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Record W2400708766 · doi:10.1001/jama.2016.5898

Association of Neighborhood Walkability With Change in Overweight, Obesity, and Diabetes

2016· article· en· W2400708766 on OpenAlexafffundabout
Maria I. Creatore, Richard H. Glazier, Rahim Moineddin, Ghazal S. Fazli, Ashley Johns, Peter Gozdyra, Flora I. Matheson, Vered Kaufman‐Shriqui, Laura C. Rosella, Douglas G. Manuel, Gillian L. Booth

Bibliographic record

VenueJAMA · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsOttawa HospitalInstitute for Clinical Evaluative SciencesUniversity of TorontoPublic Health OntarioSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsWalkabilityOverweightMedicineObesityDemographyEnvironmental healthBody mass indexGerontologyPhysical activityInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

IMPORTANCE: Rates of obesity and diabetes have increased substantially in recent decades; however, the potential role of the built environment in mitigating these trends is unclear. OBJECTIVE: To examine whether walkable urban neighborhoods are associated with a slower increase in overweight, obesity, and diabetes than less walkable ones. DESIGN, SETTING, AND PARTICIPANTS: Time-series analysis (2001-2012) using annual provincial health care (N ≈ 3 million per year) and biennial Canadian Community Health Survey (N ≈ 5500 per cycle) data for adults (30-64 years) living in Southern Ontario cities. EXPOSURES: Neighborhood walkability derived from a validated index, with standardized scores ranging from 0 to 100, with higher scores indicating more walkability. Neighborhoods were ranked and classified into quintiles from lowest (quintile 1) to highest (quintile 5) walkability. MAIN OUTCOMES AND MEASURES: Annual prevalence of overweight, obesity, and diabetes incidence, adjusted for age, sex, area income, and ethnicity. RESULTS: Among the 8777 neighborhoods included in this study, the median walkability index was 16.8, ranging from 10.1 in quintile 1 to 35.2 in quintile 5. Resident characteristics were generally similar across neighborhoods; however, poverty rates were higher in high- vs low-walkability areas. In 2001, the adjusted prevalence of overweight and obesity was lower in quintile 5 vs quintile 1 (43.3% vs 53.5%; P < .001). Between 2001 and 2012, the prevalence increased in less walkable neighborhoods (absolute change, 5.4% [95% CI, 2.1%-8.8%] in quintile 1, 6.7% [95% CI, 2.3%-11.1%] in quintile 2, and 9.2% [95% CI, 6.2%-12.1%] in quintile 3). The prevalence of overweight and obesity did not significantly change in areas of higher walkability (2.8% [95% CI, -1.4% to 7.0%] in quintile 4 and 2.1% [95% CI, -1.4% to 5.5%] in quintile 5). In 2001, the adjusted diabetes incidence was lower in quintile 5 than other quintiles and declined by 2012 from 7.7 to 6.2 per 1000 persons in quintile 5 (absolute change, -1.5 [95% CI, -2.6 to -0.4]) and 8.7 to 7.6 in quintile 4 (absolute change, -1.1 [95% CI, -2.2 to -0.05]). In contrast, diabetes incidence did not change significantly in less walkable areas (change, -0.65 in quintile 1 [95% CI, -1.65 to 0.39], -0.5 in quintile 2 [95% CI, -1.5 to 0.5], and -0.9 in quintile 3 [95% CI, -1.9 to 0.02]). Rates of walking or cycling and public transit use were significantly higher and that of car use lower in quintile 5 vs quintile 1 at each time point, although daily walking and cycling frequencies increased only modestly from 2001 to 2011 in highly walkable areas. Leisure-time physical activity, diet, and smoking patterns did not vary by walkability (P > .05 for quintile 1 vs quintile 5 for each outcome) and were relatively stable over time. CONCLUSIONS AND RELEVANCE: In Ontario, Canada, higher neighborhood walkability was associated with decreased prevalence of overweight and obesity and decreased incidence of diabetes between 2001 and 2012. However, the ecologic nature of these findings and the lack of evidence that more walkable urban neighborhood design was associated with increased physical activity suggest that further research is necessary to assess whether the observed associations are causal.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.229
Threshold uncertainty score0.456

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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".

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Citations373
Published2016
Admission routes3
Has abstractyes

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