THE VARIATION IN DAILY HABITUAL PHYSICAL ACTIVITY LINKS TO GLOBAL COGNITIVE FUNCTION IN OLDER ADULTS
Bibliographic record
Abstract
Abstract Being physically active is critical to the maintenance of physical and cognitive function into old age. Wearable sensors with the ability to monitor step counts have become popular, making it easier for individuals to track their physical activity levels from day to day. While average daily step count has traditionally been used to quantify physical activity, emerging evidence suggests that the magnitude of day-to-day variation in physical activity or other clinical outcomes may provide additional insight into health. This study aimed to investigate the relationship between the characteristics of habitual physical activity and global cognitive function in older adults. A secondary analysis from a pilot randomized controlled trial was conducted. Thirty-two older adults (79±7 y/o, 29 women) completed the Montreal Cognitive Assessment (MoCA) of global cognitive function at baseline (Total MoCA score: 25.0±2.9). Physical activity was tracked using a Fitbit wrist-worn activity tracker for two consecutive weeks with all participants having 10 complete days. Daily step count mean and standard deviation were calculated to quantify the daily average (4435.12±2768.88 steps) and daily variation (1671.05±1134.03 steps) in physical activity, respectively. After adjusting for age and sex, participants with greater daily variation in step counts exhibited better global cognitive function (r=0.52, p=0.02). In contrast, average daily steps did not significantly correlate with MoCA performance. Neither of these physical activity outcomes were significantly linked to any MoCA subscore. These results suggest that monitoring day-to-day step count variation may serve as a particularly sensitive indicator of global cognitive health in older adults.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".