Quality of Life Impacts of Bright Light Treatment, Fluoxetine, and the Combination in Patients with Nonseasonal Major Depressive Disorder: A Randomized Clinical Trial
Bibliographic record
Abstract
OBJECTIVE: Bright light therapy is increasingly recommended (alone or in combination with antidepressant medication) to treat symptoms of nonseasonal major depressive disorder (MDD). However, little is known about its impacts on quality of life (QoL), a holistic, patient-valued outcome. METHODS: = 30). QoL was assessed using the Quality of Life Enjoyment and Satisfaction Questionnaire Short Form (Q-LES-Q-SF). Treatment-related differences in QoL improvements were assessed using a repeated measures analysis of variance. The influence of potential predictors of QoL (demographic variables and change in depressive symptoms) were investigated via hierarchical linear regression. RESULTS: Q-LES-Q-SF scores significantly improved across all treatment conditions; however, no significant differences were observed between treatment arms. QoL remained poor relative to community norms by the end of the trial period: Across conditions, 70.6% of participants had significantly impaired QoL at the 8-week assessment. Reduction in depressive scores was a significant predictor of improved QoL, with the final model accounting for 54% of variance in QoL change scores. CONCLUSION: The findings of this study emphasize that improvement in QoL and reduction in depressive symptoms in MDD, while related, cannot be taken to be synonymous. Adjunctive therapies may be required to address unmet QoL needs in patients with MDD receiving antidepressant or light therapies. Further research is required to explore additional predictors of QoL in order to better refine treatments for MDD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".