Sleep Health During Recovery After Stroke
Bibliographic record
Abstract
Study Objectives: People with stroke are susceptible to developing sleep disorders, which may negatively impact recovery. Little is known about sleep health (SH) broadly and its impact on recovery after stroke. The purpose of this study was to explore factors that are associated with SH during recovery after stroke. Methods: Data were collected from 90 participants at 10-, 60-, and 90-days post stroke without moderate to severe obstructive sleep apnea. Sleep health was measured by combining data from actigraphy and self-report to create an SH score that reflected regularity, satisfaction, alertness, timing, efficiency, and duration of sleep. Factors that may be associated with SH that were collected were Patient Health Questionnaire 9 (PHQ-9), Montreal Cognitive Assessment (MOCA), Barthel Index (BI), gait speed (GS), oxygen desaturation index (ODI), age, and sex. A cumulative link mixed model (CLMM) was used to determine the association between SH and the independent factors. Results: The CLMM found that the PHQ-9 had a significant negative estimate of-0.116 (p = 0.0002) and that the BI had a significant estimate of 0.016 (p = 0.044). Indicating that higher depressive symptoms and lower functional ability are associated with poorer SH. Time post stroke was not associated with SH. Discussion: We identified depression and functional ability as significant determinants of SH. Participants exhibited poor SH while in the hospital and it remained unchanged at 60 and 90 days after stroke. Further evaluation of the likely bidirectional relationship between depression/functional impairment and SH after stroke may lead to targets to improve stroke recovery and SH.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".