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Simon MarmetAddiction Medicine, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 23, CH-1011 Lausanne, Switzerland
School of Social Work, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland

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Matthias WickiAddiction Medicine, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 23, CH-1011 Lausanne, Switzerland
Institute for Research, Development and Evaluation, Bern University of Teacher Education, Bern, Switzerland

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Marc DupuisAddiction Medicine, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 23, CH-1011 Lausanne, Switzerland
Higher Education and Research in Health Care, Lausanne University Hospital and University of Lausanne, Switzerland

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Stéphanie BaggioDivision of Prison Health, Geneva University Hospitals & University of Geneva, Geneva, Switzerland
Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland

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Magali DufourDepartment of Psychology, Université du Québec à Montréal, Montréal, QC, Canada

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Catherine GatineauDepartment of Psychology, Université du Québec à Montréal, Montréal, QC, Canada

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Gerhard GmelAddiction Medicine, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 23, CH-1011 Lausanne, Switzerland
Addiction Switzerland, Avenue Louis-Ruchonnet 14, CH-1001 Lausanne, Switzerland
Centre for Addiction and Mental Health, 1001 Queen Street West, Toronto ON M6J 1H4, Canada
University of the West of England, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, United Kingdom

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Joseph StuderAddiction Medicine, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 23, CH-1011 Lausanne, Switzerland
Department of Psychiatry, Service of Adult Psychiatry North-West, Lausanne University Hospital and University of Lausanne, Switzerland

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Open access

Abstract

Background

Video gaming is a popular activity among young people. Time spent with gaming was found to be only moderately associated with gaming disorder. However, patterns of binge gaming (playing more than 5 h consecutively) were rarely considered in research on gaming. This study explores how binge gaming frequency is related with gaming disorder and mental health.

Methods

The sample came from the Cohort study on substance use risk factors (C-SURF) and comprised 5,358 young men aged 28.26 years (SD = 1.27). ANCOVA was conducted to estimate the association between binge gaming frequency (gaming at least 5 h consecutively) and gaming disorder (measured with the Game Addiction Scale) as well as indicators of mental health.

Results

A total of 33.3% of the sample engaged in binge gaming at least once in the previous year, and 6.1% at least weekly. Frequency of binge gaming was associated with gaming disorder score in a linear dose-response relationship (linear trend = 2.30 [2.14, 2.46]) even if adjusted for time spent gaming (linear trend = 1.24 [1.03, 1.45). More frequent binge gaming was associated with lower life satisfaction and sleep quality, and with more major depression and social anxiety disorder symptoms.

Conclusions

Binge gaming patterns, especially daily or almost daily binge gaming, are important to consider with regard to gaming disorder and mental health. Asking about binge gaming may be a promising screening question for gaming related problems. Encouraging regular breaks from gaming may be a valuable prevention strategy to reduce negative outcomes of gaming.

Abstract

Background

Video gaming is a popular activity among young people. Time spent with gaming was found to be only moderately associated with gaming disorder. However, patterns of binge gaming (playing more than 5 h consecutively) were rarely considered in research on gaming. This study explores how binge gaming frequency is related with gaming disorder and mental health.

Methods

The sample came from the Cohort study on substance use risk factors (C-SURF) and comprised 5,358 young men aged 28.26 years (SD = 1.27). ANCOVA was conducted to estimate the association between binge gaming frequency (gaming at least 5 h consecutively) and gaming disorder (measured with the Game Addiction Scale) as well as indicators of mental health.

Results

A total of 33.3% of the sample engaged in binge gaming at least once in the previous year, and 6.1% at least weekly. Frequency of binge gaming was associated with gaming disorder score in a linear dose-response relationship (linear trend = 2.30 [2.14, 2.46]) even if adjusted for time spent gaming (linear trend = 1.24 [1.03, 1.45). More frequent binge gaming was associated with lower life satisfaction and sleep quality, and with more major depression and social anxiety disorder symptoms.

Conclusions

Binge gaming patterns, especially daily or almost daily binge gaming, are important to consider with regard to gaming disorder and mental health. Asking about binge gaming may be a promising screening question for gaming related problems. Encouraging regular breaks from gaming may be a valuable prevention strategy to reduce negative outcomes of gaming.

Introduction

Excessive computer gaming and gaming disorder (GD) is a matter of growing concern and received a lot of attention with the recent inclusion of gaming disorder in the ICD-11 (World Health Organization, 2019). Time spent gaming (hours spent per week) is only moderately associated with GD and not sufficient to understand problematic gaming (Demetrovics & Király, 2016). Therefore, patterns of use should be investigated. Little is known how patterns of gaming are associated with GD and related outcomes. This study explores in a large sample of young Swiss men the concept of binge gaming (BG, playing 5 or more hours consecutively) and investigates its association to GD and mental health indicators.

Conceptually, bingeing refers to episodes of excessive consumption in a short period, often accompanied by a loss of control and is associated with negative consequences for the individual engaging in bingeing behaviours (De Feijter, Khan, & van Gisbergen, 2016; Gmel, Kuntsche, & Rehm, 2011; Griffiths, 2006). Binge drinking for example is a popular concept in the alcohol field, usually defined as drinking a certain amount (usually between 4 and 8 depending on the study) on a single occasion (Gmel et al., 2011; Wechsler, Dowdall, Davenport, & Rimm, 1995). It is also a central element of the widely used AUDIT and AUDIT-C Screener (Saunders, Aasland, Babor, De la Fuente, & Grant, 1993) for alcohol use disorder. Even beyond the average alcohol use per week (volume; Rehm et al., 2010), binge drinking was found to be associated strongly with many negative alcohol-related outcomes (Ham & Hope, 2003; Kuntsche, Kuntsche, Thrul, & Gmel, 2017), and it also an important dimension to be considered for the estimation of alcohol related morbidity and mortality, especially injuries (Rehm et al., 2010). The concept of bingeing has also been used for other excessive behaviours that have been associated with negative consequences such as binge eating (American Psychiatric Association, 2013), binge watching television series (Flayelle et al., 2020), binge gambling (Cowlishaw, Nespoli, Jebadurai, Smith, & Bowden-Jones, 2018; Griffiths, 2006; Nower & Blaszczynski, 2003; Werle, Schroeder, Wolz, & Svaldi, 2021), binge internet use (Flayelle et al., 2020; Gatineau, 2021) and BG (Mahapatra, Sharma, Amudhan, & Anand, 2021). Binge episodes are considered important behavioral markers, in order to avoid the development of more severe long-term consequences, such as addiction (Cowlishaw et al., 2018; Griffiths, 2006).

The link between time spent gaming (TSG) and gaming disorder was found to be rather weak (Baggio et al., 2016; Demetrovics & Király, 2016; Pontes, Schivinski, Kannen, & Montag, 2022). In analogy to binge drinking, it may be hypothesized for gaming that a measure of BG may be relevant for negative consequences associated with intense gaming. Frequent BG may be specifically associated with specific problems, notably less time for sleep, negligence of other activities etc. Arguably, immersion into the gaming world may also be higher during very long gaming sessions, which may again increase the loss of control while decreasing the salience of activities in offline world, thus leading to preoccupation with gaming, an important marker of gaming disorder (Lemmens, Valkenburg, & Peter, 2009).

If confirmed, BG could become a significant marker to detect those who are starting to experience negative consequences due to their gaming. As a result, early intervention programs could be deployed more quickly. It is therefore important to develop the concept of BG. To the best of our knowledge, the concept of BG has not been investigated empirically.

Aims

The first aim of the study was to investigate the prevalence of BG in a large sample of young Swiss men. The second aim was to test whether BG is associated with GD as well as with other mental health indicators and to test whether these associations with BG remain significant if adjusted for TSG. We hypothesized that frequency of BG is positively associated with GD and negatively with mental health and sleep quality in a dose-response relationship.

Methods

Sample

Our sample came from the Cohort Study on Substance Use Risk Factors (C-SURF), designed to examine substance use patterns and related factors among young Swiss men (Gmel et al., 2015, 2021; Studer et al., 2013). Enrolment for the baseline assessment took place during the mandatory recruitment procedures for military service for all young Swiss men in the year 2010/11. Young men were enrolled at three of the six national military recruitment centres, which covered 21 of Switzerland's 26 cantons. A total of 7,556 participants gave their written informed consent (Gmel et al., 2021). Participation in the study was independent of whether participants served in the army or not. Participants filled out questionnaires at home, either online or on paper. Data for the present study was collected in the fourth wave (April 2019–November 2020), when participants were on average 28.26 (SD = 1.27) years old. Twelve participants were excluded due to missing values, the final sample size was 5,356.

Measures

Gaming disorder was measured with the 7-item Game addiction scale (Khazaal et al., 2016; Lemmens et al., 2009). The items were for example “how often did you play longer than intended”, and response options were “never” (0), “rarely” (1), “sometimes” (2), “often” (3) and “very often” (4), and the total score ranged from 0 to 28. Cronbach's alpha was 0.841.

TSG was measured by two questions: the frequency of gaming (recoded to days per year) and the hours played on a day when they played. The product of the frequency and quantity as hours played per week was calculated (range 0–96 h).

BG was measured as the frequency of gaming at least 5 h consecutively in one occasion (excluding short breaks for biological needs). Response options were never, less than monthly, monthly, weekly, multiple times per week, daily or almost daily. This question was adapted from the Canadian WebAdo survey (Dufour et al., 2018).

Life satisfaction was assessed using the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985) (range 5–35; Cronbach's alpha = 0.911). Sleep quality was measured with the Pittsburgh Sleep Quality Index (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989) (range 0–20). The severity of major depression symptoms over the last two weeks was measured using the Major Depression Inventory (range 0–50; Cronbach's alpha = 0.908; WHO–MDI) (Bech, Rasmussen, Olsen, Noerholm, & Abildgaard, 2001). Symptoms of social anxiety disorder (SAD) during the previous week were assessed using the Clinically Useful Social Anxiety Disorder Outcome Scale (Dalrymple et al., 2013) (range 0–48; Cronbach's alpha = 0.930).

Statistical analysis

To test the association between BG and GD, life satisfaction, sleep quality, depression and social anxiety disorder scores, ANCOVA models were estimated in SPSS 27 for differences between groups of participants (no gaming, gaming without binge, binged less than monthly, monthly, weekly, multiple times per week, daily or almost daily). Criterion variables were z-standardized (mean = 0, variance = 1). Parameters for mean differences between groups were estimated with the HC3 (heteroscedasticity consistent) robust estimator. In a first step, differences between groups of participants with different BG frequencies were tested. In a second step, models were adjusted for TSG to test whether BG frequency was still associated with BG. Partial eta-squared was estimated for BG to estimate its unique effect on top of covariates, respectively TSG. To test whether there was a dose-response relationship across categories of BG frequencies, linear trends were estimated using the polynomial contrast function. Linear trend and partial eta-squared were estimated among gamers only.

Ethics

All participants were required to give their written informed consent. The Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07) approved the research protocol.

Results

A third of the sample (33.3%) engaged in BG at least once in the past year, and 6.1% at least weekly (Table 1). ANCOVA models (Table 2, Fig. 1) showed that BG frequency is associated with GD (F5, 3940 = 467.7; P < 0.001) and that there was a linear dose-response relationship (linear trend 2.30 [2.14, 2.46]) between BG frequency and GD scores. Compared to the participants who did not engage in BG, participants engaging less than monthly in BG showed significantly higher GD scores (mean difference = 0.67 [0.61, 0.73], and this increased to 2.90 [2.41, 3.39] for those with daily or almost daily BG. These associations between BG frequency and gaming disorder score were attenuated but still significant after adjustment for TSG (hours per week), with the linear trend decreasing to 1.24 [1.03, 1.45] after adjustment (Table 2). BG explained 37.2% of the variance in GD scores, of which 14.4% were on top of TSG.

Table 1.

Descriptive statistics

nMean (SD)/%
Age5,35628.26 (1.27)
Language
 French3,11158.1%
 German2,24541.9%
Gaming frequency
 never1,40726.3%
 less than weekly1,77133.1%
 weekly1,36525.5%
 daily or almost daily81315.2%
Mean hours played per week5,3564.06 (7.95)
Time spent gaming (TSG)
 no gaming1,40826.3%
 0–0.2 h63411.8%
 0.2–0.5 h62411.7%
 0.5–1.5 h54110.1%
 1.5–3.5 h64011.9%
 3.5–7.5 h65212.2%
 7.5–15.5 h4398.2%
 15.5–30 h3476.5%
 more than 30 h711.3%
Binge gaming (BG)
 did not play1,40826.3%
 never2,16340.4%
 less than monthly1,05719.7%
 monthly3997.4%
 weekly1793.3%
 multiple times per week1041.9%
 daily or almost daily460.9%
Gaming disorder (GD)
 mean score5,3562.47 (3.68)
 Prevalence
  no5,10795.4%
  yes (4 items at least sometimes)2494.6%
Table 2.

Association of binge gaming (BG) frequency with gaming disorder and indicators of mental health: results of ANCOVA (mean difference [95% CI]), unadjusted and adjusted for time spent gaming (TSG)

No gamingGaming without BGLess than monthly BGMonthly BGWeekly BGMultiple times per week BGDaily or almost daily BGPartial eta-squared for BGLinear trend for BG
Gaming disorder
 unadjustedanaref.0.67 [0.61, 0.73]1.35 [1.23, 1.46]1.75 [1.57, 1.93]2.12 [1.83, 2.40]2.90 [2.41, 3.39]37.2%2.30 [2.14, 2.46]
 adj. for TSGbnaref.0.56 [0.50, 0.63]1.03 [0.90, 1.17]1.19 [0.97, 1.42]1.26 [0.91, 1.60]1.63 [1.09, 2.16]14.4%1.24 [1.03, 1.45]
Life satisfaction
 unadjusteda0.02 [−0.05, 0.08]ref.−0.21 [−0.28, −0.14]−0.46 [−0.57, −0.34]−0.62 [−0.79, −0.45]−0.90 [−1.14, −0.66]−1.36 [−1.75, −0.98]6.5%−1.08 [−1.27, −0.90]
 adj. for TSGb−0.01 [−0.08, 0.05]ref.−0.16 [−0.23, −0.09]−0.31 [−0.44, −0.18]−0.36 [−0.56, −0.17]−0.50 [−0.79, −0.20]−0.76 [−1.21, −0.31]1.2%−0.58 [−0.83, −0.34]
Sleep quality index
 unadjusteda−0.06 [−0.12, 0.01]ref.0.09 [0.02, 0.16]0.33 [0.22, 0.44]0.37 [0.21, 0.53]0.41 [0.20, 0.62]0.71 [0.37, 1.05]2.2%0.55 [0.36, 0.74]
 adj. for TSGb−0.04 [−0.11, 0.02]ref.0.07 [−0.01, 0.14]0.27 [0.14, 0.39]0.26 [0.08, 0.45]0.25 [−0.01, 0.51]0.47 [0.06, 0.87]0.6%0.35 [0.09, 0.60]
Depression
 unadjusteda0.05 [−0.02, 0.11]ref.0.12 [0.05, 0.19]0.32 [0.21, 0.43]0.48 [0.30, 0.65]0.66 [0.39, 0.92]1.57 [1.14, 1.99]5.1%1.15 [0.96, 1.33]
 adj. for TSGb0.06 [−0.04, 0.13]ref.0.10 [0.02, 0.17]0.25 [0.13, 0.38]0.36 [0.16 0.56]0.47 [0.16, 0.79]1.30 [0.80, 1.78]1.6%0.92 [0.67, 1.17]
Social anxiety disorder
 unadjusteda0.10 [0.03, 0.17]ref.0.07 [0.00, 0.14]0.24 [0.13, 0.35]0.34 [0.16, 0.51]0.37 [0.13, 0.60]0.57 [0.20, 0.94]1.5%0.46 [0.28, 0.65]
 adj. for TSGb0.10 [0.03, 0.17]ref.0.08 [0.00, 0.15]0.27 [0.14, 0.40]0.38 [0.16, 0.60]0.44 [0.16, 0.72]0.67 [0.22, 1.12]0.9%0.55 [0.30, 0.79]

Note: aModel only adjusted for age and linguistic region. bModel adjusted for age and linguistic region, and additionally for time spent gaming (TSG, hours per week). Criterion variables have been z-standardized and coefficients represents mean differences in standard deviation between the category of interest and the reference group (gaming without binge gaming). Partial eta-squared and linear trend as estimated without the no gaming category. na: no coefficient was estimated as the GD score for non-gamers is always 0. Bold coefficients are significant at p < 0.05.

Fig. 1.
Fig. 1.

Association of binge gaming frequency (reference group: no binge gaming) with gaming disorder and indicators of mental health

Citation: Journal of Behavioral Addictions 2023; 10.1556/2006.2022.00086

Furthermore, those who played games without engaging in BG had not significantly different or more favourable (for social anxiety disorder) indicators of mental health than those who did not play games at all (Table 2). However, for those who engaged in BG, there was a strong linear dose-response relationship between BG frequency and mental health indicators, with the largest association found for daily or almost daily BG. For all mental health indicators, these associations were attenuated after adjustment for TSG, but remained significant. BG had a significant unique contribution on top of TSG for life satisfaction (1.2%), sleep quality (0.6%), depression (1.6%) and social anxiety disorder (0.9%), and the linear trend remained significant after adjustment for TSG.

Discussion

Occasional binge gaming (playing 5 or more hours consecutively) is prevalent in our sample of young men, with about a third having binge gamed at least once in the previous year. At least weekly BG was less widespread (6.1%), and 0.9% binge gamed daily or almost daily. While GD scores were increased in all levels of BG frequency, this association was strongest for those engaging daily or almost daily in BG, thus there was a strong linear dose-response relationship between BG frequency and GD scores. Importantly, these associations remained significant if adjusted for TSG, although they were attenuated, indicating that the effect of BG on GD is in part confounded with TSG. Nevertheless, BG is associated with gaming disorder independently of TSG and should be considered on top of TSG when investigating the links between gaming behaviour and gaming disorder. Similar to earlier findings regarding other binge behaviours (Flayelle et al., 2020; Kuntsche et al., 2017), frequency of BG was related in a dose-response relationship with lower mental health, sleep quality and life satisfaction.

Overall, BG may be a potential marker of harms that may occur prior to the development of a gaming problem and of an unhealthy relationship with games. Playing 5 h or more on one occasion may increase immersion in the virtual world of the game and overstimulate rewards circuitries, which may decrease interest in offline activities, and cause mental fatigue. Gaming was also found to be associated with poor sleep (Lam, 2014), and especially long gaming sessions may decrease time available for sleep and sleep quality. In the long term, such factors may be associated with lower mental health. Playing without BG, i.e. with regular and extended breaks, may reduce these negative effects of BG and be more compatible with a healthy lifestyle.

Limitations

Our sample consisted of only young men. More research in samples also including women and a broader age range is required. Only one cut-off for BG was measured. Distributions of the outcomes deviated from normality. This was partly corrected by using robust estimators and should not have impacted our conclusions due to large sample size. The cross-sectional nature of our study does also not allow to draw conclusions about the direction of causality, longitudinal research would therefore be valuable.

Conclusions

BG frequency is associated with GD, mental health problems and poor sleep quality, even on top of TSG. BG may be a promising concept deserving further attention in research on gaming and gaming related problems. BG patterns should be assessed along with TSG, and BG could even be a valuable single-item screening question for gaming related problems. Further research is needed to establish the best threshold for hours spent gaming on one occasion to define BG. As regards the Swiss situation, it appears that BG is widespread and does deserve attention with respect to its implications for public health, especially in light of its association with mental health. Those engaging in BG may benefit from a screening for gaming related problems and/or targeted preventive interventions. Promising strategies for preventing negative consequences of BG may include promoting regular breaks from gaming to limit the time spent on gaming on a single occasion.

Funding sources

The C-SURF study was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493, and FN 33CS30_177519).

Authors’ contribution

Study concept and design: SM, GG, JS, MW. Data analysis: SM with support from GG, JS, MW, SB, MD. Interpretation of data: SM, MW, MD, SB, MD, CG, GG, JS. Writing of the manuscript: SM, MW, MD, SB, MD, CG, GG, JS. Obtained funding: GG. All authors had full access to the 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 report no financial or other relationship relevant to the subject of this article.

Acknowledgments

We thank Céline Gachoud and Christiane Gmel for their extensive work with the data collection of the C-SURF study.

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  • Khazaal, Y., Chatton, A., Rothen, S., Achab, S., Thorens, G., Zullino, D., & Gmel, G. (2016). Psychometric properties of the 7-item game addiction scale among French and German speaking adults. BMC Psychiatry, 16(1), 110. https://doi.org/10.1186/s12888-016-0836-3.

    • Crossref
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    • Export Citation
  • Kuntsche, E., Kuntsche, S., Thrul, J., & Gmel, G. (2017). Binge drinking: Health impact, prevalence, correlates and interventions. Psychology & Health, 32(8), 9761017. https://doi.org/10.1080/08870446.2017.1325889.

    • Crossref
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    • Export Citation
  • Lam, L. T. (2014). Internet gaming addiction, problematic use of the internet, and sleep problems: A systematic review. Current Psychiatry Reports, 16(4), 19. https://doi.org/10.1007/s11920-014-0444-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemmens, J. S., Valkenburg, P. M., & Peter, J. (2009). Development and validation of a game addiction scale for adolescents. Media Psychology, 12(1), 7795. https://doi.org/10.1080/15213260802669458.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahapatra, S., Sharma, M. K., Amudhan, S., & Anand, N. (2021). Binge gaming and COVID-19: A looming crisis. Journal of Mental Health and Human Behaviour, 26(1), 83. https://doi.org/10.4103/jmhhb.jmhhb_26_21.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nower, L., & Blaszczynski, A. (2003). Binge gambling: A neglected concept. International Gambling Studies, 3(1), 2335. https://doi.org/10.1080/14459790304589.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., Schivinski, B., Kannen, C., & Montag, C. (2022). The interplay between time spent gaming and disordered gaming: A large-scale world-wide study. Social Science & Medicine, 114721. https://doi.org/10.1016/j.socscimed.2022.114721.

    • Crossref
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  • Rehm, J., Baliunas, D., Borges, G. L., Graham, K., Irving, H., KehoeT., … Poznyak, V. (2010). The relation between different dimensions of alcohol consumption and burden of disease: An overview. Addiction, 105(5), 817843. https://doi.org/10.1111/j.1360-0443.2010.02899.x.

    • Crossref
    • PubMed
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  • Saunders, J. B., Aasland, O. G., Babor, T. F., De la Fuente, J. R., & Grant, M. (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption‐II. Addiction, 88(6), 791804. https://doi.org/10.1111/j.1360-0443.1993.tb02093.x.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Studer, J., Baggio, S., Mohler-Kuo, M., Dermota, P., Gaume, J., Bertholet, N., … Gmel, G. (2013). Examining non-response bias in substance use research—Are late respondents proxies for non-respondents? Drug and Alcohol Dependence, 132(1–2), 316323. https://doi.org/10.1016/j.drugalcdep.2013.02.029.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Wechsler, H., Dowdall, G. W., Davenport, A., & Rimm, E. B. (1995). A gender-specific measure of binge drinking among college students. American journal of public health, 85(7), 982985. https://doi.org/10.2105/AJPH.85.7.982.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Werle, D., Schroeder, P. A., Wolz, I., & Svaldi, J. (2021). Incentive sensitization in binge behaviors: A mini review on electrophysiological evidence. Addictive Behaviors Reports, 100344. https://doi.org/10.1016/j.abrep.2021.100344.

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  • Gatineau, C. (2021). Binge “Internet”: Un prédicteur potentiel de l’utilisation problématique d’Internet (UPI)? Conference communication at Séminaire annuel Convergence, recherche et intervention (CRI 2021)., Trois-Rivière, Canada. (virtuel).

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  • Gmel, G., Akre, C., Astudillo, M., Bähler, C., Baggio, S., Bertholet, N., … Deline, S. (2015). The Swiss cohort study on substance use risk factors–Findings of two waves. Sucht, 61(4), 251262. https://doi.org/10.1024/0939-5911.a000380.

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  • Gmel, G., Kuntsche, E., & Rehm, J. (2011). Risky single-occasion drinking: Bingeing is not bingeing. Addiction, 106(6), 10371045. https://doi.org/10.1111/j.1360-0443.2010.03167.x.

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  • Gmel, G., Mohler-Kuo, M., Studer, J., Gachoud, C., Marmet, S., Baggio, S., & Foster, S. (2021). Cohort Study on Substance Use Risk Factors (C-SURF): Fundings, key stages, participation rates, instruments and data description. https://doi.org/10.5281/zenodo.5469953.

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  • Khazaal, Y., Chatton, A., Rothen, S., Achab, S., Thorens, G., Zullino, D., & Gmel, G. (2016). Psychometric properties of the 7-item game addiction scale among French and German speaking adults. BMC Psychiatry, 16(1), 110. https://doi.org/10.1186/s12888-016-0836-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuntsche, E., Kuntsche, S., Thrul, J., & Gmel, G. (2017). Binge drinking: Health impact, prevalence, correlates and interventions. Psychology & Health, 32(8), 9761017. https://doi.org/10.1080/08870446.2017.1325889.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lam, L. T. (2014). Internet gaming addiction, problematic use of the internet, and sleep problems: A systematic review. Current Psychiatry Reports, 16(4), 19. https://doi.org/10.1007/s11920-014-0444-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemmens, J. S., Valkenburg, P. M., & Peter, J. (2009). Development and validation of a game addiction scale for adolescents. Media Psychology, 12(1), 7795. https://doi.org/10.1080/15213260802669458.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahapatra, S., Sharma, M. K., Amudhan, S., & Anand, N. (2021). Binge gaming and COVID-19: A looming crisis. Journal of Mental Health and Human Behaviour, 26(1), 83. https://doi.org/10.4103/jmhhb.jmhhb_26_21.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nower, L., & Blaszczynski, A. (2003). Binge gambling: A neglected concept. International Gambling Studies, 3(1), 2335. https://doi.org/10.1080/14459790304589.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., Schivinski, B., Kannen, C., & Montag, C. (2022). The interplay between time spent gaming and disordered gaming: A large-scale world-wide study. Social Science & Medicine, 114721. https://doi.org/10.1016/j.socscimed.2022.114721.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rehm, J., Baliunas, D., Borges, G. L., Graham, K., Irving, H., KehoeT., … Poznyak, V. (2010). The relation between different dimensions of alcohol consumption and burden of disease: An overview. Addiction, 105(5), 817843. https://doi.org/10.1111/j.1360-0443.2010.02899.x.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Saunders, J. B., Aasland, O. G., Babor, T. F., De la Fuente, J. R., & Grant, M. (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption‐II. Addiction, 88(6), 791804. https://doi.org/10.1111/j.1360-0443.1993.tb02093.x.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Studer, J., Baggio, S., Mohler-Kuo, M., Dermota, P., Gaume, J., Bertholet, N., … Gmel, G. (2013). Examining non-response bias in substance use research—Are late respondents proxies for non-respondents? Drug and Alcohol Dependence, 132(1–2), 316323. https://doi.org/10.1016/j.drugalcdep.2013.02.029.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Wechsler, H., Dowdall, G. W., Davenport, A., & Rimm, E. B. (1995). A gender-specific measure of binge drinking among college students. American journal of public health, 85(7), 982985. https://doi.org/10.2105/AJPH.85.7.982.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Werle, D., Schroeder, P. A., Wolz, I., & Svaldi, J. (2021). Incentive sensitization in binge behaviors: A mini review on electrophysiological evidence. Addictive Behaviors Reports, 100344. https://doi.org/10.1016/j.abrep.2021.100344.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • World Health Organization. (2019). International classification of diseases (11th revision) .https://icd.who.int/en.

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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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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 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

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

  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Luke CLARK (University of British Columbia, Canada)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • 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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