Pharmacological interventions to improve sleep in hospitalised adults: a systematic review
Bibliographic record
Abstract
OBJECTIVES: Patients often suffer from disturbed sleep in hospital. Poor-quality sleep in hospitalised patients has been associated with significant morbidity and pharmacological sleep aids are often prescribed. The objective of this systematic review is to evaluate the comparative efficacy and safety of pharmacological interventions used for sleep in hospitalised patients. SETTING/PARTICIPANTS: We searched MEDLINE, Embase, the Cochrane database and grey literature for prospective studies that evaluated sleep in hospitalised adults after a pharmacological intervention. PRIMARY AND SECONDARY OUTCOME MEASURES: Two reviewers assessed studies for inclusion and extracted data for efficacy outcomes, including sleep efficiency, sleep latency, sleep fragmentation and objectively measured sleep stage distribution. Risk of bias was assessed and meta-analyses were planned contingent upon homogeneity of the included studies. RESULTS: After screening 1920 citations, 15 studies involving 861 patients were included. Medications studied included benzodiazepines, nonbenzodiazepine sedatives, melatonin, propofol and dexmedetomidine. Five studies were deemed to be of high quality. Heterogeneity and variable outcome reporting precluded meta-analysis in most cases. No consistent trends with respect to sleep efficiency, quality or interruptions were observed identifying a drug or drug class as superior to another or no treatment. Benzodiazepines appeared to be better than no treatment with respect to sleep latency, but this was not consistently demonstrated across all studies. Sleep stage distribution shows that sleep in hospital is dominated by stages N1 and N2. CONCLUSIONS: There is insufficient evidence to suggest that pharmacotherapy improves the quality or quantity of sleep in hospitalised patients suffering from poor sleep. No drug class or specific drug was identified as superior even when compared to placebo or no treatment. Although 15 studies were included, the quality of evidence was limited by their quality and size. Larger, better-designed trials in hospitalised adults are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".