The effects of ibuprofen on muscle hypertrophy, strength, and soreness during resistance training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 it