Influence of leg preference on bilateral muscle activation during cycling

Abstract

The purpose of this study was to investigate asymmetry of muscle activation in participants with different levels of experience and performance with cycling. Two separate experiments were conducted, one with nine cyclists and one with nine non-cyclists. The experiments involved incremental maximal and sub-maximal constant load cycling tests. Bilateral surface electromyography (EMG) and gross and net muscle efficiency were assessed. Analyses of variance in mixed linear models and t-tests were conducted. The cyclists in Experiment 1 presented higher gross efficiency (P < 0.05), whereas net efficiency did not differ between the two experiments (21.3 ± 1.4% and 19.8 ± 1.0% for cyclists and non-cyclists, respectively). The electrical muscle activity increased significantly with exercise intensity regardless of leg preference in both experiments. The coefficient of variation of EMG indicated main effects of leg in both experiments. The non-preferred leg of non-cyclists (Experiment 2) presented statistically higher variability of muscle activity in the gastrocnemius medialis and vastus lateralis. Our findings suggest similar electrical muscle activity between legs in both cyclists and non-cyclists regardless of exercise intensity. However, EMG variability was asymmetric and appears to be strongly influenced by exercise intensity for cyclists and non-cyclists, especially during sub-maximal intensity. Neural factors per se do not seem to fully explain previous reports of pedalling asymmetries.

Keywords:

• Functional laterality
• lower extremity
• limb preference
• performance asymmetry
• motor performance
• learning

Acknowledgements

This research was supported by an International Society of Biomechanics (ISB) Travel Award to the first author, and partially supported by the University of Calgary and CAPES. The authors greatly appreciate the contribution of Dr. Brian MacIntosh and Dr. Marco Vaz for study design and data analysis. The authors also thank Giovani Cunha, Geoff Smith, and Erica Enevold for technical support during data acquisition. Many thanks also to the participants for agreeing to take part in the study.