the built environment's influence on physical activity [report]

8/9/2019 The Built Environment's influence on Physical Activity [REPORT]

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Spring 2010

The Built Environments influence on Physical Activity

Raymond Chetti

School of Architecture and PlanningUniversity at Buffalo

Abstract

Recent studies have proven that poor urban form fosters sedentary, inactive

lifestyles that discourage routine physical activity, and is associated with higher

rates of overweight and obesity. In this paper, an analysis was performed on the

correlation between characteristics of urban form such as residential density and

land use mix to determine their influence on the percentage of adults whom

achieve the Surgeon Generals recommended levels of moderate intensity physical

activity. A correlation analysis shows that physical activity has a stronger

correlation with land use mix of a Metropolitan Statistical Area (r = .42) compared

to residential density (r = .13). There is a positive correlation between rates of

population in a metro area and the built form of the metro area. These results

support the need for policies that promote increased densities and land use mix as

a means to promote active and healthier lifestyles that include walking, cycling, and

taking public transportation.

Keywords: built environment, urban form, sprawl, public health, physical activity,obesity epidemic, land use, residential density


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The Built Environments influence on Physical Activity 2

Introduction

The prevalence of the childhood obesity epidemic has grown to an alarmingrate: approximately one out of every three American children are obese (BMI

95th percentile) (Robert Wood Johnson Foundation, 2010). Obese children

are susceptible to increased risks for heart disease, diabetes, depression,

social isolation, cardiovascular disease, hypertension, high cholesterol,

orthopedic problems and the destruction of weight-bearing joints (ICF

International, 2010). Even though some may think children can grow out of

their obese phase, a study by Gunnell et.al (1998) found that obese

children are at higher risk for increased mortality risk during their adult lives;

for this reason, the prevention of childhood obesity is critical in preventing

adult diseases that are associated with obesity. The National Institute of

Diabetes, Digestive, and Kidney Diseases (2007) states, [individuals] who

are obese have a 10- to 50-percent increased risk of death from all causes,

compared with healthy weight individuals (BMI 18.5-24.9). Obesity is

associated with about 112,000 excess deaths per year in the U.S. population

relative to healthy weight individuals. Another study funded by the Medical

Research Council, the British Heart Foundation and Cancer Research UK

found that moderately obese individuals (BMI 30 to 35) lives are shortened

by three years and by about 10 for the seriously obese (BMI 40 to 50)

(Boseley, 2009).

Aside from the direct negative health effects, childhood obesity also has

significant economic impacts. The Alliance for a Healthier Generation (2010)

estimates that obesity related illnesses constitute about 9% of the total

medical costs of an average American family. About $61 billion dollars per


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year (nationally) is spent directly on medical procedures treating obesity-

related diseases while about $56 billion dollars per year is lost as an indirect

cost of obesity (such as missed work days and future earnings lost). Over 1.6

million children were unable to get the medical care they needed while 3

million children had to wait for medical care because their parents hard to

worry about the rising costs of health care.

In recent years, the impact of poor environmental design has gained the

attention of policy makers, urban planners, and public health officials. Recent

studies have proven that sprawling built form fosters sedentary, inactive

lifestyles that discourage routine physical activity, and is associated with

higher rates of overweight and obesity among adults that live in these

settings (Frumkin, 2002; Smart Growth America 2003). Smart Growth

America (2003) found that adults living in counties marked by sprawling

development are more likely to walk less and weigh more than people who

live in less sprawling counties. Smart Growth America also noted that people

who lived in more sprawling counties are more likely to suffer from chronic

diseases and hypertension.

A similar influence of the build form is also reported on children. Ewing et al.

(2006) found that the likelihood of U.S. adolescents (aged 12-17 years) being

overweight or at risk of being overweight is associated with the degree of

sprawl within their home counties. Counties where the build form is

characterized by low residential densities, single uses, and poor street

accessibility common features in a sprawling built environment promote

sedentary, inactive lifestyles while compact built form with higher densities,

mixed land uses, and greater street accessibilities encourage higher levels of

physical activity (through walking) and decrease the likelihood of obesity or

overweight among residents.


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In response to the growing recognition of the role of the built environment on

public health, this paper examines the degree to which urban form

influences physical activity among children in the United States.

About the Childhood Obesity Epidemic

American children of the 21st century are facing an obesity crisis. The

National Health and Nurtitional Examination Survey data suggests that one

out of every three American children are obese (BMI 95th percentile)

(Robert Wood Johnson Foundation, 2010). Furthermore, minorities (blacks

and Hispanics) are more susceptible to being overweight or obese since

survey data concluded that overweight or obesity prevalence is 8.9

percentage points higher among Hispanic children ages 2 to 19 and 6.6

percentage points higher for non-Hispanic Black children than among the

non-Hispanic White population.

Even though the percentage of children who were obese or overweight

dropped slightly from 31.9% in 2003-2006 to 31.7% in 2007-2008 (Robert

Wood Johnson Foundation, 2010), the obesity epidemic remains a major

health problem that needs to be addressed.

A growing body of evidence points to the growing epidemic of childhood

obesity (Gunnell et al. 1998, Boseley 2009, ICF International 2010, the

National Institute of Diabetes, Digestive, and Kidney Diseases 2007, theAssistant Secretary for Planning and Evaluation for the United States

Department of Health and Human Services 2010, and the Alliance for a

Healthier Generation 2010. Gunnell et al. (1998) found that being overweight

or obese in childhood is associated with increased mortality risk later in life.


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The authors suggest that strategies aimed at reducing weight in childhood

may positively affect ischemic heart disease heart failure due to coronary

artery disease, morbidity, and mortality. A report by ICF International (2010)

also notes that that overweight preschool children are already suffering

from precursors of negative health consequences that are typically seen only

in adults, including: depression and social isolation, type 2 diabetes,

cardiovascular disease, high blood pressure, high cholesterol, and orthopedic

problems and destruction of weight-bearing joints. These studies suggest

that childhood obesity is a growing health problem that needs to be resolved

before it leads to complicated future, adult related health problems which

are associated with an increased mortality risk.

Obesity can be addressed in a variety of settings. ICF Internationals (2010)

points to four areas where obesity prevention and intervention are possible:

child care settings, health care provider offices and settings, neighborhoods

and communities, and homes. The report calls on community planners to

fight poor urban development and sprawl by incorporating designs that

promote walking and other active modes of transportation that are notassociated with single occupancy vehicle (SOV) usage. The report concludes

that by changing the built environment to promote walkable neighborhoods,

it might be possible to promote healthier, more active lifestyles.

The National Institute of Diabetes, Digestive, and Kidney Diseases (2007)

issued a publication which answered a number of questions related to the

statistics of obesity(examples include: economics of obesity ($117 billion

total on U.S. health expenditures), benefits of physical activity, how many

deaths are associated with obesity, and more), risk factors, and problems

with being overweight and obese. This report includes a BMI chart illustrating

body weight and height while stating the appropriate BMI for the


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corresponding weight and height. This is helpful in determining whether or

not an individual may have the appropriate BMI. The statistics of this report

are vital in providing readers a basic foundation about the overweight and

obesity epidemic which the ICFI report does, while expanding upon it.

Similar to both of the preceding reports discussed, The Assistant Secretary

for Planning and Evaluation for the United States Department of Health and

Human Services (2010) also establishes common facts, statistics, and history

about the childhood obesity epidemic. While discussing the prevalence of the

childhood obesity epidemic, this report also mentioned related issues to the

childhood obesity epidemic such as childrens nutrition and eating habits,

physical inactivity and sedentary behaviors, the association between the

built environment and physical inactivity, socio-economic status and

race/ethnicitys impact on being obese, parental influences, genetics, and

advertising and marketing campaigns.

The Assistant Secretarys report notes that even though less research hasbeen done on the relationship between the physical environment and

physical activity for children than adults, the findings for children appear to

be consistent with the adult population. In other words, even though most

studies on the built environments influence on physical activity and obesity

are primarily studied on adults, the results of the built environments

influence appears to be the same for children as well. For example, if a study

concluded that adults who live in more walkable neighborhoods get more

physical activity and weigh less, that finding would hold true for children too.

The Assistant Secretarys report also discussed how sprawl and suburban

neighborhoods were areas that discouraged and prevented children and

adults from walking and bicycling; these environments typically forced their

residents to depend on their personal automobiles as their primary mode of


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travel and were associated with higher levels of body mass index (BMI) and

lower levels of physical activity.

To battle the childhood obesity epidemic, several philanthropic efforts have

emerged around the country. the American Heart Association and the

William J. Clinton Foundation established the Alliance for a Healthier

Generation (2010). Aside from an increased risk for mortality and the health

problems associated with childhood obesity, the purpose of this alliance is to

highlight the direct and indirect economic costs associated with obesity and

its related illnesses since the money used for treating obesity related

illnesses could be used for housing and other household expenses.

Built form and its Impact on Physical Activity and Obesity among

Children: A literature review

The negative effects of poor built form have been widely discussed in the

literature. For example, sprawling built form is reported to be negatively

associated with quality of life, result in depletion of natural resources and

energy, the destruction of rural and natural areas, and the depletion of our

ozone layer (European Environment Agency, 2006).Among these negative

impacts, none influence us more directly than the impact of poor built form

on our individual, personal health as well as the health of our children.

According to researchers, particular features of the built form especially

those characterized by sprawl - encourage dependency on the automobile

and reduce peoples ability to use active modes of transportation such as

walking and bicycling (Lopez 2004, Frumkin 2002; Ewing et al. 2006, Frank

et al 2004). Lopez (2004) performed a multilevel analysis (cross-sectional

study) to assess urban sprawl and obesity by gathering 2000 U.S. Census

data and individual-level data from the Behavioral Risk Factor Surveillance


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System and found that after controlling for gender, age, race/ethnicity,

income, and education, for each 1-point increase in his urban sprawl index

(as urban areas tended to be more sprawlful), the risk for being overweight

increased by .2% and the risk for being obese increased by .5%. Lopez

concluded that while the obesity epidemic has many causes, there is an

association between urban sprawl and obesity. Overall, sprawling built form

is associated with greater rates of sedentary lifestyles (i.e. little or no

physical activity) and higher degree of obesity among the population

(Frumkin 2002).

Of course, in addition to promoting sedentary lifestyles, a greater reliance on

the automobile in such communities is also associated with increased air

pollution, automobile crashes, pedestrian fatalities, decreased water quantity

and quality, and increased road rage and social isolation factors that are

clearly important, but not central to this paper.

Ewing et al. (2006) conducted a cross-sectional analysis of data from the1997 National Longitudinal Survey of Youth and found that the likelihood of

U.S. adolescents (aged 12-17 years) being overweight (85th percentile) was

associated with county sprawl. In another cross-sectional analysis, after

controlling for socio-demographic and behavioral issues, the likelihood of

young adults (aged 18-23 years), being obese was also associated with

county sprawl. The study concluded that sprawl is associated with being

overweight among U.S. youth while also finding that the relationships

discovered were comparable to those previously reported by adults. While

cross-sectional analysis proved that sprawl is associated with being

overweight, the study also concluded that longitudinal analysis did not show

any relationship between sprawl and being overweight. The authors are

unsure why a longitudinal approach gave different results but highlight how


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likely than those in more sprawling neighborhoods to achieve the Surgeon

Generals recommended 30 minutes of physical activity.

Despite the findings and conclusions associating sprawl to obesity, a study

by Eid et al (2008) questioned whether or not there was a real relationship

between urban sprawl and obesity. The results found no evidence that

sprawl causes obesity and concluded that the interest in the built

environment to combat the rise in obesity is misguided. The focus of Eid et

al. was to determine whether people in sprawling neighborhoods were

heavier because their neighborhood caused them to gain weight or whether

obese people living in sprawling neighborhoods were previously heavy to

begin with. They mention how previous studies failed to properly control for

the fact the individuals who are more likely to be obese choose to live in

more sprawling neighborhoods. The authors used Confidential Geocode Data

of the National Longitudinal Survey of Youth 1979 of the US Bureau of Labor

Statistics to match about 6,000 individuals to neighborhoods throughout the

U.S; over their study period of 6 years, 79% of these people moved at least

once. Eid et al. took note as they moved to either a sprawling or lesssprawling neighborhood and checked if changes in their neighborhood

environments led to changes in weight. Their study failed to see a change in

peoples BMI as they moved to a different neighborhood. However in

discussing their dimensions of the built environment (their sprawl index and

how they measured sprawl) the researchers only used 30-meter resolution

remote-sensing land cover data from Burchfield, Overman, Puga and Turner

(2006) to measure residential sprawl which included land cover and land use.

Second, the researchers used data from the US Census Bureau Zip Code

Business Patterns to count the number of retail shops and churches in order

to measure a neighborhoods mix of uses.


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In measuring sprawl and developing an empirical way to measure it, Jaret et

al. (2009) evaluated seven sprawl indexes and assessed the strengths and

weaknesses of each index. These indexes were used to empirically measure

sprawl. The authors assessed that the sprawl index developed by Ewing et

al. (2002) is one of the more accurate indexes with not as many

disadvantages when compared to other sprawl indexes; Ewings index

measures density, mixed land use, street accessibility, and centeredness. In

comparing the sprawl indexes of Ewing et al. (2002) and Eid et al. (2008)

Ewing et al.s has more built environment characteristics which can be used

to accurately measure sprawl as opposed to Eid et al.s method of measuring

sprawl.

Ewing et al.s report was published by Smart Growth America in 2005 which

outlined the most comprehensive effort to define, measure, and evaluate

metropolitan sprawl and its impact. The report defined sprawl as a landscape

with four dimensions: a population that is widely dispersed in low-density

development, rigidly separated homes, shops and workplaces, a network of

roads marked by huge blocks and poor access, and a lack of well-definedthriving activity centers, such as downtowns and town centers. In order to

empirically measure the four previously mentioned dimensions of sprawl, the

authors analyzed residential density, land use, the strength of metropolitan

centers, and the accessibility of the street network. The studys methodology

discussed in depth how these four dimensions of sprawl were measured

empirically and cited each source of where the data was collected.

Smart Growth Americas (2003) report was the first national study that found

a clear association between the type of place people live and their physical

activity levels, weight, and health. The study found that people living in

counties marked by sprawling development were more likely to walk less


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and weigh more than people who live in less sprawling counties; in addition

people in more sprawling counties were more likely to suffer from

hypertension. The researchers developed a county sprawl index that

ranked 448 counties in urban areas across the United States based on four

primary factors: residential density, land use mix, street connectivity, and

centeredness where lower sprawl index scores (such as 63 for Geauga

County outside of Cleveland, Ohio) represent the most sprawling counties

while higher sprawl index scores (such as 352 for Manhattan) suggest

compact neighborhood designs with less sprawl. The average score of all

counties was 100. Smart Growth Americas report suggested research

questions to perpetuate the need for further research to better understand

the relationship between our built environment and our health.

A number of studies evaluate the degree to which walkable neighborhood

designs may or may not influence physical activity and obesity levels.

Rodriguez et al. (2006) compared various measures of physical activity for

residents of a walkable, new urbanist neighborhood versus those of a

conventional suburban (high sprawling) neighborhood and found nostatistically significant differences in the measures of physical activity.

However, the authors found that residents of new urbanist neighborhoods

were more likely to be physically active in their neighborhood than residents

of the suburbs because new urbanist residents walked more for utilitarian

purposes.

As evident in the literature review, poor urban form influences public health

and perpetuates the childhood obesity epidemic by promoting sedentary

lifestyles that are associated with less physical activity and higher rates of

obesity. Previous studies have empirically measured urban form and used

different variables to measure it; as per Jaret et al. (2009), Ewing et al.s


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(2002) urban form variables are an effective way to measure a built

environments degree of sprawl.

Methodology

The empirical component of this paper investigates the influence of the built

environment on individuals level of physical activity. This study uses a

quantitative approach and deductive logic. The research design is a

quantitative case study of metropolitan statistical areas (MSA) within the

United States.i

The dependent variable in this study is the percentage of adults in a MSA

who achieve at least 30+ minutes of moderate physical activity five or more

days per week, or vigorous physical activity for 20+ minutes three or more

days per week.ii

Physical activity data was obtained from the Behavioral Risk Factor

Surveillance System (BRFSS) of the Center for Disease Control. The first year

that physical activity data could be collected was 2005. This was the first

year that the CDC started collecting physical activity data; physical activity

data for years preceding 2005 was not available online. To aid in establishingthe relationship between the independent variables and the dependent

variables, data was collected for independent variables in 2002 and in 2005

for the dependent variable.


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This study measures the independent variables (i.e. the built environment)

using indicators previously used in literature to measure the extent of sprawl

in the built environment. As noted in the preceding literature review, sprawl

can be measured in several ways. This study relies on the measures by

Ewing, Pendall, and Chen (2005). The report offers four broad measures of

sprawl which act as independent variables: residential density, mix of land

uses, street accessibility, and the concentration of development focused on a

citys center. Each of these four factors is measured using several

quantitative variables.

This study uses two of Ewing et al.s original four factors residential density

and mix of land uses. According to Ewing et al.s literature review, residential

density was concluded to be the only factor of the built environment that

previous research agreed to include. Residential density is measured by the

gross population density in persons per square mile. In addition, land use

mix was selected because of its impact on travel patterns; land use mix is

measured by the percentage of residents within satisfactory neighborhood

shopping within 1 mile of their homes because if residents live within at leastone mile of shopping, it was noted to be a good indicator to as whether or

not people would walk or drive to these destinations. Street accessibility was

not included in this study due to the time and scope limitations of this

project.

The sample for this study included 12 of the 13 metropolitan statistical areas

(MSA) that had an American Housing Survey report for the year 2002. The

excluded MSA from the study, Anaheim-Santa Ana, CA, was considered to


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significantly change the results of the study. Residential density and land use

mix variables were clearly written in the report and easy to gather.

The author conducted a correlation analysis between the dependent variable

(rates of physical activity) and each independent variable (raw data seen in

Appendix 1). The purpose of running correlation analysis on both

independent variables and the dependent variable, physical activity was to

determine how the built environment influences physical activity levels and

to what extent they are associated. This was made possible by calculating

the correlation coefficient ofr, or the degree of association between twovariables (Kachigan, 1991) in Microsoft Excel. The correlation coefficient of

this study would therefore measure the degree of association between

aspects of the built environment and the percentage of adults who achieve

the recommended levels of physical activity.

The author also generated scatter plots to aid in a pictorial representation of

raw data that was collected to illustrate linear relationships.

Limitations

The original intention of this study was to focus on the primary factors of the

built environment and their influence on childrens physical activity. Due to

the lack of data available from the CDC and the time allotted for this study,


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physical activity data had to be from adults and not all measures of the built

environment (as indicated by Ewing et al. (2002)) were taken into

consideration.

The lack of physical activity data for children by the CDC made it impossible

to gather data for them that matched the independent variables data from

metro areas. The only data made available by CDCs Youth Risk Behavior

Surveillance System (YRBSS) that pertained to childrens physical activity

only were measured at state and national levels, not community or

metropolitan levels. Attempting to correlate metropolitan levels of urban

form with state levels of physical activity would not have provided accurate

results. Nor would it have been accurate to measure the built environment at

a state level since the urban form across one state is too large and diverse to

measure. As noted by Smart Growth America (2005), the influence of the

built environment on public health is greater at community levels as opposed

to larger scales.

Due to the time span allotted for the project, it was also impossible for the

author to gather and compute street accessibility data that was necessary

for the 12 metro areas. Street shape file data was gathered by county from

ESRIs Free Data website after determining which counties composed each

respective MSA. Street shape file data was not available exclusively by MSA


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that included data on street length. The union tool was used to combine

separate countys road files to form each respective MSA (i.e.: Buffalos MSA

is composed of both Erie and Niagara counties), but after performing a

union, the street length data lost. Because of this and the time allotted for

data collection, it was impossible to calculate block lengths of MSAs which

were composed of more than one county.

Findings and Analysis

The urban form of metropolitan statistical areas was found to be associated

with the proportion of MSA residents who achieve the recommended levels of

moderate intensity physical activity. Specifically, the percentage of adults

who achieve recommended levels of physical activity is positively associated

with higher residential densities of MSAs as well as with a greater mix of land

uses. The correlation between land use mix and physical activity is higher (r

= .42) than that between residential density and physical activity (r= .13).

Figure 1. Residential Density & Physical Activity (r = .13)


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Source: American Housing Survey (2002) and Center for Disease Control (2005)

Figure 2. Land Use Mix & Physical Activity (r = .42)

Source: American Housing Survey (2002) and Center for Disease Control (2005)


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The findings of this study support the results from Frank et al. (2004) and

Frank et al. (2005). While it is evident that land use mix has a stronger,

positive correlation with physical activity as opposed to residential density,

Frank et al. (2004) also found that land use mix had the strongest

association with obesity (BMI30kg/m2) as opposed to residential density

and street connectivity.

When putting these findings into context with the built environment,

residential density can exist independently without land use mix (i.e.:

apartments that are not near any other land uses or transit). One would just

need an automobile to drive to their destination. On the other hand, if a

neighborhood has a high land use mix, it means that residents are within

walking distance of nearby shopping and are able to walk to their

destinations instead of depending on their personal automobile.

Despite the higher correlation of land use mix over density on physical

activity, both land use mix and density are closely associated. According to

the Urban Land Institute a critical mass of at least 200,000 square feet of

retail and 2,000 dwelling units within a ten-minute walk of each other is

necessary to sustain a mixed use neighborhood (Dunham-Jones et al.,

2008); therefore in order to sustain a successful mixed use neighborhood, it

is dependent on residential density.


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Conclusion

There is a clear correlation between land use mix and residential density

with the percentage of adults who achieve the recommended level of

moderate intensity physical activity in metro areas. The findings are

consistent and support previous literature (except Eid et al. (2008)) that

argues that urban form is correlated with public health. The higher the land

use mix a metropolitan statistical area is, the higher correlation there is with

being physically active. Despite the low correlation between residential

density and physical activity, there still is a correlation that supports the

notion that the higher the density a neighborhood is, the more likely it is that

its residents will be physically active. Conversely, the more sprawling in

development a neighborhood is (in terms of lower densities and a poor land

use mix), the higher the correlation there is with a lower percentage of

people achieving the recommended levels of moderate physical activity.

Based upon the literature review and the results of this study, future urbanand community policies should support higher densities and mixed use

development in specific areas of concentrated growth, namely downtowns to

promote more compact and walkable communities where residents will be

healthier and physically active. Local municipalities should make exceptions

or allow for changes in any local zoning codes that may restrict higher

density or mixed use development in a municipalitys downtown. In addition,

tax incentives should be provided to developers who are willing to invest and

redevelop downtown neighborhoods to help become mixed use and more

dense.

Endnotes


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Eid, J., Overman, H., Puga, D., and Turner, M. (2008). Fat city:

Questioning the relationship between urban sprawl and obesity.Journal of

Urban Economics, 63(2), 385-404. Retrieved from

http://www.sciencedirect.com

European Environment Agency. (2006). Urban Sprawl in Europe: The

ignored challenge. Retrieved from http://www.eea.europa.eu/

Ewing, R., Brownson, R., and Berrigan, D. (2006, December).

Relationship between urban sprawl and weight of United States youth.

American Journal of Preventive Medicine, 31(6), 464-474. Retrieved from

http://www.ajpm-online.net/

Frank, L., Andresen, M., and Schmid, T. (2004). Obesity relationships

with community design, physical activity, and time spent in cars.American

Journal of Preventive Medicine, 27(2), 87-96. Retrieved fromhttp://www.ajpm-online.net/

Frank, L., Schmid, T., Sallis, J., Chapman, J., and Saelens, B. (2005).

Linking objectively measured physical activity with objectively measured

urban form: Findings from SMARTRAQ.American Journal of Preventive

Medicine, 28(2), 117-125. Retrieved from http://www.ajpm-online.net/

Frumkin, H. (2002). Urban sprawl and public health. Public Health Reports,

117(3), 201-217. Retrieved from http://www.cdc.gov/


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Gunnell, D., Frankel, S., Nanchanal, K., Peters, T., and Smith, G.

(1998). Childhood obesity and adult cardiovascular mortality: a 57-y follow-

up study based on the Boyd Orr cohort. The American Journal of Clinical

Nutrition, 67(6), 1111-1118. Retrieved from http://www.ajcn.org

Jaret, C., Ghadge, R., Williams Reid, L., and Adelman, R. (2009). The

measurement of suburban sprawl: An evaluation. City & Community, 8(1),

65-84. Retrieved from http://www3.interscience.wiley.com/

Kachigan, K. (1991). Multivariate Statistical Analysis: A conceptual

introduction. New York, NY: Radius Press.

Lopez, R. (2004). Urban sprawl and risk for being overweight or obese.

American Journal of Public Health, 94(9), 1574-1579. Retrieved from

http://ajph.aphapublications.org/

National Institute of Diabetes, Digestive, and Kidney Diseases.

(2007, June). Statistics Related to Overweight and Obesity. Retrieved from

www.win.niddk.nih.gov

Robert Wood Johnson Foundation. (2010, January 14). Robert Wood

Johnson FoundationStatement Regarding Release of Estimates of Obesity

Prevalence Among U.S. Children and Teens. Retrieved from

http://www.rwjf.org/


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Rodriguez, D., Khattak, A., and Evenson, K. (2006). Can new urbanism

encourage physical activity? Comparing a new urbanist neighborhood with

conventional suburbs.Journal of the American Planning Association, 72(1),

43-54. Retrieved from http://www.informaworld.com/

Smart Growth America. (2005). Measuring Sprawl and its Impact.

Retrieved from http://www.smartgrowthamerica.org/

Smart Growth America. (2003, September). Measuring the Health Effects

of Sprawl; A national analysis of physical activity, obesity, and chronic

disease. Retrieved from http://www.smartgrowthamerica.org/

United States, Department of Health and Human Services; Assistant

Secretary for Planning and Evaluation. (2010). Childhood Obesity.

Retrieved from http://aspe.hhs.gov/health/reports/child_obesity/

Appendix 1. Collected Raw Data from MSAs

Metropolitan

Statistical

Area (MSA)

Physical

Activity (%)1Population

Density (personsper square mile)

Mix of Uses

(%)2

Buffalo, NY53% 746.64 86.21%

Charlotte, NC-

SC

42% 444.00 52.75%

Columbus, OH 50% 490.30 66.92%

Dallas, TX 42% 753.70 73.69%


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Fort Worth

Arlington, TX

49% 987.60 71.81%

Kansas City,

MO-KS

47% 329.80 66.92%

Miami Ft.

Lauderdale, FL

46% 1230.00 82.72%

Milwaukee, WI 54% 1027.90 72.68%

Phoenix, AZ 54% 333.80 78.47%

Portland, OR-

WA

56% 373.60 71.39%

Riverside San

Bernardino

Ontario

50% 118.80 66.33%

San Diego, CA 54% 670.00 75.36%

1 Percentage of adults who achieve 30+ minutes of moderate physical activity fiveor more days per week, or vigorous physical activity for 20+ minutes three or moredays per week.

2 Percentage of residents within satisfactory neighborhood shopping within 1 mile.

Appendix 2. Data and Data Sources

Variable Factor SourcePhysical Activity Adults with 30+

minutes of moderatephysical activity fiveor more days perweek, or vigorousphysical activity for

Center for DiseaseControl SMART: BRFSSCity and County Data(2005) used 2005 datato establish causalityand also 2005 was the


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20+ minutes three ormore days per week

most recent physicalactivity data available

Residential Density Gross populationdensity in persons persquare mile

American HousingSurvey (2002)

Land Use Mix Percentage ofresidents withinsatisfactoryneighborhoodshopping within 1 mile

American HousingSurvey (2002)

the built environment's influence on physical activity [report]

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