Effectiveness Of Regular Proactive Massage Therapy For Recreational Runners
Bibliographic record
Abstract
PURPOSE: Previous massage research has focused on short-term reactive treatment of acute muscle injury and has often failed to demonstrate positive effects. METHODS: This study evaluated a longer term massage treatment protocol by examining the influence of regular pro-active massage therapy treatments provided to novice runners as they completed a 10 week, 10 km race preparation clinic program. Twelve controls (1 male, 11 females, 33.3+4.5 y) completed the race preparation program without massage therapy while 16 participants (5 males, 11 females,34.2 + 8.1 y) completed a similar program while weekly receiving a 30 m massage geared toward their specific running and recovery needs. RESULTS: Runners in the massage group received 9.4 + 0.8 of 10 possible massages. There were no significant differences or interactions (p>0.05) between groups for running frequency, intensity, mileage, leg strength measures, pain associated with running, daily functioning and running confidence during the program. However, significant effects for time (p<.05) were found for pain while running and pain after running with both groups experiencing more pain as the running program progressed. Although, no quantitative changes were found, many qualitative differences existed with runners in the massage group generally feeling more positive about their running experiences. Only 58% of the control group actually completed the final 10 km race, while 100% of the massage group completed the race. CONCLUSIONS: These findings suggest that regular massage therapy can have positive psychological effects on novice runners that can encourage their continued participation in physical activity. Supported by a Holistic Health Research Foundation of Canada grant to PMT & KAD
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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) | 0.000 | 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) | 0.004 | 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".