Sleep and Rest-Activity Rhythms in Recovering Patients with Severe Concurrent Mental and Substance Use Disorder: A Pilot Study
Bibliographic record
Abstract
Objective: Mental health and substance use disorders are commonly associated with disrupted sleep and circadian rest-activity rhythms. How these disorders in combination relate to sleep and circadian organization is not well studied. We provide here the first quantitative assessment of sleep and rest-activity rhythms in inpatients with complex concurrent disorders, taking into account categories of substance use (stimulant vs. stimulant and opioid use) and psychiatric diagnosis (psychotic disorder and mood disorder). We also explore how sleep and rest-activity rhythms relate to psychiatric functioning. Methods: A total of 44 participants (10 female) between the age of 20–60 years (median = 29 years) wore wrist accelerometers over 5–70 days and completed standardized questionnaires assessing chronotype and psychiatric functioning (fatigue, psychiatric symptom severity, and impulsiveness). To examine potential influences from treatment, we computed (1) length of stay; (2) days of abstinence from stimulants and opioids as a measure of withdrawal; and (3) a sedative load based on prescribed medications. Results: Participants exhibited a sustained excessive sleep duration, frequent nighttime awakenings, and advanced rest-activity phase related to sedative load. Sleep disruptions were elevated in participants with a history of opioid use. Patients with a psychotic disorder showed the longest sleep and most fragmented and irregular rest-activity patterns. Non-parametric circadian rhythm analysis revealed a high rhythm amplitude by comparison with population norms, and this was associated with greater psychiatric symptom severity. Psychiatric symptom severity was also associated with greater fatigue and later MCTQ chronotype. Conclusions: This pilot study provides initial information on the prevalence and severity of sleep and circadian rhythm disturbances in individuals with severe concurrent disorders. The results underline the need for further studies to start to understand the role of sleep in the disease and recovery process in this understudied population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".