Optimising sprint interval exercise to maximise energy expenditure and enjoyment in overweight boys
Bibliographic record
Abstract
The aim of this study was to identify the sprint frequency that when supplemented to continuous exercise at the intensity that maximises fat oxidation (Fat(max)), optimises energy expenditure, acute postexercise energy intake and enjoyment. Eleven overweight boys completed 30 min of either continuous cycling at Fat(max) (MOD), or sprint interval exercise that consisted of continuous cycling at Fat(max) interspersed with 4-s maximal sprints every 2 min (SI(120)), every 1 min (SI(60)), or every 30 s (SI(30)). Energy expenditure was assessed during exercise, after which participants completed a modified Physical Activity Enjoyment Scale (PACES) followed by a buffet-type breakfast to measure acute postexercise energy intake. Energy expenditure increased with increasing sprint frequency (p < 0.001), but the difference between SI(60) and SI(30) did not reach significance (p = 0.076), likely as a result of decreased sprint quality as indicated by a significant decline in peak power output from SI(60) to SI(30) (p = 0.034). Postexercise energy intake was similar for MOD, SI(120), and SI(30) (p > 0.05), but was significantly less for SI(60) compared with MOD (p = 0.025). PACES was similar for MOD, SI(120), and SI(60) (p > 0.05), but was less for SI(30) compared with MOD (p = 0.038), SI(120) (p = 0.009), and SI(60) (p = 0.052). In conclusion, SI(60) appears optimal for overweight boys given that it maximises energy expenditure (i.e., there was no additional increase in expenditure with a further increase in sprint frequency) without prompting increased energy intake. This, coupled with the fact that enjoyment was not compromised, may have important implications for increased adherence and long-term energy balance.
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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 | 0.000 |
| 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".