Differential role of age, education, and lifestyle on cognition in late life
Bibliographic record
Abstract
Background and Objectives: The reported risk reduction effect of higher education and more active lifestyle on cognitive decline has been hypothesized to reflect better cognitive reserve (CR). This study examined the association between age, education, lifestyle, and global cognition in community-dwelling elderly in Hong Kong, and further investigated whether and how the two proxies of CR, namely education and lifestyle, affected the elderly’s cognitive performance differently. Methods: One thousand eight hundred and twenty participants aged 65 and above, not clinically demented at the time of data collection, were recruited using age-stratification random sampling method through 11 public rental housing estates in Hong Kong. Participants’ basic demographic information including age, gender and years of education was collected; their engagement in physical, mental, and social activities was assessed; and their cognitive abilities were measured by the validated Cantonese Chinese Montreal Cognitive Assessment (MoCA). Hierarchical regression model and moderation analysis were performed to evaluate the association between age, education, lifestyle and cognition. Results: Age was negatively associated with cognitive performance (r =-.48, adjusted R2 =2.33, p < .001), while both higher education (r = .41, R2 change = .075, p < .001) and more active participation in social, physical, and mental stimulating activities (r = .11, R2 change =.01, p < .001) were positively associated with more favorable cognitive performance after controlling for age. There was a weak moderation effect of age on the relationship between education and cognition (R2 change =.01, p < .05) after controlling other independent variables, but not on that between lifestyle and cognition. Conclusions: Age, education, and lifestyle are all significant predictors of cognition among elderly over the age of 65 in Hong Kong. Higher education and more active lifestyle as proxies of better CR associate with better cognitive performance, and could potentially delay the onset of dementia. Age appears to have a modulating effect between early-life education and later-life cognition, but it does not affect the relationship between lifestyle and cognition in late life.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".