Sociodemographic determinants of mobility decline among community-dwelling older adults: findings from the Canadian longitudinal study on ageing
Bibliographic record
Abstract
BACKGROUND: Mobility is fundamental to healthy ageing and quality of life. Mobility decline has been associated with functional impairment, falls, disability, dependency, and death among older adults. We explored the sociodemographic determinants of mobility decline among community-dwelling older Canadians. METHODS: This study was a secondary analysis of a six-year follow-up of the Canadian Longitudinal Study on Ageing (CLSA). Our analysis was based on 3882 community-dwelling older adults 65 years or older whose mobility was measured using timed-up and go (TUG) and 4-meter walk (4MWT) tests at baseline and follow-ups 1 and 2 after three- and six-year intervals, respectively. We analysed the cross-sectional and longitudinal association, main and interaction effects of the participants' sociodemographic characteristics on mobility decline using chi-square, Pearson's correlation, mixed-design repeated measures ANOVA, and bivariate and multivariate linear regression tests. RESULTS: At baseline, 52% of the participants were female, 70.4% were married, and the average age was 68.82 ± 2.78 years. Mean TUG and 4MWT scores were 9.59 ± 1.98 s and 4.29 ± 0.95 s, respectively. There was a strong positive longitudinal correlation between TUG and 4MWT (r = 0.65 to 0.75, p < 0.001), indicating concurrent validity of 4MWT. The multivariate linear regression (for TUG) showed that older age (β = 0.088, p < 0.001), being a female (β=-0.035, p < 0.001), retired (β=-0.058, p < 0.001), Canadian born (β=-0.046, p < 0.001), non-Caucasian (β=-0.063, p < 0.001), tenant (β = 0.050, p < 0.001), having no spouse/partner (β=-0.057, p < 0.001), household income of $50,000-$99,999 (β = 0.039, p < 0.001), wealth/investment lower than $50,000 (β=-0.089, p < 0.001), lower social status (β=-0.018,p = 0.025), secondary education and below (β = 0.043, p < 0.001), and living in certain provinces compared to others, were significant predictors of a six-year mobility decline. CONCLUSION: Our study underscored the impact of modifiable and non-modifiable sociodemographic determinants of mobility trajectory. There is a need for nuanced ageing policies that support mobility in older adults, considering sociodemographic inequalities through equitable resource distribution, including people of lower socioeconomic backgrounds.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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".