Monitoring exercise intensity during long‐term endurance exercise training in aging rats
Bibliographic record
Abstract
Chronic exercise is known to counter a number of deleterious effects of aging. There are a number of difficulties associated with long term exercise training in aged animals, such as matching appropriate training intensities to the declining exercise capacity as animals age. But there is little quantitative information available regarding exercise capacity in very old rats. We conducted a 1 year endurance exercise study in male FBN rats, starting with rats at age 24 mo and continuing daily until 36 mo of age. Rats were divided into 3 groups; high intensity (H), and moderate intensity treadmill exercise (M), and sedentary (S). M and H animals trained at 13 m/min for 30 min/day, with the H group at 5% incline. We monitored exercise capacity and its response to training by measurements of VO 2 max and blood lactate threshold (LT) every 3 mo throughout the training. The relative training intensity for the M animals, expressed by training speed as a percent of the running speed at LT, rose significantly as the animals aged (88% at 27 mo, 104% at 30 mo, 142% at 33 mo, and 325% at 36 mo). Similar results were seen with the H animals and when using VO 2 max as a measure of exercise capacity. These results indicate that even in trained animals exercise capacity declines significantly in advanced age and stress the importance of measuring exercise ability at various time points throughout a prolonged training program. Supported by: NIH AG030423
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".