VO2 KINETICS ARE PROGRESSIVELY SLOWED IN REGIONS OF UPPER MODERATE-AND HEAVY-INTENSITY EXERCISE DOMAINS
Bibliographic record
Abstract
The adaptation of VO2 has been shown to be slower for a given increment in work rate for transitions in the upper versus the lower region of the moderate-intensity exercise domain (Europ.J.Appl.Physiol. 86:125, 2001). We tested the hypothesis that muscle fibers with inherently slower kinetic characteristics are recruited progressively with increasing intensities of exercise into the heavy-intensity domain. PURPOSE To examine VO2 kinetics during identical work rate transitions into the lower (LM) and upper (UM) regions of the moderate-intensity domain, and the lower region of the heavy-intensity domain (H). METHODS Seven young males performed repetitions of constant-load cycling exercise. The protocol consisted of three consecutive steps (S1, S2, S3) of equal work rate transitions from 20W, with each step increment equal to 50% of the difference between 20W and the work rate corresponding to 90% of the estimated lactate threshold; i. e. S1: 20W → LM; S2: LM→UM; S3: UM→H. Additionally, single step increments were performed from 20W→UM (S4) and 20W→H (S5). The duration of each step was 6 min. VO2 was measured breath-by-breath. Repetitions (4–6 trials) of each protocol were ensemble averaged to yield a single response, and the on-transient phase II VO2 (τVO2) response was fit with a single exponential model. RESULTS In the multi-step protocol, the τVO2 for S1 (18±5 s) was faster than S2 (30±10 s) (p = 0.007), and S2 was faster than S3 (38±10 s) (p = 0.041). For single step protocols, the τVO2 for S5 (29±5 s) was similar to S4 (22±11 s) (p = 0.091), but slower than S1 (p = 0.044). CONCLUSION The progressively slower VO2 kinetics for a given increment in work rate throughout the moderate- and into the heavy-intensity domains supports the concept of recruitment of motor units with successively slower kinetic characteristics. Also these data support the concept that VO2 kinetics do not conform to a dynamically linear system, as the τVO2 was dependent on the region of intensity from which the exercise was initiated. Supported by NSERC, Canada
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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".