Modified sprint interval training protocols. Part I. Physiological responses
Bibliographic record
Abstract
Adaptations to sprint interval training (SIT) are observed with brief (≤15-s) work bouts highlighting peak power generation as an important metabolic stimulus. This study examined the effects of manipulating SIT work bout and recovery period duration on energy expenditure (EE) during and postexercise, as well as postexercise fat oxidation rates. Nine active males completed a resting control session (CTRL) and 3 SIT sessions in randomized order: (i) 30:240 (4 × 30-s bouts, 240-s recovery); (ii) 15:120 (8 × 15-s bouts, 120-s recovery); (3) 5:40 (24 × 5-s bouts, 40-s recovery). Protocols were matched for the total duration of work (2 min) and recovery (16 min), as well as the work-to-recovery ratio (1:8 s). EE and fat oxidation rates were derived from gas exchange measured before, during, and for 3 h postexercise. All protocols increased EE versus CTRL (P < 0.001). Exercise EE was greater (P < 0.001) with 5:40 (209 kcal) versus both 15:120 (163 kcal) and 30:240 (138 kcal), while 15:120 was also greater (P < 0.001) than 30:240. Postexercise EE was greater (P = 0.014) with 15:120 (313 kcal) versus 5:40 (294 kcal), though both were similar (P > 0.077) to 30:240 (309 kcal). Postexercise fat oxidation was similar (P = 0.650) after 15:120 (0.104 g·min−1) and 30:240 (0.116 g·min−1) and both were greater (P < 0.030) than 5:40 (0.072 g·min−1) and CTRL (0.049 g·min−1). In conclusion, shorter SIT work bouts that target peak power generation increase exercise EE without compromising postexercise EE, though longer bouts promote greater postexercise fat utilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".