The effect of low- vs high-cadence interval training on the freely chosen cadence and performance in endurance-trained cyclists
Bibliographic record
Abstract
The aim of this study was to determine the effects of high- and low-cadence interval training on the freely chosen cadence (FCC) and performance in endurance-trained cyclists. Sixteen male endurance-trained cyclists completed a series of submaximal rides at 60% maximal power (Wmax) at cadences of 50, 70, 90, and 110 r·min(-1), and their FCC to determine their preferred cadence, gross efficiency (GE), rating of perceived exertion, and crank torque profile. Performance was measured via a 15-min time trial, which was preloaded with a cycle at 60% Wmax. Following the testing, the participants were randomly assigned to a high-cadence (HC) (20% above FCC) or a low-cadence (LC) (20% below FCC) group for 18 interval-based training sessions over 6 weeks. The HC group increased their FCC from 92 to 101 r·min(-1) after the intervention (p = 0.01), whereas the LC group remained unchanged (93 r·min(-1)). GE increased from 22.7% to 23.6% in the HC group at 90 r·min(-1) (p = 0.05), from 20.0% to 20.9% at 110 r·min(-1) (p = 0.05), and from 22.8% to 23.2% at their FCC. Both groups significantly increased their total distance and average power output following training, with the LC group recording a superior performance measure. There were minimal changes to the crank torque profile in both groups following training. This study demonstrated that the FCC can be altered with HC interval training and that the determinants of the optimal cycling cadence are multifactorial and not completely understood. Furthermore, LC interval training may significantly improve time-trial results of short duration as a result of an increase in strength development or possible neuromuscular adaptations.
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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 | 0.000 |
| 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".