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The Neighborhood Energy Balance Equation: Does Neighborhood Food Retail Environment plus Physical Activity Environment = Obesity? The CARDIA Study
Journal article   Open access   Peer reviewed

The Neighborhood Energy Balance Equation: Does Neighborhood Food Retail Environment plus Physical Activity Environment = Obesity? The CARDIA Study

Janne Boone-Heinonen, Ana V. Diez-Roux, David C. Goff, Catherine M. Loria, Catarina I. Kiefe, Barry M. Popkin and Penny Gordon-Larsen
PloS one, v 8(12)
27 Dec 2013
DOI: https://doi.org/10.1371/journal.pone.0085141
PMID: 24386458
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0085141&type=printableView
Published, Version of Record (VoR) Open
url
https://doi.org/10.1371/journal.pone.0085141View
Published, Version of Record (VoR) Open

Abstract

Multidisciplinary Sciences Science & Technology Science & Technology - Other Topics
Background: Recent obesity prevention initiatives focus on healthy neighborhood design, but most research examines neighborhood food retail and physical activity (PA) environments in isolation. We estimated joint, interactive, and cumulative impacts of neighborhood food retail and PA environment characteristics on body mass index (BMI) throughout early adulthood. Methods and Findings: We used cohort data from the Coronary Artery Risk Development in Young Adults (CARDIA) Study [n=4,092; Year 7 (24-42 years, 1992-1993) followed over 5 exams through Year 25 (2010-2011); 12,921 person-exam observations], with linked time-varying geographic information system-derived neighborhood environment measures. Using regression with fixed effects for individuals, we modeled time-lagged BMI as a function of food and PA resource density (counts per population) and neighborhood development intensity (a composite density score). We controlled for neighborhood poverty, individual-level sociodemographics, and BMI in the prior exam; and included significant interactions between neighborhood measures and by sex. Using model coefficients, we simulated BMI reductions in response to single and combined neighborhood improvements. Simulated increase in supermarket density (from 25th to 75th percentile) predicted inter-exam reduction in BMI of 0.09 kg/m(2) [estimate (95% CI): -0.09 (-0.16, -0.02)]. Increasing commercial PA facility density predicted BMI reductions up to 0.22 kg/m(2) in men, with variation across other neighborhood features [estimate (95% CI) range: -0.14 (-0.29, 0.01) to -0.22 (-0.37, -0.08)]. Simultaneous increases in supermarket and commercial PA facility density predicted inter-exam BMI reductions up to 0.31 kg/m(2) in men [estimate (95% CI) range: -0.23 (-0.39, -0.06) to -0.31 (-0.47, -0.15)] but not women. Reduced fast food restaurant and convenience store density and increased public PA facility density and neighborhood development intensity did not predict reductions in BMI. Conclusions: Findings suggest that improvements in neighborhood food retail or PA environments may accumulate to reduce BMI, but some neighborhood changes may be less beneficial to women.

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
Web of Science research areas
Public, Environmental & Occupational Health
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