Pain pressure threshold of a muscle tender spot increases following local and non-local rolling massage
Bibliographic record
Abstract
BACKGROUND: The aim of the present study was to determine the acute effect of rolling massage on pressure pain threshold (PPT) in individuals with tender spots in their plantar flexor muscles. METHODS: In a randomized control trial and single blinded study, tender spots were identified in 150 participants' plantar flexor muscles (gastrocnemius or soleus). Then participants were randomly assigned to one of five intervention groups (n = 30): 1) heavy rolling massage on the calf that exhibited the higher tenderness (Ipsi-R), 2) heavy rolling massage on the contralateral calf (Contra-R), 3) light stroking of the skin with roller massager on the calf that exhibited the higher tenderness (Sham), 4) manual massage on the calf that exhibited the higher tenderness (Ipsi-M) and 5) no intervention (Control). PPT was measured at 30 s and up to 15 min post-intervention via a pressure algometer. RESULTS: At 30 s post-intervention, the Ipsi-R (24 %) and Contra-R (21 %) demonstrated higher (p < 0.03) PPT values compared with Control and Sham. During 15 min post-intervention, PPT was higher (p < 0.05) following Ipsi-R (19.2 %), Contra-R (15.9 %) and Ipsi-M (10.9 %) compared with Control. There was no difference between the effects of three deep tissue massages (Ipsi-R, Ipsi-M and Contra-R) on PPT. DISCUSSION: Whereas the increased PPT following ipsilateral massage (Ipsi-R and Ipsi-M) might be attributed to the release of fibrous adhesions; the non-localized effect of rolling massage on the contralateral limb suggests that other mechanisms such as a central pain-modulatory system play a role in mediation of perceived pain following brief tissue massage. CONCLUSION: Overall, rolling massage over a tender spot reduces pain perception. TRIAL REGISTRATION: ClinicalTrials.gov ( NCT02528812 ), August 19(th), 2015.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".