Lower-limb muscle function is influenced by changing mechanical demands in cycling
Bibliographic record
Abstract
Although cycling is a seemingly simple, reciprocal task, muscles must adapt their function to satisfy changes in mechanical demands induced by higher crank torques and faster pedalling cadences. We examined whether muscle function was sensitive to these changes in mechanical demands across a wide range of pedalling conditions. We collected experimental data of cycling where crank torque and pedalling cadence were independently varied from 13 to 44 N m and 60 to 140 rpm. These data were used in conjunction with musculoskeletal simulations and a recently developed functional index-based approach to characterise the role of human lower-limb muscles. We found that in muscles that generate most of the mechanical power and work during cycling, greater crank torque induced shifts towards greater muscle activation, greater positive muscle-tendon unit (MTU) work and a more motor-like function, particularly in the limb extensors. Conversely, with faster pedalling cadence, the same muscles exhibited a phase advance in muscle activity prior to crank top dead centre, which led to greater negative MTU power and work and shifted the muscles to contract with more spring-like behaviour. Our results illustrate the capacity for muscles to adapt their function to satisfy the mechanical demands of the task, even during highly constrained reciprocal tasks such as cycling. Understanding how muscles shift their contractile performance under varied mechanical and environmental demands may inform decisions on how to optimise pedalling performance and to design targeted cycling rehabilitation therapies for muscle-specific injuries or deficits.
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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 | 0.001 |
| 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 | 0.001 |
| 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".