Leg muscle strength and power predict rating of perceived effort during cardiopulmonary exercise testing
Bibliographic record
Abstract
The effort required to cycle progressively intensifies during an incremental exercise test. The determinants of the perceptions of leg cycling effort have not been assessed in large samples where sufficient response variation permits definitive characterization of relationships to better inform the use of ratings of perceived exertion. The perceived intensities of the effort required to cycle was rated during an incremental exercise test to symptom-limited capacity by 35,597 participants (53 ± 17 yrs, 60 % male) referred from 1988 to 2012 using a 0–10 scale (modified Borg scale). Height, weight, age, muscle strength, pulmonary function, hemoglobin, and arterialized capillary blood gases were measured and assessed for their predictive capacity for ratings of perceived exertion. In this sample, the perceived effort required to cycle was determined by cycling power (power) and the maximum cycling power output (P MAX ) according to the following equation: perceived leg cycling effort = power 2.12 • P MAX −1.86 (r = 0.8159). Forward stepwise linear regression revealed that there was additional predictive capacity with the addition of quadriceps strength to the equation while the additional inclusion of height, age and sex to the relationships contributed minimally. As the P MAX achieved was dependent on leg strength the findings of this study suggest that assessment of muscle-specific strength may be used to predict perceived leg cycling effort when completion of an incremental cycling test is not feasible. This is highly relevant knowing the technical and physiological limitations that present barriers to widespread use of incremental exercise testing.
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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 | 0.001 | 0.006 |
| 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".