Do changes in lifestyle engagement moderate cognitive decline in normal aging? Evidence from the Victoria Longitudinal Study.
Bibliographic record
Abstract
OBJECTIVE: Do lifestyle activities buffer normal aging-related declines in cognitive performance? The emerging literature will benefit from theoretically broader measurement of both lifestyle activities and cognitive performance, and longer-term longitudinal designs complemented with dynamic statistical analyses. We examine the temporal ordering of changes in lifestyle activities and changes in cognitive neuropsychological performance in older adults. METHOD: We assembled data (n = 952) across a 12-year (5-wave) period from the Victoria Longitudinal Study. Latent change score models were applied to examine whether (and in which temporal order) changes in physical, social, or cognitive lifestyle activities were related to changes in three domains of cognitive performance. RESULTS: Two main results reflect the dynamic coupling among changes in lifestyle activities and cognition. First, reductions in cognitive lifestyle activities were associated with subsequent declines in measures of verbal speed, episodic memory, and semantic memory. Second, poorer cognitive functioning was related to subsequent decrements in lifestyle engagement, especially in social activities. CONCLUSIONS: The results support the dual contention that (a) lifestyle engagement may buffer some of the cognitive changes observed in late life, and (b) persons who are exhibiting poorer cognitive performance may also relinquish some lifestyle activities.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".