Cardiovascular Responses to Eccentric Cycling Based on Perceived Exertion Compared to Concentric Cycling, Effect of Pedaling Rate, and Sex
Bibliographic record
Abstract
Interest in eccentric exercises has increased over the last decades due to its efficiency in achieving moderate–high intensity muscular work with reduced metabolic demands. However, individualizing eccentric exercises in rehabilitation contexts remains challenging, as concentric exercises mainly rely on cardiovascular parameters. To overcome this, perceived exertion could serve as an individualization tool, but the knowledge about cardiovascular responses to eccentric cycling based on perceived exertion are still scarce. For this purpose, the cardiorespiratory parameters of 26 participants were assessed during two 5 min bouts of concentric cycling at 30 and 60 rpm and two bouts of eccentric cycling at 15 and 30 rpm matched for rating of perceived exertion. With this method, we hypothesized higher exercise efficiency during eccentric cycling for a same perceived exertion. The results revealed significantly elevated heart rate and cardiac index at higher pedalling rates during concentric (p < 0.001), but not during eccentric cycling (p ≈ 1). Exercise efficiency was higher during concentric cycling (64%), decreasing with pedalling rate, while eccentric cycling exhibited increased work rates (82%), and increased by over 100% with higher pedalling rate. Hence, eccentric cycling, with lower cardiorespiratory work for the same perceived exertion, facilitates higher work rates in deconditioned populations. However, further studies are needed for effective individualization.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".