Effect of Different Frequencies of Creatine Supplementation on Muscle Size and Strength in Young Adults
Bibliographic record
Abstract
The purpose was to determine if creatine supplementation, consumed immediately before and immediately after exercise, with different dosing frequency (i.e., 2 or 3 d wk) could enhance the gains in muscle size and strength from resistance training (RT) in young adults. A group of 38 physically active, nonresistance trained university students (21-28 years) was randomly allocated to 1 of 4 groups: CR2 (0.15 g·kg creatine during 2 d wk of RT; 3 sets of 10 repetitions; n = 11, 6 men, 5 women), CR3 (0.10 g·kg creatine during 3 d wk of RT; 2 sets of 10 repetitions; n = 11, 6 men, 5 women;), PLA2 (placebo during 2 d wk of RT; n = 8, 5 men, 3 women), and PLA3 (placebo during 3 d wk of RT; n = 8, 4 men, 4 women) for 6 weeks. Before and after training, measurements were taken for muscle thickness of the elbow and knee flexor and extensor muscle groups (ultrasound), 1-repetition maximumleg press and chest press strength, and kidney function (urinary microalbumin). Repeated-measures analysis of variance showed that strength and muscle thickness increased in all groups with training (p < 0.05). The CR2 (0.6 ± 0.9 cm or 20%; p < 0.05) and CR3 groups (0.4 ± 0.6 cm or 16.4%; p < 0.05) experienced greater change in muscle thickness of the elbow flexors compared to the PLA2 (0.05 ± 0.5 cm or 2.3%) and PLA3 groups (0.13 ± 0.7 cm or 6.3%). Men supplementing with creatine experienced a greater increase in leg press strength (77.3 ± 51.2 kg or 62%) compared to women on creatine (21.3 ± 10 kg or 34%, p < 0.05). We conclude that creatine supplementation during RT has a small beneficial effect on regional muscle thickness in young adults but that giving the creatine over 3 d wk did not differ from giving the same dose over 2 d wk.
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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) | 0.001 | 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".