Creatine Supplementation during Resistance Training in Older Adults—A Meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Age-related sarcopenia and dynapenia have negative effects on strength and the ability to perform activities of daily living. Resistance training (RT) increases muscle mass and strength in older adults and is an established countermeasure for sarcopenia and dynapenia, and creatine may enhance this effect. We aimed to determine whether the addition of Cr to RT increased gains in muscle mass, strength, and function in older adults over RT alone by conducting a systematic review and meta-analysis. METHODS: PubMed and Healthstar databases were searched. Randomized, placebo-controlled trials that involved older adults supplemented with Cr and included RT regimens (>6 wk) were included. Data were analyzed using fixed or random (if data were heterogeneous) effects meta-analysis using RevMan 5. RESULTS: The meta-analysis comprised 357 older adults (average ± SD Cr: 63.6 ± 5.9 yr, Pl: 64.2 ± 5.4 yr) with 12.6 ± 4.9 wk of RT. Cr + RT increased total body mass (P = 0.004) and fat-free mass (P < 0.0001) with no effect on fat mass as compared with RT alone. Cr + RT increased chest press (P = 0.004) and leg press (P = 0.02) one-repetition maximum to a greater extent than RT alone, with no difference in the effect on knee extension or biceps curl one-repetition maximum, isokinetic or isometric knee extension peak torque. Cr + RT had a greater effect than RT alone on the 30-s chair stand test (P = 0.03). CONCLUSION: Retention of muscle mass and strength is integral to healthy aging. The results from this meta-analysis are encouraging in supporting a role for Cr supplementation during RT in healthful aging by enhancing muscle mass gain, strength, and functional performance over RT alone; however, the limited number of studies indicates further work is needed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.041 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".