Strategic creatine supplementation and resistance training in healthy older adults
Bibliographic record
Abstract
Creatine supplementation in close proximity to resistance training may be an important strategy for increasing muscle mass and strength; however, it is unknown whether creatine supplementation before or after resistance training is more effective for aging adults. Using a double-blind, repeated measures design, older adults (50-71 years) were randomized to 1 of 3 groups: creatine before (CR-B: n = 15; creatine (0.1 g/kg) immediately before resistance training and placebo (0.1 g/kg cornstarch maltodextrin) immediately after resistance training), creatine after (CR-A: n = 12; placebo immediately before resistance training and creatine immediately after resistance training), or placebo (PLA: n = 12; placebo immediately before and immediately after resistance training) for 32 weeks. Prior to and following the study, body composition (lean tissue, fat mass; dual-energy X-ray absorptiometry) and muscle strength (1-repetition maximum leg press and chest press) were assessed. There was an increase over time for lean tissue mass and muscle strength and a decrease in fat mass (p < 0.05). CR-A resulted in greater improvements in lean tissue mass (Δ 3.0 ± 1.9 kg) compared with PLA (Δ 0.5 ± 2.1 kg; p < 0.025). Creatine supplementation, independent of the timing of ingestion, increased muscle strength more than placebo (leg press: CR-B, Δ 36.6 ± 26.6 kg; CR-A, Δ 40.8 ± 38.4 kg; PLA, Δ 5.6 ± 35.1 kg; chest press: CR-B, Δ 15.2 ± 13.0 kg; CR-A, Δ 15.7 ± 12.5 kg; PLA, Δ 1.9 ± 14.7 kg; p < 0.025). Compared with resistance training alone, creatine supplementation improves muscle strength, with greater gains in lean tissue mass resulting from post-exercise creatine supplementation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".