Patient Attitudes About Light Therapy and Negative Ion Therapy for Nonseasonal Depression: An Online Survey Study
Bibliographic record
Abstract
ABSTRACT Objective No studies have yet evaluated whether light therapy or negative ion therapy can be used as maintenance treatment after acute treatment with antidepressants in patients with major depressive disorder. To address the importance of this question, we surveyed participants with depression to determine their knowledge and attitudes about light therapy and negative ion therapy, and their willingness to participate in a randomized clinical trial with these therapies substituting for antidepressants for maintenance treatment. Methods Participants with a self-reported diagnosis of depression were recruited by email, newsletters, and social media to complete an online survey with questions about awareness and effectiveness of light therapy and negative ion therapy for depression. Vignettes describing the use of these therapies for maintenance treatment were presented with follow up questions about the ease of use and reasons for wanting (and not wanting) to use the therapies instead of antidepressants. Another vignette described a randomized study with these therapies followed by questions on whether participants would likely volunteer for the study. Chi-square tests were used to examine differences in responses between therapies. Results A total of 221 participants completed the survey. Most of them were aware of both therapies, but more participants had heard of light therapy (95% compared to 62% for negative ion therapy, p<0.0001), had used light therapy (28% versus 16%, p<0.003), and regarded light therapy as effective (54% versus 37%, p<0.001). Both therapies were considered easy to use. The majority of participants (78%) thought that it was important to find non-medication therapies for maintenance treatment, and 77% responded that they would likely volunteer for a randomized study to determine efficacy of the two therapies for maintenance treatment. Conclusion People with depression are generally aware of light therapy and negative ion therapy and believe they would be good therapies to substitute for antidepressants in maintenance treatment. These findings support the importance and feasibility for a randomized relapse prevention trial with light therapy and negative ion therapy in patients 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".