A Preliminary Evaluation of the Physiological Mechanisms of Action for Sleep Restriction Therapy
Bibliographic record
Abstract
Our objective was to investigate the physiological mechanisms involved in the sleep restriction treatment of insomnia. A multiple baseline across subjects design was used. Sleep of five participants suffering from insomnia was assessed throughout the experimentation by sleep diaries and actigraphy. Ten nights of polysomnography were conducted over five occasions. The first two-night assessment served to screen for sleep disorders and to establish a baseline for dependent measures. Three assessments were undertaken across the treatment interval, with the fifth and last one coming at follow-up. Daily cortisol assays were obtained. Sleep restriction therapy was applied in-lab for the first two nights of treatment and was subsequently supervised weekly. Interrupted time series analyses were computed on sleep diary data and showed a significantly decreased wake time, increased sleep efficiency, and decreased total sleep time. Sleepiness at night seems positively related to sleep variables, polysomnography data suggest objective changes mainly for stage 2, and power spectral analysis shows a decrease in beta-1 and -2 powers for the second night of treatment. Cortisol levels seem to be lower during treatment. These preliminary results confirm part of the proposed physiological mechanisms and suggest that sleep restriction contributes to a rapid decrease in hyperarousal insomnia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".