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  • 1 Bakirkoy Training and Research Hospital for Mental Health and Neurological Disorders, Turkey
  • | 2 Izmir Tepecik Training and Research Hospital, Turkey
  • | 3 Ankara Pediatric & Pediatric Hematology Oncology Training and Research Hospital, Turkey
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Objectives

The aims of this cross-sectional study were to assess the prevalence of Internet addiction (IA) in a clinical sample of adolescents with attention-deficit hyperactivity disorder (ADHD) and to detect the moderating effects of co-occurring oppositional defiant disorder/conduct disorder (ODD/CD) on the association between ADHD and IA.

Methods

The study group comprised 119 adolescent subjects who were consecutively referred to our outpatient clinic with a diagnosis of ADHD. The Turgay DSM-IV-Based Child and Adolescent Disruptive Behavioral Disorders Screening and Rating Scale (T-DSM-IV-S) was completed by parents, and subjects were asked to complete the Internet Addiction Scale (IAS).

Results

The IAS results indicated that 63.9% of the participants (n = 76) fell into the IA group. Degree of IA was correlated with hyperactivity/impulsivity symptoms but not with inattention symptoms. As compared to the ADHD-only group (without comorbid ODD/CD), ADHD + ODD/CD subjects returned significantly higher scores on the IAS.

Conclusions

As adolescents with ADHD are at high risk of developing IA, early IA detection and intervention is of great importance for this group. In addition, adolescents with ADHD + ODD/CD may be more vulnerable to IA than those in the ADHD-only group and may need to be more carefully assessed for IA.

Abstract

Objectives

The aims of this cross-sectional study were to assess the prevalence of Internet addiction (IA) in a clinical sample of adolescents with attention-deficit hyperactivity disorder (ADHD) and to detect the moderating effects of co-occurring oppositional defiant disorder/conduct disorder (ODD/CD) on the association between ADHD and IA.

Methods

The study group comprised 119 adolescent subjects who were consecutively referred to our outpatient clinic with a diagnosis of ADHD. The Turgay DSM-IV-Based Child and Adolescent Disruptive Behavioral Disorders Screening and Rating Scale (T-DSM-IV-S) was completed by parents, and subjects were asked to complete the Internet Addiction Scale (IAS).

Results

The IAS results indicated that 63.9% of the participants (n = 76) fell into the IA group. Degree of IA was correlated with hyperactivity/impulsivity symptoms but not with inattention symptoms. As compared to the ADHD-only group (without comorbid ODD/CD), ADHD + ODD/CD subjects returned significantly higher scores on the IAS.

Conclusions

As adolescents with ADHD are at high risk of developing IA, early IA detection and intervention is of great importance for this group. In addition, adolescents with ADHD + ODD/CD may be more vulnerable to IA than those in the ADHD-only group and may need to be more carefully assessed for IA.

Introduction

In recent decades, the Internet has become an essential part of our daily lives. Despite various advantages, such as unlimited access to information exchange and social communication, some negative impacts of Internet use have also been identified (Wang et al., 2011; Zboralski et al., 2009). Excessive or problematic Internet use or Internet addiction (IA) is among the terms most commonly used when referring to maladaptive patterns of Internet use leading to clinically significant impairment or distress (An et al., 2014; Xu et al., 2012). IA is characterized by symptoms that include spending excessive time or longer than initially intended online, along with compulsive behaviors, unsuccessful attempts at cessation or control, lying about the extent of one’s use of the Internet, use to cope or escape problems, and preoccupation with the Internet when one is offline (Brezing, Derevensky, & Potenza, 2010). The estimated worldwide prevalence of adolescent IA ranges between 1.6% and 36.7% (Ko, Yen, Yen, Chen, & Chen, 2012). This wide discrepancy in prevalence rates may be explained by methodological differences and by variations in Internet access across different countries (Ho et al., 2014; Yu & Shek, 2013).

IA has been linked to various psychiatric conditions, including depression and suicidal ideation (Chang, Chiu, Lee, Chen, & Miao, 2014; Cho, Sung, Shin, Lim, & Shin, 2013; Jang, Hwang, & Choi, 2008; Kim et al., 2006; Ko, Yen, Chen, Yeh, & Yen, 2009; Lin et al., 2014; Park, Hong, Park, Ha, & Yoo, 2013; Tsitsika et al., 2011), anxiety (Cho et al., 2013), social phobia (Ko et al., 2009), obsessive–compulsive symptoms, hostility, aggression, problematic alcohol use, substance use (Ko et al., 2012), self-injurious behavior (Lam, Peng, Mai, & Jing, 2009), and, in particular, attention-deficit hyperactivity disorder (ADHD; Ko et al., 2012; Yen, Chou, Liu, Yang, & Hu, 2014; Yılmaz, Hergüner, Bilgiç, & Işık, 2015; Yoo et al., 2004). According to Bozkurt, Coskun, Ayaydin, Adak, and Zoroglu (2013), ADHD is the most prevalent (83.3%) axis-1 disorder among the adolescents referred for IA. In addition, in a two-year longitudinal study of Taiwanese adolescents, ADHD (along with hostility) was found to be the most significant predictor of IA (Ko et al., 2009).

It is well established that oppositional defiant disorder (ODD) and conduct disorder (CD) are the most prevalent comorbid psychiatric disorders among children and adolescents with ADHD (Pliszka, 2007). There is evidence that presence of comorbid ODD/CD is associated with increased risk of substance-use disorders in adolescents with ADHD (Lee, Humphreys, Flory, Liu, & Glass, 2011; Molina & Pelham, 2003). As in the case of substance-use disorders, observed rates of ODD and CD were greater in adolescents referred for IA than in the general population (American Psychiatric Association [APA], 2013; Bozkurt et al., 2013). To the best of our knowledge, however, no study has examined the effects of co-occurring disruptive behavior disorders on IA severity in adolescents with ADHD.

As mentioned above, a growing body of evidence indicates a strong link between ADHD and IA. However, as the majority of these studies have been conducted in Asia, there is a need for studies in other countries to investigate whether cultural differences influence Internet use among young people with ADHD (Weiss, Baer, Allan, Saran, & Schibuk, 2011) and whether co-occurring ODD/CD increases the risk of IA in adolescents with ADHD. The aims of this cross-sectional study were to evaluate the prevalence of IA in a clinical sample of adolescents with ADHD and to detect the moderating effects of co-occurring ODD/CD on the association between ADHD and IA.

Methods

Participants

Participants were recruited from a clinical sample of adolescents aged 12–17 years who were consecutively referred to our outpatient clinic in Istanbul, Turkey between March 2013 and September 2014 and diagnosed with ADHD. Those who were drug-free for at least 6 months or who never used any medication for ADHD were invited to participate in the study after obtaining their legal guardians’ approval. All those who were invited (138 adolescents, aged 12–17 years) agreed to participate. Of those, 19 were omitted because of incomplete data. Those receiving any ADHD medication or psychotherapeutic treatment for ADHD and those with comorbid substance use disorder or any psychotic disorder, bipolar disorder, mental retardation, or pervasive developmental disorder were not included in the study.

Diagnosis

ADHD, ODD, and CD diagnoses were based on the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; APA, 2000) criteria and comprehensive information from a clinical examination, which included interviews with both youth and parent and review of psychiatric, psychometric, and educational records. All of the interviews were conducted by child and adolescent psychiatrists with at least 9 years experience of disruptive behavioral disorders.

Data collection tools

The study data were collected using a sociodemographic information form, the Turgay DSM-IV-Based Child and Adolescent Disruptive Behavioral Disorders Screening and Rating Scale (T-DSM-IV-S), and the Internet Addiction Scale (IAS).

Sociodemographic information form

This form was prepared by the authors to collect data on the adolescent’s gender, current age, and smoking status, and on the parents’ age and educational level.

T-DSM-IV-S

This scale was developed by Turgay (1994) and was adapted for Turkish use by Ercan, Amado, Somer, and Cikoglu (2001). The T-DSM-IV-S is based on DSM-IV (APA, 2000) diagnostic criteria; 9 items assess inattention, 9 assess hyperactivity/impulsivity (HA/IM), 8 assess opposition/defiance, and 15 assess conduct disorder. Symptoms are scored by assigning a severity estimate for each on a 4-point Likert-type scale (0 = not at all, 1 = just a little, 2 = quite a bit, and 3 = very much). The scale was completed by parents in this study.

IAS

The scale consists of 35 items, with four subfactors defined as “withdrawal,” “controlling difficulty,” “disorder in functionality,” and “social isolation.” The IAS was developed in Turkish by Günüç and Kayri (2010); a pool of items was developed to determine factors responsible for addiction, and a trial scale was developed from this pool based on expert opinion during the development phase. The validity of the scale was tested by a pilot trial. A validity and reliability study was subsequently conducted with 754 high-school students from seven provinces, and the scale’s validity and reliability was found to be high (Cronbach’s α coefficient of internal consistency = .94). Attitudes were assessed on a 5-point Likert scale (5 = fully agree, 4 = agree, 3 = neutral, 2 = do not agree, and 1 = definitely do not agree). Minimum and maximum possible scores are 35 and 175, respectively.

In a study conducted in Turkey, Üneri and Tanıdır (2011) used the IAS to assess the prevalence of IA among the high-school students. Based on the results, they divided their sample into four groups. Subjects who scored over 81 were assigned to the addictive group; those who scored between 67 and 81 were assigned to the addiction risk group; those who scored between 53 and 66 were assigned to the threshold group; and those who scored less than 53 were assigned to the non-addictive group. In this study, as our sample consisted of adolescents from a similar age group, the cut-off point determined by Üneri and Tanıdır (2011) was used to identify IA.

Statistical analysis

Data were analyzed using the Statistical Package for the Social Sciences, version 16.0 (SPSS Inc., Chicago, IL). The sample was described in terms of frequencies and percentages for discrete variables and means and standard errors for continuous variables. Student’s t-test was used to compare mean values between groups, and χ2 and Fisher’s exact test were used to compare rates between groups. Pearson’s method was used for correlation analyses. One-way analysis of variance was used to compare IAS scores between ADHD subgroups. A p value of <.05 was considered to be statistically significant.

Ethics

The study was approved by the Medical Ethics Committee of the Bakirkoy Research and Training Hospital for Psychiatric and Neurological Disorders. Both adolescents and parents signed informed consent forms prior to participation in the study.

Results

The mean IAS score was 89.8 ± 26.7 (range: 35–159), and 63.9% of the participants were found to have IA (Figure 1 shows distributions of IAS scores in each ADHD subtype group.). Table 1 summarizes the participants’ demographic features, ADHD subtypes, T-DSM-IV-S scores, frequency of IA, presence of comorbid ODD and/or CD, severity of IA symptoms, Internet usage habits, and smoking status.

Figure 1.
Figure 1.

Distribution of Internet Addiction Scale (IAS) scores

Citation: Journal of Behavioral Addictions J Behav Addict 7, 2; 10.1556/2006.7.2018.46

Table 1.

Demographic features, T-DSM-IV-S scores, IAS scores, Internet-using habits and smoking status of the participants

N (%)Mean ± SD
Age11914.8 ± 1.3
Sex
 Female30 (25.2)
 Male89 (74.8)
ADHD subtypes
 Predominantly inattention48 (40.3)
 Predominantly hyperactive18 (15.1)
 Combined53 (44.5)
Comorbid ODD/CD
 Yes41 (34.5)
 No78 (65.5)
Comorbid ODD
 Yes76 (63.9)
 No43 (36.1)
Comorbid CD
 Yes27 (22.7)
 No92 (77.3)
T-DSM-IV-S
 Inattention16.97 ± 5.27
 Hyperactivity/impulsivity13.47 ± 6.33
 ODD12.93 ± 5.91
 CD5.42 ± 5.49
 Total48.89 ± 17.26
IAS89.8 ± 26.7
Internet addiction
 Yes76 (63.9)
 No43 (36)
Personal computer
 Yes76 (63.9)
 No43 (36.1)
Internet connection at home
 Yes99 (83.2)
 No20 (16.8)
Time spent on Internet
 0–471 (59.7)
 4–831 (26.1)
 ≥817 (14.3)
Smoking status
 Smoker23 (19.3)
 Non-smoker95 (79.8)

Note. SD: standard deviation; ADHD: attention-deficit hyperactivity disorder; ODD: oppositional defiant disorder; CD: conduct disorder; T-DSM-IV-S: Turgay DSM-IV-Based Child and Adolescent Disruptive Behavioral Disorders Screening and Rating Scale; IAS: Internet Addiction Scale.

No statistically significant difference was found between the IAS scores of females and males (p = .391). There was no statistically significant correlation between having a personal computer and having a home Internet connection, smoking status, ADHD subtypes, and IAS scores (p = .463, .411, .422, and .837, respectively). There was a statistically significant correlation between time spent online and presence of comorbid ODD and/or CD with IAS scores (p = .001 and .001, respectively) (Table 2). There was a statistically significant correlation between ODD/CD comorbidity and presence of IA. The odds ratios of IA in ADHD + ODD or ADHD + CD groups were 3.22 and 4.23 (95% confidence interval), respectively (Table 3).

Table 2.

The associations between demographic features, Internet-using habits, smoking status, and ADHD subtypes and presence of comorbid ODD/CD and IAS scores

IAS scorespEffect size
Sex
 Female93.4 ± 25.2.3910.18
 Male88.6 ± 27.2
Personal computer
 Yes91.2 ± 27.8.4630.14
 No87.4 ± 24.9
Internet connection at home
 Yes88.9 ± 26.9.4110.20
 No94.3 ± 26
Time spent on Internet
 0–480 ± 23.6
 4–898.5 ± 22.8.0010.23
 ≥8114.5 ± 25.3
Smoking
 Yes93.6 ± 23.8.4220.19
 No88.6 ± 27.4
ADHD subtypes
 Predominantly inattention88.1 ± 250.01
 Predominantly hyperactive89.9 ± 23.9.837
 Combined91.3 ± 29.4
Comorbid ODD/CD
 Yes95.6 ± 27.0010.68
 No78.7 ± 22.4
Comorbid ODD
 Yes95.6 ± 27.3.0010.65
 No79.4 ± 22.2
Comorbid CD
 Yes106 ± 29.8.0010.87
 No85 ± 23.8

Note. Bold values represents p < .05. ADHD: attention-deficit hyperactivity disorder; ODD: oppositional defiant disorder; CD: conduct disorder; IAS: Internet Addiction Scale.

Table 3.

The associations between ODD/CD comorbidity and presence of Internet addiction

Internet addiction
pOR95% CI
ADHD + ODD.0053.221.46–7.07
ADHD + CD.0104.231.35–13.22

Note. ADHD: attention-deficit hyperactivity disorder; ODD: oppositional defiant disorder; CD: conduct disorder; OR:odds ratio; 95% CI: 95% confidence interval.

There was no significant correlation between age, T-DSM-IV-S inattention scores, and IAS scores (r = −.90, p = .33 and r = .146, p = .112, respectively). There were statistically significant correlations between T-DSM-IV-S HA/IM, T-DSM-IV-S opposition defiance, T-DSM-IV-S conduct disorder, and T-DSM-IV-S total scores and IAS scores (r = .185, p = .043; r = .331, p = .001; r = .387, p = .001; and r = .349, p = .001, respectively) (Table 4).

Table 4.

The association between IAS scores, age, and T-DSM-IV-S scores

IAS scores
rp
Age−.90.33
T-DSM-IV-S
Inattention.146.112
HA/IM.185.043
ODD.331.001
CD.387.001
Total.349.001

Note. Bold values represents p < .05. IAS: Internet Addiction Scale; T-DSM-IV-S: Turgay DSM-IV-Based Child and Adolescent Disruptive Behavioral Disorders Screening and Rating Scale; HA/IM: hyperactivity/impulsivity; ODD: oppositional defiant disorder; CD: conduct disorder.

Discussion

In this study, 63.9% of those adolescents with ADHD (n = 76) were found to have IA, which is higher than the previously reported prevalence rates of IA among Turkish high-school students (10.1%–24.2%) (Günüç & Kayri, 2010; Üneri & Tanıdır, 2011; Yılmaz et al., 2015). Our results align with previous findings indicating an increased risk of IA in adolescents with ADHD. However, we found prevalence rates substantially higher than those reported in two earlier studies (15.7% and 32.7%) (Yen et al., 2014; Yoo et al., 2004) of children and adolescents with ADHD. Unlike previous research, this study recruited all participants from adolescents who were not receiving any type of treatment for ADHD. It was suggested in one previous report (Han et al., 2009) that an 8-week course of methylphenidate treatment could reduce the severity of Internet video gameplay among children with ADHD. In the previous samples, medical and/or psychotherapeutic treatment may have resulted in reduction of IA symptoms and frequency. In addition, our outpatient clinic mainly serves patients from lower socioeconomic backgrounds, and it has been suggested that poorer social support is linked with greater risk of IA. For that reason, participants’ socioeconomic status may account for the increase in IA in our sample (Tsai et al., 2009). As the above studies were performed in Asian countries, cultural differences may also have contributed to the discrepancy in prevalence rates.

ADHD and IA may be linked through various biological and/or psychological mechanisms. In particular, several cognitive and behavioral problems observed in individuals with ADHD have been shown to be associated with impairments in the reward processing system. Children with ADHD appear to have a strong preference for immediate rather than delayed reward, even when the delayed reward is larger. This can be explained by an aversion to waiting and a decreased sensitivity to cues that predict reward (Luman et al., 2009; Umemoto, Lukie, Kerns, Müller, & Holroyd, 2014). Yoo et al. (2004) suggested that individuals who are less satisfied with natural rewards tend to resort to substance abuse as a way of seeking enhanced stimulation of the reward pathway; in the same way, IA may serve as another “unnatural” (immediate) reward. There is also emerging evidence that Internet-use disorder is associated with structural or functional impairment of the orbitofrontal cortex, dorsolateral prefrontal cortex, anterior cingulate cortex, and posterior cingulate cortex – regions that are known to be associated with the processing of reward, motivation, memory, and cognitive controls (Park, Han, & Roh, 2017; Zhu, Zhang, & Tian, 2015). In the daily lives of children with ADHD, IA may also serve as a compensatory activity for poor social skills, interpersonal difficulties, low self-esteem, and lack of pleasure (Yen et al., 2014; Yoo et al., 2004).

In this study, symptoms of HA/IM (but not of inattention) were found to be correlated with degree of IA. As impulsivity and loss of control over Internet use are among the most prominent psychological features of individuals with IA (Young, 2004), it is unsurprising that HA/IM domains are associated with IA. In earlier studies, Cao, Su, Liu, and Gao (2007), Cao and Su (2007), and Wu et al. (2013) have reported an association between hyperactivity and impulsivity and IA among Chinese adolescents. However, the literature reports inconsistent results regarding the effects of ADHD symptom domains on degree of IA; some studies report a stronger relation to inattention, whereas others emphasize HA/IM (Bozkurt et al., 2013; Chou, Liu, Yang, Yen, & Hu, 2015; Yen et al., 2014; Yılmaz et al., 2015; Yoo et al., 2004). Similar inconsistencies arose in studies of the association between substance-use disorders and ADHD (Molina & Pelham, 2003; Lee & Hinshaw, 2006). In a meta-analytic review of prospective association of ADHD and substance-use disorders, Lee et al. (2011) hypothesized that inattention and hyperactivity may be differentially related to substance-use disorders, and the same hypothesis seems applicable to IA.

The studies have shown that 54%–84% of children and adolescents with ADHD have co-morbid ODD, and a sizeable proportion of these patients were expected to develop CD (Pliszka, 2007). Consistent with the existing data, 65.5% of the cases in our sample had comorbid ODD/CD with ADHD. As in the case of substance-use disorders (Lee et al., 2011), our findings indicate that comorbid ODD or CD increases the risk of IA in adolescents with ADHD. Dysfunctions of the prefrontal cortex are also thought to play an important role in the pathogenesis of ODD/CD (Fahim et al., 2011). Children and adolescents with conduct disorder exhibit a tendency toward risk taking and reckless behavior, indicating difficulties with decision-making and impulsivity. They are also more vulnerable to substance abuse, potentially reflecting an altered sensitivity of reward mechanisms and persistent selection of options with short-term benefits, despite negative long-term consequences (Fairchild et al., 2009). It has also been argued that ADHD + ODD substantially differs from ADHD-only in terms of neurocognitive functioning (Luman et al., 2009), and ODD/CD comorbidity is hypothesized to account for most of the associations between delay aversion and ADHD (Antrop et al., 2006). Moreover, aggression (one of the key symptoms in ODD/CD) and IA have been shown to be closely related, and IA is often accompanied by aggression. There is evidence that IA and aggression share a number of common neural substrates and neuromodulators; key neural substrates of aggression, such as the prefrontal cortex and the limbic system, are located in brain regions that also relate to IA (Hahn & Kim, 2014). Altogether with the aforementioned alterations in the brain’s reward system in IA, it seems possible that ODD/CD comorbidity may increase the risk of IA among adolescents with ADHD. In addition, poor family functioning, problems in school bonding, low academic achievement, and deviant peer relationships are among the main risk factors for substance use in adolescents with ADHD + ODD/CD (Lopez et al., 2008), and the same risk factors may also be linked to behavioral addictions in these individuals.

The prevalence of smoking in patients with ADHD ranges from 15% to 19% (Pliszka, 2007), and in line with these findings, 19.3% of our participants reported that they smoked cigarettes. Previous studies have shown that cigarette smoking increases the risk of substance use among both typically developing children and those with ADHD (Groenman et al., 2013). For that reason, Groenman et al. (2013) suggested that nicotine use could be considered a gateway to other drugs. Similarly, in a longitudinal study, Chang et al. (2014) suggested that smoking was a predictor of IA in adolescents. However, in their study of high-school students, Üneri and Tanıdır (2011) found no significant association between IA and smoking behavior, and in this study, we found no significant correlation between smoking status and IA in adolescents with ADHD.

Several limitations of this study must be acknowledged. First, as the cross-sectional design prevents us from clearly defining the direction of the casual relationship between ADHD, ODD/CD, and IA, prospective longitudinal studies are needed to clarify the temporal relationship. Second, any generalization of the results is limited by the lack of a control group comprising non-clinically referred adolescents or adolescents referred for other mental health problems. Third, IA was diagnosed on the basis of a self-report questionnaire, which may compromise the validity of diagnosis. However, as it seems more plausible that adolescents would minimize their problems with Internet use, the higher observed rate of IA in our sample seems to exclude this issue. Finally, our results were not controlled for other predictive variables such as comorbid depression and/or anxiety disorder or familial risk factors that may also have affected the prevalence and severity of IA in adolescents with ADHD.

Conclusions

IA can result in serious dysfunction in an individual’s academic and work performance, as well as in their family life, social relationships, physical health, and psychological well-being (Shek & Yu, 2012). IA may also worsen outcomes in the adolescents with ADHD who already have marked impairments in terms of scholastic adjustment and peer and familial relations (APA, 2013). In line with existing evidence (Weiss et al., 2011), the present findings confirm the significance of early detection and intervention of IA in the adolescents with ADHD. These findings also suggest that adolescents with ADHD + ODD/CD may be more vulnerable to IA than those with ADHD only and may need to be more carefully assessed in this regard. Further prospective studies are needed to more fully understand the associations between IA, ADHD, and ODD/CD. Finally, pre- and posttreatment studies evaluating IA symptoms in adolescents with ADHD only or with ADHD+ODD/CD may help to determine whether ADHD treatment reduces the risk of IA in both groups.

Authors’ contribution

HG, CT, HA, and OSU contributed to study concept and design. HG, CT, HA, AGK, CM, MKB, and MT contributed to data collection. CT and NB contributed to statistical analysis. HG, CT, AGK, CM, NB, and OSU contributed to analysis and interpretation of data. HG, CT, and MKB contributed to manuscript writing process. OSU contributed to study supervision. All authors had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Conflict of interest

The authors declare no conflict of interest.

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  • Kim, K., Ryu, E., Chon, M.-Y., Yeun, E.-J., Choi, S.-Y., Seo, J.-S., & Nam, B.-W. (2006). Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: A questionnaire survey. International Journal of Nursing Studies, 43(2), 185192. doi:10.1016/j.ijnurstu.2005.02.005

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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. Archives of Pediatrics & Adolescent Medicine, 163(10), 937. doi:10.1001/archpediatrics.2009.159

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  • Lam, L. T., Peng, Z., Mai, J., & Jing, J. (2009). The association between Internet addiction and self-injurious behaviour among adolescents. Injury Prevention, 15(6), 403408. doi:10.1136/ip.2009.021949

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, S. S., & Hinshaw, S. P. (2006). Predictors of adolescent functioning in girls with attention deficit hyperactivity disorder (ADHD): The role of childhood ADHD, conduct problems, and peer status. Journal of Clinical Child and Adolescent Psychology, 35(3), 356368. doi:10.1207/s15374424jccp3503_2

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    • Export Citation
  • Lee, S. S., Humphreys, K. L., Flory, K., Liu, R., & Glass, K. (2011). Prospective association of childhood attention-deficit/hyperactivity disorder (ADHD) and substance use and abuse/dependence: A meta-analytic review. Clinical Psychology Review, 31(3), 328341. doi:10.1016/j.cpr.2011.01.006

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    • Export Citation
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  • Lopez, B., Schwartz, S. J., Prado, G., Huang, S., Rothe, E. M., Wang, W., & Pantin, H. (2008). Correlates of early alcohol and drug use in Hispanic adolescents: Examining the role of ADHD with comorbid conduct disorder, family, school, and peers. Journal of Clinical Child & Adolescent Psychology, 37(4), 820832. doi:10.1080/15374410802359676

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  • Luman, M., Van Noesel, S. J. P., Papanikolau, A., Van Oostenbruggen-Scheffer, J., Veugelers, D., Sergeant, J. A., & Oosterlaan, J. (2009). Inhibition, reinforcement sensitivity and temporal information processing in ADHD and ADHD+ODD: Evidence of a separate entity? Journal of Abnormal Child Psychology, 37(8), 11231135. doi:10.1007/s10802-009-9334-0

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    • Search Google Scholar
    • Export Citation
  • Molina, B. S. G., & Pelham, W. E. (2003). Childhood predictors of adolescent substance use in a longitudinal study of children with ADHD. Journal of Abnormal Psychology, 112(3), 497507. doi:10.1037/0021-843X.112.3.497

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, B., Han, D. H., & Roh, S. (2017). Neurobiological findings related to Internet use disorders. Psychiatry and Clinical Neurosciences, 71(7), 467478. doi:10.1111/pcn.12422

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, S., Hong, K.-E. M., Park, E. J., Ha, K. S., & Yoo, H. J. (2013). The association between problematic Internet use and depression, suicidal ideation and bipolar disorder symptoms in Korean adolescents. Australian & New Zealand Journal of Psychiatry, 47(2), 153159. doi:10.1177/0004867412463613

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pliszka, S. (2007). Practice parameter for the assessment and treatment of children and adolescents with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 46(7), 894921. doi:10.1097/chi.0b013e318054e724

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shek, D. T. L., & Yu, L. (2012). Internet addiction phenomenon in early adolescents in Hong Kong. The Scientific World Journal, 2012, 19. Retrieved from http://www.hindawi.com/journals/tswj/2012/104304/

    • Search Google Scholar
    • Export Citation
  • Tsai, H. F., Cheng, S. H., Yeh, T. L., Shih, C. C., Chen, K. C., Yang, Y. C., & Yang, Y. K. (2009). The risk factors of Internet addiction – A survey of university freshmen. Psychiatry Research, 167(3), 294299. doi:10.1016/j.psychres.2008.01.015

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsitsika, A., Critselis, E., Louizou, A., Janikian, M., Freskou, A., Marangou, E., Kormas, G., & Kafetzis, D. (2011). Determinants of Internet addiction among adolescents: A case-control study. The Scientific World Journal, 11, 866874. doi:10.1100/tsw.2011.85

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turgay, A. (1994). Disruptive Behavior DisordersChild and Adolescent Screening and Rating Scales for Children, Adolescents, Parents and Teachers. West Blomfield, MI: Integrative Therapy Institute Publication.

    • Search Google Scholar
    • Export Citation
  • Umemoto, A., Lukie, C. N., Kerns, K. A., Müller, U., & Holroyd, C. B. (2014). Impaired reward processing by anterior cingulate cortex in children with attention deficit hyperactivity disorder. Cognitive, Affective, & Behavioral Neuroscience, 14(2), 698714. doi:10.3758/s13415-014-0298-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Üneri, Ö. Ş., & Tanıdır, C. (2011). Bir grup lise öğrencisinde internet bağımlılığı değerlendirmesi: Kesitsel bir çalışma [Evaluation of Internet addiction in a group of high school students: A cross-sectional study]. Dusunen Adam: The Journal of Psychiatry and Neurological Sciences, 24(4), 265272. doi:10.5350/DAJPN2011240402

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., Zhou, X., Lu, C., Wu, J., Deng, X., & Hong, L. (2011). Problematic Internet use in high school students in Guangdong Province, China. PLoS One, 6(5), e19660. doi:10.1371/journal.pone.0019660

    • Crossref
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  • Xu, J., Shen, L., Yan, C., Hu, H., Yang, F., Wang, L., Kotha, S. R., Zhang, L. N., Liao, X. P., Zhang, J., Ouyang, F. X., Zhang, J. S., & Shen, X. M. (2012). Personal characteristics related to the risk of adolescent Internet addiction: A survey in Shanghai, China. BMC Public Health, 12(1), 1106. doi:10.1186/1471-2458-12-1106

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The author instruction is available in PDF.
Please, download the file from HERE

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

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2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
sumbission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 850 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access
Purchase per Title  

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Publication
Programme
2021 Volume 10
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

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)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • 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 BÖTHE (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 LEJOXEUX (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)

 

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