The Association of Regular Dog Walking With Mobility, Falls, and Fear of Falling in Later Life
Bibliographic record
Abstract
BACKGROUND: It has been suggested that dog walking may protect against falls and mobility problems in later life, but little work to date has examined this. The aim of this study was to assess if regular dog walking was associated with reduced likelihood of falls, fear of falling, and mobility problems in a large cohort of community-dwelling older people. METHODS: Participants ≥60 years at Wave 5 of The Irish Longitudinal Study on Ageing were included. Regular dog walking was ≥4 days/week by self-report. The control group consisted of participants who did not own a dog or who did not regularly walk their dog. Falls and fear of falling were self-reported. Mobility was measured with Timed-Up-and-Go (TUG). Logistic regression models assessed associations between regular dog walking and outcomes of interest. RESULTS: Regular dog walkers (629/4 161, 15%) had a significantly faster TUG (10.3 (10.1-10.5) versus 11.7 (11.1-12.2) seconds, t = 2.11, p = .0343) and a lower likelihood of unexplained falls (OR 0.60 (0.38-0.96; p = .034), fear of falling (OR 0.79 (95% CI 0.64-.98); p = .032), and mobility problems (0.64 (0.45-0.91); p = .015) in fully adjusted models. Regular dog walking was also associated with a significantly lower likelihood of fear of falling (OR 0.79 (95% CI 0.64-0.98); p = .032). DISCUSSION: This study demonstrates a significantly lower prevalence of mobility impairment, falls, and fear of falling among community-dwelling older people who regularly walk their dogs. Although longitudinal and dedicated studies are required, older people should be encouraged to continue regular dog walking where feasible, as it may help in maintaining mobility and reducing falls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".