By Dominic Sagoe, Rune Aune Mentzoni, Tony Leino, Helge Molde, Sondre Haga, Mikjel Fredericson Gjernes, Daniel Hanss, and Ståle Pallesen.
Background and aims: Although alcohol intake and gambling often co-occur in related venues, there is conflicting evidence regarding the effects of alcohol expectancy and intake on gambling behavior. We therefore conducted an experimental investigation of the effects of alcohol expectancy and intake on slot machine gambling behavior.
Methods: Participants were 184 (females = 94) individuals [age range: 18–40 (mean = 21.9) years] randomized to four independent conditions differing in information/expectancy about beverage (told they received either alcohol or placebo) and beverage intake [actually ingesting low (target blood alcohol concentration [BAC] < 0.40 mg/L) vs. moderate (target BAC > 0.40 mg/L; ≈0.80 mg/L) amounts of alcohol]. All participants completed self-report questionnaires assessing demographic variables, subjective intoxication, alcohol effects (stimulant and sedative), and gambling factors (behavior and problems, evaluation, and beliefs). Participants also gambled on a simulated slot machine.
Results: A significant main effect of beverage intake on subjective intoxication and alcohol effects was detected as expected. No significant main or interaction effects were detected for number of gambling sessions, bet size and variation, remaining credits at termination, reaction time, and game evaluation.
Conclusion: Alcohol expectancy and intake do not affect gambling persistence, dissipation of funds, reaction time, or gambling enjoyment.
Sagoe, D., Mentzoni, R. A., Leino, T., Molde, H., Haga, S., Gjernes, M. F., … Pallesen, S. (2017). The effects of alcohol expectancy and intake on slot machine gambling behavior. Journal of Behavioral Addictions, 1–9. https://doi.org/10.1556/2006.6.2017.031
By Francis Markham, Martin Young, Bruce Doran, and Mark Sugden.
Background: Many jurisdictions regularly conduct surveys to estimate the prevalence of problem gambling in their adult populations. However, the comparison of such estimates is problematic due to methodological variations between studies. Total consumption theory suggests that an association between mean electronic gaming machine (EGM) and casino gambling losses and problem gambling prevalence estimates may exist. If this is the case, then changes in EGM losses may be used as a proxy indicator for changes in problem gambling prevalence. To test for this association this study examines the relationship between aggregated losses on electronic gaming machines (EGMs) and problem gambling prevalence estimates for Australian states and territories between 1994 and 2016.
Methods: A Bayesian meta-regression analysis of 41 cross-sectional problem gambling prevalence estimates was undertaken using EGM gambling losses, year of survey and methodological variations as predictor variables. General population studies of adults in Australian states and territory published before 1 July 2016 were considered in scope. 41 studies were identified, with a total of 267,367 participants. Problem gambling prevalence, moderate-risk problem gambling prevalence, problem gambling screen, administration mode and frequency threshold were extracted from surveys. Administrative data on EGM and casino gambling loss data were extracted from government reports and expressed as the proportion of household disposable income lost.
Results: Money lost on EGMs is correlated with problem gambling prevalence. An increase of 1% of household disposable income lost on EGMs and in casinos was associated with problem gambling prevalence estimates that were 1.33 times higher [95% credible interval 1.04, 1.71]. There was no clear association between EGM losses and moderate-risk problem gambling prevalence estimates. Moderate-risk problem gambling prevalence estimates were not explained by the models (I 2 ≥ 0.97; R 2 ≤ 0.01).
Conclusions: The present study adds to the weight of evidence that EGM losses are associated with the prevalence of problem gambling. No patterns were evident among moderate-risk problem gambling prevalence estimates, suggesting that this measure is either subject to pronounced measurement error or lacks construct validity. The high degree of residual heterogeneity raises questions about the validity of comparing problem gambling prevalence estimates, even after adjusting for methodological variations between studies.
Michael J.A. Wohl, Christopher G. Davis, Samantha J. Hollingshead.
In the current research, we tested the utility of a responsible gambling tool that provides players with personalized behavioral feedback about their play. We hypothesized that when the player’s estimated monetary loss is less than their actual monetary loss, subsequent expenditures will be reduced. To this end, players (N = 649) enrolled in a casino-based loyalty program were asked how much they have won or lost over a three-month period whilst using their loyalty card. They were then provided with their player-account data. Results indicated that players who under-estimated their losses (i.e., those who lost more money than they thought at Time 1) did not perceive that they had reduced their play in the 3-month follow-up period. However, data on actual play indicated that they significantly reduced the amount they wagered as well as the amount they lost during the follow-up period. Given that informed decision-making is the raison d’etre of responsible gambling tools, these results suggest that providing players with accurate information about how much they spend gambling can moderate gambling expenditures.
Wohl, M. J. A., Davis, C. G., & Hollingshead, S. J. (2017). How much have you won or lost? Personalized behavioral feedback about gambling expenditures regulates play. Computers in Human Behavior
, 437–445. https://doi.org/10.1016/j.chb.2017.01.025
Background and Aims: Flaws in previous studies mean that findings of J-shaped risk curves for gambling should be disregarded. The current study aims to estimate the shape of risk curves for gambling losses and risk of gambling-related harm (a) for total gambling losses and (b) disaggregated by gambling activity.
Design: Four cross-sectional surveys.
Setting: Nationally representative surveys of adults in Australia (1999), Canada (2000), Finland (2011) and Norway (2002).
Participants: A total of 10 632 Australian adults, 3120 Canadian adults, 4484 people aged 15–74 years in Finland and 5235 people aged 15–74 years in Norway.
Measurements: Problem gambling risk was measured using the modified South Oaks Gambling Screen, the NORC DSM Screen for Gambling Problems and the Problem Gambling Severity Index…
Source: Markham, F., Young, M., & Doran, B. (2015). The relationship between player losses and gambling-related harm: evidence from nationally representative cross-sectional surveys in four countries: Player loss risk curves for gambling harms. Addiction, n/a–n/a. http://doi.org/10.1111/add.13178