History and perspectives on interval training in sport, health, and disease
Bibliographic record
Abstract
Exercise can be conducted as low-intensity continuous training (LICT) or a variety of higher intensity work/rest formats, collectively called interval training. Interval training was developed for athletes in the early 20th century. It was systemized in Sweden as Fartlek, and in Germany as die interval Method, in the 1930s. Most contemporary forms of interval training evolved from these progenitors. In essence, interval training allows a large volume of high-intensity or race specific training to be performed while controlling the development of fatigue. Adding interval training to LICT done by athletes adds about 2%–4% to performance achievable with LICT, which represents a competitively meaningful difference in performance (e.g., 4:25 vs. 4:00 over 1 mile). More recently, interval training has been applied to health- fitness participants and even to patients with health conditions. Studies indicate that a comparatively low volume of interval training can produce substantial improvement in physiologic capacity, in as little as 20% of training time versus LICT. There are data indicating that interval training can be reasonably pleasant, have good adherence, and is safe, even in patients. Although interval training was originally designed for athletics, the fundamental patterns of work versus recovery are remarkably similar in healthy adults and patients. Although the total volume of training and both absolute and relative intensity and magnitude of homeostatic disturbance are larger in athletes, the overall pattern of effort is the same in fitness participants and patients. Interval training can thus be characterized as an important step in the evolution of exercise training.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".