During Cycling What Limits Maximum Mechanical Power Output at Cadences above 120 rpm?
Bibliographic record
Abstract
PURPOSE: A key determinant of muscle coordination and maximum power output during cycling is pedaling cadence. During cycling, the neuromuscular system may select from numerous solutions that solve the task demands while producing the same result. For more challenging tasks, fewer solutions will be available. Changes in the variability of individual muscle excitations (EMG) and multimuscle coordination, quantified by entropic half-life (EnHL), can reflect the number of solutions available at each system level. We, therefore, ask whether reduced variability in muscle coordination patterns occur at critical cadences and if they coincide with reduced variability in excitations of individual muscles. METHODS: Eleven trained cyclists completed an array of cadence-power output conditions. The EnHL of EMG intensity recorded from 10 leg muscles and EnHL of principal components describing muscle coordination were calculated. Multivariate adaptive regressive splines were used to determine the relationships between each EnHL and cycling condition or excitation characteristics (duration, duty cycle). RESULTS: Muscle coordination became more persistent at cadences up to 120 rpm, indicated by increasing EnHL values. Changes in EnHL at the level of the individual muscles differed from the changes in muscle coordination EnHL, with longer EnHL occurring at the slowest (<80 rpm) and fastest (>120 rpm) cadences. The EnHL of the main power producing muscles, however, reached a minimum by 80 rpm and did not change across the faster cadences studied. CONCLUSIONS: Muscle coordination patterns, rather than the contribution of individual muscles, are key to power production at faster cadences in trained cyclists. Reductions in maximum power output at cadences above 120 rpm could be a function of the time available to coordinate orientation and transfer of forces to the pedals.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".