Self-pacing increases critical power and improves performance during severe-intensity exercise
Bibliographic record
Abstract
The parameters of the power-duration relationship for severe-intensity exercise (i.e., the critical power (CP) and the curvature constant (W')) are related to the kinetics of pulmonary O2 uptake, which may be altered by pacing strategy. We tested the hypothesis that the CP would be higher when derived from a series of self-paced time-trials (TT) than when derived from the conventional series of constant work-rate (CWR) exercise tests. Ten male subjects (age, 21.5 ± 1.9 years; mass, 75.2 ± 11.5 kg) completed 3-4 CWR and 3-4 TT prediction trial protocols on a cycle ergometer for the determination of the CP and W'. The CP derived from the TT protocol (265 ± 44 W) was greater (P < 0.05) than the CP derived from the CWR protocol (250 ± 47 W), while the W' was not different between protocols (TT: 18.1 ± 5.7 kJ, CWR: 20.6 ± 7.4 kJ, P > 0.05). The mean response time of pulmonary O2 uptake was shorter during the TTs than the CWR trials (TT: 34 ± 16, CWR: 39 ± 19 s, P < 0.05). The CP was correlated with the total O2 consumed in the first 60 s across both protocols (r = 0.88, P < 0.05, n = 20). These results suggest that in comparison with the conventional CWR exercise protocol, a self-selected pacing strategy enhances CP and improves severe-intensity exercise performance. The greater CP during TT compared with CWR exercise has important implications for performance prediction, suggesting that TT completion times may be overestimated by CP and W' parameters derived from CWR protocols.
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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) | 0.001 | 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".