Mechanistic and methodological perspectives on the impact of intense interval training on post‐exercise metabolism
Bibliographic record
Abstract
The post-exercise recovery period is associated with an elevated metabolism known as excess post-exercise oxygen consumption (EPOC). The relationship between exercise duration and EPOC magnitude is thought to be linear whereas the relationship between EPOC magnitude and exercise intensity is thought to be exponential. Accordingly, near-maximal and supramaximal protocols such as high-intensity interval training (HIIT) and sprint interval training (SIT) protocols have been hypothesized to produce greater EPOC magnitudes than submaximal moderate-intensity continuous training (MICT). This review updates previous reviews by focusing on the impact of HIIT and SIT on EPOC. Research to date suggests small differences in EPOC post-HIIT compared to MICT in the immediate (<1 hour) recovery period, but greater EPOC values post-HIIT when examined over 24 hours. Conversely, differences in EPOC post-SIT are more pronounced, as SIT tends to produce a larger EPOC vs MICT at all time points. We discuss potential mechanisms that may drive the EPOC response to interval training (eg, glycogen resynthesis, mitochondrial uncoupling, and protein turnover among others) and also consider the role of EPOC as one of the potential contributors to fat loss following HIIT/SIT interventions. Lastly, we highlight a number of methodological shortcomings related to the measurement of EPOC following HIIT and SIT.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".