Active vs. passive recovery during an aerobic interval training session in well-trained runners
Bibliographic record
Abstract
Abstract Purpose To compare cardio-metabolic, perceptual and neuromuscular responses to an aerobic interval training (AIT) running session, with active (AR) vs. passive recovery (PR). Methods Eleven well-trained male distance runners (36.63 ± 6.93 years, 59.26 ± 5.27 mL·kg −1 ·min −1 , ⁓ 35 min in 10 km) completed the University of Montréal Track Test (UMTT) and 2 AIT sessions on track in random order, which consisted of 4 × 2 min at 100% of the maximum aerobic speed (MAS), with 2 min of AR at 80% of the velocity associated to the second ventilatory threshold (vVT 2 ), or no exercise (i.e., PR). During sessions, oxygen consumption (V̇O 2 ), heart rate (HR), blood lactate [La], rating of perceived exertion (RPE), and countermovement jump (CMJ) were continuously monitored. Results There were no differences in time spent in the “red zone” (i.e. > 90% V̇O 2max ) between sessions (222 ± 73 s AR vs. 230 ± 104 s PR, p = 0.588), although the PR exhibited a greater time spent at peak V̇O 2 close to significance (117 ± 114 vs. 158 ± 109 s, p = 0.056). However, the AR elicited a higher mean V̇O 2 (49.62 ± 5.91 vs. 47.46 ± 4.20 mL·kg −1 ·min −1 , p = 0.021). The AR favored a lower [La] after sessions (6.93 ± 2.22 vs. 6.24 ± 1.93 mmol·L −1 , p = 0.016) and a higher RPE during sessions (15 ± 0.45 vs. 14 ± 0.47, p = 0.045). Meanwhile, the CMJ was significantly potentiated during both sessions. Conclusion Considering that PR elicited lower perceptual loading for a similar cardiorespiratory response, its use would be preferable, at least, for this type of AIT running sessions.
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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 | 0.001 |
| 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".