Impact of comorbid anxiety and depressive disorders on treatment response to cognitive behavior therapy for insomnia.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of comorbid anxiety or depressive disorders on treatment response to cognitive-behavior therapy (CBT) for insomnia, behavior therapy (BT), or cognitive therapy (CT). METHOD: Participants were 188 adults (117 women; Mage = 47.4 years) with chronic insomnia, including 45 also presenting a comorbid anxiety or mild to moderate depressive disorder. They were randomized to BT (n = 63), CT (n = 65), or CBT (n = 60). Outcome measures were the proportion of treatment responders (decrease of ≥8 points on the Insomnia Severity Index; ISI) and remissions (ISI score < 8) and depression and anxiety symptoms. RESULTS: Proportion of treatment responders and remitters in the CBT condition was not significantly different between the subgroups with and without comorbidity. However, the proportion of responders was lower in the comorbidity subgroup compared to those without comorbidity in both the BT (34.4% vs. 81.6%; p = .007) and CT (23.6% vs. 57.6%; p = .02) alone conditions, although remission rates and prepost ISI change scores were not. Pre to post change scores on the depression (-10.6 vs. -3.9; p < .001) and anxiety measures (-9.2 vs. -2.5; p = .01) were significantly greater in the comorbidity subgroup relative to the subgroup without comorbidity but only for those treated with the full CBT; no difference was found for those treated with either BT or CT alone. CONCLUSIONS: The presence of a comorbid anxiety or mild to moderate depressive disorder did not reduce the efficacy of CBT for insomnia, but it did for its single BT and CT components when used alone. (PsycINFO Database Record
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".