Associations Between Potentially Modifiable and Nonmodifiable Risk Factors and Gait Speed in Middle- and Older-Aged Adults: Results From the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Gait speed is a strong predictor of morbidity and mortality in older adults. Understanding the factors associated with gait speed and the associated adverse outcomes will inform mitigation strategies. We assessed the potentially modifiable and nonmodifiable factors associated with gait speed in a large national cohort of middle and older-aged Canadian adults. METHODS: We examined cross-sectional baseline data from the Canadian Longitudinal Study on Aging (CLSA) Comprehensive cohort. The study sample included 20 201 community-dwelling adults aged 45-85 years. The associations between sociodemographic and anthropometric factors, chronic conditions, and cognitive, clinical, and lifestyle factors and 4-m usual gait speed (m/s) were estimated using hierarchical multivariable linear regression. RESULTS: The coefficient of determination, R 2, of the final regression model was 19.7%, with 12.9% of gait speed variability explained by sociodemographic and anthropometric factors, and nonmodifiable chronic conditions and 6.8% explained by potentially modifiable chronic conditions, cognitive, clinical, and lifestyle factors. Potentially modifiable factors significantly associated with gait speed include cardiovascular conditions (unstandardized regression coefficient, B = -0.018; p < .001), stroke (B = -0.025; p = .003), hypertension (B = -0.007; p = .026), serum Vitamin D (B = 0.004; p < .001), C-reactive protein (B = -0.005; p = .005), depressive symptoms (B = -0.003; p < .001), physical activity (B = 0.0001; p < .001), grip strength (B = 0.003; p < .001), current smoking (B = -0.026; p < .001), severe obesity (B = -0.086; p < .001), and chronic pain (B = -0.008; p = .018). CONCLUSIONS: The correlates of gait speed in adulthood are multifactorial, with many being potentially modifiable through interventions and education. Our results provide a life-course-perspective framework for future longitudinal assessments risk factors affecting gait speed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".