This document summarizes a study that assessed factors related to active commuting among elementary school students in Minneapolis Public Schools. The study observed student transportation modes at 39 schools to determine if rates of active commuting were related to school demographics, distance from home, or school type (neighborhood vs. magnet). Results found higher rates of active commuting at schools with lower minority enrollment and located closer to students' homes. Future research should examine additional factors like urban design and parental attitudes.

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Active Commuting in a District with a School Choice Policy
Chris L. Pulley
Master’s Project
University of Minnesota
2013

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Abstract
Objectives: The current study assessed school demographics (minority, free/reduced priced
lunch (FRPL)), school type (neighborhood vs. magnet) and distance to school, on active
commuting among 39 elementary schools in the Minneapolis Public Schools (MPS) district.
Methods: A total of 43 schools were contacted to participate in the current study. Of the 43
schools contacted, 39 participated. Observation sheets were used to record student transport
mode to and from school. Data was imported into Microsoft Excel and analyzed using STATA.
Independent t-tests were calculated to determine differences between different predictor and
outcome variables. Multiple regression models were calculated to determine whether active
commuting was related to school demographics, school characteristics (school type), and a
specific distance to school (percent within walking distance).
Results: There were no significant differences between the percentage of minority students,
percentage of students who qualify for free or reduced priced lunches, and school type.
There was no statistically significant difference in distance to school by school minority status
or percentage of students receiving FRPL. There was a significant difference in distance to
school by school type. Magnet schools are located farther away (2.2 miles; SD=0.5 miles), on
average, compared to neighborhood schools (1.7 miles; SD=0.4 miles; p=0.0016). There was a
greater percentage of active commuters (25.5%) among schools with low minority enrollment,
compared to schools with high minority enrollment (16.3%; p=0.04).
Conclusions: The proportion of students who walk or bike to school is greater at schools with a
larger proportion of students who live within walking distance, adjusted for school type
(neighborhood vs. magnet), percentage of minority students, and percentage of students eligible
for free or reduced price lunch. Future studies should assess other factors (urban form, parental
attitudes towards children actively commuting to and from school) that may influence student
transport mode.

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Introduction
In the last few decades, the prevalence of obesity among children and adolescents ages 6-
19 has increased at an alarming rate. According to the Centers for Disease Control and
Prevention (CDC), obesity is defined for children as a body mass index (BMI) at or above the
95th percentile, specific to their sex and age for children of the same age and sex (Barlow and
the Expert Committee, 2007). Obesity rates have more than tripled among children (6.5% to
19.6%) ages 6-11 and adolescents (5.0% to 18.1%) ages 12-19 (Ogden et al., 2010). Data from
the 2007-2008 National Health and Nutrition Examination Survey (NHANES) show that the
prevalence rate of obesity among adolescents was 16.9% (Ogden et al., 2010). Rising obesity
rates are a concern since obesity increases the risk for preventable causes of death, such as type
II diabetes and types of cancer (CDC, 2009a), and incurs billions of dollars in health care costs
annually (CDC, 2009b). Individuals who are overweight as adolescents are more likely to
become obese as adults (Freedman et al., 2005).
While obesity rates among children have been rising, active means of commuting to
school, such as walking or biking, have become less common over the past several decades.
Active commuting to school decreased from 47.7% in 1969 to 12.7% in 2009 among children
ages 5-14, according to data from the National Personal Transportation Survey (NPTS)
(McDonald, Brown, Marchetti, & Pedroso, 2011). In that same period, the percentage of
automobile commuting increased by 38%, from 17.1% to 55% (McDonald, 2007). Of all trips to
school made by children that are 1 mile or less, only 35.9% of these were by walking (Ham,
Macera, & Lindley, 2005), meaning the majority of children arrive to school by other means of
transport, such as bus or automobile; these transport modes decrease the opportunity for children
to engage in physical activity outside of the school day.

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The decrease in active commuting to school, and the increase in obesity rates, have led
some public health organizations to recommend active commuting as a strategy that has potential
to reduce childhood obesity (Institute of Medicine, 2009). Active commuting is among the
recommendations that the CDC has listed to prevent obesity and is among the objectives of
Healthy People 2020, which has stated that walking and bicycling to school provides a strong
opportunity where children and adolescents can increase their level of physical activity. Two of
the 15 physical activity objectives of Healthy People 2020 are increasing the proportion of trips
one mile or less made to school by walking, and two miles or less by bicycling, targeted towards
those ages 5-15 (CDC, 2010).
Physical activity guidelines recommend that children should collect at least 60 minutes of
moderate-to-vigorous physical activity (MVPA) every day (Strong et al., 2005), but less than
50% of children and adolescents in the United States meet these guidelines (CDC, 2009; Haskel
et al., 2007; Troiano et al., 2008). Girls tend to fall below the 60 minutes of recommended
MVPA by the age of 13, while boys fall below the level by the age of 15 (Nader et al., 2008).
Promoting daily physical activity, such as walking or biking to school, is an important strategy to
prevent the decline in MVPA by targeting children before they reach this age range.
Active commuting has been recommended as a strategy to increase physical activity
among students (Tudor-Locke, Ainsworth, & Popkin, 2001), and there is evidence that children
who walk or bike to school are more likely to be physically active throughout the day, compared
to children who are transported to school by other modes (Cooper et al., 2005; Davison, Werder,
& Lawson, 2008). In one study, fifth graders who walked to school five days a week recorded
24 additional minutes of daily MVPA on their physical activity monitors, compared to those who
traveled by automobile or walked less than five days a week (Sirard et al., 2005). Another study

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found that middle school girls who walked to and from school reported 14 minutes of extra
MVPA, compared to those that did not walk to and from school (Saksvig et al., 2007).
Though the majority of active commuting studies have found a positive relationship between
active commuting and physical activity, causation cannot be concluded due to the cross-sectional
nature of the studies (Faulkner, Buliung, Flora, & Fusco, 2009).
A variety of factors may prevent or discourage children from walking to school. A
primary reason why children do not often walk to school is the distance that a child lives from
school (Beck & Greenspan, 2008). The farther that a child lives from school, the less likely
he/she is to actively commute to school (Timperio et al., 2006). Distance between home and
school directly influences active commuting among children (Marshall et al., 2010). Parent
perception about the safety of the route to and from school, can also influence the transport mode
to and from school (Kerr et al., 2006; McMillan, 2007).
Data from cross-sectional studies suggest that active commuting can be an effective
strategy to increase MVPA among youth. In a review of 2003-2004 NHANES data, researchers
found that active commuting resulted in greater MVPA, compared to students who were
transported to and from by school by bus or automobile (Mendoza et al., 2011). Students who
actively commute accumulate 7.5% (4.5 minutes) more minutes of recommended MVPA
(Chillón, Evenson, Vaughn, & Ward, 2011). Increasing MVPA among youth is encouraged
since only 7.6% of adolescents ages 12-19 met the recommended MVPA requirements (Nader et
al., 2008; Troaino et al., 2008).
Besides distance from home to school, the location of a school may influence transport
mode among students (Larsen et al., 2009). Often referred to as school siting policy, this policy
is affected by total student enrollment, attendance boundary lines, and walk zone boundaries.

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School districts determine their transportation policies based on attendance boundary lines, as
well as the walk zone boundaries. The walk zone boundary is the distance where a child is
eligible for bus transportation to and from school; if the child lives outside of this distance, then
he/she must be travel to and from school by other modes, such as walking, bicycling, carpooling,
or family automobile. Students are more likely to walk to school if they live a reasonable
distance from school (i.e., within one mile). Neighborhood schools were often built and located
centrally in neighborhoods where many students lived, which would enable them to walk or bike
to school (EPA, 2003). Children had less distance to travel from home to school, providing the
opportunity to walk or bike to school.
Modern schools are often located on the edge of a neighborhood where fewer children
live, making active commuting more difficult due to a greater distance to walk from home to
school. Active commuting among students is more likely to occur when schools are centrally
located in a neighborhood (EPA, 2003) and where school enrollment is low (Davison, Werder, &
Lawson, 2008). The Environmental Protection Agency (EPA) recommends that schools should
be located within neighborhoods to encourage active commuting (EPA, 2003). Data from recent
research suggests that active commuting is related to school siting (Larsen et al., 2009; Wilson,
Marshall, Wilson, & Krizek, 2010). The CDC promotes strategies to create safe communities
that support physical activity, and encourages school districts to build new or existing schools
within a reasonable walking or biking distance of a neighborhood (CDC, 2009).
The district transportation office works with school administration to determine the
walking boundary of a school. The walking boundary, defined as the geographical area around
the school, determines whether or not students can receive bus service. If a student lives within
the walking boundary, often set as a pre-determined distance from school, then the student may

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not receive bus service from the district. The walking boundary for elementary schools in
Minneapolis Public Schools (MPS) is one-half mile or less. If a student lives beyond this
distance, he/she receives bus service. If children are not bused to and from school, then parents
must decide how their child gets to/from school. Safety is often a primary concern for parents
who want their child to be bused to and from school. Children are often driven to school instead
of walking or biking due in part to safety concerns, related to parental perception of the safety
among the route (CDC, 2005).
While school choice provides families with options of where to send their children to
school, most of these options are located farther away from the child’s home, decreasing the
likelihood that a child will commute to school by walking or bicycling. Studies that have
assessed the effect of school choice (neighborhood vs. magnet schools) on student transport
mode found higher rates of active commuting among students attending neighborhood schools,
compared to magnet schools (Wilson, Marshall, Wilson, & Krizek, 2010). There is greater active
commuting by students in neighborhood schools, which are located in the neighborhood where a
student lives, compared to magnet schools, which are not located in the neighborhood where a
student lives (Wilson, Wilson, & Krizek, 2007). School choice policy is often influenced by
factors such as academic performance and total student enrollment. This policy is relevant to the
present study, as school choice policy was enacted by MPS in the fall of 2010, before the data
from this present study was collected.
The school choice policy, known as ‘Changing School Options,’ grouped schools into
three zones across the city, according to geography and transportation needs. Under this policy,
students attend an elementary, middle, and high school located in their zone. This would

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potentially save the district money in transportation costs and encourage active commuting
among students since the school that they would attend would be located in their neighborhood.
The ecological and cognitive active commuting (ECAC) framework is an extension of the
Social Ecological Model, describing the interaction between urban form variables and
sociodemographic variables that influence school transport mode (McMillan, 2005). In a review
of active commuting studies, researchers have found that sociodemographic variables (e.g.,
race/ethnicity) may modify a parents’ decision about permitting active commuting to school
(Sirard & Slater, 2008). The ECAC model describes the interaction between the
sociodemographic and urban form factors. Urban form factors, such as sidewalk length (Oakes,
Forsyth, & Schmitz, 2009) or street lighting, may influence sociodemographic factors, such as
how parents decide their children will commute to and from school. Both neighborhood and
traffic safety, whether real or perceived, influences a parent’s decision about how their children
will travel to and from school (McMillan, 2005).
The purpose of the present study was to assess the factors related to the prevalence of
active commuting among students enrolled in elementary schools in Minneapolis Public Schools
(MPS). We specifically sought to address whether demographic variables, such as race/ethnicity
and free/reduced priced lunch status, as well as environmental variables, such as distance to
school and school type (neighborhood vs. magnet), are related to active commuting.
Methods
Target Population
Minneapolis is an urban metropolitan city in Minnesota, and there is a large diverse
student population within the district. There is great variation in school demographics by

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geographic location. Elementary schools in MPS serve a diverse population of about 15,000
students (37% African American, 30% Caucasian, 18% Hispanic American, 9% Asian
American, and 5% Native American). Some schools are charter or magnet schools, providing
parents the choice to have their children attend schools that are not usually located within their
neighborhood. As a result, children are often bussed or driven to or from these schools that are
located outside their neighborhood. Of the 72 total schools in MPS encompassing grades K-12,
all K-5 (N=24) and K-8 (N=19) public schools in Minneapolis, MN were invited to participate.
Study Design
The present study used data from a larger study designed to assess the effect of the school
choice policy on prevalence of active commuting. The study staff measured the number of
children who arrived to, and departed from, school by the different transport modes of bus, mini-
bus, automobile, walking, or bicycling, via observation sheets.
Participants
Research staff contacted the principal and transportation coordinator at each school via
letter, phone, and email to describe the study. All schools were visited and paperwork describing
the study was left with administrators. A letter that included staff contact information and
described the study was also printed in the district-wide parent newsletter. Passive consent was
used for school participation. A total of 43 schools were contacted, and four schools were
excluded from the study due to various reasons, including a school being located inside another
school, the school administration declining participation, or the school serving students above 8th
grade.
Procedures

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Observations took place in the spring of 2010, before the school choice policy change
went into effect. To avoid the effects of winter weather, the observations were performed over
two months of the spring semester (April and May 2010). Each school was observed four times:
twice during the morning arrival, and twice during the afternoon dismissal. Study staff attempted
to spread the four observations for each school across the two-month period, but this was not
possible for every school due to scheduling constraints. Observations were scheduled based on
the availability of the student observers, the research assistants, and were timed to coincide with
the arrival and dismissal time of each school.
Each observation was coordinated by a graduate research assistant, assisted by two-to-
five student observers. The number of observers varied based on the layout of the school and
level of commuter traffic. Some schools had only a small number of children actively
commuting since the majority of students were being transported by bus. Schools that had only
one or two entrances open during arrival and dismissal time required only one or two observers.
Schools that had high enrollment, large campuses, and multiple entrances required a higher
number of observers and these factors were considered in advance when assigning observers to a
given school. Before spring observations began, research assistants visited each school to
evaluate the school layout and enrollment to determine how many observers would be needed for
each school.
For morning observations, observers arrived thirty minutes before the school day began
and remained fifteen minutes afterwards to count late arrivals; for the afternoon, observers
arrived fifteen minutes before the school day ended and remained thirty minutes afterwards.
Students arriving or leaving from their designated areas were counted on a tally sheet, and their
method of commuting was recorded. Research assistants instructed the observers to pay close

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attention to whether students walking to or from their assigned area appeared to actually be
walking to or from their home, or to or from a vehicle. Individual students getting on and off
buses were not counted, but the numbers of buses were recorded. The district transportation
provided the number of students who take the bus to and from school.
Observation Tool
The measures recorded on each tally sheet included the number of students walking,
number of students biking (the biking category included a small number of students who used
rollerblades, scooters, and other human-powered vehicles), number of groups of students biking,
number of adults walking, number of students walking with adults, number of groups of students
walking with adults, number of students dropped off by cars, total number of cars, number of
buses, and number of mini-buses. This method allowed nearly every student leaving and
arriving at each school to be counted, according to the method of transportation used to get to
school. Other measures, such as temperature and weather, were also recorded at each
observation.
Statistical Analyses
Each observation sheet was tallied twice by two different data collectors, and once inter-
collector reliability was established, each sheet was then entered into a Microsoft Access data
base twice by two different data entry staff. Any inconsistencies between data inputs were
investigated and resolved. The database was then exported into SAS version 9.2 and STATA
IC/11.1 was used to analyze observation and transportation data.
Using STATA IC/11.1, independent t-tests were used to determine differences between
the different predictor and outcome variables. Factorial ANOVA were calculated to determine

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differences between distance to school and zone, the three distance categories and zone, as well
as percentage of walkers, bikers, active commuters, and auto commuters, by zone. Multiple
regression models were calculated to determine whether active commuting was related to school
demographics (race/ethnicity and free/reduced price lunches), school characteristics (school
type), and a specific distance to school (percent within walking distance). An alpha level of .05
was used to test for significance.
Independent Variables
Independent variables included mean distance to school, calculated using GIS and student
data obtained from the district transportation department (home address, school attended, and
grade). Study staff calculated the mean distance, defined as the distance between a student and
his/her school, using Arc View GIS software and the street network connecting each student to
his/her school.
Distance to school was also broken into three categories to calculate the percentage of
students living within different distances to school. Percentage within a half mile and one mile
from school was categorized as students living within the walking boundary, set by the district
transportation department. Percentage within two miles of school was categorized as students
living within the biking zone, a reasonable distance determined by the research staff. These
three distance categories are not mutually exclusive, meaning that the percentage of students
living within one distance category may also include the percentage of students living within
another distance category (i.e., percentage of students living within one mile may also include
the percentage of students living within a half mile of school).
Demographic data on race/ethnicity and free/reduced lunch status were obtained from the
district. Race/ethnicity was grouped into two categories: white and non-white/minority. School-

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level proportion of minority students was categorized as high or low, depending on the
percentage of non-white students in each school, using a threshold of 70% or more of the student
population in each school being non-white. The threshold was determined by study staff;
minority status was categorized as high if equal to, or greater than, 70% of the student population
at a school were non-white/minority, and low if 70% or less of the student population were non-
white/minority. This dichotomous variable was used in Table 2 and Table 3.
Free/Reduced Priced Lunch (FRPL) status was obtained directly from district
transportation data. Lunch status was created by dividing the number of students eligible for
FRPL by total enrollment per school. Lunch status was categorized as high if equal to, or greater
than, 70% of the student population were eligible for free/reduced priced lunches; low if 70% or
less were eligible. Minority and lunch variables were dichotomous variables used in Table 2 and
Table 3.
As part of the ‘Changing School Options’ policy, schools were categorized according to
three different zones assigned by the school district. These zones are based on geographic
location in relation to the neighborhoods within the district attendance boundaries in
Minneapolis. This is an important variable since the school choice policy that was enacted in
the fall of 2010 reorganized schools into these zones. There are a total of 13 schools located in
Zone 1, 10 schools located in Zone 2, and 16 schools located in Zone 3. Consistent with
previous studies, school type was broken into two categories; where the school was located in the
neighborhood where a student lived (neighborhood school) or not in the neighborhood (magnet
school) (Wilson, Wilson, & Krizek, 2007). Of the total 39 schools in the present study, there are
a total of 26 neighborhood schools and 13 magnet schools; school type was a dichotomous
variable used in Table 1 and Table 4.

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Outcome variables
Prevalence of active commuting was separated by the percentage of walkers and bikers
per school. Observation data included the number of children walking or biking to/from school,
as well as the number of children riding the bus or being driven to/from school. To analyze
active commuting, the number of children walking and biking before and after school (four
different numbers) was divided by the total enrollment of each school. Active commuting was
defined as the number of children walking, and the number of children biking, before and after
school, divided by total enrollment of the 39 schools. The percentage of walkers and the
percentage of bikers per school were calculated using a similar method.
Distance to school was also broken up into three categories: percentage of students living
within one-half mile of school (within walking distance), one mile of school, and two miles of
school, based on transportation data provided by the district. The number of students living
within each of the distance categories was provided by the district. The percentages were
calculated by dividing each number of students living within the different distances of school, by
total enrollment per school. This was a school-level variable, where the percentages were
calculated across all students attending each school.
Results
Demographic Data
Table 1 describes demographic data for the district. In the spring of 2010, over two-
thirds (69%) of the MPS student population were minority (non-white), and nearly two-thirds
(61%) qualified for FRPL. Across all schools (N=39), the average distance that a student lives
from school is 1.8 miles (SD=0.5 miles). Regarding distance to school, 12.3% of students live

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within a half mile from school, 31.7% live within one mile, and 64% live within two miles from
school. The total percentage of students exceeds 100% due to the distance to school not being
mutually exclusive; the percentage of students living within one mile of school also includes the
percentage of students living within a half mile of school. The percentage of students living
within two miles of school includes the percentage of students living within a half mile of school
and a mile from school. Regarding school type, there were 26 neighborhood schools and 13
magnet schools. There were no significant differences between the percentage of minority
students, percentage of students who qualify for free or reduced priced lunches, and school type
(neighborhood vs. magnet). Neither the percentage of minority students nor percentage of
students who qualify for free or reduced priced lunches were related to school type
(neighborhood vs. magnet).
District Transportation Data
Table 2 describes distance to school stratified by school characteristics. There was no
statistically significant difference in distance to school by school minority status, percentage of
students receiving FRPL, or district zone. Using the threshold that denotes schools with low
minority enrollment (<70% minority) or high minority enrollment (≥70% minority), there were
no significant differences between distance to school among schools with low minority
enrollment (1.7 miles; SD=0.4), compared to schools with high minority enrollment (1.9 miles;
SD=0.5; p=0.10). A similar relationship is observed when considering distance according to
school-determined categories. There was no significant difference between minority status and
the percentage of students living within the three school-determined distance categories (one-half
mile from school, one mile from school, and two miles from school).

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When comparing percentage of students living within the three school-determined
distance categories and lunch status, there was no difference between schools with less students
receiving FRPL (13.6%, 32.9%, and 64.5%), compared to schools with more students receiving
FRPL (10.9%, 30.3%, and 69.3%).
There was no difference in distance to school when schools were stratified by FRPL
status. There was no significant difference in distance to school among students that attended
schools with low free/reduced lunch status (<70% students qualify for free/reduced priced
lunches) (1.8 miles, SD=0.5), compared to students that attended schools with high free/reduced
priced lunch status (≥70% students qualify for free/reduced priced lunches) (1.9 miles, SD=0.5,
p=0.72). The result is not significant (t= -0.36, p=0.72). When comparing distance to school by
zone, as well as the percentage of students living within the three school-determined distances by
zone, the differences were not statistically significant.
There was a significant difference in distance to school by school type. Magnet schools
are located farther away (2.2 miles; SD=0.5 miles), on average, compared to neighborhood
schools (1.7 miles; SD=0.4 miles; p=0.0016). A similar relationship is observed when
considering the three school-determined distance categories and school type. When comparing
percentage of students living within the three school-determined distance categories and school
type, there were significant differences between magnet schools (8.7%, 22.5%, and 52.2%),
compared to neighborhood schools (14.0%, 36.2%, and 70.2%).
Observation Data
Just over one-third (33.5%) of all students that were observed, arrived to, and departed
from, school by automobile; less than 20% were active commuting (17.4% walking and 2.4%
biking), while the remaining 46.7% traveled by bus or special needs transportation.

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Table 3 describes auto and active commuting stratified by the school characteristics.
Schools with low minority enrollment had higher rates of active commuting, compared to the
high minority schools (p=0.04). The differences in active commuting between low and high
minority enrollment schools appeared to be driven by a greater percentage of students at low
minority schools that were observed bicycling to and from school, compared to high minority
schools (p< 0.01).
There was a greater percentage of active commuters (25.5%) among schools with low
minority enrollment, compared to schools with high minority enrollment (16.3%; p=0.04).
There was also a significant relationship between the percentage of bicyclists, and minority
(p=0.00) and lunch status (p=0.00). Schools with low minority enrollment had a greater
percentage of bicyclists (5.1%), compared to schools with high minority enrollment (0.7%;
p<0.001). The percentage of bicyclists was also greater in schools with less students receiving
free/reduced priced lunches (4.1%), compared to schools with more students receiving
free/reduced priced lunches (0.6%; p<0.001).
There was no significant difference in the percentage of walkers among schools with low
minority enrollment (20.4%), compared to schools with high minority enrollment (15.6%;
p=0.20). Schools with a smaller percentage of students who qualify for free/reduced priced
lunches have a greater percentage of auto commuters, active commuters, walkers, and bicyclists.
There was no significant difference in auto or active commuting by zone.
There was a significant relationship between the percentage of auto commuters and both
minority (p<0.0001) and lunch status (p<0.0001). A greater percentage of auto commuters
(42.9%) occurred in schools with low minority enrollment, compared to schools with high

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minority enrollment (27.6%; p<0.0001). A significant relationship also exists between the
percentage of active commuters and minority status (p=0.04), but not for lunch status (p=0.14).
By school type, there was no significant difference in the percentages of auto commuters
and bicyclists. There was a significant relationship between the percentage of walkers and school
type, where a greater percentage of walkers (20.2%) occurred among neighborhood schools,
compared to magnet schools (11.9%; p=0.03).
Observed Active Commuting by School and School Characteristics
Multiple regression analyses were conducted to examine the relationship between the
percentage of students who walk or bike to school (percentage of active commuters) and
different predictor variables. Table 4 describes a summary of four regression models (Model 1,
Model 2, Model 3, and Model 4), where the percentage of active commuters are compared to
different predictor variables. Model 1 shows a significant relationship between percentage of
minority students and percentage of active commuters (p=0.04). The proportion of students who
walk or bike to school is greater at schools with a smaller proportion of minority students.
However, this significance disappears after accounting for free/reduced priced lunch (Model 2).
There were no differences in the proportion of students who walk or bike to school by the
percentage of minority students in that school, or the percentage eligible for free or reduced
priced-lunch.
When school type (neighborhood vs. magnet) was accounted for (Model 3), there were
no differences in the proportion of students who walk or bike to school by the percentage of
minority students in that school, or the percent eligible for free/reduced priced lunch. The
proportion of students who walk or bike to school is significant among school type, adjusted for
percentage of minority students and percentage of students eligible for free or reduced priced

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lunch. The proportion of students who walk or bike to school is greater at schools with a larger
proportion of students who live within walking distance, adjusted for school type (neighborhood
vs. magnet), percentage of minority students, and percentage of students eligible for free or
reduced price lunch (Model 4). There were no differences in the proportion of students who
walk or bike to school by the percentage of minority students in that school, or the percent
eligible for free or reduced priced lunch when distance to school and school type were accounted
for in the model.
Discussion
There are a number of important findings in the present study, including the relationship
between distance to school and prevalence of active commuting. The average distance to school
across all schools was nearly two miles, meaning that most students do not live within an easy
walking or biking distance to school. This is interesting because even though schools are located
farther away, nearly 20% were observed actively commuting to school, which is above the
national average of 13% (McDonald, Brown, Marchetti, & Pedroso, 2011). The national average
was calculated across schools in different types of areas, not just urban. One factor could be that
Safe Routes to School, a national program that aims to increase physical activity among students
through the promotion of active commuting, had implemented strategies before and during data
collection. It may also be possible that some schools in MPS have infrastructure (i.e., sidewalks,
crosswalks, or bicycle racks) that encourages active commuting, while other schools do not have,
or have less, infrastructure. Though not assessed in the present study, the most recent data
analyzed from the 2001 and 2009 National Household Travel Surveys indicate that infrastructure
is related to active commuting (Pucher, Buehler, Merom, & Bauman, 2011). Environmental and
sociodemographic factors also influence school transport mode, so attributing one factor to the

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relationship is not plausible (McMillan, 2007). School type (neighborhood vs. magnet) was
related to active commuting, as a greater proportion of active commuters were in neighborhood
schools.
Transportation Mode
The most commonly observed transport mode was other (i.e., school bus or special needs
transportation), a finding that was not surprising as most students lived farther away from school
than what is considered a reasonable walking distance (1 mile), and the district provides bus
transportation for students living farther than one-half mile from school. Another possibility is
that barriers exist in the built environment around the schools in MPS that discourage walking or
bicycling. Aspects of the built environment, known as urban form, were not assessed in the
present study, but research shows that physical barriers (i.e., incomplete sidewalks or unmarked
crosswalks) in the urban form may affect school transport mode (McMillan, 2007).
Distance to School
More schools with low minority status and low lunch status had a greater percentage of
auto commuting, active commuting, and bicycling, compared to schools with high minority
status and high lunch status. Students attending schools with low minority status and fewer
students receiving free/reduced priced lunches had a greater percentage of active commuters,
auto commuters, and bicyclists. Parents may have safety concerns regarding their children
actively commuting to school and choose to transport them via automobile; this was not assessed
in the present study but research suggests it influences transport mode (Kerr et al., 2006; Panter,
Jones, van Sluijs, & Griffin, 2010). The average distance to school, as well as the percentage of
students living within a half mile (walking distance), one mile, or two miles of school, is not
related to minority status, lunch status, or school district zone. However, these results did not

21. Pulley 21
account for a specific distance to school or school type. Results from Table 4 suggest a
relationship between a specific distance to school (within a half-mile of school) and the
percentage of active commuters. These results are consistent with other studies that assessed
school type and active commuting (Wilson, Wilson, & Krizek, 2007; Wilson, Marshall, &
Krizek, 2010).
Active Commuting
Over half of the schools in Minneapolis Public Schools were considered high minority,
according to the threshold assigned by study staff, and just under half of the schools qualify as
having a high number of students receiving free or reduced priced lunches. There was a
significant association between percentage of active commuters and minority status, but not
lunch status. Perhaps schools with low minority enrollment have more infrastructure that
promotes active commuting, compared schools with high minority enrollment. High minority
schools have a higher prevalence of active commuting, partially due to the lack of transportation
families have to transport their children to and from school, compared to lower minority schools
(Mendoza et al., 2010).
Transport Mode
In the present study, auto commuting was related to both minority and lunch status. It
may be because of the distance to school that students were observed going to and from school
by automobile. In addition, schools that have high minority student populations and a high
percentage of students receiving FRPL are often associated with a more diverse population who
may have less access to vehicles, compared to families whose students attend low minority and
fewer students receiving FRPL. As a result, these children often must either walk or bike to
school (Mendoza et al., 2010). Though not assessed in the present study, research suggests that

22. Pulley 22
other socioeconomic factors, such as household income and car ownership, and demographic
factors (non-white ethnicity), are associated with active commuting (Pont et al., 2009).
Limitations
There are several limitations to the study that should be noted when interpreting the
findings. Outcome measures were based on a limited number of observations (four days in the
spring time) and may not reflect the patterns of transport mode to/from school throughout the
entire school year. Determining whether schools in a particular zone have a significantly higher
number of active commuters, or students living within different distances to school, cannot be
inferred from the study data gathered. There are likely characteristics of the built environment
that influence whether a child will walk or bike to school, such as traffic speed along the school
route, perception of crime in the neighborhood, and availability of sidewalks and bike trails/lanes
(Kerr et al., 2006). These physical environment characteristics were not measured in this study.
A limitation of previous active commuting studies was the frequent selection of using
surveys, which are often cited as having low validity, to measure the prevalence of active
commuting (Lee, Orenstein, & Richardson, 2008). Most researchers recommend using
instruments that are an objective measure of physical activity, such as physical activity monitors,
or accelerometers, since physical activity tends to be over-reported when measured through self-
report (Nader et al., 2008). This study design was quasi-experimental, which has become more
common in a review of recent active commuting studies (Chillón, Evenson, Vaughn, & Ward,
2011).
This study adds to the existing literature in that its unique methodology allowed for the
transport mode of almost every student in each school to be observed. Study staff selected direct

23. Pulley 23
observation of transport mode, rather than a self-report or physical activity monitor, to measure
prevalence of active commuting. This study appears to be the first to directly observe student
transport mode, and may be the first to assess the relationship between specific school
demographics (i.e., race/ethnicity, free/reduced priced lunch) and prevalence of active
commuting.
Future Recommendations
This study adds to the existing literature in active commuting in that it directly observed
students getting to and from school via different modes of transportation. Future studies should
consider using direct observations in the methodology to determine prevalence of active
commuting among students, as well as assess the effect of urban form on active commuting.
Some schools in the district continue to implement Safe Routes to School (SRTS) activities that
promote active commuting. Whether or not this program affected the prevalence of active
commuting among students cannot be determined from the present study.
Minority status, lunch status, and school type were not significantly related to active
commuting when accounting for the percentage of students who lived within walking distance.
Results in this study show that if students lived within walking distance of a school, they were
more likely to actively commute to that school. We did not find data on the number of magnet
schools MPS had 20 years ago, compared to today; however, school choice policies that include
the option of magnet schools have become more common in the past 20 years (Gorard, Fitz, &
Taylor, 2001). As active commuting was related to distance to school, where a greater
percentage of active commuters were found in schools located within a half-mile of a child’s
home, district staff could consider the student transport mode implications of a school choice
policy, as other researchers have (Wilson, Wilson, & Krizek, 2007).

24. Pulley 24
Table 1: School Characteristics (N=39)
All schools Neighborhood Schools Magnet Schools t-test p
N (%) 39 26 (67%) 13 (33%)
% Minority 69 69 68 0.06 0.95
% FRPL 61 62 60 0.24 0.81
Distance to School; mean (SD) 1.8 (0.5) 1.7 (0.4) 2.2 (0.5) 3.42 0.0016 *
% within walking distance 12.3 14.0 8.7 -2.55 0.0150 *
% within 1 mile of school 31.7 36.2 22.5 -3.08 0.0039 *
% within 2 miles of school 64.2 70.2 52 -3.11 0.0036 *
* Statistically significant (p<0.05)

25. Pulley 25
Table 2: Distance to School stratified by school characteristics (N=39)
Minority
Free or Reduced
Price Lunch
School Type
<70%
Minority
> 70%
Minority
<70%
FRPL
> 70%
FRPL
Neighborhood Magnet
Distance to School (SD) 1.7(0.4) 1.9 (0.5) 1.8(0.5) 1.9 (0.5) 1.7 (0.4) 2.2 (0.5)*
% within walking distance 13.5 11.5 13.6 10.9 14.0 8.7*
% within 1 mile of school 35.7 29.1 32.9 30.3 36.2 22.5*
% within 2 miles of school 69.6 60.8 64.5 63.9 70.2 52.2*
* Statistically significant (p<0.05)

26. Pulley 26
Table 3: Auto and active commuting stratified by school characteristics (N=39)
Minority Free or Reduced Price Lunch School Type
<70%
Minority
> 70%
Minority
<70%
FRPL
> 70%
FRPL
Neighborhood Magnet
% Auto Commuters 42.9 27.6* 40.0 26.7* 34.4 31.7
% Active Commuters 25.5 16.3* 22.9 16.6 22.6 14.2
% Walkers 20.4 15.6 18.9 15.9 20.2 11.9 *
% Bicyclists 5.1 0.7* 4.0 0.6* 2.4 2.3
* Statistically significant (p<0.05)

27. Pulley 27
Table 4: Summary of the percentage of active commuters, regressed on % minority, % free or reduced
priced lunch, school type, and % within walking distance (N=39)
Variable Model 1
% Active Commuters
b/p
Model 2
% Active Commuters
b/p
Model 3
% Active Commuters
b/p
Model 4
% Active Commuters
b/p
% Minority -0.16*
(0.08)
-0.21
(0.53)
-0.00
(0.52)
-0.39
(0.45)
% Free or Reduced
Priced Lunch
0.05
(0.52)
-0.16
(0.51)
0.27
(0.45)
School Type -8.76*
(4.31)
-2.00
(4.07)
% Within Walking
Distance
1.13***
(0.30)
Observations 39 39 39 39
Standard errors in parentheses
* p<0.05, ** p<0.01, *** p<0.001

28. Pulley 28
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