Effects of Creatine Supplementation and Resistance Training on Muscle Strength Gains in Adults <50 Years of Age: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Numerous meta-analyses have assessed the efficacy of creatine supplementation in increasing muscle strength. However, most have not considered the effect of the participants’ age, training duration, or other confounding variables on strength outcomes. Therefore, the purpose of this study was to consider the effect of these variables on the potential efficacy of creatine supplementation and resistance training for improving measures of muscle strength. Methods: Four databases were searched (MEDLINE, Scopus, Embase, and SPORTDiscus) with a search end date of 22 May 2024. Twenty-three studies were included, with 20 studies involving males (447 male participants), 2 studies involving females (40 female participants), and 1 study involving both males and females (13 male participants and 9 female participants). Results: In comparison with a placebo, creatine supplementation combined with resistance training significantly increased upper-body (WMD = 4.43 kg, p < 0.001) and lower-body strength (WMD = 11.35 kg, p < 0.001). Subgroup analyses showed a trend for greater upper-body strength improvements for males on creatine compared with females on creatine (p = 0.067, Q = 3.366). Additionally, males who consumed creatine combined with resistance training significantly increased both upper- and lower-body strength, whereas females showed no significant gains. There was a trend indicating greater lower-body strength gains from high-dose creatine compared with lower doses (p = 0.068, Q = 3.341). No other variables influenced the effect of creatine supplementation. In conclusions, creatine supplementation with resistance training enhances upper- and lower-body muscle strength in adults aged < 50, with greater benefits likely to be seen in males than females.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".