What Types of Internet Services Make Adolescents Addicted? Correlates of Problematic Internet Use
Received 25 January 2020
Accepted for publication 3 April 2020
Published 20 April 2020 Volume 2020:16 Pages 1031—1041
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Taro Kishi
Kyoung Min Kim,1 Haebin Kim,2 Jae-Won Choi,3 Soo Yeon Kim,4 Jun Won Kim5
1Department of Psychiatry, College of Medicine, Dankook University, Cheonan, Republic of Korea; 2Department of Psychiatry, Kyung Hee University Hospital, Seoul, Republic of Korea; 3Department of Psychiatry, Gyeongsang National University Hospital, Jinju, Republic of Korea; 4Department of Psychiatry, Pusan National University Hospital, Busan, Republic of Korea; 5Department of Psychiatry, Catholic University of Daegu School of Medicine, Daegu, Republic of Korea
Correspondence: Jun Won Kim
Department of Psychiatry, Catholic University of Daegu School of Medicine, 3056-6 Daemyeong-4 Dong, Nam-gu, Daegu 705-718, Republic of Korea
Tel +82 53 650 4332
Fax +82 53 623 1694
Email [email protected]
Purpose: This study investigated the prevalence and correlates of problematic internet use (PIU) in a large sample of adolescents based on the type of internet service used.
Materials and Methods: The study was conducted from 2008 to 2010, and 223,542 adolescents aged 12 to 18 years participated in the study. The participants responded to a self-report questionnaire including items for demographic factors, internet usage time, most used internet service and mental health. The PIU was assessed with the Internet Addiction Proneness Scale for Youth-Short Form.
Results: The overall prevalence rate of PIU was 5.2%, and the prevalence rates stratified by sex were 7.7% in boys and 3.8% in girls. The distribution of most used internet services was significantly different across sexes. The most commonly used internet services were gaming (58.1%) in boys and blogging (22.1%) and messenger/chatting (20.3%) in girls. The odds ratio for PIU was significantly different according to the most used internet service; using the internet mostly for pornography compared to information searching had the highest odds ratio (4.526-fold higher). Depressive episodes, suicidal ideation, and suicidal attempts were significantly associated with higher odds ratios for PIU (1.725-, 1.747- and 1.361-fold, respectively).
Conclusion: The present study identified clinically important information about PIU in adolescents. The distribution of PIU has different patterns based on sex and specific internet services. Studies of PIU with well-defined methodology and assessment tools for PIU of each specific internet service are needed.
Keywords: addiction, adolescence, sex differences, internet usage
In the last two decades, the internet has penetrated into the people’s life in a very rapid and wide way and has become an important means of daily living, such as shopping, getting news and contact with friends. US survey data reported that approximately 90% of adults had access to the internet in 2019, and the proportion of people who did not use the internet decreased from 48% in 2000 to only 10% in 2019.1 In particular, adolescents use the internet more in their daily lives than other populations. In 2018, 95% of US adolescents were reported to have access to smartphones, and 45% of teens are online on a near-constant basis.2
Although the internet provides various benefits, such as education, entertainment, social communication, convenience, and psychological well-being,3 many studies have reported negative associations of the internet with youth’s mental health, including depression, social anxiety, suicide, and cyberbullying.4–7 Notably, problematic internet use (PIU) characterized by excessive use and addictive features is one of the biggest problems with internet use in adolescent populations, of which prevalence has been reported as high up to 26.7% by previous studies.8,9
Adolescents are known to be vulnerable to PIU due to heightened impulsivity accompanied by the relative immaturity of the prefrontal cortex (PFC), especially in the early and mid-adolescent period.10–12 In addition, the emotional dysregulation in the early infant period (2 years old) has been reported to have a substantial impact on PIU in the adolescents, indicating that the inborn temperament is one of the major risk factors for PIU.13 Sex is known to be another differentiating moderator for the pattern of PIU. Boys are more likely to use internet gaming, whereas girls use more social network services than boys.14,15 In addition, environmental factors, including attachments with parents and peers are also reported as one of the predictors for PIU in adolescents. For instance, Badenes-Ribera et al16 reported that relationships with their parents influenced the level of PIU the most in early adolescents, whereas peer relationships were the most relevant factor in the older adolescent period.
Likewise, multiple studies have investigated the prevalent concerns for PIU and related risk factors in adolescents, Nevertheless, a clear definition of PIU has not been made. Researchers have investigated PIU with different terms and concepts, such as “internet addiction”,17 “compulsive internet use”,18 “problematic internet use”19 and “pathological internet use”.20 Other studies focusing on internet gaming have used the terms “problematic online game use”,21 “internet gaming addiction”22 and “internet gaming disorder”.23
Although these different terms and their definitions include a psychological construct implying a pattern of uncontrolled internet use resulting in clinical impairment,24 one reason for the lack of gold standard definition is that the internet offers a variety of content that might be associated with addictive potential such as gaming, gambling, chatting or pornography. Young25 pointed out that internet addiction covers a wide variety of behavioral impulse control problems and is categorized by five specific subtypes, including cyber-sexuality, cyber-relationships, net compulsions, information overload, and computer addiction.
Among these specific subtypes of PIU, “internet gaming disorder” and “gaming disorder” were included as a diagnosis in Section 3 of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)26 and the latest revision of the International Classification of Diseases (ICD-11) by the World Health Organization (WHO).27 Although nongaming internet activities were not considered as a formal diagnosis because of a lack of evidence,24 there are still concerns about addictive nongaming internet activities such as internet gambling,28 social networking29 and online pornography.30
However, despite these concerns about the various subtypes of PIU, studies exploring the differential addictive potentials based on specific internet services are lacking. A recent German study with 6,081 students aged 12–19 years investigated the distribution of intensely used Internet applications in PIU and non-PIU.31 In the study of Rosenkranz et al31 the most intensely used Internet applications were the social networking sites and chatting, and the most predictive Internet applications for PIU were gaming and gambling. However, studies exploring the distribution and addiction potential based on use of the specific internet service are still lacking; in fact, to our knowledge, there are no studies in Korea. Thus, the present study aimed to investigate the prevalence and correlates of PIU in a large sample of adolescents based on the subtype of internet use.
Materials and Methods
Our study was performed with data derived from the 2008, 2009 and 2010 Korean Youth Risk Behavior Web-based Survey (KYRBS). KYRBS is multiple-year cross-sectional study that has been conducted annually by the Korea Centers for Disease Control and Prevention (CDC) since 2005.32 KYRBS focuses on health-risk behaviors among adolescents. The survey was performed with a questionnaire completed by the adolescents, which consists of 125 items, including information on tobacco use, alcohol use, obesity, physical activity, sexual behaviors, substance use, Internet use, and mental health. The target population is nationally representative middle- and high-school students aged 12–18 in Korea, sampled from 400 middle and 400 high-schools in every year. The total number of participants was 223,542, and the 2008, 2009 and 2010 KYRBS included 75,238, 75,066 and 73,238 participants, respectively. Before study entry, the full instructions about the purpose and methods of the study were given to the students by trained teachers, and written informed consent was obtained from the students. Students who agreed to participate completed the anonymous questionnaire, which was presented on a computer. The CDC’s Institutional Review Board has approved the protocols for KYRBS.
To assess PIU, the Internet Addiction Proneness Scale for Youth-Short Form (KS scale) developed by Kim et al33 was used. The KS scale is a 20-item self-report scale rated on a 4-point Likert scale (1=never, 2 = sometimes, 3=often, or 4= always). It consists of six subfactors: (1) disturbance of adaptive function (6 items), (2) positive anticipation (1 item), (3) withdrawal (4 items), (4) virtual interpersonal relationship (3 items), (5) deviant behavior (2 items), and (6) tolerance (4 items). The respondent is categorized based on the scores into one of three groups: definite PIU, probable PIU, and normal user of the internet. Definite PIU is defined by a total score of 53 or above or the presence of all of the following: adaptive functioning scores of 17 or above; withdrawal scores of 11 or above; and tolerance scores of 13 or above. Probable PIU is defined by a total score between 48 and 52 or the presence of all of the following: adaptive functioning scores of 15 or above; withdrawal scores of 10 or above; and tolerance scores of 12 or above. In the present study, the PIU group was defined as the participants in the definite and probable PIU groups.
The internet usage time was asked with the item “How many hours and minutes have you used the internet on weekdays and the weekend in the last 30 days?” The internet service that was mainly used by participants was asked by the item “What service do you usually use the internet for the most?” with the choice options including information searching, messenger/chatting, gaming, watching movies, listening to music, watching videos such as user-created content, email, shopping, pornography, blogging, etc. The presence of depressive episodes, suicidal ideation, and suicidal attempts were queried by an item for each experience in the last 12 months with ‘yes’ or “no” responses as follows: “Have you ever felt sad or desperate enough to stop your daily life for two weeks in the last 12 months?” for depression, “Have you thought about suicide seriously in the last 12 months?” for suicidal ideation, and “Have you attempted suicide in the last 12 months?” for suicidal attempts.
Descriptive statistics were used for the analysis of demographic characteristics. To analyze the association between the most commonly used internet service, prevalence and correlates of PIU and descriptive statistics, the chi-square test and analysis of variance (ANOVA) were adopted. To examine the odds ratio for PIU according to the associated correlates, logistic regression with PIU as a dependent variable was used by two models. The first model included sex, grade, most used internet service, depressive episode, suicidal ideation and suicidal attempt as independent variables. Model 2 added socioeconomic status and school achievement as covariates to model 1. Statistical analyses were conducted using the software package SPSS 25.0 for Windows (SPSS Inc., Chicago, IL).
Demographic characteristics are shown in Table 1. In total, 223,542 middle- and high-school students participated in the study, and 52.5% were male. The overall prevalence of PIU was 5.8%, and the high-risk internet user group among the PIU group was 3.2%. The prevalence of PIU based on sex was 7.7% in boys and 3.8% in girls. The proportion of participants who experienced a depressive episode, suicidal ideation, and suicidal attempt were 38.0%, 19.1%, and 4.8%, respectively.
Table 1 Demographic Characteristics
Prevalence and Correlates of PIU Based on the Most Used Internet Service
Among all participants, the most commonly used internet service was internet gaming (35.0%), followed by information searching (16.2%), chatting (14.1%), and blogging (12.1%) (Table 2 and Figure 1). However, the proportions of the most used internet services were different between boys and girls (x2=9144.0; p<0.001). While the most used service in boys was internet gaming (58.1%), girls used blogging (22.1%) and chatting (20.3%) the most.
Table 2 Association Between Most Used Internet Service and Prevalence and Correlates of PIU
Figure 1 Most used internet service according to sex (%).
The prevalence rate of PIU in the users of each specific internet service was also significantly different based on the most used internet service (x2=3791.9; p<0.001). The prevalence of PIU was the highest in the adolescents who used the internet for pornography the most (19.6%), followed by gaming (9.3%) and internet community (8.4%) (Table 2 and Figure 2). The proportion of internet gaming users among the total group of those with PIU was the highest as 56.0%.
Figure 2 Prevalence of PIU according to the most used internet service (%). Abbreviations: PIU, problematic internet use; UCC, user-created content.
The proportion of participants with experiences of depressive episode, suicidal ideation and attempt was also the highest among the adolescents who used the internet for pornography the most (50.0%, 31.1% and 13.7%, respectively), followed by chatting (48.2%, 25.3%, and 7.8%, respectively) and blogging (44.8%, 22.9%, and 6.1%).
Odds Ratios of Being in the PIU Group Based on Demographics and Internet Use Variables
Table 3 shows the odds ratios for being in the PIU group based on demographics and internet use variables. The odds ratio was significantly higher in boys than in girls (OR=1.520; p<0.001). Compared to the youngest participants, the older student groups showed significantly higher odds ratios, 1.274- to 1.319-fold higher, for PIU.
Table 3 Logistric Regression for the PIU with Covariates
Compared to the adolescents using the internet for information searching the most, the odds ratio for PIU in the adolescents who used the internet for pornography the most was the highest (OR=4.526, p<0.001), followed by those using internet for the community (OR=2.822, p<0.001) and gaming (OR=2.661, p<0.001). Those using the internet the most for listening to music (OR=0.733, p<0.001) and email (OR=0.658, p=0.042) showed significantly lower odds ratios than those of the adolescents using the internet for information searching. There were no significant differences in the odds ratios between the groups using the internet mostly for information searching and the groups watching movies, online shopping and blogging.
Associations Between Psychopathology and Risk for PIU
The proportions of participants with an experience of depressive episode, suicidal ideation, and suicidal attempt in the last 12 months were the highest in groups that used the internet the most for pornography (50.0%, 31.1%, and 13.7%, respectively), followed by messenger/chatting (48.2%, 25.3%, and 7.8%, respectively) and blogging (44.8%, 22.9%, and 6.1%, respectively) (Table 2). The presence of depressive episode, suicidal ideation and suicidal attempt were also significantly associated with a higher odds ratio for PIU among the entire sample. (OR=1.725, p<0.001; OR=1.747, p<0.001; and 1.361, p<0.001, respectively) (Table 3).
Our study investigated the prevalence and correlates of PIU in a large number of adolescents based on the most commonly used internet services. In our study, the overall prevalence of PIU was 5.4%, which is comparable with previous studies conducted in other countries. Multiple previous studies of PIU reported a wide range of PIU prevalence. For instance, a study conducted across nine European countries reported a prevalence of 25%, ranging from 14% to 55% across countries.34 Another study conducted in six Asian countries reported that the prevalence of addictive internet use screened by the Internet Addiction Test (IAT) ranged from 1% in South Korea to 5% in the Philippines, and the prevalence of PIU ranged from 13% to 46%.35 Other systematic reviews of internet addiction had also reported a wide range of prevalence rates from 1% to 18.7%9 and from 0.8% to 26.7%.8 These studies argued that these wide ranges of prevalence rates for PIU might have been caused by the lack of consistency in the methodology, such as the definitions, assessment tools and cutoffs for PIU.8,36 Thus, future studies with more agreed-upon definitions and assessment tools for PIU are needed to confirm the prevalence of PIU. Nevertheless, a meta-analysis with 27 studies from 1998 to 2006 reported an average prevalence of internet gaming disorder of 4.7%, despite a wide range of prevalence rates,37 which is consistent with our study.
In our study, boys showed a higher prevalence of PIU than girls by approximately two-fold. This is a consistent finding with multiple previous studies that reported that male sex is a risk factor for PIU.38–40 However, other studies have reported the opposite pattern of sex differences for the prevalence of PIU. For instance, Durkee et al39 reported that small variations in the prevalence rate of PIU were found between the sexes in a study with adolescents from 11 European countries despite some cross-cultural differences. A Canadian study also reported no sex differences in the prevalence of PIU.15 In addition, a study with adults from 9 European countries reported that overall PIU was more prevalent in women than in men.34 These discrepancies regarding sex differences in PIU could be caused by cross-cultural differences. However, to understand these discrepancies in the sex differences in the prevalence of PIU, exploring the specific services used through the internet by both sexes should also be taken into account.
In our study, the most commonly used internet service among all participants was internet gaming followed by information searching, messenger/chatting, and blogging. However, the distribution of the most used internet services was significantly different between the sexes. Whereas the boys overwhelmingly used the internet for gaming the most, girls used the internet for blogging and messenger/chatting the most. These tendencies are consistent with findings in previous studies. Girls were reported to be more likely to use instant messaging (74%) and social network services (70%) than boys aged 15 to 17 (62% and 54%, respectively).14,41 Dufour et al15 also reported that the proportion of excessive use of social networks and blogs was higher in girls than boys. In contrast, the use of internet gaming has been consistently reported to be higher in males than in females.15,26,34,42 Although the exact reasons for these sex-related differences in internet use are not well understood,42 previous studies to explain sex differences in computer game involvement focused on aspects such as the content and design of typical games, violence of the games, competitive structures of the games, and social interactions within the games.43 Our results for the higher use of the internet for blogging and chatting and a lower use of the internet for gaming in girls than boys might be related to well-established evidence that females are more interpersonally oriented, while males are more information/task oriented.44
In our study, the number of individuals with PIU was the highest in the internet gaming users (composing more than 50% of the total PIU group), and the odds ratio for PIU was also very high in internet gaming users. These findings provide supportive evidence for the prevailing concern for internet gaming and the inclusion of internet gaming disorder in diagnostic criteria systems.26,27 Nevertheless, the addictive potential of the internet pornography should also be noted. The proportions of internet pornography as the most used internet service were not high (0.8%) and even rarer in girls (0.1%). However, the odds ratio for PIU among those who used the internet mostly for pornography was the highest, which implicates the strong addictive potential of internet pornography compared to other internet services. Of course, consuming pornography is not a problem caused by only the internet. It has been argued that excessive internet users are not internet addicts but only use the internet as a medium for other addictive behaviors.45,46 However, previous studies have pointed out that online pornography use is on the rise, and the increased “triple A” (accessibility, affordability, and anonymity) provided by the internet has enhanced the potential risk for the problematic use of online pornography.47 In addition, our findings are inconsistent with the results of the previous study of Rosenkranz et al31 which reported the relatively lower addictive potential of sexual content compared to gaming and gambling. These differential results regarding the addictive potential of sexual content between the studies might be caused by socio-environmental differences. Thus, further studies to understand and protect adolescents from the risk of problematic use of internet pornography are needed.
Another notable finding of our study was the significant association between a higher overall odds ratio for PIU and psychopathology, including depression and suicidal ideation and attempt, which is consistent with findings from a previous study24 that reported that the group of students with PIU were more likely to show more depression and suicidal and self-injurious behavior than the normal internet use group. In particular, it is interesting that the proportion of ‘yes’ responses to depressive episodes, suicidal ideation and suicidal attempts were higher in the users of messenger/chatting and blogging than the users of other services, with the exception of users of internet pornography, and this proportion was the lowest in the internet gaming users. These findings imply that depressed adolescents pursue social interaction more by the internet than entertainment. These findings are consistent with a previous study24 that also reported that there was a higher risk of depression in students with nongaming PIU than in students with gaming PIU. In addition, the proportion of “yes” responses to depressive episodes, suicidal ideation and suicidal attempt was the highest in internet pornography users. These findings suggest that using the internet primarily for pornography is associated with severe psychopathology, such as depression and suicide, as well as a strong addictive potential.
Our study has some limitations that should be noted. Although we conducted the study with a large sample of adolescents, our study is based on a cross-sectional design, which limits the interpretation of causality. For instance, depressive episodes, suicidal ideation and suicidal attempts are associated with higher odds ratios of PIU, and we cannot determine the direction of causality. Thus, future studies with a longitudinal design are warranted. Second, although we tried to include a variety of internet services that the adolescents use in the questionnaires, we did not include all services. For instance, internet gambling is one of the major concerns about internet use,28 which was not included in the questionnaires. Third, our study was based on the self-report of the adolescents alone, which could bias the report. The reporting of psychiatric symptoms is known to be discrepant among informants, such as parents and adolescents.48 Thus, obtaining information from multiple informants, including parents, is important for the exact evaluation of psychiatric symptoms. Fortunately, a previous study reported that reports based on self-report by the adolescents for the symptoms of addictive disorders such as alcohol and substance abuse were much more coincident with actual diagnoses than reports from the parents.48 In addition, we utilized simplified categorical items assessing depression, suicidal ideation, and suicidal attempts and did not include validated assessment tools. Although these simplified items were adopted to improve the response rate by a parsimonious questionnaire for a large number of participants, this might result in a lack of detailed information and distortion of the real association between PIU and adolescent psychology, such as depression and suicide. Finally, information on family characteristics, such as parent-child interactions and parenting style, was not included in the study, which is an important factor moderating PIU in adolescents.16 Thus, future studies including more detailed information on the psychopathology of adolescents and family characteristics from multiple informants are warranted to confirm the present findings.
Despite some limitations, our study identified clinically important information about PIU in adolescents. The distribution of the most used internet services has different patterns based on sex. The prevalence of PIU also showed significant differences based on the use of specific internet services. Future studies of PIU with well-defined methodology and assessment tools for each specific internet service are needed to develop strategies to protect individual adolescents from the risk of PIU.
The authors wish to thank the Ministry of Education, Ministry of Health and Welfare, and Centers for Disease Control and Prevention Korea Centers for Disease Control and Prevention, which provided the raw data.
All authors made substantial contributions to the conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (NRF-2018R1C1B5041143).
The authors report no conflicts of interest in this work.
1. Anderson M, Perrin A, Jiang J, Kumar M. 10% of Americans Don’t Use the Internet. Who are They? Washington, DC: Pew Research Center; 2019.
2. Anderson M, Jiang J. Teens, Social Media & Technology 2018. Washington, DC: Pew Research Center; 2018.
3. Gross E, Juvonen J, Gable S. Internet use and well‐being in adolescence. J Soc Issues. 2002;58:75–90. doi:10.1111/1540-4560.00249
4. Caplan SE. Relations among loneliness, social anxiety, and problematic internet use. Cyberpsychol Behav. 2006;10(2):234–242. doi:10.1089/cpb.2006.9963
5. Daine K, Hawton K, Singaravelu V, Stewart A, Simkin S, Montgomery P. The power of the web: a systematic review of studies of the influence of the internet on self-harm and suicide in young people. PLoS One. 2013;8(10):e77555. doi:10.1371/journal.pone.0077555
6. Kiriakidis SP, Kavoura A. Cyberbullying: a review of the literature on harassment through the internet and other electronic means. Fam Community Health. 2010;33(2):82–93. doi:10.1097/FCH.0b013e3181d593e4
7. Young KS, Rogers RC. The relationship between depression and internet addiction. Cyberpsychol Behav. 1998;1(1):25–28. doi:10.1089/cpb.1998.1.25
8. Kuss DJ, Griffiths MD, Karila L, Billieux J. Internet addiction: a systematic review of epidemiological research for the last decade. Curr Pharm Des. 2014;20(25):4026–4052. doi:10.2174/13816128113199990617
9. Pontes HM, Kuss DJ, Griffiths MD. Clinical psychology of internet addiction: a review of its conceptualization, prevalence, neuronal processes, and implications for treatment. Neurosci Neuroecon. 2015;4:11–23.
10. Cerniglia L, Cimino S, Ballarotto G, et al. Motor vehicle accidents and adolescents: an empirical study on their emotional and behavioral profiles, defense strategies and parental support. Transp Res F. 2015;35:28–36. doi:10.1016/j.trf.2015.09.002
11. Steinberg L. A dual systems model of adolescent risk-taking. Dev Psychobiol. 2010;52(3):216–224. doi:10.1002/dev.20445
12. Cerniglia L, Guicciardi M, Sinatra M, Monacis L, Simonelli A, Cimino S. The use of digital technologies, impulsivity and psychopathological symptoms in adolescence. Behav Sci. 2019;9(8):E82. doi:10.3390/bs9080082
13. Cimino S, Cerniglia L. A longitudinal study for the empirical validation of an etiopathogenetic model of internet addiction in adolescence based on early emotion regulation. Biomed Res Int. 2018;2018:4038541. doi:10.1155/2018/4038541
14. Lenhart A, Madden M, Macgill A, Smith A. Teens and Social Media. Washington, DC: Pew Internet & American Life Project; 2007.
15. Dufour M, Brunelle N, Tremblay J, et al. Gender difference in internet use and internet problems among Quebec high school students. Can J Psychiatry. 2016;61(10):663–668. doi:10.1177/0706743716640755
16. Badenes-Ribera L, Fabris MA, Gastaldi FGM, Prino LE, Longobardi C. Parent and peer attachment as predictors of facebook addiction symptoms in different developmental stages (early adolescents and adolescents). Addict Behav. 2019;95:226–232. doi:10.1016/j.addbeh.2019.05.009
17. 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.
18. Van der Aa N, Overbeek G, Engels RC, Scholte RH, Meerkerk GJ, Van den Eijnden RJ. Daily and compulsive internet use and well-being in adolescence: a diathesis-stress model based on big five personality traits. J Youth Adolesc. 2009;38(6):765–776. doi:10.1007/s10964-008-9298-3
19. Caplan SE. Problematic internet use and psychosocial well-being: development of a theory-based cognitive–behavioral measurement instrument. Comput Hum Behav. 2002;18(5):553–575. doi:10.1016/S0747-5632(02)00004-3
20. Kaess M, Parzer P, Brunner R, et al. Pathological internet use is on the rise among European adolescents. J Adolesc Health. 2016;59(2):236–239. doi:10.1016/j.jadohealth.2016.04.009
21. Kim MG, Kim J. Cross-validation of reliability, convergent and discriminant validity for the problematic online game use scale. Comput Hum Behav. 2010;26(3):389–398. doi:10.1016/j.chb.2009.11.010
22. Kuss DJ, Griffiths MD. Internet gaming addiction: a systematic review of empirical research. Int J Ment Health Addict. 2012;10(2):278–296. doi:10.1007/s11469-011-9318-5
23. Pontes HM, Griffiths MD. Measuring DSM-5 internet gaming disorder: development and validation of a short psychometric scale. Comput Human Behav. 2015;45:137–143. doi:10.1016/j.chb.2014.12.006
24. Strittmatter E, Kaess M, Parzer P, et al. Pathological internet use among adolescents: comparing gamers and non-gamers. Psychiatry Res. 2015;228(1):128–135. doi:10.1016/j.psychres.2015.04.029
25. Young KS. Internet addiction: evaluation and treatment. Br Med J. 1999;7:351–352.
26. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). Arlington, TX: American Psychiatric Publishing; 2013.
27. King DL, Potenza MN. Not playing around: gaming disorder in the international classification of diseases (ICD-11). J Adolesc Health. 2019;64(1):5–7. doi:10.1016/j.jadohealth.2018.10.010
28. Gainsbury SM. Online gambling addiction: the relationship between internet gambling and disordered gambling. Curr Addict Rep. 2015;2(2):185–193. doi:10.1007/s40429-015-0057-8
29. Andreassen CS. Online social network site addiction: a comprehensive review. Curr Addict Rep. 2015;2(2):175–184. doi:10.1007/s40429-015-0056-9
30. Grubbs JB, Volk F, Exline JJ, Pargament KI. Internet pornography use: perceived addiction, psychological distress, and the validation of a brief measure. J Sex Marital Ther. 2015;41(1):83–106. doi:10.1080/0092623X.2013.842192
31. Rosenkranz T, Muller KW, Dreier M, Beutel ME, Wolfling K. Addictive potential of internet applications and differential correlates of problematic use in internet gamers versus generalized internet users in a representative sample of adolescents. Eur Addict Res. 2017;23(3):148–156. doi:10.1159/000475984
32. Kim Y, Choi S, Chun C, Park S, Khang YH, Oh K. Data resource profile: the Korea youth risk behavior web-based survey (KYRBS). Int J Epidemiol. 2016;45(4):1076–1076e. doi:10.1093/ije/dyw070
33. Kim D-I, Chung YJ, Lee EA, Kim DM, Cho YM. Development of internet addiction proneness scale-short form (KS scale). Korea J Couns. 2008;9:1703–1722. doi:10.15703/kjc.9.4.200812.1703
34. Laconi S, Kaliszewska-Czeremska K, Gnisci A, et al. Cross-cultural study of problematic internet use in nine European countries. Comput Human Behav. 2018;84:430–440. doi:10.1016/j.chb.2018.03.020
35. Mak KK, Lai CM, Watanabe H, et al. Epidemiology of internet behaviors and addiction among adolescents in six Asian countries. Cyberpsychol Behav Soc Netw. 2014;17(11):720–728. doi:10.1089/cyber.2014.0139
36. Petry NM, O’Brien CP. Internet gaming disorder and the DSM-5. Addiction. 2013;108(7):1186–1187. doi:10.1111/add.12162
37. Feng W, Ramo DE, Chan SR, Bourgeois JA. Internet gaming disorder: trends in prevalence 1998-2016. Addict Behav. 2017;75:17–24. doi:10.1016/j.addbeh.2017.06.010
38. Bakken IJ, Wenzel HG, Gotestam KG, Johansson A, Oren A. Internet addiction among Norwegian adults: a stratified probability sample study. Scand J Psychol. 2009;50(2):121–127. doi:10.1111/j.1467-9450.2008.00685.x
39. Durkee T, Kaess M, Carli V, et al. Prevalence of pathological internet use among adolescents in Europe: demographic and social factors. Addiction. 2012;107(12):2210–2222. doi:10.1111/j.1360-0443.2012.03946.x
40. Tsai HF, Cheng SH, Yeh TL, et al. The risk factors of internet addiction-a survey of university freshmen. Psychiatry Res. 2009;167(3):294–299. doi:10.1016/j.psychres.2008.01.015
41. Pujazon-Zazik M, Park MJ. To tweet, or not to tweet: gender differences and potential positive and negative health outcomes of adolescents’ social internet use. Am J Mens Health. 2010;4(1):77–85. doi:10.1177/1557988309360819
42. Yau YH, Crowley MJ, Mayes LC, Potenza MN. Are internet use and video-game-playing addictive behaviors? biological, clinical and public health implications for youths and adults. Minerva Psichiatr. 2012;53(3):153–170.
43. Hartmann T, Klimmt C. Gender and computer games: exploring females’ dislikes. J Comput Mediat Commun. 2006;11(4):910–931. doi:10.1111/j.1083-6101.2006.00301.x
44. Jackson LA, Ervin KS, Gardner PD, Schmitt N. Gender and the internet: women communicating and men searching. Sex Roles. 2001;44(5):363–379. doi:10.1023/A:1010937901821
45. Griffiths M. Internet addiction - time to be taken seriously? Addict Res. 2000;8(5):413–418. doi:10.3109/16066350009005587
46. Young KS, de Abreu CN. Internet Addiction: A Handbook and Guide to Evaluation and Treatment. Hoboken, NJ: Wiley; 2010.
47. de Alarcon R, de la Iglesia JI, Casado NM, Montejo AL. Online porn addiction: what we know and what we don’t-a systematic review. J Clin Med. 2019;8(1):E91. doi:10.3390/jcm8010091
48. Cantwell DP, Lewinsohn PM, Rohde P, Seeley JR. Correspondence between adolescent report and parent report of psychiatric diagnostic data. J Am Acad Child Adolesc Psychiatry. 1997;36(5):610–619. doi:10.1097/00004583-199705000-00011
This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.