Poor Sleep Health Early After Stroke is Associated with Lower Quality of Life Later After Stroke
Bibliographic record
Abstract
Background: Factors early after stroke that are predictive of outcomes can help guide rehabilitation interventions. There is a growing understanding of the importance of sleep for recovery after stroke. Objectives: To explore factors at 10 days post stroke, including sleep health (SH), that may be associated with QOL at 60 days post stroke. Methods: Data from 62 participants were collected at 10 and 60 days post stroke. Independent variables at 10 days post stroke were the NIHSS, Barthel Index (BI), Montreal Cognitive Assessment (MOCA), Patient Health Questionnaire (PHQ-9), gait speed, Berg Balance Scale, and SH. SH was measured by combining data from actigraphy and self-report to create a score that reflected regularity, satisfaction, alertness, timing, efficiency, and duration of sleep (S-Ru-SATED). The Stroke Impact Scale (SIS) at 60 days post stroke assessed quality of life. A multiple linear regression using a leave-one-out cross validation was employed to assess the association between the independent variables at 10 days and the SIS at 60 days. Results: The leave-one-out cross-validation analysis revealed that MOCA (p=0.0068), NIHSS (p=0.0181), BI (p=0.0028), and S-Ru-SATED (p=0.000155) at 10 days post stroke were significantly associated with the SIS at 60 days post stroke. Conclusions: Participants with less stroke severity, higher cognition, better functional ability, and better SH early after stroke were more likely to have a higher QOL at 2 months post stroke. Research is needed to assess the impact of interventions to improve SH in conjunction with rehabilitation interventions after stroke.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".