Treatment Preferences for Internet-Based Cognitive Behavioral Therapy for Insomnia in Japan: Online Survey
Bibliographic record
Abstract
BACKGROUND: The internet has the potential to increase individuals' access to cognitive behavioral therapy (CBT) for insomnia at low cost. However, treatment preferences regarding internet-based computerized CBT for insomnia have not been fully examined. OBJECTIVE: The aim was to conduct an anonymous online survey to evaluate treatment preferences for insomnia among patients with insomnia and individuals without insomnia. METHODS: We developed an online survey to recruit a total of 600 participants living in the Kanto district in Japan. There were three subgroups: 200 medicated individuals with insomnia, 200 unmedicated individuals with insomnia, and 200 individuals without insomnia. The survey asked questions about the severity of the respondent's insomnia (using the Athens Insomnia Scale), the frequency of sleep medication use and the level of satisfaction with sleep medication use, the respondent's knowledge of CBT, his or her preference for CBT for insomnia before drug therapy, preference for CBT versus drug therapy, and preference for internet-based CBT versus face-to-face CBT. RESULTS: Of the 600 respondents, 47.7% (286/600) indicated that they received CBT before drug therapy, and 57.2% (343/600) preferred CBT for insomnia to drug therapy. In addition, 47.0% (282/600) preferred internet-based CBT for insomnia to face-to-face CBT. Although the respondents with insomnia who were taking an insomnia medication had a relatively lower preference for internet-based CBT (40.5%, 81/200), the respondents with insomnia who were not taking an insomnia medication had a relatively higher preference for internet-based CBT (55.5%, 111/200). CONCLUSIONS: The results of our online survey suggest that approximately half of the people queried preferred CBT for insomnia to drug therapy, and half of the respondents preferred internet-based CBT for insomnia to face-to-face CBT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".