Effect of Various Intensities of Short-Term Interval Training on Oxygen Uptake Kinetics
Bibliographic record
Abstract
Oxygen (O2) uptake kinetics reflect the rate at which an individual’s oxygen consumption (VO2) changes to meet a new metabolic demand, such as an increase or decrease in exercise intensity. O2 uptake kinetics can indicate cardiovascular and metabolic fitness and are described by the time variable, τ (tau), of the exponential equation describing the change in VO2. With training τ decreases, indicating a more rapid increase in VO2 to meet the new metabolic demand. Interval training at supramaximal (>100% VO2max) intensities has been shown to elicit similar improvements in O2 uptake kinetics to traditional endurance training. This study looked to determine the optimal intensity of interval training for eliciting improvements in O2 uptake kinetics. Fifteen recreationally active individuals (males: n=9, age = 23.3±3.3 years, VO2max = 44.2±6.5 ml O2•min-1•kg-1; females: n=6, age = 21.5±0.7 years, VO2max = 39.7±5.4 ml O2•min-1•kg-1) participated in 12 training sessions over 4 weeks. To measure O2 uptake kinetics subjects completed three step transitions from loadless (~25W) to low work-rate (~80W) cycling, prior and following training. Each subject was randomly assigned to one of three intensities - high-intensity interval training (HIIT ~120% VO2max), moderate-intensity interval training (MIIT ~90% VO2max), and low-intensity interval training (LIIT ~65% VO2max). Each session consisted of 8-12 intervals of 1-minute duration with 1-minute recovery on a stationary bicycle at the prescribed relative intensity. No significant differences between groups were observed in changes in τ (Δτ: LIIT: 3.1±8.3s; MIIT: -0.59±12.4s; HIIT: 6.2±6.0s).
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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 | 0.001 |
| 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".