Optimizing Resistance Training Prescription For Strength: A Systematic Review, Bayesian Network Meta-analysis, And Network Meta-regression
Bibliographic record
Abstract
Resistance training potently increases muscle strength. Resistance training prescription (RTx) involves numerous modifiable training-related variables. The optimal RTx for strength is based on guidelines founded on pairwise comparisons of variables where multiple combinations exist. Network meta-analysis (NMA) permits simultaneous comparisons between numerous prescriptions. PURPOSE: Determine how distinct combinations of RTx variables - load, sets, and training frequency - affect muscle strength. METHODS: MEDLINE, Embase, Emcare, SPORTDiscus, CINAHL, and Web of Science were systematically searched current to February 2022. Eligible randomized trials included healthy adults (≥18 years), compared at least two of 13 predefined conditions, spanned ≥6 weeks, and measured muscle strength. Predefined conditions were non-exercising control (CTRL) and 12 unique RTx denoted by a three-character acronym - XY# - where X is load (heavier [H] ≥80% 1-repetition maximum [1RM]; lighter [L] <80% 1RM); Y is sets (M, multi-set; S, single-set); and # is weekly frequency (3 ≥ 3 d/wk; 2 - 2 d/wk; 1 - 1 d/wk), respectively. NMA was conducted in a Bayesian framework. Network meta-regressions were run to explore the impact of age, training status, and percentage of females on network estimates. Surface under the cumulative ranking curve values were used to rank conditions. RESULTS: The NMA included 178 studies (n = 5,097 participants). The posterior standardized mean difference (95% credible interval) estimate for each RTx versus CTRL was: HM1 = 1.54 (0.81, 2.30), HM2 = 1.59 (1.28, 1.90), HM3 = 1.60 (1.38, 1.82), HS1 = 0.79 (-0.88, 2.45), HS2 = 1.15 (0.55, 1.77), HS3 = 1.22 (0.74, 1.70), LM1 = 1.07 (0.47, 1.67), LM2 = 1.23 (1.01, 1.46), LM3 = 1.07 (0.89, 1.25), LS1 = 0.75 (-0.16, 1.68), LS2 = 0.91 (0.49, 1.35), LS3 = 0.90 (0.57, 1.22). Neither age, training status, nor percentage of females meaningfully altered model fit and network estimates. HM3 (85.5%) and HM2 (83.5%) were most likely to be ranked in the top-three most effective prescriptions. CONCLUSION: All combinations of load, sets, and frequency increased muscle strength compared to no exercise; thus, healthy adults can engage in resistance training with any combination of RTx variables to increase muscle strength. Higher-load, multiset prescriptions maximized strength gains.
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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 | 0.033 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".