Mind-body therapy for treating fibromyalgia: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Fibromyalgia (FM) is a chronic and disabling condition that presents treatment challenges for both patients and healthcare providers. The objective of this review was to systematically assess the effectiveness and safety of mind-body therapies for FM. METHODS: We searched MEDLINE, EMBASE, PsycINFO, AMED, and CINAHL databases from their inception to December 2023. Eligible articles included adults diagnosed with FM participating in a mind-body therapy intervention and were published from the beginning of 2012 onwards. We assessed the quality of the studies using the Joanna Briggs Institute Critical Appraisal Checklists. RESULTS: Twenty-seven studies (1969 participants) were included, comprising 22 randomized controlled trials and 5 quasi-experimental studies. Mind-body therapies included guided imagery (n = 5), mindfulness-based stress reduction (n = 5), qi gong (n = 5), tai chi (n = 5), biofeedback (n = 3), yoga (n = 2), mindfulness awareness training (n = 1), and progressive muscle relaxation (n = 1). With the exception of mindfulness-based stress reduction, all therapies had at least 1 study showing significant improvements in pain at the end of treatment. Three or more studies on qi gong and tai chi demonstrated significant improvements in fatigue and multidimensional function, with tai chi showing the most evidence for improvement in anxiety and depression. Approximately one-third of the studies reported on adverse events. CONCLUSIONS: This systematic review found that mind-body therapies are potentially beneficial for adults with FM. Further research is necessary to determine if the positive effects observed post-intervention are sustained. STUDY REGISTRATION: Open Science Framework (https://osf.io) (September 12, 2023; https://doi.org/10.17605/osf.io/6w7ac).
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".