Objective and subjective sleep characteristics in hospitalized older adults and their associations to hospital outcomes
Bibliographic record
Abstract
Purpose: Sleep in the hospital setting is understudied, with limited literature describing measurement of sleep quality. This study among older inpatients in an acute-care hospital describes sleep characteristics both objectively and subjectively, and explores the associations of sleep with hospital outcomes. Materials and methods: = 112) at Sutter Tracy Community Hospital were enrolled from January 2016 to November 2017. Sleep prior to admission was measured subjectively [Pittsburgh Sleep Quality Index (PSQI)], while sleep during hospitalization was measured subjectively (sleep diaries) and objectively (actigraphy, averaged over all nights). Outcomes measured included change in cognition during the hospital stay (i.e., Montreal Cognitive Assessment), length of stay (LOS), discharge to a skilled nursing facility (SNF), and re-admittance to a hospital within 30 days of discharge. Results: The participants were on average 68.7 ± 6.5 years old, predominately white (77%) and 55% women. Average PSQI was high (9.1 ± 4.2) indicating poor sleep quality prior to admission. Actigraphy was well-tolerated, with most (89%) having complete data. Sleep during the hospital stay was disturbed, with low levels of total sleep time (5.6 ± 2.0 h) and high levels of fragmentation (sleep efficiency 68.4 ± 15.0%). Sleep interruption was reported on 71% of sleep diaries, with the most common reasons being due to medical care [measurement of vitals (23%), staff interruptions (22%), blood draws (21%)]. Those with lower sleep efficiency had more cognitive decline upon discharge. Although underpowered, there was a suggestion of an association with poor sleep and the likelihood of being discharged to a SNF. Those with worse self-reported sleep quality (PSQI) prior to admission had a slightly longer LOS. No associations were seen with sleep quality and likelihood of readmission. Conclusions: Collection of objective and subjective sleep measures was feasible among hospitalized older adults. Disrupted sleep was common, and was potentially related to poor hospital outcomes. Our next steps will be to leverage these results to design and implement an intervention to improve sleep in hospitalized adults.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".