The Brief Addiction Science Information Source (BASIS)

The WAGER, Vol. 15(7) – Disordered Gambling Linked with Treatment for Parkinson Disease

Patients being treated for Parkinson disease (PD) have been reported to have a disproportionately high prevalence of disordered gambling and other impulse control disorders (ICDs) such as compulsive shopping, binge-eating, and hypersexuality (e.g., Bostwick, Hecksel, Stevens, Bower, & Ahlskog, 2009; Evans, Strafella, Weintraub, & Stacy, 2009). The development of these disorders appears to be most associated with dopamine agonists, a type of medication commonly used to treat PD, but estimates of the effect have varied. This week’s WAGER reviews a large cross-sectional study designed to obtain more accurate data about ICD prevalence among those with PD and the association of ICDs with dopamine agonists (Weintraub et al., 2010).

Methods

• The authors recruited participants (n=3,090) from patients receiving their regular care at 46 movement disorder centers in the United States and Canada.
• Individual sites developed their sampling plans (e.g., attempting to recruit every third patient scheduled during a clinic) before starting.  
• Investigators included subjects in the study if they met the following criteria:  
• Between the ages of 30 to 75 years;   
• Had been taking medication for PD for >1 year and had responded to treatment;   
• Had not started or stopped dopamine agonist therapy within the past 6 months.
• Patients were assessed for current ICDs using a semi-structured interview and formal diagnostic tools.
• The Massachusetts Gambling Screen (problem gambling, 3-4 criteria endorsed; pathological gambling, 5 or more criteria) (Shaffer, LaBrie, Scanlan, & Cummings, 1994) identified disordered gambling.  
• Compulsive buying and sexual behavior were assessed by the Minnesota Impulsive Disorders Interview (Christenson, Faber, & deZwaan, 1994).  
• Investigators determined the presence of binge-eating by using the proposed research criteria from the DSM-4 TR (Diagnostic and Statistical Manual of Mental Disorders, 2000).  
• The researchers calculated odds ratios to compare ICD frequencies between PD patients on dopamine agonist therapy and those receiving other treatment; they used several statistical tests to assess the strength of the association (i.e., Cochran-Mantel-Haenszel; Breslow-Day; Wilcoxon-Mann-Whitney).

Results

• Compared to PD patients not taking a dopamine agonist, PD patients prescribed a medication in this class were 2.7 times more likely to be diagnosed with an ICD and 2.8 times more likely to be diagnosed with disordered gambling (see Table 1).
• Overall, 17.1% of patients taking dopamine agonists were diagnosed with one or more ICDs, compared with 6.9% of patients on alternative treatment. The highest relative risk for patients on dopamine agonists was for binge-eating disorder: 5.6% of patients taking dopamine agonists were diagnosed with binge-eating disorder, compared to only 1.7% of patients on alternative treatment.
• The four ICDs measured by this study occurred at similar frequencies among patients taking dopamine agonists, ranging from 4.4% for compulsive sexual behavior to 7.2% for compulsive buying.
• Many patients had more than one disorder: of the 348 subjects on dopamine agonists that were diagnosed with an ICD, the average number of ICDs was 1.38.
• On average, patients diagnosed with an ICD were younger, less likely to be married, more likely to smoke, and more likely to report a family history of alcohol abuse or gambling problems than patients not diagnosed with an ICD. There was no statistically significant association between ICD diagnosis and sex, race, or PD duration or severity.

Table 1: ICD Frequencies by Dopamine Agonist Treatment Status (adapted from Weintraub et al 2010) 

*p < 0.005 for all OR

Limitations

• This study captured ICD prevalence at a particular moment. Because side effects from PD treatment can change over time, this study might underestimate the total number of patients that will eventually develop an ICD with treatment.
• The lack of an untreated PD group or a non-PD control group prevents direct comparison with overall ICD prevalence in PD or with the population as a whole.
• The authors did not assess mediators of impulsive behavior such as motivation, mood or other disorders that could be differentially influenced by dopamine agonist treatment.

Discussion

Weintraub et al (2010) examined the association between dopamine replacement therapy and ICDs in a large sample of patients being treated for PD. Consistent with previous studies, (Bostwick et al., 2009; Evans et al., 2009) patients treated with dopamine agonists were significantly more likely to be diagnosed with one or more ICDs than those on alternative treatments. Although dopamine agonists are associated with elevated risk for ICD, more research is necessary to determine the cause of this increase and which subgroups of patients are most at risk. Clinicians should discuss the risk of ICDs with patients and their families when starting dopamine replacement therapy for PD.

-Liz Joe

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References

Bostwick, J. M., Hecksel, K. A., Stevens, S. R., Bower, J. H., & Ahlskog, J. E. (2009). Frequency of new-onset pathologic compulsive gambling or hypersexuality after drug treatment of idiopathic Parkinson disease. Mayo Clinic Proceedings, 84(4), 310-316.

Christenson, G. A., Faber, R. J., & deZwaan, M. (1994). Compulsive buying: descriptive characteristics and psychiatric comorbidity. Journal of Clinical Psychiatry, 55(1), 5-11.

Diagnostic and Statistical Manual of Mental Disorders. (2000).  (Fourth Edition, Text Revision ed.). Washington, DC: American Psychiatric Association.

Evans, A. H., Strafella, A. P., Weintraub, D., & Stacy, M. (2009). Impulsive and compulsive behaviors in Parkinson's disease. Movement Disorders, 24(11), 1561-1570.

Shaffer, H. J., LaBrie, R., Scanlan, K. M., & Cummings, T. N. (1994). Pathological gambling among adolescents: Massachusetts gambling screen (MAGS). Journal of Gambling Studies, 10(4), 339-362.

Weintraub, D., Koester, J., Potenza, M. N., Siderowf, A. D., Stacy, M., Voon, V., et al. (2010). Impulse control disorders in Parkinson disease: a cross-sectional study of 3090 patients. Archives of Neurology, 67(5), 589-595.

WAGER Vol. 15(6) – More or Less the Same: Variations on Brief Gambling Treatment

Historically, effective treatment for gambling disorders has been an elusive goal, considering that only a very small proportion of gamblers ever seek treatment (Cunningham, 2005). Brief treatment, which typically involves treatment of ten sessions or less, has been effective with alcohol related problems. Therefore, an adaptation of the brief treatment model might be equally effective for disordered gamblers unwilling to seek formal treatment. This week’s WAGER reviews a report by Hodgins, Currie, Currie, and Fick (2009) in their continuing research on brief treatment variations.

Methods

• Researchers used a randomized control study design and recruited 314 problem gamblers interested in reducing their gambling.
• At baseline, researchers gathered data about participants’ demographics, gambling history, use of public resources for gambling treatment, and gambling severity.
• Researchers assigned participants to one of four conditions:
• Brief Treatment (BT) participants received a self-help workbook after one half-hour telephone session of motivational interviewing (MI). The workbook provided self-assessments for gambling problems, practical recovery strategies, and information about local resources.  
• In addition to the initial MI and workbook, Brief Booster Treatment (BBT) participants received six more brief telephone MIs at 2, 6, 10, 16, 24, and 36 weeks.  
• Workbook Only Control (WOC) participants received the workbook without MI contact.  
• Waiting List Control (WLC) participants waited six weeks before receiving the workbook, and had no MI contact.  
• For all four conditions, researchers conducted follow-up assessments at 6, 12, 24, 36, and 52 weeks after initial contact. During these assessments, researchers collected measures of gambling prior to follow-up interviews.

Results

• After 6 weeks, BT and BBT participants reported significantly lower rates of gambling days per month than WOC and WLC participants. (M = 4.7, SD = 6.0; M = 4.8, SD = 5.9; M = 6.6, SD = 7.3; M = 5.7, SD = 6.4, respectively.)
• As Figure 1 shows, participants across the BBT, BT, and WOC conditions reported significantly lower rates of gambling at all follow-up assessments
• At the 12-month follow-up, neither BT nor BBT gambling rates differed significantly from WOC. (BT vs. WOC, χ2(1, N = 249) = 3.0, p = ns; BBT vs. WOC, χ2(1, N = 249) = 2.1, p = ns).¹

Figure 1 - Gambling Rates at Follow-Up Assessments (adapted from Hodgins et al, 2009).

 
Limitations

• For ethical reasons, researchers could not retain participants on a waiting list for the duration of the study; therefore all participants received some form of treatment during the study year.
• Participants volunteered for the clinical trial, and were interested in reducing their gambling, so the results are not generalizable to all gamblers.

Discussion

Similar to Hodgins et al.’s original study (2001), the results of this experiment highlight the efficacy of brief treatments with or without motivational therapy. Though the follow-up findings from Hodgins original study found larger differences between MI and non-MI groups (Hodgins et al., 2004) than the current study, one consistent result across these three reports is a steady decline of problem gambling, without much distinction between the types of treatment. One possible explanation is that problem gamblers naturally regress from their addiction, regardless of treatment or treatment type. An important point to consider is that all participants in these studies received follow-up interviews to collect data; therefore, another possibility is that contact, whether motivational or not, was enough to affect gambling behavior.

A noticeable similarity between the three studies is the self-selected nature of the participants. Only gamblers who expressed interest to reduce or quit their problem behavior were recruited as participants. For future studies, the inclusion of moderator and mediator variables, such as participant expectations and readiness to change, would help clarify the mechanisms through which these interventions are effecting change.

-Aaron Lim

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Footnote: Measures for WLC follow-ups after six weeks were not reported.

References

Cunningham, J.A. (2005) Little use of treatment among problem gamblers. Psychiatric Services, 56, 1024-1025.

Hodgins, D. C., Currie, S. R., & el-Guebaly, N. (2001). Motivational enhancement and self-help treatments for problem gambling. Journal of Consulting and Clinical Psychology, 69(1), 50-57.

Hodgins, D. C., Currie, S. R., el-Guebaly, N., & Peden, N. (2004). Brief motivational treatment for problem gambling: A 24-month follow-up. Psychology of Addictive Behaviors, 18(3), 293-296.

Hodgins, D.C., Currie, S.R., Currie, G., & Fick, G.H. (2009). Randomized trial of motivational treatments for pathological gamblers: More is not necessarily better. Journal of Consulting and Clinical Psychology, 77(5), 950-960.

The WAGER, Vol. 15(5) – The 2007 British Gambling Prevalence Survey: Considering Gambling Involvement

Conventional wisdom suggests that specific gambling activities, such as Internet gambling, are especially “addictive.” However, recent research suggests that the relationship between gambling and disordered gambling is more complicated than playing specific types of games. Using a United States youth sample, research suggests that gambling involvement (e.g., the number of games one plays), is a better predictor of disordered gambling than participation in any particular game (Welte, Barnes, Tidwell, & Hoffman, 2009). This week the WAGER reviews a study that further explored this phenomena by examining the association between disordered gambling and gambling involvement within the 2007 British Gambling Prevalence Survey (LaPlante, Nelson, LaBrie, & Shaffer, 2009).

Methods

• LaPlante et al. (2009) conducted secondary data analyses of the British Gambling Prevalence Survey (BGPS)¹ using weighted data of 8968 observations characteristic of the general population.
• The researchers operationally defined the following variables.  
• Disordered gambling (i.e., endorsing 3+ DSM-IV pathological gambling symptoms in the past year).   
• Gambling involvement (i.e., the number of types of gambling for which an individual reported being involved during the past year).   
• The authors conducted a series of logistic regressions using participation in each gambling type to predict past year disordered gambling.  
• Consistent with Welte et al’s. analytic strategy (2009), these logistic regressions were conducted first without controlling for involvement and then added involvement as a control. Table 1 lists the corresponding odds ratios.  

Table 1: Odd ratios for predicting disordered gambling from type of game with and without controlling for involvement (adapted from LaPlante et al. 2009)

 
Results

• When not controlling for involvement, participation in nearly every gambling type was statistically significant and positively associated with disordered gambling.
• When controlling for involvement, gambling via virtual gaming machines (e.g., virtual roulette, virtual bingo, virtual keno) was the only gambling type that remained significantly and positively associated with disordered gambling.

Limitations

• This BGPS gathers self-reported data without corroboration; therefore, this study is subject to the problems commonly associated with self-report.
• The authors only used one measure of gambling involvement.

Discussion

LaPlante et al. (2009) examined associations between participation in a particular gambling type and disordered gambling. The results indicated that when the authors controlled for gambling involvement, the association between participation in a particular gambling type and disordered gambling weakened for all types, and for 13 of 14 types, this association was no longer meaningful. These findings are consistent with an emerging body of research that suggests gambling involvement is a better predictor of gambling problems than participation in a particular game (e.g., Welte et al., 2009). One limitation of this research is that the authors used only one measure of gambling involvement (i.e., the number of types of gambling for which an individual reported being involved during the past year). More research is necessary to examine other -- and multiple -- measures of involvement to more accurately refine the meaning of gambling involvement.

-Ryan Martin

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References

American Psychiatric Association. (1994). DSM-IV: Diagnostic and statistical manual of mental disorders (Fourth ed.). Washington, D.C.: American Psychiatric Association.

LaPlante, D. A., Nelson, S. E., LaBrie, R. A., & Shaffer, H. J. (2009). The relationships between disordered gambling, type of gambling, and gambling involvement in the British Gambling Prevalence Survey 2007. European Journal of Public Health, epub ahead of print.

National Center for Social Research. (2008). British gambling prevalence survey 2007. Colchester, Essex: UK Data Archive.

Wardle, H., Sproston, K., Orford, J., Erens, B., Griffiths, M., Constantine, R., et al. (2007). British Gambling Prevalence Survey 2007. London: National Center for Social Research.

Welte, J., Barnes, G., Tidwell, M., & Hoffman, J. (2009). The association of form of gambling with problem gambling among American youth. Psychology of Addictive Behaviors, 23(1), 105-112.

--

¹The National Centre for Social Research (2008) produced the 2007 British Gambling Prevalence Survey (BGPS). The BGPS is a publicly available dataset representing interviews from 9,003 residents, randomly selected from households in England, Scotland and Wales; the response rate was 52% (Wardle et al., 2007). The BGPS assessed various gambling-related and demographic measures, including the following: past year gambling participation for 15 gambling types (e.g., lottery, online gambling, etc.) and past year DSM-IV pathological gambling criteria (American Psychiatric Association, 1994).

The WAGER, Vol. 15(4) – Sports Wagering Policies: European Athletes v. NCAA Athletes

On April 15, 2010, the European Elite Athletes Association, in conjunction with the European Gaming and Betting Association and the European Sports Security Association, announced the development of a sports betting code of conduct and educational campaign for professional athletes ("EU Athletes & European betting industry launch educational campaign to support integrity in sports," 2010).  In the U.S., the National Collegiate Athletic Association (NCAA) has a long-standing policy regarding gambling activities for its student athletes (National Collegiate Athletic Association). This week, the WAGER examines the newly released policy targeting European athletes and the NCAA policy governing sports wagering by student-athletes.

European Code of Conduct Policy for Athletes

• Development of the code was a coordinated effort of three organizations:
• The European Elite Athletes Association (i.e., a federation of independent European athlete associations including athletes from many countries and a wide variety of sports) 
• The European Sports Security Association (i.e., a betting integrity watchdog organization) 
• The European Gaming & Betting Association (i.e., a gaming and betting operators association) (EU Athletes, The European Gaming and Betting Association, & The European Sports Security Association, 2010). 
• The campaign targets professional athletes across Europe.
• The code includes the following five guiding principles:

1.  Know the rules. This principle encourages athletes to be familiar with the rules/regulations governing gambling for their particular sports in their home countries.
2. Never bet on yourself or the opponent.
3. Play safe – don’t bet on your own sport at all.
4. Be careful about handling sensitive information.
5. Fixing a competition: an absolute No-No (EU Athletes et al., 2010).

• An educational component will expound upon the code and will consist of online resources coupled with in-person presentations aimed at informing athletes about how they can maintain sports betting integrity ("EU Athletes & European betting industry launch educational campaign to support integrity in sports," 2010). The educational materials are not yet available.

NCAA Gambling Policy for Athletes

• The NCAA gambling policy applies to all student-athletes, the staff of NCAA member athletic departments, and NCAA staff. The NCAA policy prohibits the following gambling activities:
• Providing information to individuals involved in organized gambling activities concerning intercollegiate competition. 
• Soliciting a bet on any intercollegiate team. 
• Accepting a bet on any team representing the institution. 
• Soliciting or accepting a bet on any intercollegiate competition for any item (e.g., cash, shirt, dinner) of value. 
• Participating in any gambling activity that involves intercollegiate athletics or professional athletics, a bookmaker, a parlay card or any other method employed by organized gambling (National Collegiate Athletic Association). 
• The following sanctions apply to all NCAA student-athletes found to be in violation of the NCAA gambling policy:
• Betting on sports contests at either the college or professional level results in the loss of eligibility to participate in collegiate sports for one year. 
• Engaging in activities designed to change the outcome of a sporting event or betting on sports contests at ones own educational institution results in permanent loss of eligibility (National Collegiate Athletic Association). 
• The cornerstone of the NCAA “Don’t Bet on It” educational campaign is an interactive website which provides information on NCAA sports wagering policies and other gambling related resources. In addition, the campaign includes information sessions with players, coaches and officials and printed materials distributed to campuses (National Collegiate Athletic Association).

Table 1: Side-by-side comparison of the European and NCAA policies

*Athletes are subject to the rules and laws governing sports wagering in their home countries.
**Not currently implemented.

Discussion

In many ways, the new sports betting policy governing European athletes and the policy of the NCAA are very similar (see Table 1). Both policy strategies strive to protect the integrity of athletic competition by limiting behavior that can influence the outcomes of competition. Both are centered on educational campaigns with similar goals and methods of delivering their messages. There are, however, some key differences. In particular, the EU policy pertains to professional athletes and does not include sanctions on behavior, only guidelines for what is considered appropriate. In contrast, the NCAA policy has strict punitive consequences for amateur athletes that violate the gambling policy, including loss of the ability to engage in athletic competition at the collegiate level.  The newly implemented EU policy and that of the NCAA seek to protect the integrity of athletic competition from the negative influences of sports wagering by athletes. It is still unclear whether the broad guidelines of the European policy or the sanctions of the NCAA will be more successful in meeting their intended goals.

-Erica Marshall

What do you think? Please use the comment link below to provide feedback on this article.

References

EU Athletes, The European Gaming and Betting Association, & The European Sports Security Association. (2010). Code of conduct on sports betting for athletes.

EU Athletes & European betting industry launch educational campaign to support integrity in sports (Publication (2010). http://www.euathletes.info/news/news/?no_cache=1&tx_ttnews[tt_news]=503&tx_ttnews[backPid]=2

National Collegiate Athletic Association. Bylaw 10.3 and Related Cases 2010, from http://www.ncaa.org/wps/portal/ncaahome?WCM_GLOBAL_CONTEXT=/ncaa/ncaa/legislation+and+governance/eligibility+and+recruiting/sports+wagering/resources/toolkit/chapter_4_bylaw_10_3_and_related_cases

National Collegiate Athletic Association. College Sports Betting - NCAA Official Statement.   Retrieved 11/23/2009, from http://www.ncaa.org/wps/ncaa?key=/ncaa/ncaa/media+and+events/press+room/current+issues/sports+wagering

The WAGER, Vol. 15(3) – Problem gambling associations: Gambling motivations and perceptions of money-limiting strategies

Research has indicated that electronic gaming machines (EGMs) are associated with higher rates problem gambling (e.g., Breen & Zimmerman, 2002; Cox, Kwong, Michaud, & Enns, 2000). However, new research reveals that this association is not causal. When we take the extent of gambling into account (i.e., involvement), this association disappears (LaPlante, Nelson, LaBrie, & Shaffer, In press; Welte, Barnes, Tidwell, & Hoffman, 2009). Consequently, a growing body of research now indicates that EGMs are not inherently “addictive” (e.g., Blaszczynski, Sharpe, Walker, Shannon, & Coughlan, 2005; Dowling & Thomas, 2005). This week the WAGER reviews a study that considers whether self-reported EGM playing motivations and perceptions of EGM money-limiting strategies might correlate with certain players experiencing gambling problems (Nower & Blaszczynski, 2010).

Methods

• Investigators recruited study participants (n=127) as they entered the gaming area of one of four venues in a metropolitan area of Australia.
• Research assistants approached consecutive patrons and asked them to participate.  
• The authors did not randomly select participants or calculate a response rate.  
• A series of pen and paper questionnaires, nearly all of which were designed by the authors, assessed the following:
• Gambling preferences (i.e., amount spent gambling, times gambled per month, favorite gambling form, reasons for playing machines).  
• Perceptions of money-related harm reduction strategies (i.e., setting limits, using pre-loaded smart cards instead of cash).  
• Gambling severity via the Problem Gambling Severity Index of the Canadian Problem Gambling Index (Ferris & Wynne, 2001).  
• Participants were dichotomized into the following severity groups: non-problem, low risk, moderate-risk and problem.   
• The researchers conducted bivariate analyses (i.e., Pearson Chi-square, ANOVA and ANCOVA) to examine the relationships between independent variables and gambling severity groups.

Table 1: Primary reasons for gambling on EGMs by gambling severity group (adapted from Nower & Blaszczynski, 2010)  

¹Problem gamblers versus non-problem gamblers (p <.001) and moderate risk gamblers (p <.001)
²Problem gamblers versus non-problem gamblers (p <.05)

Results

• Compared to their non-problem gambling counterparts, those in the problem gambling group were significantly more likely (p < .001) to indicate that they played EGMs to earn additional income and to escape daily hassles and problems (see Table 1).
• In general, those in the problem gambling group were less likely than those in other groups to endorse using monetary limit-settings, such as smart cards, prior to EGM gambling and more likely to indicate they lost track of money spent during a session.

Limitations

• The authors utilized a convenience sample of casino patrons; the results might not be representative or generalizable to other populations.
• The power to detect significant group differences was limited by the study’s small sample size.

Discussion
Nower and Blaszczynski (2010) found that participants classified as problem gamblers were more likely to report playing EGMs to earn income or to escape daily troubles than non-problem gamblers. In addition, this study reported that problem gamblers had less favorable impressions toward EGM money limiting strategies. These characteristics might influence gambling more than interest in, or the characteristics, of a particular game. These findings are consistent with other research that has indicated gambling involvement is a better predictor of gambling problems than participation in a particular game (e.g., LaPlante et al., In press; Welte et al., 2009; Welte, Barnes, Wieczorek, Tidwell, & Parker, 2004).

References
Blaszczynski, A., Sharpe, L., Walker, M., Shannon, K., & Coughlan, M.-J. (2005). Structural characteristics of electronic gaming machines and satisfaction of play among recreational and problem gamblers. International Gambling Studies, 5, 187-198.

Breen, R. B., & Zimmerman, M. (2002). Rapid onset of pathological gambling in machine gamblers. Journal of Gambling Studies, 18(1), 31-43.

Cox, B. J., Kwong, J., Michaud, V., & Enns, M. W. (2000). Problem and probable pathological gambling: Considerations from a community survey. Canadian Journal of Psychiatry - Revue Canadienne de Psychiatrie, 45(6), 548-553.

Dowling, N., & Thomas, T. (2005). Electronic gaming machines: Are they the ‘crack-cocaine’ of gambling? Addiction, 100, 33-45.

Ferris, J., & Wynne, H. (2001). The Canadian Problem Gambling Index: Final Report. Ottawa, ON, Canada: Canadian Centre on Substance Abuse.

LaPlante, D. A., Nelson, S. E., LaBrie, R. A., & Shaffer, H. J. (In press). The relationships between disordered gambling, type of gambling, and gambling involvement in the British Gambling Prevalence Survey 2007. European Journal of Public Health.

Nower, L., & Blaszczynski, A. (2010). Gambling motivations, money-limiting strategies, and precommitment preferences of problem versus non-problem gamblers. Journal of Gambling Studies, epub ahead of print.

Welte, J., Barnes, G., Tidwell, M., & Hoffman, J. (2009). The association of form of gambling with problem gambling among American youth. Psychology of Addictive Behaviors, 23(1), 105-112.

Welte, J., Barnes, G., Wieczorek, W., Tidwell, M., & Parker, J. (2004). Risk factors for pathological gambling. Addictive Behaviors, 29(2), 323-335.

The WAGER, Vol. 15(2) - March Madness

It’s that time of the year again…March Madness. Next to the Super Bowl, this event stimulates the most sports wagering in the U.S.  During 2004, the National Collegiate Athletic Association (NCAA) conducted a national survey of gambling and other high risk behaviors among college athletes, which is described in a number of publications or reports (Ellenbogen, Jacobs, Derevensky, Gupta, & Paskus, 2008; Huang, Jacobs, Derevensky, Gupta, & Paskus, 2007a, 2007b; Petr, Paskus, & Dunkle, 2004). This survey revealed that college athletes (especially males) evidenced sports betting despite the NCAA rules prohibiting student-athletes from wagering on college or professional sports. In response to these results, NCAA president, Myles Brand, commissioned a task force of key figures in college athletics and the gambling research/treatment community to examine the data and recommend a course of action. The resulting recommendations were wide ranging and included various forms of educational outreach. During 2008, the NCAA conducted a subsequent national study of student-athletes. Analyses of these data are underway and the NCAA plan to release an initial report about the study later this year.  This week’s WAGER reviews some preliminary findings of the 2008 NCAA study (Paskus, Petr, Vicente, & Derevensky, 2009; Petr et al., 2004).

Methods

• Using a cluster or three-stage sampling design in both the 2004 and 2008 studies, researchers approached all 1000+ NCAA member colleges to participate through faculty athletics representatives. Each school was asked to survey all members of 1-3 teams selected by the NCAA through a stratified random sampling procedure, with the intent of providing samples representative of all sports and NCAA divisions.
• Participation was anonymous at both the student and school level. Based on the number of responses and experience with similar surveys, the NCAA estimated a school-level response rate of 60-65% for both 2004 and 2008. Investigators did not calculate the student-level or team response rate for either survey.
• Without the student or team response rate, we cannot calculate a study-wide response rate. However, because this rate emerges from a product of school, team and student response rates, we know that the study response rate is going to be < .60-.65.
• Rigorous data cleaning procedures were employed, including the use of item response theory (IRT) to enhance validity checks.
• To enhance comparability, investigators retroactively applied the 2008 data cleaning methods to the 2004 survey data and weighted both data sets to the NCAA’s estimate of the 2008 participation rates for 22 sports (11 male and 11 female). 
• The 2008 survey contained a single item to assess sports betting prevalence and frequency among respondents, whereas the sports betting data for the 2004 survey was based on a composite of items in the survey.
• Researchers reanalyzed the 2004 data set and the modified 2004 results to make these comparable to the 2008 results.
• The 2004 sample included 19,354 student-athletes and the 2008 sample included 19,371 student-athletes.
• Both the 2004 and 2008 surveys included 10 questions based on the DSM-IV criteria for pathological gambling (American Psychiatric Association, 1994) to assess gambling status. Each affirmative answered was scored as 1 with the total score being a summation of scores for each item.
• Researchers classified respondents as social gamblers if they received a score between 0-2, at-risk gamblers if they received a score of 3 or 4 and probable pathological gamblers if they received a score of 5 or more.

Results

• As shown in Table 1, social levels of sports wagering (i.e., defined as having gambled on sports in the past year) appear to have increased from 2004 to 2008. Researchers reported that estimates of student-athletes gambling once per month or more was similar across the two cohorts and heavy sports gambling (i.e., once/week or more) decreased since 2004.
• Analysis of the gambling-type classification according to the DSM-based gambling screen indicates that the proportion of non-gamblers and social gamblers is similar across surveys. However, the 2008 survey reveals fewer at-risk gamblers and more probable pathological gamblers.
• Overall, the proportion of male athletes endorsing any number of gambling-related problems declined from 6.7% in 2004 to 5.4% in 2008.

Table 1: Past-Year Gambling by Male Athletes

 

Limitations

• This survey gathers self-reported data without corroboration; therefore, this study is subject to the problems commonly associated with self-report.  For example, given the highly sensitive nature of the survey questions (e.g., sports gambling for college athletes is both illegal in nearly all states and violates NCAA policy), some participants might not have reported accurately their sports betting behaviors in spite of their anonymity. 
• General cohort shifts for gambling behaviors among student-athletes and the difficulties matching interventions received to specific students hampers our ability to isolate the effect of the NCAA’s educational efforts.
• Differences in the 2004 and 2008 survey instruments might have confounded the observed change in the prevalence rates of gambling on sporting events.
• The researchers chose survey procedures designed to enhance anonymity; however, these procedures make it difficult to calculate a student-level survey response rate. The NCAA estimated school level response rate of 60% to 65% sets a ceiling for the overall response rate in a study that employs multiple stage sampling. The survey response rate is likely lower because it is obtained by multiplying the school-level response rate by the team level response rate by the student-level response rate. Low response rates are a cause for concern about the representativeness of survey data and our ability to generalize its findings.
• In this case, disregarding the team level response rate, a student-level response rate lower than 80% will yield a survey-level response rate less than 50%. According to Mangione (1995, pgs. 60-61), “response rates between 50% and 60% are barely acceptable and really need some additional information that contributes to confidence about the quality of your data. Response rates below 50% really are not scientifically acceptable.” Thus, absent a very high level of team and student-level response, there are not sufficient safeguards against sampling bias.
• In addition, because evidence has shown that athlete gambling rates vary by NCAA division (e.g., Nelson et al., 2007), it is important to determine whether response rates are similar across NCAA divisions . Any inconsistencies across these rates can lead to over or under estimates of student-related gambling activities.

Conclusion
Among male college student athletes, the subsequent 2008 NCAA study shows an increase in the number of sports gamblers, a decrease in at-risk gambling behavior and an increase in the number of participants classified as a probable pathological gamblers. It is possible that prevention initiatives developed and implemented by the NCAA task force might have contributed to the changes in some measures of male college athlete gambling; however, other factors also could be at play.  Additional follow-up research is needed to have a better understanding of the effectiveness and impact of such intervention strategies.

In the meantime, the NCAA tournament will garner much attention and wagering. College students, both athletes and non-athletes, who choose to gamble, are best advised to exercise caution when wagering on the outcome.

-Erica Marshall

What do you think?  Please use the comment link below to provide feedback on this article.

References

American Psychiatric Association. (1994). DSM-IV: Diagnostic and statistical manual of mental disorders (Fourth ed.). Washington, D.C.: American Psychiatric Association.

Ellenbogen, S., Jacobs, D., Derevensky, J., Gupta, R., & Paskus, T. (2008). Gambling behavior among college student-athletes. Journal of Applied Sport Psychology, 20(3), 349-362.

Huang, J., Jacobs, D., Derevensky, J., Gupta, R., & Paskus, T. (2007a). Gambling and health risk behaviors among U.S. college student-athletes: findings from a national study. Journal of Adolescent Health, 40(5), 390-397.

Huang, J., Jacobs, D., Derevensky, J., Gupta, R., & Paskus, T. (2007b). A national study on gambling among US college student-athletes. Journal of American College Health, 56(2), 93-99.

Mangione, T. W. (1995). Mail surveys: improving the quality (Vol. 40, Applied Social Research Methods Series). Thousand Oaks, CA: SAGE Publications, Inc.

Nelson, T., LaBrie, R., LaPlante, D., Stanton, M., Shaffer, H., & Wechsler, H. (2007). Sports betting and other gambling in athletes, fans, and other college students. Research Quarterly for Exercise and Sport, 78(4), 271-283.

Paskus, T., Petr, T., Vicente, R., & Derevensky, J. (2009). Results from the 2008 NCAA Study on Collegiate Wagering: The National Collegiate Athletic Association.

Petr, T., Paskus, T., & Dunkle, J. (2004). 2003 NCAA National Study on Collegiate Sports Wagering and Associated Behaviors: The National Collegiate Athletic Association.
 

The WAGER, Vol. 15(1) – Disordered Gambling and Meaningful Correlates among Cambodian Refugees

Recent research shows that Cambodian refugees who settled in the United States suffer from disproportionately high rates of trauma (Marshall, Schell, Elliott, Berthold, & Chun, 2005). Other research shows that they also might be more prone to disordered gambling (Petry, Armentano, Kuoch, Norinth, & Smith, 2003). This week the WAGER reviews a study that examines whether socio-demographic variables and trauma are significant correlates of lifetime disordered gambling among a sample of Cambodian refugees (Marshall, Elliott, & Schell, 2009).

Methods

• Study participants (n=127) were a subsample of Cambodian refugees residing in Long Beach, California. The researchers recruited study participants by using a three stage random procedure:
  1) Screening: Census tracks identified areas with the highest concentration of Cambodian residents. The researchers screened 2,001 (97%) of the identified households for eligibility.
  2) Eligibility: Participants aged 35 to 75 were eligible if they previously lived in Cambodia during part of the Khmer Rouge regime. This process identified 719 Cambodians as eligible from 586 households (29%).
  3) Random Recruitment: One eligible person was selected randomly from each household; 490 participants agreed to participate.
• Subsample: The researchers added gambling-related questions to the core study after enrolling approximately 75% of participants. The 127 subsequent participants were used in this study.
• Face-to-face interviews assessed the following variables:
• Socio-demographic variables: age, gender, marital status, English proficiency (dichotomized as good or fair vs. poor or no ability) and household income (dichotomized as above or below the federal poverty line). 
• Pre-immigration trauma exposure: assessed 35 events using items from the 17-item Cambodian Harvard Trauma Questionnaire (Mollica et al., 1992) and the 46-item Bosnian version of the Harvard Trauma Questionnaire (Allden et al., 1998). 
• Disordered gambling classification (i.e., problem or potential pathological gambling) assessed using the South Oaks Gambling Screen (SOGS; Lesieur & Blume, 1987). A score of 3 or more indicated disordered gambling. 
• The researchers conducted bivariate and multivariate logistic regression analyses to examine the relationships between the socio-demographic and trauma variables and disordered gambling. They reported the results as odds ratios; Table 1 summarizes these findings.  

Table 1: Odds Ratios and 95% Confidence Intervals (CI) for Predicting Disordered Gambling (Adapted from Marshall et al, 2009)

 
Click image to enlarge, or adjust your browser's zoom setting.
¹Analyses are weighted and adjusted for clustering resulting from sample design
²Odds ratios are adjusted for variables with P-values less than 0.05 in bivariate models
³Per Standard Deviation
*P < 0.05
**P < 0.01

Results

• Eighteen participants (13.9%) met criteria for disordered gambling.
• Table 1 indicates that disordered gambling was significantly correlated with:
• Being male (bivariate analysis only)  
• Being married/cohabitating  
• Being exposed to high levels of trauma 

Limitations

• The rate of disordered gambling had large confidence intervals; anywhere from 4% to 24%.
• The results might not generalize to other populations, including other populations of Cambodian refugees.

Discussion

The results indicate lower rates (13.9%) of disordered gambling compared to the rates observed among Asian refugees in a similar study (i.e., 70.0%; Petry et al., 2003) that also used the SOGS with the same scoring classification. One difference between the two studies is that the Petry et al. study examined a convenience sample of refugees from three Asian communities (Laos, Cambodia, and Vietnam) at Asian community service organizations in Connecticut. Marshall et al. randomly selected a sample from a large Cambodian refugee community. Regardless, rates of disordered gambling appear to be meaningfully higher among Asian refugees than those observed in the general population (i.e., 1.4-2.9%; Kessler et al., 2008; Petry, Stinson, & Grant, 2005).

The correlation between traumatic events and disordered gambling in this study is consistent with other research (Kausch, Rugle, & Rowland, 2006); however, Marshall et al. found that marriage/cohabitation was significantly associated with disordered gambling. This finding contradicts other research (e.g., Petry et al., 2003; Petry et al., 2005), which reported positive associations between being divorced or single and disordered gambling. As a result, further examinations of disordered gambling and meaningful disordered gambling correlates among Cambodian refugees and other refugee communities are warranted.

-Tasha Chandler

What do you think? Please use the comment link below to provide feedback on this article.

References

Allden, K., Ceric, I., Kapetanovic, A., Lavelle, J., Loga, S., & Mathias, M. e. a. (1998). Harvard trauma manual: Bosnian-Herzegovina version. Cambridge, MA: Harvard Program for Refugee Trauma, Harvard Medical School.

Kausch, O., Rugle, L., & Rowland, D. Y. (2006). Lifetime histories of trauma among pathological gamblers. The American Journal on Addictions, 15(1), 35-43.

Kessler, R. C., Hwang, I., LaBrie, R. A., Petukhova, M., Sampson, N. A., Winters, K. C., et al. (2008). DSM-IV pathological gambling in the National Comorbidity Survey Replication. Psychological Medicine 38(9), 1351-1360.

Lesieur, H. R., & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144(9), 1184-1188.

Marshall, G. N., Elliott, M. N., & Schell, T. L. (2009). Prevalence and correlates of lifetime disordered gambling in Cambodian refugees residing in Long Beach, CA. Journal of Immigrant Minority Health, 11(1), 35-40.

Marshall, G. N., Schell, T. L., Elliott, M. N., Berthold, S. M., & Chun, C. A. (2005). Mental health of Cambodian refugees 2 decades after resettlement in the United States. Jama, 294(5), 571-579.

Mollica, R. F., Caspi-Yavin, Y., Bollini, P., Truong, T., Tor, S., & Lavelle, J. (1992). The Harvard Trauma Questionnaire. Validating a cross-cultural instrument for measuring torture, trauma, and posttraumatic stress disorder in Indochinese refugees. The Journal of Nervous and Mental Disease, 180(2), 111-116.

Petry, N. M., Armentano, C., Kuoch, T., Norinth, T., & Smith, L. (2003). Gambling participation and problems among South East Asian refugees to the United States. Psychiatric Services, 54(8), 1142-1148.

Petry, N. M., Stinson, F. S., & Grant, B. F. (2005). Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Journal of Clinical Psychiatry, 66(5), 564-574.

WAGER Vol. 14 (10): Generation Gap: Difference in PG symptoms based on age of onset.

Research suggests that, like OCD, the clinical characteristics and symptoms of pathological gambling (PG) (e.g., symptom severity, changes in relationships, financial trouble, comorbid disorders) can vary based on age of onset. Research has indicated differences in PG-related clinical characteristics and symptoms based on gender (Ibanez, Blanco, Moreryra, & Saiz-Ruiz, 2003; Potenza, Steinberg, Wu, Rounsaville, & O'Malley S, 2006) and age when seeking treatment (Grant et al., 2007; Lucke & Wallace, 2006; Pietrzak & Petry, 2006). To further examine the clinical characteristics of PG, Grant and colleagues (Grant, Kim, Odlaug, Buchanan, & Potenza, 2009) studied a cohort of 322 adult pathological gamblers (PGs) and focused particularly on the clinical characteristics and symptoms evidenced among participants who developed PG as older adults.

Methods

• Grant et al. (2009) recruited 322 adult participants that met current (past year) DSM-IV criteria for PG.

• Participants were recruited during a 5 year period using advertisements, and referrals from a cognitive-behavioral study, a pharmacological study, and outpatient treatment at a private or public hospital. 
• Researchers examined PG and PG-related clinical characteristics with the following instruments: 

• Structured Clinical Interview for DSM-IV Axis I Disorders and PG; 
• A semi-structured questionnaire to collect information about demographic and clinical features of PG and participant treatment history;
• The Yale Brown Obsessive Compulsive Scale Modified for PG (PG-YBOCS);
• The South Oaks Gambling Screen (SOGS);
• The Clinical Global Impression-Severity scale (CGI).
• Researchers grouped the participants by age of onset, defined as “the age when participants’ PG symptoms first met DSM-IV criteria” (Grant et al., 2009). The investigators placed them into the following three groups:

• Younger than 26 years old (N=63); 
• Between 26 and 54 years old (N=217);
• 55 years old and older (i.e., late onset; N=42).
• To test for differences among the three age groups, Grant et al. used Pearson’s chi-square, Kruskal-Wallis and post hoc Bonferroni analyses.

Results

• Compared to their younger counterparts, late onset PGs had the following significant characteristics (presented in Table 1 below with p values):

• Endorsed fewer PG-related criteria (i.e., lower SOGS scores);
• Less participation in gambling-related strategic games;
• Less participation in Gamblers Anonymous;
• More likely to seek treatment soon after experiencing PG symptoms;
• More likely to suffer from an anxiety disorder;
• Less likely to have a father with a history of a gambling problem.

Table 1. Significant differences in clinical characteristics of pathological gamblers of different ages.

Adapted from Table 2: Clinical characteristics of pathological gamblers (n=322) based on age of pathological gambling onset (Grant et al., 2009).

Characteristics	25 and younger (n=63)	26-54 (n=217)	55 and older (n=42)	p-Value
SOGS Scores, mean (± SD)	13.6 ±5.04	13.8 ±3.34	10.0 ±2.76	.004a, b
Strategic game playing, n (%)	42 (66.7)	102 (47.0)	9 (21.4)	<.001a
Gamblers Anonymous, n (%)	30 (47.6)	88 (40.6)	7 (16.7)	.004a
Seeking professional treatment, mean (± SD)	17.6 ± 11.6	8.4 ± 5.7	3.9 ± 3.7	.001a, b
Anxiety Disorder, n (%)	7 (11.1)	32 (14.7)	16 (38.1)	<.001a
Father with a gambling problem, n (%)	18 (29.0)	51 (23.7)	3 (7.1)	.025a

aBonferroni post hoc pairwise comparison (26-54 yrs) vs. (55 yrs and older) p < .05. 

bBonferroni post hoc pairwise comparison (26 and younger) vs. (55 yrs and older) p < .05. 

Limitations

• The sample lacked cultural and ethnic diversity (i.e., 94.4% were Caucasian).
• Participant history, including age of PG onset, was based on self-recall.
• Vague age of onset definition; it is unclear if participants were grouped based on the life time appearance of the first DSM criterion or the first criterion experienced in a 12-month period in which 5 or more DSM criteria were experienced.
• Using 55 years as a cut-off point is not evidence based; the authors explain that 55 years represents a reasonable extreme (13%) of the total sample and that other studies used this age as a cut off about older gamblers.

Conclusion

The study sample is limited in its diversity, which suggests a need for further research into PG-related clinical characteristics among more diverse samples of PGs, including those who experience late-onset PG. It also could be worthwhile for researchers to examine the triggers of late onset of any gambling. The study results indicate that those with late onset PG are more likely to have certain clinical characteristics, but the authors fail to explain why the two other study groups (26 and younger and 26-54) have higher rates of treatment seeking. Answering this question could explain a number of the current study results: likelihood of treatment in each group and the kinds of symptoms that each group experienced. Given the difference in the number of people per age group it’s possible that the majority of the participants (aged 54 and younger) were simply treatment seekers whose responses skewed results. 

-Ingrid Maurice

What do you think? Please use the comment link below to provide feedback on this article.

References

Grant, J. E., Kim, S. W., Odlaug, B. L., Buchanan, S. N., & Potenza, M. N. (2009). Late-onset pathological gambling: clinical correlates and gender differences. Journal of Psychiatric Research, 43(4), 380-387.

Grant, J. E., Mancebo, M. C., Pinto, A., Williams, K. A., Eisen, J. L., & Rasmussen, S. A. (2007). Late-onset obsessive compulsive disorder: clinical characteristics and psychiatric comorbidity. Psychiatry Research, 152(1), 21-27.

Ibanez, A., Blanco, C., Moreryra, P., & Saiz-Ruiz, J. (2003). Gender differences in pathological gambling. Journal of Clinical Psychiatry, 64(3), 295-301.

Lucke, S., & Wallace, M. (2006). Assessment and management of pathological and problem gambling among older adults. Geriatr Nurs, 27(1), 51-57.

Pietrzak, R. H., & Petry, N. M. (2006). Severity of gambling problems and psychosocial functioning in older adults. J Geriatr Psychiatry Neurol, 19(2), 106-113.

Potenza, M. N., Steinberg, M. A., Wu, R., Rounsaville, B. J., & O'Malley S, S. (2006). Characteristics of Older Adult Problem Gamblers Calling a Gambling Helpline. Journal of Gambling Studies.

The WAGER, Vol. 14(9) - Turning rats into gamblers: Modulating gambling behavior using laboratory animals.

Animal models of human behavior help scientists to understand pathology and develop new drugs. This week’s The WAGER reviews a study that examines an animal model of gambling behavior (Zeeb, Robbins, & Winstanley, 2009). Zeeb et al. sought to determine if rats are capable of “playing the odds” and if altering their brain chemistry (e.g., to mimic the neurochemistry of human gamblers) changes the rats’ decision-making, which according to the authors was a proxy for gambling strategy.

Method

•  Investigators trained rats (N = 32) to choose between four trays (P1 – 4) to nose-poke for food (see Figure 1).
• • Each time a rat “won”, it received one or more sugar pellets as a reward. 
  • Each time it “lost”, there was a timeout during which it could not play. 
  • In terms of long-term gain, the optimal choice was to choose P2, which was getting 2 sugar pellets with moderate waiting time. 
  • P1 represented the choice with the lowest reward (1 pellet) and the lowest probability of timeout; P4 was the choice with the highest reward (4 pellets) and the highest probability of timeout.

• After at least one week of stable baseline behavior, the researchers injected various drugs that affected (i.e., increased or decreased) the level of dopamine in brain to alter the animals' brain chemistry to mimic that of problem gamblers. Examples of drugs are:
• • Amphetamine: potentially increases the level of dopamine in the brain.
  • Eticlopride: blocks dopamine receptors.
  • SKF 81297: triggers dopamine response by the brain cell.
• Investigators conducted repeated measures ANOVA to measure how the preferences were changed following the drug injection.

Figure 1. Trial structure for the Rat Gambling Task.*

Notes:* The location of the trays was counterbalanced across rats. A picture represents one possible setting.

Results

• At baseline, animals consistently showed a preference for the optimal choice (P2), associated with a modest gain and moderate punishment.
• Amphetamine significantly increased selection of the non-optimal choice P1 (p < .01), which is associated with the least punishment but also with the least reward.
• Eticlopride significantly increased selection of the optimal choice (p < .01).
• SKF 81297 significantly increased the selection of P4 (risky with largest probability of punishment) and significantly decreased the selection of the optimal choice (p < .01). 

Limitations

• The "Rat gambling task" is not a complete analogy for human gambling behavior (e.g., rats cannot really “lose” food).
• The "decision making" task is not necessarily a proxy for gambling.
• It is not clear if dopamine leads to the avoidance of punishment or awareness of rewards.
• It is not clear if rats are relying on memory to make their choice, rather than basing their preference on the perception of risk or punishment tolerance.

Conclusion

    This study indicates that rats are capable of ‘playing the odds’. Moreover, they seem to be “risk-averse” animals, similar to human beings. The obtained data suggest that changing brain chemistry produces subsequent changes in animals “similar-to-gambling” behavior. Specifically, dopaminergic agents (drugs that influence dopamine activity in brain) can impair or improve gambling performance. The results imply that high levels of dopamine might pre-dispose non-optimal decision making, creating a risk factor for gambling-related problems. The mechanism of dopamine as a risk factor is not yet clear. However, the results are consistent with previous studies that found dopamine treatment to increase gambling drive among human patients (Abler, Hahlbrock, Unrath, Gron, & Kassubek, 2009). Taken together, these findings indicate that the rats gambling task might be a useful tool to study biological aspects of gambling.

-Julia Braverman

What do you think?  Please use the comment link below to provide feedback on this article.

References

Abler, B., Hahlbrock, R., Unrath, A., Gron, G., & Kassubek, J. (2009). At-risk for pathological gambling: imaging neural reward processing under chronic dopamine agonists. Brain: A Journal of Neurology, 132(9), 2396-2402.

Zeeb, F. D., Robbins, T. W., & Winstanley, C. A. (2009). Serotonergic and Dopaminergic Modulation of Gambling Behavior as Assessed Using a Novel Rat Gambling Task. Neuropsychopharmacology, 34(10), 2329-2343.

The WAGER, Vol. 14(8) - Video Game Playing and Gambling-Related Problems: Is there a Connection?

Research has begun to focus on how gambling impacts adolescents (e.g., Delfabbro, Lahn, & Grabosky, 2005; Welte, Barnes, Tidwell, & Hoffman, 2009). However, researchers rarely have studied the correlation between adolescent video game playing and disordered gambling behavior. This week’s WAGER reviews a study (Delfabbro, King, Lambos, & Puglies, 2009) that examined associations between gambling-related problems and types and frequency of video game play among Australian adolescents.

Methods

• Participants (N= 2,669) were students between the ages 12 and 17 attending six secondary schools (i.e., grades 8-13) in South Australia.
  • Delfabbro et al. purposely sampled schools; they selected four schools to obtain representation from each of the four major statistical districts in the metropolitan area and selected two from major regional centers.
  • All students who were present on the survey’s administration day completed the survey with the exception of students who were withdrawn by parents (i.e., withdrawers). The absentee rate reported by the schools was approximately 10%; the withdrawer rate and completion rate were not reported.
• The survey measured video-game involvement during the past year and DSM-IV Pathological Gambling (PG) items.
  • Video-game involvement measures included frequency of play, number of hours played, and type of game played, including: TV games (e.g., Xbox, Nintendo, Playstation); phone-based games; hand-held games (e.g., Nintendo, Gameboy); PC games; and arcade games.
  • Researchers assessed PG using the DSM-IV Juvenile Criteria (DSM-IV-J; Fisher, 1992); a screening instrument for gambling-related problems among adolescents modified from the DSM-IV criteria for PG.
    • A 12-item yes/no scale.
      • According to Fisher (1992), a total score of four or more indicates pathological gambling.
      • The screen asked about current problems. Five items specify “in the past year,” six items are asked in the present tense, and one item was more ambiguous (Have you fallen out with members of your family, or close friends, because of your gambling behavior?).
• The participants’ DSM-IV-J scores permitted the investigators to establish three groups: Not at risk (i.e., endorsed no items on the scale); At-risk (i.e., endorsed 1-3 items on the scale); and PGs (i.e., endorsed 4 or more items on the scale).
• The authors conducted a series of hierarchical multiple regression analyses to examine associations between gambling-related problems and the frequency and type of video game playing.

Results

• Table 1 indicates the gambling frequency (i.e., the number of video-game sessions per year) of various types of video game play among the three gambling groups.
  • The results indicate that the PG and At Risk groups played arcade, phone, hand-held, and TV games more frequently than the Not at Risk group and the At Risk group played PC games more frequently than the Not at Risk group.
• Participants who played video-games daily (n=574) had significantly higher DSM-IV-J scores (p<.001) than their counterparts who did not play or played video games less frequently (M=0.41, SD=1.25 and M=0.16, SD=0.78 respectively).

Table 1 Gambling Groups in Relation to Frequency of Video-Game Play in the Past Year (adapted from Delfabbro et al., 2009).

Click image to enlarge, or adjust your browser's zoom setting.
*p<0.01 and **p<0.001
Effect sizes: η²: 0.01-0.06 = small effect size; 0.07-0.13 = moderate effect size; 0.14+ = large effect size

Limitations

• This survey relies on self-report.
• The researchers used a cross-sectional design that does not allow for exploration of causal relationships between video-game playing and gambling. This does not take common factors (e.g., interaction between players and technology, and structural characteristics of the game such as lighting and sounds) into consideration that might account for the association between video gaming and gambling.
• Because of the selection procedures, the participants might not be representative of their schools, their classes, youthful gamblers, or youthful video game players.
• The authors used the number of students as the total degrees of freedom in their statistical analyses.  However, the cluster sampling within schools yields fewer degrees of freedom and without adjustment, the power of the statistical tests is exaggerated.
• The DSM-IV-J items shift time frames (i.e., past year, presently, and lifetime).  As a result, it is not possible to determine if gambling-related problems are current or in the past.

Conclusion

The results indicated significant associations between various types of video-gaming and gambling-related problems. However, the effect sizes were very small; this suggests that frequent video-game playing accounts for only a small part of the relationship between video game playing and gambling-related problems among adolescents. The inconsistency within the results (e.g., PC games were protective of PG, but hand-held games did not show a difference between no risk and at risk) suggests that other factors might better explain the association between video games and gambling-related problems. Future research should consider exploring additional factors (e.g., social/family influences, personality, beliefs etc.) that extend beyond frequency of playing video games that may explain why some adolescents experience gambling-related problems.

-Tasha Chandler

What do you think?  Please use the comment link below to provide feedback on this article.

References

Delfabbro, P. H., King, D., Lambos, C., & Puglies, S. (2009). Is video-game playing a risk factor for pathological gambling in Australian adolescents? Journal of Gambling Studies, 25(3), 391-405.

Delfabbro, P. H., Lahn, J., & Grabosky, P. (2005). Adolescent gambling: A report on recent ACT research. Canberra: ACT Gambling and Research Commission.

Welte, J. W., Barnes, G. M., Tidwell, M.-C. O., & Hoffman, J. H. (2009). The association of form of gambling with problem gambling among American youth. Psychology of Addictive Behaviors, 23(1), 105-112.