Objective
This study aimed to identify the association between the food environment and obesity.
Methods
BMI and waist circumference (WC) were measured in 8,076 participants from three cities. The number of fast‐food restaurants, full‐service restaurants, bars/pubs, markets, and liquor stores within 500 m of each participant was documented. The association between the food environment (ratio of fast‐food to full‐service restaurants, ratio of bars/pubs to liquor stores, and presence of markets) with obesity (BMI ≥ 30 kg/m2) and abdominal obesity (WC ≥ 102 cm for males or WC ≥ 88 cm for females) was investigated, adjusted for age, sex, education level, neighborhood deprivation, neighborhood type, and total hours per week of walking and taking into account city‐level clustering.
Results
The ratios of fast‐food to full‐service restaurants and of bars/pubs to liquor stores were positively associated with obesity (OR = 1.05 [CI: 1.02‐1.09] and OR = 1.08 [CI: 1.04‐1.13], respectively). The ratio of bars/pubs to liquor stores was positively associated with abdominal obesity (OR = 1.10 [CI: 1.05‐1.14]). There was no association between markets and either obesity or abdominal obesity.
Conclusions
Features of the food environment have varying associations with obesity. These features have an additive effect, and future studies should not focus on only one feature in isolation.
Running is one of the most popular forms of physical activity. To date, no literature explores association between the features of the built environment and running. A limited amount of literature uses walkability as a proxy for runnability, thereby misjudging the differing goals of walking and running: leisure, sport or commute. We create a rough runnability index using an affordance-based framework as a conceptual and methodological attempt to quantify features of the built environment that facilitate or hinder running as a form of leisure or sport activity. Three indices are created in the City of Surrey, British Columbia, Canada using pixelated edges. We find that areas in the downtown core and near high traffic routes show low safety and general runnability, whereas areas near parks and in low traffic, residential areas show higher safety for runners. Representing runnability using pixelated edges allows for sub-block level analysis of runnability as experienced by runners.
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