0379 A Scoping Review of Validation Studies for Commercially Available CBT-i Smartphone Applications
Bibliographic record
Abstract
Abstract Introduction Cognitive behavioral therapy for insomnia (CBT-I) remains the first line treatment for insomnia. CBT-I in comparison with sedative-hypnotics has similar efficacy, but with treatment durability and almost no adverse effects. Despite CBT-I being recognized as the best insomnia treatment, access remains limited. Digital CBT-I hopes to address the problem of scale, so as to deliver therapy to the masses. There are now multiple mobile applications available both on smartphone app stores, which claim to deliver evidence based CBT-i. These applications largely come at a cost and patients have to pay to access their services. The goal of this study is to review CBT-i smartphone applications to see if they are indeed validated. Methods We performed a search on the two most popular smartphone application platforms: Google Play and Apple Store. We used search terms: sleep, insomnia and CBT-I. We then searched for validation studies for those smartphone applications on Google Scholar. We included studies conducted in the past 10 years. Our second search consisted of reviewing PubMed and Google Scholar for validation studies for CBT-I applications. Our search terms consisted of CBT-I and smartphone, CBT-I and application and CBT-I and digital. Results Of the 9 validation studies that we initially found, 6 met our inclusion criteria. 3 were excluded as they did not solely use CBT-I in their applications. All 6 applications reported significant improvement in important sleep quality metrics such as sleep onset latency and total sleep time. 4 studies also reported on a subjective improvement in quality of sleep. 2 studies looked at populations with comorbidities including cannabis use disorder and epilepsy. Both studied again found improvement in sleep quality in those specific populations. There were concerning patterns of bias found amongst the reviewed studies. 3/6 investigators had direct relationships with companies which designed and marketed the applications. Conclusion dCBT-I offers an opportunity to increase accessibility to therapy. There are only a limited number of studies which have examined the effectiveness of the applications on the market. There remains serious concerns about the risk of bias and the quality of validation studies which claim to confirm the effectiveness of these applications. Support (if any)
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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 | 0.034 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".