Resistance Training-induced Muscle Hypertrophy Is Not Determined By Repetition-load In Resistance-trained Men
Bibliographic record
Abstract
We have previously shown that performing resistance exercise (RE) with high or low repetition-loads resulted in similar acute (protein turnover) and chronic (hypertrophy) adaptations. However, these studies were performed in RE-naïve trainees using unilateral leg exercises. Here we aimed to determine the effect of repetition-load on skeletal muscle hypertrophy and strength with a 12wk, whole-body RE intervention in RE-trained (RET) young men. We also evaluated the change and correlations of the post-RE rise in systemic hormone concentrations in relation to changes in skeletal muscle hypertrophy and strength. Forty-nine RET men (mean ± SEM, 23 ± 1 y, 86 ± 2 kg, 181 ± 1 cm) were randomly allocated into a high-repetition (lower load - 30-50% 1RM) group (HR: 20-25 repetitions/set, n=24) or a low-repetition (higher load - 70-90% 1RM) group (LR, 8-12 repetitions/set, n=25). Muscular strength increased for all exercises (p <0.01) and only the change in bench press was different between groups (HR; 9 ± 1, LR; 14 ±1 kg, p < 0.05). Lean body mass, type I and type II muscle fiber cross sectional area all increased following training (p < 0.01) with no significant differences between groups. There was no change in fibre type distribution pre- to post-intervention and no differences between groups. The acute post-RE rise in systemic hormones did not change as a result of training. The post-RE rise in total testosterone, insulin-like growth factor-1 and growth hormone had no correlation with any strength or hypertrophy outcome. These data show that in RET individuals repetition-load is a determinant of neither strength nor hypertrophic gains when RE is performed to volitional failure. In accordance with our previous findings we conclude that the post-RE rise in systemic hormones is not associated with or in any way predictive of changes in skeletal muscle hypertrophy or strength.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".