Skip to main content
PMC home page
Journal of Behavioral Addictions logoLink to Journal of Behavioral Addictions
. 2021 Aug 25;10(3):447–455. doi: 10.1556/2006.2021.00045

Between two worlds: Exploring esports betting in relation to problem gambling, gaming, and mental health problems

Loredana Marchica 1,2,*, Jérémie Richard 1, Devin Mills 3, William Ivoska 4, Jeffrey Derevensky 1
PMCID: PMC8997199  PMID: 34437298

Abstract

Background and aims

Esports betting is an emerging gambling activity where individuals place bets on an organized video gaming competition. It represents only one of several gambling activities commonly endorsed by adolescents. To date, limited research has explored the relationship between esports betting and mental health among adolescents and its convergence with both problem gambling (PG) and problem video gaming (PVG). The present study examined the relation between esports betting, PG and PVG, and both externalizing and internalizing problems among adolescents while accounting for adolescents' video gaming intensity (i.e., how often they play 2 h or more in a day) and engagement in other gambling activities.

Methods

Data was collected from 6,810 adolescents in Wood County, Ohio schools. A subset of 1,348 adolescents (M age = 14.67 years, SD = 1.73, 64% male) who had gambled and played video games during the past year were included in the analyses.

Results

Approximately 20% (n = 263) of the included sample had bet on esports during the past year. Esports betting was positively correlated with other forms of gambling, both PG and PVG, and externalizing behaviors. Mediation analyses revealed esports betting was associated to both internalizing and externalizing problems through PVG and not PG.

Conclusions

Esports betting may be particularly appealing to adolescents who are enthusiastic video gamers. As such, regulators must be vigilant to ensure codes of best practices are applied to esports betting operators specifically for underaged individuals.

Keywords: esports betting, adolescence, problem gambling, problem video gaming

Introduction

Esports are organized video gaming competitions where individuals or teams compete in leagues and/or tournaments to win monetary prizes (Greer, Rockloff, Browne, Hing, & King, 2019). Rather than betting on the outcome of traditional sports, esports betting involves bets placed on the outcomes of video gaming competitions (Abarbanel, Macey, Hamari, & Melton, 2020; Gainsbury, Abarbanel, & Blaszczynski, 2017a). The global esports market has grown dramatically, from revenues of $124 million in 2014 to over $900 million in 2018 (NewZoo, 2018). The growth in the esports market has been associated with the increases in esports betting advertising and participation (Abarbanel & Phung, 2019; Eilers & Krejcik, 2018; Juniper Research, 2018). Preliminary findings indicate that individuals betting on esports are younger, more likely to be male, report wagering on a greater number of activities, regularly play video games, and are more frequent esports spectators (Abarbanel et al., 2020; Gainsbury et al., 2017a, 2017b; Macey, Abarbanel, & Hamari, 2020; Macey & Hamari, 2019). The aim of the current study was to investigate the psychological factors associated with esports betting among adolescent youth including problem gambling, problem video gaming and youth mental health outcomes.

Problem Gambling and problem video gaming

Gambling and video gaming disorder are the two formally recognized non-substance related addictive disorders (i.e., behavioral addictions) according to the International Classification of Diseases – Eleventh Edition (ICD-11; WHO, 2019). Both disorders are defined as a persistent and recurrent engagement in the behavior (gambling or video gaming), leading to clinically significant impairment or distress. Recent research has shown that rates of problematic participation in both gambling and video gaming are higher among youth when compared to adults, with 0.2–12.3% reporting problem gambling (PG; Calado, Alexandre, & Griffiths, 2017; Emond & Griffiths, 2020) and 3–10% reporting problem video gaming (PVG; Fam, 2018; Paulus, Ohmann, von Gontard, & Popow, 2018; Stevens, Dorstyn, Delfabbro, & King, 2020). PG and PVG are comorbid with a number of mental health problems including depression, anxiety, impaired sleep, increased substance use and aggression (Lam, 2014; Männikkö et al., 2020; Mills et al., 2020; Myrseth & Notelaers, 2018). Perhaps unsurprising, both PG and PVG are associated with increased gambling and video gaming frequency, respectively (Carbonneau et al., 2015; Gentile et al., 2011). That is, those who participate more frequently and more intensely (i.e., larger bets, longer gaming sessions) in these activities are more likely to endorse symptoms of PG and PVG. Although both PG and PVG fall under the umbrella of behavioral addictions, mixed findings have been noted in the co-occurrence of PG and PVG, with some studies finding that individuals reporting symptoms of one disorder are more likely to endorse symptoms of the other (Derevensky, 2019; Floros, 2018; McBride & Derevensky, 2017) and other studies finding no direct association between PG and PVG (Delfabbro & King, 2020; Macey & Hamari, 2018) or between the frequency of involvement in gambling and gaming (Forrest, King, & Delfabbro, 2016).

Gamblification of video gaming and esports betting

Adolescents have been identified as being more likely to engage in novel forms of gambling via the Internet (Emond & Griffiths, 2020; Giralt et al., 2018; González-Roz, Fernández-Hermida, Weidberg, Martínez-Loredo, & Secades-Villa, 2017), and developers are increasingly introducing gambling-like elements into video games (Abarbanel, 2018). For instance, loot boxes, which offer the chance of obtaining rare items or in-game currency, have provided the video gaming industry with a large revenue stream over the past 5+ years. However, the lottery-style manner of loot boxes has made numerous video gamers feel as though they are gambling while playing, with loot box purchasing being significantly correlated with PG severity (Brooks & Clark, 2019; Zendle & Meyer, 2019). Another recent trend is social casino games or gambling-themed video games, which have also been found to correlate with PG measures (Gainsbury, Russell, & Hing, 2014; King, Russell, Gainsbury, Delfabbro, & Hing, 2016; Li, Mills, & Nower, 2020; Wohl, Salmon, Hollingshead, & Kim, 2017). The gamblification of video gaming has led to the emergence of a number of studies on the risks posed by loot box purchases and social casino gaming to problem gambling. Further, some studies have intended to demonstrate a possible migration from early exposure to either loot box purchases or social casino gaming to gambling with real money (Hayer et al., 2018; Macey & Hamari, 2019; Zendle, 2020; Zendle & Meyer, 2019). While there is still a need for additional research on the validity of this gateway hypothesis of video gaming to gambling (see review by Delfabbro & King, 2020), there is also a paucity of research on esports betting, which may represent further concerns related to video gaming and gambling for mental health professionals.

Unlike in-game gambling-like activities (i.e., loot boxes, social casino gaming), esports betting is external to video gaming. Indeed, although esports betting may be appealing to gamers, it is important to note that gamers are not the only individuals who engage in this type of gambling. Of note, online gamblers appear to be attracted to various forms of online wagering including, but not limited to, video game-related gambling such as esports betting (Macey & Hamari, 2018; Lelonek-Kuleta & Bartczuk, 2021). Moreover, esports betting may share some similarities to sports betting, in that individuals can believe that they are able to develop a certain degree of skill, knowledge and analysis of in-game features which will give them a betting advantage (Lelonek-Kuleta & Bartczuk, 2021; Winters & Derevensky, 2019). Based on this, individuals who play video games, similar to casual sports players, may believe that they have better chances of gambling success due to their knowledge of the game. This claim is bolstered by research findings indicating that maladaptive or distorted cognitions are implicated in both problem gambling and gaming (Brooks & Clark, 2019; Macey & Hamari, 2020).

To date, research has found that more frequent esport betting is associated with greater PG symptoms (Gainsbury, Abarbanel, & Blaszczynski, 2017b; Macey & Hamari, 2019) and one study reported esport betting as being positively associated with both PG and PVG (Zendle, 2020). Nonetheless, the link between esports betting and mental health remains unexplored. The potential elevated risk for both PG and PVG suggests that esports betting may place adolescents at-risk for mental health symptoms found to be comorbid with PG and PVG including internalizing (e.g., depression, anxiety, and low self-esteem) and externalizing problems (e.g., conduct problems, and inattention/hyperactivity) (Derevensky, 2019; Dowling et al., 2017; Giralt et al., 2018; Livazović & Bojčić, 2019; Richard, Fletcher, Boutin, Derevensky, & Temcheff, 2020). Further, given the expected growth in the esports betting market, research is needed on the potential risk esports betting represents for youth.

The present study sought to explore the effect of the frequency of participation in various gambling activities and video gaming intensity (i.e., how often do you play 2+ hours of video games in a day) onto both externalizing and internalizing problems among adolescents. Given the association between esports betting and both PG and PVG and previous research demonstrating an association between mental health outcomes and both PG and PVG (Derevensky, 2019; Richard et al., 2020), we hypothesized that increased gambling frequency and video gaming intensity would be associated with greater externalizing and internalizing problems and that these effects would be mediated by symptoms of PG and PVG. Within this broader objective, specific attention is brought to exploring the relationship between esports betting and symptoms of PG and PVG. Esports culture tends to replicate traditional sports cultures, involving elite athletes (gamers), teams, leagues, and championships. As such, similar to previous studies demonstrating that athletes (at the elite or non-elite/casual level) are more likely to engage in sports betting (Winters & Derevensky, 2019), it is hypothesized that video gamers will also be more likely to engage in esports betting, and that an increased frequency of esports betting contributes more to PVG severity in comparison to PG after accounting for other gambling activities.

Methods

The current study employed data from the 2019 Alcohol, Drug Addiction and Mental Health Services (ADAMHS) Board/Wood County Educational Service Center Survey on Alcohol and Other Drug Use among adolescents in Wood County, Ohio.

Participants

Of a total 7,573 students from grades 7 through 12 who participated in the study, 763 were excluded due to suspected insincere responding (i.e., reporting engaging in all gambling activities at all times and providing inconsistent responses). Given the purpose of the study, the sample was reduced to those who had gambled in the previous year (n = 1,752). This subsample was further reduced to exclude adolescents who gambled but did not regularly play video games (n = 404). Therefore, the final sample consisted of 1,348 adolescents (M age = 14.67 years, SD = 1.73, 64% male).

Measures

Gambling and Video Gaming Frequency. Frequency of participation in 11 common gambling activities (e.g., poker, bingo, sports betting, esports) over the previous 12 months (based on a 5-point scale ranging from “Not at All = 0” to “Daily = 4”) was assessed. Using the same 5-point scale, video gaming intensity was measured by a single item (Spent at least two hours daily playing games online or offline.). This was used in lieu of an estimate of weekly video gaming frequency due to research demonstrating inaccurate estimations (Williams, Consalvo, Caplan, & Yee, 2009; Wood & Griffiths, 2007). Additionally, given the brevity of the survey, space did not allow for a more exhaustive measure of video gaming frequency (e.g., Gentile, Lynch, Linder, & Walsh, 2004). As such, it was believed this would be the most accurate and clear assessment of how frequently adolescents' play video games intensely. Finally, the use 2+ hours daily versus 3+ or 4+ hours was based on prior research suggesting a long gaming session for both casual as well as excessive gamers would be in excess of 2 h (Kapalo, Dewar, Rupp, & Szalma, 2015; Neys, Jansz, & Tan, 2014). As such, how often they play 2+ hours (i.e., never, less than monthly, monthly, weekly, daily) would be a strong indicator of their video gaming intensity. Due to the exclusion criteria, the final sample did not include individuals who indicated “Not at all” for the video gaming intensity item meaning that the range was from 1 to 4. Item wording is provided in appendix.

NORC DSM-IV Screening for Gambling Problems (NODS-CLiP; Toce-Gerstein, Gerstein, & Volberg, 2009). The NODS-CLiP is a screening measure for gambling disorder (GD), containing three dichotomous (yes/no) questions based on the DSM-IV criteria for GD (loss of control, lying, and preoccupation). Participants who responded affirmatively to one or more items were identified as being at-risk for GD and may referred to as “problem gamblers” (PG). The NODS-CLiP has demonstrated excellent sensitivity, capturing 94% of problem gamblers identified by the NODS, while having a specificity level of 0.96 (Toce-Gerstein et al., 2009). Within the following sample, 214 participants had scores of one or higher. The internal consistency of the NODS-CLiP was 0.55 for the present sample. Item wording is provided in appendix.

Internet Gaming Disorder Scale-Short Form (IGDS-SF9; Pontes & Griffiths, 2015), is a 9-item measurement adapted from the nine core criteria that define IGD according to the DSM-5 (APA, 2013). Responses were based on a 5-point Likert scale “Never = 1” to “Very Often = 5”, with higher scores indicating greater severity of gaming disorder. In the following study the midpoint (a score of 30 or more) was considered at-risk for IGD (termed PVG) and 76 participants scored within this range. The internal consistency of the IGDS-SF9 was 0.89 for the present sample. Item wording is provided in appendix.

The Ohio Scales Youth Problem Severity Scale (PSS; Ogles, Melendez, Davis, & Lunnen, 2001). The PSS is a 20-item self-report measure that assesses internalizing and externalizing problems. Scores are calculated by summing the participants' ratings of each item on a 6-point scale during the past 30 days, ranging from “Not at All = 0” to “All the Time = 5”. Within the present sample, the internal consistency of the PSS was 0.92 (internalizing) and 0.87 (externalizing) for each subscale. Item wording is provided in appendix.

Ethics

Ethical approval was granted by the ADAMNHS ethics committee. All parents of students signed a consent form to allow students to participate in the study. Further, all students provided assent to participate and were instructed that they could leave the study at any time.

Results

Data analysis plan

Data was reviewed in SPSS version 26 (IBM Corp, 2019) with all subsequent analyses conducted in the Mplus version 8 (Muthén & Muthén, 2017). A primary objective of the present study was to assess the association between gambling frequency and video gaming intensity with both internalizing and externalizing problems, and the role either PG or PVG play in explaining this association (i.e., mediation). As such, structural equation modeling (SEM) was used to evaluate the fit of the data to the hypothesized full mediation model (Fig. 1). This assumed that the effect of gambling frequency and video gaming intensity onto both internalizing and externalizing problems would be fully accounted for by PG and PVG. Results would be reviewed to assess the need to add additional paths to improve model fit, thus relaxing the hypothesized full mediation. Given the complexity of the model, four item parcels were used to indicate the externalizing and internalizing problem latent variables (Bandalos, 2002; Little, Cunningham, Shahar, & Widaman, 2002). Finally, parameters were estimated using robust maximum likelihood as it is more robust against issues of non-normality. Missing values were estimated using full information maximum likelihood. Goodness-of-fit was assessed across several indices including the root mean square error of approximation (RMSEA; ≤ 0.05), comparative fit index (CFI; ≥ 0.95), Tucker-Lewis Index (TLI; ≥ 0.95), and standardized root mean squared residual (SRMR; ≤ 0.06) (Byrne, 2013; Kline, 2016).

Fig. 1.

Fig. 1.

Open in a new tab

Results from the hypothesized mediation model. All estimates are standardized with 95% confidence intervals in parentheses. Dashed lines were estimated but not significant (P > 0.05). * P < 0.05; ** P < 0.01; *** P < 0.001

Analyses

The data was reviewed for violations of normality with no issues detected. Results from the initial measurement model revealed high correlations between frequency of purchasing lottery and scratch tickets (r = 0.796) and frequency of engagement in fantasy sports and daily fantasy sports (r = 0.756). Two unplanned latent variables were created for ticket betting and fantasy sports betting frequency to account for these correlations. Additionally, the errors from preoccupation with video gaming and increased time playing video games were allowed to correlate based on modification indices. Results revealed an adequate fit of the data to the measurement model (χ 2 (362) = 1,096.631, P < 0.001; RMSEA = 0.039 90% (0.036, 0.041); CFI = 0.949; TLI = 0.935; SRMR = 0.039). Aside for one of the NODS-CLiP items (lied about gambling), all loadings exceeded 0.58 on their respective factor.

Bivariate correlations drawn from the measurement model were consistent with expectations. The frequency of all gambling activities was positively associated with PG (r's range from 0.347 to 0.482). Video gaming intensity was positively associated with PVG (r = 0.257). PVG was also associated with playing cards for money (r = 0.158), betting on games of skill (r = 0.158), and esports betting (r = 0.165). Externalizing problems were positively associated with all gambling activities (r's range from 0.178 to 0.253), video gaming intensity (r = 0.112), PG (r = 0.292), and PVG (r = 0.347). Internalizing problems were positively associated with ticket betting (r = 0.122), PG (r = 0.158) and PVG (r = 0.323).

Results revealed adequate fit of the data to the hypothesized full mediation model (χ 2 (376) = 1,140.734, P < 0.001; RMSEA = 0.039 90% (0.036, 0.041); CFI = 0.947; TLI = 0.935; SRMR = 0.042). A review of the modification indices did not suggest any direct paths to be added. Path estimates are in Fig. 1 with indirect effects present in Table 1. Gender and age were included as covariates. Model results indicate that frequency of participation in fantasy sports, games of skills, cards, and sports betting was significantly associated with internalizing and externalizing problems through PG, while esports betting and gaming intensity was significantly associated with internalizing and externalizing behaviors through PVG.

Table 1.

Indirect Effects from Mediation Model

Indirect Effect β P
Playing Cards for Money → Problem Gambling
 → Externalizing Problems 0.029 (0.007, 0.051) 0.011
 → Internalizing Problems 0.069 (0.030, 0.108) 0.001
Betting on Games of Skill → Problem Gambling
 → Externalizing Problems 0.016 (−0.002, 0.035) 0.078
 → Internalizing Problems 0.039 (0.003, 0.075) 0.035
Sports Betting → Problem Gambling
 → Externalizing Problems 0.011 (−0.006, 0.027) 0.205
 → Internalizing Problems 0.025 (−0.013, 0.063) 0.196
Ticket Betting → Problem Gambling
 → Externalizing Problems 0.025 (0.002, 0.047) 0.031
 → Internalizing Problems 0.059 (0.016, 0.102) 0.007
Fantasy Sports Betting → Problem Gambling
 → Externalizing Problems −0.003 (−0.024, 0.017) 0.751
 → Internalizing Problems −0.008 (−0.057, 0.041) 0.748
Esports Betting → Problem Gambling
 → Externalizing Problems 0.009 (−0.006, 0.025) 0.247
 → Internalizing Problems 0.022 (−0.015, 0.059) 0.244
Esports Betting → Problem Video Gaming
 → Externalizing Problems 0.054 (0.031, 0.077) <0.001
 → Internalizing Problems 0.048 (0.025, 0.071) <0.001
Video Gaming Intensity → Problem Video Gaming
 → Externalizing Problems 0.083 (0.057, 0.110) <0.001
 → Internalizing Problems 0.074 (0.049, 0.099) <0.001
Open in a new tab

Discussion

The current study is the first to examine the effect of adolescent participation in gambling and gaming onto externalizing and internalizing problems through PG and PVG, with a specific emphasis on esports betting as a novel form of gambling. Mediation model results indicate that the frequency of participation in fantasy sports, games of skills, cards, and sports betting were significantly associated with internalizing and externalizing problems through PG, while esports betting and video gaming intensity were significantly associated with internalizing and externalizing behaviors through PVG. Specific to esports participation, results indicate that youth who engage in more frequent esports betting are more likely to report symptoms of PVG, which in turn, result in greater externalizing and internalizing problems. These results suggest that youth who do experience mental health symptoms as a result of esports betting are likely to experience these not as a direct result of their betting behaviors, but as a consequence of PVG symptoms. With similar relationships also being present for youth reporting engagement in a greater intensity of video gaming, these results suggest that it is the presence of symptoms such as feeling preoccupied with one's gaming, losing interest in hobbies or other activities, and needing to spend greater amounts of time gaming to achieve satisfaction, which result in greater externalizing and internalizing problems. Although additional longitudinal research is necessary to evaluate the temporal sequencing of these problems, these results would be in line with the network theory of psychopathological comorbidity (Borsboom & Cramer, 2013; Fried & Cramer, 2017) whereby symptoms of different mental health conditions emerge as a result of mutually interacting and reciprocally reinforcing symptom networks.

It is significant to emphasize that in the present study, esports betting was not associated with PG in the mediation model. These results are in contrast to studies finding a significant relationship between esports betting and PG among adults (Gainsbury et al., 2017b; Macey & Hamari, 2019; Zendle, 2020). In order to explain this finding, it is possible that this discrepancy is a result of investigating these betting behaviors among adolescents who report a lower frequency of engagement in esports betting. Alternatively, it is possible that this is a result of the present sample only including youth who engaged in both gambling and video gaming over the past year. Given that both PG and PVG were included in the mediation model and that these variables were significantly correlated, it is possible that there are interactions between PG and PVG symptoms that reduced the direct effect of esports betting on PG. More specifically, in the measures assessing PG and PVG, there are overlapping symptoms (i.e., preoccupation, attempts to reduce/control, lying/deception) that may have been endorsed for PVG but not for PG depending on how one's esports betting behaviors were conceptualized. Lastly, it is possible that there was significant overlap between the game one plays and the esports games one decides to bet on. This overlap may have blurred the distinction between problems related to gaming and gambling. Additional research is required to directly investigate these possible explanations, with the potential for qualitative research among gamers who wager on esports to yield significant insights into the interaction of these problems.

The findings related to video gaming intensity and esports betting is comparable to research indicating that athletes or individuals with additional knowledge about sports (i.e., recreational sports players) are at increased risk of engaging in sport betting (Winters & Derevensky, 2019). Although shown to be false, there is a perceived influence of domain specific knowledge which is suggested to contribute to feelings of competency and ability to win wagers based on the individual's sport-specific knowledge (Khazaal et al., 2012). As such, it is possible that high intensity video gamers (or problem video gamers) exhibit a similar form of cognitive distortions and erroneous beliefs around esports betting as athletes do when betting on sports. Further, with the rapid growth in the esports market (NewZoo, 2018) and research demonstrating that esports bettors are younger and are more likely to engage in regular video gaming themselves (Abarbanel et al., 2020), it is possible that adolescents are at an increased risk of engaging in esports betting and that esports bettors represent a specific subset of individuals with problems related to gaming. The present study provides preliminary evidence for this hypothesis by demonstrating that when accounting for other gambling activities, esports bettors are more likely to exhibit externalizing and internalizing problems through PVG, but not through PG.

Limitations

This study represents an initial attempt at understanding the convergent relationship between PG and PVG among esport bettors and the impact of the prior on adolescent mental health. Nevertheless, several limitations should be noted. First, as the data is cross-sectional, it is impossible to assess causal relationships between the variables. Second, self-report data was utilized which allows for potential biases in responding. Given the sensitive nature of some of the survey questions (e.g., mental health symptoms, gambling among minors), it is possible that some individuals did not respond truthfully. A series of checks and filters were employed to confirm validity and sincerity of the responses; however, it is impossible to validate each respondent's true engagement. In a similar vein, the validity of the single item assessing video gaming intensity has not been fully explored. Nonetheless, the correlation between video gaming intensity and PVG is comparable to other studies using estimates of weekly video gaming (e.g., Lemmens et al., 2015; Pontes & Griffiths, 2015). Fourth, although the ADAMHS youth surveys are provided to all public schools within the Wood County district, selection biases may have occurred. For instance, this method of data collection may exclude adolescents who are home-schooled, go to private school, or may have dropped-out of school. Further, participation in the survey is still dependent on parent and adolescent consent. However, the large sample size and rate of public-school registration in the U.S (89%) (National Center for Education Statistics, 2019) does allow for some level of generalizability. Finally, although a large sample size was used, the data was collected from one county in the U.S. As such, it may not be representative of adolescent behaviors more broadly. Future studies should examine this relationship longitudinally in order to better evaluate the temporal nature of these relationships.

Conclusions

Given the relationship between video gaming and esports, the appeal of betting on these events can be significant for adolescents who are already at increased risk of PVG and PG. Currently, there is an influx of advertisement and content related to esports betting with little enacted regulation (Emond & Griffiths, 2020). For instance, a report in the United Kingdom found that 17% of adolescents under the age of 16 followed Twitter accounts focused on esports betting (Smith & Rossi, 2019). Regulators must be vigilant to ensure codes of best practices are applied to esports betting operators and that appropriate regulations are in place for underaged individuals, as youth esports bettors appear to be at an increased risk of PVG and both internalizing and externalizing problems. Additionally, given the results demonstrating that esports betting was related to mental health problems through PVG, it will be important for researchers and clinicians working with gamers to not only assess for gaming frequency (and potential problematic gaming behaviors) but also for the individuals level of involvement in gaming-related gambling activities including esports betting. Further, consistent with research indicating that gambling-like behaviors within video games (e.g., loot box expenditure) are more strongly associated with PVG than PG (Li, Mills, & Nower, 2019) individuals who identify esports as their main gambling activity may require assessment and treatments that are tailored more towards problem video gaming compared to problem gambling.

Funding sources

Jérémie Richard received doctoral fellowships from the Social Sciences and Humanities Research Council of Canada (SSHRC) and from the Fonds de recherche du Québec – Société et Culture (FRQSC): Programme de bourse sur le jeu responsible. The other authors have nothing to declare.

Authors' contribution

LM: Study concept and design, methods, discussion, and editing. JR: literature review and editing. DM: Statistical analysis and editing. WI: Final editing. JD: Study supervision and final editing. 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 have no conflict of interest to disclose.

Supplementary Material

jba-10-447-s001.docx (38.4KB, docx)
Open in a new tab

References

  1. Abarbanel, B. (2018). Gambling vs. Gaming: A commentary on the role of regulatory, industry, and community stakeholders in the loot box debate. Gaming Law Review , 22(4), 231–234. 10.1089/glr2.2018.2243. [DOI] [Google Scholar]
  2. Abarbanel, B. , Macey, J. , Hamari, J. , & Melton, R. (2020). Gamers who gamble: Examining the relationship between esports spectatorship and event wagering. Journal of Emerging Sport Studies , 3(1), 1–23. [Google Scholar]
  3. Abarbanel, B. , & Phung, D. (2019). Exploring gamers' perceptions of esports betting advertising. Gaming Law Review , 23(9), 640–644. 10.1089/glr2.2019.2394. [DOI] [Google Scholar]
  4. Bandalos, D. L. (2002). The effects of item parceling on goodness-of-fit and parameter estimate bias in structural equation modeling. Structural Equation Modeling , 9, 78–102. 10.1207/S15328007SEM0901_5. [DOI] [Google Scholar]
  5. Borsboom, D. , & Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology , 9, 91–121. 10.1146/annurev-clinpsy-050212-185608. [DOI] [PubMed] [Google Scholar]
  6. Brooks, G. A. , & Clark, L. (2019). Associations between loot box use, problematic gaming and gambling, and gambling-related cognitions. Addictive Behaviors , 96, 26–34. 10.1016/j.addbeh.2019.04.009. [DOI] [PubMed] [Google Scholar]
  7. Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming . Routledge. [Google Scholar]
  8. Cairns, P. (2019). Correction: Video game loot boxes are linked to problem gambling: Results of a large-scale survey. PLoS One , 14(3), e0214167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Calado, F. , Alexandre, J. , & Griffiths, M. D. (2017). Prevalence of adolescent problem gambling: A systematic review of recent research. Journal of Gambling Studies , 33(2), 397–424. 10.1007/s10899-016-9627-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Carbonneau, R. , Vitaro, F. , Brendgen, M. , & Tremblay, R. E. (2015). Trajectories of gambling problems from mid-adolescence to age 30 in a general population cohort. Psychology of Addictive Behaviors , 29(4), 1012–1021. 10.1037/adb0000102. [DOI] [PubMed] [Google Scholar]
  11. Delfabbro, P. , King, D. L. (2020). Gaming-gambling convergence: Evaluating evidence for the ‘gateway’ hypothesis. International Gambling Studies , 20(3). 10.1080/14459795.2020.1768430. [DOI] [Google Scholar]
  12. Derevensky, J. L. , Hayman, V. , & Lynette, G. (2019). Behavioral addictions. Pediatric Clinics of North America , 66(6), 1163–1182. 10.1016/j.pcl.2019.08.008. [DOI] [PubMed] [Google Scholar]
  13. Dowling, N. A. , Merkouris, S. S. , Greenwood, C. J. , Oldenhof, E. , Toumbourou, J. W. , & Youssef, G. J. (2017). Early risk and protective factors for problem gambling: A systematic review and meta-analysis of longitudinal studies. Clinical Psychology Reviews , 51, 109–124. 10.1016/j.cpr.2016.10.008. [DOI] [PubMed] [Google Scholar]
  14. Eilers & Krejcik. (2018). Esports & gambling monitor – 3Q18. Santa Ana, CA: Eilers & Krejcik Gaming, LLC. [Google Scholar]
  15. Emond, A. M. , & Griffiths, M. D. (2020). Gambling in children and adolescents. British Medical Bulletin. 10.1093/bmb/ldaa027. [DOI] [PubMed] [Google Scholar]
  16. Fam, J. Y. (2018). Prevalence of internet gaming disorder in adolescents: A meta-analysis across three decades. Scandinavian Journal of Psychology , 59(5), 524–531. 10.1111/sjop.12459. [DOI] [PubMed] [Google Scholar]
  17. Floros, G. (2018). Gambling disorder in adolescents: Prevalence, new developments, and treatment challenges. Adolescent Health, Medicine and Therapeutics , 9, 43–51. 10.2147/ahmt.s135423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Forrest, C. J. , King, D. L. , & Delfabbro, P. H. (2016). The gambling preferences and behaviors of a community sample of Australian regular video game players. Journal of Gambling Studies , 32(2), 409–420. [DOI] [PubMed] [Google Scholar]
  19. Fried, E. I. , & Cramer, A. O. J. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science: A Journal of the Association for Psychological Science , 12(6), 999–1020. 10.1177/1745691617705892. [DOI] [PubMed] [Google Scholar]
  20. Gainsbury, S. M. , Abarbanel, B. , & Blaszczynski, A. (2017a). Game on: Comparison of demographic profiles, consumption behaviors, and gambling site selection criteria of esports and sports bettors. Gaming Law Review , 21(8), 575–587. 10.1089/glr2.2017.21813. [DOI] [Google Scholar]
  21. Gainsbury, S. M. , Abarbanel, B. , & Blaszczynski, A. (2017b). Intensity and gambling harms: Exploring breadth of gambling involvement among esports bettors. Gaming Law Review , 21(8), 610–615. 10.1089/glr2.2017.21812. [DOI] [Google Scholar]
  22. Gainsbury, S. M. , Russell, A. , & Hing, N. (2014). An investigation of social casino gaming among land-based and internet gamblers: A comparison of socio-demographic characteristics, gambling and co-morbidities. Computers in Human Behavior , 33, 126–135. 10.1016/j.chb.2014.01.031. [DOI] [Google Scholar]
  23. Gentile, D. A. , Choo, H. , Liau, A. , Sim, T. , Li, D. , Fung, D. , & Khoo, A. (2011). Pathological video game use among youths : A two-year longitudinal study. Pediatrics , 127(2), e319–e329. 10.1016/j.chb.2014.01.031. [DOI] [PubMed] [Google Scholar]
  24. Gentile, D. A. , Lynch, P. J. , Linder, J. R. , & Walsh, D. A. (2004). The effects of violent video game habits on adolescent hostility, aggressive behaviors, and school performance. Journal of Adolescence , 27(1), 5–22. http://www.sciencedirect.com/science/article/pii/S0140197103000927. [DOI] [PubMed] [Google Scholar]
  25. Giralt, S. , Müller, K. W. , Beutel, M. E. , Dreier, M. , Duven, E. , & Wölfling, K. (2018). Prevalence, risk factors, and psychosocial adjustment of problematic gambling in adolescents: Results from two representative German samples. Journal of Behavioral Addiction , 1–9. 10.1093/bmb/ldaa02710.1556/2006.7.2018.37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. González-Roz, A. , Fernández-Hermida, J. R. , Weidberg, S. , Martínez-Loredo, V. , & Secades-Villa, R. (2017). Prevalence of problem gambling among adolescents: A comparison across modes of access, gambling activities, and levels of severity. Journal of Gambling Studies , 33(2), 371–382. 10.1093/bmb/ldaa02710.1007/s10899-016-9652-4. [DOI] [PubMed] [Google Scholar]
  27. Greer, N. , Rockloff, M. , Browne, M. , Hing, N. , & King, D. L. (2019). Esports betting and skin gambling: A brief history. Journal of Gambling Issues , 43. 10.4309/jgi.2019.43.8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hayer, T. , Kalke, J. , Meyer, G. , & Brosowski, T. (2018). Do simulated gambling activities predict gambling with real money during adolescence? Empirical findings from a longitudinal study. Journal of Gambling Studies , 34, 929–947. 10.1007/s10899-018-9755-1. [DOI] [PubMed] [Google Scholar]
  29. Juniper Research . (2018). Daily fantasy sports & in-game gambling: Skins and loot boxes 2018-2022 . Hampshire, UK: Juniper Research. [Google Scholar]
  30. Kapalo, K. A. , Dewar, A. R. , Rupp, M. A. , & Szalma, J. L. (2015). Individual differences in video gaming: Defining hardcore video gamers. Proceedings of the Human Factors and Ergonomics Society Annual Meeting , 59(1), 878–881. 10.1177/1541931215591261. [DOI] [Google Scholar]
  31. Khazaal, Y. , Chatton, A. , Billieux, J. , Bizzini, L. , Monney, G. , Fresard, E. , …, Khan, R. (2012). Effects of expertise on football betting. Substance Abuse Treatment, Prevention, and Policy , 7(1), 18. 10.1186/1747-597X-7-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. King, D. L. , Russell, A. , Gainsbury, S. , Delfabbro, P. H. , & Hing, N. (2016). The cost of virtual wins: An examination of gambling-related risks in youth who spend money on social casino games. Journal of Behavioral Addictions , 5(3), 401–409. 10.1556/2006.5.2016.067. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kline, R. B. (2016). Methodology in the social sciences. Principles and practice of structural equation modeling (4th ed.). Guilford Press. [Google Scholar]
  34. Lam, L. T. (2014). Internet gaming addiction, problematic use of the internet, and sleep problems: a systematic review. Current Psychiatry Reports , 16(4), 444. 10.1007/s11920-014-0444-1. [DOI] [PubMed] [Google Scholar]
  35. Lelonek-Kuleta, B. , Bartczuk, R. P. (2021). Online gambling activity, pay-to-win payments, motivation to gamble and coping strategies as predictors of gambling disorder among e-sports bettors. Journal of Gambling Studies . 10.1007/s10899-021-10015-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lemmens, J. S. , Valkenburg, P. M. , & Gentile, D. A. (2015). The internet gaming disorder scale. Psychological Assessment , 27(2), 567–82. 10.1037/pas0000062. [DOI] [PubMed] [Google Scholar]
  37. Li, W. , Mills, D. , Nower, L. (2019). The relationship of loot box purchases to problem video gaming and problem gambling. Addictive Behaviors , 97, 27-34. 10.1016/j.addbeh.2019.05.016. [DOI] [PubMed] [Google Scholar]
  38. Li, W. , Mills, D. , & Nower, L. (2020). Validation of the problematic social casino gaming scale. International Gambling Studies , 20(3), 436–451. 10.1080/14459795.2020.1777577. [DOI] [Google Scholar]
  39. Little, T. D. , Cunningham, W. A. , Shahar, G. , & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling , 9(2), 151–173. 10.1207/S15328007SEM0902_1. [DOI] [Google Scholar]
  40. Livazović, G. , & Bojčić, K. (2019). Problem gambling in adolescents: What are the psychological, social and financial consequences? BMC Psychiatry , 19(1). 10.1186/s12888-019-2293-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Macey, J. , Abarbanel, B. , & Hamari, J. (2020). What predicts esports betting? A study on consumption of video games, esports, gambling and demographic factors. New Media & Society , 23(6), 481–1505. 10.1177/1461444820908510. [DOI] [Google Scholar]
  42. Macey, J. , & Hamari, J. (2018). Investigating relationships between video gaming, spectating esports, and gambling. Computers in Human Behavior , 80, 344–353. [Google Scholar]
  43. Macey, J. , & Hamari, J. (2019). Esports, skins and loot boxes: Participants, practices and problematic behaviour associated with emergent forms of gambling. New Media & Society , 21(1), 20–41. 10.1177/1461444818786216. [DOI] [Google Scholar]
  44. Macey, J. , & Hamari, J. (2020). GamCog: A measurement instrument for miscognitions related to gamblification, gambling, and video gaming. Psychology of Addictive Behaviors , 34(1), 242–256. [DOI] [PubMed] [Google Scholar]
  45. Männikkö, N. , Ruotsalainen, H. , & Tolvanen, A. , et al. (2020). Problematic Gaming Is Associated with Some Health-Related Behaviors Among Finnish Vocational School Students. International Journal of Mental Health & Addiction , 18, 993–1007. 10.1007/s11469-019-00100-6. [DOI] [Google Scholar]
  46. McBride, J. , & Derevensky, J. (2017). Gambling and video game playing among youth. Journal of Gambling Issues , 34(34), 156–178. 10.4309/jgi.2016.34.9. [DOI] [Google Scholar]
  47. Mills, D. , Marchica, L. , Keough, M. T. , & Derevensky, J. L. (2020). Exploring differences in substance use among emerging adults at-risk for problem gambling, and/or problem video gaming. International Gambling Studies , 20(3). 10.1080/14459795.2020.1752768. [DOI] [Google Scholar]
  48. Muthén, L. K. , & Muthén, B. O. (2017). Mplus user's guide (8th ed. ). Los Angeles, CA: Muthén & Muthén. [Google Scholar]
  49. Myrseth, H. , & Notelaers, G. (2018). A latent class approach for classifying the problem and disordered gamers in a group of adolescence. Frontiers in Psychology , 9. 10.3389/fpsyg.2018.02273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. National Center for Education Statistics . (2019, ). Fast facts: Back to school statistics . https://nces.ed.gov/fastfacts/display.asp?id=372.
  51. NewZoo. (2018). 2018 Global esports market report . San Francisco, CA: NewZoo. [Google Scholar]
  52. Neys, J. L. D. , Jansz, J. , & Tan, E. S. H. (2014). Exploring persistence in gaming: The role of self-determination and social identity. Computers in Human Behavior , 37, 196–209. 10.1016/j.chb.2014.04.047. [DOI] [Google Scholar]
  53. Ogles, B. M. , Melendez, G. , Davis, D. C. , & Lunnen, K. M. (2001). The Ohio Scales: Practical outcome assessment. Journal of Child and Family Studies , 10, 199–212. [Google Scholar]
  54. Paulus, F. W. , Ohmann, S. , von Gontard, A. , & Popow, C. (2018). Internet gaming disorder in children and adolescents: A systematic review. Developmental Medicine & Child Neurology , 60(7), 645–659. 10.1111/dmcn.13754. [DOI] [PubMed] [Google Scholar]
  55. Pontes, H. M. , Griffiths, M. D. (2015). Measuring DSM-5 Internet gaming disorder: Development and validation of a short psychometric scale. Computers in Human Behavior , 45, 137–143. 10.1016/j.chb.2014.12.006. [DOI] [Google Scholar]
  56. Richard, J. , Fletcher, É. , Boutin, S. , Derevensky, J. , & Temcheff, C. (2020). Conduct problems and depressive symptoms in association with problem gambling and gaming: A systematic review. Journal of Behavioral Addictions , 9(3), 497–533. 10.1556/2006.2020.00045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Smith, J. N. A. , Rossi R., et al. (2019). Biddable youth: Sports and esports gambling advertising on Twitter: Appeal to children, young & vulnerable people. University of Bristol; 2019. https://www.bristol.ac.uk/media-library/sites/news/2019/aug/D1261_Horne_DEMOS_Management%20report_2019_web.pdf (Accessed 09 Nov, 2020). [Google Scholar]
  58. Stevens, M. W. R. , Dorstyn, D. , Delfabbro, P. H. , & King, D. L. (2020). Global prevalence of gaming disorder: A systematic review. Australian & New Zealand Journal of Psychiatry , 1–16. 10.1177/0004867420962851. [DOI] [PubMed] [Google Scholar]
  59. Toce-Gerstein, M. , Gerstein, D. R. , Volberg, R. A. (2009). The NODS–CLiP: A rapid screen for adult pathological and problem gambling. Journal of Gambling Studies , 25: 541–555. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Williams, D. , Consalvo, M. , Caplan, S. , & Yee, N. (2009). Looking for gender: Gender roles and behaviors among online gamers. Journal of Communication , 59(4), 700–725. 10.1111/j.1460-2466.2009.01453.x. [DOI] [Google Scholar]
  61. Winters, K. C. , & Derevensky, J. L. (2019). A review of sports wagering: Prevalence, characteristics of sports bettors, and association with problem gambling. Journal of Gambling Issues , 43, 102–127. 10.4309/jgi.2019.43.7. [DOI] [Google Scholar]
  62. Wohl, M. J. A. , Salmon, M. M. , Hollingshead, S. J. , & Kim, H. S. (2017). An examination of the relationship between social casino gaming and gambling: The bad, the ugly, and the good. Journal of Gambling Issues , 2017(35), 1–23. 10.4309/jgi.2017.35.1. [DOI] [Google Scholar]
  63. Wood, R. T. A. , & Griffiths, M. D. (2007). Time loss whilst playing video games: Is there a relationship to addictive behaviours? International Journal of Mental Health and Addiction , 5(2), 141–149. 10.1007/s11469-006-9048-2. [DOI] [Google Scholar]
  64. World Health Organization . (2019). International statistical classification of diseases and related health problems (11th Revision). Lausanne: World Health Organization. Retrieved from: https://icd.who.int/browse11/l-m/en. [Google Scholar]
  65. Zendle, D. , Meyer, R. , & Over, H. (2019). Adolescents and loot boxes: Links with problem gambling and motivations for purchase. Royal Society Open Science , 6(6), 190049. 10.1098/rsos.190049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Zendle, D. (2020). Beyond loot boxes: A variety of gambling-like practices in video games are linked to both problem gambling and disordered gaming. PeerJ 8(e9466). 10.7717/peerj.9466. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Journal of Behavioral Addictions are provided here courtesy of Akadémiai Kiadó

RESOURCES