Intensive Sleep Retraining and Total Sleep Deprivation for Treating Chronic Insomnia: A Randomised Controlled Trial
Bibliographic record
Abstract
The aim of the study was to compare the efficacy of intensive sleep retraining (ISR) and total sleep deprivation (TSD) against a no-treatment control condition for treating chronic insomnia and examine two potential mechanisms: the resolution of conditioned insomnia and the increase in homeostatic sleep drive produced by sleep deprivation. Thirty-four adults with chronic sleep onset insomnia (with or without sleep maintenance difficulties) were randomised to ISR, TSD, or a control condition. The ISR condition consisted of a 38.5-h period of sleep deprivation, the last 21 h of which included 42 sleep onset trials; the TSD condition consisted of an equivalent 38.5-h sleep deprivation period without any sleep onset trials, and the control condition consisted of one night of habitual sleep in the laboratory. Significant decreases in insomnia severity were observed from pre- to post-treatment in both ISR and TSD, but not in the control condition. A significant reduction in sleep onset latency was observed during the same period for the ISR condition, but not for the TSD or control conditions, with no clinically meaningful change in anxiety or depressive symptoms. Significant decreases in fatigue were also observed for both ISR and TSD conditions during the same period. These results were generally significant at 3 months after treatment. Sleep improvements produced by ISR, and to a lesser extent by TSD, suggest that both the resolution of conditioned insomnia and the increase in homeostatic sleep drive represent important mechanisms responsible for the efficacy of ISR.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".