Citation
A social ecological study examining individual, household, and neighborhood influences on children's physical activity and active commuting to/from school

Material Information

Title:
A social ecological study examining individual, household, and neighborhood influences on children's physical activity and active commuting to/from school
Creator:
Salomonsen-Sautel, Stacy
Publication Date:
Language:
English
Physical Description:
xix, 202 leaves : ; 28 cm

Subjects

Subjects / Keywords:
School children -- Transportation -- Colorado -- Denver Metropolitan Area ( lcsh )
Commuting -- Colorado -- Denver Metropolitan Area ( lcsh )
Exercise -- Physiological aspects ( lcsh )
Students -- Crimes against -- Colorado -- Denver Metropolitan Area ( lcsh )
Neighborhoods -- Colorado -- Denver Metropolitan Area ( lcsh )
Commuting ( fast )
Exercise -- Physiological aspects ( fast )
Neighborhoods ( fast )
School children -- Transportation ( fast )
Students -- Crimes against ( fast )
Colorado -- Denver Metropolitan Area ( fast )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Bibliography:
Includes bibliographical references (leaves 174-202).
General Note:
Department of Health and Behavioral Sciences
Statement of Responsibility:
by Stacy Salomonsen-Sautel.

Record Information

Source Institution:
|University of Colorado Denver
Holding Location:
|Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
747821026 ( OCLC )
ocn747821026
Classification:
LD1193.L566 2011d S34 ( lcc )

Full Text
A SOCIAL ECOLOGICAL STUDY EXAMINING
INDIVIDUAL, HOUSEHOLD, AND NEIGHBORHOOD INFLUENCES
ON CHILDRENS PHYSICAL ACTIVITY AND
ACTIVE COMMUTING TO/FROM SCHOOL
by
Stacy Salomonsen-Sautel
B.A., University of Southern Colorado, 1997
M.S., University of Denver, 2000
A thesis submitted to the
University of Colorado Denver
in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Health and Behavioral Sciences
2011


2011 by Stacy Salomonsen-Sautel
All rights reserved.


This thesis for the Doctor of Philosophy
degree by
Stacy Salomonsen-Sautel
has been approved
by
Alyson Shupe
>!
Date
Deborah S.K. Thomas


Salomonsen-Sautel, Stacy (Ph.D., Health and Behavioral Sciences)
A Social Ecological Study Examining Individual, Household, and Neighborhood
Influences on Childrens Physical Activity and Active Commuting to/from School
Thesis directed by Associate Professor John A. Brett
ABSTRACT
Children need to develop active living behaviors that will continue into
adolescence and adulthood as well as increase their physical activity to reduce the
risk of obesity and related chronic illnesses. Using a multilevel social ecological
approach, the overall aim of this study was to identify neighborhood correlates of
childrens physical activity and active commuting to/from school. Methods: A cross-
sectional research design was employed to acquire and link secondary data from
several sources at the individual and neighborhood level. The sample included 863
children, ages 5 to 14, and a household member living in 89 neighborhoods/ZIP codes
in the Denver metropolitan area. Child data were obtained from the 2005 and 2007
Child Health Survey (CHS), which was directly linked to family members data from
the 2005 and 2007 Behavioral Risk Factor Surveillance System (BRFSS).
Neighborhood data were linked to child and household data. Results: The combined
partial proportional odds model revealed that mean neighborhood safety (for five or
one to four days versus zero days) and percentage of minority residents were
significantly related to childrens active commuting to/from school. For boys, the
individual level hierarchical linear model found that family members physical
activity influenced boys physical activity, and the separate neighborhood models
revealed three neighborhood variables (percentage of individuals with at least a BA
degree, reported incidents for rape, and reported incidents for all violent offenses) that
directly influenced boys physical activity. For girls, the individual level multiple
linear regression model revealed that family members physical activity predicted
girls physical activity, and the separate neighborhood models found four
neighborhood variables (population density, reported incidents for murder, reported
incidents for robbery, and number of school playgrounds) that directly influenced
girls physical activity. Contrary to the hypothesis, parental perceptions of
neighborhood safety did not mediate the effect of total violent crime on boys or girls
physical activity or active commuting to/from school. In addition, childrens physical


activity was not correlated with their active commuting. Conclusion: Neighborhood
safety is an important factor for discretionary active commuters. Future research
should explore in greater detail the influence of crime on boys and girls physical
activity.
This abstract accurately represents the content of the candidates thesis. I recommend
its publication.
Signed
John A. Brett


DEDICATION
I dedicate this dissertation to my husband, Jeff, for your incredible support and
understanding and to my son, AJ, for your wonderful smile and laughter.


ACKNOWLEDGEMENT
I would like to acknowledge the financial assistance of the Health and
Behavioral Sciences Department. I would also like to acknowledge the agencies and
departments that provided financial contributions to pay for the physical activity,
active commuting to/from school, and neighborhood safety questions in the 2005 and
2007 CHS: Colorado Physical Activity and Nutrition Program, Colorado Department
of Public Health and Environment; Rocky Mountain Prevention Research Center,
University of Colorado Denver; Colorado Injury Control Research Center, Colorado
State University; and Maternal and Child Health Department, Colorado Department
of Public Health and Environment. Many people provided assistance and were
integral to the completion of this dissertation. My husband, Jeff, always provided
encouragement and support to complete my dissertation, and frequently, reminded me
to just get it done. My son, AJ, always provided much appreciated breaks for
playtime and made all the hard work completing my dissertation worthwhile. A
heartfelt thank you goes to my committee members John A. Brett, L. Miriam
Dickinson, Alyson Shupe, and Deborah S.K. Thomas for their much appreciated
advice and mentorship as well as wisdom. A special thank you goes to the
individuals who completed the CHS and the BRFSS, the staff at the Colorado
Department of Public Health and Environment who collected data for the CHS and
the BRFSS and generously provided the data for this dissertation, as well as all the
individuals from law enforcement and other agencies who provided crime data and
physical activity resource/GIS data for this project. Not only did these individuals
provide much needed data, they kindly answered all my many questions. I would like
to thank the GIS Coordinator of the FAST GIS lab at the University of Colorado
Denver, Sue Hawkins, M.Eng., for her assistance and willingness to answer all my
GIS questions. I doubt I would have been able to complete the GIS work without her
guidance and assistance. I would especially like to thank Cohort 10 (Aimee Ferraro,
Bridget Gaglio, Colleen Julian, and Megan Wilson) for being great role models and
for providing support and enjoyment during this process. I would like to
acknowledge a dear friend, Karen Abrahamson, who told me about the CHS, which
started my interest in this project. She was always very supportive and I deeply wish
she could have read my dissertation. I want to thank all of you very much for your
role in this dissertation because without your assistance this work would not have
been possible. Thank you.


TABLE OF CONTENTS
Figures.................................................... xv
Tables................................................... xvi
CHAPTER
1. INTRODUCTION................................................ 1
Conceptual Model and Study Framework..................... 2
Organization of the Dissertation......................... 6
2. THEORETICAL FRAMING......................................... 7
History of Social Ecological Frameworks.................. 9
Social Ecological Frameworks............................. 9
Benefits and Limitations of Social Ecological Frameworks. 11
Theory of Fundamental Social Causes................. 12
Influence of Crime on Physical Activity............. 14
Broken Windows Theory............................... 15
3. REVIEW OF THE LITERATURE FOR CHILDRENS
PHYSICAL ACTIVITY.............................................. 17
Individual Level Correlates of Childrens Physical Activity.... 18
Childrens Demographic Factors................ 19
Parental Physical Activity.................... 19
viii


Parental Body Mass Index........................ 20
Parental Demographic Factors.................... 20
Neighborhood Level Correlates of Childrens Physical
Activity.............................................. 21
Neighborhood Safety............................. 21
Neighborhood Crime.............................. 24
Neighborhood Playgrounds, Parks, Open Space,
Trails, and Recreation Facilities............... 26
Population Density.............................. 29
Neighborhood Demographic Composition............ 30
Studies Using Multilevel Frameworks to Examine Physical
Activity.............................................. 31
Studies Using Social Ecological Frameworks to Examine
Childrens Physical Activity.......................... 33
Relationship between Childrens Physical Activity and Active
Commuting to/from School.............................. 34
4. REVIEW OF THE LITERATURE FOR CHILDRENS ACTIVE
COMMUTING TO/FROM SCHOOL.................................... 36
Rates of Active Commuting to/from School.............. 37
Barriers to Active Commuting to/from School........... 38
Individual Level Correlates of Active Commuting to/from
School................................................ 38
IX


Neighborhood Level Correlates of Active Commuting to/from
School................................................ 40
Individual Level and Neighborhood Level Correlates of
Active Commuting to/from School....................... 40
Studies Using Social Ecological Frameworks to Examine
Active Commuting to/from School....................... 41
5. RESEARCH DESIGN AND METHODS.............................. 44
Research Questions and Hypotheses..................... 44
Study Design.......................................... 45
Sample................................................ 47
Data Acquisition and Measures......................... 50
Child and Household Data....................... 50
Neighborhood Data.............................. 52
Outcome Variables.............................. 55
Data Analyses......................................... 57
Data Management and Data Analysis Overview..... 57
Preliminary Analyses........................... 58
Relationship between Childrens Active Commuting
to/from School and Physical Activity........... 60
Results for Descriptive Analyses...................... 60
x


6. GIRLS AND BOYS PHYSICAL ACTIVITY.......................... 65
Primary Analyses for Girls Physical Activity.......... 65
Individual Level Analyses....................... 65
Neighborhood Level Analyses..................... 66
Results for Girls Physical Activity................... 66
Bivariate Results............................... 66
Multivariate Individual Level Results............. 70
Neighborhood Level Results........................ 72
Primary Analyses for Boys Physical Activity........... 82
Individual Level Analyses....................... 82
Neighborhood Level Analyses..................... 82
Results for Boys Physical Activity.................... 83
Bivariate Results............................... 83
Multivariate Individual Level Results............. 87
Neighborhood Level Results........................ 88
Discussion.............................................. 106
Summary of Results............................... 106
Summary of Results with Prior Research.......... 112
xi


Summary of Results for Relationship between
Childrens Physical Activity and Active Commuting
to/from School.................................. 115
Summary of Results with Theory.................. 116
Conclusions and Future Directions..................... 117
Implications and Future Directions.............. 118
Future Research................................. 121
7. ACTIVE COMMUTING TO/FROM SCHOOL........................... 123
Analyses.............................................. 123
Individual Level Analyses....................... 123
Neighborhood Level Analyses..................... 124
Results............................................... 124
Bivariate Results............................... 124
Multivariate Individual Level Results........... 128
Neighborhood Level Results...................... 130
Discussion............................................ 138
Summary of Results.............................. 138
Summary of Results with Prior Research.......... 140
Summary of Results with Theory.................. 142
Conclusions and Future Directions..................... 143
xii


Implications and Future Directions
143
Future Research.................................. 146
8. MEDIATION.................................................. 148
Mediator............................................... 148
Mediation Analyses..................................... 149
Results................................................ 149
Discussion............................................. 150
Conclusions and Future Research........................ 150
9. LIMITATIONS................................................ 152
Small Intraclass Correlation Coefficients.............. 152
Differences in Acquisition of Crime Data from Law
Enforcement Agencies................................... 154
Spatial and Temporal Differences in the Physical Activity
Resource Data.......................................... 154
Application of Census Data via ZCTAs................... 155
Using Data from the BRFSS and CHS for Another Purpose
than Designed.......................................... 155
Lack of Information on Distance to/from School......... 156
No Measure of Outside Physical Activity................ 156
xiii
10. CONCLUSIONS
157


APPENDICES
A: Denver Metropolitan Area ZIP Codes Included in
Study........................................................ 163
B: Questions from the 2005 and 2007 CHS and BRFSS............ 164
C: Additional Information on Variables....................... 168
Neighborhood Crime.................................... 168
Physical Activity Resources........................... 170
Public Elementary and Middle School Playgrounds. 170
Parks, Open Space, and Recreation Areas........ 170
Trails......................................... 171
Neighborhood Demographic Composition.................. 173
REFERENCES......................................................... 174
xiv


LIST OF FIGURES
Figure
1.1 Social Ecological Model of Childrens Physical Activity and Active
Commuting to/from School..................................... 4
1.2 Perceptions of Neighborhood Safety Mediating the Relationship
between Neighborhood Crime and Childrens Physical Activity and
Active Commuting to/from School.............................. 5
2.1 Combined Social Ecological Framework......................... 8
2.2 How Crime May Influence Physical Activity Proposed by
Loukaitou-Sideris and Eck.................................... 15
5.1 Schematic of Research Process................................ 46
5.2 Diagram of Sample............................................ 49
6.1 Separate Neighborhood Factors Related to Girls Physical Activity
Based on a Social Ecological Framework....................... 109
6.2 Separate Neighborhood Factors Related to Boys Physical Activity
Based on a Social Ecological Framework....................... 111
7.1 Combined Neighborhood Factors Related to Childrens Active
Commuting to/from School Based on a Social Ecological
Framework.................................................... 139
8.1 Baron and Kennys Mediation Relationship..................... 148
10.1 Combined Social Ecological Framework for Childrens Active
Commuting to/from School..................................... 159
10.2 Combined Social Ecological Framework for Childrens Physical
Activity..................................................... 161
xv


LIST OF TABLES
Table
5.1 Neighborhood demographic variables created from the U.S.
Census 2000 summary file 3 dataset........................... 54
5.2 Summary of variables by level of analysis, variable type, type of
measurement, and data source................................. 56
5.3 Descriptive statistics............................................. 62
6.1 Bivariate analyses with all variables and girls physical activity. 68
6.2 Results from the individual level multiple linear regression model.. 71
6.3 Results from the separate neighborhood level multiple linear
regression model for neighborhood population density, controlling
for covariates............................................... 73
6.4 Results from the separate neighborhood level multiple linear
regression model for percentage of children in poverty, controlling
for covariates............................................... 74
6.5 Results from the separate neighborhood level multiple linear
regression model for percentage of individuals in poverty,
controlling for covariates................................... 75
6.6 Results from the separate neighborhood level multiple linear
regression model for reported incidents for murder/manslaughter,
controlling for covariates................................... 76
6.7 Results from the separate neighborhood level multiple linear
regression model for reported incidents for robbery, controlling for
covariates................................................... 77
6.8 Results from the separate neighborhood level multiple linear
regression model for number of public elementary and middle
school playgrounds, controlling for covariates............... 78
xvi


6.9 Results from the separate neighborhood level multiple linear
regression model for length of trails, controlling for covariates. 79
6.10 Results from the combined neighborhood level multiple linear
regression model, controlling for covariates..................... 81
6.11 Bivariate analyses with all variables and boys physical activity.... 85
6.12 Results from the individual level hierarchal linear model......... 88
6.13 Results from the separate neighborhood level hierarchal linear
model for percentage of individuals without a high school degree,
controlling for covariates....................................... 89
6.14 Results from the separate neighborhood level hierarchal linear
model for percentage of individuals with at least a BA degree,
controlling for covariates....................................... 90
6.15 Results from the separate neighborhood level hierarchal linear
model for percentage of households at or above the median
income, controlling for covariates............................... 91
6.16 Results from the separate neighborhood level hierarchal linear
model for percentage of children in poverty, controlling for
covariates....................................................... 92
6.17 Results from the separate neighborhood level hierarchal linear
model for reported incidents for murder/manslaughter, controlling
for covariates................................................... 93
6.18 Results from the separate neighborhood level hierarchal linear
model for reported incidents for rape, controlling for covariates.... 94
6.19 Results from the separate neighborhood level hierarchal linear
model for reported incidents for robbery, controlling for
covariates....................................................... 95
6.20 Results from the separate neighborhood level hierarchal linear
model for reported incidents for aggravated assaults, controlling
for covariates................................................... 96
xvii


6.21 Results from the separate neighborhood level hierarchal linear
model for reported incidents for all violent offenses, controlling
for covariates.................................................... 97
6.22 Results from the separate neighborhood level hierarchal linear
model for size of open space, controlling for covariates.......... 98
6.23 Results from the separate neighborhood level hierarchal linear
model for length of trails, controlling for covariates............ 99
6.24 Results from the separate neighborhood level hierarchal linear
model for percentage of individuals with at least a BA degree,
controlling for three covariates only............................. 100
6.25 Results from the separate neighborhood level hierarchal linear
model for percentage of children in poverty, controlling for three
covariates only................................................... 101
6.26 Results from the separate neighborhood level hierarchal linear
model for reported incidents for all violent offenses, controlling
for three covariates only....................................... 102
6.27 Results from the separate neighborhood level hierarchal linear
model for length of trails, controlling for three covariates only. 103
6.28 Results from the combined neighborhood level hierarchal linear
model, controlling for covariates............................... 104
6.29 Results from the combined neighborhood level hierarchal linear
model, controlling for three covariates only.................... 105
6.30 Spearmans rho correlations and significance values for
neighborhood variables from combined multiple linear regression
model for girls physical activity.............................. 107
6.31 Spearmans rho correlations and significance values for
neighborhood variables from combined hierarchical linear model
for boys physical activity..................................... 107
7.1 Bivariate analyses with all variables and active commuting to/from
school.......................................................... 126
xviii


7.2 Results from the individual level partial proportional odds model... 129
7.3 Results from the separate neighborhood level partial proportional
odds model for total population, controlling for covariates.. 131
7.4 Results from the separate neighborhood level partial proportional
odds model for percentage of minority residents, controlling for
covariates................................................... 132
7.5 Results from the separate neighborhood level partial proportional
odds model for percentage of individuals in poverty, controlling
for covariates............................................... 133
7.6 Results from the separate neighborhood level partial proportional
odds model for mean neighborhood safety, controlling for
covariates and parental perceptions of neighborhood safety... 134
7.7 Results from the combined neighborhood level partial proportional
odds model, controlling for covariates and parental perceptions of
neighborhood safety.......................................... 136
xix


CHAPTER 1
INTRODUCTION
An active lifestyle in childhood has known health benefits. These benefits
include normal skeletal development and peak bone mass, increases in aerobic
fitness, HDL cholesterol, bone mineral density, psychological health (e.g., anxiety,
depression, self-esteem, and self-concept), and academic achievement, as well as
decreases in problem or delinquent behavior (1-3). In addition, research has shown
that a physically active lifestyle in childhood predicts a continuing physically active
lifestyle in adulthood (4). Therefore, establishing physically active lifestyles in
childhood may extend these healthy behaviors into adolescence and adulthood.
Alternatively, unhealthy diets and physical inactivity in children can lead to
early onset of chronic diseases (5, 6). Even though physical activity is an individual
act, physical inactivity is not solely an individual problem because social and physical
environments may influence childrens physical activity. Household and
neighborhood environments are important for physical activity because they may
promote or restrict childrens physical activity and active commuting to/from school.
Children are influenced by their parents actions and behaviors; parents act as role
models for physical activity. Children are dependent on parents for physical activity
support, such as rides to sporting events and money for these activities. Children also
follow parents rules and guidelines as well as normative behaviors; parents model
sedentary behaviors by driving children to/from school. Without positive physical
activity role models, children may face even more difficulties and obstacles when
learning and mastering active lifestyle behaviors. Additionally, neighborhoods may
influence childrens physical activity. If neighborhoods do not have parks and
playgrounds, then there are fewer opportunities and venues for children to be active.
If parents perceive their neighborhood to be unsafe, then children may not be allowed
to play outside, may have limited supervised time for outside activities, and/or may
not be allowed to walk or bicycle to/from school.
In fact, some researchers believe that social and physical environments are
largely responsible for the epidemic of inactivity (2). Therefore, a paradigm shift
from exclusive individual based models is necessary. Research outside of the
traditional focus of individual characteristics needs to be completed to determine
correlates of childrens physical activity and to develop ways to establish physically
active lifestyles that will extend into adolescence and adulthood.
More than a decade ago, social ecology models were proposed for health
promotion; although, there is still much work to fully utilize this framework in the
area of childrens physical activity and active commuting to/from school. A few


studies used a social ecology approach to examine childrens physical activity (7-11)
and another study used a social ecology framework to assess childrens walking and
bicycling (12). The former studies were published very recently (2008 2010). Only
six social ecology based studies of childrens active commuting to school have been
published and most were published in the last few years (13-18). In the past decade,
multilevel studies and multilevel analyses grew as a result of statistical analyses and
software that account for the hierarchical nature of these studies (19, 20). Even with
the recent increase in multilevel studies, there is a scarcity of studies that used a
multilevel approach to assess physical activity in children (21-28). Additionally,
there is a need for theory and models to provide possible explanations for how factors
at the neighborhood and individual levels influence childrens physical activity and
active commuting to/from school.
Even though the above studies were based on a social ecological perspective
and/or used a multilevel approach, most of the prior studies did not assess the
influence of the same household and neighborhood characteristics as this study. For
instance, some studies (9, 27) did not include objective measures of crime with
perceptions of neighborhood safety. Including both measures is important because
objective crime and perceptions of safety may impact behaviors differently.
Therefore, this study used a multilevel social ecological framework and built on
previous research by examining the separate and joint influences of many critically
important household and neighborhood factors on childrens physical activity and
active commuting to/from school,1 while controlling for individual characteristics of
children. The results of this study were framed within a social ecology model that
incorporated principles from other theories in an attempt to provide explanations for
how factors at the neighborhood and individual levels affect childrens physical
activity and active commuting to/from school. In addition, this methodological
approach identified important correlates of these behaviors, which may guide the
development of future interventions and policies aimed at increasing childrens
physical activity and active commuting to/from school.
Conceptual Model and Study Framework
The conceptual model presented below in Figure 1.1 illustrates the
hypothesized relationships between individual, household, and neighborhood factors
1 In this study, active commuting to school is defined as walking or bicycling to or from school. The
phrase active commuting to school is used as a generic term to reflect both overall active commuting to
or from school. In addition, the terms active commuting to school and active commuting to/from
school are used interchangeably.
2


examined in this study. Household factors were hypothesized to be related to
childrens physical activity. After adjustment for childrens individual
characteristics, neighborhood factors were anticipated to influence childrens physical
activity and active commuting to school. As seen in Figure 1.1, direct relationships
between neighborhood variables and childrens physical activity and active
commuting to/from school were hypothesized. Similar factors were expected to
affect both outcomes; although, active commuting to/from school and physical
activity were examined as separate outcomes due to the hypothesized relationship
between these variables and the possible different factors related to them. The fourth
primary hypothesis stated that childrens active commuting to school will be
positively correlated with their physical activity. As seen in Figure 1.2, parental
perceptions of neighborhood safety were hypothesized to mediate the relationship
between crime and childrens physical activity and active commuting to/from school.
Specifically, the following research questions and hypotheses were tested in
this study of 863 children (ages 5 to 14) and a family member living in 89 ZIP
codes/neighborhoods in the Denver metropolitan area:
1. How do family and neighborhood characteristics influence childrens physical
activity, above and beyond the individual characteristics of children?
Hypothesis la: Household factors are positively associated with
childrens physical activity. For example, family members physical
activity and low body mass index (BMI) will be related to high levels of
physical activity in children.
Hypothesis lb: Neighborhood factors will directly influence childrens
physical activity.
2. How do neighborhood characteristics influence childrens active commuting
to school, above and beyond the individual characteristics of children?
Hypothesis 2: Neighborhood factors will directly influence childrens
active commuting to school.
3. Do parental perceptions of neighborhood safety mediate the relationship
between crime and childrens physical activity and active commuting to
school?
Hypothesis 3a: Parental perceptions of neighborhood safety mediate the
relationship between crime and childrens physical activity.
Hypothesis 3b: Parental perceptions of neighborhood safety mediate the
relationship between crime and childrens active commuting to school.
4. What is the relationship between active commuting to school and physical
activity?
Hypothesis 4: Children who are physically active are more likely to
actively commute to school.
3


-fc.
Active Commuting
to/from School
Figure 1.1: Social Ecological Model of Childrens Physical Activity and Active Commuting to/from School


Research Question 3
Figure 1.2: Perceptions of Neighborhood Safety Mediating the Relationship between
Neighborhood Crime and Childrens Physical Activity and Active Commuting
to/from School
5


Organization of the Dissertation
This dissertation is organized based on the authors path of exploration
beginning with Chapter 2, which describes the theoretical basis of the study. Most of
the chapters are separated based on the two outcome measures of this study. The
literature review for childrens physical activity is discussed in Chapter 3 and Chapter
4 describes the literature for childrens active commuting to/from school. The
research questions, hypotheses, study design, sample, data acquisition, and
preliminary analyses are detailed in Chapter 5. Chapter 6 separately discusses the
analyses and results for girls and boys physical activity, and then combines the
discussion, areas for future research, and the conclusion for boys and girls. The
analyses, results, discussion, future research, and conclusion for childrens active
commuting to/from school are described in Chapter 7. The mediation analyses,
results, areas for future research, and conclusion are discussed in Chapter 8. Chapter
9 includes a summary of the limitations of this study. The final chapter summarizes
the results, implications, and future research, as well as provides a synopsis of the
theoretical principles that were combined with a social ecology model to explain the
results. This dissertation concludes with Appendices containing a map of the ZIP
codes included in this study (Appendix A), questions from the 2005 and 2007 Child
Health Survey and Behavioral Risk Factor Surveillance System (Appendix B), and
methods and procedures used to acquire and create more complex neighborhood
variables (Appendix C).
6


CHAPTER 2
THEORETICAL FRAMING
In the past, researchers and practitioners focused on individual factors, such as
cognitions, beliefs, and motivations, as determinants of physical inactivity using a
range of individual based theories (29). Some have labeled these interventions as
victim blaming due to the intense focus exclusively on individuals (30). Individual
based research reveals small associations with physical activity and cannot identify
all the correlates to improve public health interventions (29).
This history, as well as the need for broad framing models that provide a
comprehensive approach toward understanding childrens physical activity, has
stimulated interest in using social ecological frameworks to explore the numerous
factors that influence physical activity. According to Sallis and Owen, the most
promising models hypothesize that behavior is influenced by intrapersonal, social,
and physical environmental variables (Italics in original, p. 133) (2). Individual
factors are important but social and physical environmental determinants of physical
activity need to be examined because these factors frequently influence physical
activity directly and indirectly through socially learned behaviors.
This chapter begins with a brief history of social ecological frameworks and
then discusses the primary concepts of social ecological models as well as the
benefits and limitations of this approach. Because there is a scarcity of theories
explaining relationships between constructs at different levels in a social ecology
model, this study followed the example of Best et al. by developing an overarching
framework incorporating other theories with a social ecology model (31). This study
incorporated principles from three theories to support the social ecological model and
explain the results of this study. The theory of fundamental social causes, Loukaitou-
Sideris and Ecks framework of the influence of crime on physical activity, and the
broken windows theory are summarized below. Figure 2.1 displays the proposed
overarching framework combining a social ecology model with these theories.
7


<50
Theory of
Fundamental
Social Causes
Figure 2.1: Combined Social Ecological Framework


History of Social Ecological Frameworks
Social ecological frameworks originated from ecological systems theory,
which sought to understand human development (32, 33). Reciprocal interactions
between a child and his/her environment are seen as the primary catalysts of human
development. Understanding human development requires examination of
multiperson systems of interaction not limited to a single setting and must take into
account aspects of the environment beyond the immediate situation containing the
subject (p. 514) (32). Childhood development occurs as a result of the reciprocal
interactions between characteristics of the child and family processes, which are also
influenced by characteristics of the neighborhood and society (34).
According to Bronfenbrenner, 77ze ecological environment is conceived
topologically as a nested arrangement of structures, each contained within the next
(Italics in original, p. 514) (33). Based on ecological systems theory, the organization
of the environment is hierarchical, which means environmental characteristics at one
level can moderate or mediate environmental characteristics at other levels. For
example, the influence of a family members physical activity on a childs physical
activity may be strengthened or weakened by neighborhood factors, such as the
availability of neighborhood parks and playgrounds.
Social Ecological Frameworks
In general, ecology refers to relations between organisms and their
environment; social ecology examines peoples interactions with their physical and
socio-cultural environments. In contrast to human ecology, which focuses on the
interrelations among people and geographic environments, social ecology emphasizes
the social, institutional, and cultural contexts of the people-environment relations and
is based on core principles about the relationship among humans and their social and
physical environments (35, 36).
According to Sallis and Owen, ecological models are based on the following
principles and guidelines:
1) factors at multiple levels affect health behaviors and well
developed models specify how the factors interact;
2) behavior is influenced by myriad environmental features and
these environmental aspects affect behavior directly and
indirectly via perceptions of individuals; and
3) behavior specific factors should be articulated in ecological
models (37).
9


Social ecological models are important because they assert that the physical
environment has direct effects on physical activity. In other words, these
environmental influences do not need to be mediated by psychological variables.
Additionally, the social ecological perspective provides a theoretical framework for
understanding the reciprocal relationship among individuals and their social and
physical milieu (36).
The primary notion of social ecological models of health behavior is that
physical and socio-cultural environments influence behaviors by restricting or
promoting certain actions. For instance, if a parent does not feel safe walking in a
park or along neighborhood streets, then s/he probably will not walk for exercise and
may not allow his/her child to play in the neighborhood parks or school playgrounds
or actively commute to/from school. In addition, a dynamic relationship can occur as
individuals interact with the multiple social and physical environmental layers that
are part of their lives. For example, a child could prefer sedentary behaviors to the
exclusion of physical activity; although, living in a safe neighborhood with parents
who encourage active commuting to school can facilitate physical activity, which
may change the childs aversion for physical activity in other areas of his/her life.
Social ecological models are overarching frameworks or sets of theoretical
principles to aid in understanding these interrelations among individuals and their
social and physical environments, as well as the effect of these interrelations on
health and illness (36). As such the social ecological paradigm views environments
as having multiple physical, social, and cultural characteristics (36). Social
ecological frameworks do not emphasize population or societal levels to the exclusion
of individual level approaches; instead they integrate factors across multiple levels.
Social ecology frameworks add explanatory value above interpersonal and
intrapersonal factors that aid in understanding health behaviors. Flealth is assumed to
be influenced by multiple aspects of the physical environment including geography
and technology and the social environment comprising culture, family, and politics;
in addition to personal attributes, such as genetics, psychological characteristics, and
behaviors (36, 38).
Many researchers, ranging from Dzewaltowski, McLeroy, Owen, Sallis, to
Stokols, have discussed and developed social ecological frameworks2 (36-45). This
project was based on a modified social ecological framework set forth by McLeroy,
Bibeau, Steckler, and Glanz (44). Their model consists of patterned behavior as the
3 Some ecological studies address physical environmental factors only, such as access to exercise
facilities and presence of sidewalks. This study uses the term social ecology to not only refer to the
physical environment (i.e., school playgrounds and parks) but also to the social environment including
family members physical activity that may be socially learned by a child.
10


outcome, which is determined by factors in five levels. The intrapersonal level
consists of characteristics of the individual including attitudes, behaviors, and skills.
The interpersonal level includes primary groups, formal and informal social networks,
and social support systems, such as the family. The institutional level is social
institutions with organizational attributes and operational rules and regulations, such
as schools. The community level involves relationships within organizations and
informal networks with defined boundaries, such as a neighborhood. The last level,
public policy, consists of local, state, and national policies and laws. This study
combined the intrapersonal and interpersonal levels into the household and child
level (level 1) and the community level was the neighborhood level (level 2).
Benefits and Limitations of Social Ecological Frameworks
Because physical activity is influenced by a wide variety of internal and
external factors and by complex interactions of many variables (39), a comprehensive
framework for assessing factors related to childrens physical activity is critical.
Social ecological approaches provide a comprehensive framework that is unique and
beneficial for studying childrens physical activity and active commuting to/from
school. This framework is advantageous in comparison to more conventional
theoretical approaches.
Banduras social cognitive theory with an environmental perspective and
reciprocal determinism has been used to explain physical activity in children (46-50).
However, social cognitive theory asserts that environmental factors are mediated by
cognitive processes; whereas, the social ecological approach takes the perspective
that the environment has a direct influence on behavior. This direct effect is
advantageous, particularly when studying environmental influences on childrens
behavior.
The primary benefit of social ecological frameworks is their comprehensive
multilevel approach incorporating social and physical environmental factors outside
of individuals. At the same time, however, this is a limitation. Social ecological
frameworks can include many factors without determining the importance or
relevance of the factors. The everything affects everything theme of social
ecological models can be used to assert that a social ecological model includes too
many factors; by using an abundance of factors, significant relationships may be
found that are not easily interpreted or meaningful. Due to this criticism, this study
selected available individual and household variables based on prior research, such as
perceptions of neighborhood safety. This study also included other variables that had
not been jointly analyzed in previous studies including objective measures of crime,
neighborhood safety, and physical activity resources, as well as included new factors
11


consisting of aggregated neighborhood safety for childrens active commuting
to/from school.
When this project began, one of the goals was to use a multilevel social
ecological framework to determine factors related to childrens physical activity and
active commuting to/from school. At the time, there were very few studies using
multilevel analyses to test factors across levels. Most of the social ecology studies of
childrens physical activity or active commuting to/from school were published
during the time this study was completed (7-11, 13-17). As well as providing
knowledge about important correlates of childrens physical activity and active
commuting to/from school, this social ecological study adds valuable theoretical and
methodological information to this recent literature.
Theory of Fundamental Social Causes
Originally in 1995, Link and Phelan proposed the idea of social conditions as
fundamental causes of disease (51). Over the next decade, the idea evolved into the
theory of fundamental social causes (52, 53). The theory explains how individual risk
factors need to be contextualized by determining what factors put individuals at risk
initially and why social factors (i.e., socioeconomic status and social support) are
fundamental causes of disease that allow access to important resources, influence
multiple disease outcomes through multiple means, and preserve an association with
disease, even when intervening mechanisms change (51). Fundamental social causes
can be social conditions, such as relationships with intimates to social and economic
positions as well as race, socioeconomic status, gender, stressful life events of a social
nature (e.g., crime victimization), and social support (51). The theory of fundamental
social causes applies to disease, health, and health inequalities (53, 54).
A fundamental social cause (e.g., socioeconomic status) has the following
four characteristics: 1) it affects myriad disease outcomes; 2) it influences disease
outcomes via multiple risk factors; 3) the relationship between a fundamental cause
and disease is replicated over time through varying intervening means; and 4) most
importantly, fundamental social causes permit access to resources (knowledge,
money, power, prestige, and beneficial social connections) that assist in avoiding
risks or reducing the detrimental effects of disease once they occur (51, 52). In
addition, fundamental social causes influence disease even when risk factors change
because these resources can be employed in different ways in different situations. As
a consequence, the result of a fundamental cause cannot be explained solely by the
risk factors associated with a disease at any give time (51).
A primary aspect of the theory of fundamental social causes is individuals
with more resources of knowledge, money, power, prestige, and advantageous social
12


connections benefit more from new health enhancing capabilities and are more able to
decrease risks, diseases, and consequences of disease (51, 53). According to Phelan
and Link, there are at least two ways these adaptable resources are important: 1)
resources directly influence individual health behaviors by shaping whether
individuals know about, have access to, can afford, and are supported in their efforts
to have positive health behaviors and 2) resources affect access to broad contexts,
such as neighborhoods, social networks, and occupations, that vary widely in their
related risk and protective factors (53, 55). Therefore, the processes involved in the
theory of fundamental social causes occur at both the individual and contextual levels
(53). For instance, at the individual level, white collar jobs are usually safer and
include health insurance benefits in comparison to blue collar jobs. At the
neighborhood level, low income housing tends to be located in neighborhoods with
limited power and political organization, which makes residents vulnerable to
environmental hazards, pollution, and harmful social conditions (53).
To provide an example of the theory of fundamental social causes, Phelan and
Link described a causal model with death as the outcome, socioeconomic status
(SES) as the distal factor (a.k.a., fundamental social cause), and other risk factors as
proximal factors. In a traditional model, if proximal risk factors are removed, then
the relationship between SES and mortality is also expected to disappear; however,
there are several situations throughout history where this did not occur (53). Once
SES gradient causes of mortality were removed (e.g., cholera with improved water
sanitation systems), new risk factors emerged (e.g., chemical pollutants and
smoking). Those with more resources were still able to avoid these risks and/or take
advantage of the new protective factors including living in neighborhoods away from
pollutants, knowledge about lung cancer, and treatment to stop smoking, which
repeated the cycle of SES gradients in mortality (53). Thus as new advances are
made to control disease, the theory of fundamental social causes postulates that
individuals who have more resources (knowledge, money, power, prestige, and
beneficial social connections) will be able to better access and benefit from the new
knowledge, which can be used to ones health advantage (52, 54, 55). This dynamic
reproduction of the relationship between SES and disease occurs due to the adaptable
nature of resources that permit the relationship to be replicated in many varying
situations (52-55).
Phelan, Link, Diez-Roux, Kawachi, and Levin tested the theory of
fundamental social causes in mortality using the National Longitudinal Mortality
Study (52). The authors hypothesized that socioeconomic status, measured by family
income and educational attainment, will be less strongly related to mortality for less
preventable causes of death (little about prevention or treatment is known; e.g.,
arrhythmias and malignant neoplasms of the pancreas, female breast, and prostate) in
13


comparison to more preventable causes of mortality including cerebrovascular
diseases, chronic obstructive pulmonary disease, ischemic heart disease, and
malignant neoplasm of the trachea, bronchus, and lung. Their hypothesis was
supported, which corroborates the theory of fundamental social causes.
In conclusion, a fundamental social cause allows access to resources that aid
in preventing risks or decreasing the detrimental effects of disease. There are two
primary principles from the theory of fundamental social causes: 1) health advantages
can be obtained when individuals have and use these superior resources to their
benefit and 2) the means that permit advantages to accumulate change from time to
time and from place to place (55). The first principle can operate in reverse when the
lack of access to resources places individuals at greater risk of ill health. In addition,
these processes can occur at both the individual and contextual levels (53). The
theory of fundamental social causes will be used in this study to explain the effects of
neighborhood factors including education, racial/ethnic composition, and poverty as
well as household factors, such as education and income, on childrens physical
activity and active commuting to/from school.
Influence of Crime on Physical Activity
Loukaitou-Sideris and Eck proposed a framework to explain the influence of
crime on physical activity (56). As seen in Figure 2.2 below, opportunities for crime
and disorder are shaped by situational characteristics. Crime and disorder create fear
in individuals, which influences physical activity. Several co-occurring factors affect
individuals fear of crime and disorder: psychological factors like prior victimization,
familiarity with setting, and media stories; demographic factors such as gender,
race/ethnicity, age, poverty, and disability; and environmental factors involving
geographic settings, physical and social incivilities, surveillance, and lighting. The
Figure also displays some reciprocal relationships. For instance, increasing physical
activity may bring more individuals outside or to a specific setting and as a result,
may decrease fear of crime. Therefore, according to Loukaitou-Sideris and Eck,
decreasing the perception of the risk of crime is as essential as decreasing the actual
crime and risk (56). In addition, the relationships displayed in the figure depend on
the type of physical activity, type of crime or disorder, and specific situations (56).
Loukaitou-Sideris and Ecks framework will be used in this project to explain the
influence of crime and parental perceptions of neighborhood safety on childrens
physical activity and active commuting to/from school.
14


Other relationships, some of which may interact with
primary interest relationships
Figure 2.2: How Crime May Influence Physical Activity Proposed by Loukaitou-
Sideris and Eck (56)
Broken Windows Theory
Broken windows theory asserts that there is an association between disorder
and crime with small disorders leading to increasingly larger disorders and eventually
crime (57).3 This theory is based on the assumption that if environmental disorders
(e.g., a broken window) are not repaired, then there will be more environmental
disorders (e.g., more broken windows) (58). For example, if a window is broken and
is not repaired, then the remaining windows in a building will be broken because a
window that is not repaired indicates that no one cares (57). The physical
environment of a neighborhood influences individuals behaviors and a disordered
3 Disorder is defined as an incivility, boorish and threatening behavior that disturbs life, especially
urban life (p.14) (57).
15


physical environment indicates that these behaviors are tolerated. Unregulated
disorderly behavior indicates to residents that the area is unsafe. Responding
prudently, and fearful, citizens will stay off the streets, avoid certain areas, and curtail
their normal activities and associations (p.20) (57). The ultimate result of this
situation is a neighborhood whose urban life and social support/cohesion is
undermined, which leads to more disorderly behavior and serious crime (57). In this
study, broken windows theory will be used to explain a relationship between
neighborhood crime, parental perceptions of neighborhood safety, and childrens
physical activity and active commuting to/from school.
Mijanovich and Weitzman took the broken windows theory one step further to
examine school environmental disorder and feelings of safety among youth (59).
They hypothesized that disorder can indicate that no one cares about an environment.
These indications cause individuals to feel fearful, which leads to more disorder.
They found that perceived social disorder in schools was associated with feeling
unsafe.
16


CHAPTER 3
REVIEW OF THE LITERATURE FOR CHILDRENS
PHYSICAL ACTIVITY
Overall, our society is sedentary, and as a consequence, many children are
physically inactive. Physical inactivity is a public health concern due to the myriad
health consequences of an inactive lifestyle, such as possible obesity and resulting
chronic diseases. Children need to develop strong and enduring physical activity
behaviors for current and future health benefits. Increasing childrens physical
activity and continuing these healthy behaviors assists with necessary physical and
mental growth in childhood as well as may reduce the risk of obesity and related
chronic illnesses in adolescence and adulthood.
Conflicting evidence of the relationship between childrens BM1 and rates of
physical activity exists (46, 48, 60-66). Studies reported that obese children preferred
low-intensity activities, were more sedentary than non-obese children, or were
involved in fewer community organizations promoting physical activity (48, 67, 68).
Two cross-sectional studies among girls (ages 8-10 and 7-19) found that BMI was
negatively related to physical activity (60, 64). On the other hand, BMI and skinfold
thickness were not related to preschool childrens physical activity (46, 62, 63). In a
review of 108 studies, Sallis, Prochaska, and Taylor also reported that body weight
was inconsistently related to children (ages 3-12) and adolescents (ages 13-18)
physical activity (69).
Although, two recent studies revealed negative relationships with childrens
physical activity and body weight. In a study of 10 18 year olds, moderate to
vigorous physical activity was inversely related to fat mass and BMI (65). Another
study of children (ages 9-10) living in England revealed that time spent in moderate
intensity physical activity, vigorous intensity physical activity, and total physical
activity were inversely associated with waist circumference, fat mass index, and body
mass index, after controlling for sedentary time, energy intake, sleep duration, birth
weight, socioeconomic status, and maternal BMI. This relationship was strongest for
participation in vigorous intensity physical activity (66).
Children need to develop and maintain beneficial physical activity habits and
behaviors that will continue into adolescence. Increasing adolescents physical
activity is an important public health goal. Two Healthy People 2010 goals (22-6 and
22-7) are to increase the percentage of adolescents (grades 9-12) who participate in
moderate physical activity for a minimum of 30 minutes on at least five of the prior
seven days to 35% and to increase the percentage of adolescents who participate in
17


vigorous physical activity promoting cardiorespiratory fitness for 20 or more minutes
on three or more days a week to 85% (70).
The recommended levels of physical activity for children and adolescents are
to participant in at least 60 minutes of physical activity on most, preferably all, days
of the week (71). However, according to the 2007 Youth Risk Behavior Surveillance
System, only 43.7% of male and 25.6% of female students met these guidelines by
engaging in physically activity that increased their heart rate and made them breathe
hard some of the time for a total of at least 60 minutes per day on five or more days of
the week (72). In a review of physical activity studies in adolescents, Pate, Long, and
Heath found that only two-thirds of males and one-quarter of females engaged in
twenty minutes of moderate to vigorous activity three or more days a week (73).
Additionally, according to the 2008 Child Health Survey in Colorado, 0.4% of
children (ages 5 14) did not engage in any physical activity, 7.4% of children
participated in at least one hour but less than three hours of physical activity a week,
and 36.4% engaged in at least three hours but less than six hours of physical activity a
week (74).
This literature review covers five important topics related to childrens
physical activity and begins with a discussion of individual level correlates. This
discussion includes childrens demographic factors and parental influences on
childrens physical activity, such as parental physical activity, BMI, and demographic
factors. The second section consists of research involving neighborhood level
correlates, which includes neighborhood safety, neighborhood crime, physical
activity resources, population density, and neighborhood demographic composition.
Thirdly, studies using multilevel frameworks to examine physical activity are
reported. Then studies using social ecological frameworks to evaluate childrens
physical activity are discussed. Finally, this literature review concludes with a short
section on studies exploring the relationship between childrens physical activity and
active commuting to/from school. This literature review includes research based on
similar variables to this study to understand how and why analogous factors were
used.
Individual Level Correlates of Childrens Physical Activity
Numerous prior studies examined individual level correlates of childrens
physical activity. Many studies assessed childrens personal characteristics and
parental influences on childrens physical activity.
18


Childrens Demographic Factors
Several studies examined demographic factors (i.e., ethnicity/race, sex, and
age) and their relationship with childrens physical activity. Age and sex are primary
factors related to physical activity in children with boys being more active than girls
(24, 46, 50, 62, 66-69, 75-91), and as children get older their physical activity
decreases (50, 68, 69, 73, 75, 81, 84, 85, 87, 92). Research has not revealed clear
findings regarding whether or not females have a greater decrease in physical activity
than males over time. Some studies found that activity declines with age and females
have lower activity levels than males; although, females do not have a significantly
greater decrease in activity than males (50, 84, 87, 92). On the other hand, other
studies discovered a strong age related decline in physical activity, which is greater in
females than males (73, 75, 81, 85). Research assessing ethnic differences in physical
activity has not revealed consistent findings; although, when a difference was found,
non-white children were less active than white children (24, 46, 49, 69, 75, 93-95).
For instance, a study using wave 1 data from adolescents in grades 7-12 (ages 11 -
21) from the National Longitudinal Study of Adolescent Health (Add Health)
revealed that moderate to vigorous physical activity was lower for non-Hispanic
black and Hispanic adolescents compared to white adolescents (95). Conversely, a
study of ninth to twelfth grade students from Baltimore, Maryland found that African
American adolescents were more physically active than adolescents from other
racial/ethnic backgrounds (89). In summary, boys are more active than girls and
physical activity declines with age; although, research is not clear if this decrease is
greater among girls than boys, and inconclusive results exist regarding ethnic/racial
differences in childrens physical activity.
Parental Physical Activity
Parents are the most important behavioral role models for young children.
Parental modeling of physical activity is an important factor in the socialization of a
healthy lifestyle in children. As such parents physical activity behaviors are the most
commonly studied influence on childrens physical activity with the hypothesis that
physically active parents will have children who are physically active (2).
Myriad studies found a positive relationship between parents physical
activity and their childrens physical activity (48, 49, 63, 68, 79, 82, 92). Parental
physical activity was related to physical activity in preschool children during free-
play at preschool, which suggests that parental role modeling of physical activity
extends beyond the home (63). Another study reported that the amount of time
parents spent in physical activity was positively correlated with preschool childrens
19


physical activity levels (82). In fifth and sixth grade girls, direct parental modeling of
physical activity, consisting of an adult actively participating in physical activity with
a child, was positively related to girls physical activity (47). In addition, a study
using state level data from the 2003 National Survey of Childrens Health revealed
that having a mother or father who is physically inactive is associated with more days
of no vigorous physical activity and less vigorous physical activity in children (ages 6
- 17) (%).
There may be a gender component, as well as an interaction with parental
bonding, to the relationship between parents physical activity and their childrens
physical activity. Among a sample of rural fifth grade students, Trost et al. found that
mothers physical activity was related to their daughters vigorous physical activity
(49). In another study among seventh and eighth grade children, parental exercise
was related to higher levels of physical activity and had a stronger influence on
physical activity in girls than boys (79). There may be an interaction between
parental bonding and parents and childrens physical activity. A study among 57
sixth and seventh grade students found a moderating effect between childrens
bonding with their parents, parental physical activity, and childrens physical activity,
meaning that the relationship between parents and childrens physical activity
increased only when parental bonding also increased (97).
Parental Body Mass Index
Another important factor in childrens physical activity and one that may also
have a gender component is parents BMI, which was negatively related to childrens
physical activity (62, 63, 98). Two of these studies (62, 63) found this relationship to
be true with fathers but not mothers BMI. Fathers with lower BMIs had more active
preschool children (ages 2-5) than fathers with higher BMIs; although, this
relationship was not found with mothers BMI (62).
Parental Demographic Factors
Parental demographic factors, such as education and income, are related to
childrens physical activity. A study by Kimm et al. found that girls with parents who
had lower levels of education had greater declines in physical activity over time in
comparison to girls with parents who had higher levels of education (94). Among
middle and high school students, physical activity was higher for those who had a
high or medium family income and a mother with a high level of education (graduate
or professional degrees) (93). A study using a multilevel approach found that higher
family income was associated with greater physical activity in siblings (23). A study
20


using state level data from the 2003 National Survey of Childrens Health revealed
that greater household poverty was related to more days of no vigorous physical
activity in children (ages 6-17); although, this association was not found for
vigorous physical activity (96). Additionally, income affects the relationship between
parents physical activity and their childrens physical activity. Among low income
African American and Hispanic seventh grade students, family models for physical
activity had a greater influence on children as family income increased (77). In
summary, children with parents who have higher levels of education and income tend
to be more physically active in comparison to children from lower education and
income families.
Neighborhood Level Correlates of Childrens Physical Activity
Several recent studies assessed neighborhood level correlates of childrens
physical activity. These studies examined neighborhood safety, crime, physical
activity resources, population density, and neighborhood demographic characteristics
on childrens physical activity.
Neighborhood Safety
Parents perceptions of neighborhood safety have the potential to affect
childrens physical activity by limiting their time playing outdoors. There is a
positive relationship between time spent outdoors and childrens physical activity;
more outdoor activity is related to higher activity levels in children (46, 76, 98). Even
though playing outdoors is a beneficial way for children to be active, parents may
keep their children indoors due to concerns about their safety.
Prior studies examining the effects of neighborhood safety on childrens
physical activity measured the construct in slightly different ways and used parent
and/or adolescent reported measures. Additionally, only a few studies examined the
influence of childrens gender. These methodological differences may be a
contributing factor to the varying results reported below. In addition, studies reported
that certain demographic groups have greater concerns about neighborhood safety
than other groups. Children from lower SES families discussed significantly more
neighborhood hazards (traffic, trash and litter, crime, too much noise, gangs, lack of
access to parks, prejudice, and drugs) than children from higher SES families (99).
Hispanic parents reported more concerns about neighborhood safety in comparison to
black and white parents. Overall, parents reported neighborhood safety concerns
more frequently for girls (17.6%) than for boys (14.6%) (100). Another study
revealed that regardless of the racial composition of a neighborhood, African
21


Americans perceived their neighborhoods as less safe and less pleasant for physical
activity compared to whites (101).
Many studies did not find a relationship between neighborhood safety or
hazards and childrens physical activity (50, 60, 90, 99, 102-105). This lack of
association was found in studies using parent and adolescent reported perceptions of
neighborhood safety. For instance, in an Australian study of children (ages 8 9),
there was not a relationship between parental perceptions of neighborhood safety and
childrens physical activity outside school hours (105). Additionally, among
Portuguese adolescents (ages 12 18), their perceptions that crime made their
neighborhood unsafe to walk during the day or at night were not related to their
physical activity (90).
Another study revealed that parental perceptions of neighborhood safety were
related to children and adolescents physical activity but adolescents perceptions of
neighborhood safety were not related to their physical activity. A different study
found an association between perceptions of neighborhood safety and some aspects of
children and adolescents physical activity. The former study evaluated the
psychometric properties of a new survey assessing the barriers to physical activity in
neighborhood parks and streets among children (ages 5-11) and adolescents (ages
12 18). Parental reports regarding neighborhood parks and streets not being safe
because of crime were correlated with adolescents and childrens physical activity in
those venues but there was not an association with adolescents reports of safety
concerns and their physical activity. According to the authors, parental safety
concerns may be more predictive of childrens physical activity compared to
adolescents physical activity because parents are more sensitive to potential threats
to children (106).
The latter study was completed in three cities and the Neighborhood
Environment Walkability Scale for Youth was administered to parents of children
(ages 5 11), parents of adolescents, and adolescents (ages 12 18). Subscales from
this scale were compared to different types of physical activity as well as meeting
physical activity guidelines (60 minutes of physical activity five days per week or
more). After controlling for household income, gender, and race, parent reported
perceptions of neighborhood safety and crime were related to children being active in
streets and walking to shops once per week or more; however, perceptions of
neighborhood safety and crime were not related to children meeting physical activity
guidelines. Parent reported perceptions of neighborhood safety and crime were
associated with adolescents walking to parks once per week or more. Adolescent
reported perceptions of neighborhood safety and crime were related to walking to
shops once per week or more. Neither parent nor adolescent reported perceptions of
22


neighborhood safety and crime were associated with adolescents meeting physical
activity guidelines (107).
On the other hand, other studies revealed a relationship between perceptions
of neighborhood safety and childrens physical activity. These five studies used
parent reported perceptions of neighborhood safety; although, the question measuring
neighborhood safety was different. The first study used hierarchical linear modeling
to reveal that social disorder (adults loitering, adults fighting or arguing, drinking
alcohol, gangs, public intoxication, selling drugs, and prostitution) and low
neighborhood safety were related to less physical activity in youth (ages 11-16),
even after controlling for demographic variables (24). The second study found that
compared to suburban parents, inner city parents reported greater anxiety about
neighborhood safety; their childrens (ages 5-10) physical activity was negatively
correlated with their anxiety about neighborhood safety (108). A third study using
state level data from the 2003 National Survey of Childrens Health revealed that
living in an unsafe neighborhood was related to less vigorous physical activity in
children (ages 6-17); however, this association was not found for no days of
vigorous physical activity (96). This study used the same question to measure
neighborhood safety as this dissertation; although, the item was used only at the
household level. The fourth study included a sample of Canadian fifth grade students
and discovered that children living in safe neighborhoods participated in more sports
without a coach per week than children living in unsafe neighborhoods (109). The
last study was completed among fifth grade students and revealed that a favorable
social environment (collective efficacy, collective socialization of children, social
exchange, social contact, and perceived neighborhood safety) was positively related
to childrens physical activity, after adjusting for sociodemographic factors (110).
A limited number of studies examined the influence of childrens gender on
the association between neighborhood safety and physical activity. One study did not
reveal any gender differences; while, two other studies found a positive relationship
among girls. The first study examined gender similarities and differences in
adolescents (ages 12 14) moderate to vigorous physical activity. The results did
not reveal an association with male or female adolescents perceptions of
neighborhood safety and their moderate to vigorous physical activity (91). On the
other hand, Gomez, Johnson, Selva, and Sallis found that inner city seventh grade
girls perceptions of neighborhood safety were positively related to higher levels of
outdoor physical activity; although, this positive relationship was not found among
boys (111). Another study examined perceptions of neighborhood safety, exercise
aids/equipment, and the interaction of these factors on adolescents (ages 11-15)
physical activity. The results revealed a significant interaction between exercise
aids/equipment at home (e.g., swimming pool, weights, trampoline, and work out
23


videos) and girls physical activity. This significant relationship was specific to girls
living in neighborhoods perceived as less safe in comparison to neighborhoods
perceived as safe. Again, there was no relationship for male adolescents physical
activity (112).
To summarize, many studies did not find a relationship between perceptions
of neighborhood safety and childrens physical activity (50, 60, 90, 91, 99, 102-105).
Two studies revealed mixed results (106, 107) and other studies revealed a
relationship between perceptions of neighborhood safety and childrens physical
activity (24, 96, 108-112). A review of built environmental factors reported that the
inconsistent results between perceptions of neighborhood safety and physical activity
may be due to methodological differences involving the definitions of neighborhood
safety and physical activity, type of sample, and whether or not the analyses are
stratified by sex (113). Asa result of this conflicting evidence, a better understanding
is needed regarding the relationship between perceptions of neighborhood safety and
childrens physical activity. In addition, Gomez, Johnson, Selva, and Sallis revealed
that violent crime had a greater impact on girls outdoor physical activity than
neighborhood safety and these two factors independently influenced physical activity
(111). Therefore, examining neighborhood crime as well as perceptions of
neighborhood safety is necessary.
Neighborhood Crime
In some neighborhoods, parents reported that they do not allow their children
to play in the front yards or on the streets due to the chance of getting hit by stray
bullets from a drive by shooting (114). In this qualitative study of low income
African American preschoolers, there were several contextual barriers that obstructed
childrens ability to be physically active. Due to gangs, drugs, guns, and debris, the
children lived in an environment that was not inviting for physical activity, for
playing outside in their own yards, for playing in local parks, or for engaging in
formal physical activity as part of recreational community-based programs (e.g.,
YMCA). Additionally, many parents discouraged indoor physical activity due to
limited space and were not role models for physical activity. As a result, children
engaged in a lot of inside sedentary behaviors, such as television watching and video
games (114). Carlson Gielen et al.s study supported these findings by revealing that
in lower income neighborhoods parents described high rates of unpleasant walking
environments and concerns about drug dealers, crime, violence, and trash (115).
Only a handful of studies examined actual rates of crime and their association
with childrens physical activity. The studies used different measures of crime
involving crime geocoded to participants addresses, county level crime, and state
24


level crime; as a consequence, the studies revealed mixed results. Two studies did
not find an association between crime and children's physical activity; although, four
studies revealed a negative relationship between crime and childrens physical
activity.
A study of adolescents (ages 12 14) examining gender similarities and
differences in moderate to vigorous physical activity did not find an association with
crime and male or female adolescents moderate to vigorous physical activity.
Although, the lack of association may be due to the fact that crime rates were
measured at the county level and the majority of participants lived in low crime
counties (91). Another study of ninth to twelfth grade students from Baltimore,
Maryland revealed that objective and subjective (there is a lot of crime in my
neighborhood) measures of crime were not associated with adolescents physical
activity. Objective crime data included Uniform Crime Reports of type I violent
(murder, rape, aggravated assault, and robbery) and property (burglary, larceny, and
arson) crimes, which were geocoded to participants addresses (89).
Four studies reported an inverse relationship between violent crime and
childrens physical activity. The first study found that among middle and high school
students, living in a community with high crime was associated with less physical
activity (93). Another study used state level data from the 2003 National Survey of
Childrens Health and had a similar measure of violent crime rates as used in this
study but at the state level. The study revealed that violent crime rates predicted
childrens (ages 6-17) vigorous physical activity and no days of vigorous physical
activity (96). A third study used wave 1 data from adolescents in grades 7-12 (ages
11 21) from Add Health and found that total reported incidents of serious
neighborhood crime reported at the county level decreased the likelihood of
participating in moderate to vigorous physical activity (95). Lastly, a study among
inner city seventh grade girls and boys revealed that violent crime density within 14
mile of home was inversely related to girls outdoor physical activity; although, this
negative relationship was not found among boys. The authors concluded that violent
crime may be a significant barrier for girls physical activity (111).
In summary, a few studies examined actual rates of violent crime and their
association with childrens physical activity. The studies revealed inconsistent
findings with two studies reporting no association (89, 91) and four studies showing
an inverse relationship between violent crime and childrens physical activity (93, 95,
96, 111). These inconsistencies may be due to different measures of crime that were
used in the studies.
25


Neighborhood Playgrounds, Parks, Open Space,
Trails, and Recreation Facilities
Neighborhood playgrounds, parks, open space, trails, and recreation facilities
are common community features and provide a venue for children to be physically
active. Neighborhood school playgrounds (including play equipment and sports
fields) and parks provide important areas for children to play and engage in physical
activities. In addition to providing an environment for physical activity, school
playgrounds and parks are available in most neighborhoods. In San Diego,
California, school playgrounds are the most common physical activity resource in
neighborhoods; 88% of neighborhoods have at least one school playground.
Neighborhood parks are the second most available physical activity resource with
78% of neighborhoods having at least one park. Additionally, availability of these
resources is not related to SES of the neighborhood (116).
Two qualitative studies evaluated factors influencing park use, such as
distance and amenities. A Canadian study revealed that less than half (49%) of
parents/guardians watching their children at neighborhood parks frequented the park
closest to their home or daycare facility. Most traveled more than 4 km to attend the
park of their choice. For those traveling to get to the park, park amenities, such as
water attractions, shade, swings, and cleanliness, were more important than park
location. On the other hand, for those who attended parks close to their starting point,
a close location was most important (117). In another study by Corti, Donovan, and
Holman, factors influencing park use were attractiveness (i.e., trees, greenery, and
maintenance), amenities (i.e., childrens play equipment), and features (i.e., water or
birdlife) of a park (118). Park size and availability of paths were directly related to
walking or bicycling. Therefore according to these studies, parks need to have
desired amenities including play equipment, water attractions, shade, swings, and
cleanliness, be in close proximity, and be accessible with an appropriate size and
aesthetics in order for parks to be used for physical activity.
Other studies examined the existence, amount, and distance of neighborhood
playgrounds, parks, open space, trails, and recreation facilities on childrens overall
physical activity and physical activity within these locations. Studies measuring the
existence and the influence of physical activity resources on childrens physical
activity have assessed both objective and subjective resources with most of the
studies examining self-reported physical activity resources. Three studies were
completed outside the U.S. and revealed that subjectively reported physical activity
resources were associated with different types of physical activity in children.
Among Australian children (ages 10-12), beliefs that there were no parks or
sports grounds near their homes were related to fewer days walking or bicycling per
26


week (12). A Canadian study of fifth grade students discovered that children living in
neighborhoods with good access to playgrounds, parks, and recreation facilities (as
reported by parents) participated in more sports with a coach per week than children
living in neighborhoods with poor access to these facilities (109). A Portuguese study
of adolescents (ages 12-18) revealed that their perceptions of bicycle facilities in or
near their neighborhood were not related to their physical activity; although, the
perception that their neighborhood has several free or low cost recreation facilities
was associated with girls physical activity, even after adjusting for girls age (90).
Similar results were reported in four studies based in the U.S. The first study
was completed among sixth grade girls and found that perceptions of having well-lit
streets at night, having a lot of traffic in the neighborhood, having bicycle or walking
trails in the neighborhood, and having access to physical activity facilities were
related to greater non-school physical activity (119). Another study based on
adolescents (ages 12-17) self-reports revealed that access to a safe park was
positively related to regular physical activity for adolescents in urban areas compared
to rural areas (120). A third study found that parks and playgrounds were positively
correlated with physical activity in male, but not female, middle school students
(121). Lastly, a study of ninth to twelfth grade students from Baltimore, Maryland
found that park use, objective park availability, and perceived park availability were
marginally associated (p < 0.10) with adolescents physical activity (89).
These seven studies revealed the variations in the literature regarding the
influence of physical activity resources on childrens physical activity. Physical
activity resources were measured differently, ranging from having access to facilities
to actual park use, and physical activity was measured differently, ranging from
walking/bicycling and non-school physical activity to general physical activity. Even
with these methodological differences, the studies revealed that playgrounds, parks,
and trails were positively related to childrens physical activity. This synopsis is
supported by a review of built environmental factors that reported the presence of
public spaces, particularly parks and recreation centers, was related to higher physical
activity in youth (113).
Additional prior studies examined the amount, distance, and a combination of
distance and amount of neighborhood playgrounds, parks, open space, and trails on
childrens overall physical activity and physical activity within these locations. Two
studies examined amount of green space or park area on childrens physical activity.
The first study discovered that a greater proportion of park area in a neighborhood
was associated with greater physical activity in 4 7 year old boys and girls (122). In
addition, a Dutch study found that after controlling for age, sex, BMI, and maternal
education, the proportion of green space in a neighborhood was positively related to
childrens physical activity (123).
27


A few studies only examined the relationship between distance to school
playgrounds, parks, recreation centers, and open play areas on childrens physical
activity. Distance to the nearest open play area including municipal and
neighborhood playgrounds, swimming pools, athletic fields, and school playgrounds
was inversely related to inner city seventh grade boys outdoor physical activity.
Living farther from an open play space meant less outdoor physical activity for boys;
although, this negative association was not found among girls (111). A study of
adolescents (ages 12 14) examining gender similarities and differences in moderate
to vigorous physical activity found an association between male adolescents self-
reported moderate to vigorous physical activity and distance from their homes to
parks, recreation centers, and schools; although, this relationship did not exist for
female adolescents (91). Another study completed in multiple cities revealed that
sixth grade girls who lived near (within a half mile) more parks, especially parks that
are conducive to walking and had active features, participated in more non-school
physical activity than girls who lived near fewer parks (124).
Other studies assessed a combination of distance and amount of park area and
recreational facilities on childrens physical activity and physical activity to and
within these locations. The first study among children (ages 8-12) found that living
in neighborhoods with a greater percentage of park area, such as nature trails, bicycle
paths, playgrounds, athletic fields, and parks, within 0.5 miles of home was related to
more physical activity in boys, but not girls (125). A study among sixth grade girls
revealed that perceptions of easy access to basketball courts, golf courses, playing
fields, running tracks, and swimming pools and an objective Geographic Information
System (GIS) measure of the number of basketball courts within one mile of the girls
homes were related to higher levels of non-school physical activity. However,
objective GIS measures of the number of golf courses, martial arts studios, playing
fields, running tracks, skating rinks, swimming pools, and dance/gymnastic clubs
were not associated with girls non-school physical activity (126).
In three cities, parents of children (ages 5-11), parents of adolescents, and
adolescents (ages 12-18) completed the Neighborhood Environment Walkability
Scale for Youth. After controlling for household income, gender, and race, parent
reported number of recreation facilities within a ten minute walk from home were
related to children being active in parks, walking to parks, and walking to shops once
per week or more; however, parent reported number of recreation facilities were not
related to meeting physical activity guidelines. Parent reported number of recreation
facilities within a ten minute walk from home was associated with adolescents being
active in streets, being active in parks, walking to parks, walking to shops, and
walking to school once per week or more. Adolescent reported number of recreation
facilities was related to walking to parks once per week or more. Neither parent nor
28


adolescent reported number of recreation facilities was associated with adolescents
meeting physical activity guidelines (107).
To summarize, many prior studies examined the existence, amount, and
distance of neighborhood physical activity resources on childrens overall physical
activity, walking/bicycling, sports participation, non-school physical activity, and
physical activity to and within these locations. Physical activity resources were
measured differently as well as the type of physical activity. Even with these
methodological differences, the studies revealed that the presence of playgrounds,
parks, recreation/physical activity facilities, and/or trails was positively related to
children engaging in physical activity of varying types (12, 89, 90, 109, 119-121).
Prior studies also assessed the amount and/or distance of parks, open play areas,
recreation centers, physical activity facilities, schools, and green spaces in a
neighborhood on childrens overall physical activity and physical activity to and
within these locations. Overall, the studies reported that a greater proportion of these
areas, areas located closer to home, or a greater proportion of these areas in close
proximity to home were related to children engaging in more physical activity of
varying types (91, 107, 111, 122-126). In addition, some of the above relationships
were examined separately for boys and girls. When these gender comparisons were
made, significant relationships were found for boys, with the exception of one study
that reported a significant relationship among girls. The existence of parks and
playgrounds, distance to the nearest open play area, distance from home to parks,
recreation centers, and schools, and a greater percentage of park area within 0.5 miles
of home were all found to be significantly associated with boys physical activity in
separate studies (91, 111, 121, 125). On the other hand, one study revealed that
perceptions of several free or low cost recreation facilities in the neighborhood was
associated with Portuguese girls physical activity, but not boys physical activity
(90). Therefore, physical activity resources in a neighborhood are generally available
and are related to physical activity of varying kinds among children, particularly
boys.
Population Density
Only two studies examined the relationship between population density and
childrens physical activity. A Dutch study found that after controlling for age, sex,
body mass index, and maternal education, residential density in a neighborhood was
positively associated with childrens physical activity (123). Conversely, another
study among fifth grade students revealed that physical environmental factors
including traffic, physical disorder, low residential density, and less mixed land use
29


were not significantly related to childrens physical activity, after adjusting for
sociodemographic factors (110).
Studies among adults revealed that living in neighborhoods with greater
household density were related to more physical activity or walking (127, 128).
Another study discovered that adults who obtained most of their physical activity in
their neighborhoods lived in areas with high population density and high housing unit
density (129). Based on a review of transportation, urban design, and planning
literatures, residents in neighborhoods with a high population density had high rates
of walking or cycling in comparison to residents in low density neighborhoods, after
accounting for sociodemographic factors (130). In an African American
neighborhood with high density and disadvantage and many destinations within
walking distance, there were high rates of utilitarian walking among adult residents;
however, walking may not have been by choice (131).
In summary, among adults, more physical activity and walking was related to
living in neighborhoods with greater population/household density. For children,
there are a limited number of studies examining population density on childrens
physical activity, and these studies reported inconsistent results (110, 123).
Neighborhood Demographic Composition
A few studies evaluated the influence of neighborhood demographic factors
on childrens physical activity. A study using state level data from the 2003 National
Survey of Childrens Health, and a similar measure of poverty as used in this study
but at the state level, revealed that poverty and income inequality separately predicted
childrens (ages 6-17) vigorous physical activity and no days of vigorous physical
activity (96).
Another study used the Townsend Index of Deprivation (high or low
deprived) based on 2000 U.S. Census data to measure the effect of neighborhood SES
on type, location, and context of girls physical activity. The sample consisted of
sixth grade girls living in six cities across the U.S. Individual and neighborhood SES
measures were not related to girls physical activity. Although, girls living in low
SES neighborhoods were more likely to participate in moderate to vigorous physical
activities at home/neighborhood during the weekday and weekend. On the other
hand, girls living in high SES neighborhoods were most likely to engage in moderate
to vigorous physical activities at school and/or community facilities during the week
and weekend (132). In addition, a study among youth (ages 12-21) revealed that
older age, low SES, and being Hispanic were related to less physical activity;
although, neighborhood characteristics including SES, social disorganization,
30


racial/ethnic minority concentration, and urbanization were not independently
associated with physical activity (133).
To summarize, there is some support for the influence of neighborhood
demographic characteristics on childrens physical activity. State level data
measuring poverty and income inequality significantly predicted childrens vigorous
physical activity and no days of vigorous physical activity (96). Another study
among sixth grade girls revealed differences between girls living in high versus low
SES neighborhoods; although, neighborhood SES measures were not related to girls
physical activity. Girls living in low SES neighborhoods were more likely to
participate in moderate to vigorous physical activities at home/neighborhood;
whereas, girls living in high SES neighborhoods were more likely to engage in
moderate to vigorous physical activities at school and/or community facilities (132).
Additionally, another study did not report a relationship between neighborhood
characteristics and adolescents physical activity (133).
Studies Using Multilevel Frameworks
to Examine Physical Activity
Only a handful of studies used a multilevel social ecological approach to
assess physical activity or walking among adults (101, 128, 134-142). One of these
studies was completed among older adults in Denver (140). Similar to this
dissertation, a study among adults obtained neighborhood data at the ZIP code level
from the 2000 U.S. Census (101). This study revealed that neighborhood percent
black, percent used public transportation to get to work, percent walked or bicycled to
work, and median house value as well as individual level race (percent black) and
income were related to perceived pleasantness of neighborhood for physical activity.
Additionally, neighborhood percent black as well as individual race and income were
associated with perceived availability of physical activity resources.
Even fewer studies used a multilevel social ecological approach to assess
physical activity in children (21-28). Two of these multilevel studies also had a
longitudinal component. A longitudinal multilevel study by Duncan, Duncan,
Strycker, and Chaumeton revealed that higher levels of family support, single parent
status, and higher income were associated with greater levels of sibling physical
activity (23). Another multilevel longitudinal study of youth (ages 11-15) examined
neighborhood social cohesion and parental reports of childrens participation in
recreation programs as well as childrens self-reported physical activity. The study
revealed that children living in a neighborhood with lower levels of neighborhood
social cohesion were less likely to participate in recreation programs at baseline and
were not as physically active at the two year follow up, even after adjusting for other
31


individual physical activity participation factors, neighborhood education, and
availability of neighborhood youth services (26).
A multilevel study by Molnar, Gortmaker, Bull, and Buka examined
relationships between physical activity in youth (ages 11-16) and safe recreation
areas in their neighborhoods (24). They found that lower neighborhood safety and
social disorder in neighborhoods were related to less physical activity, even after
controlling for demographic variables. Beets and Foley used multilevel structural
equation modeling to examine childrens (ages 5-6) physical activity. They
discovered that at the child level, father-child time and family time playing sports
together were related to childrens physical activity. At the neighborhood level, the
effect of parent reported neighborhood quality (e.g., garbage and crime) on childrens
physical activity was mediated by parental perceptions of the neighborhood being a
safe place to play outside. This finding suggests that neighborhood quality is
important for childrens physical activity to the extent to which parents perceive
neighborhood quality to be unsafe for their children to play outdoors (27).
Kuo, Voorhees, Haythomthwaite, and Young examined the relationship
among the family environment, family support for physical activity, and
neighborhood violence (perceived and objective) on predominantly African American
ninth grade girls physical activity in Baltimore, Maryland. Objective neighborhood
violence consisted of the number of violent crimes per 1,000 residents within a
community statistical area, which was a neighborhood cluster created along census
tract boundaries. Perceived neighborhood violence was self-reported threatened or
actual violence in the past year. Using a multilevel social ecological model, this
study revealed that greater family involvement in activities, family intimacy, and
family support were associated with ninth grade girls physical activity. Both
objective and perceived neighborhood violence were not related to their physical
activity (28). In addition, another multilevel study examined the social and economic
context at the state level on adolescents (ages 10 17) physical activity, while
controlling for individual sociodemographic characteristics. State level social capital
(mutual aid and social trust) was associated with the odds of adolescents not meeting
physical activity recommendations; although, state level poverty was not related to
their physical activity (25).
In summary, a limited number of studies used a multilevel approach to assess
physical activity in children. Many of the studies examined the influence of similar
family or neighborhood factors on childrens physical activity. For instance, family
factors consisting of support, single parent status, income, father-child time, time
playing sports together, involvement in activities, and intimacy (23, 27, 28) and
neighborhood characteristics, such as social cohesion, safety, social disorder, and
quality (24-26), were associated with childrens physical activity.
32


Studies Using Social Ecological Frameworks
to Examine Childrens Physical Activity
A few studies used a social ecological approach to assess physical activity or
walking/bicycling among children. Four social ecological studies examined
environmental factors including traffic, public transportation, parks, recreation
facilities, and street connectivity on childrens physical activity or walking/bicycling.
Another study examined opportunities and barriers to physical activity; while, an
additional study explored the physical and social contexts of girls physical activity.
A qualitative study with some objectively measured neighborhood data used
an ecological framework to examine perceived opportunities and barriers to physical
activity among 59 elementary/junior high school students living in an inner city
neighborhood in Canada. Even though the neighborhood was highly walkable and
had myriad available play spaces, safety concerns limited access to outdoor physical
activity, except playing in backyards. Children were rarely allowed to play outdoors
alone; although, parent or sibling accompaniment facilitated physical activity (e.g., a
family bicycle ride) (7). Another study of sixth grade girls from multiple cities
revealed that girls physical activity outside of school, such as household chores,
walking for transportation, basketball, playing with younger children, and running or
jogging, occurred within specific physical and social contexts. Typically, these
activities were completed with at least one other person, except for household chores.
In addition, the most common location for these physical activities was the girls
homes and their neighborhood (8).
A social ecological study among Australian children (ages 5-6 and 10 12)
revealed that 5-6 year old boys with parents who reported heavy traffic in their area
were more likely than other children to walk or bicycle at least three times per week
(12). Among younger girls with parents who own more than one car and parents who
reported that public transportation was limited in their area were less likely than other
children to walk or bicycle at least three times per week. Among 10-12 year old
girls, less walking or bicycling was related to having parents who believed that their
child needs to cross several roads to reach play areas and having limited public
transportation in their area. Among older children, beliefs that there were no parks or
sports grounds near their homes were related to fewer days walking or bicycling per
week.
A Belgian study using an ecological framework and the social cognitive
theory examined perceived social and physical environmental factors on adolescents
physical activity as well as the moderating effect of self-efficacy. Social
environmental factors (modeling and social support of physical activity), higher land
use mix diversity, higher street connectivity, more attractive environments, better
33


access to recreation facilities, and higher emotional neighborhood satisfaction were
related to more active transportation to school and in leisure time. Social
environmental factors, higher perceived safety from traffic, better access to recreation
facilities, more personal physical activity equipment, and fewer electronic devices in
the bedroom were related to more leisure time sports. Many of these relationships
were moderated by self-efficacy. For example, lower perceived neighborhood safety
and poorer access to recreation facilities were only related to less active transportation
among adolescents with lower self-efficacy (9).
A Canadian study among seventh and eighth grade students revealed that
having two or more objectively (GIS) and subjectively (parent reports) measured
recreation facilities in the neighborhood was related to greater physical activity in
students, after controlling for season and demographic factors. Land use mix and
percentage of park area were not significantly related to students physical activity
(10). On the other hand, a social ecological study among high school girls found that
the number of commercial physical activity facilities was associated with vigorous
physical activity; the number of parks was associated with METs (physical activity
level expressed as multiples of basal metabolic rate) in white females (11).
In summary, the above four social ecological studies that examined
environmental factors on childrens physical activity or walking/bicycling included
different environmental factors ranging from recreation facilities and street
connectivity to social environmental factors (9-12). A few similar neighborhood
factors, such as recreation facilities and parks, were examined in the studies, and
mixed results for parks were detected. The social ecological studies reported that
better access to recreation facilities and having more recreation/physical activity
facilities was related to more active transportation, more leisure time sports, or greater
physical activity (9-11). The studies examining parks reported inconsistent results
with two studies revealing an association between parks and walking/bicycling or
physical activity (11, 12); while, another study did not find a relationship between
park area and childrens physical activity (10).
Relationship between Childrens Physical Activity
and Active Commuting to/from School
Studies revealed that active commuting to school was associated with
childrens overall physical activity levels; children who actively commuted to school
had higher levels of physical activity compared to those who traveled to school via
car or bus (143-146). A longitudinal study of fourth grade students found that boys
who actively commuted to school had slightly higher rates of physical activity at
baseline, as measured by Caltrac accelerometers, in comparison to boys who did not
34


actively commute to school; however, this difference was not found among girls
(147) . In contrast to these studies, a study among children (ages 11 14) in England
revealed that walking to and from school was not related to physical activity at school
(148) . Additionally, a study of 9 and 15 year olds in Norway, Estonia, and Portugal
did not find a relationship between walking to school and moderate to vigorous
physical activity measured with an accelerometer (149).
A systematic review revealed a positive relationship between active
commuting to school and childrens physical activity. Twelve of the 24 studies
reported that active commuters had more physical activity than non-active
commuters; four studies found mixed results based on gender and activity type. Most
studies did not differentiate whether active commuting caused more physical activity
or whether more physically active children were also more likely to actively commute
to school (150). Other studies revealed that active commuters spend an additional 24
to 45 minutes per day in moderate to vigorous physical activity than non-active
commuters (145, 146). In summary, two studies discovered that active commuting to
school was not associated with childrens physical activity (148, 149), and one study
revealed a trend towards a significant relationship, but only for boys (147). On the
other hand, the majority of studies and a systematic review reported that active
commuting to school was related to childrens physical activity; children who
actively commuted to school were more physically active (143-146, 150).
35


CHAPTER 4
REVIEW OF THE LITERATURE FOR CHILDRENS
ACTIVE COMMUTING TO/FROM SCHOOL
Active commuting (walking or bicycling) to school is an important, but
frequently overlooked, source of physical activity (151). In recent decades, active
commuting to school was replaced by more motorized transportation, especially
parents personal vehicles. The decline of active commuting to school is a public
health concern due to the loss of a widely available and easy form of physical
activity. It seems obvious that childrens physical activity can be increased and their
physical health improved, if children (and their parents) can be persuaded out of cars
and onto sidewalks.
Studies examining active commuting to school and childrens body weight
have not found many significant associations. One longitudinal study discovered that
fourth grade boys who actively commuted to school had lower BMIs and skinfolds in
comparison to boys who did not actively commute to school (147). Another study
revealed that a higher BMI (85th to less than 95th percentile) was related to a reduced
odds of walking to school among middle school students (152). However, most other
studies have not reported any associations between childrens body weight and active
commuting to school (148, 153). In addition, a systematic review reported that only
three of eighteen studies revealed an association between active commuting to school
and lower body weight, which suggests there might not be a relationship between
active commuting to school and reduced body weight or BMI (150).
Even though there is minimal evidence linking active commuting to school
with lower BMI, increasing active commuting to school is an important public health
goal. Two Healthy People 2010 goals (22-14b and 22-15b) are to increase the
percentage of walking trips (1 mile or less) to school for children and adolescents
(ages 5 15) to 50% and to increase the percentage of bicycling trips (2 miles or less)
to school for children and adolescents (ages 5 15) to 5% (70).
This literature review covers six important topics related to childrens active
commuting to school and begins with a discussion of the rates of active commuting to
school in other countries and in the Unites States. Then barriers to active commuting
to school are presented. Thirdly, the numerous studies from other countries and the
Unites States examining only individual level correlates of active commuting to
school are reported. Then the study assessing only neighborhood level correlates of
childrens active commuting to school is discussed. To tie these two sections
together, the fifth section contains studies examining both individual level and
neighborhood level influences on childrens active commuting to school. Finally, this
36


literature review concludes with a section on studies using social ecological
frameworks to examine active commuting to school.
Rates of Active Commuting to/from School
Rates of active commuting to/from school are much higher in other countries
compared to the United States. Seventy-four percent of 9 year olds and 56% of 15
year olds from Norway, Estonia, and Portugal walk to school (149). Sixty-five
percent of Spanish adolescents actively commute to school and the vast majority of
these adolescents walk to school. Their journey to school typically takes less than 15
minutes (154). In Australian children ages 5-6, 44% of boys and 48% of girls walk
or bicycle to school and among those ages 10 12, 65% of boys and 57% of girls
walk or bicycle to school (12). A Canadian study of 11 13 year olds found that
62% of students walk or bicycle to school, while 72% do so from school to home
(155). In addition, 40% of 9 10 year old English children usually walk to school
and another 9% usually bicycle to school (13, 14).
In a representative sample of U.S. children ages 5-18, 19% walk and 6%
bicycle to or from school at least once a week with similar proportions for primary
school and secondary school-aged children (156). Another study found that 9.4% of
middle school students walk to school and 4.1% bicycle to school at least one day per
week; only 3.5% walk to school five days per week (152). A multi-site study
reported that 56.2% of eighth grade girls living within 1.5 miles of their middle
school walk to/from school at least one day a week (157). According to the 2008
Child Health Survey in Colorado, 19% of children (ages 5-14) typically walk,
bicycle, or skateboard to or from school five days a week, 10% of children do so one
to four days per week, and 71% do not walk, bicycle, or skateboard to or from school
in a typical week (74).
Not only are rates of active commuting to school low in the U.S. compared to
other countries, but the rates have declined precipitously in the last three decades.
McDonald analyzed data from the National Personal Transportation Survey to
examine trends in active commuting to school over time. The following decreases in
active commuting to school were discovered. In 1969, 40.7% of 5 18 year olds
walked or bicycled to school. This proportion decreased to 23.5% in 1977. There
was another decrease to 15% in 1983. In 1990, the proportion was 19.2%; although,
there was another decrease to 11.7% in 1995. By 2001, the proportion of 5 18 year
olds walking or bicycling to school was 12.9%. Over this time period, there was an
increase in distance to school, which accounted for 47% of the decline in active
commuting to school. Distance to school also had the strongest influence on the
decision to walk or bicycle to school (158).
37


Barriers to Active Commuting to/from School
Active commuting to school may be an important source of physical activity
and is a critical public health goal; although, childrens safety and barriers must be
taken into account when examining active commuting to school and before
encouraging every child to walk or bicycle to school. A study asked parents in low
income neighborhoods about their childs pedestrian practices. Parents described
high rates of unpleasant walking environments and concerns about drug dealers,
crime, violence, and trash (115). Not only is safety a concern or barrier to active
commuting to school, Dellinger and Staunton found additional barriers to active
commuting to school: long distances (55%); traffic danger (40%); adverse weather
conditions (24%); crime danger (18%); opposing school policy (7%); other reasons
(26%); and no barriers (16%) (156). Parents who reported no barriers had children
who were six times more likely to actively commute to school in contrast to the other
children. Comparisons were also made between barriers for primary school and
secondary school-aged children; more traffic danger and crime danger barriers were
reported for the younger children. In addition, an Australian study discovered an
inverse relationship with the following statements and childrens active commuting to
school: child prefers to be driven to school; no time in the mornings; worry child will
take risks; no other children to walk with; no adults to walk with; too far to walk; and
no direct route. They also found a positive relationship with active commuting to
school and concern child may be injured in a road accident (159).
Individual Level Correlates of Active
Commuting to/from School
Numerous studies have examined only individual level correlates of active
commuting to school both in the U.S. and in other countries. Several studies in other
countries have revealed many diverse correlates of active commuting to school.
A Canadian study of children (ages 9, 13, and 16) found that girls, children
from higher income households, children of immigrants, and children living in rural
areas were less likely to walk to school (160). A study among children (ages 11 14)
in England revealed that white children, girls, and older (ages 13-14) children were
more likely to walk to/from school (148). For Spanish adolescents, low maternal
education and occupation as well as attending public schools were related to more
active commuting to school (154).
In addition to these demographic factors, level of independence, parents
perceptions and experiences, and distance to school were predictors of active
commuting to school. Among Australian primary school children (ages 5 12),
38


frequent active commuting to school was related to childrens level of independence
and to parental perceptions of the health benefits of walking as a form of travel (161).
In addition, a study of children in New Zealand revealed that the strongest predictor
of walking to school was proximity to school. Other factors that were significantly
related to childrens active commuting to school were living in a household without a
car, attending a low SES school, living in a household with three or more adults,
being in school years 4-6 compared to years 1-3, having a non-New Zealand
European ethnicity, having a parent who walked to school, and being male (162).
Lastly, a study among youth (ages 12 15) in Rotterdam, Netherlands reported that
compared to native Dutch youth, non-native Dutch youth were more likely to walk
and be a non-active computer with cyclists as the reference category. A longer
distance from home to school was positively related to not actively commuting to
school (163).
Some of these same demographic correlates (non-white ethnic background,
low SES, and older age) and shorter distance to school were related to more active
commuting to school in children living in the United States. Data from the 2001
National Household Travel Survey revealed that low income and minority youth ages
5-18, especially blacks and Hispanics, actively commuted to school at much higher
rates than white or higher income youth. After controlling for household income,
vehicle access, distance, and residential density, racial differences in active
commuting to school disappear (164).
A study in middle and high schools in North Carolina found walking or
bicycling to school was more common in boys and among non-white middle school
students. In addition, among high school students, higher parental education was
related to a reduced odds of walking to school (152). Among third to fifth grade
students, the odds of walking to school increased with age and student perceptions
that walking to school saves time or is safe. On the other hand, the farther a student
lives from school, car ownership, and access to a school bus decreased the odds of
walking to school (165).
To summarize, demographic correlates, such as older age, non-white ethnic
background, and lower SES, were found to be associated with more active
commuting to school in the United States, Canada, England, the Netherlands, Spain,
and New Zealand (148, 152, 154, 160, 162-165). There were mixed results for the
influence of gender on active commuting to school (148, 152, 160); although, the
results were not equally divided because more studies revealed that boys actively
commuted to school more than girls (152, 160). In the Netherlands, New Zealand,
and the United States, shorter distance to school was associated with more active
commuting to school (162, 163, 165).
39


Neighborhood Level Correlates of Active
Commuting to/from School
Only one study examined neighborhood level correlates exclusively. This
study assessed neighborhood factors associated with active commuting to school
among fifth grade students from public elementary schools in California. Walking
and bicycling rates were higher among neighborhoods with a greater population
density and smaller schools, after controlling for number of intersections per street
mile, percentage of students receiving public welfare, and percentage of students of
various ethnicities (166).
Individual Level and Neighborhood Level Correlates
of Active Commuting to/from School
A handful of studies examined both individual level and neighborhood level
correlates of active commuting to school in the United States and in other countries.
Myriad individual level factors and neighborhood level factors were associated with
childrens active commuting to school.
Among Portuguese adolescent girls, active commuting to school was
negatively related to their mother and fathers occupational status and fathers
education level, meaning girls from lower SES families were more likely to actively
commuting to school. Self-reported street connectivity was positively related to
Portuguese girls active commuting to school (167). A Canadian study among 11 -
13 year olds looked at active commuting to school and active commuting from school
to home; different factors were related to active commuting to school and from
school. Of the students who lived within one mile of school, shorter trips, being
male, higher land use mix, and presence of street trees was positively related to active
commuting to school. Lower residential densities and lower neighborhood incomes
were associated with actively commuting from school to home (155).
Similar to the study among adolescent Portuguese girls, street connectedness
(number of intersections) within 4,000 feet of the school was related to U.S.
adolescents active commuting to school, except in this study street connectedness
was measured objectively. In addition, the study reported that male adolescents were
more likely to actively commute to school than female adolescents (168). Kerr et al.
discovered that childrens (ages 5-18) active commuting to school was related to a
parental concern scale (e.g., crime, traffic, fast cars, sidewalks, distance to school,
time, schedules, etc.). In addition, they found that in high income neighborhoods,
children in high walkable neighborhoods in comparison to low walkable
neighborhoods were more likely to actively commute to school; although, no
40


differences were found in low income neighborhoods. Therefore, active commuting
to school was related to parental concerns, neighborhood aesthetics, and perceived
access to local stores and biking or walking facilities (169).
A study of eighth grade girls from six states across the U.S. examined
perceived and objective neighborhood features related to walking to/from school.
Girls living within 1.5 miles of their middle school were included in the analyses.
The only individual level factor examined was race/ethnicity; white girls walked to
school more frequently than Hispanic and African American girls. Girls who
perceived that it was safe to walk in their neighborhood and perceived that they had
places they liked to walk were more likely to walk to school. For objective
neighborhood measures, girls who lived closer to school, had more active destinations
in their neighborhood including basketball courts, parks, tracks, swimming pools,
walking/bicycling trails, and dance studios, lived in neighborhoods with a larger
proportion of Hispanics, and lived in neighborhoods with smaller sized blocks were
more likely to walk to/from school (157).
To summarize, these studies revealed individual level correlates, such as
lower SES, a parent concern scale, being male, and having a white ethnic
background, that were associated with more active commuting to school in the United
States, Canada, and Portugal (155, 157, 167-169). Two studies revealed that boys
actively commuted to school more than girls (155, 168), and two studies found that
street connectedness was associated with active commuting to school (167, 168). A
study in Canada and another study in the U.S. revealed that shorter trips to school
were related to more active commuting to school (155, 157). In addition, objective
neighborhood level factors including lower residential densities, lower neighborhood
incomes, more active destinations, a larger proportion of Hispanics, and
neighborhoods with smaller sized blocks were associated with more active
commuting (155, 157).
Studies Using Social Ecological Frameworks to
Examine Active Commuting to/from School
A limited number of studies used a social ecological approach to examine
childrens active commuting to school. Of the six social ecological based studies of
childrens active commuting to school, two were completed in the United States (15,
17), two studies using the same sample were based in England (13, 14), one study
was completed in Canada (16), and another study was completed in Australia (18).
The latter study was published in 2006 (18); while, the other studies were published
very recently in 2008, 2009, or 2010 (13-17).
41


The Australian study based on a social ecological model examined active
commuting to school among two age groups of children (5-6 and 10-12 year olds).
Overall, if parents reported that there were few neighborhood playmates for their
children and no lights or crossings for their child to use, then their children were less
likely to actively commute to school. Based on GIS measures, children who had a
busy road barrier en route to school were less likely to activity commute to school,
and children who only had to travel less than 800 meters to school were more likely
to actively commute to school. In addition, children (ages 5-6) whose route to
school included a steep incline and children (ages 10 12) with a direct route to
school were less likely to actively commute to school (18).
The Canadian social ecological study examined active commuting to school
among high school students. Using hierarchical linear modeling to account for
students nested within schools, the authors discovered that students who were female,
were in the 12th grade, were smokers, had low or moderate physical activity, and
attended a rural school were less likely to actively commute to school (16).
A recent social ecological study in England among children (ages 9-10)
examined attitudes, social support, and environmental perceptions of active
commuting to school as well as the moderating effect of distance to school. The
study revealed that children whose distance to school was less than one km and
whose mothers active commuted to work were more likely to walk or bicycle to
school. Boys were more likely to bicycle to school; whereas, girls were more likely
to walk to school. Parents views (convenient to drive child to school and parent
around to take child to school), social support (friend encouragement and parental
encouragement), and environmental perceptions (concerns about dangerous traffic,
concern something might happen to child, safe to walk or play in neighborhood,
neighborhood walkability score, and neighborhood sense of community) were
important for both shorter and longer distances to school. Children with peer and
parental support and who lived in supportive environments were more likely to walk
or bicycle to school. In addition, the only moderating effect of distance to school
occurred between concerns about dangerous traffic and bicycling to school (13).
Another recent social ecological study in England using the same sample as
above (13) examined neighborhood, route, and school environments on childrens
active commuting to school as well as the moderating effect of distance to school.
None of the school related measures significantly predicted active commuting to
school in the fully adjusted models. The neighborhood and route environmental
measures produced inconsistent results. Children who lived in more economically
deprived areas and had a less direct route to school were less likely to walk or bicycle
to school. Children who had routes with a high density of streetlights were less likely
to bicycle to school. On the other hand, children who lived in a neighborhood with a
42


higher density of roads were more likely to walk to school. In addition, children with
a shorter distance to school were more likely to walk or bicycle to school; however,
distance to school did not moderate any of the associations (14).
A social ecological study in the U.S. examined demographic, family, and
environmental factors related to adolescents active commuting to school. The
authors reported that adolescents most likely to actively commute to school were
males, Latinos, older, had a lower family income, attended a public school, lived in
an urban area, lived closer to school, did not have an adult present after school, and
had parents who knew little about their whereabouts after school (15).
Lastly, a social ecological study of parents/guardians of elementary school
students in Austin, Texas found that personal and social characteristics as well as
perceived physical environmental factors were related to childrens walking to/from
school. Personal and social characteristics including parents education, car
ownership, personal barriers (time constraint and convenience of driving child
to/from school), and school bus availability were negative correlates of childrens
walking to/from school. Positive personal and social correlates of childrens walking
to/from school were number of family members, parents and childrens positive
attitudes and regular walking behaviors, and supportive peer influences. In addition,
the following perceived physical environmental factors were negative correlates of
childrens walking to/from school: distance (perceived close enough distance to
walk); safety concerns; need to cross highways or freeways to get to/from school; and
the presence of convenience stores, office buildings, and bus stops on the way to/from
school (17).
In summary, these social ecological studies examined many different
neighborhood/environmental factors. The studies found specific correlates that were
negatively associated with active commuting to school, such as busy roads, no lights
or crossings, attending a rural school, crossing highways or freeways, economically
deprived areas, direct route to school, and safety concerns (14-18), and correlates that
were positively related to active commuting to school including neighborhood social
cohesion, neighborhood walkability, and density of roads (13, 14). These studies also
assessed and discovered similar individual factors, such as being male, being older,
and living in a family with a lower income/SES, that were associated with more
active commuting to/from school (13, 15-17). In addition, shorter distance to school
(perceived and measured distance) was frequently related to more active commuting
to/from school (13-15, 17, 18).
43


CHAPTER 5
RESEARCH DESIGN AND METHODS
This study used a multilevel framework because a social ecological model,
which has a hierarchical structure, was the underlying theoretical foundation. This
multilevel study had two levels; the first level contained children and family members
and the second level consisted of neighborhoods/ZIP codes. Children and their
family members were in the same level because one child was linked to one family
member and data only existed for one child per household. The following research
questions and hypotheses were tested in this study.
Research Questions and Hypotheses
1. How do family and neighborhood characteristics influence childrens physical
activity, above and beyond the individual characteristics of children?
Hypothesis la: Household factors are positively associated with childrens
physical activity. For example, family members physical activity and low
BMI will be related to high levels of physical activity in children.
Hypothesis lb: Neighborhood factors will directly influence childrens
physical activity.
2. How do neighborhood characteristics influence childrens active commuting to
school, above and beyond the individual characteristics of children?
Hypothesis 2: Neighborhood factors will directly influence childrens active
commuting to school.
3. Do parental perceptions of neighborhood safety mediate the relationship between
crime and childrens physical activity and active commuting to school?
Hypothesis 3a: Parental perceptions of neighborhood safety mediate the
relationship between crime and childrens physical activity.
Hypothesis 3b: Parental perceptions of neighborhood safety mediate the
relationship between crime and childrens active commuting to school.
4. What is the relationship between active commuting to school and physical
activity?
Hypothesis 4: Children who are physically active are more likely to actively
commute to school.
44


Study Design
A quantitative cross-sectional research design was completed to answer the
above research questions in male and female children, ages 5 to 14, in the Denver
metropolitan area. This study involved acquiring and linking secondary data from
several sources including the Colorado Department of Public Health and
Environment, law enforcement agencies, Colorado Department of Education, Denver
Regional Council of Governments, GIS maps and shapefiles from park and recreation
agencies, open space agencies, city GIS departments, and county GIS divisions, and
the U.S. Census Bureau at the individual and neighborhood levels. See Figure 5.1
below for a schematic of the research processes implemented in this study. This
study was approved by the University of Colorado Denver, Institutional Review
Board, the Human Subjects Research Committee (HSRC). The study was an exempt
protocol (HSRC Protocol 2007-070).
45


Figure 5.1: Schematic of Research Process
46


Sample
The Colorado Department of Public Health and Environment (CDPHE)
administers the Behavioral Risk Factor Surveillance System (BRFSS) and Child
Health Surveys (CHS) in Colorado. In this study, the BRFSS and CHS data were not
weighted because only data from the seven counties (Adams, Arapahoe, Boulder,
Broomfield, Denver, Douglas, and Jefferson) comprising the Denver metropolitan
area were used. This area contains numerous distinctive neighborhoods.
Neighborhoods are defined based on the U.S. Postal Services Zone Improvement
Plan (ZIP) code boundaries, rather than commonly used Census tracks and blocks,
because the BRFSS contained a question about the respondents ZIP codes (see
Appendix A for a map of the ZIP codes from the Denver metropolitan area that were
used in this study). Using ZIP codes as neighborhood boundaries means these
boundaries do not correspond to specific neighborhoods (e.g., Denvers Washington
Park neighborhood) or political boundaries.
The Council of American Survey Research Organization (CASRO) created a
response rate that takes into account the complexities of a research design, sampling
process (e.g., random digit dialing), and the practical difficulties of contacting and
assessing potential participants, such as no contacts, refusals, and partially
completed interviews. The CASRO rate is conservative and is the industry standard
for response rates in survey research. The Centers for Disease Control and
Prevention (CDC) created a BRFSS CASRO rate to account for these survey
research complexities. The BRFSS CASRO rate was 61.50% for the 2005 BRFSS
and was 60.04% for the 2007 BRFSS.
The BRFSS and CHS sample (child and household data) and the
corresponding ZIP codes/neighborhoods from this sample served as the foundation
for this project. Household data from the BRFSS were directly linked to child data
from the CHS.4 In 2005, 98.7% of respondents who completed the CHS agreed to
have their responses linked to the BRFSS. This percent was similar in 2007 with
98.6% agreeing to have their CHS responses linked to the BRFSS.
4 The term household is used because the sampling methodology for the BRFSS randomly selects
one household member to complete the survey, and the CHS is completed by the parent/guardian who
knows the most about the child (usually the mother). As a result, the BRFSS may have been
completed by a childs parent or another household member (e.g., older sibling, uncle, aunt,
grandparent, etc.). Due to the anonymity of the BRFSS and CHS, there is not a precise way to
compare the relationship of the BRFSS respondent to the CHS respondent. Rough comparisons were
completed and revealed that approximately 90% of the BRFSS respondents may have been parents;
while, the remaining were probably another family member. Therefore, the term household is used
instead of parental to describe factors derived from the BRFSS.
47


Neighborhood data were obtained from a variety of sources and linked to the
household and child data via ZIP codes. Because the merged CHS and BRFSS
dataset served as the primary dataset upon which the neighborhood data were
acquired, many steps were completed to clean and set up the data (refer to Figure
5.2).
After beginning the multilevel analyses with the 2005 CHS/BRFSS data, the
intraclass correlation coefficient (ICC) was less than 2%, p > 0.3. The ICC is the
proportion of total variance in childrens physical activity and active commuting
to/from school (outcome variables) attributed to differences between
neighborhoods/ZIP codes. A non-significant ICC means that data from participants
within one neighborhood were not more correlated than data from participants in
another neighborhood. Consequently, the multilevel analyses were not completed as
originally proposed. The very small ICCs were likely caused by one of the following
three options: 1) having a small sample and not having enough children in each
neighborhood to be able to find data similarities within each neighborhood compared
to between neighborhoods; 2) neighborhood/ZIP codes in this dataset are not
cohesive units such that participants within neighborhoods/ZIP codes are more
similar than participants in different neighborhoods/ZIP codes; or 3) ZIP codes cover
a large area, contain different people, have arbitrary boundaries, and are not good
representations of neighborhoods. A solution to the first option was to increase the
sample size by adding data from the 2007 CHS/BRFSS using only the 89 ZIP codes
in the Denver metropolitan area where there was overlap with the 2005 data. Because
increasing the sample size from 295 to 863 did not considerably change the ICCs,
slight modifications were made to the proposed statistical analyses. The questions
analyzed in this dissertation had exactly the same wording in both the 2005 and 2007
CHS and BRFSS.
The final combined sample includes 863 children, ages 5 to 14, and one
family member living in 89 neighborhoods in the Denver metropolitan area. To
summarize, the sample criteria are: 1) children and their family members living in
valid ZIP codes in the seven counties comprising the Denver metropolitan area; 2)
male and female children between the ages of 5 and 14; and 3) family members who
completed the BRFSS and CHS and agreed to have these data linked.
48


In 2005, 903 CHS
were completed and
linked to BRFSS data
in Colorado
£
A subset of 306 children
in 94 ZIP codes in the
Denver metropolitan area,
including 27 cases recoded
from Jackson to Jefferson
County, was created
Deleting cases due to
mismatches between county
and ZIP code or invalid ZIP
codes (n = 11) produced a
dataset of 295 participants
in 89 neighborhoods
In 2007, 1714 CHS
were completed and
linked to BRFSS data
in Colorado
&
814 CHS were
completed in the 89
ZIP codes that were
part of the 2005 data
£
581 CHS were
completed for
children ages 5-14
Removing respondents
who lived outside of the
Denver metropolitan area
(n = 7) and had missing
data (n = 6) created a
dataset of 568 participants
in 84 neighborhoods
The final combined sample includes 863 children,
ages 5 to 14, and one family member living in 89
neighborhoods in the Denver metropolitan area
Figure 5.2: Diagram of Sample
49


Data Acquisition and Measures
Child and Household Data
Household data were obtained from the BRFSS, which is a telephone survey
using a multistage sampling design based on random digit dialing to select potential
respondents (18 years and older). Several questions in the core BRFSS have high
reliability and validity including height, weight, BM1, and several demographic
characteristics; while, most of the remaining questions have moderate reliability and
validity (170). Most of the moderate and vigorous physical activity questions in the
BRFSS have moderate reliability (171 ).5 The CHS is a surveillance system to
monitor childrens health and risk behaviors and is an add-on to the BRFSS. One
child (ages 1 14) is randomly selected from the household. The CHS uses reports
from the parent/guardian who knows the most about the health and health practices of
the child.6
Children under age 10 have difficulties providing accurate self-report
information, particularly about duration, frequency, and intensity of physical activity
(172). Several studies have shown that parents reports of their childrens physical
activity are valid. A study compared objective measures of childrens physical
activity via a heart rate monitor to mothers reports of their childrens activities and
found that mothers are fairly accurate in calculating their childrens daily activity
patterns (173). Hager, Tucker, and Corbin found that in comparison to an objective
measure of physical activity, self-reported physical activity among children (ages 9 -
12) is not a valid measure; although, parents reports of their childrens physical
activity was more accurate (174). Refer to Appendix B for the questions that were
used from the CHS and BRFSS.
Child Data from the CHS
Child data from the CHS were obtained on children ages 5 to 14 and included
demographic factors, such as race/ethnicity, sex, and age. Childrens race/ethnicity
5 Refer to http://www.cdc.gov/brfss/pubs/mvr.htm for a complete methods, validity, and reliability
bibliography for the BRFSS.
6 Most of the 2005 and 2007 CHS respondents were parents with 79% mothers/stepmothers/adoptive
mothers, 16.9% fathers/stepfathers/adoptive fathers, 3% grandparents, 0.8% other family members,
and 0.2% other non-family member caregivers. In addition, 93.7% of the children live with the CHS
respondent full-time; while, 6.3% live with the CHS respondent part-time, every weekend.
50


was coded as non-white and white, which was the reference category, because the
majority (71.61%) of children were non-Hispanic, white. Childrens sex was either
female or male, which was the reference category. These three demographic
variables were tested as possible covariates in the statistical analyses.
Household Data from the BRFSS
Household data from the BRFSS included family members physical activity,
BMI, and demographic factors (sex, age, education, and income). Family members
physical activity and BMI were independent variables and demographic factors were
tested as possible covariates in the statistical analyses.
To obtain a continuous variable that measured the frequency and duration of
family members physical activity, a variable was created to have the same timeframe
as childrens physical activity (number of moderate and vigorous physical activity
hours per week). This variable had a very wide range of values from 0 to 73.63 hours
and revealed outliers on scatter plots. To correct for these outliers, values above the
third standard deviation away from the mean (28.14 hours) were truncated to 28 hours
of moderate and vigorous hours of physical activity per week.
Family members gender was either female or male, and their age ranged from
18-75. Family members education was a categorical variable with four levels: did
not graduate high school; graduated high school; attended college or technical school;
and graduated from college or technical school, which was the reference category.
Family members income was a categorical variable with three levels: low
income (less than $35,000); middle income ($35,000 to less than $75,000); and high
income ($75,000 or more), which was the reference category. This variable was
recoded from the original eight categories. The low income category was created and
defined based on 130% of the 2005 and 2007 poverty guidelines, which is the
eligibility criteria for the federal assistance program, Supplemental Nutrition
Assistance Program (SNAP; originally called food stamps) (175). This low income
definition was also used in a study of the Continuing Survey of Food Intakes by
Individuals (CSFII) (176). Reported income from the CHS was very similar to
reported income in the BRFSS (rho = 0.87, p = 0.00005). Therefore, due to this
similarity and to be consistent with the other family member variables, family
members income was obtained from the BRFSS.
Neighborhood Safety from the CHS
Perceptions of neighborhood safety were obtained from a question in the
CHS. Parents/guardians were asked How often do you feel your child is safe in your
51


community or neighborhood? with four possible responses (always, usually,
sometimes, and never). This question was used as an independent variable at both the
individual/household and neighborhood levels in the statistical analyses. At the
individual level, individual responses from parents/guardians were used. Due to the
small sample size in the never category (n = 5), the variable was recoded into three
levels: sometimes and never; usually; and always, which was the reference category.
Neighborhood Data
Neighborhood Safety
At the neighborhood level, individual responses based on the three category
recoded variable were aggregated to their respective neighborhood after calculating a
mean neighborhood safety rating.
Neighborhood Crime
Neighborhood crime consisted of the rate of reported incidents in 2005 that
have violent offenses (murder/nonnegligent manslaughter, forcible rape, robbery, and
aggravated assault) per 1,000 persons in each neighborhood. To calculate this
variable, the number of reported incidents for violent behavior in 2005 in each ZIP
code was obtained from law enforcement agencies in the Denver metropolitan area.
The total population in each neighborhood was obtained from the 2000 U.S. Census
summary file 3 by ZIP Code Tabulation Area (ZCTA). The rate of violent crime
per 1,000 residents was calculated for each neighborhood because these rates created
a comparable variable for neighborhoods with varying populations. Providing
support for this approach, McDonald also created indices of crime per 1,000 block
group residents (177). See Appendix C for additional details on how the crime data
were acquired and what type of data were obtained.
Physical Activity Resources
Physical activity resources (PAR) consisted of separate variables measuring:
1) number of public elementary and middle school playgrounds per neighborhood
square mile; 2) number of parks per neighborhood square mile; 3) size of parks per
neighborhood square mile; 4) number of open spaces per neighborhood square mile;
5) size of open spaces per neighborhood square mile; 6) length of trails per
neighborhood square mile; and 7) number of recreation areas per neighborhood
square mile. To adjust for the size of a neighborhood, the data were divided by land
52


area in square miles, which was obtained by the ZIP code layer from ESR1 StreetMap
Pro (178). These variables should accurately represent the physical activity resources
in each neighborhood, while accounting for the size of a neighborhood. These PAR
variables also capture two aspects of being able to access these neighborhood
resources: 1) whether a resource is present or not and 2) the concentration/size of a
resource in a neighborhood. All the PAR variables were created using GIS; although,
different methodologies were used to construct the variables (see Appendix C).
Calculating the number of parks per neighborhood square mile is beneficial
because with eleven acres per 1,000 residents, the city and county of Denver does not
have a large amount of parkland in comparison to other large cities; however, it does
have many parks distributed throughout the city. Currently, 88-96% of residents live
within six walkable blocks of a park, meaning residents can get to a park without
crossing a highway, large road, railroad track, or body of water (179). In addition,
similar variables were calculated in prior studies (135, 180).
Population Density
Population density is a measurement of population per square mile. Total
population was obtained from the 2000 U.S. Census summary file 3 via ZCTAs, and
land area in square miles was acquired from the 2000 Census ZCTA shapefiles. For
each ZCTA, population density was computed by dividing total population by land
area in square miles and was matched to neighborhood ZIP codes.
Neighborhood Demographic Composition
Neighborhood demographic composition variables obtained from the 2000
U.S. Census summary file 3 were aggregated to the neighborhood level. The 2000
U.S. Census summary file 3 consists of social, economic, and housing characteristics
collected from a sample of approximately one in six households nationwide that
received the 2000 Census long form questionnaire (181). The neighborhood
demographic composition variables included total population, education (percent
without a high school degree and percent with at least a bachelors degree),
racial/ethnic composition (percent minority), households at or above the median
income (proportion of households at or above the median income in the Denver
metropolitan area, $54,715.50 in 1999), and poverty (percentage of children less than
18 years of age in poverty and percentage of individuals in poverty) (see Table 5.1
below). Complex composite variables were not created with the 2000 U.S. Census
data because prior research based on census tracts found that a single variable
measuring percentage of persons below poverty functioned as well as more
53


complex, composite measures of economic deprivation (e.g., the Townsend index)
(182). Refer to Appendix C for further details on how these variables were created
and steps that were taken to account for ZCTA and ZIP code mismatches.
Table 5.1: Neighborhood demographic variables created from the U.S. Census 2000
summary file 3 dataset
Variable Name Variable Definition
Total population Total population (PI)
Percent minority Percent minority of the total population [100 percent whitel (P7)
Percent without high school degree Percentage of individuals ages 25 and older without a high school degree or the equivalent as a proportion of the population 25 years and over (P37)
Percent with at least a bachelors degree Percentage of individuals ages 25 and older with a bachelors degree or higher (masters, professional school, and doctorate degree) as a proportion of the population 25 years and over (P37)
Proportion of households at or above median income in Denver metropolitan area Percentage of households at or above the median income in Denver metropolitan area ($54,715.50, 1999) as a proportion of all households (P52)
Percentage of children (< 18) in poverty Percentage of children less than 18 years of age in families with total cash incomes below federal poverty levels as a proportion of the total population for whom poverty status is determined (P87)
Percentage of individuals in poverty Percentage of individuals with total cash incomes less than federal poverty levels for same size families and number of unrelated individuals with incomes below federal poverty levels for a household of one as a proportion of the total population for whom poverty status is determined (P87) |
54


Outcome Variables
Childrens Physical Activity
Childrens physical activity level was obtained from the following question in
the CHS, In a typical week, how many hours does your child spend playing sports or
doing some other physical activity like dance, roller-skating, or bicycling? An
assumption of the primary analyses is the outcome variable is normally distributed.
The variable for childrens physical activity was positively skewed and kurtotic for
everyone and separately for boys and girls. On inspection of the histograms, the
distributions looked relatively normally distributed, except for the positive skewness
primarily due to some children with many hours of physical activity per week.
Therefore, values above the 3rd standard deviation above the mean (27.1 hours) were
truncated to 25 hours. This brought in the tail of the distribution so the data were
normally distributed for everyone and for boys only; although, girls physical activity
was still slightly positively skewed and kurtotic.
Two additional variables for girls were created to assess the impact of the
positively skewed and kurtotic distribution. Sensitivity analyses were completed and
produced similar results. Therefore, all of the primary analyses for girls were
completed with the truncated physical activity variable that was slightly positively
skewed and kurtotic for the following reasons: 1) all three variables were not
normally distributed; 2) deleting outliers might remove useful information; 3) there is
no way to know if these outlying values are true, are data entry mistakes, or are
overestimates of the true values; and 4) the sensitivity analyses did not reveal large
differences.
Childrens Active Commuting to/from School
Childrens active commuting to/from school consisted of the frequency of
walking or bicycling to or from school, which was acquired from the CHS. Parents
were asked In a typical week, how many days does your child walk to or from
school? and In a typical week, how many days does your child bicycle to or from
school? These two items were summed to attain an active commuting to/from
school variable. Thirteen children were home schooled so their data were changed to
missing because they did not have the opportunity to walk or bicycle to school. This
variable was not normally distributed because 66.23% of children did not walk or
bicycle to school at least one day a week. Therefore, this variable was trichotomized
into no active commuting to/from school (zero days a week), active commuting
to/from school one to four days a week, and active commuting to/from school five
55


days a week. Timperio et al. used a similar methodology to create their three
category outcome variable measuring frequency of walking and cycling to/from
school: never; infrequent/occasional (one to four times per week); and frequent (five
or more times per week). For their variable, walking or bicycling to and from school
equaled two times (18). See Table 5.2 for a summary of all the variables with their
corresponding level of analysis, variable type, type of measurement, and data source.
Table 5.2: Summary of variables by level of analysis, variable type, type of
measurement, and data source
Level Variable Type Variable Type of Measurement Data Source
Outcome, child Dependent Hours of physical activity per week Individual CHS
Outcome, child Dependent Active commuting to/from school Individual CHS
Individual, child Covariate Demographic factors (race/ethnicity, sex, and age) Individual CHS
Individual, household Covariate Demographic factors (sex, age, education, and income) Individual BRFSS
Individual, household Independent Moderate and vigorous physical activity Individual BRFSS
Individual, household Independent BMI Individual BRFSS
Individual, household Independent Neighborhood safety Individual CHS
Neighborhood Independent Mean neighborhood safety Aggregate (from individual level) CHS
Neighborhood Independent Crime rate per 1,000 residents (reported incidents for murder/manslaughter; reported incidents for rape; reported incidents for robbery; reported incidents for aggravated assaults; and reported incidents for all violent offenses) Integral or Global Law enforcement agencies
Neighborhood Independent Number of public elementary and middle school playgrounds per neighborhood square mile Integral or Global ESR1 StreetMap Pro and Colorado Department of Education
56


Table 5.2 (Cont.)
Level Variable Type Variable Type of Measurement Data Source
Neighborhood Independent Number of parks per neighborhood square mile; size of parks (square feet) per neighborhood square mile; number of open spaces per neighborhood square mile; size of open spaces (square feet) per neighborhood square mile; and number of recreation sites per neighborhood square mile Integral or Global DRCOG shape file
Neighborhood Independent Length of trails in miles per neighborhood square mile Integral or Global Shapefiles from myriad agencies
Neighborhood Independent Population density Integral or Global 2000 U.S. Census
Neighborhood Independent Neighborhood demographic composition (total population. percent minority, percent without high school degree, percent with at least bachelors degree, proportion of households at or above median income, percentage of children in poverty, and percentage of individuals in poverty) Derived or Configural 2000 U.S. Census
Data Analyses
Data Management and Data Analysis Overview
Data for this project were derived from several sources: 1) CDPHEs 2005
and 2007 BRFSS and CHS; 2) law enforcement agencies in the Denver metropolitan
area; 3) GIS maps (ESRI StreetMap Pro); 4) Colorado Department of Education; 5)
Denver Regional Council of Governments (DRCOG); 6) GIS shapefiles from park
and recreation agencies, open space agencies, city GIS departments, and county GIS
divisions; and 7) 2000 U.S. Census Bureau. Many steps were completed to acquire
and combine all the data from these separate sources. Data from the BRFSS and CHS
were linked on a one to one basis (one family member to one child). The
neighborhood data were linked to the household and child data via ZIP codes. Data
were stored in a password protected computer and backed up every week to an
external hard drive. Merging all the data from the myriad sources, creating the
57


original dataset, and completing data management tasks including editing, cleaning
the data for missing data and out of range values, and creating composite variables
were completed using the Statistical Package for the Social Sciences (SPSS), versions
15 and 17.0.2 (183). Some of the basic analyses, such as finding covariates, were
done in SPSS. The remaining data management and primary analyses were
completed using the SAS software, version 9.2 (184).
Many analyses were completed due to the complexity of the hypotheses and
planned analyses, as well as to address the potential collinearity among the
independent variables. This may have increased the likelihood that some significant
results occurred due to chance (type I error); although, adjustments for multiple
analyses, such as the Bonferroni adjustment, were not made because they tend to be
overly conservative. The advantages and disadvantages of a type I error versus a type
II error were weighed; the decision was made to not make adjustments for multiple
analyses. Therefore, an alpha level of 0.05 was used to determine significance for
most of the statistical analyses, unless otherwise noted. Two-sided tests were used in
determining significance.
Preliminary Analyses
Validity of CHS Items
The validity of the CHS items has not been tested. Therefore, the validity of
some of the CHS items that were used in this study was tested. For childrens
physical activity, comparisons were made with prior years of the CHS. The CHS
question measuring neighborhood safety was compared to the CDCs State and Local
Areas Integrated Telephone Survey, the National Survey of Childrens Health
(NSCH) in Colorado (185). Comparisons of the prevalence estimates and confidence
intervals were made between the surveys. Some of the comparisons revealed slightly
different prevalence estimates and some comparisons were similar. Therefore, in all
likelihood, the items are measuring similar concepts, are measuring what they intend
to measure, and are not under- or over-reporting this information, or at least they are
consistently measuring these biases.
ZCTA and ZIP Code Match Analyses
Before using and merging the Census data, spatial analyses were completed to
verify that ZCTAs and ZIP codes have similar spatial boundaries, meaning
boundaries that match by 80%. These GIS analyses were completed using ArcGIS
software, version 9 (186). ZIP code boundaries were obtained from ESRI StreetMap
58


Pro (178). ZCTA boundaries were used from the U.S. Census Bureau 2000 shape
files. Union overlays, arithmetic calculations, and visual inspections were completed
to identify the overlap/matching areas between ZIP codes and ZCTAs (187).
There was not a ZCTA for ZIP code 80113; although, the ZCTA for 80110
covers the ZIP code 80113. Five ZIP codes were created after the 2000 Census
(80104 split into 80104, 80108, and 80109; 80126 split into 80126, 80129, and
80130; and 80601 split into 80601 and 80602). For these ZIP codes, the area in
square miles of the new ZIP codes and ZIP code, 80113, was calculated using union
overlays. Then the percent the new ZIP codes covered of the original ZCTAs was
calculated and applied to the Census data. Six ZIP codes (80138, 80403, 80439,
80470, 80504, and 80516) are partially outside of the Denver metropolitan area so
only Census data from the part within the Denver metropolitan area were used. After
making these adjustments, the following match rates were produced by the spatial
analyses: three ZIP codes matched by 40%; six by 50%; eleven by 60%; twelve by
70%; nineteen by 80%; and thirty-eight by 90%.
Descriptive Analyses
Descriptive statistics including distributions, frequencies, means, and standard
deviations were computed to understand the nature of the data using SPSS.
Additional descriptive analyses, such as distributions, frequencies, means, standard
deviations, and percents, and analyses to examine whether or not variables met the
assumptions of the primary statistical analyses were completed using SAS 9.2. The
descriptive analyses for all child, family members, and neighborhood variables were
completed for everyone, for girls only, and for boys only using SAS 9.2.
Covariates for Primary Analyses
Analyses were completed to screen for covariates using SAS 9.2. Specifically
for childrens physical activity, independent /-tests, Spearmans rho correlations (non-
parametric correlation), and ANOVAs were completed based on the distribution of
the possible covariates. These analyses were completed separately for girls and boys.
For childrens active commuting to/from school, Pearsons chi-square analyses,
ANOVAs, and Kruskal-Wallis tests (non-parametric alternative for one-way
ANOVA) were completed based on the distribution of the possible covariates.
The following variables were assessed for possible inclusion as a covariate:
childrens age; childrens race/ethnicity; childrens gender; family members age;
family members education; family members income; and family members gender.
If a variable was associated (p < 0.2) with one or both of the outcome variables, then
59


the variable was treated as a covariate because the same covariates for each outcome
variable were desired.
Boys had significantly more hours of physical activity per week than girls
(9.02 (SD = 5.87) hours for boys versus 7.42 (SD = 5.60) hours for girls; ^54 = -4.09,
p = 0.00005). Therefore, the primary analyses for childrens physical activity were
completed separately for boys and girls, which is consistent with prior research (47,
49, 77, 90, 91, 112, 188). There were no gender differences with childrens active
y
commuting to/from school (x 2 = 0.56, p = 0.7557). The following variables were
covariates in the primary analyses for childrens active commuting to/from school
and physical activity: childrens age; childrens race/ethnicity; family members
education; and family members income.
Bivariate Analyses
Bivariate analyses with all possible covariates and independent variables were
completed for childrens physical activity and active commuting to/from school using
SAS 9.2. For childrens physical activity, independent /-tests, one-way ANOVAs,
Pearsons correlations, or Spearmans rho correlations were completed depending on
the distribution of the child, family member, and neighborhood covariates and
independent variables. These analyses were completed including the whole sample
and separately for girls and boys. Again, depending on the distribution of the
variables, Pearsons chi-square analyses, one-way ANOVAs, or Kruskal-Wallis tests
were completed between all possible covariates and independent variables and
childrens active commuting to/from school.
Relationship between Childrens Active Commuting
to/from School and Physical Activity
One-way ANOVAs were used to compare the association between childrens
active commuting to/from school and physical activity (research question 4). Three
one-way ANOVAs were completed using Proc ANOVA in SAS 9.2; one including
the whole sample and two separate models for girls and boys.
Results for Descriptive Analyses
Descriptive statistics including frequencies, percents, means, and standard
deviations for all child, family members, and neighborhood variables are reported in
Table 5.3 for everyone, for girls only, and for boys only. The descriptive analyses
revealed that the majority of children had a white ethnic background (71.61%), and
60


there were similar proportions of boys and girls (444 (51.45%) males and 419
(48.55%) females). On average, the children were 9.78 (SD = 2.95) years old; while,
their family member who completed the BRFSS was 41.19 (SD = 8.29) years old on
average. The BRFSS respondent was more likely to be a female than a male (63.73%
versus 36.27%, respectively). Approximately half (50.98%) of the BRFSS
respondents graduated from college or technical school and only 8.81% did not
graduate from high school. Approximately half (52.31%) had a high family income
($75,000 or more); while, 25.85% had a middle family income ($35,000 to less than
$75,000) and 21.84% had a low family income (less than $35,000). Family members
had an average BM1 of 26.28 (SD = 5.23) and engaged in a mean of 5.51 (SD = 5.83)
hours of moderate or vigorous physical activity per week. Almost half (47.90%) of
the children lived in a neighborhood that their parent/guardian rated as always safe;
only 8.74% of children lived in a neighborhood rated as sometimes or never safe.
On average, children lived in neighborhoods/ZIP codes with many residents
(29,789.58, SD = 14,774.68) and with a mean population density of 2,792.41 (SD =
2,267.08). The average demographic composition of the neighborhoods was: 24.31%
minority; 11.18% without a high school degree; 2.10% of children in poverty; and
6.40% of individuals in poverty. For crime and safety, there was an average of 3.51
(SD = 2.86) reported incidents for all violent offenses per 1,000 residents, and the
mean neighborhood safety rating was 1.39 (SD = 0.25), which is slightly higher than
the truncated value for usually safe. On average, there were 0.70 (SD = 0.56) public
elementary and middle school playgrounds per neighborhood square mile, 1.78 (SD =
2.16) parks per neighborhood square mile, 1.32 (SD = 1.56) open spaces per
neighborhood square mile, 0.07 (SD = 0.10) recreation sites per neighborhood square
mile, and 1.91 (SD = 1.41) miles of trails per neighborhood square mile.
The following information describes both outcome variables. Most children
(66.23%) did not walk or bicycle to school, 10.07% walked or bicycled to school one
to four days a week, and 23.70% walked or bicycled to school five days a week.
These rates were similar for girls and boys. Children spent an average of 8.24 (SD =
5.79) hours participating in physical activity per week; girls engaged in an average of
7.42 (SD = 5.60) hours and boys spent an average of 9.02 (SD = 5.87) hours.
61


Table 5.3: Descriptive statistics
Variables Girls Only Mean (SD) or n (%) n = 419 Boys Only Mean (SD) or n (%) n = 444 Everyone Mean (SD) or n (%) n = 863
Individual Variables Children
Race/ethnicity White Non-white 287 (69.16) 128 (30.84) 326 (73.92) 115 (26.08) 613(71.61) 243 (28.39)
Gender Female Male 419 (48.55) 444 (51.45)
Age 9.72 (2.96) 9.85 (2.95) 9.78 (2.95)
Active commuting to/from school 0 days 1 - 4 days 5 days 270 (65.85) 39(9.51) 101 (24.63) 289 (66.59) 46(10.60) 99 (22.81) 559 (66.23) 85 (10.07) 200 (23.70)
Physical activity, hours per week 7.42 (5.60) 9.02 (5.87) 8.24 (5.79)
Family Members
Gender Female Male 278 (66.35) 141 (33.65) 272 (61.26) 172 (38.74) 550 (63.73) 313 (36.27)
Age 41.44 (8.56) 40.95 (8.03) 41.19(8.29)
Education Did not graduate high school Graduated high school Attended college or technical school Graduated college or technical school 45 (10.74) 67 (15.99) 98 (23.39) 209 (49.88) 31 (6.98) 71 (15.99) 111 (25.00) 231 (52.03) 76 (8.81) 138 (15.99) 209 (24.22) 440 (50.98)
Income Low Middle High 87 (21.53) 109 (26.98) 208 (51.49) 93 (22.14) 104 (24.76) 223 (53.10) 180 (21.84) 213 (25.85) 431 (52.31)
BMI 26.30 (5.27) 26.27 (5.19) 26.28 (5.23)
Physical activity, hours per week 5.67 (6.25) 5.37 (5.40) 5.51 (5.83)
Neighborhood safety Sometimes and never Usually Always 38(9.16) 193 (46.51) 184 (44.34) 37 (8.35) 179 (40.41) 227 (51.24) 75 (8.74) 372 (43.36) 411 (47.90)
62


Table 5.3 (Cont.)
Variables Girls Only Mean (SD) or n (%) n = 419 Boys Only Mean (SD) or n (%) n = 444 Everyone Mean (SD) or n (%) n = 863
Neighborhood Variables
Population
Total population 30,419.87 (15,056.58) 29,194.78 (14,495.47) 29,789.58 (14,774.68)
Population density 2,873.24 (2,292.73) 2,716.14 (2,242.52) 2,792.41 (2,267.08)
Race/Ethnicity
Percent minority 25.31 (19.62) 23.37 (18.75) 24.31 (19.19)
Education
Percentage of individuals without a high school degree 11.77 (11.53) 10.62(10.47) 11.18 (11.00)
Percentage of individuals with at least a BA degree 37.39(16.77) 38.95 (16.13) 38.19(16.45)
Income/Poverty
Percentage of households at or above the median income in Denver metro 58.55 (16.90) 60.46 (16.88) 59.53 (16.91)
Percentage of children in poverty 2.17(2.10) 2.03 (2.05) 2.10(2.08)
Percentage of individuals in poverty 6.59 (5.48) 6.22 (5.48) 6.40 (5.48)
Crime
Reported incidents for murder/manslaughter rate per 1,000 residents 0.04 (0.06) 0.04 (0.07) 0.04 (0.07)
Reported incidents for rape rate per 1,000 residents 0.53 (0.34) 0.52 (0.36) 0.53 (0.35)
Reported incidents for robbery rate per 1,000 residents 0.93 (1.03) 0.86 (1.04) 0.89(1.04)
Reported incidents for aggravated assaults rate per 1,000 residents 2.04 (1.75) 2.06 (1.86) 2.05 (1.81)
Reported incidents for all violent offenses rate per 1,000 residents 3.54(2.80) 3.48 (2.92) 3.51 (2.86)
Safety
| Mean neighborhood safety 1.38 (0.25) 1.41 (0.24) 1.39 (0.25)
63


Table 5.3 (Cont.)
Variables Girls Only Mean (SD) or n (%) n = 419 Boys Only Mean (SD) or n (%) n = 444 Everyone Mean (SD) or n (%) n = 863
Physical Activity Resources
Number of public elementary and middle school playgrounds per neighborhood square mile 0.71 (0.55) 0.69 (0.56) 0.70 (0.56)
Number of parks per neighborhood square mile 1.78 (2.08) 1.77 (2.23) 1.78 (2.16)
Size of parks per neighborhood square mile 1,652,228.22 (1,882,992.67) 1,679,225.88 (1,835,464.23) 1,666,118.10 (1,857,660.85)
Number of open spaces per neighborhood square mile 1.41 (1.65) 1.23 (1.47) 1.32 (1.56)
Size of open space per neighborhood square mile 1,220,362.35 (1,724,555.57) 1,319,336.36 (1,843,997.58) 1,271,282.93 (1,786,656.85)
Number of recreation sites per neighborhood square mile 0.06 (0.10) 0.07 (0.10) 0.07 (0.10)
Length of trails per neighborhood square mile 1.97 (1.42) 1.86(1.40) 1.91 (1.41)
64


CHAPTER 6
GIRLS AND BOYS PHYSICAL ACTIVITY
The first research question this dissertation answered was: How do family and
neighborhood characteristics influence childrens physical activity, above and beyond
the individual characteristics of children? This research question tested two
hypotheses: 1) household factors are positively associated with childrens physical
activity and 2) neighborhood factors will directly influence childrens physical
activity. Because boys and girls engaged in different amounts of physical activity
and separate analyses were completed for boys and girls, this chapter first describes
the statistical analyses used to answer this research question for girls and presents the
results of these analyses, and then discusses the statistical analyses and results for
boys physical activity. The second half of the chapter integrates the findings for
girls and boys by providing a discussion to interpret and explain the findings with
prior research and theory. Then implications, future directions, and possible
interventions are presented. Finally, the chapter concludes with a future research
agenda for girls and boys physical activity.
Primary Analyses for Girls Physical Activity
Individual Level Analyses
Multiple linear regressions were completed, instead of hierarchical linear
models as originally proposed, because there were very small neighborhood effects
(ICC < 2%, p > 0.3) for girls physical activity. A backwards elimination approach
was implemented to create a multiple linear regression model. All the individual
level independent variables and the four covariates were entered into a multiple linear
regression model using Proc Reg in SAS. Then the least significant independent
variable was removed at each subsequent step, and this process continued until every
independent variable had a p value of < 0.15.
Spearmans rho and Kendalls tau correlations among all individual level
covariates and independent variables were examined due to possible multicollinearity.
The cutoff criterion for not including a variable because it was too highly correlated
with another variable was 0.70. No variables were correlated at 0.50 or higher, so
none of the variables were removed from the final model due to multicollinearity. In
addition, all the typical assumptions for a multiple linear regression model (normality,
independence, linearity, homoscedasticity, and multicollinearity) were examined in
the final model and were upheld.
65


In addition, a sensitivity analysis was completed, due to the non-normal
distribution of the outcome variable, to determine if using the truncated outcome
variable for childrens physical activity (as mentioned above) was the correct
approach. This sensitivity analysis consisted of completing a Poisson regression
using the covariates and independent variable from the final multiple linear regression
model with the non-truncated/non-normally distributed version of childrens physical
activity.
Neighborhood Level Analyses
All the neighborhood independent variables were tested in separate multiple
linear regression models controlling for the four covariates using Proc Reg in SAS.
The multiple linear regression model with mean neighborhood safety was also
completed controlling for parental perceptions of neighborhood safety. After
completing the separate multiple linear regression models, a combined multiple linear
regression model was completed with the neighborhood variables that were related to
girls physical activity at p < 0.15 in the separate regression models.
Before adding the neighborhood variables into a final combined model,
collinearity was examined by completing Pearson, Spearmans rho, or Kendalls tau
correlations among all the covariates and neighborhood variables that were related to
girls physical activity at the p < 0.15 level in the separate models. Due to the high
correlations among these neighborhood variables (r/rho = 0.57 to 0.90), the final
combined multiple linear regression model consisted of neighborhood population
density, percentage of individuals in poverty, and reported incidents for
murder/manslaughter as well as the four covariates.
All the typical assumptions for a multiple linear regression model were
examined in the seven significant (p <0.15) separate models and in the final
combined multiple linear regression. The assumptions in all eight models were
upheld. In addition, eight Poisson regressions (sensitivity analyses) with the non-
truncated/non-normally distributed version of childrens physical activity were
completed to determine if using the truncated outcome variable was the correct
approach.
Results for Girls Physical Activity
Bivariate Results
Independent Mests, one-way ANOVAs, Pearsons correlations, or Spearmans
rho correlations were completed to assess the bivariate relationship between all
66


possible covariates and independent variables and girls physical activity. The results
of these analyses are listed in Table 6.1.
There was a negative correlation between age and girls physical activity;
older girls participated in fewer hours of physical activity per week. Girls physical
activity was positively correlated with their family members physical activity; girls
engaged in more hours of physical activity per week when their family member also
had more hours of physical activity per week. Contrary to hypothesis four, girls
active commuting to/from school was not related to their physical activity.
Several neighborhood variables were significantly correlated with girls
physical activity. Girls physical activity and percentage of residents without a high
school degree were negatively correlated; girls who lived in neighborhoods with a
smaller percentage of residents without a high school degree were more physically
active. Both measures of neighborhood poverty were negatively correlated with girls
physical activity; girls who lived in neighborhoods with a smaller percentage of
children in poverty and a smaller percentage of individuals in poverty engaged in
more hours of physical activity per week. There were negative correlations between
girls physical activity and three measures of violent crime in a neighborhood such
that girls living in neighborhoods with a greater number of reported incidents for
murder/manslaughter, robbery, and all violent offenses per 1,000 residents engaged in
fewer hours of physical activity per week. In addition, the number of public
elementary and middle school playgrounds per neighborhood square mile was
negatively correlated with girls physical activity.
These bivariate analyses revealed many significant associations with
neighborhood variables so additional analyses were conducted to verify that
completing multiple linear regressions without a random neighborhood effect was the
best approach. None of the eighteen random intercept hierarchical linear models
completed using a factor analytic covariance structure or an unstructured covariance
structure had a significant ZIP code/neighborhood variance component, and the
majority of ICCs were less than 2%, p > 0.3. Therefore, completing multiple linear
regression models without a random neighborhood effect is an appropriate analytic
strategy for girls physical activity.
67


Table 6.1: Bivariate analyses with all variables and girls physical activity
Variables Mean (SD) or n (%) Statistic p value
Individual Variables Children
Race/ethnicity White Non-white 7.53 (5.51) 7.02 (5.79) t4,2 = 0.85 0.3943
Age 9.72 (2.96) rho = -0.12 0.0129
Active commuting to/from school 0 days 1 - 4 days 5 days 7.06 (5.38) 8.13 (6.01) 8.01 (5.82) F: 406 = 1 -46 0.2343
Family Members
Gender Female Male 7.28 (5.70) 7.68 (5.41) t4i6 = -0.69 0.4912
Age 41.44(8.56) rho = -0.02 0.7310
Education Did not graduate high school Graduated high school Attended college or technical school Graduated college or technical school 7.49 (6.76) 8.22 (6.69) 7.40 (5.71) 7.15(4.86) F3 414 = 0.63 0.5988
Income Low Middle High 7.64 (6.74) 6.95 (5.57) 7.46 (4.89) F ? 400 = 0.44 0.6426
BMI 26.30 (5.27) rho = -0.06 0.2378
Physical activity, hours per week 5.67 (6.25) rho = 0.16 0.0008
Neighborhood safety Sometimes and never Usually Always 7.76 (6.01) 7.09 (5.22) 7.78 (5.86) F, 4 =0.77 0.4616
Neighborhood Variables
Population
Total population 30,420 (15,057) r = 0.01 0.8457
Population density 2,873 (2,293) r = -0.09 0.0764
Race/Ethnicity
Percent minority 25.31 (19.62) rho = -0.06 0.2112
68


Table 6.1 (Cont.)
Variables Mean (SD) or n (%) Statistic p value
Education
Percentage of individuals without a high school degree 11.77 (11.53) rho = -0.10 0.0503
Percentage of individuals with at least a BA degree 37.39(16.77) r = -0.01 0.8713
Income/Poverty
Percentage of households at or above the median income in Denver metro 58.55 (16.90) rho = 0.09 0.0632
Percentage of children in poverty 2.17(2.10) rho = -0.11 0.0266
Percentage of individuals in poverty 6.59 (5.48) rho = -0.09 0.0541
Crime
1 Reported incidents for murder/manslaughter | rate per 1,000 residents 0.04 (0.06) rho = -0.12 0.0153
Reported incidents for rape rate per 1,000 residents 0.53 (0.34) rho = -0.03 0.5748
Reported incidents for robbery rate per 1,000 residents 0.93 (1.03) rho = -0.10 0.0442
Reported incidents for aggravated assaults rate per 1,000 residents 2.04 (1.75) rho = -0.09 0.0620
Reported incidents for all violent offenses rate per 1,000 residents 3.54 (2.80) rho = -0.10 0.0531
Safety
Mean neighborhood safety 1.38 (0.25) rho = 0.01 0.8474
Physical Activity Resources
Number of public elementary and middle school playgrounds per neighborhood square mile 0.71 (0.55) r = -0.10 0.0382
Number of parks per neighborhood square mile 1.78 (2.08) rho = -0.03 0.5940
Size of parks per neighborhood square mile 1,652,228 (1,882,993) rho = 0.06 0.1891
Number of open spaces per neighborhood square mile 1.41 (1.65) rho = -0.0003 0.9956
Size of open space per neighborhood square mile 1,220,362 (1,724,556) rho = 0.005 0.9196
1 Number of recreation sites per neighborhood square mile 0.06 (0.10) rho = 0.02 0.6462
1 Length of trails per neighborhood square | mile 1.97 (1.42) r = -0.08 0.1030
69


Multivariate Individual Level Results
Using a backwards elimination approach, a multiple linear regression model
was developed to answer the first research question, part a; how family members
characteristics influence girls physical activity, above and beyond girls individual
characteristics. The results of the final multiple linear regression model are listed in
Table 6.2.
The overall multiple linear regression model significantly predicted girls
physical activity with 2% of the variability in girls physical activity explained by
girls race/ethnicity, girls age, family members education, family members income,
and family members physical activity. The final multiple linear regression model
revealed that younger girls engaged in more hours of physical activity per week and
girls had more hours of physical activity per week when their family member also
participated in more hours of physical activity per week, even after adjusting for the
other variables in the model.
The Poisson regression for the sensitivity analysis revealed similar directional
parameter estimates and significance values, in most variables, to the multiple linear
regression model. Therefore, using the truncated version of childrens physical
activity, treating the outcome variable as normally distributed, and completing
multiple linear regressions is an adequate approach for girls.
70


Table 6.2: Results from the individual level multiple linear regression model
Girls Physical Activity
Variables Estimate, B (SE) 95% C.I. t p value
Intercept 9.04(1.04)
Covariates
Children
Race/ethnicity Non-white White* -0.94 (0.70) 0 -2.31,0.43 -1.36 0.1762
Age -0.23 (0.09) -0.42, -0.05 -2.50 0.0127
Family Members
Education Did not graduate high school Other* 0.48(1.17) 0 -1.83,2.79 0.41 0.6842
Education Graduated high school Other* 1.04 (0.86) 0 -0.66, 2.74 1.21 0.2284
Education Attended college or technical school Other* 0.28 (0.71) 0 -1.13, 1.68 0.39 0.6995
Income Low Other* 0.45 (0.87) 0 -1.26,2.17 0.52 0.6020
Income Middle Other* -0.51 (0.68) 0 -1.85,0.82 -0.76 0.4506
Individual Variable
Family Members
Physical activity, hours per week 0.11 (0.04) 0.02, 0.19 2.44 0.0152
F8 39o = 2.10, p = 0.0345
R2 = 0.04
Adj. R2 = 0.02
Reference category
71


Neighborhood Level Results
Separate Neighborhood Models
All the neighborhood independent variables were tested in separate multiple
linear regression models adjusting for the four covariates. Seven neighborhood
variables were related (p <0.15) to girls physical activity, after controlling for the
covariates. These results are listed in Tables 6.3 6.9.
The following neighborhood variables revealed trends for a negative
relationship with girls physical activity: girls living in neighborhoods with fewer
children in poverty had more hours of physical activity per week; girls living in
neighborhoods with fewer individuals in poverty engaged in more hours of physical
activity per week; and girls living in neighborhoods with fewer miles of trails
engaged in more hours of physical activity per week.
Other neighborhood variables significantly predicted girls physical activity.
These models revealed negative relationships with the neighborhood variable and
girls physical activity: girls living in neighborhoods with less population density had
more hours of physical activity per week; girls living in neighborhoods with fewer
reported incidents for murder engaged in more hours of physical activity per week;
girls living in neighborhoods with fewer reported incidents for robbery participated in
more hours of physical activity per week; and girls living in neighborhoods with
fewer public elementary and middle school playgrounds participated in more hours of
physical activity per week.
The Poisson regressions for the sensitivity analyses produced similar
directional parameter estimates and significance values, in most variables, to the
seven separate neighborhood multiple linear regression models. Therefore, using the
truncated version of childrens physical activity, treating the outcome variable as
normally distributed, and completing multiple linear regressions is an adequate
approach for girls.
72


Table 6.3: Results from the separate neighborhood level multiple linear regression
model for neighborhood population density, controlling for covariates
Girls Physical Activity
Variables Estimate, B (SE) 95% C.I. t p value
Intercept 10.07 (1.02)
Covariates
Children
Race/ethnicity Non-white White* -0.71 (0.71) 0 -2.11, 0.68 -1.00 0.3161
Age -0.23 (0.09) -0.41,-0.04 -2.41 0.0166
Family Members
Education Did not graduate high school Other* 0.50(1.18) 0 -1.82,2.81 0.42 0.6723
Education Graduated high school Other* 1.32 (0.86) 0 -0.38, 3.02 1.52 0.1287
Education Attended college or technical school Other* 0.44 (0.72) 0 -0.97, 1.85 0.62 0.5373
Income Low Other* 0.58 (0.88) 0 -1.15,2.32 0.66 0.5100
Income Middle Other* -0.32 (0.69) 0 -1.66, 1.03 -0.46 0.6452
Neighborhood Variable
Population density -0.0003 (0.0001) -0.0005, 0.000003 -1.94 0.0532
F8 39o= 1.82, p = 0.0712
R2 = 0.04
Adj. R2 = 0.02 1
Reference category
73


Table 6.4: Results from the separate neighborhood level multiple linear regression
model for percentage of children in poverty, controlling for covariates
Girls Physical Activity
| Variables Estimate, B (SE) 95% C.I. t p value
| Intercept 9.93(1.01)
Covariates
Children
Race/ethnicity Non-white White* -0.66 (0.72) 0 -2.08, 0.75 -0.92 0.3584
Age -0.23 (0.09) -0.41, -0.04 -2.42 0.0158
Family Members
Education Did not graduate high school Other* 0.77 (1.19) 0 -1.57,3.11 0.65 0.5192
Education Graduated high school Other* 1.31 (0.87) 0 -0.39, 3.01 1.52 0.1304
Education Attended college or technical school Other* 0.35 (0.72) 0 -1.06, 1.75 0.48 0.6290
Income Low Other* 0.67(0.90) 0 -1.10,2.43 0.74 0.4582
Income Middle Other* -0.35 (0.69) 0 -1.69, 1.00 -0.50 0.6142
1 Neighborhood Variable
1 Percentage of children in 1 poverty -0.28 (0.16) -0.59, 0.03 -1.79 0.0738
F8 39o= 1.75, p = 0.0848
R2 = 0.03
Adj. R2 = 0.01
Reference category
74


Table 6.5: Results from the separate neighborhood level multiple linear regression
model for percentage of individuals in poverty, controlling for covariates
Girls Physical Activity
Variables Estimate, B (SE) 95% C.I. l p value
Intercept 10.10(1.02)
Covariates
Children
Race/ethnicity Non-white White* -0.72 (0.71) 0 -2.12, 0.68 -1.01 0.3146
Age -0.23 (0.09) -0.42, -0.05 -2.45 0.0146
Family Members
Education Did not graduate high school Other* 0.67(1.18) 0 -1.66,3.00 0.57 0.5718
Education Graduated high school Other* 1.23 (0.86) 0 -0.46, 2.93 1.43 0.1542
Education Attended college or technical school Other* 0.29 (0.72) 0 -1.11, 1.70 0.41 0.6832
Income Low Other* 0.65 (0.89) 0 -1.11,2.40 0.72 0.4691
Income Middle Other* -0.37 (0.68) 0 -1.72, 0.97 -0.55 0.5842
Neighborhood Variable
Percentage of individuals in | poverty -0.10(0.06) -0.22, 0.01 -1.82 0.0693
F8.39o = 1.77, p = 0.0820
R2 = 0.04
Adj. R2 = 0.02
Reference category
75


Table 6.6: Results from the separate neighborhood level multiple linear regression
model for reported incidents for murder/manslaughter, controlling for covariates
Girls Physical Activity
Variables Estimate, B (SE) 95% C.I. t p value
Intercept 9.83 (1.04)
Covariates
Children
Race/ethnicity Non-white White* -0.54 (0.72) 0 -1.97, 0.88 -0.75 0.4546
Age -0.23 (0.10) -0.42, -0.04 -2.39 0.0175
Family Members
Education Did not graduate high school Other* 0.84 (1.22) 0 -1.56,3.24 0.69 0.4899
Education Graduated high school Other* 1.06 (0.88) 0 -0.68,2.80 1.20 0.2323
Education Attended college or technical school Other* 0.43 (0.73) 0 -1.01, 1.87 0.59 0.5583
Income Low Other* 0.43 (0.90) 0 -1.35,2.21 0.47 0.6369
Income Middle Other* -0.49 (0.70) 0 -1.87, 0.89 -0.70 0.4864
Neighborhood Variable
Reported incidents for murder/manslaughter rate per 1,000 residents -10.14 (4.77) -19.53,-0.76 -2.12 0.0343
F8W= 1.70, p = 0.0977
R2 = 0.04
Adj. R2 = 0.01
*Reference category
76


Table 6.7: Results from the separate neighborhood level multiple linear regression
model for reported incidents for robbery, controlling for covariates
Girls Physical Activity
Variables Estimate, B (SE) 95% C.I. t p value
Intercept 9.79(1.04)
Covariates
Children
Race/ethnicity Non-white White* -0.54 (0.72) 0 -1.97,0.88 -0.75 0.4527
Age -0.22 (0.10) -0.41,-0.03 -2.24 0.0258
Family Members
Education Did not graduate high school Other* 0.89(1.22) 0 -1.51,3.30 0.73 0.4668
Education Graduated high school Other* 1.11 (0.88) 0 -0.63,2.85 1.25 0.2111
Education Attended college or technical school Other* 0.46 (0.73) 0 -0.98, 1.90 0.63 0.5295
Income Low Other* 0.77 (0.93) 0 -1.07,2.60 0.82 0.4107
Income Middle Other* -0.32 (0.71) 0 -1.71, 1.07 -0.46 0.6470
Neighborhood Variable
Reported incidents for robbery rate per 1,000 residents -0.68 (0.32) -1.31,-0.05 -2.13 0.0339
F8367 = 1.70, p = 0.0972
R2 = 0.04
Adi. R2 = 0.01
* Reference category
77


Table 6.8: Results from the separate neighborhood level multiple linear regression
model for number of public elementary and middle school playgrounds, controlling
for covariates
Girls Physical Activity
Variables Estimate, B (SE) 95% C.I. t p value
| Intercept 10.10(1.02)
Covariates
Children
Race/ethnicity Non-white White* -0.65 (0.71) 0 -2.05, 0.75 -0.91 0.3609
Age -0.22 (0.09) -0.40, -0.03 -2.31 0.0212
Family Members
Education Did not graduate high school Other* 0.59(1.18) 0 -1.72,2.90 0.50 0.6155
Education Graduated high school Other* 1.29 (0.86) 0 -0.40, 2.98 1.50 0.1355
Education Attended college or technical school Other* 0.48 (0.72) 0 -0.92, 1.89 0.68 0.4993
Income Low Other* 0.64 (0.88) 0 -1.09,2.37 0.73 0.4683
Income Middle Other* -0.27 (0.68) 0 -1.62, 1.07 -0.40 0.6893
Neighborhood Variable
Number of public elementary and middle school playgrounds per neighborhood square mile -1.27 (0.55) -2.35, -0.20 -2.33 0.0202
F8390 = 2.04, p = 0.0410
R2 = 0.04
Adj. R2 = 0.02
Reference category
78


Table 6.9: Results from the separate neighborhood level multiple linear regression
model for length of trails, controlling for covariates
Girls Physical Activity
Variables Estimate, B (SE) 95% C.I. t p value
Intercept 10.23 (1.04)
Covariates
Children
Race/ethnicity Non-white White* -0.83 (0.70) 0 -2.21,0.56 -1.17 0.2407
Age -0.23 (0.09) -0.42, -0.05 -2.46 0.0145
Family Members
Education Did not graduate high school Other 0.41 (1.18) 0 -1.91,2.73 0.35 0.7293
Education Graduated high school Other* 1.27 (0.86) 0 -0.43, 2.97 1.47 0.1413
Education Attended college or technical school Other* 0.40 (0.72) 0 -1.01, 1.81 0.56 0.5770
Income Low Other* 0.35 (0.87) 0 -1.37, 2.06 0.40 0.6922
Income Middle Other* -0.49 (0.68) 0 -1.83,0.84 -0.73 0.4679
Neighborhood Variable
Length of trails per neighborhood square mile -0.34 (0.20) -0.73,0.05 -1.74 0.0833
F8 390 = 1.73, p = 0.0903
R2 = 0.03
Adj. R2 = 0.01
Reference category
79


Combined Neighborhood Model
The combined neighborhood model included three neighborhood variables
(population density, percentage of individuals in poverty, and reported incidents for
murder/manslaughter per 1,000 residents) as well as the four covariates. This
combined multiple linear regression model answered the first research question, part
b; how neighborhood characteristics influence girls physical activity, above and
beyond girls individual characteristics. These results are displayed in Table 6.10.
The combined multiple linear regression model revealed a trend towards
significantly predicting girls physical activity with 2% of the variability in girls
physical activity explained by girls race/ethnicity, girls age, family members
education, family members income, population density, percentage of individuals in
poverty, and reported incidents for murder/manslaughter. The combined model
revealed that younger girls participated in more hours of physical activity per week,
even after adjusting for the other variables in the model. Even though the
neighborhood variables trended towards significance or significantly predicted girls
physical activity in the separate multiple linear regression models, the three variables
did not significantly predict girls physical activity in the combined model.
The Poisson regression for the sensitivity analysis produced similar
directional parameter estimates and significance values to the final combined
neighborhood multiple linear regression model. Therefore, using the truncated
version of childrens physical activity, treating the outcome variable as normally
distributed, and completing multiple linear regressions is an adequate approach for
girls.
80


Table 6.10: Results from the combined neighborhood level multiple linear regression
model, controlling for covariates
Girls Physical Activity
Variables Estimate, B (SE) 95% C.I. t p value
Intercept 10.17 (1.06)
Covariates
Children
Race/ethnicity Non-white White* -0.31 (0.74) 0 -1.76, 1.14 -0.42 0.6737
Age -0.21 (0.10) -0.40, -0.02 -2.19 0.0289
Family Members
Education Did not graduate high school Other* 0.84(1.22) 0 -1.56,3.24 0.69 0.4935
Education Graduated high school Other* 1.13(0.88) 0 -0.61,2.87 1.28 0.2022
Education Attended college or technical school Other* 0.47 (0.74) 0 -0.97, 1.92 0.65 0.5190
Income Low Other* 0.76 (0.93) 0 -1.06,2.59 0.82 0.4104
Income Middle Other* -0.32(0.71) 0 -1.71, 1.07 -0.45 0.6499
Neighborhood Variables
Population density -0.0002 (0.0001) -0.0005, 0.00008 -1.39 0.1662
Percentage of individuals in poverty -0.04 (0.07) -0.17,0.10 -0.52 0.6008
Reported incidents for murder/manslaughter rate per 1,000 residents -7.37(5.41) -18.01,3.26 -1.36 0.1734
F,01fi,= 1.67, p = 0.0864
R2 = 0.04
Adj. R2 = 0.02
*Reference category
81