Internet addiction: Prevalence and relation with mental states in adolescents

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1 Psychiatry and Clinical Neurosciences 2016; 70: doi: /pcn Regular Article Internet addiction: Prevalence and relation with mental states in adolescents Kentaro Kawabe, MD, PhD, 1 * Fumie Horiuchi, MD, PhD, 1 Marina Ochi, MD, 1 Yasunori Oka, MD, PhD 2 and Shu-ichi Ueno, MD, PhD 1 1 Department of Neuropsychiatry, Ehime University Graduate School of Medicine, and Center for Child Health, Behavior and Development, 2 Center for Sleep Medicine and Center for Child Health, Behavior and Development, Ehime University Hospital, Toon, Japan Aim: Internet addiction disrupts the daily lives of adolescents. We investigated the prevalence of Internet addiction in junior high school students, elucidated the relation between Internet addiction and mental states, and determined the factors associated with Internet addiction in adolescents. Methods: Junior high school students (aged years) were assessed using Young s Internet Addiction Test (IAT), the Japanese version of the General Health Questionnaire (GHQ), and a questionnaire on access to electronic devices. Results: Based on total IAT scores, 2.0% (male, 2.1%; female, 1.9%) and 21.7% (male, 19.8%; female, 23.6%) of the total 853 participants (response rate, 97.6%) were classified as addicted and possibly addicted, respectively. Total GHQ scores were significantly higher in the addicted ( ) and possibly addicted groups ( ) than in the non-addicted group ( ; P < 0.001, both groups). A comparison of the percentage of students in the pathological range of GHQ scores revealed significantly higher scores in the possibly addicted group than in the non-addicted group. Further, accessibility to smartphones was significantly associated with Internet addiction. Conclusion: Students in the addicted and possibly addicted groups were considered problematic Internet users. Use of smartphones warrants special attention, being among the top factors contributing to Internet addiction. Key words: adolescents, General Health Questionnaire, Internet addiction, suicidal ideation, Young s Internet Addiction Test. T HE INTERNET PROVIDES a new communication medium that enables access to unlimited resources of information across various topics. On the other hand, heavy Internet use has been associated with potential detrimental side-effects. In general, adolescents have been found to spend more time on the Internet than adults, 1 predisposing themselves to Internet addiction. Serious problems associated with Internet addiction among adolescents include refusal to attend school and mental *Correspondence: Kentaro Kawabe, MD, PhD, Department of Neuropsychiatry, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime , Japan. k_kentaro_k@yahoo.co.jp Received 20 January 2016; revised 7 April 2016; accepted 10 May health problems, such as loneliness, low self-esteem, insufficient sleep, anxiety and depression. 2 Students who spend more time playing computer games and using the Internet spend less time sleeping and experience higher levels of tiredness. 3 Like other addictions, Internet addiction disrupts studies, school life, and other aspects in the daily life of an individual adolescent. 2 Prevalence studies on Internet addiction among adolescents have reported large variations. A recent nationwide study in 11 countries in Europe reported a prevalence of pathological Internet users ranging from 1.2% in Italy to 11.8% in Israel. 4 In Asia, the prevalence of Internet addiction in adolescents was reported to be 13.8% in Taiwan, % in South Korea, 6 ranging from 3.0% to 6.7% in Hong Kong, 7,8 and from 405

2 406 K. Kawabe et al. Psychiatry and Clinical Neurosciences 2016; 70: % to 6.0% in China. 9,10 These large differences in the prevalence of Internet addiction were due to diverse study designs, different assessment methods, and sampling from different sub-populations in various studies. Most importantly, the level of problematic Internet addiction has not been determined. Prevalence data in Japanese adolescents have not been reported until recently. In addition, no discussions have been noted on the association between the severity of Internet addiction and mental health in Japanese adolescents. The purpose of the present study was threefold: (i) to investigate the prevalence of Internet addiction among junior high school students (aged years); (ii) to elucidate the relation between the level of Internet addiction and mental states; and (iii) to determine the factors associated with Internet addiction in adolescents. METHODS Participants The present study, which was descriptive and correlational in design, was conducted in 2014 in Masaki, a town (population: ) in the suburban area of Ehime Prefecture, Japan. All junior high school students in the area (n = 853; age, years) participated in the study. Instruments Participants answered Young s Internet Addiction Test (IAT) and the Japanese version of the General Health Questionnaire (GHQ). Participants were also asked to complete a questionnaire on their exposure or access to electronic devices (e.g., cell phones, smartphones, and laptop computers). Measure of Internet addiction The 20 items that comprise Young s Internet Addiction Test (IAT) are calibrated with scores ranging from 1 to 5 (given a total score ranging from 20 to 100), with higher scores reflecting a greater tendency toward addiction. 11 Three types of Internet user groups were identified in accordance with the original scheme of Young. 12 Respondents with IAT scores higher than 70 were classified as addicted and most likely had encountered significant life problems due to excessive Internet use. Respondents with IAT scores between 40 and 69 were classified as possibly addicted. Respondents with IAT scores below 39 were classified as non-addicted and most likely were average Internet users, only having some problems controlling Internet use. In the present study sample (n = 853), Cronbach s α coefficient was Measures of mental states Mental states of students were assessed with the 30- item Japanese version of the General Health Questionnaire (GHQ), which was validated in Japan for youth over the age of The GHQ uses six subscales to evaluate mental states and general psychopathology: General Illness, Somatic Symptoms, Sleep Disturbance, Social Dysfunction, Anxiety and Dysphoria, and Suicidal Depression (the scores of which were summed for a total score). Respondents were asked to rate each item according to the degree it was applicable during the preceding 2 or 3 weeks. Respondents were also asked to rate on a four-point scale the degree to which an adjective has applied to them: better than usual, same as usual, worse than usual, and much worse than usual. The answers much worse than usual and worse than usual are scored 1 and better than usual and same as usual are scored 0. Each of the six subscales consists of five questions; therefore, the minimum obtainable score for each scale was 0 and the maximum obtainable score, 5. In the present study (n = 853), Cronbach s α coefficient was In the GHQ subscales of General Illness, Somatic Symptoms, Sleep Disturbance, and Social Dysfunction, scores >3 were defined as pathological. In the subscale Anxiety and Dysphoria, scores >4 were defined as pathological, and in the subscale Suicidal Depression, scores 2 were defined as pathological. The cut-off point for total score of the GHQ was Measure of usage and exposure to electronic devices Students were asked to fill out a questionnaire on how they use electronic devices, such as televisions, radios, cell phones, smartphones, laptop computers, tablet computers, console games (including computer games), and portable games. Participants were also asked to respond with a yes or no to the question of whether electronic devices were freely accessible to them or placed in their own room. These questionnaires included items on the timing of first use (0 = preschool; 1 = early elementary

3 Psychiatry and Clinical Neurosciences 2016; 70: Internet addiction in adolescents 407 school; 2 = upper grades of elementary school; 3 = junior high school) and the duration of use (0 = under 30 min/day; 1 = min/day; 2 = over 60 min/day) of each electronic device. However, participants were not asked whether electronic devices connected to the Internet. Procedure This study was approved by the Institutional Review Board of Ehime University Graduate School of Medicine with the cooperation of the School Board of Masaki. The study protocol was explained to the junior high school teachers of the area. Primary caregivers were informed of this study via a letter and were asked to contact the school for clarification. In November 2014, school teachers explained the purpose of the present study to students, and the students filled out questionnaires at each class using a self-report, anonymous format in November Two students did not participate in the study, and 19 inappropriate responses were excluded. A total of 853 students (n = 425 male; n = 428 female) were included in the analysis (proper response rate, 97.6%). Data analysis Data were summarized as means ( SD) for continuous variables and as percentages for categorical variables. Descriptive statistics were used to describe the distributions of participant characteristics. χ 2 - tests were employed to compare the number of students with problematic range of GHQ scores in the non-addicted, possibly addicted, and addicted groups, complemented by adjusted residual analysis. Accessibility to electronic devices between male and female students, and at each grade To elucidate the main factors that affect Internet addiction, variables such as grade, sex, and total scores of GHQ were entered into multiple regression analysis as covariates. Backward stepwise regression was performed. One-way analysis of variance (ANOVA) was conducted to compare the GHQ scores between the three IAT groups with post hoc analysis with Bonferroni. P-values >0.05 were used for variable removal. Logistic regression models were used to assess the independent relations between Internet addiction and accessibility of certain electronic devices. We first performed a series of correlational analyses individually. If variables were found to be significant, they were subjected to the multivariable model. All tests were two-sided and assumed a 5% significance level. All data were analyzed using SPSS (IBM Corp., Armonk, NY, USA) for Windows, version RESULTS The mean age of participants was years (male, ; female, years). Of the 853 students, 279 (32.7%) were first-grade, 271 (31.8%) were second-grade, and 303 (35.5%) were third-grade junior high school students. Prevalence of Internet addiction The mean score of total IAT was (male, ; female, ). Based on total IAT scores, 2.0% (male, 2.1%; female, 1.9%) and 21.7% (male, 19.8%; female, 23.6%) of the participants were classified as addicted and possibly addicted, respectively (Table 1). The mean IAT score was in the addicted, in the possibly addicted, and in the nonaddicted groups. The percentage of non-addicted individuals in the first grade was 85.3%, in the second grade it was 74.2%, and in the third grade it was 70.0%. Multiple regression analysis of the total score of IAT and various factors showed that the total score of the IAT correlated significantly with the grade (B = 1.705, β = 0.490, P = 0.001) and Table 1. Prevalence of Internet addiction Nonaddicted Possibly addicted Addicted Total, n (%) 651 (76.3) 185 (21.7) 17 (2.0) Sex Male, n (%) 332 (78.1) 84 (19.8) 9 (2.1) Female, n (%) 319 (74.5) 101 (23.6) 8 (1.9) Junior high school grade First, n (%) 238 (85.3) 36 (12.9) 5 (1.8) Second, n (%) 201 (74.2) 64 (23.6) 6 (2.2) Third, n (%) 212 (70.0) 85 (28.0) 6 (2.0) Score of IAT IAT, Young s Internet Addiction Test.

4 408 K. Kawabe et al. Psychiatry and Clinical Neurosciences 2016; 70: Table 2. Analysis of variance for GHQ subscale scores in the three groups GHQ scores Total Non-addicted n = 651 (A) Possibly addicted n = 185 (B) Addicted n = 17 (C) F P Total score <0.001 General illness <0.001 Somatic symptoms <0.001 Sleep disturbance <0.001 Social dysfunction <0.001 Anxiety and dysphoria <0.001 Suicidal depression <0.001, Adjusted for multiple comparison. The score of A is significantly lower than both that of B and C. The score of B is significantly lower than that of C. GHQ, General Health Questionnaire. Table 3. Performance of GHQ subscale scores over a range of cut-off points in the three groups GHQ score Total n = 853 Non-addicted n = 651 (A) Possibly addicted n = 185 (B) Addicted n = 17 (C) χ 2 P-value General illness, n (%) 62 (7.3) 30 (4.6) 25 (13.5) 7 (41.2) 46.5 <0.001, Somatic symptoms, n (%) 132 (15.5) 72 (11.1) 52 (28.1) 8 (47.1) 45.2 <0.001, Sleep disturbance, n (%) 68 (8.0) 31 (4.8) 34 (18.4) 3 (17.6) 38.6 <0.001, Social dysfunction, n (%) 65 (7.6) 31 (4.8) 29 (15.7) 5 (29.4) 36.1 <0.001, Anxiety and dysphoria, n (%) 109 (12.8) 54 (8.3) 49 (26.5) 6 (35.3) 50.7 <0.001, Suicidal depression, n (%) 157 (18.4) 87 (13.4) 58 (31.4) 12 (70.6) 62.5 <0.001, Statistically significant association by adjusted residual analysis, P < The percentage of A is significantly lower than both that of B and C. The percentage of B is significantly lower than that of C. with the total score of the GHQ (B = 1.208, β = 0.481, P < 0.001), but not with sex (P = 0.92). Mental state of children with Internet addiction The mean total score of the GHQ was in the non-addicted, in the possibly addicted, and in the addicted groups (Table 2). The total score of the GHQ differed significantly between the three groups by one-way ANOVA (F = 77, P < 0.001) and showed group differences between the non-addicted and possibly addicted and also non-addicted and addicted groups. All subscale scores of the GHQ differed significantly between the three groups. Post-hoc analyses revealed that Suicidal Depression scores significantly differed among all three groups. The General Illness, Somatic Symptoms, Sleep Disturbance, Social Dysfunction, and Anxiety and Dysphoria scores significantly differed between the non-addicted and possibly addicted and between the non-addicted and addicted groups. Table 3 shows the percentage of students above the cut-off scores of the GHQ in the non-addicted, possibly addicted, and addicted groups. Findings revealed that the higher the addicted scores, the higher the scores of mental states. Accessibility of electronic devices Table 4 shows the number and rate of students to whom each electronic device was freely accessible. The accessibility of cell phones, smartphones, and laptop computers in male students was significantly lower than that in female students. On the other hand, the accessibility of console games and

5 Psychiatry and Clinical Neurosciences 2016; 70: Internet addiction in adolescents 409 Table 4. Characteristics of participants on accessibility of electronic devices Accessibility of Sex Grade electronic devices Male (%) Female (%) χ 2 P-value First (%) Second (%) Third (%) χ 2 P-value Television Radio <0.001** Cell phone <0.001** Smartphone * Laptop computer <0.001** ** Tablet computer Console game ** Portable game ** *P < **P < Table 5. Association of Internet addiction with electronic device factors Internet addiction Crude Adjusted Total Electronic devices N Number % OR 95%CI OR 95%CI Television Radio Cell phone * Smartphone ** ** Laptop computer * Tablet computer Console game * Portable game *P < **P < Adjusted for other factors in multiple logistic regression analyses with stepwise elimination procedure at the P = 0.05 significance level for entry into the model. CI, confidence interval; Internet addiction, addicted and possibly addicted; OR, odds ratio. portable games in male students was significantly higher than that in female students. The higher the grade, the greater the number of students who had laptop computers and radio devices (Table 4). Factors that contribute to Internet addiction As shown in Table 5, univariate logistic regression analyses revealed that four variables cell phones, smartphones, laptop computers, and console games were significantly associated with Internet addiction for students. Using a multivariable model with Internet addiction of students as the dependent variable, we found that the accessibility of smartphones was the most significant factor. For the association between the total scores of IAT and the usage of smartphones, we noted no apparent correlations between the IAT score and first usage of smartphones (r = 0.03, P = 0.65), although the duration of using smartphones was moderately associated with the total IAT score (r = 0.24, P < 0.001). DISCUSSION Adolescence is a period of rapid psychological maturation and susceptibility to Internet attraction. 15 Excessive Internet use was found to be detrimental to family relationships, self-esteem, life satisfaction, and academic performance. 19 The present study revealed that mental states in

6 410 K. Kawabe et al. Psychiatry and Clinical Neurosciences 2016; 70: adolescents are significantly associated with Internet addiction. Prevalence of Internet addiction indicates an initial clue in defining the target of intervention. According to a press release of the Japan Ministry of Internal Affairs and Communications (MIC) (2013), 7.6% of junior high school students responding to the IAT were judged as addicted and 35.7% were judged as possibly addicted. 20 The present study showed a lower prevalence than that reported by the MIC. One possible reason is that the MIC collected data from online samples from 257 male and 261 female participants. Meanwhile, in the present study, school teachers distributed questionnaires to individual students at each class. In a Greek study using the IAT for adolescents (n = 897), the prevalence of problematic Internet use was 12.8% and that of addictive use was 1.0%. 15 The prevalence of Internet addiction in Japan was higher than that in Greece. Age and sex differences are also important issues in terms of Internet addiction. In the present study, Internet addiction correlated significantly with grade, but not with sex. To our knowledge, this is the first study to compare the tendency of Internet addiction for each grade of junior high school. Our findings show the higher the grade, the greater the number of students with Internet addiction. This study also indicated that the percentage of those who owned radios and laptop computers was significantly different for each grade. However, we will need to conduct longitudinal studies in order to identify the causal relation of the factors. In a previous study, sex differences were found in the severity of online gaming addiction among male subjects. 21 In the present study, male students made use of games (such as console and portable games) more than female students; while female students made use of computers (including laptop and tablet computers) and mobile phones (such as cell phones and smartphones) more than male students. According to Chou and colleagues, no consensus has been reached on whether Internet addiction is associated with sex difference. 22 The most important issue in Internet addiction in adolescents is associated with the deterioration of mental states related to Internet addiction. The present study revealed that students with Internet addiction experienced more severe mental states. The addicted and possibly addicted groups showed more disturbed mental states than the non-addicted group in all subscales of the GHQ. In particular, the addicted group had more severe depression and suicidal ideation than the other groups. The relatively high IAT scores in the possibly addicted group were positively correlated with depression and suicidal ideation. 2 On the other hand, the percentage of subjects within the pathological range was greater in the possibly addicted group than in the non-addicted group. These findings indicate the necessity of paying attention not only to addicted but also to possibly addicted adolescents. Previous studies reported that symptoms of Internet addiction were comorbid with psychiatric conditions, including suicidality and depressive symptoms, 8 and that adolescents with problematic Internet use experienced a higher incidence of depression at 9 months follow-up compared to other adolescents. 23 Intervention of Internet addiction may be beneficial in the treatment of psychiatric disorders associated with Internet addiction. 24 The findings of the present study suggest that Internet addiction contributes to depression. It remains unknown which adolescents might be good candidates for intervention that could prevent comorbid mental problems associated with Internet addiction. It is necessary to prevent Internet addiction in order to attenuate these detrimental effects on the mental health of adolescents. Finally, we need to determine an effective target of intervention in terms of electronic devices. We suggest that accessibility to smartphones is one of the most important factors in Internet addiction. Smartphones are very convenient as they can be used while lying in bed to surf the Internet or watch videos. The superior handiness and light weight of smartphones as compared with other electronic devices (such as laptop computers) makes them particularly attractive. The rate of smartphone ownership is currently increasing rapidly. In the USA, the percentage of the general population who owned a smartphone increased from 35% in 2011 to 56% in In Japan, according to a Cabinet Office report, the number of junior high school students who owned a smartphone increased from 2.6% in 2010 to 47.4% in In the present study, 38.8% of male and 46.2% of female junior high school students owned a smartphone. Moreover, the duration, rather than the first-time usage of smartphones, is significantly associated with Internet addiction in early teens. In order to prevent Internet addiction, health-care professionals, school administrators, and parents should be aware of this finding.

7 Psychiatry and Clinical Neurosciences 2016; 70: Internet addiction in adolescents 411 In addition, the mental health education curriculum for junior high school students should consider proper use of the Internet, and how misuse of the Internet might affect students mental health. Although the present study suggests a correlation between Internet addiction and severe mental states among junior high school students, due to the cross-sectional nature of our study we cannot conclude whether Internet usage causes poor quality of school life. Limitations The present study has several limitations. First, study participants (adolescents) were recruited from public junior high schools in suburban areas (between urban and rural communities). Although the recruitment area is suburban and relatively intermediate in terms of socioeconomics, our findings may not be generalizable to the entire Japanese adolescent population. Second, because the present study is crosssectional, it cannot indicate cause-and-effect relations like longitudinal studies. A longitudinal study is needed to support our findings. Third, our assessment of Internet use patterns relied on adolescents self-reports; therefore, our findings may underestimate the true prevalence of Internet addiction and associated mental problems. Furthermore, validation of the Japanese IAT has not yet been performed. Validation of the Japanese IAT is important for the detection of Internet addiction in clinical practice. In addition, our assessment tools did not ask participants whether their electronic devices connected to the Internet. Fourth, although we pointed out that Internet addiction was associated with mental problems, we did not control for confounding factors, such as development or mental disorders, or domestic environment, including economic status. Conclusion The prevalence of problematic Internet use in Japanese adolescents was more than 20% and we observed a strong correlation between Internet addiction and mental problems, such as suicidal depression. Because smartphones are most closely associated with Internet addiction, controlled usage of smartphones could prevent or at least minimize Internet addiction in adolescents. ACKNOWLEDGMENTS The authors thank all of the participants, teachers and the School Board of Masaki and Ehime Prefecture who participated in this study. This work was supported by a Health and Labor Sciences Research Grant ( ). DISCLOSURE STATEMENT The authors declare no conflicts of interest in preparing this article. There are no funders to report for this submission. AUTHOR CONTRIBUTIONS K. K. designed the study, managed data collection, conducted statistical analysis, and prepared the initial manuscript. F. H. contributed to study design, data collection, statistical analysis, and revision of the manuscript critically for important intellectual content. M. O. contributed to data collection and data input. Both Y. O. and S. U. provided critical comment, which significantly enhanced the final draft. REFERENCES 1. Kraut R, Kiesler S, Boneva B, Cummings J, Helgeson V, Crawford A. Internet paradox revisited. J. Soc. Issues 2002; 58: Kim K, Ryu E, Chon MY et al. Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: A questionnaire survey. Int. J. Nurs. Stud. 2006; 43: Van den Bulck J. Television viewing, computer game playing, and internet use and self-reported time to bed and time out of bed in secondary-school children. Sleep 2004; 27: Durkee T, Kaess M, Carli V et al. Prevalence of pathological internet use among adolescents in Europe: Demographic and social factors. Addiction 2012; 107: Yang SC, Tung CJ. Comparison of internet addicts and non-addicts in Taiwanese high school. Comput. Human Behav. 2007; 23: Park SK, Kim JY, Cho CB. Prevalence of internet addiction and correlations with family factors among south Korean adolescents. Adolescence 2007; 43: Lai CM, Mak KK, Watanabe H, Ang RP, Pang JS, Ho RC. Psychometric properties of the internet addiction test in Chinese adolescents. J. Pediatr. Psychol. 2013; 38: Fu KW, Chan WS, Wong PW, Yip PS. Internet addiction: Prevalence, discriminant validity and correlates among adolescents in Hong Kong. Br. J. Psychiatry 2010; 196:

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