Computerised Cognitive Behavioural Therapy for Insomnia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Computerised cognitive behavioural therapy (CCBT) is an innovative mode of delivering services to patients with psychological disorders. The present paper uses a meta-analysis to systematically review and evaluate the effectiveness of CCBT for insomnia (CCBT-I). METHOD: A comprehensive search was conducted on 7 databases including MEDLINE, PsycINFO, EMBASE, CINAHL, Cochrane Library, Social Sciences Citation Index and PubMed (up to March 2011). Search terms covered 3 concepts: (1) [internet, web, online, computer-aided, computer-assisted, computer-guided, computerized OR computerised] AND (2) [CBT, cognitive therapy, behavio(u)ral therapy OR behavio(u)r therapy] AND (3) [insomnia, sleep disorders OR sleeping problem]. RESULTS: 533 potentially relevant papers were identified, and 6 randomised controlled trials (RCTs) that met the selection criteria were included in the review and analysis. Two RCTs were done by the same group of investigators (Ritterband and colleagues) using the same internet programmes. Post-treatment mean differences between groups showed that the effects of CCBT-I on sleep quality, sleep efficiency, the number of awakenings, sleep onset latency and the Insomnia Severity Index were significant, ranging from small to large effect sizes. However, effects on wake time after sleep onset, total sleep time and time in bed were non-significant. On average, the number needed to treat was 3.59. The treatment adherence rate for CCBT-I was high (78%). CONCLUSION: The results lend support to CCBT as a mildly to moderately effective self-help therapy in the short run for insomnia. CCBT-I can be an acceptable form of low-intensity treatment in the stepped care model for insomnia.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".