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Record W4293104216 · doi:10.1371/journal.pone.0272299

Jog with your dog: Dog owner exercise routines predict dog exercise routines and perception of ideal body weight

2022· article· en· W4293104216 on OpenAlexaffabout
Sydney Banton, Mike von Massow, Júlia Guazzelli Pezzali, Adronie Verbrugghe, Anna K. Shoveller

Bibliographic record

VenuePLoS ONE · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOverweightMultinomial logistic regressionMedicineObesityDemographyLogistic regressionBody weightGerontologyPsychologyStatisticsInternal medicine

Abstract

fetched live from OpenAlex

Canine obesity is becoming an increasingly prevalent concern among companion animal veterinarians and professionals alike. A number of sociodemographic, dietary, and exercise related variables have been shown to be predictive of a dog's bodyweight, however, all previous surveys designed to address these variables have been focussed on only one area of the world at a time. The objective of this survey was to investigate how an owner's exercise routine influences their dog's exercise routine and which of the owner's dietary and exercise habits influence their perception of their dog's body weight. The survey included respondents across France, Germany, the United Kingdom, Canada and the United States. The survey was distributed online via Qualtrics (Qualtrics XM, Utah, USA) and a total of 3,298 responses were collected, equally distributed across country and between sexes. Comparison of column proportions and multinomial logistic regression were performed in SPSS Statistics (Version 26, IBM Corp, North Castle, New York, USA). Respondents from Germany were more likely to exercise their dog for a longer amount of time, rank the importance of exercise as extremely important, report that their dog is an ideal body weight, and were less likely to report that someone (including a veterinarian) had told them their dog was overweight. Results from linear regression revealed that those who had been told their dog was overweight, those who restrict their dog's food intake to control weight, those who select a weight control diet and those who give their dog more other foods (treats, table scraps, fruits/vegetables) on a daily basis were all less likely to believe that their dog is an ideal body weight. In contrast, only those who reported doing more vigorous exercise themselves or those who reported that their dog performs vigorous exercise were more likely to believe that their dog is an ideal body weight. The results highlight owner's perceptions of healthy weight and the role of nutrition and exercise. Owner's intentions and attitudes towards the value of exercise and promoting an ideal body weight in their dog should be explored, but may require a One Health approach to ensure successful weight management among both dogs and their owners.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.278
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2022
Admission routes2
Has abstractyes

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