Effects of Creatine and Caffeine Supplementation During Resistance Training on Body Composition, Strength, Endurance, Rating of Perceived Exertion and Fatigue in Trained Young Adults
Bibliographic record
Abstract
The primary purpose was to determine the separate and combined effects of creatine and caffeine supplementation during resistance training on body composition and muscle performance in trained young adults. Twenty-eight participants were randomized to supplement with creatine and caffeine (CR-CAF; n = 9, 22 ± 4 years; 0.1 g·kg−1·d−1 of creatine monohydrate + 3 mg·kg−1·d−1 of caffeine anhydrous micronized powder); creatine (CR; n = 7, 22 ± 4 years, 0.1 g·kg−1·d−1 of creatine + 3 mg·kg−1·d−1 of micronized cellulose), caffeine (CAF; n = 6, 19 ± 1 years, 3 mg·kg−1·d−1 of caffeine + 0.1 g·kg−1·d−1 of maltodextrin) or placebo (PLA; n = 6, 23 ± 7 years, 0.1 g·kg−1·d−1 of maltodextrin + 3 mg·kg−1·d−1 micronized cellulose) one hour prior to performing resistance training for 6 weeks. Before and after training and supplementation, fat-free and fat mass (air-displacement plethysmography), muscle thickness (elbow and knee flexors and extensors; ultrasound), muscle strength (1-repetition maximum [1-RM] for the leg press and chest press), and endurance (one set of repetitions to volitional fatigue using 50% baseline 1-RM for leg press and chest press) were assessed. There was a group x time interaction (p = 0.049) for knee extensor muscle thickness with CR experiencing an increase over time with no changes in the other groups. There were no other between group differences for any variable. In conclusion, creatine supplementation and resistance training results in a small improvement in knee extensor muscle accretion in trained young adults.
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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 | 0.001 |
| 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".