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  • 1 Kaohsiung Medical University Hospital, Taiwan
  • 2 School of Medicine, and Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Taiwan
  • 3 Ditmanson Medical Foundation Chia-Yi Christian Hospital, Taiwan
  • 4 Yuli Hospital, Ministry of Health and Welfare, Taiwan
  • 5 National Dong Hwa University, Taiwan
  • 6 Kaohsiung Municipal Hsiao-Kang Hospital, Taiwan
  • 7 School of Medicine, University of Washington, USA
  • 8 Seattle Children’s Hospital, USA
  • 9 Tainan Municipal Hospital (Managed by Show Chwan Medical Care Corporation), Taiwan
Open access

Aim

To examine the relationship between borderline personality symptoms and Internet addiction as well as the mediating role of mental health problems between them.

Methods

A total of 500 college students from Taiwan were recruited and assessed for symptoms of Internet addiction using the Chen Internet Addiction Scale, borderline personality symptoms using the Taiwanese version of the Borderline Symptom List and mental health problems using four subscales from the Symptom Checklist-90-Revised Scale (interpersonal sensitivity, depression, anxiety, and hostility). Structural equation modeling (SEM) was used to test our hypothesis that borderline personality symptoms are associated with the severity of Internet addiction directly and also through the mediation of mental health problems.

Results

SEM analysis revealed that all paths in the hypothesized model were significant, indicating that borderline personality symptoms were directly related to the severity of Internet addiction as well as indirectly related to the severity of Internet addiction by increasing the severity of mental health problems.

Conclusion

Borderline personality symptoms and mental health problems should be taken into consideration when designing intervention programs for Internet addiction.

Abstract

Aim

To examine the relationship between borderline personality symptoms and Internet addiction as well as the mediating role of mental health problems between them.

Methods

A total of 500 college students from Taiwan were recruited and assessed for symptoms of Internet addiction using the Chen Internet Addiction Scale, borderline personality symptoms using the Taiwanese version of the Borderline Symptom List and mental health problems using four subscales from the Symptom Checklist-90-Revised Scale (interpersonal sensitivity, depression, anxiety, and hostility). Structural equation modeling (SEM) was used to test our hypothesis that borderline personality symptoms are associated with the severity of Internet addiction directly and also through the mediation of mental health problems.

Results

SEM analysis revealed that all paths in the hypothesized model were significant, indicating that borderline personality symptoms were directly related to the severity of Internet addiction as well as indirectly related to the severity of Internet addiction by increasing the severity of mental health problems.

Conclusion

Borderline personality symptoms and mental health problems should be taken into consideration when designing intervention programs for Internet addiction.

Introduction

The emergence of the Internet in recent decades has largely changed modern life. The Internet allows for the rapid search of information, facilitates sharing of information, and offers a whole new way of communication between people from all over the world. Although increased access to the Internet has brought much convenience to society, it has also resulted in maladaptive patterns of use and led to decreased life quality, negative mood, and impaired daily function in real life (Anderson, 2001; Festl, Scharkow, & Quandt, 2013; Young, 1998).

The presentations of problematic Internet use, or Internet addiction, described in the literature mainly include a loss of control over Internet use, a preoccupation with being “on-line,” a perceived need for increasing amounts of time on the Internet, withdrawal symptoms, and persistent use despite negative consequences (Block, 2008; Chen, Weng, Su, Wu, & Yang, 2003; Ko, Yen, Chen, Yang, et al., 2009; Shapira et al., 2003; Young, 1998). A review of recent studies focusing on larger populations revealed that the prevalence of Internet addition ranged from 0.8% in Italy to 26.7% in Hong Kong (Kuss, Griffiths, Karila, & Billieux, 2014). College students in particular have been shown to be the most avid users, spending about six times the amount of time spent on the Internet than other users (Anderson, 2001). This population may be especially more prone to Internet addiction because they are in a stage of developmental transition and because they often have easy access to the medium (Kandell, 1998). Research has found that 12.3%–15.3% of college students in Taiwan (Lin, Ko, & Wu, 2011; Yen, Ko, Yen, Chen, & Chen, 2009) and 6.4% of first-year university students in China (Ni, Yan, Chen, & Liu, 2009) are addicted to the Internet. Individuals with Internet addiction have been shown to have a high rate of sleep problems and daytime drowsiness (Anderson, 2001; Choi et al., 2009), low social competence, poor social integration skills, low life satisfaction, and increased role impairment at work, in marriage and at school (Young, 2004).

Certain personality traits and behavioral styles have been shown to correlate with Internet addiction, such as hostility/aggression (Festl et al., 2013; Yen, Ko, Yen, Wu, & Yang, 2007), poor impulse control (Cao, Su, Liu, & Gao, 2007; Özdemir, Kuzucu, & Ak, 2014), and low sociability (Festl et al., 2013). Researchers have found hostility to be associated with maladaptive coping strategies and more situations that could trigger substance use (McCormick & Smith, 1995), and reversely the deindividuation effects according to the social identity model may promote the severity of hostility and poor impulse control (Reicher, Spears, & Postmes, 1995). Interpersonal problems that resulted from hostility and low sociability may also trigger individuals to use the Internet as a means of coping with the distress. Some of those personality traits, especially hostility/aggression and poor impulse control, are also seen in borderline personality disorder (BPD) (Leichsenring, Leibing, Kruse, New, & Leweke, 2011), which is present in 10%–14% of clinical samples with Internet addiction (Bernardi & Pallanti, 2009; Floros, Siomos, Stogiannidou, Giouzepas, & Garyfallos, 2014). Individuals with BPD have a higher risk of developing substance-related disorders than that in general population, with a lifetime prevalence rate up 78% (Tomko, Trull, Wood, & Sher, 2014). Multiple theories may explain the high prevalence of substance-related disorders in patients with BPD. The impulsivity frequently seen in patients with BPD may be associated with a tendency for seeking immediate reward rather than waiting for a larger but delayed reward (Coffey, Schumacher, Baschnagel, Hawk, & Holloman, 2011). The self-medication hypothesis argues that patients with BPD often experience intense and suffering emotions and thus are prone to use substance to reduce the negative mood state (Kienast, Stoffers, Bermpohl, & Lieb, 2014). Studies have shown that behavioral addiction shares similar symptoms and neurobiological abnormalities with substance-related disorder (Robbins & Clark, 2015), and the fifth edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-5) has included pathological gambling in the category of substance-related disorders (American Psychiatric Association, 2013). As Internet addiction has also been conceptualized as a possible form of behavioral addiction (Robbins & Clark, 2015) and included in the Section III of DSM-5 (APA, 2013), its relationship with BPD has received growing interest in recent years. A recent study found that people with mild or severe Internet addiction have more prominent borderline personality features than those who are not addicted to the medium (Dalbudak, Evren, Aldemir, & Evren, 2014). Moreover, the severity of borderline personality symptoms has been shown to correlate with the severity of Internet addiction and to be predictive of Internet addiction after controlling for conditions that frequently coexist with BPD, such as depression, anxiety, dissociation, and child abuse (Dalbudak et al., 2014). A recent longitudinal study further demonstrated that borderline personality features were predictive of an increase in severity of Internet addiction in the next year (Wu, Ko, Tung, & Li, 2016). Nevertheless, little is known about the ways in which borderline personality symptoms are associated with Internet addiction.

Wu et al. (2016) found that the expectancy of tension reduction and disinhibition when using the Internet mediates the relationship between Internet addiction and borderline personality features. They assumed that people with borderline personality features frequently experience negative affects and that they seek to alleviate those affects through Internet usage. However, more evidence is needed to support this view. Moreover, psychiatric symptoms have been shown to be more severe in individuals with Internet addiction than in those without (Yang, Choe, Baity, Lee, & Cho, 2005; Yen et al., 2008). Studies have shown that depression and hostility are predictive of both the severity of Internet addiction (Yen et al., 2008) and the occurrence of Internet addiction 2 years later (Ko, Yen, Chen, Yeh, & Yen, 2009). Anxiety symptoms (Dalbudak et al., 2014) and unstable interpersonal relationships (Milani, Osualdella, & Di Blasio, 2009) have also been shown to be associated with the severity of Internet addiction. In contrast, there is evidence that lower levels of hostility and interpersonal sensitivity are predictive of remission of Internet addiction (Ko, Yen, Yen, Lin, & Yang, 2007). For individuals with these mental health problems, the Internet may serve as a means of coping with negative feelings because they expect positive outcomes such as tension reduction and disinhibition from the Internet (Brand, Laier, & Young, 2014; Wu et al., 2016), which increases the risk of developing Internet addiction. Negative emotional states, such as depression, may also decrease the capability of self-control (LaRose, Lin, & Eastin, 2003), thereby increasing the risk of Internet addiction (Özdemir et al., 2014).

Numerous mental health problems are frequently seen in individuals with BPD (Grant et al., 2008). Among those, depression and anxiety were reported to be the most common comorbidities (Grant et al., 2008). Instability of interpersonal relationships is an important and disabling symptom in patients with BPD, who are also more likely to present with interpersonal sensitivity (Bungert et al., 2015). Hostility is another key feature of BPD (Skodol et al., 2002) and is mostly regarded as the result of that personality trait (Gardner, Archer, & Jackson, 2012). Indeed, because BPD is a pervasive and enduring condition in most cases, the associated mental health problems may be the result of their multiple maladaptive cognitions and behaviors.

The aims of this study were to examine the association of borderline personality symptoms with Internet addiction as well as the mediating role of mental health problems for the association between borderline personality symptoms and Internet addiction. Given the correlation between BPD symptoms and Internet addiction demonstrated in previous studies (Dalbudak et al., 2014; Wu et al., 2016), the strong association between BPD and mental health problems (Bungert et al., 2015; Gardner et al., 2012; Grant et al., 2008), and the association between mental health problems and increased risk of Internet addiction (Ko, Yen, Chen, Yeh, et al., 2009; Özdemir et al., 2014; Yen et al., 2008), we hypothesize that borderline personality symptoms are directly associated with Internet addiction and also indirectly associated with Internet addiction through the mediation of mental health problems.

Methods

Participants

Participants were recruited using an advertisement posted on websites targeting college students aged from 20 to 30 years. A total of 500 college students (238 men and 262 women) participated in this study. The mean age of the participants was 22.1 years [standard deviation (SD), 1.8 years]. Among the majors of study, the categories of “health professions and related programs” (n = 172, 34%), “science, math, and technology” (n = 105, 21%), and “arts and humanities” (n = 55, 11%) have the greatest number of students. The participants were paid 500 NT dollars after completing the study procedures.

Measures

Taiwanese version of the Borderline Symptom List (BSL-23). The self-rated BSL-23 (Bohus et al., 2009) was developed by selecting the items from the original BSL-95, a self-rating scale assessing the severity and multiple dimensions of borderline personality symptomatology showing good psychometric properties including high level of sensitivity to change and a high ability to discriminate borderline patients from other patient groups (Bohus et al., 2001). The BSL-23 also had a high correlation with the BSL-95, a high internal consistency, and a high validity to discriminate patients with BPD from those with a DSM-IV axis I diagnosis (Bohus et al., 2009). A higher total score of the BSL-23 indicates more severe borderline personality symptoms. In this study, the authors first translated the English version of the BSL-23 into the Taiwanese version using the standard forward-, backward-, and pretest-step methods. The Cronbach’s α of the Taiwanese version of the BSL-23 was .93. One hundred participants in this study completed the Taiwanese version of the BSL-23 twice, once at study entry, and then again 3 months later. The 3-month test–retest reliability (Pearson’s r) was .71 (p < .001). The Pearson’s correlation of the Taiwanese version of the BSL-23 with the total score of the Beck Depression Inventory-II (Beck, Steer, & Brown, 1996) was .72.

Symptom Checklist-90-Revised Scale (SCL-90-R). The SCL-90-R is a self-reported scale measuring the severity of mental health problems (Derogatis, Lipman, & Covi, 1973). A total of four subscales in the SCL-90-R were applied to measure participants’ interpersonal sensitivity, depression, anxiety, and hostility in the previous week. Each item in the subscales was rated on a 5-point rating scale ranging from “not at all” (0) to “extreme” (4) (Derogatis et al., 1973; Tsai, Wen, Lin, Soong, & Chen, 1978). Its validity in assessing mental health problems among people in Taiwan has previously been established (Tsai et al., 1978). Higher total scores on the subscales indicate more severe mental health problems. Recent studies using factor analysis showed that the bifactor model had the closest fit with the SCL-90-R, but the global distress factor was strong and specific symptom factors were relatively weaker (Urban, Arrindell, Demetrovics, Unoka, & Timman, 2016; Urban et al., 2014). The Cronbach’s α of the four subscales in this study ranged from .76 to .89.

Chen Internet Addiction Scale (CIAS). We used the self-administrated CIAS to assess participants’ severity of Internet addiction in the month preceding the study (Chen et al., 2003). The CIAS contains 26 items rated on a 4-point Likert scale with scaled scores ranging from 26 to 104 (Chen et al., 2003). A higher total score indicates a more severe level of Internet addiction. The internal reliability (Cronbach’s α) of the CIAS in this study was .93.

Procedure and statistical analysis

Research assistants explained the procedures and methods of completing the research questionnaires to the participants individually. The participants could ask any question when they had problems completing the questionnaires, and the research assistants would resolve the problems for them.

Our hypothesized model of the relationship between borderline personality symptoms, mental health, and Internet addiction is illustrated in Figure 1. Structural equation modeling (SEM) was used to estimate parameters, test the adequacy of the model, and evaluate the adequacy with which the data matches the model (Jöreskog & Sörbom, 1993). The goodness-of-fit index was examined with IBM SPSS Amos 18.0 (SPSS Inc., Chicago, IL, USA). Maximum-likelihood estimation was used to estimate the parameters of the model. Several statistics, including the non-normed fit index (NNFI), the comparative fit index (CFI), incremental fit index (IFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR) were also used to evaluate the goodness of fit of the model (Hu & Bentler, 1998). Based on the requirement of goodness of fit, NNFI and CFI should be higher than 0.9, and RMSEA and SRMR should be lower than 0.05 (Hu & Bentler, 1998). A two-tailed p value of less than .05 was considered to indicate statistical significance.

Figure 1.
Figure 1.

The path estimated coefficients of the model of borderline personality symptoms, mental health problems, and Internet addiction. *t > 1.96. p < .05

Citation: Journal of Behavioral Addictions J Behav Addict 6, 3; 10.1556/2006.6.2017.053

Ethics

Informed consent was obtained from all participants prior to assessment. The study was approved by the Institutional Review Board of the Kaohsiung Medical University Hospital.

Results

The correlation matrix of measured variables is shown in Table 1, and the results indicated that there were significant correlations among measured variables except for age. The high correlations among interpersonal sensitivity, depression, anxiety, and hostility (r = .61∼.84) indicate that these four mental health problem indicators are highly interactive. To examine whether mental health could be represented by four indices, including interpersonal sensitivity, depression, anxiety, and hostility, confirmatory factor analysis (CFA) was applied to examine the measured model of mental health. The results of the CFA supported that these four indicators could be integrated into a mental health problem indicator (Table 2).

Table 1.

The correlation matrix of measurement variables

Mean (SD)1234567
1. Age22.1 (1.8)
2. Borderline personality symptoms12.0 (10.3)−0.06
3. Interpersonal sensitivity0.8 (0.7)−0.080.62**
4. Depression0.7 (0.6)−0.080.70**0.84**
5. Anxiety0.7 (0.6)−0.060.62**0.77**0.84**
6. Hostility0.6 (0.5)−0.040.48**0.61**0.63**0.62**
7. Internet addiction55.6 (13.9)−0.080.36**0.32**0.35**0.33**0.25**

Note. SD: standard deviation.

p < .01.

Table 2.

Regression weight of the measurement model

EstimateSECRp
HostilityMental health1.000
AnxietyMental health1.4030.07917.649<.001
DepressionMental health1.4500.07718.856<.001
Interpersonal sensitivityMental health1.5120.08517.692<.001

Note. CR: critical ration; SE: standard error.

Through skew analysis, we found all of the variables, that is,  interpersonal conflict (skew = 1.077), anxiety (skew =1.430), depression (skew = 1.230), hostility (skew = 1.615), and BSL-23 (skew = 1.899) showed negative skew except CIAS (skew = 0.071). Thus, the effects of negative skew were minimized by centralized limitation in SEM. The goodness-of-fit indices for the hypothesized model on the relationships among borderline personality symptoms, mental health, and Internet addiction are as following: NNFI, IFI, and CFI are all 1.00, and both SRMR and RMSEA are 0.01. The goodness-of-fit indices for the hypothesized model were satisfactory. The results also indicate that all paths in the hypothesized model are significant (Figure 1). The severity of borderline personality symptoms was positively associated with the severity of Internet addiction as well as positively associated with the severity of mental health problems. In addition, the severity of mental health indicators was positively associated with the severity of Internet addiction. The results indicate that the severity of borderline personality symptoms is directly related to the severity of Internet addiction and that the severity of Internet addiction is mediated by the severity of mental health problems.

We used Sobel test to identify which mental health variable plays as the strongest mediator on the association between BSL and Internet addiction. We found depression showed the strongest mediating effect on Internet addiction (t = 7.7999, p = 0), followed by anxiety (t = 7.15, p = 0), interpersonal conflict (t = 7.00, p = 0), and hostility (t = 5.34, p = 0).

Through bootstrapping analysis, we found that standardized total effect on Internet addiction was 48.30%. Standardized direct effects of BSL on mental health and on Internet addiction were 71.9% and 20.3%, respectively. Standardized direct effect of mental health on Internet addiction was 22.2%. The standardized indirect effect of BSL on Internet addiction was 16%. By bias-corrected percentile method, we found the standardized regression weight of BSL–mental health, mental health–Internet addiction, and BSL–Internet addiction were 0.719 (95% CI = 0.652–0.78), 0.22 (95% CI = 0.108–0.344), and 0.203 (95% CI = 0.085–0.309), respectively.

Discussion

Few studies have directly explored the relationship between BPD symptoms and Internet addiction (Dalbudak et al., 2014; Wu et al., 2016). To the best of our knowledge, this is the first study to examine the role of mental health problems in mediating the relationship between BPD symptoms and Internet addiction. Our results demonstrate that BPD symptoms directly increase the severity of Internet addiction and that mental health problems mediate the relationship between BPD symptoms and Internet addiction.

Our finding that BPD symptoms are positively associated with Internet addiction is consistent with findings reported in previous studies (Dalbudak et al., 2014; Wu et al., 2016). This study provides further evidence for the assumptions mentioned in previous studies that people with BPD symptoms may be more prone to Internet addiction when they have more severe mental health problems (Dalbudak et al., 2014; Wu et al., 2016). Dalbudak et al. (2014) argued that the anonymity of the Internet may facilitate social interaction in those with mental health problems such as depression, anxiety, or unstable interpersonal relationships, which are commonly seen in BPD. Caplan (2003) also suggested that people with psychological problems favored online interaction rather than face-to-face social activities because of impaired social skills. In addition, it has been suggested that Internet addiction is associated with dissociative experiences (Canan, Ataoglu, Ozcetin, & Icmeli, 2012), which are very common in patients with BPD (Korzekwa, Dell, & Pain, 2009). People with mental health problems, including those with BPD symptoms, may use the Internet to dissociate from their emotional pain (Dalbudak et al., 2014). People with mental health problems may also use the Internet as an attempt to alleviate (LaRose et al., 2003) or regulate their negative mood states (Caplan, 2002). In a longitudinal study, Wu et al. (2016) found that positive expectancy for tension reduction and disinhibition mediates the relationship between BPD features and Internet addiction. Similarly, they explained their finding by hypothesizing that individuals with BPD features may use the Internet as a platform to alleviate the negative emotions frequently experienced by people with BPD. Therefore, based on the evidence noted above, for people with BPD and mental health problems, the Internet may either serve as a means for compensation of low social skills or as a way of coping with psychological discomfort, through dissociation, escape, or relief from negative feelings. When this works, expectations of tension reduction became a reinforcement and may further heighten the risk of Internet addiction, and low self-control possibly also plays a role in this process (Gamez-Guadix, Villa-George, & Calvete, 2012; Özdemir et al., 2014).

Wu et al. (2016) found that positive expectancy fully mediates the relationship between BPD features and Internet addiction. However, this study shows that there is a direct correlation between BPD symptoms and Internet addiction and that mental health problems partially mediate the effect. These findings may reflect the fact that personality traits that are characteristic of BPD predispose patients with BPD to developing an addiction to the Internet. Impulsivity, a prominent symptom of BPD, is a well-known risk factor for substance abuse (Perry & Carroll, 2008) and also Internet addiction (Cao et al., 2007). In addition, a chronic feeling of emptiness, which is one of the DSM-5 (APA, 2013) criteria used to establish a diagnosis of BPD, and feelings of isolation and loneliness are also common features of BPD (Pazzagli & Monti, 2000). Of those symptoms, loneliness has been found to be related to Internet addiction through low self-control (Özdemir et al., 2014). Since the causes of Internet addiction are multifactorial, it is not surprising that multiple pathways are associated with Internet addiction.

The treatment of Internet addiction is still evolving. Cognitive behavioral therapy and pharmacotherapy, especially antidepressants, are the most common approaches used to treat Internet addiction (Przepiorka, Blachnio, Miziak, & Czuczwar, 2014), and both show promise in improving the severity of the disorder (Winkler, Dörsing, Rief, Shen, & Glombiewski, 2013). The results of this study suggest that whether direct management of comorbid mental health problems, especially in patients with BPD symptoms, is beneficial for treating Internet addiction needs to be investigated in future studies. According to our model and the evidence reviewed above, improving mental health problems including depression, anxiety, interpersonal sensitivity, and hostility in individuals with Internet addiction may improve their social interaction and decrease their need for the Internet as a way of coping, thus interrupting the loop of positive reinforcement. Of course, more evidence is needed to confirm this view. In addition, further studies that focus on the effect of treating mental health problems in people with BPD symptoms as a preventative measure against the development of Internet addiction are warranted.

There are several limitations in this study that need to be considered when interpreting the results. First, all participants were college students and the findings in this study might not be generalizable to other populations. Second, the participants were volunteers who were recruited through advertisement. Therefore, individuals with very severe Internet addiction may have been less likely to be recruited because they may have little interest in activities other than using the Internet. In addition, the rating scales in this study are self-administered, hence the validity may be less than structured, interview-based assessments. The Taiwanese version of BSL-23 may also require validation from further studies. Moreover, there may be other mental health problems that were not been included in this analysis but which might play a role in the mediating effect on Internet addiction. However, based on a literature review of mental health problems related to Internet addiction, we believe that the four mental health items chosen in this study are among the most important ones. Finally, although the possible mechanism and the supporting evidence of the proposed model were discussed in this study according to literature, the cross-sectional nature of this study made confirmation of casual relationships difficult.

Conclusions

This study demonstrated the positive association between BPD symptoms and Internet addiction as well as the mediating effect of mental health problems between them. Further study is needed to confirm the generalizability of the result and the causal relationship between BPD symptoms, mental health problems, and Internet addiction, as well as to explore the effect of managing mental health problems in people with BPD symptoms suffering from Internet addiction.

Authors’ contribution

W-HL and K-HL: analysis and interpretation of data, drafting the manuscript, and statistical analysis. C-HK and RCH: conception and design of study, and acquisition of data. H-FH: conception and design of study, acquisition of data, and study supervision. C-FY: conception and design of study, acquisition of data, study supervision, and statistical analysis. Dr. W-HL and Dr. K-HL contributed equally to this study.

Conflict of interest

The authors declare no conflict of interest.

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  • Kandell, J. J. (1998). Internet addiction on campus: The vulnerability of college students. CyberPsychology & Behavior, 1(1), 1117. doi:10.1089/cpb.1998.1.11

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  • Kienast, T., Stoffers, J., Bermpohl, F., & Lieb, K. (2014). Borderline personality disorder and comorbid addiction: Epidemiology and treatment. Deutsches Ärzteblatt International, 111(16), 280286. doi:10.3238/arztebl.2014.0280

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  • Ko, C. H., Yen, J. Y., Chen, C. S., Yeh, Y. C., & Yen, C. F. (2009). Predictive values of psychiatric symptoms for Internet addiction in adolescents: A 2-year prospective study. Archives of Pediatrics and Adolescent Medicine, 163(10), 937943. doi:10.1001/archpediatrics.2009.159

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  • Ko, C. H., Yen, J. Y., Yen, C. F., Lin, H. C., & Yang, M. J. (2007). Factors predictive for incidence and remission of Internet addiction in young adolescents: A prospective study. CyberPsychology & Behavior, 10(4), 545551. doi:10.1089/cpb.2007.9992

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  • Kuss, D. J., Griffiths, M. D., Karila, L., & Billieux, J. (2014). Internet addiction: A systematic review of epidemiological research for the last decade. Current Pharmaceutical Design, 20(25), 40264052. doi:10.2174/13816128113199990617

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  • Lin, M. P., Ko, H. C., & Wu, J. Y. (2011). Prevalence and psychosocial risk factors associated with Internet addiction in a nationally representative sample of college students in Taiwan. Cyberpsychology, Behavior, and Social Networking, 14(12), 741746. doi:10.1089/cyber.2010.0574

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  • Przepiorka, A. M., Blachnio, A., Miziak, B., & Czuczwar, S. J. (2014). Clinical approaches to treatment of Internet addiction. Pharmacological Reports, 66(2), 187191. doi:10.1016/j.pharep.2013.10.001

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  • Robbins, T. W., & Clark, L. (2015). Behavioral addictions. Current Opinion in Neurobiology, 30, 6672. doi:10.1016/j.conb.2014.09.005

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  • Tomko, R. L., Trull, T. J., Wood, P. K., & Sher, K. J. (2014). Characteristics of borderline personality disorder in a community sample: Comorbidity, treatment utilization, and general functioning. Journal of Personality Disorders, 28(5), 734750. doi:10.1521/pedi_2012_26_093

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  • Grant, B. F., Chou, S. P., Goldstein, R. B., Huang, B., Stinson, F. S., Saha, T. D., Smith, S. M., Dawson, D. A., Pulay, A. J., Pickering, R. P., & Ruan, W. J. (2008). Prevalence, correlates, disability, and comorbidity of DSM-IV borderline personality disorder: Results from the Wave 2 National Epidemiologic Survey on Alcohol and Related Conditions. Journal of Clinical Psychiatry, 69(4), 533545.

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    • Export Citation
  • Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Chicago, IL/Hillsdale, NJ: Scientific Software International/Lawrence Erlbaum Associates.

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  • Kandell, J. J. (1998). Internet addiction on campus: The vulnerability of college students. CyberPsychology & Behavior, 1(1), 1117. doi:10.1089/cpb.1998.1.11

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kienast, T., Stoffers, J., Bermpohl, F., & Lieb, K. (2014). Borderline personality disorder and comorbid addiction: Epidemiology and treatment. Deutsches Ärzteblatt International, 111(16), 280286. doi:10.3238/arztebl.2014.0280

    • Search Google Scholar
    • Export Citation
  • Ko, C. H., Yen, J. Y., Chen, C. S., Yeh, Y. C., & Yen, C. F. (2009). Predictive values of psychiatric symptoms for Internet addiction in adolescents: A 2-year prospective study. Archives of Pediatrics and Adolescent Medicine, 163(10), 937943. doi:10.1001/archpediatrics.2009.159

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ko, C.-H., Yen, J.-Y., Chen, S.-H., Yang, M.-J., Lin, H.-C., & Yen, C.-F. (2009). Proposed diagnostic criteria and the screening and diagnosing tool of Internet addiction in college students. Comprehensive Psychiatry, 50(4), 378384. doi:10.1016/j.comppsych.2007.05.019

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ko, C. H., Yen, J. Y., Yen, C. F., Lin, H. C., & Yang, M. J. (2007). Factors predictive for incidence and remission of Internet addiction in young adolescents: A prospective study. CyberPsychology & Behavior, 10(4), 545551. doi:10.1089/cpb.2007.9992

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Korzekwa, M. I., Dell, P. F., & Pain, C. (2009). Dissociation and borderline personality disorder: An update for clinicians. Current Psychiatry Reports, 11(1), 8288. doi:10.1007/s11920-009-0013-1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuss, D. J., Griffiths, M. D., Karila, L., & Billieux, J. (2014). Internet addiction: A systematic review of epidemiological research for the last decade. Current Pharmaceutical Design, 20(25), 40264052. doi:10.2174/13816128113199990617

    • Crossref
    • Search Google Scholar
    • Export Citation
  • LaRose, R., Lin, C. A., & Eastin, M. S. (2003). Unregulated Internet usage: Addiction, habit, or deficient self-regulation? Media Psychology, 5(3), 225253. doi:10.1207/S1532785XMEP0503_01

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leichsenring, F., Leibing, E., Kruse, J., New, A. S., & Leweke, F. (2011). Borderline personality disorder. The Lancet, 377(9759), 7484. doi:10.1016/S0140-6736(10)61422-5

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, M. P., Ko, H. C., & Wu, J. Y. (2011). Prevalence and psychosocial risk factors associated with Internet addiction in a nationally representative sample of college students in Taiwan. Cyberpsychology, Behavior, and Social Networking, 14(12), 741746. doi:10.1089/cyber.2010.0574

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCormick, R. A., & Smith, M. (1995). Aggression and hostility in substance abusers: The relationship to abuse patterns, coping style, and relapse triggers. Addictive Behaviors, 20(5), 555562. doi:10.1016/0306-4603(95)00015-5

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milani, L., Osualdella, D., & Di Blasio, P. (2009). Quality of interpersonal relationships and problematic Internet use in adolescence. CyberPsychology & Behavior, 12(6), 681684. doi:10.1089/cpb.2009.0071

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ni, X., Yan, H., Chen, S., & Liu, Z. (2009). Factors influencing Internet addiction in a sample of freshmen university students in China. CyberPsychology & Behavior, 12(3), 327330. doi:10.1089/cpb.2008.0321

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Özdemir, Y., Kuzucu, Y., & Ak, Ş. (2014). Depression, loneliness and Internet addiction: How important is low self-control? Computers in Human Behavior, 34, 284290. doi:10.1016/j.chb.2014.02.009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pazzagli, A., & Monti, M. R. (2000). Dysphoria and aloneness in borderline personality disorder. Psychopathology, 33(4), 220226. doi:10.1159/000029147

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perry, J. L., & Carroll, M. E. (2008). The role of impulsive behavior in drug abuse. Psychopharmacology, 200(1), 126. doi:10.1007/s00213-008-1173-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Przepiorka, A. M., Blachnio, A., Miziak, B., & Czuczwar, S. J. (2014). Clinical approaches to treatment of Internet addiction. Pharmacological Reports, 66(2), 187191. doi:10.1016/j.pharep.2013.10.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reicher, S. D., Spears, R., & Postmes, T. (1995). A social identity model of deindividuation phenomena. European Review of Social Psychology, 6(1), 161198. doi:10.1080/14792779443000049

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robbins, T. W., & Clark, L. (2015). Behavioral addictions. Current Opinion in Neurobiology, 30, 6672. doi:10.1016/j.conb.2014.09.005

  • Shapira, N. A., Lessig, M. C., Goldsmith, T. D., Szabo, S. T., Lazoritz, M., Gold, M. S., & Stein, D. J. (2003). Problematic Internet use: Proposed classification and diagnostic criteria. Depression and Anxiety, 17(4), 207216. doi:10.1002/da.10094

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skodol, A. E., Siever, L. J., Livesley, W. J., Gunderson, J. G., Pfohl, B., & Widiger, T. A. (2002). The borderline diagnosis II: Biology, genetics, and clinical course. Biological Psychiatry, 51(12), 951963. doi:10.1016/S0006-3223(02)01325-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tomko, R. L., Trull, T. J., Wood, P. K., & Sher, K. J. (2014). Characteristics of borderline personality disorder in a community sample: Comorbidity, treatment utilization, and general functioning. Journal of Personality Disorders, 28(5), 734750. doi:10.1521/pedi_2012_26_093

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsai, M. T., Wen, J. K., Lin, H. N., Soong, W. T., & Chen, C. C. (1978). Application of self-rating symptom scales to psychiatric outpatients. Bulletin of the Chinese Society of Neurology and Psychiatry, 4, 1322.

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  • Urban, R., Arrindell, W. A., Demetrovics, Z., Unoka, Z., & Timman, R. (2016). Cross-cultural confirmation of bi-factor models of a symptom distress measure: Symptom Checklist-90-Revised in clinical samples. Psychiatry Research, 239, 265274. doi:10.1016/j.psychres.2016.03.039

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  • Urban, R., Kun, B., Farkas, J., Paksi, B., Kokonyei, G., Unoka, Z., Felvinczi, K., Olah, A., & Demetrovics, Z. (2014). Bifactor structural model of symptom checklists: SCL-90-R and Brief Symptom Inventory (BSI) in a non-clinical community sample. Psychiatry Research, 216(1), 146154. doi:10.1016/j.psychres.2014.01.027

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  • Winkler, A., Dörsing, B., Rief, W., Shen, Y., & Glombiewski, J. A. (2013). Treatment of Internet addiction: A meta-analysis. Clinical Psychology Review, 33(2), 317329. doi:10.1016/j.cpr.2012.12.005

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  • Impact Factor (2018): 4.873
  • Scimago Journal Rank (2018): 1.624
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  • SJR Quartile Score (2018): Q1 Clinical Psychology (26/293)
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Language: English

Founded in 2011
Publication: One volume of four issues annually

Publishing Model: Gold Open Access
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Publication Programme: 2020. Vol. 9.

Senior editors

Editor(s)-in-Chief: Zsolt Demetrovics

Assistant Editor(s): Csilla Ágoston

Associate Editors

  • Judit Balázs (ELTE Eötvös Loránd University, Hungary)
  • Joel Billieux (University of Lausanne, Switzerland)
  • Matthias Brand (University of Duisburg-Essen, Germany)
  • Anneke Goudriaan (University of Amsterdam, The Netherlands)
  • Daniel King (Flinders University, Australia)
  • Ludwig Kraus (IFT Institute for Therapy Research, Germany)
  • Anikó Maráz (Humboldt University of Berlin, Germany)
  • Astrid Müller (Hannover Medical School, Germany)
  • Marc N. Potenza (Yale University, USA)
  • Hans-Jurgen Rumpf (University of Lübeck, Germany)
  • Attila Szabó (ELTE Eötvös Loránd University, Hungary)
  • Róbert Urbán (ELTE Eötvös Loránd University, Hungary)
  • Aviv M. Weinstein (Ariel University, Israel)

Editorial Board

  • Max W. Abbott (Auckland University of Technology, New Zealand)
  • Elias N. Aboujaoude (Stanford University School of Medicine, USA)
  • Hojjat Adeli (Ohio State University, USA)
  • Alex Baldacchino (University of Dundee, United Kingdom)
  • Alex Blaszczynski (University of Sidney, Australia)
  • Kenneth Blum (University of Florida, USA)
  • Henrietta Bowden-Jones (Imperial College, United Kingdom)
  • Beáta Bőthe (University of Montreal, Canada)
  • Wim van den Brink (University of Amsterdam, The Netherlands)
  • Gerhard Bühringer (Technische Universität Dresden, Germany)
  • Sam-Wook Choi (Eulji University, Republic of Korea)
  • Damiaan Denys (University of Amsterdam, The Netherlands)
  • Jeffrey L. Derevensky (McGill University, Canada)
  • Naomi Fineberg (University of Hertfordshire, United Kingdom)
  • Marie Grall-Bronnec (University Hospital of Nantes, France)
  • Jon E. Grant (University of Minnesota, USA)
  • Mark Griffiths (Nottingham Trent University, United Kingdom)
  • Heather Hausenblas (Jacksonville University, USA)
  • Tobias Hayer (University of Bremen, Germany)
  • Susumu Higuchi (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David Hodgins (University of Calgary, Canada)
  • Eric Hollander (Albert Einstein College of Medicine, USA)
  • Jaeseung Jeong (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser Khazaal (Geneva University Hospital, Switzerland)
  • Orsolya Király (Eötvös Loránd University, Hungary)
  • Emmanuel Kuntsche (La Trobe University, Australia)
  • Hae Kook Lee (The Catholic University of Korea, Republic of Korea)
  • Michel Lejoyeux (Paris University, France)
  • Anikó Maráz (Eötvös Loránd University, Hungary)
  • Giovanni Martinotti (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Frederick Gerard Moeller (University of Texas, USA)
  • Daniel Thor Olason (University of Iceland, Iceland)
  • Nancy Petry (University of Connecticut, USA)
  • Bettina Pikó (University of Szeged, Hungary)
  • Afarin Rahimi-Movaghar (Teheran University of Medical Sciences, Iran)
  • József Rácz (Hungarian Academy of Sciences, Hungary)
  • Rory C. Reid (University of California Los Angeles, USA)
  • Marcantanio M. Spada (London South Bank University, United Kingdom)
  • Daniel Spritzer (Study Group on Technological Addictions, Brazil)
  • Dan J. Stein (University of Cape Town, South Africa)
  • Sherry H. Stewart (Dalhousie University, Canada)
  • Attila Szabó (Eötvös Loránd University, Hungary)
  • Ferenc Túry (Semmelweis University, Hungary)
  • Alfred Uhl (Austrian Federal Health Institute, Austria)
  • Johan Vanderlinden (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. Voiskounsky (Moscow State University, Russia)
  • Kimberly Young (Center for Internet Addiction, USA)

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

Including gaming disorder in the ICD-11: The need to do so from a clinical and public health perspective

Commentary on: A weak scientific basis for gaming disorder: Let us err on the side of caution (van Rooij et al., 2018)