Higher- and lower-load resistance exercise training induce load-specific local muscle endurance changes in young women: a randomised trial
Bibliographic record
Abstract
The effect of resistance training with higher- and lower-loads on muscle mass and strength has been extensively studied, while changes in muscle endurance have received less attention. This trial aimed to assess the effect of training load on absolute muscle endurance (AME) and relative muscle endurance (RME). Sixteen untrained women (22.7 ± 3.3 yr: mean ± SD) had one arm and leg randomly assigned to train with higher loads (HL; 80–90% 1RM), and the contralateral limbs trained with lower loads (LL; 30–50% 1RM) thrice weekly to volitional fatigue for 10 weeks. Heavy and light load AME and RME, strength, and muscle mass were assessed pre- and post-training. Strength increased more in the HL compared to LL leg ( P < 0.01), but similar increases in strength were observed between upper body conditions ( P = 0.46). Lower body heavy and light load AME improved in both conditions, but HL training induced a larger improvement in heavy load AME (HL: 9.3 ± 4.3 vs. LL: 7.5 ± 7.1 repetitions, time × limb P < 0.01) and LL training induced a larger improvement in light load AME (LL: 24.7 ± 22.2 vs. HL: 15.2 ± 16.7 repetitions, time × limb P = 0.04). In the upper body, HL and LL training induced similar increases in both heavy (time × limb P = 0.99), and light load (time × limb P = 0.16) AME. Dual-energy X-ray absorptiometry showed no change in leg fat-and-bone-free mass (FBFM) for either condition, and an increase in only LL arm FBFM. AME improved in a manner specific to the training loads used. ClinicalTrials.gov (NCT04547972).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".