Massage Therapy Modulates Inflammatory Mediators Following Sprint Exercise in Healthy Male Athletes
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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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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) | 0.001 | 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) | 0.000 | 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