A randomized controlled trial of a digital cognitive behavioral therapy for insomnia for older adults
Bibliographic record
Abstract
Older adults with insomnia face considerable challenges accessing treatment given limited availability to first-line therapy (Cognitive-Behavioral Therapy for Insomnia, CBT-I). This study evaluated the efficacy of Sleep Healthy Using the Internet for Older Adults Suffering with Insomnia and Sleeplessness (SHUTi OASIS), a tailored CBT-I internet intervention for older adults with insomnia, in a 3-arm randomized controlled trial (SHUTi OASIS alone, SHUTi OASIS + stepped support, online patient education [PE]). 311 participants (ages 55-95) were randomized to receive SHUTi OASIS (alone n = 105; with stepped support n = 102), with both conditions reporting significant improvements across post, 6-month, and 12-month follow-ups in insomnia severity compared to those receiving PE (n = 104). Clinically meaningful indices of insomnia response and remission were also higher among those receiving SHUTi OASIS. Those who received SHUTi OASIS also significantly outperformed those receiving PE on secondary outcomes, including sleep onset latency, wake after sleep onset, sleep efficiency, number of awakenings, sleep quality, and fatigue, across most timepoints. Results indicate that digital CBT-I provides important benefits for older adults, offering strong potential to expand access to insomnia treatment for this underserved population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.011 | 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".