Investigating the antidepressant effects of CBT-I in those with major depressive and insomnia disorders
Bibliographic record
Abstract
Cognitive behavioral therapy for insomnia (CBT-I) is a highly effective treatment for insomnia disorder that also helps with myriad clinically relevant, non-sleep specific symptoms - most notably, depression. Studies evaluating depression change after CBT-I suggest that CBT-I is an effective therapy for depression. Subsequently, empirical efforts have started investigating the mechanisms by which CBT-I exerts an antidepressant effect. The present study replicates the efficacy of CBT-I on depressive complaints and examines whether changes in sleep-specific variables predict depression outcome after CBT -I. Seventy participants presenting with comorbid insomnia and major depressive disorders (MDD-I) completed four sessions of CBT-I over eight weeks. Participants completed daily sleep diaries and self-report measures at baseline and post-treatment to assess changes in sleep and mood-related variables. CBT-I was associated with large improvements in depression (d = 0.8). Tendencies to ruminate in response to fatigue predicted post-treatment depression improvements (β = 0.294). Other predictors of post-treatment mood improvement included younger age (β = -0.191) and lower baseline depression (β = -0.472). The study was an open trial without a control group, restricting conclusions that can be made. Participants who joined the trial received insomnia-specific treatment; therefore, questions relevant to those who are primarily seeking mood treatment cannot be addressed. The results suggest that younger MDD-I participants with moderate depression symptoms may benefit most from the antidepressant effects of CBT-I. Additionally, targeting the tendency to ruminate in response to fatigue is an important endeavor in CBT-I, as it produces depression improvement.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".