Examining whether Changes in Sleep Habits Predict Long-Term Sustainment of Treatment Gains in Individual Remitted from Insomnia after CBT-I
Bibliographic record
Abstract
Objectives Providers of Cognitive-Behavioral Therapy for Insomnia (CBT-I) are often asked whether the behavioral recommendations (e.g., stimulus control, sleep restriction) must be adhered to ad infinitum. We examined whether changes in sleep habits/behaviors are a life sentence, or whether patients who remit can relax their adherence while maintaining their treatment gains at 1-year follow-up (FU).Methods Participants (N = 179) completed 2 weeks of sleep diaries and measures of insomnia severity and safety behaviors at baseline and following four sessions of CBT-I. Of the 137 patients that achieved remission, 77 completed these measures at 1-year FU.Results Improvements in insomnia severity and total wake time (TWT) at post-treatment were maintained at FU (ps ≥ .52). Similarly, reductions in safety behaviors were maintained at FU (p – 1.00), whereas lingering in bed reduced during treatment (p < .001) but increased at FU (p < .001). Changes in sleep habits after treatment did not predict insomnia severity at FU. However, increases in time in bed positively predicted TWT at FU (p = .001).Conclusions Those who remit after CBT-I may generally relax their adherence to behavioral recommendations without significantly impacting their perceived insomnia symptoms 1 year after treatment despite some increases in TWT. Results increase our confidence in CBT-I as a brief and durable intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".