View More View Less
  • 1 Faculty of Social Studies, Masaryk University, Brno, Czech Republic
Open access

Background and aims

Association between substance use and excessive play of online games exists both in theory and research. However, no study to date examined playing online games under the influence of licit and illicit drugs.

Methods

We questioned a convenient online sample of 3,952 Czech online gamers on their experiences and motives of using caffeine, alcohol, tobacco, psychoactive pharmaceuticals, and illicit drugs while playing massive multiplayer online games (MMOGs).

Results

The results showed low prevalence of illicit drug use while playing online games. Substance use was positively associated with intensity of gaming and both addiction and engagement; psychoactive substances with stimulating effect were linked to higher engagement and gaming intensity, whereas use of sedatives was associated with higher addiction score. Substance use varied slightly with the preference of game genre.

Discussion

Drug use while playing appears as behavior, which is mostly not related to gaming – it concerns mostly caffeine, tobacco, alcohol, or cannabis. For some users, however, drug use was fueled by motivations toward improving their cognitive enhancement and gaming performance.

Abstract

Background and aims

Association between substance use and excessive play of online games exists both in theory and research. However, no study to date examined playing online games under the influence of licit and illicit drugs.

Methods

We questioned a convenient online sample of 3,952 Czech online gamers on their experiences and motives of using caffeine, alcohol, tobacco, psychoactive pharmaceuticals, and illicit drugs while playing massive multiplayer online games (MMOGs).

Results

The results showed low prevalence of illicit drug use while playing online games. Substance use was positively associated with intensity of gaming and both addiction and engagement; psychoactive substances with stimulating effect were linked to higher engagement and gaming intensity, whereas use of sedatives was associated with higher addiction score. Substance use varied slightly with the preference of game genre.

Discussion

Drug use while playing appears as behavior, which is mostly not related to gaming – it concerns mostly caffeine, tobacco, alcohol, or cannabis. For some users, however, drug use was fueled by motivations toward improving their cognitive enhancement and gaming performance.

Introduction

In general, the concepts of behavioral addiction, particularly online gaming addiction, and substance use are closely related on many levels. Although the discussion did not yet reach complete consensus (see Kuss, Griffiths, & Pontes, 2017 and other papers in the issue for the summary of the debate), addictive behaviors appear to share similar sets of correlates and risk factors (Fisoun, Floros, Siomos, Geroukalis, & Navridis, 2012; Yen, Yen, Chen, Chen, & Ko, 2007), similar neurobiological manifestations (Grant, Brewer, & Potenza, 2014; Ko, Yen, Yen, Chen, & Chen, 2012), and similar symptomatology (Petry et al., 2014). Studies found positive relationship between addiction to some online applications including games and both licit and illicit drugs use (Walther, Morgenstern, & Hanewinkel, 2012), even in the longitudinal perspective (Zhang, Brook, Leukefeld, & Brook, 2016). Some researchers approach substance use and excessive use of online applications in terms of psychiatric comorbidity (Starcevic & Khazaal, 2017), while others try to explain the co-occurrence of both conditions within the framework of problem behavior theory (Ko et al., 2008). High risk of Internet addiction was found associated with negative academic outcomes, truancy, lifetime use of tobacco, alcohol, and/or drugs, suicidal thoughts, self-harming, and delinquent behaviors (Evren, Dalbudak, Evren, & Ciftci Demirci, 2014). Substance use is also associated with increased duration of Internet use (Secades-Villa et al., 2014); excessive gamers are often excessive caffeine users (Porter, Starcevic, Berle, & Fenech, 2010). These studies typically measure any use of the substance within certain period of time or during the lifetime; no data exist on actual gaming under the influence of psychoactive substances.

Gaming under the influence of psychoactive substances is, however, described for pathological gambling, where acute intoxication leads to worsened gambling outcomes. For instance, effects of concurrent alcohol use include disinhibition and risk-taking behaviors leading to increased losses (Ellery, Stewart, & Loba, 2005; Lyvers, Mathieson, & Edwards, 2015; Wiebe, Single, & Falkowski-Ham, 2001), while use of stimulant-type drugs, especially amphetamines, allows players to gamble for longer periods of time, to operate more slot-machines at once, and to stay focused longer (Hart et al., 2008). Tobacco, alcohol, caffeine, cannabis, and methamphetamine are the most commonly used substances among the patients in treatment for gambling disorder in the Czech Republic, with nearly one fifth using the latter two (Mravčík, Černý, Roznerová, Licehammerová, & Tion Leštinová, 2015). Griffiths and Barnes (2008) note that addictive behaviors online may elevate these risks given the 24/7 availability and a lack of social control.

Our study aims to explore levels and patterns of online gaming under the influence and to describe what substances are used by the gamers while playing, what are the subjective reasons for use, whether there is any relationship between intoxicated play, time spent gaming, and gaming addiction severity. We also acknowledge the variability of acute effects of individual substances (Miller, 2013) and of the gaming genres (Lemmens & Hendriks, 2016) and assume different drug preferences among players of different genres as some games may involve more competitive and achievement-oriented gaming, whereas others may be more suitable for relaxation and escape from daily routines.

Methods

Data collection and sampling

The data come from the second wave of the three-wave longitudinal online survey among the Czech and Slovak players of massive multiplayer online games and were collected in winter 2013. The questionnaire was published online in the Czech language using the Lime Survey platform. Gamers were recruited through advertisement at online forums, Facebook pages, and guild sites and in specialized magazines that targeted the core of the Czech and Slovak gaming community, with heavy players expected to be of the highest proportion in the sample. Respondents were asked to provide an e-mail address to participate in follow-up data collection. The questionnaire contained a set of sociodemographic questions, several items on gaming intensity, patterns, and preferences, the Addiction–Engagement Questionnaire (AEQ), and a voluntary module on drug use. At least one prevalence question in the drug module was answered by 4,004 respondents aged 11–59 years (10.2% were 15 years old or younger, 25.5% were 17 years old or younger, and 55.0% stated age of 21 years or less), who represented 83.1% of the total sample of 4,821 gamers. Compared with non-respondents, those who responded to drug module were by 0.9 years older [M = 22.16, SD = 6.41, t(4,819) = −3,500, p < .001] were less often male [92.4%, compared with 95.2%; χ2 (1, N = 4,821) = 8.159, p = .004], and did not differ in hours spent gaming per week [M = 29.67, SD = 16.35, t(4,354) = −0.897, p = .370].

Measures and analyses

Online gaming addiction was measured by AEQ, a 24-item tool with response options on a 4-point scale (ranging from 1 – strongly agree to 4 – strongly disagree). The scale was designed to distinguish between online gaming addiction (12 items) and high engagement in online games (12 items) (Charlton & Danforth, 2007, 2010). Addiction scale covers the components, such as tolerance, conflict, and withdrawal, whereas engagement refers more to salience and mood changes. Since it was not validated for discriminatory purposes, we do not use cutoff points to distinguish addicted and non-addicted gamers, but rather work with the scale as a continuum. Both subscales had sufficient internal consistency (Cronbach’s α = .83 for addiction and .74 for high engagement). We created two new combined variables for addiction and engagement as mean scores of the respective subscales ranging from 1 to 4 (MADD = 1.84, SDADD = 0.54; MENG = 2.82, SDENG = 0.42). The frequency of online gaming, expressed in weekly playing hours, was constructed as a combined measure using two open-ended questions asking the number of hours spent playing during average weekday and on a day off. Respondents who did not play in the last 3 months (i.e., obtained zero in the combined frequency variable) were excluded from the analysis. Gaming genre was identified on the basis of the favorite game title – three most popular genres were compared [role-playing games (RPG), multiplayer online battle arena (MOBA), and first/third-person shooter games (FPS)].

Drug use while gaming was measured using two items. Question on prevalence asked whether respondents have played games under the influence of following substances once, repeatedly, or never in the last 12 months: caffeine, tobacco, alcohol, cannabis or cannabis resin, amphetamine or methamphetamine, Ecstasy/MDMA, cocaine, stimulant pharmaceuticals (e.g., Ritalin), hallucinogens (LSD or psilocybin), sedatives and tranquilizers, mephedrone, and other legal highs. Legal highs were defined as “legal drugs and other enhancers from smart shop.” Since the numbers of users were generally low, the responses were coded as 0 (not used) and 1 (used). Mephedrone use was reported by only two individuals and was not, therefore, regarded in further analyses. A dummy drug Relevin was also included to identify false-positive answers – three respondents were excluded from the analysis on the basis of this item. Motivations to use the substance were measured using multiple response set asking why the respondents used the substance: to concentrate better, to stay up, for courage, to enjoy more, to calm down, to suppress hunger, to fall asleep, not related to gaming, and no reason.

Descriptive statistics were calculated for all variables; t-test was used to compare addiction, engagement, and frequency levels between users and non-users; χ2 test (or Fisher’s exact test for drugs with small number of users) was employed to assess the distribution of users and non-users by gaming genre. Given the exploratory nature of the study, we report statistics and effect sizes for all relationships (Cohen, 1988).

Ethics

The study did not require approval of the ethics committee. In line with the university ethical guidelines (CTT, 2015), details about the study aims, procedures, and the data collected were provided on the first page of the questionnaire. Participation was voluntary and all information provided was confidential. The participation was solicited by online advertisement and parents of underage children could not be addressed directly; therefore, minors were requested to confirm that they would participate in the survey with parental approval.

Results

Caffeine was, with 74.2% of positive responses, the most common stimulant-type substance used during gaming. Tobacco products were used by 25.3% and alcohol by 50.4% of the sample; 2.8% played online games under the influence of psychoactive pharmaceuticals; 1.8% mentioned legal highs; and 14.5% stated that they used at least one of the list of illicit substances while gaming (14.2% reported cannabis use and 1.9% used other drugs; see Table 1).

Table 1.

Proportion of gamers using the substances while gaming, overall and by genre (%)

Genres (% of users)Effect size
RPGMOBAFPS/TPSOthersTotalχ2(df)Cramer’s VN
Caffeine73.575.376.472.174.24.35(3)0.033,941
Tobacco28.324.324.523.225.38.50(3)0.053,933
Alcohol48.654.445.548.350.416.67(3)0.073,935
Cannabis/resin12.916.715.11114.216.41(3)0.073,930
Amphetamines0.40.51.10.70.63.03*(3)0.033,938
Ecstasy/MDMA0.90.50.70.20.63.50*(3)0.063,933
Cocaine0.50.11.30.40.410.16*(3)0.023,928
Stimulant-type pharmaceuticals0.40.20.20.20.31.31*(3)0.023,943
Hallucinogens (LSD/psilocybin)1.41.20.70.91.12.02(3)0.043,939
Sedatives and tranquilizers3.52.21.62.62.66.66(3)0.023,938
Legal highs1.81.91.11.81.81.21(3)0.023,936

Note. RPG: role-playing games; MOBA: multiplayer online battle arena; FPS/TPS: first/third-person shooter game.

For tables having the cells with expected values less than 5, the Fisher’s exact test is reported.

Those who played under the influence of almost any substance spent more hours per week gaming than the non-users – the difference was highest for stimulant-type pharmaceuticals (+9.8 hr/week), Ecstasy/MDMA (+9.6), sedatives (+6.9), and amphetamines (+6.2) and lowest for cannabis, alcohol, and hallucinogens. Caffeine users played on average 3.8 more hours per week. Addiction scores were higher mainly for users of legal highs, sedatives and tranquilizers, ecstasy, and caffeine; engagement grew for users of caffeine and ecstasy; cocaine users were less engaged than non-users. The effect sizes ranged from very small to medium (Table 1). In terms of game genres, rather small differences in substance use while gaming were observed with overall trivial effects sizes. Nevertheless, players of FPS and MOBA games showed slight tendency toward stimulant-type drugs, whereas RPG players used more tobacco products, hallucinogens, and ecstasy (Table 2).

Table 2.

Gaming addiction, engagement, and frequency of play among gamers using the substances while gaming and among non-users

Not usedUsedt-test
MSDNMSDNDifftdfCohen’s d
Addiction score
Caffeine1.730.501,0111.880.552,910−0.15−8.141,898.50.29
Tobacco1.840.542,9211.860.54992−0.02−1.033,911.00.04
Alcohol1.820.551,9411.870.531,974−0.04−2.593,896.20.08
Cannabis/resin1.840.543,3561.890.51554−0.05−2.103,908.00.10
Amphetamines1.840.543,8941.860.5924−0.01−0.123,916.00.02
Ecstasy/MDMA1.840.543,8921.980.6821−0.14−1.213,911.00.26
Cocaine1.840.543,8921.760.48170.080.603,907.00.15
Stimulant-type pharmaceuticals1.840.543,9121.880.4911−0.04−0.223,921.00.07
Hallucinogens (LSD/psilocybin)1.840.543,8761.910.4843−0.07−0.833,917.00.13
Sedatives and tranquilizers1.840.543,8172.110.54102−0.27−5.043,917.00.51
Legal highs1.840.543,8472.130.6169−0.29−4.433,914.00.54
Engagement score
Caffeine2.740.421,0062.850.412,910−0.11−7.483,914.00.27
Tobacco2.810.422,9182.860.41990−0.04−2.843,906.00.10
Alcohol2.810.431,9382.840.401,972−0.03−2.523,879.80.08
Cannabis/resin2.820.423,3532.840.39552−0.02−1.31773.00.06
Amphetamines2.820.423,8942.770.49240.050.623,911.00.13
Ecstasy/MDMA2.820.423,8872.900.5221−0.08−0.873,906.00.19
Cocaine2.820.423,8872.710.39170.121.183,902.00.29
Stimulant-type pharmaceuticals2.820.423,9072.800.35110.020.193,916.00.06
Hallucinogens (LSD/psilocybin)2.820.423,8722.890.4842−0.07−1.093,912.00.17
Sedatives and tranquilizers2.820.423,8122.900.41102−0.08−1.893,912.00.19
Legal highs2.820.423,8422.890.4969−0.07−1.1269.80.16
Weekly hours of play
Caffeine26.8715.5895230.6216.532,754−3.75−6.311,743.70.23
Tobacco29.1116.122,76531.2116.98933−2.09−3.393,696.00.13
Alcohol30.1216.631,81529.2216.121,8860.901.683,699.00.06
Cannabis/resin29.6016.373,17530.1516.42522−0.55−0.713,694.00.03
Amphetamines29.6216.343,68035.7822.4423−6.16−1.3222.10.38
Ecstasy/MDMA29.6016.283,67839.1924.4521−9.59−1.8020.10.59
Cocaine29.6116.343,67834.4719.0417−4.86−1.223,693.00.30
Stimulant-type pharmaceuticals29.6416.373,69939.4417.209−9.80−1.793,706.00.60
Hallucinogens (LSD/psilocybin)29.6516.343,66430.4818.3740−0.83−0.323,702.00.05
Sedatives and tranquilizers29.4816.253,60636.3818.9197−6.91−3.5699.90.42
Legal highs29.5816.323,63735.5318.8364−5.95−2.883,699.00.36

Note. Diff: mean difference.

Most often, the use of the substance was not related to gaming (71.4% of those who stated at least one reason for substance use) or there was no particular reason for use (30.1%). More than half of the sample (59.6%) stated only reasons that were not related to gaming; game-related motives were mentioned by 40.4% respondents and involved avoiding sleep (25.8%), increased concentration (15.6%), enhanced enjoyment (13.8%), tension management (7.3%), increased courage (4.1%), avoiding hunger (2.7%), and insomnia management (2.0%). The differences by game genre were negligible.

Discussion

Drug use while playing appears to be a very specific behavior, likely less prevalent than any drug use in the same population. Although not representative of the gaming community, our results suggest that the use of mildly stimulating caffeine while gaming is a rather common behavior, whereas any type of cognitive enhancement by misused pharmaceuticals and/or illicit substances appears to be rare.

Every third gamer who plays intoxicated has used the substance for reasons related to playing. This outcome may suggest that, at least for some gamers, the association between substance use and gaming severity cannot be fully explained using traditional approaches of psychiatric comorbidity (Starcevic & Khazaal, 2017) or problem behavior theory (Ko et al., 2008), as it seems a pragmatic choice instead of an uncontrolled behavior. This may be especially true for high achievers and competitive gamers, as the use of “smart drugs” is increasingly observed in gaming sports (Dance, 2016). This hypothesis would also be supported by the fact that players of the more competitive genres (such as FPS and MOBA games) showed higher tendency to use substances with stimulating effects, and that cocaine and caffeine users scored higher on the engagement subscale of the AEQ. The observation that the users of legal highs, a category that may also include smart drugs, averaged higher in weekly frequency of play and on the addiction subscale may be explained by the acute effects involving, among others, tunnel vision and increased immersion (Petersen, Nørgaard, & Traulsen, 2015).

On the other hand, majority of the sample stated reasons unrelated to the game itself, suggesting that they would use the substance anyway or they just got to play already intoxicated. This may be self-evident for the high proportion of caffeine, tobacco, and alcohol users in the sample, but it may be linked to deeper underlying problems for users of illicit and addictive drugs as parallels with pathological gamblers would suggest (Cunningham-Williams, Cottler, Compton, Spitznagel, & Ben-Abdallah, 2000; Goudriaan, Oosterlaan, de Beurs, & van den Brink, 2006). Gaming addiction was associated with symptoms of depression and worse mood, and addicted gamers might, therefore, tend to self-medicate for mood management (Charlton & Danforth, 2010). Due to the exploratory nature of this study and limited contextual data, we cannot test this hypothesis.

Our results may also be viewed as an indication of association between intoxicated gaming and gaming frequency and severity of online gaming addiction symptoms. Such association has already been described for any use of psychoactive substances and addiction to online applications (Fisoun et al., 2012; Yen et al., 2007) as well as comorbidity of pathological gambling and substance (typically alcohol) addiction (Geisner et al., 2016). Nevertheless, it should be stressed that our data do not provide insight in severity of substance use; we only refer to any intoxication while gaming within the last 12 months.

Any conclusions beyond our sample should be, however, made with caution due to a number of limitations. The self-nominated sample of online gamers is not representative and the results should not be generalized. The actual drug use while gaming may be underreported, since it is a sensitive issue, the data collection was part of a longitudinal project and we collected e-mail addresses to distribute the follow-up questionnaires; the sense of anonymity/confidentiality might have been affected. Also, the available information on interfering variables and covariates is limited, as we only aimed for exploratory screening within broader online gaming addiction project. This is also why we do not report p values, as we did not aim to test any hypotheses within the exploratory design.

Conclusions

Use of legal stimulants and mood/cognitive enhancements, specifically of alcohol, tobacco, and caffeine products, appears to be rather normalized and widespread. Drug use while playing, a specific behavior often not related to gaming, concerns mostly caffeine, tobacco, alcohol, or cannabis. For some users, however, drug use was fueled by motivations toward improving their cognitive enhancement and gaming performance. Nevertheless, intoxicated gaming may increase the risks of development of gaming problem for some players and excessive use of legal stimulants, such as caffeine products, and medicines in the parenting context may serve as an indicator of growing engagement into the gaming. To examine these hypotheses would, though, require more focused research.

Authors’ contribution

KŠ was involved in drafting paper and data preparation. LB contributed to study concept and design. KŠ and AŤ contributed to statistical analysis. KŠ and LB contributed to interpretation of data. 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.

Acknowledgements

The authors acknowledge the support of the Czech Science Foundation (GA15-19221S).

References

  • Charlton, J. P., & Danforth, I. D. W. (2007). Distinguishing addiction and high engagement in the context of online game playing. Computers in Human Behavior, 23(3), 15311548. doi:10.1016/j.chb.2005.07.002

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charlton, J. P., & Danforth, I. D. W. (2010). Validating the distinction between computer addiction and engagement: Online game playing and personality. Behaviour & Information Technology, 29(6), 601613. doi:10.1080/01449290903401978

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J. (1988). Statistical power analyses for the social sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.

  • CTT. (2015). Nakládání s výzkumnými daty na Masarykově univerzitě [Use of research data at Masaryk University]. Brno, Czech Republic: Centrum pro transfer technologií, Masarykova univerzita.

    • Search Google Scholar
    • Export Citation
  • Cunningham-Williams, R. M., Cottler, L. B., Compton, W. M., Spitznagel, E. L., & Ben-Abdallah, A. (2000). Problem gambling and comorbid psychiatric and substance use disorders among drug users recruited from drug treatment and community settings. Journal of Gambling Studies, 16(4), 347376. doi:10.1023/A:1009428122460

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dance, A. (2016). Smart drugs: A dose of intelligence. Nature, 531(7592), S2S3. doi:10.1038/531S2a

  • Ellery, M., Stewart, S. H., & Loba, P. (2005). Alcohol’s effects on video lottery terminal (VLT) play among probable pathological and non-pathological gamblers. Journal of Gambling Studies, 21(3), 299324. doi:10.1007/s10899-005-3101-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evren, C., Dalbudak, E., Evren, B., & Ciftci Demirci, A. (2014). High risk of Internet addiction and its relationship with lifetime substance use, psychological and behavioral problems among 10th grade adolescents. Psychiatria Danubina, 26(4), 330339. Retrieved from https://hrcak.srce.hr/file/239140

    • Search Google Scholar
    • Export Citation
  • Fisoun, V., Floros, G., Siomos, K., Geroukalis, D., & Navridis, K. (2012). Internet addiction as an important predictor in early detection of adolescent drug use experience – Implications for research and practice. Journal of Addiction Medicine, 6(1), 7784. doi:10.1097/ADM.0b013e318233d637

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geisner, I. M., Huh, D., Cronce, J. M., Lostutter, T. W., Kilmer, J., & Larimer, M. E. (2016). Exploring the relationship between stimulant use and gambling in college students. Journal of Gambling Studies, 32(3), 10011016. doi:10.1007/s10899-015-9586-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goudriaan, A. E., Oosterlaan, J., de Beurs, E., & van den Brink, W. (2006). Psychophysiological determinants and concomitants of deficient decision making in pathological gamblers. Drug and Alcohol Dependence, 84(3), 231239. doi:10.1016/j.drugalcdep.2006.02.007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, J. E., Brewer, J. A., & Potenza, M. N. (2014). The neurobiology of substance and behavioral addictions. CNS Spectrums, 11(12), 924930. doi:10.1017/S109285290001511X

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffiths, M., & Barnes, A. (2008). Internet gambling: An online empirical study among student gamblers. International Journal of Mental Health and Addiction, 6(2), 194204. doi:10.1007/s11469-007-9083-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hart, C. L., Gunderson, E. W., Perez, A., Kirkpatrick, M. G., Thurmond, A., Comer, S. D., & Foltin, R. W. (2008). Acute physiological and behavioral effects of intranasal methamphetamine in humans. Neuropsychopharmacology, 33(8), 18471855. doi:10.1038/sj.npp.1301578

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ko, C. H., Yen, J. Y., Yen, C. F., Chen, C. S., & Chen, C. C. (2012). The association between Internet addiction and psychiatric disorder: A review of the literature. European Psychiatry, 27(1), 18. doi:10.1016/j.eurpsy.2010.04.011

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ko, C. H., Yen, J.-Y., Yen, C. F., Chen, C. S., Weng, C. C., & Chen, C. C. (2008). The association between Internet addiction and problematic alcohol use in adolescents: The problem behavior model. CyberPsychology & Behavior, 11(5), 571576. doi:10.1089/cpb.2007.0199

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuss, D. J., Griffiths, M. D., & Pontes, H. M. (2017). Chaos and confusion in DSM-5 diagnosis of Internet gaming disorder: Issues, concerns, and recommendations for clarity in the field. Journal of Behavioral Addictions, 6(2), 103109. doi:10.1556/2006.5.2016.062

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemmens, J. S., & Hendriks, S. J. F. (2016). Addictive online games: Examining the relationship between game genres and Internet gaming disorder. Cyberpsychology, Behavior, and Social Networking, 19(4), 270276. doi:10.1089/cyber.2015.0415

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lyvers, M., Mathieson, N., & Edwards, M. S. (2015). Blood alcohol concentration is negatively associated with gambling money won on the Iowa gambling task in naturalistic settings after controlling for trait impulsivity and alcohol tolerance. Addictive Behaviors, 41, 129135. doi:10.1016/j.addbeh.2014.10.008

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, P. M. (2013). Principles of addiction: Comprehensive addictive behaviors and disorders (Vol. 1). San Diego, CA: Academic Press.

    • Search Google Scholar
    • Export Citation
  • Mravčík, V., Černý, J., Roznerová, T., Licehammerová, Š., & Tion Leštinová, Z. (2015). Charakteristiky léčených problémových hráčů v ČR: průřezová dotazníková studie [Characteristics of problem gamblers in Treatment in the Czech Republic: A Cross-Sectional Questionnaire Survey]. Adiktologie, 15(4), 322333. Retrieved from http://casopis.adiktologie.cz/cs/casopis/4-15-2015

    • Search Google Scholar
    • Export Citation
  • Petersen, M. A., Nørgaard, L. S., & Traulsen, J. M. (2015). Pursuing pleasures of productivity: University students’ use of prescription stimulants for enhancement and the moral uncertainty of making work fun. Culture, Medicine, and Psychiatry, 39(4), 665679. doi:10.1007/s11013-015-9457-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petry, N. M., Rehbein, F., Gentile, D. A., Lemmens, J. S., Rumpf, H.-J., Mößle, T., Bischof, G., Tao, R., Fung, D. S., Borges, G., Auriacombe, M., González Ibáñez, A., Tam, P., & O’Brien, C. P. (2014). An international consensus for assessing Internet gaming disorder using the new DSM-5 approach. Addiction, 109(9), 13991406. doi:10.1111/add.12457

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porter, G., Starcevic, V., Berle, D., & Fenech, P. (2010). Recognizing problem video game use. Australian and New Zealand Journal of Psychiatry, 44(2), 120128. doi:10.3109/00048670903279812

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Secades-Villa, R., Calafat, A., Fernández-Hermida, J. R., Juan, M., Duch, M., Skärstrand, E., Becoña, E., & Talic, S. (2014). Duration of Internet use and adverse psychosocial effects among European adolescents. Adicciones, 26(3), 247253. doi:10.20882/adicciones.6

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Starcevic, V., & Khazaal, Y. (2017). Relationships between behavioural addictions and psychiatric disorders: What is known and what is yet to be learned? Frontiers in Psychiatry, 8, 53. doi:10.3389/fpsyt.2017.00053

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walther, B., Morgenstern, M., & Hanewinkel, R. (2012). Co-occurrence of addictive behaviours: Personality factors related to substance use, gambling and computer gaming. European Addiction Research, 18(4), 167174. doi:10.1159/000335662

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wiebe, J., Single, E., & Falkowski-Ham, A. (2001). Measuring gambling and problem gambling in Ontario. Retrieved from http://www.responsiblegambling.org/rg-news-research/rgc-centre/research-and-analysis/docs/research-reports/measuring-gambling-and-problem-gambling-in-ontario

    • Search Google Scholar
    • Export Citation
  • Yen, J.-Y., Yen, C.-F., Chen, C.-C., Chen, S.-H., & Ko, C.-H. (2007). Family factors of Internet addiction and substance use experience in Taiwanese adolescents. CyberPsychology & Behavior, 10(3), 323329. doi:10.1089/cpb.2006.9948

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., Brook, J. S., Leukefeld, C. G., & Brook, D. W. (2016). Longitudinal psychosocial factors related to symptoms of Internet addiction among adults in early midlife. Addictive Behaviors, 62, 6572. doi:10.1016/j.addbeh.2016.06.019

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charlton, J. P., & Danforth, I. D. W. (2007). Distinguishing addiction and high engagement in the context of online game playing. Computers in Human Behavior, 23(3), 15311548. doi:10.1016/j.chb.2005.07.002

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charlton, J. P., & Danforth, I. D. W. (2010). Validating the distinction between computer addiction and engagement: Online game playing and personality. Behaviour & Information Technology, 29(6), 601613. doi:10.1080/01449290903401978

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J. (1988). Statistical power analyses for the social sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.

  • CTT. (2015). Nakládání s výzkumnými daty na Masarykově univerzitě [Use of research data at Masaryk University]. Brno, Czech Republic: Centrum pro transfer technologií, Masarykova univerzita.

    • Search Google Scholar
    • Export Citation
  • Cunningham-Williams, R. M., Cottler, L. B., Compton, W. M., Spitznagel, E. L., & Ben-Abdallah, A. (2000). Problem gambling and comorbid psychiatric and substance use disorders among drug users recruited from drug treatment and community settings. Journal of Gambling Studies, 16(4), 347376. doi:10.1023/A:1009428122460

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dance, A. (2016). Smart drugs: A dose of intelligence. Nature, 531(7592), S2S3. doi:10.1038/531S2a

  • Ellery, M., Stewart, S. H., & Loba, P. (2005). Alcohol’s effects on video lottery terminal (VLT) play among probable pathological and non-pathological gamblers. Journal of Gambling Studies, 21(3), 299324. doi:10.1007/s10899-005-3101-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evren, C., Dalbudak, E., Evren, B., & Ciftci Demirci, A. (2014). High risk of Internet addiction and its relationship with lifetime substance use, psychological and behavioral problems among 10th grade adolescents. Psychiatria Danubina, 26(4), 330339. Retrieved from https://hrcak.srce.hr/file/239140

    • Search Google Scholar
    • Export Citation
  • Fisoun, V., Floros, G., Siomos, K., Geroukalis, D., & Navridis, K. (2012). Internet addiction as an important predictor in early detection of adolescent drug use experience – Implications for research and practice. Journal of Addiction Medicine, 6(1), 7784. doi:10.1097/ADM.0b013e318233d637

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geisner, I. M., Huh, D., Cronce, J. M., Lostutter, T. W., Kilmer, J., & Larimer, M. E. (2016). Exploring the relationship between stimulant use and gambling in college students. Journal of Gambling Studies, 32(3), 10011016. doi:10.1007/s10899-015-9586-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goudriaan, A. E., Oosterlaan, J., de Beurs, E., & van den Brink, W. (2006). Psychophysiological determinants and concomitants of deficient decision making in pathological gamblers. Drug and Alcohol Dependence, 84(3), 231239. doi:10.1016/j.drugalcdep.2006.02.007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, J. E., Brewer, J. A., & Potenza, M. N. (2014). The neurobiology of substance and behavioral addictions. CNS Spectrums, 11(12), 924930. doi:10.1017/S109285290001511X

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffiths, M., & Barnes, A. (2008). Internet gambling: An online empirical study among student gamblers. International Journal of Mental Health and Addiction, 6(2), 194204. doi:10.1007/s11469-007-9083-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hart, C. L., Gunderson, E. W., Perez, A., Kirkpatrick, M. G., Thurmond, A., Comer, S. D., & Foltin, R. W. (2008). Acute physiological and behavioral effects of intranasal methamphetamine in humans. Neuropsychopharmacology, 33(8), 18471855. doi:10.1038/sj.npp.1301578

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ko, C. H., Yen, J. Y., Yen, C. F., Chen, C. S., & Chen, C. C. (2012). The association between Internet addiction and psychiatric disorder: A review of the literature. European Psychiatry, 27(1), 18. doi:10.1016/j.eurpsy.2010.04.011

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ko, C. H., Yen, J.-Y., Yen, C. F., Chen, C. S., Weng, C. C., & Chen, C. C. (2008). The association between Internet addiction and problematic alcohol use in adolescents: The problem behavior model. CyberPsychology & Behavior, 11(5), 571576. doi:10.1089/cpb.2007.0199

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuss, D. J., Griffiths, M. D., & Pontes, H. M. (2017). Chaos and confusion in DSM-5 diagnosis of Internet gaming disorder: Issues, concerns, and recommendations for clarity in the field. Journal of Behavioral Addictions, 6(2), 103109. doi:10.1556/2006.5.2016.062

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemmens, J. S., & Hendriks, S. J. F. (2016). Addictive online games: Examining the relationship between game genres and Internet gaming disorder. Cyberpsychology, Behavior, and Social Networking, 19(4), 270276. doi:10.1089/cyber.2015.0415

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lyvers, M., Mathieson, N., & Edwards, M. S. (2015). Blood alcohol concentration is negatively associated with gambling money won on the Iowa gambling task in naturalistic settings after controlling for trait impulsivity and alcohol tolerance. Addictive Behaviors, 41, 129135. doi:10.1016/j.addbeh.2014.10.008

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, P. M. (2013). Principles of addiction: Comprehensive addictive behaviors and disorders (Vol. 1). San Diego, CA: Academic Press.

    • Search Google Scholar
    • Export Citation
  • Mravčík, V., Černý, J., Roznerová, T., Licehammerová, Š., & Tion Leštinová, Z. (2015). Charakteristiky léčených problémových hráčů v ČR: průřezová dotazníková studie [Characteristics of problem gamblers in Treatment in the Czech Republic: A Cross-Sectional Questionnaire Survey]. Adiktologie, 15(4), 322333. Retrieved from http://casopis.adiktologie.cz/cs/casopis/4-15-2015

    • Search Google Scholar
    • Export Citation
  • Petersen, M. A., Nørgaard, L. S., & Traulsen, J. M. (2015). Pursuing pleasures of productivity: University students’ use of prescription stimulants for enhancement and the moral uncertainty of making work fun. Culture, Medicine, and Psychiatry, 39(4), 665679. doi:10.1007/s11013-015-9457-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petry, N. M., Rehbein, F., Gentile, D. A., Lemmens, J. S., Rumpf, H.-J., Mößle, T., Bischof, G., Tao, R., Fung, D. S., Borges, G., Auriacombe, M., González Ibáñez, A., Tam, P., & O’Brien, C. P. (2014). An international consensus for assessing Internet gaming disorder using the new DSM-5 approach. Addiction, 109(9), 13991406. doi:10.1111/add.12457

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porter, G., Starcevic, V., Berle, D., & Fenech, P. (2010). Recognizing problem video game use. Australian and New Zealand Journal of Psychiatry, 44(2), 120128. doi:10.3109/00048670903279812

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Secades-Villa, R., Calafat, A., Fernández-Hermida, J. R., Juan, M., Duch, M., Skärstrand, E., Becoña, E., & Talic, S. (2014). Duration of Internet use and adverse psychosocial effects among European adolescents. Adicciones, 26(3), 247253. doi:10.20882/adicciones.6

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Starcevic, V., & Khazaal, Y. (2017). Relationships between behavioural addictions and psychiatric disorders: What is known and what is yet to be learned? Frontiers in Psychiatry, 8, 53. doi:10.3389/fpsyt.2017.00053

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walther, B., Morgenstern, M., & Hanewinkel, R. (2012). Co-occurrence of addictive behaviours: Personality factors related to substance use, gambling and computer gaming. European Addiction Research, 18(4), 167174. doi:10.1159/000335662

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wiebe, J., Single, E., & Falkowski-Ham, A. (2001). Measuring gambling and problem gambling in Ontario. Retrieved from http://www.responsiblegambling.org/rg-news-research/rgc-centre/research-and-analysis/docs/research-reports/measuring-gambling-and-problem-gambling-in-ontario

    • Search Google Scholar
    • Export Citation
  • Yen, J.-Y., Yen, C.-F., Chen, C.-C., Chen, S.-H., & Ko, C.-H. (2007). Family factors of Internet addiction and substance use experience in Taiwanese adolescents. CyberPsychology & Behavior, 10(3), 323329. doi:10.1089/cpb.2006.9948

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., Brook, J. S., Leukefeld, C. G., & Brook, D. W. (2016). Longitudinal psychosocial factors related to symptoms of Internet addiction among adults in early midlife. Addictive Behaviors, 62, 6572. doi:10.1016/j.addbeh.2016.06.019

    • Crossref
    • Search Google Scholar
    • Export Citation
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
  • PsychInfo
  • PubMed Central
  • SCOPUS
  • Medline
  • CABI
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)

 

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
May 2021 0 16 17
Jun 2021 0 27 37
Jul 2021 0 17 14
Aug 2021 0 14 18
Sep 2021 0 16 17
Oct 2021 0 17 33
Nov 2021 0 0 0