Dog Walking as Physical Activity and Multi-Level Correlates of Dog Walking among Adolescents
Bibliographic record
Abstract
PURPOSE: To assess the association of dog ownership and dog walking with moderate to vigorous physical activity (MVPA) and BMI among adolescents and examine correlates of dog walking. METHODS: Participants were adolescents (n=928) from the Baltimore, MD and Seattle, WA regions, ages 12-17 years. Measures included 7 days of accelerometer monitoring, GIS measures of the built environment, and surveys of psychosocial and environment characteristics. Minutes/day of MVPA and BMI were compared among adolescents (1) without a dog (n=441) and those with a dog who (2) do (n=300) or (3) do not (n=184) walk it. Among adolescents with a dog (n=484), models assessed relations between dog walking (days/week) and (1) demographic, (2) psychosocial (physical activity self-efficacy), (3) home environment (electronic device ownership), (4) perceived neighborhood environment, and (5) objective neighborhood walkability factors. All models were adjusted for participant demographic factors. RESULTS: Adolescent dog walkers obtained 4-5 more minutes/day of MVPA than those with dogs but were non-dog-walkers (p=.044) and those who lived in non-dog households (p=.025). Adolescents who walked their dog were more likely to have a more educated parent (p=.056), own more personal electronics (p=.047), have better perceived neighborhood aesthetics (p=.016), live in a more walkable neighborhood (p=.045), and live in a multifamily home (p =0.003) compared to adolescents with dogs who did not walk them. Adolescents’ age interacted with physical activity self-efficacy (p=.045) and perceived neighborhood aesthetics (p<.001), where self-efficacy and aesthetics were positively associated with dog walking among younger adolescents and not associated in older adolescents. Adolescent BMI was not associated with dog walking or dog ownership. CONCLUSIONS: Dog walking was an important contributor to overall MVPA, where dog walkers obtained 7-8% more minutes/day of MVPA than those who did not walk them, and correlates of dog walking were found at multiple levels of influence. Results suggest interventions to increase dog walking at the environmental and psychosocial levels are worthy of evaluation, particularly given that nearly 50% of US households own dogs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".