ABSTRACT
COLLEGE: A TIME TO INCREASE KNOWLEDGE AND ATTENUATE HEALTH? A
STUDY INTO COLLEGE’S INFLUENCES ON STUDENTS’ ALCOHOL CONSUMPTION,
PHYSICAL ACTIVITY, AND, DIET
by Lauren M. Kincheloe
Many behavior choices for one’s adult life are established during the college experience, a time
in which health behaviors often. Multiple health behavior change research examines the
relationship of two or more health behaviors in people, and may be a method of providing more
efficient health interventions. Using data from 321 students' first semester of college, this study
examines the relationship between and changes students' readiness to change their physical
activity patterns, dietary choices, and alcohol consumption utilizing the Transtheoretical Model
framework. Across the semester, significant changes were observed in the students' readiness to
change certain health behaviors and gender differences were observed. Binge drinking and high
fat food avoidance stages of change, and exercise and fruit and vegetable intake stages of
change, showed the only significant health behavior relationships. Further understanding
changes in students' readiness to change their health behaviors may aid universities in improving
students' health behavior patterns.
COLLEGE: A TIME TO INCREASE KNOWLEDGE AND ATTENUATE HEALTH? A
STUDY INTO COLLEGE’S INFLUENCES ON STUDENTS’ ALCOHOL CONSUMPTION,
PHYSICAL ACTIVITY, AND, DIET
A Thesis
Submitted to the
Faculty of Miami University
in partial fulfillment of
the requirements for the degree of
Master of Science
Department of Kinesiology and Health
by
Lauren M. Kincheloe
Miami University
Oxford, OH
2012
Advisor: __________________________
Rose Marie Ward, PhD
Reader: ___________________________
Susan Lipnickey, PhD, JD
Reader: ___________________________
Mark S. Walsh, PhD
Table of Contents
Chapter 1: Proposal
Page Number
Introduction
1
Multiple Health Behavior Change Research
3
Transtheoretical Model of Change
8
Alcohol Use and College Students
13
Physical Activity and College Students
22
Dietary Patterns and College Students
28
Methods
35
Participants
35
Procedure
35
Measures
36
Proposed Analysis
37
Statement of Purpose
37
Research Questions
37
References
44
Chapter 2: Manuscript
Introduction
59
Transtheoretical Model
60
Multiple Health Behavior Change Research in College Students
61
Methods
62
Participants
62
Procedure
62
Measures
63
Results
64
Discussion
67
Limitations
70
Implications and Future Research
71
References
73
Bibliography
80
ii
Tables
100
Figures
111
Appendix A: Consent Form
115
Appendix B: Debrief Sheet for Time 1
116
Appendix C: Debrief Sheet for Time 2
117
Appendix D: Measures/Questions
118
iii
List of Tables
Page Number
Table 1: The Processes of Change
100
Table 2: Studies Examining Physical Activity Patterns in College Students
102
Table 3: Fruit and Vegetable Consumption in College Students
104
Table 4: Single Behavior Stage of Change Movement From Time 1 to Time 2
105
Table 5: Multiple Health Behavior Relationships at Time 1
107
Table 6: Correlation Table for Health Behavior Change in Stage of Change Variables
108
Table 7: Percent of Students Moving the Same Number of Stages of Change from
109
Time 1 to Time 2
Table 8: Gender Breakdown of Change in Stage of Change from Time 1 to Time 2
iv
110
List of Figures
Page Number
Figure 1: Stage distribution of students for binge drinking at time 1 and time 2
111
Figure 2: Stage distribution of students for intention to exercise at time 1 and time 2
112
Figure 3: Stage distribution of students for high fat food avoidance at time 1 and time 2
113
Figure 4: Stage distribution of students for fruit and vegetable consumption at time
114
1 and time 2
v
Chapter One
Proposal
Transitioning into college is a significant time period in a student’s life, and health
behaviors often change due to decreased parental influence (Hogan & Aston, 1986), social
influences, changed physical environment, altered self-efficacy in a new environment, varied
academic expectations, and altered social norms (Gruber, 2008; Nelson, Kocos, Lytle, & Perry,
2009; Quintiliani, Allen, Marino, Kelly-Weeder & Li, 2010; Von Ah, Ebert, Ngamvitroj, Park, &
Kang, 2004). Many behavior habits for one’s adult life are established during the college
experience and are pertinent in leading a healthy lifestyle (Von Ah et al., 2004). Unfortunately,
not all of these chosen behaviors are healthy with many chronic disease risk factors (U.S.
Department of Health and Human Services, 1991) such as weight gain, physical inactivity, high
alcohol consumption, and poor eating patterns all becoming more prevalent on college campuses
in the United States (Douglas et al., 1997; Nelson et al., 2009).
Throughout college, the presence of risky health behaviors increases alongside students’
increasing knowledge (Douglas et al., 1997; Patrick, Covin, Fulop, Calfas, & Lovato, 1997;
Racette, Deusinger, Strube, Highstein, & Deusinger, 2005; Spencer, 2002). Looking at multiple
behaviors at a New Jersey university 50% percent or more of college students regularly
consumed a diet high in saturated fats, and engaged in binge drinking, while 30% did not
consume the recommended dosage of fiber, or partake in enough cardiovascular exercise (less
than 2 days per week) (Spencer, 2002). Moreover in California, in the 30 days prior to
completing a health behavior survey, 70 percent of students had consumed at least one alcoholic
drink, and 20 percent had consumed alcohol on more than ten days in the preceeding month
(Patrick et al., 1997). Additionally, only 44% of all students partook in at least 20 minutes of
aerobic physical activity three or more days in the past week and even less (31.3%) had
participated in stretching exercises (Patrick et al., 1997).
This pattern of poor health behaviors also is demonstrated through the American College
Health Association’s annual National College Health Assessment (ACHA-NCHA-II). Over 70%
of students reported not participating in moderate aerobic or cardio exercise more than three days
a week, and over 85% not partaking in strength training activities more than three days a week
(National College Health Association, 2010). Additionally, in relation to dietary patterns, almost
three-fourths (73.7%) of college students do not consume five or more servings of fruits and
1
vegetables per day, while about one-fifth report consuming three or more high fat foods the
previous day (Douglas et al., 1997). For college students the four years after high school
graduation consist of an influx of risky health behaviors including a steady decrease in the rate of
physical activity and daily intake of fruit, in addition to an increase in the number of alcohol
drinking days and overall binge drinking (Cullen et al., 1999). These unhealthy habits put
students at risk for chronic disease and more steps need to be taken to improve the health of
college youth (Douglas et al., 1997; Spencer, 2002).
Potentially compounded by the changes in exercise, alcohol, and dietary habits, weight
fluctuates upon entering college and is a continuous health issue for college students. Females
gain an average of 1.7 pounds, and males gain an average of 4.2 pounds from entering college to
the end of senior year; in addition, the average student does not meet the recommended exercise
and dietary guidelines (Racette, Deusinger, Strube, Highstein, & Deusinger, 2008). In 2006, the
National College Health Assessment of four-year universities demonstrated about 31% of
students were overweight (using self-reported height and weight), with about 30% of these being
obese (American College Health Association, 2007). More recently, in the Spring 2010 ACHANCHA-II this percentage increased with over 35% of students reporting being overweight
(American College Health Association, 2010). Weight gain is often associated with dietary
patterns (Mozaffarian, Hao, Rimm, Willett, & Hu, 2011) and lack of physical activity (Lee,
Djousse, Sesso, Wang, & Buring, 2010), two health behaviors addressed in this study.
Gender differences also exist, with college men consistently having greater rates of risky
health behavior participation, including a greater frequency and quantity of alcohol consumption
than females (Cullen et al., 1999; Johnson, Nichols, Sallis, Calfras, & Hovell, 1998; Patrick et
al., 1997; Spencer, 2002). Weight gain is less for females throughout the four years of college
(Economos, Hildebrandt, & Hyatt, 2008; Racette et al., 2008) and females consume more fruits
and vegetables than males (Economos et al., 2008). Furthermore, more males than females
report consuming fast food, and also at a significantly higher frequency (Driskell & Morse,
2009). Interventions aimed at college students should take note of these gender effects, as sexspecific interventions may be necessary for optimal effect.
Each of these behaviors seen in college student can lead to more serious health issues
such as heart disease (Spencer, 2002), obesity, or addiction problems later in life for these young
adults (Douglas et al., 1997). The greatest rates of mortality and morbidity in the United States
2
are due to cancer and heart disease, which are most often as a result of multiple risky health
behaviors such as alcohol abuse, lack of physical activity, and inadequate diets (Prochaska,
Spring, & Nigg, 2008). Utilizing the 2001 National Health Interview Survey, researchers looked
at multiple chronic disease risk factors in the United States population (N = 29,183) (Fine,
Philogene, Gramling, Coups, & Sinha, 2004). Four common factors were found with three being
the health behaviors in question; risky alcohol consumption, physical inactivity, and unhealthy
dietary practices. Gaining a deeper understanding of college students’ health behaviors will
allow administrators and health professionals to develop successful interventions aimed at
improving the health of college students, and mitigating the observed increase in poor health
behaviors upon entering college. This study examines the changes in freshmen college students’
alcohol consumption, physical activity, and diet upon entering university, in addition to the
interrelationships amongst these three health behaviors.
Multiple Health Behavior Change Research
Healthy People 2020 is a “set of goals and objectives … designed to guide national
health promotion and disease prevention …” in the United States (U.S. Department of Health,
2010). The objectives refer to physical activity, diet, tobacco and substance abuse, as well as a
plethora of other risk factors and diseases focused on altering multiple health behaviors in order
to achieve prime health (U.S. Department of Health, 2010). Multiple health behavior change
(MHBC) research is a developing field focused on studying numerous health behaviors and
identifying interrelationships between them. The findings from this research can potentially lead
to more successful and efficient health programs focused on multiple health behaviors, as
opposed to just one. Understanding these health behaviors, the associated attitudes and beliefs,
as well as how health behavior constructs relate to one another (Noar, Chabot, & Zimmerman,
2008) can help us achieve these Health People 2020 initiatives and become a nation in optimum
health.
Research in the MHBC area raises questions with regard to intervention structuring.
There have been inquiries into whether MHBC programs should focus on the multiple behaviors
sequentially or simultaneously (Hyman, Pavlik, Taylor, Goodrick, & Moye, 2007). Hyman et al.
(2007) examined whether long term changes in smoking, reducing dietary sodium levels, and
increasing physical activity were more effective counseling all three behaviors at one time,
addressing new behaviors sequentially, or continuing with standard procedures (control).
3
Addressing behaviors simultaneously may be more beneficial and effective than sequential
counseling. Additional studies (Prochaska, Delucchi, & Hall, 2004; Prochaska et at al., 2005;
Smeets, Kremers, De Vries, & Brug, 2007) have found similar support for simultaneous
counseling with multiple health behaviors which may be pertinent in further intervention
program designs.
Contrastingly, evidence that MHBC occurs sequentially is demonstrated in multiple
studies (King et al., 1996; Noar et al., 2008; Spring et al., 2004). The rationale for sequential
interventions is that success in improving one health behavior can produce an overall increase in
self-efficacy and motivation to change a subsequent behavior. Moreover, succeeding in one goal
may make the next seem manageable and attainable (Emmons, Marcus, Linnan, Rossi, &
Abrams, 1994; King et al., 1996). Vandelanotte et al. (2007) found success in a computer based
physical activity and fat consumption intervention in both the sequential and simultaneous
multiple health behavior focused programs. There was slightly better long-term maintenance in
the sequential intervention, but the researchers suggested further research to determine the better
mode of implementation (Vandelanotte et al, 2007). This consistent dichotomy of findings for
styles of interventions demands further research into the best method of implementing MHBC
programs.
Interactions between multiple health behaviors are very common, and recognizing these
co-occurrences between health behaviors may help in creating successful and efficient
interventions (Prochaska, Nigg, Spring, Velicer, & Prochaska, 2010; Prochaska et al., 2008).
Multiple health behavior change interventions, focus on two or more behaviors individuals
engage in that effect their health, and may be focused on the individual themselves, or a whole
population (Prochaska et al., 2008). These studies often demonstrate that one risky behavior is
directly linked to another behaviors with underlying mental processes, attitudes, or issues
potentially producing both behaviors (Nigg, Lee, Hubbard, & Min-Sun, 2009; Prochaska et al.,
2010). As a result, programs focusing on one behavior may result in positive effects for an
untargeted behavior (Johnson et al., 1998). There are multiple hypotheses in MHBC research of
the effects health behavior interventions may have on participants. The first is the concept of
overburdening (Clark et al., 2005; Clark et al., 2002). The premise is that single risk factor
interventions show greater increases in positive health behaviors, than the multiple behavior
focused programs. This stems from the idea that focusing on multiple behaviors may overwhelm
4
participants and is found mostly true for action-stage-oriented stage (see Transtheoretical Model
section) interventions (Clark et al., 2005).
The second hypothesis, the enhancement hypothesis, states multiple health behavior
interventions show greater increases in positive health behaviors than single risk factor
interventions (Clark et al., 2005; Clark et al., 2002). Targeting multiple behaviors at one time is
more successful than focusing on individual behaviors, perhaps as a result of an interaction
between the health behaviors. The last hypothesis is the idea of additivity (Clark et al., 2005;
Clark et al., 2002). Contrasting to the idea of enhancement, additivity assumes there is no
interaction effect between the multiple health behaviors and the interventions work
independently of each other (Clark et al., 2005; Clark et al., 2002). Both the idea of
enhancement and additivity are viewed as desirable outcomes because one MHBC intervention
produces changes in more than one behavior and demonstrates the idea of “more bang for the
buck” (Clark et al., 2002). Recently, MHBC interventions have proven to be successful at
producing behavior change, shown to be more cost effective than single health behavior
programs, as well as produce an increase in one’s self-efficacy to change risky behaviors
(Prochaska et al., 2008; Prochaska, 2008).
Multiple health behavior change research is a growing field and it has only been within
the past ten to fifteen years that studies examining multiple health behavior relationships have
become more prevalent. In 1999 the Behavior Change Consortium (BCC) was developed,
consisting of 15 National Institute of Health (NIH) funded behavior change projects (Ory,
Jordan, & Bazarre, 2002). These projects aimed at reducing smoking, improving physical
activity and nutrition, as well as promoting other health related behaviors (Ory et al., 2002).
Most projects in the BCC focus on multiple behavior and multiple theoretical based interventions
to further understand interrelationships between different health-related factors, especially
nutrition and diet with physical activity (Ory et al., 2002). Each individual study produced its
own findings, in addition to the consortium as a whole creating the RE-AIM Model for planning
and implementing successful health behavior change interventions (Glasgow, Klesges,
Dzewaltowski, Bull, & Estabrooks, 2004) as well as the overall emphasis on the importance of
treatment fidelity (Bellg et al., 2004).
In Rhode Island, the SENIOR project consisted of a 12 month Transtheoretical Model
based multiple health behavior intervention project in older adults (Clark et al., 2005). The
5
interventions focused on nutrition, namely increasing fruit and vegetable consumption (Clark,
Nigg, Greene, Riebe, Saunders, 2002) and exercise together, as opposed to single behavior
interventions, and looked at whether individuals whom were ready to change one behavior are
more likely to change another, based upon the Transtheoretical Model of Health Behavior
Change (Clark et al, 2005; Clarke et al., 2002).
The SENIOR project revealed that the Stages of Change for exercise were significantly
associated with multiple physical activity measures and servings of fruit per day and that those in
the action or maintenance stage of one behavior were more likely to be in the same stage in the
other (Clark et al., 2005). However, only weak associations were found between exercise and
diet in older adults and it was found that these two behaviors are not very correlated in this
specific population (Clark et al., 2005). In contrast, however, other studies have shown the
success of MHBC interventions portray interrelationships between exercise and diet behaviors in
older adults (Tucker & Reicks, 2002). Even though the SENIOR project did not show these
significant relationships, it was still very valuable in the knowledge and experiences gained from
the research in older adults, and is a hallmark study for MHBC research.
Another success in MHBC research studied the relationships between smoking, low
physical activity, and high dietary fat intake in manufacturing workers, Emmons et al. (1994)
showed that smokers were more likely than nonsmokers to have the other risk factors.
Moreover, individuals with low dietary fat intake were less likely to exemplify the other two
behaviors, showing that for this population a reduction in dietary fat intake is associated with
other positive health behaviors (Emmons et al., 1994). Additionally, smokers trying to refrain
from smoking have shown significantly more confidence in their success when on a regular
exercise regime opening up the discussion on the relationship between these two variables;
physical activity and smoking (King, Marcus, Pinto, Emmons, & Abrams, 1996).
Examining health risk factors for coronary artery disease patients, researchers sought to
determine the best method for patients to improve on risk behaviors (Allegrante, Peterson,
Boutin-Foster, Ogedegbe, & Charlson, 2008). Participants had the option of choosing behaviors
to change based on their personal risk reports assessing their risk factors. Results demonstrated
greater success in patients whom chose health behaviors they were more likely to succeed in
changing, but may not produce the greatest health benefits. This finding lead researchers to
believe clients should be guided to select behaviors to based on ease of altering, in order to
6
increase confidence in success potentially leader to changes in more health beneficial behaviors
(Allegrante et al., 2008). Moreover, over 50 percent of subjects chose to focus on increasing
their physical activity (specifically aerobic exercise and strength training) pointing to the fact
that physical activity is an important behavior to focus on in MHBC for its role as a gateway
behavior in increasing the patient’s behavioral momentum for subsequent behaviors (Allegrante
et al., 2008).
MHBC research is valid in a number of age groups, not only adults. In adolescents,
while studying the relationships of physical activity, fruit and vegetable consumption, and
amount of television watching in elementary, middle, and high school students researchers found
that being at risk for one behavior almost always significantly increased the odds of being at risk
for another (Driskell, Dyment, Mauriello, Castle, & Sherman, 2008). Across all three age groups
the majority of students were at risk for multiple health behaviors, with the percentage at risk for
at least two factors increasing during the progression from elementary (62%) to high school
(73%) (Driskell et al., 2008). In California, researchers found health risk behaviors co-occuring
in 29% of males and 40% of female adolescents (mean age 14.4 years), with physical inactivity
and low fruit and vegetable consumption occurring in both males and females (Mistry,
McCarthy, Yancey, Lu, & Patel, 2009). Although these findings may point to health behavior
interventions being more successful in adolescents than college students, the limitation is that
most elementary and high school students are not as independent (transportation, financially,
food availability, etc.) as college students. Upon entering college, young adults primarily are the
ones in control of their behavior choices and are a prime population for MHBC interventions.
Multiple health behavior change research in a college student population.
Acknowledging the multitude of chronic disease risk factors college students possess, MHBC
research has expanded to the college student population. The same ideology of MHBC research
presented to other age groups could be beneficial to this population in recognizing
interrelationships and co-occurrences of health behaviors. Co-occurrences in health behaviors
have been portrayed in both male and female college students, with 65% of female college
students having more than 2 unhealthy behaviors (Quintiliani et al., 2010). In Germany cooccurrences of health risk behaviors occurred in over 85% of first year college students (Keller et
al., 2008). Often, college women who have reported low levels of fruit and vegetable intake and
physical activity (mainentance behaviors) have been shown to report higher consumption of
7
alcohol, an at-risk behavior (Quintiliani et al., 2010). Moreover, female college students whom
eat healthier have been found to participate in more vigorous activity, and those strength training
ate fewer fatty foods (Johnson, et, al., 1998). Additionally, men show co-occurrences of health
behaviors as those who partake in more vigorous, moderate, and flexibility exercise have been
seen to eat healthier foods (Johnson et al., 1998). For both genders of college students
relationships between levels of alcohol consumption and physical activity rates were seen (Nigg
et al., 2009), as well as an association between alcohol consumption and eating unhealthy,
calorie dense foods (Nelson et al., 2009). Students who drank alcohol showed greater rates of
physical activity, and more active students drank alcohol more frequently (Nigg et al., 2009).
Furthermore, it has been demonstrated that excessive alcohol consumption in college students
interferes with other health behaviors and that regular physical activity or exercise may improve
alternative health behaviors (Werch et al., 2007).
Recognizing relationships such as these can help in developing adequate interventions
and programs to increase the health of our college youth. College should be a time of growth
and development, mentally, physically, and emotionally, not a period of putting oneself at
greater health risks. Greater attention needs to be brought to preventing these behaviors and
bettering the wellbeing of college students. Understanding multiple health behavior clusters and
where one is mentally in the change process, can help develop successful health behavior
interventions in the college arena (Prochaska et al., 2008). This study examines the relationships
between college students’ alcohol consumption, physical activity and eating patterns, and their
relationship to the Transtheoretical Model (TTM) of change framework throughout the students’
freshman year of college.
Transtheoretical Model of Behavior Change
The Transtheoretical Model (TTM) was developed from components of numerous
leading theories in behavior change and is a framework for the health behavior change process
(Prochaska & Velicer, 1997). The TTM involves a progression through five stages of change in
order to reach the ultimate goal of behavior or lifestyle change and can be used for both the
acquisition and cessation of behaviors (Prochaska, DiClemente, & Norcross, 1992). There are
multiple constructs in the model including processes of change, decisional balance between the
pros and cons of the targeted behavior, and self-efficacy (Prochaska & DiClemente, 1984;
Prochaska & Velicer, 1997). By employing the TTM practitioners can help patients progress
8
through the five stages of change to their goal of behavior modification (Prochaska & Velicer,
1997).
Stages of change. The TTM’s core construct is the stage of change, or the five stages of
change an individual progresses through when attempting behavioral alteration (Glanz, Rimer, &
Viswanath, 2008). According to the TTM, change does not just have a beginning and end point,
but instead focus is on personal development throughout the whole change progression
(Prochaska & Veliver, 1997). The first stage is Precontemplation (PC; Prochaska & Velicer,
1986). Individuals in PC are not thinking about changing their behavior within the next six
months, in addition to often avoiding thinking about their high risk or unhealthy behavior(s)
(Prochaska & Velicer, 1986). Most people in this stage are unaware of their problem(s), and
they often enter therapy or group behavioral change sessions due to pressure from a friend or
loved one (Prochaska et al., 1992). The next stage is Contemplation (C). In this stage,
individuals plan on changing within the next six months (Prochaska & Velicer, 1986). Serious
thought has been put into modifying or ceasing a behavior but no action has been made
(Prochaska et al., 1992). People can become stagnant in this stage for some time, due to their
views of the pros and cons of changing a behavior being equivalent (Prochaska & Velicer, 1986).
Preparation (PR) involves the intention of changing a behavior within the foreseeable future,
usually within the next month (Prochaska & Velicer, 1986). Generally, methods for the change
have already mentally been developed by the indivdual (Prochaska & Velicer, 1986) and the
individual may previously have taken small steps to ready themself for change (Prochaska et al.,
1992). This stage is ultimately when the decision to change is made; individuals either move
forward to action, or maintain reside where they are (Prochaska et al., 1992). The Action (A)
stage is when behaviors, environments, or variables in one’s life are overtly modified in order to
support behavior change (Prochaska & Velicer, 1986; Prochaska et al., 1992). These changes
must have occurred within the past six months to be classified in the action stage (Prochaska et
al., 1992). Following action is Maintenance (M). This stage involves sustaining the changes
incorporated in the action stage. Preventing relapse is the objective of this period and as time
goes on confidence of success increases (Prochaska & Velicer, 1986). For some attempting
behavioral change maintenance may last for the remainder of one’s life (Prochaska et al., 1992).
The duration of the maintenance stage is dependent upon the nature of behavior change and how
successful one is in coping with temptations, or urges to engage in a specific behavior when in
9
difficult situations, and success in developing alternatives for the unwanted behavior (Prochaska
& DiClemente, 1986).
Research shows distribution amongst the stages of change for people partaking in at risk
behaviors is approximately 40% in Precontemplation, 40% in Contemplation, and 20% in
Preparation stage (Prochaska & Velicer, 1997; Rossi, 1992; Velicer, Fava, & Prochaska, 1995).
The majority of health behavior change programs are focused on the minority of patients in the
Preparation or Action stages which would be technically ineffective with these populations
(Prochaska, 1991). Therapy has been proven to be more successful if interventions are matched
to the same stage of change the desired individual/population are currently in. Additionally,
many times patients get stuck in a stage and have difficulty progressing forward (Prochaska &
DiClemente, 1986). Moreover, the stages of change have a spiral relationship in that one may
regress to a previous stage, then move forward, then back again (Prochaska et al., 1992). This
spiral pattern occurs most often and proves that most people learn from their mistakes and setbacks (Prochaska, 1991).
In college students, temptations from peers and their environment greatly affect health
behavior change processes and current stage of change. The temptations of easily accessible
unhealthy foods can affect one’s maintenance of a healthy eating pattern, the prevalence of
exercise amongst one’s peers can affects workout patterns, and the prevalence of alcohol in
social gatherings can affect maintenence of healthy behaviors (Nelson et al., 2009). When
examining health behavior changes, temptations are variables that should be controlled for and
new healthier behaviors need to be conditioned in order to promote successful long-term change
(Sun, Prochaska, Velicer, & Laforge, 2007).Understanding the dynamics of the stages of change
college students are in with regards to their health behaviors can help universities better develop
successful programs and improve the well-being of their students.
Processes of Change. Another construct of the TTM are the ten processes of change
(POC) that aid in progression from one stage of change to the next have been defined for the
majority of behaviors (Prochaska & Velicer, 1986). The POC utilize overt and covert actions
that assist in altering relationships, thoughts, or behaviors associated with an unwanted behavior
(Prochaska & Velicer, 1986). Most processes are directly integrated with progressing through
specific stages, and can be viewed as the independent variables leading to change (Armitage,
2009). The POC (Prochaska & Velicer, 1986) are listed in Table 1. When each POC is used
10
strategically at the proper point in the change process, the individual is more likely to be
successful in progressing through the stages of change (Prochaska & Velicer, 1997). Due to time
constraints in the survey, the POC processes will not be examined in the current study, but still
are recognized as a valid construct of the TTM.
Decisional balance. Decisional balance is the next construct of the TTM and focuses on
evaluating the pros and cons of a behavioral change (Prochaska & Velicer, 1997). Depending
upon the current SOC, the balance between the “believed” pros and cons shifts to where one may
outweigh the other (Prochaska & DiClemente, 1986). In PC, the participant view the cons of
changing as more important than the pros of changing. Usually in the C stage, the pros and cons
equal each other (Prochaska, 2008); in PR, a crossover occurs where the pros now outweigh the
cons (Prochaska, Wright, & Velicer, 2008). Ultimately, from PC to A, the pros successfully
increase with each stage resulting in a greater gap between the pros and cons of changing
(Prochaska, 2008). The same balancing relationship between prevalence of pros and cons has
been found spanning more than fifty health behaviors and in order for permanent change to
occur, the pros of the desired behavior need to outweigh the cons of the target behavior
(Prochaska, 2008).
College is a time when students often develop unhealthy risky behaviors (Douglas et al.,
1997; Nelson et al., 2009); until these young adults can visualize and believe the benefits (or
pros) of a healthy lifestyle, change to a healthier lifestyle is less likely to occur. Following the
theory behind decisional balance in the TTM, the positives of responsible moderate drinking will
need to outweigh the positives associated with heavy drinking (and the cons of reducing heavy
drinking), before students will contemplate and take action to decrease their binge drinking
(Migneault, Pallonen, & Veliver, 1997). Moreover, students need to recognize the benefits of
regular physical activity, outweigh the time and effort it takes, before exercise becomes a regular
routine in their daily schedule. Lastly, the pros of a lower fat and overall healthier eating pattern
will need to be outweigh the extra effort and lowered convenience (cons) this way of life may
present. Universities must begin to understand students’ views of the pros and cons of the latter
health behaviors in order to successfully intervene on the health patterns of our college youth.
Self-efficacy and temptations. The final construct in the TTM is the evaluation of selfefficacy in the context of tempting situations. Self-efficacy refers to the personal confidence the
individual has concerning his or her success in implementing a behavior or lifestyle change (i.e.,
11
PC to A; Prochaska & Velicer, 1986). Self-efficacy increases with progress across the stages of
change (Prochaska & DiClemente, 1986). Increased self-efficacy allows one to manage
temptations or desires to partake in an undesired behavior (Prochaska & Velicer, 1986), and an
indirect correlation is seen between self-efficacy and temptations from precontemplation to
maintenance (Prochaska & DiClemente, 1986). For most behaviors it is not until the
advancement from action to maintenance that the level of self-efficacy overrides that of
temptations and one is able to better control behavioral cues (Prochaska & DiClemente, 1986).
Higher levels of perceived self-efficacy in college students is a predictor of lower chances of
drinking, and greater participation in health-promoting activities (i.e., healthy diets and physical
activity; Von Ah et al, 2004), and lower levels of perceived ability have been associated with
reduced rates of healthy behaviors (Boekeloo & Griffin, 2009; Joffe &Radius, 1993). Using this
knowledge and working with students to increase their self-efficacy, may aid in reducing the
poor patterns of alcohol consumption, physical activity levels, and eating patterns observed in
college students.
The TTM is a valuable tool when implementing health promotion or behavior change
programs. This model is focused on the specific type of behavior change and developing
relevant interventions for target groups. Depending upon the individual’s stage of change, level
of self-efficacy, and beliefs in the pros and cons of lifestyle change, differing strategies may be
implemented in treatment. The TTM has been relevant in over forty-eight health related
behaviors (Hall & Rossi, 2008) including alcohol consumption, physical activity, and eating
behaviors, as well as effective in generalizing across a multitude of target populations (Prochaska
et al., 1994).
Although college students are the target population of the current study, the TTM has
been used in various other arenas. The formerly mentioned SENIOR project implemented stagespecific interventions for exercise and fruit and vegetable consumption and portrayed that
readiness to change health behaviors is behavior specific in older adults, meaning health
behaviors (and readiness to change those behaviors) are relatively independent of each other
(Clark et al., 2005). Stage-based multiple behavior interventions have also been administered in
primary care patients to quit smoking, eat healthier, receive regular mammograms, and prevent
skin cancer (Prochaska et al., 2005). After two years, a stage based program showed
significantly greater improvements in both dietary behaviors, use of sunscreen and avoiding sun
12
exposure, and reductions in smoking when compared to the control group, once again
demonstrating the TTM validity in multiple health behavior research (Prochaska et al., 2005). In
diabetic patients, a stage based intervention has a significantly greater effect than usual diabetic
treatment on moving patients to the action/maintenance stage in self-monitoring blood glucose
levels, healthier eating, and quitting smoking (Jones et al., 2003). In attempts to reduce weight
in an obese population, individual behavior specific health improvement reports based on the
TTM constructs (stages of change, self-efficacy, decisional balance, and processes of change)
and subjects’ assessments were periodically distributed to participants over one year. Obese
individuals participating in this multiple weight-related behaviors stage-based intervention
showed greater progression to action/maintenance for healthy eating, exercising, emotional
distress control, and weight than the control group (Johnson et al., 2008). Employing the TTM
to create MHBC interventions framed on the TTM may be valuable in creating successful
programs without overwhelming participants (Driskell et al., 2008) and creating synergistic
health behavior change (Johnson et al., 2008). Using the TTM in the current study with college
student health behaviors will provide valuable information on changes students experience in
their readiness to change health behaviors during their first semester.
Using the TTM framework the proposed study examines the relationship between college
students’ readiness to change their alcohol consumption, physical activity levels, and eating
behaviors during their freshmen year of college. The model will be used to examine college’s
effects on the constructs of the TTM in these three health behaviors throughout students’ first
semester of college, as well as the corresponding interrelationships these behaviors may have on
each other.
Alcohol Use and College Students
In 1949 Yale initiated the first major national college student alcohol study (Straus,
1953). At this time, an average of 74% of students reported using alcohol (across all schools)
(Staus, 1953), and alcohol use since has increased to 84% of students (Wechsler et al., 1994). In
the late 1940s over two-fifths of men, and the majority of women who consumed alcohol, did so
no more than once per month, never were drunk, and consumed smaller rather than larger
quantities of alcohol at each sitting (aka; did not heavily drink; Straus, 1953). Over the years the
proportion of students drinking and quantity of alcohol consumed by students has increased
(Hingson, Heeren, Winter, & Wechsler, 2005; Wechsler, Dowdall, Maenner, Gledhill-Hoyt, &
13
Lee, 1998). From 1998-2005 in college students ages 18-24 there was an increase from 41.7% to
44.7% of students who reported heavy episodic or binge drinking (Hingson, Zha, & Weitzman,
2009) and only a small percentage (16-19%) of students identified themselves as abstainers from
alcohol (Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994; Wechsler et al., 1998).
Alcohol use in colleges is a national problem that is highlighted in Healthy People 2020
(U.S. Department of Health and Human Services, 2010), as well as the USDA’s 2010 Dietary
Guidelines (USDA & U.S. Department of Health and Human Services, 2010). The new
objectives for Healthy People 2020 released in January 2011, specifically target the college
student population with objective SA-14.2 stating, “Reduce the proportion of students engaging
in binge drinking during the past two weeks”. (U.S. Department of Health and Human Services,
2010). Learned alcohol use at a young age only foreshadows the substance abuse likely to
continue into one’s adult years (The National Center on Addiction and Substance Abuse, 2011).
If universities nationally begin to develop successful interventions aimed at alcohol use and other
health behaviors in college youth, there is hope for mitigating college students’ poor health
behaviors.
Many consequences result from alcohol abuse including problems with the law, lower
academic performance, social relationship strains, unplanned sexual activity, injury, and
secondary effects on those surrounding the problem drinkers (Wechsler et al., 1994). Around
92.7% of heavy drinking college students experience careless behavior consequences (missing
class, perform actions they later regret, got into a fight, argued with friends, etc.), and an earlier
onset of drinking being associated with more serious alcohol use consequences such as problems
with the law (Vik et al., 2000). From 1999-2005, the proportion of college students ages 18-24
driving under the influence of alcohol increased by 7%, and the rate of unintentional non-traffic
alcohol related injury deaths increased 25.6% with total deaths around 1,825 college students
(Hingson et al., 2009). Injuries are often common consequences of alcohol use with about one in
ten college students being hurt or injured because of their drinking and 12% having been hurt or
assaulted by another drinking peer (Hingson et al., 2005). Alcohol use also affects students’
academics with missed class and lower ability to perform well due to hangovers (Wechsler et al.,
1994).
Binge drinking is often portrayed alongside social aspects of the American college life in
media and popular culture, yet this exposure to alcohol in the media is associated with alcohol
14
consumption in young people (Morgenstern, Isensee, Sargent, & Hanewinkel, 2011; Smith and
Foxcroft, 2009). Binge drinking also is correlated with less studying and lower grades, yet this is
often “offset” but more parties, larger friend groups, more sexual partners, and greater
socialization (Weschsler, Dowdall, Davenport, & Castillo, 1995). Wechsler & Austin (1998)
describe the term binge drinking as a type of "heavy episodic drinking characteristic of college
students” and give a numerical definition of five or more drinks for men and four or more drinks
for women (i.e., the 5/4 measure) on one occasion during the previous two weeks. A standard
drink is one 12 ounce beer, one five ounce glass of wine, or one 1.5 ounces of 80 proof spirits
(NIAAA, 2005). The binge drinking pattern brings one’s blood alcohol concentration (BAC) to
0.08 or above resulting in a greater danger to oneself and society (Friedman, Robinson, &
Yelland, 2011; Moskowitz & Fiorentino, 2000). Frequent binge drinkers are those partaking in
this three or more times in the past three weeks, and occasional binge drinkers partake less that
once every two weeks (Wechsler & Nelson, 2001).
Students whom binge drink are more likely to be harmed by alcohol and its effects than
those whom do not (Wechsler et al., 1994; Wechsler et al., 1998; Wechsler et al., 2000) with
47% of frequent binge drinkers experiencing five or more alcohol-related problems compared to
only 3% of non-bingers (Wechsler & Austin, 1998). Frequent binge drinkers are more likely to
engage in unplanned sexual activity, experience hangovers, miss classes, perform actions they
regret, and forget where they were or what they did than non-binge drinkers (Wechsler et al.,
1994). Heavy alcohol consumption also leads to risky sexual behaviors with over 2,700 vaginal
or anal sexual events being reported from only 177 heavy drinking college students and less
condom use during heavy drinking episodes and sex with steady partners (Scott-Sheldon, Carey
M., & Carey K., 2010). Underage drinkers are more likely to have negative consequences
including doing things they regret, forgetting where they were, causing property damage, get in
trouble with the police, and acquire injuries due to alcohol use (Wechsler et al., 2002).
Interestingly, these same students are less likely to drive under the influence, miss a class, and
engage in unprotected sexual activity than those ages 21-23 while under the influence of alcohol
(Wechsler et al., 2002). Despite the plethora of consequences students experience, less than one
percent of frequent binge drinkers defined themselves as problem drinkers, and about one fifth
believe they have a drinking problem (Wechsler et al., 1994).
15
Alcohol not only affects those consuming it, but it also affects surrounding peers and
colleagues. College freshmen males’ GPAs are reduced by 0.18 points their first year of college
by having a roommate who frequently drank in high school and by 0.43 points by their second
year of college (Kremer & Levy, 2003). Additionally, non-binge drinkers at high binge drinking
colleges are two times as likely to be harassed by a student using alcohol, three times as likely to
have property damage, and three times as likely to have their studying or sleep disturbed than
non-binge drinkers at low-binge drinking prevalence colleges (Wechsler & Nelson, 2001). From
2001 to 2005 around 97,000 college students were victims of alcohol-related sexual assault or
date rape annually (Hingson et al., 2009).
The college environment is where most young adults acquire lifestyle preferences both
from fellow peers and personal experience (Kremer & Levy, 2003). Unfortunately, college is
also a locus of substance abuse with about 40% of students binge drinking in the past two weeks
(Wechsler, Lee, Kuo, & Lee, 2000) and 45.4% of students drinking alcohol 2-4 times per month
(Goldstein, Flett, & Wekerle, 2010). Moreover, the percentage of first-year students consuming
alcohol has been shown to increase throughout the first year of college (Kasparek, Corwin,
Valois, Sargent, & Morris, 2008). In first year college students, most alcohol consumption
occurs at the beginning of each semester and on weekends with less drinking during exam
periods (Tremblay et al., 2010). The highest drinking days in the first year of college seems to
occur on Halloween, New Year’s Eve, and St. Patricks’s Day (Tremblay et al., 2010) following
the pattern that greater levels of alcohol is consumed resulting in higher levels of intoxication
during celebratory drinking (Glindemann, Wiegand, & Geller, 2007). Despite the drinking age
being 21 years old, drinking patterns in college students under 21 do not differ from those
college students ages 21-23 (Wechsler et al., 1994) with 43.6% of underage students being
classified as binge drinkers (Wechsler, Lee, Nelson, & Kuo, 2002). Fifty percent of underage
students report that alcohol is “very easy” to obtain with binge drinkers reporting an increased
perceived availability of 56.9% (Wechsler et al., 2002). Underage students are more likely to
“drink to get drunk,” get drunk three or more times in a month, and consume more drinks on an
average drinking occasion than of drinking age students (Wechsler, 2002).
Furthermore, the college environment seems to cause an increase in drinking rates with a
greater percentage of 18-24 year old college students drinking five or more drinks on a single
occasion in the past month when compared to the same age non-college group (Hingson et al.,
16
2005). Moreover, while in high school, those whom will later be attending college have lower
rates of drinking than those who will not become college students; however, after college
graduation and into the following year, college students’ heavy drinking increases more and
surpasses that of their non-college student peers (O’Malley & Johnston, 2002). These effects of
the college environment can be attributed to the fact that many college students often do not live
with their parents as college students whom still live at home tend to drink less than non-college
high school graduates living with their parents (O’Malley & Johnston, 2002). Heavy alcohol use
rates are higher in the Northeast and North Central regions and lower in the South and West
colleges (O’Malley & Johnston, 2002) and the lowest rates of binge drinking tend to be in
students living in substance free dorms or off campus with their parents (Wechsler et al., 2002).
Drinking is not always a social occasion in college students. Some students tend to drink
alone, which has been linked to greater alcohol issues later in life (Christiansen, Vik, & Jarchow,
2002). Although almost two-thirds of college students classify themselves as social-heavy
drinkers, about 15% do report heavy drinking when alone (Christiansen et al., 2002). These
students report more negative drinking consequences, as well as higher depression scores, more
alchol expectancies, less self-efficacy and motivation to reduce drinking, and greater rates of
regaulr drinking than social-heavy drinkers (Christiansen et al., 2002).
Greek system involvement is associated with greater alcohol use and binge drinking
(Cashin, Presley, & Meilman, 1998; Scott-Sheldon, Carey K., & Carey M., 2008) especially
during the first two years of college (Capone, Wood, Borsari, & Laird, 2007). Joining a
fraternity often increases the proportions of alcohol use with 45% of students in a fraternity
binge drinking more than once a week over the past year and 73% reporting drinking more than
once a week (Kremer & Levy, 2003). A similar pattern is seen in sororities with a significantly
greater heavy drinking seen in students belonging to a sorority when compared to those not
involved with the sorority (Cashin et al., 1998). Residing in a fraternity or sorority house only
increases these percentages with 81.1% of residents reporting binge drinking (Wechsler et al.,
1998). Only 25% of these students’ non-fraternity counterparts reported binge drinking more
than once a week over the past year, and 37% of non-Greek students reported drinking more than
once a week over the past year (Kremer & Levy, 2003). Greek members also view alcohol as a
means to friendships, sex, and social activities to a greater extent than non-Greek members
(Cashin et al., 1998). Although Greek life plays an important role in alcohol use on college
17
campuses, this is not a topic of the current study due to time constraints and the primary focus
being on the relationship amongst health behaviors in students and not social effects.
There are gender differences in alcohol drinking patterns with males drinking more
quantities and frequently, (Kremer & Levy, 2003; O’Malley & Johnston, 2002) in addition to
having different drinking motives than females (Goldstein et al., 2010; O’Malley et al., 2002).
Males have higher rates of binge drinking with 50-51.6% of males and 23.4-39% of females
binging at least once a month (Goldstein et al., 2010; Wechsler et al., 1994). In addition, 39% of
women and 50% of men binged in the past two weeks (Wechsler et al., 1995). Interestingly,
both males and female drinking students are more likely to binge drink, than not (Wechsler et al.,
1994). Of the drinking students, only 35% of males and 45% of women are non-binge drinkers
(Wechsler et al., 1994). Heavy drinking males (96.9%) are more likely to experience careless
behavior consequences from their drinking than female heavy drinkers (89.1%), in addition to
greater rates of risk behaviors such as unprotected sex, unplanned sex, and problems with the
police (73% of heavy drinking men vs. 49.2% of heavy drinking females; Vik, Carrello, Tate, &
Field, 2000).
As far as drinking motives, women tend to drink moreso for the desire of positive
experiences, reduced social anxiety, mating/interactions with the opposite sex, and for mood
management/using alcohol as a “pick me up” (Smith & Berger, 2010). Similarly, males also
place emphasis on the effects of drinking on reducing social anxiety (DeJong, DeRicco, &
Schneider, 2010) yet rate meeting members of the opposite sex as more important than females
(Pedersen, LaBrie, & Kilmer, 2009). Both males and females often report drinking in positive
situations, but women are more likely than men to report drinking to cope with adverse emotions
in negative situations (Norberg, Norton, Olivier, & Zvolensky, 2010). In addition, higher stress
levels are often seen in female college students and they are more likely to drink to cope with
this stress when compared to men (Rice & Van Arsdale, 2010).
Pre-gaming and drinking games prior to a planned night’s activities are also newer trends
in the college social environment with anywhere from 30-65% of college students prepartying
prior to going out and encompass fast paced drinking during short periods of time (Borsari et al.,
2007; DeJong et al., 2010). These drinking practices increase students’ intoxication level
(Blood Alcohol Level: BAL) for the night, in addition to increasing drinking consequences in
both males and females (LaBrie & Pedersen, 2008). Because of the fast-paced nature of
18
prepartying, students may reach dangerous levels of intoxication prior to even going out or
feeling the effects of the alcohol until in public resulting in even greater risks when drinking
more at the intended destination (Pedersen et al., 2009). Prepartying most often occurs with
friends while getting ready to go out, before going to a bar or party, while playing drinking
games in a dorm before going out, and before going to a concert or sporting event (Pedersen et
al., 2009). There are a multitude of motives for prepartying including saving money, being able
to drink if underage, arriving to a social function already intoxicated, and making the night more
interesting (Pedersen et al., 2009). Men often have additional/differing reasons than females for
prepartying such as higher ratings of meeting females, greater sex opportunities, enjoying
concerts and sporting events more, and conforming to social pressures (Pedersen et al., 2009).
Current norms pertaining to college and drinking, as well as students’ perceptions of
alcohol affect individuals’ drinking patterns (Osberg, Insana, Eggert, & Billingsley, 2011;
Perkins & Wechsler, 1996). Students who are more permissive with their views of alcohol (and
most likely who were raised in a permissive environment) have greater rates of personal alcohol
abuse than those with more stringent perceptions (Perkins & Wechsler, 1996). Additionally, a
student’s perception of the campus norm regarding alcohol contributes to his/her actions with
regards to alcohol. Those perceiving the campus environment to be permissive of alcohol usage
are more likely to abuse alcohol, even when controlling for the student’s own attitude toward
alcohol (Perkins & Wechsler, 1996). Similarly, in pertaining to prepartying and drinking games,
perceptions of drinking game and prepartying frequency and quantity were associated with
mirror effects in actual behaviors in both male and female students (Pedersen & LaBrie, 2008).
The College Life Alcohol Salience Scale (CLASS) recently was developed to examine the extent
students believe that alcohol is pertinent to their college experience (Osberg et al., 2010). These
beliefs are often predictive of college freshmen’s alcohol drinking and associated consequences
at two time points and can be key in targeting alcohol prevention interventions for freshmen
college students (Osberg et al., 2011). Perceptions of one’s friends’ acceptance of alcohol
consumption and alcohol related behaviors have a predictive nature of reflecting one’s personal
belief about alcohol’s role in the college experience (Osberg et al., 2011). Further understanding
these perceived norms can help reduce the alcohol problems observed in colleges today.
The excessive binge drinking demonstrated in college student results in immediate
consequences in addition to potential unforeseen consequences later in life. Students may
19
become dependent on alcohol or face more serious health issues. For example, they might
experience areduction in brain cell size, liver disease, depression, coordination problems,
weakened heart, pancreatitis, kidney problems, and even death (NIAA, 2011). Understanding
college’s impacts on students’ readiness to change alcohol consumption, self-efficacy, and
perceptions of the pros and cons of their drinking behavior can help mitigate this growing
problem on our county’s college campuses. Employing the TTM framework to examine
freshmen college students’ alcohol consumption, eating habits, and physical activity patterns will
provide further understanding and help our nation work towards the objectives of improving the
health of young adults.
Alcohol use and the Transtheoretical Model. The TTM was originally developed and
tested on smokers and since has been applied to alcohol and other substance abusers (Hall &
Rossi, 2008; Migneault, 1995; Migneault, Adams, & Read, 2005). Numerous studies support the
efficacy of the TTM in relation to alcohol use and the standard themes in relationships of the
TTM constructs are demonstrated. The pros and cons of drinking are associated with the stages
of change and these factors change over time as one progresses towards altering their alcohol
abuse behavior (Migneault et al., 2005). Additionally, more alcohol use and earlier stages of
change are associated with greater reports of temptation to drink and lower self-efficacy
(Migneault et al., 2005). These relationships have been studied in the adult population and to a
lesser extent in college students. Further examining and understanding students’ drinking
patterns in relation to their readiness to change, self-efficacy, and decisional balance will
produce valid insight into promoting safer and less excessive drinking habits in our nation’s
college environment.
SOC in college students has been correlated with the typical, number of drinks per week,
number of binge drinking occasions, and alcohol related problems (Shealy, Murphy, Borsari, &
Correia, 2007), with those having greater than average past drinking portraying lower readiness
to change (Kaysen, Lee, LaBrie, & Tollison, 2009). Anywhere from 58% to two-thirds of heavy
college student drinkers are in the PC stage, even despite past consequences from their alcohol
abuse, and one third report evidence of tolerance to alcohol (Harris, Walters, & Leahy, 2008;
Vik, Culbertson, & Sellers, 2000). Around 17.9% of heavy drinkers are contemplators, or those
whom acknowledged a need to reduce their drinking (Harris et al., 2008). These contemplators
drink more than precontemplators (24%) (Harris et al., 2008) and those in the action stage
20
(Shealy et al., 2007; Vik et al. 2000). The contemplators report greater alcohol related
consequences than precontemplators, but less than action students, leading to the idea that the
greater problems (or cons) associated with alcohol lead to stage progression and initiation of
action (Shealy et al., 2007; Vik et al., 2000). Despite experiencing alcohol related problems,
83% of heavy student drinkers have not committed to change, supporting the idea that multiple
variables/concepts come into play with behavior change and negative consequences may not be
enough to initiate action to change behavior in college students (Vik et al., 2000). In first year
college students only one in five binge drinkers report having taken any action to change their
drinking (Vik et al., 2000).
The decisional balance construct of the TTM focuses on perceptions and balance of the
pros and cons of a behavior change (Prochaska & Velicer, 1986). Typical American college
students recognize two main cons of drinking: those associated with concrete/physical harm and
those associated with emotional consequences (Migneault, Velicer, Prochaska, & Stevenson,
1999). As supported by the TTM, those perceiving greater cons of drinking are more likely to
drink less, and those reporting greater pros from drinking have greater drinking rates (Migneault
et al., 1999). Students reporting little alcohol use and alcohol related problems are more likely to
portray less motivation to change their current alcohol behaviors, most likely due to the lack of
experienced cons of drinking (Shealy et al., 2007). More interpersonal and academic problems
associated with alcohol use in first year college students is also correlated with a higher readiness
to change binge drinking patterns (McGeea et al., 2010). Moreover, higher levels of depression
and anxiety (greater cons) in heavy college drinkers are been correlated with a greater readiness
to change alcohol consumption patterns (Smith & Tran, 2007). As students progress through the
stages of change, their pros of drinking decrease and their perceived cons of drinking increase,
suggesting that TTM based stage matched interventions are applicable to college students and
should focus on reducing the pros of drinking (Migneault et al., 1999; Ward & Scielke, 2011).
Most college students believe that their alcohol problems will resolve themselves
through“natural recovery,” the process of resolving alcohol problems without formal treatment,
yet self-efficacy is key in order for this to occur (Vik, Cellucci, & Ivers, 2003). Greater levels of
self-efficacy, or confidence in success, has been associated with decreases in temptation and
greater overall success alcohol abstinence (Carbonari & DiClemente, 2000). Unfortunately,
“natural recovery” does not happen for most, and only about one-fifth (22%) of college students
21
with a history of drinking in high school have been shown to reduce their drinking while in
college without formal treatment (Vik et al., 2003). Current bingers unable to reduce their
drinking, however, show lower self-efficacy to resist drinking under social pressure and more
ambivalence about change than natural reducers (Vik et al., 2003). In other words, current
bingers exemplify a lower ability and confidence to reduce temptation than those students
reducing their alcohol consumption without formal treatment.
Alcohol abuse in college is an emerging issue that needs to be addressed. These changes
in alcohol use seem to occur upon entering the college atmosphere and students’ readiness and
confidence to change these behaviors changes within the first semester of college. Behaviors
developed during this period of life often mimic those later in life and can result in serious health
consequences including death. The TTM has been employed sparingly in studying college
students’ drinking behaviors and further examining the TTM framework in relation to students’
alcohol consumption patterns in their first semester can help develop successful future
interventions to reduce this widespread alcohol abuse. Moreover, examining students’ alcohol
behaviors in relation to their eating habits and physical activity patterns can produce pertinent
insight into the health behavior changes experienced upon entering.
Physical Activity and College Students
Currently, the US Department of Health and Human Services recommends “at least 150
minutes (2 hours and 30 minutes) a week of moderate-intensity, or 75 minutes (1 hour and 15
minutes) a week of vigorous-intensity aerobic physical activity” for optimal health benefits (US
Department of Health and Human Services, 2008). Additionally, the recent release of the Health
People 2020 objectives state objectives to “reduce the number of adults whom engage in no
leisure-time physical activity” and increase the amount of physical activity the average American
partakes in on most days of the week (US Department of Health and Human Services, 2011).
The Centers for Disease and Control and Prevention (CDC) defines physical activity as, “any
bodily movement produced by the contraction of skeletal muscle that increases energy
expenditure above a basal level” (CDC, 2011).
The focus on physical activity participation has its roots in the vast benefits one can
experience from these activities. Regular physical activity and/or exercise is known to reduce
risk of premature death and chronic disease, prevent weight gain, benefit body composition and
blood lipid levels (Sacheck, Kuder, & Economos, 2010) improve cardiorespiratory and muscular
22
fitness, improve bone health, reduce depression, and improve cognitive function (Hallal, Victora,
Azevedo, & Wells, 2006; US Department of Health and Human Services, 2011; US Department
of Health and Human Services, 2008; Warburton, Nicol, & Bredin, 2006). Specifically in
college students physical activity has also been associated with improved blood lipid levels
(Sacheck, Kuder, & Economos, 2010). Moreover, evidence shows that a sedentary lifestyle can
lead to increase risk of premature death, decreased functional ability, obesity (leading to many
secondary diseases), and a decrease in health in each of the forementioned areas (US Deparment
of Health and Human Services, 2008).
Despite this knowledge, the US Surgeon General reports that the majority of American
(more than 60%) adults do not participate in regular physical activity and 20% participate in no
activity at all. This trend in inactivity is not only seen in adults, but also in adolescents and
young adults, including college students. The largest declines in physical activity is
demonstrated from the period of adolescene to young adulthood, and then again to a greater
extent during the college experience (Bray & Born, 2004; Lake, Townshend, Alvanides, Stamps,
& Adamson, 2009; Nelson, Gortmaker, Subramanian, & Wechsler, 2007; Racette et al., 2005;
Racette et al., 2008) Juniors and senior college students participate in approximately three fewer
exercise bouts per week than freshmen and sophomores (Reed & Phillips, 2005). Physical
education class in high school may not make a difference in students’ continuing physical
education patterns, as well. College students whom were athletes in high school and did not take
part in physical education class report engaging in more cardiovascular exercise than students
whom were non athletes and required to take physical education in high school (Everhart et al.,
2005). With this in mind, one understands why the transition to college is a pertinent time for
young adults to establish proper physical activity patterns (Stephens, Jacobs, & White, 1985).
From 1996-2008 average physical fitness in college students declined while body fatness
increases throughout students’ college careers (Pribis, Burtnack, McKenzie, & Thayer, 2010)
and the proportion of overweight/obese students increases throughout the four years in college
(Racette et al., 2005; Racette et al., 2008). Lack of physical activity during these young years
may lead to greater health issues and poor health behaviors in the adult years (Von Ah et al.,
2004). Understanding the relationship physical activity and other health behaviors have in
common is pertinent in improving the amount of exercise our nation’s young adults receive.
23
Moreover, examining the effects the college environment has on physical activity patterns and
readiness to change their behaviors is a crucial step in this process.
The most recent ACHA-NCHA in fall 2010 examined 106 college campuses and found
that only 19.2% (17.9% females, 21.4% males) of college students report moderate-intensity
cardio/aerobic exercise for at least 30 min on 5-7 days of the week and 58% report only 1-4 days of the
week, with 22.5% reporting none on any day of the week (American College Health Association,
2011). Overall, only 46.7% of the students from 42 U.S. college campuses met the physical
activity guidelines in a fall 2010 survey (American College Health Association, 2011), yet a
similar pattern is seen in many previous years (see Table 2). College students are more active on
weekdays than on weekends, including more walking during the week (Sisson, McClain, &
Tudor-Locke, 2008) and which may be advantageous information when implementing physical
activity programs (Behrens & Dinger, 2003; Dinger & Behrens, 2006). Exercise specific
patterns show that males are more likely than females to participate in muscle strengthening
exercises and vigorous physical activity (Lowry et al., 2000) and females are more likely than
males to perform moderate or vigorous cardiovascular or aerobic exercise (American College
Health Association, 2011). Table 2 presents data on a range of studies examining physical
activity patterns in the college population.
Sedentary activity may also replace time that college students would otherwise be
devoting to physical activities, and includes driving, reading, music, art, talking on the phone,
computer time, video games, or in other words “inactive behaviors.” Studying is a necessary
component of the college lifestyle with 62% of students reporting doing 1-2 hours of homework
during the weekdays and a total of almost 30 hours of sedentary behaviors throughout the week.
(Lake et al., 2009). Additionally, with the constant increase in recreational technology (i.e., cell
phones, television, computers, etc) more time is spent at sedentary behaviors and less at physical
activities. During the week, students watch TV for an average of 3.18 hours daily (Clement,
Schmidt, Berriaix, Covington, & Can, 2004) and on the weekends 51% of students spend 3-4
hours watching TV, videos, DVDs, or listening to music (Lake et al., 2009). Males show
patterns of greater sedentary activity (television/video watching, computer time, etc.) than
females, yet at the same time men report greater levels of exercise (Dinger & Behrens, 2006;
Lake et al., 2009). Negative correlations between sedentary activities and exercise are
demonstrated for both male and female college students (Lake et al., 2009). In females,
24
television watching is negatively associated with most physical activity patterns, however,
studying is positively correlated with the duration of exercise. For males, computer use is
negatively correlated with physical activity behaviors and may be seen as a competing factor for
physical activity in college males (Lake et al., 2009). Understanding these gender specific
behavior patterns that interfere/compete with physical activity, can help administrators tailor sex
specific physical activity programs aimed at increasing the number of active students on our
college campuses. Not undermining the importance of this area of research, and solely due to
the scope of the current study, specific sedentary behaviors will not be examined, and instead the
focus will be on patterns in physical activity.
Students exercise for a variety of reasons and both intrinsic and extrinsic motives are
pertinent factors in college students’ decision to participate in physical activity (Wang, Liu, Bian,
& Yan, 2011). Sport participation has more intrinsic motives such as enjoyment and challenge,
while exercise motivations are more extrinsic focusing on appearances, strength, endurance, and
stress management (Kilpatrick, Hebert, & Bartholomew, 2005). Specifically with weight
training, intrinsic beliefs regarding its importance, usefulness, and pure interest in the exercise
predict students’ intention for further participation (Gao, 2008). Both male and females
exemplify the same level of feelings of obligation to exercise and higher feelings of obligation
lead to greater self-reported physical activity (Chu, Bushman, & Woodard, 2008). Each of these
play a role in the varying degrees of physical activity demonstrated in college students.
Once more, gender differences are relevant in regards to college students’ physical
activity. Males are more motivated by activities incorporating performance factors (Chu et al.,
2008; Greene et al., 2011; Kilpatrick et al., 2005), whereas females view weight management
and physique as higher motivators (Kilpatrick et al., 2005; Lowry et al., 2000). Examining
students’ exercise choices support this evidence in that positive correlations are seen between
men’s obligation to exercise and resistance training and women’s obligation to exercise and
cardiorespiratory activity (Chu et al., 2008). Females are more likely than males to participate in
low-intensity physical activity, and males are more likely to partake in strength training
(Kasparek et al., 2008). In order to develop successful interventions increasing the activity of
college students, these motivators and interests must be taken into consideration for successful
behavioral changes and adherence. Although not examined in the study at hand, future
interventions should acknowledge these gender differences in college students.
25
The campus built environment (i.e., human built areas such as buildings, roads,
sidewalks, parks) and surrounding community also affect the frequency and duration of college
students’ physical activities. About two-thirds of students engage in physical activity less than
two-thirds of a mile from where they live, and a positive correlation exists between the distance
between a student’s residence and location of exercise and the duration and intensity of the
activity (Reed & Phillips, 2005). In males, frequency of physical activity has a negative
correlation with proximity of the activity’s location and females often initiate physical activity
closer to home. Freshman and sophomore college students engage in physical activity closer to
their residences and participate in three more bouts of exercise per week than juniors and seniors
(Reed & Phillips, 2005).
Car access on campus, public transportation, and support for walking (sidewalks, daily
traffic, curb lamps, lighting, etc.) all impact daily physical activity and can explain variances we
see amongst different college’s physical activity participation (Sisson et al., 2008). Students at
colleges with greater perceived walk ablility accumulate significantly more time (70 min/day vs.
37 min/day) walking than those from a less “walker friendly” campus (Sisson et al., 2008).
Although campus built environment is not examined in the current study it is a factor affecting
college students’ physical activity patterns. College administrators and health promoters looking
to increase the activity of their students should bring attention to their campuses built
environment and surrounding geographic terrain for natural methods to increase the activity of
their students.
Physical activity is a pertinent component of overall health, especially in college students.
These individuals are at an age where health behaviors are developed for the adult life, and
developing healthy physical activity patterns should be an important part of college life.
Unfortunately, pas t research shows the opposite, with physical activity patterns decreasing upon
entering college. Additionally, gender differences in physical activity patterns exist between
males and females and should be addressed in programs attempting to increase students’ activity.
Understanding students’ readiness to change this behavior, as well as the change college causes
in relation to this is an important part of developing these successful interventions.
Physical activity and the Transtheoretical Model. The TTM describes individual’s
readiness to change various health behaviors and is applicable to adults’ physical activity and
exercise patterns (Marshall & Biddle, 2001; Nigg et al., 2011; Prochaska & Marcus, 1994;
26
Prochaska et al., 1994; Sarkin, Johnson, Prochaska, & Prochaska, 2001; Spencer, Adams,
Malone, Roy, & Yost, 2006). Tailoring interventions to individuals’ specific stage of change has
helped people progress to action/maintenance in exercise behaviors (Johnson et al., 2008). The
application of the framework to college students’ physical activity behaviors, however, has not
been thoroughly examined. College students in different stages of change differ in the various
TTM constructs with regards to physical activity (Cardinal, Tuominen, & Rintala, 2004). Those
in PC report the lowest level of self-efficacy and pros and those in M report the highest
(Cardinal, Keis, & Ferrand, 2006).
Furthermore, gender differences exist as those in the earlier stages of change (i.e., PC, C,
and PR) for general physical activity are more likely to be women than men (Cardinal et al.,
2009; Suminski & Petosa, 2002) ,and males often report more pros and have greater self-efficacy
than women for muscular fitness promoting behavior (Cardinal et al., 2006). Understanding how
students’ view their readiness to change their physical activity patterns, their beliefs regarding
the pros and cons of physical activity, as well as their self-efficacy are all factors that may help
educators improve the health of college youth.
Supporting the use of the TTM in college students, research demonstrates an increase in
weekly exercise level as students progress through the stages of change (Cardinal et al., 2009;
Daley & Duda, 2006). The distribution of students in the various stages of change is 2-6.8% in
PC, 4.9-22% in C, 17-26.2% in PR, 22-23.8% in A, and 33.5-38.3% in M (Daley & Duda, 2006;
Wadsworth & Hallam, 2007; Zizzi, Keeler, & Watson, 2006). Students in the higher stages of
change for physical activity exemplify greater physical activity levels, as well as higher overall
health status (Clement et al., 2004). As previously discussed, students physical activity levels
decline as they progress through college. The same pattern is observed for the SOC. As students
progress from freshmen to sophomore year of college there a significant reduction in the number
of students in maintenance for aerobic exercise (Racette et al., 2005).
Mixed findings have been reported with regards to self-efficacy levels in college students
while progressing through the SOC. While examining the TTM constructs and physical activity
in American and South Korean college students, Cardinal et al. (2009) demonstrated an increase
in self-efficacy in the later SOC, supporting previous TTM research. Additionally, students’
beliefs about their capabilities (or their self-efficacy) has proven to be a significant predictor of
their performance and efforts exerted (Gao, 2008). Levy and Cardinal (2006), however, found
27
contradicting evidence supporting that increases in self-efficacy did not accompany increases in
exercise stages. Decisional balance research in college students provides mixed findings as well.
Levy and Cardinal (2006) found that there are no significant effects for cons of exercise
behavior, whereas Cardinal et al. (2009) observe an increase in pros of physical activity in the
later SOC.
The effects of the first semester of college on a students’ readiness to change their
physical activity patterns when incorporating their alcohol consumption and eating habits has not
been examined. Employing multiple health behavior research in investigating the
interrelationships amongst these three behaviors, how the college environment affects them, and
using the framework of the TTM will allow a clearer picture of effectively improving the health
behaviors of college students. Employing the TTM enables an understanding of college
students’ readiness to change their health behaviors allowing the tailoring of stage specific
interventions. Students with different mental preparedness respond differently to various
interventions, and programs should be created accordingly, focusing on stage specific attitudes
and beliefs.
Dietary Patterns and College Students
As of 2009, the CDC estimates 27.6 % of American adults are obese, a condition known
to lead to many other serious diseases/disorders including psychological issues with poor selfconfidence (Centers for Disease Control and Prevention, 2010). The primary cause of this
phenomenon is simply energy imbalance between calories consumed and calories expended
(World Health Organization, 2011). While this is the primary cause, it may not be the only
factor contributing to this weight gain with lifestyle factors (Mozaffarian et al., 2011) and
genetics (Elks et al., 2010; Frayling et al., 2007; Loos et al., 2008; Thorleifsson et al., 2009;
Willer et al., 2009) playing a role, as well. Nevertheless, food consumption impacts weight
changes, as well as overall health (Mozaffarian et al., 2011; US Department of Health and
Human Services, 2011). A well-balanced diet has shown to reduce risk for vitamin and mineral
deficiencies, heart disease, diabetes, hypertension, overweight and obesity, and other chronic
conditions (US Department of Health and Human Services, 2011). Diets high in fruits and
vegetables specifically, in addition to low fat diets, have demonstrated a decreased risk of many
chronic diseases, including coronary heart disease, and helpful to individuals trying to lose
weight (CDC, 2011; Joshipura et al., 2001; Terry, Terry, & Wolk, 2001; USDA, 2010).
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At present, Americans consume less than 50% of the daily recommendation of vegetables
and less than 60% of the daily recommended fruits (USDA, 2010). A plethora of government
agencies are developing programs and initiatives to improve the diets of the American people to
reduce the increase in obesity and diminishing health. Healthy People 2020 specifically
addresses Americans’ eating patterns in objectives NWS 14, 15, and 17 by stating goals to
increase the quantity and variety of fruits and vegetables in one’s diet, in addition to reducing the
percentage of daily calories from solid fats and added sugars (US Department of Health and
Human Services, 2011). The USDA recently released the 2010 Dietary Guidelines for
Americans and specifically recommending an average of 2 cups of fruits and 2 ½ cups of
vegetables per day, in addition to a diet with fat totaling no more than 20-35% of total calories
for optimal health (USDA, 2010).
College is a point in time to adopt these healthful eating patterns and unfortunately is
often a time when eating habits changes are not always for the better (Racette et al., 2008).
Often college students exemplify unhealthy eating practices including skipping meals (Huang,
Song, Schemmel, & Hoerr, 1994), excessive dieting (Lowry et al., 2000), frequent snacking, and
high fat/ low vitamin and mineral diets (Johnston, Solomon, & Corte, 1998). Eventually, these
common unhealthy eating patterns may lead to weight gain (Racette et al., 2008) and/or nutrient
deficient diets (Zive, Nicklas, Busch, Myers, & Berenson, 1996). The Fall 2010 American
College Health Association NCHA survey reveals that only 4.8% of college students meet the
recommended five or more fruits or vegetables per day with 68.2% of students consuming 2 or
less per day (American College Health Association, 2011). Table 3 lists similar findings for fruit
and vegetable consumption rates in other studies. These rates of fruit and vegetable consumption
are only seen to decrease throughout the college years (Kasparek et al., 2008; Larson, NeumarkSztainer, Hannan, & Story, 2007), a time of change and adaptation for these students (Dyson &
Renk, 2006; Nelson, Story, Larson, Neumark-Sztainer, & Lytle, 2008).
The availability of fruits and/or vegetables in students’ living environments are positively
associated with consumption; in other words, if the food is readily present and available it is
more likely to be consumed (Chung & Hoerr, 2005; Neumark-Sztainer, Wall, Perry, & Story,
2003). Further understanding these patterns and changes that occur in eating patterns upon
entering college can help administrators develop successful interventions that improve the diets
of our nation’s college student, and hopefully produce long-term changes for the best.
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High fat consumption is another dietary concern in college students and intake of total
fat, in addition to saturated fat, is often higher than recommended levels for optimal health
(Brevard & Ricketts, 1996; Hampl & Betts, 1995; Hendricks & Herbold, 1998). According to
Brevard and Ricketts (1996) on average college students consume 34-46% of their total calories
from fat, a higher percentage than the USDA recommended 20-35% (USDA, 2010). Ground
beef, a food product high in saturated fat, is reported as the primary source of dietary fat for both
male and female students (Hampl & Betts, 1995). Moreover, cakes, pies, doughnuts, cookies,
and ice cream are consumed an average of 1.1 times per day and 60% of students consume these
items 1-2 times per day (Clement et al., 2004). Fried foods are common occurrences in dining
halls and more than 50% of freshmen report eating fried food at least three times in the past week
(Racette et al., 2005). Students with a high fast food intake and/or fried food intake also
consume less fruits and vegetables, leading to the belief that these unhealthy options often
replace the nutrient dense fruits and vegetables in college students’ diets (Racette et al., 2008).
Students with high-fat diets are at greater risk for lower overall health status (Clement et al.,
2004) and often show weight gain through their four years in college (Levitsky, Halbmaier, &
Mrdjenovic, 2004; Pliner & Saunders, 2008; Racette et al.,2008).
Dietary patterns in college student differ by gender and males often consume less fruits
and vegetables than females (ACHA, 2011; Chung & Hoerr, 2005; Erinosho, Thompson, Moser,
& Yaroch, 2011; Greene et al., 2011), whereas females are less likely to eat high fat foods and
diets than males (Chunch & Hoerr, 2005; Huang et al., 1994; Lowry et al., 2000). Over a quarter
of males report consuming fast food several times per week, whereas around 12% of females do
(Laska, Pasch, Lust, Story, & Ehlinger, 2009). Candy bars, pizza, chips, and french fries are all
eaten more often by men than women (Huang et al., 1994) and males report lower intention to
consume healthy diets (Deshpande, Basil, & Basil, 2009). As a result of these poor diets, all
essential vitamins and minerals are often lacking from students’ daily intakes (Hendricks &
Herbold, 1998). Females more commonly than males report nutrient intakes less than 2/3 of the
recommended dietary allowance (RDA), yet also demonstrate more nutrient rich diets (in
relation to nutrient/kcals) than males (Zive et al., 1996).
There are multiple potential factors leading to these dietary patterns observed in college
students including availability of food on campus and dorm rooms, lack of time, prioritization,
stress, and alcohol consumption (Nelson et al., 2009). Although these are not studied in the
30
current study due to the lack of data, gaining a further understanding of these factors, in addition
to other behaviors influencing dietary pattern in college students, may help administrators curb
the unhealthy diet changes seen in college students.
Diet and the Transtheoretical Model. The TTM has been shown to be valid for various
eating behaviors in a range of populations (Di Noia & Prochaska, 2010). Applying the TTM to
assessing fruit and vegetable intake, shows that persons in the highest stage of change (M)
consume the recommended five or more servings per day, whereas those in A eat one more
serving (3.68 servings/day) than those in PR (2.68 servings/day) (Van Duyn et al., 1998).
Moreover, individual interventions tailored to individual’s specific stage of change in fruit and
vegetable intake and “healthy eating” have shown to help participants progress to the A and M in
these behaviors (Johnson et al., 2008). Prochaska et al. (1994) demonstrated the validity of the
decisional balance aspect of the model with respect to high-fat diets and the SOC. In the earlier
SOC, the cons of change were shown to outweigh the pros, yet as one progressed to PR and A,
the pros of change began to outweigh the cons of reducing fat in one’s diet (Prochaska et al.,
1994).
Studies with college students, dietary habits, and the TTM show more than 55% of
students are in the PC stage for adopting healthier eating patterns both at the beginning of
freshman year and the end of sophomore year (Racette et al., 2005) and only 5% of all ages of
college students are in both the A and M stages combined together (Cummins, Johnson, Paiva,
Dyment, & Mauriello, 2006). Average fruit and vegetable intakes for college students in PC, C,
and PR are lower than the recommended daily servings, and are significantly lower than those in
A/M, data supported by the TTM (Chung, Hoerr, Levine, & Coleman, 2006). For fruit and
vegetable consumption specifically stages distributions are often PC (0-40.4%), C (5.6-37.3%),
PR (7.0-40.8%), A (1.2-7.0%), and M (5.1-50%) (de Oliveira, Anderson, Auld, & Kendall, 2005;
Richards, Kattlemann, & Ren, 2006; Soweid, Kak, Major, Karam, & Rouhana, 2003). There is a
negative correlation between high calorie/high fat foods and the stage of change students are in
(Clement et al., 2004; Lamb & Joshi, 1996), and a positive correlation between fruit and
vegetable consumption and stage of change (Clement at al., 2004). As with other populations,
stage matched interventions have proven to be successful in increasing the fruit and vegetable
consumption in college age students (Cummins et al., 2006; Johnson et al., 2008; Soweid et al.,
2003), as well as reducing fat intake and increasing readiness to change (Finchenor & Byrd31
Bredbenner, 2000; Franko et al., 2008). Gender differences also exist in the stage distributions
for fruit and vegetable consumption in college age individuals. For fruit stage of change, more
males are in PC, while more females are in the A/M stage, and when solely examining solely
vegetable stage of change more males are in PC than females (Horacek, 2002). In relation to
dietary fat consumption, females are more likely than males to be in the later stages, and thus
females are more willing to give up fat in their diets (Lamb & Joshi, 1996).
Balancing the pros and cons of behavioral change has also been examined in college
students readiness to change their eating patterns. Between the C/PR and A/M stages, the pro
scores of fruit/vegetable consumption can predict males’ stage categorization supporting the
application of the decisional balance construct to college students (Horacek et al., 2002).
Moreover, the pros and cons of fruit and vegetable consumption have shown to be predictive of
SOC in both males and females and may be a pertinent focus for successful interventions
(Cummins et al., 2006).
The self-efficacy component of the TTM has also been examined in relation to fruit and
vegetable intakes in college students. Self-efficacy is a positive predictor of fruit and vegetable
intake, as well as nutrition protective behavior (Franko et al., 2008; Horacek et al., 2002; Von Ah
et al, 2004), and those with past successful attempts at increasing fruit and vegetable intake have
higher self-efficacy than those without successful attempts (Chung & Hoerr, 2005). Once again,
those in the later SOC show greater self-efficacy than those in the early stages (Cummins et a;.,
2006) and employing motivational methods to increase self-efficacy has been productive in
increasing college students’ fruit and vegetable consumption (Franko et al., 2008; Richards et al.,
2006). Interventions should attempt to maximize self-efficacy in students for extended periods
in to help them maximize their full health potential.
Researchers have yet to examine the changes in students’ readiness to change their
alcohol consumption, eating habits, and physical activity patterns across the first semester of
freshman year. Additionally, understanding the relationships amongst these three behaviors on
each other can help develop subsequent multiple health behavior change interventions curbing
the demise in health behaviors upon entering college. Applying various matched components of
the TTM to interventions targeting college students’ health behaviors can potentially mitigate the
poor lifestyle changes observed in college students today.
32
Alcohol Consumption, Diet, and Physical Activity and Multiple Health behavior Change
Research
As presented, the three behaviors in focus have been studied widely as individual
behaviors in the college student population. In addition, studies have examined relationships
between two of these variables, but none have looked at all three behaviors together, especially
in relation to students’ readiness to change and the TTM constructs. Cluster analysis on college
student health behaviors shows that certain health behaviors coincide with others (Laska et al.,
2009), yet students’ readiness to change has not yet been examined in relation to the three
behaviors in question.
Alcohol consumption, physical activity have been examined in a multitude of studies
(see Multiple Health Behavior Research in a College Student Population for further references)
looking at the relationship amongst the two of the three behaviors, yet not with all three in
relation to students’ readiness to change. Musselman & Rutledge (2010) report a positive
association between alcohol consumption and physical activity in college students, and same
relationship is portrayed in numerous other studies showing that more active nonathletes drink
more alcohol than less active students (Dunn & Wang, 2003; Moore & Werch, 2008). What’s
more, intercollegiate athletes (more physically active students by nature) are more likely to drink
than nonathletes (Leichliter, Meilman, Presley, & Cashin, 1998; Martens, Dams-o’Connor, &
Beck, 2006). In contrast, some view exercise as an alternative substance free activity that can
replace and/or help reduce alcohol consumption in college students (Correia, Benson, & Carey,
2005; Weinstock, 2010; Murphy, Pagano, & Marlatt, 1986), yet this idea is still questioned by
researchers (Barry & Piazza, 2010). The current study will test for a positive or negative
relationship between readiness to change physical activity and readiness to change alcohol
consumption in college students, in addition to examining the changes that occur in these
behaviors over the first semester of college.
Dietary habits and alcohol consumption are related in college students. Students
consuming low-fat diets also consume more alcohol than other students (Hampl & Betts, 1995).
Qualitative research presents students’ reports that alcohol greatly affects their dietary habits
(Nelson et al., 2009). Alcohol-related eating, including both eating prior to drinking to minimize
the alcohol’s effects and eating late at night due to “munchies,” affects’ students diets and often
leads to greater consumption of take-out and higher fat foods (Nelson et al., 2009). Physical
33
activity patterns have also been linked to dietary habits in college students. Students reporting
greater levels of sedentary behaviors (watching TV/DVDs, listening to music, driving, etc) have
also reported a greater percentage of energy from fat and greater total energy intake (Lake et al.,
2009). One study examined the stages of change in relation to diet and physical activity and
portrayed similar findings with a negative correlation between high calorie/high fat food intake
and physical activity stage of change score (Clement et al., 2004). Moreover, fruit and
vegetable consumption was positively correlated with the physical activity stage score (Clement
et al., 2004) and regular physical activity has shown to be associated with fruit intake (Chung &
Hoerr, 2005). Those attempting weight control, recognize the importance of both diet and
physical activity with 54% of females and 41% of males using both methods to lose weight
(Lowry et al., 2000).
The combination of high alcohol consumption, poor diet, and lack of regular physical
activity has great implications for long-term health. TTM tailored multiple health behavior
change intervention have proven to be more effective than individual behavior interventions,
especially in relation to diet and exercise (Johnson et al., 2008). Gaining a clear understanding
of the relationships between these three behaviors and how college affects students’ readiness to
change each behavior is pertinent in developing successful interventions for this age group. The
multiple questions asked while examining these behaviors in college students are listed below.
Research Question 1: Do college students’ readiness to change their alcohol consumption,
physical activity, and diet change throughout their first semester of college, and do
complementary changes exist in students’ decisional balance for these behaviors? Do
freshmen college students’ quantity of alcohol and fruits and vegetables consumed change
over their first semester of college?
Each behavior will be examined individually in relation to the TTM stage of change at
both Time 1 and Time 2. Dietary patterns will include stages of change for both fruit and
vegetable consumption, as well as high fat food intake. It is expected over the course of
freshman students’ first semester of college their readiness to change alcohol consumption will
decrease and their readiness to participate in more physical activity will decrease. Additionally,
it is expected freshmen college students’ readiness to eat more fruits and vegetables and less
fatty foods will decrease.
34
Decisional balance will be measured for each behavior at both Time 1 and Time 2, and it
is expected the cons of changing behaviors will decrease over the first semester of college.
Research Question 2: Are the stages of change for each health behavior related; are they
dependent on each other? Are there interrelationships between these three health
behaviors with respect to students’ stage of change status at both Time 1 and Time 2?
It is expected there will be significant associations between alcohol consumption,
physical activity, and dietary stages of change over the course of a semester in freshman college
students.
Research Question 3: Does the change in stage of change form Time 1 to Time 2 for each
health behavior in question differ between males and females?
It is expected there will be significant differences between males and females in
magnitude of behavioral stage of change changes throughout freshman students’ first semester of
college.
Methods
Data was collected from 321 first-year undergraduate students enrolled in an introductory
psychology course at a mid-sized Midwestern university campus. Prior to collecting data, the
primary investigator received approval from the institutional review and students completed an
informed consent form (Appendix A). Data collection was part of a larger longitudinal study and
students completed a survey inquiring about more than 20 health behaviors at two time points
fifteen weeks apart during their first semester of college. Surveys were administered in a large
lecture hall on campus both during the first day of fall semester (Time 1) and during the week
before finals the same semester (Time 2). Seven versions of the survey were available to help
counterbalance any progressive error, and it took participants anywhere from 30-60 minutes to
complete the questionnaire. After completing the questionnaire participants were verbally
debriefed for any further questions and future reference.
Measures
The questionnaire used included questions regarding the behaviors of interest, alcohol
consumption, physical activity, and dietary patterns, in addition to a variety of other health
behaviors. Only gender and questions pertaining to the three latter behaviors will be used in this
study and all are listed in Appendix D.
35
To assess alcohol stages of change (Laforge, Maddock, Rossi, 1998), the questions, “In
the last month, have you had 5 or more (for males) or 4 or more (for females) drinks in a row”
and , “Did you drink until you got drunk or intoxicated at least once in the last month” are used.
Six one-sentence answer options were given assessing the current alcohol stage of change.
Average frequency of alcohol use at in the past month was also examined at both Time 1 and
Time 2 by asking “Think back over the last month. How many times have you consumed 5 or
more drinks in one day?” A drink was clearly defined for the participants as “one bottle of beer,
one ounce of liquor, or a four-ounce glass of wine.”
To assess alcohol decisional balance, the decisional balance scale (Laforge, Maddock, &
Rossi, 1999) was used measuring the students’ pros and cons of drinking alcohol. Five measures
for both the pros and cons were used and students were asked how important various aspects of
drinking were in their decision to drink (i.e., Drinking helps me keep my mind off problems) by
choosing from a scale of five options (Not at all important, Not important, Somewhat important,
Very important, or Extremely important).
In order to measure physical activity stages of change, regular exercise was first defined
as “any planned physical activity (e.g., brisk walking, aerobics, jogging, bicycling, swimming,
rowing, etc.) performed to increase physical fitness. Such activity should be performed 3 or
more times per week for 20 or more minutes per session at a level that increases your breathing
rate and causes you to break a sweat.” The question asked to measure stages of change was “Do
you regular exercise according to the definition above” and 5 answer options were given
(Marcus, Selby, Niaura, & Rossi, 1992).
Dietary stage of change was measured by looking at both high fat food and fruit and
vegetable stage of change. High fat food was assessed with the question “Do you consistently
avoid eating high fat foods?” and six one-sentence answer options (Greene & Rossi, 1998).
General fruit and vegetable consumption was measured at both Time 1 and Time 2 by asking
“How many servings of fruits and vegetables do you usually eat each day?” Fruit/vegetable
stages of change was measured with the two questions (1) Have you been eating 5 or more
servings of fruits and vegetables a day for more than 6 months and (2) Do you intend to start
eating 5 or more servings of fruits and vegetables a day in the next 6 months (Laforge, Greene, &
Prochaska, 1994).
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Proposed Analysis
The purpose of this study is to examine the changes and interrelationships students
experience in their readiness to change their alcohol consumption, physical activity, and diet
during two time points their first semester of college. Differences between males and females
change in readiness to change these behaviors across the semester will be examined to identify if
any gender differences exist. The researcher hypothesizes that there are relationships existing
amongst readiness to change these behaviors, supporting the use of simultaneous multiple health
behavior interventions in the college student population. Below are the research questions being
examined and the proposed methods of analysis for each.
Research Question 1: Do college students readiness to change their alcohol consumption,
physical activity, and diet change throughout the first semester of college? Do freshmen
college students’ quantity of alcohol and fruits and vegetables consumed change over their
first semester of college?
This research question will be answered by examining if the Time 1 stage of change for
each health behavior individually is different from that at Time 2. The variables will be the stage
of change at both Time 1 and Time 2 for each health behavior. The program SPSS (Statistical
Package for the Social Sciences) will be used to run a Chi-square analysis for overall sample
population, to see if the stage of change differs from Time 1 to Time 1within each health
behavior. Some students may experience changes over their first semester, while others
behaviors may remain static. Moreover, students may progress in their readiness to change one
health behavior, while digressing in another.
Research Question 2: Are the stages of changes for each health behavior related; are they
dependent on each other? Are there interrelationships between these three behaviors with
respect to their stage of change status at both Time 1 and Time 2? Do the stages of change
for all three health behaviors change across time together?
This research question will be answered by examining if there is a relationship between
the stages of change for alcohol consumption, physical activity, and diet in freshman college
students. Because the variables are categorical a Chi-square test of independence will be used to
examine if each variable (the stage of change for each health behavior) is dependent on the
others. This analysis will test:
Does alcohol stage of change affect physical activity stage of change? (and vice versa)
37
Does alcohol stage of change affect eating stage of change? (and vice versa)
Does physical activity stage of change affect diet stage of change? (and vice versa)
Knowledge on the interrelationships that may or may not exist is significant in the development
of successful multiple health behavior interventions.
Research Question 3: Does the change in stage of change from Time 1 to Time 2 for each
health behavior in questions differ between males and females?
To answer this question the variables are the change in stage of change from Time 1 to
Time 2 for each health behavior. New variables will be created using the difference between
Time 2 and Time 1 for each health behavior in both males and females. For example, with
alcohol consumption, the formula Alcohol Stage of Change time 2 – Alcohol Stage of Change Time
1 = Alcohol Stage of Change Change will be used for both males and females creating a new
change variable. The program SPSS will be used to run a Chi-square analysis comparing these
two change variables across gender for each health behavior in question. The magnitude of stage
of change for both genders will be examined to see if the direction of change is stable across all
behaviors and both genders.
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Chapter Two
A Time to Increase Knowledge and Attenuate Health? A Study into College’s Influence on
Students’ Physical Activity, Diet, and Alcohol Consumption
Introduction
The college transition is a significant time period in a student’s life, and during this time
health behaviors often change due to decreased parental influence (Hogan & Aston, 1986), social
influence, changes in physical environment, changes in social norms, varying academic
standards, and associated stressors (Gruber, 2008; Nelson, Kocos, Lytle, & Perry, 2009;
Quintiliani, Allen, Marino, Kelly-Weeder, & Li, 2010; Von Ah, Ebert, Ngamvitrtoj, Park, &
Kang, 2004). Many health behavior habits for one’s adult life are established during the college
experience and are pertinent in leading a healthy lifestyle (Von Ah et al., 2004). These health
behavior changes are not always for the better including weight gain, with over 35% of students
being overweight (ACHA, 2010), low fruit/vegetable and high-fat food diets (Douglas et al.,
1997), low physical activity levels (Racette, Deusinger, Strube, Highstein, & Deusinger, 2008),
and an increase in alcohol consumption (Kasparek, Corwin, Valois, Sargent, & Morris, 2008).
Males show greater rates of health risk behaviors than females including more alcohol
consumption (Cullen et al., 1999; Johnson, Nichols, Sallis, Calfras, & Hovell, 1998; Patrick,
Covin, Fulop, Calfas, & Lovato, 1997; Spencer, 2002), more weight gain (Economos,
Hildebrandt, & Hyatt, 2008; Racette et al., 2008), less fruit and vegetable intake (Economos et
al., 2008), and more fast food intake (Driskell & Morse, 2009). Each of these poor college
student health behaviors can lead to more serious health issues such as heart disease (Spencer,
2002), obesity, or addiction problems later in life for these young adults (Douglas et al., 1997).
The greatest rates of mortality and morbidity in the United States are due to cancer and heart
disease, which are most often as a result of multiple risky health behaviors such as alcohol abuse,
lack of physical activity, and inadequate diets (Prochaska, Spring, & Nigg, 2008).
Multiple health behavior change research is a field focusing on studying numerous health
behaviors and interrelationships amongst them and may lead to more successful health behavior
interventions. Co-occurrences in health behaviors have been portrayed in both male and female
college students, with 65% of female college students having more than 2 unhealthy behaviors
(Quintiliani et al., 2010). Past research shows that one risky behavior may be directly linked to
another behavior with underlying mental processes, attitudes, or issues potentially producing
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both behaviors (Nigg, Lee, Hubbard, & Min-Sun, 2009; Prochaska, Nigg, Spring, Velicer, &
Prochaska, 2010). Further understanding these relationships in college students’ readiness to
change their alcohol consumption, physical activity, fruit and vegetable consumption, and high
fat food avoidance may help produce more successful interventions in the future.
Transtheoretical Model (TTM)
The four behaviors in question were examined using the Transtheoretical Model
framework. The Transtheoretical Model (TTM) was developed from components of numerous
leading theories in behavior change and is a framework for the health behavior change process
(Prochaska & Velicer, 1997). The TTM involves a progression through five stages of change
(precontemplation, contemplation, preparation, action, and maintenance) in order to reach the
ultimate goal of behavior or lifestyle change and can be used for both the acquisition and
cessation of behaviors (Prochaska, DiClemente, & Norcross, 1992). There are multiple
constructs in the model including processes of change, decisional balance between the pros and
cons of the targeted behavior, and self-efficacy (Prochaska & DiClemente, 1984; Prochaska &
Velicer, 1997).
The central construct of the model is the stages of change (Glanz, Rimer, & Viswanath,
2008), which allow researchers and practioners to gauge where someone is in the change
process. The first stage is Precontemplation. Individuals in PC are not thinking about changing
their behavior within the next six months in addition to often avoiding thinking about their high
risk or unhealthy behavior(s) (Prochaska & Velicer, 1997). The next stage is Contemplation (C)
in which individuals plan on changing within the next six months (Prochaska & Velicer, 1997).
At this time, serious thought has been put into modifying or ceasing a behavior but no action has
been made (Prochaska et al., 1992). Preparation (PR) involves the intention of changing a
behavior within the foreseeable future, usually within the next month (Prochaska & Velicer,
1997). Generally, methods for the change have already mentally been developed by the
individual (Prochaska & Velicer, 1997), and the individual may previously have taken small
steps to ready him- or herself for change (Prochaska et al., 1992). This stage is ultimately when
the decision to change is made; individuals either move forward to action, or stay where they are
(Prochaska et al., 1992). The Action (A) stage is when behaviors, environments, or variables in
one’s life are overtly modified in order to support behavior change (Prochaska & Velicer, 1997;
Prochaska et al., 1992). These changes must have occurred within the past six months to be
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classified in the action stage (Prochaska et al., 1992). Following action is Maintenance (M).
This stage involves sustaining the changes incorporated in the action stage. Preventing relapse is
the objective of this period and as time goes on confidence of success increases (Prochaska &
Velicer, 1997). During the process of behavior change, one may progress and regress through
these stages in a spiral like pattern until M is reached (Prochaska et al., 1992).
The TTM has been applied to over forty-eight health related behaviors (Hall & Rossi,
2008) and is effective in generalizing across a multitude of target populations (Prochaska et al.,
1994). Specifically in the college student population, the TTM has shown to be valid with
alcohol consumption (e.g., Harris, Walters, & Leahy, 2008; Kaysen, Lee, LaBrie, & Tollison,
2009; Shealy, Murphy, Borsari, & Correia, 2007; Vik, Culbertson, & Sellers, 2000), physical
activity (e.g., Cardinal, Tuominen, & Rintala, 2004; Cardinal, Keis, & Ferrand, 2006; Cardinal et
al., 2009; Clement, Schmidt, Berriaix, Covington, & Can, 2004; Daley & Duda, 2006; Racette,
Deusinger, Strube, Highstein, & Deusinger, 2005; Wadsworth & Hallam, 2007; Zizzi, Keeler, &
Watson, 2006), and dietary patterns (e.g., Chung, Hoerr, Levine, & Coleman, 2006; Clement et
al., 2004; Cummins, Johnson, Paiva, Dyment, & Mauriello, 2006; de Oliveira, Anderson, Auld,
& Kendall, 2005; Finchenor & Byrd-Bredbenner, 2000; Franko et al., 2008; Horacek, 2002;
Lamb & Joshi, 1996; Racette et al., 2005; Richards, Kattlemann, & Ren, 2006; Soweid, Kak,
Major, Karam, & Rouhana, 2003). TTM tailored multiple health behavior change intervention
also have been proven to be more effective than individual behavior interventions, especially in
relation to diet and exercise (Johnson et al., 2008). Even so, students’ readiness to change,
however, has not yet been examined in relation to alcohol consumption, physical activity, and
dietary behaviors together in college students. The current study examined the stages of change
in college students' health behaviors at two time points across the first semester of college.
Multiple Health Behavior Change Research in College Students
Previously, cluster analysis on college student health behaviors shows that certain health
behaviors coincide with others (Laska, Pasch, Lust, Story, & Ehlinger, 2009). For example,
Musselman and Rutledge (2010) report a positive association between alcohol consumption and
physical activity in college students. However, other researchers view exercise as an alternative
substance free activity that can replace and/or help reduce alcohol consumption in college
students (Correia, Benson, & Carey, 2005; Murphy, Pagano, & Marlatt, 1986; Weinstock, 2010).
Additionally, students consuming low-fat diets also consume more alcohol than students not on a
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low-fat diet (Hampl & Betts, 1995), and physical activity patterns have also been linked to
college student dietary habits (Clement et al., 2004; Chung & Hoerr, 2005; Lake, Townsend,
Alvanides, Stamps, & Adamson, 2009). Gaining a clear understanding of the relationships
between these three health behaviors and how college affects students’ readiness to change each
behavior is pertinent in developing successful interventions for this age group.
Summary and Research Questions
This study examined if the first semester of college had an effect on freshmen students'
health behaviors. Changes in readiness to change alcohol consumption, physical activity, and
diet were examined, in addition to the interrelationships and general patterns amongst these
behaviors. Differences in behavior patterns by gender also was examined to see if health
behavior interventions for college students should be specifically tailored to gender.
Methods
Participants
Data was collected from 321 first year undergraduate students enrolled in an introductory
psychology course at a mid-sized Midwestern university campus. Over 78% of the sample (n =
256) completed the questionnaire at both Time 1 and Time 2. Of the initial sample, 227
participants (70.70%) were female and 94 (29.30%) were male. The participants had an average
age of 18.06 (SD = .44). The average height of participants was 66.83 inches (SD = 3.71) and
weight of 141.76 (SD = 28.59). This was a fairly uniformed ethnic sample consisting of
Caucasian (91.00%), Asian/Asian American (5.30%), Hispanic/Latino (2.80%), Black/African
American (1.20%), American Indian/Alaska Native (0.90%), and Other (0.90%).
Procedures
Prior to collecting data, the primary investigator received approval from the institutional
review board. Data collection was part of a larger longitudinal study, and during their first
semester of college, students completed a survey inquiring about more than 20 health behaviors
at two time points fifteen weeks apart. Surveys were administered in a large lecture hall on
campus both during the first day of fall semester (Time 1) and during the week before finals the
same semester (Time 2). At both time points students were read an explanation of the study and
completed an informed consent form, prior to completing the questionnaire. Seven versions of
the survey were available to help counterbalance any progressive error. Participants took
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between 30-60 minutes to complete the questionnaire. After completing the questionnaire
participants were verbally debriefed for any further questions.
Measures
The questionnaire included basic demographic questions (i.e., gender, race, and age) to
gain an understanding of the sample population. Questions assessing frequency of alcohol and
fruit and vegetable consumption behavior patterns were also asked, in addition to specific stage
of change questions for binge drinking, intention to exercise, fruit and vegetable consumption,
and high fat food avoidance at both time points.
Alcohol stage of change (Laforge, Maddock, & Rossi, 1998). To measure the students'
binge drinking stage of change the question, “In the last month, have you had 5 or more (for
males) or 4 or more (for females) drinks in a row” was used. It had six one-sentence answer
options that placed students in categories from precontemplation to non-bingers. For instance
the answer corresponding to Precontemplation was, "Yes, and I do not intend to stop drinking 5
or more (4 or more) drinks in a row." A drink was defined for the participants as “one bottle of
beer, one ounce of liquor, or a four-ounce glass of wine.”
Exercise stage of change (Marcus, Selby, Niaura, & Rossi, 1992). In order to measure
physical activity stages of change, regular exercise was first defined as “any planned physical
activity (e.g., brisk walking, aerobics, jogging, bicycling, swimming, rowing, etc.) performed to
increase physical fitness. Such activity should be performed 3 or more times per week for 20 or
more minutes per session at a level that increases your breathing rate and causes you to break a
sweat.”
This definition was consistent with the American College of Sports Medicine (ACSM)
and the American Heart Association’s (AHA) description of vigorous physical activity (Haskell
et al., 2007) at the time of data collection. The participants were asked “Do you regular exercise
according to the definition above” and 5 answer options were presented representing the five
stages of change. For example, the answer corresponding to Contemplation was, "No, but I
intend to in the next 6 months."
Fruit and vegetable stage of change (Laforge, Greene, & Prochaska, 1994).
Fruit/vegetable stages of change was measured with the two questions (1) Have you been eating
5 or more servings of fruits and vegetables a day for more than 6 months and (2) Do you intend
to start eating 5 or more servings of fruits and vegetables a day in the next 6 months, with
answers corresponding to the stages of change.
63
High fat food avoidance stage of change (Greene & Rossi, 1998). Readiness to change
high fat food avoidance was assessed with the question “Do you consistently avoid eating high
fat foods?” and five one-sentence answer options parallel to the TTM stages of change. For
example, the answer correlating with Precontemplation was, "No, and I do not intend to in the
next 6 months."
Results
Descriptives of alcohol and fruit and vegetable consumption patterns. Initial
frequencies were run at both time 1 and time 2 on alcohol and fruit and vegetable consumption in
the freshmen college students. At time 1 on the first day of classes, freshmen year, 87.9% of the
sample had drunk an alcoholic beverage at some point in their life. In a typical week, students
reported drinking on fewer, rather than more, days of the week (0-1 day = 56.3%, 2-3 days =
37.1%, and 4 or more days = 5.2%). On a typical drinking day, students consumed an average of
4.64 drinks (SD = 3.38). In addition, the students had an average of 7.56 drinks (SD = 5.60) on
their highest drinking occasion in the last 30 days. The majority of the students (62.0%) reported
they had drank until intoxicated in the past and plan to again. In addition, almost half of the
students (49.5%) in the Precontemplation SOC for intention to stop drinking until intoxicated.
Additionally, at time 1 the majority of participants had binge drank at least once in the past
month (never = 39.9%, once = 10.6%, 2-3 times = 21.2%, 4-6 times = 15.3%, 7 or more times =
12.1%). With regards to fruit and vegetable consumption the majority of students (57.8%)
consumed only 1-2 servings of fruits and vegetables per day.
At time 2 the week before first semester finals, 91.7% of the sample reported ever
previously drinking an alcoholic beverage. Students often drank at least one alcoholic drink on
multiple days in a typical week (0-1 day = 38.2%, 2-3 days = 50.8%, and 4 or more days =
9.4%). On a typical drinking day, students reported consuming an average of 3.96 drinks (SD =
2.92), and an average of 6.27 drinks (SD = 5.35) on their highest drinking occasion in the last 30
days. Almost three-fourths of the students (72.0%) reported they had drank until intoxicated in
the past and plan to again, significantly more than at time 1. Additionally, the proportion of
students in Precontemplation intentions to stop drinking until intoxicated increased to 63%.
Similarly to time 1, at time 2 the majority of participants had binge drank at least once in the past
month (never = 28.7%, once = 9.1%, 2-3 times = 18.5%, 4-6 times = 18.9%, 7 or more times =
24.0%). Lastly, the majority of students (56.3%) still only consumed 1-2 servings of fruits and
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vegetables per day at time 2. Frequencies were run at both time 1 and time 2 for the stage
distributions in each health behavior (see figures 1-4).
Health behavior change using stage of change measures. To assess the movement of
the stages across time, chi-square tests were used for each individual health behavior at time 1
and time 2. From time 1 to time 2, participants showed significant changes in readiness to
change their binge drinking, χ2(25, n = 251) = 220.50, p < 0.001, exercise patterns, χ 2(16, n =
256) = 136.79, p < 0.001, and high-fat food avoidance, χ 2(16, n = 255) = 140.36, p < 0.001.
However, for binge drinking, exercise, and high-fat avoidance, 60.5%, 57.1%, and 44.4% of
students stayed in the same stage of change for both time 1 and time 2. For binge drinking,
almost a third of students (29.6%) regressed and 10.0% progressed in their readiness to change
from time 1 to time 2. With respect to intention to exercise, 27.1% regressed and 16.1%
progressed in stage of change across the first semester of college. The least amount of students
(20.9%) regressed, and the most progressed (35%) across the two time points in readiness to
avoid high fat foods. No significant (p > .05) changes were observed from time 1 to time 2 in
students’ changes in readiness to change their fruit and vegetable consumption, yet there were
differences in servings consumed per day. At time 2, 10.1% of students consumed five or more
servings of fruits and vegetables per day compared to 3.0% at time 1. Moreover, over the first
semester of college significantly more students had increased the number of fruit/vegetable
servings consumed per day (20.8%), as opposed to decreased (17.7%), χ 2(16, n = 256) = 105.13,
p < 0.001. These results are shown in Table 4.
Health behavior change interrelationships. To examine the interrelationships amongst
binge-drinking, exercise, fruit and vegetable consumption, and high fat food avoidance stages of
change, chi-square tests were use for behavior pairs at both time 1 and time 2. Stage of change
for exercise and high fat food avoidance was the only behavior pair to present significant (p =
.006) relationships at both time 1, χ 2(16, n = 320) = 33.95, p = 0.006, and time 2, χ 2(16, n = 255)
= 50.59, p < 0.001. At time 1, 27.3% (n = 87) of participants were in the same stage of change
for both of the latter behaviors, 59.77% of whom were in Maintenance. At time 2, this number
increased to 34.80% of participants being in the same stage of change for both intention to
exercise and high fat food avoidance, yet the percentage of this in Maintenance for both
behaviors dropped to 48.31%. In addition at time 1, 16.3% of students were in Precontemplation
for limiting fat intake, yet at the opposite end of the spectrum in Maintenance for intention to
65
exercise. At time 2, however, 39.2% of students were in an action stage of change (Action or
Maintenance) for both health behaviors.
At time 2, readiness to change binge drinking and high fat food avoidance also were
significantly related, χ 2(20, n = 253) = 45.35, p = 0.001. At this time,16.2% of students were in
the same stage of change for both behaviors, 92.68% of whom were in Precontemplation.
Interestingly at time 2, 30% of students were also in an action stage of change (Action or
Maintenance) for avoiding high fat foods, yet in Precontemplation for binge-drinking at the same
time. Further results of the multiple health behavior relationships at time 1 are presented in
Table 5.
To examine if the stage of change for all three health behaviors changed across time
together, a correlation was performed using the changes in stage of change variables for each
behavior (T2 SOC-T1 SOC). The only significant correlation was between intention to exercise
and binge drinking stages of change, r(249) = -.156, p = .013. That is, as one progresses in their
readiness to change their alcohol consumption, they regress in their readiness to change their
exercise patterns and vice versa (↑Alcohol SOC = ↓ Exercise SOC). The correlation table is
presented in Table 6.
A chi-square examined all behaviors’ change variables together, yet this resulted in no
significant findings. As a result, chi-squares were run between each pair of change variables for
the multiple health behaviors; a total of six chi-squares. The only significant relationships were
between the change in stage of change for binge-drinking and high fat avoidance together, χ2 (80,
n = 250) = 110.11, p = 0.01, and intention to exercise and fruit and vegetable consumption, χ2
(28, n = 256) = 41.72, p < 0.05. Over a third of participants (34%) moved the same number of
stages from time 1 to time 2 in binge drinking and high fat food avoidance, 90.59% of whom
maintained their stage of change in both behaviors from time 1 to time 2. Additionally, 10% of
students maintained their stage of change in binge drinking, yet they also progressed one stage in
high-fat food avoidance from time 1 to time 2. From time 1 to time 2, 54.3% (n = 139) of
students did not change in readiness to change both their exercise and fruit and vegetable intake.
One-tenth of students did not change in readiness to change their fruit and vegetable
consumption, yet regressed one stage in exercise. Similarly, 13.3% maintained their readiness to
change their fruit and vegetable consumption, yet progressed one stage in intention to exercise.
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The percentage of students moving the same number of stages for each health behavior from
time 1 to time 2 are presented in Table 7.
Gender comparison. To explore the differences in change in stage of change from time
1 to time 2 between males and females, chi-squares were run between each behavior’s change
variable and gender. Results from this analysis are shown in Table 8. The only significant
difference between males and females was in their change in readiness to change high fat food
avoidance, χ 2(8, n = 255) = 28.95, p < 0.001. Females were more likely than males to progress
in readiness to avoid high fat foods across the first semester of college, and a higher percentage
of males (56.2%) than females (39.6%) maintained their stage of change from time 1 to time 2.
Discussion
Across the first semester of college, freshmen are less likely to be ready to change some
behaviors (e.g., alcohol consumption and exercise) and more likely to progress to adopting some
healthy behaviors (e.g., high fat food avoidance). This result is similar to past research on
entrance into college’s impact on students' health behaviors (Douglas et al., 1997; Nelson et al.,
2009; Patrick, Covin, Fulop, Calfas, & Lovato, 1997; Racette, Deusinger, Strube, Highstein, &
Deusinger, 2005; Spencer, 2002) and readiness to change these health patterns (Racette et al.,
2005). In the current sample, the only behavior not showing significant changes in readiness to
change across the semester was fruit and vegetable intake. However, the data shows most
students were in Preparation to increase their fruit and vegetable intake at both time points.
Even when students increase their readiness to change over time, the students' health
behaviors did not always improve to meet the recommended guidelines at the time. Given that
the average age of participants was 18 years of age, legally, none of the students should have
been drinking alcohol. The majority of students, however, had consumed alcohol at some point
in their life, and they reported consuming alcohol during the study. Moreover, the current
average number of drinks per drinking occasion surpassed four, whereas the United States
Department of Agriculture's Dietary Guidelines define moderate drinking as up to one drink per
day for females and two drinks per day for males (USDA, 2010). At the end of the first semester
of college this number of drinks per drinking occasion did not significantly decrease, and
significantly more student had regressed (29.6%) in their readiness to change their binge
drinking patterns, than progressed (10%). At time 2, the majority of students were in PC, similar
to findings from past research (Harris, Walters, & Leahy, 2008; Vik, Culbertson, & Sellers,
67
2000). This heavy episodic drinking and alcohol abuse is often associated with many negative
consequences including problems with the law, lower academic performance, social relationship
strains, unplanned sexual activity, injury, and secondary effects on those surrounding the
problem drinkers (Wechsler et al., 1994). The regression seen in readiness to change binge
drinking patterns in this study suggests that current alcohol consumption intervention programs
in place at the university may not be as effective as previously thought. Moreover, these findings
support the hypothesis that changes in students readiness to change their alcohol consumption
patterns does change over the course of the first semester of college.
The majority of students at both time points did not meet the recommended intake of five
servings of fruit and vegetables per day, putting them at risk for various vitamin and mineral
deficiencies, chronic diseases, and more difficult weight maintenance (USDA, 2010). Nutrient
deficiencies may also lead to greater health problems later in life and have been shown to affect
academic performance (Ross, 2010). In addition to low fruit and vegetable intake, at time 1 and
time 2, only 44.2% and 52% of students avoided intake of high fat foods. Throughout their first
semester, however, more students had progressed to the action and maintenance stages of change
and less were in the precontemplation for fat avoidance. If unmonitored, constant high fat food
intake can lead to weight gain (Levitsky, Halbmaier, & Mrdjenovic, 2004; Pliner & Saunders,
2008; Racette et al., 2008) chronic health issues, and lower overall health status (Clement et al.,
2004). In contrast to past research (Racette et al., 2005) significant changes in stage of change
for fruit and vegetable consumption across the first semester of college were not demonstrated.
An overwhelming 94.1% of students were in Preparation and less than 1% were in Maintenance
at both time points. Racette et al. (2005) showed the majority of students (55%) were in
Precontemplation at both the beginning of freshmen year and the end of sophomore year.
However, the current results may differ due to university specific culture and/or the time points
of data collection.
On a more positive note, at both the beginning and end of their first semester of college
about two-thirds of students reported the recommended level (were in the Action or Maintenance
stage of change) of 20 minutes of physical activity most days of the week. In the current study at
both time points, a higher percentage of students were in both Action and/or Maintenance, than
other studies examining stages of change in exercise in college students (Daley & Duda, 2006;
Wadsworth & Hallam, 2007; Zizzi, Keeler, & Watson, 2006). Even so, as supported by past
68
research (Racette et al., 2005), more students regressed than progressed in readiness to change
during this time suggesting that the college environment does negatively affect students' physical
activity patterns. Efforts should be made to research variables leading to these changes in
behavior, and implement programs to mitigate this regression.
A limited number of significant relationships were found between the studied health
behaviors in these freshmen college students at both time points of data collection. Readiness to
increase physical activity and avoid high fat foods were found to be interrelated at both time
points, leading to the conclusion that interventions aimed at one behavior may have an
potentially inadvertent positive effect on the other behavior. Past research with college students
supports this finding showing that whom eat healthier have been found to participate in more
vigorous activity, and those strength training ate fewer fatty foods (Johnson et al., 1998).
Clement et al. (2004) also found a relationship between these two variables, with a negative
correlation between high calorie/high fat food intake and physical activity stage of change score.
Lastly, Lake et al. (2009) also show that students with greater levels of sedentary activity, report
a greater percentage of energy from fat and total energy intake.
Similar to this previous relationship, at time 2 only, binge drinking and high fat food
avoidance stages of change were significantly interrelated suggesting that one behavior may
affect the other and multiple health behavior change interventions can be an efficient method of
health promotion for this population. Past research (Nelson et al., 2009) supports this finding
stating that alcohol-related eating drastically affects students' diets and often leads to greater
consumption of take-out and higher fat foods.
Change in stage of change in the multiple behaviors, also produced significant results. A
negative correlation was found between change in binge drinking and exercise stage of change,
meaning that as one regressed in readiness to change one behavior, they progressed in the other.
Both Laska et al. (2009) and Nigg et al. (2009) presented similar results stating that students
whom drank alcohol had greater rates of physical activity. This finding may suggest
compensation effects and a more complicated relationship between these two behaviors, as
supported by past research in college student health behavior relationships (Laska, Pasch, Lust,
Story, & Ehlinger, 2009; Nigg et al., 2009). Future research is warranted to further explore this
relationship.
69
In addition to this correlation, there were significant relationships between change in
stage of change of binge drinking and high fat food avoidance, as well as change in stage of
change of intention to exercise and consume fruits and vegetables. These findings are supported
by past research by both Johnson et al. (1998), whom showed relationship between students
exercise patterns and healthier eating, and Nigg et al. (2009) whom demonstrated an association
between alcohol consumption and eating unhealthy, calorie dense foods. In contrast, and not
supported by current findings, Clement et al. (2204) demonstrated a negative correlation between
high calorie/high fat food intake and physical activity stage of change score.
Contrary to gender difference expectations and previous gender difference findings
(Cardinal et al., 2009; Horacek, 2002; Suminski & Petosa, 2002), high fat food avoidance
portrayed the only significant difference in change in stage of change between male sand
females. Lamb and Joshi (1996) had similar findings showing that females are more likely than
males to be in the later stages of change for dietary fat consumption. Given this, TTM based
health promotion programs aimed at college student binge drinking, physical activity, and fruit
and vegetable consumption may need not be gender specific, allowing for more efficient (both
financially and timely) interventions.
Although some changes were seen in health behavior stage of change, the vast majority
of students remained stable in readiness to change their health behaviors from time 1 to time 2.
Additionally, with regards to the multiple health behavior relationships fewer significant
interrelationships were found in contrast to past research. Upon running the stage of change
analysis for all behaviors together across the first semester in college, no significant relationships
were found. This lack of significance, potentially provides disconfirming evidence for the
hypothesis that a multiple health behavior change cluster exists between these behaviors stage of
change in this sample population. This is the first study of its kind examining the four health
behaviors in question using Stages of Change, which may be a reason for the lack of behavior
interrelationships. The conclusion may be that there are indeed interrelationships in the
behavioral patterns, but not in readiness to change these patterns. Moreover, the current results
may be specific to the university studied, and results may be different at a larger or more urban
or more diverse university.
70
Limitations
Although this study has numerous interesting findings, there were limitations to it. First,
two data points throughout students first semester of college were used to collect data. Although
both the beginning and end of the semester behavior patterns were collected, both time points
were key points during the semester (the first day of classes and before finals week). During
these times, students do not have as standard and predicable schedules and priorities, as they may
throughout the middle of the semester, which may result in altered behavior. Past research (Del
Boca, Darkes, Greenbaum, & Goldman, 2004; Lee, Maggs, & Rankin, 2006) shows that alcohol
consumption in college students is moderated around finals, which means the current study's
time 2 data may not show quite the extent of true behavior changes. Moreover, collecting data
on a Monday versus a day later in the week may alter data, as past research (Del Boca et al.,
2004) shows students drink heavier Thursday, Friday, and Saturday.
Additionally, the beginning of the semester and end of semester can potentially have very
different weather patterns (August vs. December) which may skew the results. Past research
shows that weather is indeed a determinant of exercise and physical activity patterns (Dishman,
Sallis, & Oreinstein, 1985), and weather may also affect the other health behavior patterns
studied in these students. Collecting at more time points would produce a clearer and more
accurate depiction of the health behavior changes that occur throughout the first semester of
college.
Another limitation was the method of data collection. Although there are a plethora of
students from various majors in introductory psychology, this may have not been a truly
representative sample of the freshmen student population. This method, however, did aid in
participant follow through at time two, because of the structured classroom setting the
questionnaire was administered in. Perhaps future research could sample from introductory
courses in a variety of disciplines in order to more closely resemble the class demographics.
Implications and Future Research
The majority of students in the current study did not meet national recommendations for
alcohol intake, fruit and vegetable consumption, and physical activity at both times of data
collection. This suggests that universities should consider placing more emphasis on personal
health and wellness, in addition to the standard curriculum of a college student. Although gender
specific programs may not be necessary for all behaviors, programs aimed at limiting high fat
71
foods may be more successful if specifically targeting males or females, since past research
shows genders respond differently to nutrition interventions (Wong, Gucciardi, Ri, & Grace,
2005). To further explore the efficacy of gender specific programs, future researcher should
compare gender neutral to gender specific TTM based health behavior interventions in college
students.
In addition, in relation to multiple health behavior change in stage of change, future
research should perform random data collection and collect at more time points throughout
freshmen year. As supported by previous research, employing the TTM to create MHBC
interventions framed on the TTM may be valuable in creating successful programs without
overwhelming participants (Driskell et al., 2008) and creating synergistic health behavior change
(Johnson et al., 2008). Therefore truly understanding students' readiness to change throughout
the whole of their first year of college may aid universities in improving the shown health
behavior patterns, as well as creating more efficient interventions aimed at health patterns
showing the most regression.
This study emphasizes the regression in health behaviors students experience their first
semester of college. Alcohol consumption increases, fruit and vegetable intake decreases, and
student regress in their readiness to participate in physical activity. Significant relationships
exist between readiness to change high fat food avoidance and physical activity in these students
in addition to binge drinking and high fat food avoidance. Both relationships may be valuable
information for future college health program planning. Gender differences also came into play
with relation to change in stage of change for high fat food avoidance. These findings all
emphasize the complicated nature of the college environment and the health behavior patterns
resulting from it, and future qualitative research may be necessary in fully understanding these
health behavior changes and patterns. In the meantime, university health professionals and
government agencies should employ this information to aid in creating successful health
programs for college freshmen students.
72
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Zizzi, S.J., Keeler, L.A., & Watson II, J.C. (2006). The interaction of goal orientation and stage
of change on exercise behavior in college students. Journal of Sport Behavior, 29(1), 96110.
99
Table 1
The Processes of Change
Name
Definition
Examples (for someone attempting to
reduce alcohol consumption)
Consciousness
Increased knowledge and
Educating college students on the
raising
understanding about the causes,
negative effects of binge drinking and
consequences, and treatment for steps that can be taken to minimize the
a behavior
behavior.
Expressing feelings about the
Talking with friends/family about your
target behavior and possible
feelings (anxiety, anger, depression, etc.)
solutions
regarding your binge drinking behaviors.
Recognizing consequences and
Reflect upon how you view yourself when
self-image with and without the
you are drinking. Is this different from
behavior
the sober you? Why/why not?
Environmental
Realization of the effect one’s
Reflect upon how your drinking may
reevaluation
behavior has on their social
affect the social situations you put
environment and others around
yourself in, your friends and/or those
Dramatic relief
Self-reevaluation
close to you.
them
Social liberation
Changing the social
Look at the social environment (friends,
environment, policies, or social
living situation, work, recreational
norms to help one move
environment, etc.) you place yourself in
towards the target behavior
on a day to day basis. Does this allow for
more drinking? Does it increase your
drinking?
Self liberation
Belief that one can change and
Make a verbal/written statement to
committing to that change
yourself that you can and will reduce your
drinking. Believe that this is a possible
and attainable goal.
Stimulus control
Controlling environmental cues
Not attending parties where there will be
that would have previously lead
excessive drinking in order to minimize
to the target behavior
the temptation to participate.
100
Contingency
Use of punishment and
Rewarding oneself for not binge drinking
management
reinforcement to promote the
on the weekends. Maybe putting aside the
desired behavior and inhibit the
money one would have spent on alcohol
target behavior; reinforcement
and punishments can be
administered by self or others
and saving up for some greater reward in
the future (long-term) or using that money
to reward oneself instantly (short-term).
Helping
Use of social support during the Communicating with friends what your
relationships
change process
goals are and asking for them to support
you and help monitor your behavior.
Counterconditioning
Learning to substitute
Substituting soda or sparkling water
alternative behaviors for
instead of alcohol when at a party.
undesired behavior
101
Table 2
Studies Examining Physical Activity Patterns in College Students
Author
Year
Gender
Physical Activity Patterns
(M/F)
Brevard &
1996
M&F
Ricketts
Lowry et al.
29% of students living on campus and 28% off
campus are sedentary or lightly active
2000
M&F
37.6% participate in vigorous physical activity ≥3
days/week
29.9% participate in muscle strengthening exercises
≥3 days/week
19.5% participated in 30 minutes of moderate
physical activity such as walking/biking ≥ 5
days/week
Suminski &
2002
M&F
Petosa
Huang et al.
47% did not engage in vigorous physical activity in
the past month
2003
M&F
11% of males and 21.5% of females report no aerobic
exercise per week
33% participate in no strength training per week
Average weekly participation in aerobic exercise 2.8
days/week
Average weekly participation in strength training is
2.2 days per week
Clement et al.
2004
F
46.6% regularly engage in strenuous physical activity
72.4% participate in exercise or labor 3+ times/week
Kilpatrick et
2005
M&F
al.
Average weekly participation in exercise 3.58
days/week
Average weekly participation in sport 2.14 days/week
Racette et al.
2005
M&F
About half engaged in regular aerobic exercise
102
30% of students engaged in no physical activity
Racette et al.
2008
M&F
In college freshmen, 59% of students engaged in
aerobic exercise, 45% in strengthening, 31% in
stretching regularly while 29% did not exercise
regularly.
In college seniors, 25% did not exercise regularly.
Laska et al.
2009
M&F
57.8% females participate in ≥20 minutes vigorous
physical activity on <3 days/week
56.1% males participate in ≥20 minutes vigorous
physical activity on <3 days/week
Greene et al.
2011
M&F
54.2% report high activity level and physically fit
103
Table 3
Fruit and Vegetable Consumption of College Students
Author
Year
Gender Findings
Lowry et al.
2000
M&F
26.3% ate ≥5 servings/day of fruits and vegetables per
day
Huang et al.
2003
M&F
69.4% ate <5 servings/day of fruits and vegetables per
day
Mean fruit and vegetable intake = 4.2 servings per day
Clement et al. 2004
F
Mean vegetable intake = 1.68 servings per day
Mean fruit intake = 1.34 servings per day
Racette et al.
2005
M&F
71% freshmen ate <5 servings/day of fruits and
vegetables per day
Racette et al.
2008
M&F
71% seniors ate <5 servings/day of fruits and vegetables
per day
Keller et al.
2008
M&F
95% ate <5 servings/day of fruits and vegetables per day
Laska et al.
2009
M&F
64.6% ate <5 servings/day of fruits and vegetables per
day
ACHA-
Spring
NCHA
2010
M&F
0 servings/day = 5.6%
1-2 servings/day = 58.4%
3-4/day = 30%
5+ servings/day = 6%
Erinosho et
2011
M&F
al.
Greene et al.
Mean vegetable intake = 3.2 servings per day
Mean fruit intake = 2.2 servings per day
2011
M&F
5.5% ate ≥5 servings/day of fruits and vegetables per day
104
Table 4
Single behavior stage of change movement from time 1 to time 2
Binge
Drinking (%)
p <0.001
n = 251
Exercise
(%)
p<0.001
n = 256
Fat (%)
p<0.001
n = 255
Fruit/Vegetable
(%)
p > .05
n = 256
Stable
PC
39.80
0.40
18.40
---
C
0.80
1.60
1.20
4.30
5.90
7.80
2.40
0.00
1.20
94.10
6.30
---
38.70
16.10
0.00
NB (Non-bingers)
0.00
17.10
Total Stable (%)
60.50
--57.10
--44.40
--94.10
PR
A
M
Progressed
PC→C
PC→PR
2.00
0.40
0.80
5.10
0.00
2.00
-----
PC→A
PC→M
PC→NB
C→PR
C→A
C→M
C→NB
PR→A
0.40
0.00
0.40
0.80
0.40
0.80
0.40
2.00
0.00
0.00
3.50
---
2.40
---
---
---
---
2.00
0.80
0.80
---
5.90
3.90
4.30
2.00
--5.10
2.70
--0.00
-----
PR→M
PR→NB
0.00
0.40
2.30
1.20
0.80
---
---
---
A→M
A→NB
M→NB
Total
Progressed(%)
Regressed
0.00
0.40
1.60
3.50
5.50
-----
-----
-----
10.00
16.10
35.00
3.50
6.80
1.20
2.40
---
0.80
2.80
4.70
2.70
2.00
2.00
1.20
---
C→PC
PR→C
PR→PC
105
A→PR
A→C
A→PC
M→A
M→PR
M→C
M→PC
NB→M
NB→A
NB→PR
NB→C
NB→PC
Total Regressed
(%)
0.80
2.80
3.60
0.80
0.80
1.20
1.20
1.20
2.40
0.40
1.20
2.80
29.60
2.70
1.60
0.80
-----------
2.70
0.40
1.60
5.50
2.00
1.20
1.20
-----------
--------0.40
0.00
-----------
27.10
20.90
2.40
5.10
5.90
2.30
0.80
106
Table 5
Multiple health behavior relationships at time 1.
Binge Drinking
Exercise
(p > .05)
FruitVegetable
(p > .05)
High Fat
Avoidance
(p > .05)
% in
Stage of
Change
PC
C
PR
A
M
C
PR
M
PC
PC
0.60
3.80
7.60
6.30
21.80
0.60
39.10
0.30
11.40
C
0.60
1.30
1.30
1.90
5.70
0.30
10.40
0.00
2.20
PR
0.00
0.30
1.90
0.90
3.80
0.60
6.30
0.00
2.20
A
0.00
1.30
1.30
0.30
6.00
0.00
8.80
0.00
2.50
M
0.00
0.00
1.30
1.30
2.80
0.00
5.40
0.00
1.60
NB
1.60
3.80
6.00
6.30
10.40
0.90
27.10
0.00
11.40
C
PR
A
M
7.60
3.20
6.90
11.00
1.30
0.90
2.80
3.50
0.00
0.90
2.20
1.60
0.60
1.60
0.90
3.20
0.60
0.60
0.90
1.60
4.40
2.50
3.50
6.30
Exercise
FruitVegetable
(p > .05)
High Fat
Avoidance
(p = .006)
High Fat
Avoidance
(p > .05)
Stage of
Change
C
PR
PC
0.00
2.80
C
0.90
9.30
PR
0.30
19.30
A
0.60
16.50
M
0.60
49.20
M
PC
0.00
2.20
0.00
4.10
0.00
6.90
0.00
2.20
0.30
16.30
C
0.30
1.30
2.50
4.40
5.90
3.10
4.10
2.80
1.60
4.40
4.70
4.40
7.50
16.30
PR
A
M
0.00
0.60
0.00
1.60
0.30
2.80
Fruit-Vegetable
Stage of
Change
PC
C
C
0.30
0.00
PR
30.90
14.40
M
0.30
0.00
PR
A
M
0.00
1.30
0.90
9.70
16.30
25.90
0.00
0.00
0.00
107
Table 6
Correlation table for health behavior change in stage of change variables
T2-T1
exercise
SOC
T2-T1 exercise SOC
T2-T1 Fat
SOC
T2-T1 Fruit
and Veggie
SOC
--
T2-T1 Fat SOC
-.01
--
T2-T1 Fruit and Veggie SOC
-.06
-.03
--
-.16*
-.02
-.05
T2-T1 Alcohol SOC
Note: *Correlation is significant at the 0.05 level (2-tailed).
108
Table 7
Percent of students moving the same number of stages of change from time 1 to time 2
BingeExercise
(p >0.05)
n = 251
Exercise- Fruit/Veg.BingeBinge-Fat ExerciseFat
Fat
Fruit/Veg. Avoidance Fruit/Veg. Avoidance Avoidance
(p >0.05) (p = 0.014) (p = 0.046) (p >0.05)
(p >0.05)
n = 251
n = 250
n = 256
n = 255
n = 255
Change in
SOC (T2-T1)
-4
---
---
0.00
---
---
---
-3
-2
-1
0
1
2
3
0.00
0.00
1.20
35.10
1.20
0.00
0.00
--0.00
0.80
57.00
0.00
0.00
---
0.00
0.00
1.20
30.80
1.60
0.40
0.00
--0.00
0.00
54.30
0.00
0.00
---
0.00
0.00
2.70
26.70
3.50
0.40
0.00
--0.00
0.40
40.40
0.40
0.00
---
4
Total (%)
0.00
37.50
--57.80
0.00
34.00
--54.30
0.00
33.30
--41.20
109
Table 8
Gender breakdown of change in stage of change from time 1 to time 2
Binge Drinking
p > 0.05
Exercise
p > 0.05
Fruit/Vegetable
p > 0.05
High Fat
Avoidance
p < 0.001
Change
in SOC
from
Time 1 to
Time 2
(T2-T1) Males Females Males Females Males Females Males Females
-5
1.40
3.30
-------------4
1.40
2.80
--------4.10
0
-3
4.30
5.50
0.00
1.10
----6.80
1.1
-2
7.10
9.40
2.70
3.30
1.40
0.00
0.00
4.9
-1
5.70
12.20
9.60
13.10
1.40
2.20 11.00
14.3
0 71.40
56.40 61.60
55.20 91.80
95.10 56.20
39.6
1
5.70
6.60 13.70
13.70
4.10
2.20 12.30
22.5
2
1.40
1.10
6.80
10.40
1.40
0.50
1.40
9.9
3
1.40
1.70
2.70
3.30
----5.50
5.5
4
0.00
0.60
2.70
0.00
----2.70
2.2
5
0.00
0.60
-----------
110
Figure 1
Stage distribution of students for binge drinking at time 1 and time 2
Students in Each Stage (%)
60
50
40
30
Time 1
20
Time 2
10
0
PC
C
PR
A
Stage of Change
111
M
NB
Figure 2
Stage distribution of students for intention to exercise at time 1 and time 2
Students in Each Stage (%)
60
50
40
30
Time 1
Time 2
20
10
0
PC
C
PR
Stages of Change
112
A
M
Figure 3
Stage distribution of students for high fat food avoidance at time 1 and time 2
Students in Each Stage of Change (%)
35
30
25
20
Time 1
15
Time 2
10
5
0
PC
C
PR
Stage of Change
113
A
M
Figure 4
Stage distribution of students for fruit and vegetable consumption at time 1 and time 2
Students in Each Stage of Change (%)
Fruit and Vegetable Consumption
120
100
80
60
Time 1
40
Time 2
20
0
C
PR
Stage of Change
114
M
Appendix A: Consent Form
College Health Study II
Dear Participant:
You have been asked to take part in the research project described below. The researcher
will explain the project to you in detail. If you have any questions, please feel free to call Dr.
Rose Marie Ward, the person mainly responsible for the study.
The purpose of the study is to gather information from students about issues of health
behavior change. Responses to these items will be completely anonymous. At no time will
your name be tied to your responses. Only project personnel will have access to the survey
responses.
1. YOU MUST BE AT LEAST 18 YEARS OLD to be in this research project.
2. If you decide to take part in this study, your participation will involve filling out a survey
pertaining to attitudes towards different aspects of healthy living. The survey will ask you
questions about your sleep, diet, exercise, smoking, drinking, teeth, and other health
behaviors. The survey will take approximately 50 minutes to complete.
3. The possible risks or discomforts of the study are minimal, although you may feel some
embarrassment answering some of the questions about private matters.
4. Although there are no direct benefits of the study, your answers will help increase the
knowledge regarding the status of problems in psychology.
5. Your part in the study is confidential. That means your answers to all questions are private.
No one else can find out what your answers are. Scientific reports will be based on group
data and will not identify you or any individual as being in this project. You will be assigned
a participant number for tracking purposes only.
6. The decision to participate in this research is up to you. You do not have to participate and
you can refuse to answer any question.
7. Participation in this study is not expected to be harmful or injurious to you. However, if this
study causes you any injury, you should write or call Dr .Rose Marie Ward at (513) 5293751.
If you have questions about the study, you can contact the investigator, Dr. Rose Marie
Ward, 513-529-3751 or [email protected].
If you have any questions or concerns about your rights as a subject, you may contact
Miami University's Office for the Advancement of Research and Scholarship, (513) 529-3734 or
[email protected].
You are at least 18 years old. You have read the consent form and your questions have
been answered to your satisfaction. Your filling out the survey implies your consent to
participate in this study.
If these questions are upsetting and you want to talk, please use the phone numbers below:
Miami University Student Counseling Service 529-4634
Psychology Clinic Benton Hall 529-2423
Community Counseling and Crisis Center 523-4146
Thank you,
Rose Marie Ward, Ph.D.
Principal Investigator
115
Appendix B: Debrief Sheet at Time 1
Thank you for participating in our experiment. You have finished the first part of a twopart study. The second part of the study will be later in the semester. We will contact you to
remind you of your commitment.
The study in which you have just participated in was designed to examine attitudes and
beliefs about health behaviors among college students. Each of you was asked the same series of
questions. Specifically, we will be examining the situations in which college students feel most
comfortable expressing their health beliefs. We will also examine the prevalence rates of certain
health behaviors among college students. We hope to use this data to add to the body of
literature concerning college student health behavior. We are interested in seeing if this decision
to practice health behaviors occurs in a stage-like progression that is readily identifiable.
We appreciate your participation in this study.
If you would like more information concerning our theories, please read:
Prochaska, J. O. (2003). Treating entire populations for behavior risks for chronic
disease. Homeostasis, 42, 1-12.
Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. In search of how people change:
Applications to addictive behaviors. Social Psychology of Health: Psychology
Press: NY.
If you have questions/comments, or if you are interested in getting information about the results,
please call Dr. Ward at 529-3751 or e-mail [email protected].
116
Appendix C: Debrief Sheet at Time 2
Thank you for participating in our experiment. You have finished the second part of a two-part
study. The first part of the study was earlier in the semester.
The study in which you have just participated in was designed to examine attitudes and beliefs
about health behaviors among college students. Specifically, we asked you questions about your
smoking practices, alcohol consumption, eating practices, exercise habits, teeth care habits, and
many other health related behaviors. Each of you was asked the same series of questions.
Specifically, we will be examining the situations in which college students feel most comfortable
expressing their health beliefs. We will also examine the prevalence rates of certain health
behaviors among college students. We hope to use this data to add to the body of literature
concerning college student health behavior. We are interested in seeing if this decision to
practice health behaviors occurs in a stage-like progression that is readily identifiable. We are
also interested in examined which health behaviors seem to impact the practice of other health
behaviors. For example, are you more or less willing to exercise when you have had little sleep.
We appreciate your participation in this study.
If you would like more information concerning our theories, please read:
Prochaska, J. O. (2003). Treating entire populations for behavior risks for chronic
disease. Homeostasis, 42, 1-12.
Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. In search of how people change:
Applications to addictive behaviors. Social Psychology of Health: Psychology
Press: NY.
If you have questions/comments, or if you are interested in getting information about the results,
please call Dr. Ward at 529-3751 or e-mail [email protected]. Results will be available in
the Spring of 2005.
117
Appendix D: Measures
Alcohol Consumption Stage of Change (Laforge, Maddock, & Rossi, 1998 )
A drink is defined as one bottle of beer, one ounce of liquor, or a four-ounce glass of wine.
In the last month have you had 5 or more (for males) or 4 or more (for females) drinks in a
row?
o Yes, and I do not intend to stop drinking 5 or more (4 or more) drinks in a row.
o Yes, but I intend to stop drinking 5 or more (4 or more) drinks in a row during the
next 6 months.
o Yes, but I intend to stop drinking 5 or more (4 or more) drinks in a row during in
the next 30 days.
o No, but I have had 5 or more (4 or more) drinks in a row in the past 6 months.
o No, and I have not had 5 or more (4 or more) drinks in a row in the past 6 months.
o No, I have never had 5 or more (4 or more) drinks in a row.
Think back over the last month. How many times have you consumed 5 or more drinks in
one day?
o Never
o Once
o 2 to 3 times
o 4 to 6 times
o 7 or more times
Did you drink until you got drunk or intoxicated at least once in the last month?
o No, I have not been drunk in the last 6 months.
o No, I have not been drunk in the last month, but I have been drunk in the last 6
months.
o Yes, but I intend to stop getting drunk in the next 30 days.
o Yes, but I intend to stop getting drunk in the next 6 months.
o Yes, and I do not intend to stop getting drunk
118
Alcohol Decisional Balance (Laforge, Maddock, & Rossi, 1999)
How important are the following in your decision about how much to drink?
1= Not At All Important
2 = Not Important
4 = Very Important
3 = Somewhat Important
5 = Extremely Important
1. Drinking could get me addicted to alcohol.
2. I do not like myself as much when I drink.
3. I feel happier when I drink.
4. I can talk with someone I am attracted to better after a few drinks.
5. Drinking helps keep my mind off problems.
6. Drinking makes me more relaxed and less tense.
7. Drinking helps me have fun with friends.
8. Drinking too much could make me do things I regret.
9. I could accidentally hurt someone because of my drinking.
10. I am setting a bad example for others with my drinking.
Physical Activity Stage of Change (Marcus, Selby, Niauea, & Rossi, 1992)
Regular exercise is any planned physical activity (e.g., brisk walking, aerobics, jogging,
bicycling, swimming, rowing, etc.) performed to increase physical fitness. Such activity should
be performed 3 or more times per week for 20 or more minutes per session at a level that
increases your breathing rate and causes you to break a sweat.
Do you exercise regularly according to the definition above?
o
Yes, I have been for more than 6 months.
o Yes, I have been, but for less than 6 months.
o No, but I intend to in the next 30 days.
o No, but I intend to in the next 6 months.
o No, and I do not intend to in the next 6 months.
Fruit and Vegetable Consumption Stage of Change (Laforge, Greene, & Prochaska, 1994)
How many servings of fruits and vegetables do you usually eat each day?
o
Zero
o One to Two
o Three to Four
119
o Five
o Six or more
Have you been eating 5 or more servings of fruits and vegetables a day for more than 6
months?
o Less than 6 months
o More than 6 months
Do you intend to start eating 5 or more servings of fruits and vegetables a day in the next
6 months?
o No, and I do not intend to in the next 6 months
o Yes, and I intend to in the next 6 months
o Yes, and I intend to in the next 30 days
High Fat Food Avoidance Stage of Change (Greene & Rossi, 1998)
Do you consistently avoid eating high fat foods?
o No, and I do not intend to in the next 6 months
o No, but I intend to in the next 6 months
o No, but I intend to in the next 30 days
o Yes, and I have been, but for less than 6 months
o Yes, and I have been for more than 6 months
120