Digital cognitive behavior therapy for insomnia improving sleep quality: a real-world study
Bibliographic record
Abstract
BACKGROUND: Digital cognitive behavior therapy for insomnia (dCBT-I) is an effective treatment in alleviating insomnia. This study examined the effect of dCBT-I for improving sleep quality in patients with insomnia complaints from a clinical population in a real-world setting. METHODS: The study included 6,002 patients aged 18 years and above with primary complaints of dissatisfying sleep from a sleep clinic in a psychiatric hospital from November 2016 to April 2021. Patients were diagnosed with insomnia, anxiety disorders, or anxiety comorbid with insomnia or depression according to ICD-10. A mobile app was developed for self-reported assessment and delivering dCBT-I interventions and treatment prescriptions to participants. The primary outcome was change in global sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI). At 8- and 12-week follow-up, 509 patients were reassessed. Data were analyzed with non-parametric tests for repeated measures. RESULTS: Patients treated with dCBT-I monotherapy were younger, with a more frequent family history of insomnia compared to those with medication monotherapy and those with combined dCBT-I and medication therapy. Improvements of sleep quality from baseline to 8-week follow-up were significant in each treatment type. Compared to 8-week follow-up, PSQI scores at 12-week were significantly decreased in the depression group receiving combined therapy and in the anxiety group treated with dCBT-I monotherapy and with combined therapy. A time-by-treatment interaction was detected in anxiety patients indicating differential reduction in PSQI scores over time between different treatment options. CONCLUSION: The current findings suggest dCBT-I is a practical and effective approach for lessening insomnia symptoms, especially for patients with anxiety symptoms suggesting with a more extended intervention period (i.e., 12 weeks). TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR1900022699).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".