The effects of chronic stretch training on musculoskeletal pain
Bibliographic record
Abstract
PURPOSE: One of the primary mechanisms for the increase in range of motion following stretching is an increase in pain/stretch tolerance. However, it remains unclear whether stretching can reduce pain in patients suffering from musculoskeletal pain. Therefore, the purpose of this systematic review was to investigate whether chronic stretch training can decrease pain in patients suffering from musculoskeletal pain. METHODS: In our search, we included three databases (PubMed, Scopus, and Web of Science) and after removing duplicates, screened 797 papers. Six papers were found to be eligible for this review. The inclusion criteria were controlled or randomized controlled trials that involved any type of chronic stretch training with participants experiencing musculoskeletal pain and where at least one pain output parameter was reported (e.g. visual analogue scale). RESULTS: Of the six studies reviewed, four focused on the effects of stretching interventions on pain in patients, while the other two examined pain prevalence during the stretching period. The interventions lasted between 4 weeks and 6 months and involved either static or dynamic stretching techniques with in total 658 participants. Five of the six studies reported a significant decrease in pain scores or a reduction in the prevalence or severity of pain following the observation period. CONCLUSION: The findings indicate that stretching can alleviate pain by enhancing range of motion and reducing muscle stiffness, which may ease nerve pressure and lower muscle spindle activity. Although results were somewhat mixed, the evidence overall supports stretching as an effective intervention for relieving musculoskeletal pain.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".