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Clémence Cabelguen Department of Addiction Science and Liaison Psychiatry, Nantes University Hospital, Nantes, France

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Bruno Rocher Department of Addiction Science and Liaison Psychiatry, Nantes University Hospital, Nantes, France

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Juliette Leboucher Clinical Investigation Unit “Behavioral Addictions/Complex Affective Disorders”, Addictology and Psychiatry Department, Nantes University Hospital, Nantes, France

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Benoît Schreck Department of Addiction Science and Liaison Psychiatry, Nantes University Hospital, Nantes, France
SPHERE, Inserm 1246, University of Nantes and Tours, Nantes, France

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Gaëlle Challet-Bouju Clinical Investigation Unit “Behavioral Addictions/Complex Affective Disorders”, Addictology and Psychiatry Department, Nantes University Hospital, Nantes, France
SPHERE, Inserm 1246, University of Nantes and Tours, Nantes, France

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Jean-Benoît Hardouin SPHERE, Inserm 1246, University of Nantes and Tours, Nantes, France

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Marie Grall-Bronnec Department of Addiction Science and Liaison Psychiatry, Nantes University Hospital, Nantes, France
Clinical Investigation Unit “Behavioral Addictions/Complex Affective Disorders”, Addictology and Psychiatry Department, Nantes University Hospital, Nantes, France
SPHERE, Inserm 1246, University of Nantes and Tours, Nantes, France

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Open access

Abstract

Background and aims

Since June 2018, gaming disorder (GD) has been recognized as a disease. It is frequently associated with attention deficit hyperactivity disorder (ADHD), as there are common vulnerability factors and bidirectional interactions between the two disorders. This study aims to evaluate the presence of ADHD symptoms and predictive factors of ADHD among patients with GD.

Methods

Ninety-seven patients ≥16 years old referred to the University Hospital of Nantes between 2012 and 2020 for GD were included. The diagnosis of GD was given a posteriori in accordance with the new ICD-11 GD definition. ADHD was screened using the Adult-ADHD Self-Report Scale and the Wender-Utah Rating Scale. A multivariate logistic regression model was used to identify explanatory factors for ADHD-GD comorbidity.

Results

The rate of GD patients who screened positive for ADHD was 39%. Predictive factors of ADHD-GD comorbidity were impulsivity (higher score on the negative urgency dimension) and low self-esteem.

Discussion

The rate of ADHD found among patients with GD is consistent with that from the literature on internet GD but higher than that found for other behavioural addictions. The identification of a higher negative urgency score and low self-esteem as predictive factors of AHDH-GD comorbidity indicates that gaming could be considered a dysfunctional way to cope with emotional dysregulation in ADHD or to virtually escape.

Conclusions

Comorbid ADHD must be taken into consideration to minimize its functional impact on GD patients and gaming-related damage. In contrast, the evaluation of gaming habits in patients with ADHD could be useful for both prevention and care.

Abstract

Background and aims

Since June 2018, gaming disorder (GD) has been recognized as a disease. It is frequently associated with attention deficit hyperactivity disorder (ADHD), as there are common vulnerability factors and bidirectional interactions between the two disorders. This study aims to evaluate the presence of ADHD symptoms and predictive factors of ADHD among patients with GD.

Methods

Ninety-seven patients ≥16 years old referred to the University Hospital of Nantes between 2012 and 2020 for GD were included. The diagnosis of GD was given a posteriori in accordance with the new ICD-11 GD definition. ADHD was screened using the Adult-ADHD Self-Report Scale and the Wender-Utah Rating Scale. A multivariate logistic regression model was used to identify explanatory factors for ADHD-GD comorbidity.

Results

The rate of GD patients who screened positive for ADHD was 39%. Predictive factors of ADHD-GD comorbidity were impulsivity (higher score on the negative urgency dimension) and low self-esteem.

Discussion

The rate of ADHD found among patients with GD is consistent with that from the literature on internet GD but higher than that found for other behavioural addictions. The identification of a higher negative urgency score and low self-esteem as predictive factors of AHDH-GD comorbidity indicates that gaming could be considered a dysfunctional way to cope with emotional dysregulation in ADHD or to virtually escape.

Conclusions

Comorbid ADHD must be taken into consideration to minimize its functional impact on GD patients and gaming-related damage. In contrast, the evaluation of gaming habits in patients with ADHD could be useful for both prevention and care.

Introduction

In 2013, internet gaming disorder (IGD) was integrated into the 5th edition of the Diagnostic and Statistical Manual (DSM-5) (American Psychiatric Association, 2013). Since June 2018, the World Health Organization (WHO) has recognized gaming disorder (GD) as a stand-alone diagnosis in the 11th version of the International Classification of Diseases (ICD-11) (World Health Organization, 2019). GD is defined by 3 criteria: (1) impaired control over gaming; (2) increasing priority given to gaming over other activities to the extent that gaming takes precedence over other interests and daily activities; and (3) continuing or escalating gaming despite the occurrence of negative consequences. The behaviour pattern must be of sufficient severity to result in significant impairments in personal, family, social, educational, occupational or other important areas of functioning and normally evident for at least 12 months. In comparison with IGD, the criteria of tolerance, withdrawal, symptom preoccupation, concealing the time spent gaming or gaming as a way to relieve negative moods were not included in the ICD-11. Thus, the choice of either the DSM-5 or ICD-11 definition of the disorder may influence prevalence estimates.

A review of the literature analysing data from 27 worldwide studies reports a prevalence of IGD of approximately 4.7% (Feng, Ramo, Chan, & Bourgeois, 2017). The recent appearance of GD in international nomenclatures (ICD-11) means that prevalence data have not yet been collected.

Several risk factors for excessive gaming (online or not) have been identified (González-Bueso et al., 2018; Schou Andreassen et al., 2016). Sociodemographic factors included male sex, early age, single-parent family and maltreatment. Clinical factors include mood and anxiety disorders. Finally, psychopathological factors include impulsivity, withdrawal, reduced social and empathic skills, difficulties in emotional regulation, and attention problems. The influence of these factors on gaming is likely bidirectional (Gentile et al., 2011).

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder (American Psychiatric Association, 2013) characterized by a clinical triad of inattention, hyperactivity and impulsivity to varying extents. In 2018, González-Bueso et al. conducted a systematic review of 53,889 children and adults in the general population worldwide. Among the 24 included studies, only 8 used ADHD measurements, with an association between ADHD and IGD found in 7 of them (González-Bueso et al., 2018). In 2017, Yen et al. conducted a cross-sectional study on 174 adults aged 20–30 years old in Taiwan suffering from GD and found that the prevalence was 39% (OR = 13.5) (Yen et al., 2017). Another cross-sectional study found a 32.2% prevalence among internet addict students aged 18–27 years old in Taiwan (OR = 4.5) (Ko, Yen, Chen, Chen, & Yen, 2008). Measurements of ADHD can nevertheless be biased by the use of various screening or diagnostic tools. In the review of González-Bueso et al. (2018), the authors used the ADHD Self-Report Scale (ASRS), the Strengths and Difficulties Questionnaire, Dupaul’s ADHD scale or the ADHD DSM-IV-TR criteria. Yen et al. (2017) proposed an interview based on DSM-IV-TR criteria and assessed childhood ADHD history with the Kiddie-Schedule for Affective Disorders and Schizophrenia. Ko et al. (2008) developed a semistructured diagnostic tool for adult ADHD based on DSM-IV criteria. It is important to note that those three studies did not specifically evaluate ADHD in treatment-seeking samples. Thus, the prevalence rate may differ from studies that focus on patients seeking specific care for GD. Bias in prevalence estimates can also result from the difference between research on the complete syndrome of ADHD and research on only symptoms and to whether factors commonly associated with the disorder (e.g., increased risk in case of associated dyslexia) are considered.

To broadly analyse the link between ADHD and GD, we selected several domains of interest. On the one hand, we explored individual traits through innate abilities (e.g., temperament) or acquired abilities (e.g., self-esteem). We focused on impulsivity, which is a core feature of ADHD and a risk factor for developing behavioural addictions (Zilberman, Gal, Efrati, Neumark, & Rassovsky, 2018). We were particularly interested in the identification of low self-esteem, which was found to be strongly associated with ADHD (Harpin, Mazzone, Raynaud, Kahle, & Hodgkins, 2016) and is likely to interfere with gaming practice. On the other hand, we investigated social skills (Ryu et al., 2018) through relationship abilities and social anxiety.

To our knowledge, no work has studies the presence of ADHD symptoms and associated predictive factors among individuals diagnosed as pathological gamers according to the new ICD-11 definition. The analysis of factors associated with ADHD-GD comorbidity is of high importance to better identify the profiles of patients at risk for this comorbidity and to propose a more comprehensive and adjusted treatment for them. This study aims to fill this gap and analyse factors associated with ADHD-GD comorbidity.

Materials and methods

Procedure

Patients were recruited from the EVALuation of behavioural ADDictions (EVALADD) cohort (NCT01248767), which proposes a prospective follow-up of patients over 15.25 years old (the line separating paediatric and adult care in our hospital) initializing specific care for behavioural addiction at Nantes University Hospital, France. As part of the EVALADD procedure, patients completed a multiaxial psychological assessment through a face-to-face structured interview and self-administered questionnaires. The structured interview was conducted by trained research staff with experience with behavioural addictions. In our study, data were collected at the initiation of addictive care.

Participants

We selected 125 outpatients who were referred for excessive video game use (according to the patient him or herself or to his or her relatives) from January 2012 to January 2020. After exclusion of those who did not match the GD ICD-11 criteria or those for whom data were not exploitable, 97 patients were included in the analysis (Fig. 1).

Fig. 1.
Fig. 1.

Flowchart

Citation: Journal of Behavioral Addictions 10, 4; 10.1556/2006.2021.00074

Measures

Among the EVALADD procedures (see Fauconnier et al., 2020), only the measures specific to the domains of our study are detailed.

Gaming

A thorough investigation of the medical records of the 125 patients identified allowed us to search for the presence or absence of the three ICD-11 criteria during the 12 months preceding inclusion. Functional impairment was also systematically sought in the medical records. Only the presence of the 3 criteria and a functional impairment could lead to a diagnosis of GD and thus inclusion in the study. All cases were analysed by a researcher and the referred psychiatrist in a double-blind manner. Motives for gaming were explored using the Videogame Motivation Questionnaire (VMQ). The VMQ includes the same items and uses the same scoring system as the Gambling Motives Questionnaire (GMQ) (Stewart & Zack, 2008), which was developed from the Drinking Motives Questionnaire (DMQ). Only the instructions differ between the GMQ and the DMQ to be appropriate for the behaviour studied. The GMQ is characterized by high internal consistency factors (alpha = 0.91, 0.81 and 0.86 for the 3 subscales, see Stewart & Zack, 2008). In our study, these Cronbach’s alpha values were 0.76, 0.73 and 0.72, respectively.

ADHD

Each patient completed the 25-item Wender-Utah Rating Scale (WURS), a self-administered questionnaire that retrospectively explores symptoms of ADHD during childhood on a 5-point Likert Scale (Ward, Wender, & Reimherr, 1993). Patients ≥18 years old also completed the ASRS V1.1, a screening test based on the 18 diagnostic criteria of the DSM-IV-TR (Kessler et al., 2005). To consider ADHD symptoms present, patients <18 years old should have a WURS score ≥46/100. Patients ≥18 years old were considered to have ADHD symptoms if they had a WURS score ≥46/100 and a positive screening on the ASRS.

Psychopathology

Patients completed the Temperament and Character Inventory (TCI-125) (Cloninger, Przybeck, & Svrakic, 1994), the UPPS-P impulsivity behaviour scale (Lynam, Smith, Whiteside, & Cyders, 2006), the Rosenberg Self Esteem scale (RSE) (Rosenberg, 1965), the Liebowitz Social Anxiety Scale (LSAS) (Liebowitz, 1987) and the Relationship Scales Questionnaire (RSQ) (Griffin & Bartholomew, 1994).

Statistical analysis

The presence of ADHD symptoms in patients with GD was estimated by a proportion calculation. The analysis of predictive factors of comorbid ADHD-GD was realized by a two-step multivariate logistic regression model. First, we selected clinically relevant variables to explain the comorbidity of ADHD-GD. Given that ADHD is a neurodevelopmental condition, we decided to focus on several traits that appear during cognitive and psycho affective development in a diachronic way. We removed redundant variables for the analysis to avoid problems with collinearity (RSE – total, LSAS – diagnosis, LSAS – fear of performance, LSAS – avoidance of performance, LSAS – fear of social interaction and LSAS – avoidance of social interaction, RSQ – preoccupied, RSQ – fearful and RSQ – dismissive). Selected variables were compared with bivariate tests between patients with or without ADHD symptoms (Table 1). Two-by-two comparisons were performed by means of two sample t-tests for quantitative variables and Chi2 or Fisher’s exact tests (when appropriate) for qualitative variables. Second, variables significant at 0.20 in bivariate analyses were included in the multivariate logistic regression, and then variables nonsignificant at 0.05 were removed one at a time, starting with the least significant variable (backward procedure, Mickey & Greenland, 1989). The corresponding odds ratios (ORs) and associated 95% confidence intervals (95% CIs) were estimated to quantify the strength of the association between the predictive factors selected and the characteristics of interest.

Table 1.

Clinically relevant variables to explain the comorbidity of ADHD-GD (n = 97)

Total (n = 97) ADHD+ (n = 37) ADHD- (n = 58) OR P
Mean (SD) or n (percentage)
SOCIODEMOGRAPHIC CHARACTERISTICS
Family history of addiction 61 (63%) 27 (73%) 33 (57%) 2.045 0.116
GAMING
VMQ – coping (/20) 15 (3.5) 17 (3.0) 14 (3.4) 1.279 0.001
VMQ – positive reinforcement (/20) 15 (3.1) 16 (3.1) 14 (3.1) 1.142 0.071
VMQ – social (/20) 9 (2.8) 10 (3.3) 9 (2.4) 1.161 0.059
PSYCHOPATHOLOGY
TCI – novelty seeking (%) 54 (20.9) 61 (20.7) 50 (20.1) 1.028 0.014
TCI – harm avoidance (%) 57 (24.7) 63 (22.6) 53 (25.4) 1.018 0.058
TCI – reward dependence (%) 51 (17.5) 50 (17.3) 52 (17.8) 0.993 0.570
TCI – persistance (%) 32 (28.0) 31 (30.4) 33 (26.5) 0.997 0.668
UPPS-P – negative urgency (/16) 10 (3.1) 12 (3.1) 9 (2.5) 1.421 0.00006
UPPS-P – positive urgency (/16) 11 (2.9) 12 (2.8) 10 (2.7) 1.340 0.0007
UPPS-P – lack of premeditation (/16) 9 (2.8) 10 (3.2) 8 (2.4) 1.216 0.016
UPPS-P – lack of perseverance (/16) 10 (3.1) 11 (3.2) 10 (2.9) 1.182 0.023
UPPS-P – sensation seeking (/16) 10 (3.3) 11 (3.4) 10 (3.1) 1.127 0.079
RSE – low (<30/40) 69 (73%) 32 (87%) 37 (64%) 3.632 0.020
LSAS – total (/144) 51 (27.3) 61 (27.0) 45 (25.6) 1.024 0.006
RSQ – secure (/25) 15 (4.4) 14 (4.3) 16 (3.5) 0.896 0.064

Variables in grey were retained to be entered in the multivariate logistic regression model (i.e., variables significant at 0.20).

Abbreviations: Videogame Motives Questionnaire (VMQ); Temperament and Character Inventory (TCI); Urgency, Premeditation (lack of), Perseverance (lack of), Sensation seeking, Positive urgency (UPPS-P); Rosenberg Self-Esteem Scale (RSE); Liebowitz Social Anxiety Scale (LSAS); Relationship Scales Questionnaire (RSQ).

Ethics

Participants were informed of the research and provided written informed consent, including consent from parents or guardians for the participants under age 18, prior to their inclusion in the EVALADD cohort. The study was conducted in accordance with Good Clinical Practice Guidelines and the Declaration of Helsinki, with approval from the Ethics Committee in 2012, amended in 2018.

Results

Population characteristics

Among the 97 included patients, 91 (94%) were male. The average age was 23 years old (SD = 8). According to the Mini International Neuropsychiatric Interview (Sheehan et al., 1998), 49% of patients had major depressive disorder (current or past), and 26 (27%) were at risk of suicide (current or past). Among anxiety disorders, social phobia was the most common, found in 21 patients (22%). Nicotine use disorder was found in 30 patients (31%), and alcohol and cannabis use disorders were found in 16 patients (17%). No participant took methylphenidate, the only psychostimulant available in France for ADHD treatment.

Presence of ADHD symptoms in patients with GD

The rate of ADHD symptoms in patients with GD was estimated at 39% (SD = 5, 95% CI [29–50]) (missing data (MD), n = 2). Figure 1 shows the flowchart of our study and details the rates of symptoms of ADHD by patient age. These rates were 42% (95% CI [20–64]) in patients <18 years old and 38% (95% CI [27–49]) in patients ≥18 years old (MD = 2). Among patients ≥18 years old, two-thirds presented a combined type, and one-third presented an inattentive predominant type. None presented a hyperactive/impulsive predominant type.

Factors associated with ADHD-GD comorbidity

The final multivariate logistic regression model (pseudo R2 = 0.24) revealed the variables that were significantly associated with ADHD comorbidity in patients with GD: a higher score on the dimension “negative urgency” of the UPPS-P (OR = 1.52 95% CI [1.25–1.85], P < 0.0001) and low self-esteem as estimated by the RSE (OR = 6.60 95% CI [1.76–24.82], P < 0.005).

Discussion

Main results

The objective of the study was to evaluate the presence of ADHD symptoms among patients with GD diagnosed based on the new ICD-11 criteria and then to identify the predictive factors of GD-ADHD comorbidity. The rate of 39% is similar to rates found by Yen et al., in 2017 (39.1%) and Ko et al., in 2008 (32.2%) among patients with IGD, despite different diagnostic tests being used. Our results are also in line with those of two other studies examined in the review of González-Bueso et al. (2018). Panagiotidi et al. (2017) found a significant correlation between the ASRS and the Problem Video-Game Playing Test in 205 healthy adults aged 27.4 years old on average (R 2 = 0.22). Vadlin, Åslund, Hellström, and Nilsson (2016) identified an association between symptoms of ADHD and problematic gaming in two cohorts of 1868 and 242 Swedish adolescents in the general population aged 12–18 (OR = 2.43, 95% CI [1.44–4.11]). The results of the other studies retained by Gonzàlez-Bueso are difficult to compare with ours for several reasons: the tests used to identify ADHD symptoms were different, the sample comprised children and adults (the estimated prevalence of ADHD in children is almost twice that in adults), and study designs were of different types.

The 39% rate of ADHD symptoms among patients with GD is particularly high compared to those of other behavioural addictions: 20–25% among patients with gambling disorder (Fatseas et al., 2016), 25% among patients with sex addiction (Reid, Carpenter, Gilliland, & Karim, 2011), and one-third among patients with food addiction (Davis et al., 2011). Contrary to these studies, we focused on a young, treatment-seeking sample, which limits the comparison between them. However, beyond common vulnerability factors between ADHD and diverse behavioural addictions (male sex, neurocognitive traits such as deficient inhibitory control, poor decision making and attentional bias, for example), this high prevalence could be explained by early and easy access to gaming. Moreover, gaming can enhance hyperfocus, which has been reported by many patients with ADHD (Hupfeld, Abagis, & Shah, 2019). Video games offer an immersive experience to patients while they remain active, contrary to other addictive behaviours. This could suggest to patients that some aspects of ADHD symptomatology may be positive (Sedgwick, Merwood, & Asherson, 2019). This comforting impression may act as a positive reinforcer of gaming and explain the choice of that addiction object for these patients.

Among the many factors evaluated as predictive of comorbid ADHD-GD, only high negative urgency and low self-esteem were significant. Persons with a marked negative urgency trait, when subjected to negative emotions, feel compelled to act immediately, in an inadequate way, to dispose of this trait (Anestis, Selby, & Joiner, 2007). In gaming, high negative urgency has been found to be associated with problematic use of MMO-RPGs in players interviewed in cybercafés (Billieux et al., 2011). High urgency was also a characteristic found in a cluster of gamers displaying signs of problematic online gaming use (Billieux et al., 2015). Emotional dysregulation is a core feature associated with the clinical triad (inattention, hyperactivity, impulsivity) of ADHD (Shaw, ArgyrisStringaris, Joel, & Leibenluft, 2014) and is often neglected. Among patients with ADHD, gaming could be used to fulfil an urgent need to calm negative feelings.

Furthermore, among ADHD-GD patients, low self-esteem could lead to the use of video games to feel accomplished, to feel control, or to reassure themselves of their skills (King & Delfabbro, 2014). It can also provide an escape from unpleasant feelings secondary to negative beliefs about themselves, or it can provide support from their gaming community.

Strengths and limitations

This study has several limitations. It is a French, single-centre study. The cross-sectional design of our study limits the interpretation of data and exploration of the causal link between ADHD and GD. The sample size was small compared to those of previous studies. This feature and our design could have hindered the identification of other predictive variables of GD-ADHD comorbidity. ADHD evaluation was heterogeneous between patients <18 and ≥18 years old. However, we decided to include younger patients to enhance the size of our sample and to increase the scope of our results. Furthermore, we used the VMQ, which is a nonvalidated scale, to explore the motives of gamers.

These limitations are compensated by the strengths of the work. We used a clinical sample of typical patients with GD (only outpatients, the large majority were male, and they were 24 years old on average at inclusion). GD was explored clinically, not via self-assessment, as commonly done in studies of gaming. The innovative nature of the study is based on the use of the new ICD-11 definition for GD. Moreover, evaluations of gaming and psychopathology characteristics were based on a clinical interview. The ASRS V1.1 and the WURS are both recommended for screening ADHD, especially in a French sample (Weibel et al., 2020). These scales, which have been mainly validated in the general population, have a higher rate of false positives in clinical samples. We were particularly stringent with patients ≥18 years old with a screening estimated positive if WURS + ASRS were both positive, as we complied with the condition of use of ASRS (Kessler et al., 2005). This may have limited the risk of over screening. Nevertheless, diagnosing ADHD requires a clinical interview, and our procedure permitted the identification of only ADHD symptoms.

Conclusions

The rate of ADHD symptoms among patients with GD was estimated to be 39%. This association opens up new clinical and therapeutic opportunities. First, the screening of ADHD in patients with GD appears essential. Conversely, a rigorous evaluation of video game practice in ADHD patients seems indispensable. This increased vigilance is legitimized by specific efficient care that exists for ADHD. The early identification/treatment of ADHD may help minimize its functional impact on GD patients as well as mitigate gaming-related damage. Moreover, patients suffering from ADHD and GD seem to present low self-esteem and a higher level of negative urgency, which can make them more vulnerable to mood disorders (Sowislo & Orth, 2013) and addictive disorders (Anestis, Selby, & Thomas, 2007), respectively. In contrast, the evaluation of video game use in ADHD patients could be useful for both prevention and care. It might be relevant for some patients with ADHD to add specific therapeutic tools targeted to GD (e.g., individual or group cognitive behavioural therapy (King et al., 2017)) to their medical care for ADHD.

Funding sources

No financial support was received for this study.

Authors’ contribution

Original idea, study design: MGB, JBH. Assessments: BR, JL, GCB. Statistical analysis and interpretation of data: CC, JBH, MGB. First draft of the manuscript: CC. EVALADD cohort supervision: GCB, MGB. Contribution to the final manuscript: MGB, JBH, BR, JL, GCB, BS. 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

Authors declare no conflict of interest.

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  • King, D. L. , Delfabbro, P. H. , Wu, A. M. S. , Yim Doh, Y. , Kuss, D. J. , Pallesen, S. , … Sakuma, H. (2017). Treatment of internet gaming disorder: An international systematic review and CONSORT evaluation. Clinical Psychology Review, 54, 123133. https://doi.org/10.1016/j.cpr.2017.04.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ko, C. H. , Yen, J. Y. , Chen, C. S. , Chen, C. C. , & Yen, C. F. (2008). Psychiatric comorbidity of internet addiction in college students: An interview study. CNS Spectrums, 13(2), 147153. https://doi.org/10.1017/s1092852900016308.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebowitz, M. R. (1987). Social phobia. Modern Problems of Pharmacopsychiatry, 22, 141173. https://doi.org/10.1159/000414022.

  • Lynam, D. R. , Smith, G. T. , Whiteside, S. P. , & Cyders, M. A. (2006). The UPPS-P: Assessing five personality pathways to impulsive behavior. Technical report. West Lafayette: Purdue University.

    • Search Google Scholar
    • Export Citation
  • Mickey, R. M. , & Greenland, S. (1989). The impact of confounder selection criteria on effect estimation. American Journal of Epidemiology, 129(1), 125137. https://doi.org/10.1093/oxfordjournals.aje.a115101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Panagiotidi, M. (2017). Problematic video game play and ADHD traits in an adult population. Cyberpsychology, Behavior and Social Networking, 20(5), 292295. https://doi.org/10.1089/cyber.2016.0676.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reid, R. C. , Carpenter, B. N. , Gilliland, R. , & Karim, R. (2011). Problems of self-concept in a patient sample of hypersexual men with attention-deficit disorder. Journal of Addiction Medicine, 5(2), 134140. https://doi.org/10.1097/ADM.0b013e3181e6ad32.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenberg, M. (1965). Society and the adolescent self-image (pp. 560562). Princeton.

  • Ryu, H. , Lee, J.-Y. , Choi, A. , Park, S. , Kim, D.-J. , & Choi, J.-S. (2018). The relationship between impulsivity and internet gaming disorder in young adults: Mediating effects of interpersonal relationships and depression. International Journal of Environmental Research and Public Health, 15(3). https://doi.org/10.3390/ijerph15030458.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schou, A. , Cecilie , Billieux, J. , Griffiths, M. D. , Kuss, D. J. , Demetrovics, Z. , … Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors, 30(2), 252262. https://doi.org/10.1037/adb0000160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sedgwick, J. A. , Merwood, A. , & Asherson, P. (2019). The positive aspects of attention deficit hyperactivity disorder: A qualitative investigation of successful adults with ADHD. Attention Deficit and Hyperactivity Disorders, 11(3), 241253. https://doi.org/10.1007/s12402-018-0277-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shaw, P. , Stringaris, A. , Nigg, J. , & Leibenluft, E. (2014). Emotion dysregulation in attention deficit hyperactivity disorder. The American Journal of Psychiatry, 171(3), 276293. https://doi.org/10.1176/appi.ajp.2013.13070966.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheehan, D. V. , Lecrubier, Y. , Sheehan, K. H. , Amorim, P. , Janavs, J. , Weiller, E. , … Dunbar, G. C. (1998). The mini-international neuropsychiatric interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59(Suppl 20), 2233 quiz 34–57. PMID 9881538.

    • Search Google Scholar
    • Export Citation
  • Sowislo, J. F. , & Orth, U. (2013). Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies. Psychological Bulletin, 139(1), 213240. https://doi.org/10.1037/a0028931.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stewart, S. H. , & Zack, M. (2008). Development and psychometric evaluation of a three-dimensional gambling motives questionnaire. Addiction (Abingdon, England), 103(7), 11101117. https://doi.org/10.1111/j.1360-0443.2008.02235.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vadlin, S. , Åslund, C. , Hellström, C. , & Nilsson, K. W. (2016). Associations between problematic gaming and psychiatric symptoms among adolescents in two samples. Addictive Behaviors, 61, 815. https://doi.org/10.1016/j.addbeh.2016.05.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ward, M. F. , Wender, P. H. , & Reimherr, F. W. (1993). The Wender Utah Rating Scale: An aid in the retrospective diagnosis of childhood attention deficit hyperactivity disorder. The American Journal of Psychiatry, 150(6), 885890. https://doi.org/10.1176/ajp.150.6.885.

    • Search Google Scholar
    • Export Citation
  • Weibel, S. , Menard, O. , Ionita, A. , Boumendjel, M. , Cabelguen, C. , Kraemer, C. , … Lopez, R. (2020). Practical considerations for the evaluation and management of attention deficit hyperactivity disorder (ADHD) in adults. L’Encephale, 46(1), 3040. https://doi.org/10.1016/j.encep.2019.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • World Health Organization (2019). ICD-11 : International statistical classification of diseases and related health problems. 11th version.

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    • Export Citation
  • Yen, J.-Y. , Liu, T.-L. , Wang, P.-W. , Chen, C.-S. , Yen, C.-F. , & Ko, C.-H. (2017). Association between internet gaming disorder and adult attention deficit and hyperactivity disorder and their correlates: Impulsivity and hostility. Addictive Behaviors, 64, 308313. https://doi.org/10.1016/j.addbeh.2016.04.024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zilberman, N. , Yadid, G , Efrati, Y. , Neumark, Y. , & Rassovsky, Y. (2018). Personality profiles of substance and behavioral addictions. Addictive Behaviors, 82, 174181. https://doi.org/10.1016/j.addbeh.2018.03.007.

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  • King, D. L. , & Delfabbro, P. H. (2014). The cognitive psychology of internet gaming disorder. Clinical Psychology Review, 34(4), 298308. https://doi.org/10.1016/j.cpr.2014.03.006.

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  • King, D. L. , Delfabbro, P. H. , Wu, A. M. S. , Yim Doh, Y. , Kuss, D. J. , Pallesen, S. , … Sakuma, H. (2017). Treatment of internet gaming disorder: An international systematic review and CONSORT evaluation. Clinical Psychology Review, 54, 123133. https://doi.org/10.1016/j.cpr.2017.04.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ko, C. H. , Yen, J. Y. , Chen, C. S. , Chen, C. C. , & Yen, C. F. (2008). Psychiatric comorbidity of internet addiction in college students: An interview study. CNS Spectrums, 13(2), 147153. https://doi.org/10.1017/s1092852900016308.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebowitz, M. R. (1987). Social phobia. Modern Problems of Pharmacopsychiatry, 22, 141173. https://doi.org/10.1159/000414022.

  • Lynam, D. R. , Smith, G. T. , Whiteside, S. P. , & Cyders, M. A. (2006). The UPPS-P: Assessing five personality pathways to impulsive behavior. Technical report. West Lafayette: Purdue University.

    • Search Google Scholar
    • Export Citation
  • Mickey, R. M. , & Greenland, S. (1989). The impact of confounder selection criteria on effect estimation. American Journal of Epidemiology, 129(1), 125137. https://doi.org/10.1093/oxfordjournals.aje.a115101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Panagiotidi, M. (2017). Problematic video game play and ADHD traits in an adult population. Cyberpsychology, Behavior and Social Networking, 20(5), 292295. https://doi.org/10.1089/cyber.2016.0676.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reid, R. C. , Carpenter, B. N. , Gilliland, R. , & Karim, R. (2011). Problems of self-concept in a patient sample of hypersexual men with attention-deficit disorder. Journal of Addiction Medicine, 5(2), 134140. https://doi.org/10.1097/ADM.0b013e3181e6ad32.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenberg, M. (1965). Society and the adolescent self-image (pp. 560562). Princeton.

  • Ryu, H. , Lee, J.-Y. , Choi, A. , Park, S. , Kim, D.-J. , & Choi, J.-S. (2018). The relationship between impulsivity and internet gaming disorder in young adults: Mediating effects of interpersonal relationships and depression. International Journal of Environmental Research and Public Health, 15(3). https://doi.org/10.3390/ijerph15030458.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schou, A. , Cecilie , Billieux, J. , Griffiths, M. D. , Kuss, D. J. , Demetrovics, Z. , … Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors, 30(2), 252262. https://doi.org/10.1037/adb0000160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sedgwick, J. A. , Merwood, A. , & Asherson, P. (2019). The positive aspects of attention deficit hyperactivity disorder: A qualitative investigation of successful adults with ADHD. Attention Deficit and Hyperactivity Disorders, 11(3), 241253. https://doi.org/10.1007/s12402-018-0277-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shaw, P. , Stringaris, A. , Nigg, J. , & Leibenluft, E. (2014). Emotion dysregulation in attention deficit hyperactivity disorder. The American Journal of Psychiatry, 171(3), 276293. https://doi.org/10.1176/appi.ajp.2013.13070966.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheehan, D. V. , Lecrubier, Y. , Sheehan, K. H. , Amorim, P. , Janavs, J. , Weiller, E. , … Dunbar, G. C. (1998). The mini-international neuropsychiatric interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59(Suppl 20), 2233 quiz 34–57. PMID 9881538.

    • Search Google Scholar
    • Export Citation
  • Sowislo, J. F. , & Orth, U. (2013). Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies. Psychological Bulletin, 139(1), 213240. https://doi.org/10.1037/a0028931.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stewart, S. H. , & Zack, M. (2008). Development and psychometric evaluation of a three-dimensional gambling motives questionnaire. Addiction (Abingdon, England), 103(7), 11101117. https://doi.org/10.1111/j.1360-0443.2008.02235.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vadlin, S. , Åslund, C. , Hellström, C. , & Nilsson, K. W. (2016). Associations between problematic gaming and psychiatric symptoms among adolescents in two samples. Addictive Behaviors, 61, 815. https://doi.org/10.1016/j.addbeh.2016.05.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ward, M. F. , Wender, P. H. , & Reimherr, F. W. (1993). The Wender Utah Rating Scale: An aid in the retrospective diagnosis of childhood attention deficit hyperactivity disorder. The American Journal of Psychiatry, 150(6), 885890. https://doi.org/10.1176/ajp.150.6.885.

    • Search Google Scholar
    • Export Citation
  • Weibel, S. , Menard, O. , Ionita, A. , Boumendjel, M. , Cabelguen, C. , Kraemer, C. , … Lopez, R. (2020). Practical considerations for the evaluation and management of attention deficit hyperactivity disorder (ADHD) in adults. L’Encephale, 46(1), 3040. https://doi.org/10.1016/j.encep.2019.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • World Health Organization (2019). ICD-11 : International statistical classification of diseases and related health problems. 11th version.

    • Search Google Scholar
    • Export Citation
  • Yen, J.-Y. , Liu, T.-L. , Wang, P.-W. , Chen, C.-S. , Yen, C.-F. , & Ko, C.-H. (2017). Association between internet gaming disorder and adult attention deficit and hyperactivity disorder and their correlates: Impulsivity and hostility. Addictive Behaviors, 64, 308313. https://doi.org/10.1016/j.addbeh.2016.04.024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zilberman, N. , Yadid, G , Efrati, Y. , Neumark, Y. , & Rassovsky, Y. (2018). Personality profiles of substance and behavioral addictions. Addictive Behaviors, 82, 174181. https://doi.org/10.1016/j.addbeh.2018.03.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
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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

Indexing and Abstracting Services:

  • Web of Science [Science Citation Index Expanded (also known as SciSearch®)
  • Journal Citation Reports/Science Edition
  • Social Sciences Citation Index®
  • Journal Citation Reports/ Social Sciences Edition
  • Current Contents®/Social and Behavioral Sciences
  • EBSCO
  • GoogleScholar
  • PsycINFO
  • PubMed Central
  • SCOPUS
  • Medline
  • CABI
  • CABELLS Journalytics

2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

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
submission  
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 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
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

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

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)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • 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)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • 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 (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Astrid MÜLLER  (Hannover Medical School, Germany)
  • 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)
  • Róbert URBÁN  (ELTE Eötvös Loránd University, Hungary)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN  (Ariel University, Israel)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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