Dog ownership and adults’ objectively-assessed sedentary behaviour and physical activity
Bibliographic record
Abstract
Evidence suggests a positive effect of dog ownership on physical activity. However, most previous studies used self-reported physical activity measures. Additionally, it is unknown whether owning a dog is associated with adults' sedentary behaviour, an emerging health risk factor. In this study, physical activity and sedentary behaviour were objectively collected between 2013 and 2015 from 693 residents (aged 40-64 years) living in Japan using accelerometer devices. Multivariable linear regression models were used, adjusted for several covariates. The means of total sedentary time and the number of long (≥ 30 min) sedentary bouts were 26.29 min/day (95% CI - 47.85, - 4.72) and 0.41 times/day (95% CI - 0.72, - 0.10) lower for those who owned a dog compared to those not owning a dog, respectively. Compared with non-owners, dog-owners had significantly higher means of the number of sedentary breaks (95% CI 0.14, 1.22), and light-intensity physical activity (95% CI 1.31, 37.51). No significant differences in duration of long (≥ 30 min) sedentary bouts, moderate, vigorous, and moderate-to-vigorous-intensity physical activity were observed between dog-owners and non-owners. A novel finding of this study is that owning a dog was associated with several types of adults' sedentary behaviours but not medium-to-high-intensity physical activities. These findings provide new insights for dog-based behavioural health interventions on the benefits of dog ownership for reducing sedentary behaviour.
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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 | 0.001 |
| 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".