Psychiatry Investig Search


Psychiatry Investig > Volume 11(4); 2014 > Article
Lee, Shin, Cho, and Shin: Psychosocial Risk Factors Associated with Internet Addiction in Korea



The aim of this study was to examine the prevalence of Internet addiction in middle school students and to identify associated psychosocial risk factors and depression.


This study was part of a larger epidemiological study on childhood psychiatric disorders conducted in Osan, a city of Republic of Korea. We used IAS for internet addiction, K-YSR for subjects' emotional and behavioral problems and K-CDI for depressive symptoms. We used the data of n=1217 completed cases. We put on independent variables, which are sex, age, smoking and alcohol experiences, economic status, age of first Internet use, K-YSR and K-CDI score.


The subjects consisted of addicted users (2.38%), over users (36.89%) and normal Internet users (60.72%). Attention problems, sex, delinquent problems, K-CDI scores, thought problems, age and aggressive behavior were predictable variables of internet addiction. Age of initial Internet use negatively predicted Internet addiction.


This result showed similar to other researches about sociodemographic, emotional or behavioral factors related to internet addiction. Generally, subjects with more severe internet addiction had more emotional or behavioral problems. It means that they already have had various difficulties when we found internet addiction of adolescents. Therefore it is necessary to evaluate whether the subjects have any emotional or behavioral troubles and to intervene to prevent internet addiction.


South Korea has one of the most advanced IT systems in the world, with a fast internet speed and overall nationwide easy internet accessibility. Therefore, the adaptation of internet use in their lives has become a common phenomenon. A survey on the use internet revealed that 99.9% of adolescents.1 Internet addiction has been recognized as an international problem. Studies in other countries have also been conducted. The prevalence rate of internet addiction in the USA is 9.8-15.2% among people in their teens and twenties.2 In Greece, prevalence rate of potential problematic internet use (PIU) is 19.4% and the PIU rate is 1.5%. In this study, potential PIU is defined as internet use which fulfils some, but not all, of the propose criteria of PIU. They used Young Internet Addiction Test for grouping internet use characteristics. PIU means an individual's inability to control one's use of the internet, so have marked distress and/or functional impairment. In Taiwan, the prevalence rate of the internet addiction is 15.3% among university students.3 A number of studies have revealed the incidence rates of Internet addiction among Korean adolescents to be between 2.6 and 14.9%.1,4,5 A number of factors such as location, screening tool and target age may have contributed to the differences of incidence rates in these studies.
Internet addiction disorder (IAD) is defined as one's inability to control his use of the internet, which could leads up to physical, psychological, social difficulties.6 In 1998, Goldberg suggested IAD to be a psychiatric disorder based on pathological gambling as described in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). Along with pathological gambling, IAD show features similar to those of substance dependency like salience, mood modification, tolerance, withdrawal symptoms, conflict and relapse.6 Clinical concern requiring intensive evaluation and treatment on Internet addiction has magnified over the past few years. But it was debated whether it should be added as a disorder in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). It is not clear whether internet addiction has manifested from a preexisting disorder, or is truly a discrete disease entity. As of 2013, 'Internet use gaming disorder' has been incorporated into DSM-V Section 3, a category of disorders needing further research.7
Internet addiction has demonstrated a correlation with depression, attention-deficit hyperactivity disorder (ADHD) and impulse control disorder.8,9,10,11 Of the 1618 students aged 13 to 18 years, 6.4% were at a moderate to high risk of pathological internet use. Those with pathological use were 2.5 times more likely to experience depression at the 9-month follow-up compared with the control group. This result suggests that young people who are initially free of mental health problems but pathologically use the internet are at risk of developing depression.11 On the other hand, depression was one of the preceding mental health issues of Internet addiction as reported by Young.6
The development of internet addiction was shown to be higher in the population with ADHD. In a report by Yoo et al.,12 it was stated that elementary students with internet addiction had a higher rate of ADHD symptoms. The adolescent population has also shown an associations between ADHD and internet addiction.13 It is suggested that to satisfy their need for immediate gratification, child and adolescents with ADHD seek comfort in the internet probably leading to internet addiction. Because they have a harder time to retain their interest and have an aversion for delayed reward, this usually results in poor academic function and difficulties in peer relationships. This is probably one of the main causes of their internet addiction because internet activities usually provide multimodal stimuli, immediate response and reward.
In addition, exposure to the internet at an early age and poor family cohesion, adaptability and communication were environmental factors of internet addiction.13 Ni et al.14 stated that the age of first exposure to internet use was significantly associated with internet addiction. Surveys on other addictions such as pathologic gambling and alcohol drinking suggest that early age exposure is closely related to its severity or dependency.15,16,17 If we take into account the results of these researches, exposure to the internet at a young age may be an associated factor to Internet addiction.
In this study, our aim was to examine 1) the prevalence of problematic internet use and the extent of internet addiction, 2) the factors associated with internet addiction.



This study was a part of a larger epidemiological study on child psychiatric disorders conducted in Osan, a city southwest of Seoul, Republic of Korea. The Child Mental Health Care Center performed the survey during a periodic health examination of local children, collecting the data in 2006. The investigators explained to students, and their parents about objects of this study and benefits by letters and they signed the informed consents, and gave assurance of confidentiality. The students were requested to complete the questionnaires in a classroom under supervision of a research assistant. Total 1857 students participated in this research and 640 students were excluded due to incomplete questionnaires, resulting in 1217 students.


Sociodemographic data

The participants completed a general questionnaire covering family structure, parental education and economic status, smoking experience, alcohol drinking experience and age of initial Internet use as well as age and sex. Economic status was divided into three categories based on family income.

Internet Addiction Scale

The degree to which the subject is involved in Internet usage was determined using the Korean version of the Internet Addiction Scale (IAS).6,12 The IAS consists of 20 questionnaire items. Each item is rated on a 5-point scale; a higher overall score indicates greater Internet addiction. According to Young, a score of 70 or above in the IAS indicates apparent Internet addiction, and a score over 40 indicates overuse of the Internet, which may cause some problems in daily life. The IAS has been established as a reliable and valid tool.18 Cronbach's alpha was 0.91 in the present study, indicating excellent internal consistency.

Korean-Youth Self Report

Achenabch19 developed this self-report scale (YSR) which adolescents use for self-reporting their own adaptability and emotional and behavioural problems for the last 6 months. It was developed for adolescents between the ages of 11 and 18 years. The YSR yields age and gender-based T-scores for 13 empirically derived subscales, such as anxious/depressed, attention problems, aggressive behaviours, externalizing and internalizing problems, etc. The YSR has been reported to have adequate psychometric properties. We used the Korean version of YSR that was devised by Oh et al.20 that is considered to have similar adequate psychometric properties in Korean adolescents. K-YSR has also been normed for gender- and age-specific groups and has been used widely for clinical and research purposes in Korea.

Korean Children's Depression Inventory

We used the CDI to assess depressive symptoms. The CDI consists of 27 self-rated questions scored on a 3-point Likert scale from 0 (not present) to 2 (present and marked); the total score range is from 0 to 54.21,22 The item domains include negative mood, interpersonal problems, negative self-esteem, ineffectiveness, and anhedonia.21,22 The Korean version of the CDI was standardized in 1990, and its validity and reliability in Korean samples have been well established and reported elsewhere. A total score of 29 is considered the cutoff point for severe depressive symptoms in the K-CDI.23

Statistical analysis

First, we separated the subjects into three groups-Internet addicted users, overusers and normal Internet users-based on the total scores on the IAS and compared sociodemographic characteristics and K-YSR scores among the three groups with a chi-square test and a Kruskal-Wallis test. We used this non-parametric method because this study did not show normal distribution.
Second, the primary purpose of the present study was to assess the effects of emotional and behavioral problems and other family or socio-economic conditions on the addictive us-age of the Internet using stepwise multiple regressions. Our main independent variables were sex, age, smoking and alcohol experience, economic status, age of first internet use, sub-items of the K-YSR and K-CDI scores. The dependent variables included the IAS score-Internet addicted users, overusers, and normal Internet users. We used SPSS ver. 17.0 for the analyses.


Among the 1217 middle school students enrolled in this study, it was proven that 29 subjects (2.38%) were internet addicted users, 449 subjects (36.89%) were overusers and 739 (60.72%) were normal internet users (Table 1). Sex, age, smoking experience and age of initial internet use differed among the subgroups but alcohol drinking and economic status sh-owed little difference (Table 1).
The average scores were 77.41±7.80 of internet addicted users, 49.42±7.65 of overusers and 30.20±5.13 of normal internet users (Table 2). In the higher internet addicted group, the score of sub-item of K-YSR was high and the differences were statistically significant (p<0.01) except withdrawn item. There was no difference between overuser and internet addicted user but normal internet user showed a difference from the other two in the withdrawn item. In K-CDI, the higher internet addicted group showed a correlation with higher K-CDI scores and the difference among the three groups was significant (p<0.01) (Table 2).
Internet addiction was related to K-YSR total and sub-items and also to K-CDI (Table 3, p<0.01). Factors that could explain the severity of internet addiction were attention problems (β=0.578, t=3.36), delinquent problems (β=0.900, t=4.02), thought problems (β=0.727, t=3.80) and aggressive behaviour (β=0.264, t=3.25) in K-YSR and sex (β=5.498, t=8.65), age (β=1.591, t=4.29), K-CDI scores (β=0.382, t=6.50) (Table 4). Age of first internet use showed counter correlation with internet addiction (β=-0.090, t=-3.71). It meant that when we start using internet younger age, we are easily addicted to internet (Table 4). Above eight items consisted of 31.5% of factors explaining internet addiction [R2=0.315, F(8)=68.41, p<0.01] (Table 4).


This study was about the internet addiction rate in middle school students and related socioeconomic characteristics, emotional and behavioural factors.
Males were more closely related to internet addiction than females which had consistent results with other studies.1,3,8,9,24 When multiple regression was conducted, the male gender was a strong predictor of internet addiction (Table 4).
This study proved there was a relationship between internet addiction and the older the subject were. Studies in other countries have also concluded that internet addiction is highly prevalent in adolescents.25,26,27 But there has been no in-depth study into the reason why internet addiction affects adolescents between the ages of thirteen and fifteen. Generally, high school students are more likely to be addicted to the internet than middle and elementary school students. Therefore we speculate that as middle school students near high school age, their internet addiction becomes more prominent.5,28
The younger the age of first internet use showed a higher tendency of a more severe internet addiction. A research in China on the starting age of internet use (age 8-12) in university freshman confirmed an internet addiction.14 There is no definite reason but this result may mean that exposing children to the internet later in age could be a protective factor of internet addiction. To protect children from excessive internet exposure, the family environment is important. Parents have to enforce measurements on the internet use of their children. Younger children are influenced easily by their parents' internet use.
Other family environmental factors also influence internet addiction. Kim et al.29 reported that communication problems within the family and a weak family cohesion were related to severe internet addiction. A survey showed that the atmosphere surroundings within the family would probably be an important factor in reducing internet addiction.30
Internet addiction was correlated with a high K-CDI score and depression/anxiety and social problems: sub-items of K-YSR which was consistent with the results of other studies.26,27,31,32,33,34 Based on Khantzian's hypothesis, we suggest that the cyber-world as a method of self medication can easily regulate the affection, self-esteem, relationship or self-care of the user even though they suffer from depression in the real world.36 Lee et al.34 stated that those with severe internet addiction sh-owed problems with adjusting to school life and low self-efficacy. Therefore, adolescents with depression or social problems are more likely to use the internet as a means of escaping problems in the real world.
Items associated with a social problem of K-YSR include not getting along, being teased and not liked, feeling persecuted, and being the target of fights and attacks.
The result that attention problem was a predicting factor with internet addiction was a replication of results of other stu-dies.26,37,38 Ko et al.32 also reported that ADHD was the strongest predictor of internet addiction in a prospective study over two years. ADHD patients cannot endure one thing for an extended period of time and they have difficulties waiting for delayed reward and only respond to immediate reward. So they become easily addicted to games that are immediately rewarding.39 Being able to multitask while on the internet is another attractive trait to ADHD patients.
In this study, delinquent problems, externalizing problems and aggressive behavior were associated with internet addiction (Table 2), moreover delinquent problems and aggressive behavior independently were predictors of internet addiction (Table 4). There are several researches which reported that impulsivity and delinquency are associated with internet addiction regardless of having an attention problem.9,24,40,41 Adolescents with aggressive or delinquent behavior have difficulties forming a relationship in the real world but find it easier to form and break in the cyber-world. But it is not clear whether aggression or delinquent behavior is a direct cause of internet addiction, more focus on this topic is needed.
Adolescent internet use could not be seen as a wasteful activity but as an indirect expression of difficulties associated with school adjustment and peer relationship. Internet addiction can be a sign of depression, anxiety or ADHD so the evaluation of comorbidity is needed. We have to also evaluate the severity of the internet addiction and its implication and give the internet addicts help.
This research has some limitations and the first limitation is geographical limitation because subjects were in a city in Korea, making it hard to generalize the results. The second limitation is that we couldn't explore more about correlation of depression, K-YSR sub-items or sociodemographic data with contents of internet use due to no data about the contents. It is possible that correlations between them are associated with internet contents. The third limitation is about to thought problems of YSR. There has been no definite explanation about the relationship between thought problems and internet addiction yet. This study couldn't explain the association either. It could be a method like exploring about the contents of internet user to explain it. The third limitation is that problematic behaviors were not psychiatric disorders. So we cannot think that a person with high score in attention problems is an ADHD patient or a person with high delinquent problems is a patient with conduct disorder. The forth is that this study is cross-sectional study so we cannot explain exact causal relationship.
This result showed similar to other researches about sociodemographic, emotional or behavioral factors related to internet addiction. Male, older age, attention problems, delinquent problems, aggressive behavior, K-CDI scores and age of first internet use were related to internet addiction. Generally, subjects with more emotional or behavioral problems showed more severe internet addiction. It means that they already have had various difficulties when we found internet addiction of adolescents. Therefore it is necessary to evaluate whether the subjects have any emotional or behavioral troubles and to intervene to prevent internet addiction.


1. National Information Society Agency. The Survey of Internet Addiction 2011. Seoul: National Information Society Agency; 2012.

2. Moreno MA, Jelenchick L, Cox E, Young H, Christakis DA. Problematic internet use among US youth: a systematic review. Arch Pediatr Adolesc Med 2011;165:797-805. PMID: 21536950.
crossref pmid pmc
3. Lin MP, Ko HC, Wu JY. Prevalence and psychosocial risk factors associated with internet addiction in a nationally representative sample of college students in Taiwan. Cyberpsychol Behav Soc Netw 2011;14:741-746. PMID: 21651418.
crossref pmid
4. National Internet Development Agency of Korea. Survey on the Computer and Internet Usage. Report about the second half of the year 2005. National Internet Development Agency of Korea: Seoul; 2006.

5. National Internet Development Agency of Korea. Survey on the Computer and Internet Usage. Report about the second half of the year 2007. Seoul: National Internet Development Agency of Korea; 2008.

6. Young KS. Caught in the Net: How to Recognize the Signs of Internet Addiction--and a Winning Strategy for Recovery. New York: John Wiley & Sons; 1998.

7. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5. Washington DC: American Psychiatric Pub Incorporated; 2013.

8. Yen JY, Yen CF, Chen CS, Tang TC, Ko CH. The association between adult ADHD symptoms and internet addiction among college students: the gender difference. Cyberpsychol Behav 2009;12:187-191. PMID: 19072077.
crossref pmid
9. Cao F, Su L, Liu T, Gao X. The relationship between impulsivity and Internet addiction in a sample of Chinese adolescents. Eur Psychiatry 2007;22:466-471. PMID: 17765486.
crossref pmid
10. Yen CF, Ko CH, Yen JY, Chang YP, Cheng CP. Multi-dimensional discriminative factors for Internet addiction among adolescents regarding gender and age. Psychiatry Clin Neurosci 2009;63:357-364. PMID: 19566768.
crossref pmid
11. Lam LT, Peng ZW. Effect of pathological use of the internet on adolescent mental health: a prospective study. Arch Pediatr Adolesc Med 2010;164:901-906. PMID: 20679157.
crossref pmid
12. Yoo HJ, Cho SC, Ha J, Yune SK, Kim SJ, Hwang J, et al. Attention deficit hyperactivity symptoms and internet addiction. Psychiatry Clin Neurosci 2004;58:487-494. PMID: 15482579.
crossref pmid
13. Ju SJ, Jwa DH. A prediction model for internet game addicted adolescents: focusing on socioeconomic and family related traits. Korean J Youth Stud 2011;18:165-190.

14. Ni X, Yan H, Chen S, Liu Z. Factors influencing internet addiction in a sample of freshmen university students in China. Cyberpsychol Behav 2009;12:327-330. PMID: 19445631.
crossref pmid
15. Buchmann AF, Schmid B, Blomeyer D, Becker K, Treutlein J, Zimmermann US, et al. Impact of age at first drink on vulnerability to alcohol-related problems: testing the marker hypothesis in a prospective study of young adults. J Psychiatr Res 2009;43:1205-1212. PMID: 19332346.
crossref pmid
16. Jenkins MB, Agrawal A, Lynskey MT, Nelson EC, Madden PA, Bucholz KK, et al. Correlates of alcohol abuse/dependence in early-onset alcohol-using women. Am J Addict 2011;20:429-434. PMID: 21838841.
crossref pmid pmc
17. Rahman AS, Pilver CE, Desai RA, Steinberg MA, Rugle L, Krishnan-Sarin S, et al. The relationship between age of gambling onset and adolescent problematic gambling severity. J Psychiatr Res 2012;46:675-683. PMID: 22410208.
crossref pmid pmc
18. Shapira NA, Goldsmith TD, Keck PE Jr, Khosla UM, McElroy SL. Psychiatric features of individuals with problematic internet use. J Affect Disord 2000;57:267-272. PMID: 10708842.
crossref pmid
19. Achenbach TM. Manual for the Youth Self-Report and 1991 Profile. Burlington, VT: University of Vermont, Department of Psychiatry; 1991.

20. Oh KJ, Hong KE, Lee HR, Ha EH. Koran-Youth Self Report (K-YSR). Seoul, Korea: Chung Ang Aptitude Research Center; 1997.

21. Kovacs M, Beck AT. An Empirical-Clinical Approach toward a Definition of Childhood Depression. In: Schulterbrandt JG, Raskig A, editor. Depression on Children: Diagnosis, Treatment, and Conceptual Models. New York: Raven Press. 1977, p. 1-25.

22. Kovacs M. The Children's Depression Inventory: A self-rated depression scale for school-aged youngsters 1983;Unpublished Manuscript.

23. Cho SC, Lee YS. Development of the Korean form of the Kovacs; children's depression inventory. J Korean Neuropsychiatr Assoc 1990;29:943-956.

24. Carli V, Durkee T, Wasserman D, Hadlaczky G, Despalins R, Kramarz E, et al. The association between pathological internet use and comorbid psychopathology: a systematic review. Psychopathology 2013;46:1-13. PMID: 22854219.
crossref pmid
25. Cao H, Sun Y, Wan U, Hao J, Tao F. Problematic internet use in Chinese adolescents and its relation to psychosomatic symptoms and life satisfaction. BMC Public Health 2011;11:802PMID: 21995654.
crossref pmid pmc
26. Yen JY, Ko CH, Yen CF, Wu HY, Yang MJ. The comorbid psychiatric symptoms of Internet addiction: attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility. J Adolesc Health 2007;41:93-98. PMID: 17577539.
crossref pmid
27. Ko CH, Yen JY, Chen CS, Yeh YC, Yen CF. Predictive values of psychiatric symptoms for internet addiction in adolescents: a 2-year prospective study. Arch Pediatr Adolesc Med 2009;163:937-943. PMID: 19805713.
crossref pmid
28. Korea Agency for Digital Opportunity and promotion. The Survey of Internet addiction 2007. Seoul: Korea Agency for Digital Opportunity and promotion; 2008.

29. Kim HS, Chae KC, Rhim YJ, Shin YM. Familial characteristics of internet overuse adolescents. J Korean Neuropsychiatr Assoc 2004;43:733-739.

30. Siomos K, Floros G, Fisoun V, Evaggelia D, Farkonas N, Sergentani E, et al. Evolution of Internet addiction in Greek adolescent students over a two-year period: the impact of parental bonding. Eur Child Adolesc Psychiatry 2012;21:211-219. PMID: 22311146.
crossref pmid
31. Kim TH, Ha EH, Lee ES, Cho SJ, Song DH. Emotional and behavioral problems related with internet addiction in adolescence. J Korean Neuropsychiatr Assoc 2005;44:364-370.

32. Ko CH, Yen JY, Yen CF, Chen CS, Chen CC. The association between Internet addiction and psychiatric disorder: a review of the literature. Eur Psychiatry 2012;27:1-8. PMID: 22153731.
crossref pmid
33. Park MS, Park SE. The relationship between internet immersion and social competences and behavior development of elementary school children. Korean J Educ Psychol 2004;18:313-327.

34. Lee MS, Moon JW, Park JS. A Study on the relationship of middle and high school students' internet addiction level and school life adjustment. J Korean Soc Sch Health 2010;23:42-52.

35. Fioravanti G, Dettore D, Casale S. Adolescent Internet addiction: testing the association between self-esteem, the perception of Internet attributes, and preference for online social interactions. Cyberpsychol Behav Soc Netw 2012;15:318-323. PMID: 22703038.
crossref pmid
36. Khantzian EJ. The self-medication hypothesis of substance use disorders: a reconsideration and recent applications. Harv Rev Psychiatry 1997;4:231-244. PMID: 9385000.
crossref pmid
37. We JH, Chae KM. Internet addiction disorder of attention-deficit hyperactivity disorder adolescents, psychological characteristics. Korean J Clin Psychol 2004;23:397-416.

38. Yoo HJ, Woo Si, Kim J, Ha J, Lee CS, Sohn JW. Relationship between attention deficit hyperactivity disorder symptoms and internet addiction in high school students. Korean J Psychopathol 2003;12:85-94.

39. Hong KE. Korean Textbook of Child Psychiatry. Seoul: Chungang Munwhasa; 2005.

40. Shin HS, Lee JS, Lee HG, Shin JS. Sex-differences in the effects of comorbidity of depression/anxiety and aggression on adjustment problems of juvenile delinquents. Korean J Couns Psychother 2004;16:491-510.

41. Kormas G, Critselis E, Janikian M, Kafetzis D, Tsitsika A. Risk factors and psychosocial characteristics of potential problematic and problematic internet use among adolescents: a cross-sectional study. BMC Public Health 2011;11:595PMID: 21794167.
crossref pmid pmc
Table 1
Comparison of sociodemographic characteristics among addiction, overuser and normal user group
Table 2
Comparison of K-YSR/K-CDI scores among addiction, overuser, and normal user group

**p<0.01. K-YSR: Korean-Youth Self Report, K-CDI: Korean Children's Depression Inventory, IAS: Internet Addiction Scale

Table 3
Correlation between K-YSR, K-CDI and IAS score

**p<0.01. K-YSR: Korean-Youth Self Report, K-CDI: Korean Children's Depression Inventory, IAS: Internet Addiction Scale

Table 4
Stepwise multiple regression analysis on internet addiction

**p<0.01. K-CDI: Korean Children's Depression Inventory


Browse all articles >

Editorial Office
#522, 27, Seochojungang-ro 24-gil, Seocho-gu, Seoul 06601, Korea
Tel: +82-2-717-0892    E-mail:                

Copyright © 2024 by Korean Neuropsychiatric Association.

Developed in M2PI

Close layer
prev next