Inclusion of sprints during moderate-intensity continuous exercise enhances post-exercise fat oxidation in young males
Bibliographic record
Abstract
To assess the physiological demands of including high-intensity efforts during continuous exercise, we designed a randomized crossover study, in which 12 physically active young males executed 3 different exercises in random order: FATmax – continuous exercise at the highest fat oxidation zone; 2min-130% – FATmax interspersed by a 2-minute bout at 130% of the maximal oxygen uptake associated intensity (iV̇O2max); and 20s:10s-170% – FATmax interspersed by four 20-s bouts at 170%iV̇O2max interpolated by 10s of passive recovery. We measured oxygen uptake (V̇O2), blood lactate concentration ([LAC]), respiratory exchange rate (RER), and fat and carbohydrate (CHO) oxidation. For statistical analyses, repeated-measures ANOVA was applied. Although no differences were found for average V̇O2 or carbohydrate oxidation rate, the post-exercise fat oxidation rate was 37.5% and 50% higher during the 2min-130% and 20s:10s-170% protocols, respectively, compared with the FATmax protocol, which also presented lower values of RER during exercise compared with 2min-130% and 20s:10s-170% (p < 0.001 in both), and higher values post-exercise (p = 0.04 and p = 0.002, respectively). [LAC] was higher during exercise when high-intensity bouts were applied (p < 0.001 for both) and was higher at post-exercise during the intermittent bouts compared with FATmax (p = 0.016). The inclusion of high-intensity efforts during moderate-intensity continuous exercise promoted higher physiological demands and post-exercise fat oxidation. Novelty: The inclusion of 2-minute efforts modifies continuous exercise demands. Maximal efforts can increase post-exercise fat oxidation. 2-minute maximal efforts, continuous or intermittent, presents similar demands.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".