Comprehensive Assessment of Insomnia in Recovering Alcoholics Using Daily Sleep Diaries and Ambulatory Monitoring
Bibliographic record
Abstract
BACKGROUND: Many alcoholics continue to experience disrupted sleep after quitting drinking. Previous studies with recovering alcoholics have relied on retrospective questionnaires and a single night of polysomnography to document sleep problems. In the present study, sleep was assessed at home over the course of a week using both subjective and objective sleep measures. METHODS: A mixed gender group of alcoholics in recovery (n = 63) experiencing insomnia underwent a multimodal sleep assessment including a structured interview, daily sleep diaries, questionnaires, and ambulatory sleep monitoring. Alcoholics with short-term (<12 months) and long-term (>12 months) abstinence had their sleep monitored prospectively and were compared. RESULTS: Over half of the participants reported sleep problems that predated the onset of alcohol dependence. Alcoholics with short- and long-term abstinence had similarly disturbed sleep. Overall, problems with getting to sleep were worse than sleep maintenance difficulties. Severity of insomnia was unrelated to drinking history and modestly correlated with the current level of depressive symptoms. Poor sleep hygiene was evident in the sample. CONCLUSIONS: Alcoholics can experience sleep problems for many months after quitting drinking. Implications for sleep interventions with this population are discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".