Acquiring a Dog and Walking It: A Preliminary Examination of the Possible Physical Activity and Health Benefits
Bibliographic record
Abstract
Abstract In light of the detrimental health consequences associated with insufficient physical activity, there is growing concern about the low percentage of adults who are sufficiently active. Given that some researchers have recommended that acquiring a dog should be promoted as a means of increasing physical activity, this longitudinal study examined whether acquiring a dog and walking it leads to an increase in physical activity. Results revealed that participants in the acquired-dog group ( n = 17) increased their moderate- to vigorous-intensity physical activity in 10-minute bouts from baseline to 8 months, while there was no change in the control group ( n = 28). The present study also examined whether, if dog owners become more physically active, this results in health benefits. Although individuals in the acquired-dog group increased their physical activity, they did not experience any improvements in their physical or psychological health over the course of the study relative to the control group. However, it is noteworthy that the majority of the acquired-dog group perceived that acquiring a dog had positively affected their health. Taken together, these findings suggest that acquiring a dog and walking it merits further attention as a way of increasing physical activity and there is a need for additional research on the possible physical activity-related health benefits from dog walking.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".