Encouraging General Practitioners to Refer Patients With Insomnia to a Digital Therapeutic (Sleepio): Feasibility Repeated-Measures Intervention Study
Bibliographic record
Abstract
Background: Sleepio, a digital therapeutic offering digital cognitive behavioral therapy for insomnia, has been recommended by the National Institute for Health and Care Excellence in the United Kingdom as an alternative to offering sleep hygiene or sleeping pills. However, understanding of the referral behavior of general practitioners (GPs) regarding Sleepio is lacking. Objective: The aim of this study was to investigate the feasibility of using an intervention targeting GPs in Scotland to increase referrals of patients with insomnia to Sleepio. Methods: GPs working in primary care in Scotland were invited to join the study. GPs were recruited through the Primary Care Research Network in Scotland from June 10, 2024, to October 13, 2024. The behavior change wheel (BCW) was used to inform the design of the intervention. During the intervention, GPs reviewed an orientation on using Sleepio and received a visual reminder midway through the intervention. The primary outcome was the number of Sleepio referrals every 2 weeks over 2 months. The secondary outcome was the change in the GPs' reported confidence level that Sleepio will be successful in reducing patients' insomnia symptoms, and confidence in recommending Sleepio to patients. Results: Of the 23 GPs who joined the study, 16 completed all stages. Overall, 68.8% (11/16) of participants were females, and the mean age was 42 (SD 8) years. The total number of Sleepio referrals in 2 months was 96 for all 16 GPs. In the first 2 weeks of the intervention, the mean referral rate to Sleepio was 22.4% for all 16 GPs, but this rate increased to 45% by the end of week 8. A repeated measures analysis indicated there was no statistically significant difference in GPs' referral rates across 4 data points. GPs' reported confidence level in recommending Sleepio increased significantly (z=-3.436; P<.001), from a mean of 5.44 (SD 1.7; somewhat confident) to 8.13 (SD 2; very confident). Conclusions: This study explored the feasibility and impact of an intervention aimed at supporting GPs to refer patients with insomnia to the digital therapeutic, Sleepio. Improvements were seen in GP-reported confidence levels at recommending Sleepio. A large-scale intervention and a longer study duration could provide useful information concerning how long the intervention effect on GPs' behavior toward Sleepio referrals might be maintained.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".