Functional data analysis reveals asymmetrical crank torque during cycling performed at different exercise intensities
| Main Author: | |
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| Publication Date: | 2021 |
| Other Authors: | , , , , , , |
| Format: | Article |
| Language: | por |
| Source: | Repositório Institucional da UFMG |
| Download full: | https://doi.org/10.1016/j.jbiomech.2021.110478 http://hdl.handle.net/1843/56793 https://orcid.org/0000-0003-1520-7330 https://orcid.org/0000-0001-8923-4855 https://orcid.org/0000-0002-5216-7342 https://orcid.org/0000-0002-2057-199X https://orcid.org/0000-0003-1448-5670 https://orcid.org/0000-0001-5207-9230 https://orcid.org/0000-0001-9670-4894 |
Summary: | Pedaling asymmetry is claimed as a factor of influence on injury and performance. However, the evidence is still controversial. Most previous studies determined peak torque asymmetries, which in our under standing does not consider the pattern of movement like torque profiles. Here we demonstrate that asymmetries in pedaling torque at different exercise intensities can be better described when the torque profiles are considered using functional analysis of variance than when only the peak values are analyzed. We compared peak torques and torque curves recorded while cyclists pedaled at submaximal intensities of 60%, 80%, and 95% of the maximal power output and compared data between the preferred and non preferred legs. ANOVA showed symmetry or rather no difference in the amount of peak torque between legs, regardless of pedaling intensity. FANOVA, on the other hand, revealed significant asymmetries between legs, regardless of cycling intensity, apparently for different sections of the cycle, however, not for peak torque, either. We conclude that pedaling asymmetry cannot be quantified solely by peak torques and considering the analysis of the entire movement cycle can more accurately reflect the biome chanical movement pattern. Therefore, FANOVA data analysis could be an alternative to identify asym metries. A novel approach as described here might be useful when combining kinetics assessment with other approaches like EMG and kinematics and help to better understand the role of pedaling asym metries for performance and injury risks. |