Optimal Resistance Training Prescriptions For Skeletal Muscle Hypertrophy: A Systematic Review, Bayesian Network Meta Analysis And Meta Regression
Bibliographic record
Abstract
Resistance training prescription (RTx) to achieve muscle hypertrophy involves combining resistance training (RT) variables: load, sets, and training frequency. Evidence-based recommendations on the optimal RTx for hypertrophy are derived from numerous pairwise meta-analyses of individual RT variables. Network meta-analysis allows for the simultaneous comparison of several interventions. PURPOSE: To leverage network meta-analysis to comprehensively compare the impact of several RTx on muscle hypertrophy. METHODS: Eligible studies were randomized controlled trials that included healthy adults (≥18 years old), ≥6 weeks RT, compared at least 2 of 11 predefined interventions, and measured hypertrophy. Predefined interventions were non-exercise control (CTRL) and 10 unique RTx denoted by a three-character acronym - XY# - where X is load (heavy [H] ≥80% 1-repetition maximum [1RM]; light [L] <80% 1RM); Y is sets, (multiple [M] ≥2 sets/exercise; single [S] 1 set/exercise); and # is weekly frequency (3, ≥3 days/wk; 2, 2 days/wk; 1, 1 day/wk), respectively. Six databases were systematically searched up to February 2022. Network meta-analysis was implemented in a Bayesian framework. We conducted univariate network meta-regressions to explore the influence of pre-specified covariates on our findings. RESULTS: 118 articles (n = 3348) were included. The posterior mean (95% credible interval [CrI]) for each RTx versus CTRL was: HM3 = 0.53(0.36,0.69), HM1 = 0.41(0.34, 1.18), HM2 = 0.70(0.50, 0.89), HS3 = 0.35(0.02, 0.72), HS2 = 0.10(0.58, 0.78), LM3 = 0.51(0.39, 0.63), LM1 = 0.57(0.19, 0.95), LM2 = 0.60(0.45, 0.75), LS3 = 0.38(0.17, 0.59), LS2 = 0.50(0.21, 0.80). The top 2 RTx for hypertrophy were (mean rank [95% CrI]): HM2 (2.05 [1 to 5]) and LM2 (3.40 [1 to 7]). Adjusting for pre-specified covariates did not reduce the posterior between-trial standard deviation or result in 95% CrI coefficients that did not cross zero. CONCLUSIONS: All RTx were effective in promoting skeletal muscle hypertrophy, demonstrating that adults can increase skeletal muscle mass by engaging in various RT programs. RT-induced muscle hypertrophy is most effectively accomplished by performing multiple sets/exercise twice weekly, regardless of load. Our analysis yields insight into minimal ‘doses’ of RT to produce hypertrophy.
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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 | 0.026 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.003 | 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".