Interaction of Physical Activity, Sleep, and Cognitive Function in Stroke: SEM and ML Approaches
Bibliographic record
Abstract
Abstract Cognitive impairment is prevalent among stroke survivors, highlighting the importance of exploring modifiable lifestyle factors such as physical activity and sleep, along with their biological underpinnings. This study aimed to investigate how physical activity and sleep influence cognitive function in stroke survivors, examining potential biological mediators. Cross-sectional data collected at hospital admission from 262 stroke survivors (First Hospital of Jilin University, 2024) were analyzed. Measures included self-reported weekly physical activity (exercise frequency/duration), nighttime sleep (duration, latency), cognitive function (Montreal Cognitive Assessment [MOCA]), emotional status (Hamilton Depression Rating Scale [HAMD]), and Serum neurological (homocysteine [Hcy]) and inflammatory (hs-CRP) biomarkers. Participants were aged 65.4±8.2 years, 43% female, and 58% showed cognitive impairment. Correlation and regression analyses examined associations, and structural equation modeling (SEM) explored causal pathways. Machine learning (ML) methods identified key predictors of cognitive impairment. Frequent physical activity (r = 0.35, p < 0.001) and adequate sleep (r = 0.25, p < 0.001) significantly correlated with higher cognitive scores. Depression severity (r=-0.40, p < 0.001) and elevated Hcy (r=-0.36, p < 0.001) negatively correlated with cognition. Regression analyses confirmed exercise frequency (β = 0.34, p < 0.001) and sleep duration (β = 0.29, p < 0.001) as independent positive predictors of cognitive function. SEM demonstrated direct beneficial effects of physical activity (β = 0.41, p < 0.001) on cognition and indirect effects mediated by improved sleep and reduced inflammation (CFI>0.9). ML predicted cognitive impairment (accuracy>80%), identifying age, education, Hcy level, physical activity, and sleep as crucial predictors. Physical activity and sleep synergistically enhance cognition in stroke survivors via inflammatory and neurological pathways. Promoting regular exercise, optimizing sleep, and managing biomarkers may preserve cognitive function.
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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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".