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

被引:52
|
作者
Boone-Heinonen, Janne [1 ]
Diez-Roux, Ana V. [2 ]
Goff, David C. [3 ]
Loria, Catherine M. [4 ]
Kiefe, Catarina I. [5 ]
Popkin, Barry M. [6 ]
Gordon-Larsen, Penny [6 ]
机构
[1] Oregon Hlth & Sci Univ, Dept Publ Hlth & Prevent Med, Portland, OR 97201 USA
[2] Univ Michigan, Dept Epidemiol, Ann Arbor, MI 48109 USA
[3] Wake Forest Univ, Bowman Gray Sch Med, Dept Epidemiol & Prevent, Winston Salem, NC USA
[4] NHLBI, Div Epidemiol & Clin Applicat, Bethesda, MD 20892 USA
[5] Univ Massachusetts, Sch Med, Dept Quantitat Hlth Sci, Worcester, MA USA
[6] Univ N Carolina, Carolina Populat Ctr, Dept Nutr, Chapel Hill, NC 27599 USA
来源
PLOS ONE | 2013年 / 8卷 / 12期
基金
美国国家卫生研究院;
关键词
BODY-MASS INDEX; ARTERY RISK DEVELOPMENT; BUILT ENVIRONMENT; LONGITUDINAL-ASSOCIATIONS; HEALTHY FOODS; DIET QUALITY; URBAN SPRAWL; LAND-USE; WEIGHT; DENSITY;
D O I
10.1371/journal.pone.0085141
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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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页数:11
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