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Research Report

Editor's Choice - Free Access

Addictive Potential of Internet Applications and Differential Correlates of Problematic Use in Internet Gamers versus Generalized Internet Users in a Representative Sample of Adolescents

Rosenkranz T. · Müller K.W. · Dreier M. · Beutel M.E. · Wölfling K.

Author affiliations

Outpatient Clinic for Behavioral Addictions, Department of Psychosomatic Medicine and Psychotherapy, University Medical Center, Mainz, Germany

Corresponding Author

Tabea Rosenkranz

Outpatient Clinic for Behavioral Addictions, Department of Psychosomatic Medicine and Psychotherapy, University Medical Center

Untere Zahlbacher Strasse 8, DE-55131 Mainz (Germany)

E-Mail tarosenk@uni-mainz.de

Related Articles for ""

Eur Addict Res 2017;23:148-156

Abstract

Aims: This paper examines the addictive potential of 8 different Internet applications, distinguishing male and female users. Moreover, differential correlates of problematic use are investigated in Internet gamers (IG) and generalized Internet users (GIU). Method: In a representative sample of 5,667 adolescents aged 12-19 years, use of Internet applications, problematic Internet use, psychopathologic symptoms (emotional problems, hyperactivity/inattention, and psychosomatic complaints), personality (conscientiousness and extraversion), psychosocial correlates (perceived stress and self-efficacy), and coping strategies were assessed. The addictive potential of Internet applications was examined in boys and girls using regression analysis. MANOVAs were conducted to examine differential correlates of problematic Internet use between IG and GIU. Results: Chatting and social networking most strongly predicted problematic Internet use in girls, while gaming was the strongest predictor in boys. Problematic IG exhibited multiple psychosocial problems compared to non-problematic IG. In problematic Internet users, GIU reported even higher psychosocial burden and displayed dysfunctional coping strategies more frequently than gamers. Conclusion: The results extend previous findings on the addictive potential of Internet applications and validate the proposed distinction between specific and generalized problematic Internet use. In addition to Internet gaming disorder, future studies should also focus on other highly addictive Internet applications, that is, chatting or social networking, regarding differential correlates of problematic use.

© 2017 S. Karger AG, Basel


Introduction

Internet gaming disorder (IGD) has been included in the current DSM-5 as a condition warranting further research [1], which is associated with a number of psychosocial and psychopathological problems ranging from sleep disturbance, lower academic performance to comorbidity with depression, anxiety, and attention deficit/hyperactivity disorder (ADHD), to cite a few [2,3,4,5,6]. Recently, other Internet applications have been investigated regarding their addictive potential, encouraging the view of different subtypes of Internet addiction besides gaming, such as the problematic use of social networking sites (SNSs), pornography, gambling, or shopping platforms [7,8]. It has been proposed that these specific forms of problematic Internet use (SPIU) can be described as addictive behaviors that could be engaged in offline (with SNSs being an exception, see [9] for a discussion), but are possibly enhanced by the Internet. Furthermore, they should be distinguished from a generalized form of problematic Internet use (GPIU), which refers to a rather aimless use of the Internet, engaging in multiple activities without clear preferences [9,10,11]. This early differentiation of 2 conceptually distinct forms of Internet addiction by Davis [11] in 2001 has since received empirical support by a number of studies [7,10,12,13], demonstrating that it is meaningful to distinguish specific forms of Internet use from generalized use.

It has further been argued that Internet applications differ in their addictive potential due to their range of rewarding properties [14,15,16]. Specifically, Internet gaming [16], online sex offers [17], and online chatting [14,18,19] are thought to harbor a high addictive potential, which can be defined as a high number of problematic users in proportion to non-problematic users. Furthermore, recent studies have demonstrated that users differ on several levels, such as sociodemographics, individual predispositions, and psychosocial symptoms, according to their preferred Internet application [7,20]. For instance, it can be assumed that the addictive potential of specific Internet applications varies with gender. Of the few studies that have compared the addictive potential of different Internet applications within the same sample, most of them drawing on data from large multicenter EU projects [19,21], only one has taken gender differences into account [22]. Published in 2012, this study demonstrated significant gender differences regarding the addictive potential of downloading content, playing online single-user games, reading/posting news/discussion groups, and social networks. Other studies have reported that female gender predicts problematic SNS use [2] and male gender predicts problematic gaming [2,23]. Based on these findings, it can be hypothesized that girls might be more prone to develop problematic SNS use, while boys are more susceptible to the addictive potential of video games. More research is necessary to investigate differences in the addictive potential of specific Internet applications as a function of relevant sociodemographic variables such as gender.

Furthermore, differences in individual predispositions and psychosocial symptoms between SPIUs should be more carefully studied in order to identify possible risk factors. To date, however, most studies have either examined a unidimensional construct of Internet addiction without further differentiations of used applications, or one specific Internet application, such as gaming, SNSs, or pornography has been investigated with regard to associated risk factors and psychosocial variables. For instance, low conscientiousness and low extraversion were identified as specific risk factors for problematic gaming when compared to a group of pathological gamblers and healthy controls [24]. Another study further compared problematic gamers to pathological gamblers and substance users, reporting that problematic gamers specifically differed in 4 characteristics, showing increased irritability/aggression, social anxiety, ADHD, and lower self-esteem [25].

Some studies have also directly compared aspects of the problematic use of different Internet applications. One study investigated mutual and differential aspects between problematic Internet gamers (IG) and problematic Internet pornography users and found that shyness and low life satisfaction were specific predictors for problematic Internet gaming [26]. Symptoms of problematic Internet gaming and Internet pornography use did not correlate, therefore supporting the idea of distinct conditions. Another recent study reported an association between symptoms of depression and problematic gaming, while problematic use of SNS was linked more strongly to anxiety and symptoms of obsessive compulsive disorder [2]. Furthermore, both forms of SPIU were predicted by ADHD symptoms, even though this association was lower in problematic gaming compared to problematic SNS use.

Studies that have specifically compared GPIU and SPIUs have found that GPIU is associated with higher symptom severity [7] than one distinct SPIU and that GPIU is related to a greater risk of axis I comorbidities [27] compared to IGD. Investigating differential aspects of SPIUs and GPIU could advance the development of specialized treatments significantly.

Moreover, the role of dysfunctional coping in the development and maintenance of GPIU and IGD has been highlighted. Testing a theoretical model of GPIU based on Davis' cognitive behavioral model, Brand et al. [10] demonstrated that dysfunctional coping styles and Internet use expectancies mediated the relationship between psychosocial symptoms and GPIU. Another study found that specifically media-focused and substance-related coping predicted problematic Internet use in young adults [28].

In sum, the reviewed findings suggest the importance of gender differences when examining the addictive potential of different Internet applications. Based on findings of current studies, it is highly relevant to differentiate between Internet applications in order to reveal predispositions and psychosocial symptoms associated with problematic Internet use. The lack of such distinctions is also mentioned as a limitation by other authors. For instance, Parker et al. [29] did not find gender differences for Internet use, hypothesizing that gender differences for specific Internet applications might have cancelled each other out.

Consequently, the first aim of this paper is to examine the addictive potential of specific Internet applications taking gender into account. In line with the outlined research, we hypothesize that Internet applications differ in terms of addictive potential in general, with gaming and the use of sexual content exhibiting the highest addictive potential. We expect that addictive potentials of Internet applications differ as a function of gender: gaming should exhibit a higher addictive potential for boys, whereas chatting and social networking should exhibit higher addictive potentials for girls.

Secondly, since IGD is included in the current DSM-5 as a condition in need of further research, psychosocial symptoms and coping strategies are examined for problematic IG compared to generalized Internet users (GIU). Based on previous research, we hypothesize that problematic IG and problematic GIU differ in their pattern of psychosocial problems, with the latter showing increased psychosocial burden. Overall, these findings would further validate the idea of distinct Internet addiction subtypes and contribute to the advancement of specialized treatments.

Material and Methods

Participants and Procedure

In total, 42 schools of rural and urban areas in the German federal state of North Rhine-Westphalia were selected by random probability sampling methods after stratification for school type and population density. There was no systematic difference regarding school type or region between participating (54.3%) and non-participating schools. The study was approved by the local Ethics Committee according to the Declaration of Helsinki. Participation in the study was voluntary and written informed consent was given by all participants and, if they were underage, their legal guardians. Overall, 6,081 students filled out a questionnaire during school class provided by the school for data acquisition. The dataset was checked for plausibility resulting in a sample reduction of 6.3% (n = 382). Cases with more than 10% (>2) missing items in the main instrument, Assessment of Internet and Computer game Addiction-Scale (AICA-S), were excluded. This resulted in a sample size of 5,667 students (50.3% male) aged 12-19 years (mean 15.44, SD 1.73) attending grades 7-13 (mean 9.3, SD 1.56). Sociodemographic variables are shown in Table 1.

Table 1

Sociodemographic data of participants

/WebMaterial/ShowPic/853528

Instruments

Internet Addiction. The AICA-S [30] is a self-report comprising 16 items. A clinical score is calculated based on 14 items, ranging between 0 and 27. Scores >7 indicate problematic use and scores >13.5 indicate Internet addiction. Diagnostically relevant items are based on diagnostic criteria of IGD as in DSM-5 and substance use disorders, such as craving, tolerance, withdrawal symptoms, loss of control, preoccupation, and negative consequences in important life domains. Two open-ended questions assessed the time spent actively online per day during the week and during the weekend or holidays. Also, the frequency of use (0 = “never,” 1 = “seldom,” 2 = “often,” 3 = “very often”) regarding 8 Internet applications (gaming, shopping, chatting, e-mailing, sexual content, gambling, social networks, and information research) was assessed. Internal consistency of the AICA-S is reported at α = 0.89 [30]. A sensitivity of 80.5% and a specificity of 82.4% in assessing Internet addiction has been reported in a clinical sample [31].

Extraversion/Conscientiousness. Extraversion/conscientiousness was assessed using the 2 corresponding subscales of the well-validated NEO-FFI [32]. Each scale consists of 12 statements, which are rated on a 5-point Likert-scale from 0 = “strongly disagree” to 4 = “strongly agree.” Good internal consistency has been reported for the German population (extraversion: α = 0.80; conscientiousness: α = 0.85) [33].

Perceived Stress. The Perceived Stress Scale [34,35] is a 10-item questionnaire assessing feelings of stress due to lack of control or unpredictable stressful events during the last month. Internal consistency for the German version is reported at Cronbach's α = 0.84 and correlations with depression (r = 0.59), anxiety (r = 0.59), and fatigue (r = 0.57) indicate constructive validity of the Perceived Stress Scale-10 [36].

Self-Efficacy. Self-efficacy was assessed with the General Perceived Self-Efficacy Scale [37], which consists of 10 items ranging from 1 = “do not agree” to 4 = “agree.” Good internal consistency has been reported in a German validation study (α = 0.92) [38].

Psychiatric Symptoms. The strengths and difficulties questionnaire [39] is a screening instrument for deficits in 4 different domains: emotional symptoms (anxiety/depression), conduct problems (conduct disorder), hyperactivity/inattention (ADHD), and peer problems. A total difficulties score can be computed by adding the scores of the 4 scales, while a fifth scale assesses prosocial behavior. This paper focuses only on the 4 problem scales. Each scale comprises 5 items, which can be answered with 0 = “not true,” 1 = “somewhat true,” or 2 = “certainly true.” The strengths and difficulties questionnaire has been shown to detect conduct, hyperactivity, depressive, and anxiety disorders in a community sample using multiple informants with good (70-90%) sensitivity [40]. In this study, the self-report version was used. Internal consistency ranges from 0.45 (conduct problems) to 0.70 (emotional problems) [41], which is similar to values in our study (0.51 for conduct problems to 0.74 for emotional problems). Scales with poor (<0.60) internal consistency were excluded; therefore, emotional problems (0.74) and hyperactivity/inattention (0.64) remained for further analysis.

Coping Strategies. The BriefCOPE [42] consists of 6 subscales and 12 items. Four subscales identify dysfunctional coping strategies (e.g., self-blame, denial) and 2 subscales identify functional coping (positive reframing, active coping). Since media-focused coping has been identified as a common coping strategy in Internet addiction [43,44], we included 2 items, which assess media use as another dysfunctional coping strategy. Again, only subscales with α ≥0.60 were included for further analysis, namely denial (0.62), behavioral disengagement (0.65), self-blame (0.78), and media use (0.82).

Statistical Analysis and Operationalization

Statistical analysis was conducted with SPSS version 23 (IBM). Addictive potentials of Internet applications were retained by linear regression analysis with backwards selection to avoid suppressor effects.

Intense use of an Internet application was defined as indicating the highest (“very often”) use of the specific application. IGs were defined as participants who reported intense Internet gaming and indicated using less than 4 other Internet applications intensely. GIUs were defined as participants who reported the intense use of 5 or more Internet applications. Two one-way MANOVAs were computed to compare non-problematic vs. problematic IG, and problematic IG vs. problematic GIU, respectively. Differences in sociodemographic variables were examined using χ2 tests.

Results

Prevalence of Internet Addiction

The prevalence of Internet addiction was somewhat high with 2.3% (n = 128). Another 11% (n = 624) of students were identified as exhibiting problematic Internet use. Both addicted and problematic users were classified as problematic Internet users (PIU), resulting in a subsample of 752 students (13.3%). In the subsample of IG (n = 563), 202 participants were positively screened as problematic IG (3.6% of n), whereas in the sample of GIU (n = 106), 51 participants were classified as problematic GIU (0.9% of n). The prevalence of problematic users that only indicated intense use of Internet games without any other application was 0.05% (n = 29).

Intense Use of Internet Applications

Overall, a high proportion of participants indicated intense use of SNS (PIU: 83.7%, non-PIU: 74.3%; Fig. 1) and chatting (PIU: 76.6%, non-PIU: 60.7%). Information research was the third most frequently reported Internet application among non-PIU (34.3%), while gaming was the third most frequently reported activity in PIU (35.4%). E-shopping (PIU: 8.1%, non-PIU: 4.0%) was the second least frequently indicated application, followed by gambling (PIU: 3.6%, non-PIU: 0.7%).

Fig. 1

Distribution of intensely used Internet applications in problematic Internet users (PIU) and non-PIU in the total sample.

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Addictive Potential of Internet Applications

Corresponding to the first aim of this paper, the addictive potential of specific Internet applications was examined by conducting regression analyses for the total sample and for boys and girls separately.

Regression analysis for the total sample indicated that all Internet applications contributed significantly to the explanation of problematic Internet use (Table 2). Internet gaming exerted the highest influence on the model, followed by social networking and chatting. Frequent information research exhibited the least important influence in the model, showing a slightly negative relationship with problematic use. Furthermore, gambling, visiting websites with sexual content, shopping, e-mailing, and information research had low effects sizes (β < 0.10), indicating that they only explained a low amount of variance in the model.

Table 2

Internet applications as predictors for problematic Internet use in boys and girls

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When male and female users were examined separately, the order of predictors contributing to the explanation of problematic Internet use changed. For boys, gaming remained the strongest predictor and exhibited an even higher influence in the model, followed by social networking, chatting, gambling, and visiting websites with sexual content. E-mailing and shopping had a weaker influence on the model, while information research was excluded in a second step as it had no significant effect on the model.

For girls, chatting and social networking both exhibited comparably strong influences in the model. Interestingly, for girls, the third most important predictor was gaming. Gambling, visiting websites with sexual content, and shopping had a weaker influence on the model. E-mailing did not contribute to the model significantly.

Psychosocial Correlates of Problematic Use in GIU and IG

In accordance with the second aim of this paper, the specific problematic Internet use of gaming was examined by comparing non-problematic and problematic IG and by comparing problematic IG to problematic GIU.

Sociodemographic Variables

Sociodemographic characteristics were compared between non-problematic and problematic IG and between problematic IG and problematic GIU. In the sample of IG, problematic users were more likely to have at least one parent with migration background (χ2 (1, 532) = 5.02, p < 0.05, ϕ = 0.10) compared to non-problematic users. No significant differences were found between the 2 problematic (IG vs. GIU) groups. Descriptively, a higher percentage of migration background (2nd generation migration; GIU: 43.1%; IG: 35.1%) and change of school (GIU: 42.6%; IG: 28.1%) was evident in problematic GIU compared to problematic IG.

Psychosocial Variables and Psychiatric Symptoms

All compared groups did not differ regarding age or gender. Therefore, a one-way (non-problematic vs. problematic use) MANOVA was conducted for the group of IG. Problematic use was associated with lower conscientiousness, higher perceived stress, emotional problems, as well as higher hyperactivity/inattention and more psychosomatic complaints compared to non-problematic IG (Table 3). Significant differences were also found regarding coping strategies: problematic IG employed dysfunctional strategies more frequently than non-problematic IG. A medium effect size (ηp2 = 0.12) indicated that problematic IG coped by media use significantly more often than non-problematic IG. Medium effect sizes were also found for perceived stress, psychosomatic complaints, and hyperactivity/inattention (ηp2 = 0.07-0.08).

Table 3

Psychosocial correlates of problematic use in Internet gamers and generalized Internet users

/WebMaterial/ShowPic/853526

A one-way MANOVA (problematic IG vs. problematic GIU) revealed higher psychiatric symptoms in problematic GIU than in problematic IG. Problematic GIU reported significantly higher scores in emotional problems, as well as more psychosomatic complaints. At the same time, problematic GIU indicated using the coping strategies behavioral disengagement and denial significantly more frequently than problematic IG.

Discussion

The present study pursued 2 main goals: first, to examine the addictive potential of 8 different Internet applications, while taking gender into account. The second purpose was to further investigate problematic Internet gaming by comparing a broad range of psychosocial deficits, psychiatric symptoms, personality traits, and coping styles between problematic and non-problematic IG, as well as problematic generalized users.

Consistent with our hypotheses, Internet gaming was found to show the highest addictive potential. Moreover, in line with recent research, social networking and chatting exhibited the second and third highest addictive potentials. Contrary to expectations, even though the use of sexual content significantly predicted problematic Internet use, it produced only a small effect on the model. This finding might be explained by the fact that the sample consisted of adolescents and data were assessed during school class. It is possible that this topic is associated with more shame in this age period and that participants might have answered in a more socially desirable way, since the classroom environment might have compromised privacy of given answers. In boys, linear regression yielded an even stronger addictive potential for Internet gaming and a slightly lower importance, but nevertheless high addictive potential, for chatting and social networking. In girls, social networking and chatting contributed to the regression model most strongly. While gambling and the use of sexual content exhibited a strong influence in male users, they showed less significant impact on the prediction of problematic Internet use in female users. Information research had a negative relationship with problematic Internet use, therefore suggesting that problematic users use the Internet less for information research than non-problematic users. E-shopping also exerted a less significant impact on the model compared to other Internet applications, while its impact was comparable between boys and girls.

These results replicate findings of other studies (e.g., [2,22,23]) and confirm the assumption that the addictive potential depends on the specific Internet application and the sociodemographic characteristics of its users. An implication of these findings is that future studies examining Internet addiction should consider that effects might cancel each other out when Internet applications are not examined separately and gender or other sociodemographic variables (e.g., age) are not taken into account as a confounding factor. The finding that gaming was the highest predictor for problematic Internet use in boys and chatting was the highest predictor in girls is consistent with previous studies [5,45]. One unanticipated finding was that gaming exhibited the third highest addictive potential in girls. As of now, gaming has always been viewed as a male domain. Nevertheless, about 10% of IG were found to be female, and this proportion was equal in both non-problematic and problematic IG. It is likely that there are certain video game genres which attract both boys and girls equally. For instance, it has been shown that there are no gender differences in addictive use of free-to-play games [46].

So far, IGD has been included in the DSM-5 as a condition warranting further research as it seems to be the most prevalent subtype of Internet addiction in studied populations. The fact that Internet gaming showed the highest addictive potential in our study further supports the idea of including IGD as a formal disorder in international classification systems such as the DSM or the ICD. Still, other Internet applications, such as chatting and social networking, should also be taken into account as other highly addictive subtypes of Internet addiction. Further evidence is required in order to support our claim that different subtypes of Internet addiction exist, with affected populations differing in specific sociodemographic characteristics and psychiatric symptoms.

Furthermore, psychosocial variables were examined regarding problematic Internet gaming and generalized Internet use. Results show that problematic IG scored significantly lower in conscientiousness than non-problematic IG. This is consistent with other studies, which have found low conscientiousness to be a key characteristic of IGD [24]. At the same time, no significant differences were found regarding extraversion in problematic IG compared to non-problematic IG. Previous studies have shown that problematic IG scored significantly lower regarding extraversion when compared to pathological gamblers [24] or substance abusers [25]. The reason why no significant differences were found in this study can most likely be identified in the comparison group. In our study, problematic IG were not compared to another clinical sample, but to users of the same Internet application. Therefore, there might be no significant differences within the group of IG and low extraversion cannot be seen as a specific predictor of problematic Internet gaming.

Generally, problematic IG indicated higher psychosocial burden, which can be seen in significantly higher perceived stress, higher scores in emotional problems and hyperactivity, and more psychosomatic complaints compared to non-problematic IG. Moreover, problematic IG applied dysfunctional coping strategies significantly more often than non-problematic IG, with differences in media-related coping yielding the highest effect size. These findings contribute to current research regarding IGD, showing that problematic IG are significantly impaired in their daily life and suffer from a variety of psychosocial symptoms [20,47]. It is possible, that some of these symptoms might be the cause of dysfunctional coping strategies. Further studies should investigate the role of dysfunctional coping in the development of Internet addiction.

No differences were found regarding personality variables between problematic IG and problematic GIU. However, even though effect sizes were rather small, problematic GIU displayed even higher emotional problems and psychosomatic complaints than problematic IG. Moreover, problematic GIU engaged significantly more often in dysfunctional coping strategies such as denial and behavioral disengagement. These findings can be interpreted in the light of Davis' cognitive behavioral model [11]. Davis postulates that GPIU is linked to the addictive potential of the Internet itself, particularly to its communicative character. It has been assumed that GPIU is associated with a lack of social support and perceived social isolation in real life (cf. [10,48]). The excessive use of specific Internet applications, however, is assumed to be associated with an individual predisposition that can also lead to excessive behavior offline and is intensified by the opportunities the Internet has to offer. The GIU sample in this study was found to primarily engage in chatting, social networking, and other communicative applications, which is consistent with Davis' model. Therefore, this study shows that problematic GIUs, who can be characterized as using a variety of communication-intense applications excessively, are impaired in multiple areas of their daily life and report higher burden than problematic IG.

A limitation to our findings is the operationalization of the sample of IG. Since subjects were not asked to indicate which Internet application they engaged in the most intensely, the sample of IG could also include users who would not classify themselves primarily as IG despite indicating the highest possible answer. The second most intensely used Internet application in this sample was chatting (51.9%). Generally, a high overlap can be observed between chatting (voice chats) and Internet gaming in this age group. As voice chats are often used parallel to Internet gaming to augment the gameplay, Internet gaming can be regarded as the primarily used Internet application. Therefore, we consider the amount of falsely classified IG in this sample as rather low. Another limitation is the cut-off criterion for classifying problematic users. Although the diagnostic screening instrument is one of the few clinically validated instruments for assessing Internet addiction, self-report data are always subject to participants' perceptions and there is always some chance of misclassification. We decided to include both users with problematic and addictive use to yield a larger subsample. Accordingly, the studied samples encompass persons with subclinical Internet addiction who might not be diagnosed with Internet addiction or IGD in a clinical setting.

A noteworthy aspect of this study is its representative character, which allows for the generalization of the findings to all German adolescents. Another strength of this study is the inclusion of specific demographic variables such as gender, which might have compromised findings of previous cross-sectional studies.

This study replicates the finding that Internet gaming shows the highest addictive potential for users. At the same time, communication-intense applications such as chatting and social networking should be recognized as other highly addictive Internet applications. While girls are more inclined to use these applications in an addictive way, boys are more prone to show addictive use of Internet gaming. Further research should acknowledge the fact that users differ in their sociodemographic characteristics and individual predispositions depending on the preferred Internet applications. In line with recent evidence and current debates concerning the conceptualization of IGD and Internet addiction, the findings of this study emphasize the need to differentiate between specific types of problematic Internet use and urge future studies to distinguish between GPIU and SPIUs.

Acknowledgments

The study was funded by the Ministry of Health, Equalities, Care, and Ageing of the state North Rhine-Westphalia. This paper is part of the doctoral thesis of the first author.

Disclosure Statement

The authors state that there is no conflict of interest.


References

  1. American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). American Psychiatric Publishing, 2013.
  2. Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, Pallesen S: The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behav 2016;30:252-262.
  3. Bischof G, Bischof A, Meyer C, John U, Rumpf H: Prävalenz der Internetabhängigkeit - Diagnostik und Risikoprofile (PINTA-DIARI), 2013;1-9.
  4. Gentile DA, Choo H, Liau A, Sim T, Li D, Fung D, Khoo A: Pathological video game use among youths: a two-year longitudinal study. Pediatrics 2011;127:e319-e329.
  5. Müller KW, Janikian M, Dreier M, Wölfling K, Beutel ME, Tzavara C, Richardson C, Tsitsika A: Regular gaming behavior and internet gaming disorder in European adolescents: results from a cross-national representative survey of prevalence, predictors, and psychopathological correlates. Eur Child Adolesc Psychiatry 2015;24:565-574.
  6. Rehbein F, Kliem S, Baier D, Mößle T, Petry NM: Prevalence of internet gaming disorder in German adolescents: diagnostic contribution of the nine DSM-5 criteria in a state-wide representative sample. Addiction 2015;110: 842-851.
  7. Laconi S, Tricard N, Chabrol H: Differences between specific and generalized problematic Internet uses according to gender, age, time spent online and psychopathological symptoms. Comput Human Behav 2015;48:236-244.
    External Resources
  8. Starcevic V, Billieux J: Does the construct of Internet addiction reflect a single entity or a spectrum of disorders? Clin Neuropsychiatry 2017;14:5-10.
  9. Griffiths MD, Pontes HM: Internet addiction disorder and Internet gaming disorder are not the same. Addict Res Ther 2014;5:e124.
    External Resources
  10. Brand M, Laier C, Young KS: Internet addiction: coping styles, expectancies, and treatment implications. Front Psychol 2014;5:1256.
  11. Davis RA: A cognitive-behavioral model of pathological Internet use. Comput Human Behav 2001;17:187-195.
    External Resources
  12. Montag C, Bey K, Sha P, Li M, Chen YF, Liu WY, Zhu YK, Li CB, Markett S, Keiper J, Reuter M: Is it meaningful to distinguish between generalized and specific Internet addiction? Evidence from a cross-cultural study from Germany, Sweden, Taiwan and China. Asia-Pacific Psychiatry 2015;7:20-26.
  13. Rehbein F, Mößle T: Video game and internet addiction: is there a need for differentiation? Sucht 2013;59:129-142.
    External Resources
  14. Kormas G, Critselis E, Janikian M, Kafetzis D, Tsitsika A: Risk factors and psychosocial characteristics of potential problematic and problematic internet use among adolescents: a cross-sectional study. BMC Public Health 2011;11:595.
  15. Meerkerk GJ, van den Eijnden RJJM, Franken IHA, Garretsen HFL: Is compulsive internet use related to sensitivity to reward and punishment, and impulsivity? Comput Human Behav 2010;26:729-735.
    External Resources
  16. van Rooij AJ, Schoenmakers TM, van de Eijnden RJ, van de Mheen D: Compulsive Internet use: the role of online gaming and other Internet applications. J Adolesc Heal 2010;47:51-57.
  17. Meerkerk GJ, van den Eijnden RJ, Garretsen HF: Predicting compulsive Internet use: it's all about sex! Cyberpsychology Behav 2006;9:95-103.
  18. Morrison CM, Gore H: The relationship between excessive internet use and depression: a questionnaire-based study of 1,319 young people and adults. Psychopathology 2010;43:121-126.
  19. Tsitsika A, Janikian M, Schoenmakers TM, Tzavela EC, Olafsson K, Wójcik S, Macarie GF, Tzavara C, Richardson C: Internet addictive behavior in adolescence: a cross-sectional study in seven European countries. Cyberpsychol Behav Soc Netw 2014;17:528-535.
  20. Mérelle SYM, Kleiboer AM, Schotanus M, Cluitmans T, Waardenburg CM, Kramer D, Van de Mheen D, van Rooij AJ: Which health-related problems are associated with problematic video-gaming or social media use in adolescents? A large-scale cross-sectional study. Clin Neuropsyschiatry 2017;14:11-19.
  21. Strittmatter E, Parzer P, Brunner R, Fischer G, Durkee T, Carli V, Hoven CW, Wasserman C, Sarchiapone M, Wasserman D, Resch F, Kaess M: A 2-year longitudinal study of prospective predictors of pathological Internet use in adolescents. Eur Child Adolesc Psychiatry 2016;25:725-734.
  22. Durkee T, Kaess M, Carli V, Parzer P, Wasserman C, Floderus B, Apter A, Balazs J, Barzilay S, Bobes J, Brunner R, Corcoran P, Cosman D, Cotter P, Despalins R, Graber N, Guillemin F, Haring C, Kahn JP, Mandelli L, Marusic D, Mészáros G, Musa GJ, Postuvan V, Resch F, Saiz PA, Sisask M, Varnik A, Sarchiapone M, Hoven CW, Wasserman D: Prevalence of pathological internet use among adolescents in Europe: demographic and social factors. Addiction 2012;107:2210-2222.
  23. Király O, Griffiths MD, Urbán R, Farkas J, Kökönyei G, Elekes Z, Tamás D, Demetrovics Z: Problematic internet use and problematic online gaming are not the same: findings from a large nationally representative adolescent sample. Cyberpsychol Behav Soc Netw 2014;17:749-754.
  24. Müller KW, Beutel ME, Egloff B, Wölfling K: Investigating risk factors for internet gaming disorder: a comparison of patients with addictive gaming, pathological gamblers and healthy controls regarding the big five personality traits. Eur Addict Res 2013;20:129-136.
  25. Walther B, Morgenstern M, Hanewinkel R: Co-occurrence of addictive behaviours: personality factors related to substance use, gambling and computer gaming. Eur Addict Res 2012;18:167-174.
  26. Pawlikowski M, Nader IW, Burger C, Stieger S, Brand M: Pathological Internet use - it is a multidimensional and not a unidimensional construct. Addict Res Theory 2014;22:166-175.
    External Resources
  27. King DL, Delfabbro PH, Zwaans T, Kaptsis D: Clinical features and axis I comorbidity of Australian adolescent pathological Internet and video game users. Aust N Z J Psychiatry 2013;47:1058-1067.
  28. Kuss DJ, Dunn TJ, Wölfling K, Müller K, Hędzelek M, Abstract JM: Excessie Internet use and psychopathology: the role of coping. Clin Neuropsychiatry 2017;14:73-81.
  29. Parker JDA, Taylor RN, Eastabrook JM, Schell SL, Wood LM: Problem gambling in adolescence: relationships with internet misuse, gaming abuse and emotional intelligence. Pers Individ Dif 2008;45:174-180.
    External Resources
  30. Müller KW, Glaesmer H, Brähler E, Woelfling K, Beutel ME: Prevalence of internet addiction in the general population: results from a German population-based survey. Behav Inf Technol 2014;33:757-766.
    External Resources
  31. Müller KW, Beutel ME, Wölfling K: A contribution to the clinical characterization of Internet addiction in a sample of treatment seekers: validity of assessment, severity of psychopathology and type of co-morbidity. Compr Psychiatry 2014;55:770-777.
  32. Costa PT, McCrae RR: Neo PI/FFI Manual Supplement. Odessa, Psychological Assessment Resources, 1989.
  33. Borkenau P, Ostendorf F: NEO-Fünf-Faktoren-Inventar (NEO-FFI) nach Costa und McCrae (Handanweisung). Göttingen: Hogrefe, Neo-Fünf-Faktoren Inventar nach Costa und McCrae, 1993.
  34. Cohen S, Kamarck T, Mermelstein R: A global measure of perceived stress. J Health Soc Behav 1983;24:385-396.
  35. Cohen S, Williamson G: Perceived stress in a probability sample of the United States; in Spacapan S, Oskamp S: (eds), Newbury Park, Sage. Soc Psychol Heal Claremont Symp Appl Soc Psychol 1988, pp 31-67.
  36. Klein EM, Brähler E, Dreier M, Müller KW, Schmutzer G, Wölfling K, Beutel ME: The German version of the Perceived Stress Scale - Psychometric characteristics in a representative German community sample. BMC Psychiatry 2016;16:159.
  37. Schwarzer R, Jerusalem M: Generalized Self-Efficacy scale; in Weinman J, Wright S, Johnston M: (eds) Meas Heal Psychol A user's portfolio Casual Control beliefs, Windsor, NFER-NELSON, 1995, pp 35-37.
  38. Hinz A, Schumacher J, Albani C, Schmid G, Brähler E: Bevölkerungsrepräsentative Normierung der Skala zur Allgemeinen Selbstwirksamkeitserwartung. Diagnostica 2006;52:26-32.
    External Resources
  39. Goodman R: The Strengths and Difficulties Questionnaire: a research note. J child Psychol psychiatry 1997;38:581-586.
  40. Goodman R, Ford T, Simmons H, Gatward R, Meltzer H: Using the Strengths and Difficulties Questionnaire (SDQ) to screen for child psychiatric disorders in a community sample. Br J Psychiatry 2000;177:534-539.
  41. Essau CA, Olaya B, Anastassiou-Hadjicharalambous X, Pauli G, Gilvarry C, Bray D, O'callaghan J, Ollendick TH: Psychometric properties of the Strength and Difficulties Questionnaire from five European countries. Int J Methods Psychiatr Res 2012;21:232-245.
  42. Carver CS: You Want to Measure Coping but Your Protocol's Too Long: Consider the Brief COPE. Int J Behav Med 1997;4:91-100.
  43. Batthyány D, Müller KW, Benker F, Wölfling K: [Computer game playing: clinical characteristics of dependence and abuse among adolescents]. Wien Klin Wochenschr 2009;121:502-509.
  44. Wölfling K, Müller KW, Giralt S, Beutel ME: Emotionale Befindlichkeit und dysfunktionale Stressverarbeitung bei Personen mit Internetsucht. SUCHT 2011;57:27-37.
    External Resources
  45. Hormes JM, Kearns B, Timko CA: Craving Facebook? Behavioral addiction to online social networking and its association with emotion regulation deficits. Addiction 2014;109:2079-2088.
  46. Dreier M, Wölfling K, Duven E, Giralt S, Beutel ME, Müller KW: Free-to-play: About addicted Whales, at risk Dolphins and healthy Minnows. Monetarization design and Internet Gaming Disorder. Addict Behav 2017;64:328-333.
  47. Kim, NR, Hwang SSH, Choi JS, Kim DJ, Demetrovics Z, Király O, Nagygyörgy K, Griffiths MD, Hyun SY, Youn HC, Choi SW: Characteristics and psychiatric symptoms of internet gaming disorder among adults using self-reported DSM-5 criteria. Psychiatry Investig 2016;13:58-66.
  48. Morahan-Martin J, Schumacher P: Loneliness and social uses of the Internet. Comput Human Behav 2003;19:659-671.
    External Resources

Author Contacts

Tabea Rosenkranz

Outpatient Clinic for Behavioral Addictions, Department of Psychosomatic Medicine and Psychotherapy, University Medical Center

Untere Zahlbacher Strasse 8, DE-55131 Mainz (Germany)

E-Mail tarosenk@uni-mainz.de


Article / Publication Details

First-Page Preview
Abstract of Research Report

Received: July 05, 2016
Accepted: April 23, 2017
Published online: June 16, 2017
Issue release date: July 2017

Number of Print Pages: 9
Number of Figures: 1
Number of Tables: 3

ISSN: 1022-6877 (Print)
eISSN: 1421-9891 (Online)

For additional information: http://www.karger.com/EAR


Copyright / Drug Dosage / Disclaimer

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References

  1. American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). American Psychiatric Publishing, 2013.
  2. Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, Pallesen S: The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behav 2016;30:252-262.
  3. Bischof G, Bischof A, Meyer C, John U, Rumpf H: Prävalenz der Internetabhängigkeit - Diagnostik und Risikoprofile (PINTA-DIARI), 2013;1-9.
  4. Gentile DA, Choo H, Liau A, Sim T, Li D, Fung D, Khoo A: Pathological video game use among youths: a two-year longitudinal study. Pediatrics 2011;127:e319-e329.
  5. Müller KW, Janikian M, Dreier M, Wölfling K, Beutel ME, Tzavara C, Richardson C, Tsitsika A: Regular gaming behavior and internet gaming disorder in European adolescents: results from a cross-national representative survey of prevalence, predictors, and psychopathological correlates. Eur Child Adolesc Psychiatry 2015;24:565-574.
  6. Rehbein F, Kliem S, Baier D, Mößle T, Petry NM: Prevalence of internet gaming disorder in German adolescents: diagnostic contribution of the nine DSM-5 criteria in a state-wide representative sample. Addiction 2015;110: 842-851.
  7. Laconi S, Tricard N, Chabrol H: Differences between specific and generalized problematic Internet uses according to gender, age, time spent online and psychopathological symptoms. Comput Human Behav 2015;48:236-244.
    External Resources
  8. Starcevic V, Billieux J: Does the construct of Internet addiction reflect a single entity or a spectrum of disorders? Clin Neuropsychiatry 2017;14:5-10.
  9. Griffiths MD, Pontes HM: Internet addiction disorder and Internet gaming disorder are not the same. Addict Res Ther 2014;5:e124.
    External Resources
  10. Brand M, Laier C, Young KS: Internet addiction: coping styles, expectancies, and treatment implications. Front Psychol 2014;5:1256.
  11. Davis RA: A cognitive-behavioral model of pathological Internet use. Comput Human Behav 2001;17:187-195.
    External Resources
  12. Montag C, Bey K, Sha P, Li M, Chen YF, Liu WY, Zhu YK, Li CB, Markett S, Keiper J, Reuter M: Is it meaningful to distinguish between generalized and specific Internet addiction? Evidence from a cross-cultural study from Germany, Sweden, Taiwan and China. Asia-Pacific Psychiatry 2015;7:20-26.
  13. Rehbein F, Mößle T: Video game and internet addiction: is there a need for differentiation? Sucht 2013;59:129-142.
    External Resources
  14. Kormas G, Critselis E, Janikian M, Kafetzis D, Tsitsika A: Risk factors and psychosocial characteristics of potential problematic and problematic internet use among adolescents: a cross-sectional study. BMC Public Health 2011;11:595.
  15. Meerkerk GJ, van den Eijnden RJJM, Franken IHA, Garretsen HFL: Is compulsive internet use related to sensitivity to reward and punishment, and impulsivity? Comput Human Behav 2010;26:729-735.
    External Resources
  16. van Rooij AJ, Schoenmakers TM, van de Eijnden RJ, van de Mheen D: Compulsive Internet use: the role of online gaming and other Internet applications. J Adolesc Heal 2010;47:51-57.
  17. Meerkerk GJ, van den Eijnden RJ, Garretsen HF: Predicting compulsive Internet use: it's all about sex! Cyberpsychology Behav 2006;9:95-103.
  18. Morrison CM, Gore H: The relationship between excessive internet use and depression: a questionnaire-based study of 1,319 young people and adults. Psychopathology 2010;43:121-126.
  19. Tsitsika A, Janikian M, Schoenmakers TM, Tzavela EC, Olafsson K, Wójcik S, Macarie GF, Tzavara C, Richardson C: Internet addictive behavior in adolescence: a cross-sectional study in seven European countries. Cyberpsychol Behav Soc Netw 2014;17:528-535.
  20. Mérelle SYM, Kleiboer AM, Schotanus M, Cluitmans T, Waardenburg CM, Kramer D, Van de Mheen D, van Rooij AJ: Which health-related problems are associated with problematic video-gaming or social media use in adolescents? A large-scale cross-sectional study. Clin Neuropsyschiatry 2017;14:11-19.
  21. Strittmatter E, Parzer P, Brunner R, Fischer G, Durkee T, Carli V, Hoven CW, Wasserman C, Sarchiapone M, Wasserman D, Resch F, Kaess M: A 2-year longitudinal study of prospective predictors of pathological Internet use in adolescents. Eur Child Adolesc Psychiatry 2016;25:725-734.
  22. Durkee T, Kaess M, Carli V, Parzer P, Wasserman C, Floderus B, Apter A, Balazs J, Barzilay S, Bobes J, Brunner R, Corcoran P, Cosman D, Cotter P, Despalins R, Graber N, Guillemin F, Haring C, Kahn JP, Mandelli L, Marusic D, Mészáros G, Musa GJ, Postuvan V, Resch F, Saiz PA, Sisask M, Varnik A, Sarchiapone M, Hoven CW, Wasserman D: Prevalence of pathological internet use among adolescents in Europe: demographic and social factors. Addiction 2012;107:2210-2222.
  23. Király O, Griffiths MD, Urbán R, Farkas J, Kökönyei G, Elekes Z, Tamás D, Demetrovics Z: Problematic internet use and problematic online gaming are not the same: findings from a large nationally representative adolescent sample. Cyberpsychol Behav Soc Netw 2014;17:749-754.
  24. Müller KW, Beutel ME, Egloff B, Wölfling K: Investigating risk factors for internet gaming disorder: a comparison of patients with addictive gaming, pathological gamblers and healthy controls regarding the big five personality traits. Eur Addict Res 2013;20:129-136.
  25. Walther B, Morgenstern M, Hanewinkel R: Co-occurrence of addictive behaviours: personality factors related to substance use, gambling and computer gaming. Eur Addict Res 2012;18:167-174.
  26. Pawlikowski M, Nader IW, Burger C, Stieger S, Brand M: Pathological Internet use - it is a multidimensional and not a unidimensional construct. Addict Res Theory 2014;22:166-175.
    External Resources
  27. King DL, Delfabbro PH, Zwaans T, Kaptsis D: Clinical features and axis I comorbidity of Australian adolescent pathological Internet and video game users. Aust N Z J Psychiatry 2013;47:1058-1067.
  28. Kuss DJ, Dunn TJ, Wölfling K, Müller K, Hędzelek M, Abstract JM: Excessie Internet use and psychopathology: the role of coping. Clin Neuropsychiatry 2017;14:73-81.
  29. Parker JDA, Taylor RN, Eastabrook JM, Schell SL, Wood LM: Problem gambling in adolescence: relationships with internet misuse, gaming abuse and emotional intelligence. Pers Individ Dif 2008;45:174-180.
    External Resources
  30. Müller KW, Glaesmer H, Brähler E, Woelfling K, Beutel ME: Prevalence of internet addiction in the general population: results from a German population-based survey. Behav Inf Technol 2014;33:757-766.
    External Resources
  31. Müller KW, Beutel ME, Wölfling K: A contribution to the clinical characterization of Internet addiction in a sample of treatment seekers: validity of assessment, severity of psychopathology and type of co-morbidity. Compr Psychiatry 2014;55:770-777.
  32. Costa PT, McCrae RR: Neo PI/FFI Manual Supplement. Odessa, Psychological Assessment Resources, 1989.
  33. Borkenau P, Ostendorf F: NEO-Fünf-Faktoren-Inventar (NEO-FFI) nach Costa und McCrae (Handanweisung). Göttingen: Hogrefe, Neo-Fünf-Faktoren Inventar nach Costa und McCrae, 1993.
  34. Cohen S, Kamarck T, Mermelstein R: A global measure of perceived stress. J Health Soc Behav 1983;24:385-396.
  35. Cohen S, Williamson G: Perceived stress in a probability sample of the United States; in Spacapan S, Oskamp S: (eds), Newbury Park, Sage. Soc Psychol Heal Claremont Symp Appl Soc Psychol 1988, pp 31-67.
  36. Klein EM, Brähler E, Dreier M, Müller KW, Schmutzer G, Wölfling K, Beutel ME: The German version of the Perceived Stress Scale - Psychometric characteristics in a representative German community sample. BMC Psychiatry 2016;16:159.
  37. Schwarzer R, Jerusalem M: Generalized Self-Efficacy scale; in Weinman J, Wright S, Johnston M: (eds) Meas Heal Psychol A user's portfolio Casual Control beliefs, Windsor, NFER-NELSON, 1995, pp 35-37.
  38. Hinz A, Schumacher J, Albani C, Schmid G, Brähler E: Bevölkerungsrepräsentative Normierung der Skala zur Allgemeinen Selbstwirksamkeitserwartung. Diagnostica 2006;52:26-32.
    External Resources
  39. Goodman R: The Strengths and Difficulties Questionnaire: a research note. J child Psychol psychiatry 1997;38:581-586.
  40. Goodman R, Ford T, Simmons H, Gatward R, Meltzer H: Using the Strengths and Difficulties Questionnaire (SDQ) to screen for child psychiatric disorders in a community sample. Br J Psychiatry 2000;177:534-539.
  41. Essau CA, Olaya B, Anastassiou-Hadjicharalambous X, Pauli G, Gilvarry C, Bray D, O'callaghan J, Ollendick TH: Psychometric properties of the Strength and Difficulties Questionnaire from five European countries. Int J Methods Psychiatr Res 2012;21:232-245.
  42. Carver CS: You Want to Measure Coping but Your Protocol's Too Long: Consider the Brief COPE. Int J Behav Med 1997;4:91-100.
  43. Batthyány D, Müller KW, Benker F, Wölfling K: [Computer game playing: clinical characteristics of dependence and abuse among adolescents]. Wien Klin Wochenschr 2009;121:502-509.
  44. Wölfling K, Müller KW, Giralt S, Beutel ME: Emotionale Befindlichkeit und dysfunktionale Stressverarbeitung bei Personen mit Internetsucht. SUCHT 2011;57:27-37.
    External Resources
  45. Hormes JM, Kearns B, Timko CA: Craving Facebook? Behavioral addiction to online social networking and its association with emotion regulation deficits. Addiction 2014;109:2079-2088.
  46. Dreier M, Wölfling K, Duven E, Giralt S, Beutel ME, Müller KW: Free-to-play: About addicted Whales, at risk Dolphins and healthy Minnows. Monetarization design and Internet Gaming Disorder. Addict Behav 2017;64:328-333.
  47. Kim, NR, Hwang SSH, Choi JS, Kim DJ, Demetrovics Z, Király O, Nagygyörgy K, Griffiths MD, Hyun SY, Youn HC, Choi SW: Characteristics and psychiatric symptoms of internet gaming disorder among adults using self-reported DSM-5 criteria. Psychiatry Investig 2016;13:58-66.
  48. Morahan-Martin J, Schumacher P: Loneliness and social uses of the Internet. Comput Human Behav 2003;19:659-671.
    External Resources
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