Green Light Exposure Improves Pain and Quality of Life in Fibromyalgia Patients: A Preliminary One-Way Crossover Clinical Trial
Bibliographic record
Abstract
OBJECTIVE: Fibromyalgia is a functional pain disorder in which patients suffer from widespread pain and poor quality of life. Fibromyalgia pain and its impact on quality of life are not effectively managed with current therapeutics. Previously, in a preclinical rat study, we demonstrated that exposure to green light-emitting diodes (GLED) for 8 hours/day for 5 days resulted in antinociception and reversal of thermal and mechanical hypersensitivity associated with models of injury-related pain. Given the safety of GLED and the ease of its use, our objective is to administer GLED as a potential therapy to patients with fibromyalgia. DESIGN: One-way crossover clinical trial. SETTING: United States. METHOD: We enrolled 21 adult patients with fibromyalgia recruited from the University of Arizona chronic pain clinic who were initially exposed to white light-emitting diodes and then were crossed over to GLED for 1 to 2 hours daily for 10 weeks. Data were collected by using paper surveys. RESULTS: When patients were exposed to GLED, but not white light-emitting diodes, they reported a significant reduction in average pain intensity on the 10-point numeric pain scale. Secondary outcomes were assessed by using the EQ-5D-5L survey, Short-Form McGill Pain Questionnaire, and Fibromyalgia Impact Questionnaire and were also significantly improved in patients exposed to GLED. GLED therapy was not associated with any measured side effects in these patients. CONCLUSION: Although the mechanism by which GLED elicits pain reduction is currently being studied, these results supporting its efficacy and safety merit a larger clinical trial.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".