Author: Dalton, Stacy, L Title: Sleep-Obesity Association among UW-Stout College Students through

Assessment of Weight Status and Sleep and Breakfast Behaviors The accompanying research report is submitted to the University of Wisconsin-Stout, Graduate School in partial

completion of the requirements for the

Graduate Degree/ Major: MS Food and Nutritional Science

Research Adviser: Ann Parsons, Ph.D.

Submission TennN ear: August, 2011

Number of Pages: 65

Style Manual Used: American Psychological Association, 6th edition

RECEIVED AUG 08 2011

GRADUATE SCHOOL

[8J I understand that this research report must be officially approved by the Graduate School and that an electronic copy of the approved version will be made available through the University Library website [8J I attest that the research report is my original work (that any copyrightable materials have been used with the permission of the original authors), and as such, it is automatically protected by the laws, rules, and regulations of the U.S. Copyright Office.

STUDENT'S NAME: Stacy Dalton

STUDENT'SSIGNATURK ~~\J= DATE: '2 /31 [\ ADVISER'S NAME (Committee Chair ifMS Plan A or EdS Thesis or Field Project/Problem): Ann Parsons, PhD

ADVISER'S SIGNATURE: A- ~ ~~ DATE: 4~ 3, Chl/ ......... _--------_ .. _-_ .. _------------- .. ------......... --_ ..... .. _----_ .............. __ ...... _------------_ .. _--------------- .. --_ .. _---------_ .. _------_ .. _ ....... ... This section for MS Plan A Thesis or EdS ThesislField Project papers only Committee members (other than your adviser who is listed in the section above)

1. CMTE MEMBER'S NAME: Charlene Schmidt, PhD, RD, CD

SIGNATURE: ~1~~ DATE: ~ -3-\\ 2. CMTE MEMBER'S NAME: Carol Seaborn, PhD, RD, CD, CFCS

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This section to be completed by the Graduate School This fInal research report has been approved by the Graduate School.

(Director, Office of Graduate Studies) (Date)

2

Dalton, Stacy, L. Sleep-Obesity Association among UW-Stout College Students through

Assessment of Weight Status and Sleep and Breakfast Behaviors

Abstract

Obesity among the United States population has increased considerably over the last 40

years, while sleep duration has noticeably decreased. An association between sleep and obesity

is evident and previous research has shown this to be true. This study aimed to examine the

sleep-obesity association among UW-Stout college students through the assessment of weight

status and sleep and breakfast behaviors. Three-hundred thirty-five students participated in the

study through the completion of an online survey about usual sleep and breakfast behaviors and

perceptions. Sleep patterns changed when school was in session and out of session relative to

the week/weekend, with students sleeping the longest durations on the weekends when school

was not in session. During the week 62.8% (n=209) of students sleep more when school was in

session than not in session, conversely during the weekend 60.7% (n=197) slept the same when

school was in session than out of session. Breakfast eating was 12% higher in the ―Yes, most of

the time‖ category when school was not in session compared to in session (33%, 21%,

respectively). A difference in sleep patterns and BMI was observed between short sleep

durations (9 hours), with the

greatest difference between 9 hours. Responses about the importance of breakfast

and sleep were found to correlate with behavior. Furthermore, the observed correlation between

student behaviors and student perceptions suggest that school sleep and breakfast education

programs should support improvement in students’ sleep patterns and breakfast eating behaviors.

3

Acknowledgements

This research project would not have been possible without the support of many key

people in my life. I wish to express much gratitude to my committee chair, Dr. Ann Parsons for

all the support, encouragement and assistance she provided throughout this process. I could not

have done this without her guidance. I also have much gratitude to my other committee

members Dr. Charlene Schmidt and Dr. Carol Seaborn for all of their assistance throughout the

last few years and especially with the completion of this research project.

I would also like to thank Susan Greene for all her help with the statistical analysis, Josh

Hachmeister for his assistance with Qualtrics, and the librarians at the University of Wisconsin-

Stout for helping me get started with my research.

Finally, I would like to thank the two key people in my personal life that supported me in

countless ways throughout this journey: my mom for believing in me and always encouraging

me, and Dustin for loving me and supporting me though this long, and often stressful process. I

love you both so much and could not have done this without you!

4

Table of Contents

.................................................................................................................................................... Page

Abstract ............................................................................................................................................2

List of Tables ...................................................................................................................................6

List of Figures ..................................................................................................................................7

Chapter I: Introduction ....................................................................................................................8

Statement of the Problem ...................................................................................................10

Purpose of the Study ..........................................................................................................10

Assumptions of the Study ..................................................................................................11

Definition of Terms............................................................................................................11

Limitations of the Study.....................................................................................................12

Methodology ......................................................................................................................13

Chapter II: Literature Review ........................................................................................................15

Obesity in the United States ...............................................................................................15

Obesity among College Students .......................................................................................16

Sleep in the United States ..................................................................................................17

Sleep and College Students ................................................................................................18

Association among Sleep and Obesity: An Overview .......................................................19

Sleep Time Defined ...........................................................................................................22

Sleep Measure Methods .....................................................................................................23

Chapter III: Methodology ..............................................................................................................24

Subject Selection and Description .....................................................................................24

Survey Instrument ..............................................................................................................24

5

Data Collection Procedures ................................................................................................25

Data Analysis .....................................................................................................................25

Limitations .........................................................................................................................26

Chapter IV: Results ........................................................................................................................28

Response Rate ....................................................................................................................28

Demographic Analysis .......................................................................................................28

Student Sleep Patterns........................................................................................................29

Student Breakfast Habits....................................................................................................33

Sleep and Breakfast Comparison .......................................................................................35

Height and Weight of Participants .....................................................................................35

Body Mass Index Comparisons .........................................................................................36

Student Sleep Habits Compared to Sleep Perceptions .......................................................37

Student Breakfast Habits Compared to Importance of Breakfast ......................................38

Chapter V: Discussion ...................................................................................................................39

Limitations .........................................................................................................................39

Conclusions ........................................................................................................................40

Recommendations ..............................................................................................................43

References ......................................................................................................................................46

Appendix A: IRB Approval ..........................................................................................................56

Appendix B: Research Survey ......................................................................................................57

Appendix C: Email to Students.....................................................................................................63

Appendix D: Consent Form ..........................................................................................................64

6

List of Tables

Table 1: Age and Year in School of Participating Students ..........................................................29

Table 2: Frequency Table of Sleep Patterns ..................................................................................30

Table 3: Sleep Differences between Weekend and Week .............................................................32

Table 4: Sleep Differences between In Session and Not In Session..............................................33

Table 5: BMI Distribution .............................................................................................................36

Table 6: In Session and Not in Session Sleep Habits Compared to Sleep Perceptions .................38

Table 7: UW-Stout Student Weight Status Compared with 2008 NHANES Data........................40

7

List of Figures

Figure 1: Do you usually eat breakfast when you wake up?—in session ......................................34

Figure 2: Do you usually eat breakfast when you wake up?—not in session................................34

Figure 3: In session sleep and breakfast ........................................................................................35

Figure 4: In session and not in session nighttime sleep and BMI ..................................................37

8

Chapter I: Introduction

Overweight and obesity are highly impactful health issues among a large percentage of

the United States population. As of 2008 over 30% of adults in the U.S. were considered to be

obese in most age and sex groups (Flegal, Carroll, Ogden, & Curtin, 2010). Over the last 20

years there has been an alarming increase in obesity in the United States. Obesity rates have

been rising slowly in the United States since 1960; however within the last 20 years obesity has

raised alarmingly fast with twice as many adults now classified as obese compared to the 60’s

(Philipson, Dai, Helmchen, & Variyam, 2004). The Centers for Disease Control and Prevention

(CDC) reports that the obesity rate of Americans by state ranges from 21-34%, with 36 states

having a prevalence of obesity 25% or higher (Centers for Disease Control and Prevention

[CDC], 2011b). Similar research found the greatest increases in overweight and obesity to occur

among young adults between the ages of 18-29 years (Mokdad et al., 1999). Obesity prevention

has become the center of much of the current health research, and sleep deprivation, one of the

known risk factors associated with obesity, is also being investigated.

Sleep deprivation is a major health issue in the United States affecting more than a

quarter of the population and has been indirectly guided by the constant demands of the

industrialized modern society (Spiegel, Knutson, Leproult, Tasali, and Van Cauter, 2005).

Current sleep recommendations for adults are between 7-9 hours of sleep per night (National

Sleep Foundation [NSF], 2011). Over the last few decades the self-reported sleep duration of

United States Americans has decreased by 1.5-2 hours (NSF, 2002). According to the CDC, a

recent survey showed that just over 35% of the United States adult population (>18 years)

reported getting less than 7 hours of sleep a night (McKnight-Eily et al., 2011). A 2010 national

9

college health survey reported that roughly 61% of college students surveyed felt tired 3-7 days

out of the week (American College Health Association [ACHA], 2011).

Sleep deprivation or short sleep duration (SSD) is a lack of restorative sleep and has been

found to have several negative health and nutritional implications on the body, including a

negative association with weight gain and obesity (Chaput, Brunet, & Tremblay, 2006; Chen,

Beydoun & Wang, 2008; Lopez-Garcia et al., 2008; Taheri, Lin, Austin, Young, & Mignot,

2004; Vorona et al., 2005). SSD is a problem in the United States across all age and social

groups (Anic, Titus-Ernstoff, Newcomb, Trentham-Dietz, & Egan, 2010; Kohatsu, et al., 2006;

Lopez-Garcia et al., 2008; Spiegel et al., 2005). SSD has also been found to influence hormonal

changes that can alter energy balance in the body and lead to increased appetite and cravings for

carbohydrates (Nedeltcheva et al., 2009b; Taheri et al., 2004; Speigel, Leprouit, & Van Cauter,

1999). The latter of these can contribute to poor nutritional breakfast habits and may cause

weight gain.

Current research has established an association between SSD and obesity; however, the

association is not fully understood. As the percentage of obesity has grown throughout the years

in the United States, the average hours of sleep per night has declined (Jones, Johnson, &

Harvey-Berino, 2008). Associations with SSD and increased BMI have been found among

children, adolescents and adults (Chaput et al., 2006; Kripke, Garfinkel, Wingard, Klauber, &

Marler, 2002; Taheri et al., 2004; Vorona et al., 2005). Along with SSD, inadequate sleep

quality and alterations in sleep patterns have been found to be contributing factors to obesity

(Kohatsu et al., 2006; Patel et al., 2008; Taheri et al., 2004).

Obesity in the adolescence years is the leading predictor of obesity in adulthood and this

makes the college-aged students an important population to look at in regards to the obesity

10

epidemic (Engeland, Bjorge, Tverdal, & Soggard, 2004). The college years are a major

transition time for many young adults as many are moving away from home and living on their

own for the first time. College students are faced with many new challenges including meal

planning, sleeping schedules, and managing school, work and social commitments. College

students typically get insufficient sleep during the school week (ACHA, 2011; Brown, Buboltz,

& Soper, 2002) and either take naps or sleep longer hours during the weekend to make up for

lost sleep (Brown et al., 2002; Forquer, Camden, Gabriau, & Johnson, 2008).

Statement of the Problem

There is an increasing amount of literature available showing a correlation between sleep

patterns and obesity. With the continued rise in obesity in this country there is need for further

research to be conducted in regards to this topic. Much of the current research on the association

between sleep and obesity does not look specifically at college students. College students are a

unique population as there are added stresses of balancing the demands of college courses,

school deadlines, extracurricular activities, work, and social activities. The college years are

transitional years for students as many are living on their own for the first time and learning to

manage the demands of life. These stresses and demands put college students at an increased

risk for sleep deprivation. For these reasons research involving college students sleep behaviors

and body mass index (BMI) is needed. This study looks at college students at the University of

Wisconsin-Stout, their self-reported sleep habits, breakfast consumption, BMI and perceptions.

Purpose of the Study

The purpose of this study was to examine sleep patterns, breakfast consumption and BMI

of University of Wisconsin-Stout college students. To the knowledge of the investigator current

research has not investigated a college student population in regards to SSD, BMI and breakfast

11

consumption. The main objectives for this research study were to identify if there was an

association between sleep patterns and breakfast habits, sleep patterns and weight status and to

identify if student’s perceptions correlate with weight status and behaviors. This study looked at

usual weekday versus usual weekend sleep patterns when school was in session versus out of

session. At the conclusion of this investigation implications were made from the results and

recommendations are made for continued research in this topic.

Assumptions of the Study

It is assumed that the research participants answered the survey questions honestly,

accurately, and to the best of their knowledge. It is also assumed from the literature that self-

reported usual sleep patterns are an effective way to collect data on sleep behavior (Taheri et al.,

2004). In addition, it is assumed that self reported height and weight were based off of recent

measurement rather than perception.

Definition of Terms

Actigraphy. A device used to study sleep and wake patterns.

Body Mass Index (BMI). A common way to assess overweight and obesity using a

weight-to-height ratio: BMI=weight (kg)/height2

(m2). There are four BMI classifications and

they are as follows: underweight (< 18.5), normal (18.5-24.9), overweight (25.0-29.9), and

obesity (30.0+).

Circadian rhythm. A daily cycle of biological activity that is present in humans and

based on a 24-hour period. Circadian rhythm is influenced by regular variations in the

environment (i.e. light-dark cycles) and can be disrupted by changes in daily schedules. It is also

referred to as a biological clock.

Ghrelin. A hormone that stimulates hunger.

12

Leptin. A hormone that assists in the regulation of energy intake and energy

expenditure, including appetite and metabolism. The level of circulating leptin in the body is

directly proportional to the total amount of fat in the body.

Obesity. A BMI of 30.0 or higher.

Overweight. A BMI between 25.0 and 29.9.

Qualtrics. An internet based survey software.

Rapid eye movement (REM) sleep. A stage of sleep in a normal sleep cycle

distinguished by movement of the eyes.

Short sleep duration (SSD). A shorter than average amount of restorative sleep. In this

paper SSD refers to sleep durations shorter than 7 hours per night. Sleep curtailment and sleep

deprivation are used interchangeably with SSD throughout this paper.

Sleep apnea. A sleep disorder where there is one or more episodes of abnormal breathing

pauses which disrupt a person’s normal sleep cycle.

Sleep efficiency. The ratio of the total time spent asleep to the amount of time spent in

bed.

Limitations of the Study

This study investigated the self-reported usual sleep patterns of UW-Stout college

students. This study did not, however, have students keep a sleep diary to record actual sleep nor

did it use any type of electronic sleep device, such as an actigraphy, to record sleep data. Other

limitations to this study are factors not investigated that may also have influences on sleep,

obesity and breakfast habits such as alcohol usage, media usage, and the use of caffeinated

beverages and supplements.

13

This study was limited to the self-reported voluntary responses by students. No

anthropometric data was taken and all participants’ BMI’s were calculated using students’ self-

reported height and weight. Self-reporting leaves room for error as students may under- or over-

estimate their height and weight. The adult BMI height-to-weight equation was used to evaluate

all students’ weight status. The CCD currently recommends that BMI for children and teens (2-

20 years) be calculated using a body mass index-for-age (BMI-for-age) and gender since the

amount of body fat changes with age and varies between boys and girls (CDC, 2011a). The

CDC states that healthy weight ranges, such as that for the adult BMI ranges cannot be

accurately provided for children and teens because weight ranges change monthly with age and

with height increases. Due to the fact that the survey made students chose from a range of ages,

it was decided to use the adult calculation for BMI evaluation for all participants. A limitation to

using BMI to evaluate weight status is that it does not take body composition into account and so

might overestimate body fat in people with muscular build and underestimate body fat in people

who have lost muscle.

Methodology

An online survey was created by the researcher through Qualtrics, an online survey tool.

The research proposal (Appendix A) and the questionnaire (Appendix B) were submitted to the

University of Wisconsin-Stout’s Institutional Review Board (IRB) for approval. Following

approval from IRB the questionnaire was sent out to a 25% random sample (2199) of University

of Wisconsin-Stout students via email addresses that were obtained through Planning,

Assessment, Research and Quality [PARQ] at the University of Wisconsin-Stout. Students were

emailed (Appendix C) regarding the opportunity to participate in the survey and were sent a copy

of the research survey consent form (Appendix D) a brief description of the research and a link

14

that would take them directly to the survey on Qualtrics. Completion of the survey implied

consent of participation in the research and was clearly stated on the survey.

All surveys were collected within Qualtrics and results were analyzed by the researcher

with the assistance of a statistician from the University of Wisconsin-Stout Applied Research

Center. Statistical analysis was completed using Statistical Package for Social Science (SPSS)

software program version 17.0.

15

Chapter II: Literature Review

Within this research literature review trends, health concerns and risk factors for obesity

and sleep in the United States will be discussed along with the association between the two

topics. Obesity and sleep will also be examined among the college student population.

Obesity in the United States

Obesity is an extremely impactful health issues among the U.S. population and in many

other industrialized countries; and it is one of the leading causes of death in the United States

(Resta et al., 2003). Over the last 20 years there has been an alarming increase in obesity in the

United States. As of 2008 research from the National Health and Nutrition Examination Survey

(NHANES) showed that over 30% of men and women in the U.S. are considered to be obese in

most age and sex groups (Flegal et al., 2010). This prevalence in obesity is observed in all but

one of the U. S. states. Obesity is a risk factor for an array of different health issues, including

hypertension, type 2 diabetes, insulin resistance, stroke, coronary heart disease, sleep disorders

and psychological problems (Bjorntorp, 1998; Gangwisch et al., 2006; Resta et al., 2003).

Obesity has also been found to affect daytime sleeping, life expectancy and overall general

health (Resta et al., 2003; Olshansky et al., 2005).

There are many things that contribute to weight gain and obesity such as sedentary

lifestyles, unhealthy eating habits, and socioeconomic status. A variety of eating habits can

influence weight gain including frequency of eating during the day, eating on the go or away

from home and not eating breakfast (Huang, Hu, Fan, & Tsai, 2010; Ma et al., 2003). Ma et al.

(2003) found an increased risk of obesity among those who did not eat breakfast and among

those who ate more frequently away from home. Socioeconomic status is associated with

overweight and obesity, as people of a higher socioeconomic status tend to have a greater

16

awareness of health and weight and place a higher importance on maintaining a healthy lifestyle

and weight (Nelson, Gortmaker, Subramanian, Cheung, & Wechsler, 2007).

Obesity among College Students

The general health effects of obesity usually become apparent later in the adult life;

however, the root causes and poor lifestyle behaviors develop in the earlier years (Buckworth &

Nigg, 2004; Engeland et al., 2004). Obesity in adolescence years is a leading predictor of

obesity in adulthood and this makes the college students an important population to look at in

regards to obesity prevention. College is a major transition time for many young adults. College

students are faced with many new challenges as they move away from home and begin managing

daily schedules on their own. Weight gain and unhealthy behavioral patterns during the college

years may contribute to overweight and obesity in adulthood (Engeland et al., 2004).

The college environment unintentionally supports an unhealthy lifestyle. Environmental

influences such as buffet cafeterias, living arrangements, sleep schedules, and sedentary

lifestyles support college obesity (Boyle & LaRose, 2008; Levitsky & Youn, 2004; Nelson et al.,

2007; Ying, Lee, Tam Bridges, & Keating, 2007). College students have reported spending over

30 hours per week in sedentary positions mostly due to studying and media usage such as

television, video games, and computers (Buckworth et al., 2004; Nelson et al., 2007). Racette et

al. (2005) found that 70% of students had a significant weight gain during the first two years in

college. This was attributed to unhealthy dietary behaviors and inactivity, including eating high

fat or fast food at least three times a week. A similar study found 27% of students to be either

overweight or obese and the contributing factors to be unhealthy dietary habits (i.e., low fruit,

vegetable and fiber intake) and low physical activity (Huang et al., 2003). These activities and

17

environmental influences promote unhealthy, unbalanced lifestyles and can lead to significant

weight gain during college.

Sleep in the United States

Depriving the body of sleep is a common health issue in the United States. Over the last

few decades the average sleep duration of a United States adult has decreased by 1.5-2 hours

(NSF, 2002), with 35% of adults reporting less than seven hours of sleep a night (Wheaten, Liu,

Perry, & Croft, 2011). The National Sleep Foundation currently recommends that adults get

between 7-9 hours of sleep per night (NSF, 2011). Although restorative sleep can vary

considerably for each individual and range outside of these recommendations, the majority of

adults on average will need between 7-9 hours a night.

There are several health issues that are associated with short sleep duration (SSD). SSD

has been found to have negative health implications on total cholesterol, HDL-cholesterol,

triglycerides, energy expenditure, systolic blood pressure, diabetes, hypertension and obesity

(Bjorvatn et al., 2007; Chaput et al., 2006; Chen et al., 2008; Taheri et al., 2004; Vorona et al.,

2005). These health issues along with cardiovascular disease and respiratory disorders may have

associations with both short and long sleep durations (CDC, 2011c; Gangwisch et al., 2006;

Stranges et al., 2008b). Sleep duration is a predictor in susceptibility in the development of colds

as insufficient sleep affects the immune system negatively and causes the body to have a lower

resistance to illness (Cohen, Doyle, Alper, Janicki-Deverts, & Turner, 2009). Sleep restriction

has been shown to decrease glucose tolerance, compromise insulin sensitivity, and cause weight

gain (Choi et al., 2008; Nedeltecheva et al., 2009b; Vorona et al., 2005).

The constant demands of the industrialized society have driven Americans to shorter

sleep durations (Spiegel et al., 2005). Modern cultural changes such as increased television

18

viewing, computer usage, cellular phones and gaming are demanding more and more time and

impacting sleep patterns (Suganuma et al., 2007). These types of media usage prior to night time

sleeping have been shown to decrease sleep time. Self-perceived insufficient sleep after media

usage was found to be between 45-48% and younger respondents between the ages of 15-19

years more frequently attributed media usage prior to sleep as a factor in their perceived sleep

deficiency. Other factors attributed to decreased sleep are caffeine (beverages and supplements),

alcohol use, physical activity relative to sleep time and obesity (Resta et al., 2003; Suganuma et

al., 2007; Vgontzas et al., 2008). Obesity is a risk factor for several different sleep problems

such as sleep apnea and poor sleep quality. Compared with those of a healthy weight obese

individuals spend less time in the rapid eye movement sleep stage and obese individuals have

less sleep efficiency compared to healthy weight individuals (Resta et al., 2003; Vgontzas et al.,

2008).

Sleep and College Students

College students have a unique lifestyle in which they must balance work, social and

school activities with sleep. Sleep often times is neglected to make more time in the day for

other necessary activities. The American College Health Association (ACHA) developed a

survey, the National College Health Assessment (NCHA) which is designed to collect

information about college students on a wide range of health topics. From the NCHA Fall 2010

survey 61% of college students surveyed ―felt tired, dragged out or sleepy during the day‖ 3-7

days out of the week and 18% reported that sleep difficulties were a major factor in affecting

individual academic performance (ACHA, 2011).

This suggests that college students are either not getting enough sleep or not getting

quality sleep at night. Some studies have found that repeated sleep deprivation leads to a decline

19

in cognitive functioning and can have a negative effect on performance, higher levels of stress,

attention deficit and difficulty learning (Kronholm et al, 2009; Kim, Kim, Park, Choi, & Lee,

2011). Poor sleep quality among college students has also been attributed to environmental

noise, worrying while trying to fall asleep, varying schedules and going to bed thirsty (Brown et

al., 2002).

Association among Sleep and Obesity: An Overview

A clear association between the rise of obesity and the decline in sleep duration is evident

from current research. As the percentage of obesity has grown throughout the years in the

United States, the average hours of sleep per night has declined (Jones et al., 2008). Sleep

duration has yet to be supported as a causal factor in obesity and although the exact association is

unknown one does exists (Anic et al., 2007). Associations with SSD and obesity have been

found across all social groups and spans from children to older adults (Chaput et al., 2006; Chen

et al., 2008; Kripke et al., 2002; Lopez-Garcia, 2008; Taheri et al., 2004; Vorona et al., 2005).

Several studies have looked at the association between SSD and obesity in adults

(Bjorvatn et al., 2007; Kohatsu et al., 2006; Lauderdale et al., 2008; Lopez-Garcia, 2008;

Moreno, Louzada, Teixerra, Borges & Lorenzi-Filho, 2006; Stranges et al., 2008b; Taheri et al.,

2004; Vorona et al., 2005). Vorona et al. (2005) found that total sleep time decreased as BMI

increased, except in the severely obese (BMI 40+). Their findings also included a sleep

difference between normal BMI and BMI’s 25+ to be 16 minutes a day (1.86 hours a week).

This data suggests that adding as little as 16 extra minutes of sleep per day could potentially lead

to weight loss. Another study found an association with BMI and sleep less than 8 hours per

night but did not see this association with sleep greater than 8 hours per night (Taheri et al.,

20

2004). Kohatsu et al. (2006) found an association with BMI and sleep in a rural adult population

among those reporting less than 6 hours of sleep a night.

Both short and long sleep duration (LSD) have been found to be associated with obesity

in elderly women (Lopez-Garcia et al., 2008). Other adult studies have also found a u-shape

association with obesity and SSD and LSD (Bjorvatn et al., 2007; Stranges et al., 2008b).

Bjorvatn et al. (2007) also looked at the effect of SSD during the week combined with LSD

during free time (i.e. weekends) and found no association with obesity. One study saw a change

in energy expenditure accompanied by increased hunger, reduced fat loss and increased loss of

fat free body mass in participants exposed to a 2 week energy and sleep restriction (Nedeltcheva,

Kilkus, Imperial, Schoeller & Penev, 2010). The results of this research emphasize the

importance of sleep for the maintenance of a healthy, lean body during periods of calorie

restriction.

Along with SSD, both inadequate sleep quality and sleep pattern alterations in sleep have

been found to be risk factors for weight gain and obesity (Kohatsu et al., 2006; Patel et al. 2008;

Speigel et al., 2005; Taheri et al., 2004). Current research suggests that adequate sleep duration

and sleep quality are important for normal metabolic and hormonal functioning as well as for

appetite regulation (Van Cauter, Spiegel, Tasali, & Leproult, 2008). SSD has been found to

influence hormonal changes that can alter energy balance in the body and lead to increased

appetite, hunger, snacking and cravings for carbohydrates (Nedeltcheva, Kessler, Imperial, &

Penev, 2009a; Spiegel et al., 1999; Spiegel, Tasali, Penev, & Van Cauter, 2004). This can lead

to poor nutritional habits especially with food choices.

Ghrelin and leptin are two hormones which help regulate appetite that are currently being

studied in regards to SSD. Alterations in these hormones might be involved in the observed

21

increases in BMI that are associated with SSD (Crisprim et al.2007; Schmid, Hallschmid, Jauch-

Chara, Born & Schultes, 2008; Speigel et al., 1999; Taheri et al., 2004; Vorona et al., 2005). The

exact association is unknown, however, the combination of a reduction in leptin and an increase

in ghrelin result in increased hunger and food intake (Speigel et al., 1999; Crisprim et al., 2007).

A single night of total sleep deprivation was found to increase ghrelin levels and feelings of

hunger in healthy men of normal weight (Schmid et al., 2008). Similar increases in ghrelin

levels were seen after two consecutive days of reduced sleep duration to 4 hours a night. (Spiegel

et al., 2004). Also observed was a reduction in the hormone leptin after sleep curtailment and an

observed increase in hunger in participants especially for high carbohydrate, calorie-dense foods.

Likewise, short sleep duration was found to be associated with decreased leptin (15.5%) and

elevated ghrelin (14.9%) when comparing 5 hours per night of sleep with 8 hours per night of

sleep (Taheri et al., 2004). Similar to the previously mentioned research this combination of

hormone fluctuations leads to an increase in appetite. Not all research has observed associations

between sleep patterns, BMI, and the hormones ghrelin and leptin (Littman et al., 2007).

Hormone fluctuations in the body, such as those mentioned above, are thought to be

linked with the body’s circadian rhythm. Current epidemiological studies provide evidence to a

link between circadian rhythm, sleep and metabolism (Crispim et al., 2007; Gangwisch, 2009;

Taheri et al., 2004; Yildiz, Suchard, Wong, McCann & Licinio, 2004). Ghrelin levels have been

found to increase from midnight to dawn, however, this was only observed in the participants

who were thin or of normal weight (Yildiz et al., 2004). This observation was not seen in obese

individuals thus suggesting the obese may have a flawed or varied circadian rhythm.

Ancestral reasons may be an explanation as to why SSD would trigger metabolic changes

in the body increasing calorie intake and fat deposition (Gangwisch, 2009; Huang, Ramsey,

22

Marcheva, & Bass, 2011). In the past the human body was more closely synched with light

exposure from the sun and the changing of the seasons. Increased daylight in summer months,

for instance, would lead to shorter nights of sleep in summer and vice versa, decreased daylight

in winter months would lead to longer nights of sleep in winter (Gangwisch, 2009). A higher

availability of food in summer months would have led to increases in food consumption to

prepare the body for those winter months with lower food availability. Therefore, SSD could

trigger metabolic changes in the body to eat more and store energy for winter months. Today’s

culture with electricity, artificial lights and around the clock entertainment is different. The days

are extended past daylight hours leading to problems with SSD year round.

Sleep Time Defined

There are different ways in which sleep time has been defined and studied in the current

research. Sleep duration is one way to define sleep time, and studies have looked at the effects

of both short and long sleep durations (Bjorvatn et al., 2007; Lopez-Garcia et al., 2008; Stranges

et al., 2008b). Nighttime sleep and/or total sleep in a 24-hour period has also been studied

(Vorona et al., 2005). Sleep alterations have also been observed focusing on changes in sleeping

behavior. Timing and quality of sleep time or sleep efficiency has also been studied which

focuses on the quality of the sleep time opposed to the length of sleep time (Bjorvatn et al., 2007;

Buxton et al., 2009). Another way researchers have studied sleep time is by the timing of sleep.

This method focuses on when participants are going to bed and when they rise and accounts for

the body’s circadian rhythm. Few studies have looked at student sleep pattern differences when

school is in session versus out of session.

23

Sleep Measure Methods

There are several methods that accurately measure sleep patterns. One way is by using

an actigraphy. While using this method the participant wears a small actimetry sensor usually

around the wrist that will measure total motor activity throughout the 24-hour day (Ancoli-Israel

et al., 2003; Littner et al., 2002). This method can monitor circadian rhythm patterns and sleep

disturbances during the night. This method, however, can be expensive and therefore is not used

as often as other methods. The other main ways of collecting sleep data rely on participants to

self-report behavior. These methods include a) sleep journals where participants record their

actual sleep, b) usual sleep patterns where participants record what their usual sleep pattern is

like, and c) sleep recalls where participants recall how much they have slept in the past few days

or for the past week (Taheri et al., 2004). All three self-reporting methods have been found to

provide accurate, stable sleep data and research has shown self-reporting data and lab readings to

be quite consistent with one another.

24

Chapter III: Methodology

Included in this chapter is a description of the survey tool used for this research as well as

a description of participants and how they were selected. Additionally, this chapter contains an

explanation of the analysis of the data collected and limitations of the methodology used.

Subject Selection and Description

UW-Stout college students were chosen for this study due to convenience factors for the

researcher and interest in local student health and behaviors. Prior to data collection approval

from the University of Wisconsin-Stout Institutional Review Board (IRB) was obtained

(Appendix A). Following IRB approval a Survey Declaration form was completed and reviewed

by UW-Stout’s Survey Clearinghouse in Planning, Assessment, Research and Quality (PARQ).

Following this review PARQ sent the researcher an email with a distribution list of 2199 UW-

Stout students. All 2199 students from the distribution list were sent an email regarding the

survey (Appendix B) and the invitation to participate in the present study (Appendix C). Within

the email sent to students was a brief description about the researcher, survey length (5-10

minutes), a link to take students directly to the survey on Qualtrics and a copy of the implied

consent form (Appendix D). A statement regarding implied consent was clearly written within

the email sent to students and on the survey itself saying that completion of the survey implied

agreement to participate in the present research project. A total of 335 students responded to the

email by participating in the Qualtrics survey.

Survey Instrument

The online survey used in this project was developed specifically for the present research.

Survey questions were written following a review of previous sleep and obesity related research

and some questions were adapted from existing surveys (Kohatsu et al., 2006; Stamatakis, &

Brownson, 2008; Steptoe, Pearey & Wardle, 2006). The final survey was entered online through

25

Qualtrics, an internet based survey software. Demographic information was collected at the

beginning of the survey and included: age, gender, race, height, weight, major, and year in

school (Appendix B). The next set of survey questions involved usual sleep behaviors when

school is in session and when school is out of session including questions specific to the week

and weekend. Questions about normal breakfast habits, physical activity, feeling rested and

feeling tired upon waking were also asked in regards to when school is in session and out of

session. The final set of survey questions involved student perceptions about general health

including importance of sleep, sacrificing sleep, importance of breakfast, and exercise.

Data Collection Procedures

A link to the 31 question survey was emailed to 2199 (25% random sample) University

of Wisconsin-Stout students in April 2011. Students used the link in the email to obtain access

to the survey. Upon completion the survey was automatically submitted to the Qualtrics

database from which the research was able to access the data for analysis.

Data Analysis

There were a variety of statistical methods used for analyses in this study. Statistical

analysis was completed using the Statistical Package for Social Science (SPSS) software

program version 17.0. Demographic analysis was done using frequency tables for age, gender,

ethnicity, major, year in school, height and weight. Height and weight means were expressed as

mean + standard deviation. Frequency tables were also used to analyze nighttime and 24-hour

sleep times during the week and weekend both when school was in session and not in session.

The Wilcoxcon signed ranks test and paired sample t-test were used to compare differences

between sleep hours during the week and weekend. The same two tests were used to analyze

26

sleep hours when school was in session and not in session. Student perceptions on sleep were

analyzed using Pearson correlation.

Breakfast habits were analyzed using frequency. A one-way ANOVA (with Tukey’s post

hoc test) was used to evaluate the relationship between breakfast habits and sleep patterns. A

Pearson correlation was used to analyze the strength of the relationship between breakfast habits

when school was in session versus out of session. A Pearson correlation was also used to

analyze the relationship between breakfast habits and the importance of breakfast.

A one-way ANOVA (with Tukey’s post hoc test) was used to evaluate the relationship

between BMI and sleep patterns. Sleep responses were converted to numerical values so that

ANOVA analysis could be run on the data. Numeric values used in place of sleep ranges for

ANOVA were 1-5 with 1 representing 9. Once values

were calculated using this scale the numbers were then converted back into hours with 2.5 = 6.5

hours, 3.2 = 7.2 hours, etc.

Limitations

This study examined the self-reported usual sleep, height, and weight of UW-Stout

college students. This study did not have students keep a sleep diary to record actual sleep nor

did it use any type of electronic sleep device, such as an actigraphy, to record sleep data. Self-

reported sleep has been found to be an effective way to collect sleep data (Taheri et al., 2004).

This study also used self-reported height and weight to calculate BMI of participants. No

anthropometric data was physically collected by the researcher. Using BMI to observe student’s

weight status is another limitation to this study as BMI does not take body composition into

account and so may over- or underestimate a person’s adiposity. All participants’ BMI’s were

calculated using their self-reported height and weight and evaluated using BMI for adults. The

27

CCD currently recommends that BMI for children and teens (2-20 years) be calculated using a

body mass index-for-age (BMI-for-age) percentile since the amount of body fat changes with age

(CDC, 2011a). The CDC has separate BMI-for-age growth charts for girls and boys, because the

amount of fat varies between boys and girls. The CDC states that healthy weight ranges, such as

that for the adult BMI ranges cannot be accurately provided for children and teens because

weight ranges change monthly with age and with height increases.

Work week and weekend were not differentiated for breakfast consumption. Another

limitation to the methodology was within the age categories. The age categories for students to

choose from were listed on the survey with 6 separate options. The age 30 was mistakenly listed

in two of those choices once in 27-30 years and once in 30-35 years.

28

Chapter IV: Results

The purpose of the present study was to examine the sleep patterns, weight status, and

breakfast habits of University of Wisconsin-Stout college students. To the knowledge of the

investigator current research has not looked at a college student population in regards to sleep

deprivation, BMI and breakfast habits. The main objectives for this research study were to

identify if there was an association between sleep patterns and weight status, to identify if there

was an association between sleep patterns and breakfast habits and to identify if student

perceptions correlate with behavior. This study also looked at usual weekday versus usual

weekend sleep patterns, as well as usual sleep patterns when school was in session versus out of

session. Sleep patterns and breakfast habits of UW-Stout college students were also investigated

to see if there was a correlation with BMI.

Response Rate

Of the 2199 students that received the email there were 379 (17.2%) student responses.

Thirty-six respondents did not go past the initial demographic questions and 8 respondents did

not answer at least 4 of the major research questions. These 44 responses were eliminated from

the data analysis (n=335). Not all respondents answered every question as each question was

voluntary, so total n varies from question to question.

Demographic Analysis

Thirty-seven percent of the respondents were male (n=123) and 63% were female

(n=211). Ninety-three percent of respondents were White/Caucasian (n=313). The remaining

seven percent were Hispanic 1.2 % (n=4), Asian 2.7% (n=9), Native American 1.5% (n=5), and

3 respondents chose not to respond (0.9%).

29

Seventy percent of the population was between the ages of 18-23. Table 1 shows the

distribution of all age groups. Freshman respondents made up 13.5% (n=45) of the total

responses. Sophomores represented 15.6% of the population (n=52). Juniors and seniors

represented 23.4% (n=78) and 23.1% (n=77), respectively. Eight percent of the population were

5+ year seniors (n=27), 13% were graduate students (n=43), and 3% were other (n=11). Of the

59 bachelors and master degree programs offered at UW-Stout 53 were represented in the data.

Table 1

Age and Year in School of Participating Students

Age

Year in School

Age in years n % Class n %

18-20 110 32.8

Freshman 45 13.5

21-23 123 36.7

Sophomore 52 15.6

24-26 34 10.1

Junior 78 23.4

27-30 16 4.8

Senior 77 23.1

30-35 12 3.6

5+ year Senior 27 8.1

35+ 40 11.9

Graduate 43 12.9

Total 335 100

Other 11 3.3

Total 333 100

Student Sleep Patterns

When school was in session the hours slept per night varied from week to weekend

(Table 2). During the school week 53.0% (36.8%, 7-7.9; 16.2%, 8-8.9) of students (n=177)

reported getting the recommended 7-9 hours of sleep per night. On the weekend when school

was in session 60.8% (38.3%, 7-7.9; 23.4%, 8-8.9) of students (n=199) reported getting 7-8.9

hours of sleep per night.

30

Table 2

Frequency Table of Sleep Patterns

Hours of Sleep

Week Weekend

n % n %

In Session Night

31

6-6.9) getting less than 7 hours within a 24-hour period (n=105). During the weekend when

school was in session 15.3% (2.8%,

32

than during the week. Again, a similar pattern was observed for the 24-hour sleep when school

was not in session (58.9%, 33.3%, and 7.8%, respectively).

A paired sample t-test showed a difference in nighttime sleep (-1.02 hours, p

33

During the weekend the majority of students (60.7%, n=198) slept the same amount of

nighttime sleep when school was in session and not in session, 12.9% (n=42) slept more when

school was in session than not in session, and 26.4% (n=86) slept less. A similar pattern of in

session and not in session differences was observed for the 24-hour sleep on the weekend

(62.6%, 15.3%, and 22.1%, respectively).

A paired sample t-test showed a significant difference in nighttime sleep (-.92 hours,

p

34

Responses varied from in session to out of session with a slight shift towards ―Yes, most of the

time‖ when school was not in session compared to in session (21%, 33%, respectively). The

responses ―Yes, most of the time‖ and ―Yes, all the time‖ together increased 10% between in

session and not in session (55% and 65%, respectively); whereas the combined responses ―No,

almost never‖ and ―Yes once in awhile‖ decreased 15% between in session and not in session

(37% and 22%, respectively).

Figure 1. Do you usually eat breakfast when you wake-up?—in session

Figure 2. Do you usually eat breakfast when you wake-up?—not in session

20%

17%

9%21%

34%

In Session

No, almost never

Yes, once in awhile

Yes, some of the time

Yes, most of the time

Yes, all the time

9%

13%

14%

33%

32%

Not in Session

No, almost never

Yes, once in awhile

Yes, some of the time

Yes, most of the time

Yes, all the time

35

Sleep and Breakfast Comparison

A one-way ANOVA (with a Tukey’s post hoc test) was used to analyze and compare

sleep responses with breakfast eating (Figure 3). A difference in breakfast and sleep patterns

was observed during the week when school was in session for both nighttime sleep and 24-hour

sleep (p

36

weeks (n=199) and 88% of the respondents had checked their weight within the last three months

(n=296). Only, three percent of the population had checked their weight over a year ago or did

not remember when they checked their weight (n=10).

Body Mass Index Comparisons

Body mass index (BMI) classified participants into underweight, normal weight,

overweight and obese. Table 5 shows 54.6% (n=178) of total participants to be of normal weight

(61.5% female, 43.0% male). Males had the highest percentage of overweight (35.5%) and

obesity (19.8%). Overweight and obesity for the total population were 27.9% and 16.6%,

respectively.

Table 5

BMI Distribution

BMI Category Both Sexes Female Male

n % n % n % Underweight 3 0.9 1 0.5 2 1.7

Normal weight 178 54.6 126 61.5 52 43.0

Overweight 91 27.9 48 23.4 43 35.5

Obese 54 16.6 30 14.6 24 19.8

Total 326 100 205 100 121 100

Figure 4 displays student responses for in session and not in session nighttime sleep

compared to student BMI’s. During in session weekend sleep there was a significant difference

in BMI for those reporting 6 hours of

sleep a night. Differences were also seen during the week when school was not in session

between those reporting 8-8.9 hours and >9 hours with those reporting

37

similar difference is seen between >9 hours of sleep and

38

Table 6

In Session and Not in Session Sleep Habits Compared to Sleep Perceptions

Categories Getting enough sleep

is important to me

I regularly sacrifice

sleep

In Session Week

Night -.406* .499*

24-hours -.426* .457*

Weekend hours

Night -.206* .089

24-hours -.211* .072

Not In Session Week

Night -.278* .173*

24-hours -.279* .157*

Weekend hours

Night -.188* -.004

24-hours -.160* .012

*=significant relationship, p

39

Chapter V: Discussion

Increasing evidence has pointed towards an association between sleep and obesity. This

chapter contains limitations of the study and survey tool, conclusions made from the data and a

discussion of the findings from the research. This chapter ends with recommendations and

implications for further research.

Limitations

It should be noted that this study has several limitations some of which have been

identified in previous chapters. First, this study was not demographically diverse. Although the

respondents to the survey represented the demographics of the total population of UW-Stout,

over 90% were White/Caucasian. Thus, the results presented here cannot be generalized to all

college students in the United States. A more diverse population would have offered information

on ethnic differences if any exist.

A second limitation was that the online survey tool used was not validated prior to data

collection. Validation of the survey would have provided strength and confidence to the study.

In addition to this no pilot experiment was conducted prior to implementing the final survey,

which may have impacted the results of the study. A pilot study would have improved the

research design and strengthened statistical significance.

This study also had a relatively small sample size of 335 respondents completing the

online survey. A larger sample size would have given strength to the study and would have

allowed for generalizations to have been made in regards to age and year in school. Surveying

students from other universities would have provided a larger sample size and would have

increased demographic diversity.

40

Conclusion

The present research provided information on sleep patterns, breakfast habits and BMI of

UW-Stout college students, as well as some student perceptions of these topics. Similar to

previous studies, this study showed an association between short sleep duration of

41

The 2008 NHANES data showed that 68.3% of adults in the US are considered to be

overweight and obese (Flegal et al., 2010). This suggests that UW-Stout college students have

an average percentage of overweight and obesity below that of the national average (Table 7).

Table 7 displays the differences in overweight and obesity from the 2008 NHANES data and

data of the present study including the data specific to the Caucasian population being that 96%

of the present study’s population was White/Caucasian.

Wheaten et al. (2011) reports that 35% of adults get less than seven hours of sleep a

night. The present study showed that 43% of the population got less than seven hours of

nighttime sleep during the week with only 15% of the population getting less than seven hours of

sleep during the weekend when school was in session. This suggests that sleep patterns change

from week to weekend for college students and similar changes may be observed with non-

college students. When school was not in session sleep hours increased, with fewer students

reporting less than seven hours of sleep per night during the week and weekend. This suggests

that when school is not in session a higher percentage of college students are getting the

recommended 7-9 hours (NSF, 2011) and suggests that college student sleep patterns change

with respect to school session. This also suggests that there are lifestyle changes between when

school is in session versus not in session. In addition, sleep duration >9 hours increased on the

weekends when compared to the week when school was both in session and not in session. This

change between week and weekend sleep behaviors suggest that students may be trying to catch

up on lost sleep during the weekends.

Previous studies found an association between total sleep time and BMI, where shorter

sleep times were accompanied with higher BMI’s (Bjorvatn et al., 2007; Kohatsu et al., 2006).

Bjorvatn et al. (2007) saw a gradual increase in average BMI as sleep hours decreased from 7-7.9

42

hours (BMI=25.05) to 9 hours to 30.24 in

43

study more than half of students said that they ate breakfast most or all of the time regardless of

whether school was in session or not. The present study also showed that breakfast habits

correlated with the value of the importance of breakfast, showing that the more a student valued

the importance of eating breakfast the more often they ate breakfast.

Recommendations

Results of the current research indicate that further studies on college student sleep, BMI

and breakfast habits need to be done. Sleep and obesity are associated but a causal relationship

has yet to be established, this makes research of these topics difficult and variables hard to

control. Many things have been found to influence both sleep and obesity such as sedentary

lifestyle, lack of physical activity, socioeconomic status, breakfast skipping, media usage,

caffeine and a variety of environment influences (Buckworth et al. 2004; Huang et al., 2003;

Huang et al., 2010; Ma et al., 2003; Nelson et al., 2007; Stamatakis et al., 2008); and as this

study showed school session and weekday may influence this relationship.

Eating patterns can influence both sleep and obesity. This study looked at usual breakfast

habits and importance of breakfast, however it did not investigate the location of breakfast eating

(i.e., at home, in the car, at a restaurant/cafeteria). Location of eating can be especially important

when trying to understand college student behaviors and trends and comparing on campus and

off campus student breakfast habits also warrants focus for future studies.

Combining media usage with this study would have provided further information on

influences to short sleep duration. Media use has been found to influence sleep when used prior

to bedtime and can lead to a sedentary lifestyle (Suganuma et al., 2007). College students are

often on their computers for school at all hours of the day which may influence sleep patterns.

44

Further research is needed in this area to observe the possible influences computer and other

media usage has on student sleep patterns and BMI.

Observing how students change throughout their college career is important to the

understanding the influences the college student lifestyle can have on obesity long-term.

Previous studies have shown that poor lifestyle in adolescence is a risk factor in adulthood

obesity (Buckwork et al., 2004; Engeland et al., 2004). Following the same students from

freshman year to senior year and perhaps even beyond may offer some unique benefits to

understanding theses influences on obesity and may assist in the prevention of obesity.

Previous research found sleep hygiene awareness to be related to sleep behaviors and

overall sleep quality and illustrated that sleep hygiene education can influence sleep behaviors.

(Brown et al., 2002). The present study observed sleep behaviors in relation to perceptions and

found that in most cases sleep perceptions matched behaviors, however, it did not investigate the

possible influences sleep hygiene awareness has on sleep behaviors. Nor did it look at sleep

hygiene education. Addressing the educational level of sleep hygiene would have offered further

understanding into the influences of sleep behavior and could assist in the development of an

educational tool for improving sleep hygiene among college students. In addition, education on

the importance of breakfast may also influence breakfast eating behavior as this study found the

correlation factor between breakfast habits and the importance of breakfast to be strong.

This study used self-reported usual sleep to collect information on student sleep patterns.

Sleep diaries and laboratory data were not collected in this study. Although usual sleep has been

shown to be relatively consistent with sleep diaries and lab recorded data (Taheri et al., 2004),

sleep diaries would provide a record of actual sleep versus usual sleep and laboratory data could

further the understanding of sleep quality.

45

This study used self-reported height and weight, which provided an easy way to gain

information from students without having to have students physically come on to campus to be

measured. However, self-reporting may have skewed data from student’s under- or over-

estimating their height or weight. Height and weight were used to calculate the BMI of

respondents in order to investigate a possible association between sleep duration and obesity.

However, BMI does not take body composition into account and may over- or under-estimate a

person’s BMI. Using skin-fold measurements or a Dual-emission X-ray absorptiometry (DEXA)

is an alternative way to collect data on weight status that would be more accurate and take body

composition into account. Using the DEXA can be a long and time consuming process which

would require a larger time commitment from both the participants and researcher. Collecting

skin-fold measurements can also be time-consuming and rely on the accuracy of the person

doing the measurement. However, body composition data would be more precise when using

one these methods compared to BMI and both should be considered with future research.

46

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APPENDIX A: IRB Approval

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57

APPENDIX B: Research Survey

Q33 ―This research has been approved by the UW-Stout IRB as required by the Code of Federal

regulations Title 45 Part 46.‖ By completing the following survey you agree to participate in the

project entitled Sleep and Nutrition Beliefs, Perceptions and Habits of UW-Stout College

Students and are providing your informed consent. If you are under the age of 18 you cannot take

this survey. Thank you!

Q1 Age: 18-20 (1), 21-23 (2), 24-26 (3), 27-30 (4), 30-35 (5), >35 (6)

Q2 Gender: Male (1), Female (2)

Q7 Race/Ethnicity White/Caucasian (1), African American (2), Hispanic (3), Asian (4), Native American (5), Pacific Islander (6),

Other (7), No Response (8)

Q8 What year in school are you? Freshman (1), Sophomore (2), Juni