Massage Therapy Modulates Inflammatory Mediators Following Sprint Exercise in Healthy Male Athletes
Bibliographic record
Abstract
Massage therapy is a common postexercise muscle recovery modality; however, its mechanisms of efficacy are uncertain. We evaluated the effects of massage on systemic inflammatory responses to exercise and postexercise muscle performance and soreness. In this crossover study, nine healthy male athletes completed a high-intensity intermittent sprint protocol, followed by massage therapy or control condition. Inflammatory markers were assessed pre-exercise; postexercise; and at 1, 2, and 24 h postexercise. Muscle performance was measured by squat and drop jump, and muscle soreness on a Likert scale. Significant time effects were observed for monocyte chemoattractant protein-1 (MCP-1), interleukin-8 (IL-8), interleukin-6 (IL-6), interleukin-10 (IL-10), tumor necrosis factor alpha (TNFα), drop jump performance, squat jump performance, and soreness. No significant effects for condition were observed. However, compared with control, inflammatory marker concentrations (IL-8, TNFα, and MCP-1) returned to baseline levels earlier following the massage therapy condition (p < 0.05 for all). IL-6 returned to baseline levels earlier following the control versus massage therapy condition (p < 0.05). No differences were observed for performance or soreness variables. MCP-1 area under the curve (AUC) was negatively associated with squat and drop jump performance, while IL-10 AUC was positively associated with drop jump performance (p < 0.05 for all). In conclusion, massage therapy promotes resolution of systemic inflammatory signaling following exercise but does not appear to improve performance or soreness measurements.
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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".