Lower average daily step count is associated with poorer executive function and rurality in a veteran cohort
Bibliographic record
Abstract
Abstract Background An active lifestyle is associated with better cognitive health in older individuals. This relationship is understudied in U.S. Veterans, a population at risk of cognitive impairment due to the high prevalence of associated risk factors and comorbidities. Passive monitoring of daily activities provides objective measures of activity that may serve as a sensitive index of day‐to‐day function and dementia risk. Method Participants (age > 57) were enrolled as part of Collaborative Aging Research using Technology (CART), a multi‐site study examining the feasibility of unobtrusive remote sensing and monitoring of physical, cognitive, and health‐related activities. The Veteran cohort consists of volunteers living in largely rural communities in the Pacific Northwest, self‐identified as being a Veteran, and included their cohabitant, if applicable. Daily step counts were acquired using a wrist‐worn device. Baseline one‐month averages were compared with rurality and cognitive function. Result 114 nondemented participants residing in 67 homes underwent neuropsychological assessment and passive monitoring of daily activities (55% male, age 70.7, MOCA 23.4). 70% resided in a rural area (rural‐urban commuting area (RUCA) score > 4 ) and 29% had > three vascular risk factors. Participants with 14+ days of gait activity measured within a one‐month period near baseline cognitive assessments not using a walker in the home were included (n = 107). Average daily step count obtained over an average of 27.7 days was 3,065 (median 2,515) and was greater in large rural towns compared with small‐isolated rural (p = 0.07) or urban (p = 0.04) towns. After adjusting for potential confounders, lower average daily steps were associated with worse performance in executive function, a relationship observed in Veterans in large rural, but not urban or small rural towns. Conclusion In a cohort comprised primarily of rural Veteran’s and their spouses, lower average number of steps per day is associated with poorer executive function and this relationship varied by rurality. Real‐world monitoring of daily activities may identify those at greatest risk of cognitive decline for interventional studies aimed at dementia prevention in older individuals, and is of particular relevance in rural settings, where access to specialty care is limited.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".