Innovative Digital Cognitive Behavioral Treatment for Insomnia Disorder in Adults (dCBT-i): Framework Development
Bibliographic record
Abstract
BACKGROUND: Chronic insomnia, or insomnia disorder, is a major health issue with a prevalence of up to 15%. The recommended first-line treatment is cognitive behavioral therapy for insomnia (CBT-i), which, unfortunately, remains insufficiently accessible. Digitalization has the potential to reduce health care access inequalities by offering more flexible and accessible care options. Digital CBT-i (dCBT-i) has been shown to be as effective as in-person CBT-i, highlighting its potential for broader implementation. OBJECTIVE: This study aimed to develop an evidence-based dCBT-i program grounded in theoretical and clinical knowledge, designed for efficient integration into health care systems, and to establish it as the first prescribed digital treatment in France. METHODS: The program was constructed based on validated CBT-i theory and practice, incorporating the latest scientific data on CBT for insomnia. It was designed as a robust multicomponent therapy, integrating an initial standardized assessment and daily intelligent adaptation to enable digital phenotyping and provide personalized treatment. RESULTS: We developed an innovative digital solution that combines scientific rigor with practical application. The program includes a standardized initial evaluation and dynamic personalization through intelligent algorithms. These features allow for the adaptation of therapy based on patient progress and needs, ensuring individualized care. CONCLUSIONS: The development of this dCBT-i program represents a significant milestone in digital health care, offering a scalable solution to the accessibility challenges of traditional CBT-i. Future steps involve conducting clinical studies to further evaluate its effectiveness and optimize its implementation within health care systems.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".