INVESTIGATING THE CAMPUS CYCLING ENVIRONMENT

OF A LARGE SOUTHEASTERN UNIVERSITY FROM

AN ECOLOGICAL PERSPECTIVE

by

MARGARET M. SHIELDS

ANGELIA PASCHAL, COMMITTEE CHAIR STUART USDAN

MELANIE TUCKER BRIAN GORDON JAMES LEEPER

A DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Health Sciences

in the Graduate School of The University of Alabama

TUSCALOOSA, ALABAMA

2015

Copyright Margaret M. Shields 2015 ALL RIGHTS RESERVED

ii

ABSTRACT

Cycling is an effective method to address physical, psychological, and environmental

health. As an alternative mode of transport, it can also be more economical compared to motor

vehicles. Despite these benefits, cyclists run a moderately high risk of being injured on the road

or in a vehicular accident. According to a survey completed by the National Highway Traffic

Safety Administration, 88% of cyclists felt most threatened by motorists on the road and 37%

perceived uneven walkways and roadways were a threat to personal safety.

The purpose of this study was to examine college student perceptions of safety and

factors contributing to campus cycling from an ecological perspective. Intrapersonal,

interpersonal, and institutional factors associated with safety and campus cycling were assessed

as well as how they interacted with each other. A survey was developed and administered to

students on a large southeastern public university.

The sample of 356 participants indicated that certain intrapersonal level factors were

predictors of cycling, including bike specific issues (e.g., concerns about general bike

maintenance) and personal appearance (e.g., looking “silly while wearing a helmet).

Interpersonal cycling factors (e.g., concerns about interacting with motor vehicle drivers) were

not statistically significant. However, institutional cycling factors, including institutional

barriers (e.g., busy roads) and institutional facilitators (e.g., establishing more bike lanes and

covered parking), were predictors of cycling.

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The study findings provide guidance to university stakeholders about what specific

factors are prioritized and deemed more likely to facilitate cycling among students. Cycling has

been found to be very beneficial, as it has been associated with reduced traffic, improved

parking, and better ease of transport on campus. Therefore, these suggestions have implications

for environmental and structural changes, policy development, and program planning.

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DEDICATION

This dissertation is dedicated to my nieces and nephews. I hope this hard work inspires

them to believe nothing is impossible.

“Congratulations! Today is your day. You’re off to great places! You’re off and away!

And will you succeed? Yes! You will indeed (98¾% guaranteed).

Kid, you’ll move mountains!”

-Dr. Seuss

v

LIST OF ABBREVIATIONS AND SYMBOLS

a Cronbach’s index of internal consistency

ACHA American College Health Association

ACSM American College of Sports Medicine

BAG Bike Advisory Group

BFU Bicycle Friendly University

CDC Centers for Disease Control and Prevention

CI Confidence Interval

f Frequency

HBM Health Belief Model

LAB League of American Bicyclists

NHTSA National Highway Transportation Administration

OR Odds ratio: an association between an exposure and an outcome p Probability associated with the occurrence under the null hypothesis of a value as

extreme as or more extreme than the observed value SEM Social Ecological Model UA University of Alabama

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ACKNOWLEDGMENTS

“The journey of a thousand miles begins with a single step,” (Japanese Proverb) and the

journey of a dissertation begins with a single word. I am first and foremost thankful for my

husband, Kevin, who kept reminding that I would make it through and continually supported me.

Second, to my family (both blood and adopted) that cheered me on, called me, and took me out

for meals. Every bit is remembered and dear to my heart. I am grateful for all of the little voices

in my life that cheered me on during this chapter. From my nieces, Nana and Grandpa, athletes,

students, and friends who spoke words of comfort and love. It takes a village to raise a child as

well as complete a degree and they are the best! Finally, I am so thankful for every 9:30 phone

call from dad after I got off work. Whether it was words of encouragement or about the weather,

it was always nice knowing that he is always there for me.

To my committee chair, Dr. Paschal has been my mentor and friend in this journey. “It’s

not where you go, its who you meet along the way!” I am grateful for all of the help along the

way and words of encouragement. Every meeting we had, I came out feeling a little better about

the process. To Dr. Tucker, thank you for helping me from day one and reminding me that I am

good enough to be in the program. To Dr. Leeper, thank you for helping me sort out my

promiscuous stats. To Drs. Usdan and Bolland, you were encouragement, direction and support

along the way. Dr. Gordon, thank you for stepping in and helping when I needed it! I am

grateful to all of you!

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CONTENTS

ABSTRACT ............................................................................................................ ii

DEDICATION ....................................................................................................... iv

LIST OF ABBREVIATIONS AND SYMBOLS ................................................... v

ACKNOWLEDGMENTS ..................................................................................... vi

LIST OF TABLES .................................................................................................. x

1. INTRODUCTION .............................................................................................. 1

a. Purpose & Significance ...................................................................................... 2

b. Implications ......................................................................................................... 3

c. Research Questions ............................................................................................. 5

d. Assumptions ........................................................................................................ 5

e. Delimitations ....................................................................................................... 6

f. Limitations ........................................................................................................... 7

g. Terminology ........................................................................................................ 8

2. REVIEW OF LITERATURE .......................................................................... 11

a. Introduction ...................................................................................................... 11

b. Bikeability ........................................................................................................ 12

c. History of the Bicycle ...................................................................................... 13

d. The Bikeable Environment .............................................................................. 14

e. Population that Currently Cycles ...................................................................... 20

f. Health ................................................................................................................. 21

viii

g. Physical Activity ............................................................................................... 22

h. Healthy Campus 2020 ....................................................................................... 25

i. Health Statistics ................................................................................................. 26

j. How Cycling Improves University Campuses ................................................... 30

k. Challenges to Cycling on Campus .................................................................... 34

l. Current Research on Cycling ............................................................................. 42

m. Social Ecological Model .................................................................................. 46

n. Health Belief Model .......................................................................................... 50

o. Purpose .............................................................................................................. 52

3. METHODOLOGY ........................................................................................... 53

a. Institutional Review Board Approval ............................................................... 53

b. Participant Recruitment .................................................................................... 53

c. Data Collection .................................................................................................. 55

d. Survey Instrument ............................................................................................. 55

e. Piloting the Survey ............................................................................................ 62

f. Survey Completion ............................................................................................ 62

g. Data Analysis .................................................................................................... 63

h. Research Questions ........................................................................................... 63

i. Summary ............................................................................................................ 65

4. RESULTS ......................................................................................................... 67

a. Data Source ...................................................................................................... 68

b. Sample Characteristics ..................................................................................... 68

c. Missing Data .................................................................................................... 72

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d. Analysis of Research Questions ....................................................................... 73

e. Summary .......................................................................................................... 91

5. DISCUSSION & CONCLUSIONS .................................................................. 93

REFERENCES ................................................................................................... 108

APPENDICES .................................................................................................... 130

x

LIST OF TABLES

2.1 Bikeability Standards for Diamond and Platinum Statuses ............................ 16

2.2 2014 Bikeability Rates for Alabama ............................................................... 18

3.1 Questions Specific to the Health Belief Model ............................................... 58

3.2 Survey Questions: Health Belief Model Constructs ....................................... 60

3.3 Survey Questions: Social Ecological Model Constructs ................................ 61

4.1 Student Demographics ................................................................................... 69

4.2 Student Health Status & Physical Activity ..................................................... 70

4.3 Safety Perceptions of Students ........................................................................ 71

4.4 Cycling Prevalence in Student Sample ........................................................... 74

4.5 Cyclist Demographics ..................................................................................... 76

4.6 Logistic Regression Analysis of the Population in Comparison of Cyclists vs. Non-Cyclists ............................................................................... 77

4.7 Multivariate Logistic Regression Analysis of the Population in Comparison of Cyclists vs. Non-Cyclists ...................................................... 78

4.8 Intrapersonal Cycling Factors ......................................................................... 79

4.9 Intrapersonal Cycling Factor Results .............................................................. 81

4.10 Interpersonal Cycling Factors ....................................................................... 82

4.11 Interpersonal Cycling Factor Results ............................................................ 83

4.12 Institutional Cycling Factors ......................................................................... 84

4.13 Institutional Cycling Factor Results .............................................................. 86

xi

4.14 Descriptives of Each of the Scored Questions .............................................. 88

4.15 Descriptives of Each of the Social Ecological Model Levels ....................... 89

4.16 Suggestions from the 2014 Cycling Survey for the University of Alabama ............................................................................... 90

4.17 Additional Comments from the 2014 Cycling Survey for the University of Alabama .............................................................................. 91

1

CHAPTER ONE

INTRODUCTION

In 2012, cycling accounted for 726 traffic deaths in the U.S., which was a six percent

increase from the previous year (NHTSA, 2014). Of the cycling deaths, 69% occurred in urban

areas, 60% occurred at non-intersection areas of the road, and 48% occurred between 4:00 p.m.

and 11:59 p.m. Ninety-five of the people killed in cycling accidents were between 16 and 24

years old; cycling rates among male cyclers were four times higher than female cyclers

(NHTSA, 2014). Another 49,000 cyclists were injured during the same year.

Between 2002 and 2011, there were a total of 2,041 reported injuries and 72 fatalities in

the state of Alabama (CAPS, 2012). Alabama ranked 50th in the nation for bikeability in 2013,

which was a decrease from the previous year (League of American Bicyclists, 2014a).

Alabama’s highest performing categories were “Policies & Programs” and “Education &

Encouragement” which scored between 20-40% out of 100%. For the purpose of this paper,

bikeability is defined as an assessment of an area or community for numerous aspects of bicycle

use including the laws and policies, safety, education, and promotion as well as the general

acceptance or use of bicycles in the area (Lowry, Callister, Gresham & Moore, 2012).

National efforts to improve bikeability have been made by a number of federal agencies

including the US Department of Transportation, Environmental Protection Agency and Centers

for Disease Control and Prevention (CDC) (Lynott et al. 2009; EPA, 2009; American Academy

of Pediatrics, 2009). Each of these agencies promotes cycling as a way to improve health,

2

reduce air pollution and the consumption of non-renewable energy, and an economic mode of

transportation. However, despite efforts, there has only been a 0.09% increase in the total of

cyclists in the United States since 1994 (Flusche, 2010). The total cycling population increased

from 9,463 to 24,428 (US Census, 2010b, c).

Researchers in a number of cities and universities worldwide have conducted studies

about cycling as a method of active transport. However, the majority of these studies are

international (Scheeper et al., 2013; Molina-Garcia, Sallis & Castillo, 2014; Agarwal & North,

2012); only a handful were conducted in the United States (Shepherd, 2008; Sallis, Frank,

Saelens, & Kraft, 2004; Troped, Saunders, Pate, Reininger, & Addy, 2003). Minimal studies

have documented active transport on a college campus in the U.S. (Bopp, Kaczynski, &

Wittman, 2011; Bonham & Koth, 2010; Sisson & Tudor-Locke, 2008), which presents a

significant knowledge gap about active transport among this population. The proposed study

addressed this gap, utilizing the Health Belief Model and the Social Ecological Model.

Purpose and Significance

The University of Alabama student population has grown substantially over the past

years. For instance, the campus population increased from 20,969 in 2004 to 34,852 in 2013

(University of Alabama, 2013; University of Alabama, 2014; UA News, 2013). In turn, this

growth has increased the motor vehicle traffic on campus. These trafficking concerns and

consequent safety issues have prompted the University to address these problems. There is

currently an initiative on campus in coordination with the Bike Advisory Group (BAG), which

is employees of the facilities, recreation and police departments, to increase the public safety

of pedestrians and cyclists and to decrease the amount of motor vehicles (Crimson White Staff,

2014). This effort will help reduce the incidence of pedestrian and cyclist injuries, and it

3

coincides with the Healthy People 2020 initiatives to increase physical activity and

environmental health for the campus (USDHHS, 2014c).

Before campus stakeholders can curb the rate and population of motor vehicle traffic, it

is important to address active commuting issues and safety on university campuses (Mapes,

2007). Data collected from the campus population will help identify the necessary perceived

changes and barriers of cycling. The current study will provide information from the

perspectives of students about factors that are helpful, serve as barriers, and that are needed to

promote cycling. The results will provide the UA Bike Advisory Group and other campus

stakeholders with information needed to improve campus cycling.

The purpose of this study was to examine college student perceptions of safety and

factors contributing to campus cycling from an ecological perspective. Intrapersonal,

interpersonal, and institutional factors associated with safety and campus cycling were assessed,

as well as how these influences interacted with each other. Recommendations for improving

safety in university cycling were also obtained.

Implications

This study consisted of a sample of students from the University of Alabama to

investigate the factors associated with campus cycling and to provide potential strategies for

change. Currently, there is no research that provides this information from a southeastern

university. By analyzing ecological levels, researchers are able to look beyond the use of other

models which focus on individual factors that contribute to behavior, and they are able to

consider other levels of influence, including environmental and behavioral factors (Stokals,

1996). This approach brings multiple facets of research together and analyzes the relationship

between individual levels. The resulting information will help stakeholders of the university and

4

health educators to successfully disseminate health campaigns throughout the population to

create or change beliefs and attitudes to reduce the barriers surrounding cycling for health and

wellness.

Furthermore, stakeholders from the University of Alabama are advocating for the

importance and benefits of cycling on campus. The current research provides UA stakeholders

information from the students’ perception to help make informed decisions that would affect the

environmental and physical wellness of the university. To accomplish this feat, the population

and stakeholders need to move toward an environment of sustainable and active transport. This

study will help campus planners better understand the needs and desires of the population. The

information will also help make informed changes to community and policy levels as a method

of intervention.

Increased prevention efforts are needed to address chronic disease (Wu & Green, 2000).

Physical activity, including cycling, can be used as a means for reducing and preventing the rates

of chronic disease (Nelson et al., 2007; King et al., 1995). The results of this study will

potentially benefit the field of health promotion by making a connection between cycling and

student perceptions of safety. This data can then be used to address community and policy levels

of the Social Ecological Model to advocate for physical activity (King et al., 1995). Additionally,

campus programs which incorporate a theoretical framework such as the Health Belief Model

and that target student perceptions could help the UA campus and others like it to better

understand how cycling could be incorporated throughout campus life as a means of chronic

disease prevention.

5

Research Questions

The following research questions were investigated.

Research Question 1: Of the population sampled, what is the prevalence of on-campus

cycling at the University of Alabama?

Research Question 2: How does campus cycling differ between demographic subgroups

at the University of Alabama?

Research Question 3: Which factors facilitate or hinder cycling behaviors at the

University of Alabama?

Subquestion A: Which intrapersonal factors facilitate or hinder cycling behaviors?

Subquestion B: Which interpersonal factors facilitate or hinder cycling behaviors?

Subquestion C: Which institutional factors facilitate or hinder cycling behaviors?

Research Question 4: What strategies or suggestions for improving campus cycling are

recommended by students at the University of Alabama?

Assumptions

Assumptions are the elements of the study that are identified and accepted by the

researchers without control or burden of proof (Simon, 2011). In this study, there are four

assumptions:

1. Theory and model assumption: It is assumed that the Health Belief Model and the

Social Ecological Model will adequately describe the cycling perception of the

University of Alabama student population and explain the adoption of cycling on

campus.

6

2. Instrument assumption: It is assumed that the “Cycling Survey for the University of

Alabama” will capture the perceptions of the campus student population that are

needed for data collection.

3. Respondent assumption: It is assumed that the students will be able to complete the

instrument to the satisfaction of the researchers.

4. Distribution assumption: It is assumed that the professors at the University of

Alabama will adequately promote and distribute the survey to the student population.

Delimitations

Delimitations are the elements that limit the scope and define boundaries the study which

is controlled by the research (Simon, 2011). These factors include the objectives, research

questions, theoretical framework, and population of interest. One of the delimitations of this

study is the subject of the research. Research on cycling a southeastern university has not

previously been conducted. Since stakeholders of the university are working to make the

campus ‘cycling friendly’, this is an appropriate time for this to take place.

In addition, another delimitation is the research questions, which informed the survey

instrument questions. The survey questions are simple and anticipate participant perceptions

while addressing both the social ecology and perceptions of safety.

Another delimitation is the combination of model and theory used for the study. The

Social Ecological Model (SEM) provides a framework for understanding human behavior and its

interaction with the social and physical environments (Bronfenbrenner, 1979). The SEM helps

stakeholders to better understand how the population views themselves within various levels of

ecology. The Health Belief Model (HBM) provides knowledge to researchers regarding how

7

perceptions of safety influence a person’s decision to cycle (Quine, Rutter & Arnold, 1998). This

model weighs perceived threat of safety against the perceived benefits of cycling to determine

attitudes and beliefs of health and cycling. Both the SEM and HBM are compatible in

researching cycling in this study.

The final delimitation is the population of interest. This study consists of a population of

students, ages 17-24 years, at a large southeastern United States university. All participants must

be currently enrolled in the university to take part in the study.

Limitations

There are a number of limitations regarding this research. This data was collected from a

convenience sample, which is one of the most common forms of data collection (Farrokhi,

2012). Although convenience sampling in this study can lead to adequate data collection the

findings may not be fully representative of the campus population. Convenience sampling is less

expensive, easy to implement, and can result in a random sample (Freedman, 2009). However,

this can also result in a biased sample and its findings may not be applicable to the population as

a whole. Given the nature of this investigation, which also presents minimal risk to student

participants, convenience sampling is acceptable.

Data are self-reported, which could be problematic in recall or reluctance in answering

questions (Northrup, 1996). The survey instrument is designed to provoke honesty from the

individual for the best study results. In addition, misreporting is associated with sensitivity and

threat. Since the questions are non-sensitive, it is reasonable to conclude that misreporting will

be minimal (Northrup, 1996).

8

Terminology

General Study Terms

Active Transport. The act of walking or cycling to work.

Bikeability. An assessment of an area or community for numerous aspects of bicycle use

including the laws and policies, safety, education, and promotion and the general acceptance or

use of bicycles in the area.

Cycling. The act of utilized a human propelled one, two, or three-wheeled cycle for the

act of exercise, leisure, utility or commute.

Cyclist. A person, male or female, that engages in cycling.

Distracted Driving. Operating a motor vehicle while texting, talking on the phone,

listening to music, eating and drinking, grooming or putting on makeup, using a navigation

system, or watching a video.

Motor Vehicle. Any mode of transportation that requires any form of motor, electric, or

fuel. This includes passenger vehicles, utility vehicles, and motorcycles.

Velodrome. A track designed for cycling (Merriam-Webster, n.d.)

Social Ecological Model Definitions

Social Ecological Model. A health model that considers that interplay of five complex

factors: intrapersonal, interpersonal, institutional, community, and policy (Bronfenbrenner,

1979).

Intrapersonal. A level of the Social Ecological Model that refers to biological and

personal factors that contributes to health behaviors.

9

Interpersonal. A level of the Social Ecological Model that refers to closer relationships

that influence behaviors, including close social peers, partners, and family members

(Bronfenbrenner, 1979).

Institutional. A level of the Social Ecological Model that refers to the organizational

characteristics, formal rules, and operative regulations of a social institution. This includes

campus climate, schedules, built environment, and common spaces (Bronfenbrenner, 1979).

Community. A level of the Social Ecological Model that refers to settings in which a

person dwells, such as schools, workplaces, church, and neighborhoods (Bronfenbrenner, 1979).

Policy. A level of the Social Ecological Model that refers to local, state, and national

laws or policies (Bronfenbrenner, 1979).

Health Belief Model Definitions

Health Belief Model: A model for the prediction of health behavior (Rosenstock, 1974).

Cues to Action: A construct of the Health Belief Model that refers to the reminders and

other elements that remind an individual of a new health behavior (Glanz, 2008).

Perceived Barriers: A construct of the Health Belief Model that refers to the belief that

an action or factor will have negative consequences on the individual (Glanz, 2008).

Perceived Benefits: A construct of the Health Belief Model that refers to the belief or

attitude that an action or factors will result in positive benefits (Glanz, 2008).

Perceived Severity: A construct of the Health Belief Model that refers to the degree to

which a behavior or factor will disrupt daily routine (Glanz, 2008).

Perceived Susceptibility: A construct of the Health Belief Model that refers to the belief

that an individual might be affected by a behavior or factor (Glanz, 2008).

10

Perceived Threat: A combination of the Perceived Susceptibility and Perceived Severity

constructs (Glanz, 2008).

Self-Efficacy: A construct of the Health Belief Model that refers to the personal

confidence that a person has toward a new behavior or factor (Glanz, 2008).

11

CHAPTER TWO

REVIEW OF LITERATURE

Introduction

Cycling is an effective method to address physical activity, psychological and

environmental health, and economic considerations (Cavill & Davis, 2003). However, cyclists

are at a moderately high risk of being injured on the road or involved in a vehicular accident

(Albert, 1999). Motor vehicles are the predominant mode of transportation, often with short

commute times in many areas, and contribute to congestion, stress, and other complications

(Bhalla, Ezzati, Mahal, Salomon & Reich, 2007). The modern traffic system is designed to be

compatible with automobiles, but not with cyclists in many areas of the country (Wegman et al.,

2010). As a result, people in the United States cycle less than any other country. Other factors

that impact low cycling rates include the lack of bike lanes and the need of cyclists to share the

road with motor vehicles (Wegman et al., 2010). Even when bicycle paths and lanes are

available, many cyclists do not utilize them due to poor construction or deterioration of the lanes,

or a lack of convenience to them.

Personal safety is an important component to using a bicycle as a main method of

transportation. According to a survey completed by the National Highway Traffic Safety

Administration (2008), 88% of cyclists felt most threatened by motorists on the road, and 37%

felt that uneven walkways and roadways were a threat to personal safety. Possible

improvements to infrastructure would likely encourage more cycling in the area. Many states,

12

cities, and college campuses across the United States encourage a bicycle friendly atmosphere

(Shinkle & Teigan, 2008). According to the American Community Survey (US Census Bureau,

2010), from 2000 to 2008 bicycle-commuting increased 43%; however, less than 1%

(approximately 0.55%) use cycling as their predominant form of transportation. Many cities

rarely exceed 5%, even though cities like Minneapolis once had a bicycle population that made

up over 20% of its normal downtown traffic (Mapes, 2009).

In 2010, six fatalities and 167 injuries to cyclists were reported in the state of Alabama.

Of these reported events, the majority of injuries and deaths occurred to riders 16 years and

older. Among others, this age group includes college students and the working class, who would

be more likely to use cycling as a form of exercise and/or alternative transportation (NHTSA,

2010).

Alabama currently ranks 50th among all other states for being cyclist friendly. Alabama

also has no strategic highway safety plan or aggressive fund for bicycle safety projects (LAB,

2012). The state has made progress by passing a “texting while driving” ban as a step towards

creating safer streets by reducing distracted driving (Alabama Department of Public Safety,

2012). However, many of the organizations that form the laws and policies of the state have yet

to assess the bikeability of the natural and built environment.

Bikeability

The term “bikeability” came from a program that was used in the United Kingdom to

improve participation in cycling and active commuting (Wahlgren & Schantz, 2011). However,

in recent years, it has become a scale as to how safe an area is for the individual cyclist. It

considers the amount and kind of bike lanes that are present, the speed limit and proximity of

motor vehicles to those on bicycles, how structurally safe the lanes are, and how accessible they

13

are from various parts of an area. To simplify, bikeability assesses an area or community’s laws

and policies, infrastructure, safety, education and promotion, as well as the general acceptance or

use of bicycles in an area (Wahlgren & Schantz, 2011).

A university’s bikeability study could help by analyzing the population that presently

cycles, the availability of parking or storage for bicycles on campus, and law enforcement.

Regulations have been in place for a number of years regarding active transport, but not the

safety of cyclists while sharing the road with motor vehicles. For example, a motor vehicle is

required to drive at least three feet from a cyclist on any given street, which is the responsibility

of the person in the car or truck (Alabama Department of Public Safety, 2014); however, there is

no control over the physical space that is given to a cyclist on the street or the behavior of the

cyclist. This has been a challenge since most infrastructures were originally built around the use

of bicycles in the early 1900s.

History of the Bicycle

Early bicycle versions had a tall stature that made riding awkward and unsafe, especially

for females (Herlihy, 2004). The “safety bicycle” was introduced in the late 1880s and became

what is known today as the modern bicycle. This design was primarily marketed to women as a

means of empowerment and freedom from their daily mundane lives. The main benefit was

people could put their feet on the ground at stop signs or for added stability. This created an

affordable and practical way to commute throughout cities. For women, driving cars was not

acceptable but cycling was encouraged. This opened doors for freedom and control in everyday

life (McClean, 2012).

By the late 1800s, bicycles had become common, with an estimated four million people

cycling. For example, by 1906, 20% of the commuters in Minneapolis were cyclists (Pfaum,

14

2011). This gave cyclists a right to the road with power in numbers. Nearly every state claimed

that bicycles had the same right to the road as motor vehicles (Mapes, 2007).

With the boom of the motor vehicle, the bicycle became less popular and recreation for

children with periodic revivals throughout the following decades. During the Great Depression

and World War II, bicycles were popular again with economic strife and gasoline rations

(McClean, 2012). The final jump was in the 1970s when the number of bikes increased by 8.3

million in two years due to the cultural shift exemplified by the first Earth Day and to the rise of

gas prices in this decade.

Currently, in the United States, cycling is more often than not for recreation and thrill

(Mapes, 2007). As a mode of transportation, it has fallen below 5% in all cities. Because of this,

the United States government has developed multi-faceted bike policies to encourage more bike-

friendly communities (US Department of Transportation, 2014).

The Bikeable Environment

Each year, the League of American Bicyclists (LAB) reviews and ranks the 50 United

States to determine which are making contributions to bike friendliness in the nation. As

previously stated, Alabama is rated 50th while other states nearby are rated much higher,

including Arkansas (38th), Mississippi (31st), Louisiana (32nd), Georgia (26th) and Tennessee

(22nd) (League of American Bicyclists, 2014a). Rankings are updated each year for states,

communities, universities, and businesses, and are conducted through the use of LAB’s version

of the Social Ecological Model called the “5 E’s.”

The LAB’s 5 E’s are enforcement, education, engineering, evaluation, and education

dependent on the percentage of increase or decrease of three key outcomes: ridership (people

commuting by bicycle), crashes (per 10,000 daily commuters), and fatalities (per 10,000 daily

15

commuters). These are then ranked into five categories (bronze, silver, gold, platinum, and

diamond) (League of American Bicyclists, 2014b). For the diamond status, ridership must have

20% of the people commuting by bicycle, 50 or less crashes per 10,000 daily commuters, and 0.2

fatalities per 10,000 daily commuters. Each of the 5 E’s is broken down into subcategories to

determine appropriate ranking. Table 2.1 provides the 2014 qualifiers for platinum and diamond

statuses (League of American Bicyclists, 2014a). The following explains this with the criteria

needed for platinum or diamond statuses.

Enforcement is determined by whether or not the community or state has a law

enforcement or cycling liaison and if there are bicycle friendly laws and ordinances in place.

Education is the ratings of public education outreach and the annual offering of adult bicycling

skills classes as well as the percentage of primary and secondary schools offering bicycling

education. The engineering category is decided by a rating of bike access to public

transportation, percentages of total bicycle network mileage to total road network mileage, and

arterial streets with bike lanes. Evaluation is deduced by the number of bike program staff and a

bike plan that is current and being implemented. Finally, encouragement is assessed by active

bike clubs and signature events, bike month and bike to work events, active bicycle advisory

committee, active advocacy group, and recreational facilities like bike parks and velodromes

(League of American Bicyclists, 2014b). Each of these categories parallels levels associated

with the Social Ecological Model.

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Table 2.1 Bikeability Standards for Diamond and Platinum Statuses Platinum Diamond Enforcement Law Enforcement/Cycling Liaison Yes Yes Bicycle-Friendly Lanes/Ordinances in Place Very Good Excellent Education Public Education Outreach Excellent Excellent Annual Offering of Adult Bicycling Skills Classes

Quarterly Monthly

Percent of Primary & Secondary Schools Offering Bicycling Education

60% 80%

Engineering Bike Access to Public Transportation Very Good Excellent Total Bicycle Network Mileage to Total Road Network Mileage

45% 70%

Arterial Streets with Bike Lanes 78% 90% Evaluation One (1) Bike Program Staff Person Per 20K Citizens Per 10K Citizens Bike Plan is Current & Being Implemented Yes Yes Encouragement Active Bike Clubs & Signature Events Yes Yes Bike Month & Bike to Work Events Excellent Excellent Active Bicycle Advisory Committee Yes Yes Active Advocacy Group Yes Yes Recreational Facilities Like Bike Parks & Velodromes

Very Likely Yes

Source: League of American Bicyclists, 2014a

Currently, neither the state of Alabama nor the University of Alabama has reached any of

the above rankings. However, the community of Auburn currently holds the bronze ranking and

the University of Alabama has received an honorable mention for the strides made in the past

years to establish a more friendly cycling environment (League of American Bicyclists, 2014c).

These are steps towards improvement, but are insufficient to remove Alabama from its bottom

position.

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Each year, bike friendliness is reevaluated among the divisions of LAB in order to keep

stakeholders, cyclists, and the recognized entities current with the given standard. In addition,

states are given report cards for any change that has taken place over the last year. Currently,

Washington State holds the top position of all 50 states with an overall value of 66.8 out of 100

points (League of American Bicyclists, 2014d). Among the signs of success and scores given to

the state, Washington indicated evidence of nine out of the ten signs, only missing the “People

Commuting by Bike (More than 1%)” and qualitative feedback for the state included

accountability and expansion of funding, laws and investments for the non-motorized population.

Overall, Washington State was given a silver status.

Conversely, Alabama is currently ranked last with an overall score of 17.4 out of 100,

which is a five-point increase from the previous year. Of the top ten signs of success, Alabama

has sustained both an active state advocacy group and a state bicycle plan that was adopted in

2004 (League of American Bicyclists, 2014e). In addition, Alabama introduced bicycle

education for police, bicycle safety emphasis in strategic highway safety planning and top ten

state for congestion mitigation, and air quality spending in 2014. LAB (2014e) suggested

Alabama adopt a safe passing law, add specific training and policies for engineering, aid in

funding a cycling friendly environment, and include language, state websites and campaigns to

address and advocate cycling in the state (Table 2.2).

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Table 2.2 2014 Bikeability Rates for Alabama United States Ranking 50 Regional Ranking (South) 13 Category Scores 5 = High 1 = Low Bicycle-Friendly Lanes/Ordinances in Place Very Good Excellent Education Public Education Outreach Excellent Excellent Annual Offering of Adult Bicycling Skills Classes

Quarterly Monthly

Percent of Primary & Secondary Schools Offering Bicycling Education

60% 80%

Engineering Bike Access to Public Transportation Very Good Excellent Total Bicycle Network Mileage to Total Road Network Mileage

45% 70%

Arterial Streets with Bike Lanes 78% 90% Evaluation One (1) Bike Program Staff Person Per 20K Citizens Per 10K Citizens Bike Plan is Current & Being Implemented Yes Yes Encouragement Active Bike Clubs & Signature Events Yes Yes Bike Month & Bike to Work Events Excellent Excellent Active Bicycle Advisory Committee Yes Yes Active Advocacy Group Yes Yes Recreational Facilities Like Bike Parks & Velodromes

Very Likely Yes

Currently, nine out of ten of the top national universities are ranked among the Bicycle

Friendly University (BFU) Program (League of American Bicyclists, 2014f). Of the universities

recognized, only two are ranked at the platinum level: Stanford University and the University of

California at Davis (UC Davis) (League of American Bicyclists, 2014g). Platinum colleges and

universities indicate excellence in all categories. These campuses have a comfortable and safe

bike network, excellent bike parking, education and support programs for bikes, and a large

proportion of the campus population utilizing bikes as a regular method of transportation.

19

Upon further investigation of the sustainable transportation on college campuses,

researchers have determined that the higher education setting is a viable target to impact the

population and areas surrounding the campus (Balsas, 2003). Cycling is currently improving

quality of life on campuses (Clarke, 2000) as seen in many areas of the United States. Students

often walk and cycle in conjunction with motorized transportation. In addition, college students

tend to be physically more fit, have restricted budgets, live close to campus, and already own a

bicycle or are willing to purchase one (Tolley, 1996).

UC Davis is among those universities that strongly advocate for campus cycling. The

university has wide offerings of cycling resources to encourage non-motorized transport. UC

Davis was awarded BFU status by the League of American Bicyclists in 2005. To build

enthusiasm for the cause, the university maintains wider streets, well-marked bike lanes, inviting

pathways, abundant bike parking, and mutual respect between cyclists and motorists, which has

encouraged the numbers of bikes per capita for the area. This is accomplished through various

programs to build and advocate for cycling (UC Davis, 2012).

To further this movement, UC Davis educates students on a wide variety of notions

including how to choose the appropriate bike, theft prevention, appropriate bicycle parking,

general rules of the road, and advice for the new cyclist (UC Davis, 2012). Because of this and

general heightened awareness for the cycling population, the university boasts of 15,000 to

20,000 cyclists during peak use and has additional services specific for the cyclist. These

include daily use and summer bicycle storage, cycling classes, commuter showers, and bike

repair stations located around campus (UC Davis, 2012). Overall, these components encourage

an environment and population of cycling for a holistically more healthy campus.

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Population that Currently Cycles

Most U.S. cyclists are male; research indicates that more than 50% of men participate in

cycling annually (Harris, 2011) with higher use rate in males ages 25-64 years (Pucher, Buehler

& Seinen, 2011). Nationwide, 0.8% of men and 0.3% of women utilize cycling as a mode of

active transport (McKenzie, 2014). Increased numbers of female cyclists are found in areas

where there is a wider separation between the cyclist and traffic or with separated cycling

facilities (Baker, 2009; Garrard, Rose & Lo, 2008).

According to the annual American Community Survey, there is a reported increase of

commuters that cycle nationwide. In 1990, there were only 9,643 reported cyclists commuting,

but between 2006 and 2008 there was about 24,428 (US Census, 2010a, b, c). This is a 153%

increase from 1990 to 2008. In Alabama, there are approximately 2,300 cycle commuters (0.2%)

out of the 4.8 million residents in the state, which is the third lowest proportion of United States

commuters traveling by bike (McKenzie, 2014). Of those commuters, 1,454 are male and 868 are

female.

Nationally, the younger population (16-24 years) is more likely to use cycling as a mode

of commute at a rate of 1.0%. However, as the age groups increase, this rate quickly declines to

a rate of 0.3% in those 55 and older (McKenzie, 2014). Age and gender are not the only

predictors of cycling for active transport. Race also plays a role; African Americans have the

lowest percentage of cyclers (0.3%), and those who identified with two or more races have the

highest (0.8%) (McKenzie, 2014). Finally, there is a convex trend in cycling and education.

Those with less than a high school degree and those with a graduate or professional degree have

the highest proportion of cyclers (0.7% and 0.9%, respectively) (McKenzie, 2014).

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Health

While the United States is one of the wealthiest countries in the world, the health of the

population does not parallel this trend (Institute of Medicine of the National Academies, 2013).

In comparison to other high-income countries, the United States ranks poorly in heart disease,

obesity, and diabetes. Of the top seventeen high-income countries, it ranks 17th in life

expectancy for males and 16th for females (Ho & Preston, 2010). Each of these health risks is

directly correlated with physical activity and can be prevented.

Healthy People 2020

The Healthy People Initiatives began with the 1979 Surgeon General’s Report. In the

early 1980s, the US Department of Health and Human Services along with the Centers for

Disease Control and Prevention, the Food and Drug Administration, the National Institutes of

Health, and other leading U.S. agencies unveiled Healthy People 1990 as a means of eliminating

or reducing health disparities, addressing quality health care, and improving availability of

health-related information (USDHHS, 2014b).

One of the objectives of Healthy People 2020, the fourth generation of initiatives since

the 1979 Surgeon General’s Report, encourages an “increase [in] trips to work made by cycling”

by 10% from 2010 (USDHHS, 2014d, EH 2.1). University campuses can help meet this Healthy

People objective (CDC, 2010). The college environment plays an important role in impacting the

current and future health behaviors of university students (Bopp, Kaczynski & Wittman, 2011).

Active transport impacts the physical, psychological, and environmental health of university

students (Kaczynski, Bopp & Wittman, 2010).

This national set of guidelines also includes objectives for physical activity, which are to

reduce the proportion of adults who do not participate in physical activity in their free time

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(USDHHS, 2014c, PA-1), as well as increase the amount of adults that meet the national

guidelines for both aerobic and muscle-strengthening activities (USDHHS, 2014d, PA-2).

Meeting these objectives would help lower the risk of many of the above-mentioned diseases

including obesity, CVD, high blood pressure, and type 2 diabetes (USDHHS, 2008).

Physical Activity

In 2008, 32% of adults reported being inactive, and 19% were active but did not meet the

recommended guidelines for physical activity. By 2011, less than half (48%) met the

recommendations for physical activity for aerobic or strength training (CDC, 2011). Of these,

men were more likely to complete all of the guidelines than women.

Physical activity is important for any age group. Not only can it control weight and

increase the ability to perform daily activities, but it can also reduce risk of chronic diseases such

as cardiovascular disease (CVD), type 2 diabetes, and some cancers (CDC, 2011). Physical

activity also helps strengthen muscles and bones and improves overall mental health and mood

(CDC, 2011).

Recommendations

The World Health Organization (WHO) provides physical activity recommendations for

all nations. It is recommended that individuals strive to participate in 30 minutes of physical

activity each day. This can be achieved at one time or at various times throughout the day with

two 15-minute or three 10-minute periods of activity (World Health Organization, 2002). This

includes purposeful times of exercise or utilizing a 15-minute bike ride to commute each way to

work.

The American College of Sports Medicine (ACSM) recommends physical activity based

on four major categories: cardiorespiratory, resistance, flexibility, and neuromotor exercises

23

(ACSM, 2011). All of these reduce the risk of cardiovascular and other chronic diseases. While

these contribute to benefits of the body, the overall ACSM recommendation is to participate in

150 minutes or more of moderate exercise every week. Each category of activity has individual

recommendations for it, but the bottom line is that individuals stay active or “keep moving”.

ACSM states, “People unable to meet these minimums can benefit from some

activity…Sedentary behaviors—sitting for long periods of time—are distinct from physical

activity and has been shown to be a health risk in itself (ACSM, 2011).” Exercise can be

accomplished in big sections of time (i.e. one hour of Zumba class) or in smaller sections (i.e. 10

minutes of walking at lunch and after work). Either way is acceptable (USDHHS, 2012).

Importance of Physical Activity

Since the 1950s researchers have focused on physical activity and found a strong

correlation to good health (Morris, Heady, Raffle, Roberts & Parks, 1953). In 1996, the United

States Surgeon General released a report that highlighted the importance of physical activity

(USDHHS, 1996). According to the US Surgeon General, benefits of physical activity included

reductions in heart disease, hypertension, colon cancer, and diabetes. Additional benefits

included increased maintenance of bones, muscles, and joints, and reduced rates of anxiety and

depression. The report also stated physical activity promotes mental and emotional well-being,

aids in maintenance of body weight, contributes to lean muscle, and lowers body fat (US DHHS,

1996).

There have been numerous studies throughout the history of health research that pointed

to improved health with physical activity. Participating in such daily physical activity routines

such as walking were correlated with reduced mortality in the Honolulu Heart Program

(Anderson, Schnohr, Schroll & Hein, 2000). The study found that men who walked less than one

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mile per day had twice the mortality than those who walked more than two miles (Hakim et al.,

1998). Anderson, Schnohr, Schroll and Hein (2000) found that both men and women who stayed

active during their leisure time, cycling included, had lower associations with all-cause mortality.

It is not only circulatory diseases and overall mortality that are positively affected by

physical activity, but cancer and diabetes are also affected. The risk of colon cancer on average

is reduced by 40-50% (Lee & Blair, 2002). There is a reduction of the risk of breast cancer

(Luoto, Latikka, Pukkala, Hakulinin & Vihko, 2000). In addition, physical activity has shown to

have a protective health effect on those with lung cancer (Lee & Blair, 2002).

Walking and cycling, whether for active transportation or leisure, have been found to

reduce the risk factors of type 2 diabetes (Wannamethee, Shaper & Alberti, 2000). This disease

was once found primarily in adults but is now found in many children and adolescents with the

rise of obesity (Kaufman, 2002; USDHHS, 1996).

Physical activity benefits do not solely act on body structure but also aid in psychological

health and well-being. Regardless of socioeconomic status, the effects can be pinpointed in all

ages and populations for improvements of mood and emotion (Biddle, 2000). Physical activity

has been associated with improvements in self-esteem, self-perception, and depression (Fox,

2000). While the exact mechanisms or rates are still unknown for these diseases, researchers are

confident in the beneficial effects of physical activity on disease (Fox, 1999).

As physical activity improves the health of an individual, physical inactivity creates a

myriad of complex problems. One of the major risk factors for type 2 diabetes is physical

inactivity which can increase the risk of the disease by as much as 50% (Wannamethee, Shaper

& Alberti, 2000). In addition, physical inactivity has deleterious impact on mental and

psychological well-being. Those who spend less time engaged in physical activity are more

25

likely to become depressed over time (Camacho, Roberts, Lazarus, Kaplan & Cohen, 1991;

Penedo & Dahn, 2005). Keeping the population in motion is important for the prevention of

many diseases and for lowering the risk of early mortality. This can be accomplished on

university campuses through the Health Campus initiatives (ACHA, 2014).

Healthy Campus 2020

To promote and accompany Healthy People 2020, the American College Health

Association (ACHA) formed a coalition to propose objectives that would mirror the efforts of the

US Department of Health and Human Services but target the college population. In 2010, upon

the announcement of the Healthy People 2020 objectives, the Healthy Campus Coalition was

created. It was comprised of health professionals from over 25 higher education professional

organizations who wrote student and employee objectives for better campus health (ACHA,

2013).

The college health initiative utilized portions of the Healthy People 2020 guidelines that

pertained to the college age population and demographics. It consists of 54 objectives and 11

topic areas. While these do not directly address cycling, the initiative addressed many of the

major concerns of this population and considers topics helpful to the public health of students in

relationship to academic progress. These objectives include stress, sleep, anxiety, work, and

common communicable diseases such as influenza or strep throat (ACHA, 2013).

As another important component of health on campuses, the initiatives of Healthy

Campus 2020 also address the health of faculty and staff (ACHA, 2013). These do not directly

affect the health of students beyond communicable diseases, but indirectly affect those that work

within the university system within the classroom environment to create herd immunity.

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Health Statistics

Alabama Health Statistics

The state of Alabama is faced with many chronic health challenges. Heart disease,

cerebrovascular diseases, diabetes mellitus, and hypertension were included in the top 15 causes

of death (Alabama Center for Health Statistics, 2013). Alabama was ranked 47th in the United

States for all health outcomes in 2012 (United Health Foundation, 2012).

According to the Alabama Department of Public Health, nearly one-third of the adult

population in Alabama has high blood pressure and even more (39.3%) has high blood

cholesterol. Above the two-thirds national average, 68.1% of the population categorized as

overweight and obese and 12.2% diagnosed with diabetes (ADPH, 2010). Each of these is a risk

factor of cardiovascular disease (CVD), which is a component of heart disease, and is the highest

cause of death not only in Alabama but the United States.

Heart disease and stroke led to 33% of Alabama deaths in 2005. High blood pressure and

cholesterol were reported in over one-third of the population in 2007 (ADPH, 2010). Together

these problems kill over 15,000 people in the state per year and cost the U.S. $503.20 billion

annually (ADPH, 2010). Heart disease and stroke are the first and third causes of death in the

United States, respectively, mainly due to poor lifestyle choices including diet and physical

activity (CDC, 2008).

While not mentioned as a leading cause of death, obesity is a large contributor; it is

associated with an unhealthy lifestyle. In 2007, 66.6% of adults in Alabama were described as

overweight or obese. This is more than three percent above the national average (CDC, 2008).

Additionally, 17% of high school students are reported obese, which is four percent above the

national average (American Heart Association, 2011), and many of these students are entering

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the university population. In Tuscaloosa, the rate is even higher with 71% of the population

being obese or overweight (ADPH, 2010).

Obesity is an epidemic across the nation and in Alabama, and can be treated with an

active lifestyle. Physical activity is important for many reasons. It not only reduces the amount

of obesity in a population but also improves the health of individuals. Alarmingly, in the state of

Alabama, 58% of adults report insufficient physical activity (CDC, 2008). Thirty-one percent of

the state population and 32% of the population in the Tuscaloosa area claimed they were

inactive, defined as no physical activity in the last 30 days (CDC, 2008).

National University Health Statistics

The American College Health Association (ACHA) collects and reports on student data

collected from 153 national universities to provide a valid snapshot each year of campus health

(ACHA, 2013). Overall, the university student self-reports indicate a higher perception of health

than those in the state or national population. When asked about overall health, 58.7% of all

students reported having very good or excellent health in the spring of 2013 (ACHA, 2013).

When asked what factors affect their individual academic performance, students replied they

were affected by anxiety (19.6%), a chronic health problem or serious illness (3.5%), depression

(12.6%), difficulty sleeping (19.4%), and stress (28.6%) (ACHA, 2013).

The percentage of overweight and obesity in this population is also on the rise.

Overweight and obesity in children 12-19 years old saw a growth rate of 5% to 21% from 1980

to 2012 (Ogden, Carroll, Kit & Flegal, 2014). Those with overweight or obese parents are at

even greater risk (Agras, Hammer, McNicholas & Kraemer, 2004). The dilemma becomes

multifactorial in the wellness spectrum of these individuals. Extra weight on the body affects the

skeletal and organ systems and increases the rate of comorbidities (Daniels et al., 2005;

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Freedman, Zuguo, Srinivasan, Berenson & Dietz, 2009; Li, Ford, Zhao & Mokdad, 2009). Many

diseases once only seen in older adults, such as type 2 diabetes, cardiovascular disease,

hypertension, hypercholesterolemia, and sleep apnea, are now being identified in the younger

population (Kennedy, Sloman, Douglass & Sawyer, 2007). Beyond the physical issues, there are

many emotional and psychological diseases an obese person will encounter including bullying,

low self-esteem, and depression (Stoler, 2013).

University of Alabama Health Statistics

The University of Alabama (UA) participated in the university specific National College

Health Assessment. Compared to the national physical activity rate, UA is above the standard.

When asked how many times in the past seven days a student participated in moderate activity

for 30 minutes, 61.9% (4.4% above the national average), 23.4% reported five to seven days

(3.9% above the national average), and only 14.6% reported that they participated in no

moderate activity per week, which is 8.4% below the national average (ACHA, 2011). The same

questions were asked regarding vigorous activity for 20 minutes. UA students answered 31.9%

did vigorous activity one to two days per week (0.8% above the national average), 40.2%

claimed three to seven days per week (9.5% above the national average), and 28% claimed to not

participate in vigorous activity weekly (9.5% below the national average) (ACHA, 2011).

The data above indicates a change in the university chronic health problems. Like

physical activity levels, the University of Alabama rates better for levels of chronic diseases.

The rate of students diagnosed or treated for diabetes in the last 12 months was 0.9% (0.1%

below the national average), and 1.9% for high cholesterol (1.1% below the national average).

In the past 12 months, 91.9% of students claimed health did not affect their academic status

compared to the 90% national average, and 6.7% experienced a health issue, which did not affect

29

them negatively. This was slightly above the 6.3% national average (ACHA, 2011). The health

problems of the student population are unique due to the pressures of the academia and college

life. However, like those of the general population, these too can be treated with physical

activity (ACHA, 2012), which not only has a physical impact on people but also an economic

impact.

Economic Impact of Physical Activity

Quantified benefits of physical activity vary widely based on physical outcomes and

methods used to value them. There are a number of less obvious costs directly and indirectly

correlated with physical inactivity (Bidwell, 2012). In the United States, the direct health care

costs of inactivity are estimated to range between $24.3 billion and $37.2 billion. This accounts

for 2.4% to 3.7% of health care costs and jumps to 9.4% when obesity is included (Colditz,

1999). The cost of diseases related to lack of physical activity and poor nutrition habits is a

concern with $174 billion spent in relation to diabetes in 2007 (American Diabetes Association,

2008) and $14.1 billion in direct costs related to childhood obesity in 2009 (Cawley, 2010). This

will later impact the university population. The physically inactive will spend an average of

$330 more per year on health costs compared to a physically active counterpart.

Cycling is an inexpensive and sustainable form of transport (Gatersleben & Appleton,

2007). It not only requires a low consumption of energy but aids in the health of the cyclist and

environment. Cycling is also a relatively fast method of transportation for short commute

distances and is both reliable and affordable for the population (Lumsdon & Tolley, 2001).

Bicycle commuting saves money in various other methods as well. According to the

Office of the Federal Environmental Executive, cycling for commute decreases work

absenteeism, increases employee productivity, and reduces maintenance costs. Utilizing a bike

30

does not require a person to fill up on gas, requires less space than the motorized counterpart,

and causes little wear on the roads. Saving money combined with other health dimensions could

be used to advocate for cycling or physical activity, in general (Davis, 2010).

How Cycling Helps Health

Cycling helps environmental, mental, and physical dimensions of health (Woodcock et

al., 2007). The relationship between cycling as a mode of active transportation and health

benefits is complex. Various studies have concluded that the duration to and from work

combined with the intensity of physical output are sufficient to improve health in an individual

more than walking (Oja et al., 1991; Vuori, Oja & Paronen, 1994). Cycling maintains health

benefits even when combined with local transportation systems (Oja, Vuori & Paronen, 1998).

A number of health benefits are specific to cycling. It is a low impact exercise, which

creates less strain on and injury to joints and prevents further injury to damaged joints (Klein et

al., 2007). Klein et al. (2007) rated cycling as allowable after serious hip and knee surgeries.

This qualifies the activity for many other stages and kinds of injury. Additionally, a 150-pound

cyclist burns approximately 410 calories when pedaling 12 miles an hour (Conway, 2012). If

bicycling were incorporated into daily routines and commute, the campus community would be

healthier, save money, and decrease healthcare costs (Davis, 2010).

How Cycling Improves University Campuses

Cycling as a method of transportation improves the health of the individual cyclist,

mitigates climate change, improves environmental health, and benefits the entire population in a

manner that is socially equitable (Macmillan et al., 2014). Bicycle commuting offers a “non-

polluting, non-congesting, physically active form of transportation in a country (Mapes, 2013).”

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Since 40 percent of trips in the U.S. are within two miles of a person’s home, cycling is adequate

for those trips and can easily replace motor vehicle transportation (Mapes, 2013).

Reduces Parking Issues

According to the University of Alabama, the campus population has grown by nearly

15,000 students from 2004 (enrollment - 20,969) to 2013 (enrollment - 34,852). This growth has

caused challenges for parking on campus (University of Alabama, 2013; University of Alabama,

2014). Those that commute to campus are challenged to find adequate parking in the perimeter

parking zones and take another form of transportation to specific buildings or classes while

students that live on campus have specific parking hubs.

Lack of parking creates acute problems in densely populated areas that are not able to

provide adequate parking for the population (Arnott & Inci, 2006). When university officials are

not prepared for population surges, small parking areas and transportation become troublesome

to those that work, study, or live in the area (Balsas, 2003; Shang, Lin & Huang, 2007). This is

due to population size and the outdated design of the university. This is the case of the University

of Alabama, which was founded in the early 1800s.

The first Campus Master Plan was released in 2007 and outlined the physical and

historical foundations of the university campus (University Planning, 2007). There is a growth in

campus toward a more pedestrian and cyclist friendly layout as well as sustainable expansions.

According to the original plan, the president and campus planners wanted to increase pedestrian

activity and support bicycle use. “By encouraging walking and cycling for general accessibility,

the University supports more active lifestyles for students, which is essential for their full

intellectual and social growth” (University Planning, 2007).

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With the 2010 purchase of land to expand the University of Alabama, planners saw the

need to continue to move parking to the perimeter of the physical campus and make it “a

walkable campus” that will “support bicycle use” (University Planning, 2012). The plan

dedicates land on the campus outskirts for short-term parking and reduced the congestion of

central campus by eliminating smaller parking lots. There is more availability for bike racks in

centralized areas and nearer to frequented buildings by making actions toward a more cycle

friendly campus.

Reduces Motor Vehicle Traffic

Increased cycling and reduced motor vehicle traffic on campus reduce noise and physical

danger for the student population (Woodcock et al., 2009). Motor vehicle traffic contributes to

high levels of campus noise, which can also be a contributor of stress. Not only has noise been

found to be a contributor of elevated blood pressure and other psychiatric illness, traffic noise is

specifically correlated with decrease in work quality, increase in physical tension, increase in

blood pressure, and notable change in hormones (Ising, Dienel, Gunther & Markert, 1980). By

reducing motor vehicle traffic, stress can be reduced.

Motor vehicle traffic is negatively correlated with non-motor vehicle traffic (Dill &

Voros, 2007). Heavy motor vehicle traffic leads to greater perceived risk of accidents,

frustration with cyclist and vehicle interaction, and insecurity while travelling. Streets with

lower speed limits are perceived to be safer by cyclists and reduce the amount of crashes

involving cyclists. Lowered speeds and reduced volume of traffic allow for more reaction time

to prevent accidents between vehicles and cyclists (Retting, Ferguson & McCartt, 2003). Traffic

calming including such methods as narrowing lanes, speed tables, and protected pedestrian

33

islands are further methods that can make traffic seem safer and increase the amount of cycling

traffic (Smith & Appleyard, 1981).

Cycling itself helps in multiple ways to reduce the amount of motor vehicles on the road.

Basically, the more people who ride bikes, the less people would drive cars (Jacobsen, 2003).

However, there are more contributing factors. Jacobsen (2003) reports increased walking and

cycling does not change the number of accidents but helps to alter the behavior of the motorist.

Increasing the number of cyclists creates power in numbers and improves perceived safety in the

population (Jacobsen, 2003).

Improved Air Quality

Air pollution negatively affects the health of human population each year, especially in

areas that are densely populated (WHO, 2006; COMEAP, 2010). While industry and

commercial activity contribute to the problem, road traffic also affects the amount of air

pollution present (Caiazzo, Ashok, Waitz, Yim, & Barrett, 2013). Exposure to unhealthy air

particles contributed to approximately 130,000 premature deaths in 2005 (Fann et al., 2012) and

160,000 in 2012 (US EPA, 2011).

Cycling is an environmentally sustainable and pollution-free mode of transport (Cavill &

Davis, 2003). According to Rowell and Fergusson (1991), if the rate of cycling increased by ten

times the current rate, it would prevent as much as 750,000 tons of carbon monoxide, 100,000

tons of nitrogen dioxide, and 16,000,000 tons of carbon dioxide from being emitted. While it is

commonly thought that those walking and cycling are exposed to higher amounts of pollutants, it

is also true that those caught in slow moving traffic are at the same risk or higher (van Wijnen,

Verhoeff, Jans & van Bruggen, 1995). In fact, driving can be a risk three times greater than a

34

person cycling due to the proximity of the driver to the polluted air and quantity of emissions

(Department of the Environment Transport and the Regions, 1998).

A common approach recommended by many health professionals is to reduce exposure to

air pollution through increased means of physical activity in areas of high pollutant

concentrations (Sharma, 2005). Individuals often refrain from exposure to air pollution when

amounts are increased, which can deter from using bicycles as a means of commute.

Challenges to Cycling on Campus

Cycling is a great option for physical activity, as it improves psychological and

environmental health, and softens economic hardships (for instance, the need to purchase gas is

decreased). However, there are problems that often affect the cycling community (Mapes,

2009). Since the popularity of the car in the last half of the 1900s, the transportation system has

continued to invest money into the motor vehicle infrastructure system. This is far different than

the laws of 1900 in which states claimed that bicycles and vehicles had the same rights of the

road (Mapes, 2009).

Cyclists run a moderately high risk of being injured on the road or in a motor vehicle

accident, which is a big concern to many cyclists (Bracher, 1989; CTC, 1997). The modern

traffic system is designed to be friendly to the automobile but not to the cyclist in many areas of

the state and city (Wegman et al., 2010). As a result of this and other factors, people in the U.S.

cycle less than any other country as of 2003. This can be attributed to lack of bike lanes, bike

lanes in poor repair, or the need to share the road with motor vehicles.

The University of Alabama is not free from the injury list. From April to December of

2013, there were a total of nine reported accidents on the campus (UAPD, 2013). All of these

listed the bicyclist at fault, typically due to refusal to stop before the pedestrian crosswalk.

35

Seven of the cases resulted in minor injuries while two had no injuries at all. As of late August

2014, there have only been two cases reported with no injury (UAPD, 2014). It was not listed

whether the injury was the cyclist or another person involved.

Another challenge in the United States is urban sprawl (Moudon et al., 2005). Unlike

some areas of Europe or heavily populated cities in which much of the city is close in proximity,

cities like Tuscaloosa are spread out and increase the commute time for bicycles and motorists

alike. This makes a challenge for city government officials and developers to encourage an

environment that is safe for cyclists. It is also important to have end-of-trip facilities like bicycle

racks or lockers to encourage commuting (Mapes, 2009).

Perceptions of risk and safety tend to further the barriers of cycling. This affects how

often and who is willing to participate. In the populations that utilize cycling as a means of

commute as well as those that use it for recreation, the perceived risk of cycling is as significant

as the experience of an accident (Xing, Handy & Buehler, 2008; Dill & Voros, 2007). Cyclists

determine this through safety in numbers (Jacobsen, 2003). For example, the more cyclists take

to the road, the more people feel safe and visible on the road, and the less accidents are reported.

Infrastructure also changes the perception among cycling participants.

Taylor, Kingham, and Koorey (2009) argued that off-road or separated paths increase the

perception that cycling is more safe and enjoyable. In addition, the perceived fear for safety also

needs to be addressed when developing, planning, and implementing policies. In the 2002

National Survey of Pedestrian and Bicyclist Attitudes and Behaviors, more than 10% of the

participants felt their personal safety was threatened as they cycled (Royal & Miller-Steiger,

2008), 88% of them reported that they felt threatened by motor vehicles and 37% had concerns

about the road or sidewalk conditions.

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Traffic System

Infrastructure has a lot of influence on the atmosphere of cycling in various cities, both in

and around university campuses. Infrastructure can either encourage or discourage the

likelihood of cycling. A study from the Harvard School of Public Health analyzed the use and

risk of injury on bicycle specific trails versus a bicycle lane on the side of the street. Researchers

noted that physically separated cycle tracks are as safe or safer than in-road bike lanes (Lusk et

al., 2011). Separate cycling paths were used 2.5 times more than nearby on-street bicycle lanes

and the relative risk of injury was 28% lower. Of these, the safest cycle tracks were on streets

with the least amount of motor vehicle traffic. This research discussed a major limitation of

increased quantities of shared roads. One of these is the amount of parking that is taken from the

space with bike lanes, causing more motor vehicles to use the bike lane as a parking space (Lusk

et al., 2011).

Researchers have found that there is no single way to better the traffic system and

increase bicycle commuting by the population (Moritz, 1997; Nelson & Allen, 1997; Ogilvie,

Egan, Hamilton & Petticrew, 2004). Mouden et al. (2005) stated, “Frequently mentioned

environmental changes that can encourage cycling included: more bike lanes and trails

(mentioned by almost 49% of the respondents), good lighting at night (33%), and bicycle racks

at destinations (31%).” This is discussed further in other literature. Pucher, Dill, and Handy

(2010) reported that appropriate and aggregated infrastructure would facilitate an increase in

cycling. Even more, the increase of bicycle infrastructure is “positively and significantly

correlated with higher rates of bicycle commuting” (Dill & Carr, 2003). However, bike lanes

and paths alone did not increase the number of cyclists without connectivity to off-campus

destinations (Dill & Carr, 2003; Nelson & Allen, 1997).

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Laws Around Cycling

Law enforcement is an influential component when increasing bikeability in a

community. This includes helmet laws, speed limits, and general driving by both cyclists and

motor vehicles. These laws are found to increase the population’s comfort and perception of

safety (McClean, 2012). However, not all laws are advantageous in increasing the cycling

population.

While helmet laws are important, the enforcement of these laws can cause the number of

cyclists to decrease (McClean, 2012). This is reportedly due to how clumsy and awkward the

helmet feels while riding, reducing the comfort level of the individual. In addition, there have

been added complaints of storage issues with a helmet for fear that the helmet could be stolen or

where to store it when in a building.

Enforcement of reduced speed, speed tables, and areas with traffic calming has been

attributed to increased rates of cycling. Lowered speed limits improve both the perceived and

actual safety for the cyclists (McClean, 2012). Simultaneously, this reduces air and noise

pollution, making the area more environmentally friendly, and increases the relative speed of

cycling to driving. When each of these is mandated, the efficiency of cycling rivals the

efficiency of driving.

Personal Safety

Personal safety is an important component to using a bicycle as a main method of

transportation. Concern for safety is a prevalent barrier and safe conditions are not always

present (Chan, 2009; Pucher & Buehler, 2009). According to a survey completed by the

National Highway Traffic Safety Administration (2008), 88% of cyclists felt more threatened by

motorists while on the road and 37% felt that uneven walkways and roadways were a threat to

38

personal safety. Concern for safety is often stated as a significant barrier that affects commuting

habits (Pucher, Dill & Handy, 2010; Boelte, 2010).

There are other reasons that cyclists do not utilize bicycle paths and lanes. For many,

these reasons include poor construction, deterioration of lanes, or lack of convenience, meaning

that those planning the infrastructure did not effectively plan bicycle routes (Pucher,

Thorwaldson, Buehler & Klein, 2010). Taylor, Kingham, and Koorey argue that off-road or

separated lanes increase the perception that cycling is safe and more enjoyable (2009). However,

perceived fears need to be considered when developing, planning, and implementing

interventions and policies for better cycling environments.

Further challenges to cycling include the underreporting of accidents whether they occur

with motor vehicles or not (de Hartog, Boogaard, Nijland & Hoek, 2010). This is often due to

embarrassment from one or both parties or the desire to keep law enforcement and insurance

companies from being involved. This is common in those accidents that do not include fatalities

and leads to inaccurate statistics and problems of cycling safety.

Perceptions of Cycling

Overall, cycling can be a way for individuals to get healthy as well as contribute to the

environment (Pooley & Turnbull, 2000). However, one of the major deterrents of cycling in the

United States is the perceived amount of danger (Zeeger, 1994). When riding next to heavily

trafficked or in high-speed areas, a person can feel that the chance of injury is greater. This is

not always the case. Forrester (2001) argues that when a population becomes adjusted to a

higher rate of cycling, the roadways also become safer as seen in many European cities. There is

also often an increase in facilities to aid in cycling such as bike lanes, bike paths, or parking

facilities (Pucher, 2001).

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In further research, the perceived improvement in cycling safety has helped to increase

the cycling population. This is identified as safety in numbers (Elvik, 2009; Robinson, 2005).

As rates of cyclists rise and as they see more of each other on the road, the number of injury

levels fall (Pucher, Thorwaldson, Buehler & Klein, 2010). Additionally, the more people in a

community who cycle, the more probable that drivers are also cyclists and will heed safety

concerns.

People feel cycling with traffic can put oneself in the way of air pollution and emissions

from motor vehicles (Pucher & Dijkstra, 2003). However, research has shown cycling can

remove a person from the emissions of heavy traffic, and the increased number of trips by

bicycle reduces the number of motor vehicles on the roadway thereby reducing the amount of

emissions and air pollution from traffic (Rojas-Rueda, de Nazelle, Tainio & Nieuwenhuijsen,

2011). Additionally, many people feel that cycling is neither convenient nor an attractive way to

commute from place to place (Pucher & Dijkstra, 2003), as one may perspire or be exposed to

natural debris, like dirt, puddles, or rain.

Visual assumptions guide cyclists. When measured, the perception of the road, alertness,

and responsiveness changed given the quality of the physical path (Vansteenkiste, Zeuwts,

Cardon, Philippaerts & Lenoir, 2014). Cyclists often spend more time during a regular commute

looking at the surrounding environment instead of paying attention to the roadway (Foulsham,

Walker & Kingstone, 2011). When the texture of the path changes, the gaze and perception of

the person follows suit (Pelz & Rothkopf, 2007). This changes the acuity of the individual and

reaction time in relation to a person’s surroundings. When the only available and functional

space for cycling is not designed or maintained for the purpose of cycling, the changed

perception and acuity of the person will lead to more accidents.

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Bicyclist fatalities have fallen in the United States by 24% between 1980 and 2000 given

the awareness of non-motorists on the roadway (Pucher & Dijkstra, 2000). It is also safe to say

that with positive advocacy for the mode of transportation, a cycling culture will flourish.

Policies & Laws

Since the last half of the 1900s, the transportation system has continued to pour money

into the motor vehicle infrastructure system, with little investment towards cycling (Mapes,

2009). The Intermodal Surface Transportation Efficiency Act of 1991 was passed to increase

highway safety and mass transit in an energy efficient manner (US Department of Congress,

1991). Yet, no funding was made available for cycling. This funding disparity does not appear

to support the laws in most states, which claimed bicycles and vehicles had the same rights of the

road (Mapes, 2009).

Internationally, Germany, Denmark, and the Netherlands have taken innovative measures

and put key policies in place (Pucher & Buehler, 2009). These countries have bicycling

networks that include separated cycling facilities, modified intersections with priority traffic

systems, traffic calming, specific bike parking, traffic education, and protective laws intended for

the non-motor vehicle transportation. The bicycle networks of Northern Europe are

multifaceted, available to and utilized by all income classes, genders, and ages, and focus on

education and safety for active commuters (Pucher & Buehler, 2009).

Many federal, state, and city level agencies have tried in the past two decades to

implement policies and initiatives to improve the cycling atmosphere in the prospective areas.

The first was the federal Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991.

This act was introduced to provide highway and transit funding in collaboration with various

planning requirements to provide more non-motorized commuter trails through high priority

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corridors in the National Highway System (US Congress, 1991). During the course of its era

until it expired in 1997, 80 corridors across the United States were improved.

Following the ISTEA, the Transportation Equity Act for the 21st Century (TEA-21) of

1998 began. TEA-21 was authorized to fund specific regional transportation plans provided it

met seven planning factors. These included an increase in the safety and security for both

motorized and non-motorized users, promotion of energy conservation and improvement of

quality of life for the population, and integration of connectivity across and between various

modes of transportation (US Congress, 1998). This guaranteed federal funds for surface

transportation through the 2003 fiscal year.

The Emergency Economic Stabilization Act of 2008 became Public Law 110-343. A

section commonly known as the “Bicycle Commuter Act (Section 211)” was added by Oregon

representative Earl Blumenaurer which included “transportation fringe benefit to bicycle

consumers.” While it was unsuccessfully attached to other bills, the Bicycle Commuter Act helps

to benefit the companies and organizations that add in bicycle improvements, repair, and storage

by offering a tax break for the various improvements (Public Law 110-343, 2008)

In March of 2010, the Policy Statement on Bicycle and Pedestrian Accommodation

Regulations and Recommendations was released to establish the integration of active

transportation networks and support these as equally important to the motorized counterparts (US

Department of Transportation, 2014). Legislation and regulations require the inclusion of

bicycle and pedestrian projects in order to foster safer cycling, more livable and people friendly

communities, promote health and physical activity, and help environmental health by reducing

emissions and the use of fuel. Furthermore, this policy encourages transportation agencies to

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take a proactive role in providing safe and convenient facilities that foster increased pedestrian

and cyclist use of all ages.

Policies change the amount of motor vehicle use in and around university campuses.

They hold a lot of potential to solve the need for parking (Shannon, Giles-Corti, Pikora, Bulsara,

Shilton & Bull, 2006), increase quality of life and financial benefits (Toor & Havlick, 2004), and

reduce the amount of traffic congestion related to the campus (Delmelle & Delmelle, 2012).

This has already been observed in small and large universities by increased parking fees

(Delmelle & Delmelle, 2012) and increased financial perception of driving (Shannon et al, 2006)

as effective policies to shifting the mode from motor vehicle use to forms of active transport.

Currently, UA has does not have a specific list of laws for cyclists but promotes those in

the state of Alabama. Bike registration is free and optional to students and faculty (University

Recreation, 2014). While this policy is optional, it helps the University of Alabama Police

Department to find and return stolen bicycles. According to the Alabama Code, bicyclists “shall

be granted all of the rights and shall be subject to all of the duties applicable to the driver of a

vehicle…except as to those provisions…which by their nature have no application” (Alabama

Code, 1975).

Current Research on Cycling

Many states and colleges campuses across the United States are working to encourage a

bicycle friendly atmosphere (Handy, Heinen and Xing, 2011; Heinen and Handy, 2012;

Nankervis, 2012; Paez & Whalen, 2010; Rybarczyk & Gallagher, 2014). According to the

American Community Survey, bicycle-commuting trends from 2000 to 2008 have increased 43%

(League of American Bicyclists, 2009).

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Non-University Studies

In a study of six U.S. cities, Handy, Heinen and Xing (2011) analyzed the decision to

commute by bicycle and barriers that current and potential commuters face on a regular basis.

They found that several factors affected the choice to choose or refrain from cycling, including

socioeconomic factors, attitude, self-selection, social-environmental factors, and physical

environment factors. Their research led to the conclusion that the social environment of the

workplace, attitudes, and self-selection are important considerations affecting the likelihood to

commute by cycle. Even more so, the more the employer supports cycling and the individual

feels comfortable with it, the more likely that active commute by cycling will increase.

In a comparison study of Davis, California and Delft, Netherlands, the similarities and

differences between attitudes and beliefs about the decision to commute were examined (Heinen

& Handy, 2012). By comparing a person’s attitude toward a behavior, the norms of the social

environment, and the perception to the extent a person is able to perform the behavior, a greater

understanding of whether a person will or will not commute by bicycle can be achieved. The

researchers interpreted that when a person believes and adds importance to it, there is a reason or

motivation to cycle. For example, if a person believes that commuting by bicycle will help him

get in shape and that is an important factor, then a person is more inclined to commute by

bicycle. However, when a person has a negative perception or does not find cycling to be

socially acceptable, then the likelihood of using this as a regular form of transportation quickly

declines.

Weather has often been assumed as a barrier to active commute by bicycle but reports

offer little data to support this (Replogle, 1992; Goldsmith, 1992). However, research conducted

in Melbourne, Australia, researchers presented data on the effect of weather conditions on

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cycling (Nankervis, 2012). The assumption of weather, especially rain, made the conditions of

cycling unfeasible. While weather did affect the number of cyclists, Nankervis found the

difference to be small. Weather and climate were not found to be a strong barrier and cyclists

generally resumed daily routine. Dill and Carr (2003) concluded that research was inconclusive

of the impact of rain and other weather on commuter cyclists.

Interventions to increase cycling safety and the number of cyclists were conducted in the

already high cycling population cities of Munster (Germany), Freiberg (Germany) and

Amsterdam (Netherlands) (Beatley, 2000). These cities have taken action to calm motor vehicle

traffic, build and integrate bike networks, construct bike paths, improve signage, and discourage

the use of motor vehicles. This was not only found to increase the number of cyclists but also

works to make a more sustainable environment.

University Studies

Around the world, universities are working to promote a sustainable campus environment

by reducing the amount of automobile use (Paez & Whalen, 2010). Rising school enrollments,

like that of the University of Alabama, have led to issues with air and noise pollutions, motor

vehicle accidents, and congestion (Barata, Cruz & Ferreira, 2011). This has led a number of

universities to conduct studies to better understand the use of sustainable transport as a means of

active living and friendlier university environment.

At the University of Michigan in Flint, research was conducted to understand the

untapped potential for active transportation in and around the campus (Rybarczyk & Gallagher,

2014). Researchers used an attitudinal survey to gather the perception of cycling from the

students, staff, and faculty of the university. As one of the fastest growing universities in

Michigan, university leaders were encouraged to change the transportation schematic in order to

45

manage parking, reduce the amount of congestion on the campus, and increase sustainability. By

exploring the attitudes toward cycling and barriers, officials were able to gauge activity on the

campus. In the end, students agreed that safer routes, better lighting, and more attractive

destinations would improve the amount of cycling. Faculty and staff did not agree with this but

all parties agreed that living more than five miles from the university would be a barrier

(Rybarczyk & Gallagher, 2014). Members of the faculty, staff, and students that lived within

five miles agreed that safer routes, repair/air stations, bicycle classes, and increased parking costs

and fees would help increase the amount of cycling on the campus.

In a large, Midwestern university, researchers disseminated a survey to better understand

the mode of travel and influences on commuting. Overall, students responded with higher

amounts of active commuting behavior than did faculty or staff (Bopp, Kaczynski & Wittman,

2011). The most prominent explanation in the variance of travel decisions was based on

psychological set. When the current economic and ecological perceptions pointed to cycling or

walking to be a beneficial action, the individual was more likely to participate.

In 2009, research was conducted at the University of Maryland in College Park, MA.

Through a web-based survey, researchers analyzed travel patterns and specific issues

surrounding bicyclists (Akar & Clifton, 2009). The survey included a wide variety of subjects

including program innovations, improvement in infrastructure, and policies on the campus. The

findings of the study identified issues and concerns campus wide. The whole population, cyclists

and non-cyclists alike, agreed infrastructure is important to encourage cycling. Furthermore, the

results also indicated that nonmotorized vehicle adoptions may be more time sensitive given the

perception of cycling for exercise, time saving and economic purposes. This helped university

46

stakeholders to make policy and transportation improvements to create a more bike friendly

environment.

In a study to assess the knowledge, beliefs, and attitudes of the Georgia State University

students, participants completed surveys (Pope, 2010). The purpose was to assess the bicycling

trends and examine the beliefs and attitudes between those who cycle and those who do not. Of

the sample, 11% identified themselves as cyclists and half of those reported using bicycles as a

regular mode of transport to and from campus. Overall, cyclists believed that riding a bike was a

pleasant experience, had social support among cyclists, and had a higher rate of self-efficacy

than those who did not cycle. The largest barrier was the distance from home to campus and

many felt that bringing a bike to campus was not convenient. This is better understood through

the ecology of the population.

Social Ecological Model

The Social Ecological Model (SEM) provides a framework for understanding human

behavior and its interaction with the social and physical environments (Bronfenbrenner, 1979).

The model postulates that human behavior is influenced by multiple factors, which occur at

various levels of influence. The factors interact both within and between these levels to

influence or shape the health behavior or pattern of disease or injury. Although different

versions have been used to demonstrate the SEM levels, typically the factors are categorized into

the following levels: intrapersonal, interpersonal, institutional, community and policy levels

(Sallis, Cervero, Ascher, Henderson, Kraft & Kerr, 2006; Stokols, 2000).

The individual level includes biological and personal factors that might affect a given

health behavior such as campus cycling (e.g., a cyclist’s sex, age, history of illness, physical

fitness, attitudes, beliefs, knowledge, and skills) (Stokols, 2000). The interpersonal level

47

includes social groups, networks, and relationships that might influence whether campus cycling

occurs (e.g., the cyclist’s family, friends, athletic team, or faculty-student relationship). The

institutional level addresses specific institution/settings, which might influence the behavior. For

example, the university is an institution/setting. Specific characteristics of this institution that

might influence “campus cycling” could include the university’s free parking areas and hours,

availability of biking stations on campus, university specific traffic laws, the efforts of a

particular university committee, or the university’s busing/transportation system.

The community level in the SEM includes factors that go beyond the individual, primary

relationship, and a particular institution/setting (Stokols, 1996). In the case of campus cycling,

community level factors might include settings/organizations outside of the university and their

characteristics, as well as the relationship of the university with these settings/organizations (e.g.,

an adjacent neighborhood’s infrastructure including bike paths; a community’s traffic volume

and commute time; availability of buses in the community). The community level may also

include broader social, economic, cultural, health, and environmental factors or conditions that

impact campus cycling (e.g., the “cycling culture” of a city, gas prices, rate of physical inactivity

in a community, or socioeconomic status of a particular population).

Finally, the policy level includes laws, policies, regulations, and processes at the local,

state, or national levels that affect a given health behavior, such as campus cycling (Stokols,

1996). Examples of factors at this level might include the city’s biking zones, the state’s

transportation laws, the level of state funding for cycling lanes/safety, and support from the

national health agenda on cycling such as Healthy People 2020.

The Social Ecological Model blends well with the proposed study for a number of

reasons. First, SEM draws on the environment for having an influence on the physical, social,

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and cultural dimensions that play a role in health and wellness (Stokols, 1996). Thousands of

students walk on the University of Alabama every day. Improvement in the environmental

health of the campus will also influence the overall health of the student population.

Health and wellness are interdisciplinary (Pongsiri & Roman, 2007) and the movement

to promote health considers the interdependencies in each of the levels of social ecology

(Stokols, 1996). This helps to integrate community-wide change, strategies for prevention, and

encompass epidemiology in coordination with making an active presence on the campus.

A greater understanding of the effects of policies, the physical environment, and the

social influences on physical activity (in this case, campus cycling) can be gained from social

ecological models (Giles-Corti & Donovan, 2002). Because campus cycling is understudied, the

SEM is a suitable framework to assess factors that influence this behavior and to identify

recommended strategies for improving bikeability on campuses. The information obtained can

help build effective programs and initiatives.

A number of studies have reported the importance of addressing cycling from an

ecological perspective. When considering active transport only on an individual level,

researchers found that the amount of staff and students of an Australian university that utilized

active transport, namely walking and cycling, was comparable. However, when comparing rates

of physical activity between those who walk or cycle and those who do not. Rissel, Mulley and

Ding (2013) found that those who use active transport were more likely to be active in general.

This gave program planners direction for the future programs and initiatives for the university

(Rissel, Mulley & Ding, 2013).

The RESIDential Environmental Study (RESIDE) was a five-year research program that

took place in Australia to understand why people started to cycle for both recreation and

49

transport by evaluating the impact of the Department of Planning’s Livable Neighborhood guide

(Badland, Knuiman, Hooper & Giles-Corti, 2013). Researchers found that enhancing self-

efficacy in the intrapersonal level, social support in the interpersonal level, and available

infrastructure in the community level helps to increase cycling levels (Badland, Knuiman,

Hooper & Giles-Corti, 2013).

TravelSmart, another Australian cycling program, was funded to target the staff and

students of a campus community (Hancock & Nuttman, 2014). Researchers were interested in

promoting cycling and gaining feedback from the population simultaneously and encompassed

the individual and organizational facets. By utilizing the interpersonal, institutional, and

community levels, the university was able to build a coordinated sustainability management team

to influence the individuals and policies, as these were found to be two of the largest barriers.

Furthermore, this pushed the university to address the carbon footprint and improve the overall

health of the university population and environment (Hancock & Nuttman, 2014).

When trying to understand choice of modality, Lavery, Paez and Kanaroglou (2013)

found that three main factors were influential: the demographic descriptors of the person, attitude

and the space or land use of the university. Those who were identified as active travelers were

more enthusiastic for their method of transportation and likely to use it on a regular basis. The

density of the built environment swayed those who used cars and public transit, with a positive

relationship between the rate of density and use of car. Finally, demographics contributed to the

mode of transport, whether a person could afford a personal vehicle, understood the important of

feasible alternatives or was willing to utilize public transport (Lavery, Paez & Kanaroglou,

2013).

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Health Belief Model

Rosenstock and Hochbaum (1958) developed the Health Belief Model, which was based

on Lewin’s Field Theory of 1935 that founded the idea of barriers in behavior change. This

theory also utilizes the Value-Expectancy Theory, which discusses the value of the outcome or

an action and the expectation that the action may bring assuming that the population is interested

in health and preventing disease.

The Health Belief Model (HBM) is the most commonly used health promotion theory

with all influences on an intrapersonal level (Yarbrough & Braden, 2001). With beginnings in

subjective expected utility models, the theory combines knowledge, attitudes beliefs, history,

culture, and skills through four perceptions that serve as constructs for the theory.

While there are seven total constructs for the Health Belief Model, the four distinct ones

are perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. As

the theory was expanded, cues to action, motivating factors, and self-efficacy were added to the

constructs. The three later added constructs are cues to action, self-efficacy, and modifying

factors level (Champion & Skinner, 2008).

Perceived severity is a person’s notion of how serious danger or condition may be on

one’s life. Three factors were found to influence this perception in relation to personal safety

and bicycles: commute distance, number of traffic signals passed, and familiarity with the law

(John & Najafi, 2011). The greater the distance and higher the number of traffic signals, the

greater the perception of severity.

Perceived susceptibility is a person’s belief about the chances of getting a disease or

health condition. If a person does not believe that there is a risk, there is little chance for change.

In a 2011 study, 37 of 100 individuals surveyed reported always wearing a helmet. Of these,

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62% reported a personal experience, 81% knew someone who had been involved, and 65% had

witnessed a bicycle accident (John & Najafi, 2011). These three factors influence the perceived

susceptibility of the individual.

Perceived threat is a combination of perceived susceptibility and perceived severity.

While cues to action were found to affect the perception of threat, perceptions of threat were

found to be a strong influence on the attitude, intentions, and behaviors toward bicycle helmets

(Witte, Stokols, Ituarte & Schneider, 1993).

Perceived benefits are the believed values in a behavior. One of the main perceived

benefits is the ecological-economic awareness (de Geus, De Bourdeaudhuij, Jannes & Meeusen,

2007). Many cyclists reported cycling is cheaper than driving a motor vehicle and better for the

environment which was found to be a major benefit of the activity. In addition, bike lane

connectivity and rapidity of cycling were also found to be reported by those who regularly

cycled (Titze, Stronegger, Janschitz & Oja, 2008).

Perceived barriers are the believe obstacles that stand in the way of a person changing his

behavior. This is one of the most important constructs to determine change because barriers that

are too strong will not allow for change in a person no matter the level of the other constructs

(Janz & Becker, 1984). In order for healthy behaviors to start or flourish, the perceived benefits

must outweigh the perceived barriers. Researchers found that cyclists would be more likely to

wear helmets if manufacturers are able to make a helmet that is aesthetically pleasing with

convenient storage for little to no extra cost (John & Najafi, 2011). Both the look and

cumbersome qualities are barriers to a person wearing a helmet on a regular basis if at all.

Cues to action are the people, events, or things that prompt a person to change. This can

be as simple as a commercial on TV or being winded at the top of the stairs or as complex as a

52

diagnosis of disease or death of a loved one (Hayden, 2009). Helmet manufacturers and

educational campaigns were found to be effective cues to action for cyclists through the

promotion and aesthetics of the items (John & Najafi, 2011).

Self-efficacy is the belief and confidence in oneself to be able to complete conquer a

challenge. In this theory, it is also addressed for improvement through goal setting,

reinforcement, or monitoring. Campaigns aimed at increasing self-efficacy were found to be

effective at also increasing cycling for transportation (de Geus, De Bourdeaudhuij, Jannes &

Meeusen, 2007).

Modifying factors are those intrapersonal variables that can be swayed through education,

enlightenment, or change of environment. These factors come from a wide variety of sources.

Modifying factors of cycling are directly correlated with safety regulations. Fraser and Lock

(2010) reported positive associations with policies promoting safe cycling lane construction.

This theory has been well validated and studied in numerous interventions. Janz and

Becker found that the greatest results were those that came when barriers and susceptibility are

targeted (1984). This is also useful in a large population intervention. In a recent study, the

Health Belief Model and associated scale defined 52% of the variance associated with helmet use

(Ross, Thomson Ross, Rahman & Cataldo, 2010).

Purpose

The purpose of this study was to examine college student perceptions of safety and

factors contributing to campus cycling from an ecological perspective. Intrapersonal,

interpersonal, and institutional factors associated with campus cycling were assessed as well as

how these influences interact with each other. Recommendations for improving cycling were

obtained to address future needed campus changes.

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CHAPTER THREE

METHODOLOGY

The purpose of this study was to examine college student perceptions of safety and

factors contributing to campus cycling from an ecological perspective. This was a non-

experimental, descriptive, correlational study. The main purpose was to describe the cycling

population of the University of Alabama, including subdemographic descriptives. Additionally,

factors at the intrapersonal, interpersonal, and institutional ecological levels were evaluated

while exploring possible relationships among them in the university population. This chapter

details the study design, population recruitment, instrument development, data collection, and

statistical analysis with the use of the Social Ecological Model and the Health Belief Model.

Institutional Review Board Approval

The Institutional Review Board (IRB) at the University of Alabama (UA) approved a

proposal with expedited review on September 19, 2014 (see Appendix B). Modifications were

made to the protocol based on the feedback of the dissertation committee. The application was

submitted for additional modifications and approved November 7, 2014.

Participant Recruitment

The study consisted of a sample of students from a large southeastern public university,

the University of Alabama. The inclusion criteria for study participation were: currently a

graduate or undergraduate student of the University of Alabama (UA), 17-24 years old, and able

54

to read and write English. These criteria were selected because they were characteristic of most

students at the university and would be helpful for recruitment purposes.

Alabama law states that those under the age of 19 years are considered minors. Due to

this, minors were involved in the study since the inclusion criteria for age range begins at 17

years. Due to the amount of students who live away from home and the inclusion of minors in

the study, a waiver of parental consent was approved for this study. Additionally, written

consent was waived for all participants to ensure anonymity. Those who did not fall within the

inclusion criteria or did not agree to the conditions of the survey were not included in the

statistical analysis.

A power analysis was conducted with G*Power Version 3.1.9.2 (Faul, Erdfelder,

Buchner & Lang, 2009) to calculate the needed sample size for a multiple binary logistic

regression with parameters (OR = 1.4; effect size = 0.2; α = 0.05; power = 0.8). This yielded a

sample size of n=450, which is comparable to a recent United States university study which

yielded a population of 457 students (Bopp, Kaczynski & Wittman, 2011).

The researcher sent recruitment emails to UA instructors to inform them of the study and

to gain permission to enter classes for participant recruitment. For classes where the instructor

allowed the researcher admittance, the researcher scheduled appointments to attend the class,

describe the study, and encourage participation. The researcher informed potential participants

that there were no repercussions for not participating in the study. The link to the Qualtrics

survey was sent to the instructors and posted to Blackboard for the use of participants in the

classroom.

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Data Collection

Data collection took place over the course of four weeks between November and

December 2014. Surveys were completed in the privacy of the student’s choosing and time. The

study’s purpose and requirements were explained at the beginning of the electronic survey. The

participants accepted these to continue the study. All data were compiled through Qualtrics

survey software.

Qualtrics is a web-based survey and data collection software tool that is used worldwide

for marketing, research, and education (Qualtrics, 2014). This program was available through

the University of Alabama and allowed the researcher to easily add and edit instruments

developed through the website. This service then made a weblink available to students to easily

access the survey from any computer or smartphone.

Survey Instrument

The Cycling Survey for the University of Alabama included questions that addressed

demographic information including gender, age, rank in school, and distance from home to

campus (see appendix C). This instrument assessed the general public safety of campus,

perceptions of cycling, facilitations, and barriers of cycling based on the levels and constructs of

the Social Ecological Model and Health Belief Model. Questions were based on the US

Department of Transportation: National Highway Traffic Safety Administration’s Bikeability

Checklist, UC Davis’ Travel Survey, and California State University at Channel Islands’ Bike

and Transportation Survey. These were used to assess bikeability and active transport in

communities and campuses. Questions were based on either a 5-point Likert scale or “Check all

that apply” format to collect accurate information.

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The questions from these surveys included 15 items addressing bicycle ownership. The

initial question was, “Do you own a bicycle?” If answered, “No,” then participants skipped

forward to other factors that influence cycling. If answered, “Yes,” the participants are asked to

describe cycling habits through a number of categorical questions. Examples included, “Is your

bicycle registered? (yes/no).” “When did you last ride your bike? (this week, within the last

month, within the last year, more than a year ago, never).” “Have you used the bike repair

stations on campus? (yes, no, didn’t know that there were any).”

In addition, questions were also paired with the intrapersonal, interpersonal, and

institutional levels of the Social Ecological Model. Those that were intrapersonal and

interpersonal included three “check all that apply” questions with the “other” type-in option to

cover the intrapersonal factors associated with cycling. These included the perception of factors

of the physical bicycle that may deter a person from cycling. Example statements included, “I

don’t know how to work the gears,” “I can never get my brakes working right,” or “I’m not

confident in using a bike lock.” Factors that influence cycling decisions included statements

such as “I don’t know how to ride a bike,” “I don't know cycling rules,” “my cycling skills are

poor,” or “I had an accident or scare/near-miss on a bike in the past.” Finally, statements of

appearance were addressed with such statements as, “looking/feeling silly on a bike,” “helmets

mess up my hair,” or “others look fit and I don’t.”

Those survey questions that were categorized as “institutional” included three “check all

that apply” questions with the “other” type-in option to cover the institutional factors associated

with cycling. These included statements about the perception of institutional factors such as,

“there are inadequate bike lanes or bike paths in my area,” “I dislike car fumes,” and “there is no

place to change my clothes after cycling to campus.” Statements that targeted traffic perceptions

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included, “speed of automobiles,” “vehicles turning right in front of me when I’m going

straight,” “insufficient enforcement of both cycling and traffic laws,” and “vehicles hitting me

from behind when I’m cycling.” Factors relating to services and facilities that would encourage

cycling included “covered parking,” “locker/shower facilities,” “better markings/signage,” and

“more connectivity to downtown/shopping.”

Personal safety perceptions of the population were addressed through questions from the

Bicycle Helmet Attitudes Scale (Ross et al., 2010). These had all been used to assess

perceptions of severity, susceptibility, barriers, and benefits as well as cues to action based on the

constructs of the Health Belief Model. The survey included eighteen 5-point Likert scale

statement to address the perception of cycling safety among the campus population (Table 3.1).

These included perceived threat statements such as, “I do not go fast enough to need head

protection in a crash,” “bicycling is dangerous on slippery/wet roads,” and “if I injured my head

while riding my bike, it could seriously affect my ability to function at school.” Perceived

benefits were addressed through statements such as, “wearing a helmet would make me feel less

anxious when I ride a bike” and “I believe that wearing a helmet can prevent a serious head

injury if I have a bicycle accident.” Perceived barriers were addressed by statements such as, “I

would feel embarrassed wearing a bicycle helmet,” “quite frankly, wearing a helmet looks

stupid,” or the cost of helmets is generally more than they are worth.” Lastly, cues to action

were measured by statements such as, “I recall seeing commercials, ads or posters about wearing

a helmet while bicycling.”

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Table 3.1 Questions Specific to the Health Belief Model Question Construct I do not go fast enough to need head protection in a crash. Perceived Threat I feel that helmets are unnecessary for very short rides. Perceived Threat Bicycle helmets are less important for those who ride their bike infrequently.

Perceived Threat

When I’m bicycling, I am at risk of being injured by motor vehicles.

Perceived Threat

Bicycling is dangerous on slippery/wet roads. Perceived Threat There is a good chance that I could get hurt while riding my bicycle.

Perceived Threat

If  I  am  injured  while  riding  my  bike,  it  could  seriously  affect  my  social  life.    

Perceived  Severity  of  Harm  

If  I  am  injured  while  riding  my  bike,  it  could  seriously  affect  my  ability  to  function  at  school.      

Perceived  Severity  of  Harm  

Wearing  a  helmet  would  make  me  feel  less  anxious  when  I  ride  a  bike.    

Perceived  Benefits  

Wearing  a  helmet  while  bicycling  makes  me  feel  safer.     Perceived  Benefits  

I  believed  that  wearing  a  helmet  can  prevent  a  serious  head  injury  if  I  have  a  bicycle  accident.    

Perceived  Benefits  

I  would  feel  embarrassed  wearing  a  bicycle  helmet.     Perceived  Barriers  

Quite  frankly,  wearing  a  helmet  looks  stupid.     Perceived  Barriers  

The  cost  of  helmets  is  generally  more  than  they  are  worth.     Perceived  Barriers  

I  would  not  want  to  spend  money  to  buy  a  bicycle  helmet.     Perceived  Barriers  

I  have  several  friends  that  routinely  wear  helmets  when  they  ride.    

Cues  to  Action  

My  parents  never  made  me  wear  a  helmet  when  I  was  a  child.     Cues  to  Action  I  recall  seeing  commercials,  ads  or  posters  about  wearing  a  helmet  while  bicycling.    

Cues  to  Action  

Physical activity of the population was addressed through questions from the American

College Health Association’s National College Health Assessment (ACHA, 2008). These had

been used to assess the frequency and type of physical activity in the college population.

Questions were based on an eight-point scale. The survey included three questions addressing

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physical activity. These were through a zero to seven-day scale and addressed how many times a

student had participated in moderate intensity, vigorous intensity, or resistance training in the

past week.

Finally, survey questions included primary and secondary modes of transportation, bike

ownership and uses, common bike practices on the campuses and in the area, cycling skills and

confidence, facility design and maintenance, bike and equipment, clothing and fashion, and other

influences. Finally, there were a number of questions regarding what would encourage a person

to ride a bike (“How important would these factors be in encouraging you to bicycle to campus

more often?”/”If UA offered the following, would it encourage you to bicycle to campus?”).

Participants responded on a scale of 1 (Not at all important) to 5 (Very Important) or 1 (Very

Likely) to 5 (Very Unlikely).

Commonly measured and relevant demographic variables were included in the survey,

such as gender, age, graduate/undergraduate status, year in school, area of study, race/ethnicity,

place of residence, length of commute, mode of transportation (primary and secondary), and

local zip code. These variables defined the characteristics of the sample and sub-groups in

further analyses.

The questions and statements above were directly related to the constructs of both the

Health Belief Model (Table 3.2) and the Social Ecological Model (Table 3.3). This connection

aided the researchers in better understanding the ecological perspective of the data and how this

could be utilized within the study.

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Table 3.2 Survey Questions: Health Belief Model Constructs Question Construct

Where do you live? Perceived Benefit/Barrier If off campus, how many miles is your commute one-way to UA?

Perceived Benefit/Barrier

What state/country were you living in before you came to UA?

Perceived Benefit

Do you own or rent a bicycle? Perceived Benefit Is your bicycle registered? Perceived Threat How many miles a week do you ride your bike?

Perceived Barrier

Do you bicycle in combination with other transportation modes to campus?

Perceived Barrier

Do you lock your bike? Perceived Threat Do you wear a helmet? Perceived Threat Do you ride with the flow of traffic? Perceived Threat Do you stop at red lights/stop signs? Perceived Threat Have you ever had your bike stolen? Perceived Threat How would you rate bike parking on campus?

Modifying Factors

Have you used the bike repair stations on campus?

Modifying Factors

Are you willing to pay a facilities fee on campus for better bike paths and resources?

Perceived Benefits/ Modifying Factors

Personal Safety Factors Self-Efficacy Cycling in Traffic Self-Efficacy/ Perceived Threat Services/Facilities Perceived Benefits Bike & Equipment Modifying Factors Personal Appearance Self-Efficacy/ Perceived Barriers Factors that encourage cycling Perceived Benefits Classes that would encourage cycling Cue to Action Perceptions of Cycling Safety All Physical Activity Perceived Benefits

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Table 3.3 Survey Questions: Social Ecological Model Constructs Question Ecological Level Gender Intrapersonal Age Intrapersonal What is your race or ethnicity? Intrapersonal Are you an undergraduate or a graduate? Intrapersonal What year are you in college? Intrapersonal What is your major? Intrapersonal What is your area of study? Intrapersonal Where do you live? Community If off campus, how many miles is your commute one-way to UA?

Community

What state/country were you living in before you came to UA?

Community

Primary/secondary mode of transportation Intrapersonal Do you own of rent a bicycle? Intrapersonal Is your bicycle registered? Intrapersonal/Policy How many miles a week do you ride your bike?

Intrapersonal

Do you bicycle in combination with other transportation modes to campus?

Intrapersonal/Community

When did you last ride your bike? Intrapersonal How often do you ride your bicycle to campus?

Intrapersonal

Do you lock your bike? Intrapersonal Do you wear a helmet? Intrapersonal/Policy Do you ride with the flow of traffic? Policy Do you stop at red lights/stop signs? Policy Have you ever had your bike stolen? Intrapersonal How would you rate bike parking on campus? Community Have you used the bike repair stations on campus?

Community

Are you willing to pay a facilities fee on campus for better bike paths and resources?

Institutional/Policy

Personal Safety Factors Intrapersonal Institutional Barriers Institutional Cycling in Traffic Interpersonal

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Institutional Facilitators Institutional Bike & Equipment Intrapersonal Personal Appearance Intrapersonal/ Interpersonal Factors that encourage cycling Community Classes that would encourage cycling Institutional Where would you cycle? Community Perceptions of Cycling Safety Intrapersonal/Interpersonal/Institutional Physical Activity Intrapersonal Suggestions/Comments Intrapersonal

Piloting the Survey

A pilot study is a “small scale version or trial run in preparation for a major study” (Polit,

Beck & Hungler, 2001). It is often used as a pretest for an instrument (Baker, 1994). Although a

pilot study does not guarantee the success of the study, the process ensures that the study purpose

is understood, instructions are clear, wording is appropriate to the population, survey questions

are understood, important issues are covered, and that the question flow is logical (Simon,

2011).

A panel of 10 students and stakeholders of the university piloted the survey instrument

used for the study. All participants included in the piloting were either stakeholders of the

university from the Bike Advisory Group or part of the participating student population. Each

person independently read the survey and gave feedback. Changes to the instrument were made

accordingly.

Survey Completion

Each participant who chose to take the survey filled out the web-administered survey.

The first screen of the survey was the informed consent section (Appendix B). If a participant

chose not to consent, he/she was taken to the final page of the survey, thanked for his/her

consideration, and the survey closed. By filling out the surveys, respondents indicated they

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understood their responsibility in the survey, they were between the ages of 17 and 24, they were

currently enrolled at the University of Alabama, and they read and understand English.

Participants were allowed to skip questions if they did not understand or feel comfortable

completing the question. Surveys lasted approximately 20 minutes and consisted of 65 items.

At the end of the survey, each participant were thanked for his/her time on the last page and

provided contact information to the researcher and the UA Institutional Review Board for

questions.

Data Analysis

All data analysis was conducted using the Statistical Package for the Social Sciences

(SPSS), version 21 for Macintosh (SPSS Inc., 2013) in coordination with Qualtrics automatic

conversion. Various levels of analysis were performed on the completed and cleaned data set.

Descriptive statistics, including frequencies and Chi Square, were generated on all study

variables. Surveys that did not meet criteria or contain inconsistencies were not included in the

final analyses.

Based on preliminary analyses, a series of univariate and multivariate logistic regressions

were used to analyze the data and determine relationships between participants who cycled and

those who did not. This was appropriate to examine the categorical outcome variable in the

study. The a priori was set at less than or equal to .05.

Research Questions

The following research questions were addressed:

Research Question 1: Of the population sampled, what was the prevalence of on-campus

cycling at the University of Alabama?

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This research question focused on the prevalence of cycling at the University of

Alabama. To examine the prevalence of this behavior among college students sampled at UA,

simple frequencies and descriptive analysis were run in SPSS. Cycling was a categorical

variable based on bike ownership or rental (YES/NO) and frequency of cycling is a categorical

variable (never to daily).

Cycling prevalence among the UA sample was analyzed by examining frequencies,

percentiles, and a bar chart. Frequency of cycling was analyzed by examining descriptives to

determine distribution, variability, and measures of central tendency.

Research Question 2: How did campus cycling differ between demographic subgroups at the

University of Alabama?

This research question focused on the prevalence of cycling among demographic

subgroups at the University of Alabama. To examine the prevalence between subgroups among

college students sampled at UA, Chi Square was used to analyze each predictor of the outcome

in SPSS. Multivariate logistic regression was used for further in-depth analysis.

Predictors included gender (male/female), race/ethnicity (non-Hispanic white, Asian,

Hispanic/Latino, Black/African American, American Indian/Alaska Native, Native

Hawaiian/Pacific Islander, Other), campus living (On/Off), school status

(graduate/undergraduate), and age.

Research Question 3: Which factors facilitate or hinder cycling behaviors at the University of

Alabama?

Subquestion A: Which intrapersonal factors facilitate or hinder cycling behaviors?

Subquestion B: Which interpersonal factors facilitate or hinder cycling behaviors?

Subquestion C: Which institutional factors facilitate or hinder cycling behaviors?

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This research question and its subquestions focused on factors correlated with each level

of the Social Ecological Model and the preference of those factors between participants who

cycled and those who did not. Each of the questions was analyzed with a chi square analysis.

Univariate and multivariate logistic regressions were used for further in depth analysis.

Predictors for these research questions were all categorical and included subjects such as

environmental factors, cycling skills and confidence, bike and equipment and perceptions of

cycling safety.

Research Question 4: What strategies or suggestions for improving campus cycling were

recommended by students at the University of Alabama?

This question focused on strategies or suggestions for improving cycling at the University

of Alabama which the survey participants reported. Participants were asked to write in their

suggestions. Simple frequencies and descriptive analysis were used to analyze the data.

Summary

Chapter three summarizes the research methods and questions used to analyze data

collected from the Campus Cycling Survey for the University of Alabama. Descriptive statistics

were used to organize and summarize the population and prevalence of campus cycling (Daniel,

2004). These included age, gender, race/ethnicity, participating college, and year in college.

Further, cyclists were asked about simple cycling practices such as their practices at stoplights,

use of bike locks, and use of bike helmets. Researchers used univariate and multivariate logistic

regression to analyze factors at the intrapersonal, interpersonal, and institutional levels of the

Social Ecological Model. This was done to determine which levels were most influential for the

cyclist and non-cyclist alike. The a priori for this analysis was set at .05, determining that there

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was a 5% chance of a Type 1 error. Finally, participants were asked for suggestions for cycling

improvement and additional comments.

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CHAPTER FOUR

RESULTS

Chapter four presents the study results of statistical data analysis. A number of studies

have attempted to explore bikeability on campuses (Bopp et al, 2011; Bonham & Koth, 2010;

Sisson & Tudor-Locke, 2008; Rybarczyk & Gallagher, 2014; Akar & Clifton, 2009). However,

these lacked theory and framework to support health behavior change needed by stakeholders.

The purpose of this study was to examine college student perceptions of safety and factors

contributing to campus cycling from an ecological perspective. Intrapersonal, interpersonal, and

institutional factors associated with safety and campus cycling were assessed as well as how

these influencers interacted with each other.

To explore the issues above, the study examined the prevalence and descriptives of

campus cycling and factors that influence or hinder cycling behavior on the intrapersonal,

interpersonal, and institutional levels. Finally, strategies and suggestions were reported for

further research. The results are organized in four sections based on the research questions.

Chapter four describes the study results. All data were analyzed using the Statistical

Package for the Social Sciences, version 21 for Macintosh (SPSS Inc., 2013). Descriptive

statistics describe the characteristics of the sample and prevalence of cycling. Chi square

analysis, univariate and multivariate logistic regressions evaluate the demographic subgroups

and factors that facilitate or hinder cycling behaviors The a priori criteria to indicate significance

of a predictor is less than or equal to .05.

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Data Source

A total of 516 university students responded to the 2014 Cycling Survey for the

University of Alabama. The study was delimited to participants who responded to survey

questions about rent or ownership of a cycle, frequency of cycling, gender, ethnicity, age, and

university status.

Sample Characteristics

The resulting analyzed pool was a sample of 356 participants. Of the respondents, 73.9%

were female. Over half of the students were 21-22 years of age (54.4%) and 76.1% of the

population described themselves as white/non-Hispanic. Of the sample, 93.3% were

undergraduates, 82.7% were juniors or seniors, and 32.1% had academic majors in General

Health Studies, Exercise & Sport Science, or Public Relations. Table 4.1 highlights the

demographic characteristics of the student sample.

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Table 4.1 Student Demographics (N=356)

Variable N % Gender

Male 93 26.1% Female 263 73.9%

Age 18 20 5.6% 19 36 10.1% 20 70 19.7% 21 118 33.1% 22 76 21.3% 23 23 6.5% 24 13 3.7% Race/Ethnicity American Indian/Alaska Native 9 2.5% Asian 11 3.1% Black/African American 53 14.9% Hispanic/Latino 7 2.0% Native Hawaiian/Pacific Islander 2 0.6% White/Non-Hispanic 271 76.1% Other 3 0.8% University Status

Undergraduate 332 93.3% Graduate 24 6.7%

Year in College 1 22 6.2% 2 37 10.9% 3 91 26.7% 4 191 56.0% Top 5 Undergraduate Majors General Health Studies 48 13.5% Public Relations 38 10.7% Exercise & Sport Science 28 7.9% Advertising 15 4.2% Athletic Training 12 3.4% Telecommunication & Film 12 3.4%

Table 4.2 presents additional characteristics of the sample. Of the sample, 71.3% reported

their overall health to be good or very good, 45.7% reported participating in moderately intense

exercise four or more days per week for at least 30 minutes, 27.5% participated in vigorously-

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intense exercise four or more days per week for at least 20 minutes, and 23.0% participated in

eight to 10 strength training exercises for eight to 12 repetitions each four or more days per

week.

Table 4.2 Student Health Status & Physical Activity

Variable N % General Health

Very Poor/Poor 10 2.8% Fair 91 25.6% Good/Very Good 251 71.3%

Moderate Intensity Cardio or Aerobic Exercise for at Least 30 Minutes in the Last 7 Days 0-2 133 37.4% 3-4 103 28.9% 5-7 112 31.5% Vigorous Intensity Cardio or Aerobic Exercise for at Least 20 Minutes in the Last 7 Days 0-2 202 56.8% 3-4 79 22.1% 5-7 66 18.5% Strength Training (8-10) for 8-12 Repetitions in the Past 7 Days

0-2 232 66.9% 3-4 63 17.7%

5-7 82 14.5%

Table 4.3 presents perceptions of safety while cycling and influences of safety on

cycling. Of those who reported, 19.9% agreed or strongly agreed that they do not go fast enough

to need head protection, 31.4% reported that helmets are unnecessary for very short rides, 69.6%

agreed that they are at risk of being injured by motor vehicles, 64.9% agreed that bicycling is

dangerous on slippery or wet roads, and 55.6% agreed that they could get hurt while riding their

bicycle. When asked about the effect of injury while cycling, 32.3% believed that an injury

while cycling could seriously affect their social life and 48.8% believed that it could affect the

ability to function at school.

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Table 4.3 Safety Perceptions of Students

Survey Statement N % I do not go fast enough to need head protection in a crash.

Strongly Disagree/Disagree 183 51.4% Neither Agree nor Disagree 94 26.4% Agree/Strongly Agree 71 19.9%

I feel that helmets are unnecessary for very short rides. Strongly Disagree/Disagree 145 40.7% Neither Agree nor Disagree 88 24.7% Agree/Strongly Agree 112 31.4% Bicycle helmets are less important for those who ride their bike infrequently. Strongly Disagree/Disagree 196 55.1% Neither Agree nor Disagree 107 30.1% Agree/Strongly Agree 45 12.7% When I’m bicycling, I am at risk of being injured by motor vehicles.

Strongly Disagree/Disagree 33 9.3% Neither Agree nor Disagree 67 18.8%

Agree/Strongly Agree 248 69.6% Bicycling is dangerous on slippery/wet roads. Strongly Disagree/Disagree 33 9.3% Neither Agree nor Disagree 81 22.8% Agree/Strongly Agree 231 64.9% There is a good chance that I could get hurt while riding my bicycle. Strongly Disagree/Disagree 54 15.1% Neither Agree nor Disagree 96 27.0% Agree/Strongly Agree 198 55.6% If I am injured while riding my bike, it could seriously affect my social life. Strongly Disagree/Disagree 102 28.6% Neither Agree nor Disagree 129 36.2% Agree/Strongly Agree 115 32.3% If I am injured while riding my bike, it could seriously affect my ability to function at school. Strongly Disagree/Disagree 64 18.0% Neither Agree nor Disagree 105 29.5% Agree/Strongly Agree 174 48.8% Wearing a helmet would make me feel less anxious when I ride my bike. Strongly Disagree/Disagree 120 33.7% Neither Agree nor Disagree 133 37.4% Agree/Strongly Agree 92 25.8% Wearing a helmet while bicycling makes me feel safer. Strongly Disagree/Disagree 83 23.3% Neither Agree nor Disagree 117 32.9% Agree/Strongly Agree 143 40.1%

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I believe that wearing a helmet can prevent a serious head injury if I have a bicycle accident. Strongly Disagree/Disagree 23 6.4% Neither Agree nor Disagree 64 18.0% Agree/Strongly Agree 258 72.5% I would feel embarrassed wearing a bicycle helmet. Strongly Disagree/Disagree 69 19.3% Neither Agree nor Disagree 101 28.4% Agree/Strongly Agree 174 48.9% Quite frankly, wearing a helmet looks stupid. Strongly Disagree/Disagree 83 23.3% Neither Agree nor Disagree 101 28.4% Agree/Strongly Agree 161 45.3% The cost of helmets is generally more than they are worth. Strongly Disagree/Disagree 154 43.2% Neither Agree nor Disagree 137 38.5% Agree/Strongly Agree 53 14.9% I would not want to spend money to buy a bicycle helmet. Strongly Disagree/Disagree 109 30.6% Neither Agree nor Disagree 110 30.9% Agree/Strongly Agree 125 35.1% I have several friends that routinely wear helmets when they ride. Strongly Disagree/Disagree 177 49.7% Neither Agree nor Disagree 98 27.5% Agree/Strongly Agree 69 19.4% My parents never made me wear a helmet when I was a child. Strongly Disagree/Disagree 195 54.7% Neither Agree nor Disagree 76 21.3% Agree/Strongly Agree 75 21.0% I recall seeing commercials, ads or posters about wearing a helmet while bicycling. Strongly Disagree/Disagree 88 24.7% Neither Agree nor Disagree 104 29.2% Agree/Strongly Agree 152 42.7%

Missing Data

Missing data can create underrepresentation in terms of variables that can lead to

limitations in the research (Schneider, Clark, Rakowski & Lapane, 2012; Raghunathan, 2004).

Participants of the 2014 Cycling Survey for the University of Alabama provided information that

gives insight to researchers about the prevalence of cycling at UA and cycling demographics

such as gender, age, race/ethnicity, university status, and bike use. Statistically, the usefulness of

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these variables is limited by the number of participants who refused to answer said questions

(N=163). Participants who refused to answer these questions were deleted from the sample.

Nonetheless, the resulting data were present in sufficient numbers to make analysis possible, as

the sample size was sufficient for statistical analysis.

Analysis of Research Questions

Research Question 1: Of the population sampled, what is the prevalence of on-campus cycling

at the University of Alabama?

The prevalence of on-campus cycling was analyzed through frequency and descriptive

statistics. Of the participants, 28.1% responded that they own or rent a bike on the University of

Alabama campus (N = 100) with a confidence interval of (23.4, 32.8). Table 4.4 presents the

data that was collected from those who responded, “yes” to ownership or rental of bikes on the

university campus, including registration, purpose of cycling, frequency of cycling, average

number of miles cycled per week, last time used, and directed questions of cycling safety and

security.

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Table 4.4 Cycling Prevalence in Student Sample (N=100)

Question N % Is your bicycle registered with the university?

Yes 53 53.0% No 46 46.0%

How many miles a week do you ride your bike? 0 12 12.0% 1-25 68 68.0% 26-50 9 9.0% 51+ 4 4.0% How do you use your bike? Work 31 31.0% School 85 85.0% Shopping 8 8.0% Exercise 43 43.0% Recreation 46 4.0% Other 2 2.0% How often do you bike in combination with other transportation modes?

Never 53 53.0% Rarely/Sometimes 34 34.0% Often/All of the Time 13 13.0%

When did you last ride your bike? More than a year ago 6 6.0% Within the last year 19 19.0% Within the last month 14 14.0% This week 61 61.0% How often do you ride a bike to campus? Never, live off campus 22 22.0% Never, live on campus 13 13.0% A couple times per year 9 9.0% A couple times per month 9 9.0% A couple times per week 18 18.0% Daily 29 29.0% Do you lock your bike? Yes 94 94.0% No 6 6.0% Do you wear a helmet? Yes 13 13.0% No 87 87.0% Do you ride with the flow of traffic? Yes 81 81.0% No 18 18.0% Do you stop at red lights?

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Yes 82 82.0% No 18 18.0% Do you stop at stop signs? Yes 63 63.0% No 36 36.0% Have you ever had your bike stolen? Yes 22 22.0% No 78 78.0% How would you rate bike parking on campus? Very Poor/Poor 28 28.0% Fair 47 47.0% Good/Very Good 26 26.0% Have you every used the bike repair stations on campus? Yes 19 19.0% No 40 40.0% Do not know there were any 41 41.0% Are you willing to pay a fee on campus for better bike paths and resources? Yes 67 67.0% No 33 33.0%

Research Question 2: How does campus cycling differ between demographic subgroups at the

University of Alabama?

Demographic subgroups were identified as gender, age, race/ethnicity, university status,

and year in college (Table 4.5). These were analyzed with univariate logistic regressions of the

cycling sample.

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Table 4.5 Cyclist Demographics (N=100) Variable

N % of Variable

Cycling Gender Male 38 40.9% Female 62 23.6% Age 18 8 40.0% 19 12 33.3% 20 25 35.7% 21 28 23.7% 22 17 22.4% 23 6 26.1% 24 4 30.8% Race/Ethnicity White/Non-Hispanic 84 31.0% Black/African American 5 9.4% Other 11 34.4% University Status Undergraduate 93 28.0% Graduate 7 29.2% Year in College Freshmen 8 36.4% Sophomore 11 29.7% Junior 25 27.5% Senior 51 26.7% Colleges College of Arts & Sciences 19 33.3% Culverhouse College of Commerce 12 33.3% College of Communication & Information

Sciences 19 27.1%

College of Education 8 18.6% College of Engineering 14 60.9% College of Human Environmental Sciences 15 18.1% Capstone College of Nursing 0 0.0% School of Social Work 1 50.0%

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In the overall analysis of females, 23.6% of the total female sample owned or rented a

bike on campus while 40.9% of the total male participants owned or rented a bike (Table 4.6).

This relationship is significant. Gender had an effect on the likelihood of cycling (O.R. = 2.24).

This indicated males were approximately twice as likely to cycle as their female counterparts.

Table 4.6 Logistic Regression Analysis of the Population in Comparison of Cyclists vs. Non-Cyclists Variable p-value O.R. C.I. Gender p = .002** 2.24 1.36, 3.70 Age p = 0.08 0.86 0.73, 1.02 Race/Ethnicity p = .009** African-American/Black Other p = .003** 4.31 1.66, 11.22 White/Non-Hispanic p = .007** 5.03 1.55, 16.28 Undergraduate/Graduate p = .903 0.95 0.38, 2.35 Year in College p = .806 Freshmen Sophomore p = .341 1.57 0.62, 3.96 Junior p = .705 1.16 0.54, 2.52 Senior p = .891 1.04 0.59, 1.82 * p ≤ .05 ** p ≤ .01 *** p ≤ .001 Race/ethnicity was found to be statistically significant (p = .009). The non-Hispanic

white population was five times more likely than the African American/Black population to

cycle (C.I. = 1.66, 11.22) and all other races were four times more likely than the African

American/ Black population to cycle (C.I. 1.55, 16.28).

A multivariate logistic regression was performed to ascertain the effects of gender, age,

and race/ethnicity on the likelihood that participants are cyclists. Significance was found in

gender (p ≤ .001), non-Hispanic white population and other combined races (both p = .002), and

age (p = .034). The logistic regression model explained 11.5% of the variance in cycling (Table

4.7).

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Table 4.7 Multivariate Logistic Regression Analysis of the Population in Comparison of Cyclists vs. Non-Cyclists Variable p-value O.R. C.I. Gender p ≤ .001*** 2.63 1.55, 4.46 Age p = .034* 0.83 0.69, 0.99 Race/Ethnicity p = .002** African-American/Black Other p = .574 0.80 0.36, 1.77 White/Non-Hispanic p = .002** 0.15 0.04, 0.49

Research Question 3: Which factors facilitate or hinder cycling behaviors at the University of

Alabama?

Crosstabs and univariate logistic regression analyses calculated the strength of the

association between factors correlated with each level of the Social Ecological Model and the

preference of those factors between those who cycled and those who did not. Each of the three

ecological levels is analyzed independently below.

Subquestion A: Which intrapersonal factors facilitate or hinder cycling behaviors?

Intrapersonal factors were compared between cyclists and non-cyclists in the sample,

including personal safety factors, bike specific issues, and appearance. Of these, 56.0% of the

cycling sample indicated the weather is unsuitable while 20.7% of the non-cycling sample

reported the same. Conversely, all but one of the statements in the appearance category

indicated a lower percentage from the cycling sample than the non-cycling sample. Table 4.8

presents intrapersonal perceptions of safety while cycling and influences of other personal

factors that facilitate cycling, including number and percentage of cyclists and non-cyclists.

These questions reflect the personal perceptions of the individual.

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Table 4.8 Intrapersonal Cycling Factors

Question Cyclist (N=100)

Non-Cyclists (N=256)

N % N % Personal Safety Factors What factors influence your cycling decisions?

I don’t know how to ride a bike. 0 0.0% 16 2.8% The distance is too far. 29 29.0% 79 30.9%

The weather is not suitable. 56 56.0% 53 20.7% I have personal safety/security concerns. 16 16.0% 42 16.4% I don’t know cycling rules. 5 5.0% 41 16.0% My cycling skills are poor. 2 2.0% 37 14.5% I can’t afford a bicycle. 1 1.0% 29 11.3% I’m not in shape to ride a bicycle. 2 2.0% 12 4.7% I’m concerned about aggressive/distracted

drivers. 43 43.0% 81 31.6%

I don’t like being assertive with drivers. 10 10.0% 32 12.5% I don’t like to ride after dark. 30 30.0% 67 26.2% I had an accident or scare/near-miss on a

bike in the past. 13 13.0% 16 6.3%

I don’t like riding close to traffic. 22 22.0% 87 34.0% Bike Specific Issues What specific issues about the bicycle influence your cycling behaviors? I don't know how to work the gears. 5 5.0% 24 9.4% I’m afraid that I’ll get stranded with a flat

tire. 7 7.0% 31 12.1%

I’m not confident in using a bike lock. 5 5.0% 39 15.2% I can never get my brakes working right. 5 5.0% 19 7.4% I don’t know how to carry books or other

items on my bike. 9 9.0% 75 29.3%

I’m afraid my bike will get stolen. 8 8.0% 61 23.8% I wish I knew more about general bike

maintenance. 15 15.0% 42 16.4%

Appearance What appearance related factors influence your cycling decisions?

My clothing while riding 24 24.0% 90 35.2% Difficulty bringing spare clothing. 15 15.0% 42 16.4% Appearance after cycling 35 35.0% 102 39.8% Helmets mess up my hair. 19 19.0% 88 34.4% I feel silly with a helmet on. 32 32.0% 97 37.9% I feel or look silly on a bike. 5 5.0% 63 24.6% No other people in the area cycle. 5 5.0% 11 4.3% Other cyclists look fit and I don’t. 6 6.0% 25 9.4% My shoes are inappropriate. 9 9.0% 30 11.7%

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Chi-square analysis was performed on all of the intrapersonal cycling factors (Table 4.9).

Of these, 14 statements were statistically significant. Of the personal safety factors, weather and

perception of cycling were the most significant barriers (p ≤ .001 and p = .001, respectively). Of

the bike specific issues, knowing how to carry books or cargo was the most significant (p ≤

.001). Of the appearance issues, feeling silly on a bike was the most significant (p ≤ .001).

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Table 4.9 Intrapersonal Cycling Factor Results

Question Personal Safety Factors p-value What factors influence your cycling decisions?

I don’t know how to ride a bike. p = .011** The distance is too far. p = .732

The weather is not suitable. p ≤ .001*** I have personal safety/security concerns. p = .926 I don’t know cycling rules. p = .005** My cycling skills are poor. p = .001** I can’t afford a bicycle. p = .002** I’m not in shape to ride a bicycle. p = .241 I’m concerned about aggressive/distracted

drivers. p = .043*

I don’t like being assertive with drivers. p = .511 I don’t like to ride after dark. p = .466 I had an accident or scare/near-miss on a

bike in the past. p = .036*

I don’t like riding close to traffic. p = .027* Bike Specific Issues What specific issues about the bicycle influence your cycling behaviors? I don't know how to work the gears. p = .175 I’m afraid that I’ll get stranded with a flat

tire. p = .161

I’m not confident in using a bike lock. p = .008** I can never get my brakes working right. p = .413 I don’t know how to carry books or other

items on my bike. p ≤ .001***

I’m afraid my bike will get stolen. p = .002** I wish I knew more about general bike

maintenance. p = .745

Appearance What appearance related factors influence your cycling decisions?

My clothing while riding p = .043* Difficulty bringing spare clothing. p = .249 Appearance after cycling p = .399 Helmets mess up my hair. p = .004** I feel silly with a helmet on. p = .299 I feel or look silly on a bike. p ≤ .001*** No other people in the area cycle. p = .774 Other cyclists look fit and I don’t. p = .257 My shoes are inappropriate. p = .460

* p < .05 ** p < .01 *** p < .001

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Subquestion B: Which interpersonal factors facilitate or hinder cycling behaviors?

Interpersonal factors were compared between cyclists and non-cyclists in the sample,

including traffic interaction concerns. Greatest differences between the percentages of chose

statements were with concern about vehicles turning in from of the person while cycling, in

which cyclists’ response was 51.0% and non-cyclists’ response was 36.3%. Table 4.10 presents

interpersonal perceptions of safety while cycling and influences of other personal factors that

influence cycling, including number and percentage of cyclists and non-cyclists. These address

the perceptions of the sample regarding interaction with traffic and drivers.

Table 4.10 Interpersonal Cycling Factors

Question Cyclist (N=100)

Non-Cyclist (N=256)

N % N % Traffic Interaction Concerns What are you specific concerns about bicycling in traffic?

Volume of motor vehicles 31 31.0% 100 39.1% Speed of automobiles 47 47.0% 146 57.0%

Moving vehicles 38 38.0% 82 32.0% Distracted driving 63 63.0% 150 58.6% Possibility of bike getting stolen while it is

parked 18 18.0% 56 21.9%

Insufficient enforcement of both cycling and traffic laws

19 19.0% 29 11.3%

Motorists who run red lights and stop signs 42 42.0% 88 34.4% Vehicles turning right in front of me when

I’m going straight 51 51.0% 93 36.3%

Crossing at intersections 47 47.0% 87 34.0%

Chi-square analysis was performed on all of the interpersonal cycling factors (Table

4.11). Of these, two statements were statistically significant. Of the traffic interaction concerns,

vehicles turning in front of the cyclists and crossing intersections were statistically significant (p

= .011 and p = .023, respectively).

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Table 4.11 Interpersonal Cycling Factor Results

Question Traffic Interaction Concerns p-value What are you specific concerns about bicycling in traffic?

Volume of motor vehicles p = .156 Speed of automobiles p = .088

Moving vehicles p = .284 Distracted driving p = .446 Possibility of bike getting stolen while it is

parked p = .418

Insufficient enforcement of both cycling and traffic laws

p = .057

Motorists who run red lights and stop signs p = .179 Vehicles turning right in front of me when

I’m going straight p = .011*

Crossing at intersections p = .023* * p < .05 ** p ≤ .01 *** p ≤ .001

Subquestion C: Which institutional factors facilitate or hinder cycling behaviors?

Institutional factors were compared between cyclists and non-cyclists in the sample,

including institutional barriers and institutional facilitators. Of the barriers, 51.0% of cyclists

indicated inadequate bike lanes while 24.2% of non-cyclists responded the some. The only

barrier chosen more by non-cyclists was difficulty riding to transit (7.0%) versus the cyclist

response (3.0%). Similarly, the percentage of institutional facilitators were higher among the

cycling sample than the non-cycling sample except for the ladies-only cycling class option which

was chosen by the non-cycling sample than the cycling sample (16.4% and 13.0%, respectively).

Table 4.12 presents institutional factors of safety while cycling and influences of other personal

factors that facilitate cycling, including number and percentage of cyclists and non-cyclists.

These are perceived factors of the built environment that encourage or inhibit students from

cycling.

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Table 4.12 Institutional Cycling Factors

Question Cyclist (N=100)

Non-Cyclist (N=256)

N % N % Institutional Barriers What community or organizational factors influence your cycling decisions?

There are inadequate bike lanes or bike paths in my area.

51 51.0% 62 24.2%

The local roads are too busy for me to cycle on them.

39 39.0% 101 39.5%

The University of Alabama is not well connected to the city of Tuscaloosa.

23 23.0% 25 9.8%

Some of my routes are not well lit. 24 24.0% 51 19.9% The roads are in terrible shape. 19 19.0% 39 15.2% It’s difficult to ride my bike to transit. 3 3.0% 18 7.0% I dislike car fumes. 10 10.0% 11 4.3% There is nowhere to park my bike. 20 20.0% 22 8.6% There are no facilities for locking or securing

my bike. 11 11.0% 16 6.3%

There is no place to change my clothes after cycling to campus.

13 13.0% 15 5.9%

Institutional Facilitators What services/facilities would you be interested in using at UA if they were offered?

Covered parking 56 56.0% 137 53.5% Indoor bike parking 46 46.0% 55 21.9% Locker/shower facilities 18 18.0% 34 13.3% Bike lockers 30 30.0% 39 15.2% Bike repair classes 17 17.0% 24 9.4% Bike safety classes 18 18.0% 35 13.7% Ladies-only cycling classes 13 13.0% 42 16.4% Organized social cycling events 21 21.0% 16 6.3% Beginner cycling classes 15 15.0% 38 14.8% More direct routes 54 54.0% 86 33.6% More security cameras on bike racks 33 33.0% 55 21.5% More bike lanes 70 70.0% 88 34.4% Wider lanes on the road 43 43.0% 87 34.0% Better markings/signage 33 33.0% 49 19.1% More connectivity to downtown/shopping 35 35.0% 50 19.5% Better lighting along routes 35 35.0% 82 32.0% More bike racks near buildings 53 53.0% 51 19.9% More bike racks near commutes parking lots 32 32.0% 28 10.9% Ability to bring bike on Crimson Ride 21 21.0% 23 9.0%

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Chi-square analysis was performed on all of the institutional barriers and facilitators

(Table 4.13). Of the barriers, five statements were statistically significant. Of these, inadequate

bike lanes and connectedness were the most significant barriers (p ≤ .001 and p = .001,

respectively). Of the facilitators, 11 statements were significant.

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Table 4.13 Institutional Cycling Factor Results

Question Institutional Barriers p-value What community or organizational factors influence your cycling decisions?

There are inadequate bike lanes or bike paths in my area.

p ≤ .001***

The local roads are too busy for me to cycle on them.

p = .937

The University of Alabama is not well connected to the city of Tuscaloosa.

p = .001**

Some of my routes are not well lit. p = .396 The roads are in terrible shape. p = .387 It’s difficult to ride my bike to transit. p = .147 I dislike car fumes. p = .040* There is nowhere to park my bike. p = .003** There are no facilities for locking or securing

my bike. p = .128

There is no place to change my clothes after cycling to campus.

p = .024*

Institutional Facilitators What services/facilities would you be interested in using at UA if they were offered?

Covered parking p = .428 Indoor bike parking p ≤ .001*** Locker/shower facilities p = .257 Bike lockers p = .002** Bike repair classes p = .043* Bike safety classes p = .303 Ladies-only cycling classes p = .424 Organized social cycling events p ≤ .001*** Beginner cycling classes p = .970 More direct routes p ≤ .001*** More security cameras on bike racks p = .024* More bike lanes p ≤ .001*** Wider lanes on the road p = .112 Better markings/signage p = .005** More connectivity to downtown/shopping p = .002** Better lighting along routes p = .592 More bike racks near buildings p ≤ .001*** More bike racks near commutes parking lots p ≤ .001*** Ability to bring bike on Crimson Ride p = .002**

* p < .05 ** p ≤ .01 *** p ≤ .001

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The six questions that were asked for the three levels for the Social Ecological Model

were scored by the total options selected by each individual and analyzed independently as well

as in combination with the three levels.

Univariate logistic regressions were performed to ascertain the effects of personal safety

factors, bike specific issues, personal appearance, traffic interaction concerns, institutional

barriers, and institutional facilitators scores on the likelihood that participants are cyclists.

Personal safety factors and traffic interaction concerns were not statistically significant. Of the

six scores, four were found to be statistically significant: institutional barriers (p ≤ .001)

explained 5.0% of the variance, institutional facilitators (p ≤ .001) explained 11.8% of the

variance, bike specific issues (p ≤ .001) explained 8.2% of the variance, and personal appearance

(p = .006) explained 3.3% of the variance. Table 4.14 presents the descriptives of each of the

scores from the coordinating scored questions and their categories. Additionally, table 4.14

presents the descriptives of the scored questions based on cycling status, including mean and

standard deviation of both samples.

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Table 4.14 Descriptives of Each of the Scored Questions Question Category p-value O.R. C.I. Personal Safety Factors p = .997 1.00 0.87, 1.15 Bike Specific Issues p ≤ .001*** 0.58 0.44, 0.75 Appearance p = .006** 0.83 0.73, 0.95 Traffic Interaction Concerns p = .170 1.07 0.97, 1.18 Institutional Barriers p ≤ .001*** 1.28 1.12, 1.46 Institutional Facilitators p ≤ .001*** 1.22 1.13, 1.31 Descriptives of Scored Questions Based on Cycling Status

Cyclist Non-Cyclist Question Category Mean St. Dev. Mean St. Dev. Personal Safety Factors 2.04 1.64 2.04 1.74 Bike Specific Issues 0.57 0.80 1.16 1.28 Appearance 1.54 1.62 2.18 2.05 Traffic Interaction Concerns 3.64 2.14 3.25 2.49 Institutional Barriers 2.15 1.82 1.44 1.56 Institutional Facilitators 5.42 3.61 3.25 2.91 * p < .05 ** p ≤ .01 *** p ≤ .001

Table 4.15 presents the descriptives of levels from the scores of the Social Ecological

Model. Univariate logistic regressions were performed to ascertain the effects of intrapersonal,

interpersonal, and institutional scores on the likelihood that participants are cyclists. The

interpersonal level was not statistically significant. However, the intrapersonal and institutional

level scores were found to be statistically significant (p ≤ .001). The intrapersonal level

explained 3.2% of the variance of the model and the institutional level explained 11.5% of the

variance of the model. Additionally, table 4.15 presents the descriptives of the scored levels

based on cycling status, including mean and standard deviation of both samples.

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Table 4.15 Descriptives of Each of the Social Ecological Model Levels Level # of options Mean St. Dev. Variance Intrapersonal 16 5.03 3.78 14.30 Interpersonal 9 3.35 2.40 5.77 Institutional 24 5.50 4.40 19.83 Question Category p-value O.R. C.I. Intrapersonal p = .006** 0.91 0.85, 0.97 Interpersonal p = .170 1.07 0.97, 1.18 Institutional p ≤ .001*** 1.16 1.09, 1.22 Descriptives of Scored Levels Based on Cycling Status Cyclist Non-Cyclist Question Category Mean St. Dev. Mean St. Dev. Intrapersonal 4.15 3.10 5.38 3.97 Interpersonal 3.64 2.14 3.25 2.49 Institutional 7.57 4.93 4.69 3.90 * p < .05 ** p ≤ .01 *** p ≤ .001

Research Question 4: What strategies or suggestions for improving campus cycling are

recommended by students at the University of Alabama?

Table 4.16 presents suggestions that were offered by participants of the 2014 Cycling

Survey for the University of Alabama. Students were asked to write in their suggestions. One-

hundred and sixty-seven statements (N=167) were provided. Themes were identified within the

written statements. For each theme (or suggestion), the frequency, or number of statements made

for each theme, were assessed. The top three suggestions from participants were: more bike lanes

on campus including two-way bike lanes (f = 53; 33.13%), improved law enforcement/clarity of

rule (f = 28; 17.50%), and wider/improved bike lanes (f = 25; 15.63%).

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Table 4.16 Suggestions from the 2014 Cycling Survey for the University of Alabama Statement Frequency (f) % More bike lanes on campus including two-way bike lanes 53 33.13% Improved law enforcement/clarity of rule 28 17.50% Wider/improved bike lanes 25 15.63% More bike racks on campus 17 10.63% Improved safety for cyclists including lighting 16 10.00% Improved bike education/cycling mindfulness 14 8.75% More bike lanes throughout Tuscaloosa 7 4.38% Better connectivity/routes 7 4.38% Covered parking/bike lockers 6 3.75% Offer more bike rental options/have bike rentals around campus

4 2.50%

Less/slower vehicular traffic 4 2.50% Bike racks closer to buildings 3 1.88% Improve road quality/infrastructure 3 1.88% Improve bike security 2 1.25% Improved motorist mindfulness 2 1.25% Add shower/changing facilities to campus 2 1.25% Be able to take bike on transit 2 1.25% Bike repair assistance/classes 1 0.63% Add a bicycle coordinator/staff to campus 1 0.63%

Table 4.17 presents additional comments that were offered by participants of the 2014

Cycling Survey for the University of Alabama. The top three suggestions from participants were

improved law enforcement for both cyclists and motor vehicle drivers (f = 7; 18.92%), build up a

cycling culture at the University of Alabama/raise awareness for cycling (f = 6; 16.22%), and

necessity for motor vehicle drivers to be more mindful of cyclists (f = 5; 13.51%).

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Table 4.17 Additional Comments from the 2014 Cycling Survey for the University of Alabama Statement Frequency (f) % Improve law enforcement for both cyclists and motor vehicle drivers

7 18.92%

Build up a cycling culture at the University of Alabama/raise awareness for cycling

6 16.22%

Drivers need to be more mindful of cyclists 5

13.51%

I like/would like cycling. 5 13.51% Build more infrastructure for cycling at the University of Alabama

3 8.11%

Improved safety for cyclists/pedestrians at the University of Alabama

3 8.11%

Use common sense while cycling/be smart 2 5.41% Increase the amount of bikes and bike racks on the University of Alabama campus

2 5.41%

Decrease motor vehicle traffic 1 2.70% Offer daily rental bikes 1 2.70%

Summary

The statistical analysis of the data indicated that 100 of the 356 participants (28.1%)

either rent or own a bike at the University of Alabama. Of those, 53.0% reported they had

registered their bikes with the university, 61% had ridden their bike in the last week, and 68%

indicated that they ride their bikes 25 miles or less per week. The top three reasons for cycling

were school (85.0%), exercise (43.0%), and work (31.0%).

Additionally, gender, race/ethnicity, and participating college were statistically

significant, indicating that females, the non-Hispanic white sample were most likely to cycle.

While none of the interpersonal factors of the Social Ecological Model were statistically

significant, the scores of the intrapersonal and institutional levels indicated significance. Of

those, institutional factors (p ≤ .001), institutional facilitators (p ≤ .001), bike specific issues (p ≤

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.001) and personal appearance (p = .006) were statistically significant. Finally, clear and

consistent law enforcement and improved bike lanes were the most important reported items.

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CHAPTER FIVE

DISCUSSION & CONCLUSIONS

Cycling impacts the physical, psychological, and environmental health of university

students (Cavill & Davis, 2003; Kaczynski, Bopp, & Wittman, 2010; Rybarczyk & Gallagher,

2014; Akar & Clifton, 2009). It is also a sustainable form of transport, both physically and

monetarily (Gatersleben & Appleton, 2007). Cycling is relatively convenient for short

distances or commutes, and is both reliable and affordable for most of the population

(Lumsdon & Tolley, 2001). Yet, despite the positive benefits of cycling, limited studies have

explored bikeability on university campuses (Bopp et al., 2011; Bonham & Koth, 2010;

Sisson & Tudor-Locke, 2008). Among the university studies documented, they lacked theory

and framework to support health behavior change needed by stakeholders.

The purpose of this study was to examine college student perceptions of safety and

factors contributing to campus cycling from an ecological perspective. Intrapersonal,

interpersonal, and institutional factors associated with safety and campus cycling were

assessed as well as how these influencers interacted with each other.

This chapter provides further discussion of the study results, specifically as they pertain

to the research questions. The limitations of the study are also discussed. The chapter highlights

practical implications for campus improvement and future research. Finally, the chapter

addresses the implications of study results for health education and promotion.

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University of Alabama Cyclists

Findings from this study indicated that the prevalence of on-campus cycling at the

University of Alabama was 28.1%. Of the University of Alabama (UA) cyclists, most cyclists

were female (73.9%), which contrasts with national statistics, which indicated the male

population cycles more (Harris, 2011). However, given the majority of UA commuting was a

short distance (less than five miles) and the speed limit was 35 miles per hour or lower, the

finding is consistent with international studies that indicated a negative relationship between

gender and speed. Stigell (2011) reported that slower velocity of motor vehicles often correlates

with more female cyclists, as they feel safer on the roadway.

In addition, there were significant differences in race/ethnicity among cyclists, and the

findings were consistent with former research (McKenzie, 2014). For instance, non-Hispanic

white students (84.0%) made up the majority of cyclists on campus. African American students

represented the lowest percentage of cyclists (5.0%). Finally, those who identified with two or

more races represented 11.0% of the cycling sample. These findings have implications for

targeted interventions.

In order to better understand who cycles, as well as the primary use of cycles, on the UA

campus, a number of questions were asked of cyclists only. The findings indicated that cycling

was utilized for school (85.0%), exercise (43.0%), and work purposes (31.0%). Nearly two-

thirds of cyclists reported they had cycled within the last week. A little over one-half (53%) said

they cycled exclusively or that they never combined cycling with other modes of transportation.

These cycling behaviors are important to stakeholders to foster a community and culture that

supports cyclists.

Intrapersonal Cycling Factors

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Several intrapersonal factors were examined in this study. Factors at the intrapersonal

level were categorized as personal safety factors, bike-specific issues, and personal appearance.

“Personal safety factors” included concerns about riding too close to traffic, aggressive/distracted

drivers, riding after dark, unsuitable weather, distance being too far to cycle, and others. When

the individual “personal safety factors” were analyzed as a single grouped factor, personal safety

factors were not found to be statistically significant. Therefore, as a whole, “personal safety

factors” were not an important predictor of cycling. Similarly, the study did not indicate

statistically significant findings for either of the individual “personal safety factors” alone.

Nonetheless, the information obtained from this level of analysis can be used to address

barriers identified by students (cyclists and non-cyclists), which were of most concern. For

example, both cyclists and non-cyclists mentioned concerns about aggressive/distracted drivers.

Alabama state law has passed a texting while driving ban (Alabama Department of Public

Safety, 2012); therefore, this might be an issue to address by law enforcement and university

stakeholders.

Wearing helmets while cycling was an additional “personal safety factor.” Research

demonstrates that mortality rates are lower among those who wear helmets while cycling

compared to those who do not (Insurance Institute for Highway Safety Highway Loss Data

Institute, 2015). Helmet protection was a safety concern indicated among students in this study.

About 55% of the total sample agreed that bicycle helmets are important even if a person cycles

infrequently. About 52% of the cyclists indicated they go fast enough to need head protection in

the case of an accident. Moreover, approximately 70% of the cyclists indicated they were at risk

for being injured by a motor vehicle while cycling. Cues to action and perceived threat are found

to be a strong influence on attitude, intentions, and behaviors toward bicycle helmets, which is

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consistent with the Health Belief Model (Ross et al., 2010; Witte, Stokols, Ituarte & Schneider,

1993). Yet, despite the concerns among cyclists and non-cyclists, only 13% of the cyclists

indicated they wore helmets all of the time. These findings indicate that knowledge is not always

sufficient to promote change in behavior. The findings have implications for program planning.

Perhaps offering bike training sessions or promoting awareness among motor vehicle drivers

about the importance of being attentive, especially on campus because of cyclists could be

effective strategies.

Other “personal safety factors” were examined in this study. Research indicates that

traffic violations committed by cyclists (e.g., not stopping at stop signs) and perceptions of or

experiences with stolen bicycles might negatively influence cycling (Lawson, Pakrashi, Ghosh &

Szeto, 2013). When the students in this study (cyclists and non-cyclists) were asked about the

general practices of cyclists on campus, 94.0% perceived that cyclists locked their bikes on a

regular basis, 81.0% believed cyclists rode with the flow of traffic, and 82.0% of the sample

believed that cyclists stopped at red lights or stop signs. Despite these positive perceptions,

22.0% of cyclists indicated they had bikes stolen, 81.0% said they ride with the flow of the

traffic, and 63.0% of the cyclists at UA reported that they stop at stop signs. Being aware of

these perceptions and actual cycling practices (including discrepancies between them) might help

campus stakeholder better understand what, if any, relevant and targeted interventions are

needed.

“Bike specific issues” pertained to concerns about one’s bike getting stolen, not knowing

about general bike maintenance, not feeling confident about using a lock, etc. When the

individual “bike specific issues” were analyzed as a single grouped factor, bike specific issues

were found to be statistically significant. Therefore, taken together, bike specific issues were an

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important predictor of cycling, which is consistent with current literature (Telfer, Rissel, Bindon,

& Bosch, 2006). Although none of the individual “bike specific issues” statements were

statistically significant, they provide insight into what contributed to the significant finding when

grouping the factors (p ≤ .001), as well as what might be important for program planning. The

findings indicated that among both cyclists and non-cyclists, the desire to know something more

about general bike maintenance was among the most cited barriers (Pucher, Dill, & Handy,

2010). The findings have implications for program planning. Promoting current bike

maintenance resources already available on campus, or considering how to improve these, might

be a suitable strategy.

Finally, “personal appearance” factors pertained to concerns about looking silly while

wearing a helmet, the condition of one’s clothes while riding and afterwards, one’s hair being

disarrayed or messed up after riding, and others. When the individual “personal appearance

factors” were analyzed as a single grouped factor, appearance was found to be statistically

significant (p = .006). Therefore, taken together, personal appearance was an important predictor

of cycling. Although none of the “personal appearance factors” individually were statistically

significant, they provide insight into what contributed to the significant finding when grouping

the factors, as well as what might be important for program planning. The most frequently cited

factors for both cyclists and non- cyclists were concerns about one’s appearance after cycling,

looking “silly” while wearing a helmet, and concerns about clothes while riding (Dill & McNeil,

2013). Considering how to address this issue among college students might be a next step to

consider.

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Interpersonal Cycling Factors

“Interpersonal cycling factors” pertained to concerns about interacting with motor vehicle

drivers or traffic interaction concerns. For example, these included speed of automobiles,

crossing at intersections, distracted drivers, and other related concerns. When these individual

“interpersonal cycling factors” were analyzed as a single grouped factor, interpersonal cycling

factors were not found to be statistically significant (p = .170). Therefore, as a whole,

“interpersonal cycling factors” were not an important predictor of cycling. Similarly, the study

did not indicate statistically significant findings for either of the individual “interpersonal cycling

factors” alone.

Nonetheless, the information obtained from this level of analysis can be used to address

the traffic interaction concerns which were identified by students (cyclists and non-cyclists) and

that were of most concern. For example, comparable concerns were reported among both

cyclists and non-cyclists. Among those concerns that were cited most frequently (and which

were also in the top three for both cyclists and non-cyclists) were distracted drivers, vehicles

turning in front of cyclists, and the speed of automobiles (Jacobsen, Racioppi & Rutter, 2009;

Tilahun, Levinson, & Krizek, 2007). The findings have implications for program planning. For

instance, media campaigns for the general student population might be needed to target drivers

about safety for all who share the roads with them, including cyclists (Pucher, Dill, & Handy,

2010).

To continue with the positive efforts of the University of Alabama in addressing cycling

safety and in promoting cycling, it is recommended that university planners rely on standards set

by the League of American Bicyclists for the 5 E’s and minimize issues that ensue with negative

interactions with motor vehicles (League of American Bicyclists, 2014b). The 5 E’s were

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engineering, education, encouragement, enforcement, evaluation, and planning. In addition, more

education and regulation needs to be continually aimed toward campus drivers to minimize the

impacts of distracted driving and avert traffic injuries to cyclists, drivers, and pedestrians

(Jacobson & Gostin, 2010).

Institutional Cycling Factors

Institutional cycling factors included those identified as cycling barriers and facilitators.

“Institutional barriers” pertained to inadequate bike lanes or bike paths, local roads being too

busy, routes not be well lit, car fumes, lack of facilities for locking or securing bikes, etc. When

the individual “institutional barriers” were analyzed as a single grouped factor, the barriers were

found to be statistically significant (p ≤ .001). Therefore, taken together, institutional barriers

were an important predictor of cycling.

Although none of the “institutional barriers” individually were statistically significant,

they provide insight into what contributed to the significant finding when grouping the factors,

as well as what might be important for program planning. The findings indicated that among the

“institutional barriers”, the top three concerns for both cyclists and non-cyclists included busy

local roads, inadequate bike lanes or bike paths, and routes not well lit or lighted. These findings

support previous research, which explains that facilities and infrastructure make cycling easier,

more effective, and even attractive to the individual (Dill, 2004). Cyclists in the current study

indicated inadequate bike lanes (51.0%), lack of covered or indoor bike parking (56.0% and

46.0%, respectively), need for more direct routes (54.0%), and need for better markings or

signage (33.0%) as institutional barriers for cycling.

Students were also asked to respond to questions about potential factors at the

institutional level that would facilitate cycling (i.e., “institutional facilitators”). Institutional

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facilitators included covered parking, indoor bike parking, locker/shower facilities, beginner

cycling classes, more bike racks, ability to bring bike on Crimson Ride, etc. When the individual

“institutional facilitators” were analyzed as a single grouped factor, the facilitators were found to

be statistically significant (p ≤ .001). Therefore, taken together, institutional facilitators were an

important predictor of cycling.

Although none of the “institutional facilitators” individually were statistically significant,

they provide insight into what contributed to the significant finding when grouping the factors,

as well as what might be important for program planning. Among those factors students believed

would help facilitate cycling and that were the most frequently cited among cyclists and non-

cyclists – included developing more bike lanes and covered parking. Other institutional factors

that would facilitate cycling, according to both groups, were indoor bike parking and more direct

routes. These findings have implications for structural changes at the university and/or for

promotion and awareness of available facilities and resources.

Pucher, Dill, and Handy (2010) reported that appropriate and aggregated infrastructure

would facilitate an increase in cycling. As previously stated, streets with lower speed limits are

perceived to be safer by cyclists and reduce the amount of crashes involving cyclists. Lowered

speeds and reduced volume of traffic allow for more reaction time to prevent accidents between

vehicles and cyclists (Retting, Ferguson & McCartt, 2003). Moreover, traffic calming including

such methods as narrowing lanes, speed tables, and protected pedestrian islands are further

methods that can make traffic seem safer and increase the amount of cycling traffic (Smith &

Appleyard, 1981). Also effective, separate cycling paths are typically used 2.5 times more than

nearby on-street bicycle lanes and the relative risk of injury is about 28% lower. The increase of

bicycle infrastructure is “positively and significantly correlated with higher rates of bicycle

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commuting” (Dill & Carr, 2003). By adopting more protective infrastructure and traffic calming

methods, both perceived and actual cycling safety can improve (McClean, 2012).

Student Suggestions and Comments

Several suggestions were provided from students regarding what could be done to

improve campus cycling. The top three suggestions that were recommended included the

following items: (1) more bike lanes on campus, including two-way bike lanes (suggested by a

third of the respondents who responded to this question); (2) improved law enforcement/clarity

of rules (17.50%); and (3) wider/improved bike lanes (15.63%). Although reported with less

frequency, the following three most cited suggestions included more bike racks on campus,

improved safety for cyclists including lighting, and improved bike education/cycling

mindfulness. Many of these factors overlapped with concerns expressed at the intrapersonal,

interpersonal, and institutional levels. They provide guidance to stakeholders about what specific

factors are prioritized and deemed more likely to facilitate cycling among students. Therefore,

these suggestions have implications for environmental and structural changes, policy

development, and program planning for the university.

One of the major deterrents of cycling in the United States is the perceived amount of

danger (Zeeger, 1994). The same was indicated in this study. When riding next to heavily

trafficked or in high-speed areas, the perceived chance of injury is greater. There is a need for

facilities that aid in cycling such as bike lanes, bike paths, or parking facilities (Pucher, 2001).

Law enforcement is an influential component when increasing bikeability in a

community as seen in the suggestions and additional comments given by the respondents. This

includes helmet laws, speed limits, and general driving by both cyclists and motor vehicles.

These laws increase the population’s comfort and perception of safety (McClean, 2012).

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Enforcement of reduced speed, speed tables, and areas with traffic calming is attributed to

increased rates of cycling. Lowered speed limits improve both the perceived and actual safety

for the cyclists (McClean, 2012). When each of these is mandated, the efficiency of cycling

rivals the efficiency of driving.

Study Limitations

A number of limitations to this study must be acknowledged, including its overall

exploratory nature. Surveys collected for this research were done through convenience sampling

by presenting the study to various classes and by word of mouth. While this is an inexpensive

method of collecting data, the data can be skewed. In this study, the greatest number of

respondents was from the College of Human Environmental Sciences even though the College of

Arts and Sciences is the largest of the colleges on campus. The students’ area of study may skew

the data collected. In addition, the majority of classes were upper division classes (junior and

senior level courses), which did not adequately capture the freshmen and sophomore

demographic.

The survey was administered during November & December. The responses to a couple

of survey items regarding cycling frequency (e.g., average # of times cycle per week) could have

been influenced by the weather during that time of the year. For example, some students might

have reported less number of times they cycle on average given the survey was administered

during the winter months when many typically cycle less. Administering the survey during a

different time of year might yield different responses to these specific survey items. Nonetheless,

the weather in Alabama was relatively mild. For instance, Tuscaloosa’s average temperature in

November was 68 degrees Fahrenheit (U.S. Climate Data, 2015).

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Data was collected from a large residential campus. This limits how much the study can

be compared to neighboring urban universities. While they may have similar demographics and

academic structure, the infrastructure may differ enough that they are incomparable. The study

findings may be more meaningful and generalizable to similar residential and spacious campuses

that are self-contained and have low population density.

Furthermore, the study findings may only be limited to campus populations and not be

inferable to surrounding municipalities. The student population at this university may not

represent the general community. For the past few years, the number of out-of-state students in

the freshmen class surpassed in-state students. Last year, this was the case for total student

enrollment (University of Alabama, 2014b). This demographic change shifts the economic and

cultural background of the university as the majority of students are from other states or

countries.

Missing data of any kind typically underrepresents the issue that researchers must accept

in terms of limitation (Field, 2005). A number of respondents answered questions about their

cycling behavior and personal safety perceptions but provided limited if any demographic

information, including gender, age, and race which were key variables. The number of

participants who refused to answer these questions lessened the statistical usefulness of these

variables, but present data were sufficient to render possible analysis.

The cross-sectional nature of this study was another limitation given the information was

from one snapshot in time. This design allows only for correlations but does not allow for

causation over time. In addition, some variables (e.g., “perception of health” and “perception of

safety”) may be results of cycling as much as they may be predictors of it. Cross-sectional

studies are both acceptable and respected as a method of research. However, they are designed to

104

give information about a sample at a given point with further research to follow or as a baseline

for health promotion (Lundberg, 2003).

Implications for Health Education and Promotion

The findings concerning cycling behavior for the campus will be presented to campus

stakeholders to plan programs and campaigns aimed at increasing the number of cyclists. To

further promote cycling, future researchers and stakeholders should focus on the numerous

benefits derived from cycling including the physical, environmental, and economical benefits.

This will help curb the amount of motor vehicles present on campus.

By collecting the opinions of both cyclists and non-cyclists, university stakeholders will

be able to improve the infrastructure throughout the existing campus as well as integrate cyclist

friendly elements into future construction. In addition, knowing the student opinion will help

health educators build a sense of campus mindfulness.

A vital component of cycling for the campus is connecting the university to the

surrounding areas, including downtown, shopping areas, and off-campus work. These efforts

will enable students that live off campus to feel safer cycling the short commute to and from

campus as well as to the surrounding areas. If the university stakeholders and planners join

forces with city planners to create bikeways, it can lead to improved campus connectivity with

the city and overall infrastructure.

This research will also help other collegiate entities better understand the cycling

environment on large university campuses. With the number of people and motor vehicles

present on campuses and the urge to create a sustainable environment, it is important for

universities to partner in future research (Hall & Vredenburg, 2012). This research is

105

encouraged given the ease of cycling in a university environment of closely located amenities,

activities, and housing (Delmelle & Delmelle, 2012).

Finally, this study also acts as a baseline and insight for future Healthy Campus

objectives. The current objectives cover general guidelines for aerobic physical activity and

muscle-strengthening activity (ACHA, 2014). Additionally, there are no objectives that address

the importance of a healthy campus environment. Cycling studies like this and future studies can

impact the American College Health Association to also include environmental health as a future

objective.

Recommendations for Future Research

Future research for campus cycling at this university and others is highly recommended.

The purpose of this study was to examine college student perceptions of safety and factors

contributing to campus cycling from an ecological perspective. Results of this research will give

the Bike Advisory Group and other campus stakeholders the student perception of campus

cycling. Further studies to improve and better understand campus cycling is strongly

encouraged, specifically in relation to personal safety, community factors and improved policies.

In addition, studies should include the working population of the university, including faculty

and staff, to better infer the results to the entire population and not only the students.

The advancement of campus cycling from a health promotion and education perspective

could benefit tremendously from a larger study population to better infer to the total campus

population and have a better perspective of campus cycling. This will also help stakeholders to

address the largest two levels of the Social Ecological Model: community and policy. In

addition, stakeholders need to consider the interaction of motor vehicles and cyclists to create a

106

social norm of drivers and cyclists coexisting (Stigell, 2011). This could be collected through

data collected describing the perception of cycling from a motorist.

Research should also be conducted on other similar campuses in order to compare

populations and areas of success. This also includes further investigation of sub-demographics

including the international student population and Greek organizations. By regional or state

universities collaborating, it widens the overall picture of a more sustainable environment and

improves safety perceptions. These studies should also address qualitative information from

cyclists for improved routes and infrastructure (Stigell, 2011). Conducting surveillance of

cyclists will aid stakeholders in future decisions.

Summary

This chapter highlights the conclusions, implications, recommendations for future

research, and limitations of the present study. Based on the results of the research questions that

were investigated, conclusions were drawn about the influence of ecological levels and personal

safety perceptions on cycling, which have led to an increased understanding of the campus

cycling environment. Practical significance and final research results may expand understanding

of active commute from the student perception. The limitations of this study were delineated.

Finally, recommendations for future research described the need for additional research efforts as

well as important direction to advance campus cycling.

Each of the identified predictors, including perceived safety and improved infrastructure,

may be used in interventions to increase cycling and are receptive to modification. Information

about significant predictors of cycling in students can help target campus campaigns and other

interventions. Application of the Health Belief Model and Social Ecological Model may be

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useful in developing such programs in order to relate to the population and infrastructure of the

university environment.

108

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APPENDIX A

INSTITUTIONAL REVIEW BOARD APPROVAL

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APPENDIX B

INFORMED CONSENT

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APPENDIX C

SURVEY

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GENERAL INFORMATION ABOUT YOU AND HOW YOU GET TO CAMPUS

1. Gender Male Female 2. Age _____________ years 3. What is your race or ethnicity? Check all that

apply. American Indian/Alaska Native

Asian Black/African American Hispanic/Latino Native Hawaiian/Pacific Islander White Other:-

_________________________________________

Student 4. Are you an undergraduate or a graduate? Undergraduate

Skip to 5

Graduate

Skip to 7 Undergraduate 5. What year are you in college? Freshman

Sophomore

Junior

Senior

6. What is your major?

_________________________ Skip to 8

Graduate 7. What is your area of

study? _________________________

8. Where do you live? On Campus Residence Hall or Apartment.

Skip to 10

Off Campus

9. If off campus, how many miles is your commute

one-way to UA? _________ What is your home zip code?

___________________ Do not have

2nd mode Walk Bike Skate-

board Tuscaloosa Trolley

UA Bus

Carpool Automobile

Motorcycle

10. What is your primary mode of transportation to campus?

If you have a secondary mode of transportation, what is it?

11. Do you own or rent a bicycle here at UA? Yes

Skip to 12

No

Skip to 23

12. Is your bicycle registered? Yes

No

How many miles a week do you ride your bike?

_______miles

13. How do you use your bike? Check all that apply. Work

School

Shopping

Exercise

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14. Do you bicycle in combination with other transportation modes (Tuscaloosa Trolley, Crimson Ride) to campus?

Often

Some-times

Rarely

Never

15. When did you last ride your bike?

This week

Within the last month

Within the last year

More than a year

ago

Never

16. How often do you ride your bicycle to campus? Daily

A couple times per

week

A couple times per month

A couple times per year

Never, live on campus

Never, live off campus

17. Do you lock your bike? Yes

No

Do you wear a helmet? Yes

No

18. Do you ride with the flow of traffic?

Yes

No

Do you stop at red lights? Yes

No

19. Have you ever had your bike stolen?

Yes

No

20. How do you rate bike parking on campus?

Excellent

Good

Fair

Poor

Very poor

21. Have you used the bike repair stations on campus?

Yes

No

Didn’t know that there were any  

22. Are you willing to pay a facilities fee on campus for better bike paths and resources if most students or employees considered the fee acceptable?

Yes

No

Cycling Skills & Confidence 23. What factors influence your cycling decisions? (Check all that apply.)

I don’t know how to ride a bike. The distance is too far. The weather is not suitable. I have personal safety/security concerns. I don’t know cycling rules. My cycling skills are poor. I can’t afford a bicycle. I’m not in shape to ride a bicycle.

I’m concerned about aggressive/distracted drivers (e.g., drivers who text or talk on the phone while driving).

I don’t like being assertive with drivers. I don’t like to ride after dark. I had an accident or scare/near-miss on a bike

in the past. I don’t like riding close to traffic. NONE of the above applies to me.

Environmental Factors 24. What community or organizational factors influence your cycling decisions? (Check all that apply.)

There are inadequate bike lanes or bike paths in my area.

The local roads are too busy for me to cycle on them.

The University of Alabama is not well connected to the city of Tuscaloosa.

Some of my routes are not well lit. The roads are in terrible shape.

It’s difficult to ride my bike to transit. I dislike car fumes. There is nowhere to park my bike. There are no facilities for locking or securing

my bike There is no place to change my clothes after

cycling to campus. NONE of the above applies to me.

25. What are your specific concerns about bicycling in traffic? (Check all that apply.)

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Volume of motor vehicles Speed of automobiles Moving vehicles Distracted driving (e.g., individuals who talk on

the phone, text, or use other devices while driving.) Possibility of bike being stolen while it’s parked Insufficient enforcement of both cycling and

traffic laws

Motorists who run red lights and stop signs. Vehicles turning right in front of me when

I’m going straight

Vehicles hitting me from behind when I’m cycling

Crossing at intersections NONE of the above applies to me.

26. What services/facilities would you be interested in using at UA if they were offered? (Check all that apply.)

Covered Parking Indoor Bike Parking Locker/shower Facilities Bike Lockers Bike Repair Classes Bike Safety Classes Ladies-only cycling classes Organized social cycling events Beginner cycling classes

More Direct Routes More bike lanes Wider lanes on the road Better Markings/Signage More Connectivity to Downtown/Shopping Better lighting along routes More bike racks near buildings More bike racks near commuter parking lots Ability to bring bike on Crimson Ride Other:

__________________________________________

Bike & Equipment 27. What specific issues about the bicycle influence your cycling decisions? (Check all that apply.)

I don’t know how to work the gears. I’m afraid that I’ll get stranded with a flat tire. I’m not confident in using a bike lock.

I can never get my brakes working right. I don’t know how to carry stuff on my bike. I am afraid that my bike will get stolen. NONE of these factors bother me.

Appearance 28. What appearance related factors influence your cycling decisions? (Check all that apply.)

My clothing while riding. Difficulty bringing spare clothing. Appearance after cycling (e.g., being red,

sweaty, disheveled). Helmets mess up my hair.

Looking/Feeling silly on a bike. No other people in area cycle. Other cyclists look fit and I don’t Inappropriate shoes NONE of the above.

How important would these factors be in

encouraging you to bicycle to campus more often?

Not at all Important

Not too Important

Neutral Somewhat

Important

Very Important

29. Need to purchase a bike/better bike 30. Bike racks closer to office/classes

31. More bike lanes on major streets.

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32. Shower and changing facilities at the gym 33. Less or slower vehicular traffic on route to

campus. 34. Ability to take bicycle on Crimson Ride bus. 35. Other _______________________

If UA offered the following would it encourage you to bicycle to campus?

Very Likely Likely Unsure Unlikely Very Unlikely

36. Bicycle technique and safety classes 37. Bicycle maintenance classes

BIKE SHARE INFORMATION The University of Alabama provides free bicycles to use ON CAMPUS. If you checked one out, how often would you use them to…

Very Often Often Unsure

Sometimes

Not Very Often

38. Get from parking lots to classrooms or offices?

39. Get from offices/classrooms to other locations on campus (e.g. residence halls, The Ferg, library, The Strip, etc.)?

40. How likely would you be to use them to get from campus to other locations (Cobb Theater, Midtown Plaza, University Mall, Target, Walmart, other)?

Perceptions of Cycling Safety Disagree Somewhat

Disagree Unsure Somewhat

Agree Agree

41. I do not go fast enough to need head protection in a crash.

42. I feel that helmets are unnecessary for very short rides.

43. Bicycle helmets are less important for those who ride their bike infrequently.

44. When I’m bicycling, I am at risk of being injured by motor vehicles.

45. Bicycling is dangerous on slippery/wet roads. 46. There is a good chance that I could get hurt

while riding my bicycle.

47. If I am injured while riding my bike, it could seriously affect my social life.

48. If I am injured while riding my bike, it could seriously affect my ability to function at school.

49. Wearing a helmet would make me feel less anxious when I ride a bike.

50. Wearing a helmet while bicycling makes me feel safer.

51. I believe that wearing a helmet can prevent a serious head injury if I have a bicycle accident.

52. I would feel embarrassed wearing a bicycle helmet.

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53. Quite frankly, wearing a helmet looks stupid. 54. The cost of helmets is generally more than

they are worth.

55. I would not want to spend money to buy a bicycle helmet.

56. I have several friends that routinely wear helmets when they ride.

57. My parents never made me wear a helmet when I was a child.

58. I recall seeing commercials, ads or posters about wearing a helmet while bicycling.

Physical Activity & Health On how many of the past 7 days did you: 0 1 2 3 4 5 6 7 59. Do moderate-intensity cardio or aerobic

exercise (such as a brisk walk) for at least 30 minutes?

60. Do vigorous-intensity cardio or aerobic exercise (such as jogging) for at least 20 minutes?

61. Do 8-10 strength-training exercises (such as resistance weight machines) for 8-12 repetitions each?

Poor Fair Good Very Good Excellent 62. How would you describe your general health?

63. What suggestions do you have for improving biking at UA?

Thank you!