The impact of work-matched interval training on <i>V</i>O<sub>2peak</sub> and <i>V</i>O<sub>2</sub> kinetics: diminishing returns with increasing intensity
Bibliographic record
Abstract
High-intensity interval training (HIIT) improves peak oxygen uptake (V̇O2peak) and oxygen uptake (V̇O2) kinetics, however, it is unknown whether an optimal intensity of HIIT exists for eliciting improvements in these measures of whole-body oxidative metabolism. The purpose of this study was to (i) investigate the effect of interval intensity on training-induced adaptations in V̇O2peak and V̇O2 kinetics, and (ii) examine the impact of interval intensity on the frequency of nonresponders in V̇O2peak. Thirty-six healthy men and women completed 3 weeks of cycle ergometer HIIT, consisting of intervals targeting 80% (LO), 115% (MID), or 150% (HI) of peak aerobic power. Total work performed per training session was matched across groups. A main effect of training (p < 0.05) and a significant interaction effect was observed for V̇O2peak, with the change in V̇O2peak being greater (p < 0.05) in the MID group than the LO group; however, no differences were observed between the HI group and either the MID or LO groups (ΔV̇O2peak; LO, 2.7 ± 0.7 mL·kg(-1)·min(-1); MID, 5.8 ± 0.7; HI, 4.2 ± 1.0). The greatest proportion of responders was observed in the MID group (LO, 8/12; MID, 12/13; HI, 9/11). A nonsignificant relationship (p = 0.26; r(2) = 0.04) was found between the changes in V̇O2peak and τV̇O2. These results suggest that training at intensities around V̇O2peak may represent a threshold intensity above which further increases in training intensity provide no additional adaptive benefit. The dissociation between changes in V̇O2peak and V̇O2 kinetics also reflects the different underlying mechanisms regulating these adaptations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".