The effectiveness of stimulus control in cognitive behavioural therapy for insomnia in adults: A systematic review and network meta‐analysis
Bibliographic record
Abstract
Stimulus control is part of the widely used cognitive behavioural therapy for insomnia. However, there is a lack of knowledge about its mechanisms of action and effectiveness when used alone. This systematic review with network meta-analysis aimed to evaluate stimulus control efficacy when used alone compared with cognitive behavioural therapy for insomnia or its components. The review also documented stimulus control mechanisms of action proposed by the authors. A search was conducted in several bibliographic databases (MEDLINE, PsycINFO, Embase, CINAHL, Psychology Behavioural Sciences Collection, Web of Science, and Cochrane Library) and in two registers from 1972 to June 2022. Randomised studies with adult participants presenting a diagnosis of insomnia and including at least one stimulus control instruction in a treatment group were included. Risk of bias was assessed with the Quality Assessment of Controlled Intervention Studies. Twenty-three studies were included and three network meta-analyses were conducted. The quality of included studies was generally poor. Results indicate that stimulus control is an effective intervention to improve insomnia compared with control conditions. Not all stimulus control instructions seem essential, especially those known to recondition the bedroom for sleep. The review challenges the classical conditioning hypothesis. Results should be interpreted cautiously given the small number of studies included, bias risk, and inconsistencies in the network meta-analysis. Rigorous research is needed in evaluating stimulus control efficacy and mechanisms.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.022 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".