The effects of a pre-exercise meal on postexercise metabolism following a session of sprint interval training
Bibliographic record
Abstract
Sprint interval training (SIT) has demonstrated reductions in fat mass through potential alterations in postexercise metabolism. This study examined whether exercising in the fasted or fed state affects postexercise metabolism following acute SIT. Ten active males performed a bout of modified SIT (8 × 15-s sprints; 120 s recovery) in both a fasted (FAST) and fed (FED) state. Gas exchange was collected through 3 h postexercise, appetite perceptions were measured using a visual analog scale, and energy intake was recorded using dietary food logs. There was no difference in energy expenditure between conditions at any time point (p > 0.329) or in total session energy expenditure (FED: 514.8 ± 54.9 kcal, FAST: 504.0 ± 74.3 kcal; p = 0.982). Fat oxidation at 3 h after exercise was higher in FED (0.110 ± 0.04 g·min−1) versus FAST (0.069 ± 0.02 g·min−1; p = 0.013) though not different between conditions across time (p > 0.340) or in total postexercise fat oxidation (FED: 0.125 ± 0.04 g·min−1, FAST: 0.105 ± 0.02 g·min−1; p = 0.154). Appetite perceptions were lower in FED (–4815.0 ± 4098.7 mm) versus FAST (–707.5 ± 2010.4 mm, p = 0.022); however, energy intake did not differ between conditions (p = 0.429). These results demonstrate the fasted or fed state does not augment postexercise metabolism following acute SIT in a way that would favour fat loss following training. Novelty Energy expenditure was similar between conditions, while fat oxidation was significantly greater in FED at 3 h after exercise. Appetite perceptions were significantly lower in FED; however, energy intake was not different between conditions. Current findings suggest that performing SIT in the fed or fasted state would not affect fat loss following training.
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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 | 0.000 |
| 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".