PREDICTORS OF DRIVING EXPOSURE IN A NATIONWIDE SAMPLE OF OLDER WOMEN FINDINGS FROM THE WHIMS STUDIES
Bibliographic record
Abstract
The high prevalence of cognitive impairment among older adults necessitates identifying modifiable factors that can prevent/delay progression of cognitive decline.Regular physical activity is one modifiable factor of high impact.Prior observational studies used self-reported physical activity measures, which often do not accurately measure frequency, duration and intensity of activity performed.We determined the cross-sectional association between accelerometry-assessed physical activity/sedentary behavior and domain-specific cognition among the Women's Health Initiative-Objective Physical Activity and Cardiovascular Health in Older Women (WHI-OPACH) participants.In total, 660 females (78-94 years) wore an ActiGraph GT3X+ accelerometer continuously over 7 days to estimate minutes of sedentary and moderate-to-vigorous physical activity (MVPA).Memory, language, attention, working memory and executive function domains were measured via a standardized and validated telephone assessment in the WHI-Memory Study.Cognitive test scores were standardized to domain z-scores.Multivariable linear regression models estimated domain-specific associations of quartiles of sedentary time and MVPA, adjusted for age, ethnicity, education, and accelerometer wear-time.Participants in the lowest quartile of sedentary time had a 0.6 and 0.7 standard deviation (SD) higher z-score in attention and working memory compared to those in the upper quartile.Women in the lowest quartile of MVPA had a 0.2 and 0.3 SD lower z-score in attention and working memory compared to those in the upper quartile.Sedentary time/MVPA were not associated with memory, language, or executive function.Limited data on objectively-measured physical activity/sedentary behavior in relation to cognition necessitates using accelerometer data across population-based studies to confirm these findings and explore plausible mechanisms.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".