Self-Help Treatment for Insomnia: a Randomized Controlled Trial
Bibliographic record
Abstract
STUDY OBJECTIVES: Insomnia is a prevalent health complaint that often remains untreated. Several interventions are efficacious but they are not widely available. This study evaluated the efficacy of a self-help behavioral intervention for insomnia. DESIGN: The study used a 2 (conditions; self-help treatment, no treatment control) x 3 (assessments; pretreatment, posttreatment, 6-month follow-up) mixed factorial design. SETTING: This study was part of a larger epidemiologic study conducted with a randomly selected sample of 2001 adults of the province of Quebec in Canada. PARTICIPANTS: One-hundred ninety-two adults (n = 127 women, 65 men; mean age, 46 years) with insomnia, selected from a larger community-based epidemiologic sample, were randomly assigned to self-help treatment (n = 96) or no-treatment control (n = 96). INTERVENTIONS: The self-help intervention included 6 educational booklets mailed weekly to participants and providing information about insomnia, healthy sleep practices, and behavioral sleep scheduling and cognitive strategies. MEASUREMENTS AND RESULTS: Participants completed sleep diaries and questionnaires at pretreatment, posttreatment, and 6-month follow-up. Significant but modest improvements were obtained on subjective sleep parameters for treatment but not control participants. Treated participants averaged nightly gains of 21 minutes of sleep and a reduction of 20 minutes of wakefulness, with a corresponding increase of 4% in sleep efficiency. Improvements were also obtained on measures of insomnia severity (Insomnia Severity Index) and of sleep quality (Pittsburgh Sleep Quality Index), and those changes were maintained at follow-up. CONCLUSIONS: A self-help behavioral intervention was effective in alleviating a broad range of insomnia symptomatology in a community sample. Self-help may be a promising approach to make effective intervention more widely available.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".