Patient Attitudes About Light Therapy and Negative Ion Therapy for Nonseasonal Depression: An Online Survey Study
Bibliographic record
Abstract
OBJECTIVE: No studies have evaluated the feasibility of light therapy or negative ion therapy as maintenance treatments after acute antidepressant treatment in major depressive disorder. To address this gap, we surveyed people with depression about their knowledge and attitudes about light therapy and negative ion therapy, and their willingness to participate in a clinical trial of maintenance treatment with these therapies. METHODS: Participants with a self-reported diagnosis of depression completed a researcher-generated online survey, created for this study, examining awareness and effectiveness of light therapy and negative ion therapy, which included vignettes describing the use of these therapies for maintenance treatment. Participants were asked about the feasibility and reasons for wanting (and not wanting) to use the therapies instead of antidepressants. Response frequencies were compared using chi-square tests. RESULTS: A total of 193 participants completed the survey. Most were aware of both therapies, but significantly more participants had heard of light therapy (95% versus 63% for negative ion therapy, p<0.001), had used light therapy (29% versus 17%, p<0.001), and regarded light therapy as effective (54% versus 37%, p<0.001). Both therapies were considered easy to use. Most participants (81%) placed importance on finding non-medication therapies for maintenance treatment; 77% responded that they would likely volunteer for a randomized study of maintenance treatment. CONCLUSION: People with depression are aware of light therapy and negative ion therapy and support their usage as substitutes for antidepressants in maintenance treatment. This supports the importance and feasibility of a randomized relapse prevention trial with light therapy and negative ion therapy in people with depression.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".