Massage therapy slightly decreased pain intensity after habitual running, but had no effect on fatigue, mood or physical performance: a randomised trial
Bibliographic record
Abstract
QUESTION: Does massage therapy reduce pain and perceived fatigue in the quadriceps, and improve the mood and physical performance of runners after habitual sporting activity (10-km run)? DESIGN: Randomised controlled trial with concealed allocation, intention-to-treat analysis and blinded assessment. PARTICIPANTS: Seventy-eight runners after sporting activity (10-km run). INTERVENTION: The experimental group received 10 minutes of massage to the quadriceps aimed at recovery following sport practice, and the control group received a sham joint mobilisation. OUTCOME MEASURES: Pain and perceived fatigue were each assessed using a 0-to-10 numerical rating scale; pain behaviour via the McGill Pain Questionnaire; mood profile via Brunel Mood Scale; quadriceps muscle flexibility using maximal knee flexion angle via inclinometer; isometric muscle strength of knee extensors via hand-held dynamometry; and vertical jump performance using jump height via My Jump 2 app. Evaluations were carried out immediately before and after the intervention, and at 24, 48 and 72 hours after the intervention. Generalised estimating equations were used to estimate a between-group difference (95% CI) using data across all time points. RESULTS: The experimental group had significantly lower scores than the control group on the numerical rating scale for pain by 0.7 points (95% CI 0.1 to 1.3). There were no significant between-group differences for any of the other outcome measures. CONCLUSION: Massage therapy was effective at reducing pain intensity after application to the quadriceps of runners compared to a sham technique, but the magnitude of the effect was small. There were no significant effects on perceived fatigue, flexibility, strength or jump performance. TRIAL REGISTRATION: Brazilian Registry of Clinical Trials, RBR-393m7m.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".