Association between changes in habitual stepping activity and cognition in older adults
Bibliographic record
Abstract
Advancing age is associated with declines in cognitive function. Although physical activity is thought to protect against this decline, it is unclear how a short-term uptake in daily steps or a decline in day-to-day step variability may contribute to cognition among older adults. We tested associations between changes in step counts, day-to-day step variability and executive cognitive functions among older adults taking part in a physical activity intervention. Thirty-seven older adults (33 females; 71.4 ± 6.3 years) completed a 10-week personalized physical activity intervention. Participants wore a Fitbit to measure daily step counts throughout the study. They also completed a computerized Stroop task before and after the intervention. Average step counts and step count variability via average-real-variability (ARV) were determined. Compared to pre-intervention, step counts increased (p < 0.001) and step variability decreased post-intervention (p = 0.04). Models describing the changes in step counts and ARV over the 10-weeks were cubic (both, p < 0.04). Reaction times during the simple (p = 0.002) and switching (p = 0.04) conditions were faster post-intervention. Change in step variability was positively associated with the change in reaction time for the switching condition (β = 0.029, p = 0.002). On average, a reduction in day-to-day step variability was associated with improvements in cognitive flexibility.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".