Muscle hypertrophy and strength gains after resistance training with different volume-matched loads: a systematic review and meta-analysis
Bibliographic record
Abstract
The purpose of this paper was to conduct a systematic review and meta-analysis of studies that compared muscle hypertrophy and strength gains between resistance training protocols employing very low (VLL < 30% of 1-repetition maximum (RM) or >35RM), low (LL30%–59% of 1RM, or 16–35RM), moderate (ML60%–79% of 1RM, or 8–15RM), and high (HL ≥ 80% of 1RM, or ≤7RM) loads with matched volume loads (sets × repetitions × weight). A pooled analysis of the standardized mean difference for 1RM strength outcomes across the studies showed a benefit favoring HL vs. LL and vs. ML and favoring ML vs. LL. The LL and VLL results showed little difference. A pooled analysis of the standardized mean difference for hypertrophy outcomes across all studies showed no differences between training loads. Our findings indicate that when the volume load is equal between conditions, the highest loads induce superior dynamic strength gains. Alternatively, hypertrophic adaptations were similar irrespective of the load magnitude. Novelty: Training with higher loads elicits greater gains in 1RM muscle strength when compared to lower loads, even when the volume load is equal between conditions. Muscle hypertrophy is similar irrespective of the magnitude of the load, even when the volume load is equal between conditions.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".