Comparing the Effects of Single‐ and Multiple‐Component Therapies for Insomnia on Sleep Outcomes
Bibliographic record
Abstract
BACKGROUND: Single- and multiple-component therapies are recommended in professional guidelines for managing chronic insomnia. Systematic reviews point to insufficient evidence of the comparative effectiveness of these therapies, which is required for treatment decision making. PURPOSE: To compare the effectiveness of three single-component and one multiple-component therapies on short-term sleep outcomes. METHODS: The data were obtained from 517 persons with chronic insomnia, enrolled in a partially randomized preference trial. They were allocated to the single-component therapies: sleep education and hygiene (SEH), stimulus control therapy (SCT), and sleep restriction therapy (SRT), or the multiple-component therapy (MCT). The outcomes, perceived insomnia severity and sleep parameters, were assessed with established measures at pre and posttest. Repeated measure analysis of variance was used to compare the outcomes across therapy groups over time. The clinical relevance of the therapies' effects was evaluated by examining the effect size and remission rate. RESULTS: The four therapies differed in their effectiveness in reducing perceived insomnia severity and improving sleep outcomes. SEH was least effective. SCT, SRT, and MCT were moderately effective. SCT and SRT demonstrated slightly higher remission rates than MCT for perceived insomnia severity and some sleep parameters. LINKING EVIDENCE TO ACTION: SCT and SRT are viable single-component therapies that produce clinical benefits. Single-component insomnia treatment may be more convenient to implement in the primary care setting due to the reduced number of treatment recommendations compared to MCT.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".