1294 What Factors Influence Sleep Health During Recovery After Stroke?
Bibliographic record
Abstract
Abstract Introduction People post stroke (PPS) exhibit a variety of sleep disorders. PPS with poor sleep health (SH) often have worse outcomes. However, the factors that may contribute to poor SH in PPS are poorly understood. The purpose of study was to explore factors that influence SH after stroke. Methods Data were collected at 10- (in the hospital), 60-, and 90-days post stroke. SH was assessed based on the components of the Ru-SATED scale by combining data from actigraphy: regularity, timing, efficiency, and duration of sleep; and self- report of satisfaction and alertness. This provided a surrogate Ru-SATED (S-Ru-SATED) SH score between 0 and 12, with higher scores indicating better SH. Common stroke outcomes were taken at each time point: Montreal Cognitive Assessment (MOCA), Barthel Index (BI), Patient Health Questionnaire 9 (PHQ-9), and self-selected gait speed (GS). To assess the relationship between the S-RU-SATED and potentially predictive covariates, we used a cumulative link mixed model. This method combines features of ordinal regression with mixed effects modeling. We estimated the cumulative probabilities of the response falling into or below each category of the S-Ru-SATED as a function of fixed effects (fixed predictive covariates included PHQ-9, BI, GS, MOCA, sex, age, and time post-stroke), and subject-specific random effects which captures the variability across subjects accounting for time dependencies in the data. Results Data from 90 PPS were used in the analysis. For the fixed effects, PHQ-9 had a significant negative estimate of -0.1327 (p < 0.001), indicating that for each unit increase in PHQ-9, the odds of being in a higher category of S-Ru-SATED decreased by approximately 12.4% holding other variables constant. The other predictors were not statistically significant. Conclusion Our findings suggest that people with greater levels of depression may have poorer sleep health during the first 3 months after stroke. Interestingly, differences between 60/90 days versus inpatient periods did not significantly influence SH. We would expect that SH would improve once participants were out of the hospital. Addressing depression early after stroke may be an important to improve SH in people who are recovering after stroke. Support (if any) National Institutes of Health NINR, Award Number R01NR018979.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".