Lifestyle Engagement Affects Cognitive Status Differences and Trajectories on Executive Functions in Older Adults
Bibliographic record
Abstract
The authors first examined the concurrent moderating role of lifestyle engagement on the relation between cognitive status (cognitively elite, cognitively normal [CN], and cognitively impaired [CI]) and executive functioning (EF) in older adults. Second, the authors examined whether baseline participation in lifestyle activities predicted differential 4.5-year stabilities and transitions in cognitive status. Participants (initial N = 501; 53-90 years) were from the Victoria Longitudinal Study. EF was represented by a 1-factor structure. Lifestyle activities were measured in multiple domains of engagement (e.g., cognitive, physical, and social). Two-wave status stability groups included sustained normal aging, transitional early impairment, and chronic impairment. Hierarchical regressions showed that baseline participation in social activities moderated cognitive status differences in EF. CI adults with high (but not low) social engagement performed equivalently to CN adults on EF. Longitudinally, logistic regressions showed that engagement in physical activities was a significant predictor of stability of cognitive status. CI adults who were more engaged in physical activities were more likely to improve in their cognitive status over time than their more sedentary peers. Participation in cognitive activities was a significant predictor of maintenance in a higher cognitive status group. Given that lifestyle engagement plays a detectable role in healthy, normal, and impaired neuropsychological aging, further research in activity-related associations and interventions is recommended.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".