Treating Depression and Anxiety with Digital Cognitive Behavioural Therapy for Insomnia: A Real World NHS Evaluation Using Standardized Outcome Measures
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that insomnia may be an important therapeutic target to improve mental health. AIMS: Evaluating changes in symptoms of depression and anxiety after supported digital cognitive behavioural therapy (dCBT) for insomnia delivered via a community-based provider (Self Help Manchester) of the Improving Access to Psychological Therapies (IAPT) service. METHOD: Supported dCBT for insomnia was delivered to 98 clients (mean age 44.9 years, SD 15.2, 66% female) of Self Help Manchester. All clients received six support calls from an eTherapy coordinator to support the self-help dCBT. During these calls levels of depression (Patient Health Questionnaire, PHQ-9) and anxiety (Generalized Anxiety Disorder, GAD-7) were determined. RESULTS: Depression (M difference-5.7, t(70) = 12.5, p < .001) and anxiety [Generalized Anxiety Disorder-7 (GAD-7), M difference-4.1, t(70) = 8.0, p < .001] were reduced following supported dCBT for insomnia. This translated into an IAPT recovery rate of 68% for depression and anxiety. CONCLUSIONS: These results suggest that dCBT for insomnia alleviates depression and anxiety in clients presenting with mental health complaints in routine healthcare.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 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".