Does moderate-intensity continuous training promote muscle hypertrophy? A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
The effects of moderate-intensity continuous training (MICT) on muscle hypertrophy remain controversial, largely due to the absence of non-exercise control groups in several trials and considerable heterogeneity in training protocols, assessment methods, and participant characteristics. This systematic review and meta-analysis aimed to examine the effects of MICT on skeletal muscle hypertrophy. Following PRISMA guidelines, searches were conducted in MEDLINE, Embase, Web of Science, Scopus, and the Cochrane Library for chronic human interventions comparing MICT to non-exercise controls, reporting both whole-body and localized muscle mass outcomes (lower limb lean mass, muscle cross-sectional area, fiber cross-sectional area, and muscle thickness) in healthy adults without chronic diseases or musculoskeletal injuries. A random-effects model compared MICT versus control for total and regional muscle mass outcomes combined and separately. Thirteen studies met the inclusion criteria. The standardized mean difference was 0.050 (95% CI: -0.147 to 0.248) for total and regional outcomes combined and 0.080 (95% CI: -0.277 to 0.437) for regional outcomes alone. In conclusion, the current evidence does not consistently support a hypertrophic effect of MICT. However, this conclusion should be interpreted with caution due to variability in study design, populations, and measurement sensitivity.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".