The effects of exercise intensity and duration on the relationship between the slow component of VO<sub>2</sub> and peripheral fatigue
Bibliographic record
Abstract
Abstract Aim If the development of the oxygen uptake slow component (V̇O 2SC ) and muscle fatigue are related, these variables should remain coupled in a time‐ and intensity‐dependent manner. Methods: 16 participants (7 females) visited the laboratory on 7 separate occasions: (1) three 6‐minutes moderate‐intensity cycling exercise bouts proceeded by a ramp incremental test; (2‐3) 30‐minutes constant power output (PO) exercise bout to determine the maximal lactate steady state (MLSS); (4‐7) constant‐PO exercise bouts to task failure (TTF), pseudorandomized order, at (i) 15% below the PO at MLSS; (ii) 10 W below MLSS; (iii) MLSS; (iv) 10 W above MLSS (first intensity and randomized order thereafter). Neuromuscular fatigue was characterized by isometric maximal voluntary contractions and femoral nerve electrical stimulation of knee extensors to measure peripheral fatigue at baseline, at min 5, 10, 20, 30 and TTF. Pulmonary oxygen uptake (V̇O 2 ) was continuously recorded during the constant‐PO bouts and V̇O 2SC was characterized based on each individual V̇O 2 kinetics during moderate transitions. Results The development of V̇O 2SC and peripheral fatigue were correlated across time ( r 2 adj range of 0.64‐0.80) and amongst each exercise intensity ( r 2 adj range of 0.26‐0.30) (all P < .001). Also, TTF was correlated with V̇O 2SC and neuromuscular fatigue parameters ( r 2 adj range of 0.52‐0.82, all P < .001). Conclusion The V̇O 2SC and peripheral fatigue development are correlated throughout the exercise in a time‐ and intensity‐dependent manner, suggesting that the V̇O 2SC may depend on muscle fatigue even if the mechanisms of reduced contractile function are different amongst intensities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".