The effects of Internet addiction on the lifestyle and dietary behavior of Korean adolescents

Abstract

We performed this study to examine lifestyle patterns and dietary behavior based on the level of Internet addiction of Korean adolescents. Data were collected from 853 Korean junior high school students. The level of Internet addiction was determined based on the Korean Internet addiction self-scale short form for youth, and students were classified as high-risk Internet users, potential-risk Internet users, and no risk Internet users. The associations between the students' levels of Internet addiction and lifestyle patterns and dietary behavior were analyzed using a chi-square test. Irregular bedtimes and the use of alcohol and tobacco were higher in high-risk Internet users than no risk Internet users. Moreover, in high-risk Internet users, irregular dietary behavior due to the loss of appetite, a high frequency of skipping meals, and snacking might cause imbalances in nutritional intake. Diet quality in high-risk Internet users was also worse than in potential-risk Internet users and no risk Internet users. We demonstrated in this study that high-risk Internet users have inappropriate dietary behavior and poor diet quality, which could result in stunted growth and development. Therefore, nutrition education targeting high-risk Internet users should be conducted to ensure proper growth and development.

Full text

Nutrition Research and Practice (Nutr Res Pract) 2010;4(1):51-57
DOI: 10.4162/nrp.2010.4.1.51
The effects of Internet addiction on the lifestyle and dietary behavior of Korean
adolescents
Yeonsoo Kim1*, Jin Young Park2*, Sung Byuk Kim3, In-Kyung Jung4, Yun Sook Lim5 and Jung-Hyun Kim4§
1School of Human Ecology, Nutrition and Dietetics Program, Louisiana Tech University, LA 71272 USA
2Graduate school of Education, Chung-Ang University, Seoul 156-756, Korea
3Ministry for Health, Welfare and Family Affairs, Seoul 110-793, Korea
4Department of Home Economics Education, Chung-Ang University, Heukseok-dong, Dongjak-gu, Seoul 156-756, Korea
5Department of Food and Nutrition, Kyung Hee University, Seoul 130-701, Korea
Abstract
We performed this study to examine lifestyle patterns and dietary behavior based on the level of Internet addiction of Korean adolescents. Dat
a
were collected from 853 Korean junior high school students. The level of Internet addiction was determined based on the Korean Internet addictio
n
self-scale short form for youth, and students were classified as high-risk Internet users, potential-risk Internet users, and no risk Internet users. The
associations between the students’ levels of Internet addiction and lifestyle patterns and dietary behavior were analyzed using a chi-square test.
Irregular bedtimes and the use of alcohol and tobacco were higher in high-risk Internet users than no risk Internet users. Moreover, in high-ris
k
Internet users, irregular dietary behavior due to the loss of appetite, a high frequency of skipping meals, and snacking might cause imbalances
in nutritional intake. Diet quality in high-risk Internet users was also worse than in potential-risk Internet users and no risk Internet users. We
demonstrated in this study that high-risk Internet users have inappropriate dietary behavior and poor diet quality, which could result in stunte
d
growth and development. Therefore, nutrition education targeting high-risk Internet users should be conducted to ensure proper growth and development
.
Key Words: Internet addiction, dietary behavior, diet quality, adolescents
Introduction8)
The Internet has become an important tool for social
interaction, information, and entertainment [1]. However, as the
Internet has moved into homes, schools, Internet cafes, and
businesses, the prevalence of Internet addiction has been
increasing rapidly. Internet addiction is characterized as poorly
controlled Internet use, and can lead to impulse-control disorders
[2]. Recently, Internet addiction, especially among adolescents,
has been recognized as an important social issue in various
countries because of the high prevalence of depression, aggressive
behavior, psychiatric symptoms, and interpersonal problems
associated with this addiction [3,4]. The incidence of Internet
addiction in adolescents was estimated to be approximately 11%
in China [2], 8% in Greece [5], and 18.4% in Korea [1].
Adolescents are more vulnerable to Internet addiction than
adults, and the social performance, psychology, and lifestyle
habits of Internet addicts can be affected by this addiction [6].
Numerous cross-sectional studies have shown that Internet
addiction has an adverse effect on several lifestyle-related factors
in adolescents; it can result in irregular dietary habits, extended
periods of time spent on the Internet [7], physical inactivity, short
duration of sleep [2], and increased use of alcohol and tobacco
[2,8,9]. Some studies have reported that the change in lifestyle-
related factors caused by heavy Internet use could have an
adverse impact on the growth and development of Internet addicts
[2,7].
Nutritional status also plays a crucial role in growth and
development during adolescence. Several studies have shown that
malnutrition or unbalanced nutritional intake can reduce weight
gain and decrease leg length in adolescents [9,10]. Optimal
nutrition is therefore important for adolescents to grow and
develop properly. Moreover, once dietary habits are formed
during childhood, they tend to be carried on throughout adulthood,
thus teaching adolescents to develop healthy eating habits is of
critical importance [11].
Numerous studies have showed associations between Internet
addiction and mental health problems, such as depression and
psychiatric symptoms, among adolescents. However, information
on the effects of Internet addiction on the dietary behavior of
* Yeonsoo Kim and Jin Young Park are Co-first authors.
§Corresponding Author: Jung-Hyun Kim, Tel. 82-2-820-5278, Fax. 82-2-817-7304, Email. jjhkim@cau.ac.kr
Received: November 17, 2009, Revised: February 16, 2010, Accepted: February 16, 2010
ⓒ2010 The Korean Nutrition Society and the Korean Soci ety of Community Nutrit ion
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)
which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

52
The effect of Internet addiction on dietary behavior
adolescents is limited. Therefore, in this study, we examined the
dietary behavior of Korean adolescents according to their level
of Internet addiction.
Subjects and Methods
Subjects
This cross-sectional study included 1,000 adolescents from
grades 7 through 9 living in Seoul, Korea. Of 1,000 participants,
800 students were recruited from eight junior high schools. The
remaining 200 subjects were recruited from the Korean Youth
Counseling Institute (KYCI), where they had been diagnosed and
were being treated as Internet addicts. The study was conducted
from October 2008 to November 2008. The Institutional Review
Board of Chung-Ang University (Seoul, Korea) deemed this
study exempt from the requirement for informed consent. Of the
1,000 surveys administered and collected, 147 were excluded due
to incomplete responses and difficulty in assessing the level of
Internet addiction, thus a total of 853 samples were analyzed
in this study.
Korean Internet addiction test (KS scale)
Internet addiction was evaluated using the Korean version of
the Internet addiction self-scale short form (KS scale) for youth,
which was developed by the Korea Agency for Digital
Opportunity and Promotion [12]. In brief, the KS scale for
adolescents is a 20-item self-report questionnaire, consisting of
six core components: disturbance of daily routines, self-esteem,
withdrawal, virtual interpersonal relationship, deviant behavior,
and tolerance. Response to each question is on 4-point Likert
scale where 1 corresponds to “not at all”, 2 corresponds to
“sometimes”, 3 corresponds to “frequently”, and 4 corresponds
to “always”. The level of Internet addiction was categorized as
either high-risk, potential-risk, or no risk based on the total score
and the score for the three components of disturbance of daily
routines, withdrawal, and tolerance. Subjects were classified as
high-risk Internet users if their total score was the same or greater
than 52, and/or if the score for disturbance of daily routine,
withdrawal, and tolerance was greater than 16, 10, and 12,
respectively. Subjects were classified as potential-risk Internet
users if their total score was greater than or equal to 48 and
less than 52 and/or if their score for disturbance of daily routine,
withdrawal, and tolerance was greater than 14, 9, and 11,
respectively. Subjects were classified as no risk Internet users
if their total score was less than 48.
Subject characteristics and lifestyle patterns
The following socio-demographic characteristics of subjects
were used in this analysis: age at the time of recruitment, family
income per month, and the education level of the parents. A
lifestyle habit questionnaire assessed the regularity of bedtime,
sleep disturbance, and the use of alcohol and tobacco.
Dietary behaviors and diet quality
The dietary behavior questionnaire assessed recent changes in
meal size, appetite, eating speed, frequency and reasons for
skipping meals, and the frequency, type, and reasons for
snacking. Diet quality was assessed by a 10-item mini-dietary
assessment index. The mini-dietary assessment index was used
to assess overall dietary quality based on the 2005 Dietary
Guidelines and Food Tower for Koreans [13]. This index includes
four food groups that should be consumed, four food groups that
limited amounts of should be consumed, and two items regarding
varied and regular diet. Responses to food items of which
sufficient amounts should be consumed were reported using a
5-point Likert scale where 1=seldom, 3=sometimes, and 5=always.
Responses to food items of which limited quantities should be
consumed were also reported using a 5-point Likert scale where
1=always, 3=sometimes, and 5=seldom. The maximum possible
score for diet quality is 50. In this study, diet quality was defined
as “good” if the total score was greater than or equal to 30 [14].
Statistical analyses
All analyses were performed with a significance level of α
=0.05 using the SPSS software package version 12.0 (SPSS Inc,
Chicago, IL, USA). Relationships between levels of Internet
addiction and socio-demographic characteristics, lifestyle patterns,
and dietary behavior were analyzed using the chi-square test. The
relationship between dietary quality and level of Internet addiction
based on the self-scale rating system were analyzed using
one-way ANOVA followed by Duncan’s multiple range test for
multiple comparisons.
Results
General characteristics of subjects
The general characteristics of the participants and the relationships
between the level of Internet addiction and general characteristics
are provided in Table 1. Subjects were between the ages of 13
and 15 years with a mean age of 14.0 years. More boys were
high-risk Internet users than girls (31.4% vs.14.0%), and more
girls were no risk Internet users than boys (74.7% vs. 58.9%).
Younger adolescents were significantly more likely to be high-
risk Internet users than older adolescents (P< 0.001). Household
monthly income was significantly related to the level of Internet
addiction; adolescents from households with a low monthly
income (< 1,000 K won and 1,000 K-1,999 K won) were more
likely to be high-risk Internet users (57.5% and 31.7%, respectively)

Yeonsoo Kim et al.
53
Table 1. Subject char acteristics based on level of Internet addiction
High risk (n=186) Potential risk (n=90) No risk (n=577) Total (n=853) P-value
Gender
Boys 120 (31.4)1) 37 (9. 7) 225 (58.9) 382 (100.0) < 0.001
Girls 66 (14.0) 53 (11.3) 352 (74.7) 471 (100.0)
Age (years)
13 72 (33.8) 15 (7.0) 126 (59.2) 213 (100.0) < 0.001
14 58 (18.3) 46 (14.5) 213 (67.2) 317 (100.0)
15 56 (17.3) 29 (9.0) 238 (73.7) 323 (100. 0)
Monthly income (Korean Won)2)
< 1,000K 23 (57.5) 3 (7.5) 14 (35.0) 40 (100.0) < 0. 001
1,000K-1,999K 38 (31.7) 15 (12.5) 67 (55. 8) 120 (100.0)
2,000K-2,999K 35 (22.2) 25 (15.8) 98 (62. 0) 158 (100.0)
3,000K-3,999K 29 (15.9) 14 (7.7) 139 (76.4) 182 (100.0)
≥4,000K 43 (15.6) 28 (10.1) 205 (74.3) 276 (100.0)
Father’s education
High school graduate & under 79 (27.2) 28 (9.7) 183 (63.1) 290 (100.0) < 0. 001
College graduate 61 (17.3) 37 (10.5) 254 (72.2) 352 (100.0)
Graduate school graduate 17 (15.2) 17 (15.2) 78 (69. 6) 112 (100.0)
Others 12 (57.1) 1 (4.8) 8 (30.1) 21 (100.0)
Mother’s education
High school graduat e & under 92 (22.4) 41 (10.4) 261 (66.2) 394 (100.0) 0.008
College graduate 55 (18.0) 31 (10.1) 220 (71.9) 306 (100.0)
Graduate school graduate 7 (13.5) 9 (17.3) 36 (69.2) 52 (100.0)
Others 11 (47.8) 3 (13.1) 9 (39.1) 23 (100. 0)
1) N (%)
2) 1,250 Korean won = 1US dollar
Table 2. KS-scale scores based on the level of Internet addiction
Components Maximum score High risk ( n=186) Potential risk (n=90) No risk (n=577) Total (n=853)
Disturbance of daily routine 24 14.97 ± 3.211)a2) 13. 90 ± 3.25b9.32 ± 2.21c11.04 ± 3.59
Self-esteem 4 2.41 ± 0.94a1.69 ± 0.84b1.32 ± 0.61c1.60 ± 0.85
Withdrawal 16 10.56 ± 2.59a5.22 ± 2.21c5.49 ± 1.50b6.88 ± 2.82
Virtual i nterpersonal relationship 12 7.23 ± 2.54a4.56 ± 1.89b3.78 ± 1.41c4.62 ± 2.58
Deviant behavi or 8 5.16 ± 1.53a3.93 ± 1.46b2.87 ± 1.07c3.48 ± 1.55
Tolerance 16 10.61 ± 2.97a8.76 ± 2.64b5.90 ± 2.04c7.23 ± 3.07
Total 80 50.95 ± 8.41a41.06 ± 5.29b28.69 ± 6.36c34.90 ± 11. 48
1) Mean ± S.D
2) Values with different superscript letters within a row are significantly different after Duncan’s multiple range test (
P
<0.05).
than adolescents from households with a higher monthly income.
Adolescents from households with high monthly incomes
(3,000K-3,999K won and ≥4,000K won) were more likely to
be no risk Internet users (76.4% and 74.3%, respectively).
Parents’ educational status also affected the level of Internet
addiction. High-risk Internet users had parents whose highest
level of education was high school graduation or less (27.2%
in father and 22.4% in mother, respectively). In contrast, a high
proportion of no risk Internet users had parents who were college
graduates (72.2% in father and 71.9% in mother, respectively).
KS-scale score
The total KS-scale score and the scores of the six components
of the KS-scale are presented in Table 2. High-risk Internet users
had significantly higher total KS-scale scores and scores for the
six main components than potential-risk Internet users and no
risk Internet users (P<0.05).
Lifestyle patterns
Lifestyle patterns, including bedtime, sleep disturbance,
alcohol use, and tobacco use according to the level of Internet
addiction are shown in Table 3. No risk Internet users had regular
bedtime patterns (10.4% always had a regular bedtime and 41.8%
often had a regular bedtime) while high-risk Internet users
complained of irregular bedtime patterns (13.6% reported often
irregular bedtimes and 11.4% reported always irregular bedtimes).
Both high- and potential-risk Internet users suffered from sleep
disturbances (81.1% and 76.7%, respectively). Similarly, 66% of

54
The effect of Internet addiction on dietary behavior
Table 3. Lifest yle patterns based on the level of Internet addiction
High risk
(n=186)
Potential
risk
(n=90)
No risk
(n=577)
Total
(n=853) P-value
Bedtime
Always regular 20 (10.9)1) 15 (16.7) 60 (10.4) 95 (11.2) < 0.001
Often regular 49 (26.6) 25 (27. 8) 241 (41.8) 315 (37.0)
Neither regul ar or
irregular
69 (37.5) 30 (33.3) 229 (39.7) 328 (38.5)
Often irregular 25 (13.6) 14 (15.6) 32 (5.5) 71 (8.3)
Always irregular 21 (11.4) 6 (6.7) 15 (2. 6) 42 (4.9)
Sleep disturbance
Yes 150 (81.1) 69 (76.7) 278 (48.3) 497 (58.4) < 0. 001
No 35 (18.9) 21 (23.3) 298 (51.7) 354 (41.6)
Alcohol use
Yes 123 (66.5) 58 (64.4) 252 (43.7) 433 (50.8) < 0. 001
No 62 (33.5) 32 (35.6) 325 (56.3) 419 (49.2)
Tobacco use
Yes 97 (52.4) 28 (31.1) 90 (15.6) 215 (25.2) < 0.001
No 88 (47.6) 62 (68.9) 897 (84.4) 637 (74.8)
1) N (%)
Table 4. Recent changes in dietary habi ts based on the level of Internet
addiction
High risk
(n=186)
Potential
risk
(n=90)
No risk
(n=577)
Total
(n=853) P-value
Changes in meal si ze
Increased 54 (29.0)1) 29 (32.2) 164 (28.6) 247 (29.1) 0.019
Decreased 62 (33.3) 20 (22.2) 127 (22.2) 209 (24.6)
No change 70 (37.6) 41 (45.6) 282 (49.2) 393 (46.3)
Changes in appetit e
Worse 25 (13.4) 7 (7.8) 21 (3.7) 53 (6.2) 0.001
Bad 30 (16.1) 11 (12.2) 80 (13.9) 121 (14.2)
No change 72 (38.7) 43 (47.8) 254 (44.2) 369 (43.4)
Better 17 (9.1) 8 (8.9) 78 (13.6) 103 (12.1)
Do not know 42 (22.6) 21 (23.3) 142 (24.7) 205 (24.1)
Changes in eating speed
Fast 64 (34.4) 37 (41.1) 173 (30.0) 274 (32.2) 0. 002
Average 71 (38.2) 33 (36.7) 271 (47. 0) 375 (44.0)
Slow 32 (17.2) 11 (12.2) 109 (18.9) 152 (17.8)
Irregular 19 (10. 2) 9 (10.0) 23 (4.0) 51 (6.0)
1) N (%)
Table 5. Snacking patterns based on the level of Internet addict ion
High risk
(n=186)
Potential
risk
(n=90)
No risk
(n=577)
Total
(n=853) P-value
Skipping breakfast 0.683
Yes 88 (47.3)1) 43 ( 48.3) 228 (40.1) 359 (42.6)
No 98 (52.7) 46 (51.7) 340 (59.9) 484 (57.4)
Skipping Lunch 0.177
Yes 16 (8.6) 6 (6.8) 34 (6.0) 56 (6.7)
No 170 (91.4) 82 (93.2) 531 ( 94.0) 783 (93.3)
Skipping Dinner 0.049
Yes 38 (20.4) 15 (17.1) 80 (14.1) 133 (17.0)
No 148 (79.6) 73 (82.9) 486 ( 85.9) 707 (82.8)
Reasons for meal skipping
Oversleep 49 (28.3) 22 (26. 2) 112 (21.3) 183 (23.4) 0.026
No appetite 34 (19.7) 20 (23.8) 122 ( 23.2) 176 (22.5)
Indigestion 6 (3.5) 6 (7.1) 29 (5.5) 41 (5.2)
Snacking before
a meal
8 (4.6) 5 (6.0) 21 (4.0) 34 (4.3)
Weight loss 10 (5.6) 8 (9.5) 38 (7.2) 56 (7.2)
Saving money 2 (2.9) 0 (0.0) 2 (0.4) 7 (0.9)
Lack of time 25 (14.5) 10 (11.9) 118 (22.4) 153 (19.5)
Habit 18 (10.4) 6 (7.1) 40 (7.6) 64 ( 8.2)
Others 18 (10.4) 7 (8.3) 44 (8. 4) 69 (8. 8)
Frequency of snacking
≥3 times/day 29 (15.8) 13 (14.4) 55 (9.7) 97 ( 11.5) 0.004
1-2/day 104 (56.5) 65 (72.2) 396 (69.8) 565 (67.2)
None 51 (27.7) 12 (13.3) 116 (20.5) 179 (21.3)
Snack items
Confectionery 86 (55.5) 50 (60.2) 239 (47.2) 375 (50.4) 0.245
Soda 4 (2.6) 4 (4.8) 38 (7.5) 46 (6.2)
Ttokbokki ,
rameon,
fried f oods
21 (13.5) 8 (9.6) 73 (14.4) 102 (13.7)
Fast foods 12 (7.7) 3 (3.6) 26 (5.1) 41 (5.5)
Fruits 14 (9.0) 9 (10.8) 61 (12.1) 84 (11.3)
Milk 15 (9.7) 8 (9.6) 55 (10.9) 78 (10.5)
Others 3 (1.9) 1 (1.2) 14 (2.8) 18 (2.4)
Reasons for snack ing
Hunger 86 (46.7) 46 (51. 1) 319 (55.6) 451 (53.2) 0.057
Lack of time for
a meal
10 (5.4) 1 (1.1) 30 (5.2) 41 (4.8)
Habit 28 (15.2) 22 (24. 4) 79 (13.8) 129 (15. 2)
Boredom 33 (17.9) 14 (15.6) 98 (17. 1) 145 (17.1)
Social event 17 (9.2) 5 (5.6) 34 (5.9) 56 (6.6)
Others 10 (5.4) 2 (2.2) 14 (2.4) 26 (3.1)
1) N (%)
high-risk Internet users and 64% of potential-risk Internet users
had used alcohol. Fifty-two percent of high-risk Internet users
had used tobacco while only 15.6% of no risk Internet users
had used tobacco.
Dietary behavior and diet quality
Recent changes in eating habits among adolescents are provided
in Table 4. More of high-risk Internet users answered that their
dietary habits had been changed to have small meal sizes, a poor
appetite, and irregular eating speeds than no risk Internet users
(P=0.019, 0.001, and 0.002, respectively). High-risk Internet
users had a high prevalence of skipping dinner (Table 5).
High-risk Internet users snacked frequently, often snacking more
than three times per day (15.8% vs. 9.7 % for no risk Internet
users). Favorite snacks and reasons for snacking were not signi-
ficantly different among adolescents based on levels of Internet
addiction.
Diet quality based on levels of Internet addiction is shown

Yeonsoo Kim et al.
55
Table 6. Diet qual ity1) based on t he level of Internet addi ction
High risk
(n=186)
Potential risk
(n=90)
No risk
(n=577)
Total
(n=853)
I eat more than one
serving of milk or
dairy products every
day.
2.72 ± 1.722)a3) 3.36 ± 1.36b3.40 ± 1.52b3.25 ± 1.58
I eat several servings
of meat, fish, egg,
bean, or tof u every
day.
2.86 ± 1.50a3.04 ± 1.48a3.35 ± 1.41b3.21 ± 1.44
I eat vegetables and
Kimchi every meal.
2.83 ± 1.63a3.11 ± 1.48ab 3.43 ± 1.45b3.26 ± 1.51
I eat one serving of
fruit or fruit j uice every
day.
2.91 ± 1.69a3.38 ± 1.49b3.45 ± 1.55b3.32 ± 1.59
I eat three meals a
day on a regular
basis.
2.58 ± 1.56a2.98 ± 1.63b3.32 ± 1.59c3.12 ± 1.62
I eat a variety of foods
every day.
2.86 ± 1.60a2.98 ± 1.48a3.38 ± 1.45b3.16 ± 1.42
I eat fried or stir-fried
foods most of the
time.
2.85 ± 1.57a2.78 ± 1.42a3.35 ± 1.45b3.18 ± 1.49
I eat fatty meat most
of the ti me.
2.72 ± 1.50a2.73 ± 1.50a3.28 ± 1.56b3.10 ± 1.58
I add table salt or soy
sauce to foods most
of the ti me.
3.26 ± 1.67a3.07 ± 1.59a3.53 ± 1.52b3.42 ± 1.57
I eat ice cream, cake,
and/or drink soda
between meals.
2.80 ± 1.72a2.80 ± 1.50a3.29 ± 1.54b3.13 ± 1.59
Total 28.38 ± 6. 34a30.22 ± 6.79b33.75 ± 6. 01c32.20 ± 6.57
1) Diet quality was assessed by using 10-item mini-dietary assessment index
developed by Kim [ 14].
2) Mean ± SD
3) Values with different superscript letters within a row are significantly different
(P< 0.05) after Duncan’s multiple range test.
in Table 6. The diet quality of high-risk Internet users was
significantly lower than that of potential-risk Internet users and
no risk Internet users, respectively (P<0.05).
Discussion
In this study, we demonstrated that high-risk Internet users eat
smaller meals, have less of an appetite, skip meals, and snack
more than their potential-risk and normal-risk Internet user
counterparts. Moreover, the diet quality of high-risk Internet users
is poorer than that of potential-risk Internet users and no risk
Internet users.
The frequency of skipping dinner in high-risk Internet users
was significantly higher than that in no risk Internet users. This
finding is consistent with a study by Kim and Chun that reported
a high incidence of meal skipping in Internet addicts [7]. The
high frequency of skipping dinner could be related to snacking;
more frequent snacking was observed in high-risk Internet users
than no risk Internet users. Savige et al. also reported that
adolescent heavy snackers skipped dinner more frequently than
their non- or light-snacker adolescent counterparts [15]. Moreover,
the favorite snacks of our participants were confectionery and
fast food, which are nutritionally poor foods with high calories
provided by fats and simple sugars but with few other nutrients
such as vitamins and minerals. Thus high-risk Internet users have
improper dietary behaviors that could impact their growth and
development.
The quality of the diet of high-risk Internet users as measured
using a mini-dietary assessment index was poor. The mini-dietary
assessment index that we used is a Korean version of the Healthy
Eating Index in which scores over 30 indicate a good quality
diet. In high-risk Internet users, the average total score was 28.38,
which indicates an “inappropriate” diet quality. High-risk Internet
users had the lowest meal regularity score, reflected by a higher
rate of skipping dinner in high-risk Internet users than no risk
Internet users. Moreover, high-risk Internet users did not consume
enough milk and dairy products, meat and fish, and fruits and
vegetables compared with no risk Internet users. Proper intake
of milk and dairy products as major sources of calcium during
childhood is crucial for achieving optimal peak bone mass and
maintaining and repairing bone tissue [16]. In addition, low
consumption of fruits and vegetables in high-risk Internet users
suggests low intake of vitamins, minerals, and fiber in these
individuals. Vitamins and minerals play a crucial role in energy
production, maintenance of bone health, adequate immune
function, and protection against oxidative stress [17,18]. Several
studies have shown that proper fruit and vegetable intake can
prevent health problems such as obesity and cardiovascular
diseases [19-21].
High-risk Internet users not only consumed too little of the
recommended food groups; they consumed more than the
recommended daily quantities of fatty foods, fried foods, salt,
and foods high in simple sugars. High fat and simple sugar intake
increase the chance of being overweight or obese. Obese children
and adolescents can have various adverse health outcomes,
including diabetes, hypertension, dyslipidemia, and metabolic
syndrome [22-24]. Furthermore, obese children have a higher risk
of cardiovascular mortality when they reach adulthood [22,23].
The diet of high-risk Internet users, though it may meet their
energy requirements, is lacking in nutritional value, and may
therefore not support the growth spurt during adolescence and
may cause nutrition-related health problems.
High-risk Internet users drank and smoked more and had a
poorer quality diet and higher frequency of meal skipping than
no risk Internet users. Results from two cross-sectional studies
on Korean high school students [8] and Taiwanese high school
students [2] found a strong association between Internet addiction
and high use of alcohol and tobacco. Alcohol and tobacco
companies use the Internet to promote and advertise their
products by using themes and icons of youth popular culture,
games and contests, and commercially-sponsored websites and
homepages [25]. Therefore, because high-risk Internet users are
more likely to be exposed to tobacco and alcohol advertisements,

56
The effect of Internet addiction on dietary behavior
they are more likely to drink and smoke than other Internet users.
Furthermore, high frequency of use of tobacco and alcohol can
exacerbate diet-related problems, because smoking and drinking
are negatively associated with diet quality and dietary behaviors
such as meal regularity [26,27].
High-risk Internet users reported more irregular sleep patterns
and more episodes of sleep disturbance than no risk Internet
users. This is consistent with a previous study of Korean
adolescents that showed that Internet addiction was associated
with insomnia, apnea, and nightmare [8]. In addition, sleep
disturbance could increase the risk of mental health problems
as well as substance abuse [6,28,29,30]. Hence, high-risk Internet
users are more likely to experience physical and mental health
problems.
This study has some limitations. First, this study was a
cross-sectional study, therefore we could not confirm causal
associations between Internet addiction and dietary behavior.
Second, the questionnaire was self-reported. It is therefore
possible that some of the adolescents may not have admitted
to using alcohol and tobacco due to social restrictions, even
though this study was anonymous.
High-risk Korean adolescent Internet users had improper
dietary behavior and a poorer diet quality than their no risk
Internet counterparts. To ensure that the growth and development
of high-risk Internet users is not adversely impacted, their diets
should be supplemented with the nutrients that they are lacking.
Interventions to improve both dietary behavior and treat Internet
addiction may have synergistic health benefits.
In conclusion, the results of this study suggest that children
should be educated as to what a balanced diet and optimum
physical activity routine is to remain healthy and grow. Furthermore,
the government should take an active role in designing and
evaluating Internet addiction-related health intervention strategies.
Given the likely adverse effects of Internet addiction on adolescents’
development because of poor dietary behavior, it is critical to
raise awareness about Internet addiction. Close attention should
be paid to students at risk of Internet addiction, as well as students
at low risk to prevent them from becoming addicted to the
Internet.
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