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Record W4312460283 · doi:10.1079/hai.2018.0011

An Examination of the Possible Physical Activity and Short-Term Health Benefits Associated With Dog Walking

2018· article· en· W4312460283 on OpenAlexaffabout
Nikolina M. Duvall Antonacopoulos, Timothy A. Pychyl

Bibliographic record

VenueHuman-animal interaction bulletin · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsMoodHealth benefitsPsychologyPhysical healthPhysical therapyGerontologyMedicinePhysical medicine and rehabilitationMental healthSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract The purpose of this study was to examine whether dog walking is beneficial for dog owners’ health by providing sufficient physical activity for health benefits and improving their psychological health immediately after dog walking. The sample was comprised of 61 dog walkers who wore an accelerometer for one week on all of their dog walks and completed seven visual analogue scales assessing their psychological health before and after each of their dog walks. Slightly more than half of the time spent dog walking was at the moderate- to vigorous-intensity level recommended for health benefits. Furthermore, through dog walking alone, approximately two in five dog walkers met Canada’s 150-minute physical activity guideline for health benefits. Analyses comparing dog walkers’ psychological health before and after their dog walks revealed an improvement on six out of seven psychological health measures. Dog walkers felt less stressed after walking their dog and experienced an increase in their energy, self-esteem, social life satisfaction and overall life satisfaction as well as a more positive mood. Taken together, these findings provide objective evidence that dog walking is a viable means of attaining the physical activity needed for health benefits and highlight the need to further explore the short-term health benefits of dog walking.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.639

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 head0.033
GPT teacher head0.366
Teacher spread0.333 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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