Longitudinal Associations Between Walking Frequency and Depressive Symptoms in Older Adults: Results from the VoisiNuAge Study
Bibliographic record
Abstract
BACKGROUND: Cross-sectional studies show that walking is associated with depression among older adults, but longitudinal associations have rarely been examined. The aim of this study was to investigate longitudinal associations between walking frequency and depressive symptoms in older adults to determine which variable is the stronger prospective predictor of the other. DESIGN: Longitudinal; four repeated measures over 5 years. SETTING: Population-based sample of urban-dwelling older adults living in the Montreal metropolitan area. PARTICIPANTS: Participants from the VoisiNuAge study aged 68 to 84 (N=498). MAIN EXPOSURES: depressive symptoms (Geriatric Depression Scale) and number of walking days in previous week (Physical Activity Scale for the Elderly). Covariates: age, education, and number of chronic illnesses. Cross-lagged panel analyses were performed in the entire sample and in sex-stratified subsamples. RESULTS: Depressive symptoms predicted walking frequency at subsequent time points (and more precisely, higher depressive symptoms were related to fewer walking days), but walking frequency did not predict depressive symptoms at subsequent time points. Stratified analyses revealed that prospective associations were statistically significant in women but not men. CONCLUSION: The longitudinal association between walking frequency and depressive symptoms is one in which depressive symptoms predict reduced walking frequency later. Higher depressive symptoms are more likely a cause of reduced walking because of time precedence than vice versa. Future research on longitudinal relationships between meeting physical activity recommendations and depression are warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".