Influence of leg preference on bilateral muscle activation during cycling
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".