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ANALYZING AWARENESS OF OBESITY AMONG STUDENTS IN THE

SCHOOL OF AGRICULTURE & CONSUMER SCIENCES AT TENNESSEE

STATE UNIVERSITY

A Thesis

Submitted to the Graduate School

of

Tennessee State University

in

Partial Fulfillment of the Requirementsfor the Degree ofMaster of Science

Graduate Research Series No. ______

Kashin A. Thompson

August, 2011


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ANALYZING AWARENESS OF OBESITY AMONG STUDENTS IN THE

SCHOOL OF AGRICULTURE & CONSUMER SCIENCES AT TENNESSEE

STATE UNIVERSITY

A Thesis

Submitted to the Graduate School

of

Tennessee State University

in

Partial Fulfillment of the Requirementsfor the Degree ofMaster of Science

Kashin A. ThompsonAugust 2011


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To the Graduate School:

We are submitting a thesis by Kashin A. Thompson entitled "Analyzing

Awareness of Obesity Among Students in the School of Agriculture & Consumer

Sciences at Tennessee State University" We recommend that it be accepted in partial

fulfillment of the requirements for the degree, Master of Science in Agricultural Sciences.

_Dr_Eisshea Tegegne__________________Chairperson

__Dr Barbara Canada__________________Committee Member

__Dr. Lan Li_________________________Committee Member

_Dr.Surendra P. Singh_________________Committee Member

Accepted for the Graduate School:

_Alex Sekwat _________________Dean of the Graduate School


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i

DEDICATION

This thesis is dedicated to my father, Ray Thompson who passed away on June

17, 2011. He will be missed and forever in my heart. It is also dedicated to my mother,

Kim Thompson without her encouraging words and support I would have never made it

to this point in my life.


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ii

ACKNOWLEDGEMENTS

This thesis took the work of many family members, friends, and professors. First

and foremost I would like to thank God. When things seemed rough, I referred to the

scripture "look to thehills from which your help cometh", because through him all things

are possible. I would like to express my sincere thanks to my committee members, Dr. S.

Singh, Dr. F. Tegegne, Dr. B. Canada and Dr. L. LI for sharing their knowledge on

strengthening the thesis. Assistance by Dr. E. Ekanem is also acknowledged.

I would also like to acknowledge my colleagues Derrick, Steven, and Simba, who

have been supportive, checking on me to make sure I was making progress every other

hour if not every hour, and aiding me in areas that I struggled. I would also like to

acknowledge my wonderful friend Angela Knowlton who made sacrifices, and provided

positive attitude, and letting me utilize her computer. I would like to extend a very special

thanks to Denise Mitchell, and Mrs. Mary Ekanem. Last but not least I would like to

thank my family who gave me motivation and encouragement.


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ABSTRACT

KASHIN THOMPSON Analyzing Awareness of Obesity Among Students in the School

of Agriculture & Consumer Sciences at Tennessee State University (under the direction

of Dr. FISSEHA TEGEGNE)

Obesity is excessive accumulation of body fat, which can lead to an adverse effect on

health, resulting in reduced life expectancy and/or increased health problems. It is a

major public health problem found in men, women, and children of all ages, races, ethnic

background, and socioeconomic groups in the United States and around the world. The

objectives of this study are as follows: 1) To assess awareness levels about obesity and

related issues among students in the school of Agriculture and Consumer Sciences at

Tennessee State University, 2) To characterize the prevalence of obesity and knowledge

about it among the students, 3) To discuss suggestions by the students involved in this

study. This study can contributes to a better understanding of factors related to students

awareness about obesity and their suggestions to tackle the problem. In order to

accomplish the objectives of this study data were collected from primary and secondary

sources. Face- to-Face surveys of graduate and undergraduate students at Tennessee State

University in the School of Agriculture and Consumer Sciences were conducted in spring

2011 A total of one hundred forty one completed responses were received. The data were

checked for completeness coded and entered into the computer. Spss-Pc was used to

conduct descriptive and statistical analysis of the data. Results show that there are

various factors affecting awareness about obesity and strategies to tackle it.


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TABLE OF CONTENTS

Acknowledgments.............................................................................................................ii

Abstract.............................................................................................................................iii

Table of Contents..............................................................................................................iv

List of Tables......................................................................................................................v

Chapter I. Introduction......................................................................................................1

Statement of the problem....................................................................................10

Objectives............................................................................................................11

Hypothesis...........................................................................................................11

Significance of the Project..................................................................................12

Chapter II. Review of Literature......................................................................................14

Chapter III. Methodology................................................................................................21

Chapter IV. Results and Discussion.................................................................................23

Chapter V. Summary and Recommendations...................................................................44

Recommendations for future research..................................................................45

References........................................................................................................................47


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LIST OF TABLES

Table 1 Prevalence of Obesity among adults, by black/white race or

Hispanic ethnicity, census region and sex-behavioral Risk Factor

Surveillance System surveys, United States, 2006-2008

3

Table 2 U.S. Obesity Trends Amongst the States 5

Table 3 Prevalence and trends data in Tennessee 7

Table 4 Prevalence of overweight, obesity and Class II Obesity by Socio-

Demographic Characteristics

15

Table 5 Classification and Exercise Habits Cross Tabulation 25

Table 6 Classification and Aware that Poor Diets Put Health at Risk Cross

Tabulation

26

Table 7 Plans to Change Eating Habits and Which would Encourage plans to

change eating habits Cross Tabulation

27

Table 8 Classification and Plan to Change Your Eating Habits Cross

Tabulation

29

Table 9 Distribution Frequencies of Awareness of Obesity Problems among

designed groups

30

Table 10 Results of Chi-Square Tests of Awareness of Obesity Among

designed Groups

32

Table 11 Anova Results of Awareness Level of Obesity Problems 35

Table 12 BMI Among Students 43


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CHAPTER I

INTRODUCTION

Obesity is a growing problem in the United States. It is an excessive accumulation of

body fat, which can lead to adverse effects on health, resulting in reduced life expectancy

and/or increased health problems. It is considered to be a chronic illness requiring

lifelong treatment and management (Mauro 2008). Obesity is usually associated with

other conditions that can be controlled but not cured, such as high blood pressure and

diabetes. When dealing with weight there are several topics that come into play varying

from child to adult, they are:

Extreme obesitya BMI (body mass index) greater than or equal to 40

Obesityan excessive amount of body fat in relation to lean body mass or a body

weight that is 30 percent over the ideal weight for a specified height; BMI of 30or greater.

Normal Weightideal weight per height measurements; a classification of BMIof 18.5-24.9.

Underweightweighing less than normal, healthy, or required; BMI less than18.5.

Obesity is a major public health problem found in men, women, and children of all

ages, race, ethnic backgrounds, and socioeconomic groups in the United States and

around the world. According to the United States Center for disease control (CDC) more

than one third of adult Americans are obese. Approximately 300,000 deaths a year due to

obesity, which puts it as the second leading cause of preventable deaths in the United

States (Carson- Dewitt, MD 2009)


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Even though obesity is found in men, women, and children, it is more prevalent

among women. The forty to fifty year age group has the largest percentage of obesity

with, 53% of Black women and 51% of Mexican women and almost 39% of White

women. Racial/ethnic differences in obesity rates are not found in men. Table 1 provides

some obesity statistics by various taxonomies for different regions of the United States.

American children and teens are also affected by the most common nutritional disorder.

Children have become heavier in the past 30 years, and the prevalence of childhood obesity has

more than doubled among children ages 2-5 (5.0% to 13.9%), has tripled among youth ages 6-11

(6.5% to 18.8%), and has more than tripled among adolescents ages 12-19 (5.0% to 17.4%)

(Barnes 2011). Once again Black and Hispanics are more likely to be overweight than White

children. However, recent data suggest that the rate of overweight in children did not increase

significantly between 1999 and 2008, except in the heaviest boys. This rate, though, remains

alarmingly high; all are drastic changes that occurred over a thirty year span. The twenty to

seventy-four age groups showed the highest increases compared to the two to five and six to

eleven age groups. The World Health Organization (WHO 2008) estimated that 1.5 billion

adults, 20 and older were overweight in 2008. Of these over 200 million men and almost 300

million women were obese.


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______________________________

Source: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5827a2.htm#tab1

Nearly 43 million children under the age of five were overweight in 2010. WHO refers

to the global epidemic as globesity. Figure 1 shows thepercentages of obesity across

the states.

Table 1: Prevalence of obesity among adults, by black/white race or hispanic

ethnicity, census region, and sex - Behavioral Risk Factor Surveillance

System surveys, United States, 20062008

Census regions White, non-Hispanic

(n = 900,629)

Black, non-Hispanic

(n = 84,838)

Hispanic

(n = 63,825)

Percent (95% CI) Percent (95% CI) Percent (95% CI)

Overall

Both sexes 23.7 (23.523.9) 35.7 (35.036.3) 28.7 (28.029.5)

Men 25.4 (25.125.7) 31.6 (30.632.7) 27.8 (26.728.9)

Women 21.8 (21.622.1) 39.2 (38.540.0) 29.4 (28.530.3)

Northeast

Both sexes 22.6 (22.223.0) 31.7 (30.033.4) 26.6 (25.028.3)

Men 25.0 (24.425.6) 26.5 (24.029.1) 26.9 (24.329.6)

Women 20.0 (19.620.5) 36.1 (34.038.3) 26.0 (24.128.0)

Midwest

Both sexes 25.4 (25.125.8) 36.3 (34.937.9) 29.6 (27.431.9)

Men 27.0 (26.527.6) 32.1 (29.734.5) 29.7 (26.433.1)

Women 23.8 (23.324.2) 40.1 (38.342.0) 29.2 (26.631.9)

South

Both sexes 24.4 (24.124.7) 36.9 (36.237.7) 29.2 (28.130.3)Men 26.3 (25.826.8) 32.6 (31.433.9) 28.3 (26.630.1)

Women 22.5 (22.122.9) 40.6 (39.741.5) 29.7 (28.331.1)

West

Both sexes 21.0 (20.621.5) 33.1 (29.736.7) 29.0 (27.730.3)

Men 22.1 (21.522.8) 34.1 (29.039.6) 27.3 (25.529.2)

Women 19.8 (19.320.4) 32.0 (28.236.1) 30.4 (28.732.1)


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Figure 1: U.S. Obesity Trend

Source: U.S. Obesity Trends 19852009, CDC, August, 2010
http://www.cdc.gov/obesity/data/trends.htmlhttp://www.cdc.gov/obesity/data/trends.htmlhttp://www.cdc.gov/obesity/data/trends.htmlhttp://www.cdc.gov/obesity/data/trends.html


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______________________Source: Extracted and modified from U.S. Obesity Trends 19852009, CDC, August,2010.

Table 2: U.S. Obesity Trends Amongst the States

State Percent State PercentAlabama 31.0 Illinois 26.5

Alaska 24.8 Indiana 29.5

Arizona 25.5 Iowa 27.9Arkansas 30.5 Kansas 28.1California 24.8 Kentucky 31.5Colorado 18.6 Louisiana 33.0Connecticut 20.6 Maine 25.8Delaware 27.0 Maryland 26.2Washington DC 19.7 Massachusetts 21.4Florida 25.2 Michigan 29.6

Georgia 27.2 Minnesota 24.6Hawaii 22.3 Mississippi 34.4Idaho 24.5 Missouri 30.0Montana 23.2 Rhode Island 24.6 Nebraska 27.2 South Carolina 29.4 Nevada 25.8 South Dakota 29.6 New Hampshire 25.7 Tennessee 32.3 New Jersey 23.3 Texas 28.7 New Mexico 25.1 Utah 23.5 New York 24.2 Vermont 22.8 North Carolina 29.3 Virginia 25.0

North Dakota 27.9 Washington 26.4Ohio 28.8 West Virginia 31.1Oklahoma 31.4 Wisconsin 28.7Oregon 23.0 Wyoming 24.6Pennsylvania 27.4
http://www.cdc.gov/obesity/data/trends.htmlhttp://www.cdc.gov/obesity/data/trends.htmlhttp://www.cdc.gov/obesity/data/trends.htmlhttp://www.cdc.gov/obesity/data/trends.html


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Figure 2: Percentages of Overweight and Obesity in Tennessee

__________________________

Source: (BRFSS Prevalence and Trends Data www.thecenterformichigan.net/wp-

content/uploads/2010/01/Obesity.xls)
http://www.thecenterformichigan.net/wp-content/uploads/2010/01/Obesity.xlshttp://www.thecenterformichigan.net/wp-content/uploads/2010/01/Obesity.xlshttp://www.thecenterformichigan.net/wp-content/uploads/2010/01/Obesity.xlshttp://www.thecenterformichigan.net/wp-content/uploads/2010/01/Obesity.xls


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Notes and Definitions of Terms:

P=Prevalence and Trends Data

T= Tennessee Available Years

BMI=Weight classification by Body Mass Index (BMI)

Ov= Overweight and Obesity (BMI)

Percent (%) = Weighted Percentage

CI = Confidence Interval

n = Cell Size (Numerator

Table 3: Prevalence and Trends Data in Tennessee

Year: Tennessee

Percent CI n

1995 18.4 (16.6-20.2) 359

1996 17.4 (15.8-19.0) 508

1997 17.7 (16.1-19.3) 510

1998 19.2 (17.5-20.9) 556

1999 20.5 (18.8-22.2) 596

2000 22.9 (21.1-24.7) 656

2001 23.4 (21.6-25.2) 653

2002 24.5 (22.7-26.3) 740


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____________________

Source: BRFSS Prevalence and Trends Data available at

www.thecenterformichigan.net/wp-content/uploads/2010/01/Obesity.xls

Notes and Definitions of Terms:

P=Prevalence and Trends Data

T= Tennessee Available Years

BMI=Weight classification by Body Mass Index (BMI)

Ov= Overweight and Obesity (Ov-BMI)

Percent (%) = Weighted Percentage

CI = Confidence Interval

n = Cell Size (Numerator)

Table 3: Continued

Year: Tennessee

Percent CI n

2003 25 (23.0-27.0) 616

2004 27.2 (25.2-29.2) 933

2005 27.4 (25.4-29.4) 1245

2006 28.8 (26.8-30.8) 1195

2007 30.7 (28.5-32.9) 1428

2008 31.2 (29.0-33.4) 1453

2009 32.9 (30.9-34.9) 1674

2010 31.7 (29.7-33.7) 1730
http://www.thecenterformichigan.net/wp-content/uploads/2010/01/Obesity.xlshttp://www.thecenterformichigan.net/wp-content/uploads/2010/01/Obesity.xlshttp://www.thecenterformichigan.net/wp-content/uploads/2010/01/Obesity.xlshttp://www.thecenterformichigan.net/wp-content/uploads/2010/01/Obesity.xls


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Many American communities are characterized by unhealthy options when it comes

to dieting and exercising. According to the Centers for Disease Control and prevention

(CDC 2008), public health approaches that make healthy options affordable and easily

available for Americans are needed. The main focus of the CDCs division of nutrition,

physical activity and obesity (DNPAO) is to create policy and environmental changes to

increase physical activity, consumption of fruits and vegetables, breastfeeding and to

reduce television viewing, consumption of sugar, sweetened beverages and high energy

dense foods. Tennessee had a population of 6,346,105 in 2010 (metro pulse 2011).

Bress 2009 says about 4.8 million of the population are adults of which thirty-six percent

are considered overweight and thirty-two percent are classified as obese.

Three in ten Tennessee adults report no leisure time for physical activity:

Only twenty-three percent of adults eat fruits and vegetables at least 5 times a day

Sixteen percent of Tennessee youth ninth through twelve grades are overweight and

another sixteen percent are obese according to 2009 youth risk behavior survey data

(http://www.cdc.gov/obesity/stateprograms/fundedstates/tennessee.html)

Only twenty-four percent of the youth ninth through twelve grade meet physical

activity recommendation levels which are 60 or more minutes of physical activity a

day

Only eighteen percent eat fruits and vegetables five times a day

Forty one percent drank one plus non diet soda a day

Thirty-eight percent watched three or more hours of television a day
http://www.cdc.gov/obesity/stateprograms/fundedstates/tennessee.htmlhttp://www.cdc.gov/obesity/stateprograms/fundedstates/tennessee.html


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Tennessees obesity task force received CDC funding to develop a state nutrition,

physical activity and obesity plan, Eat well, play more Tennessee.

BMI

In classifying an individual as obese or not Body Mass Index (BMI) is observed

that is a heuristic proxy for human body fat based on an individuals height and weight.

Body mass index is defined as the individuals body weight divided by the square of his

or her height.

Statement of the problem

Tennessee is ranked second out of the fifty states in terms of obesity. The

epidemic of obesity took off from about 1980 and in almost all countries has been rising

excessively ever since. When the Society of Actuaries (SOA) (U.S. world news 2011)

researchers separated the economic cost of overweight and obesity to the United States in

2009, they found that it was $72 billion for overweight and $198 billion for obesity. The

findings are based on a review of papers published primarily between January 1980 and

June 2009. Tennessee alone spends $1.5 billion each year on obesity-related health costs

(Sanchez 2010). According to data from the Behavioral Risk Factor Surveillance System

(BRFSS), no state met the Healthy People 2010 objective of 15 percent, and 30 states

were 10 or more percentage points away from the objective

(http://www.obesity.org/resources-for/obesity-statistics.htm). Obesity increase risks of

acquiring diabetes, heart disease and other chronic illnesses. Obesity is now being
http://www.health.gov/healthypeoplehttp://www.obesity.org/resources-for/obesity-statistics.htmhttp://www.obesity.org/resources-for/obesity-statistics.htmhttp://www.health.gov/healthypeople


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considered a disease, and sixty nine percent of adults and thirty nine percent of children

in Tennessee are considered obese.

Objectives

The objectives of this study are as follows:

To assess awareness levels about obesity and related issues among students in the

school of Agriculture and Consumer Sciences at Tennessee State University,

To characterize the prevalence of obesity and knowledge about it among the

students,

To discuss suggestions by the students involved in this study.

Hypothesis

It is hypothesized that awareness about obesity and its prevalence among college

students will be related to: (a) access to information about it, (b) characteristics of the

students, (c) reading food labels, (d) familiarity with USDA dietary guidelines, (e) eating

habits, and (f) exercise habits. The hypothesis will be tested using primary data collected

on the above factors these variables were looked at based on literature from Weicha et al

2006, Hasse et al 2004 and Edman et al 2005.


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Significance of the problem

This study can contribute to understanding about factors that are related or

contribute to obesity among college students in Tennessee. The study can also raise

awareness levels of obesity amongst college students. Responses received from the

participants can suggest how to go about addressing the obesity issue, in regards to as

exercise facilities, watching what is consumed; attending classes that pertain to healthy

living and dieting. This study can contribute to a better understanding of students

awareness about obesity and their suggestions to tackle the problem. Research focusing

on college students is limited in general and at the historically black colleges and

universities in particular.

Figure 3: Prevalence Percentages of Obesity Among Men and Women

The graph shows trends in the prevalence of obesity for adults aged 20to 74 years in the

United States from 1960 to 2000 as percentage of the total population.


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The rest of the thesis is organized as follows: chapter II will present review of

relevant literature that will highlight what has been done. Chapter III focuses on

methodology. This provides the hypothesis to be tested, description of data collected and

methods used in analyzing it. The last chapter will analyze data, present and discuss the

results including their implications.


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CHAPTER II

LITERATURE REVIEW

According to Nelson (2007) the transition from adolescence to adulthood is one

developmental period that may be a critical stage for weight gain. Weicha et al (2006)

came to a conclusion that television viewing is associated with exposure to food and

beverage advertising and with between meal snacking. Majority of youth and college

students watch television in their leisure time Hasse (2004). Nelsons study is the first to

examine prevalence, trends and social disparities, overweight, obesity, and class II

obesity in a nationally representative sample of college students in the United States. The

purpose of their study was to examine social disparities and behavioral correlates of

overweight and obesity overtime among college students. They took a sample of 24,613

college students 12,786 in 1993 and 11,827 in 1999 all under the age of twenty five

(mean 20.4 S.D 1.6). The questions focused on physical activity, television viewing, and

BMI analysis. Respondents reported current height in feet and inches and weight in

pounds. Self-report measures of height and weight are generally considered to be valid

and reliable for large-scale surveillance surveys. Their results were descriptive analyses

and cross tabulations which came from a program similar to SPSS and SAS. They ran

tests on gender and overweight then added television viewing to the analyses; they also

added information that pertained to the objectives. They concluded overweight rose

21.7% in 1993 to 26.8% in 1999, obesity rose 4.1% in 1993 to 6.5% in 1999, and class II

obesity rose 0.9% to 1.9%. Significantly higher rates of overweight and obesity occurred


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among students in their later years of college. Males were significantly more likely to be

overweight and obese, than their female colleagues, and out of the race classification

African American males were more obese as seen in the table below.

Table 4: Prevalence of Overweight, Obesity and Class II Obesity by

Socio-Demographic Characteristics

SelectedDemography

Sample Size OverweightBMI>=25

ObesityBMI>=30

Class IIobesity

BMI>=35

Year 1993 1999 1993 1999 1993 1999 1993 1999GenderFemale 7369 7258 13.5 20.0 2.9 5.4 1.0 2.0

Male 5417 4569 30.8 35.0 5.4 7.8 0.8 1.8

RaceWhite 10624 9307 21.5 26.7 3.9 6.2 0.7 1.7African American 568 633 33.3 38.3 11.2 13.9 4.4 5.3Asian 849 978 13.6 16.4 2.0 2.3 0.2 0.6 Native/American 745 909 23.9 30.6 3.4 8.2 0.6 2.1Hispanic 733 743 25.0 30.2 2.8 8.3 0.4 2.2

Socioeconomic positionBoth ParentsAttended College

7454 7412 20.4 25.0 3.6 5.9 0.7 1.6

One Parent (notboth) AttendedCollege

3256 2841 23.4 29.1 4.6 7.6 1.2 2.0

Neither ParentAttended College

2076 1574 23.5 31.4 4.9 7.2 1.2 2.9

Years in SchoolFirst Year 2864 2993 18.9 23.0 3.1 5.2 0.7 1.7Sophomore 2648 2845 19.5 27.3 4.0 6.7 0.8 2.5Junior 3110 2912 22.0 27.6 4.3 7.2 0.9 1.5Senior 3045 2382 23.8 27.5 4.3 5.7 1.0 1.3Fifth Year 1119 695 28.7 37.2 5.7 10.9 1.2 3.7

___________________________

Source: Disparities in overweight and obesity among U.S. college students. AmericanJournal of Health Behavior. Volume 31 issue 4, 1 July 2007, 363-373Toben F Nelson


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A study carried out by Anderson et al (2003), the transition from high school to

college may be such a critical period, because it is associated with many lifestyle changes

that can lead to weight gain, such as changes in eating habits and increased alcohol

intake. A total of 192 individuals were weighed on a digital scale and their heights were

taken in September. Only 76% of participants returned to the lab in December and

provided data on weight and eating changes during their first semester in college. At that

time they were weighed again and completed follow up questionnaires as in September.

Of the 76%, 135 provided complete data at both times they were interviewed. A subset

of participants came back and provided the same information in May. In conclusion the

percentage of participants defined as overweight or obese from September to May

doubled, so for the group provided itssafe to say the freshman year of college could be

considered a critical period for weight gain Anderson et al (2003). Anderson also stated

identifying critical periods for weight gain such as the freshman year of college and the

factors that influence them may lead to the development of effective obesity prevention

programs.

In a study carried out by Levi et al (2007) 358 college students were surveyed at

state university in the western United States to test the applicability of involvement on

issues of obesity and eating habits. What they found was that women focused more on

the types of food they ate than men. They concluded that mens food choices are fixed in

the ideology of what it means to be male and female in American society.


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In a study done by Anne house et al (2004) a cross sectional survey was carried

out with 19,298 university students from 23 countries varying in culture and level of

economic development. The study focused on leisure time physical activity, health

beliefs, and health knowledge. The study showed prevalence of inactivity in leisure time

varied with cultural and economic developmental factors, 23% north Western Europe and

United States, 30% central and Eastern Europe, 39% Mediterranean, 42% pacific Asian

and 44% developing countries. They concluded that knowledge about activity and health

was disappointing with only 40-60% of students being aware that physical activity was

relevant to risk of heart disease. In the Journal of American College Health a study was

carried out by Terry et al (2003) that zeroed in on 738 college students aged 18 to 27 to

assess overweight, obesity, dietary habits, and physical activity. They used BMI >

25kg/m^2 or BMI>85th percentile and BMI>30 kg/m^1 or BMI>95th percentile to

estimate overweight and obesity for ages less than up to 19. For the ages 20 and up they

used BMI > 25kg/m^2 and BMI>30 kg/m^2. The study gave results of overweight rates

of 21.6% and obesity rates of 4.9%, 69% of the respondents reported less than five

servings of fruits and vegetables per day, and more than 67% reported less than twenty

grams of fiber per day. The respondents also indicated that physical activity was done

less than three days a week. Most college students are not meeting dietary and physical

activity guidelines Terry et al (2003). These authors suggested the need for prevention,

interventions and increased understanding of overweight in college students.


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Edman (2005) wanted to examine the relationship between negative emotions,

body dissatisfaction, exercise, and disordered eating attitudes and behaviors among obese

college students. The participants were 190 students of which are 88 male and 102 are

female those that had a BMI above 30, were all required to take surveys. In this study

compared to Real men dont read food labels the females reported higher levels of

disorderly eating, uncomfortable with body size, and more frequent dieting, while the

males worked out more. The significance of the study carried out by Edman et al (2005)

was that body discomfort, anger discomfort, and self-discouragement went with the drive

for thinness for both males and females. They also concluded that anger discomfort is the

only factor that could predict disorderly eating in both genders. Anger management

may be an important component in treatment of disordered eating among obese young

adults Edman et al (2005).

The objectives of the study titled association between the body mass index of

first year female university students and their weight-related perceptions and practices,

psychological health physical activity and other physical health indicators. Cilliers et al

(2006) were investigating the association between the weight status of freshman female

students and various weight management-related characteristics to identify possible

components of a weight management program for students. What they looked at were

weight measured in light clothes with no shoes, and height taken with no shoes, blood

pressure, physical activity, body shape, eating attitudes, and self concept. Additional

questions included previous schooling, medication use, chronic disease, and smoking

habits. What they came up with was the management program should include


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information about supplement use, smoking, realistic weight goals, safe and sound

weight loss methods, weight cycling, body shape, perceptions, eating attitudes and

behaviors, self concept and physical activity Cilliers et al (2006).

In the study conducted by Sira et al (2010) they used a cross-sectional survey to

investigate the rates of overweight and obesity and eating attitudes among 582 students

with 106 male and 420 female college students. The respondents came from a

southeastern university whose age ranged from 18-25, heights and weights were self

reported for BMI calculations. Sira et al (2010) used the chi-square to determine if the

students with a BMI greater than 25 between gender and ethnic background were

significant. Males had significantly higher mean BMI than females (48.1% Vs 28.9%)

Chi-square = 15.26 with a degrees of freedom =1and p less than 0.001. According to the

report about a third (29.8%) of college students were overweight or obese. Sira et al

(2010) conducted a study of students who tried to lose weight but went about it

incorrectly. These findings call for obesity prevention intervention, lifestyle

modification, and outreach programs among college students. The study further

highlights the importance of the college years as an excellent time for health promotion.

Morrow et al (2006) wanted to investigate changes in body weight, BMI, body

composition, and fat among freshman women during first year of college. They surveyed

137 women and found they gained about 2.4 pounds, and not the myth of fifteen pounds

associated with freshmen women. Grahm et al (2002) reported that the fixation on the

freshman fifteen myth is responsible for freshmen students having negative feelings


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20

about their body weight and image while being more likely to categorize themselves as

being overweight.

Racette et al (2008) conducted a survey to assess height and weight changes,

exercise and dietary behaviors among college students from freshman year to the end of

the senior year. The major finding of the study was BMI increased significantly through

the four years of college. The result also suggests that if weight gain of freshman year

continued throughout the four years, it will be a dramatic increase in the incidence of

overweight and obesity among young adults.


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CHAPTER III

METHODOLOGY

Data for this study were collected from primary and secondary sources to analyze

objectives of this study. The secondary sources include books, journals, publications by

CDC, and others. Face to Face survey of graduate and undergraduate students in the

school of Agriculture and Consumer sciences were conducted in spring 2011. A total of

one hundred forty-one completed responses were received. Seventy-seven percent of

respondents were female with the balance being male.

Data Collection

Data collection involved face-to-face surveys of Agriculture and Consumer

Science students at Tennessee State University. The survey instrument was pre-tested

using a few students to get feedback to finalize it. The questions in the survey included

types of food consumed and where, height and weight, exercise habits, socioeconomic

background, awareness levels about obesity and knowledge of USDA dietary guidelines.

The data were coded, entered into the computer and analyzed using the Statistical

Package for the Social Sciences (SPSS). Descriptive and inferential statistics including

frequencies, chi-square test and analysis of variance (ANOVA) were used in explaining

results of the findings.


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The BMI data was compiled on excel spreadsheet using the following formula:

BMI=Weight (kg)/Height (M)2. For example, the BMI for an individual who is 57 and

weighs 150 pounds is given below:

BMI = Weight (kg) Height2

(m)An individual who is 5'7'' in height and weighs 150 pounds

Weight conversion (lb to kg):To convert from lbs to kg: weight in lbs divided by 2.2Example: 150 lb 2.2 = 68 kg

Height conversion (inches to meters):To convert from inches to meters: (height in inches x 2.54) 100Example: 67 x 2.54 = 170170 100 = 1.70 meters

BMI = 68 (1.7)2 = 68 2.89 = 23.5Source:http://www.acefitness.org/fitnessqanda/fitnessqanda_display.aspx?itemid=324)


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CHAPTER IV

RESULTS AND DISCUSSION

Sixty-one percent of respondents had urban background with the remaining

coming from rural areas. Of the 39% from rural areas, 10.6% were male and 28.4% were

female. For the urban background, 12% were male and 48.9% were female. In terms of

age, the results showed the following: 18-22 year old (57.7%), 23-27 (27.0%), 28-32

(7.1%), 33-37 (3.5%) and 38-42 (2.1%) only a miniscule proportion (2.1%) accounted for

those beyond the age of forty-two.

Regarding parents education 32.6% indicated their mothers had a high

school/GED level of education, 26.2% attended college but did not finish, 20.6% hold a

college degree, and 19.1% received a graduate degree. When dealing with fathers

educational level, 38.3% indicated that they received a high school diploma or GED,

18.4% had some college education, 22.0% finished college and 12.1% attained a graduate

degree.

Students who participated in the survey varied in terms of department and

concentrations. Of the 141 respondents, 73 (51.8%) were agricultural science students

and 48.2% were enrolled in the Department of Family and Consumer Sciences. Figure 4

below shows the percentage distribution of respondents in the different concentrations in

the two departments.


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Figure 4: Distribution of students enrolled in different concentrations

Not only did the students vary in terms of department and concentrations in which

they were enrolled but also differed in classification. Graduate students accounted for

27.0%. In the undergraduate category the distribution is as follows: seniors (24.8%),

juniors (22.7%), sophomore (16.3%), 9.2% and freshman. Research shows that 96.5% of

students that had taken the survey consume snacks, and over 90% of these students

consume snacks 1-10 times a day. A small proportion (3.5%) stated they consume no

snacks.

When the participants were asked about reading food labels and the types of food

they buy, a combined 74.5% indicated that they often make it a point to read food labels

or seldom read food labels. In terms of food purchased 46.1% and 26.2% purchased low

fat foods and low calorie food respectively. This may reflect that they have some

concern about their diet and healthy living.

0 10 20 30 40 50

1

2

3

4

5

Agribusiness

22.7

Food & Nutrition

7.1

Family and early

childhood

education 40.4

Plant & soil

science 17.7

Animal Science

12.1

Series1


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Table 5 shows the workout habits of the individuals in the two departments by

classification. Of the five classifications the graduate class has the most individuals with

28.9% that workout daily. The senior class that workout often 34.3%, the junior class

had 43.8% that workout sometimes, and the sophomore class works out often 21.7%.

The freshman class had the lowest total of individuals 9.2% who attempted exercising.

Table 5: Classification and Exercise Habits Cross Tabulation

Classification Daily Often Sometimes Rarely Not at all TotalFreshman (Count) 3 4 5 0 1 13

(%) within Classification 23.1 30.8 38.5 .0 7.7 100

(%) within Exercise habits 12.0 9.8 10.6 .0 14.3 9.2

(%) of Total 2.1 2.8 3.5 .0 .7 9.2

Sophomore (Count) 1 5 10 5 2 23

(%) within Classification 4.3 21.7 43.5 21.7 8.7 100

(%) within Exercise habits 4.0 12.2 21.3 23.8 28.6 16.3

(%) of Total .7 3.5 7.1 3.5 1.4 16.3

Junior (Count) 6 8 14 3 1 32



(%) of Total 4.3 5.7 9.9 2.1 .7 22.7

Senior (Count) 4 12 11 7 1 35



(%) of Total 2.8 8.5 7.8 5.0 .7 24.8

Graduate (Count) 11 12 7 6 2 38

(%) within Classification 28.9 31.6 18.4 15.8 5.3 100.0

(%) within Exercise habits 44.0 29.3 14.9 28.6 28.6 27.0(%) of Total 7.8 8.5 5.0 4.3 1.4 27.0

Total Count 25 41 47 21 7 141


(%) within Exercise habits 100.0 100.0 100.0 100.0 100 100

(%) of Total 17.7 29.1 33.3 14.9 5.0 100


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When the individuals were questioned as to whether or not they were aware of

poor diets can jeopardize health, 124 responded yes (87.9%) and 17 no (12.1%). The

graduate class had the most yes responses with 33 (86.8%) and the freshman class had the

fewest no responses, with 0 indicating that individuals are aware of poor diet but obesity

doesnt concern them. Therefore they had no reason to inquire about the issue.

Table 6: Classification Aware that Poor Diets Put Health at Risk CrossTabulation

ClassificationAware that poor diets put health at risk

TotalYes No

Freshman 13 0 13

Sophomore 19 4 23

Junior 28 4 32

Senior 31 4 35

Graduate 33 5 38

Total 124 17 141


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27

Table 7: Plans to Change Eating Habits Which Would Encourage Plans to

Change Eating Habits Crosstabulation

Selected Variables

Which would encourage change in eating habits

Nutrition

Education

Availabil

ity of

low cost

exercise

facilities

Reduced

portion

sizes of

foods Other Total

Yes (Count) 47 28 18 9 102

Percent within Plan to

change your eating habits

46.1 27.5 17.6 8.8 100.0

Percent within Which

would encourage change

in eating habits

71.2 82.4 75.0 60.0 73.4

Percent of Total 33.8 20.1 12.9 6.5 73.4

No (Count) 19 6 6 6 3



51.4 16.2 16.2 16.2 100.0



in eating habits

28.8 17.6 25.0 40.0 26.6

Percent of Total 13.7 4.3 4.3 4.3 26.6

Total Count 66 34 24 15 139



47.5 24.5 17.3 10.8 100.0



in eating habits

100.0 100.0 100.0 100.0 100.0

Percent of Total 47.5 24.5 17.3 10.8 100.0


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A question in the survey inquired about changing eating habits, and what would

help encourage change. Of those responding, 47.5 percent indicated that nutrition

education can help change eating habits while, 24.5 percent agreed that availability of

low cost exercise facilities and 17.3percent indicated reduced portion sizes of food can

help change eating habits. The category other is selected by 10.8 percent of the

respondents. The importance of more events that inform about healthy eating, free

exercise facilities, reading labels, eating more fruits and vegetables, and eating less fast

foods are underscored by the respondents.


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Table 8: Classification: Plan to Change Your Eating Habits Cross Tabulation

Plan to changeyour eating habits

Classification Yes No Total

Freshman (Count) 8 5 13

Percent within Classification 61.5 38.5 100.0

Percent within Plan to change your eating habits 7.7 13.5 9.2

Percent of Total 5.7 3.5 9.2

Sophomore (Count) 21 2 23




Junior (Count) 23 9 32




Senior Count 27 8 35




Graduate (Count) 25 13 38Percent within Classification 65.8 34.2 100.0



Total Count 104 37 141




When the students were asked if they had planned to change their eating habits,

73.8 said yes and 26.2 responded no. The individuals who agreed can be said to have

some knowledge of healthy diet, or unhappy with their current body build. Those who

responded no either dont care about their body build, they could be physically fit, or are

content with their current diet status.


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Table 9: Distribution Frequencies of Awareness of Obesity Problems Among

Designed Groups

Awareness Levels of ObesityProblems

A lot Some VeryLittle

None Total

Total Number of respondents 74 51 11 5 141

Percent 52.5 36.2 7.8 3.5 100.0

Awareness of the Effects of Poor DietsYes (# of resp.) 71 42 7 4 124

Percent 57.3 33.9 5.6 3.2 100.0 No (# of resp.) 3 9 4 1 17

Percent 17.6 52.9 23.5 5.9 100.0

Knowledge of Dietary GuidelinesYes (# of resp.) 52 23 0 3 78

Percent 66.7 29.5 .0 3.8 100.0 No (# of resp.) 22 28 11 2 63

Percent 34.9 44.4 17.5 3.2 100.0

Reading Food LabelsOften (# of resp.) 33 15 4 0 52

Percent 63.5 28.8 7.7 .0 100.0Seldom (# of resp.) 31 17 2 3 53

Percent 58.5 32.1 3.8 5.7 100.0 Never (# of resp.) 10 19 5 2 36

Percent 27.8 52.8 13.9 5.6 100.0


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Table 9: Continued

Distribution of Frequencies Awareness Levels of Obesity Problems

A Lot Some VeryLittle

None Total

Vegetable Consumption Habits

Yes (# of resp.) 56 26 3 2 87

Percent 64.4 29.9 3.4 2.3 100.0

No (# of resp.) 18 25 8 3 54

Percent 33.3 46.3 14.8 5.6 100.0

Exercise Habits

Daily (# of resp.) 15 8 1 1 25

PercentOften (# of resp.)

60.023

32.016

4.02

4.00

100.041

PercentSometimes (# of resp.)

56.126

39.017

4.93

.01

100.047

Percent 55.3 36.2 6.4 2.1 100.0

Seldom (# of resp.) 8 8 4 1 21

PercentNever (# of resp.)

38.12

38.12

19.01

4.82

100.07

Percent 28.6 28.6 14.3 28.6 100.0

Plan to Change Eating HabitsYes (# of resp.) 58 39 5 2 104

Percent 55.8 37.5 4.8 1.9 100.0

No (# of resp.) 16 12 6 3 37

Percent 43.2 32.4 16.2 8.1 100.0


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Table 10: Results of Chi-Square Tests of Awareness of Obesity Problems Among

Designed Groups

Pearson Chi-Square Test Likelihood Ratio Test2-Stat DF P-Value 2-Stat DF P-Value

Awareness of the Effect of Poor Diet

12.40 3 0.006 11.73 3 0.008

Knowledge of Dietary Guidelines

22.51 3 0.000 26.86 3 0.000

Reading Food Labels

14.87 6 .021 16.995 6 .009

Vegetable Consumption Habits

15.11 3 .002 15.26 3 .002

Exercise Habits

21.38 12 .045 14.70 12 .258

Plan to Change Eating Habits

8.50 3 .037 7.503 3 .057

Analysis of Variance of Awareness of Obesity Problem

The one-way Analysis of Variance (ANOVA) was applied to assess how the

awareness levels of obesity vary among different groups that was defined according to a

given group or categorical variable. ANOVA uses least squares to fit the linear models.

The model of the one-way ANOVA is:

(1)

Yij = m+a2 +a3 + ...+aj + ...+aJ +eij

Where Yij denotes the dependent variable-the awareness level of obesity, ranging from 1

to 4, for each individual i in the sample that belongs to group j (j=1,,J). The sample


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can be divided intoJgroups according to a categorical variable. For example, the sample

can be divided into three groups: respondents who often, seldom and never read food

labels. Where is the intercept to be estimated, 2 J is fixed-effects coefficients to be

estimated, which measures the deviation of the average awareness level of group j from

the average awareness of the omitted group. Group 1 is the omitted or base group to be

compared with. ij represents the error term which is assumed to be independently and

identically distributed (i.i.d).

Define

Yjas the average awareness of obesity of group j, and

Y is the overall

average awareness of the whole sample. The total variation in obesity awareness among

respondents in the sample is measured by the total sum of squares (TSS) as

(2)

SST= (Yij -Y)2

ji

The between-group sum of squares (SS), or equivalently SS of the model (1)

(SSR) measures the variation in obesity awareness between groups, and is defined as

(3)

SSR = (Yj -Y)2

j

The variation of obesity awareness within a group is measured by the within-

group SS. It is also the SS of residuals of the model (1) (SSE), which indicates the

variation in obesity awareness due to factors, observed or unobserved, that are not

included in model (1). SSEis computed as SSE= SST SSR.R2 measures to what extent

the total variation in obesity awareness in the sample is explained by differences in

obesity awareness between groups, i.e.,R2 = SSR/SST.


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The F-test is applied to test whether the average awareness levels of obesity

problems are jointly equal among groups. The null hypothesis of F-test can be written as

2= 3= =J=0. The F-test is also equivalent to testing whether or not the between-

group variation in obesity awareness is significant in explaining the total variation in

obesity awareness across individuals in the entire sample (Snedecor and Cochran, 1989).

F-test statistic is the ratio ofSSR/J 1 overSSE/NJ.

Furthermore, the F-test serves as a robustness check to the Pearson Chi-square

test, which assumes specific distribution assumption. In comparison, F-test is not limited

by specific distributions, and is applied in this study because the sample has sufficient

number of observations. If the results of F-tests are consistent with those of the Pearson

Chi-square test, it suggests that the findings are robust to the tests and test statistics.

In the following analysis, the ANOVA models was estimated by designating the

awareness of obesity problems as the dependent variables as specified in model (1), and

selecting the knowledge of USDA dietary guidelines, awareness of the effects of poor

dieting, reading of food labels, vegetable consumption, exercise habits, and intention to

change eating habits, respectively, as the categorical variable by which the sample was

divided into definite groups.

The results reported in table 11 include (i) the sum-of-squares results of ANOVA,

andR2 as the indicator for goodness-of-the-fit of the model; and (ii) the results, i.e., F-

statistics and the resultant p-values, for the F-test for the model significance or the

significance of between-group differences.


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Table 11: ANOVA Results of Awareness Level of Obesity Problems

Sum of Squares (SS) F-Test

DF SS R2 F-Stat P-Value

Awareness of the Effects of Poor Dieting

Between-group (SSR) 1 5.90 6.93 10.35 0.00

Within-group (SSE) 139 79.18 93.04

Total (SST) 140 85.08 100

Knowledge of Dietary Guidelines



Total (SST) 140 85.08 100

Reading Food Labels



Total (SST) 139 84.69 100

Vegetable Consumption



Total (SST) 140 85.08 100

Exercise HabitsBetween-group (SSR) 4 7.45 8.76 3.27 0.01


Total (SST) 140 85.08 100

Plan to Change Eating Habits



Total (SST) 140 85.08 100

The first set of results show that the awareness levels of obesity are significantly

heterogeneous, at a better than 1 significance level, between those who were aware that

poor dieting could put health at risk and those who had no awareness. This result is

consistent with that of previous Chi-squared tests. This between-group difference


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contributed a small 6.9 percent of the overall differences in obesity awareness across

individuals in the sample. It should be noted that this implication is drawn based on the

one-way ANOVA that analyzes one categorical variable, while leaving other observed

and unobserved factors in the residual term in the model (1). This applies to all of the

following analysis.

The survey question was designed to see whether people would associate poor

diets and its adverse effect with obesity. The premise is that individuals who are aware of

obesity problems would consent that poor diets could put health at risk, and vice versa.

Therefore, it is expected that variations in awareness of the adverse effects of poor diets

would explain little of variations in awareness of obesity problems among respondents.

The low R2 measure supports this hypothesis. The evidence in the frequency table, in

addition, supports this hypothesis. Of all 141 respondents, 113 had a lot or some

awareness of obesity knew about the adverse effects of poor diets; and 5 had very little or

none awareness of obesity also knew nothing about the adverse effects of poor diets.

The second set of results indicates that respondents who had knowledge about

dietary guidelines and who did not had a significantly different degree of obesity

awareness. The null hypothesis ofF-test that average awareness is equal between these

two groups is rejected at a better than 1 significant level. R2 indicates that the difference

in obesity awareness between two groups contributed 9.38 percent of the overall

differences in obesity awareness across individuals in the sample. Though with moderate

degree based on R2 measure, this between-group difference in obesity awareness has

statistical significance in explain the total sample variation in obesity awareness


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according to F-test. The result of F-test, which suggests heterogeneity in obesity

awareness between respondents with and without the knowledge of dietary guidelines, is

consistent with that of the Pearson Chi-square test. The findings are, therefore, shown to

be robust to different tests and test statistics.

The results on distribution frequencies further show how the respondents in these

two groups differ in their awareness of obesity problems. More than half of the

respondents (78 out of 141 respondents) knew about the dietary guidelines, and majority

of them had high (52 respondents) or some (23 respondents) awareness of obesity

problems, and only a few had little (zero) (11 respondents) or none awareness of obesity

problems. On the other hand, three-quarters of the rest 68 respondents, declaring no

knowledge of dietary guidelines, were aware of obesity problem to some degree or more.

Specifically, 22, 28, 11, 2 respondents had a lot, some, little, and none awareness of

obesity.

Taken the analysis of distribution frequencies and ANOVA together, the results

and findings are summarized as follows. First, the results suggest that respondents had a

higher degree of awareness of obesity problems than of dietary guidelines. Second,

majority (88.65 percent) of the respondents had a lot or some degree of awareness of

obesity problems, regardless of whether they knew or did not know about dietary

guidelines. Therefore, the difference in obesity awareness between these two groups did

not contribute much to explaining the overall variation across individuals in the sample.

The modestR2 measure is, hence, expected.


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Third, the significant difference in obesity awareness between respondents with

and without the knowledge of dietary guidelines, according to F-test, is observed within

two awareness levels: (i) for those 74 respondents who were highly aware of obesity

problems, two-thirds of them knew dietary guidelines; and (ii) all 11 respondents who

knew very little about obesity problems did not know about dietary guidelines. This

finding suggests respondents who knew about dietary guidelines were more likely to

know a lot about obesity problems; and those who had no knowledge of dietary

guidelines mostly likely knew little about obesity problem. The evidence implies a

positive association between the knowledge of dietary guidelines and the awareness of

obesity problem.

The analysis above examines the linkage between the awareness of obesity

problems with knowledge and awareness of poor or healthy diets; and the following

analysis assesses whether and how the awareness of obesity problems is associated with

observed behavior or actions, namely reading food labels, consuming vegetables, and

exercise. This confirms the hypothesis that individuals who are more aware of obesity

problems would be more likely to read food labels, consume vegetables on a daily basis,

and exercise regularly.

The ANOVA results pertaining to reading food labels show that the awareness of

obesity is significantly different among these three groups that never, seldom, or often

read food labels. Nevertheless, the difference in awareness levels among these three

groups only explained 7.17 percent of the overall differences in awareness levels across

individuals. From the results of the distribution frequencies, the evidence was not strong


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or clear that the awareness of obesity were different between those who made a point to

read labels and who seldom read food labels. However, it was evident that the awareness

of obesity differed among those who often read labels and those who never did.

Therefore, the heterogeneity in the obesity awareness most likely is explained by the

difference between those who often read labels and those who did not.

The results show that individuals who were aware of obesity problem reflected

their awareness to some extent in behavior as reading food labels in this case. Forty-

seven out of 124 respondents (37.9 percent) who had some or a lot awareness of obesity

said that they often made a point to read labels. Nevertheless, the rest seldom or never

read food labels even though they were aware of obesity problems. On the other hand, the

result was as expected that most respondents who had little or no awareness of obesity

problems seldom or never read labels. Four respondents who had little awareness gave

some surprising answers that they often read food labels, which might be motivated by

other causes to read labels.

In the case of vegetable eating habits, the R2 measure indicates that the difference

in obesity awareness respondents who eat two servings of vegetables daily or not

contributed only 9.3 percent of the total variations in obesity awareness across

individuals in the sample; whereas other factors explain the rest 90.7 percent of the

variations in obesity awareness. The results show that the null hypothesis of the F-test is

rejected at the 5 significant level (P value = 0.000), which indicates that the awareness

levels of obesity are significantly different between people with different vegetable

consumption behavior. Taken together with R2 measure, the results suggest that obesity


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awareness was significantly different between the two groups; however, this between-

group difference contributed merely 9.3 percent of the overall differences in obesity

awareness across individuals in the sample.

Based on the results of distribution frequencies, this study found that awareness of

obesity problems is positively associated with vegetable consumption habits, with a

higher degree than with label behavior. That is 65.5 percent of respondents who had some

or a lot awareness of obesity also consumed two servings of vegetables daily. In addition,

82 out of 87 respondents who consumed two servings of vegetables daily, and 43 out of

54 who did not consume two servings of vegetables daily all had a lot or some awareness

of obesity problems. Hence, the difference in vegetable consumption behavior did not

contribute much in explaining the differences in obesity awareness across individuals,

and therefore, the lowR2 measure.

When the ANOVA test was ran on exercise habits, this study found a significant

heterogeneity in obesity awareness among these five groups. The results show that the

null hypothesis that average mean will be equal amongst groups of the F-test is rejected

with a 1.4 percent significance level, which indicates that the awareness levels of obesity

are significantly different between individuals whose exercise habits vary. This between-

group difference contributed merely 8.7 percent of the overall differences in obesity

awareness across individuals in the sample. The results of distribution frequencies show

that individuals who were aware of obesity problem were very likely to engage in some

exercise routine. Out of 125 respondents who had some or a lot of obesity awareness, 105

(84) were involved in exercises daily, often, or sometimes. In comparison, 50 or 8 out of


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16 respondents who had little or no awareness of obesity rarely or never exercises at all.

Because the majority of respondents who practiced some exercise routine also had some

or a lot of awareness of obesity problems, it is expected that differences in awareness

levels among groups with different exercise habits would explain in a mild degree the

overall variations in obesity awareness.

Finally, the study examined whether and how individuals awareness of obesity

problems would associate with individuals intention to alter their behavior, explicitly the

survey question asks about individuals plan to change their eating habit. The awareness

levels of obesity are significantly different between two groups of those who planned to

change dietary habits and those who did not. This between-group difference contributed a

minuscule 4.2 percent of the overall differences in obesity awareness across individuals

in the sample. The results show that the more awareness of the obesity problem, the more

individuals are prone to change their eating habits. For the 74 individuals who had a high

degree of awareness, 58 indicated that they planned to change; and for the 51 respondents

who had some awareness, 39 indicated they intended to change. The respondents who

had a lot or some awareness of obesity problem may already have healthy eating habits.

Therefore, the evidence that the rest of 28 who responded that they did not plan to change

did not necessarily indicate that awareness did not affect their dietary habits. For those 16

respondents who had little or no awareness of obesity, seven indicated that they would

change their eating habits.


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Conclusions:

The analysis of distribution frequencies, Chi-squared tests, and ANOVA

estimations were conducted to examine whether and how individuals awareness of

obesity problems are associated with their knowledge about healthy or poor diets, with

their observed behavior or life style, and with their intentions to change their current

habit.

First, the study examined the linkage between the awareness of obesity problems

with knowledge and awareness of the adverse effects of poor diets and knowledge of the

dietary guidelines. The results show that most respondents associated obesity problems

with the adverse effects of poor diets. The results also indicate respondents who knew

about dietary guidelines were more likely to know a lot about obesity problems; and

those who had no knowledge of dietary guidelines mostly likely knew little about obesity

problem. The evidence suggests that respondents had a higher degree of awareness of

obesity problems and the adverse effects of poor diets, than their awareness of dietary

guidelines. Consider dietary guidelines provide information on healthy diet, whereas

obesity problems relate to the adverse effects of poor diet. It implies that respondents

were more aware of obesity problems, poor diets and ensuing adverse effects, than of

healthy diets and related effects.

Second, this study assesses whether and how the awareness of obesity problems is

associated with observed behavior or actions, namely reading food labels, consuming

vegetables, and exercise. The hypothesis that individuals who are more aware of obesity

problems would be more likely to read food labels, consume vegetables on a daily basis,


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and exercise regularly was tested. The results show that individuals who were aware of

obesity problems revealed their awareness in their life-style choices and behavior.

Namely, for respondents who had some or a lot awareness of obesity problems, 84

engaged in exercises daily, often, or sometimes, 65.5 percent consumed two servings of

vegetables daily, and 37.9 percent often made it a point to read food labels.

Finally, the study evaluated the association between the awareness of obesity

problem with stated intention to change behavior in the future. The findings indicate that

individuals who had some and high awareness of obesity problems intended to make

changes in their dietary habits, regardless whether or not they were practicing healthy

diets at the time. In contrast, 50 of those who had very little or no awareness of obesity

problem indicated that they had no plans to change their eating habits. This suggests that

the awareness levels of obesity problem are likely to influence individuals intention to

change their current dietary behavior.

Table: 12 BMI Among Students

Frequency Percent Valid Percent

Cumulative

Percent

Valid .00 37 26.2 26.2 26.2

1.00 104 73.8 73.8 100.0

Total 141 100.0 100.0

Table 12 shows that of the 141 respondents 26.2 percent (37) of them are classified as

obese, and the other 73.8 percent (107) did not respond to the weight question, are

overweight, underweight or normal weight. Of the 26.2 percent it can be assumed that

they have little or no awareness about the obesity issue, simply because they are obese.


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CHAPTER V

SUMMARY AND RECOMMENDATIONS

This study focused on the awareness levels of obesity amongst Tennessee State

University Agricultural and Consumer science students. Chapter I described the obesity

issue nationally and in Tennessee the United States Center for Disease Control (CDC)

states more than one third of adult Americans are obese. Approximately 300,000 deaths

a year are due to obesity, which puts it as the second leading cause of preventable deaths

in the United States. Economic cost of overweight and obesity to the United States in

2009 was $72 billion and $198 billion respectively. Of 6,346,105 people in Tennessee

about 4.8 million are adults of which 36 percent are considered overweight and 32

percent are classified as obese. The above data show the magnitude of the obesity

problem and the need to increase awareness about it. In chapter II literature pertaining to

awareness levels of obesity, and measures that should be taken to address the issue

amongst college students are examined. The literature review also helped in identifying

research gaps involving university students in general and those attending Historically

Black Colleges and Universities in particular.

Chapter III outlined the methodology used in this study. The data were collected

in spring semester of 2011 through face to face survey of graduate and undergraduate

students in the school of Agriculture and Consumer Sciences. Hypothesis were

developed; data were analyzed using SPSS-PC, in addition to generalizing descriptive


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45

statistics, Chi-square test and Analysis of Variance (ANOVA) were used to conduct

quantitative analyses of the data.

The first objective was assessing awareness levels of obesity amongst Tennessee

State University Agricultural and consumer science students. The study provided that

Tennessee State University agriculture and consumer science students who read food

labels, exercise, have knowledge of healthy eating guidelines, were aware of the obesity

problem. The second objective determined the obesity levels amongst the students in the

two departments. The study found that 27 of the 141 respondents fall in the obese

category.

The final objective was to discuss the opinions of survey respondents of ways

they feel that will raise awareness levels of obesity. The findings indicated that

availability of more free exercise facilities, less consumption of food from restaurants,

attending nutrition classes, and more consumption of healthier foods can contribute to

dealing with the obesity issue.

Recommendations for Future research

This study can provide some insights on the subject of awareness about obesity

and strategies to tackle it based on responses received from the study group/college

students. An expanded study covering a mix of universities and states can yield

comparative results. Given that factors contributing to obesity are many, a concerted

effort involving different Government and non government agencies, communities,


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university researchers, outreach professionals and schools, is critical to make progress in

tackling the obesity problem.


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