Creatine supplementation combined with blood flow restriction training enhances muscle thickness and performance: a randomized, placebo-controlled, and double-blind study
Bibliographic record
Abstract
This study aimed to compare the effects of an 8-week creatine (CR) or placebo (PL) supplementation on muscle strength, thickness, endurance, and body composition employing different training paradigms with blood flow restriction (BFR) vs. traditional resistance training (TRAD). Seventeen healthy males were randomized between the PL ( n = 9) and CR ( n = 8) groups. Participants were trained unilaterally utilizing a within-between subject bicep curl exercise where each arm was allocated to TRAD or BFR for 8 weeks. Muscular strength, thickness, endurance, and body composition were evaluated. Creatine supplementation promoted increases in muscle thickness in TRAD and BFR compared with their placebo counterparts, however, without a significant difference between treatments ( p = 0.349). TRAD training increased maximum strength (1 repetition maximum (1RM)) compared with BFR after 8 weeks of training ( p = 0.021). Repetitions to failure at 30% of 1RM were increased in the BFR-CR group compared with the TRAD-CR group ( p = 0.004). Repetitions to failure at 70% 1RM were increased from weeks 0–4 ( p < 0.05) and 4–8 ( p < 0.05) in all groups. Creatine supplementation exerted a hypertrophic effect when utilized with TRAD and BFR paradigms and increased muscle performance at 30% 1RM when utilized in conjunction with BFR. Therefore, creatine supplementation seems to amplify muscle adaptation following a BFR program. Registered in the Brazilian Registry of Clinical Trials (ReBEC), under the registration number: RBR-3vh8zgj.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".