Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2009 Aug 21.
Published in final edited form as: Am J Health Promot. 2009;23(3):203–209. doi: 10.4278/ajhp.071214133

Obesity and the Built Environment: Does the Density of Neighborhood Fast-Food Outlets Matter?

Fuzhong Li 1, Peter Harmer 2, Bradley J Cardinal 3, Mark Bosworth 4, Deb Johnson-Shelton 5
PMCID: PMC2730045  NIHMSID: NIHMS138445  PMID: 19149426

Abstract

Purpose

To examine variation in obesity among older adults relative to the joint influences of density of neighborhood fast-food outlets and residents' behavioral, psychosocial, and sociodemographic characteristics.

Design

Cross-sectional and multilevel design.

Setting

Census block groups, used as a proxy for neighborhoods, within the metropolitan region's Urban Growth Boundary in Portland, Oregon.

Subjects

A total of 1,221 residents (mean age=65 years old) recruited randomly from 120 neighborhoods (48% response rate).

Measures

A Geographic Information System-based measure of fast-food restaurant density across 120 neighborhoods was created. Residents within the sampled neighborhoods were assessed with respect to their body mass index (BMI), frequency of visits to local fast-food restaurants, fried food consumption, levels of physical activity, self-efficacy of eating fruits and vegetables, household income, and race/ethnicity.

Analyses

Multilevel logistic regression analyses.

Results

Significant associations were found between resident-level individual characteristics and the likelihood of being obese (BMI≥30) for neighborhoods with a high-density of fast-food restaurants in comparison to those with a low density: odds ratios [OR] for obesity, 95% confidence interval [CI] were: 1.878 (CI=1.006-3.496) for weekly visits to local fast-food restaurants; 1.792 (CI=1.006-3.190) for not meeting physical activity recommendations; 1.212 (CI=1.057-1.391) for low confidence in eating healthy food; and 8.057 (CI=1.705-38.086) for non-Hispanic black residents.

Conclusion

Increased density of neighborhood fast-food outlets was associated with unhealthy lifestyles, poorer psychosocial profiles, and increased risk of obesity among older adults.

Keywords: Fast Food, Environment Design, Community Health, Obesity, Policy, Prevention Research, Manuscript format: research, Research purpose: modeling/relationship testing, Study design: non-experimental, Outcome measure: biometric, Setting: local community, Health focus: fitness/physical activity, Strategy: built environment, Target population age: adults/seniors, Target population characteristics: geographic location

Introduction

The increasing trend in obesity prevalence in the U.S. adult population1-5 has made the problem of overweight and obesity a leading health concern for this country.6-9 Obesity in adults is associated with increased risk of a number of hypokinetic diseases and metabolic abnormalities including coronary heart disease, type 2 diabetes, hypertension, certain cancers, and osteoarthritis.8,10,11 Apart from physical ailments, people suffering from obesity also face psychological problems including depression, appearance consciousness, and lack of self-confidence. Being obese decreases life expectancy12 and increases medical and social costs.13

While obesity is a complex health issue related to lifestyle and genetic factors, there is growing evidence linking overweight and/or obesity to unhealthy environments (e.g., land use patterns, food availability, processing, and marketing).14-18 One particular environmental factor that is receiving increased attention is the availability of local area fast-food restaurants. Recent data reveal that the number of fast-food restaurants in the U.S. has increased significantly over the past decade,19,20 particularly in low income and predominantly black urban neighborhoods.20,21 The data appear to coincide with a marked upward trend in total spending on fast food19,22 and energy intake derived from away-from-home sources, in particular fast-food outlets.19,23,24,25 With respect to obesity, studies have found associations between fast food consumption and increased body mass index (BMI) and weight gain.26-30 For example, in a 15-year study, adults who ate fast food more than twice a week gained 10 pounds more, and their insulin resistance increased twice as fast, compared to those who ate fast food less than once a week.30 On a broader scale, an association has been found between the density of neighborhood fast food restaurants, the number of residents per restaurant, and state obesity rates,16 and between residents who reside in areas possessing a higher relative number of fast-food outlets to full-service restaurants and a higher weight status.31

While existing studies have led to more closely examining how an obesogenic environment32 is related to overweight or obesity, the majority of studies have focused on direct or independent contributions of either neighborhood-level or individual-level factors related to obesity health risk. Because weight gain and/or obesity have increasingly been recognized to be the result of the interplay among biological, behavioral, and environmental factors,32 it is reasonable to argue that individual characteristics are naturally correlated with those of their social or neighborhood contexts, suggesting that neighborhood- and individual-level characteristics might interact to jointly influence obesity.33

This study, therefore, was designed to examine whether associations between selected individual-level behavioral, psychosocial, and sociodemographic characteristics and obesity were moderated by the availability of local area fast-food outlets. Specifically, within a multilevel analytic framework, we tested the working hypothesis that the strength of the association between individual-level measures of frequency of weekly visits to fast-food restaurants, self-efficacy of eating fruits and vegetables, fried food consumption, levels of physical activity, and obesity would vary as a function of density of fast-food outlets in the neighborhood where older adults live. On the basis of past research,20,21,34 sociodemographic measures of household income and race/ethnicity were also included in the analyses.

Methods

Design

A cross-sectional, multistage, stratified sampling design was employed to obtain a regionally representative sample of adults between 50-75 years old from randomly sampled U.S. census block groups geographically located within the Portland, Oregon, metropolitan region's Urban Growth Boundary (www.metro-region.org). Census block groups were used as a proxy for neighborhoods and are, therefore, the Primary Sampling Unit (PSU) in this study. The sampling procedures involved three stages: (1) randomly selecting block groups that represented a wide variety of urban forms (land use mix), with socio-economically (median household income) and ethnically diverse populations; (2) randomly selecting households in selected block groups; and (3) identifying and recruiting eligible residents from selected households. The study was conducted between 2006-2007 and the research protocol was approved by the Oregon Research Institute Institutional Review Board.

Sample

A total of 120 neighborhoods, stratified on the variables of urban form, median household income, and race/ethnicity, were selected in 2006. Single and multiple family households (i.e., houses, apartments) within the selected neighborhoods were identified from a database purchased through a commercial vender (www.surveysampling.com). The dataset was compiled from local telephone directories (primary data source), voter registration, and driver's licence information (secondary data source) from 2006, providing the targeted age sample and contact information (name, address, and phone number) for the study recruitment. Proportional allocation was used to determine the size of the sample selected from each block group, with sample sizes varying from 5-8 residents for small population block groups to 9-21 residents for medium-to-large population block groups. In accordance with the study's inclusion/exclusion criteria, the target population was comprised of adults who were between 50 and 75 years old, English-speaking, independently ambulatory (including cane use), and with no major mental deficits.

Each selected household/resident was approached initially through direct mail, followed by a telephone contact. Non-responding or declining households were replaced by continued random sampling of households until the target number of households within each neighborhood was recruited. The sampling efforts resulted in a total of 1,221 participants being recruited, representing a response rate of 48%. An in-person-interview was used to collect a wide range of data, including sociodemographic characteristics, dietary and physical activity behaviors, body weight and height, and perceptions of the neighborhood built environment. All participants provided written, informed consent before assessments and were paid $25-30 plus transportation costs for their participation.

Measures

Individual-Level Measures

Body Mass Index (BMI)

Data on weight (in pounds) and height (in inches) were obtained through objective anthropometric measures. These data were used to calculate BMI by dividing weight (in kilograms) by height (in meters) square (weight/height2). Because of the substantive focus on obesity in this study, BMI was classified into two categories: 1 = obese (BMI ≥ 30); 0 = otherwise (BMI < 30).

Eating-out behavior

Two questions indicative of the frequency of adult weekly fast-food visits were asked: “How often do you eat food from a place like McDonalds, Burger King, KFC, Pizza Hut, or some other fast-food restaurant?” and “How often do you go to buffet-type restaurants?” The response categories were: (1) never, (2) less than once per week, (3) 1-2 times a week, (4) 3-4 times a week, (5) five times a week, and (6) every day. For the purpose of analysis, a binary score of eating out at fast food restaurants/buffet-type was created: 1= 1-2 times or more each week; 0 = never or less than once per week. A 12-month test-retest reliability measure for the two-item scale was .75.

Eating self-efficacy

Adapted from Resincow et al.35 the efficacy to eat more fruits and vegetables scale was used. This scale consisted of 10 items, assessing the extent to which participants believed they could eat more fruits or vegetables. Each of the efficacy items was measured using response categories ranging from 1 (Not at all confident) to 10 (Completely confident). Validity and reliability of the scale were established by the scale developers.35 Cronbach's alpha internal-consistency reliability coefficient was .85 for the scale. For ease of interpretation, the raw scores were reversed with higher scores indicating lower confidence eating more fruits and vegetables.

Fried food consumption

A single question was asked of respondents: “How many servings of fried food (e.g., deep fried in oil, pan fried in oil or butter, etc.) do you have in a typical week?” The number of servings was recorded. For the analysis, a binary score of fried food consumption was created: 1 = 1 or more servings; 0 = no servings.

Fruits and vegetables intake

The Fruit and Vegetable screener (the “All-Day” version)36 was used to assess the frequency and portion size intake of fruits and vegetables. Validity and reliability of the scale were established by the scale developers.36 Scores were computed per scoring guidelines established by the National Cancer Institute,37 with higher scores indicating higher levels of fruit and vegetable intake.

Physical activity

Levels of physical activity were assessed using the Behavioral Risk Factor Surveillance System Survey (BRFSS).38 The BRFSS physical activity questions assess the number of days per week and total time spent per day in moderate and vigorous physical activity. Physical activity levels were computed into three categories per the American College of Sports Medicine and Centers for Disease Control and Prevention (CDC) recommendations for physical activity:39,40,41 (a) met guidelines for moderate physical activity (i.e., ≥5 times per week, ≥30 minutes per day) or vigorous physical activity (i.e., ≥ 3 times per week, ≥20 minutes per day); (b) insufficiently active (i.e., some physical activity but less than the guidelines recommend); or (c) inactive (i.e., no physical activity reported). For the purpose of this analysis, the latter two categories were combined to create a dichotomous variable of 1 = not meeting the recommended physical activity levels; 0 = meeting the recommended level of physical activity.

Sociodemographic characteristics

These measures included and were operationalized as: (a) age (continuous variable: 50-75 years old); (b) gender (1 = males; 0 = females); (c) education (1 = high school diploma or lower; 0 = some college or higher); (d) household income (1 = $29,999 or less; 0 = $30,000 or above); (e); race/ethnicity (1 = non-Hispanic black; 0 = otherwise); (f) employment status (1 = employed; 0 = not employed); (g) home ownership (1 = yes; 0 = no); (h) alcohol use (1 = current user; 0 = never or no use); (i) tobacco use (1 = current user; 0 = never or no use), and (j) health status (5 = excellent; 1 = poor).

Neighborhood Measures

Fast-food restaurants

Commercial business data were purchased via infoUSA (www.infousa.com). The dataset, which was updated in 2006, contained information on types of fast food restaurants among sampled block groups. The data were compiled using proprietary 4-digit extensions to the Standard Industrial Classification (SIC) codes, which included various fast food chain restaurants or franchises such as McDonald's, Burger King, and Wendy's. The compiled data were then spatially geocoded and integrated within a Geographic Information System (GIS) using ArcView software.42 For the analysis, the number of fast food outlets was divided by area in square miles to obtain a density measure of fast-food outlets for each of the 120 neighborhoods. To enhance the interpretability of the odds ratio, the measure was standardized. Resulting scores were further divided into high or low quartiles (determined by the interquartile range) with the high quartile representing high-density and low-quartile representing low-density of neighborhood fast-food outlets.

Land use mix

Data were generated using existing geographic databases managed by Portland's Regional Land Information System (RLIS; www.metro-region.org), which contains complete and current street address information as well as other data layers (i.e., tax assessor's data, regional land use data from digital aerial photography, employment data, census data). All parcel data were spatially integrated within the GIS to characterize the urban built environment of the sampled study area. To generate a land use mix index, the formula developed by Frank et al.43 was used. Values near zero on this index reflected single-use environments (such as residential suburbs) whereas values near 1 reflected maximal mixed usage. Therefore, the latter represented greater neighborhood land use mix.

Residential density

A residential density measure was developed by calculating the number of persons per residential acre in each block group.

Other sociodemographics

Three additional measures were developed using 2000 census data to represent neighborhood-level socioeconomic characteristics: (a) median household income, (b) percentage of non-Hispanic black residents, and (c) percentage of Hispanic residents. These socioeconomic and land use type measures (land use mix, residential density) were used as study covariates.

Analysis

Given the multilevel design of the study, a multilevel random effects logistic model (with logit link function) was constructed with neighborhoods as level-2 units and residents nested within each neighborhood as level-1 units. The two-level logistic regression model consisted of the binary response variable of obesity (1 = BMI ≥30; 0 = BMI < 30) that was predicted from the neighborhood-level independent variables of density of fast-food outlets, land use mix, and sociodemographics (i.e., median household income, percentage of non-Hispanic black residents, percentage of Hispanic residents), and individual-level independent variables of eating-out at fast-food restaurants, eating self-efficacy, fried food consumption, physical activity, fruit and vegetable intake, and sociodemographic variables (i.e., age, gender, education, household income, race/ethnicity, home ownership, employment status, health status). A number of a priori specified cross-level interaction terms were constructed in addition to main effects; specifically, density of fast-food outlets by: (a) eating out at fast-food restaurants, (b) meeting recommended physical activity, (c) eating fruit and vegetable self-efficacy, (d) fried food consumption, (e) race/ethnicity, and (f) household income. Model testing was conducted using Hierarchical Linear and Nonlinear Modeling software.44

Results

The majority of study participants were male (57%) and non-Hispanic white (92%), with a mean age of 62 (±7) years, some college or higher (77%), and a household income of $30,000 or higher (73%). Of the total sample for this study, 38.2% (n = 466) were classified as obese (BMI ≥ 30). Furthermore, approximately 36% of the respondents reported not meeting physical activity recommendations. A majority of the participants (72%) reported fried food consumption (servings range: 1-23 times per week) and approximately 24% of the sample (n = 296) reported visiting their neighborhood fast-food restaurants 1-2 times weekly or more. The sample had a low mean score of 2.57 (±2) on the self-efficacy of eating fruits and vegetables measure.

Descriptive statistics on individual characteristics across the low- and high-quartile range of neighborhood fast-food outlet density are presented in Table 1. As can be seen, a shift from the low- to high-quartiles of neighborhood fast-food density was associated with an increase in the rate of obesity among residents who made frequent visits to fast-food restaurants, did not meet recommended levels of physical activity, and who had lower self-efficacy in eating fruits and vegetables. Also notable was the observation that the prevalence of obesity among non-Hispanic black residents increased for neighborhoods with a high-density of fast-food outlets.

Table 1.

Aggregated Descriptive Statistics on Obesity by Individual Characteristics Across High- and Low-Density of Neighborhood Fast-Food Outlets

Density of Neighborhood Fast-Food Restaurants
Low-Density High-Density
Aggregated Individual-Level Measures at the Neighborhood level Prevalence of Obesity (number of neighborhoods)
Visits to fast-food restaurants
 Yes (n = 113) 36.08 (n = 58) 43.40 (n = 55)
 No (n = 7) 23.75 (n = 2) 41.71 (n = 5)
Meeting recommended physical activity
 Not meeting (n = 115) 35.80 (n = 56) 42.87 (n = 59)
 Meeting (n = 5) 33.75 (n = 4) 36.67 (n = 1)
Low self-efficacy in eating fruits and vegetables
 Above or at median (n = 62) 35.51 (n = 26) 45.79 (n = 36)
 Below median (n = 58) 35.78 (n = 34) 39.47 (n = 24)
Fried food consumption
 Yes (N = 120) 35.67 (n = 60) 43.26 (n = 60)
 No 0 0
Race/ethnicity
 Non-Hispanic black (n = 29) 33.34 (n = 11) 47.19 (n = 18)
 Others (n = 91) 36.19 (n = 49) 41.54 (n = 42)
Open in a new tab

Of the six a priori specified interaction terms in the original model, density of fast-food outlets by fried food consumption and by household income were not found to be statistically significant (p = .43, p = .21, respectively). The model was reestimated by removing these two non-significant interaction terms. Odds ratios (interaction terms) generated from the multilevel logistic model testing are shown in Table 2. The results showed a number of statistically significant cross-level interaction effects, adjusting for the various resident- and neighborhood-level covariates. The estimated odds ratios indicate that residents living in high-density fast-food outlet neighborhoods who visited fast-food or buffet restaurants 1-2 times weekly were 1.878 (95% CI: 1.006, 3.496, p < .05) times more likely to be obese than those who lived in low-density fast-food outlet neighborhoods. Similar results for high-density fast-food outlet neighborhoods compared to low-density fast-food outlet neighborhoods were found for residents that did not meet recommended levels of physical activity, OR=1.792 (95% CI: 1.006, 3.190, p < .05); reported low self-efficacy in eating healthy food, OR =1.212 (95% CI: 1.057, 1.391, p < .005); or were non-Hispanic black residents, OR =8.057 (95% CI: 1.705, 38.086, p < .005).

Table 2.

Multilevel Logistic Regression Analyses of Associations between Obesity and Resident-Level Measures of Characteristics and Neighborhood-Level Measures of Density of Fast-Food Outlets

Obesity (BMI ≥ 30)
Cross-level interaction effect b coefficient Standard Error t-value Odds Ratio 95% CI
Density of fast-food outlets by visits to fast-food restaurants 0.629 0.318 1.980 1.878 1.006, 3.496
Density of fast-food outlets by not meeting recommended physical activity 0.583 0.294 1.982 1.792 1.006, 3.190
Density of fast-food outlets by self-efficacy of eating fruits and vegetables 0.193 0.070 2.752 1.212 1.057, 1.391
Density of fast-food outlets by race/ethnicity (non-Hispanic Black) 2.087 0.792 2.633 8.057 1.705, 38.086
Open in a new tab
*

Odds ratios were adjusted for the following variables: at the resident-level: age, gender, education, household income, employment status, home ownership, alcohol and tobacco use, vegetable intake, and fried food consumption; at the neighborhood-level: land use mix, residential density, median household income; percentage of non-Hispanic black residents; and percentage of Hispanic residents.

Discussion

This study builds on previous research by exploring the hypothesis that older adult obesity may be the result of the interaction between individual eating habits, not meeting physical activity guidelines, self-efficacy in eating healthy food, and ethnicity, and the built-environment feature of density of local area fast-food outlets. Findings provided support for the individual-environment cross-level interaction hypothesis in that, among neighborhoods with higher fast-food restaurant density, there was a strong association among older adults visiting fast-food or buffet style restaurants, lower self-efficacy of eating fruits and vegetables, not meeting physical activity guidelines, and the likelihood of being obese.

Results from this study suggest that, while older adult lifestyles and psychological factors may contribute to increased weight and obese risk, the availability of fast-food outlets in neighborhoods where they live may further increase the likelihood of being obese. The multilevel influences of the built environment and individual characteristics on the obesity problem have not been reported in part because, methodologically, most studies lack a multilevel design and level-specific measures that are delineated at each level of the data hierarchy. The use of the multilevel design in this study allows the relative importance of both neighborhood- and resident-level factors in relation to obesity to be examined simultaneously.45

While findings from this study are in agreement with various ecologic-based predictions33,46,47 and congruent with empirical findings that frequent fast-food consumption is associated with higher weight status,16,26,28,30,31 the current study extends the extant research by specifically examining cross-level interactions between individual-level behavioral and psychological factors and local area fast-food outlets at the neighborhood level that are hypothesized to jointly contribute to obesity. The study findings also add to the growing body of knowledge on the race/ethnicity and obesity relationship commonly examined,20,21,34 demonstrating that African-Americans living in neighborhoods with greater densities of fast-food outlets are more likely to be obese compared to other races/ethnicities.

Limitations

Findings in the current study are limited by the cross-sectional design, which precludes drawing conclusions about causality. For example, we could not conclude that the distribution of restaurant availability determines the frequency with which individuals make weekly trips to fast-food outlets and, consequently, increase body weight. A more rigorous approach in future studies in this line of research would be to design longitudinal studies that specify a temporal lag between the cause (i.e., obesogenic environment and individual characteristic factors) and effect (i.e., change in BMI or obesity), so that, for example, a longitudinal analysis of change in restaurant density and individuals' eating-out behavior, and change in weight status can be conducted to infer causal processes associated with restaurant availability, individual behavior, and obesity. While change in modifiable individual lifestyles (e.g., food consumption, physical activity) can be made through short observation units (e.g., months), it may require substantially longer periods (i.e., years) to observe change in built environment characteristics, if any. This poses a great challenge in the study of interaction effects of individual and environmental food outlet factors on obesity.

Additionally the focus on characteristics of the built environment associated with the presence of fast food outlets presents limitations. For example, we have not considered information regarding the built environment surrounding participants' places of work or homes, such as the absence of neighborhood sidewalks that may contribute to being more sedentary or more likely to want to order food in or drive to fast-food outlets. Similarly, neighborhood environment features, such as being automobile-dependent or live-and-work, suburban-style environments, could also affect individuals choosing to eat lunch out or patronize restaurants after work. Future studies should consider modeling these factors into the overall built environment, individual eating-out behaviors, and obesity.

Finally, this study was limited by the use of participant self-reports as measures of fast-food restaurant visits. Also, because the exact location of each restaurant visit was not recorded, we could not verify whether the reported locations were within the sampled study area. Greater confidence may be placed in the findings if future studies use GIS-based measures to capture whether the locations of fast-food outlets visited are within a specified radius of respondents' residences and linking them to the neighborhood-level density of fast-food outlets.

Significance and Implications

The public health significance of this study is the indication that individuals' unhealthy lifestyles (i.e., physical inactivity, poor dietary habits) and lack of confidence about eating healthy food may be exacerbated by an obesogenic environment, such as, in this case, the increasing density of neighborhood fast-food outlets. In this regard, the findings add to the growing body of literature about obesity and healthy living communities, suggesting the need to emphasize the important role of built environment influences that facilitate unhealthy lifestyles and the obesity epidemic. From a public health perspective, the results suggest the need for public health authorities to develop better food and land use regulations that limit the growth of fast-food outlets in neighborhoods, and/or encourage a more healthful eating environment.31 Unless consideration of obesogenic aspects of the built environment are addressed, the success of community-wide educational programs informing individuals about healthy lifestyles, including healthy eating practices and increasing physical activity, is likely to be muted.

Conclusion

This study is a step in alleviating the paucity of multilevel research on evaluating the influence of neighborhood built environment factors, specifically fast-food outlets, on obesity in middle-aged and older adults. The findings underscore the importance of examining the availability of fast-food restaurants in moderating the relationship between individual-level lifestyle indicators, self-efficacy, and other well-established demographic characteristics (e.g., race/ethnicity) and obesity. Collectively, these finding suggest that a high concentration of fast-food outlets in community neighborhoods, combined with unhealthy lifestyles and poor psychosocial profiles, contribute to an increased risk of obesity. Public health promotion that targets reducing obesity needs to incorporate policy change or regulation initiatives that restrict the proliferation of fast-food outlets in neighborhoods.

So What?

This study extends previous research by showing how the density of neighborhood fast-food outlets moderates the relationship between individual-level behavioral, psychological and sociodemographic characteristics, and obesity in middle aged and older adults. The findings highlight the importance of considering the role of an obesogenic environment – density of fast-food outlets, and the mechanisms through which it may influence behavior and health. By better understanding the interaction between food environments, lifestyle and other salient characteristics, we are more likely to develop effective land use and public health policies that limit the impact of detrimental elements of the local food or nutrition environment on the public's health.

Contributor Information

Fuzhong Li, Oregon Research Institute, Eugene, Oregon

Peter Harmer, Department of Exercise Science, Willamette University.

Bradley J. Cardinal, Department of Exercise Science, Oregon State University

Mark Bosworth, Metro Regional Services, Portland, Oregon 97232

Deb Johnson-Shelton, Oregon Research Institute, Eugene, Oregon

References

  • 1.Centers for Disease Control and Prevention. U.S. obesity trends: 1985-2006. [October 1, 2007]; Available at: http://www.cdc.gov/nccdphp/dnpa/obesity/trend/maps/index.htm.
  • 2.Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999-2000. JAMA. 2002;288:1723–1727. doi: 10.1001/jama.288.14.1723. [DOI] [PubMed] [Google Scholar]
  • 3.Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM. Prevalence of overweight and obesity among US children, adolescents, and adults, 1999-2002. JAMA. 2004;291:2847–2850. doi: 10.1001/jama.291.23.2847. [DOI] [PubMed] [Google Scholar]
  • 4.Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999-2004. JAMA. 2006;295:1549–1555. doi: 10.1001/jama.295.13.1549. [DOI] [PubMed] [Google Scholar]
  • 5.Wang Y, Beydoun MA. The obesity epidemic in the United States – gender, age, socioeconomic, racial/ethnic, and geographic characteristics: A systematic review and meta-regression analysis. Epidemiologic Rev. 2007 doi: 10.1093/epirev/mxm007. [DOI] [PubMed] [Google Scholar]
  • 6.Flegal KM, Graubard BI, Williamson DF, Gail MH. Excess deaths associated with underweight, overweight, and obesity. JAMA. 2005;293:1861–1867. doi: 10.1001/jama.293.15.1861. [DOI] [PubMed] [Google Scholar]
  • 7.Institute of Medicine, Committee on Prevention of Obesity in Children and Youth. Preventing Childhood Obesity: Health in the Balance. Washington DC: National Academies Press; 2005. [DOI] [PubMed] [Google Scholar]
  • 8.National Task Force on the Prevention and Treatment of Obesity. Overweight, obesity, and health risk. Arch Intern Med. 2000;160:898–904. doi: 10.1001/archinte.160.7.898. [DOI] [PubMed] [Google Scholar]
  • 9.U.S. Department of Health and Human Services. The Surgeon General's Call to Action to Prevent and Decrease Overweight and Obesity. Rockville MD: U.S. Public Health Services, Office of the Surgeon General; 2001. [PubMed] [Google Scholar]
  • 10.Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The disease burden associated with overweight and obesity. JAMA. 1999;282:1523–1529. doi: 10.1001/jama.282.16.1523. [DOI] [PubMed] [Google Scholar]
  • 11.Patterson RE, Frank LL, Kristal AR, White E. A comprehensive examination of health conditions associated with obesity in older adults. Am J Prev Med. 2004;27(5):385–390. doi: 10.1016/j.amepre.2004.08.001. [DOI] [PubMed] [Google Scholar]
  • 12.Fontaine KR, Redden DT, Wang C, et al. Years of life lost due to obesity. JAMA. 2003;289:187–193. doi: 10.1001/jama.289.2.187. [DOI] [PubMed] [Google Scholar]
  • 13.Finkelstein EA, Fiebelkorn IC, Wang G. National medical spending attributable to overweight and obesity: How much and who's paying. Health Aff. 2003;(Suppl):W3, 219–26. doi: 10.1377/hlthaff.w3.219. [DOI] [PubMed] [Google Scholar]
  • 14.Ewing R, Schmid T, Killingsworth R, Zlot A, Raudenbush S. Relationship between urban sprawl and physical activity, obesity, and morbidity. Am J Health Promot. 2003;18:47–57. doi: 10.4278/0890-1171-18.1.47. [DOI] [PubMed] [Google Scholar]
  • 15.Frank LD, Andresen MA, Schmid TL. Obesity relationships with community design, physical activity, and time spent in cars. Am J Prev Med. 2004;27:87–96. doi: 10.1016/j.amepre.2004.04.011. [DOI] [PubMed] [Google Scholar]
  • 16.Maddock J. The relationship between obesity and the prevalence of fast food restaurants: State-level analysis. Am J Health Promot. 2004 Nov-Dec;19(2):137–143. doi: 10.4278/0890-1171-19.2.137. [DOI] [PubMed] [Google Scholar]
  • 17.Morland K, Roux AV, Wing S. Supermarkets, other food stores, and obesity: The Atherosclerosis Risk in Communities Study. Am J Prev Med. 2006;30(4):333–339. doi: 10.1016/j.amepre.2005.11.003. [DOI] [PubMed] [Google Scholar]
  • 18.Rundle A, Diez Roux AV, Freeman LM, Miller D, Neckerman KM, Weiss CC. The urban built environment and obesity in New York City: A multilevel analysis. Am J Health Promot. 2007;21(4 Supplement):326–334. doi: 10.4278/0890-1171-21.4s.326. [DOI] [PubMed] [Google Scholar]
  • 19.Paeratakul S, Ferdinand DP, Champagne CM, Ryan DH, Bray GA. Fast-food consumption among US adults and children: Dietary and nutrient intake profile. J Am Diet Assoc. 2003;103:1332–1338. doi: 10.1016/s0002-8223(03)01086-1. [DOI] [PubMed] [Google Scholar]
  • 20.Powell LM, Chaloupka FJ, Bao Y. The availability of fast-food and full-service restaurants in the United States: Associations with neighborhood characteristics. Am J Prev Med. 2007;33(4S):S240–S245. doi: 10.1016/j.amepre.2007.07.005. [DOI] [PubMed] [Google Scholar]
  • 21.Block JP, Scribner RA, DeSalvo KB. Fast food, race/ethnicity, and income. Am J Prev Med. 2004;27:211–7. doi: 10.1016/j.amepre.2004.06.007. [DOI] [PubMed] [Google Scholar]
  • 22.Schlosser E. Fast Food Nation. New York: Houghton Miffin; 2001. [Google Scholar]
  • 23.Guthrie JF, Lin BH, Frazao E. Role of food prepared away from home in the American diet, 1977-78 versus 1994-96: Changes and consequences. J Nutr Edu Beh. 2002;34:140–150. doi: 10.1016/s1499-4046(06)60083-3. [DOI] [PubMed] [Google Scholar]
  • 24.Nielsen SJ, Siega-Riz AM, Popkin BM. Trends in food locations and sources among adolescents and young adults. Prev Med. 2002;35:107–113. doi: 10.1006/pmed.2002.1037. [DOI] [PubMed] [Google Scholar]
  • 25.Stewart H, Blisard N, Bhuyan S, Nayga RM., Jr The demand for food away from home full-service or fast food? United States Department of Agriculture. Agricultural Economic Report Series (No. 829) 2004 [Google Scholar]
  • 26.Bowman SA, Vinyeard BT. Fast food consumption of U.S. adults: Impact on energy and nutrient intakes and overweight status. J Am Coll Nutr. 2004;23:163–168. doi: 10.1080/07315724.2004.10719357. [DOI] [PubMed] [Google Scholar]
  • 27.Binkley JK, Eales J, Jekanowski M. The relation between dietary change and rising U.S. obesity. Int J Obes. 2000;24:1032–1039. doi: 10.1038/sj.ijo.0801356. [DOI] [PubMed] [Google Scholar]
  • 28.French SA, Harnack L, Jeffery RW. Fast food restaurant use among women in the Pound of Prevention study: Dietary, behavioral and demographic correlates. Int J Obes. 2000;24:1032–1039. doi: 10.1038/sj.ijo.0801429. [DOI] [PubMed] [Google Scholar]
  • 29.Jeffrey RW, French SA. Epidemic obesity in the United States: Are fast foods and television viewing contributing? Am J Public Health. 1998;88:277–280. doi: 10.2105/ajph.88.2.277. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Pereira MA, Kartashov AI, Ebbeling CB, Van Horn L, Slattery ML, Jacobs DR, Jr, Ludwig DS. Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet. 2005;365(9464):36–42. doi: 10.1016/S0140-6736(04)17663-0. [DOI] [PubMed] [Google Scholar]
  • 31.Mehta NK, Chang VW. Weight status and restaurant availability - A multilevel analysis. Am J Prev Med. 2008;34(2):127–133. doi: 10.1016/j.amepre.2007.09.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Egger G, Swinburn B. An “ecological” approach to the obesity pandemic. BMJ. 1997;315:477–480. doi: 10.1136/bmj.315.7106.477. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Li F, Fisher KJ, Ory MG, et al. Neighborhood influences on physical activity in older adults: A multilevel perspective. J Aging Physi Activ. 2005;13:32–58. doi: 10.1123/japa.13.1.87. [DOI] [PubMed] [Google Scholar]
  • 34.Morland K, Wing S, Diez RA, Poole C. Neighborhood characteristics associated with the location of food stores and food service places. Am J Prev Med. 2002;22:23–29. doi: 10.1016/s0749-3797(01)00403-2. [DOI] [PubMed] [Google Scholar]
  • 35.Resnicow K, Campbell MK, Carr C, et al. Body and soul: A dietary intervention conducted through African-American churches. Am J Prev Med. 2004;27:97–105. doi: 10.1016/j.amepre.2004.04.009. [DOI] [PubMed] [Google Scholar]
  • 36.Thompson FE, Kipnis V, Subar AF, Krebs-Smith SM, Kahle LL, Midthune D, Potischman N, Schatzkin A. Evaluation of 2 brief instruments and a food-frequency questionnaire to estimate daily number of servings of fruit and vegetables. Am J Clin Nutr. 2000;71(6):1503–1510. doi: 10.1093/ajcn/71.6.1503. [DOI] [PubMed] [Google Scholar]
  • 37.National Institutes of Health. Fruit & Vegetable Screeners: Scoring Procedures. [November 2007]; Available at: http://riskfactor.cancer.gov/diet/screeners/fruitveg/scoring.
  • 38.Centers for Disease Control and Preventions. Behavioral Risk Factor Surveillance System. [November 1, 2007]; Available at: www.cdc.gov/brfss.
  • 39.Haskell WL, Lee IM, Pate RR, et al. Physical activity and public health: Updated recommendation for adults from American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc. 2007;39(8):1423–1434. doi: 10.1249/mss.0b013e3180616b27. [DOI] [PubMed] [Google Scholar]
  • 40.Nelson ME, Rejeski WJ, Blair SN, et al. Physical activity and public health in older adults: Recommendation from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc. 2007;39:1435–1445. doi: 10.1249/mss.0b013e3180616aa2. [DOI] [PubMed] [Google Scholar]
  • 41.Pate RR, Pratt M, Blair SN, et al. Physical activity and public health. A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. JAMA. 1995;273:402–407. doi: 10.1001/jama.273.5.402. [DOI] [PubMed] [Google Scholar]
  • 42.ArcView [computer program]. Version 9.2. Redlands, CA: Environmental Systems Research Institute (ESRI); 2007. [Google Scholar]
  • 43.Frank LD, Schmid TL, Sallis JF, Chapman J, Saelens BE. Linking objectively measured physical activity with objectively measured urban form: Findings from SMARTRAQ. Am J Prev Med. 2005;28(2S2):117–125. doi: 10.1016/j.amepre.2004.11.001. [DOI] [PubMed] [Google Scholar]
  • 44.Raudenbush SW, Bryk A, Cheong YF, Congdon R. HLM6: Hierarchical Linear and Nonlinear Modeling. Lincolnwood, IL: Scientific Software International; 2004. [Google Scholar]
  • 45.Diez Roux AV. The examination of neighborhood effects on health: Conceptual and methodological issues related to the presence of multiple levels of organization. In: Kawachi I, Berkman LF, editors. Neighborhoods and Health. New York: Oxford University Press; 2004. pp. 45–64. [Google Scholar]
  • 46.Ewing R. Can the physical environment determine physical activity levels? Exerc Sport Sci Rev. 2005;33:69–75. doi: 10.1097/00003677-200504000-00003. [DOI] [PubMed] [Google Scholar]
  • 47.Sallis JF, Owen . Ecological models of health behavior. In: Glanz K, Lewis FM, Rimer BK, editors. Health Behavior and Health Education: Theory, Research and Practice. 3d. San Francisco: Jossey-Bass; 2002. pp. 426–484. [Google Scholar]

RESOURCES