Influence of Recovery Mode on the Maximum Number of Intervals Until Exhaustion During an Aerobic Interval Training Session
Bibliographic record
Abstract
ABSTRACT: Varela-Sanz, A, Sánchez-Otero, T, Tuimil, JL, Boullosa, D, and Iglesias-Soler, E. Influence of recovery mode on the maximum number of intervals until exhaustion during an aerobic interval training session. J Strength Cond Res 37(9): e510-e520, 2023-We analyzed work capacity, cardiometabolic, perceptual, and neuromuscular responses to an aerobic interval training (AIT) running session until exhaustion, with active (AR) vs. passive recovery (PR). Eight well-trained male endurance runners (36.88 ± 7.14 years, 58.22 ± 3.39 ml·kg -1 ·minute -1 ) randomly completed, after familiarizations and the University of Montreal Track Test (UMTT), 2 AIT track running sessions until exhaustion consisting in 2-minute bouts at 100% of maximum aerobic speed (MAS), with 2 minutes of recovery at 80% of the velocity associated to the second ventilatory threshold (vVT 2 ) (i.e., AR), or no exercise (i.e., PR). Oxygen consumption (V̇O 2 ), heart rate (HR), blood lactate [La], rating of perceived exertion (RPE), and countermovement jump (CMJ) were continuously monitored during sessions. The level of statistical significance was set at p ≤ 0.05. PR resulted in longer time to exhaustion during sessions (13.9 vs. 11.6 bouts, p = 0.045), but lower HR ( p < 0.01) when compared with AR. Time spent over 90% of maximum oxygen consumption (V̇O 2max ), blood lactate concentrations, neuromuscular performance, and RPE did not differ between AR and PR ( p > 0.05). Thus, PR allowed runners to perform more work intervals and, therefore, to accumulate a greater volume. On the other hand, when training goals are focused on reaching a higher chronotropic stress (i.e., higher HR) during the training session, athletes would obtain more benefits from AR. This study also demonstrates that the current volume recommendations for AIT are far below (54-64.5%) the maximum training capacity of well-trained runners.
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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) | 0.002 | 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".