Impact of Eccentric or Concentric Training on Body Composition and Energy Expenditure
Bibliographic record
Abstract
PURPOSE: To compare the effects of 8-wk eccentric (ECC) versus concentric (CON) training using downhill and uphill running in rats on whole body composition, bone mineral density (BMD), and energy expenditure. METHODS: Animals were randomly assigned to one of the following groups: 1) control (CTRL), 2) +15% uphill-running slope (CON), 3) -15% downhill-running slope (ECC15), and 4) -30% downhill-running slope (ECC30). Those programs enabled to achieve conditions of isopower output for CON and ECC15 and of iso-oxygen uptake (V˙O2) for CON and ECC30. Trained rats ran 45 min at 15 m·min five times per week. Total body mass, fat body mass, and lean body mass (LBM) measured through EchoMRI™, and 24-h energy expenditure including basal metabolic rate (BMR) assessed using PhenoMaster/LabMaster™ cage system were obtained before and after training. At sacrifice, the right femur was collected for bone parameters analysis. RESULTS: Although total body mass increased in all groups over the 8-wk period, almost no change occurred for fat body mass in exercised groups (CON, -4.8 ± 6.18 g; ECC15, 0.6 ± 3.32 g; ECC30, 2.6 ± 6.01 g). The gain in LBM was mainly seen for ECC15 (88.9 ± 6.85 g) and ECC30 (101.6 ± 11.07 g). ECC was also seen to positively affect BMD. An increase in BMR from baseline was seen in exercise groups (CON, 13.9 ± 4.13 kJ·d; ECC15, 11.6 ± 5.10 kJ·d; ECC30, 18.3 ± 4.33 kJ·d) but not in CTRL one. This difference disappeared when BMR was normalized for LBM. CONCLUSIONS: Results indicate that for iso-V˙O2 training, the impact on LBM and BMD is enhanced with ECC as compared with CON, and that for isopower but lower V˙O2 ECC, an important stimulus for adaptation is still observed. This provides further insights for the use of ECC in populations with cardiorespiratory exercise limitations.
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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) | 0.001 | 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 | 0.000 |
| 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".