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Record W2311762804 · doi:10.2105/ajph.2016.303096

Neighborhood Walkability and Body Mass Index Trajectories: Longitudinal Study of Canadians

2016· article· en· W2311762804 on OpenAlexafffundabout
Rania Wasfi, Kaberi Dasgupta, Heather Orpana, Nancy A. Ross

Bibliographic record

VenueAmerican Journal of Public Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsWalkabilityBody mass indexDemographyQuartileConfidence intervalMedicinePopulationNational Health and Nutrition Examination SurveyOverweightRegression analysisGerontologyStatisticsEnvironmental healthPhysical activityMathematicsPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the impact of neighborhood walkability on body mass index (BMI) trajectories of urban Canadians. METHODS: Data are from Canada's National Population Health Survey (n = 2935; biannual assessments 1994-2006). We measured walkability with the Walk Score. We modeled body mass index (BMI, defined as weight in kilograms divided by the square of height in meters [kg/m(2)]) trajectories as a function of Walk Score and sociodemographic and behavioral covariates with growth curve models and fixed-effects regression models. RESULTS: In men, BMI increased annually by an average of 0.13 kg/m(2) (95% confidence interval [CI] = 0.11, 0.14) over the 12 years of follow-up. Moving to a high-walkable neighborhood (2 or more Walk Score quartiles higher) decreased BMI trajectories for men by approximately 1 kg/m(2) (95% CI = -1.16, -0.17). Moving to a low-walkable neighborhood increased BMI for men by approximately 0.45 kg/m(2) (95% CI = 0.01, 0.89). There was no detectable influence of neighborhood walkability on body weight for women. CONCLUSIONS: Our study of a large sample of urban Canadians followed for 12 years confirms that neighborhood walkability influences BMI trajectories for men, and may be influential in curtailing male age-related weight gain.

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)0.004
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 score0.056
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.043
GPT teacher head0.340
Teacher spread0.297 · 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

Citations73
Published2016
Admission routes3
Has abstractyes

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