Physical activity attenuates the effect of poor sleep quality on cognitive function in adults with chronic stroke
Bibliographic record
Abstract
Abstract Background Good sleep quality is important for cognitive health. Individuals with chronic stroke are at an increased risk for dementia and about half have insomnia. Stroke survivors are a target population in need of intervention strategies to promote sleep quality to preserve cognitive function. Physical activity is a promising approach. Hence, in this cross‐sectional analysis, we examined whether physical activity moderates the effect of poor sleep quality on cognitive function in adults with chronic stroke. Method This is a cross‐sectional analysis of baseline data acquired from 121 community‐dwelling older adults with chronic stroke (age: 70.73 ± 8.56 years; 38.30% female) enrolled in a 6‐month randomized control trial. Sleep quality was assessed with the MotionWatch8 (MW8) and physical activity was measured with the Life Space Questionnaire (LSQ; 0‐120). Participants were split into higher or lower physical activity by the median LSQ score (median = 64.00). Cognitive performance was measured with the 13‐item Alzheimer’s Disease Assessment Scale (ADAS‐Cog; 0‐85), with higher scores indicating worse cognitive performance. A linear regression was performed to first assess the independent association of MW8‐measures of sleep quality (i.e., sleep duration, fragmentation, efficiency, latency, and awakenings) and ADAS‐Cog score, after accounting for age and Montreal Cognitive Assessment score (MoCA) score. Based on the results of this linear regression, we performed a second linear regression with ADAS‐Cog score as the dependent variable, and sleep latency (minutes), physical activity level, and the interaction of sleep latency * physical activity level as independent variables, controlling for age and MoCA score. Result Greater sleep latency (β1 = 0.22, p = 0.017) was significantly associated with greater ADAS‐Cog score, after controlling for age and MoCA (R2 = .50, F(3, 105) = 34.74, p<.01). Physical activity significantly moderated the association between sleep latency (β1 = 0.38, p = 0.033) and ADAS‐Cog score (R2 = .53, F(5, 103) = 22.93, p<.01). Specifically, those in the higher physical activity group showed lower ADAS‐Cog scores with higher sleep latency (r = .67, p = .627) and those in the lower physical activity group showed higher ADAS‐Cog scores with higher sleep latency (r = .75, p = .007). Conclusion Physical activity attenuates the effect of poor sleep quality on cognitive function in adults with chronic 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".