Intraindividual variability in sleep schedule: effects of an internet-based cognitive-behavioral therapy for insomnia program and its relation with symptom remission
Bibliographic record
Abstract
STUDY OBJECTIVES: Sleep schedule consistency is fundamental to cognitive-behavioral therapy for insomnia (CBT-I), although there is limited evidence suggesting whether it predicts treatment response. This analysis tested whether: (1) an Internet-based CBT-I program affects intraindividual variability (IIV) in sleep schedule and (2) sleep schedule IIV predicts insomnia symptom remission. METHODS: This secondary analysis compares participants (N = 303) randomized to an Internet-based CBT-I program (SHUTi-Sleep Healthy Using the Internet) or Internet-based patient education (PE). Participants reported daily bedtimes and rising times on 10 online sleep diaries collected over 2 weeks at baseline and 9-week post-intervention assessment. Participants completed the Insomnia Severity Index (ISI) at post-assessment and 6-month follow-up; symptom remission was defined by ISI < 8. Mixed effects location scale modeling was used to examine the effect of SHUTi on bedtime and rising time IIV; a novel two-staged analysis examined the effect of bedtime and rising time IIV on insomnia symptom remission. RESULTS: At post-assessment, SHUTi participants reported about 30% less bedtime and 32% less rising time variability compared to PE (ps < 0.03). Bedtime and rising time IIV was not independently associated with likelihood of insomnia symptom remission at the subsequent time point (ps > 0.18), nor did sleep schedule IIV moderate treatment response (ps > 0.12). CONCLUSIONS: Findings demonstrate that an Internet-delivered CBT-I program can effectively increase users' sleep schedule consistency relative to an educational control. This consistency, however, was not related to treatment outcome when defined by insomnia symptom remission, suggesting that enforcing rigid sleep schedules for patients may not be necessary for treatment success. CLINICAL TRIAL REGISTRATION: NCT00328250.
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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 | 0.001 | 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) | 0.001 | 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".