P3‐214: Longitudinal relations among walking activity, gait speed, and cognitive functioning over a 10‐year period: Findings from the health, aging, and body composition study
Bibliographic record
Abstract
Previous studies have linked both physical activity and functional mobility to future changes in cognition among older adults. However, few studies have assessed all three constructs longitudinally in order to determine their interrelations over time and whether physical activity and functional mobility have independent associations with cognition. This study examined self-reported time spent walking, gait speed, and general cognition as measured by the Modified Mini Mental State Test (3MS) over a ten-year period. 2527 older adults (mean age = 73.5 years at year 1) with complete data at year 1 from the Health ABC study, a biracial cohort were included. Self-reported time spent walking (mins/week), 3MS, and gait speed over a 20-meter walk were assessed at several time points from year 1 to 10. Joint longitudinal models were constructed using Mplus 7.3 and maximum likelihood estimation with robust standard errors. Covariates included clinical site, age, education, race, BMI, sex, smoking and drinking status, and prevalent diabetes, cerebrovascular and cardiovascular disease. A covariate-adjusted latent growth curve model examined the longitudinal relationships among walking activity, gait speed, and cognition (Figure 1). Higher initial gait speed predicted slower decline in cognition and walking activity from year 1 to 10 (ps<.001). Initial cognition and walking activity were not significant cross-domain predictors. After accounting for the predictive effects of the baseline scores, decline in walking, cognition and gait speed were interrelated (ps<.01). In follow-up analyses, the correlation between change in walking and cognition became non-significant after accounting for changes in gait speed; however, the correlation between change in cognition and change in gait speed was unmitigated after controlling for changes in walking. In a follow-up piecewise model (Figure 2), early decline in gait speed predicted later decline in cognition (p<.05), but early decline in cognition did not predict later decline in gait speed. Standardized results of latent growth curve model assessing longitudinal relations among routine walking, gait speed, and general cognitive functioning. To reduce model complexity, observed variables and covariates are not shown. Covariates include education, year 1 age, clinical site, race, gender, smoking and drinking status, and prevalent cerebrovascular disease, cardiovascular disease, and diabetes. All latent variables are regressed on the covariates. Standardized estimates are shown. Predictive associations are shown in red and correlations are shown in purple. To be considered significant, the p value must be less than .05 in both unadjusted and adjusted models. 3MS = Modified mini mental state test. ∗p < .05 ∗∗ p < .01 ∗∗∗ p < .001 Model fit indices: RMSEA = .024 (.022, .026); CFI = .976; TLI = .969. Standardized results from piece-wise latent change regression model. To reduce model complexity, covariates are not shown. Covariates include education, year 1 age, clinical site, race, gender, smoking and drinking status, and prevalent cerebrovascular disease, cardiovascular disease, and diabetes. All latent variables are regressed on the covariates. Standardized estimates are shown. Predictive associations are shown in red and correlations are shown in purple. To be considered significant, the p value must be less than .OS in both unadjusted and adjusted models. 3MS = Modified mini mental state test. ∗p < .05; ∗∗ p < .01; ∗∗∗ p < .001 These results suggest that gait speed is more closely related to changes in cognition than walking activity. Moreover, early changes in gait speed predict later changes in cognition, but not vice versa. Thus, declining gait speed appears to be a leading indicator for cognitive decline 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".