Creatine and strength training in older adults: an update
Bibliographic record
Abstract
Abstract Aging is associated with numerous physiological, musculoskeletal, and neurological impairments including a loss of muscle, strength, function, bone mineral, and cognition. Strength training is an effective intervention to counter these age-associated declines. In addition, creatine supplementation is purported to enhance strength training gains in lean tissue mass, muscular strength, and function. There is emerging evidence that creatine combined with strength training can alter bone geometry and cognitive performance. The purpose of this review is to update previous meta-analyses examining creatine combined with strength training on lean tissue mass and bone density compared to strength training and placebo. A secondary purpose was to explore the effects of creatine and strength training on cognition. Updated meta-analyses revealed that creatine enhances lean tissue mass (mean difference [MD]: 1.18 kg, 95 % CI: 0.70–1.67; p<0.00001) and upper body muscular strength (standard mean difference [SMD]: 0.24, 95 % CI: 0.05–0.43; p=0.02) compared to strength training and placebo. Creatine combined with strength training had no greater effects compared to strength training and placebo on lower body muscular strength (SMD: 0.17, 95 % CI: −0.03–0.38; p=0.09), whole-body (MD: −0.00 g cm −2 ; 95 % CI: −0.01–0.00, p=0.32), femoral neck (MD: −0.00 g cm −2 ; 95 % CI: −0.01–0.00, p=1.00), or lumbar bone mineral density (MD: 0.00 g cm −2 , 95 % CI: −0.01–0.01; p=045). There is preliminary evidence that combining strength training and creatine is an effective strategy to improve bone geometry in postmenopausal females and cognitive function in older adults. Overall, the combination of creatine and strength training has favorable effects on lean tissue mass and upper body strength. In contrast, creatine combined with strength training does not enhance lower-body strength or bone mineral.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".