The Brief Addiction Science Information Source (BASIS)

The WAGER, Vol. 18(5) – Should I sleep on it? How mental fatigue affects risky behavior

Factors such as mental fatigue and the outcomes of previous bets might influence how we make decisions about risk. However, previous research reveals inconsistent findings about these factors. Being  “cognitively-depleted”—in other words, being mentally fatigued—sometimes promotes riskier behavior and sometimes promotes more conservative decision making (Freeman & Muraven, 2010; Unger & Stahlberg, 2011). Likewise, it is unclear how the outcome of previous bets influences risky decisions (Demareee, Burns, DeDonno et al., 2012). This week’s WAGER reviews a study of the effects of both cognitive depletion and with the outcome of previous bets on risky gambling behavior (Kostek & Ashrafioun, 2013).

Methods

• The authors recruited 81 students (average age = 19.1, SD = 1.62, 53% female) from a large Midwestern university to participate in a lab-based gambling study.
• The authors randomly assigned participants to one of four groups: Cognitive-depletion/Winning, Cognitive-depletion/Losing, Control/Winning, and Control/Losing.
  • The authors first manipulated cognitive depletion using a writing task: those in the cognitive depletion conditions completed a writing task without using the letters ‘a’ or ‘n,’ and those in the control conditions completed a writing task without any letter restrictions.
  • Then, the authors manipulated betting outcomes using pre-programmed blackjack hands: those in the winning condition won 75% of blackjack hands played, and those in the losing condition won 25% of blackjack hands played.
• The authors measured decisions about risk in two ways:
  • Participants completed the Domain-Specific Risk-Taking scale (DOSPERT, Blais & Weber, 2006) which measures the likelihood of engaging in a variety of risky financial behaviors, like ‘Betting a day’s income at the horse races.”
  • At the end, participants could wager their five-dollar study compensation one dollar at a time for a chance to win $50. The odds of winning were 1:200 for each dollar wagered.

Results

• First, the authors examined participants’ reported likelihood of taking financial risks.
  • The authors uncovered an interaction between the two factors. In the control condition, those who won in blackjack said they were more likely to take financial risks than those who lost. But among participants who were cognitively depleted, those who lost in blackjack reported a greater likelihood of risk taking than those who won (Figure 1).
• Second, the authors examined how much people bet in the final bet. In this case, the two factors had independent effects on behavior:
  • Those in the cognitive depletion condition bet less (mean = $0.76, SD = 1.02) than people than those in the control condition (mean = $1.40, SD = 1.81).
  • Those in the winning condition bet significantly more (mean = $1.40, SD = 1.70) than those in the losing condition (mean = $0.76, SD = 1.04).

Figure 1: Likelihood of taking financial risks (mean scores) in the four conditions. Adapted from Kostek & Ashrafioun, 2013.

Note: *group differences significant at p<0.05

Limitations

• The study’s sample size is small (n=81), which means we should consider the findings preliminary.
• The writing task may have had other effects besides making participants cognitively depleted, such as frustration or boredom. This might have accounted for betting variations.
• We do not know if other groups of people, beyond college students, would act the same way.
• The 20-hand gambling scenario was brief and might not approximate real-life betting outcomes.

Conclusion

Understanding situational factors could be important to preventing gambling-related problems. In this study, self-reported financial risk varied in a complicated manner-- control subjects reported that they were more likely to engage in risky behavior after doing well at blackjack. In contrast, mentally fatigued participants were more drawn to risk after a series of losses. However, this effect was not apparent at a statistically significant level when the outcome was actual bets—participants bet more when not mentally fatigued and after a series of wins. This highlights the inconsistency between self-reports and actual behavior. Additional research could help to explain this inconsistency.

- Jed Jeng

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References

Blais, A., & Weber, E. (2006). A domain-Specific Risk-Taking (DOSPERT) Scale for Adult Populations. Judgment and Decision Making, 1(1).

Demareee, H., Burns, K., DeDonno, M., Agarwala, E., & Everhart, D. (2012). Risk dishabituation: In repeated gambling, risk is reduced following low-probability "surprising" events (wins or losses). Emotion, 12(3), 495-502.

Freeman, N., & Muraven, M. (2010). Self-Control Depletion Leads to Increased Risk Taking. Social Psychological & Personality Science, 1(2), 175-181.

Kostek, J., & Ashrafioun, L. (2013). Tired Winners: The Effects of Cognitive Resources and Prior Winning on Risky Decision Making. Journal of Gambling Studies, Advance online publication.

Unger, A., & Stahlberg, D. (2011). Ego-Depletion and Risk Behavior. Social Psychology, 42(1), 28-38.

The WAGER, Vol. 18(4) – The influence of expanded gambling opportunities in Iowa on disordered gambling rates

The expansion of gambling opportunities raises some important public health issues. Researchers previously thought that expanded gambling opportunities would lead to increased rates of gambling and gambling problems. A growing body of evidence, however, suggests that the relationship between gambling opportunities and gambling problems is more complicated (LaPlante & Shaffer, 2007). Iowa provides a setting to examine this relationship further. Iowa first legalized riverboat casino gambling in 1990, and then land-based casinos in 2005. There are currently 21 casinos in the state, up from just seven in 1994. . Today’s WAGER reviews a study (Black, McCormick, Losch, Shaw, Lutz, & Allen, 2012) exploring trends in gambling and disordered gambling over time in Iowa using data from three cross-sectional studies spanning more than 20 years.

Methods

• Researchers analyzed data from three telephone studies of gambling behavior conducted among randomly selected households in 1989, 1995, and 2012 [1],[2].
  • All surveys collected demographic information, as well as the South Oaks Gambling Screen (SOGS) to assess gambling and disordered gambling.
    • Gambling participation includes any gambling regardless of consequences; it is defined by a SOGS score of 1 or higher.
    • Recreational gambling includes gambling without experiencing negative consequences; it is defined as a SOGS score of 1 or 2.
    • Problem gambling is defined by a SOGS score of 3 or 4.
    • Pathological gambling, the most severe form of disordered gambling, is defined as a SOGS score of 5 or greater.
  • The analytic sample for the 1989, 1995, and 2012 studies were 750, 1,500, and 356, respectively.
  • Response rates were 74%, 57%, and 11% for 1989, 1995, and 2012, respectively.
• Researchers then compared the rates of lifetime gambling behavior from the 2012 study to rates from the 1989 and 1995 studies using chi-squared and Fisher’s Exact Test. Researchers did not report statistical comparisons between the 1989 and 1995 studies; however, we calculated these statistics and report them below.

Results

• The rates of gambling participation increased significantly between 1989 (13.6%) and 1995 (28.3%; χ² (1) = 60.0, p < .001). The rate dropped significantly between 1995 and 2012 (17.1%; χ² (3) = 18.7, p < .001). The 2012 rates were still significantly higher than in 1989, Fishers Exact Test, p < .05.
• In the 2012 study, 17.1% of the sample reported lifetime gambling participation. This represents a significant drop from 1995 (28.3%, χ² (3) = 18.7, p < .001), but a significant increase from 1989 (13.6%, Fishers Exact Test, p < .05). The 1995 rate was significantly higher than the 1989 rate, χ² (1) = 60.0, p < .001
• The rates of problem gambling increased significantly between 1989 (1.7%) and 1995 (3.5%; χ²(1) = 6.7, p < .05). The rates decreased from 1995 to 2012 (2.2%), but the difference did not reach significance, χ² (1) = 1.5, ns. Problem gambling rates increased between 1989 and 2012, but this different did not reach significance, χ² (1) = 0.57, ns.
• The rates of the pathological gambling increased significantly between 1989 (0.1%) and 1995 (1.9%; Fisher’s Exact Test p < .05). The rates decreased marginally between 1995 and 2012 (1.4%; χ² (1) = 0.4, ns). Pathological gambling rates increased significantly between 1989 and 2012, Fisher’s Exact Test p < .05.
• Figure 1 shows these trends over time.

Figure 1 – Percent of participants meeting criteria for SOGS score groups over time. 

Note: The dotted lines are added for clarity only. They do not indicate the pattern of gambling and gambling problems in between survey years. 

Limitations

• The sample size for the 2012 study was relatively small compared to similar random-digit dialing studies. This is particularly problematic for conditions with low base-rates like disordered gambling; in the 2012 study, only 5 people qualified for pathological gambling, and 8 for problem gambling.
• This study relies on self-report data; some participants might misrepresent or lack insight into their gambling problems.
• The response rate for the 2012 study was very low. Only 11% of those invited to participate were included in the analytic dataset.
• Repeated cross-sectional analyses cannot provide the same richness of information related to impact as a longitudinal design. By following the same group of participants over many years, longitudinal designs help eliminate many sources of bias, and can show us how individuals change and react over time.

Conclusions

Despite continued gambling expansion in Iowa since the legalization of casinos during 1990, there is not a linear increase in gambling participation rates; the percent of residents gambling rose significantly from 1989 to 1995 but dropped significantly from 1995 to 2012. The rates of problem and pathological gambling showed the same pattern. Rates rose significantly between 1989 and 1995, but decreased marginally between 1995 and 2012. . Overall, these results are consistent with an adaptation model proposed by Shaffer (2005) and LaPlante & Shaffer (2007), which suggests that gambling participation and problems increases initially with more exposure but peaks and stabilizes at a lower rate over time. However, true longitudinal research is needed to fully support or dispel this hypothesis. By following one large, representative group of people, we can learn how people react over time to expanded gambling and avoid the ambiguity associated with repeated cross-sectional surveys. For example, a longitudinal design is required to see if the same people have disordered gambling over a period time, or if they recover and others develop problems.

 -Daniel Tao

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References

Black, D.W., McCormick, B., Losch, M.E., Shaw, M., Lutz, G., Allen, J. (2012). Prevalence of problem gambling in Iowa: Revisiting Shaffer’s adaptation hypothesis. Annals of Clinical Psychiatry, 24(4), 279-284.

LaPlante, D.A., Shaffer, H.J. (2007). Understanding the influence of gambling opportunities: Expanding exposure models to include adaptation. American Journal of Orthopsychiatry, 77(4), 616-623.

Shaffer, H.J. (2005). From disabling to enabling the public interest: Natural transitions from gambling exposure to adaptation and self-regulation. Addiction, 100, 1227-1230. 

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[1] The data collection period for the 2012 study was from 2007-2008.

[2] A 2011 study in Iowa used the NODS, a different measure of disordered gambling. The results from this study are not directly comparable to the previous studies, which used the SOGS. 

The WAGER, Vol. 18(3) – Three roads to pain: Testing the pathways model for problem gambling

During 2002, Blaszczynski and Nower proposed that there are three pathways to problem and pathological gambling. In their first pathway, problem gambling develops as a matter of behavioral conditioning, not as a matter of psychopathology. In their second pathway, factors such as mood disorders,  poor coping skills, and traumatic life events contribute to the production of an “emotionally vulnerable gambler” who gambles to relieve pain (Blaszczynski & Nower, 2002). In their third pathway, emotional vulnerability combines with impulsivity and attention and personality disorders to create many problems, including gambling. Today’s WAGER reports on research (Nower, Martins, Lin, & Blanco, 2012) that searches for symptom clusters corresponding to these three pathways  using data from of the National Epidemiological Survey on Alcohol and Related Conditions.

Methods

• The study used data from the U.S. National Epidemiological Survey on Alcohol and Related Conditions (NESARC) (Grant & Dawson, 2006).
  • The data were reduced to include only and all disordered gambling participants (n = 581).[1]
  • The researchers analyzed participants’ responses to questions about alcohol use, tobacco use, other medications and drugs, psychological disorders, family history of addiction and psychological problems, gambling, medical conditions and victimization.
• The researchers used latent class analysis to identify subgroups within the NESARC disordered gambling population based on a combination of 17 clinical indicators.  
  • They evaluated models with between 1 and 5 classes to identify the optimal model (i.e. the model that best fit the observed data).
  • The researchers identified the optimal model by examining statistics (i.e. log-likelihood statistics, AIC and BIC)..

Results

• Out of the five models, the optimal one was the one with three classes.
• The probability that someone in Class 1 (n = 295) had a prior-to-past-year experience with a substance use disorder was 55%. The probabilities for most other indicators of psychological problems were under 10%. Participants in this group reported relatively high mental health functioning.
  • Based on these clinical indicators, researchers suggested that Class 1 corresponds to the first Blaszczynski and Nower (2002) pathway.
• The probability that someone in Class 2 (n = 117) had a prior-to-past-year experience with a substance use disorder was 74%. Other probabilities of note were those of physical fights due to drinking (33%), anti-social personality disorder (23%), panic disorder or general anxiety disorder (22%), and onset of the first episode of depression in the past year (22%).
  • Because of the elevated probabilities of signs of emotional vulnerability, the researchers suggested that Class 2 was generally consistent with the second Blaszczynski and Nower (2002) pathway.
• The probability that someone in Class 3 (n = 169) had a prior-to-past-year experience with a substance use disorder was 78%. Additionally, the probabilities of a history of antisocial personality disorder, past-year substance abuse disorder, and involvement in drinking-related physical fights were 49%, 43% and 40%, respectively.
  • Class 3 had the highest probabilities out of the three classes for 12 out of the 17 indicators. The researchers drew parallels between Class 3 and the third Blaszczynski and Nower (2002) pathway.
• Figure 1 is a 3-dimensional graph with the probabilities for prior-to-past-year alcohol abuse, prior-to-past-year history of drinking-related physical fights, and lifetime anti-social personality disorder, for the three classes. These three example indicators and the three locations in space illustrate the differences between Class 1, Class 2, and Class 3.

Figure 1: Probabilities of lifetime anti-social personality disorder, prior-to-past-year abuse or dependence of alcohol or drugs, and prior-to-past-year history of physical fights due to drinking, for each of the three classes. Adapted from Nower et al., 2013.

Limitations

• It is not possible to test some aspects of the three-pathway model. For example, Blaszczynski and Nower (2002) proposed that the pathways are dependent on the age at which individuals are introduced to gambling. However, these data was not part of the NESARC.
• Likewise, it is not possible to determine from the data which symptoms of drug abuse and other issues preceded the onset of gambling problems and which ones followed it. This order of events is necessary for distinguishing among the pathways.

Conclusion

The three classes found in the latent class analysis somewhat correspond to the three pathways outlined in Blaszczynski and Nower’s (2002) earlier work. They represent different levels of complicating factors that can interfere with or obstruct attempts at intervention or treatment for disordered gambling. If clinicians can identify their patients’ pathways, they might be able to adapt or tailor different treatment programs to their own special needs and personalities.

– Matthew Tom

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References

Blaszczynski, A., & Nower, L. (2002). A pathways model of problem and pathological gambling. Addiction, 97(5), 487–499. doi:10.1046/j.1360-0443.2002.00015.x

Grant, B. F., & Dawson, D. A. (2006). Introduction to the national epidemiologic survey on alcohol and related conditions. Alcohol Health & Research World, 29(2), 74.

Nower, L., Martins, S. S., Lin, K.-H., & Blanco, C. (2012). Subtypes of disordered gamblers: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Addiction (Abingdon, England). doi:10.1111/add.12012

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[1] All participants were asked if they had gambled at least five times in any 1 year of their lives. Those who responded affirmatively and then met three or more DSM-IV criteria for pathological gambling were classified as disordered gambling participants.

The WAGER, Vol. 18(2) – Potential pathways to problem gambling: The relationships among life stress, gambling motives, and gambling behavior

Research suggests that some people might develop addictive behavior through their attempts to escape or avoid experiences associated with stressful life events (Blaszczynski & Nower, 2002; Cooper, Frone, Russell, & Mudar, 1995). Other contributing factors to developing addictive behavior include proximity to and accessibility of potential objects of addiction (Cox, Yu, Afifi, & Ladouceur, 2005; Shaffer et al., 2004). This week’s WAGER reviews a study that examined how these factors  intersect and relate to gambling motivation, frequency and gambling-related problems (Thomas, Allen, Phillips, & Karantzas, 2011).

Methods

• Three hundred forty seven (229 females) current gamblers who use electronic gambling machines (EGM) recruited through flyers in various public message boards participated in the study.
• Participants completed a survey regarding their demographic information and frequency of EGM gambling.
• Participants also reported whether they experienced stress (defined as a major life change or loss) since commencing EGM and whether they ever escaped from their problems by using behavior such as drinking alcohol, using drugs or eating (0: never to 4: a lot of time) (i.e., general avoidance coping).
• Next, participants completed the following measures:
  • The EGM motivation scale (Thomas, Allen, & Phillips, 2009) , which consists of three subscales assessing avoidance (e.g., Gambling provides a break from worrying), accessibility (e.g., Venues are close) and social (e.g., I can meet new people) motivation. Response options ranged from 0 (Doesn't apply to me) to 5 (Applies to me almost always).
  • Problem gambling severity index (Ferris & Wynne, 2001) - e.g., Have you bet more than you could really afford to lose? Response options ranged from 0 (Never) to 3 (Almost always).

Results

• Figure 1 depicts significant (p < .05) correlations among measured variables. Specifically,
  1. Self-reported stress correlated with all three types of gambling motivation.
  2. Self-reported stress related to general avoidance coping, which in turn related to avoidance-motivated gambling.
  3. Those who reported stronger avoidance and accessibility motives for gambling reported having more gambling-related problems and playing electronic gambling machines more frequently.

Figure 1: The relationships among stress, avoidance coping, gambling motivations, frequency and problems. 

Limitations

• This is a correlational study; therefore, we cannot conclude if avoidance motivation leads to gambling frequency and problems or vice versa, or if a third variable is related to both factors.
• Measures have the usual limitations of self-report and therefore may not accurately reflect individual’s situational stressors, motivations, and actual gambling behavior.

Conclusion   

The results revealed that experiencing a stressful life event was positively related to avoidance, accessibility, and social motives for gambling. However, only avoidance and accessibility motives for gambling were related to frequency of gambling and gambling-related problems; social motivation did not relate to gambling related problems. This implies that to predict how stressful life events relate to potential future gambling-related problems, it is important to understand typical gambling motivations. The results also demonstrated that the tendency to escape from problems using drugs and alcohol is related to avoidance-motivated gambling, which is in turn related to gambling frequency and problems. This supports the view of problem gambling as a phenomenon that shares common antecedents with other expressions of addiction (Shaffer, et al., 2004). Future research designs that include experimental manipulations (e.g., of stress or gambling motivation) might shed light on causal mechanisms.

-Julia Braverman

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References

Blaszczynski, A., & Nower, L. (2002). A pathways model of problem and pathological gambling. Addiction, 97(5), 487-499.

Cooper, M. L., Frone, M. R., Russell, M., & Mudar, P. (1995). Drinking to regulate positive and negative emotions: A motivational model of alcohol use. Journal of Personality and Social Psychology, 69(5), 990-1005.

Cox, B. J., Yu, N., Afifi, T. O., & Ladouceur, R. (2005). A National Survey of Gambling Problems in Canada. The Canadian Journal of Psychiatry / La Revue canadienne de psychiatrie, 50(4), 213-217.

Ferris, J., & Wynne, H. J. (2001). The Canadian Problem Gambling Index: Final report.

Shaffer, H. J., LaPlante, D. A., LaBrie, R. A., Kidman, R. C., Donato, A. N., & Stanton, M. V. (2004). Toward a syndrome model of addiction: Multiple expressions, common etiology. Harvard Review of Psychiatry, 12, 367-374.

Thomas, A. C., Allen, F. C., & Phillips, J. (2009). Electronic gaming machine gambling: Measuring motivation. Journal of Gambling Studies, 25(3), 343-355.

Thomas, A. C., Allen, F. L., Phillips, J., & Karantzas, G. (2011). Gaming machine addiction: The role of avoidance, accessibility and social support. Psychology of Addictive Behaviors, 25(4), 738-744.

The WAGER, Vol. 18(1) - Disorder or distraction? Experiential Avoidance and problem gambling behaviors

Experiential avoidance (EA) refers to an individual’s attempt to avoid internal experiences related to an event, such as thoughts, feelings, and physical sensations, by engaging in some other activity (Riley 2012). Researchers have suggested that people can engage in EA through a number of activities, including sex, drug use, exercise, and gambling (Riley 2012). EA might therefore be related to the development of gambling problems. This week’s WAGER reviews a study that explored the relationship between EA and problem gambling, as well as the relationships between these constructs and related constructs: thought suppression and mindfulness (Riley 2012).  

Methods

A convenience sample of 103 treatment-seeking problem gamblers from an outpatient facility in South Australia completed a battery of measures to assess their levels of problem gambling and mindfulness, their tendency to suppress difficult thoughts, and their inability to take action when faced with “negatively evaluated private events.” Participants completed the following measures:

• Dispositional EA: Acceptance and Action Questionnaire (AAQ-II; Bond et al. 2011)[1]
  • In this scale, higher scores indicate lower levels of EA.
• Dispositional thought suppression: White Bear Suppression Inventory (WBSI; Wegner and Zanakos 1994)[2]
• Dispositional mindfulness: Mindfulness and Attention Awareness Scale (MAAS; Brown and Ryan 2003)[3]
• Problem gambling symptomology: Problem Gambling Severity Index (PGSI; Ferris and Wynne 2001)[4]

The researchers completed multiple regression analyses to explore the relationships among problem gambling, thought suppression, and mindfulness; and, to examine whether EA mediated a relationship between thought suppression and problem gambling.

Results

Table 1 below shows the relationship (zero order correlations) between the variables in question. Thought suppression and experiential avoidance were positively associated with problem gambling (r’s = .33 and -.43; both p’s < .01). Mindfulness, on the other hand, was negatively associated with problem gambling (-.39; p < .001).  

Table 1: Zero order correlations of study variables. (This table is adapted from the original article).

	Experiential avoidance	Problem gambling	Mindfulness	Thought suppression
Experiential avoidance	-	-.43; p < .001	.51; p < .001	-.63; p < .001
Problem gambling	-	-	-.39; p < .001	.33; p < .01
Mindfulness	-	-	-	.48; p < .001
Thought suppression	-	-	-	-

The researchers conducted linear regression analyses to examine potential mediating relationships. As shown in Tables 2 and 3, EA appears to mediate the relationships between both thought suppression and mindfulness with problem gambling. When controlling for EA, the standardized regression coefficients between the other variable (mindfulness or thought suppression) and problem gambling decreased and became non-significant.

Table 2: Linear Regression with Problem Gambling as the dependent variable w/ thought suppression and experiential avoidance as independent variables

	B	Standardized B	p	Sobel z;
Thought supression	0.04	0.11	0.91	2.73; p < .001
Experiential avoidance	-0.15	-0.35	0.22

Table 3: Linear Regression with Problem Gambling as the dependent variable w/ mindfulness and experiential avoidance as independent variables

	B	Standardized B	p	Sobel z
Mindfulness	-0.82	-0.19	.085	-2.82; p < .001
Experiential avoidance	-0.15	-0.35	.001

Limitations

• The study may not be generalizable to other populations.
  • 103 is a relatively small sample size and all individuals came from the same outpatient facility in South Australia
  • The researchers used a convenience sample so the results may not be applicable to all problem gamblers
    • The participants were treatment seeking problem gamblers, perhaps those individuals for whom EA functions differently would not be seeking treatment.
• Due to the cross-sectional nature of the analysis it is not possible to determine causal relationships between EA, thought suppression, and mindfulness and problem gambling.

Discussion

This article suggests that pathological gambling is related to experiential avoidance and thought suppression. The mediational findings suggest a model whereby people experience intrusive thoughts and attempt to cope with the distress these thoughts cause by gambling. A more mindful state, however, might decrease the urge to escape distress and gamble. These models can be applied to more targeted, and therefore more effective, gambling treatments. Specifically, incorporating mindfulness-based therapies and acceptance practices into gambling recovery might help counteract experiential avoidance and draw into focus the underlying problem that is leading to the problematic behavior.

- Emily Shoov

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References

Bond, F. W., Hayes, S. C., Baer, R. A., Carpenter, K. C., Guenole, N., Orcutt, H. K., et al. (2011). Preliminary psychometric properties of the acceptance and action questionnaire—II: A revised measure of psychological flexibility and acceptance. Behavior Therapy, 42, 676–688.

Brown, K. W., & Ryan, R. M. (2003). The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology, 84(4), 822–848.

Ferris, J. A., & Wynne, H. J. (2001). The Canadian problem gambling index: Final report. Canada: Canadian Centre on Substance Abuse.

Marotta, J. J. (2002). Experiential avoidance as a functional process of problem gambling. In Marotta, J. J., Cornelius, J. A., & Eadington, W. R. (Eds.), The downside of problem and pathological gambling (pp. 69–91). Reno, Nevada, USA: University of Nevada Press.

Riley, B. (2012). "Experiential Avoidance Mediates the Association Between Thought Suppression and Mindfulness with Problem Gambling." Journal of Gambling Studies.. Epub 30 October 2012.

Wegner, D. M., & Zanakos, S. (1994). Chronic thought suppression. Journal of Personality, 62(4), 615–640.

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[1] A 10-item self-report measuring “an individual’s tendency to make negative evaluations of unwanted private events, unwillingness to be in contact with them and a desire to alter their form or frequency to the extent that they are unable to take action in the face of such events.”

[2] A 15-item self-report measuring “an individual’s tendency to suppress unwanted negative thoughts.”

[3] A 15-item self-report measuring “an individual’s dispositional mindfulness” (i.e., the existence or absence of an individual’s “attentional presence” to an experience)

[4] A 9 item self-report measure of problem gambling. 4 items that assess problem gambling behaviors and 5items  that assess consequences of gambling.

The WAGER, Vol. 17(10) – Up close: Gambling and the telescoping effect

Researchers have observed that women tend to start gambling later in life but progress to gambling-related problems more quickly compared to men (Nelson et al., 2006). Piazza, Vrbka and Yeager (1989) refer to this phenomenon as the telescoping effect. Although the causes of telescoping remain unclear, previous studies investigating gender differences in the development of addiction suggest that anxiety disorders might relate to telescoping (Ibanez, 2003; Kushner, 2011); people who have a co-morbid anxiety tend to progress more quickly from recreational to hazardous drinking. This week’s WAGER reviews a study that tested whether co-morbid anxiety also helps explain gender differences in the progression of pathological gambling  (Grant, 2012).

Method

• Researchers recruited 71 individuals seeking treatment for pathological gambling (PG) from a clinical research trial:

• The study excluded people with lifetime psychotic or bipolar disorder and people with current (past 12 months) substance abuse or dependence.
• The study compared 34 women (51.6 years old [SD=10.6] years) with 37 men (44.2 years old [SD=12.7] years).

• The researchers recorded the following variables through semi-structured clinical interviews:

• Gambling behaviors, measured by the Structured Clinical Interview for Pathological Gambling (Grant et al., 2004);
• Age of gambling initiation;
• Time from initiation to meeting criteria for PG;
• Current and lifetime Axis-1 psychiatric disorders (First, 1995); and,
• Demographic features

• The researchers used the Cox proportional hazards regression test to examine gender differences in the time to developing PG symptoms, controlling for demographic variables, type of gambling, and psychiatric comorbidity.

Results

• Women had a higher mean age at gambling initiation (Mean = 31.3 years, SD = 13.0) compared with men (Mean = 22.4 years, SD = 7.9); t(69)=2.668, p < .001.
• There were no significant differences in rates of psychiatric comorbidity in men and women.
• Figure 1 shows that women had a significantly shorter time from initiation of gambling to meeting the criteria for PG (8.33 years [SD= 8.7] compared with 11.97 years [SD= 9.1];  log-rank chi-square (1) = 3.92p < .05), controlling for demographic variables, type of gambling, and psychiatric comorbidity.
• These results were in the absence of certain lifetime anxiety, impulse control, affective, alcohol use and substance use disorders.

Figure 1.  Time from Initiation of Gambling to Problem Gambling


Note: adapted from data presented in Grant et al. (2012)

Limitations

• Researchers derived measures of gambling activity from retrospective self-report and these might not accurately reflect actual behaviors, feelings, or thoughts.  

• Researchers excluded people with past-year substance disorders, lifetime psychotic disorders and lifetime bipolar disorders, thus diminishing the generalizability of the findings—particularly given that PG is highly comorbid with other substance use disorders (Kessler et al., 2008).

• The sample may be potentially biased because it is made up of treatment seekers. People who seek treatment represent only a small slice of those with PG, and may not be representative of the overall PG population.

Conclusion  

Consistent with previous research, compared with men, women had a significantly shorter time from their initiation of gambling behavior to meeting the criteria for pathological gambling. Furthermore, this pattern was evident even after controlling for the potential confounding effects of major lifetime psychiatric comorbidity. The persistence of the telescoping effect suggests that certain characteristics, apart from comorbidity, might better account for the temporal course of problem gambling behavior. Women might experience the telescoping effect because of gender-specific social or biological pressures or because they start gambling later (Nelson et al., 2006). Gender is one small point in a possible constellation of psychosocial factors that may affect the trajectory of PG. Future research could provide greater clinical focus on the gender differences of gambling behavior, while controlling for a greater range of psychiatric and demographic variables.

-Kat Belkin

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References

First, M., Spitzer, RL, Gibbon, M, & Williams JBW. (1995). Structured clinical interview for DSM-IV-patient edition (SCID-I/P, Version 2.0). New York: Biometrics Research Department, New York State Psychiatric Institute.

Grant, J., Odlaugh, BL, & Mooney, ME. (2012). Telescoping phenomenon in pathological gambling: Association with gender and comorbidities. Journal of Nervous Mental Disorders, 200, 996-998.

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

Kessler, R. C., Hwang, I., LaBrie, R., Petukhova, M., Sampson, N. A., Winters, K. C., et al. (2008). DSM-IV Pathological Gambling in the National Comorbidity Survey Replication. Psychological Medicine.

Kushner, M., Maurer, E, Menary, K, & Thuras, P. (2011). Vulnerability to the rapid ("telescoped") development of alcohol dependence in individuals with anxiety disorder. Journal for the Studies of Alcohol and Drugs, 72, 1019-1027.

The WAGER, Vol. 17(9) - Full tilt: Poker experience and reactive betting

Tilting is a poker term used to describe the choice of sub-optimal and risky betting strategies due to frustration, anger and other reactions to gambling outcomes. Previous studies have shown that skills necessary to control tilting (e.g.,  regulating negative emotions) might be acquired with experience (Palomaki, Laakasuo, & Salmela, 2012a). However, no study has directly investigated the phenomenon of tilting. This week’s WAGER reviews a study that examined if poker experience and emotional sensitivity are associated with “tilting” betting behavior (Palomaki, Laakasuo, & Salmela, 2012b).

Method

Four hundred seventeen poker players recruited from Finnish online poker communities completed an online survey.

• The researchers assessed the following variables:
  • Poker experience measured by a 3-item scale (e.g., “What is the rough estimate of how many poker hands you have played during your life?”; 1 = “0–50,000”; 10 = “more than 5 million”).
  • Emotional sensitivity to losses measured by an 11-item Likert-type scale (e.g., “I feel losing is unfair”; 1 = “Completely disagree” to 7 = “Completely agree”).
  • Perceived effect of experience on tilting measured by a 4-item Likert-type scale (e.g. “The more poker experience I have gained, the less frequently I have tilted”; 1 = “Completely disagree” to 7 = “Completely agree”).
  • Severity of tilting measured by a 4-item Likert-type scale capturing intensity, frequency and perceived harmfulness (e.g., “On estimate, how many times have you tilted within your last 6 months of active poker playing?”; 1 = 0; 7 = ‘More than 10’).
• The researchers built a multiple regression model predicting severity of tilting from poker experience, emotional sensitivity, perceived effect of experience on tilting, and their interaction. 

Results

• More experienced poker players reported tilting more severely (B = 0.22, t(412) = 5.75, p < .001).
• Higher emotional sensitivity to losses was associated with more severe tilting (B = 0.598, p < .001).
• Participants who believed their experience had reduced their tilting, not surprisingly reported less severe tilting in the past 6 months (B = -0.25, t(412) = -5.44, p < .001)
• Figure 1 shows a significant interaction between poker experience and the perceived effect of poker experience on tilting (B = -0.12, t (412) = -5.61, p < .001). Players with little poker experience reported similar severity of tilting regardless of their perception of the effect of experience on tilting. However, relatively experienced players reported more severe tilting if they reported low perceived effect of poker experience on tilting.

Figure 1. Interaction effect of perceptions and poker experience on self-reported tilting behavior

Note. PEET = perceived effect of experience on tilting.

Limitations

• This is a correlational study; therefore we cannot conclude if poker experience leads to severe tilting or people who are inclined to severe tilting tend to play poker more.
• Measures of gambling activity are derived from self-report and might not accurately reflect behavior, feelings, or thoughts. In particular, participants’ perceptions of the effect of experience on tilting likely relate to their reports of past 6-month tilting because both questions measure perceptions.

Conclusion   

Experienced poker players reported more severe tilting than inexperienced players did. Experienced players might encounter more tilting-provoking situations because they play more tables, rounds, and hands of poker, compared to novice players. Another explanation of the positive association between poker experience and severity of tilting is that experienced players might have a different perception of what tilting means compared to that of less experienced players. For example, experienced players may believe they are “losing control” in the same situations in which non-experienced players believe they have full control. It might also be that those who think their poker experience helps to control tilting also think they tilt less. Future research could conduct a true experiment, manipulating experience or a longitudinal study to assess the temporal relationship between poker experience and tilting. Future studies should also use more objective measures of tilting (e.g., by evaluating actual wagering behavior).

-Julia Braverman

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

References

Palomaki, J., Laakasuo, M., & Salmela, M. (2012a). "Don't Worry, It's Just Poker!"-Experience, Self-Rumination and Self-Reflection as Determinants of Decision-Making in On-Line Poker. Journal of Gambling Studies.

Palomaki, J., Laakasuo, M., & Salmela, M. (2012b). Losing More by Losing It: Poker Experience, Sensitivity to Losses and Tilting Severity. Journal of Gambling Studies, (Epub ahead of print).

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Notes

1. The researchers defined tilting to participants as “a strong negative emotional state elicited by elements of the poker game (e.g., “bad beats” or a prolonged “losing-streak”) that is characterized by losing control, and due to which the quality of decision-making in poker has decreased.”

The WAGER, Vol. 17(8) - Silencing the siren’s call: Can pop-up messages help slot machine players stick to their pre-set spending limits?

Previous research suggests that many people who seek treatment for gambling problems report developing their problems while playing slot machines. Therefore, researchers have been seeking ways to make slot machine gambling safer, including encouraging players to set and keep a firm limit on expenditure. While many players understand the importance of preset limits, many have trouble adhering to them (Wohl, Christie, Matheson, & Anisman, 2010). This may be due a psychological phenomenon known as dissociation, where a person is so engaged in an activity that they lose track of their surroundings. Today’s WAGER reviews a study that examines whether pop-up messages help people follow their preset monetary limits, and whether they do so by interrupting dissociation  (Stewart & Wohl, 2012).  

Methods

• Researchers recruited 59 college students from a pool of 1,584 students at a university in Eastern Canada who had previously completed a screen for problem and pathological gambling.
  • Researchers randomly selected participants from each of three levels of gambling symptomology to obtain their sample:
    • Recreational gamblers (n=17; 30% of the sample),
    • Problem gamblers (n=26, 45%),
    • Pathological gamblers (n=16, 25%).
  • All selected participants chose to participate in the study.
  • Participants were then randomized into two groups: pop-up and control.
• Researchers gave participants $20 worth of credits with which to gamble within a virtual reality (VR) gambling environment. Participants had to insert all of the $20 into a VR slot machine, but were told they could stop at anytime and keep whatever money they had left.
• After depositing their money, participants set a limit for how much they were willing to lose prior to playing. Participants then began to use the slot machine. Researchers rigged the machine so each spin was identical for each participant.
  • Participants in the pop-up condition were presented with a pop-up when their losses hit their predetermined limit and asked to complete a questionnaire about their dissociation during the session (Jacobs, 1988). Participants could then choose to stop and keep their remaining money or continue gambling.
  • Participants in the control group did not see any pop-ups, and continued to gamble until they lost all their money or chose to stop. At this point, control participants completed the same dissociation questionnaire. 

Results

• The groups did not differ in the amount of their preset limits.
• However, more than double the number of participants in the pop-up group (89.6%) adhered to their limits, compared to the control group (43.3%), χ² (1, n = 59) = 14.12, p < .001.
• Both gambling symptoms and dissociation decreased adherence to monetary limits, regardless of group (symptomology: Wald’s χ² (1) = 5.13, p = .02, Odds Ratio=0.74; dissociation: Wald’s χ² (1) = 5.14, p = .02, OR=0.37).
• Researchers then determined whether dissociation was a mediator of the relationship between gambling symptoms and limit adherence. Figure 1 shows a schematic of the proposed mediation model.
  • For participants in the control condition, those with more severe gambling problems showed higher levels of dissociation, which in turn led to lower limit adherence.
  • No mediation effect was found for the pop-up group.

Figure 1: Schematic of the tested mediation model (Stewart & Wohl, 2012)

Limitations

• Participants in the pop-up group took the questionnaire immediately after the pop-up. In essence, the intervention wasn’t simply a pop-up, but a pop-up and questionnaire. This could interrupt the gambling experience for those who wished to continue gambling, and could introduce a confounding factor.
• The study occurred in a lab setting. The participants’ behavior and the effects of the pop-up messages might be different with their own money in a casino environment, with lights, sounds, and other distracting stimuli.

Conclusions

This study showed that pop-up messages on slot machine terminals can be an effective way to increase user adherence to preset monetary limits. In the control condition, participants with gambling problems dissociated more, which made them less likely to stick to their limits; this relationship did not hold in the pop-up condition, suggesting it interrupted the process of dissociation for those with problems. However, in both conditions, participants with higher levels of gambling problems were still more likely to ignore their limits than participants with fewer symptoms. This suggests that pop-up limit messages might be an effective tool for reducing harms associated with gambling, but might not be effective for people with high levels of problems.

-Daniel Tao

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

References

Jacobs, D.F. (1988). Evidence for a common dissociative-like reaction among addicts. Journal of Gambling Behavior, 4, 27-37 doi:10.1007/BF01043526.

Stewart, M.J. & Wohl, M.J.A. (2012). Pop-up messages, dissociation, and craving: How monetary limit reminders facilitate adherence in a session of slot machine gambling. Online First doi:10.1037/a0029882.

Wohl, M.J.A., Christie, K., Matheson, K. & Anisman, H. (2010). Animation-based education as a gambling prevention tool: Correcting erroneous cognitions and reducing the frequency of exceeding limits among slot players. Journal of Gambling Studies, 26, 469-486. doi:10.1080/14459790802405855.

The WAGER, Vol. 17(7) - You’re cut off! Can casinos identify problem gamblers?

“Hey buddy, I think you’ve had too much. Let me call you a cab.” We are all familiar with this bar scene. The bartender decides when a drinker should be cut off, based on how many drinks he has had and how he is acting. But, unless a gambler is suspected of cheating, we might not see similar behavior from a dealer in a casino. Why not? Unlike problem drinking or drugging, problem gambling does not produce specific, easily observable, and predictable behavior. This week, the WAGER reviews a study that tested whether casino employees could use their knowledge of regular players to identify whether these players had gambling problems (Delfabbro, Borgas, & King, 2012).

Methods

• Researchers intercepted 303 (195 women, 108 men) gaming machine gamblers in seven south Australian gambling venues.
• Gamblers completed a survey that included questions about their gambling behavior, as well as the Problem Gambling Severity Index (PGSI: Ferris & Wynne, 2001).
• Venue staff, after researchers pointed out the gambler in question, completed surveys about each participating gambler, indicating how well they knew the gambler, how often they thought he or she gambled at the venue, and whether they thought the gambler had any problems with gambling.

Results

• Almost fifty percent (49.8%) of the gamblers who agreed to participate in the study played gambling machines weekly or more frequently, compared to less than 5% of the southern Australia general population.
• The PGSI classified 31% of the sample as at moderate or higher risk for gambling problems, and 8.6% of the sample as actual problem gamblers.
• Staff were familiar with three quarters of the participants in the study; those participants who were familiar to staff were more likely to report being frequent gamblers at the casino than other participants (58% compared to 20%).
• As Table 1 shows, the staff identified 35 patrons as having at least some gambling problems. Most (30) of them reported being at some risk for gambling problems.
• However, as Table 1 also shows, staff failed to identify many of the gamblers who reported problems. 

Table 1. Staff and Participant Report of Gambling Problems (adapted from Delfabbro et al., 2012)

Staff Reported Status	Gambler Self-Reported (PGSI) Status
	No Problems	Low Risk	Moderate Risk	Problem Gambler
No Problems	77	55	49	14
Some Problems	2	8	10	7
Problem Gambler	3	2	2	1
Sensitivity (# of actual problem gamblers correctly identified by staff / # of actual problem gamblers): For problem gambling (1/22): 5% For some problems/moderate problems (20/83): 24%
Specificity (# of people with no gambling problems correctly identified by staff / # of people with no gambling problems): For no problems – 77 / 82 = 94% For no problems / low risk – 132/147 = 90%

Note. Table 1 N = 230 – only gamblers whom staff recognized.

Limitations

• The sample was not representative of gaming machine gamblers, because only a small proportion of those recruited agreed to participate.
• Identifying problem gamblers for a research project is not the same as identifying them for intervention; staff might be unwilling to act on those assessments if such action would have consequences for the gambler or the venue.

Discussion

This study suggests that the gamblers whom the staff identifies as having problems are very likely to be problem gamblers. However, staff identify very few of these individuals – the overwhelming majority go undetected. If these findings are replicated, interventions administered by trained venue staff might be more effective than originally thought. Though many problem gamblers would be missed, those targeted would likely be at risk for gambling problems. However, any intervention employed would need to be carefully designed and evaluated to minimize unintended consequences (see Op-Ed, March 21, 2007).

-Sarah Nelson

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

Reference

Delfabbro, P., Borgas, M., & King, D. (2012). Venue staff knowledge of their patrons' gambling and problem gambling. Journal of Gambling Studies, 28, 155-169.

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

The WAGER, Vol. 17(6) - Fun with Friends: The influence of social networks on gambling problems

Researchers increasingly have become interested to investigate the environmental and social factors surrounding those with addiction. One of the tools used in this investigation is social network analysis (SNA). Fundamentally, SNA rests upon the assumption that the people a person interacts with regularly can influence that person’s behaviors and attitudes. SNA has been applied extensively to alcohol use and smoking (e.g., Christakis & Fowler, 2008), but it has not been applied to gambling behavior until now. This week’s WAGER reviews the first attempt to apply SNA to gambling behavior and related problems (Fortune, MacKillop, Miller, Campbell, Clifton & Goodie, 2012). 

Methods

• Researchers used newspaper advertisements to recruit 128 participants from Athens, Georgia who reported gambling at least once a week.
• An interviewer guided participants through a questionnaire. This questionnaire included the Diagnostic Interview for Gambling Severity (DIGS; Winters, Specker & Stinchfield, 1997), which assesses DSM-IV criteria for pathological gambling.
• Participants also estimated gambling frequency and expenditure for their parents, siblings, and five of their closest friends. Researchers also asked participants to indicate their confidence in these estimates, and only used estimates in which participants indicated some confidence. (On average, participants were confident in their estimates: M=2.5 on a 1 [no confidence] to 3 [high confidence] scale.)  

Results

• Researchers found a significant positive correlation between participants’ DIGS scores and their estimations of friends’ gambling frequency (r = .31, p < .001) and expenditure (r = .30, p < .001).
• Among biological relatives, the only significant relationship was between DIGS scores and estimated gambling frequency for the fathers (r = .30, p < .05).

Table 1: Correlation coefficients between participants’ DIGS score and gambling frequency and expenditure for different social network members.


*p < .05
** p < .001 

Limitations

• The study is purely correlational; it cannot tell us whether people with gambling problems tend to find like-minded friends, or whether having gambling friends influenced PG symptoms.
• The study relies entirely on participant’s perceptions of how others behave. It is possible that the study measures how participants’ perceptions of their friends’ gambling behavior varies as a function of participant DIGS score, not how their friends’ actual behavior varies.
• The “friend” category was relatively heterogeneous, containing significant others, non-first degree biological relatives (e.g., uncles and aunts), among others. There was no measure of the strength of the relationship, or how often they interacted. 

Discussion

This study provides a first investigation of the association between social networks and gambling problems. While the study found that gambling-related problems are correlated with the projected gambling behavior of friends, it is important to note that this study relies entirely on participants’ projections and assumptions about their friends’ behavior. This makes it impossible to isolate participants’ perceptions from their friends’ actual behavior. Traditionally, SNA utilizes objective data gathered directly from a participant’s friends and family (Christakis & Fowler, 2008),. Further studies need to use rigorous SNA to address the major limitations and gaps in knowledge and create a more thorough understanding of how interactions with other people might influence gambling behaviors and problems. 

-Daniel Tao

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

References

Christakis, N.A., Fowler, J.H. (2008). The collective dynamics of smoking in a large social network. The New England Journal of Medicine, 358, 2249-2258.

Fortune, E.E., MacKillop, J., Miller, J.D., Campbell, W.K., Clifton, A.D., Goodie, A.S. (2012). Social density of gambling and its association with gambling problems: An initial investigation. Journal of Gambling Studies, Online First. DOI:10.1007/s10899-012-9303-3.

Winters, K.C., Specker, S., Stinchfield, R.D. (1997). Diagnostic Interview for Gambling Severity (DIGS). Minneapolis: University of Minnesota.