P129 A substantial percentage of patients with Chronic Insomnia do not appreciably increase Total Sleep Time after Cognitive Behavioural Therapy for Insomnia
Bibliographic record
Abstract
Abstract Introduction Total sleep time (TST) does not exceed baseline for the majority of patients after CBT-I. However by follow-up, TST increases by almost 1 hour on average. The current study investigated the extent to which this TST improvement is common and assessed for baseline predictors of increased TST after CBT-I. Methods This study is an archival analysis of data from a randomised clinical trial comparing acute CBT-I to acute CBT-I plus maintenance therapy (N = 80). The percent of patients that exceeded baseline TST by ≥30 minutes was assessed at post treatment and 3, 6, 12, and 24 months following treatment. Linear mixed models were conducted to assess the effect of patient demographics (age, sex, ethnicity, marital status), and baseline Sleep Diary-reported sleep continuity and Insomnia Severity Index (ISI) scores on changes in TST. Results 17% of patients achieved an appreciable increase in TST by treatment end, and this proportion only increased to 58% over time. Sleep Diary-reported sleep latency, wake after sleep onset, early morning awakenings, total wake time, TST, and sleep efficiency at baseline were associated with greater increases in TST after CBT-I (interaction ps < .03). Demographics and ISI scores were not significant predictors (interaction ps > .07). Conclusion A substantial proportion of patients do not appreciably increase TST after CBT-I, but patients with more severe sleep continuity disturbances at baseline exhibited the largest improvements. Whether all patients could increase their TST even further after CBT-I is a topic for further investigation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".