical constraints.module; non-negative matrix factorization; motor control; cycling THE REDUNDANCY of the musculoskeletal system (Bernstein 1967) implies vast degrees of freedom. This provides great flexibility but makes the control of these degrees of freedom extremely complex. Consequently, the question of how the central nervous system coordinates activity among numerous muscles is central to understanding motor control. Low-dimensional modules formed by muscles activated in synchrony, named muscle synergies, have been proposed as building blocks that could simplify the construction of motor behaviors (2005) reported that combinations of a small number of synergies accounted for a large fraction of the variation in the EMG patterns observed during jumping, swimming, and walking in frogs. However, due to different muscle architectures and fiber type composition (and thus to different mechanical advantages) among muscles participating in the same mus...

Our aim was to determine whether muscle synergies are similar across trained cyclists (and thus whether the same locomotor strategies for pedaling are used), despite interindividual variability of individual EMG patterns. Nine trained cyclists were tested during a constant-load pedaling exercise performed at 80% of maximal power. Surface EMG signals were measured in 10 lower limb muscles. A decomposition algorithm (nonnegative matrix factorization) was applied to a set of 40 consecutive pedaling cycles to differentiate muscle synergies. We selected the least number of synergies that provided 90% of the variance accounted for VAF. Using this criterion, three synergies were identified for all of the subjects, accounting for 93.5+/-2.0% of total VAF, with VAF for individual muscles ranging from 89.9+/-8.2% to 96.6+/-1.3%. Each of these synergies was quite similar across all subjects, with a high mean correlation coefficient for synergy activation coefficients (0.927+/-0.070, 0.930+/-0.052, and 0.877+/-0.110 for synergies 1-3, respectively) and muscle synergy vectors (0.873+/-0.120, 0.948+/-0.274, and 0.885+/-0.129 for synergies 1-3, respectively). Despite a large consistency across subjects in the weighting of several monoarticular muscles into muscle synergy vectors, we found larger interindividual variability for another monoarticular muscle (soleus) and for biarticular muscles (rectus femoris, gastrocnemius lateralis, biceps femoris, and semimembranosus). This study demonstrated that pedaling is accomplished by the combination of the similar three muscle synergies among trained cyclists. The interindividual variability of EMG patterns observed during pedaling does not represent differences in the locomotor strategy for pedaling.

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Background Lockdown has been one of the major worldwide strategies to control the spread of coronavirus disease 2019 (COVID-19). Its consequences on the well-being of individuals needs to be better understood. The objective of this work was to evaluate the impact of lockdown on the well-being of a general population and the factors associated with this potential impairment of well-being in a population that has been only lightly affected by COVID-19 such as in Reunion island, an overseas French department. Methods An online survey was proposed to the population of Reunion Island between the 35 th and 54 th days of lockdown relative to pre- and per-lockdown periods. Well-being was measured by the 5-item World Health Organization Well-Being Index, with some questions about sleep habits (Pittsburgh questionnaire), weekly physical activity (IPAQ), health, and lifestyle. Results Four hundred volunteers answered the survey. They reported a 15.7% decrease in well-being (p<0.001), accompanied by increased anxiety (p<0.001), decreased weekly physical activity (p<0.001), delayed and poorer quality sleep (p<0.001). Multivariate logistical analysis showed that impairment in well-being during lockdown was independently associated with an increase in anxiety (odds ratio (OR): 4.77 (3.26–6.98), p<0.001), decrease in weekly physical activity (OR: 0.58 (0.43–0.79), p<0.001), and poor-quality sleep (OR: 0.29 (0.19–0.43), p<0.001). Conclusions This study suggested an impairment in well-being during lockdown, associated with anxiety, lack of physical activity and sleep disruptions. Public policies must consider these factors as levers for improving the well-being of the population in order to effectively combat the spread of COVID-19.

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The present study was designed to quantify the effect of power output on muscle coordination during rowing. Surface electromyographic (EMG) activity of 23 muscles and mechanical variables were recorded in eight untrained subjects and seven experienced rowers. Each subject was asked to perform three 2-min constant-load exercises performed at 60, 90 and 120% of the mean power output over a maximal 2,000-m event (denoted as P60, P90, and P120, respectively). A decomposition algorithm (nonnegative matrix factorization) was used to extract the muscle synergies that represent the global temporal and spatial organization of the motor output. The results showed a main effect of power output for 22 of 23 muscles (p values ranged from <0.0001 to 0.004) indicating a significant increase in EMG activity level with power output for both untrained and experienced subjects. However, for the two populations, no dramatic modification in the shape of individual EMG patterns (mean r (max) value = 0.93 ± 0.09) or in their timing of activation (maximum lag time = -4.3 ± 3.8% of the rowing cycle) was found. The results also showed a large consistency of the three extracted muscle synergies, for both synergy activation coefficients (mean r (max) values range from 0.87 to 0.97) and muscle synergy vectors (mean r values range from 0.70 to 0.76) across the three power outputs. In conclusion, despite significant changes in the level of muscle activity, the global temporal and spatial organization of the motor output is very little affected by power output on a rowing ergometer.

SUMMARYMuscle fatigue is an exercise-induced reduction in the capability of a muscle to generate force. A possible strategy to counteract the effects of fatigue is to modify muscle coordination. We designed this study to quantify the effect of fatigue on muscle coordination during a cyclic exercise involving numerous muscles. Nine human subjects were tested during a constant-load rowing exercise (mean power output: 217.9±32.4W) performed until task failure. The forces exerted at the handle and the footstretcher were measured continuously and were synchronized with surface electromyographic (EMG) signals measured in 23 muscles. In addition to a classical analysis of individual EMG data (EMG profile and EMG activity level), a non-negative matrix factorization algorithm was used to identify the muscle synergies at the start and the end of the test. Among the 23 muscles tested, 16 showed no change in their mean activity level across the rowing cycle, five (biceps femoris, gluteus maximus, semitendinosus, trapezius medius and vastus medialis) showed a significant increase and two (gastrocnemius lateralis and longissimus) showed a significant decrease. We found no change in the number of synergies during the fatiguing test, i.e. three synergies accounted for more than 90% of variance accounted for at the start (92.4±1.5%) and at the end (91.0±1.8%) of the exercise. Very slight modifications at the level of individual EMG profiles, synergy activation coefficients and muscle synergy vectors were observed. These results suggest that fatigue during a cyclic task preferentially induces an adaptation in muscle activity level rather than changes in the modular organization of the muscle coordination.