Cadence, power, and muscle activation in cycle ergometry
Bibliographic record
Abstract
PURPOSE: Based on the resistance-rpm relationship for cycling, which is not unlike the force-velocity relationship of muscle, it is hypothesized that the cadence which requires the minimal muscle activation will be progressively higher as power output increases. METHODS: To test this hypothesis, subjects were instrumented with surface electrodes placed over seven muscles that were considered to be important during cycling. Measurements were made while subjects cycled at 100, 200, 300, and 400 W at each cadence: 50, 60, 80, 100, and 120 rpm. These power outputs represented effort which was up to 32% of peak power output for these subjects. RESULTS: When all seven muscles were averaged together, there was a proportional increase in EMG amplitude each cadence as power increased. A second-order polynomial equation fit the EMG:cadence results very well (r2 = 0.87- 0.996) for each power output. Optimal cadence (cadence with lowest amplitude of EMG for a given power output) increased with increases in power output: 57 +/- 3.1, 70 +/- 3.7, 86 +/- 7.6, and 99 +/- 4.0 rpm for 100, 200, 300, and 400 W, respectively. CONCLUSION: The results confirm that the level of muscle activation varies with cadence at a given power output. The minimum EMG amplitude occurs at a progressively higher cadence as power output increases. These results have implications for the sense of effort and preferential use of higher cadences as power output is increased.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".