The effects of ibuprofen on muscle hypertrophy, strength, and soreness during resistance training
Bibliographic record
Abstract
High doses of ibuprofen have been shown to inhibit muscle protein synthesis after a bout of resistance exercise. We determined the effect of a moderate dose of ibuprofen (400 mg x d(-1)) consumed on a daily basis after resistance training on muscle hypertrophy and strength. Twelve males and 6 females (approximately 24 years of age) trained their right and left biceps on alternate days (6 sets of 4-10 repetitions), 5 d x week(-1), for 6 weeks. In a counter-balanced, double-blind design, they were randomized to receive 400 mg x d(-1) ibuprofen immediately after training their left or right arm, and a placebo after training the opposite arm the following day. Before- and after-training muscle thickness of both biceps was measured using ultrasound and 1 repetition maximum (1 RM) arm curl strength was determined on both arms. Subjects rated their muscle soreness daily. There were time main effects for muscle thickness and strength (p < 0.01). Ibuprofen consumption had no effect on muscle hypertrophy (muscle thickness of biceps for arm receiving ibuprofen: pre 3.63 +/- 0.14, post 3.92 +/- 0.15 cm; and placebo: pre 3.62 +/- 0.15, post 3.90 +/- 0.15 cm) and strength (1 RM of arm receiving ibuprofen: pre 18.6 +/- 2.8, post 23.4 +/- 3.5 kg; and placebo: pre 18.8 +/- 2.8, post 22.8 +/- 3.4 kg). Muscle soreness was elevated during the first week of training only, but was not different between the ibuprofen and placebo arm. We conclude that a moderate dose of ibuprofen ingested after repeated resistance training sessions does not impair muscle hypertrophy or strength and does not affect ratings of muscle soreness.
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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 | 0.000 | 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 | 0.000 | 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".