The WAGER, Vol. 19(8) – I can’t believe it’s not gambling: Experience with simulated gambling and gambling-related problems among adolescents

July 23, 2014

The Internet gambling marketplace is diverse and evolving. In addition to traditional gambling, the Internet also provides access to experiences that look and feel like gambling, but involve no monetary exchange (i.e., simulated gambling). Although traditional Internet gambling is age-restricted, simulated gambling experiences are open to anyone and available in many different formats (e.g., as smartphone and social networking apps, embedded within video games). This week, as a part of our Special Series on Addiction and Technology, The WAGER reviews research that examines adolescents’ gambling-related problems and technology-enabled experience with simulated gambling (King et al., 2014).

Methods

  • Researchers recruited seven co-educational schools from 50 randomly selected secondary schools in South Australia, to participate in a survey study.
    • 1,287 students (ages 12-17; 50% male) from these schools participated.
  • The survey included items to record demographic information, as well as other key variables such as electronic media use and activity, gambling behavior, simulated gambling behavior, and gambling problems, and other mental health characteristics.
    • With respect to gambling behavior, for each type of game listed, participants reported whether they had ever played it for money, whether they had ever played it without money, and whether they played via the Internet.
  • Analyses included examination of correlates of gambling disorder status, using binary logistic regression .

Results

  • More than 30% of the sample (N=405) reported ever participating in a simulated gambling activity.
  • Adolescents who reported more current monetary gambling activities, more current simulated gambling activities, and more lifetime simulated gambling activities were more likely to be “at-risk” for having gambling-related problems (i.e., report 1 or more symptoms). (See Table 1.)
    • Gender and age were not associated with at-risk gambling.

Debi july WAGER
Figure. Logistic Regression (odds ratios and confidence intervals) Predicting At-risk Gambling. Adapted from King et al., (2014). Note: bolded rows indicate effects that are significant at p < .05. Click image to enlarge.

Limitations

  • This cross sectional survey cannot determine whether gambling problems preceded simulated gambling experiences, or vice versa.
  • The multivariate analyses examined simulated gambling experiences as a group, which precludes determining whether different simulated gambling experiences are better predictors of gambling problems.
  • A poor response rate at the school level (7 of 50) severely limits the generalizability of the findings.
    • The researchers did not report student-level response rate.
  • The threshold for gambling-related problems was extremely low. It is unclear whether students who met this threshold experienced any negative consequences of their gambling.

Conclusion
Nearly a third of adolescents in this study reported engaging in simulated gambling experiences via digital or social media. Unfortunately, simulated gambling engagement appears to be a strong predictor of the experience of gambling-related problems. These findings suggest that researchers should devote more attention to this issue, generally, and among adolescents, specifically. Although this study provides interesting evidence related to the associations between simulated gambling and gambling-related problems, its methodological limitations prevent definitive statements about how one event might lead to another. A prospective longitudinal study, in particular, would provide valuable evidence related to the potential role of simulated gambling experiences in the development of gambling-related problems.

– Debi LaPlante

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Reference

King, D. L., Delfabbro, P. H., Kaptsis, D., & Zwaans, T.  (2014). Adolescent simulated gambling via digital and social media: An emerging problem. Computers in Human Behavior, 31, 305-313.