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Lijuan Shi School of Education, Hunan University of Science and Technology, Xiangtan, Hunan, China

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Yuanyuan Wang The National Clincial Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China

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Hui Yu Division of Psychology, Faculty of Health and Life Sciences, De Montfort University, Leicester, UK

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Amanda Wilson Division of Psychology, Faculty of Health and Life Sciences, De Montfort University, Leicester, UK

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Stephanie Cook Division of Psychology, Faculty of Health and Life Sciences, De Montfort University, Leicester, UK

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Zhizhou Duan School of Health Sciences, Wuhan University, Wuhan, Hubei Province, China

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Ke Peng The George Institute for Global Health, UNSW, Sydney, Australia
School of Public Health, The University of Sydney, Sydney, Australia

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Zhishan Hu Faculty of Health Sciences, University of Macau, Macau SAR, China

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Jianjun Ou Department of Psychiatry and Mental Health Institute, Second Xiangya Hospital, Central South University, Chinese National Clinical Research Centre on Mental Disorders, Changsha, Hunan, China

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Suqian Duan Department of Psychiatry, Southern Medical University Nanfang Hospital, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brian Science and Brain-Inspired Intelligence, Guangzhou, Guangdong, China

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Yuan Yang Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

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Jiayu Ge School of Education, Hunan University of Science and Technology, Xiangtan, Hunan, China

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Hongyan Wang School of Education, Hunan University of Science and Technology, Xiangtan, Hunan, China

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Li Chen School of Education, Hunan University of Science and Technology, Xiangtan, Hunan, China

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Kaihong Zhao School of Education, Hunan University of Science and Technology, Xiangtan, Hunan, China

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Runsen Chen The National Clincial Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
Department of Psychiatry and Mental Health Institute, Second Xiangya Hospital, Central South University, Chinese National Clinical Research Centre on Mental Disorders, Changsha, Hunan, China
Division of Psychology, Faculty of Health and Life Sciences, De Montfort University, Leicester, UK

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Abstract

Background

The aim of this study was to investigate the mechanisms of Internet gaming disorder (IGD) and the associated interaction effects of childhood trauma, depression and anxiety in college students.

Methods

Participants were enrolled full-time as freshmen at a University in the Hunan province, China. All participants reported their socio-demographic characteristics and undertook a standardized assessment on childhood trauma, anxiety, depression and IGD. The effect of childhood trauma on university students' internet gaming behaviour mediated by anxiety and depression was analysed using structural equation modelling (SEM) using R 3.6.1.

Results

In total, 922 freshmen participated in the study, with an approximately even male-to-female ratio. A mediation model with anxiety and depression as the mediators between childhood trauma and internet gaming behaviour allowing anxiety and depression to be correlated was tested using SEM. The SEM analysis revealed that a standardised total effect of childhood trauma on Internet gaming was 0.18, (Z = 5.60, 95% CI [0.02, 0.05], P < 0.001), with the direct effects of childhood trauma on Internet gaming being 0.11 (Z = 3.41, 95% CI [0.01, 0.03], P = 0.001), and the indirect effects being 0.02 (Z = 2.32, 95% CI [0.00, 0.01], P = 0.020) in the pathway of childhood trauma-depression-internet gaming; and 0.05 (Z = 3.67, 95% CI [0.00, 0.02], P < 0.001) in the pathway of childhood trauma-anxiety-Internet gaming. In addition, the two mediators anxiety and depression were significantly correlated (r = 0.50, Z = 13.54, 95% CI [3.50, 5.05], P < 0.001).

Conclusions

The study revealed that childhood trauma had a significant impact on adolescents' Internet gaming behaviours among college students. Anxiety and depression both significantly mediated the relationship between childhood trauma and internet gaming and augmented its negative influence. Discussion of the need to understand the subtypes of childhood traumatic experience in relationship to addictive behaviours is included.

Abstract

Background

The aim of this study was to investigate the mechanisms of Internet gaming disorder (IGD) and the associated interaction effects of childhood trauma, depression and anxiety in college students.

Methods

Participants were enrolled full-time as freshmen at a University in the Hunan province, China. All participants reported their socio-demographic characteristics and undertook a standardized assessment on childhood trauma, anxiety, depression and IGD. The effect of childhood trauma on university students' internet gaming behaviour mediated by anxiety and depression was analysed using structural equation modelling (SEM) using R 3.6.1.

Results

In total, 922 freshmen participated in the study, with an approximately even male-to-female ratio. A mediation model with anxiety and depression as the mediators between childhood trauma and internet gaming behaviour allowing anxiety and depression to be correlated was tested using SEM. The SEM analysis revealed that a standardised total effect of childhood trauma on Internet gaming was 0.18, (Z = 5.60, 95% CI [0.02, 0.05], P < 0.001), with the direct effects of childhood trauma on Internet gaming being 0.11 (Z = 3.41, 95% CI [0.01, 0.03], P = 0.001), and the indirect effects being 0.02 (Z = 2.32, 95% CI [0.00, 0.01], P = 0.020) in the pathway of childhood trauma-depression-internet gaming; and 0.05 (Z = 3.67, 95% CI [0.00, 0.02], P < 0.001) in the pathway of childhood trauma-anxiety-Internet gaming. In addition, the two mediators anxiety and depression were significantly correlated (r = 0.50, Z = 13.54, 95% CI [3.50, 5.05], P < 0.001).

Conclusions

The study revealed that childhood trauma had a significant impact on adolescents' Internet gaming behaviours among college students. Anxiety and depression both significantly mediated the relationship between childhood trauma and internet gaming and augmented its negative influence. Discussion of the need to understand the subtypes of childhood traumatic experience in relationship to addictive behaviours is included.

Introduction

In the current Cyber age, there are many studies focused on the association between problematic behaviours or symptoms and Internet engagement (Brand, Young, Laier, Wolfling, & Potenza, 2016; Sigerson, Li, Cheung, Luk, & Cheng, 2017). However, the umbrella term ‘Internet addiction’ is over-simplistic. It fails to specify the various problematic behaviours resulting from Internet engagement and how these diverse behaviours could be caused by different underlying mechanisms (Kuss, Griffiths, & Pontes, 2017). The Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) have proposed that there is a need to assess Internet gaming disorder (IGD), adding IGD to the ‘emerging measures and models’ section of mental conditions in need of further research (American Psychiatric Association, 2013). According to the DMS-5, IGD refers to problematic and repetitive use of Internet-based games. Internet gaming disorder was also proposed in the 11th revision of International Classification of Diseases (ICD-11) (Rumpf et al., 2018). Hence, the research area is attracting increasing attention.

Over the past decade, research on IGD has sharply increased. Research has demonstrated that IGD has a negative impact on mental health, well-being and everyday functioning (Sarda, Begue, Bry, & Gentile, 2016). There are a handful of studies that report the prevalence of IGD, however, this research is inconsistent, with the prevalence varying from 1.2% to 46% (Kircaburun, Griffiths, & Billieux, 2019; Lemmens, Valkenburg, & Gentile, 2015; Muller et al., 2015; Rehbein, Kliem, Baier, Mossle, & Petry, 2015; Wan & Chiou, 2006). Studies have also identified factors associated with IGD/problematic Internet use, which include depression, anxiety and childhood trauma (Kircaburun et al., 2019; Sarda et al., 2016; Yates, Gregor, & Haviland, 2012). Individuals with disorders such as depression and anxiety were reported to be at a higher risk of developing problematic gaming (Kircaburun et al., 2019). Furthermore, Sarda et al. (2016) reported that IGD was significantly associated with depression and anxiety. A previous study in University students further found that having a history which included a traumatic experience increased the likelihood of problematic Internet use (Dalbudak, Evren, Aldemir, & Evren, 2014). Moreover, Yates et al. (2012) suggested that college students with maltreatment during childhood were at a disproportionally high risk for excessive Internet use. As a result the researchers suggested that emotion regulation should be the focus when providing therapeutic prevention and intervention to students (Yates et al., 2012). Kardefelt-Winther (2014) proposed that Internet addiction could be regarded as a compensatory coping strategy for psychological symptoms such as depression, anxiety and traumatic events (Kardefelt-Winther, 2014). IGD could then in return act as a maladaptive coping strategy when dealing with traumatic life events (Kircaburun et al., 2019). Thus, based on the literature, childhood trauma, depression and anxiety have been associated with IGD, while childhood trauma has appeared to result in depression and anxiety.

Despite abundant studies on IGD, studies using a framework that explains the relationships amongst IGD, depression, anxiety and childhood trauma are non-existent. We therefore aimed to conduct a study to investigate the relationship between childhood trauma, depression, anxiety and IGD in Chinese college students. The current study examined the direct and indirect relationships between childhood trauma and IGD through the effect of depressive symptoms and anxiety symptoms. We hypothesised that childhood trauma would be directly associated with IGD and indirectly associated with IGD via depression and anxiety.

Methods

Participants

A total of 941 Chinese students were recruited at a local Hunan University, China, between October 2018 and December 2018. Out of the 941 students, 922 completed the questionnaires. All eligible participants were full-time freshmen enrolled at the university.

Materials

Socio-demographic characteristics of the college students were collected, including ethnicity, gender, age, height, weight, whether they were an only child, yearly family income and the history of family mental illness.

The Childhood Trauma Questionnaire Short Form (CTQ-SF)

The Childhood Trauma Questionnaire Short Form (CTQ-SF) consists of 28 items (25 clinical items and 3 validation items) on a 5-point Likert scale, which are scored from 1 (never true) to 5 (very often true). It covers five types of Childhood trauma: emotional abuse, emotional neglect, sexual abuse, physical neglect and physical abuse. The sum scores of the CTQ-SF ranges from 25 to 125 points, with a higher score indicating a higher severity of Childhood trauma (Bernstein et al., 2003). The reliability of the Chinese version of CTQ-SF scale has been confirmed (the Cronbach's alpha was 0.77) (Zhao, Zhang, Li, Zhou, Li, & Yang, 2005). Cronbach's alpha was 0.82 in the current sample.

The Generalized Anxiety Disorder-7 (GAD-7)

Screening of anxiety symptoms was implemented by the GAD-7 scale. It is comprised of 7 items on a 4-point Likert scale. The total score of the GAD-7 ranges from 0 to 21, with a higher score indicating a more severe level of anxiety. It has been reported that the Chinese version of GAD-7 exhibits good validity and reliability, with a Cronbach's alpha of 0.88 (Yu et al., 2016). Cronbach's alpha was 0.86 in the current sample.

Figure 1.
Figure 1.

Final model with the standardized coefficients, and unstandardized coefficients presented in the parentheses. Note. *P < 0.05, **P < 0.01, ***P < 0.001

Citation: Journal of Behavioral Addictions 9, 1; 10.1556/2006.2020.00002

The Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR)

The level of depressive symptoms was measured by the QIDS-SR scale, which contains nine types of depressive symptoms. The QIDS-SR consists of 16 items on a 4-point Likert scale and the total score ranges from 0 to 27, with a higher score indicating a more severe level of depressive symptoms. The reliability of the Chinese version of QIDS-SR has been confirmed by (Liu et al., 2013) (the Cronbach's alpha was 0.74). Cronbach's alpha was 0.70 in the current sample.

The Internet Gaming Disorder Scale (IGD)

The 9-item IGD scale can assess Internet addiction based on the DSM-5 IGD criteria (Sigerson et al., 2017), which is the short version of the 20 item IGD scale (Pontes, Kiraly, Demetrovics, & Griffiths, 2014). Each item is answered by either yes or no (yes = 1 and no = 0). The sum of the IGD score ranges from 0 to 9, with a higher score indicating a higher possibility of IGD. The Chinese version of the IGD scale has been validated in China, with a Cronbach's alpha of 0.91 (Sigerson et al., 2017). Cronbach's alpha was 0.65 in the current sample.

Statistical Analysis

The data was analysed using R for Mac, version 3.6.1. We first ran the descriptive statistics of the socio-demographic characteristics among the college student population. Then, correlation tests were performed to explore the associations among CTQ, Anxiety, Depression and IGD. Finally, the model analysis was conducted using R LAVAAN package (Rosseel, 2012). The significance level was set as α = 0.05 (two-tailed) in all data analysed.

Ethics

Ethical approval was obtained from the Ethics Committee of Second Xiangya Hospital, Central South University, China. All participants were fully informed about the purpose of this investigation, agreed to participate in this survey and provided written informed consent voluntarily.

Results

Sample Characteristics

Table 1 shows the socio-demographic data of the 922 individuals who participated in this study. Most of the Chinese college students were Han ethnic (92.3%), and the male-to-female ratio was approximately 50%. Most of the participants were 18–19 years old (71.8%) with a normal BMI (77.3%). About two-thirds of the participants had siblings (67.9%). Only 4.0% of the students reported that their family income was inadequate and 7.0% reported that they had a family history of mental illness. 5.5% of the individuals met the criteria for IGD (cut-off score of 5).

Table 1.

Socio-demographic characteristics of college students

Characteristic Number Percent(%)
Ethnic
Han 851 92.3
Others 71 7.7
Gender
Men 447 48.5
Women 475 51.5
Age
16–17 178 19.3
18–19 662 71.8
≥20 82 8.9
BMI*
Thin 72 7.8
Normal 713 77.3
Overweight 107 11.6
Obesity 26 2.8
Being the only child
Yes 295 32.1
No 625 67.9
Yearly family income
Low 37 4.0
Average 442 48.0
High 441 48.0
History of family mental illness
Yes 64 7.0
No 856 93.0
IGD
Yes 50 5.5
No 862 94.5

Note: IDG = Internet Gaming Disorder.

*Different gender using different criteria.

Descriptive Statistics

Results from the correlation analysis showed that childhood trauma (M = 35.32, SD = 8.10) was significantly correlated with anxiety (M = 3.14, SD = 3.22), r = 0.24, P < 0.001 and with depression (M = 4.61, SD = 3.17), r = 0.33, P < 0.001 and with internet gaming disorder (M = 1.38, SD = 1.61), r = 0.19, P < 0.001. Internet gaming disorder was also significantly correlated with anxiety, r = 0.20, P < 0.001 and with depression, r = 0.25, P < 0.001. In addition, anxiety and depression were also highly correlated, r = 0.59, P < 0.001.

An independent t-test was conducted to test the gender difference in IGD scores. Result revealed that female students (M = 1.43, SD = 1.70) reported similar levels of Internet gaming disorder in comparison to male students (M = 1.34, SD = 1.52), t (910) = 0.90, P = 0.368.

Mediation Analysis

A mediation model with anxiety and depression being the mediators between childhood trauma and internet gaming, allowing anxiety and depression to be correlated was tested using SEM (Duan et al., 2019). The SEM analysis revealed that a standardised total effect of childhood trauma on Internet gaming disorder was 0.18, (Z = 5.60, 95% CI [0.02, 0.05], P < 0.001), with the direct effects of childhood trauma on Internet gaming being 0.11 (Z = 3.41, 95% CI [0.01, 0.03], P = 0.001), and the indirect effects being 0.02 (Z = 2.32, 95% CI [0.00, 0.01], P = 0.020) in the pathway of childhood trauma-depression-Internet gaming disorder; and 0.05 (Z = 3.67, 95% CI [0.00, 0.02], P < 0.001) in the pathway of childhood trauma-anxiety-Internet gaming disorder. In addition, the two mediators, anxiety and depression, were significantly correlated (r = 0.50, Z = 13.54, 95% CI [3.50, 5.05], P < 0.001) (Fig. 1).

Discussion

We investigated the relationship between childhood trauma and IGD via the mediation effects of depression and anxiety. The results showed that childhood trauma impacted IGD directly and indirectly via depression and anxiety. Anxiety and depression partially mediated the effects of childhood trauma on IGD. These results are meaningful and have practical implications when conceptualized into a mediation framework. The proposed framework could be used by researchers in the field, such as clinicians to improve the prevention and treatment of IGD.

In the current sample, the prevalence of IGD in college students was 5.5%. As briefly mentioned in the introduction, the prevalence of IGD was reported in different studies ranging from 1.2% to 46%. The reported variations in prevalence could be due to the different settings, measurements and samples. Also, the DSM-5 stated that IGD was more prevalent in Asian countries compared to North America and Europe (American Psychiatric Association, 2013). The result in our report indicated that among university students in a semi-controlled environment (Chinese university students live in a communal building on the campus), the prevalence of Internet gaming is rather restricted. Previous studies also found that males were more likely to engage in Internet related problematic behaviours (Durkee et al., 2012; Schimmenti et al., 2017), but we did not find gender difference in IGD.

Consistent with previous study, we found that childhood trauma was a strong predictor of depression and anxiety (Chen, Gillespie, Zhao, Xi, Ren, & McLean, 2018; Dalbudak et al., 2014). Childhood trauma could also affect IGD directly and indirectly via symptoms of depression and anxiety. It is likely that people with previous traumatic experiences may use maladaptive coping strategies (Ehlers & Clark, 2000), such as internet gaming. The addiction to Internet gaming could be used to avoid focussing on trauma and trauma related distress, such as depression and anxiety. However, the application of dysfunctional coping strategies could impede the cognitive processing of traumatic experiences and would be detrimental to the healing process (Schimmenti et al., 2017). In addition, previous study indicated that childhood trauma was associated with other problematic internet use such as cyberbullying, while childhood trauma was directly and indirectly associated with cyberbullying via Cluster B personality traits (i.e. antisocial, narcissistic, histrionic and borderline) (Kircaburun, Demetrovics, Király, & Griffiths, 2018). The different personality trait could play an important mediating role in the relationship between childhood trauma and IGD. Future following up studies on IGD should examine the effect of personality traits.

The current results have significant clinical implications. Our results suggest that addressing childhood trauma, depression and anxiety together could more effectively help college students reduce their IGD. We recommended that IGD among college students be further explored using both clinical and epidemiological methods. Clinical interventions should strive to address the motives behind Internet related problems (Schimmenti et al., 2017), and tailored interventions should focus on the whole picture including the individual's past experiences rather than merely aiming to treat the IGD. We call for education institutes to provide treatment for students with IGD. For example, mindfulness-based intervention is reported to be effective in treating IGD symptoms (Sanacora et al., 2017), possibly because mindfulness training aims to achieve a deep understanding of the self and forgiveness of the past.

There are several limitations to the current study that should be noted. First, causality among the variables cannot be guaranteed due to the cross-sectional design. Future longitudinal studies are required to explore the relationship between childhood trauma and IGD. Second, the sample was collected in one university, which may limit the generalizability. Third, the core addiction indicators in the different IGD screening instruments were inconsistent from each other (Torres-Rodriguez, Griffiths, & Carbonell, 2018). Researchers have questioned the suitability of specific instruments for different settings, since studies may emphasize different aspects of IGD (King, Haagsma, Delfabbro, Gradisar, & Griffiths, 2013). Future studies with representative samples and more robust measures are needed to verify the findings. Fourth, although the model was significant, the effect sizes were relatively small. IGD could be impacted by a lot of other variables. Internet-related disorders should be considered within an integrative theoretical framework, covering the aspects of biopsychological factors, social factors, psychopathological factors, personality traits and other specific motivations (Brand, Young, Laier, Wolfling, & Potenza, 2016; Kircaburun, Demetrovics, Király, & Griffiths, 2018). We recommended that future studies use comprehensive measurements covering multiple factors.

In conclusion, our research sheds new light on the understanding of IGD using a mediation framework model, which explores the relationship between childhood trauma, depression, anxiety and IGD. The results can be applied to clinical practice in order to facilitate IGD prevention and promote treatment interventions. Future studies should investigate other potential underlying mechanisms that may lead to the development of IGD, providing a more comprehensive understanding of the issue.

Author's contribution

Study design: SL, OJ, CR, GJ, WH, CL, ZK. Data collection: SL, GJ, WH, CL, ZK. Data analysis: WY, WA, DZ, PK, HZ, WH, OJ, YH, SQ. Data interpretation: WY, WA, DZ, PK, HZ, CR, ZK. Manuscript preparation: SL, WY, WA, YY, CR, CS, PK, HZ, SQ. 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.

Funding sources

This research was supported by Scientific Research Fund of Hunan Provincial Education Department (18B219). The funder had no role in the study design, collection, analysis or interpretation of the data and writing the manuscript.

Conflict of interest

The authors declare no conflict of interest.

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  • Torres-Rodriguez, A., Griffiths, M. D., & Carbonell, X. (2018). The treatment of Internet Gaming Disorder: A brief overview of the PIPATIC program. International Journal of Mental Health and Addiction, 16(4), 10001015. https://doi.org/10.1007/s11469-017-9825-0.

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  • Yates, T. M., Gregor, M. A., & Haviland, M. G. (2012). Child maltreatment, alexithymia, and problematic internet use in young adulthood. Cyberpsychology, Behavior, Social Networking, 15(4), 219225.

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  • Schimmenti, A., Passanisi, A., Caretti, V., La Marca, L., Granieri, A., Iacolino, C., et al. (2017). Traumatic experiences, alexithymia, and Internet addiction symptoms among late adolescents: A moderated mediation analysis. Addictive Behaviors, 64, 314320. https://doi.org/10.1016/j.addbeh.2015.11.002.

    • Search Google Scholar
    • Export Citation
  • Sigerson, L., Li, A. Y. L., Cheung, M. W. L., Luk, J. W., & Cheng, C. (2017). Psychometric properties of the Chinese Internet Gaming Disorder Scale. Addictive Behaviors, 74, 20-26. https://doi.org/10.1016/j.addbeh.2017.05.031.

    • Search Google Scholar
    • Export Citation
  • Torres-Rodriguez, A., Griffiths, M. D., & Carbonell, X. (2018). The treatment of Internet Gaming Disorder: A brief overview of the PIPATIC program. International Journal of Mental Health and Addiction, 16(4), 10001015. https://doi.org/10.1007/s11469-017-9825-0.

    • Search Google Scholar
    • Export Citation
  • Wan, C. S., & Chiou, W. B. (2006). Psychological motives and online games addiction: A test of flow theory and humanistic needs theory for Taiwanese adolescents. Cyberpsychology & Behavior, 9(3), 317324. https://doi.org/10.1089/cpb.2006.9.317.

    • Search Google Scholar
    • Export Citation
  • Yates, T. M., Gregor, M. A., & Haviland, M. G. (2012). Child maltreatment, alexithymia, and problematic internet use in young adulthood. Cyberpsychology, Behavior, Social Networking, 15(4), 219225.

    • Search Google Scholar
    • Export Citation
  • Yu, Y., Hu, M., Liu, Z. W., Liu, H. M., Yang, J. P., Zhou, L., et al.. (2016). Recognition of depression, anxiety, and alcohol abuse in a Chinese rural sample: A cross-sectional study. BMC Psychiatry, 16, 93. https://doi.org/10.1186/s12888-016-0802-0.

    • Search Google Scholar
    • Export Citation
  • Zhao, X. F., Zhang, Y. L., Li, L. F., Zhou, Y. F., Li, H. Z., & Yang, S. C. (2005). Reliability and validity of the Chinese version of childhood trauma questionnaire. Chinese Journal of Clinical Rehabilitation, 9, 105107.

    • 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

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  • 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

  • Sophia ACHAB (Faculty of Medicine, University of Geneva, Switzerland)
  • Alex BALDACCHINO (St Andrews University, United Kingdom)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Maria BELLRINGER (Auckland University of Technology, Auckland, New Zealand)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Damien BREVERS (University of Luxembourg, Luxembourg)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Julius BURKAUSKAS (Lithuanian University of Health Sciences, Lithuania)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Silvia CASALE (University of Florence, Florence, Italy)
  • Luke CLARK (University of British Columbia, Vancouver, B.C., Canada)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Geert DOM (University of Antwerp, Belgium)
  • Nicki DOWLING (Deakin University, Geelong, Australia)
  • Hamed EKHTIARI (University of Minnesota, United States)
  • Jon ELHAI (University of Toledo, Toledo, Ohio, USA)
  • Ana ESTEVEZ (University of Deusto, Spain)
  • Fernando FERNANDEZ-ARANDA (Bellvitge University Hospital, Barcelona, Spain)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Sally GAINSBURY (The University of Sydney, Camperdown, NSW, Australia)
  • Belle GAVRIEL-FRIED (The Bob Shapell School of Social Work, Tel Aviv University, Israel)
  • Biljana GJONESKA (Macedonian Academy of Sciences and Arts, Republic of North Macedonia)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Joshua GRUBBS (University of New Mexico, Albuquerque, NM, USA)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • 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)
  • Zsolt HORVÁTH (Eötvös Loránd University, Hungary)
  • Susana JIMÉNEZ-MURCIA (Clinical Psychology Unit, Bellvitge University Hospital, Barcelona, Spain)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Chih-Hung KO (Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Taiwan)
  • Shane KRAUS (University of Nevada, Las Vegas, NV, USA)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Bernadette KUN (Eötvös Loránd University, Hungary)
  • Katerina LUKAVSKA (Charles University, Prague, Czech Republic)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Gemma MESTRE-BACH (Universidad Internacional de la Rioja, La Rioja, Spain)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Ståle PALLESEN (University of Bergen, Norway)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Michael SCHAUB (University of Zurich, Switzerland)
  • 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)
  • Hermano TAVARES (Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN (Ariel University, Israel)
  • Anise WU (University of Macau, Macao, China)

 

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