Arterial Stiffness and Age Moderate the Association Between Physical Activity and Cognition in Older Adults
Bibliographic record
Abstract
Abstract Background: Evidence supports that time spent on physical activity has beneficial effects on cognition in older adults. Nevertheless, this beneficial effect is likely to change in function of individual modifying factors like age and level of arterial stiffness. This study aims to reveal whether arterial stiffness and age modulate the positive impact of physical activity on cognition by developing a double moderation model. Methods: 110 healthy older adults aged 60 to 75 years old were examined for arterial stiffness (carotid-femoral Pulse Wave Velocity [cf-PWV]), global cognition (composite score of Montreal Cognitive Assessment, and Mini-Mental State Examination), and self-reported physical activity (PACED diary). Using PROCESS macro for SPSS, we evaluated if cf-PWV (moderator 1), and age (moderator 2) moderate the relationship between physical activity (X) and global cognition (Y). The threshold for high stiffness was set at 8.5 m/s based on previous studies that reported this cut-off more appropriate for classifying cerebrovascular risk groups. Results: The interaction of arterial stiffness x age moderated the effect of physical activity on global cognition (β=-.89, SE=.42, p=.037) (Model: R2=.15, p=.018). Physical activity had a positive effect on cognition in younger-older adults (aged 60 to 68.5 years) with cf-PWV>8.5 m/s (β=.57, SE=.222, p=.011, 95% CI.133 to 1.014) and on older-older adults (aged 68.6 to 75 years) with cf-PWV<8.5 m/s (β=.49, SE=.190, p=.010, 95% CI=.116 to .869). Conclusions: Identifying the right age groups and arterial stiffness levels at which physical activity can have beneficial effects on cognition is a key step in providing tailored behavioral interventions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".