Examining the relationships among built environment, physical activity, and body mass index in El Paso, TX
Introduction
Obesity has been implicated in the development of a myriad of diseases as well as psychological distress [1], [2]. In addition, being overweight shortens life expectancy by 1–3 years and increases the risk from all cause mortality by 50–100% [3]. Obesity is the seventh leading cause of death in the US. and contributes to at least 280,000 preventable deaths each year [3]. The health care costs associated with obesity have been estimated at US$117 billion dollars a year, which exceeds the health care costs spent on smoking and alcoholism combined [4].
Obesity is caused by a combination of increased caloric intake and decreased caloric expenditure resulting in a state of “positive” energy balance. Recently, researchers have begun to examine the effects of the environment on the rising rates of overweight and obesity in the US. The terms “obesogenic” and “toxic” have been used to describe the environment that many researchers feel is primarily responsible for the US obesity epidemic [5], [6].
One aspect of the environment's role in obesity that has received recent attention is the way in which neighborhoods and communities are designed to promote or discourage different kinds of physical activity [7] such as moderate walking in neighborhoods and vigorous aerobic exercise in gym facilities [8]. Several aspects of the built environment (urban design, land-use, and transportation systems) have been examined by transportation researchers in order to understand which factors influence an individual's decision to walk or bike for transportation. Based on this literature, it would be expected that individuals living in traditional urban neighborhoods would have lower body mass index (BMI) because they self-report walking and biking for transportation more frequently than individuals living in suburban neighborhoods [9], [10].
The research that has examined the direct influence of the built environment on obesity has been inconclusive. Ewing et al. [11] found that increasing sprawl (low population density and low connectivity/street accessibility) was related to increasing obesity and BMI. However, another study found that individuals living in urban areas (high population density) were more likely to be overweight or obese [12]. These inconsistent results may be due to the different measures or different populations used in these studies, or that the effect of the built environment on BMI is not a direct, clear relationship.
It is possible that the built environment works to affect BMI through mediators such as differing levels of physical activity [9], [10], or is magnified by confounding factors such as socioeconomic status (SES), general health, or intake of fruits and vegetables. An example of this complex relationship would be if living in an urban environment was associated with a greater number of sidewalks, which were a predictor of walking for exercise, but were also an area for high crime, which is a deterrent for walking. Therefore, it may be that different aspects of the urban environment interact to influence whether or not a person was overweight or obese. Complicating this relationship would be the confounder of SES, given that SES is related to all of these variables: people with higher income are more likely to exercise, are less likely to be overweight, and, depending upon the city, may prefer to live in urban environments where they can walk to work. It is just as likely, however, that urban areas are associated with low SES, high crime, and unemployment, all leading to less self-reported walking.
The purpose of this study was to examine the complex relationships among built environment variables, physical activity, and BMI given many demographic and health variables that might be potential confounders of these relationships. We conducted the study in El Paso, TX, a large US/Mexico border region with variation in neighborhood characteristics such as having dense inner city housing, suburban housing peripheral to the city, and outlying rural, agricultural areas. Structural Equation Modeling (SEM) was used to model these relationships because this technique can examine confounding, mediating, and moderating variables all at once, whereas path analysis or multiple regression must use a variety of “steps” that cannot include all confounding, mediating, or moderating variables in the model at once [13].
Based upon the current literature, it was expected that the following variables would potentially confound a relationship between the built environment and BMI: more time spent watching TV, worse overall self-reported health, greater number of children, older age, lower acculturation, lower SES, decreased fruit and vegetable consumption, and more self-reported morbidities. Built environmental variables would predict BMI when controlling for these confounds such that fewer physical activity facilities, greater distance to physical activity facilities, fewer sidewalks, and more change in elevation (slope) would be related to higher BMI. Finally, the relationship between the built environment and BMI would be mediated by greater self-reported barriers to physical activity, and less time spent in light, moderate, and vigorous physical activities. Exploratory relationships were also evaluated between BMI and transportation variables (density, intersection density, percentage of cul-de-sacs, percentage of four-way intersections, and land-use).
Section snippets
Participants
Individuals were randomly selected from El Paso County using random digit dial methodology. A list of phone numbers was generated by a national phone survey company (Survey Sampling Inc., Fairfield, CT) for use by local phone surveyors (Signius, El Paso, TX). These phone lists were designed to provide an even distribution of residential phone numbers across all zip code areas in El Paso County. A total of five attempts were made to contact each phone number on the list if it belonged to a
Participants
Participant characteristics are shown in Table 1. Participants were on average 42 ± 17 years old, were slightly overweight with a BMI of 26.6 ± 5.6 kg/m2 (41% normal weight, 37% overweight, and 21% obese), were moderately acculturated 3.1 ± 1.2 (possible range of 1–5), had average SES levels of semi-skilled workers 28 ± 17 (possible range of 8–66), had 1 ± 1 child living in the home, had self-reported good health, ate at least one serving of fruits or vegetables a day, and had less than one
Discussion
Increasing land-use mix was found to have a positive association with increasing BMI, such that individuals who lived in areas with more commercial and industrial buildings had higher BMIs. Only one other study found that individuals living in urban areas (high population density) were more likely to be overweight or obese [12]. For the most part, our findings are in contrast to some recent studies that have found increased land-use mix related to decreased rates of obesity. Saelens et al. [30]
Acknowledgments
We would like to thank the Paso del Norte Health Foundation, its director, Ann G. Pauli, and Juanita Galaviz, Tommy Tinajero, and Dan Green for their assistance with all data for this study. In addition, we would like to thank Eugenia Martinez for her hard work in data entry and verification throughout the evaluation for the walking initiative. We would also like to thank the Planning, Research, and Development Department of El Paso City Hall and the El Paso Parks and the El Paso Recreation
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