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Journal of American College Health
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/vach20
Healthy lifestyle status related to alcohol and food
addiction risk among college students: a logistic
regression analysis
Cheong Hoon Kim, Kyung-Ah Kang & Sunhwa Shin
To cite this article: Cheong Hoon Kim, Kyung-Ah Kang & Sunhwa Shin (2023) Healthy
lifestyle status related to alcohol and food addiction risk among college students: a
logistic regression analysis, Journal of American College Health, 71:3, 775-781, DOI:
10.1080/07448481.2021.1908302
To link to this article: https://doi.org/10.1080/07448481.2021.1908302
Published online: 11 Mar 2022.
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MAJOR ARTICLE
Healthy lifestyle status related to alcohol and food addiction risk among college
students: a logistic regression analysis
Cheong Hoon Kim, DPT
a
, Kyung-Ah Kang, RN, Ph.D
b
, and Sunhwa Shin, RN, Ph.D
b
a
Department of Physical Therapy, Sahmyook University, Seoul, South Korea;
b
College of Nursing, Sahmyook University, Seoul,
South Korea
ABSTRACT
Objective: This study aimed to determine whether low healthy lifestyle (HL) status was associated
with alcohol and food addiction risks among college students.
Method: The data were gathered through an online survey questionnaire from 311 college stu-
dents. The students were divided into either a lower or a higher HL status group, based on HL
mean score, and the major statistical method used was a binary logistic regression.
Results: There were significant differences in alcohol and food addiction score between the two
groups. The lower HL status group showed a 3.06 times higher risk of problematic drinking and a
2.44 times higher risk of food addiction compared with the higher HL status group.
Conclusions: The results of this study suggest the importance of HL in the prevention of alcohol
and food addiction. HL information can be used to develop health education programs aimed at
preventing addiction for college students.
ARTICLE HISTORY
Received 17 October 2019
Revised 8 May 2020
Accepted 21 March 2021
KEYWORDS
Addiction risk; alcoholism;
college students; food
addiction; healthy lifestyle
Everyone has their own unique lifestyle. Lifestyle is the inte-
gration of habits that are carried out on a routine basis, and
lifestyle choices are closely related to health.
1,2
Healthy life-
style (HL) involves the performance of those activities that
maintain and optimize physical and mental health and facili-
tate the fulfillment of individual needs.
2
Maintaining a HL
lowers the risk of serious illness and early death, and can
improve quality of life.
3,4
College students are in a crucial phase of their lives. For
most, the college years are a time when an individual
becomes independent from his or her parents and takes
responsibility for his or her own life.
5
Previous studies have
demonstrated that most college students have unhealthy life-
styles due to decreased physical activity, irregular sleep,
drinking, smoking, and inadequate weight management.
5
Additionally, these unhealthy lifestyle habits lead to psycho-
logical stress.
6
Moreover, an unhealthy lifestyle not only
affects physical health, but also negatively affects psycho-
logical and spiritual health.
1
Therefore, it is very important
that college students consistently and routinely practice
healthy living.
Previous studies have reported that nutrition, rest, exer-
cise, water intake, amount of sunshine, spiritual or religious
beliefs, amount of fresh air, stress management, and inter-
personal relationships are all determining factors of a HL.
2,7
Factors for evaluating the HL habits of college students, as
reported by previous studies, include fitness and exercise,
healthy eating, relaxation, self-development, participation in
leisure activities, and social participation.
3,7
The importance
of assessing amount of sunshine (to determine underexpos-
ure or overexposure) has also been stressed.
8
In addition, several studies have suggested that diets that
are high in fats, red meat, fast foods, desserts, and sugars
carry a notably higher risk for obesity, heart disease and
type 2 diabetes.
9
These foods are also highly processed with
added fats and/or refined carbohydrates, and may trigger an
addiction-like response in college students.
10
Food addiction
is associated with lifestyle-related disorders such as obesity
and binge eating.
11
The condition is activated by a metabolic
reward system that pursues pleasure behavior as per the
individual’s choice and willingness, and threatens health by
destroying the system’s homeostasis. This process is similar
to alcohol addiction.
11
Sixty different medical conditions have been found to be
related to alcohol. Alcohol contributes to 4% of the global
burden of disease, which accounts for about as much death
and disability globally as tobacco and hypertension.
Treatment research has demonstrated that prompt interven-
tion in primary care is effective and feasible.
12
Alcohol initi-
ation and abuse during adolescence must be reframed as
part of the typical reward-seeking and risk-taking behaviors
that characterize this period of life.
13
Many studies have reported that college students who fail
to practice HL habits are more at risk for alcohol, food, or
substance addiction, which, in turn, results in negative con-
sequences throughout life.
14
As negative emotions increase,
students are more likely to engage in addictive behavior.
15,16
However, there is insufficient evidence to determine which
CONTACT Sun-Hwa Shin shinsh@syu.ac.kr College of Nursing, Sahmyook University, Nursing Department, 815 Hwarangro, Nowon-gu, Seoul, 01795,
South Korea
ß2021 Taylor & Francis Group, LLC
JOURNAL OF AMERICAN COLLEGE HEALTH
2023, VOL. 71, NO. 3, 775–781
https://doi.org/10.1080/07448481.2021.1908302
specific lifestyle behaviors or habits play a role in predispos-
ing individuals to addiction and require professional advice
or intervention from a health professional.
Assessing the lifestyle behaviors and habits of college stu-
dents and understanding how to assist them in transitioning
to a HL are crucial for any health professional who consults
with students with alcohol or food addiction.
17,18
Health
professionals advice individuals to modify their lifestyle hab-
its, to promote a speedy recovery.
19,20
Moreover, changes in
lifestyle are necessary as preventive measures against
addiction.
14,21
This study aimed to examine whether HL status affects
alcohol or food addiction risks and to identify the HL fac-
tors that affect addictive behavior. The purposes were to (a)
identify the differences in HL status (lower HL status group
or higher HL status group) among college students, accord-
ing to their general characteristics, (b) identify the differen-
ces in HL status between college students with low-risk
drinking habits (lower risk group) and college students with
problematic drinking habits (problematic drinking group) in
terms of their general characteristics, (c) identify the differ-
ences in HL status between college students with low risk of
food addiction (non-food addiction risk group) and college
students with high risk of food addiction (food addiction
risk group) in terms of their general characteristics, (d)
identify the influence of HL status on the risk of alcoholism
among college students, and (e) identify the influence of HL
status on the risk of food addiction among college students.
Methods
Research design
This was a descriptive study to examine the HL status on
alcohol and food addiction risks among college students.
Participants
The participants were college students at S University in
Seoul, Korea. Participation in the online survey was volun-
tary. The exclusion criteria included not being an officially
registered student with the school and being a graduate stu-
dent. Power analyses using the GPower 3.1.9.2 program
(Heinrich-Heine-Universit€
at D€
usseldorf, D€
usseldorf,
Germany) indicated that with a significance level of .05,
effect size of 0.15 (middle), and power (1-b) of 0.95, the
minimum number of cases required in a multiple linear
regression with 11 independent variables was 178. The total
number of participants in this study was 311. Hence, this
study met the minimum number of required cases.
Measures
Healthy lifestyle
Healthy lifestyle (HL) variables were measured with a HL
screening tool.
7
The tool consists of 36 questions in 9
domains (sunshine, water, air, rest, exercise, nutrition, tem-
perance, beliefs, and physical condition). Answers are based
on a 4-point Likert scale, ranging from 1 (“not at all”)to4
(“always”). Each domain consists of 4 questions. The partici-
pants were asked to answer the questions based on their
experiences during the past 7 days. A higher score indicates
a healthier lifestyle. The mean total HL score was 92.47 (SD
¼8.84) and ranged from 64 to 120. The mean total HL
score was used to divide the participants into a lower HL
status group and a higher HL status group for statistical
analysis. Changes in this score would indicate risk, prompt
changes in treatment, and demonstrate possible health bene-
fits.
22
In a study by Kim and Kang,
7
the tool’s intrinsic reli-
ability Cronbach’s⍺value was .71. In this study, the value
was .73.
Alcohol addiction
Alcohol addiction was measured with the Alcohol Use
Disorders Identification Test (AUDIT) developed by the
World Health Organization,
23
and was translated into
Korean.
24
Responses our based on a 5-point Likert scale,
ranging from 0 (“never’)to4(“daily or almost daily”). A
higher total score indicates a higher level of alcohol depend-
ence. Participants scoring 0–7 are classified as low-risk
drinkers, 8–11 as risk drinkers, 12–19 as high-risk drinkers,
and 20 or more as drinkers with alcohol use disorder. In
this study, individuals with a score of 0–7 points were classi-
fied into the low risk group and individuals with 8 points or
more were classified into the problematic drinking group,
and analyzed as a binary variable. Intrinsic reliability
Cronbach’s⍺for the Korean version of the AUDIT
24
was
determined to be .86. In this study, the value was .88.
Food addiction
Food addiction was measured with the Yale Food Addiction
Scale (YFAS).
25
The YFAS has been translated into
Korean.
26
The scale consists of 25 items. Responses for items
1 to 16 are based on a 5-point Likert scale, ranging from 0
(“not at all”)to4(“every day or above 4 times per week”).
Responses for items 17 to 24 are based on a 2-point scale,
ranging from 0 (“no”)to1(“yes”). For the last item (item
25), participants are asked to select or write down the food
that caused a problem. Participants were classified into the
food addiction risk group if they had a clinically significant
score on item 15 or 16 (a score of 1 on either item) and a
symptom count of 3 or above. Others were classified into
the non-food addiction risk group. The intrinsic reliability
Cronbach’s⍺of the Korean version of the YFAS was .88.
26
In this study, the value was .92.
Data collection and ethical considerations
The data collection period was from June 2018 to May 2019.
Prior to data collection, research approval (2-7001793-AB-
N-012018031HR) was obtained from the Research Ethics
Review Committee of the University, where the research was
conducted. The review committee confirmed that the study
did not violate human rights and that all processes
776 C.-A. KANG AND S.-H. SHIN
conformed to standard ethics requirements, including issues
of voluntary participation, anonymity, and confidentiality.
The data were gathered through an online survey. The
questionnaire used in the survey included the HL screening
tool, AUDIT, YFAS, and items on the general characteristics
of the participants. Prior written informed consent was
obtained from each participant. The participants were
informed that the data would be treated in a confidential
manner, and their identities would not be revealed.
Statistical analysis
The data was encoded after review for errors and missing
answers and then transferred to a file. Statistical analyses
were conducted with SPSS version 23.0 software (IBM
Corp., Armonk, NY, USA). Averages and standard devia-
tions, skewness, and kurtosis were calculated for the general
characteristics of the participants. Statistical analyses of HL
status, alcohol addiction, and food addiction were also con-
ducted. The differences between the variables pertaining to
the general characteristics were analyzed by v
2
test, t-test,
and analysis of variance, and post-hoc analyses were con-
ducted using the Scheff
e test. A binary logistic regression
analysis was performed to determine the HL status on alco-
hol and food addiction. In addition, the odds ratio (OR)
and 95% confidence interval (CI) were calculated. For all
statistical analyses, significance was set at .05.
Results
Difference in healthy lifestyle according to general
characteristics
Data on the general characteristics of the participants appear
in Table 1. The participants were 70 men (22.5%) and 241
women (77.5%), and the academic year distribution was 116
freshmen (37.3%), 71 seniors (22.8%), 63 sophomores
(20.3%), and 61 juniors (19.6%). Most of participants
(72.3%) were in families with middle-income status. One
hundred eighty-nine (60.8%) were satisfied with their
current life situation. Body mass index (BMI) was calculated
from mass and height.
The majority (189, 60.8%) of the participants had normal
weight. Ninety-five (30.5%) participants had low weight.
Twenty-seven (8.7%) participants had overweight. There were
182 (58.5%) participants in the low-risk group and 129 (41.5%)
in the problematic drinking group. With respect to food addic-
tion, 235 (75.6%) participants were in the non-addiction risk
group and 76 (24.4%) were in the addiction risk group.
Differences in HL status were analyzed in terms of general
characteristics (Table 1). There were significant gender differences;
men had higher HL scores compared with women (t ¼2.58,
p¼.010). There were no significant differences based on academic
year (F ¼1.86, p¼.136) or family economic status (F ¼0.50,
p¼.605). HL score showed significant differences based on the
level of current satisfaction (F ¼30.42, p<.001). The score was
significantly higher in the satisfied group than in the moderately
satisfied group or dissatisfied group. In alcohol addiction, the low-
risk group had significantly higher HL status scores than the prob-
lematic drinking group (t ¼31.40, p<.001). For food addiction,
the non-addiction risk group had significantly higher HL status
scores than the addiction risk group (t ¼15.45, p¼.001).
With regard to the characteristics of the measured variables
(Table 2), the mean score for alcohol addiction was 0.76 points
(± 0.72), and the mean score for food addiction was 0.67
points (±0.53). The mean score for HL status was 2.59 points
(±0.25). Physical condition scored the highest among the com-
ponents at 3.09 points (±0.48), followed by beliefs at 2.89
points (±0.54), sunshine at 2.74 points (±0.47), temperance at
2.64 points (±0.50), air at 2.50 points (±0.43), exercise at 2.46
points(±0.54),waterat2.36points(±0.46),restat2.35points
(±0.45), and nutrition at 2.26 points (±0.59).
Correlation of healthy lifestyle, alcohol addiction and
food addiction
Correlations between HL status, alcohol addiction, and food
addiction were analyzed (Table 2). Alcohol addiction and
food addiction had a positive correlation (r¼.16, p<.001).
Table 1. Healthy lifestyle according to the subjects’characteristics (N¼311).
Characteristics Categories
Total Healthy lifestyle
n (%) M ± SD t/F (p)
Gender Male 70(22.5) 2.66 ± 0.24 2.58
(.010)Female 241(77.5) 2.57 ± 0.25
Grade Freshmen 116(37.3) 2.63 ± 0.26 1.86
(.136)Sophomore 63(20.3) 2.56 ± 0.26
Junior 61(19.6) 2.54 ± 0.25
Senior 71(22.8) 2.59 ± 0.24
Family economic status High 35(11.3) 2.63 ± 0.25 0.50
(.605)Middle 225(72.3) 2.58 ± 0.25
Low 51(16.4) 2.59 ± 0.27
Current satisfaction in life Satisfaction
a
189(60.8) 2.67 ± 0.24 30.42
(<.001)
a>b,c
Moderate
b
91(29.3) 2.48 ± 0.22
Unsatisfaction
c
31(10.0) 2.41 ± 0.24
Body mass index Low weight 95(30.5) 2.61± 0.26 1.71
(.182)Normal weight 189(60.8) 2.59 ± 0.24
Over weight 27(8.7) 2.51 ± 0.29
Alcohol addiction Low risk group 182(58.5) 2.65 ± 0.26 31.40
(<.001)Problematic drinking group 129(41.5) 2.50 ± 0.21
Food addiction Non-addiction risk group 235(75.6) 2.62 ± 0.24 15.45
(<.001)Addiction risk group 76(24.4) 2.49 ± 0.26
JOURNAL OF AMERICAN COLLEGE HEALTH 777
HL status and alcohol addiction had a significant negative
correlation (r¼–.32, p<.001). Among the components of
HL, water (r¼–.14, p¼.015), rest (r¼–.12, p¼.035), nutrition
(r¼–.13, p¼.023), temperance (r¼–.57, p<.001), beliefs
(r¼–.21, p<.001), and physical condition (r¼–.12, p¼.034)
had negative correlations with alcohol addiction.
HL status and food addiction had a significant negative
correlation (r¼–.24, p<.001). Among the components of HL
air (r¼–.13, p¼.027), nutrition (r¼–.16, p¼.004), temper-
ance (r¼–.19, p¼.001), and physical condition (r¼–.24,
p¼.001) had negative correlations with food addiction.
Differences in alcohol addiction and food addiction
according to the participants’characteristics
There was no significant difference in general characteristics
between the low-risk alcohol drinking group and the prob-
lematic alcohol drinking group (Table 3). For food addic-
tion, there were significant differences between the two
genders in the non-addiction risk group compared with the
addiction risk group (v
2
¼6.56, p¼.006).
Influence of healthy lifestyle on alcohol addiction
Differences in HL status between the low-risk group and
problematic drinking group were analyzed (Table 4). The
OR in the problematic drinking group was 3.06 times higher
(OR ¼3.06, 95% CI [1.97, 4.90]) than in the low-risk group.
According to the analysis of components of HL, college stu-
dents with lower scores on water, rest, nutrition, temper-
ance, and beliefs had a significantly higher OR than the
those with higher scores on the above-mentioned variables.
After controlling for the gender of the participants, a bin-
ary logistic regression analysis was performed to examine
the HL status on alcohol addiction (Table 5). The value of
the final model 2 log likelihood test with predictors were
significantly decreased (v
2
¼22.94, p<.001), the model was
described at 9.6% (Negelkerke R
2
¼.096). The Hosmer-
Lemeshow goodness-of-fit test had a significant probability
greater than 0.05 (v
2
¼0.55, p¼.760), so the regression model
was considered appropriate. When gender was controlled,
the OR was 3.12 times higher in the group with low HL
scores (OR ¼3.12, 95% CI [1.94, 5.04]).
Influence of healthy lifestyle on food addiction
Differences in HL status between the non-food addiction
risk group and the food addiction risk group were analyzed
(Table 4). The OR in the addiction risk group was 2.44
times higher (OR ¼2.44, 95% CI [1.42, 4.20]) than in the
non-addiction risk group. According to the analyses of
Table 2. Descriptive statistics and correlation of healthy lifestyle, alcohol addiction and food addiction (N¼311).
Variables
Alcohol addiction Food addiction
M ± SD Skewness Kurtosisr(p)r(p)
Alcohol addiction 1 .16(<.001) 0.76 ± 0.72 1.31 1.68
Food addiction .16(<.001) 1 0.67 ± 0.53 1.39 2.05
Healthy lifestyle –.32(<.001) –.24(<.001) 2.59± 0.25 –0.07 0.34
Sunlight –.06(.308) –.09(.123) 2.74 ± 0.47 –0.13 0.56
Water –.14(.015) –.08(.140) 2.36 ± 0.46 0.20 –0.45
Air –.10(.066) –.13(.027) 2.50 ± 0.43 –0.15 0.08
Rest –.12(.035) –.08(.189) 2.35 ± 0.45 0.27 0.61
Exercise .01(.859) –.05(.393) 2.46 ± 0.54 0.10 –0.31
Nutrition –.13(.023) –.16(.004) 2.26 ± 0.59 0.22 0.09
Temperance –.57(<.001) –.19(.001) 2.64 ± 0.50 –0.21 0.06
Beliefs –.21(<.001) –.08(.187) 2.89 ± 0.54 –0.35 0.04
Physical condition –.12(.034) –.24(<.001) 3.09± 0.48 –0.15 0.33
Table 3. Differences of alcohol addiction and food addiction according to the subjects’characteristics (N¼311).
Characteristics Categories
Alcohol addiction Food addiction
Low risk
group
(n ¼182)
Problematic
drinking
group
(n ¼129)
v
2
(p)
Non-addiction
risk group
(n ¼235)
Addiction
risk group
(n ¼76)
v
2
(p)n (%) n (%) n (%) n (%)
Gender Male 41(58.6) 29(41.4) 0.01
(1.000)
61(87.1) 9(12.9) 6.56
(.006)Female 141(58.5) 100(41.5) 174(72.2) 67(27.8)
Grade Freshmen 71(61.2) 45(38.8) 2.51
(.474)
92(79.3) 24(20.7) 3.21
(.361)Sophomore 33(52.4) 30(47.6) 48(76.2) 15(23.8)
Junior 33(54.1) 28(45.9) 41(67.2) 20(32.8)
Senior 45(63.4) 26(36.6) 54(76.1) 17(23.9)
Family
economic status
High 20(57.1) 15(42.9) 0.96
(.618)
26(74.3) 9(25.7) 0.05
(.974)Middle 129(57.3) 96(42.7) 170(75.6) 55(24.4)
Low 33(64.7) 18(35.3) 39(76.5) 12(23.5)
Current
satisfaction in life
Satisfaction 115(60.8) 74(39.2) 1.07
(.585)
149(78.8) 40(21.2) 2.97
(.227)Moderate 50(54.9) 41(45.1) 65(71.4) 26(28.6)
Unsatisfaction 17(54.8) 14(45.2) 21(67.7) 10(32.3)
Body mass index Low weight 57(60.0) 38(40.0) 0.45
(.797)
75(78.9) 20(21.1) 1.77
(.414)Normal weight 108(57.1) 81(42.9) 142(75.1) 47(24.9)
Over weight 17(63.0) 10(37.0) 18(66.7) 9(33.3)
778 C.-A. KANG AND S.-H. SHIN
components of HL, college students with lower scores on
water, rest, temperance, and physical condition had a signifi-
cantly higher odds ratio than higher scoring col-
lege students.
After controlling the gender of the participants, a binary
logistic regression analysis was performed to examine the
HL status on alcohol addiction (Table 5). The value of the
final model 2 log likelihood test with predictors were sig-
nificantly decreased (v
2
¼16.31, p<.001), the model was
described at 7.6% (Negelkerke R
2
¼.076). The Hosmer-
Lemeshow goodness-of-fit test had a significant probability
greater than 0.05 (v
2
¼2.39, p¼.303). Hence, the regression
model was considered appropriate. When gender and HL
status were combined, women had a 2.34 times higher risk
of food addiction than men (OR ¼2.34, 95% CI [1.09,
5.04]). The OR was 2.28 times higher in the group with low
HL scores (OR ¼2.28, 95% CI [1.32, 3.95]).
Discussion
The college years are a very important time to prepare for
the future and for life as a healthy and independent adult.
However, most college students are exposed to various
addiction risks due to lack of self-management and control.
This study examined HL status as a basic and essential fac-
tor in the prevention of alcohol and food addiction, in order
to promote healthy futures among college students.
College students with lower HL score are at 3.06 times
more risk of developing problematic drinking habits com-
pared with college students with higher HL score.
Alcoholism is a major challenge to public health. Moreover,
even moderate drinking consumption is known to have
potential health risks.
12
This shows that problematic drink-
ing is a negative predictor of HL status.
Among the components of HL examined in this study,
water, rest, nutrition, temperance, and beliefs were found to
be related to alcohol addiction risk. Water intake mediates
the reduction of cravings in alcohol use disorders. However,
alcohol consumption inhibits the absorption of water in the
body, which can, in turn, cause health problems.
27,28
Students with high temperance scores have been found to
have significantly low risk of alcohol addiction, suggesting
the importance of temperance in addiction problems. A pre-
vious study
29
suggested that goal-focused self-regulation may
be an important explanatory variable in predicting alcohol
addiction. Self-enhancement and anger management work-
shops are included in most alcohol cessation programs, as
most drinkers or alcoholics are reported to have low self-
awareness and high levels of depression, and to lack motiv-
ation for change.
17,30
This implies that the practice of tem-
perance and self-control, including managing one’s negative
emotions and behavior, are essential in alcohol drinking ces-
sation and the management of relapse. A meta-analysis on
23 articles demonstrated that interventions delivered via
electronic means (e.g., text messages) can support college/
university students in reducing their daily drinking.
31,32
A
previous study showed that individuals with spiritual or reli-
gious practices use fewer addictive substances, such as ciga-
rettes and alcohol, compared with those who not practice or
follow any religion.
21
This supports the idea that beliefs is
one of the factors associated with addiction behavior.
The students in the lower HL group were 2.44 times
more at risk for food addiction than the students in the
higher HL group. Given that the physical condition compo-
nents demonstrated the most significant relationships with
food addiction risk, it can be concluded that improving the
management of individual health is crucial for the preven-
tion of food addiction among college students.
33
In this study, water, temperance, and rest had statistically
significant influence on food addiction risk, confirming that
Table 4. Differences of healthy lifestyle on alcohol addiction and food addiction (N¼311).
Variables Group
Alcohol addiction Food addiction
Low risk
group
(n ¼182)
Problematic
drinking group
(n ¼129)
pOR 95% CI
Non-addiction
risk group
(n ¼235) n (%)
Addiction
risk group
(n ¼76) n (%) pOR 95% CI
n (%) n (%)
Healthy lifestyle Low 72(39.6) 86(66.7) <.001 3.06 [1.91, 4.90] 107(69.0) 51(67.1) .001 2.44 [1.42, 4.20]
High 110(60.4) 43(33.3) 128(54.5) 25(32.9)
Sunlight Low 71(39.0) 56(43.4) .437 1.20 [0.76, 1.90] 93(39.6) 34(44.7) .426 1.24 [0.73, 2.08]
High 111(61.0) 73(56.6) 142(60.4) 42(55.3)
Water Low 81(44.5) 84(65.1) <.001 2.33 [1.46, 3.71] 116(49.4) 49(64.5) .023 1.86 [1.09, 3.18]
High 101(55.5) 45(34.9) 119(50.6) 27(35.5)
Air Low 107(58.8) 84(65.1) .259 1.31 [0.82, 2.09] 142(60.4) 49(64.5) .529 1.19 [0.69, 2.03]
High 75(41.2) 45(34.9) 93(39.6) 27(35.5)
Rest Low 86(47.3) 77(59.7) .031 1.65 [1.05, 2.61] 115(48.9) 48(63.2) .032 1.79 [1.05, 3.04]
High 96(52.7) 52(40.3) 120(51.1) 28(36.8)
Exercise Low 77(42.3) 58(45.0) .642 1.11 [0.71, 1.76] 95(40.4) 40(52.6) .063 1.64 [0.97, 2.75]
High 105(57.7) 71(55.0) 140(59.6) 36(47.4)
Nutrition Low 101(55.5) 88(68.2) .024 1.72 [1.07, 2.76] 139(59.1) 50(65.8) .303 1.33 [0.77, 2.28]
High 81(44.5) 41(31.8) 93(40.9) 26(34.2)
Temperance Low 62(34.1) 91(70.5) <.001 4.63 [2.85, 7.54] 106(45.1) 47(61.8) .012 1.97 [1.16, 3.35]
High 120(65.9) 38(29.5) 129(54.9) 29(38.2)
Beliefs Low 73(40.1) 68(52.7) .028 1.66 [1.06, 2.62] 103(43.8) 38(50.0) .348 1.28 [0.76, 2.15]
High 109(59.9) 61(47.3) 132(56.2) 38(50.0)
Physical condition Low 100(54.9) 83(64.3) .098 1.48 [0.93, 2.35] 129(54.9) 54(71.1) .014 2.02 [1.15, 3.53]
High 82(45.1) 46(35.7) 106(45.1) 22(28.9)
OR ¼Odds ratio; CI ¼Confidence interval.
JOURNAL OF AMERICAN COLLEGE HEALTH 779
overall ability to control food consumption and adequate
rest are necessary to prevent food addiction. The more a
college student engages in a HL that includes regular phys-
ical activity, adequate rest, proper stress management, and a
nourishing diet, the less likely he or she will be to develop a
food addiction. In contrast, insufficient sleep can adversely
affect the regulation of food intake, resulting in unhealthy
food choices; unhealthier foods may appear more appealing
and rewarding to the college student with insufficient
sleep.
34
According to a previous study, physical condition
and temperance are two factors that have a significant effect
on food addiction. College students have been found to
have very low interests in health promotion.
35,36
A study
conducted in Saudi Arabia reported that the majority of uni-
versity students did not participate in programs on health
care.
37
Consistent interest in one’s physical condition should
be considered during the college years.
The present study observed significant differences in HL
status scores based on gender. Previous studies have
reported higher HL status in men compared with women.
Several studies
35,36
have found that men are better at prac-
ticing a health-promoting lifestyle than women and have
higher levels of regular exercise and physical activity.
6,35
This evidence suggests that men generally have healthier
lifestyles than women.
This study had several limitations. First, because the par-
ticipants of this study were all from the same university,
caution must be used when generalizing the results.
Therefore, it is necessary to expand the scope and the valid-
ity of the study results through further studies on various
populations. Second, alcohol addiction was divided into two
variables (low-risk drinking and problematic drinking) based
on AUDIT scores, and nondrinkers were included in the
low-risk group. Third, there have been few studies on the
correlation between HL status and addiction among college
students, and, therefore, the comparison with other studies
was limited.
The results of this study suggest the need for more life-
style-based health education for college students. Most
health education programs on the topic are well carried out
for students in lower age groups, and provision of health
education for adults is an integral part of the prevention
and management of chronic diseases. However, health edu-
cation that promote HLs among college students are scarce.
In addition, because college students do not generally
experience symptoms of physical illness, they do not feel it
necessary to practice health management and are therefore
less likely to perform health-promoting activities.
38
Given
that health-management is a lifelong requirement and
college students’health lifestyles determine their later health,
promoting self-control and developing lifestyle behaviors are
crucial during the college years. Moreover, health programs
should be conducted periodically to train students to rou-
tinely and consistently practice healthy behaviors.
In conclusion, this study examined the HL status on the
risk of alcoholism and food addiction among college stu-
dents and found that HL status is a major factor affecting
addiction behavior and that high interest in one’s health and
self-control over one’s behavior are essential to prevent alco-
hol and food addiction. The results of the study can poten-
tially serve as evidence-based data for the development of
alcohol and food prevention programs for college students.
The college years are a crucial period in which individuals
develop lifestyle behaviors that can last throughout
their lifetime.
Conflict of interest disclosure
The authors have no conflicts of interest to report. The authors con-
firm that the research presented in this article met the ethical guide-
lines, including adherence to the legal requirements, of the Republic of
Korea, and received approval from the institutional review board of
S University.
Funding
The authors have no funding or conflicts of interest to disclose.
ORCID
Kyung-Ah Kang http://orcid.org/0000-0002-3799-9554
Sunhwa Shin http://orcid.org/0000-0003-4052-9542
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