INVESTIGATING WITHIN-PERSON MEDIATION OF GAIT-COGNITION ASSOCIATIONS IN A WALKING INTERVENTION FOR OLDER ADULTS
Bibliographic record
Abstract
Abstract Objective Between-person associations between gait and cognitive function are well-documented, with select interventions (e.g., exercise) known to benefit both physiological function and cognitive health. Comparatively few studies, however, have investigated links between within-person changes in gait and cognition in the context of exercise interventions, or the mechanisms that might underlie this relationship. Method: The current study employed data from the Healthy Bodies Healthy Minds study -- a longitudinal walking intervention for initially-sedentary older adults (N = 118, 65-87 years). The study employs an intensive repeated-measures design, with physiological and cognitive function indexed at 5 assessments: baseline, 6, 9, 12, and 16 weeks. Gait velocity was assessed using a GAITRite Computerized Walkway, executive functioning was measured via the Groton Maze Learning (GML) Test, with aerobic capacity indexed by converting performance on the Rockport 1-Mile Walk Test to a V02 max proxy. Results Employing longitudinal multilevel mediation (1-1-1) models, we explored the within-person relationship between gait velocity and executive functioning, with particular consideration of aerobic capacity as a mediator. The indirect effect (ab+σajbj=-0.25) was significant, with aerobic capacity mediating 14.5% of the within-person gait-cognition time-varying association. Consistent with hypotheses, increases in gait velocity were associated with increases in aerobic capacity, which, in turn, conferred downstream benefits on GML test performance. Conclusions: These results further our understanding of the gait-cognition relationship, identifying aerobic health as an important within-person mediator in the context of a walking intervention. Key implications include the potential benefits of simple lifestyle interventions for promoting 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".