Prior moderate and heavy exercise accelerate oxygen uptake and cardiac output kinetics in endurance athletes
Bibliographic record
Abstract
Cardiorespiratory interactions at the onset of dynamic cycling exercise are modified by warm-up exercises. We tested the hypotheses that oxygen uptake (Vo(2)) and cardiac output (Q) kinetics would be accelerated at the onset of heavy and moderate cycling exercise by warm-up. Nine male endurance athletes (peak Vo(2): 60.5 +/- 3.2 ml.min(-1).kg(-1)) performed multiple rides of two different 36-min cycling protocols, involving 6-min bouts at moderate and heavy intensities. Breath-by-breath Vo(2) and beat-by-beat stroke volume (SV) and Q, estimated by Modelflow from the finger pulse, were measured simultaneously with kinetics quantified from the phase II time constant (tau(2)). One novel finding was that both moderate (M) and heavy (H) warm-up bouts accelerated phase II Vo(2) kinetics during a subsequent bout of heavy exercise (tau(2): after M = 22.5 +/- 2.7 s, after H = 22.1 +/- 2.9 vs. 26.2 +/- 3.2 s; P < 0.01). Q kinetics in heavy exercise were accelerated by both warm-up intensities (tau(2): M = 22.0 +/- 4.1 s, H = 23.8 +/- 5.6 s vs. 27.4 +/- 7.2 s; P < 0.05). During moderate exercise, prior heavy-intensity warm-up (one or two bouts) accelerated Vo(2) kinetics and elevated Q at exercise onset, with no changes in Q kinetics. A second novel finding was a significant overshoot in the estimate of SV from Modelflow in the first minutes of each moderate and heavy exercise bout. These findings suggest that the acceleration of Vo(2) kinetics during heavy exercise was enabled by the acceleration of Q kinetics, and that rapid increases in Q at the onset of moderate and heavy exercise might result, in part, from an overshoot of SV.
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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) | 0.001 | 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".