CYBERPSYCHOLOGY, BEHAVIOR, AND SOCIAL NETWORKING Volume 17, Number XX, 2014 ª Mary Ann Liebert, Inc. DOI: 10.1089/cyber.2013.0382

ORIGINAL ARTICLE

Internet Addictive Behavior in Adolescence: A Cross-Sectional Study in Seven European Countries Artemis Tsitsika, MD, PhD,1 Mari Janikian, PhD,1 Tim M. Schoenmakers, PhD,2,3 ´ lafsson, PhD,4 Szymon Wo´jcik, MS,5 George Florian Macarie, PhD,6 Eleni C. Tzavela, MS,1 Kjartan O 1 Chara Tzavara, PhD, The EU NET ADB Consortium, and Clive Richardson, PhD 7

Abstract

A cross-sectional school-based survey study (N = 13,284; 53% females; mean age 15.8 – 0.7) of 14–17-year-old adolescents was conducted in seven European countries (Greece, Spain, Poland, Germany, Romania, the Netherlands, and Iceland). The aim of the study was to investigate the prevalence of Internet addictive behavior (IAB) and related psychosocial characteristics among adolescents in the participating countries. In the study, we distinguish two problematic groups: adolescents with IAB, characterized by a loss of control over their Internet use, and adolescents ‘‘at risk for IAB,’’ showing fewer or weaker symptoms of IAB. The two groups combined form a group of adolescents with dysfunctional Internet behavior (DIB). About 1% of adolescents exhibited IAB and an additional 12.7% were at risk for IAB; thus, in total, 13.9% displayed DIB. The prevalence of DIB was significantly higher among boys than among girls (15.2% vs. 12.7%, p < 0.001) and varied widely between countries, from 7.9% in Iceland to 22.8% in Spain. Frequent use of specific online activities (e.g., gambling, social networking, gaming) at least 6 days/week was associated with greater probability of displaying DIB. Multiple logistic regression analysis indicated that DIB was more frequent among adolescents with a lower educational level of the parents, earlier age at first use of the Internet, and greater use of social networking sites and gaming sites. Multiple linear regression analysis showed that externalizing (i.e., behavioral) and internalizing (i.e., emotional) problems were associated with the presence of DIB.

isolation and neglect of social, academic, and recreational activities, and personal health.3 IAB has received growing research attention and various terms have been used to describe it, such as ‘‘excessive,’’ ‘‘problematic,’’ and ‘‘pathological’’4 among others. There is no consensus within the international scientific community, including the American Psychiatric Association, on whether this behavioral pattern constitutes a ‘‘true addiction,’’ and further investigation is recommended.5 Currently, researchers and clinicians have identified symptoms of IAB that share similarities with behavioral addictions and correspond to diagnostic criteria for impulse control disorders, mainly pathological gambling.3,6 Two types of IAB, specific and generalized, have been identified in the literature.7 An individual displaying generalized IAB uses the Internet as a whole in a compulsive way,

Introduction

A

dolescents have embraced the Internet as a tool providing multiple options and unique opportunities for communication, education, and entertainment. While most adolescents use the Internet without significant problems, some report dysfunctional ways of using the Internet.1 They report symptoms that are akin to behavioral addictions or substance addictions. These symptoms vary in number and frequency. Dysfunctional Internet users have an addictionlike pattern of Internet use, which negatively impacts their important everyday activities, relationships, and psychological well-being.2 In the current study, Internet addictive behavior (IAB) is defined as a behavioral pattern characterized by loss of control over Internet use. This behavior potentially leads to 1

Adolescent Health Unit, Second Department of Pediatrics, National and Kapodistrian University of Athens, Athens, Greece. IVO Addiction Research Institute, Rotterdam, The Netherlands. Erasmus University Medical Center, Rotterdam, The Netherlands. 4 University of Akureyri, Akureyri, Iceland. 5 Nobody’s Children Foundation, Warsaw, Poland. 6 Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania. 7 Panteion University of Social and Political Sciences, Athens, Greece. 2 3

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whereas one with specific IAB is drawn to particular online activities such as gaming or the use of social networking sites (SNS).8,9 It is difficult to establish prevalence rates of IAB since there is a lack of a universal definition and diagnostic criteria. In the scientific literature, estimates of IAB prevalence among adolescents vary greatly between European countries. For example, among Greek adolescents, IAB was estimated to be 1%,10 whereas in the United Kingdom, it was estimated at 18%.3 However, most European estimates fall within the lower part of the range, between 1% and 8%.1,8,11–15 In particular, research in similar age-groups has found an IAB prevalence of 1.2% in Italy,1 3.7% in the Netherlands,16 4.6% in Romania,1 5.1% in Germany,17 and 5.8% in Poland.15 IAB rates among adolescents seem to differ between countries possibly depending on numerous factors, including the age group sampled and the assessment instruments used. Examples of instruments are the Young Diagnostic Questionnaire for Internet addiction12 and the Compulsive Internet Use scale.8 Whereas most instruments are based on DSM-IV criteria for pathological gambling1,3,8,10,12,16 and some on substance use disorder,7 the operationalization of criteria and wording of items differ between questionnaires.18 Also, the selection of criteria may differ, possibly in relation to the number of items in the questionnaire; for example, the Compulsive Internet Use scale, with 14 items,8,16 does not contain items on tolerance, whereas the Young scale, with 20 items, does.1,3,10,12 IAB has been associated with multiple internalizing comorbid conditions, such as anxiety, depression, and suicidal ideation,19 as well as social isolation,20 impaired social skills,21 and social difficulties.22 IAB among adolescents has also been related to externalizing disorders, including conduct problems, aggression, and hostile behavior, as well as consequent impaired peer relations.23 The causal relationship between IAB and emotional and behavioral problems has not been adequately established yet. The literature to date suggests both directions of causality, although evidence is somewhat stronger for internalizing problems being an antecedent of IAB.24 Internalizing problems can increase adolescent vulnerability to developing IAB, as the Internet can be remarkably reinforcing.9,25 The Internet offers ways of avoiding difficulties encountered in the offline world; adolescents can cope with life circumstances by using the Internet as an escape mechanism.7 Notwithstanding, there is some evidence that people with IAB develop mental health problems, such as depression.2 The primary objective of the present investigation was to explore the prevalence of IAB among adolescents in seven European countries. There is a gap in the literature in the assessment of emotional and behavioral characteristics of adolescents with IAB using empirically derived self-report instruments validated in each country. Consequently, the secondary objective was to evaluate the association between IAB and related emotional and behavioral problems among the study population. Materials and Methods Study design and study population

A cross-sectional study was conducted by the EU NET ADB consortium in seven European countries: Spain,

TSITSIKA ET AL.

Poland, Germany, the Netherlands, Romania, Iceland, and Greece. Data were collected from October 2011 to May 2012. The study protocol was approved by the appropriate ethics committees in each participating country. Written informed consent was required from the parents or legal guardians of all eligible participants before participation in the study. Each country drew a clustered probability sample, with school classes as primary sampling units. The national official and complete school and class register was the sampling frame for each country, and about one hundred 9th- and 10thgrade classes were sampled per country. All students attending the selected classes on the day of data collection were included in the procedure, subject to parental permission. The detailed protocol of the EU NET ADB study has been published elsewhere.26 Data collection

Self-report anonymous questionnaires were self-completed by participating adolescents in class during school hours. The questionnaire included items pertaining to personal characteristics, family status, and Internet use. Participants were asked to complete the questionnaire anonymously to help ensure confidentiality and to minimize any potential reporting bias. A total of 13,708 adolescents completed the survey. In total, 85% of students who were on the class registers participated. Further, 424 participants were excluded because of missing data for age or gender or for age out of the permitted range (14–17 years). This resulted in a total sample of 13,284 adolescents (F/M: 7,000/6,284; mean age 15.8 – 0.7) included in the analyses. Measures

A structured questionnaire was administered to adolescents, which included the Internet Addiction Test (IAT3) and the Youth Self Report (YSR27). The 20-item IAT evaluates the degree of preoccupation with the Internet, its compulsive use, behavioral problems and emotional changes linked with Internet use, and the impact of Internet use on functioning. Young3 has linked Internet addiction most closely to pathological gambling, an impulse control disorder according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition.28 Originally, the IAT was designed based on symptoms for impulse control disorder and was used as a one-factor scale. In a pilot study within this project, the phrasing of three IAT items was modified for adolescents and contemporary Internet use. For example, item 3, ‘‘How often do you prefer the excitement of the Internet to intimacy with your partner?’’ was modified to ‘‘How often do you prefer the excitement of the Internet to being with your boyfriend or girlfriend?’’ The test showed high reliability (Cronbach’s a = 0.92). Response scores to each IAT item range from 0 to 5, where a score of 0 is defined for responses of ‘‘never/not applicable,’’ 1 is defined as ‘‘rarely,’’ and a score of 5 as ‘‘always.’’ The total IAT score may range from 0 to 100 points. In order to assess IAB, the following cutoff scores as recommended by Young3 were applied: (a) 0–19: no signs of IAB; (b) 20– 39: mild, yet nonproblematic signs of IAB; (c) 40–69: at risk for IAB; (d) 70–100: IAB. The first two categories are both considered as functional Internet behavior (0–39 points),

INTERNET ADDICTIVE BEHAVIOR IN ADOLESCENCE

while the latter two together are considered as dysfunctional Internet behavior (DIB; 40–100 points). For the IAT scale (20 items), two missing values per subject were permitted and were replaced by the country-specific median. The YSR is an empirically derived and widely used 112item, self-report instrument for adolescents 11–18 years of age with excellent psychometric properties.29 The YSR is used in both clinical and school settings in assessing adolescents’ behavioral and emotional problems as well as their competences. Adolescents rate how true each item is for themselves (now or within the past 6 months) using a threepoint scale (0 = absent, 1 = occurs sometimes, 2 = occurs often). The YSR generates a total of 25 scores of competences and problem behaviors. Scale and subscale output scores are organized into two groups: (a) competence scales (activities, social competence, academic performance, total competence); (b) empirically based syndrome scales (anxious/depressed, withdrawn/depressed, somatic complaints, social problems, thought problems, attention problems, rule-breaking [delinquent] behavior, aggressive behavior). The syndrome scales cluster into broadband scales—internalizing problems and externalizing problems—and a total problems scale. In this report, raw scores were used. Higher scores indicated more problems (in the syndrome scales) or greater competence (in the competence scales). Data were collected on Internet use. Adolescents were asked whether they belonged to at least one SNS. If they did, they were asked for how long they used SNS on a typical weekday (‘‘normal school day’’) and on weekends or during vacation (‘‘nonschool day’’) in the past 12 months. Response options ranged from ‘‘not at all’’ to ‘‘more than 4 hours.’’ The weighted average of weekday and weekend use provided a single estimate of daily SNS use throughout the week. The median response ‘‘2 hours per day’’ was used to dichotomize the frequency of SNS into moderate SNS use (< 2 hours daily) and heavier SNS use (q2 hours daily). Adolescents were further asked about the daily frequency of gaming (‘‘How often do you play computer games?’’), with response options ranging from ‘‘less than 1 hour’’ to ‘‘more than 6 hours.’’ Educational attainment of parents was used as a proxy measure of socioeconomic status. Specifically, educational attainment was measured by the highest qualification earned between the two parents. Two categories were created, low/ middle (primary or secondary school) and high (postsecondary or tertiary education) educational level. Missing values for an item arose when the respondent did not tick any of the response options or ticked ‘‘Don’t know/prefer not to say’’ if this option was available. Statistical analysis

Categorical variables are presented with relative frequencies and 95% confidence intervals (95% CI). For the comparison of proportions, Pearson’s chi-square tests of independence were used. Odds ratios (ORs) with 95% CIs were computed in order to show the effect of every online activity on having DIB. Since our measure for IAB assumes a gradual increase in addiction-like patterns from nonproblematic, mild signs of IAB to problematic signs of IAB, which encompasses both risky behavior and addictive behavior, we chose to analyze all persons with problematic

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signs in order to find factors associated with IAB: that is, everyone showing dysfunctional Internet use (IAT scores 40–100). To explore which participant characteristics and known risk applications for DIB (i.e., gaming and SNS use; see Introduction) were associated with DIB in our sample, a multiple logistic regression analysis was used. First, we included demographic variables into the analysis: gender, age, and socioeconomic status as indicated by the highest educational level of the parents. Second, we included the following independent variables: age at first use of the Internet,6 use of SNS, and computer gaming. For the latter two, in order to distinguish lighter from heavier users, dichotomous variables were used: namely, using those applications more or less than 2 hours per day. Adjusted ORs are shown from the results of the logistic regression analyses. In order to explore the association between YSR and DIB, linear regression analyses were conducted with dependent variable, each YSR subscale, and independent variables, the presence of DIB, age, gender, parental educational level, and country. All p-values reported are two-tailed. Analyses were conducted using the Complex Samples procedure in SPSS statistical software (version 18.0) with countries as strata and classes as clusters so that the computation of all statistical tests and CIs correctly took into account the complex sample design. Results

Table 1 presents the demographic characteristics of the sample by gender, age, parental educational level, and country. Table 2 shows that the prevalence of IAB in the sample was 1.2% (0.9% in girls vs. 1.6% in boys) and the prevalence of at risk for IAB was 12.7% (11.8% in girls, 13.6% in boys). In total, the prevalence of DIB was 13.9%. DIB was more frequent among boys, in those aged over 15 years and in adolescents whose parents’ highest educational level was low or middle ( p < 0.001 in each case). Overall, the highest rate of DIB was found in Spain (22.8%) and the lowest in Iceland (7.9%). Comparisons between pairs of countries using a Bonferroni adjusted 5% significance level showed that the percentages in Spain and Romania were significantly higher than in all other countries and, in addition, the percentage in Iceland was significantly lower than that in Poland and Greece. Table 3 presents the ORs for the relationships between various online activities and DIB for all countries. Gambling and using SNS had the strongest relation with DIB, followed by playing games for monetary prizes, and visiting chat rooms and Internet forums. The weakest relationships were found for e-mail, news sites, hobbies, purchasing goods, and single-player games (e.g., solitaire, backgammon). Watching videos or movies was not significantly related to DIB, while doing homework or research online was negatively associated with DIB. Table 4 presents a multiple logistic regression analysis with DIB as a dependent variable and country, gender, age, parental educational level, age at first use of the Internet, use of SNS, and gaming as the independent variables. All the independent variables, with the exception of gender and age, were independently associated with DIB (Table 4). The prevalence of DIB was higher among adolescents whose

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TSITSIKA ET AL.

Table 1. Demographic Characteristics of the Sample by Gender, Age, Parental Educational Level, and Country Gender Female, N (%) All adolescents Greece Spain Romania Poland Germany The Netherlands Iceland

7,000 1,010 1,024 1,021 1,030 1,281 625 1,009

(52.7) (51.3) (51.7) (55.8) (52.1) (54.4) (50.0) (52.4)

Age Male, N (%)

6,284 957 956 809 948 1,073 624 917

(47.3) (48.7) (48.3) (44.2) (47.9) (45.6) (50.0) (47.6)

Parental education

14–15 years, N (%)

16–17 years, N (%)

Low/middle, N (%)

8,156 1,375 1,296 486 1,468 1,351 480 1,700

5,128 592 684 1,344 510 1,003 769 226

4,165 819 713 775 767 575 188 328

parents have lower educational attainment, among those who started using the Internet at an earlier age, and, importantly, among adolescents who spent more than 2 hours per day on gaming or SNS as compared with adolescents who spent less time on these applications. Table 5 presents the results of multiple linear regression analyses with dependent variable, each YSR subscale in turn, and independent variables, the presence of DIB, age, gender, parental educational level and country. The regression was fitted first without and then with DIB, so that the contribution of DIB to the explained variation could be seen. We found that the presence of DIB, controlling for the other independent variables, significantly predicted higher scores (greater problems) on all scales of emotional and behavioral problems (regression coefficients range from 1.6 to 7.9; all p’s < 0.001). On the contrary, DIB significantly but very

(61.4) (69.9) (65.5) (26.6) (74.2) (57.4) (38.4) (88.3)

(38.6) (30.1) (34.5) (73.4) (25.8) (42.6) (61.6) (11.7)

High, N (%)

(37.3) (44.4) (40.5) (48.6) (50.0) (30.4) (19.8) (20.5)

7,007 1,024 1,046 820 766 1,319 763 1,269

(62.7) (55.6) (59.5) (51.4) (50.0) (69.6) (80.2) (79.5)

weakly predicted lower scores (lower competence) on activities, social competence, academic performance, and total competence score (regression coefficients range between 0.2 and 1.1; all p’s < 0.001 except for social competence, p = 0.028). With the exception of social competence, the regression coefficients all remain statistically significant at p < 0.01 after carrying out a simple Bonferroni correction to allow for the multiple testing of the 12 scale scores. Discussion

The present study assessed the prevalence of IAB and its association with psychosocial characteristics among adolescents in seven European countries. The findings indicated that approximately 1% of the adolescents examined exhibited IAB. This prevalence is consistent with that reported in other

Table 2. Percentage of Adolescents with Functional and Dysfunctional Internet Behavior by Gender, Age, Parental Educational Level, and Country Functional Internet behavior (N = 11,029)

All adolescents Gender Female Male Age 14–15 years 16–17 years Parental education Low/middle High Spain Romania Poland Greece The Netherlands Germany Iceland

Dysfunctional Internet behaviora (N = 1,778)

% No signs of IAB (95% CI)

% Mild signs of IAB (95% CI)

% At risk for IAB (95% CI)

% IAB (95% CI)

% Total dysfunctional Internet behavior (95% CI)

47.2 (46.1–48.3)

38.9 (37.9–39.9)

12.7 (12.0–13.4)

1.2 (1.0–1.5)

13.9 (13.1–14.7)

48.5 (47.1–50.0) 45.7 (44.3–47.2)

38.8 (37.4–40.1) 39.0 (37.7–40.3)

11.8 (11.0–12.7) 13.6 (12.7–14.6)

0.9 (0.7–1.2) 1.6 (1.3–2.0)

12.7 (11.8–13.6) 15.2 (14.2–16.3)

48.0 (46.7–49.4) 46.0 (44.3–47.6)

39 (37.7–40.2) 38.8 (37.3–40.3)

12 (11.2–12.8) 13.8 (12.7–14.9)

1.1 (0.8–1.4) 1.5 (1.2–1.9)

13.0 (12.2–14.0) 15.2 (14.2–16.4)

45.3 47.4 19.3 45.5 50.3 59.0 45.3 53.9 56.2

38.4 39.9 57.9 36.8 36.4 28.3 42.5 35.4 35.9

14.9 11.6 21.3 16.0 12.0 11.0 11.4 9.7 7.2

1.4 1.0 1.5 1.7 1.3 1.7 0.8 0.9 0.8

16.3 12.6 22.8 17.7 13.2 12.7 12.2 10.6 7.9

(43.4–47.1) (46.0–48.9) (17.2–21.6) (42.6–48.5) (47.9–52.8) (56.2–61.7) (42.1–48.5) (51.1–56.7) (53.5–58.9)

(36.7–40.1) (38.6–41.3) (55.4–60.5) (34.3–39.3) (34.3–38.7) (26.0–30.8) (39.5–45.6) (33.0–38.0) (33.3–38.5)

(13.6–16.3) (10.8–12.5) (19.2–23.5) (13.9–18.4) (10.5–13.7) (9.4–12.9) (9.3–13.9) (8.2–11.5) (5.9–8.7)

(1.1–1.9) (0.8–1.4) (0.9–2.3) (1.1–2.4) (0.8–1.9) (1.1–2.5) (0.4–1.5) (0.6–1.4) (0.4–1.6)

a Differences in % of DIB by gender, age, parental education, and country are all statistically significant at p < 0.001. CI, confidence interval; DIB, dysfunctional Internet behavior; IAB, Internet addictive behavior.

(14.9–17.8) (11.8–13.5) (20.6–25.1) (15.5–20.1) (11.6–15.0) (10.9–14.8) (10.0–14.7) (9.0–12.5) (6.4–9.7)

INTERNET ADDICTIVE BEHAVIOR IN ADOLESCENCE

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Table 3. Odds Ratios and 95% Confidence Intervals for Relationships Between Online Activities and Dysfunctional Internet Behavior %a Gambling Social networking sites (e.g., Facebook) Monetary prize games Chat rooms Internet forums Searching for sexual information Making personal Web sites or blogging Instant messaging (e.g., MSN) Downloading movies Downloading music Real-time strategy games Downloading games Downloading software Multiplayer role-playing games Searching for medical information Shooter games E-mail News sites Hobbies Purchasing goods Single-player games (e.g., solitaire, backgammon) Watching videos or movies Doing homework or research

ORb

Table 4. Factors Independently Associated with Dysfunctional Internet Behavior in Multiple Logistic Regression: Adjusted Odds Ratio and 95% Confidence Interval

95% CI

8.4 2.97 2.52–3.49 92.5 2.62 1.95–3.51 15.5 60.0 50.4 35.4 28.6

2.58 2.45 2.44 2.40 2.31

2.26–2.95 2.16–2.79 2.18–2.74 2.16–2.67 2.07–2.59

85.6 66.9 87.8 33.8 51.3 61.2 44.8 43.2 48.0 84.3 74.5 82.7 53.1 64.2

2.29 2.16 2.15 2.11 2.00 2.00 1.82 1.80 1.66 1.57 1.48 1.42 1.31 1.26

1.86–2.81 1.87–2.50 1.72–2.70 1.89–2.36 1.79–2.24 1.78–2.25 1.63–2.04 1.62–2.00 1.49–1.86 1.31–1.87 1.31–1.69 1.22–1.66 1.17–1.47 1.13–1.41

97.5 1.01 0.68–1.48 93.3 0.68 0.57–0.83

a

Proportion of adolescents reporting the specific activity at least weekly. b All ORs are statistically highly significant ( p < 0.001) except for ‘‘watching videos or movies’’ ( p = 0.98). ORs, odds ratios.

adolescent populations using the same instrument.10,12 Boys had a higher prevalence of IAB (1.6%) than girls (0.9%). Similar gender differences concerning the nature of Internet use have been reported elsewhere.30,31 The observed gender differences may be attributed to the effect of the differential frequency of Internet utilization between genders. Specifically, since boys spend more time on the Internet than girls,10 the average weekly hours of Internet use may serve as a potential confounder for the development of IAB. Overall, the prevalence rates of IAB were higher in the Southern and Eastern/Middle European countries from the sample, and lower in the Northern European countries. The same trend held for the prevalence of DIB, which includes IAB as well as adolescents ‘‘at risk for IAB.’’ The country with the highest rate of DIB was Spain, which had a rate (23%) almost three times higher than Iceland’s (8%). Future studies are needed to elucidate the cross-cultural variations in the phenomenon’s prevalence. In addition, using the current dataset, differences between countries in scores on predicting variables for DIB could be studied. Since the number of adolescents with IAB in the sample was rather low for subgroup analyses, further analyses were restricted to DIB. Adolescents with this behavior scored relatively high on symptoms for IAB and thus experienced a significant number of problems related to their Internet use. Corroborating previous research,8,32–34 the present results

Country Spain Romania Poland The Netherlands Greece Germany Iceland Gender Female Male Age 14–15 years 16–17 years Parental education Low/middle High Age at first use of the Internet (years) Daily use of SNSs No use/< 2 hours q2 hours/day Average hours of playing games per weekday No gaming/< 2 hours q2 hours/day

OR (95% CI)

p

4.94 (3.71–6.58) 2.68 (1.94–3.70) 1.94 (1.43–2.61) 1.80 (1.25–2.59) 1.54 (1.12–2.11) 1.44 (1.04–1.99) 1.00a

< 0.001 < 0.001 < 0.001 0.002 0.008 0.030

1.00 0.95 (0.82–1.10)

0.49

1.00 1.01 (0.88–1.16)

0.89

1.00 0.85 (0.74–0.98) 0.94 (0.91–0.98)b

0.028 0.001

1.00 3.47 (3.05–3.94)

< 0.001

1.00 2.34 (1.99–2.75)

< 0.001

a

Indicates reference category. For 1-year increase. SNSs, social networking sites.

b

showed that adolescents who gambled at least a few times a week had increased likelihood of exhibiting DIB compared with those who never engaged in online gambling. It is maintained that the Internet may provide a readily accessible medium for adopting gambling patterns,35 and the sustainment of such behaviors through this medium may in turn lead to the development of IAB.32,33 Thus, adolescents who participated even infrequently in Internet gambling had an increased likelihood of presenting with DIB. On the other hand, those who used the Internet to carry out homework or research activities were less likely to present with DIB. Earlier studies have shown that SNS and gaming are the strongest predictors of IAB.8,36 Therefore, these were included in a multivariate analysis of DIB. Results showed that DIB was correlated somewhat stronger with SNS than with online gaming. The maintenance of online networks and the need to stay connected are the key factors explaining increased attraction to SNS.37 In a Greek sample, Kormas et al.36 found a similar pattern, whereas in a Dutch sample, Van Rooij et al.8 found that social online games were actually more predictive of compulsive Internet use than social activities such as SNS, and also more than other types of online games. In the present analysis, however, social games and other online games were not distinguished, thereby possibly masking a stronger effect of social online games.

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Table 5. Regression Coefficients for the Presence of Dysfunctional Internet Behavior in Multiple Linear Regressions with the Dependent Variable the YSR Scores, Adjusting for Age, Gender, Parental Educational Level, and Country

Dependent variable Activities Social competence Academic performance Total competence score Anxious/depressed Withdrawn/depressed Somatic complaints Social problems Thought problems Attention problems Rule-breaking (delinquent) behavior Aggressive behavior Internalizing problems score Externalizing problems score Total problem score

R2 for control R2 when variables DIB only added

Regression coefficient for DIBa

0.189 0.256 0.102 0.272 0.081 0.109 0.087 0.021 0.040 0.039 0.043

0.193 0.263 0.111 0.278 0.136 0.148 0.128 0.088 0.113 0.115 0.122

- 0.79 – 0.12b - 0.17 – 0.08 - 0.18 – 0.02 - 1.10 – 0.18 2.90 – 0.16 1.55 – 0.10 1.79 – 0.12 2.07 – 0.12 2.52 – 0.14 2.64 – 0.12 3.30 – 0.16

0.053 0.084

0.142 0.149

4.54 – 0.20 6.20 – 0.33

0.034

0.138

7.89 – 0.33

0.063

0.177

22.3 – 0.96

a Adjusted for age, gender, parental educational level, and country. All coefficients are statistically highly significant ( p < 0.001) except for social competence ( p = 0.028). b Standard error. YSR, Youth Self Report.

Adolescents whose parents had higher educational level had a lower probability of showing dysfunctional Internet use. This finding agrees with earlier studies that showed a significant association between dysfunctional Internet use and lower parental educational attainment.38 A possible pathway that may partly account for these differences between low and high parental educational attainment in DIB is parental involvement.39 Lower levels of parental engagement have been correlated with IAB9 as well as with various negative behavioral outcomes in adolescents, such as poorer academic performance and delinquency.40 The study findings indicated that DIB was significantly associated with marked psychosocial and emotional maladjustment. This evidence for the concomitant occurrence of DIB with both internalizing and externalizing problems is consistent with previous research, indicating that adolescents with DIB engage in more aggressive actions41 and display increased social isolation and impaired communication skills21 as well as depressive and anxiety symptoms, among others.42,43 DIB was further found to be weakly related to decreased scores on the competence scales. Adolescents engaging in such online behavioral patterns are potentially neglecting alternative leisure activities, hobbies, and social contacts, spending more time online. As the amount of online time increases, possible offline events that could serve as sources of reward are experienced less often. In consequence, adolescents may retreat into their familiar and comfortable world of the Internet.8 It has been proposed that poorly adjusted adolescents may engage in DIBs in order to cope with emotional turmoil.44

However, DIB has been observed to lead to unsuccessful life-coping mechanisms,45,46 thus creating a vicious circle centered upon Internet usage and psychosocial maladjustment. It is possible that DIB may exacerbate preexisting psychosocial symptomatology present among adolescents, thus adversely affecting their functioning and performance within both their academic and social environments. Results of the study should be interpreted in the light of a number of research limitations. First, as is typical of schoolbased surveys of adolescents, adolescents who do not attend school or were absent on the day of data collection were not represented. Second, the data are cross sectional, and so do not indicate the direction of relationships or provide information about preceding influences or long-term outcomes of IAB. Third, surveys of this magnitude must rely on selfreport data, limiting the measurement of Internet behavior to individual perceptions possibly tainted by recall bias. Finally, seven European countries participated in the study; findings from this study may not be generalizable to Europe as a whole or to other parts of the world. A major strength of this study is the large sample of adolescents sampled from randomly selected schools, across seven European countries. The students were surveyed using consistent procedures in each country. The present study is the first of its kind to assess the emotional and behavioral characteristics of adolescents using the YSR, an empirically derived and widely applied self-report instrument validated in each country. To sum up, our main findings indicated that DIB is associated with internalizing and externalizing problems among adolescents. On the basis of the results of the current crosssectional study, we cannot draw conclusions about the direction of any causal relations between DIB and psychological or behavioral problems. Further research using longitudinal designs is needed to address issues of causality. Knowledge of comorbidity with DIB is, however, interesting from a preventative and therapeutic perspective; it shows that the problem addressed often does not stand by itself but is accompanied and possibly exacerbated by other problems. Acknowledgments

This project was funded under the Safer Internet plus program (SIP-KEP-4101007), a European multiannual community program on promoting safer use of the Internet and new online technologies. The authors would like to thank Elena Critselis for her contributions in conceptualizing the study and George Antonogeorgos for statistical support; the project’s International Advisory Board members, especially Dr. Ellen Helsper, for their continuous support and contribution; and the external advisors Dr. Donald Greydanus, Dr. Elisabeth Staksrud, and Dr. Hatim Omar for their valuable feedback and evaluation. Author Contribution

A.T. participated in the study design, supervision, and coordination. M.J. participated in the study design, article composition, and interpretation of the data. T.M.S. participated in the article composition and interpretation of the data. E.C.T. coordinated the data acquisition and data entry and participated in the editing of the article. K.O. helped critically revise the article for intellectual content. S.W.

INTERNET ADDICTIVE BEHAVIOR IN ADOLESCENCE

participated in the editing and revision of the article. G.F.M. participated in the editing of the article and revision. C.T. performed the statistical analysis and interpretation of the data. C.R. participated in supervision of the statistical analysis and in the editing of the final article. The EU NET ADB consortium (member list: www.eunetadb.eu) was responsible for the design, data collection, and data interpretation. Each author listed on the article has seen and approved the submission of this version of the article and takes full responsibility for the article. The corresponding author affirms that she has listed everyone who contributed significantly to the work in the Author Contribution section. Previous oral or poster presentations at local, regional, national, or international meetings where part of the data was presented include the following: International conference of the Safer Internet Day: The Effects of Internet on Children and Young People, Budapest, Hungary, 2012; 6th International Conference: Keeping Children and Young People Safe Online, Warsaw, Poland, 2012; 17th European Annual Meeting of the International Association of Adolescent Health, Antalya, Turkey, 2012; Insafe Conference, Larnaca, Cyprus, 2012. Author Disclosure Statement

No competing financial interests exist. References

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Address correspondence to: Dr. Artemis Tsitsika Adolescent Health Unit Second Department of Pediatrics National and Kapodistrian University of Athens Leoforos Mesogeion 24 Goudi 11527, Athens Greece E-mail: [email protected]