bioRxivNeurosciencePreprint
Neuromechanical Justification of Lower-Limb Functional Tests for a Return to Running: A Muscle Coordination Analysis
Hiroki SaitoiD, Ayu Yamano, Nanae Suzuki, Kazuya MatsushitaiD, Hikaru YokoyamaiD, Atsushi SasakiiD, Tatsuya TakahashiiD, Kimitaka NakazawaiD
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Abstract
IntroductionThis study aimed to explore the shared muscle synergies between running and functional tests that are commonly used when considering the return to running (RTR) after sports-related injuries. We hypothesized that shared muscle synergies would differ among tasks, providing insights into prioritizing functional tests in the context of RTR. MethodsTen healthy male participants were recruited to perform running and 9 functional tasks and their 16 lower limb and trunk muscle activities were recorded using electromyography (EMG). Non-negative matrix factorization (NMF) was applied to the collected EMG data to explore shared muscle synergies between running and the functional tasks. We compared the percentages of shared synergies and temporal patterns between running and each functional test. ResultsAlthough all functional tests exhibited shared muscle synergies with running, the walk (75% [40%-100%]), single leg hops with 30% of maximum distance (SLH30) (60% [20%-100%]), and stepup (63% [0%-100%]) tasks displayed significantly higher percentages of shared synergies compared to other tests. However, significant differences in temporal patternss were observed between running and all functional tasks, indicating varying activation profiles of shared muscle synergies. ConclusionAlthough all functional tests shared muscle synergies with running, variations in the degree of shared synergies and temporal patterns imply that walking, SLH30, and step-up tests may be the most beneficial in predicting running behavior post-ACL injuries. However, functional tests cannot fully replicate running dynamics, suggesting the need for a careful interpretation when assessing readiness for RTR.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
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