Longitudinal Associations Between Walking Speed and Amount of Self-reported Time Spent Walking Over a 9-Year Period in Older Women and Men
Bibliographic record
Abstract
Background: Age-related limitations in mobility and decreased physical activity appear to be linked cross-sectionally; however, large-scale, longitudinal analyses of the associations between age-related changes in mobility and engagement in physical activity are lacking. In this longitudinal study, we hypothesized that early mobility limitations would contribute to later decreases in physical activity to a larger degree than the reciprocal association of early decreases in physical activity to later mobility limitations. Methods: Participants were 2,876 initially well-functioning community-dwelling older adults (aged 70-79 years at baseline; 52% women; 39% black) studied over a 9-year period. Usual walking speed and self-reported physical activity (based on minutes per week of walking) were assessed at Years 0 (ie, baseline), 4, and 9. A cross-lagged, longitudinal model assessed the bidirectional associations between walking speed and physical activity over time. Results: Early change in walking speed between Years 0 and 4 predicted late change in physical activity between Years 4 and 9 (β = .13 p < .001). However, early change in physical activity did not predict late change in walking speed (β = -.01, p = .79). The difference between these two predictive associations was highly significant (p < .001). Associations were independent of baseline demographic and physical health variables, as well as longitudinal changes in grip and quadriceps strength. Conclusions: The results suggest declining walking speed as a precursor to declining engagement in physical activity, but the converse association was not evident. Improving walking speed may be a method to increase physical activity among elderly individuals.
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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 | 0.001 | 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".