Stepped care for insomnia in primary care using digital and face-to-face cognitive behavioral therapies: A pragmatic nonrandomized clinical trial
Bibliographic record
Abstract
To evaluate the effectiveness of a stepped-care intervention for insomnia in primary care. In this non-randomized pragmatic clinical trial, patients from primary care clinics and with chronic insomnia disorder were allowed to choose between continuing their usual treatment (prescribed sleep medication) or receiving digital CBT-I (dCBT-I), either alone or in combination with medication. After the first treatment step, non-remitters were provided with the choice of receiving face-to-face CBT-I (FtFCBT-I), medication, or no additional treatment. The primary outcome was insomnia symptoms as measured by the Insomnia Severity Index. Among 154 adults with insomnia, 73 were allocated to dCBT-I, 66 to combined treatment and 15 to medication alone based on their preference. When compared to medication alone, first-step treatment with dCBT-I or combined treatment both produced significantly larger effects on reducing insomnia severity (dCBT-I vs Med, difference in the mean changes = -3.3; Comb vs Med, -3.7), and led to higher percentages of responders (dCBT-I vs Med, 54.8% vs 16.0%, OR = 6.38; Comb vs Med, 53.6% vs 16.0%, OR = 6.07) and remitters (dCBT-I vs Med, 65.8% vs 9.4%, OR = 18.61; Comb vs Med, 67.5% vs 9.4%, OR = 20.13. Adding FtFCBT-I as second-step treatment offered an added value for non-remitters after the first-step treatment. Improvements achieved at post-treatment were sustained through the 6-month follow-up for most of the treatment sequences. These findings demonstrated the feasibility and efficiency of implementing digital and in-person CBT-I within a stepped-care model in primary care practice. • Digital CBT-I applied in primary care is an effective treatment delivery modality for patients with chronic insomnia, and non-remitters could be further stepped up to a more intensive treatment (e.g., in-person CBT-I). • Implementing a stepped-care intervention for insomnia in primary care could facilitate the provision of guideline care, optimize treatment effects, and enhance accessibility of evidence-based interventions. • It is important to involve patients in the decision-making process when selecting among different treatment options within a stepped-care intervention.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".