Music as a Sleep Aid in Fibromyalgia
Bibliographic record
Abstract
BACKGROUND: Interventions to improve sleep in fibromyalgia may generalize to improvements in multiple symptom domains. Delta-embedded music, pulsating regularly within the 0.25 Hz to 4 Hz frequency band of brain wave activity, has the potential to induce sleep. OBJECTIVES: To assess the effects of a delta-embedded music program over four weeks for sleep induction in patients with fibromyalgia. METHODS: The present unblinded, investigator-led pilot study used a within-subject design. Analysis was based on 20 individuals with fibromyalgia who completed the study, of the 24 recruited into the study. The primary outcome variables were the change from baseline in Fibromyalgia Impact Questionnaire (FIQ) and Jenkins Sleep Scale scores. A patient global impression of change was measured on a seven-point Likert scale. Secondary outcome measures, comprised of items 5, 6 and 7 of the FIQ, were used as indicators of pain, tiredness and being tired on awakening. RESULTS: The FIQ median score of 76.4 (95% CI 61.3 to 82.1) at baseline improved to 60.3 (95% CI 53.1 to 72.0; P=0.004). The Jenkins Sleep Scale median value of 17.5 (95% CI 15.5 to 18.5) at baseline fell to 12.5 (95% CI 8.5 to 14.5; P=0.001) at study completion. The outcomes of the patient global impression of change ratings were mostly positive (P=0.001). Being tired on awakening declined significantly from a median of 9.0 (95% CI 8.0 to 10.0) to 8.0 (95% CI 5.5 to 9.0; P=0.021). However, there was no significant improvement in pain level (baseline median 7.5 [95% CI 7.0 to 8.5] versus study completion median 7.0 [95% CI 6.5 to 8.0]; P=0.335) or tiredness (baseline median 9.0 [95% CI 8.0 to 9.5] versus study completion median 8.0 [95% CI 6.0 to 8.5]; P=0.061). There were no serious adverse events. CONCLUSIONS: Delta-embedded music is a potential alternative therapy for fibromyalgia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".