Analgesic benefits of pharmacological and nonpharmacological sleep interventions for adults with fibromyalgia: a systematic review and meta-analyses
Bibliographic record
Abstract
ABSTRACT: Fibromyalgia is a chronic musculoskeletal pain condition affecting 2% to 8% of the population. Most people with fibromyalgia experience poor sleep, and sleep problems consistently predict pain severity. Given the bidirectional relationship between sleep and pain, improving sleep may reduce fibromyalgia pain. This review identified trials of sleep-focused pharmacological and nonpharmacological interventions to evaluate whether they improved pain outcomes for adults with fibromyalgia. A systematic search of 7 electronic databases, 2 trial registries, and the reference lists of related studies retrieved 5728 records. Three independent reviewers completed study screening, data extraction, and risk of bias assessments. Nineteen studies met the inclusion criteria. Sleep interventions were evaluated against inactive and active control groups in a total of 10 trials with self-reported pain outcomes. Sodium oxybate significantly improved pain compared with placebo at postintervention (4.5 g: MD = -8.53, 95% CI [-12.08, -4.98]; 6.0 g: SMD = -0.46, 95% CI [-0.60, -0.31]). Cognitive behavioural therapy (CBT) for insomnia did not significantly improve pain compared with usual care at postintervention ( MD = -4.73, 95% CI [-10.89, 1.43]). Comparisons of CBT for insomnia with sleep hygiene training, and combined CBT for insomnia and pain with CBT for pain showed no significant postintervention differences. Other interventions (eg, melatonin and zopiclone) were identified but had insufficient data to allow meta-analysis. This review provided preliminary support for the analgesic benefits of some sleep interventions for adults with fibromyalgia. Long-term effects of sleep interventions on pain should be investigated to further inform treatment planning efforts.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".