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Record W2944053927 · doi:10.1016/j.jshs.2019.05.004

Differences in transportation and leisure physical activity by neighborhood design controlling for residential choice

2019· article· en· W2944053927 on OpenAlexafffundabout
Gavin R. McCormack, Mohammad Javad Koohsari, Koichiro Oka, Christine M. Friedenreich, Anita Blackstaffe, Francisco Uribe Alaniz, Brenlea Farkas

Bibliographic record

VenueJournal of sport and health science/Journal of Sport and Health Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsAlberta Health ServicesUniversity of Calgary
FundersCentrum för Miljövetenskaplig Forskning, Umeå UniversitetJapan Society for the Promotion of ScienceCanadian Institutes of Health ResearchMinistry of Education, Culture, Sports, Science and TechnologyFlorence University of the Arts
KeywordsSocioeconomic statusRecreationWalkabilityDestinationsMarital statusOddsDemographyOdds ratioQuality of life (healthcare)PsychologyGerontologyGeographyEnvironmental healthPhysical activityMedicineLogistic regressionSociologyTourismPopulationPhysical therapy

Abstract

fetched live from OpenAlex

Cross-sectional studies provide useful insight about the associations between the built environment and physical activity (PA), particularly when reasons for neighborhood choice are considered. Our study analyzed the relationship between levels of weekly transportation and leisure PA among 3 neighborhood designs, statistically adjusting for sociodemographic characteristics and reasons for neighborhood choice. A stratified random sample of adults (age ≥20 years) living in Calgary (Canada) neighborhoods with different neighborhood designs (grid, warped-grid, and curvilinear) and socioeconomic status completed a self-administered questionnaire capturing PA, sociodemographic characteristics, and reasons for neighborhood choice (response rate = 10.1%; n = 1023). Generalized linear models estimated associations between neighborhood design and transportation and leisure PA outcomes (participation (any vs. none) and volume (metabolic equivalent: h/week)), adjusting for neighborhood socioeconomic status, sociodemographic characteristics (gender, age, ethnicity, education, household income, marital status, children, vehicle access, dog ownership, and injury), and reasons for neighborhood choice (e.g., proximity and quality of recreational and utilitarian destinations, proximity to work, highway access, aesthetics, and sense of community). Overall, 854 participants had resided in their neighborhood for at least 12 months and provided complete data. Compared with those living in curvilinear neighborhoods, grid neighborhood participants had greater odds (p < 0.05) of participating in any transportation walking (odds ratio (OR) = 2.17), transportation and leisure cycling (OR = 2.39 and OR = 1.70), active transportation (OR = 2.16), and high-intensity leisure PA (≥6 metabolic equivalent; OR = 1.74), respectively. There were no neighborhood differences in the volume of any transportation or leisure PA undertaken. Adjustment for neighborhood selection had minimal impact on the statistical or practical importance of model estimates. Neighborhood design is associated with PA patterns in adults, independent of reasons for neighborhood choice and sociodemographic factors.

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)0.014
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.382
Teacher spread0.320 · 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 designObservational
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

Citations17
Published2019
Admission routes3
Has abstractyes

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