Overweight dogs exercise less frequently and for shorter periods: results of a large online survey of dog owners from the UK
Bibliographic record
Abstract
Abstract Canine obesity is now the number one health concern in dogs worldwide. Regular physical activity can improve health, and owners are advised to exercise their dogs on a regular basis. However, limited information exists about associations between overweight status of dogs and walking activity. An online survey was conducted between June and August in 2014, coinciding with the broadcast of a national UK television programme, exploring dog behaviour. Information gathered included signalment, overweight status, and owner-reported information on duration and frequency of dog walking. The University of Liverpool Ethics Committee approved the project, and owners consented to data use. Simple and multiple logistic regression analyses were used to determine associations between overweight status and dog walking activity. Data were available from 11 154 adult dogs, and 1801 (16·1 %) of these were reported as overweight by their owners. Dogs reported to be overweight dogs were more likely to be neutered (P < 0·0001) and older (P < 0·0001). Various breeds were over-represented including beagle, Cavalier King Charles spaniel, golden retriever, Labrador retriever and pug (P < 0·0001 for all). Both frequency and duration of walking were negatively associated with overweight status (P < 0·0001 for both). On multiple regression analysis, duration and frequency were independently and negatively associated with the odds of being overweight, along with a range of other factors including age, neuter status and breed. This study has identified associations between overweight status and exercise. In the future, studies should determine the reason for this association, and whether changes in walking activity can influence weight status.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".