Priming Exercise Induced Attenuation Of VO2 Slow Component Is Associated With Changes In Muscle EMG Activity
Bibliographic record
Abstract
The precise mechanisms for the development of the VO2 slow component during heavy exercise and the increase in oxygen cost during moderate exercise that follows heavy warm-up remain uncertain. PURPOSE: We tested the hypothesis that changes in muscle activity are related to changes in slow component amplitude during heavy exercise and elevated steady state VO2 during moderate exercise following a heavy warm-up. METHODS: Eight male endurance athletes performed two repetitions of two cycling protocols involving 6-min bouts of heavy and moderate intensity. VO2 was measured breath-by-breath and muscle activity was assessed by surface EMG. RESULTS: During heavy exercise, prior moderate and heavy exercise had a graded effect, attenuating the slow component amplitude by 19% and 40%, (prior moderate: 455 ± 52; prior heavy: 341 ± 54 vs. no warm-up: 564 ± 71 ml/min; P < 0.01 for both). Similarly, prior warm-up modified EMG activity between the 2nd and 6th min of exercise, shifting the increase in integrated EMG (iEMG) during control, to a smaller increase after a moderate bout and a decrease after heavy exercise. Principle components analysis showed a moderate positive correlation between the slow component amplitude and the changes iEMG of the knee extensor muscles (r = 0.45, P = 0.03). During moderate exercise, mean power frequency (MPF) was augmented by one or two prior heavy bouts, but no changes in iEMG were observed. CONCULSION: The attenuation of slow component amplitude by moderate and heavy warm-up and the elevated moderate exercise steady state VO2 following a heavy warm-up appear to be related to some changes in surface EMG activity and this may be an indication of altered muscle fibre recruitment induced by the priming exercise.
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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 | 0.000 |
| 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) | 0.002 | 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".