Age-related decline of cognitive resources precedes and explains the decline in physical activity
Bibliographic record
Abstract
Objective: This study aimed to test whether the level of cognitive resources explain the engagement in physical activity across aging and whether the age-related decline of cognitive resources precede the decline in physical activity. Methods: Data from 105,206 adults aged 50 to 90 years from the Survey of Health, Ageing and Retirement in Europe (SHARE) were used in adjusted linear mixed models to examine whether the engagement in moderate physical activity and its evolution across aging was dependent on cognitive resources. Cognitive resources and physical activity were measured 5 times over a 12-year period. Delayed recall, verbal fluency, and the level of education were used as indicators of cognitive resources. The frequency of engagement in moderate physical activity was self-reported. Dynamic structural equation models (SEM) were used to assess the temporal precedence of changes in cognitive resources and physical activity. Results: Results showed that lower cognitive resources were associated with lower levels and steeper decreases in moderate physical activity across aging. Results further revealed a time-ordered effect with a stronger influence of cognitive resources (delayed recall and verbal fluency) on subsequent changes in moderate physical activity than the opposite. Conclusion: These findings suggest that, after age 50, the level of engagement in moderate physical activity and its trajectory depend on the availability of cognitive resources.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".