Physiological basis of interval training for performance enhancement
Bibliographic record
Abstract
NEW FINDINGS: What is the topic of this review? This review considers the physiological basis of interval training for performance enhancement, with an emphasis on the capacity for aerobic energy provision. What advances does it highlight? It highlights advances regarding the effect of interval training on primary physiological determinants of aerobic energy provision, which are associated with performance. ABSTRACT: Interval training refers to an intermittent style of exercise, in which bouts of more intense effort are interspersed with recovery periods within a given training session. Physiological responses to interval training depend on numerous factors, including the specific nature of the intervention and the initial training state of the individual. Interval training improves performance in part by enhancing the capacity for aerobic energy provision, even in those who are already trained. Two primary mechanisms in this regard are an increased whole-body maximal oxygen uptake and an enhanced capacity for oxidative metabolism in skeletal muscle owing to an increase in mitochondria. In comparison to moderate-intensity continuous exercise, interval training can elicit superior responses when total work is matched, and similar responses despite a reduced training volume and time commitment.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".