Physical Activity Over the Life Course and Its Association with Cognitive Performance and Impairment in Old Age
Bibliographic record
Abstract
OBJECTIVE: To determine how physical activity at various ages over the life course is associated with cognitive impairment in late life. DESIGN: Cross-sectional study. SETTING: Four U.S. sites. PARTICIPANTS: Nine thousand three hundred forty-four women aged 65 and older (mean 71.6) who self-reported teenage, age 30, age 50, and late-life physical activity. MEASUREMENTS: Logistic regression was used to determine the association between physical activity status at each age and likelihood of cognitive impairment (modified Mini-Mental State Examination (mMMSE) score >1.5 standard deviations below the mean, mMMSE score</=22). Models were adjusted for age, education, marital status, diabetes mellitus, hypertension, depressive symptoms, smoking, and body mass index. RESULTS: Women who reported being physically active had a lower prevalence of cognitive impairment in late life than women who were inactive at each time (teenage: 8.5% vs 16.7%, adjusted odds ratio (AOR)=0.65, 95% confidence interval (CI)=0.53-0.80; age 30: 8.9% vs 12.0%, AOR=0.80, 95% CI=0.67-0.96); age 50: 8.5% vs 13.1%, AOR=0.71, 95% CI=0.59-0.85; old age: 8.2% vs 15.9%, AOR=0.74, 95% CI=0.61-0.91). When the four times were analyzed together, teenage physical activity was most strongly associated with lower odds of late-life cognitive impairment (OR=0.73, 95% CI=0.58-0.92). However, women who were physically inactive as teenagers and became active in later life had lower risk than those who remained inactive. CONCLUSIONS: Women who reported being physically active at any point over the life course, especially as teenagers, had a lower likelihood of cognitive impairment in late life. Interventions should promote physical activity early in life and throughout the life course.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".