Impact of 4 weeks of interval training on resting metabolic rate, fitness, and health-related outcomes
Bibliographic record
Abstract
Resting metabolic rate (RMR) and substrate oxidation (respiratory exchange ratio; RER) are important indicators of health. The effects of interval training on RMR have not been thoroughly investigated, which was the purpose of the present study. Thirty men and women (mean ± SD age and maximal oxygen uptake: 28.8 ± 7.6 years and 33.0 ± 8.3 mL·kg−1·min−1) completed 4 weeks of Wingate-based sprint interval training (SIT), repeated 1-min high-intensity intervals (HIIT), or served as controls. Before and after training, RMR, resting RER, maximal oxygen uptake, body composition, physical activity, and energy intake were recorded. Data were analyzed using a repeated-measures ANOVA. RMR increased in response to 4 weeks of SIT training (1789 ± 293 to 1855 ± 320 kcal·day−1; p = 0.003) but did not increase after HIIT (1670 ± 324 to 1704 ± 329 kcal·day−1; p = 0.06). While SIT increased RMR by ∼2× the magnitude of HIIT, the difference was not significant (p = 0.5). Fasting substrate oxidation and RER did not change (p > 0.05). Maximal oxygen uptake increased, and small changes were also observed in percent body fat and fat mass (p < 0.05 for all). In conclusion, SIT provided a time-efficient stimulus to increase RMR after 4 weeks in healthy adults. However, the clinical relevance of the changes observed in this study remains to be determined. Further studies should be conducted in obese individuals and those with diabetes or insulin resistance to examine if interval training (≥4 weeks) influences resting metabolic rate in magnitudes similar to that reported here.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".