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 (V̇O2SC) 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 (V̇O2) was continuously recorded during the constant‐PO bouts and V̇O2SC was characterized based on each individual V̇O2 kinetics during moderate transitions. Results The development of V̇O2SC and peripheral fatigue were correlated across time (r2adj range of 0.64‐0.80) and amongst each exercise intensity (r2adj range of 0.26‐0.30) (all P < .001). Also, TTF was correlated with V̇O2SC and neuromuscular fatigue parameters (r2adj range of 0.52‐0.82, all P < .001). Conclusion The V̇O2SC and peripheral fatigue development are correlated throughout the exercise in a time‐ and intensity‐dependent manner, suggesting that the V̇O2SC may depend on muscle fatigue even if the mechanisms of reduced contractile function are different amongst intensities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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".