Anxiolytic impact of cognitive behavioural therapy for insomnia in patients with co‐morbid insomnia and generalized anxiety disorder
Bibliographic record
Abstract
BACKGROUND: Cognitive behavioural therapy for insomnia (CBT-I) is an effective treatment for chronic insomnia that also improves non-sleep symptoms, such as mood and anxiety. Identifying sleep-specific variables that predict anxiety change after CBT-I treatment may support alternative strategies when people with generalized anxiety disorder (GAD) do not improve from standard GAD treatment. AIMS: To investigate CBT-I on changes in anxiety and evaluate whether changes in sleep-specific variables predict anxiety outcomes. METHODS: Seventy-two participants presenting with insomnia and GAD (GAD-I) completed four sessions of CBT-I. Participants completed daily diaries and self-report measures at baseline and post-treatment. RESULTS: CBT-I in a co-morbid GAD-I sample was associated with medium reductions in anxiety, and large reductions in insomnia severity. Subjective insomnia severity and tendencies to ruminate in response to fatigue predicted post-treatment anxiety change, in addition to younger age and lower baseline anxiety. CONCLUSIONS: The findings suggest that younger GAD-I participants with moderate anxiety symptoms may benefit most from the anxiety-relieving impact of CBT-I. Reducing perceived insomnia severity and the tendency to ruminate in response to fatigue may support reductions in anxiety in those with GAD-I.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".