Temporal dynamics of subjective sleep profiles predicting mood improvements during adjunctive light therapy combined with sleep rescheduling
Bibliographic record
Abstract
Light therapy yields inconsistent results in people with non-seasonal depression, which may in part be due to heterogeneous responses and the temporal dynamics of changes in sleep, daytime functioning and mood. This study assessed the timeline of the antidepressant effects of light therapy and sleep rescheduling relative to changes in sleep and daytime sleep-related factors, and sought to identify predictors of treatment response in young people with depression. Twenty-four individuals with depression (mean±SD: 21.2±1.0 years old;17% male) underwent adjunctive morning light therapy with a wake-up phase advance over four weeks. They completed the Beck Depression Inventory-II (BDI-II) and Leeds Sleep Evaluation Questionnaire. On average, BDI-II scores decreased significantly after four weeks of intervention (F(2,32)=3.5, p=.044, ηp2=0.18). After two weeks, improvements in the ease of getting to sleep and sleep quality were significantly associated with BDI-II improvements (F(2,21)=6.3, p=.007). From two to four weeks, improvements in daytime sleep-related factors were significantly associated with BDI-II improvements (F(2,12)=6.0, p=.015). More sleep-related difficulties prior to the intervention tended to predict BDI-II improvements across the four weeks of light therapy (F(2,14)=3.7, p=.053). Open-label design and small sample size. Sleep-enhancement emerging in the early phase of light therapy and sleep rescheduling may subsequently alleviate sleep-related daytime dysfunctions, which may in turn further improve mood. Controlled trials are required to confirm whether the antidepressant effects of light may be linked to the attenuation of sleep-related difficulties, and whether sleep difficulties may be useful predictors of the antidepressant response to light therapy.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".