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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

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

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

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).

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)

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)

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)

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

Pulley 28

References

Barlow, S. E., & The Expert Committee. (2007). Expert committee recommendations regarding

the prevention, assessment, and treatment of child and adolescent overweight and obesity:

Summary report. Pediatrics, 120(suppl 4), S164–S192.

Beck, L. F., & Greenspan, A. I. (2008). Why don't more children walk to school? Journal of

Safety Research, 39(5), 449-452.

Centers for Disease Control and Prevention (2005). Barriers to children walking to or from

school—United States, 2004. Morbidity and Mortality Weekly Report, 1(4), 949-952.

Centers for Disease Control and Prevention. (2009). Trends in Leisure-Time Physical Inactivity

by Age, Sex, and Race/Ethnicity — United States, 1994–2004. Morbidity and Mortality

Weekly Report, 54(39), 991–994.

Centers for Disease Control and Prevention. (2009). Recommended Community Strategies and

Measurements to Prevent Obesity in the United States. Morbidity and Mortality Weekly

Report, 58(RR-07), 1-26.

Chillón, P., Evenson, K.R., Vaughn, A., & Ward, D.S. (2011). A systematic review of

interventions for promoting active transportation to school. International Journal of

Behavioral Nutrition and Physical Activity, 8(1), 10-26.

Cooper, A. R., Andersen, L. B., Wedderkopp, N., Page, A. S., & Froberg, K. (2005). Physical

activity levels of children who walk, cycle, or are driven to school. American Journal of

Preventive Medicine, 29(3), 179-184.

Davison, K., Werder, J., & Lawson, C. (2008). Children’s Active Commuting to School:

Current Knowledge and Future Directions. Preventing Chronic Disease, 5(3), A100.

Pulley 29

Environmental Protection Agency. (2003). EPA 231-R-03-004. Travel and environmental

implications of school siting. Washington, DC. Retrieved from

http://www.epa.gov/smartgrowth/pdf/school_travel.pdf

Faulkner, G. E. J., Buliung, R. N., Flora, P. K., & Fusco, C. (2009). Active school transport,

physical activity levels and body weight of children and youth: A systematic review.

Preventive Medicine, 48(1), 3-8.

Freedman, D. S., Khan, L. K., Serdula, M. K., Dietz, W. H., Srinivasan, S. R., & Berenson, G. S.

(2005). The relation of childhood BMI to adult adiposity: The Bogalusa heart study.

Pediatrics, 115(1), 22-27.

Gorard, S., Fitz, J., & Taylor, C. (2001). School choice impacts: what do we know? Educ. Res.,

30(7), 18–23.

Ham, S., Macera, C., & Lindley, C. (2005). Trends in Walking for Transportation in the United

States, 1995 and 2001. Preventing Chronic Disease, 2(4), A14.

Haskell W., Lee I., Pate R., et al. (2007). Physical Activity and Public Health: Updated

Recommendation for Adults from the American College of Sports Medicine and the

American Heart Association. Medicine & Science in Sports & Exercise, 39(8),1423–1434.

Healthy weight: Assessing your weight: BMI: Adult BMI calculator: English | DNPAO | CDC.

Retrieved from

http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/english_bmi_calculator/bmi_cal

culator.html

IOM (Institute of Medicine). 2009. Local Government Actions to Prevent Childhood

Obesity. Washington, DC: The National Academies Press.

Pulley 30

Kerr, J., Rosenberg, D., Sallis, J. F., Saelens, B. E., Frank, L. D., & Conway, T. L. (2006).

Active commuting to school: Associations with environment and parental concerns.

Medicine & Science in Sports & Exercise, 38(4), 787-794.

Larsen, K., Gilliland, J., Hess, P., Tucker, P., Irwin, J., & He, M. (2009). The Influence of the

Physical Environment and Sociodemographic Characteristics on Children's Mode of Travel

to and From School. American Journal of Public Health, 99(3), 520-526.

Lee, M.C., Orenstein, M. R., & Richardson, M. J. (2008). Systematic review of active

commuting to school and children's physical activity and weight. Journal of Physical Activity

and Health, 5(6), 930-949.

Marshall, J., Wilson, R.D., Meyer, K., Santhoshk, R., McDonald, N.C., & Wilson, E.J. (2010).

Vehicle Emissions during Children's School Commuting: Impacts of Education Policy.

Environmental Science & Technology, 44(5), 1537-1543.

McDonald, N. (2007). Active Transportation to School: Trends among U.S. Schoolchildren,

1969–2001. American Journal of Preventive Medicine, 32(6), 509–516.

McDonald, N. (2008). Critical Factors for Active Transportation to School

Among Low-Income and Minority Students: Evidence from the 2001 National Household

Travel Survey. American Journal of Preventive Medicine, 34(4), 341-344.

McDonald, N., Brown, A., Marchetti, L., & Pedroso, M. (2011). U.S. School Travel 2009: An

Assessment of Trends. American Journal of Preventive Medicine, 41(2), 146-151.

McMillan, T.E. (2005). Urban form and a child’s trip to school: the current literature and a

framework for future research. J Planning Lit., 19(4), 440-456.

McMillan, T.E. (2007). The Relative Influence of Urban Form on a Child’s Travel Mode to

School. Transportation Research Part A: Policy and Practice, 41(1), 69–79.

Pulley 31

Mendoza, J.A., Watson, K., Baranowski, T., Nicklas, T.A., Uscanga, D.K., Nguyen, N., &

Hanfling, M.J. (2010). Ethnic Minority Children’s Active Commuting to School and

Association with Physical Activity and Pedestrian Safety Behaviors. Journal of Applied

Research on Children: Informing Policy for Children at Risk, 1(1), 1-23.

Mendoza, J.A., Watson, K., Nguyen, N., Cerin, E., Baranowksi, T., & Nicklas, T.A. (2011).

Active Commuting to School and Association with Physical Activity and Adiposity Among

US Youth. J Phys Act Health, 8(4), 488-495.

Nader, P. R., Bradley, R. H., Houts, R. M., McRitchie, S. L., & O'Brien, M. (2008). Moderate-to-

vigorous physical activity from ages 9 to 15 years. JAMA: The Journal of the American

Medical Association, 300(3), 295-305.

Oakes, J.M., Forsyth, A.,& Schmitz, K.H. (2007). The Effects of Neighborhood Density and

Street Connectivity on Walking Behavior: The Twin Cities Walking Study. Epidemiologic

Perspectives & Innovations, 4(16),

Obesity and overweight: Contributing Factors | DNPAO | CDC. (2009a). Retrieved from

http://www.cdc.gov/obesity/childhood/causes.html

Obesity and overweight: Economic Consequences | DNPAO | CDC. (2009b). Retrieved from

http://www.cdc.gov/obesity/causes/economics.html

Ogden, C. L., Carroll, M.D., Curtin, L.R., Lamb, M.M., & Flegal, K. M. (2010). Prevalence of

High Body Mass Index in US Children and Adolescents, 2007-2008. JAMA: The Journal of

the American Medical Association, 303(3), 242-249.

Owen N., Leslie E., Salmon J., & Fotheringham, M.J. (2000). Environmental determinants of

physical activity and sedentary behavior. Exerc Sport Sci Rev.,28(4), 153-158.

Pulley 32

Panter, J. R., Jones, A. P., van Sluijs, E. M. F., & Griffin, S. J. (2010). Attitudes, social support

and environmental perceptions as predictors of active commuting behaviour in school

children. Journal of Epidemiology and Community Health, 64(01), 41-48.

Physical Activity | Healthy People 2020 Objectives | CDC. (2010). Retrieved from

http://www.healthypeople.gov/2020/topicsobjectives2020/pdfs/PhysicalActivity.pdf

Pont, K., Ziviani, J., Wadley, D., Bennett, S., & Abbott, R. (2009). Environmental correlates of

children's active transportation: A systematic literature review. Health & Place, 15(3), 849-

862.

Pucher, J., Buehler, R., Merom, D., & Bauman, A. (2011). Walking and cycling in the united

states, 2001-2009: Evidence from the national household travel surveys. American Journal of

Public Health, 101(suppl 1), S310–S317.

Saksvig, B. I., Catellier, D. J., Pfeiffer, K., Schmitz, K. H., Conway, T., Going, S., et al. (2007).

Travel by walking before and after school and physical activity among adolescent girls.

Archives of Pediatrics Adolescent Medicine, 161(2), 153-158.

Sirard, J.R., Riner, W. F.J., McIver, K.L., & Pate, R.R. (2005). Physical activity and active

commuting to elementary school. Medicine & Science in Sports & Exercise, 37(12), 2062-

2069.

Sirard, J.R., & Slater, M.E. (2008). Walking and Bicycling to School: A Review. American

Journal of Lifestyle Medicine, 2(5), 372-396.

Strong, W. B., Malina, R. M., Blimkie, C. J. R., Daniels, S. R., Dishman, R. K., Gutin, B., et al.

(2005). Evidence based physical activity for school-age youth. The Journal of Pediatrics,

146(6), 732-737.

Pulley 33

Timperio, A., Ball, K., Salmon, J., Roberts, R., Giles-Corti, B., Simmons, D., et al. (2006).

Personal, family, social, and environmental correlates of active commuting to school.

American Journal of Preventive Medicine, 30(1), 45-51.

Troiano R., Berrigan D., Dodd K., et al. (2008). Physical Activity in the United States Measured

by Accelerometer. Medicine and Science in Sports and Exercise, 40(1), 181–188.

Tudor-Locke C., Ainsworth, B.E., & Popkin, B.M. (2001). Active commuting to school: an

overlooked source of children’s physical activity? Sports Med., 31(5), 309-313.

Wilson, E. J., Wilson, R., & Krizek, K. J. (2007). The implications of school choice on travel

behavior and environmental emissions. Transp. Res. Part D: Transport Environ., 12(7),

506–518.

Wilson, E.J., Marshall, J., Wilson, R., & Krizek, K.J. (2010). By foot, bus or car: children's

school travel and school choice policy. Environment and Planning A, 42(), 2168–2185.