The Prevalence of Massage Therapy Utilization for Musculoskeletal Conditions: A Systematic Review
Bibliographic record
Abstract
Background: Massage therapy is a popular treatment for musculoskeletal conditions globally. As the efficacy for massage therapy grows over time, it is becoming a more acceptable form of therapy alongside conventional medicine. The aim of this systematic review is to highlight the prevalence of massage therapy utilization specifically for the treatment of musculoskeletal conditions. Methods: A comprehensive search of health databases using keywords mapped to massage and musculoskeletal conditions identified 38 studies. An assessment of the quality of these studies was undertaken using a validated quality appraisal instrument. Results: Overall, the prevalence of massage use ranged from 2% to 81.2%. The range narrowed marginally from 2.2% to 56% in larger studies (n ≥ 1,000). Prevalence was higher among younger individuals, ranging from 12% to 56%. The prevalence of use among women ranged from 7.7% to 56%. The highest prevalence for conditions was for lower back pain/back pain, ranging from 10.5% to 68.1%, and for patients with chronic pain, ranging from 17.6% to 56%. The lowest prevalence was reported in Australia, ranging from 2% to 56%, and the highest in North America, from 2.2% to 81.2%. Conclusions: Our findings indicate that 74% of studies in this review relating to prevalence of massage therapy utilization for musculoskeletal conditions are reported within studies focusing on complementary medicine more generally. Further studies on massage as an independent treatment modality would be useful to provide improved evidence on prevalence for massage use for musculoskeletal conditions. While the range of prevalence reported here is wide, inpatients and outpatients with specific musculoskeletal conditions including pain are high users of massage therapy. Despite the growing interest in research, there is a gap in the literature around men and their use of massage therapy. Further high-quality research in these areas will better inform the knowledge base around these participant cohorts.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".