A prospective evaluation of non-interval- and interval-based exercise training progressions in rodents
Bibliographic record
Abstract
Non-interval and interval training progressions were used to determine (i) the mean rate at which treadmill speed could be incremented daily using a non-interval training progression to train rats to run continuously at different intensities and (ii) the number of training days required for rats to run continuously at different exercise intensities with non-interval- and interval-based training progressions to establish methods of progressive overload for rodent exercise training studies. Rats were randomly assigned to mild-intensity (n = 5, 20 m·min(-1), 5% grade), moderate-intensity (n = 5, 30 m·min(-1), 5% grade), and heavy-intensity non-interval groups (n = 5, 40 m·min(-1), 5% grade) or a heavy-intensity interval (n = 5, 40 m·min(-1), 5% grade) group and ran 5 days·week(-1) for 6 weeks. Non-interval training involved a daily increase of treadmill speed, whereas interval training involved a daily increase of interval time, until the animal could run continuously at a prescribed intensity. In mild-, moderate-, and heavy-intensity non-interval-trained rats, treadmill speed was increased by 0.6 ± 0.7 m·min(-1)·day(-1), 0.6 ± 0.2 m·min(-1)·day(-1), and 0.8 ± 0.1 m·min(-1)·day(-1), respectively. Target training intensity and duration were obtained following 0.4 ± 0.5 days, 17 ± 3 days, and 23 ± 3 training days (p < 0.05) in mild-, moderate-, and heavy-intensity groups, respectively. In contrast, interval-trained rodents required 11 ± 1 training days. These data demonstrate that rodents will tolerate an increase in treadmill speed of ∼0.7 ± 0.1 m·min(-1)·day(-1) and that this progression enables rats to run continuously at moderate and heavy intensities with 3-4 weeks of progressive overload. Interval training significantly reduces the number of training days required to attain a target intensity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".